Energy-Efficient Near-Field Integrated Sensing and Communication: A Comprehensive Review

Abstract

The pervasive scale of networks brought about by smart city applications has created infeasible energy footprints and necessitates the inclusion of sensing sustained operations with minimal human intervention. Consequently, integrated sensing and communication (ISAC) is emerging as a key technology for 6G systems. ISAC systems realize dual functions using shared spectrum, which complicates interference management. This motivates the development of advanced signal processing and multiplexing techniques. In this context, extremely large antenna arrays (ELAAs) have emerged as a promising solution. ELAAs offer substantial gains in spatial resolution, enabling precise beamforming and higher multiplexing gains by operating in the near-field (NF) region. Despite these advantages, the use of ELAAs increases energy consumption and exacerbates carbon emissions. To address this, NF multiple-input multiple-output (NF-MIMO) systems must incorporate sustainable architectures and scalable solutions. This paper provides a comprehensive review of the various methodologies utilized in the design of energy-efficient NF-MIMO-based ISAC systems. It introduces the foundational principles of the latest research while identifying the strengths and limitations of green NF-MIMO-based ISAC systems. Furthermore, this work provides an in-depth analysis of the open challenges associated with these systems. Finally, it offers a detailed overview of emerging opportunities for sustainable designs, encompassing backscatter communication, dynamic spectrum access, fluid antenna systems, reconfigurable intelligent surfaces, and energy harvesting technologies.

1. Introduction

The 6G of wireless communication systems is expected to integrate a paradigm shift in the use-cases of telecommunication infrastructures. 5G systems primarily focused on enhanced mobile broadband, ultra-reliable low-latency communications, and massive machine-type communications [1,2], which enabled communication services for human-centric and machine-centric networks [3]. However, owing to the mass inclusion of machines in wireless networks, the energy usage has scaled infeasibly [4]. The technical standards of 5G systems do not provide ecologically sustainable architectures to meet the growing demands of diversified applications [5]. Furthermore, modern internet-of-things applications require high-precision sensing as a key-functionality [6]. Consequently, ISAC has been included in the key-usage scenarios for 6G systems by the international telecommunication union (ITU) [7]. This integration will enable wireless networks to simultaneously provide connectivity and environmental awareness, which will fundamentally change how telecommunication architectures interact with the physical world.

ISAC refers to the joint operation of communication and sensing functionalities using shared network resources e.g., hardware, spectrum and power [6,8]. It unifies traditionally separate sensing capabilities e.g., radar, wireless sensing and environmental monitoring, etc., and communication functions. Recent technological advancements in signal processing algorithms, antenna design structures, and computational capabilities have enabled the practical implementation of ISAC systems. This integration of sensing capabilities in 6G networks is expected to provide an unprecedented precision in environmental awareness applications. Multiple-input multiple-output (MIMO) technology is a key enabler for ISAC owing to its ability to leverage multiple antennas for spatial multiplexing [9,10]. Spatial multiplexing exploits the multipath reflections of the wireless link such that multiple data streams are communicated over the same time-frequency resources. This is realized using beamforming, which is a signal processing technique where a transmitter (receiver) with multiple antennas steers (combines) the transmitted (received) signal in (from) a specific direction by applying precise phase and amplitude weights to each antenna element. This makes MIMO technology transformative for interference management and improves system capacity. In ISAC systems, MIMO can enable simultaneous transmission of communication signals and sensing waveforms by using beamforming to mitigate inter-function interference, hence, enhancing spectral efficiency. However, conventional MIMO systems have limited effectiveness in ISAC systems owing to their stringent interference management and multiplexing demands. Furthermore, MIMO performance is significantly affected by environmental factors, e.g., path obstructions and interference from other wireless devices. To provide feasible functionality and mitigate these issues, large-scale MIMO antenna arrays are required. The spatial multiplexing and beamforming gains of MIMO arrays increase with an increase in the number of antennas, and therefore, more precise beams can be steered towards targets and users of interest. Furthermore, 6G networks are expected to utilize significantly higher frequency bands, e.g., millimeter wave (mmWave) within the range 30–300 GHz, and terahertz (THz) bands [11], which correspond to shorter wavelengths, and extend the Rayleigh distance, which models the boundary between the NF and far-field (FF) regions [12]. The precise beamforming enabled by the increase in the frequency and supported by larger antenna arrays gives rise to communication in the NF which is introduced in the following text.

Motivated by the above, ELAAs are emerging as promising technology for 6G systems. ELAAs are massive antenna arrays with large physical apertures and provide ultra-high spatial multiplexing gains in wireless systems [13,14]. They support a higher number of independent data streams with more precise beam focusing capabilities, maximizing spectral efficiency without increasing bandwidth. Furthermore, by focusing energy precisely on intended users and targets, ELAAs reduce power wastage and enhance energy efficiency while also decreasing interference to other users. However, as antenna arrays become larger, the FF planar wave approximation becomes increasingly suboptimal. The large apertures of ELAAs makes the NF region far-reaching [14]. This necessitates the development of complex spherical wave models for feasible and practical modeling of next-generation systems.

Near-field integrated sensing and communication (NF-ISAC) systems can offer tremendous gains over conventional FF systems [15]. The spherical wavefronts of NF propagation enable precise focusing of electromagnetic energy in three-dimensional space. The NF channel has increased degrees-of-freedom (DoFs) and can focus energy at different distances in the same direction [16]. This immensely improves the sensing and communication performance of ISAC systems through reduced interference and minimal energy wastage. However, the implementation of NF-ISAC systems introduces significant energy efficiency challenges as the complex channels NF propagation require more sophisticated signal processing algorithms for channel estimation and precoding. Furthermore, the dual functionality of ISAC systems introduces competing energy demands between sensing and communication operations. Addressing these energy challenges in NF-ISAC systems requires advanced power allocation, beam focusing algorithms, and resource management. The development of energy-efficient hardware implementations and adaptive resource allocation algorithms is crucial for a practically feasible and scalable deployment of NF-ISAC systems.

1.1. Related Works

In this section, we provide a comprehensive review of the related works. The list of abbreviations is provided in

Table 1. Table of Abbreviations.

Abbreviations	Explanation
AoA	Angle of Arrival
AT	Assisting Transceiver
BCD	Block Coordinate Descent
BS	Base-Station
BSUM	Block Successive Upper bound Minimization
CoMP	Coordinated Multipoint
CR	Cognitive Radio
CRB	Cramer-Rao Bound
CSI	Channel State Information
CU	Communication Users
DC	Difference of Convex
DFRC	Dual-Function Radar and Communication
DFT	Discrete Fourier Transform
DOA	Direction-of-Arrival
DoF	Degrees-of-Freedom
DRT	Deterministic-Random Trade-off
EE	Energy Efficiency
EKF	Extended Kalman Filter
ELAA	Extremely Large Antenna Array
EM	Electromagnetic
ER	Energy Receiver
FAS	Fluid Antenna Systems
FD	Full-Duplex
FF	Far-Field
FIM	Fisher Information Matrix
FPA	Fixed-Position Antenna
HAD	Hybrid-Analog-and-Digital
IoT	Internet-of-Things
IR	Information Receiver
ISAC	Integrated Sensing and Communication
ISABC	Integrated Sensing and Backscatter Communication
ISACP	Integrated Sensing, Communications, and Powering
ISCC	Integrated Sensing, Communication and Computing
ISEAC	Integrating Sensing, Energy and Communication
ISPAC	Integrated Sensing, Positioning, and Communication
ITU	International Telecommunication Union
JCAS	Joint Communication and Sensing
JWOD	Joint Waveform Optimization and Design
LoS	Line-of-Sight
MIMO	Multiple-Input Multiple-Output
MM	Majorization-Minimization
mmWave	Millimeter wave
MUSIC	Multiple Signal Classification
NF	Near-Field
NF-ISAC	Near-field Integrated Sensing and Communication
NOMA	Non-Orthogonal Multiple Access
OFDM	Orthogonal Frequency Division Multiplexing
PAPR	Peak-to-Average Power Ratio
PCCP	Penalty Convex-Concave Procedure
PDD	Penalty Dual Decomposition
PEB	Position Error Bound
PLS	Physical Layer Security
PMN	Perceptive Mobile Network
PWM	Plane Wave Model
QoS	Quality-of-service
RCG	Reimannian Conjugate Gradient
RE	Reflecting Elements
RF	Radio-Frequency
RIS	Reconfigurable Intelligent Surface
SCA	Successive Convex Approximation
SDR	Semi-Definite Relaxation
SEE	Sensing-centric Energy Efficiency
SINR	Signal-plus-Interference-to-Noise Ratio
SNR	Signal-to-Noise Ratio
SOCP	Second-Order Cone Programming
SWIPT	Simultaneous Wireless Information and Power Transfer
SWM	Spherical Wave Model
THz	Terahertz
TTD	True-Time-Delay
UAV	Unmanned Aerial Vehicle
USW	Uniform Spherical Wave
XL-MIMO	Extremely large scale Multiple-Input Multiple-Output
XL-RIS	Extremely Large-scale Reconfigurable Intelligent Surface
XL-STAR-RIS	Extremely Large-scale Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface
ZZB	Ziv-Zakai Bound

. We note that while the literature for ISAC and NF technologies has independently matured, the exploration of efficient designs in NF-ISAC systems remains limited. The existing related literature can be structured into three major categories: FF ISAC, NF communication, and NF-ISAC. Far field ISAC systems have been extensively explored for next-generation systems [17,18,19,20,21,22,23,24,25,26,27]. Fundamental principles, system architectures, and waveform designs are reviewed for FF systems in [24,25,26]. Data-driven and learning based designs are also explored for FF ISAC systems [20]. Plane wave approximations dominate in these systems and the spatial resolution is limited to the angular domains. Models without spherical wavefront considerations, distance-dependent beamforming, and spatial non-stationarity are not applicable in NF systems. Studies on ISAC systems have been extensively reviewed in literature [17,18,19,20,21,22,23,24,25,26,27].

In [17], the authors present a systematic classification of traditional sensing technologies and ISAC systems within a unified framework. They survey the fundamental limits of both ISAC and conventional sensing, demonstrating that standard bounding techniques used in sensing and communications cannot be directly combined to study these limits. Additionally, they offer new insights into device-free and device-based sensing, including expressions and interpretations of the Cramér-Rao bounds for these modalities.

The authors in [18] detail the fundamental principles of ISAC and metasurfaces, followed by an analytical review of the literature on metasurfaces-assisted ISAC in RCC and DFRC systems. This review is further enhanced by an examination of ISAC architectures that integrate metasurfaces within both the ISAC transmitter and receiver.

An exhaustive review of joint communication and radar systems from a signal processing perspective is presented in [19]. It systematically examines the typical system and signal models employed in both transmission and reception, subsequently shifting its focus solely to signal reception. Within this context, the article rigorously surveys joint design and optimization methodologies as documented in recent literature.

The authors in [20] systematically review the application of machine learning algorithms within the domain of ISAC in the current literature. The review encompasses a broad spectrum of application scenarios, including THz communication, vehicular communications, radar systems, beamforming, tracking, localization, spectrum sensing, and edge computing. Their exploration aims to highlight the potential of intelligent techniques in advancing ISAC.

In [21], the authors provide a comprehensive review of current research in perceptive mobile networks (PMNs). They begin by delineating the limitations associated with the coexistence of communication and radar systems, particularly in relation to interference cancellation. Their discussion then introduces joint communication and sensing (JCAS) and its applications within mobile networks, presenting three distinct types of JCAS. Subsequently, the authors propose a PMN framework that supports the three sensing operations discussed in the article. Additionally, they examine the modifications necessary to enable sensing capabilities in current communication-only deployments. Finally, the authors address various PMN challenges and their corresponding solutions, concluding with a summary of open research problems.

In [22], the authors emphasize the importance of accurate ISAC channel modeling and provide a comprehensive survey of radar and channel modeling. This is further extended by the survey of both statistical and deterministic target and clutter radar cross section modeling. The article is concluded by suggesting future research directions for the modeling of ISAC channels.

The review presented in [23] comprehensively examines the ISAC research landscape, identifying key journals and thematic areas through keyword analysis. It encompasses two decades of scholarly work, analyzing annual publication trends, citation metrics, and contributions. Furthermore, the study also proposes a structured framework to enhance future research in this field.

The authors in [24] present a comprehensive review of the ISAC literature from the perspective of mobile communication systems, with a focus on ISAC signal design, processing, and optimization. Additionally, the review examines radar signal processing methodologies relevant to ISAC, including the channel information matrix method, spectrum lines estimator method, and super-resolution techniques. Finally, the authors categorize ISAC signal optimization into peak-to-average power ratio (PAPR) optimization, interference management, and adaptive signal optimization.

In [25], the authors examine various waveform design paradigms for ISAC, categorizing them into communication-centric design, sensing-centric design, and joint waveform optimization and design (JWOD). They provide a detailed overview of each paradigm and critically reviews the associated literature. In particular, the authors focus on communication-centric waveform designs, detailing the integration of ISAC with prevalent waveforms such as orthogonal frequency division multiplexing (OFDM) and single-carrier systems etc. Additionally, the review encompasses several JWOD schemes, addressing aspects of waveform optimization, spatial beamforming, and joint time/frequency designs. Finally, the authors compare these design techniques based on various features and discusses prospective research directions and challenges.

An in-depth investigation of ISAC is provided in [26] by examining various design paradigms, including communication-centric and sensing-centric approaches. It further explores enabling techniques such as transmit waveform design, signal processing, and data processing, as well as potential future applications. Additionally, the authors detail tools available for sensing data collection and highlight publicly accessible datasets that can support further research and development. Finally, the article describes current challenges in ISAC and proposes new avenues for future research.

The authors in [27] offer a comprehensive overview of recent advances in ISAC systems, emphasizing their foundational principles, physical-layer design, and networking aspects. They begin by deriving the theoretical limits of ISAC systems from an information-theoretic perspective, and subsequently address critical issues such as clock synchronization, phase offset, and Pareto optimality. Their discussion further explores the concept of sensing-based cellular networks, which paves the way for new cross-layer resource management strategies and transmission protocols. Additionally, the article examines security and privacy concerns within ISAC systems and concludes with an analysis of a multi-object, multi-task recognition problem using wireless signals.

NF communication systems are also being explored in the context of next-generation systems [28,29,30,31,32,33,34]. They highlight the effects of antenna aperture and frequency on NF channels and delineate spherical wavefronts, non-linear phase shifts, and spatial non-stationarity [32,33,34]. Channel estimation and beamfocusing designs are also explored in the context of communication [32,33,34]. Extremely large scale MIMO (XL-MIMO) and ELAAs have made the NF far-reaching, therefore, their fundamentals, channel models and designs are also being explored [28,29,30,31]. The consideration of a large number of antennas in ELAAs has also directed attention towards the energy footprint of these systems. Keeping in view the size and energy requirements of NF systems, energy efficient solutions for near-field communications have also been reviewed [35]. While these surveys and reviews provide the fundamentals of NF operation in communication systems, they largely omit the sensing functions, especially in energy efficient systems, which are expected to be integrated in 6G systems. NF studies have been extensively surveyed in the literature [28,29,30,31,32,33,34,36].

In [28], the authors provide a comprehensive survey of the literature on extremely XL-MIMO systems. They detail four distinct XL-MIMO architectures, with particular emphasis on uniform linear array-based and uniform planar array-based configurations, which encompass both point and continuous aperture antennas. They then examine the electromagnetic characteristics of the NF, leading to the discussion of NF channel models. Furthermore, the article addresses various signal processing schemes for XL-MIMO, particularly low-complexity approaches and deep learning variants, to facilitate practical implementations. Finally, it explores the integration of XL-MIMO with other enabling technologies for 6G networks and outlines prospective research directions.

The opportunities and challenges associated with XL-MIMO systems are examined in [29]. The authors propose a novel paradigm of cell-free XL-MIMO and employ multi-agent deep reinforcement learning to address challenges in high-dimensional signal processing and energy consumption. In order to meet the increasing data rate requirements and adapt to diverse operational scenarios, the study also discusses antenna selection and power control algorithms. Finally, future directions for the development of green cell-free XL-MIMO systems are outlined.

