Search Results (569)

This paper presents a compact magneto-electric dipole (MED) antenna optimized for wideband circularly polarized (CP) radiation for 5G applications. It incorporates a staircase-shaped electric dipole with trimmed corners to excite orthogonal modes for enhanced CP performance. The proposed single-layer MED antenna achieves a $20.6\%$ wide-impedance bandwidth ($|S_{11}| <-10$ dB, $22.97$–$28.12$ GHz) and $21.9\%$ CP bandwidth ($AR<3$ dB, $22.23$–$27.83$ GHz) with a compact footprint of $15\times 15\times 1.6{\mathrm{mm}}^3$. There is a symmetrical radiation pattern with a co-to-cross polarization ratio $>23$ dB and a stable gain of $8.8$ dBi. An equivalent circuit model is optimized via particle swarm optimization (PSO). The optimized MED antenna is utilized to investigate various CP-MIMO configurations and wideband sequential arrays. Next, a $1\times 2$ CP-MIMO antenna system is developed, employing polarization diversity in parallel and mirror configurations. Isolation is improved by etching a ground slot between the MED elements, yielding isolation levels of below $-20$ dB and $-23$ dB, respectively. Further, a $2\times 2$ CP-MIMO configuration is designed and evaluated. This arrangement demonstrates an envelope correlation coefficient (ECC) of $1\times {10}^{-3}$ and a diversity gain of approximately 10 dB across the operating bandwidth. Finally, a sequential array is designed that applies a ${90}^{\circ }$ sequential rotation and phase excitation to MED elements for high-gain CP 5G communications. Here, various array sizes are evaluated, with an $8\times 8$ MED array providing CP radiation ($AR\le 1$ dB) from 20 to 30 GHz with enhanced impedance and axial ratio bandwidths and stable gain with a peak value of $27.47$ dBi. Full article

In recent years, the scientific community has increasingly focused on state-of-the-art techniques, such as MIMO and mmWave transmission, aimed at enhancing the performance of telecommunication channels both quantitatively and qualitatively through various approaches. These efforts often rely on channel models designed to more accurately represent real-world conditions, thereby ensuring that the results are objective and practically applicable. In the present study, we employ one of the most scientifically reliable system- level simulators, Vienna SLS Simulator, to evaluate the performance of a wireless channel that we configure based on the latest standards (3GPP TR 36.873). We take into account the well-known non-symmetrical behavior of mMIMOs, where m stands for microwave MIMOs, in wireless communication systems and analyze the resulting changes in key performance metrics including average cell throughput, average user spectral efficiency and signal-to-interference-plus-noise ratio (SINR). We vary specific parameters such as transmission power, antenna polarization, ratio of indoor to outdoor users, and others with the aim of validating or challenging existing scientific assumptions. Particular attention is given to studying how variations in the aforementioned factors affect channel geometry and spatial uniformity, emphasizing the role of antenna geometry, polarization and user distribution in shaping channel asymmetries in mmWave MU-MIMO systems. Overall, this study provides insights into designing more balanced and efficient wireless systems in realistic urban environments. Full article

This paper proposes a quantum multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) framework for image transmission, which combines quantum multi-qubit encoding with spatial and frequency diversity to enhance noise resilience and image quality. The system utilizes joint photographic experts group (JPEG), high efficiency image file format (HEIF), and uncompressed images, which are first source-encoded (if applicable) and then processed using classical channel encoding. The channel-encoded bitstream is mapped into quantum states via multi-qubit encoding and transmitted through a 2 × 2 MIMO system with varied diversity schemes. The spatially mapped qubits undergo the quantum Fourier transform (QFT) to form quantum OFDM subcarriers, with a cyclic prefix added before transmission over fading quantum channels. At the receiver, the cyclic prefix is removed, the inverse QFT is applied, and the quantum MIMO decoder reconstructs spatially diverged quantum states. Then, quantum decoding reconstructs the bitstreams, followed by channel decoding and source decoding to recover the final image. Experimental results show that the proposed quantum MIMO-OFDM system outperforms its classical counterpart across all evaluated diversity configurations. It achieves peak signal-to-noise ratio (PSNR) values up to 58.48 dB, structural similarity index measure (SSIM) up to 0.9993, and universal quality index (UQI) up to 0.9999 for JPEG; PSNR up to 70.04 dB, SSIM up to 0.9998, and UQI up to 0.9999 for HEIF; and near-perfect reconstruction with infinite PSNR, SSIM of 1, and UQI of 1 for uncompressed images under high channel noise. These findings establish quantum MIMO-OFDM as a promising architecture for high-fidelity, bandwidth-efficient quantum multimedia communication. Full article

