Sensing-Assisted Communication for mmWave Networks: A Review of Techniques, Applications, and Future Directions

Abstract

The emergence of 6G wireless systems marks a paradigm shift toward intelligent, context-aware networks that can adapt in real-time to their environment. Within this landscape, Sensing-Assisted Communication (SAC) emerges as a key enabler, integrating perception into the communication control loop to enhance reliability, beamforming accuracy, and system responsiveness. Unlike prior surveys that treat SAC as a subfunction of Integrated Sensing and Communication (ISAC), this work offers the first dedicated review of SAC in Millimeter-Wave (mmWave) and Sub-Terahertz (Sub-THz) systems, where directional links and channel variability present core challenges. SAC encompasses a diverse set of methods that enable wireless systems to dynamically adapt to environmental changes and channel conditions in real time. Recent studies demonstrate up to 80% reduction in beam training overhead and significant gains in latency and mobility resilience. Applications include predictive beamforming, blockage mitigation, and low-latency Unmanned Aerial Vehicle (UAV) and vehicular communication. This review unifies the SAC landscape and outlines future directions in standardization, Artificial Intelligence (AI) integration, and cooperative sensing for next-generation wireless networks.

1. Introduction

The evolution towards 6G wireless networks marks a departure from conventional communication-centric designs toward intelligent, context-aware infrastructures. A core enabler of this transformation is ISAC, wherein shared Radio Frequency (RF) waveforms and hardware are leveraged to simultaneously perform data transmission and environmental perception [1,2]. By merging sensing and communication functionalities, ISAC enhances spectral efficiency, reduces hardware redundancy, and equips wireless systems with real-time awareness of surrounding dynamics—an essential capability for applications such as autonomous driving, aerial robotics, and industrial automation [3,4]. This integration addresses fundamental spectrum scarcity by reusing radio resources for dual purposes, while also drawing on principles of joint estimation and communication in information theory. In essence, ISAC can be viewed as an evolution of traditional RF system design, where capacity and sensing accuracy are co-optimized rather than treated in isolation.

Despite these advantages, ISAC alone does not fully address the challenges of high-frequency wireless systems, particularly in mmWave and Sub-THz bands. These bands offer massive bandwidth and support highly directional transmission, but they are also extremely sensitive to signal blockage, fast-varying channels, and frequent beam misalignment [5,6,7]. Conventional solutions such as exhaustive beam training or periodic sweeping are inefficient under such conditions, introducing latency and overhead that are especially detrimental in high-mobility scenarios like Vehicle-to-Infrastructure (V2I) or UAV communications [5,7]. To overcome these limitations, the concept of SAC has gained attention as a complementary and communication-driven realization of the ISAC paradigm. SAC leverages real-time environmental information—obtained from Radio Detection and Ranging (Radar), Light Detection and Ranging (LiDAR), Global Positioning System (GPS), inertial sensors, or through jointly designed ISAC hardware systems—to aid and improve communication decisions such as beam steering, handover management, and link adaptation [8,9,10]. By tightly integrating sensing into the communication process, SAC enables systems to anticipate and react to environmental changes, offering significant gains in responsiveness, reliability, and efficiency [5,6].

While previous studies have considered SAC a secondary component of ISAC, in this review, we highlight SAC as an emerging research area with its unique challenges, methodologies, and opportunities, thus warranting independent investigation. Specifically, the focus is placed on SAC in the context of mmWave and Sub-THz systems, where its impact is most pronounced. SAC methodologies are classified into model-based estimators (e.g., Kalman Filters (KFs), Particle Filters (PFs)), geometric and radar-based sensing frameworks, and learning-based approaches that use deep or reinforcement learning for beam prediction and channel estimation [5,9,10,11]. Key procedures such as predictive beamforming, overhead reduction, and mobility-aware beam alignment are also examined.

The key contributions of this review are summarized as follows:

• A comprehensive review is conducted focusing specifically on SAC for mmWave and Sub-THz wireless systems, clearly distinguishing it from general ISAC and emphasizing its role in enhancing beamforming, reliability, and adaptability.
• SAC methodologies are classified and analyzed across both model-based and data-driven approaches, including KFs, PFs, radar-guided prediction, and deep learning frameworks [5,9].
• A structured taxonomy of SAC techniques is introduced, organized by sensing modality, prediction algorithm, and deployment integration, to serve as a foundation for comparative evaluation.
• Real-world applications are highlighted and discussed where SAC plays a transformative role, such as in Vehicle-to-Everything (V2X) communication, UAV connectivity, and high-frequency Wireless Local Area Networks (WLANs) with particular emphasis on predictive beamforming and training overhead reduction [5,6,7].
• Open challenges and future directions are outlined, underscoring SAC’s potential as a foundational component of context-aware and low-latency 6G network architectures [1,3].

To position this review within the context of the existing literature,

Table 1. Comparison of recent ISAC and SAC review articles.

Paper	ISAC Focus	SAC-Specific	Taxonomy	Application Domains	Beamforming Integration
[1]	✓	✓	Partial	Broad (6G)	No
[2]	✓	✕	No	General ISAC	No
[5]	✕	✓	No	V2X vehicular only	Partial
[10]	✕	✓	No	V2X, radar-centric	Yes
[6]	✓	✓	No	Beam alignment in mmWave	Partial
This review	✓	✓	SAC-focused	V2X, UAV, Indoor mmWave	Core to SAC

provides a comparative analysis of recent survey papers related to ISAC and SAC. As shown, most prior works [1,2,5,6,10] either address SAC only as a subset of broader ISAC discussions or focus on narrow technical domains such as vehicular radar or beam alignment. In contrast, this work presents a dedicated, SAC-centric review that offers a structured taxonomy, explores diverse sensing modalities, and emphasizes the role of SAC in enhancing communication robustness in mmWave and Sub-THz systems. Furthermore, beamforming integration—central to the SAC paradigm—is addressed here as a core design axis rather than a peripheral topic.

The remainder of the paper is organized as follows and illustrated in Figure 1: Section 2 reviews radar as a foundational sensing function. Section 3 outlines the ISAC framework and its architectural variants. Section 4 presents the core methodologies, applications, and advantages of SAC. Section 5 discusses open challenges and outlines future research directions, while Section 6 provides the concluding remarks.

2. Radar as a Foundational Sensing Function

Radar has long served as a foundational technology in wireless sensing, offering precise estimation of environmental parameters such as range, velocity, and angle. By transmitting RF signals and analyzing their echoes, radar systems enable essential sensing functionalities—detection, localization, and tracking—that are critical to intelligent and adaptive wireless environments [12]. With the emergence of ISAC, radar has re-emerged as a natural candidate for embedding sensing capabilities into wireless communication systems. This resurgence is largely attributed to the architectural and algorithmic parallels shared between radar and communication domains. Commonalities in waveform structures (e.g., Orthogonal Frequency-Division Multiplexing (OFDM)), antenna architectures (e.g., Multiple-Input Multiple-Output (MIMO) arrays), and signal processing techniques (e.g., matched filtering and Fast Fourier Transform (FFT)) facilitate seamless integration using shared hardware platforms [2,13].

Early ISAC investigations demonstrated the feasibility of superimposing communication data on radar waveforms, enabling dual-function systems without requiring additional spectrum or transceiver chains [14]. This principle of waveform duality laid the foundation for co-design approaches that minimize hardware redundancy, enhance spectral efficiency, and simplify system deployment. These efficiencies are becoming increasingly critical, particularly as communication demand and spectrum congestion escalate in the mmWave and Sub-THz bands [15,16]. Furthermore, radar inherently supports key physical-layer strategies—such as beamforming, spatial filtering, and MIMO transmission—that are equally vital to high-frequency wireless communication. This functional synergy enables unified transceiver architectures that support both sensing and communication objectives, facilitating operations such as joint beam design, proactive link adaptation, and channel-aware transmission. Within this context, radar not only complements communication functions but also enables paradigms such as SAC and Communication-Assisted Sensing (CAS), which jointly enhance system awareness and responsiveness [1]. Accordingly, radar is positioned not merely as a compatible modality but as a strategic cornerstone of ISAC. It enables spectrum co-utilization, real-time environmental awareness, and hardware consolidation—capabilities that are essential for realizing context-aware and resource-efficient 6G wireless networks.