The authors in [30] provide a comprehensive examination of the hardware design of XL-MIMO systems, addressing critical aspects such as channel modeling, performance analysis, and signal processing. The discussion subsequently addresses current challenges in XL-MIMO systems and explores potential solutions. Two case studies are presented focusing on hybrid propagation channel modeling and effective degrees of freedom calculation. Subsequently, the effective degrees of freedom are determined for a scenario involving non-parallel XL-MIMO configurations and multiple user equipment, using the proposed methodologies. The article concludes with a discussion of several promising future research directions.

In [31], the authors explore the near field channel models of holographic MIMO and highlight their features and challenges. To address the inherent channel measurement and computational complexities involved in channel estimation, the electromagnetic domain channel models are also presented and discussed. Finally, the future research directions are discussed in the conclusion of the article.

A comprehensive review of NF-MIMO systems is presented in [32]. It begins with an introduction to the spherical wavefront characteristics inherent to NF propagation, followed by a discussion on NF channel modeling and recent advancements in antenna array technology. The article examines multiple channel models and evaluates their performance using metrics such as degrees of freedom and transmission rate. Furthermore, it addresses the signal processing aspects of NF communication by detailing various channel estimation, beamforming, and beam-training techniques. Finally, the review thoroughly explores the sensing, security, and massive connectivity dimensions of NF systems.

The authors in [33] provide a thorough investigation of the technical aspects of NF communication through an extensive survey encompassing channel modeling, channel estimation, NF beam focusing, and NF sensing. Two case studies are presented to illustrate the potential of NF spatial multiplexing and enhanced positioning accuracy. The article concludes with a discussion of prospective future research directions.

The unique characteristics of the spherical wavefronts allow them to have distance resolution in addition to angular resolution in XL-MIMO systems. In [37], the authors draw distinctions between NF and FF communication enabled using large antenna arrays. The challenges and unique properties of the near field are discussed in the context of user localization and channel estimation. Algorithms presented in the literature for the mitigation of these problems are thoroughly reviewed and future directions are suggested. In [36], the authors review the literature on NF-ISAC systems. They describe and illustrate the differences between NF and FF systems while also considering different NF-ISAC designs. The authors review case-studies and existing NF-ISAC literature to study the methods involved and potential of current NF-ISAC systems. Finally, they also describe future directions, challenges and potential applications. Considering the above literature, the motivation for the study of energy efficient NF-ISAC systems is presented in the following section.

1.2. Motivations

Despite the expanding literature on NF communications and ISAC systems, a comprehensive review which unifies the fundamental principles, advantages, challenges, opportunities, and state-of-the-art designs of NF-ISAC systems has not been conducted. Existing surveys predominantly focus on FF ISAC systems and realize the planar wavefront approximations with conventional system models which are not feasible in NF-ISAC systems. These reviews detail waveform designs, resource allocation, channel estimation, and dual-functional processing in ISAC systems but do not consider the spherical wave characteristics of NF systems.

Conversely, literature on NF communications thoroughly examine channel estimation, codebook design, beamtraining, beamfocusing, waveform designs, and resource allocation in XL-MIMO systems, but these investigations predominantly consider communication systems, and exclude the sensing functions. Hence, a consolidated account of the state-of-the-art in NF-ISAC systems is imperative to sustain quality-of-service (QoS) requirements of next-generation systems. In ultra-dense deployments where XL-MIMO arrays provide service to a diverse set of devices for versatile applications, the sensing and communication must be co-designed under fundamentally different wave propagation models from traditional systems. Furthermore, emerging technologies such as continuous aperture MIMO and reconfigurable intelligent surfaces (RISs) are also expected to operate in the NF region, which further necessitate modern models and effective designs.

1.3. Contributions

This is the first comprehensive review that unifies the foundational principles, challenges, opportunities and design methodologies of NF-ISAC systems with energy-efficiency considerations. Existing ISAC surveys are predominantly based on FF assumptions and fail to capture the electromagnetic characteristics and signal processing demands of spherical wave propagation in NF. Simultaneously, research in NF communication largely omits sensing functionality. In light of these research gaps, this work aims to bridge ISAC and NF research by conducting an in-depth survey. Our main contributions are summarized as follows:

• We offer a detailed review of the challenges associated with energy efficiency in conventional ISAC systems, including interference management, resource allocation, hardware constraints, and latency. These challenges form a foundation for understanding the additional complexities introduced by NF propagation.
• We delineate the differences between NF and FF wave propagation, and evaluate their impact on ISAC system performance. Specifically, we highlight how spherical wavefronts, spatial non-stationarity, and depth resolution affect the beamforming, codebook design, and beamtraining in NF systems.
• We systematically categorize the energy efficiency challenges unique to NF-ISAC systems, e.g., channel modeling, channel estimation, codebook design, beamtraining and interference management, etc.
• We provide an extensive survey of energy efficient NF-ISAC system designs, with a structured analysis of power-centric, sensing-centric, communication-centric, and joint designs.
• We introduce emerging research directions for sustainable NF-ISAC systems, including wireless power transfer, RIS-aided ISAC, backscatter technology, fluid antenna and cognitive radios, etc.

1.4. Paper Organization

The remainder of this article is organized as follows:

• Section 2 discusses energy efficiency in traditional ISAC systems, and delineates key challenges such as hardware implementation, interference management, and latency, etc. It also introduces energy-efficient designs that emphasize power and performance considerations.
• Section 3 focuses specifically on energy-efficient NF-ISAC. It begins with a classification of wave propagation regions and outlines the physical and operational differences between FF and NF-ISAC systems. These differences then elaborate the fundamental changes in system designs for NF-ISAC systems. This is followed by an in-depth review of the energy efficiency challenges in NF-ISAC systems, e.g., channel modeling, channel estimation, codebook design, beamforming, and hardware constraints. This section also provides a thorough survey of energy efficient designs of NF-ISAC systems classified as power-centric, joint ISAC, sensing-centric, and communication-centric designs.
• Section 4 presents a road-ahead discussion on sustainable NF-ISAC systems. Modern technologies such as integrated sensing, communication and powering, RIS-assisted ISAC, passive backscattering, fluid antennas, and cognitive radio are discussed in the context of NF-ISAC systems.
• Section 5 concludes the paper with a summary of major findings and implications for 6G and beyond.

A summary of the organization also provided in Figure 1.

2. Energy Efficiency in Integrated Sensing and Communication

The integration of sensing and communication functions introduces significant energy efficiency (EE) challenges, as the requirements of high-performance sensing and ultra-reliable communication strain the limited power resources. Sensing tasks, e.g., radar sensing and environment mapping, require higher signal strength and more computational resources to provide precise parameter estimation and low latency. Furthermore, communication systems demand spectral efficiency and signal reliability, which can be realized using adaptive modulation and error correction coding. These techniques further increase processing requirements. These stringent demands of dual-functions increase energy costs, which infeasibly increase the carbon footprint of wireless infrastructures, and necessitate energy efficient designs to meet net-zero targets of the future. Additionally, sensing is heavily dependent on line-of-sight (LoS) links which require dense ISAC deployments in urban environments with distributed architectures, further complicating the energy consumption.

2.1. Challenges of Energy Efficiency in ISAC

The dual-functioning of ISAC systems introduces numerous challenges in their practical implementation, as shown in Figure 2. The following text describes the key challenges of ISAC systems from an energy efficiency perspective.

2.1.1. Interference Management

ISAC systems utilize the same spectral resources for the dual-functions, which complicates interference management beyond traditional wireless systems [38,39,40]. ISAC systems can experience the following types of interference:

• Inter-function interference: This occurs when transmitted sensing signals interfere with communication signals and vice versa. This causes performance degradation for both functions.
• Multi-user interference: In multi-user systems, signals intended for one user may interfere with signals intended for another user. This is particularly pronounced in FF communications, where beamforming limitations and user proximity can exacerbate interference.
• Multi-target interference: Multiple reflecting objects or targets can cause undesired signal superposition, which makes it difficult to distinguish the target-of-interest. This is pronounced in urban environments where buildings, vehicles, and other obstacles introduce additional signal reflections.
• Clutter interference: Unwanted reflections from environmental objects, such as walls, buildings, trees, and foliage, can cause significant interference in both sensing and communication [6,39,41]. This is particularly challenging in FF ISAC scenarios, where limited resolution of beamforming action can degrade detection accuracy.

Conventional interference management is insufficient for ISAC systems owing to the extreme levels of undesired signals. Therefore, novel methods are imperative to the feasible functioning of ISAC systems. Effective interference mitigation requires advanced optimization methods that can address the unique characteristics of each type of interference while maintaining acceptable performance for both sensing and communication functions. However, these techniques can increase the computational workload on the hardware, which can consequently increase the energy consumption of ISAC systems. Hence, sustainable designs of interference management frameworks are of vital importance to the scalability of ISAC systems.

2.1.2. Hardware Implementation

Conventional communication and sensing systems have specialized hardware optimized for data transmission and sensing, separately. Merging these functionalities into a single hardware architecture introduces integration challenges, as advanced antennas must be designed to support both sensing and communication with specialized radiation patterns for each function. Reconfigurability is also required to switch between communication and sensing modes for simultaneous operation. Furthermore, these architectures must be able to manage different power levels, bandwidths, and signal processing requirements as communication and sensing functions different in each of these aspects. Furthermore, adaptive signal processors must be integrated to handle the computational demands of dual-purpose operation without introducing excessive delays or power consumption [42]. Synchronization is another challenge for ISAC systems and requires specialized hardware to accommodate wider bandwidths and larger dynamic ranges for dual-function systems [27]. These stringent requirements exponentially increase the requirements and design considerations of hardware, which further hinders the energy efficiency of ISAC systems. Achieving this integration without compromising efficiency, cost, or size requires sustainable innovations in circuit design, and system architecture.

2.1.3. Resource Constraints

Power consumption represents a critical concern for ISAC systems, especially for battery-operated devices which are deployed in remote or resource-constrained environments. While the integration of sensing and communication can reduce overall energy consumption by eliminating redundancies, the simultaneous operation of sensing and communication functionalities has increased power requirements compared to single-function systems. The energy efficiency challenges demand a dynamic balance between sensing and communication functions. For example, in vehicle-2-vehicle (V2V) networks, vehicles prioritize sensing in dense traffic scenarios whereas they prioritize communication in open terrains. Mitigating these challenges requires innovations in low-power circuit design, efficient signal processing algorithms, and intelligent resource management frameworks. These advancements must consider the end-to-end energy consumption of the system while meeting the performance constraints for both sensing and communication functions. Achieving optimal energy efficiency while preserving high performance is a fundamental challenge that impacts the practical viability of ISAC systems.

2.1.4. Channel Estimation and Modeling

ISAC require accurate channel models which account for both communication and sensing requirements. Models optimized for communication systems are unsuited for capturing the interactions between sensing service stations and objects-of-interest, especially in non-LoS scenarios in environments with significant scattering and diffraction. Communication channels are concerned with the input-output relationships between the transmitters and the receivers. In contrast, sensing systems typically requires detailed modeling of signal propagation paths, and reflections from targets or objects of interest. Developing unified channel models which can accurately represent both aspects is challenging [43,44]. Furthermore, in next-generation systems, mmWave and THz bands are expected to be utilized, which further complicate real-time channel estimation and modeling. Channel estimation for communication systems assists in reliable functioning of the system. However, channel estimation is a key performance function in sensing systems as target parameters can be estimated from the channel. These functions create high processing overheads, which increases energy consumption. Accurate and efficient channel estimation techniques are required to mitigate these issues. In the presence of imperfect CSI, channel estimation methods have to be considered that have mechanisms to suppress the imperfect nature of the channel estimation techniques. It may be noted that the source of the imperfect nature of the channel estimation maybe be due to the low-complexity nature of the algorithms involved even in LoS scenarios. These algorithms are necessary for fast-converging solutions for channel estimation in situations where the channel coherence time is extremely low. The large number of antennas in the antenna arrays allowing NF communications need high speed algorithms that may provide reasonable estimate of the channels between all of the antennas and the intended receiver.

2.1.5. Performance Trade-Off

ISAC systems realize complex performance trade-offs between sensing and communication functions. Communication-centric waveforms are optimized for spectral efficiency and throughputs, whereas sensing-centric waveforms prioritize range and doppler resolutions. Therefore, joint waveforms design for these competing objectives are a challenging design task. ISAC systems also experience the deterministic-random trade-off (DRT) [45,46,47,48,49,50]. Sensing tasks demand deterministic signals with repetitive, predictable properties. Deterministic signals create clear patterns which enable the measurement of target parameters. They are also more robust to interference and noise. In contrast, communication tasks demand randomness to maximize signal entropy which ensures secure and efficient data transmission. Hence, ISAC signals must be designed to optimize this trade-off. This also complicates energy efficiency in ISAC systems as sensing functions require strong deterministic signals, but these signals become noise and interference for communication functions. The communication functions consequently require more processing power to mitigate the increased interference from sensing functions.

2.1.6. Latency

The majority of sensing applications require real-time tracking and positioning, which necessitates ultra-low latency architectures. In ISAC systems, the joint processing of sensing and communication signals introduces additional computational complexity which can increase end-to-end latency beyond acceptable thresholds for time-critical applications. Furthermore, sensing applications usually require precise synchronization between transmitter and receivers. Even small timing offsets can severely degrade sensing performance for applications requiring high-resolution or precise sensing. Additionally, sensing systems are heavily dependent on LoS links for feasible functioning, which creates dense and distribute. Addressing these challenges requires innovations in distributed clock synchronization, and offset correction algorithms. However, these solutions will increase energy consumption, and hinder system sustainability. Hence, energy efficient designs must be developed to provide scalable and functional ISAC deployments.

2.1.7. Dynamic Environments

ISAC systems must operate reliably in dynamic environments characterized by user mobility, target movements, multipath propagation, and environmental clutter. ISAC systems must be able to adapt to these changes in real-time to sustain feasible sensing and communication performance. These challenges intensify in high-mobility scenarios, e.g., vehicular networks, drone applications and train systems. Reliable communication and precise sensing with rapidly varying channel conditions demands advanced mobility management protocols which must consider the Doppler effects and signal handovers. These protocols require advanced hardware and processing capabilities, which further increase the energy consumption.

2.1.8. Security and Privacy

Integrating sensing and communication raises new security and privacy concerns beyond those of conventional single-function systems [8,51,52,53]. The sensing functionality of ISAC systems can collect sensitive data about individuals and environments, which raises privacy issues. These are particularly alarming for high-resolution sensing systems deployed in public and residential areas. Furthermore, the dual-function nature of ISAC systems makes them vulnerable to novel cyberattacks which can disrupt both sensing and communication operations. This can give rise to jamming attacks, which can create critical safety risks for commercial and mission critical applications such as autonomous vehicles. Developing robust security frameworks is challenging and requires specialized hardware, which increases energy consumption and complicates ISAC system sustainability. In NF-ISAC systems, the high directionality and spatial resolution achieved through NF beamfocusing can inadvertently result in beam leakage, however, due to the beam focusing ability of NF this might be present only in the direct vicinity of the user as opposed to the signal leakage in the FF. Moreover, the integration of sensing opens the possibility for covert surveillance, where adversaries exploit the sensing function to infer spatial or behavioral information without explicit authorization. This becomes a problem in commercially available signal transmitting devices where a malicious actor may utilize specialized signals for covert sensing while appearing as a legitimate user to the other communication users.

2.1.9. Network Architecture

Fundamental changes in the network architectures must be realized to sustain feasible functioning of ISAC systems. Communication centric designs treat inter-cell signals as interference, which degrades system performance severely. However, sensing functions require different perspectives for environment reconstruction and precise target parameter estimation. Therefore, the network designs must adapt for sensing functions, which will further complicate interference management, and require advanced hardware, higher processing power and more energy consumption. Furthermore, sensing functions are heavily dependent on LoS links, therefore, wireless system architectures must realize novel deployments for a uniform coverage of ISAC systems. Coordinated cellular network and distributed antennas aided ISAC systems are promising approaches for network-level implementations. These architectures can leverage multiple perspectives for improved sensing while maintaining communication coverage. However, they excessively increase the energy usage and hinder network sustainability. Therefore, developing effective and optimized network architectures for ISAC systems remains an open challenge.