The advent of Sixth Generation (6G) wireless communication systems demands unprecedented data rates, ultra-low latency, and massive connectivity to support emerging applications such as extended reality, digital twins, and ubiquitous intelligent services. These stringent requirements call for the use of massive Multiple-Input Multiple-Output (m-MIMO) systems with hundreds or even thousands of antennas, which introduce substantial challenges for signal detection algorithms. Conventional linear detectors, especially the linear Minimum Mean Square Error (MMSE) detectors, face prohibitive computational complexity due to high-dimensional matrix inversions, and their performance remains inherently restricted by the limitations of linear processing. The current research suggested an Iterative Signal Detection (ISD) algorithm with significant limitations being occupied with the combination of Deep Q-Network (DQN) and Quasi-Newton algorithms. The method incorporates the Broyden-Net, which could be faster with less memory training than the model in the case of spatially correlated channels, a Quasi-Newton method, and DQN to improve the m-MIMO detection. The proposed techniques support the computational efficiency of realistic 6G systems and outperform linear detectors. The simulation findings proved that the DQN-improved Quasi-Newton algorithm is more appropriate than traditional algorithms, since it combines the reward design, limited memory updates, and adaptive interference mitigation to shorten convergence time by 60% and increase the confrontation to correlated fading. Full article

Unmanned aerial vehicles (UAVs) equipped with antenna arrays can deliver high-capacity, high-throughput, and low-latency communication services. Considering a UAV-assisted mmWave multi-input and multi-output (MIMO) system, a two-stage beamforming scheme based on a budgeted combinatorial multi-armed bandit (BC-MAB) is proposed to improve the system’s spectral efficiency (SE). The pre-beamformer design problem is initially formulated as a BC-MAB problem. In this framework, the reward is the received energy, while the cost corresponds to the energy consumed by each RF chain and the budget is represented by the residual energy of the UAV. To achieve a favorable trade-off between the number of communication slots and the energy acquired per slot, a pre-beamforming scheme based on the bang-per-buck ratio is introduced to optimize the number of activated RF chains, therefore maximizing the cumulative reward. The second stage utilizes the reduced-dimensional instantaneous channel state information to design and optimize the beamformer to achieve maximum system SE. The proposed scheme achieves more than 7.1% improvement in SE compared to the benchmark schemes. Simulations validate the superiority of the proposed scheme. Full article

As the global deployment of fifth-generation (5G) networks matures, the research community is conceptualising sixth-generation (6G) systems, projected for deployment around 2030. This article presents a comprehensive, evidence-based examination of the technological innovations and applications that characterise this transition, informed by a scoping review of 57 sources published between January 2020 and August 2025. The transition to 6G signifies a fundamental transformation from a mere communication utility to an intelligent, sensing, and globally integrated cyber-physical continuum, propelled by a strategic reassessment of the network’s societal function and the practical insights gained from the 5G era. We critically analyse the foundational physical layer technologies that facilitate this vision, including Reconfigurable Intelligent Surfaces (RIS), Terahertz (THz) communications, and the transition to Extremely Large-Scale MIMO (XL-MIMO), emphasising their interdependencies and the fundamental shift towards near-field physics. The analysis encompasses the architectural transformation necessary to address this new complexity, elucidating the principles of the AI-native network, the seamless integration of Non-Terrestrial Networks (NTN) into a cohesive three-dimensional framework, and the functional convergence of communication and sensing (ISAC). We also look at how these changes affect the real world by looking at data from trials and case studies in smart cities, intelligent transportation, and digital health. The article synthesises the overarching challenges in security, sustainability, and scalability, arguing that the path to 6G is defined by two intertwined grand challenges: building a trustworthy and sustainable network. By outlining the critical research imperatives that stem from these challenges, this work offers a holistic framework for understanding how these interconnected developments are evolving wireless networks into the intelligent fabric of a digitised and sustainable society. Full article