The remainder of this section explores, in detail, the radar-specific factors that underpin its central role in ISAC-enabled systems. The subsequent subsections examine: (i) architectural variations in radar design and their integration potential with communication systems; (ii) geometric configurations such as monostatic, bistatic, and multistatic setups; and (iii) signal processing techniques that enable radar to support dual-function operation with high precision and low latency.

2.1. Radar Architectures

Radar architectures vary significantly in their design complexity, spatial resolution, and suitability for co-integration with communication systems. Within the ISAC paradigm, several radar types have emerged as prominent candidates due to their alignment with communication hardware, signal processing techniques, and spectral characteristics.

• Phased-Array Radar: This architecture employs electronically steered antenna arrays, in which each element is driven with programmable phase shifts to form directional beams without requiring mechanical movement [17]. Phased-array systems enable agile beam steering, high array gain, and support for multi-beam operation. These properties are especially advantageous in ISAC applications operating at mmWave and Sub-THz frequencies, where rapid spatial adaptation is critical [18].
• Frequency-Modulated Continuous-Wave (FMCW) Radar: FMCW radar transmits a continuously swept-frequency waveform (chirp) and extracts target range and velocity by analyzing the frequency difference between transmitted and received signals [19]. The low peak power and high resolution of FMCW systems make them well-suited for short-range sensing and compact ISAC transceiver implementations.
• MIMO Radar: In MIMO radar systems, orthogonal waveforms are transmitted from multiple antennas, and received signals are jointly processed to synthesize a large virtual aperture [20]. This architecture significantly enhances angular resolution and is inherently compatible with MIMO communication platforms, thereby facilitating hardware reuse and co-design for ISAC [2,21].
• Passive Radar: Passive radar systems exploit ambient RF signals—such as cellular, television, or Wi-Fi transmissions—as illuminators of opportunity [22]. Although such systems do not have control over the transmitted waveform, they offer spectral efficiency, low implementation cost, and reduced probability of intercept. These features make passive radar particularly attractive for covert or interference-sensitive ISAC scenarios in dense wireless environments [23].
• Pulse-Doppler Radar: This classical radar configuration transmits periodic pulses and applies matched filtering combined with Doppler analysis to simultaneously extract range and velocity information [17]. Widely used for moving target indication and clutter suppression, pulse-Doppler radar can be adapted for coexistence with communication signals in dual-function ISAC transceivers [24,25].

2.2. Radar Sensing Topologies

The spatial configuration of transmit and receive components in a radar system plays a pivotal role in determining its sensing capability, resolution, and feasibility for integration within ISAC frameworks. Key parameters such as antenna separation, synchronization constraints, angular diversity, and deployment flexibility vary significantly across geometric arrangements, influencing both performance and implementation complexity [17]. A systematic understanding of these topologies is crucial for the design of ISAC systems that jointly optimize communication reliability and situational awareness, especially in dynamic or infrastructure-constrained scenarios.

• Monostatic Radar: In monostatic radar, the transmitter and receiver are co-located, often sharing a common antenna through a circulator or duplexer. This geometry simplifies transceiver design and synchronization, while providing strong target echoes and high Signal-to-Noise ratios (SNRs), particularly at short to medium ranges [17]. Monostatic systems are widely adopted in automotive, aerospace, and fixed-node applications. Within ISAC, this geometry enables a single transceiver to perform both communication and environmental sensing using the same waveform and hardware chain [2].
• Quasi-Monostatic Radar: This configuration serves as an intermediate case in which transmit and receive antennas are spatially separated by a short distance, typically within the same device enclosure. While maintaining much of the synchronization and calibration simplicity of monostatic systems, quasi-monostatic radar offers improved angular diversity and enhanced spatial resolution. Recent ISAC studies have employed this setup in benchmark experiments to assess Angle-of-Arrival (AoA) estimation and near-field beam prediction [26].
• Bistatic Radar: Bistatic radar employs a transmitter and receiver at geographically distinct locations, separated by a considerable distance, ranging from tens to hundreds of meters. This arrangement enhances spatial diversity and provides robustness to multipath and Non-Line-of-Sight (NLoS) conditions [27]. It also offers increased stealth, as the receiving node operates passively without emitting signals. However, challenges such as time synchronization, waveform knowledge at the receiver, and propagation path modeling must be addressed. In cooperative ISAC systems, bistatic radar configurations are particularly useful when sensing and communication are functionally decoupled between distributed nodes [27].
• Multistatic Radar: Multistatic architectures generalize the bistatic approach by utilizing multiple transmitters and/or receivers deployed across a wide area. This geometry enhances robustness by leveraging angular diversity and sensor fusion to improve tracking accuracy in cluttered environments or under occlusion [28,29]. Multistatic radar is especially beneficial in vehicular and smart infrastructure networks, where distributed ISAC nodes collaboratively build an environmental map while maintaining data connectivity. However, this configuration imposes stringent requirements on inter-node synchronization, low-latency communication links, and scalable data fusion algorithms [30,31].

A visual summary of these topologies is provided in Figure 2, adapted from fundamental radar classification principles [17], while their comparative features are summarized in

Table 2. Comparison of radar topologies: Monostatic, Quasi-Monostatic, Bistatic, and Multistatic.

Configuration	Tx/Rx Arrangement	Advantages	Challenges
Monostatic	Co-located transmitter and receiver	Simplified design; strong echoes; mature technology [2,17]	Limited angular diversity; easier to detect [17]
Quasi-monostatic	Slight Tx/Rx separation within a common node	Better angular resolution; simple synchronization [26]	Less diversity than bistatic; moderate added complexity [26]
Bistatic	Spatially separated Tx and Rx	Covert reception; geometric diversity; stealth detection [27]	Requires synchronization and waveform knowledge [27]
Multistatic	Distributed Tx/Rx nodes with overlapping coverage	Robust detection; spatial diversity; cooperative sensing [28,30,31]	High coordination and data fusion overhead [29,31]

.

2.3. Localization Techniques in Wireless Sensing

Localization has long been recognized as a cornerstone of wireless networks, since effective communication requires knowledge of the position and mobility of users and infrastructure nodes. Position and motion awareness directly support mobility management, handover, and resource allocation, and in high-frequency systems such as mmWave and Sub-THz, they are indispensable for beam alignment and blockage mitigation. In fact, localization is increasingly regarded as a fundamental quality enabler for next-generation networks, complementing connectivity and capacity objectives [32]. As networks evolve toward 6G, accurate localization is becoming not only a service but also a foundational building block for integrated communication and sensing frameworks [33].

Radar inherently produces environmental measurements—range, angle, and velocity—that can be transformed into position and orientation estimates [34,35]. Several classes of localization techniques have been extensively studied. AoA methods exploit antenna arrays to infer directionality and are well suited to monostatic or MIMO-based radar topologies; their accuracy improves with aperture size, but they require careful calibration and are sensitive to multipath [35]. Time-of-Arrival (ToA) techniques determine distance from absolute propagation delay and achieve high precision with wideband signals, although they depend on tight synchronization across transceivers [36]. Time-Difference-of-Arrival (TDoA) alleviates clock bias by comparing delays across distributed receivers, making it attractive for bistatic and multistatic architectures, but at the cost of synchronization and coordination complexity [37]. Received Signal Strength Indicator (RSSI)-based methods are simpler and cost-effective, requiring no additional synchronization, but they suffer from low accuracy under fading and shadowing conditions [32]. Finally, hybrid approaches—such as combining AoA with TDoA or AoA with RSSI—integrate complementary modalities to enhance robustness in NLoS and high-mobility scenarios, though at the expense of computational overhead [36].