2.2. Energy-Efficient Designs

Energy-efficient designs for ISAC systems are shown in Figure 3. Power-focused communication-centric designs seek to maximize data throughput per unit of energy by designing transmit waveforms and beamforming weights to the channel conditions. Power-focused sensing-centric designs adjust waveform parameters and receiver processing to maximize sensing information per joule. Power-focused joint designs identify Pareto optimal operating points which balance communication throughput and sensing accuracy under minimal power consumption. Power-centric designs concentrate on minimizing total transmit power while satisfying basic quality-of-service requirements for both sensing and communication functions. Performance-focused communication-centric designs maximize metrics such as sum-rate, SINR or spectral efficiency under an overall energy budget. Performance-focused sensing-centric designs pursue peak beam-pattern gain, sensing SINR or CRB performance while respecting power and communication constraints. Performance-focused joint designs coordinate sensing and communication objectives through weighted objective functions or direct Pareto-boundary maximization, treating energy efficiency as a secondary evaluation criterion. The classifications of energy-efficient designs provide insights to the performance trade-offs experienced by ISAC systems. They are summarized in

Table 2. Overview of design objectives and their corresponding performance constraints.

Type of Design	Objective	Optimized	Constrained
Power focused designs	Maximize communication energy efficiency [54,55,56,57,58,59,60,61]	Communication performance, power	Sensing performance
	Maximize sensing energy efficiency [62,63,64]	Sensing performance, power	Communication performance
	Maximize joint sensing and communication energy efficiency [65,66,67,68,69,70,71,72,73,74]	Communication & sensing performance, power	Power
	Minimize power consumption [6,75]	Power	Communication & sensing performance
Performance focused designs	Maximize communication performance [76,77,78]	Communication performance, power	Sensing performance
	Maximize sensing performance [52,79,80,81,82]	Sensing performance, power	Communication performance
	Maximize joint sensing and communication performance [83,84]	Communication & sensing performance, power	Power

, and further detailed in the following:

2.2.1. Power-Focused Designs

Power-focused systems actively target efficient power allocation in ISAC systems. They can be broadly classified into the categories shown in

Table 3. Overview of power-focused energy-efficient designs of ISAC systems.

Type	Work	Motivations
Energy efficient beam management	[6,55,56,57,59,62,67,68,69,71,72,75]	The simultaneous operation of sensing and communication while meeting the QoS requirements of modern communication systems is only possible owing to optimized beamforming design.
Energy efficient waveform design	[54,65]	Waveform design is necessary for the joint operation of integrated sensing and communication. An optimized waveform facilitates efficient spectrum utilization and enhances the accuracy of target detection while maintaining robust communication performance.
Secure communications	[58,61]	Physical layer security is highly dependent on sensing the eavesdroppers so that their respective signal-plus-interference-to-noise ratio (SINR) can be minimized. The sensing functionality of ISAC provides an efficient procedure to sense eavesdroppers and therefore naturally enables secure communications.
mmWave & THz communications	[60,64]	Communication systems operating at a high frequency produce narrow beams and can therefore frequently face the problem of beam alignment. ISAC provides an efficient solution to the problem by allowing both high speed communication and accurate target sensing and tracking for enhanced beam alignment.
Cell-free communications	[63,74]	Line of sight operation is necessary for accurate sensing, this requirement is adequately fulfilled in the case of cell-free communications.
NOMA enabled communications	[66,70]	As in the case of ISAC, NOMA also focuses on efficient spectrum utilization at the cost of increased processing. It can be used to further enhance the spectrum utilization of ISAC.
UAV enabled communication	[73,85]	UAVs have extremely high mobility making them prone to beam misalignment. ISAC is enabled by the line of sight operation provided by UAVs, and can offer a viable solution to the problem of beam misalignment in UAV enabled systems.

. The details of each of these categories are elaborated in the following text.

Communication-centric Designs: Communication-centric designs aim to optimize the energy efficiency with respect to communication performance metrics while meeting the sensing QoS requirements. These designs prioritize the minimization of energy usage per transmitted bit without degrading the sensing precision and accuracy.

Detection and tracking of undesired targets in situations involving requirements on secure communications can benefit from the joint communication and sensing design of ISAC systems. In [61], the authors utilize the sensing signal for the tracking of undesired eavesdroppers while also using the same signal as artificial noise to further enhance secure communication between the base station and receiver. They utilize semi-definite relaxation (SDR) and successive convex approximation to solve the problem while proving that the relaxation of SDR remains tight. They also provide simulation results to confirm the effectiveness of their schemes. In light of the increasing complexity of applications and their supporting systems, there is consensus on the necessity of enhancing energy efficiency of wireless communication systems in addition to optimizing communication performance [86]. In the context of ISAC, although numerous studies have addressed the detection of static targets, there remains a critical need to focus on the detection of moving targets within communication systems. In [54], the authors address a communication-centric, energy-efficient waveform design problem for ISAC, aiming to maximize communication performance across multiple users while satisfying constraints on transmit power and the Cramer-Rao bound (CRB). They employ Dinkelbach’s algorithm in conjunction with a semidefinite relaxation-based solution, thereby demonstrating the effectiveness of the proposed approach in minimizing sum-power consumption.

The energy-efficient operation of integrated sensing and communication is of paramount importance for its widespread adoption. In [55], the authors consider the problem of energy efficient beamforming in an ISAC system with multiple communication users and a radar target. The energy efficient solution involves fulfilling communication SINR requirements of all users while also providing the minimum beampattern gain. The authors use the power normalized system throughput as the objective function and derive an equivalent formulation of the original problem. They use techniques such as successive convex approximation and SDR to obtain the solution. They also show that the performed relaxation in the SDR is tight.

Owing to their movement versatility and flexibility in use-cases, unmanned aerial vehicles (UAV) can act as mobile access points in many emergency scenarios. The movement of the UAVs, however, makes them prone to beam misalignment and jitter. In [56], the authors propose an energy-efficient ISAC-based beam alignment and beam tracking scheme. The beam alignment is performed based on the extended Kalman filtering. Chaning the number of active antennas constrols the beamwidth, which in turn is decided based on the prediction error derived from the extended Kalman filtering. For energy-efficient operation, the optimal number of activated antennas is selected using block coordinate descent algorithm. The proposed scheme is compared with suitable benchmark schemes to verify its effectiveness.

Vehicular networks are an active area of study aiming for the development of effective algorithms and protocols for communication between vehicles and infrastructure. Owing to the nature of the environment, sensing is of paramount interest in vehicular communication alongside reliable communication under rapidly changing channel conditions. In [57], the authors aim to realize energy efficient optimal beamforming under bounded channel estimation errors. The authors relax the rank-1 constraints and utilize Shur complement and S-Procedure to convexify the CRB and channel estimation error constraints. Later the Lagrangian dual function and Karush-Kuhn-Tucker conditions are used to prove that the optimal beamforming solution is rank-1. The authors show that the proposed algorithm converges quickly and can combat the presence of channel estimation errors.

Sensing is an integral part of secure communication as sensing and tracking legitimate users can help in making legitimate communication more secure. In [58], the authors tackle the problem of energy efficient secure integrated sensing and communication in the presence of multiple sensing targets where one of the targets is the eavesdropper. They aim to optimize the transmit and receive beamforming vectors and also design the artificial noise, in the presence of sensing and secure rate constraints. Successive convex approximation, SDR and fractional programming are used for the convexification of the non-convex problem. They show through numerical results that energy efficient operation is achieved while satisfying all constraints.

As the requirements of the next generation of wireless communication systems are expected to increase, the efficient utilization of the spectrum has attracted considerable attention. Integrated sensing and communication has emerged as one of the key enabling techniques addressing effective spectrum utilization. However, the coexistence of communication and sensing while sharing resources introduces interference into the system and must be managed effectively. In [59], the authors consider the problem of interference cancellation while considering an energy efficient beamforming design for a single target multi-user communication system. They aim to efficiently reduce the communication interference and the sensing to communication interference. The energy efficient communication-centric interference cancellation problem is made convex using the Dinkelbach-based scheme and SDR. The numerical analysis shows that the energy efficient proposed scheme outperforms classical schemes and have higher energy efficiency.

With the increase in the frequency of operation in the mmWave domain, accurate and energy efficient beamforming is necessary for the effective and energy efficient operation of wireless communication systems. In [60] the authors consider a hybrid beamforming supported integrated sensing and communication mmWave multiple-input multiple-output system with multiple communication users and multiple targets. The authors aim to maximize the energy efficiency of the system while considering the communication SINR and the sensing beamforming gain of the targets. Dinkelbach’s method is used for the conversion of the fractional problem to a subtracted form, then the problem is relaxed into a digital beamforming problem using semi-definite relaxation. These digital beamformers are then used in a penalty based manifold optimization method along with alternating optimization to determine the baseband and analog beamformers. The proposed scheme is then compared with benchmark methods to demonstrate its performance.

Sensing-centric Designs: Sensing-centric designs aim to maximize the energy efficiency with respect to sensing performance metrics while meeting the communication QoS requirements. These designs focus on minimizing the energy consumed per unit of sensing information, without degrading the reliability and throughput of communication.

The sensing aspect in integrated sensing and communication can require significant computational resources. These resources might not be present at the sensing device due to which advances inferences from the sensing data might be difficult to make. Considering this problem, the authors in [62] present an integrated sensing and communication system where the sensing data from the base station is communicated to multiple edge servers for remote processing. The problem is formulated as an energy efficiency maximization problem where the beamforming vectors for sensing and communication are optimized. To solve the non-convex problem, the authors convert it into a feasibility checking problem and then to a difference of convex problem (DC). The proposed algorithm is shown to have superior performance compared to the benchmark scheme.

The communication systems envisioned to support the operation of next generation technologies not only have bandwidth and latency requirements but are expected to support a massive scale of deployment. As networks continue to grow, availability and energy requirements scale accordingly. In [63], the authors consider a cell-free integrated sensing and communication system and highlight the need for energy efficient sensing especially in the case of cell-free architecture. They consider a sensing-centric energy efficiency maximization problem while also meeting power budget and communication constraints. The authors employ the quadratic transformation and semidefinite programming methods to transform and solve the non-convex problem. Numerical results show that the proposed algorithm provides an energy-efficient solution in line with the theoretical analyses.

The coexistence of communication and sensing operations within ISAC systems necessitates the study and optimization of related systems for their energy efficient operation. In the current research landscape sensing oriented energy efficiency has yet to be thoroughly explored. The authors in [64] aim to mitigate the spatial wideband effect in THz wideband enabled ISAC systems while enhancing the sensing energy efficiency. They redefine a generalized metric, referred to as sensing-centric energy efficiency (SEE), and maximize its value by optimizing the hybrid beamforming while meeting the sensing mutual information, rate constraints and power budget. They use penalty dual decomposition by combining the Dinkelbach, minorization-maximization, successive convex approximation and element-wise optimization techniques. The proposed scheme is shown to perform 40% better in terms of SEE compared to benchmark schemes.

Joint Designs: Joint designs maximize the energy efficiency of both sensing and communication functions simultaneously by optimizing the waveforms, power allocation, and spectral distribution, They achieve a dual-function energy-efficient system design and minimize the combined energy usage per transmitted bit and per sensed information unit. These designs also meet minimum sensing and communication QoS thresholds.

In ISAC systems, sensing and communication signals are designed to share frequency, time, and hardware resources. In addition to satisfying the individual requirements of sensing and communication, these signals must also manage their mutual interference while sharing hardware resources. To ensure the feasibility of the ISAC-enabled paradigm, energy-efficient solutions for both communication-centric and sensing-centric designs are essential. In this context, the authors in [65] address the problem of energy efficiency in ISAC-enabled systems. They optimize communication-centric energy efficiency for two scenarios—point-like target estimation and extended target estimation—by using the quadratic-transform-Dinkelbach method. Concurrently, sensing-centric energy efficiency is optimized for the same scenarios following the development of a novel metric that integrates the CRB with transmit energy. Finally, a joint optimization problem is formulated using a Pareto optimization technique to maximize communication-centric energy efficiency under a sensing-centric energy constraint, which is solved using an SCA-based iterative algorithm. Numerical results demonstrate that the proposed communication-centric and sensing-centric schemes outperform their benchmark counterparts.

The intricate coupling of different resources in integrated sensing, communication and computing (ISCC) is an open challenge that is necessary to be overcome for the widespread adoption of ISCC. In particular, Power IoT is anticipated to facilitate sustainable development in power systems. The authors in [66] propose a full-duplex (FD) non-orthogonal multiple access (NOMA) enabled ISCC framework, aiming to optimize the transmit beamformer, receive beamformer, uplink power control, task offloading decisions, and computing resource allocation. Given the strong correlation between uplink and downlink channels, an alternating optimization approach is employed. The problem is decomposed into two subproblems via a linearly constrained minimum variance algorithm, which are then solved iteratively. Numerical results demonstrate that the proposed algorithm significantly outperforms the corresponding benchmark schemes.

LoS operation in ISAC systems is necessary for their effective operation. Cell-free communication systems provide a viable architecture for the support of sensing operations within ISAC. In [74] the authors consider a cell-free ISAC system where the transmitters are sending information to their respective users while also estimating the location of a single target. They aim to minimize the total power consumption while also fulfilling their communication and sensing requirements. The authors propose semi definite relaxation and Cramer-Rao lower bound approximation for the solution of the non-convex problem. Numerical results show a reduction in power consumption as compared to the benchmark solutions.

The energy-efficient operation of nodes in an IoT ecosystem requires the optimal utilization of transmission resources. The integration of communication and sensing functions into a single system necessitates their joint design and optimization. In [67], the authors focus on minimizing the total energy consumption at the transceiver while achieving the required data rate and satisfying the CRB constraint, all while maintaining the “on” transmission state duration within specified limits. This framework is evaluated in the context of a special case involving MIMO sensing only, where the communication constraint is deemed negligible. The findings indicate that the optimal solution under these conditions is to employ isotropic transmission with the shortest feasible “on” duration during the on-off operational cycle, thereby minimizing non-transmission energy consumption. Under typical operational constraints, the authors employ the Lagrange duality method to derive a semi-closed form solution. The resulting optimal transmission policy is characterized by a full-rank transmit covariance matrix, appropriate power allocations across different eigenmodes, and an optimal “on” duration for communication. Numerical results demonstrate that the proposed scheme significantly outperforms benchmark approaches, particularly under stringent communication and sensing constraints.

ISAC has been explored within the domain of green communications due to its capability to optimize resource allocation based on real-time environmental data. In [68], the authors examine a multi-user full-duplex communication system underpinned by a green ISAC architecture that concurrently facilitates target detection. They formulate a minimum energy efficiency maximization problem that jointly optimizes uplink, downlink, and radar beamforming designs, as well as uplink transmit power, while accounting for norm-bounded channel state information (CSI) errors and QoS constraints. The problem is addressed using successive convex approximation and second-order cone programming, yielding near-optimal resource allocation. Comparative analyses reveal that the proposed scheme outperforms benchmark spectral efficiency maximization approaches.

ISAC enabled by coordinated multipoint (CoMP) is a challenging paradigm owing to the interference within and among base stations. The optimal management of resources for proper interference management can result in better sensing and communication performance as compared to non-CoMP systems. In [69], the authors aim to develop an energy efficient CoMP-ISAC system through the collaborative optimization of the of the base station mode selection, receiver base station selection, and the covariance matrix of communication and radar signals, while satisfying communication constraints, sensing constraints and total transmit power budget. The authors utilize a branch-and-bound optimization algorithm to solve the mixed Boolean problem of base-station and user association. The focus is directed towards decreasing the multi-user interference at each branching step. Zero forcing along with alternating optimization is used for interference suppression. The dual quadratic transform is used to ensure the convexity of the objective function, and the simulation results are used to show the effectiveness of the proposed algorithm.