With the increasing sophistication of electromagnetic environments in modern combat platforms, joint sensing and communication (JSAC) technology has emerged as a critical research frontier. Among these, JSAC waveform design plays a crucial role, as it enables the simultaneous achievement of both sensing and communication functions using the same transmit waveform. This paper presents a novel waveform design for a multi-input multi-output (MIMO) JSAC system. The proposed design leverages orthogonal frequency division multiplexing (OFDM) to reduce signal interference through low cross-correlation characteristics. Linear frequency modulation (LFM) is used as the carrier waveform, effectively narrowing the main lobe width of the autocorrelation function. We introduce phase perturbation into binary phase shift keying (BPSK) signals to enhance waveform performance, formulating the resulting problem as a high-dimensional, non-convex optimization challenge. To address this, we propose a hybrid optimization algorithm QGPV combining a quantum genetic algorithm (QGA), quantum particle swarm optimization (QPSO), and variable neighborhood search (VNS). The simulation results demonstrate that the proposed algorithm achieves superior performance compared with several typical methods. Notably, the peak sidelobe level (PSL) can be suppressed to around −21 dB with five iterations, highlighting the efficiency of the optimization process. These results validate the effectiveness of the proposed approach, showing improved waveform characteristics with an acceptable trade-off in communication performance. Full article

Multiple-Input Multiple-Output (MIMO) antenna systems are key to improving wireless channel capacity and reliability. Yet, their inherent need for compact configurations introduces a significant challenge: electromagnetic coupling between closely placed radiating elements. This undesirable phenomenon diminishes efficiency, increases signal correlation, and compromises electromagnetic isolation. To mitigate these issues, researchers have proposed diverse isolation techniques, such as Defected Ground Structures (DGS), metamaterials, fractal geometries, and neutralization lines. These techniques are crucial for boosting isolation and facilitating antenna miniaturization without compromising overall electromagnetic performance, making them indispensable for modern compact communication systems. This article provides a comprehensive review of these techniques, dissecting their fundamental operating principles and analyzing the electromagnetic isolation results previously documented in the literature. Furthermore, experimental findings derived from the fabrication and characterization of prototypes, aiming to confirm the practical efficacy of these isolation methods, are presented. Full article

In-band full-duplex (IBFD) communication systems offer a promising means of improving spectral efficiency by enabling simultaneous transmission and reception on the same frequency channel. Despite this advantage, self-interference (SI) remains a major challenge to their practical deployment. Among the different SI cancellation (SIC) techniques, this paper focuses on digital SIC methodologies tailored for multiple-input multiple-output (MIMO) wireless transceivers operating under digital beamforming architectures. Two distinct digital SIC approaches are evaluated, employing a generalized memory polynomial (GMP) model augmented with Itô–Hermite polynomial basis functions and a phase-normalized neural network (PNN) to effectively model the nonlinearities and memory effects introduced by transmitter and receiver hardware impairments. The robustness of the SIC is further evaluated under both single off-line training and closed-loop real-time adaptation, employing estimation techniques such as least squares (LS), least mean squares (LMS), and fast Kalman (FK) for model coefficient estimation. The performance of the proposed digital SIC techniques is evaluated through detailed simulations that incorporate realistic power amplifier (PA) characteristics, channel conditions, and high-order modulation schemes. Metrics such as error vector magnitude (EVM) and total bit error rate (BER) are used to assess the quality of the received signal after SIC under different signal-to-interference ratio (SIR) and signal-to-noise ratio (SNR) conditions. The results show that, for time-variant scenarios, a low-complexity adaptive SIC can be realized using a GMP model with FK parameter estimation. However, in time-invariant scenarios, an open-loop SIC approach based on PNN offers superior performance and maintains robustness across various modulation schemes. Full article