Beyond single-user positioning, recent studies have highlighted the importance of multi-user and network-wide localization. For example, multi-user positioning and beamforming in mmWave MIMO systems can be jointly optimized, underscoring the link between localization accuracy and communication performance [38]. Similarly, mmWave MIMO localization can simultaneously provide position and orientation information, thereby strengthening the integration of sensing and communication [35]. These contributions indicate that localization is not merely auxiliary but central to predictive beamforming, mobility-aware adaptation, and proactive handover in next-generation systems.

Table 3. Comparison of representative localization techniques, their applicable topologies, advantages, and challenges.

Ref	Technique	Applicable Topologies	Advantages	Challenges
[36]	AoA	Monostatic, MIMO, Multistatic	High angular resolution with large arrays; aligned with MIMO architectures	Sensitive to calibration errors and multipath
[36]	ToA	Bistatic, Multistatic	Direct range estimation; high accuracy with wideband signals	Requires tight synchronization; prone to NLoS bias
[37]	TDoA	Bistatic, Multistatic	Eliminates clock bias; robust with distributed receivers	High synchronization and coordination complexity
[32]	RSSI	Monostatic, Bistatic	Simple, low-cost, no extra synchronization	Poor accuracy; vulnerable to fading and blockage
[34,36]	Hybrid (AoA+TDoA, AoA+RSSI)	Multistatic	Combines complementary modalities; robust under NLoS and mobility	Higher computational burden; requires fusion algorithms
[38]	Multi-user Positioning & Beamforming	MIMO, Multistatic	Joint optimization of positioning and communication; enhances SAC beam alignment	Requires joint design of sensing and communication resources

summarizes representative localization techniques, their applicability to radar topologies, and their main advantages and challenges.

2.4. Radar Signal Processing Techniques

The performance, resolution, and real-time adaptability of radar systems are fundamentally determined by the underlying signal processing algorithms. These techniques transform raw echoes into actionable information—such as range, velocity, and angular position—and are critical for the integration of radar into ISAC systems. The following methods represent the foundational processing strategies enabling radar to serve as a high-resolution sensing modality within next-generation wireless networks:

• Matched Filtering and Pulse Compression: Matched filtering maximizes the SNR by correlating received waveforms with their transmitted counterparts [39]. To maintain both long-range detection capability and fine range resolution, pulse compression techniques are applied using Linear Frequency-Modulated (LFM) chirps or phase-coded sequences. These methods, though classical, remain essential for high-accuracy sensing in cluttered environments.
• Doppler Processing: Doppler processing leverages the frequency shift of received echoes across successive pulses to extract radial velocity information. Through coherent pulse integration and FFT analysis, Moving Target Indication (MTI) is achieved, enabling the radar to distinguish dynamic objects from stationary clutter [40]. Despite its long-standing use, Doppler processing remains a cornerstone for ISAC scenarios involving mobile users or vehicles, where motion-sensitive feedback enhances beam adaptation.
• FMCW Radar Processing: In FMCW radar, a continuous chirp signal is transmitted, and the frequency difference (beat frequency) between the transmitted and received waveforms is analyzed to determine both range and velocity. The linearity of the frequency sweep facilitates high-resolution ranging with relatively low power and compact hardware [40]. FMCW is widely adopted in automotive and short-range ISAC systems due to its efficiency and scalability.
• MIMO Radar Processing: MIMO radar exploits spatial diversity by transmitting orthogonal waveforms from multiple antennas and jointly processing the echoes at multiple receivers. This configuration synthesizes a large virtual aperture, improving angular resolution without requiring dense physical antenna arrays. Beamspace Direction-of-Arrival (DoA) estimation techniques—such as Multiple Signal Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT)—are employed for high-precision target localization [41]. The alignment of MIMO radar processing with MIMO communications makes it a natural fit for ISAC platforms.
• Compressive Sensing(CS): CS leverages the sparsity inherent in radar scenes—i.e., the presence of relatively few dominant scatterers—to recover target parameters from a reduced set of measurements, often sampled below the Nyquist rate [42]. CS reduces the Analog-to-Digital Converter (ADC) sampling burden and computational cost, and is particularly useful for low-power ISAC devices. It also supports simultaneous sensing and communication by sharing subcarriers or time slots while maintaining recovery accuracy.
• AI-Enhanced Cognitive Radar: Cognitive radar architectures employ adaptive sensing strategies guided by machine learning algorithms. These systems dynamically adjust waveforms, transmit power, and beam directions in response to environmental feedback. Learning-based models—ranging from supervised classifiers for clutter rejection to reinforcement learning agents for waveform adaptation—enable robust performance in complex, time-varying conditions [22]. In ISAC contexts, AI-enhanced radar modules can support real-time situational awareness while reducing reliance on static beam training or deterministic signal models.

These advanced processing techniques form the algorithmic core of radar’s adaptability, precision, and real-time responsiveness. Within ISAC systems, foundational methods such as matched filtering and Doppler analysis provide reliable baseline functionality, while advanced tools such as MIMO processing, compressive sensing, and AI-driven radar unlock high-resolution spatial awareness and low-complexity integration. As ISAC architectures evolve toward tighter integration and shared infrastructure, this layered set of signal processing strategies will play a pivotal role in achieving low-latency, low-power, and context-aware wireless systems [22,40].

3. ISAC: The Road to Integration

ISAC represents a transformative paradigm in wireless system design, where communication and sensing functions are performed jointly using the same hardware, spectrum, and signaling resources [3]. Traditionally, radar and communication systems have evolved independently, resulting in separate infrastructure, spectrum allocation, and waveform design. However, the exponential growth of wireless devices, spectrum scarcity, and the increasing demand for environment-aware intelligent services have catalyzed the convergence of these two functionalities [13]. Consequently, the integration of sensing capabilities into the communication infrastructure offers several compelling advantages. First, spectrum reuse leads to higher efficiency and coexistence between diverse applications [15]. Second, hardware reuse minimizes deployment cost and form factor, especially critical for mobile and embedded platforms. Third, ISAC enables novel applications such as radio-based Simultaneous Localization and Communication (SLAC), vehicular object tracking with connectivity, and ambient sensing in smart environments [1].

Furthermore, the evolution of ISAC has been driven by technological breakthroughs across multiple domains. These include the development of reconfigurable intelligent surfaces (RIS), mmWave and Sub-THz frequency utilization, massive MIMO (mMIMO) systems, and AI-enabled signal processing [43,44]. Collectively, these innovations allow joint transceivers to operate with high spatial resolution, directional beamforming, and predictive environment modeling, thereby supporting both high-capacity data transmission and precise target detection.

In addition, several 6G initiatives have recognized ISAC as a foundational pillar in the future network architecture [31]. The design challenge lies not only in the cohabitation of radar and communication tasks but also in achieving an optimal balance between them. To this end, recent research has explored trade-offs in power allocation, waveform duality, and interference management to maximize joint performance [22]. Ultimately, ISAC is envisioned as a critical enabler for ubiquitous intelligence, where devices continuously sense and adapt to their environment while maintaining robust connectivity. Accordingly, the next subsections delve into ISAC architectures, waveform design strategies, benefits, and challenges in greater detail.

3.1. ISAC Architectures

The architectural taxonomy of ISAC systems is pivotal to their scalability, functional versatility, and deployment feasibility in 6G networks. Figure 3 presents a four-level classification of ISAC architectures based on integration depth. This hierarchy reflects a systematic evolution from loosely coupled coexistence to tightly unified, network-level integration, each with distinct design trade-offs and implementation challenges.

• Level 1–Coexistence: At the foundational level, sensing and communication subsystems merely share spectral resources and possibly front-end hardware, but remain functionally independent in waveform design, signaling, and processing. This architecture is commonly applied in automotive radar systems that operate alongside wireless communication modules [45]. While it allows low-complexity retrofitting of legacy systems, it often suffers from spectrum contention and uncoordinated interference [43].
• Level 2–Shared Hardware: This level introduces partial physical integration where both subsystems share antennas or RF front-end modules, while maintaining separate baseband chains. It enables moderate hardware reuse and simplified deployment, particularly in constrained or cost-sensitive environments [19].
• Level 3–Joint Signaling and Processing: In this architecture, unified waveform design and coordinated baseband signal processing are implemented, allowing radar and communication to operate over shared time-frequency resources. This tight integration supports advanced beamforming, joint channel estimation, and environmental awareness, which are essential for applications such as UAV navigation and V2X systems [1,43]. However, achieving this level requires rigorous synchronization and joint optimization algorithms.
• Level 4–Network-Level Integration: The highest degree of integration is achieved at the network level, where ISAC functionalities are holistically optimized across distributed nodes. Through deep cooperation in task allocation, data fusion, and dynamic resource management, this architecture enables scalable, context-aware sensing-communication platforms for mission-critical applications in smart factories, vehicular networks, and infrastructure monitoring [19,43].