The 6G wireless communication systems are the key enablers for the formation and sustenance of the future era of ubiquitous intelligence. In this evolution, the sensing functionality of wireless communication systems will prove to be indispensable. As the number of wirelessly connected devices continue to increase, innovative solutions are required for efficient utilization of the limited available spectrum. Considering this motivation, both NOMA and integrated sensing and communication provide efficient spectrum utilization while extending legacy functionality. In [70], the authors propose a hybrid NOMA enabled ISAC system that adaptively manages the additional sensing and communication interference. The authors investigate the effect of the number of users on the system with a fixed number of transmit antennas. They demonstrate that when the number of communication users (CU) is less than or equal to the number of antennas then the proposed framework mostly experiences the interference due to ISAC. When the number of CUs becomes greater than the number of antennas then the interference due to NOMA is added to the ISAC induced interference. In the underloaded scenario, the hybrid NOMA-ISAC scheme form a transmission power minimization problem to optimize the SIC and BF design. To solve this problem, an ideal case is assumed and the insights from the ideal case are used to determine the binary SIC option at the CU while the remaining problem is optimized using SDR. For the overloaded scenario, the authors formulate a joint BF design, SIC option and power allocation problem for the minimization of the transmit power. This problem is then solved using alternating optimization. The numerical results show that the underloaded scenario provides the same performance as the ISAC scheme and the overloaded scenario consumes the least power than the other benchmarks.

The increase in the number of users and the projected growth of wireless enabled devices has motivated the study of spectrally efficient wireless communication technologies. Integrated sensing and communication also aims to take advantage of shared resources in order to provide sensing capabilities. In [71], the authors investigate an energy-efficient channel sharing aided ISAC system where the base-station (BS) can sense multiple targets. The authors construct a problem for multi-target scheduling, transmit beamforming and receive beamforming for each sensing target. The objective is to maximize the energy efficiency of the radar sensing while also guaranteeing the minimum required performance at the communication users. The authors utilize Dinkelbach’s method to address the fractional nature of the problem. SDR and Lagrange’s duality are then used for determining the beamforming vectors. Swap matching is used for the determination of the sensing scheduling after reformulating the problem as a matching game. The numerical results clearly indicate the superiority of the channel sharing based ISAC scheme over other schemes.

With the increase in the demand for high data rate and reliable communication, mmWave and ISAC have emerged as key enabling technologies owing to their capability of supporting high bandwidth and reliable communication respectively. It is, therefore, beneficial for both of these technologies to take advantage of the capabilities enabled by the other. The power and locality limitations of a single BS restrict their ability for effective sensing and communication. Hence, the authors in [72] consider the problem of energy-efficient collaborative multi-base station beamforming for sensing and communication using mmWave. As the unrestricted collaboration of multiple BS for sensing and dual-function radar and communication (DFRC) would increase the amount of power being used for these functionalities, an energy efficient collaborative process has to be realized. The authors formulate a non-linear mixed integer optimization problem for task allocation, beam scheduling and transmit power control. Numerical results show that the energy usage of the network is significantly reduced as compared to the scheme without multi-BS cooperation or with the utilization of DFRC waveforms.

The versatility of unmanned aerial vehicles is complemented by the sensing ability of integrated sensing and communication systems. Hence, the authors in [73] consider a UAV enabled integrated sensing and communication system where a UAV senses users on the ground and relays that information back to the BS. The radar mutual information is used as the sensing constraint. On the basis of the sensing fairness, the authors consider an energy efficiency and average mutual information maximization problem by jointly optimizing the user scheduling, transmit power and UAV trajectory. The problem is decomposed into 3 sub-problems involving user scheduling optimization, transmit power minimization and UAV trajectory optimization. Relaxation techniques along with successive convex approximation and fractional programming are used to solve the sub-problems. Alternating optimization is used to iterate over the 3 sub-problems to get the solution of the original problem. Simulation results show that the energy-efficiency is being maximized while ensuring sensing fairness.

Power-centric Designs: Power-centric designs aim to minimize the power consumption of the ISAC systems while meeting the sensing and communication constraints. The energy utilization is minimized but the performance of sensing and communication functions is not maximized. Sensing enabled through integrated sensing and communication systems can allow for the detection of targets in systems employing dynamic service providing entities such as UAVs. To tackle the issue of resource allocation and UAV trajectory design, the authors in [85], propose an alternating optimization algorithm utilizing semi-definite relaxation, the big-M method and successive convex approximation. They aim to minimize the average power consumption through the optimization of the transmit beamforming, the UAV hovering time slots, the UAV trajectory and velocity. Simulation results show that significant power savings are achieved by using the proposed schemes as compared to the benchmark schemes.

The integrated sensing and communication systems typically studied in literature consider a FD sensing scenario and a half-duplex communication scenario. In [75], the authors consider a FD communication and sensing system with the aim of power minimization and sum-rate maximization independently, by optimizing the downlink transmit signal, uplink receive beamformers and the transmit power of the uplink users. In both of these cases, the optimal receive beamformer is derived with respect to the transmit beamforming at the base-station. These results are used to develop iterative algorithms for the solution of the optimization problems. Numerical results indicate the superiority of the optimized FD-ISAC scheme compared to typical ISAC scenarios.

The sensing capabilities of an internet-of-things (IoT) network are of vital importance in order to realize a truly intelligent and sustainable ecosystem of IoT devices. In [6] the authors consider a multi-user multi-target full duplex integrated sensing and communication system with the aim of minimizing the total power consumption at the base-station while fulfilling the communication and sensing requirements. Semi-definite relaxation and generalized Rayleigh quotient-based optimization is used for the joint design of the optimal radar signal, transmit beamforming vectors and receive combiners. Simulation results show the effectiveness of the algorithm compared to the existing benchmarks in different simulation scenarios.

2.2.2. Performance-Focused Designs

Performance-focused designs focus on the optimized utilization of power to maximize the performance of ISAC systems. Within a limited power budget, the increase in the performance of the system realizes a more efficient system operation. They can be broadly classified into the following as shown in

Table 4. Overview of performance-focused energy-efficient designs of ISAC systems.

Type	Work	Motivations
Energy efficient beam management	[79,83,84]	Waveform and beamforming designs are essential for the proper functioning of ISAC systems owing to their joint operation. Without waveform design and beamforming neither of functionalities promised by ISAC would fulfill the requirements of modern communication systems.
Secure communication	[52,76,77]	Secure communication systems are highly reliant on the detection and tracking of malicious entities trying to intercept sensitive communications. The sensing functionality of ISAC provides a viable opportunity to cater for this requirement.
UAV communcations	[78,82]	Simultaneous sensing and communication allows for the deployment and efficient operation of versatile service providing entities like UAVs. Beamforming and beam alignment are important requirements within systems formed by such entities and can greatly benefit from the sensing capabilities of ISAC.
NOMA communications	[81,82]	Simultaneous sensing and communication are required for the efficient utilization of the available spectrum without incurring more hardware costs. NOMA is also a transformative technology that allows for the efficient spectrum utilization and can complement the operation of ISAC.

:

Communication-centric Designs: Communication-centric designs aim to maximize throughput, reliability, and spectral efficiency while meeting the sensing constraints and total power budget. The broadcast nature of the wireless mediums makes them inherently vulnerable to potential security breaches. This vulnerability is further exacerbated by the integration of sensing and communication as the radar systems must illuminate the targets with high power to have stronger echo signals which provide desired sensing information, e.g., angle and velocity. Furthermore, sidelobe suppression should be realized to avoid unfavorable clutter. To enable secure high-precision sensing and high-reliability communication, authors in [76] propose a robust and secure resource allocation design for a multiuser ISAC system which maximizes the sum secrecy rate of the system subject to the communication QoS threshold of legitimate users, the transmit power budget constraint, and the sensing performance requirement defined by desired beampattern matching. The authors design the duration of signal snapshots, the beamforming vectors, and the covariance matrix of the artificial noise to provide a robust design. They utilize block coordinate descent (BCD)-based iterative algorithm to obtain a suboptimal solution. Their results outperform conventional multi-stage ISAC system designs.

Target sensing functionality is necessary for effective secure communications. Integrated sensing and communication has emerged as a transformative technology that allows the simultaneous operation of sensing and communication. The authors in [77], consider the problem of secure communication in an ISAC aided UAV enabled communication system. The system consists of multiple eavesdroppers and therefore also contains a jamming UAV that directs the jamming signal towards the eavesdroppers. During the ISAC process, the source UAV relays the information of the eavesdroppers to the jamming UAV. The jamming UAV uses this information during the communication process, in which the eavesdroppers try to intercept the information. The problem of secure communication rate maximization is considered by optimizing the user scheduling, transmit power and secure UAV trajectory. The original optimization problem is broken down into 3 optimization problems which are iteratively optimized. The numerical results indicate that a significant secure transmit rate is achieved.

The practical realization and sensing requirements of real-world applications may require infrequent sensing as opposed to frequent communication. In article [78], the authors introduce integrated periodic sensing and communication where the system achievable rate is maximized by jointly optimizing the UAV trajectory, user association, target sensing selection and transmit beamforming while fulfilling the sensing requirements. The authors derive the optimal beamforming design and the tight lower bound on the achievable rate. Based on the above results the authors propose a two-layer optimization design for the considered problem and study the edge case of the number of antennas approaching infinity. Numerical results showcase the effectiveness of the proposed algorithms.

Sensing-centric Designs: Sensing-centric designs aim to maximize sensing accuracy, detection probability and beampattern gain while meeting the communication constraints and total power budget. Integrated sensing and communication, owing to its sensing capabilities, is envisioned to be one of the key enablers of secure communication systems. Typically in a system with eavesdroppers, the channel from the transmitter to the eavesdroppers is not known due to their passive operation. The sensing capabilities of ISAC can be utilized to alleviate this limitation. In [52], the authors propose a sensing aided secure communication system, where the eavesdroppers are located using the sensing beam and secure communication is then carried out using the information collected from sensing. During the sensing stage, the number of sensing beams are decided with a greater number of beams returning a tighter CRB. The authors convert the two-stage joint optimization problem into two decoupled sub-problems and solve them by using backward induction. Numerical results are also shown to verify the robustness of the proposed scheme.

The operational advantages of UAV rely on their spatial versatility combined with their robust and energy efficient operation. In [80], the authors tackle the problem of joint path and beamforming optimization of an energy constrained UAV. Sensing information is assumed to be collected by the UAV using the optimal beamformers, following the optimal path while also guaranteeing the QoS. The authors solve this non-convex problem using approximate dynamic programming methods and semidefinite relaxation. Simulation results are also presented to show the effectiveness of the proposed scheme.

The move towards higher frequencies in the next generation of wireless communication systems also motivates the adoption of massive MIMO arrays. The authors in [79] also consider a massive MIMO integrated sensing and communication system with the aim of maximizing the sensing energy. They employ a structured beamforming consisting of communication precoding and sensing beamforming. Each user is guaranteed to have a minimum signal-to-interference-plus-noise ratio and the problem is solved with instantaneous and statistical CSI to develop two optimization algorithms, both with different but affordable computational complexities. Numerical examples are provided to illustrate the effectiveness of the proposed schemes.

Owing to the versatility of ISAC, it can be used in conjunction with a number of other technologies to take full advantage of the available spectrum. NOMA also aims to maximize the communication spectrum efficiency to effectively utilize the available spectrum. In [81], the authors consider a NOMA enabled ISAC system where the communication users are served using NOMA and sensing is also performed on a group of sensing targets using ISAC. The authors aim to maximize the sensing efficiency of ISAC systems while satisfying the communication, power and sensing quality requirements of each user and target. To achieve this objective the authors perform the joint optimization of the beamforming vectors, the NOMA transmission duration and the sensing scheduling of the sensing targets. The authors take advantage of successive convex approximation (SCA) and the penalty function method to convert the non-convex problem into a convex optimization problem. The NOMA transmission duration is determined using the bi-section search method and finally, the sensing scheduling is done using a cross-entropy learning based algorithm. Compared to the benchmark schemes, the proposed method showcases better communication and sensing performance.

ISAC aims to take full advantage of the available spectrum through the simultaneous operation of communication and sensing using he same spectrum, time and hardware resources. Inefficient communication methods can degrade the advantage provided by ISAC, therefore, the authors in [82] consider a FD NOMA enabled ISAC system. The base-station performs simultaneous sensing and communication while also supporting uplink/downlink communication. The performance of single-target detection with perfect CSI is optimized using transmit and receive beamforming designs. And the same optimization variables are used for the case of multiple targets with statistical CSI. The objective of the optimization problem is to maximize the sensing signal-to-interference-plus-noise ratio while fulfilling the SINR requirements of the uplink and downlink communication users. The authors formulate an alternating optimization algorithm for the optimization of the non-convex problem. Numerical results show the superiority of the proposed scheme over the orthogonal multiple access scheme enabled systems in terms of communication and sensing performance.

Joint Designs: Joint designs maximize the sensing and communication performance and provide high performance gains while meeting the power budget. While power utilization is constrained, these systems enhance energy-efficiency by optimizing the utilization of resources. The energy efficient operation of ISAC is necessary for its adoption in future wireless communication systems owing to its spectral efficiency advantages. In [83], the authors consider the problem of the optimization of weighted communication and sensing metrics in an integrated sensing and communication systems. The sum-rate and CRB are used as the communication and sensing metrics respectively. They consider 3 scenarios where the solutions to each of the scenarios is approached in a different manner depending on the complexity. The first two scenarios consist of a single-user and a multi-user sub-problem where the channels of the users are orthogonal to each other and the optimal closed form expression of the solutions are formulated. In the general multi-user scenario, the authors propose a novel branch and bound algorithm based on the McCormick relaxation. Then a graph neural network is designed for the pruning of the unnecessary computations in the branch and bound algorithm. Simulation results show that the proposed algorithm and its GNN-based variant display efficient performance.

Integrated sensing and communication has emerged as a key enabler of next generation wireless communication systems and has attracted considerable research attention. Different aspects of ISAC enabled ecosystems have been considered and studied. In [84], the authors consider a multi-user multi-target ISAC system with the aim of jointly designing the transmit beamforming vectors for radar-centric optimization and then for joint optimization. The radar centric optimization utilizes beam pattern approximation to achieve the required sensing efficiency while the communication users are guaranteed to have a signal-to-interference-plus-noise ratio above a certain threshold. A joint radar plus communication optimization algorithm using a regularization term is also optimized. A rate-splitting based beamforming design for the maximization of the sensing effectiveness while having a minimum downlink communication sum-rate is also proposed and numerical results are used to show the effectiveness of the proposed schemes.

The above overview of the works optimizing different types of energy efficient design objectives provide a comprehensive idea of the challenges prevalent in energy efficient ISAC systems. In the following text we detail the integration of NF in ISAC systems, their differences with FF systems and finally some examples of energy efficient system designs in NF-ISAC systems.

3. Energy Efficient Near-Field Integrated Sensing and Communication

NF-ISAC systems can offer unprecedented capabilities in 6G wireless networks. This is attributed to their ability to exploit distance-aware beamfocusing and spatially selective signal processing enabled by spherical wavefronts. While they have advanced spatial diversity gains, they can face critical challenges in the feasible implementations. This section presents the fundamentals of NF propagation, its challenges in ISAC systems, and the state-of-the-art energy-efficient designs for NF-ISAC systems.

The reactive near field is the area between the antenna and radiative near field. The reactive field is characterized by energy oscillations to and from the antenna, appearing as reactance and therefore does not radiate energy as in the case of the radiative NF or FF. The characteristics of the reactive NF are heavily dependent on distance, as changes in distance from the antenna within the field are of the order of the operational wavelength [87]. The waveforms in the reactive NF are not fully developed and exhibit a non-uniform power density. These properties invalidate the planar wave assumptions in this region.

The non-radiating nature of the reactive NF lends unique properties to the behavior of the waves within the region. The energy of the waves does not radiate outward and experiences rapid degradation within the region with distance. The spatial distribution of the power within the region is also spatially non-uniform. The oscillating nature of the field also contributes to non-linear phase changes as opposed to the radiating regions where the phase changes linearly. The reactive near field area is defined by the Fresnel distance, which, like radiative regions, depends on the frequency of operation and is defined as follows:

$$d_{fr}=0.62\sqrt{{\displaystyle \frac{D^3}{\lambda }}},$$

(1)

where D and $\lambda$ are the largest antenna dimension and the wavelength of the transmitted wave respectively. This equation implies that the Fresnel region expands with the increase in the antenna dimensions and with the increase in the frequency of operation, as is the trend in the case of modern wireless communication systems. The region defined by the distance greater than the Fresnel distance include the radiative NF and FF which are associated with typically studied radiative waves. All the regions of wave propagation and their respective distances are illustrated in Figure 4.

3.1. Differences of Near-Field and Far-Field ISAC Systems

The shift to ELAAs and higher frequency bands has made the NF region relevant for next-generation systems. This section discusses the fundamental differences between NF and FF ISAC systems emphasizing channel modeling and energy efficiency considerations. The different channel models can directly affect system design, performance optimization, and resource allocation in ISAC systems. We discuss the differences in detail next.

3.1.1. Wave Propagation Differences

The most fundamental differences between NF and FF regions are the wave propagation characteristics. In the FF region, electromagnetic waves can be feasibly approximated as planar wavefronts, i.e., the wavefront appears flat at the receiver as the distance from the transmitter is substantial and the relative angle-of-arrival or angle-of-departure is approximately constant. Conversely, in the NF region, the waves cannot be approximated as planar and realize spherical wavefronts which maintain their curvature at the receiver. This necessitates different channel modeling for practical accuracy. The FF plane wave model (PWM) assumes that the signals arrive at the receiver with identical phase shifts across receiving antenna elements. This is sufficient for conventional systems with limited array aperture sizes. However, as antenna array dimensions increase significantly in 6G systems, the spherical wave model (SWM) is required to accurately describe wave propagation characteristics. The SWM considers the angular and distance-dependent phase variations across array elements which results in additional DoFs which can be exploited for both sensing and communication purposes. The differences in wave propagation are detailed in

Table 5. Comparison of reactive NF, radiating NF, and FF regions.

Aspect	Reactive Near-Field	Radiating Near-Field	Far-Field
Energy Behavior	Non-radiative, oscillatory energy storage	Partial energy radiation with spherical wavefronts	Fully radiative with stable wave propagation
Field Distribution	Highly non-uniform, dominated by reactive components	Transitioning from reactive to radiative fields	Uniform field distribution with well-defined propagation
Wavefront Formation	Not fully developed, no clear propagation pattern	Formed but exhibits significant curvature	Fully developed and approximated as planar waves
Phase Variation	Non-linear and complex, preventing simple approximations	Non-linear across the aperture due to spherical curvature	Linear with respect to the aperture, enabling simplified modeling
Power Distribution	Localized with rapid field strength decay	More uniform but still affected by distance-dependent variations	Uniform and governed by free-space path loss
Beamforming Capability	Ineffective due to high field reactivity	Enables beamfocusing at specific locations in space	Enables beamsteering with stable angular properties
Frequency Dependence	Higher frequency extends the reactive NF range	Higher frequency extends the radiating NF region	Lower frequency shifts the FF region closer to the antenna
Boundary Definition	Fresnel distance: $d_f={\displaystyle \frac{1}{2}}\sqrt{{\displaystyle \frac{D^3}{\lambda }}}$	Rayleigh distance: $d_r={\displaystyle \frac{2D^2}{\lambda }}$	Beyond Rayleigh distance where planar wave approximation holds

.

3.1.2. Beam Squint

In wideband and NF systems, different frequencies can observe a deviation in the effective beam direction, which substantially degrades the performance of the system [88,89,90,91,92,93,94]. This phenomenon is called the beam squint effect. In FF ISAC systems with planar wave models, the applied phase shifts, which define the beam directions across the antenna array steer a beam towards the desired angle, and all the frequencies of the band have minimal angular variations in the beam directions. However, in the NF systems, the spherical wave models introduce additional dependence on transmitter-receiver distance, which cause the focal points of the beams to shift in angular and range dimensions across the bandwidth of the signals. Consequently, the phase shifts and precoding vectors which steer and focus the beams on the desired spatial regions become frequency selective and complicate beamfocusing by demanding advanced designs that jointly address both angular and range variations. These designs must mitigate beam squint to ensure uniform service and effective utilization of resources by concentrating energy on the desired focal point.

3.1.3. Spatial Non-Stationarity

In NF systems, the spherical wavefronts lead to significant changes in channel characteristics over different elements of the antenna array. In FF ISAC systems, the uniform plane wave assumption realizes identical amplitudes and phases across the different elements of the array which result in spatially homogeneous channels. In contrast, NF propagation creates more pronounced variations in the channel amplitudes and phases across the elements of the array [33,95,96,97,98]. Non-stationary makes XL-MIMO designs extremely challenging, as large-scale arrays can have extreme variability in channel realizations. While this non-stationarity complicates channel modeling and precoding designs, it also provides additional DoFs which can be utilized to provide advanced interference mitigation and higher spatial multiplexing gains. By exploiting these expanded DoFs, NF-ISAC systems can enable more efficient and effective utilization of resources.

3.1.4. Depth Resolution

Owing to the spatial non-stationarity and spherical wave characteristics, NF systems are capable of distinguishing targets, users and scatterers based on their distances from the transmitter. This depth resolution is inherently absent in FF systems. The linear phase of the FF wave propagation is determined by the angle-of-arrival or angle-of-departure of the signals, and therefore, it completely masks the range or distance of the scatterer or end-device. The additional distance-dependent phase information of NF systems can be exploited to resolve targets and scatterers which are not only differing in their relative orientation, but also their range [99,100]. This depth resolution can have disruptive advantages in ISAC systems, as it can facilitate precise energy focusing and improved target localization, thus enabling dual sensing and communication service for users and targets with similar angular directions but different distance.

3.2. Challenges of Energy Efficiency in NF-ISAC

The challenges of NF-ISAC systems are described as follows:

3.2.1. Channel Modeling

The mathematical representation of signal propagation through wireless mediums is referred to as channel modeling. These models are the foundational blocks of wireless communication theory, and formulate the basis for system design, performance analysis, and algorithm development. Owing to the spherical wave nature of NF systems, their channel models are fundamentally different from FF models. Furthermore, NF region contains both radiative and reactive components that vary non-linearly with distance, further complicating channel modeling. Accurate and precise channel modeling for NF MIMO is being explored in the context of 6G systems [96,101,102,103,104].

Channel modeling for NF-ISAC systems constitutes a significant challenge because it must provide a unified representation that simultaneously supports both communication and sensing objectives with SWM considerations which include the angular and distance-dependent phase variations resulting in additional DoFs. The mutual interaction of communication and sensing waveforms further complicates this task, as aperture effects and Doppler shifts impact sensing metrics and communication performance in distinct ways. Moreover, the inherent coupling of angular and range dependencies in the near field complicates precise modeling. Inaccurate or overly simplified models degrade system performance, leading to suboptimal resource allocation, increased energy waste, and impaired energy efficiency. Conversely, model reductions which lower computational or energy costs often compromise either sensing resolution or communication throughput, thereby undermining overall system efficacy.

3.2.2. Channel Estimation

Channel estimation is the process of determining the characteristics of the link between the transmitter and receiver. Accurate channel estimation lays the foundation of all functional wireless links and is imperative for effective resource allocation and advanced signal processing techniques such as beamforming and precoding. In ISAC systems, accurate channel estimation does not only enable data transfer for the communication-centric devices but is essential for sensing accuracy and object detection probability.

NF channel estimation is exceptionally challenging owing to the spherical wavefronts characteristics which render the PWM ineffective and unsuitable for NF systems leading to errors in beamforming and sensing tasks [105,106,107,108,109,110]. In the NF region, the channel estimation parameter space expands to include both angular and distance information for each end-device e.g., communication users or sensing targets. The expanded parameter space therefore induces an exponential increase in estimation complexity relative to conventional FF systems with PWMs. Moreover, due to the intrinsic coupling of angle and range, NF channels exhibit heightened sensitivity to user or target mobility, where minor spatial displacements produce substantial variations in the channel response. The dual-functionality of ISAC systems compounds these challenges as channel estimation must be performed for communication-centric and sensing-centric devices, which may interfere with each other during the channel estimation process.

NF channel estimation algorithms have high algorithmic complexity owing to the SWM with joint angle-range dependency, which increases computational requirements and leads to higher energy consumption. Furthermore, next-generation systems have extreme demands, which necessitates the development of XL-MIMO arrays. These arrays have extended NF regions and require more frequent channel estimations for feasible functioning of communication and sensing functions, significantly increasing the energy overhead [105,106]. The incorporation of mmWave and THz technologies further complicates channel estimation complexity. These bands suffer extreme path loss, necessitating highly directional beams produced by ELAAs. THz and mmWave channel estimation is being explored for functional next-generation deployments [111,112,113,114,115]. It is expected that next-generation systems will have scatterers in NF and FF regions simultaneously. Therefore, efficient hybrid-field channel estimation is also being explored in the context of 6G systems [116,117,118]. Reconfigurable intelligent services are emerging as a key-enabler of 6G systems due to their ability to dynamically manipulate the wireless propagation environment, enabling enhanced coverage, higher energy efficiency, and improved system capacity [4,5,119,120,121,122,123]. Owing to the extreme demands of 6G systems, extremely large-scale RISs (XL-RISs) are gaining traction in the research community. They further complicate channel estimation owing to their passive nature. Recent works are exploring efficient XL-RIS channel estimation techniques [124,125,126,127,128].

3.2.3. Codebook Design

Codebooks are collections of predefined precoding vectors which can be exploited to bypass explicit channel estimation methods and jointly perform beamforming and channel estimation in wireless systems. Codebooks can provide efficient beam search and reduce channel estimation overhead through structured sampling of the parameter space for wireless links. NF codebook design is inherently difficult because beam patterns must cover both angle and distance dimensions owing to the SWM, vastly increasing the codebook’s size. Conventional codebook designs prove inadequate for NF scenarios, as they fail to model the intrinsic curvature of spherical wavefronts. Recent works are exploring efficient codebook designs of NF systems which provide effective design and storage without prohibitive overhead [129,130,131,132,133,134]. Exhaustive search can be computationally infeasible in NF systems, therefore, state-of-the-art works are exploring hierarchical codebook designs where the angle and range domains are resolved separately [135,136,137,138,139]. The wide bandwidth of THz and mmWave systems can undergo significant beam split effects, where beams can point in slightly different directions for different frequencies. Researchers are exploring efficient codebook designs for wideband systems to mitigate the performance degradation caused by beam split effects [140,141,142,143]. The expanded search space of NF systems increases the number of pilot transmissions during codebook training, which increases energy consumption. For ISAC systems, unified codebook designs must balance fine angular resolution against distance resolution while supporting both functions. This further increases the storage capacity and increases the processing power for beam search, which directly impacts the energy-efficiency of NF-ISAC systems.

3.2.4. Beamtraining

Beamtraining is the process of scanning the codebook to obtain the optimal beamforming directions for establishing reliable wireless links. The access point or base stations sequentially test all the predefined beampatterns corresponding the codewords of the codebooks and compute the best codewords which can improve channel quality and system performance with minimal processing overheads. NF spherical wavefronts depend on both angle and distance, hence, beamforming codebooks must span a two-dimensional grid. As the codebook resolution increases, the number of entries grows quadratically, and exhaustive beamtraining searches over all angle-range pairs, exponentially increasing the computational complexity. This range dependency in NF systems renders traditional FF beamtraining protocols inefficient. Furthermore, end-device mobility has a greater impact on beam alignment in NF regions, as small spatial position changes can cause substantial signal power fluctuations that necessitate frequent retraining. This makes real-time beam alignment under mobility conditions infeasable. Beamtraining in NF-ISAC systems is further complicated by the difference in communication-oriented and sensing-oriented demands. Communication-oriented beamtraining algorithms focus on maximizing signal strength at user locations, while sensing-oriented beamtraining techniques prioritize parameter estimation of potential targets-of-interest. Integrating these functions within a unified framework further exacerbates training overheads and computational complexity.

The expanded search space in NF beamtraining directly increases energy consumption through more extensive pilot signal transmission and processing requirements. Furthermore, XL-MIMO systems with extended NF regions require more frequent beam retraining due to increased sensitivity to mobility, which further increases energy overhead. Furthermore, suboptimal beam selection resulting from imprecise codebook designs and inefficient beamtraining algorithms increase power consumption. Hence, state-of-the-art works are exploring efficient and effective beamtraining algorithms for NF systems which cause minimal processing overheads [137,144,145,146,147].

3.2.5. Beamfocusing and Precoding

Beamfocusing is the NF variant of FF beamsteering. It concentrates signal energy in specific spatial regions by exploiting precoding vectors. This forms the basis of spatial multiplexing in wireless systems. NF beamfocusing requires precise control in both angular and range dimensions owing to the SWM range-angle dependency, substantially increasing computational complexity compared to FF techniques. Moreover, the heightened sensitivity to user and target mobility necessitates real-time, frequent NF precoding updates. Trade-offs between sensing and communication objectives must be considered in the efficient and effective precoder designs of NF-ISAC systems. They must focus energy for communication reliability while maintaining sufficient spatial exposure for target detection and parameter estimation. NF precoding has intensive computational demands which directly increases energy consumption. Furthermore, suboptimal precoding methods cause underutilization of resources and indirectly impact energy efficiency. Additionally, the increased spatial interference from undesired clutter, targets and users in multi-user and multi-target scenarios exacerbates the complexity of precoding and impacts the system’s overall energy efficiency. Efficient NF beamforming and precoding algorithms are being explored in latest works [107,148,149,150,151,152,153].

3.2.6. Interference Management

NF systems exhibit more complex spatial patterns than FF systems and exacerbate the challenges of interference mitigation of ISAC systems. This is further worsened by user and target mobility which rapidly varies the interference patterns in NF regions, demanding advanced and dynamic mitigation techniques. Spatial non-stationarity and SWM of NF channels further complicates interference prediction and mitigation owing to position-dependent variations in path gain, phase, and delay across the array aperture, which invalidate conventional stationary covariance assumptions. Communication and sensing functions have varied interference tolerance thresholds and requirements, which demand multi-objective interference management methods. Managing the dual-function demands motivates the development of advanced joint designs that balance performance across both domains. Complex interference management algorithms for NF-ISAC increase the computational demands, and consequently, expand the energy footprints. Higher transmit power may be required to overcome residual interference which further reduces energy efficiency. Hence, latest works are exploring advanced interference management methods for practical NF systems [154,155,156,157].

3.2.7. Hardware Implementation

6G systems are expected to have physically large antenna arrays which extend the NF region significantly and require precise hardware designs with tighter tolerances. Higher frequencies of operation in next-generation systems further complicates hardware design and necessitates accurate beam control and timing synchronization. ISAC systems require more complex hardware architectures which can adapt to different operational modes. However, the higher precision and dynamic range required for the efficient utilization of XL-MIMO arrays not only requires more specialized hardware, thereby increasing cost, but also faster and more complex control mechanisms that high energy requirements in order to provide higher fidelity. Therefore, efficient and viable hardware designs of NF systems are actively being developed for practical deployments [28,30].

Mutual coupling arises from the electromagnetic interaction between antenna elements in an array that affects their radiation patterns and impedance characteristics. This influences the performance of multi-antenna transmission and reception and hinders service reliability and sensing accuracy. Since XL-MIMO systems have densely packed antennas, mutual coupling effects become significantly more pronounced and complex to model. More sophisticated models are required to model mutual coupling effects to mitigate the performance degradation. Optimizing array design to minimize mutual coupling becomes more challenging with dual functionality requirements since optimal array geometries can differ for sensing and communication operations. Mutual coupling can also be controlled using more complex signal processing techniques at the cost of greater computational resources that may not only require additional power but also more sophisticated processing capabilities which may lead to an increase in deployment and operational cost as mentioned earlier. Recent works are exploring efficient designs and accurate models of NF systems which mitigate these effects [158,159,160,161].

3.3. System Designs for NF-ISAC

Energy-efficient designs of NF-ISAC systems are delineated in this section. The fundamental principles of NF-ISAC systems have been descussed in recent works [14,162,163]. In [14], the authors introduce the fundamentals of NF propagation and elaborate typical applications of NF-ISAC. The key-advantages and technical challenges of ELAA-empowered ISAC are also discussed. Similarly, an ELAA-assisted NF-ISAC system with larger bandwidths is explored in [162]. The authors introduce wideband sensing and communication channels, and elaborate the angular-delay correlations alongside non-uniform Doppler frequencies in the NF. They also present Doppler-domain signal multiplexing and velocity sensing which is enabled by wideband systems. As ISAC systems will transform base stations with ELAAs, ref. [163] systematically explores the potentials and advantages of NF-ISAC systems technology. They discuss the foundational principles of NF propagation, and analyze their corresponding technological frameworks through comprehensive simulations. Their simulations demonstrate the advantages of NF-ISAC. They conclude with open challenges and potential future research directions corresponding these systems.