This hierarchical view provides system designers with a conceptual roadmap to transition from legacy coexistence schemes to fully integrated, software-defined ISAC networks. The choice of integration level is typically dictated by system requirements, regulatory limitations, and available hardware-software capabilities [1,45].

3.2. Geometric Configurations for ISAC

The spatial geometry between transmit and receive elements plays a fundamental role in shaping the performance, resolution, and deployment flexibility of ISAC systems. As ISAC evolves from classical radar foundations, it inherits three well-established geometric configurations—monostatic, bistatic, and multistatic—which are reinterpreted in the context of wireless communication networks [2,45].

Monostatic ISAC systems co-locate the transmitter and receiver, often within a single transceiver unit such as a base station. By analyzing the echoes of its own transmitted signals, the node can infer environmental features such as object range and motion. This architecture simplifies timing synchronization and hardware design but is typically limited by reduced angular diversity and line-of-sight dependence [2]. Monostatic setups are commonly used in access point-based sensing and cellular infrastructure.

Bistatic ISAC architectures distribute the transmit and receive nodes spatially, allowing the sensing unit to capture reflections from signals it did not transmit. For example, a user device or passive reflector may receive downlink communication signals and analyze their echoes for environmental insight. While this arrangement increases synchronization complexity, it improves spatial coverage and robustness to blockage, making it ideal for cooperative vehicular or device-to-device ISAC [46,47].

Multistatic ISAC extends this idea by deploying multiple, spatially distributed transmitters and receivers that operate collaboratively. These configurations enable rich scene understanding through sensor fusion, spatial diversity, and wide-area coverage. However, they demand tight coordination, data sharing, and distributed processing. Multistatic ISAC is especially promising for vehicular networks, smart cities, and large-scale indoor sensing [48,49,50].

Additionally, in communication scenarios involving Multiple Access Channels (MAC) or Broadcast Channels (BC), transmitter and receiver roles may switch dynamically based on traffic or sensing needs. This role fluidity introduces new opportunities for adaptive geometric deployment in ISAC systems.

Table 4. Comparison of ISAC geometric configurations.

Configuration	Synchronization Complexity	Spatial Coverage	Typical Use Cases
Monostatic	Low (self-synchronized) [2,45]	Local, directional [2]	Base station sensing, indoor localization, industrial robotics [2]
Bistatic	Medium (requires coordination) [46,47]	Moderate (dual-node coverage) [46,47]	V2X sensing, cooperative edge devices, stealth detection [46,47]
Multistatic	High (multi-node synchronization) [49,50]	Wide-area, multi-angle [49,50]	Smart city infrastructure, vehicular networks, environmental monitoring [49,50]

presents a comparison of typical ISAC geometric configurations, including monostatic, bistatic, and multistatic, in terms of synchronization complexity, spatial coverage, and relevant use cases. This classification highlights how different architectures trade off between implementation complexity and sensing capability.

3.3. Signal and Waveform Design

Waveform design lies at the core of ISAC systems, as it governs the ability to fulfill both sensing and data transmission requirements using a unified signal structure. Effective waveform strategies must simultaneously preserve radar-specific attributes—such as ambiguity resolution, Doppler sensitivity, and range estimation accuracy—while maintaining communication-centric features like spectral efficiency, low peak-to-average power ratio (PAPR), and resilience to interference [51,52]. Several key waveform design approaches have emerged in the recent ISAC literature:

• OFDM-Based Waveforms: OFDM has gained significant traction due to its inherent compatibility with modern wireless systems and its suitability for dual-functionality. By embedding radar pilot tones within the communication subcarriers, OFDM enables simultaneous range and Doppler estimation while supporting data transmission [46]. However, challenges such as high PAPR and inter-carrier interference must be addressed for robust ISAC performance.
• LFM Chirps and Phase-Coded Sequences: Traditional radar waveforms such as LFM chirps and Golay complementary codes offer excellent autocorrelation properties, making them ideal for accurate time-delay and Doppler estimation [47]. These waveforms can be adapted for communication by overlaying modulation or employing time-division multiplexing. Golay sequences, in particular, have been adopted in IEEE 802.11ay standards for joint localization and data delivery.
• Multi-Objective Optimization: Joint radar-communication waveform design is framed as a multi-objective optimization problem, where conflicting metrics such as communication rate, radar resolution, and interference suppression are balanced [49]. Recent approaches include Pareto optimality, weighted sum methods, and reinforcement learning models that dynamically tune waveform parameters based on environment and task priorities.
• Adaptive Waveform Selection: In scenarios where environmental conditions or application goals vary over time, ISAC systems may switch between predefined waveform families. This approach is especially effective in UAV-based sensing or vehicular networks where LoS conditions, mobility, and interference profiles fluctuate dynamically [50].
• Spectral and Spatial Precoding: Precoding techniques in both frequency and spatial domains are used to tailor waveforms for angular selectivity and interference minimization. For example, directional precoding combined with MIMO beamforming allows ISAC systems to focus sensing energy while maintaining communication reliability [53].

To aid in comparative design decisions,

Table 5. Comparison of ISAC waveform design strategies.

Waveform Design Strategy	Key Benefits	Challenges	Application Scenarios
OFDM-Based ISAC [46]	Enables dual-functionality in mmWave	High PAPR, sensitive to interference	Vehicular networks, 802.11ad/ay WLANs
LFM and Phase-Coded [47]	Strong radar accuracy, low sidelobes	Limited spectral efficiency	Short-range sensing, industrial IoT
Multi-Objective Design [49]	Balanced sensing and communication	Computational overhead, design complexity	Adaptive 6G base stations, smart factories
Adaptive Selection [50]	Flexibility under changing conditions	Requires feedback and control logic	UAV navigation, mission-driven sensing
Spectral Precoding [53]	Improved angular resolution	Needs accurate CSI and calibration	MIMO arrays, beam-aware ISAC

summarizes key trade-offs among major ISAC waveform strategies in terms of implementation complexity, sensing performance, and communication compatibility.

3.4. Surface and Antenna Platforms for ISAC

Beyond signal and waveform co-design, the choice of physical aperture is pivotal for ISAC at mmWave and Sub-THz frequencies. Conventional phased arrays face escalating power and cost as element counts increase, driven by dense phase-shifter networks, high-speed DAC/ADC chains, and stringent thermal constraints. Wideband operation further exacerbates beam squint and calibration complexity. In ultra-massive MIMO regimes, near-field effects enlarge the focal region and impose tighter synchronization and linearity requirements. These demands become especially critical when a single front end must simultaneously deliver high-fidelity sensing (range/angle/Doppler) and reliable data transmission [29,51,54].

To address these limitations, metasurface-based apertures such as Reconfigurable Intelligent Surfaces (RIS) and Intelligent Omni-Surfaces (IOS) have been proposed. By shifting wavefront control to large arrays of low-power elements, such metasurfaces reduce per-element RF power consumption. As a result, they can not only steer illumination toward users but also shape echo fields to enhance observability for sensing, thereby enabling the joint optimization of surface states and ISAC waveforms. Compared with fully active phased arrays, RIS/IOS platforms perform environment-level beamforming that is advantageous under blockage and NLoS conditions, while also reducing front-end power requirements [29,51]. This potential is supported by prototype evidence: an International Mobile Telecommunications (IMT)-2030 RIS-based testbed at mmWave demonstrates multi-stream ultra-massive MIMO with substantially reduced RF-chain power relative to conventional arrays, confirming feasibility for large-aperture ISAC deployments [55].