3.3.1. Practical Designs

While ELAAs offer unprecedented spatial DoFs, enabling substantial increase of channel capacity with the number of elements, their practical implementations must carefully balance the complexity, energy and capacity trade-offs. Practical systems adopt hybrid beamforming, heirarchical codebook designs, and advanced signal processing techniques to maintain performance with lower power budgets. Incorporating low-resolution and low-power RF components further reduces per-element energy costs. Effective array design thus balances hardware efficiency, processing complexity, and array geometry to maximize bits-per-Joule. Experimental and practical validation of NF MIMO systems is being conducted to prove the efficacy of the advanced degrees-of-freedom [96,97,164,165,166,167,168,169]. In [164], the authors propose a near-field signal model for the uniform circular array to avoid the plane-wave model mismatch during channel estimation in the near-field region. They investigate the model’s ability to estimate path parameters with its ambiguity function. They also experimentally validate the model. In [96], the authors focus on the spatial non-stationarity characteristics of massive MIMO systems. Owing to this non-stationarity, statistical channel modeling, and deterministic models are infeasible to implement. The authors utilize a 0.5 m radius virtual uniform circular array (UCA) to perform a 6 GHz-bandwidth millimeter-wave (mmWave) indoor channel measurement where non-stationarity is evident. They then propose a channel modeling framework for massive MIMO which captures the observed spatial non-stationarity from the dominant multipaths of mmWave channels. They validate the efficacy of the proposed method using ray-tracing simulations. Their framework advances the practical implementation of massive MIMO systems with NF effects. Experimental validation of efficient ray-tracing models for NF spatial non-stationary mmWave channels is performed in [97]. The authors focus on accurate ray tracing simulations for massive MIMO systems. To enable efficient ray tracing, they use a coarse-refinement strategy which is capable of capturing NF non-stationary characteristics. They simulate the channel using sparsely located array elements and then interpolate onto other elements, which significantly reduces complexity while maintaining accuracy. Their proposed strategy performs similar to the brute-force method with a drastic reduction in complexity through experimental validation. A radar sensing problem is considered using a large number of antennas in [165]. The authors utilize the orthogonal frequency division multiplexing (OFDM) waveform and show that large arrays enable accurate localization in the array near-field region. Their findings are validated using experiments with a massive MIMO testbed operating at 3.5 GHz and 18 MHz OFDM bandwidth in an indoor environment. Their experiments report a median accuracy of (3.4, 5.6) cm in the near-field. They conclude that NF radar sensing can be incorporated into next-generation massive MIMO deployments, even when they operate at low carrier frequencies and within narrow bandwidth constraints. An efficient calibration scheme for mmWave radars is proposed in [166] using frequency-modulated continuous wave. They conduct over-the-air experiments with 1.3 million transceiver pairs, which validate the accuracy of the developed calibration method and its effectiveness in practical applications. Hence, experimental investigations have validated NF communication and sensing architectures across a range of antenna geometries and carrier frequencies, demonstrating that accurate channel-modeling frameworks can enhance the performance of large-antenna array systems. Collectively, these studies affirm that integrated sensing and communication in the near field can be implemented with existing hardware and processing techniques, positioning NF-ISAC as a robust enabler for 6G and beyond.

3.3.2. Power-Centric Designs

Power-centric designs utilize power in the key-objectives of the system design. These design problems either directly impact power e.g., power minimization, or indirectly minimize power utilization e.g., communication energy efficiency or sensing energy efficiency maximization. Latest works in power-centric designs of NF-ISAC systems are explored as follows. Multi-target detection in NF-ISAC systems is explored in [170]. They consider a FD BS to sense the targets while communicating with the users. They formulate a transmit power minimization problem which satisfies the communication and sensing rates. The authors design the transmit beamforming for joint functions and receive beamforming for sensing. An iterative algorithm is proposed to solve the non-convex problem. Their scheme provides a 6 dB gain over FF benchmarks using 255 BS antennas. Since power consumption is a major challenge in NF-ISAC systems, the authors in [171] propose a novel low-power beamforming scheme in a CoMP ISAC network. CoMP is a technique in which multiple base-stations or access-points cooperate to improve network performance. The authors realize base-station cooperation to improve coverage and augment the sensing and communication beams to reduce power consumption. They formulate a power minimization problem while meeting communication rate and object detection constraints. They utilize SCA to solve the non-convex problem and devise a fast-converging algorithm. Their results validate the effectiveness of the proposed scheme in low-power ISAC systems. As the integration of XL-arrays introduces far-reaching NF uniform spherical wave (USW) propagation and the stringent high-rate requirements necessitate wideband signal models, in [172], the authors discuss the principles of NF wideband sensing and communication and describe the fully digital and hybrid analog-digital precoding techniques to enable high accuracy in target range and angular direction estimation. They also discuss the future trends and directions in wideband NF systems.

3.3.3. Joint ISAC Designs

An overview of the joint design of energy efficient NF-ISAC systems is provided in

Table 6. Overview of joint designs in energy-efficient NF-ISAC systems.

Paper	Key Focus	Techniques/Algorithms
[14]	Fundamentals of NF propagation and ISAC. Discussion of typical NF-ISAC applications.	Survey/discussion
[162]	Wideband channel characteristics, angular-delay correlations, and Doppler effects.	Analysis of angular-delay correlations, Doppler-domain signal multiplexing, velocity sensing
[163]	Potentials and advantages of NF-ISAC systems. Discussion of technological frameworks supporting NF-ISAC.	Simulation results
[170]	Joint transmit/receive beamforming for multi-target detection and multi-user communication with power minimization.	Iterative non-convex optimization, generalized Rayleigh ratio quotient, Semi-definite Relaxation, Gaussian randomization method
[171]	Low-power transmit beamforming in CoMP ISAC networks.	SCA, Novel fast-converging algorithm
[172]	NF wideband sensing and communication, high-accuracy target estimation.	Fully-digital and hybrid precoding design
[173]	NF-ISAC framework for DL/UL, effects of aperture and polarization on channel models.	Rate derivations, scaling law analysis
[174]	Accurate channel model-based NF-ISAC framework incorporating effective aperture loss.	Closed-form expression derivations
[175]	NF-ISAC framework with effective aperture loss and polarization mismatch.	Closed-form derivations
[99]	Transmit symbol design for weighted joint sensing and communication performance.	2D MUSIC algorithm for target estimation
[176]	NF-ISAC beamforming schemes (conjugate sensing, null-space sensing, joint design).	Derivation of closed-form rate expressions
[177]	Joint communication channel estimation and target parameter estimation overcoming phase nonlinearity.	High-dimensional linear model formulation
[178]	Overview of NF-ISAC techniques: comm-assisted sensing, sensing-assisted comm, and joint designs.	Comparative discussion and analysis
[179]	Hybrid beamforming for NF THz-ISAC, mitigating beam squint.	Alternating optimization
[180]	Impact of Doppler and spatial wideband effects on NF-ISAC performance.	CRB and Ziv-Zakai bound derivations, joint position-velocity estimation
[181]	Multi-user NF-ISAC with delay alignment modulation for target echo sensing.	SCA, PCCP, and SOCP

.

In [173], the authors evaluate appropriate channel models in ISAC systems to account for NF effects. They propose an ISAC framework for downlink and uplink scenarios, and analyze the impacts of aperture and polarization of antennas. They also study three different designs: communications-centric, sensing-centric, and Pareto optimal, and derive the sensing and communication rates. They also consider the scaling laws related to the number of antennas. Their results show that with an increase in array sizes, sensing and communicate rates saturate under the proposed model, but diverge under conventional models. Authors propose an accurate channel model based NF-ISAC framework in [174] with effective aperture loss considerations. They analyze sensing and communication rate performances under three beamforming designs: communications-centric, sensing-centric, and Pareto-optimal. They also derive closed-form expressions for achievable sensing-communication rate regions. Their results show that the rate performance converges as the number of antennas at the BS increases. Accurate channel model-based NF-ISAC framework is also proposed in [175], which considers effective aperture loss and polarization mismatch. Authors analyze sensing and communication rate performance under communications-centric, sensing-centric, and Pareto-optimal designs. They also derive closed-form expressions for the sensing and communication rates. Their results show that the adopted models are more accurate than conventional models.

As the NF region enables precise focusing of energy at specific spatial regions, it can enable the simultaneous computation of target direction and range from a single base station. To leverage this ability, authors in [99] design a transmit symbol vector to maximize weighted sensing and communication performances subject to total power constraints with multiple targets and users. Target parameters are estimated using the 2D multiple signal classification (MUSIC) algorithm. In mmWave and THz scenarios, the NF EM wave propagation changes the dimensionality of the antenna steering vector from a single angle dependency to a dual distance and angle dependence. A steering vector of an antenna array models the phase shifts across antenna elements for a signal arriving from, or departing to a particular direction. Therefore, effective NF beamforming schemes must be designed for feasible ISAC deployments. In [176], authors propose three different beamforming schemes: conjugate sensing beamforming, null-space sensing beamforming, and joint communication and sensing beamforming. They also analyze the complexities and application scenarios of the three schemes, and derive closed form expressions for communication and sensing rates. Their results show that the proposed schemes have lower complexity compared with the state-of-the-art methods. The communication scatterers and radar targets are expected to reside closer to the ISAC transceivers, therefore, an ELAA based NF-ISAC system design is explored in [177]. The authors propose a feasible solution to address the non-linear phase relationship of NF systems which render the Fourier analysis ineffective for target detection and communication channel estimation. Fourier analysis decomposes a signal into a sum of sinusoidal components and transforms a signal from spatial or time domain to the frequency domain. As the phase varies linearly at the array elements in FF systems, applying the discrete Fourier transform (DFT) can enable channel estimation and target parameter estimation. However, DFT can not resolve range, which is required by NF systems. Therefore, the authors demonstrate that the phase nonlinearity can be expressed linearly in high-dimensions. They utilize this model to formulate a joint communication channel estimation and target parameter estimation problem with high precision.

Motivated by the integration of XL-MIMO in future wireless systems, authors in [178] study NF-ISAC. They discuss the advantages of NF communication and sensing, and introduce three different techniques for NF-ISAC: communication-assisted NF sensing, sensing-assisted NF communication and joint NF communication and sensing. They also analyze the different research opportunities presented by NF-ISAC, and the corresponding design issues. They also discuss attractive future directions for NF-ISAC. ISAC and THz are two key technologies expected to be integrated in 6G systems. Furthermore, prior works predominantly utilize narrowband ISAC, which degrades beamforming performance. To tackle severe signal attenuation, the authors in [179] introduce an alternating optimization technique for hybrid beamforming in NF THz-ISAC systems. They mitigate and compensate for the NF beam-squint via baseband beamformers. Their results show higher spectral efficiency performance. The impact of doppler and spatial wideband effects on sensing performance using communication signals in high mobility scenarios can degrade system functionality drastically. Therefore, authors in [180] study these two effects for NF wideband mmWave systems. They first propose a channel model for a high-speed moving user using an OFDM signal, and then derive the CRB for six parameter estimation to evaluate the impact of NF parameters and velocity on the CRB positioning is quantified. They also derive the derive the Ziv-Zakai bound (ZZB) for positioning and formulate a joint position and velocity parameter estimation algorithm by performing DFT.

As opposed to joint ISAC designs, sensing-centric and communication centric designs have also been considered which focus on sensing-focused objectives with communication performance constraints and communication-focused objectives with sensing focused constraints respectively. In order to extract the best energy efficient sensing performance, the power provided to the sensing signal has to be optimized so as the interference does not degrade the communication performance below the required threshold while also providing the best sensing performance per unit energy. Similarly, the power allocated to communication in the case of communcation-centric designs should be such that the interference is minimized while maintaining the best possible performance per unit energy, and the echo signal from the target is sufficient to provide the required sensing performance. The energy-efficient constraints become increasingly important with the increase in the number of antennas, as a the additional degrees of freedom in NF provide opportunities for better optimized ISAC performance. Studies on both of these types of designs have been discussed in the following text.

3.3.4. Sensing-Centric Designs

An overview of the sensing-centric designs for energy-efficient NF-ISAC systems is given in

Table 7. Overview of sensing-centric designs in energy-efficient NF-ISAC systems.

Paper	Key Focus	Techniques/Algorithms
[15]	Joint waveform optimization to maximize sensing performance subject to communication constraints.	Two-stage optimization and SDR
[182]	NF wideband ISAC for multiuser and multi-target sensing, CRB minimization.	CRB derivation, iterative convex optimization
[183]	Sensing SINR maximization using NF beamforming while satisfying communication QoS.	Rayleigh entropy theory and SDR
[184]	ELAA-based NF-ISAC design to maximize the minimum beam pattern gain for radar sensing.	SDR and low-complexity algorithm
[185]	Joint waveform design to minimize beampattern matching error for sensing.	Majorization-minimization (MM) algorithm
[100]	Integrated sensing, positioning, and communication (ISPAC) using a double-array at the BS.	PDD-based double-loop and alternating optimization
[186]	Joint angle and distance CRB minimization in a double-array NF-ISAC system.	PDD-based double-loop iterative algorithm
[37]	Localization via NF beamforming for receiver positioning.	Localization algorithm with Monte-Carlo simulations
[88]	Mitigation of beam squint in NF systems using TTD lines for localization.	TTD implementation and trajectory equation derivation

.

Sensing-centric designs maximize the performance of the sensing subsystem by maximally utilizing the available resources while meeting the communication QoS constraints. The sensing-centric state-of-the-art designs of NF-ISAC systems are described as follows. Motivated by the inclusion of ELAAs and high frequencies, e.g., mmWave and THz to meet the extreme communication demands and higher sensing resolution., the mismatch between FF and NF designs is explored in [15]. To improve the practicality of ISAC designs, the authors propose a NF framework to jointly optimize the joint ISAC waveform to maximize the sensing performance subject to communication constraints. They solve the problem using two-stage optimization and semidefinite relaxation. Their results reinforce the gains of NF region over the FF region. In [182], authors propose a NF wideband ISAC framework for multiuser communication and multi-target sensing. They derive the expression for the CRB of direction-of-arrival (DoA) and distance estimations, and minimize it subject to communication constraints of users. They relax the problem into an iterative convex problem to solve it. Their results show that sparse transceiver designs improve sensing performance without degrading communication performance.

The potential of NF beamforming is explored for ISAC systems with extremely large arrays in [183]. The authors analyze the NF beam focusing ability, and the mismatch between NF and FF, using models of NF spherical waves. They also discuss the performance degradation caused by the conventional FF beamforming in the near field region. They then formulate a beamforming problem to maximize the sensing SINR while meeting the communication QoS and transmit power budget constraints. They utilize Rayleigh entropy theory and SDR to solve the non-convex problem. Simulation results show that the proposed scheme can mitigate co-angle interference and enhance sensing and communication performance. ELAA-based NF-ISAC is considered in [184]. The authors maximize the minimum beam pattern gain for radar sensing while meeting the communication requirements. They solve the problem using SDR and provide a low-complexity solution. Their results show that NF beamfocusing can detect multiple targets located at identical angles.

Beamforming design for NF-ISAC systems is also exploerd in [185]. The authors jointly design the communication and sensing waveforms to minimize the beampattern matching error for sensing while meeting the communication QoS and transmit power budget constraints. They utilize the MM algorithm to solve the non-convex problem. Their results verify the effectiveness of the proposed scheme in NF regions. In [100], the authors propose a NF ISPAC framework which serves multiple communication users and senses multiple targets. They utilize a novel double-array to enable the NF-ISPAC at the BS. They colocate an assisting transceiver (AT) to the large-scale main transceiver which enables sensing and positioning in the communication system. A joint angle and distance CRB is derived, which is then utilized in a CRB minimization problem subject to communication rate thresholds in downlink and uplink scenarios. They utilize a penalty dual decomposition (PDD)-based double-loop algorithm to solve the non-convex problem for the downlink system, and utilize alternating optimization to solve the uplink problem. Their results show that the ISPAC system can resolve both angle and distance domains by utilizing a single BS. Similarly, in [186], authors formulate and solve a joint angle and distance CRB minimization problem for a double-array downlink NF-ISAC system. Their solution satisfies the communication QoS requirements and the hybrid-analog-and-digital (HAD) structure constraint. They utilize a PDD based double-loop iterative algorithm to solve the non-convex problem. Their results reinforce that NF-ISAC systems can locate the target in both angle and distance domains.