In parallel, holographic MIMO has emerged as a complementary approach. Holographic apertures treat the array as quasi-continuous, with sub-wavelength sampling that enables near-field focusing and provides higher spatial degrees of freedom than conventional discrete arrays. These features benefit both fine angular and range resolution, as well as multi-stream communications [33]. Nevertheless, they also introduce hybrid near–far field propagation effects that can degrade the performance of conventional estimators. Recent advances in hybrid near–far field channel estimation have begun to mitigate power diffusion and calibration challenges in holographic apertures [56]. Building on these capabilities, holographic ISAC (H-ISAC) leverages reconfigurable holographic surfaces to realize low-power, high-diversity apertures for joint sensing and data transmission. Early system-level analyses and experimental demonstrations indicate reduced power consumption relative to phase-shifter arrays, while preserving high spatial resolution [51,57,58,59]. For ISAC operation beyond 30 GHz, metasurface and holographic platforms (i) lower per-element RF power by concentrating control in passive or programmable elements, (ii) enhance angle–range resolution through near-field focusing, and (iii) enable new joint designs that couple surface states, waveforms, and estimators with predictive control in mobility-constrained scenarios.

3.5. Applications of ISAC

ISAC is a cornerstone technology for emerging 6G networks, enabling real-time environmental perception alongside high-throughput data exchange. Its ability to unify sensing and communication on shared spectral and hardware resources unlocks significant advantages in latency, scalability, and energy efficiency. Key application domains include the following:

• Automotive Systems: In V2X communication networks, ISAC facilitates cooperative perception and motion awareness by reusing sidelink or downlink signals for NLOS detection and obstacle tracking [52,60]. Unlike traditional automotive radar, ISAC reduces hardware redundancy and improves safety through shared real-time situational awareness.
• Aerial Sensing and UAVs: UAVs benefit from ISAC by leveraging communication waveforms for terrain mapping, obstacle avoidance, and autonomous navigation [61]. Beyond outdoor missions, UAVs are increasingly explored for indoor applications, such as automated inspection in warehouses and factories, infrastructure monitoring in large public venues (e.g., airports or stadiums), and emergency response in GNSS-denied environments (e.g., collapsed buildings or underground facilities). In these scenarios, ISAC-enabled UAVs can simultaneously use communication signals for connectivity and localization while employing radar or vision-assisted sensing for obstacle detection and collision avoidance, thereby enabling safe and reliable operation of lightweight, multi-purpose aerial platforms [61,62,63].
• Smart Infrastructure: ISAC-enabled mmWave and Wi-Fi access points can perform dual functions such as occupancy detection, human activity recognition, and gesture classification, all while maintaining wireless connectivity [64]. This enables cost-effective, privacy-preserving ambient sensing in smart homes, offices, and urban deployments.
• Defense and Emergency Response: In adversarial or disaster-prone scenarios, ISAC systems support low-probability-of-intercept (LPI) communication while simultaneously sensing terrain, movement, or trapped individuals [63]. These dual-function devices enable search-and-rescue operations, stealth communications, and real-time situational feedback with minimal infrastructure.

A comparative summary of latency demands, sensing resolution, and core ISAC benefits across these domains is provided in

Table 6. Representative applications of ISAC across domains.

Application Domain	Latency Requirement	Sensing Resolution	ISAC Benefits
Automotive (V2X)	≤10 ms	≤0.1 m (range); ≤5° (angle)	NLOS detection; reduced hardware overhead; joint localization
UAV and aerial sensing	10–50 ms	0.5–1 m (range); ≤10° (angle)	GNSS-independent mapping; low-payload integration
Smart infrastructure	50–100 ms	1–2 m (range); medium angular resolution	Human detection; occupancy monitoring; gesture tracking
Defense and rescue	≤20 ms	Environment-dependent	LPI communication; passive sensing; robust in degraded conditions

.

3.6. Benefits and Performance Gains

ISAC offers a wide range of system-level benefits that address fundamental challenges in spectrum scarcity, infrastructure complexity, and environment-aware connectivity. By unifying communication and sensing on a shared physical layer, ISAC provides the following key advantages:

• Spectrum Efficiency: ISAC enables full spectrum reuse by embedding sensing and communication functionalities within the same waveform and frequency bands. This drastically reduces the need for dedicated radar spectrum and enhances coexistence across heterogeneous wireless services [3].
• Hardware and Energy Efficiency: By using shared RF front ends, antennas, and baseband processors, ISAC systems eliminate the duplication of hardware components. This significantly reduces power consumption, footprint, and overall deployment costs, particularly in dense access networks and mobile edge environments [1].
• Low-Latency Adaptation: ISAC’s real-time environmental awareness facilitates proactive beam steering, resource allocation, and mobility prediction. This minimizes handover delays and improves robustness in high-mobility or obstructed scenarios such as vehicular and UAV networks [3].
• Situational Awareness and Environmental Intelligence: Continuous radar-based sensing allows ISAC transceivers to detect obstacles, track users, and model spatial features. This contextual knowledge enables environment-aware services such as smart infrastructure control, gesture recognition, and adaptive coverage planning [57].

These benefits are concisely summarized in

Table 7. Comparison between traditional wireless communication systems and ISAC.

Feature	Traditional Systems	ISAC Approach
Spectrum Use	Dedicated to either sensing or communication	Shared spectrum for dual functionality
Hardware Architecture	Separate RF chains and baseband paths	Unified transceiver and baseband processing
Environmental Awareness	Absent or limited (feedback-based)	Continuous radar-based spatial awareness
Latency Handling	Reactive handovers	Proactive beam and resource adaptation
Cost and Energy Efficiency	High due to redundant components	Improved via reuse of spectrum and hardware

, which contrasts ISAC with conventional wireless communication systems across several critical performance dimensions.

These benefits are particularly evident when comparing the conventional architecture with separate radar and communication in Figure 4a to the integrated ISAC architecture in Figure 4b. The conventional design in Figure 4a requires distinct hardware and spectrum resources for sensing and communication, leading to redundancy and higher latency. In contrast, the ISAC design in Figure 4b consolidates functionalities on a shared physical layer, enabling spectrum and hardware reuse, reducing cost and energy consumption, and providing real-time environmental awareness with low-latency adaptation.

3.7. Limitations of ISAC

Despite its clear advantages in spectrum reuse, hardware efficiency, and situational awareness, ISAC still faces notable limitations that hinder large-scale deployment. At the algorithmic level, joint optimization of sensing and communication objectives is inherently complex, as improving radar resolution may compromise spectral efficiency while maximizing communication throughput can reduce sensing accuracy. These trade-offs are further aggravated by the synchronization and signal-processing overhead required when multiple ISAC nodes cooperate in real time [45,51,54]. From an environmental perspective, ISAC systems operating in mmWave and Sub-THz bands are highly sensitive to adverse propagation effects such as blockages, rain fading, and gaseous absorption. Moreover, NLoS conditions and multipath scattering complicate radar echo interpretation and joint channel estimation, leading to degraded sensing resolution and less reliable communication links in cluttered or dynamic scenarios [29,57]. On the hardware side, wideband RF chains, high-resolution Analog-to-Digital Converters (ADCs)/Digital-to-Analog Converters (DACs), and large antenna arrays impose stringent cost, power, and scalability constraints, particularly in mobile and edge platforms where energy efficiency is critical [50,51]. These limitations highlight the need for trade-off aware designs and adaptive architectures, while also motivating complementary approaches such as SAC, where environmental perception is primarily exploited to enhance communication robustness and adaptability.

3.8. Quantitative Benchmarking

Benchmarking constitutes a fundamental process for validating ISAC systems and enabling comparability across different implementations. Unlike conventional communication benchmarking, which primarily focuses on throughput, latency, and reliability, ISAC requires a joint assessment of both communication and sensing functionalities. Recognizing this dual requirement, standardization bodies such as the International Telecommunication Union Radiocommunication Sector (ITU-R), the 3rd Generation Partnership Project (3GPP), and the European Telecommunications Standards Institute (ETSI) have introduced measurable key performance indicators (KPIs) specifically tailored for ISAC.

At the IMT-2030 level, Recommendation ITU-R M.2160 establishes new capability classes for 6G systems, explicitly encompassing positioning and sensing-related metrics alongside conventional communication KPIs [65]. In parallel, 3GPP TS 22.137 (Release 19) specifies functional and performance requirements for integrated sensing services within 5G networks [66], while TR 22.837 [67] and ETSI ISG ISAC deliverables [68] emphasize the importance of use-case-driven benchmarking. Complementing these standardization efforts, academic contributions highlight that ISAC benchmarking must simultaneously capture communication and sensing dimensions to reflect system performance [34,43].

Representative KPIs for ISAC, as defined in ITU-R and 3GPP specifications, are summarized in

Table 8. Representative KPIs for ISAC as defined by ITU-R, 3GPP, and ETSI [65,66,67,68].

KPI Dimension	Indicative Values	Notes
Positioning accuracy	1–10 cm; 0.5–1 m	High-precision vs. general scenarios
Velocity accuracy	0.03–0.1 m/s	Resolution and range dependent
Range resolution	0.1–1 m	Separation of close targets
Angle resolution/accuracy	0.1°–1°	Antenna aperture dependent
Latency	10–1000 ms	Sub-100 ms for critical cases
Update rate	0.5–50 Hz	Slow vs. fast dynamics
Detection reliability	<1–5% errors	Confidence 95–99%
Availability	90–99%	Environment and deployment dependent

include (i) positioning accuracy ranging from 1–10 cm for high-precision applications to 0.5–1 m in general scenarios; (ii) velocity estimation accuracy in the order of 0.03–0.1 m/s; (iii) range resolution between 0.1–1 m for distinguishing closely spaced targets; (iv) angular resolution or accuracy between 0.1°–1°, depending on the antenna aperture and system configuration; (v) sensing service latency ranging from 10–1000 ms, with sub-100 ms targets for safety-critical applications; (vi) update rates spanning from 0.5 Hz for slow-changing environments to 50 Hz for highly dynamic use cases such as UAV or vehicular detection; (vii) detection reliability, with missed detection and false alarm probabilities typically constrained below 1–5% and confidence levels between 95–99%; and (viii) service availability typically in the range of 90–99%. These multidimensional KPIs highlight the inherent use-case dependency of ISAC benchmarking, whereby vehicular safety requires centimeter-level positioning and ultra-low latency, while environmental monitoring prioritizes robustness and availability at lower update rates.

SAC has not yet been associated with standardized KPIs in ITU-R or 3GPP. Its evaluation remains research-oriented, where sensing information is exploited to support communication tasks such as beam management, channel estimation, and mobility handling. Reported indicators include beam prediction accuracy, CSI overhead reduction, spectral efficiency improvement, and robustness to blockage or mobility [34,43]. While these results demonstrate SAC’s potential, they remain fragmented and scenario-dependent, reflecting the absence of harmonized benchmarks and highlighting a key avenue for future standardization.

These benchmarks underline the maturity of ISAC as a paradigm where sensing and communication are evaluated in tandem. Yet in practice, communication remains the primary objective, with sensing often playing a supportive role. This motivates SAC, an ISAC-derived concept where environmental awareness is chiefly exploited to enhance communication. The next section presents SAC in depth, outlining its principles, methodologies, and applications in high-frequency wireless networks.

4. Sensing-Assisted Communication (SAC)

Building upon the ISAC framework, SAC emphasizes communication performance as the primary goal, while exploiting sensing capabilities as a supportive mechanism. By embedding environmental awareness into the communication control loop, SAC enhances adaptability, reduces latency, and improves link reliability—particularly in dynamic mmWave and Sub-THz environments. This section outlines the core principles, methodologies, and emerging applications that characterize SAC as a communication-centric evolution of ISAC.

4.1. Fundamentals of SAC

SAC refers to the incorporation of real-time environmental awareness—derived from modalities such as radar, LiDAR, inertial sensors, or vision-based systems—into the communication control loop to enhance system performance, particularly in high-frequency and mobile wireless environments [26]. SAC is now regarded as a cornerstone of next-generation ISAC systems, especially within the evolving 6G architecture [29,31]. In contrast to conventional wireless systems, which rely solely on communication-layer feedback mechanisms (e.g., pilot signals, Acknowledgment (ACK)/Negative Acknowledgment (NACK)), SAC leverages a richer information domain—termed the “sensing space”—encompassing user mobility, spatial geometry, and environmental context. This complements classical “communication space” metrics such as Channel State Information (CSI) and SNR, enabling predictive and proactive adaptation strategies [69].

Furthermore, high-frequency wireless links operating in the mmWave and Sub-THz bands are fundamentally constrained by severe path loss, which scales with frequency due to reduced diffraction and shorter wavelengths. This effect, compounded by frequent LoS blockages in dynamic environments, poses significant challenges for beam alignment, link robustness, and mobility management. SAC addresses these issues by introducing sensing-derived side information into the communication stack, enabling systems to proactively adapt transmission strategies to counteract path loss and blockage [70]. For instance, SAC can support dynamic beam steering toward viable paths, select alternative sub-bands, or adjust transmit power only when required. Unlike conventional feedback-driven schemes, which respond reactively after degradation occurs, SAC empowers communication systems to anticipate propagation challenges and allocate resources preemptively. A particularly impactful realization of this paradigm is Sensing-Assisted Beamforming (SAB), where radar or other sensing modalities embedded at the base station track user mobility or environmental dynamics in real time. By leveraging this side information, the system can preemptively refine beamforming vectors, thereby reducing training overhead, mitigating blockage, and enabling seamless mobility support [10,71,72,73,74,75]. While a full taxonomy of SAB strategies lies beyond the scope of this review, it is acknowledged as a rapidly evolving research direction that builds upon the SAC foundation.

Overall, SAC refines the ISAC vision by placing communication performance at the center, while still leveraging sensing as a proactive enabler. This evolution allows high-frequency networks to operate with foresight, flexibility, and robustness, thereby laying the groundwork for intelligent 6G systems capable of real-time adaptation in complex, dynamic, and user-centric environments.

4.2. Technical Aspects and Methodologies

At the core of SAC lies the ability to anticipate channel dynamics and proactively optimize beamforming through environmental sensing. A diverse set of technical approaches has emerged, broadly categorized into model-based estimation, geometric sensing, learning-based architectures, and hybrid solutions. Each method leverages contextual information—position, velocity, orientation, and spatial patterns to integrate environmental intelligence into communication decision-making.

4.2.1. Model-Based Estimation Techniques

Model-based SAC strategies rely on recursive state estimation to predict channel behavior. One of the most prevalent tools is the EKF, used to estimate dynamic parameters such as AoD, AoA, and their derivatives in mmWave channels [76]. The system is modeled as a nonlinear state-space process:

$$\begin{array}{cc}\hfill {\mathbf{x}}_k& =f\left({\mathbf{x}}_{k-1}\right)+{\mathbf{w}}_{k-1},\hfill \end{array}$$

(1)

$$\begin{array}{cc}\hfill {\mathbf{z}}_k& =h\left({\mathbf{x}}_k\right)+{\mathbf{v}}_k,\hfill \end{array}$$

(2)

where ${\mathbf{x}}_k$ is the state vector at time k, ${\mathbf{z}}_k$ the observed measurement, and ${\mathbf{w}}_{k-1}$, ${\mathbf{v}}_k$ are zero-mean noise processes. As shown in Equations (1) and (2), the EKF models state transitions and observations through nonlinear functions corrupted by noise. EKF-based SAC enables efficient beam prediction with minimal training overhead. In [76], a second-order motion model with two-beam training per frame achieved robust tracking in high-mobility vehicular scenarios. To handle non-Gaussian noise or nonlinearity, PFs, also known as Sequential Monte Carlo (SMC) methods, provide a more flexible alternative [35]. In this framework, the posterior distribution of the beam state is approximated by a weighted set of particles $\{{\mathbf{x}}_k^{\left(i\right)},{\omega }_k^{\left(i\right)}\}$, where ${\omega }_k^{\left(i\right)}$ denotes the importance weight of the i-th particle at time k. These particles are propagated and updated as

$$\begin{array}{cc}\hfill {\mathbf{x}}_k^{\left(i\right)}& \sim p\left({\mathbf{x}}_k\right|{\mathbf{x}}_{k-1}^{\left(i\right)}),\hfill \end{array}$$

(3)

$$\begin{array}{cc}\hfill {\omega }_k^{\left(i\right)}& \propto {\omega }_{k-1}^{\left(i\right)}·p\left({\mathbf{z}}_k\right|{\mathbf{x}}_k^{\left(i\right)}).\hfill \end{array}$$

(4)

Equations (3) and (4) describe the sampling and weighting steps of the PF algorithm. This method supports blind tracking and prediction in the presence of ambiguous or noisy feedback. PF-based beam tracking, as demonstrated in [35], showed robust performance even with severe mobility and partial observation.