The high frequency and highly directional communications expected in next-generation systems offer improved sensing resolution and enable a unified sensing and communication approach. To enable a communicate-to-sense system, authors in [37] present a localization algorithm which can exploit beamforming to estimate the location of the receiver. This algorithm can be implemented using large intelligent surfaces and large antenna arrays. They provide performance evaluation with static and mobile users using Monte-Carlo simulations. The empirical cumulative distribution function for both static and mobile users show the effectiveness of their algorithm. To mitigate the beam squint of wideband MIMO systems, the authors in [88] utilize true-time-delay lines (TTDs) to control the range and direction of the beam squint in NF communications systems. By utilizing this control, the authors propose to localize users. They derive the trajectory equation for the beam squint and design a controlling scheme. With this scheme, the different angles and different distances will realize different beams at different frequencies and subcarriers. Hence, the beam sweeping overhead is drastically reduced compared to conventional beam searches, and multiple users can be localized using this method. The simulation results reinforce the efficacy of the proposed methods in terms of overhead and localization.

A multi-user NF-ISAC system is explored in [181] with delay alignment modulation. They formulate a signal-to-noise ratio (SNR) maximization problem for the target echo sensing which ensures communication SINR and transmit power budget constraints. The problem is non-convex and the authors utilize successive convex approximation, penalty convex-concave procedure (PCCP) and second-order cone programming (SOCP) to solve it. The results show the impact of channel model mismatch on the NF-ISAC system.

3.3.5. Communication-Centric Designs

An overview of the communication-centric designs for energy-efficient NF-ISAC systems is given in

Table 8. Overview of communication-centric designs in energy-efficient NF-ISAC systems.

Paper	Key Focus	Techniques/Algorithms
[187]	Secure NF-ISAC in a NOMA scenario, joint beamforming for secure communication and sensing.	SDR and SCA, CRB derivation
[188]	Joint trajectory design and ground base station beamforming, UAVs, location prediction, secrecy rate, high time efficiency	Alternating optimization, successive convex approximation
[189]	Joint trajectory design and beamforming, STAR-RIS, transmission/reflection matrices, UAVs, sum rate maximization	Alternating optimization, successive convex approximation

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Communication-centric designs maximize the performance of the communication subsystem by maximally utilizing the available resources while meeting the sensing QoS constraints. The integration of sensing and communication functions creates security challenges owing to the higher transmit powers required for sensing functions. Therefore, next-generation systems are attracting more attention for physical layer security (PLS). In [187], PLS of NF-ISAC systems is explored in a NOMA scenario. The base-station transmits private messages to multiple communication users and performs simultaneous target sensing. They formulate a joint transmit beamforming design to enable secure communication and meet sensing requirements. They also derive the CRB for joint distance and angle sensing in the NF, and maximize the secrecy rate for communication users under the CRB constraint. They utilize SDR and SCA to solve the problem. Their results show that the proposed scheme enhances the performance of secure communication.

Security and privacy issues of NF-ISAC systems are also tackled in [188]. Authors study a novel secure UAV-aided NF-ISAC system. A ground base station is deployed with a large aperture antenna array which communicates with the communication and eavesdropper UAVs. The authors utilize artificial noise for jamming and sensing. They utilize the doppler shift variations over the spatial NF channel to estimate the 3D velocities from the echo signals of the target. Extended Kalman filter (EKF) is exploited to correct the location prediction errors. Utilizing the location of the eavesdropper UAV, the ground base station beamforming problem is formulated to maximize the secrecy rate while satisfying the sensing constraint. The authors utilize alternating optimization and SCA to solve the non-convex problem. Their results show that the proposed scheme outperforms the benchmarks.

The effect of NF is explored in a STAR-RIS aided air-ground ISAC system in [189]. The authors propose a STAR-RIS enabled air-ground NF-ISAC system where an UAV is deployed as a mobile station to mitigate the high path loss effects of high frequency systems. They formulate a weighted sum rate maximization problem which jointly provides communication and sensing gains by designing the reflection/transmission matrices, beamforming vectors and UAV trajectory. Three subproblems are devised and solved using successive convex approximation and alternating optimization. Their results validate the effectiveness of their scheme.

The above section summarizes the studies on energy efficient system designs for NF-ISAC where the FF channel model is replaced by its NF equivalent with the adoption of larger arrays and higher number of antenna elements. The optimization of these systems takes into account the additional degrees of freedom provided by the spherical wavefronts and the complexity of a higher number of antenna elements at the base station.

As more enabling technologies are added to the ever growing landscape of emerging communication systems, hybrid systems capable of supporting and/or enhancing the operation of NF-ISAC technologies need to be studied. Some potential sustainable technologies capable of integrating with NF-ISAC are presented and discussed in the following text.

4. Sustainable Near-Field Integrated Sensing and Communication

In the following, we detail the emerging technologies for sustainable next-generation systems. The sustainable designs of NF-ISAC systems are summarized in

Table 9. Overview of sustainable NF-ISAC systems.

Technology	Articles	Motivations
Integrated Sensing, Communications and Powering	[190,191,192,193,194]	Combines sensing, data transfer and RF power delivery to mitigate energy and spectrum bottlenecks in ultra-dense 6G networks.
RIS-aided Sustainable ISAC	[195,196,197,198,199,200]	Employs passive beamforming to direct energy and extend coverage with minimal RF hardware.
Integrated Passive Backscattering and Sensing	[41,201,202,203]	Uses ultra-low-power backscatter tags for scalable, cost-effective sensing and communications without active RF chains.
Fluid Antenna–assisted ISAC	[9,204,205,206,207,208]	Dynamically reconfigures antenna element positions to maximize spatial multiplexing gains.
Cognitive Radio with ISAC	[209,210]	Leverages dynamic spectrum access to boost spectral efficiency for joint sensing and communication.

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4.1. Integrated Sensing, Communications and Powering

The key requirements proposed by the International telecommunication union for 6G systems include a network density of ${10}^8$ nodes per km² with a traffic capacity of 1Gbps per m² [211]. These stringent demands can create energy and spectrum bottlenecks. The resource bottlenecks are further exacerbated by the existence of radar systems in bands below 10 GHz e.g., S-band (2–4 GHz) and C-band (4–8 GHz), which complicate interference management [190]. Furthermore, the vision of pervasive sensing applications, which motivates ISAC systems, increases the workload on hardware and complicates energy efficiency. To mitigate these issues, wireless power transfer is emerging as a disruptive technique which can enable sustainable but dense information systems. Wireless power transfer conveys energy via radio-frequency (RF) signals over the air such that an energy harvesting circuit can convert incident RF power into energy at the receiver [212,213,214,215]. This has motivated the exploration of simultaneous wireless information and power transfer (SWIPT) systems [213,214,215]. A typical scenario illustrating an integrated sensing, communication and powering system is shown in Figure 5.

Since concurrent sensing and communication functions consume RF energy and therefore, can rapidly exhaust the limited power resources, exploiting the information and probing waveform for energy harvesting can immensely improve the energy efficiency of the system. Hence, state-of-the-art works are exploring the convergence of ISAC and SWIPT systems [190,191,192,193]. In [190], the authors propose the design of an integrated sensing and SWIPT system which drastically improves spectral efficiency. The authors consider a base-station which transmits signals to perform downlink multiuser communication, radar target sensing and energy harvesting. They formulate a beampattern matching error minimization problem which optimizes the sensing performance while meeting the total transmit power budget and QoS constraints of all users. The users have communication and energy harvesting QoS requirements. They utilize a DC functions representation to solve the non-convex problem. Their results show the trade-off between the sensing requirements and the performance of communication and energy harvesting in integrated sensing and SWIPT systems.

In [191], the authors converge ISAC and SWIPT to formulate an integrated sensing, communications, and powering (ISACP) system. They propose a federated learning scheme to maximize the energy-efficiency of massive MIMO ISACP networks. To solve the non-tractable problem, they propose an actor-critic enabled multi-agent federated learning algorithm. An integrating sensing, energy and communication (ISEAC) system is considered in [192]. The authors consider a BS that serves one legitimate single-antenna information receiver (IR) at a known location, while several single-antenna energy receivers (ERs) acting as eavesdroppers are at unknown positions. The locations of the eavesdroppers must be sensed. The authors formulate a transmit beamforming problem to maximize the secrecy rate, while meeting the ERs’ energy-harvesting requirements and the sensing constraints. They utilize semidefinite relaxation and Taylor expansion to yield convex approximations and solve the problem. Their results verify the efficacy of the proposed beamforming scheme. A convergence of ISAC and SWIPT systems is explored to meet the stringent QoS requirements of next-generation traffic in [193]. The authors introduce an ISACP framework which exploits massive MIMO beamforming to jointly support sensing, communication, and energy harvesting in next-generation wireless networks. They estimate the angle-of-arrival of mobile users by utilizing the CRB, and maximize the energy-efficiency while satisfying both sensing and communication constraints. The effectiveness of ISAC and SWIPT in [190,191,192,193,194] motivates the design of NF-ISACP systems. In [194], authors study secure communication in an ISCAP system with ELAAs. The BS sends messages to a single communication receiver and senses a point target. It also charges multiple energy receivers which enhance the energy efficiency of the system. A joint transmit beamforming design is considered which maximizes communication performance while meeting the powering and sensing requirements. The sensing waveform acts as artificial noise and is effective in jamming eavesdroppers. The authors utilize CRB constraints for target sensing, and exploit SDR and fractional programming to solve the non-convex problem. Their results show that angular and distance domain resolutions can further enhance secure communication while satisfying sensing and powering requirements. However, NF systems can further complicate energy-efficient ISACP designs. Beam squint in wideband NF systems requires frequency-selective precoders which jointly mitigate squint and optimize power transfer. Furthermore, spatial non-stationarity demands unified channel models and estimation methods capable of real-time computations without prohibitive complexity. Furthermore, beamfocusing in NF ICASP systems would require higher precision to deliver competing objectives e.g., ensuring sensing accuracy, and delivering sufficient harvesting power.

The integration of sensing and communication along with powering can eliminate the need for dedicated hardware for providing the same set of services, making it more feasible to not only deploy but to adopt. More compact deployments, owing to the integrated operation will enable the system to provide more ubiquitous support for the required communication, sensing and powering services. Experimental verification of the gains provided by NF beamfocusing [216], ISAC [217] and SWIPT [218] have been independently verified. The proposed solutions for ISCAP have a growing body of research but lack experimental insights that may substantiate the theorized gains.

4.2. Reconfigurable Intelligent Surfaces Aided Sustainable ISAC

Reconfigurable intelligent surfaces are planar surfaces composed of densely packed passive elements which are capable of precisely manipulating electromagnetic (EM) waves to create programmable phase shifts which enhance end-to-end wireless link performance [119,123,219,220]. By precisely configuring the phase shifts of the reflecting waves, the RIS can provide signal energy focusing and signal energy nulling towards desired directions without complete RF chains. These passive beamforming gains can drastically improve the energy efficiency of wireless systems and enable sustainable dense networks. The energy-efficient operation of RISs motivates their inclusion in ISAC systems, enabling efficient resource sharing through the joint operation of sensing and communication functions [195,196,197]. A typical RIS enabled ISAC system is illustrated in Figure 6.

In [195], the authors consider an RIS enabled ISAC system where the goal of the ISAC base-station is to maximize the sum achievable throughput of the wireless power communication network while meeting the communication and sensing constraints. Maximizing the sum-achievable throughput while limiting the maximum transmit power, also minimizes the energy spent per bit of information and hence, contributes to the energy efficiency of the system. The energy beamforming matrix, radar beamforming matrix, phase shift matrix of the RIS, and transmission slot of the UL and DL are the optimization variables. The problem is divided into 2 sub-problems where the energy and radar beamforming matrices and phase shift matrix are optimized using the penalty-based successive convex approximation and Reimannian conjugate gradient (RCG) algorithm for first sub-problem, and the polar point method and RCG algorithm are used to optimize the timeslot and phase shift matrix of the uplink transmissions in the second sub-problem. Numerical results show the effectiveness of RIS in improving the wireless power transfer and communication performance and the superior performance of the optimization algorithm in improving the sensing performance and throughput as compared to the benchmarks. Passive RIS designs have been studied in detail in the literature in energy constrained systems. There may, however, be instances where signal amplification may be required in addition to the reconfigurability offered by the RIS. In [196], the authors consider an active RIS enabled integrated sensing and communication system. The maximization of the radar signal SINR is performed by optimizing the transmit beamforming matrix at the base-station and the reconfiguration matrix of the active RIS. The majorization-minimization algorithm is used for the non-convex radar SINR objective. The scaling order of the radar SINR is derived and the transmit power allocation problem along with the deployment strategy of the active RIS are studied. Numerical results indicate the superiority of the active RIS scheme over the passive RIS. In [197], the authors propose two design frameworks addressing distinct operational objectives. The first framework aims to develop a spectrally efficient ISAC system by jointly optimizing the transmit beamforming vectors, radar signal covariance matrix, and RIS reflection coefficients, with the objective of maximizing the weighted sum rate for communication while minimizing the radiation pattern approximation error. The second framework focuses on energy efficiency, employing the same optimization variables to optimize power allocation subject to communication constraints. The resulting non-convex optimization problems are solved using fractional programming, second-order cone programming, and semidefinite relaxation techniques. Furthermore, a low-complexity localized search algorithm is proposed for determining the optimal RIS configuration. Numerical results demonstrate that the proposed schemes yield improvements in both the weighted sum rate and the sensing beam pattern compared to conventional approaches.

Furthermore, as next-generation systems are expected to utilize mmWave and THz bands, free-space path loss and blockages hinder link reliability and coverage. To mitigate these challenges, the RIS can address these issues by dynamically creating virtual LoS paths around obstacles. XL-RISs can be utilized to provide passive beamfocusing where the nonlinear phase is exploited to provide precise energy focusing in the angular and distance domain while keeping energy consumption in feasible limits. This has transformative potential for NF-ISAC systems as it can provide high resolution sensing at a fraction of the conventional MIMO power consumption.

Owing to the potential of RIS in next-generation systems, a novel RIS-aided NF-ISAC system is investigated in [198]. The sensor elements are mounted on the RIS to enable sensing functions. The authors derive a new expression of position error bound (PEB), and devise a cost function to balance the sensing performance and the sensor deployment costs. They then formulate a cost minimization problem which jointly designs the number of sensor elements at the RIS and the passive beamformers. A joint geometric programming element-wise algorithm is formulated using SCA and geometric programming to solve the problem. Their results show that the proposed scheme provides the least PEB and cost function values compared to the benchmarks.

A semi-passive RIS aided NF-ISAC system is proposed in [199]. The authors utilize active elements for sensing target, and passive elements for communication to derive the Fisher Information Matrix (FIM) for unknown 3D coordinates of the targets. The FIM is used to compute the CRB. The authors formulate and solve a CRB minimization problem while ensuring minimum achievable rate for each communication use and the total transmit power budget. They utilize alternating optimization to solve the problem. Their results show that the proposed scheme can improve the sensing performance compared with benchmark schemes.

In [200], authors propose a semi-passive RIS-aided NF-ISAC system and compute the optimal beam direction for both sensing and communication. They perform downlink beamscanning using passive reflecting elements (REs) which finds the optimal reflected beam and assists the signal transmission from the base station to the communicating user. Furthermore, the IRS can estimate the target parameters by analyzing the reflected echo signals. They design a NF codebook by using the cascaded NF steering vectors of the IRS, and find the optimal codeword using the exhaustive training process. A CRB analysis is also provided which provides information of the sensing performance. Their results show a monotonic decrease in the CRB with respect to the beamscanning duration.

RIS, owing to their low power operation, have the potential for widespread deployment and enable a diverse set of services. The integration of ISAC with RIS will further enhance the sensing capabilities of the wireless networks, needed for the support of next generation services. The scalability of RIS enabled systems is further enhanced by their minimal adoption overhead in current cellular systems, this is due to the fact that they can be retroactively deployed in current systems without major changes to the existing infrastructure. A practical analysis of the performance of RIS in the NF to allow for beamfocusing is provided in [216] where the benefits provided by the operation of the RIS in the NF are compared to the performance of the RIS in the conventional far-field.