4.2.2. Geometric Sensing and Environmental Awareness

Geometric sensing constitutes a key enabler of SAC, leveraging spatial and kinematic information, such as user position, velocity, AoA, and AoD, to enable predictive and environment-aware beamforming. Unlike conventional CSI-based techniques, which depend on reactive feedback and pilot signaling, geometric sensing facilitates anticipatory beam management through the real-time inference of environmental geometry. This approach harnesses data from infrastructure-side sensors, such as mmWave radar and LiDAR, as well as user-side devices including GPS and inertial measurement units (IMUs). By integrating these sensing sources, SAC systems can estimate the relative positioning and motion of transceivers and surrounding scatterers. This allows for proactive beam steering, reduced alignment overhead, and increased resilience to channel variations in mobile and dynamic environments.

In this context, reference [77] presents a predictive beamforming strategy utilizing mmWave radar at the base station, combined with a Bayesian estimator to track user motion and dynamically update beam directions. The effectiveness of spatial awareness in guiding initial beam alignment is further supported by studies examining the use of GPS and IMU data in vehicular channels [78,79]. Out-of-band sensing and learning-based approaches have also been proposed to extract spatial features from sub-6 GHz channels to assist mmWave beam prediction and blockage detection [80]. A broader survey of radar-assisted communication in mmWave and terahertz (THz) vehicular networks highlights the growing relevance of geometric sensing in high-frequency mobile scenarios [58].

As illustrated in Figure 5 provides a visual representation of the geometric sensing process in SAC systems, highlighting the cooperative integration of infrastructure-side and user-side sensing modalities. The base station, equipped with a mmWave radar, performs high-resolution detection of object velocity and angular position, enabling anticipatory beam steering. This is depicted through a radar sensing cone, representing the spatial region being actively scanned for motion and AoA estimation. In parallel, mobile users such as vehicles and wearable devices supply motion and positioning data through GPS and IMU sensors. These measurements are fed back to the base station via sub-6 GHz or other low-frequency control channels, illustrated as environmental feedback in the figure. By combining these two streams of spatial information, the SAC system can proactively align mmWave beams—indicated in red—toward target users with minimal reliance on pilot signaling or reactive CSI-based feedback. This figure emphasizes the architectural advantage of geometric sensing in dynamic environments: It enables rapid beam acquisition, mitigates blockage and misalignment, and supports high-frequency communication with greater resilience to mobility. The visual elements complement the concepts discussed in this subsection and illustrate how environmental awareness is operationalized in practice through multi-modal sensing [58,77,78,79,80].

Overall, geometric sensing introduces a scalable mechanism for SAC systems to minimize training overhead, accelerate beam acquisition, and maintain high-quality links under mobility by embedding environmental awareness directly into the beamforming process.

4.2.3. Learning-Based SAC Architectures

Machine learning enables SAC systems to learn direct mappings from sensed input to optimal beam or channel parameters. Convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and RL agents have been trained on features such as radar images, motion vectors, or beam history [80,81]. These architectures excel in environments with nonlinear mobility patterns, NLOS propagation, or limited labeled data. For instance, ref. [80] introduced a neural predictor that outputs beam indices based on raw radar and trajectory data, achieving faster recovery from link blockage than EKF-based approaches. RL-based beam management, on the other hand, has shown adaptability in unknown and changing environments.

4.2.4. Hybrid Architectures and Practical Realizations

In practice, robust SAC systems often blend the strengths of various paradigms. For example, radar-based sensing is used for initial user localization, followed by EKF/PF for continuous estimation and deep learning for uncertainty mitigation or handover prediction [10]. Hybrid SAC systems demonstrate how proactive environmental modeling leads to better spectral efficiency, reliability, and latency control. As illustrated in Figure 6, SAC techniques span a wide design space, classified by their sensing modalities, inference methods, and level of system integration.

To support this taxonomy,

Table 9. Key literature on SAC techniques and their trade-offs in accuracy, latency, and complexity.

Ref(s)	SAC Technique Category	Key Contribution(s)	Accuracy	Latency	Complexity
[35,76]	Model-based (EKF, PF)	EKF for beam tracking; PF for non-Gaussian mobility	EKF: Medium; PF: High	EKF: Low; PF: High	EKF: Low; PF: Medium
[58,77,78,79,80]	Geometric sensing	Radar/GPS/IMU beam prediction; sub-6 GHz out- of-band learning	Medium	Medium	Medium
[80,81]	Learning-based	Neural nets and RL for dynamic beam adaptation	High	Medium	High
[10]	Hybrid architectures	Fusion of radar, EKF/PF, and deep learning	High	Medium	High
[58]	Application-domains	V2X, UAV and IoT	V2X: High; UAV: Medium; IoT: Low	V2X: High; UAV: Medium; IoT: Low	Domain-dependent
[10,58]	Integration levels	From coexistence to fully integrated ISAC	High	Medium	High

provides a mapping between representative references and their associated SAC categories and contributions. In addition to summarizing the foundational literature, the table highlights analytical trade-offs in terms of accuracy, latency, and computational complexity, offering a more comparative perspective on the strengths and limitations of different SAC approaches. Together, these methodologies represent the technical foundation of SAC, transforming it from a reactive add-on into a proactive and intelligent paradigm within next-generation wireless networks.

4.3. Applications and Advantages

SAC offers significant performance gains across a wide range of advanced wireless communication scenarios, particularly those characterized by high mobility, narrow beamwidths, and stringent latency or reliability requirements. This subsection highlights key technical advantages and representative use cases enabled by SAC.

4.3.1. Predictive Beamforming and Reduced Link Interruptions

SAC’s predictive beamforming, implemented through model-based or AI-driven estimators, enables systems to preempt beam misalignment before signal degradation occurs based on anticipated channel evolution. Traditional mmWave and Sub-THz systems employ reactive beam management, modifying direction only after signal degradation is detected. In contrast, SAC integrates filtering or machine learning-based predictors, such as EKF or PFs, with environmental awareness to estimate future beam directions before degradation occurs [35,76]. In V2I deployments, for instance, SAC-enabled base stations can detect turning vehicles or occlusions and switch beam paths preemptively, avoiding link outages. This foresight is critical for latency-sensitive applications such as autonomous driving or high-speed train connectivity, where continuous links must be preserved [35].

4.3.2. Enhanced Link Reliability in High-Mobility Environments

SAC significantly improves communication robustness in high-mobility scenarios, including UAV communications, mobile base stations, and dynamic small-cell deployments. In such cases, traditional beam training may become infeasible due to rapid variations in AoD or AoA. SAC methods—via radar, inertial sensors, or GPS—enable millisecond-level tracking and beam update rates, maintaining link quality even under abrupt mobility or channel fading [82]. Moreover, trajectory prediction enables cooperative beamforming and coordinated handover among distributed access points, enhancing connectivity and reducing control overhead in multi-user networks. This is particularly relevant for vehicular or aerial communication nodes that require seamless transitions between coverage zones.

4.3.3. Efficient Resource Usage and Overhead Reduction

A key advantage of SAC is its ability to reduce beam training and channel estimation overhead. By using sensing-derived priors, SAC systems restrict the beam search space to a narrow set of probable directions. For example, PF-based tracking in [35] demonstrated an over 80% reduction in training overhead compared to exhaustive beam sweeping, leading to more efficient use of spectral resources. This becomes even more critical in the THz regime, where beamwidths are exceptionally narrow, and misalignment incurs significant delays. SAC frameworks can also optimize beam training schedules, refine channel probing strategies, and improve power efficiency through context-aware transmission control [80].

4.3.4. Context-Aware Beamforming for 6G and V2X Systems

Beyond predictive beam tracking, next-generation wireless networks will increasingly adopt context-aware beamforming strategies driven by cooperative sensing and cross-layer feedback. SAC plays a central role in this process by enabling environmental context to be shared between mobile users and infrastructure in real-time. In cooperative driving scenarios, vehicles equipped with onboard sensors—such as LiDAR, radar, and inertial units—can detect road geometry, occlusions, or motion patterns and transmit this information to roadside units (RSUs). RSUs, in turn, integrate these inputs with their own sensing data to pre-configure beam patterns or schedule proactive handovers [81]. This approach exemplifies multi-agent cooperation, where infrastructure-side and user-side sensing are jointly fused to support environmental awareness and beam control. SAC also extends to indoor mmWave/THz WLAN scenarios, where user orientation and motion—captured through embedded IMUs or vision-based systems—can guide rapid beam reconfiguration to sustain high-rate services such as extended reality (XR) and augmented reality (AR) [13,80]. By enabling real-time integration of spatial context into the communication stack, SAC advances beyond traditional feedback-based control to offer resilient, low-latency beamforming solutions tailored for highly dynamic 6G environments.

4.3.5. Comparative Analysis with Traditional Systems

To quantify the practical benefits of SAC,

Table 10. Comparison between conventional and SAC-enabled wireless systems.

Performance Metric	Traditional Systems	SAC-Enabled Systems
Beam Training Overhead	High (exhaustive/codebook search)	Low (sensing-guided prediction)
Beam Alignment Latency	Reactive (100–200 ms typical)	Proactive (10–50 ms with prediction)
Accuracy of Beam Tracking	Moderate (reliant on CSI quality)	High (enhanced by environmental sensing)
Robustness to Mobility	Limited (frequent re-alignment needed)	High (trajectory-aware beam control)
Spectral Efficiency	Lower (frequent training consumes slots)	Higher (more slots available for data)
Energy Efficiency	Lower (redundant scans, retries)	Higher (fewer re-transmissions)
Latency in Handover	Reactive, delay-prone	Anticipatory, low-latency
Adaptability to Dynamics	Low (based on past CSI)	High (real-time environmental awareness)

provides a direct comparison between traditional communication systems and SAC-enabled architectures. The comparison highlights gains in overhead, beam accuracy, latency, and adaptability that are critical in high-mobility and THz environments. SAC fundamentally augments communication systems with a predictive, environmentally aware control layer. It enhances link robustness under mobility, reduces training and feedback overhead, and improves system responsiveness to dynamic propagation conditions. These features are essential for enabling ultra-reliable low-latency communication (URLLC) in 6G use cases. Figure 7 visualizes representative SAC applications, including predictive beamforming, UAV tracking, proactive handover at the cell edge, and ISAC-based environmental sensing for communication assistance.

4.4. Limitations of SAC

Although SAC has demonstrated significant gains in predictive beamforming, mobility management, and overhead reduction, some limitations constrain its robustness and scalability. From an algorithmic perspective, the accuracy of SAC frameworks strongly depends on the performance of state-estimation and prediction models such as EKF, PF, and DRL/RL architectures [35,76,80,81]. Model-based approaches are sensitive to non-linearities and noisy feedback, while data-driven methods may suffer from generalization gaps when deployed in unseen environments. Environmentally, SAC inherits the propagation challenges of high-frequency bands, where atmospheric absorption, rain fading, and blockage severely impair both sensing quality and link reliability, especially in dense vehicular or urban scenarios. In addition, NLoS conditions and multipath scattering often introduce erroneous or delayed sensing feedback, leading to misaligned beams and degraded SAC performance in highly dynamic environments [57,58,77,78,79].

On the hardware side, embedding radar, LiDAR, or inertial sensing into communication nodes raises complexity, energy consumption, and cost, which is particularly critical for resource-constrained platforms such as UAVs and IoT devices [58,82]. Furthermore, real-time fusion of heterogeneous sensing modalities requires substantial computational resources that may exceed the capabilities of edge devices. These limitations highlight that while SAC is a powerful complement to ISAC, its practical deployment demands lightweight, adaptive, and hardware-efficient designs capable of maintaining robustness in dynamic real-world environments.

5. Future Directions

Despite recent advances, several pivotal challenges and opportunities remain to fully realize SAC within ISAC-enabled 6G networks:

• Standardization and Protocol Integration: Realizing SAC at scale demands explicit support across the PHY and MAC layers. Emerging standards must define unified sensing and communication reference signals, joint feedback and scheduling mechanisms, and adaptive frame structures that minimize latency while enabling dual-purpose operation. Ongoing activities in IEEE 802.11bf (WLAN sensing), IEEE 802.15.4a, and early 3GPP ISAC study items illustrate initial steps in this direction, while testbeds such as RIS-based IMT-2030 provide experimental validation platforms [52,83].
• High-Frequency Implementation at mmWave/THz: At extreme frequencies, SAC performance hinges on accurate sensing despite severe path loss, sparse multipath, and mobility-induced beam misalignment. Solutions must address real-time tracking with low training overhead and scalable hardware implementations. Recent prototypes of holographic MIMO and Sub-THz communication further highlight both feasibility and the remaining hardware challenges.
• AI-Augmented Sensing and Inference: Emerging SAC frameworks will likely combine statistical estimators with neural architectures, enabling predictive and adaptive capabilities. Balancing generalization, real-time execution, interpretability, and power constraints remains an open design space.
• Security and Privacy Preservation: The exploitation of sensing data introduces new vulnerabilities—e.g., spoofing, eavesdropping on motion data, or adversarial interference. Future SAC systems must integrate encrypted sensing, secure localization, and robust authentication mechanism.
• Toward Distributed and Cooperative SAC: This review focused on single-node SAC systems. However, cooperative SAC, where base stations and users share sensing information, promises enhanced spatial awareness, reduced redundancy, and coordinated beamforming. Such architectures will require novel synchronization and information fusion techniques. Early vehicular and UAV SAC pilots already suggest the potential of cooperative sensing, but new synchronization and fusion mechanisms are required for large-scale adoption.

6. Conclusions

In light of these future directions, this review concludes by synthesizing the foundational insights and practical implications of SAC in ISAC-enabled wireless systems. The integration of communication and environmental awareness through ISAC marks a fundamental paradigm shift in the architecture and operation of future wireless systems, particularly 6G. This review systematically examined the foundations and evolution of ISAC, tracing its trajectory from independent radar-based sensing toward tightly co-designed frameworks where sensing and data transmission cohabit the same infrastructure and spectrum. Within the broader ISAC vision, SAC emerges as a key operational enabler. SAC techniques transform real-time environmental and geometric information—such as user motion, location, and reflectivity signatures—into actionable inputs for beam alignment, channel estimation, and protocol adaptation. In high-frequency systems where beam misalignment and blockage are prevalent, SAC enables preemptive adaptation and robust link continuity.

This review presented a structured taxonomy of SAC techniques, encompassing model-based estimators, geometry-aware sensing, and learning-based frameworks. For example, SAC implementations using PF in vehicular networks have demonstrated over 80% reduction in beam training overhead compared to exhaustive search [69], while predictive radar-guided methods have reduced beam alignment latency to below 50 ms [5]. In highly dynamic environments, SAC has been shown to maintain link robustness with fewer handovers and reduced protocol signaling, particularly in V2X and UAV systems [10,71].

Ultimately, SAC offers a scalable and pragmatic bridge between communication performance and environmental cognition—positioning it as a foundational component of intelligent, ultra-reliable, and low-latency 6G wireless networks.