4.3. Integrated Passive Backscattering and Sensing

As discussion in Section 2.1, ISAC systems experience resource and architectural challenges owing to the reliance of sensing on LoS links and the higher workload on network hardware for dual functioning. These challenges hinder scalability of ISAC systems, and complicate network planning. To this end, backscatter technology is emerging as a promising candidate for ISAC systems [221,222]. Backscatter devices piggyback information on the reflected RF signals by dynamically modulating the impedance of an antenna or reflective surface [41]. This passive operation provides transformative energy efficiency gains by consuming negligible power. Furthermore, these devices have low form-factor and do not have active RF components which drastically lowers the manufacturing costs and enables scalability. These devices harvest energy from existing RF signals and eliminate the need for additional spectrum allocations or dedicated infrastructure. Additionally, the passive operation has minimal processing overhead and yields faster response times with reduced system latency. Despite these advantages, backscatter technology predominantly enables short range communication, thus restricting large-scale deployment. Furthermore, the reliance on modulated RF signal reflections constrains the achievable throughput, thus rendering it unsuitable for high data-rate applications. As backscatter devices rely on reflected signals much like the radar sensing functions, it can inherently enable ISAC applications. This convergence can improve spectral efficiency and hardware overhead by dense deployments to form distributed sensing arrays which improve performance in NLoS conditions. A typical system with integrated passive backscatter and communication is illustrated in Figure 7. It may, however, be noted that due to the extremely low energy operation of backscatter nodes, the signals reflected by the nodes are extremely vulnerable to line of sight operation and operate using very low data rate protocols in order to transmit their information. These low data rates severely hinder the type of applications enabled by this technology. Backscatter communication maximizes its utility in environments where ambient signals are a reliable resource for the corresponding tags.

Owing to its advantages, backscatter aided ISAC systems are being explored in literature [41,201,202,203]. A single-user single-tag integrated sensing and backscatter communication (ISABC) system is explored in [201]. The authors consider a full-duplex base-station and employ maximum ratio transmission to provide beamforming gains. They consider a dedicated radar waveform and receive uplink data transmission from the backscatter devices. This waveform is also utilized to perform monostatic sensing. Their results reinforce that power allocation is critical in ISABC systems. In [41], authors present a green transceiver design for a QoS-aware ISABC. They formulate a power minimization problem which jointly designs the precoding vector, receive combiners and radar signal while meeting the communication and sensing performance constraints. The formulated problem is non-convex, therefore, the authors utilize alternating optimization, and semi-definite relaxation to obtain a tractable solution. A single-tag, single-user system is investigated in [203]. The authors analyze the comparative sensing performances of ISAC and ISABC systems and conclude that, as the tags experience reflection losses, the ISAC systems provide higher sensing rates. The authors also discuss the applications and challenges associated with ISABC systems. A radar-aided backscatter communication system is investigated in [202]. The authors exploit ambient signals of the radar at the backscatter devices to enable information transfer. They also investigate the effects of clutter interference and reveal a graceful degradation of the probability of error with respect to the increase in transmission rate. The passive nature of backscatter tags makes them highly susceptible to interference. NF-ISAC systems can mitigate this by providing advanced interference management through precise beamfocusing capabilities which can provide drastic improvements in backscatter communication throughput.

In practical deployment scenarios, one of the major considerations is energy cost and provisioning. This problem is inherently tackled by the low-energy operation of backscatter systems. These systems remain operational for long periods of time due to their low operational energy and allow for massively scalable infrastructures without concerns of energy management. Practical implementations of ISABC have emerged which demonstrate the efficacy of the systems utilizing the benefits of ISAC and backscatter communications. In [223], the authors design a system allowing for both upkink and downlink backscatter communication with radar sensing and backscatter localization. The authors in [224] also demonstrate the performance of a zero-power backscatter communication and sensing system.

4.4. Fluid Antenna Assisted ISAC

Fluid antenna systems (FASs) are emerging as a disruptive technology for high capacity next-generation networks. Conceptually, an FAS is an antenna architecture in which the spatial locality of its constituent elements can be dynamically adjusted and reconfigured to obtain the desired radiation pattern. This provides an additional degree-of-freedom over conventional fixed-position antenna (FPA) systems, and drastically improves the spatial multiplexing gains [9,204,205,208,225,226,227,228]. This spatial reconfiguration allows FAS to select the position which maximizes received signal strength or minimizes interference. This flexibility is motivating the investigation of fluid antennas for ISAC systems [9,204,205,206,207]. The interference cancellation capabilities and enhanced degrees of freedom of FAS have also motivated the study of energy efficiency in multi-user FAS [229]. The enhanced diversity provided by FAS can enable systems to meet QoS requirements while maintaining minimal energy consumption. A typical fluid antenna enabled ISAC system is illsutrated in Figure 8.

In [206], the authors propose a fluid antenna assisted ISAC system. They utilize a FAS-enabled base-station which transmits the ISAC signal to the communication user and the sensing target. They formulate a rate maximization problem for the downlink communication user while meeting the sensing beampattern gain requirements and adhering to the power budget. The formulated problem is non-convex, therefore, the authors utilize alternating optimization to obtain a tractable solution. Similarly, in [9], the authors investigate the green design of a QoS-aware fluid-antenna aided ISAC system. They jointly design the antenna position vector transmit beamformers, radar signal and receive combiners to minimize the power consumption at the fluid-antenna enabled base-station while meeting the QoS requirements of multiple communication users and multiple radar targets. The formulated problem is non-convex, therefore, the authors utilize generalized Rayleigh quotient, SDR and alternating optimization to obtain a tractable solution.

In [204], the authors improve the performance of an ISAC system using a 2-dimensional FAS with multiple ports mounted at the base-station. The authors jointly optimize the locations of the antennas and the precoders to maximize the sum-rate in a multi-user MIMO system. The formulated problem is non-convex, therefore, the authors utilize a primal-dual based learning algorithm to obtain a solution. They also explore the effects of partial CSI on the performance of the proposed solution. In [205], the authors minimize the transmit power in an fluid-antenna enabled ISAC system by jointly optimizing the transmit beamformers and port selection for FAS while meeting the communication and sensing constraints. They utilize an iterative algorithm based on sparse optimization to solve this problem. A rate maximization problem for a fluid-antenna aided ISAC system is investigated in [207]. The authors jointly design the beamformer and the antenna position vector while keeping the radar probing power in the desired direction above a threshold. The non-convex problem is solved using block successive upper bound minimization (BSUM) and majorization-minimization.

Owing to the stringent demands of next-generation systems, authors propose an extremely large-scale simultaneously transmitting and reflecting reconfigurable intelligent surface (XL-STAR-RIS) aided FAS is [208]. They precisely estimate the distance and angle of arrival (AoA), while simultaneously providing service to communication users. They formulate a joint distance CRB and AoA estimation error minimization problem, and optimize the sensing signal covariance matrix, communication beamformer, XL-STAR-RIS phase shifts, and the FA position vector to meet the desired communication performance. A PDD and BCD method to solve the non-convex problem. Their results validate the effectiveness of the proposed scheme.

Experimental verification of the efficacy of FAS is an ongoing process due to the very recent introduction of the technology. However, the preliminary analysis of practical prototypes has indicated promising results that reduce complexity and improve overall performance. The earlier prototypes based on metallic and non-metallic liquid antennas are presented in [230], and the more recent electromagnetically reconfigurable FAS are demonstrated in [231]. Due to the nature of the technology, the deployment of FAS systems is expected to replace current antenna systems, however, their compatibility with traditional infrastructure can ease adoption. Further development of different types of FAS can also enhance scalability of these systems.

4.5. Cognitive Radio with ISAC

Cognitive radio (CR) technology improves spectral efficiency by employing dynamic spectrum access to maximize the utilization of available spectrum. Traditional wireless systems have utilized fixed spectrum access where a specific band of the available spectrum is allocated to one or more dedicated users. However, this allocated spectrum can only be utilized by the dedicated devices and no other devices are allowed to utilize it whether or not the dedicated devices are using the spectrum. This extreme under-utilization of spectrum drastically degrades spectral efficiency. To mitigate this, CR technology employs dynamic spectrum allocation where the radio spectrum is shared between two types of devices: primary devices and secondary devices [209]. The primary devices are priority devices and should not experience interference from secondary devices. The secondary devices are the unlicensed devices and can opportunistically use the spectrum only if it does not interfere with the primary devices’ functionality. This enables enhanced utilization of spectrum and makes CR a promising candidate for mass-scale systems. As NF-ISAC systems have to perform dual functions, resource utilization should be maximized to enable sustainability. In addition to the spectral efficiency gains, CR networks can also be explored for energy efficient operation by effectively managing the transmit power, channel access and interference threshold [232,233]. The coordination of the primary and secondary users enabled by the base station can effectively enhance energy operation while also mitigating spectrum under-utilization. CR can be a transformative approach in NF-ISAC systems as it can provide drastic spectral efficiency gains and take advantage of the superior beam focusing and sensing capabilities of NF-ISAC for better interference management. Owing to its potential, CR-based NF-ISAC systems are being explored in latest literature [210]. In this work, the secondary device is an ISAC transmitter which tries to simultaneously communicate and sense using spectrum that is unlicensed. The primary receiver is a critical device and must not experience any interference from the ISAC transmitter. To reduce the computational complexity caused by NF channel models, the authors propose two suboptimal beamforming strategies: zero forcing-based, and maximum ratio transmission-based designs. They also develop a robust beamforming design under imperfect CSI. They formulate a transmit power minimization problem which meets the communication and sensing QoS demands. Their results validate the effectiveness of the proposed schemes. A typical ISAC system enhanced using cognitive radio is illustrated in Figure 9. Cognitive radio with ISAC can enable efficient spectrum utilization and the literature on the subject provides a solid foundation for the development of efficient algorithms and protocols. However, the practical verifications and real-world evaluations are required to verify the feasibility of such protocols. The efficient spectrum utilization greatly motivates the adoption of such systems as the deployment of cognitive radio systems does not require additional equipment and is also highly scalable owing to its cellular nature.

5. Lessons Learned and Perspectives

The proliferation of antenna elements, the migration to higher frequency bands, and the integration of sensing capabilities in next-generation wireless communication systems diversity the design considerations which must be addressed to ensure network sustainability. As the complexity of the underlying systems is expected to increase owing to the stringent demands of the future networks, the associated technologies are also evolving to efficiently manage and allow the scalable support of mass-scale, scalable deployments of wireless infrastructures. The literature surrounding both NF and ISAC systems is still in its early stages. As more progress is made to solidify their theoretical and practical foundations, the following challenges and future research directions need to be considered. Near-field operation unlocks substantial spatial DoFs and increases channel capacity as array sizes grow. However, achieving these gains requires a careful trade-off of complexity, energy consumption and capacity. Complexity encompasses the computational and algorithmic overhead required for accurate channel modeling, exhaustive beamtraining, and high-resolution codebook searches. Energy consumption is expanded by the increase in signal processing units and RF chains to support NF communication and sensing operations. The additional DoFs increase capacity but require advanced channel estimation, modeling, and beamfocusing to utilize the performance gains, which, in turn, drives up energy consumption. Therefore, system designers must balance these three factors while delivering acceptable network performance. We observe that reliable NF channel models are required for the analysis and evaluation of NF aided designs [96,101,102,103,104]. Increasingly sophisticated models have been proposed that consider the varying polarization, effective area and distance between different elements of an antenna array in the NF [234]. However, more reliable channel models are required to properly evaluate the effect of non-LoS channels within the NF. Future wireless communication systems increasingly employ larger antenna arrays, and as operating frequencies rise, the number of required antenna elements increases correspondingly. An increase in the number of array elements escalates the computational complexity of channel estimation and necessitates more precise estimation techniques to fully exploit the spatial DoFs in NF ISAC systems. The expanded channel estimation parameter space, which includes both angle and distance variables, increases computational complexity and energy consumption in NF-ISAC systems [105,106,107,108,110]. It also makes NF-ISAC systems highly sensitive to mobility, which further exacerbates computational challenges and synchronization. codebook design and beamtraining require two-dimensional searches over angle and range, expanding storage requirements, pilot overhead, and processing demands [129,130,131,133,134,137,144,145,146,147]. Hardware implementation at mmWave and terahertz bands demands precise timing synchronization, tight component tolerances, and mitigation of mutual coupling effects, all of which impact energy efficiency and practical feasibility. Advanced channel estimation algorithms, codebook design, and beam-training strategies can facilitate efficient channel state information retrieval for both communication and sensing.Advanced signal processing and machine learning techniques can substantially alleviate the inherent complexities of near-field ISAC systems. Hybrid beamforming architectures reduce hardware costs and training overhead by combining a limited number of RF chains with analog phase shifters. Hierarchical codebook designs enable beamtraining by progressively refining beam directions through multi-resolution search stages which cuts down on pilot transmission and feedback requirements. Meanwhile, deep learning and artificial intelligence-based methods can learn underlying channel states and spatial correlations to perform rapid, data-driven channel estimation and beam selection, even under imperfect hardware and non-ideal propagation conditions. Collectively, these approaches enable efficient exploitation of spatial DoFs for concurrent communication and sensing in large-scale, high-frequency, high density networks.

The beam focusing effect of NF beamforming benefits from the added distance resolution in addition to the standard angular resolution apparent in the FF. This added DoF, however, introduces complexity to the beamforming design and low-complexity methods have to be formulated that allow for the fast and efficient beamforming. The simultaneous communication and sensing operation makes this process even more complex owing to the trade-off between communication and sensing. The sensing and communication service requirements can vary depending on the use-case and therefore, as explored in this review, different problem formulations have to be considered allowing for varying solutions for different application scenarios. The comparative performance of NF systems is experimentally explored through spherical-wave signal models in different array structures, demonstrating accurate path-parameter estimation and reduced model mismatch. Further studies investigate spatial non-stationarity in massive MIMO via virtual array measurements and efficient ray-tracing strategies to maintain performance with lower complexity. Practical radar sensing experiments confirm centimetre-level localization accuracy, validating the viability of integrated NF communication and sensing on existing hardware. In practical systems, more robust solutions are required that either cater for a multitude of usage scenarios or are easily tunable to specific types of use-cases.

Future research on NF ISAC systems should develop unified frameworks that jointly optimize communication and sensing objectives under realistic channel models and hardware constraints. Low-complexity and adaptive algorithms leveraging machine learning and artificial intelligence, are needed to manage expanded parameter spaces and mobility dynamics. Energy-efficient architectures such as hybrid beamforming and low-power RF components will be critical for sustainable large-scale deployments. Robust calibration and synchronization techniques must be developed for operation at mmWave and terahertz frequencies. Prototyping and field trials in diverse environments will validate theoretical models and inform practical standardization.

6. Conclusions

In this paper, we have provided a comprehensive review of energy-efficient NF-ISAC systems. We first provided a detailed overview of the energy-efficiency challenges in conventional ISAC systems. We then delineated the fundamental distinctions between near-field and far-field wave propagation, and discussed the effects of spherical wavefronts, spatial non-stationarity, and depth resolution on beamforming, codebook design, and beamtraining. We then systematically discussed energy-efficient NF-ISAC systems and classified them into power-centric, sensing-centric, communication-centric, and joint designs. Finally, we identified emerging directions for sustainable NF-ISAC systems, e.g., wireless power transfer, RISs, backscatter technology, fluid antennas, and cognitive radios. Joint NF-ISAC designs have demonstrated that precise modeling of near-field propagation with spherical wavefronts and spatial non-stationarity considerations enables simultaneous high-resolution sensing and high-throughput communication within practical power budgets. Algorithmic frameworks such as closed-form beamforming, hybrid frameworks, intelligent and iterative optimization architectures effectively balance sensing accuracy and communication demands. Scalability is attained through hybrid analog-digital architectures and coarse-refinement techniques which reduce computational complexity without sacrificing performance. However, systems must utilize advanced signal processing, hybrid beamforming, and hierarchical codebook and beamtraining designs to optimize the energy-capacity trade-off. Experimental validations confirm centimeter-level localization precision and robust multiplexing gains under realistic hardware constraints. Future efforts should emphasize unified channel modeling and low-complexity calibration methods to accelerate the deployment of NF-ISAC in 6G and beyond. Collectively, this work provides a standalone review of energy-efficient NF-ISAC systems.