Beyond-5G wireless systems increasingly rely on distributed massive multiple-input multiple-output (MIMO) architectures to achieve high spectral efficiency, low latency, and wide coverage. A key challenge in such networks is that cooperating base stations (BSs) often possess different levels of channel state information (CSI) due to fronthaul constraints, user mobility, or hardware limitation. In this paper, we propose two novel detectors that enable cooperation between BSs with differing CSI availability. In this setup, some BSs have access to instantaneous CSI, while others only have long-term channel information. The proposed detectors—termed the coherent/non-coherent (CNC) detector and the differential CNC detector—integrate coherent and non-coherent approaches to signal detection. This framework allows BSs with only long-term information to actively contribute to the detection process, while leveraging instantaneous CSI where available. This approach enables the system to integrate the advantages of non-coherent detection with the precision of coherent processing, improving overall performance without requiring full CSI at all cooperating BSs. We formulate the detectors based on the maximum likelihood (ML) criterion and derive analytical expressions for their pairwise block error probabilities under Rayleigh fading channels. Leveraging the pairwise block error probability expression for the CNC detector, we derive a tight upper bound on the average block error probability. Numerical results show that the CNC and differential CNC detectors outperform their respective single-BS baseline-coherent ML and non-coherent differential detection. Moreover, both detectors demonstrate strong resilience to mid-to-high range correlation at the BS antennas. Full article

In massive multiple-input and multiple-output (mMIMO) systems, superimposed channel state information (CSI) feedback is developed to improve the occupation of uplink bandwidth resources. Nevertheless, the interference from this superimposed mode degrades the recovery performance of both downlink CSI and uplink data sequences. Although machine learning (ML)-based methods effectively mitigate superimposed interference by leveraging the multi-domain features of downlink CSI, the complex interactions among network model parameters cause a significant burden on system resources. To address these issues, inspired by sensing-assisted communication, we propose a novel superimposed CSI feedback method assisted by inactive sensing information that previously existed but was not utilized at the base station (BS). To the best of our knowledge, this is the first time that inactive sensing information is utilized to enhance superimposed CSI feedback. In this method, a new type of modal data, different from communication data, is developed to aid in interference suppression without requiring additional hardware at the BS. Specifically, the proposed method utilizes location, speed, and path information extracted from sensing devices to derive prior information. Then, based on the derived prior information, denoising processing is applied to both the delay and Doppler dimensions of downlink CSI in the delay—Doppler (DD) domain, significantly enhancing the recovery accuracy. Simulation results demonstrate the performance improvement of downlink CSI and uplink data sequences when compared to both classic and novel superimposed CSI feedback methods. Moreover, against parameter variations, simulation results also validate the robustness of the proposed method. Full article

A compact fork-shaped four-element multiple-input multiple-output (MIMO) antenna system with wide bandwidth for 5G millimeter-wave (mmWave) applications is presented. The antenna elements are arranged orthogonally to achieve a compact footprint of $20\times 26{\mathrm{mm}}^2$. To enhance the gain, a frequency selective surface (FSS) is placed above the MIMO system, providing an average gain improvement of 1.5 dB across the entire operating band and achieving a peak gain of 7.5 dB at 41 GHz. The proposed design operates in the Ka-band (22–46 GHz), making it well suited for 5G communications. The antenna exhibits an isolation greater than 20 dB and radiation efficiency exceeding 80% across the band. Moreover, key MIMO performance metrics, including diversity gain (DG ≈ 10) and envelope correlation coefficient (ECC < 0.05), meet the required standards. A prototype of the proposed system was fabricated and measured, with the experimental results showing good agreement with simulations. Full article

Intelligent Reflective Surface (IRS)-aided Multiple-Input Multiple-Output (MIMO) systems have emerged as a promising solution to enhance spectral and energy efficiency in future wireless communications. However, accurate channel estimation remains a key challenge due to the passive nature and high dimensionality of IRS channels. This paper proposes a lightweight hybrid framework for cascaded channel estimation by combining a physics-based Bilinear Alternating Least Squares (BALS) algorithm with a deep neural network named ConvTrans-ResNet. The network integrates convolutional embeddings and Transformer modules within a residual learning architecture to exploit both local and global spatial features effectively while ensuring training stability. A series of ablation studies is conducted to optimize architectural components, resulting in a compact configuration with low parameter count and computational complexity. Extensive simulations demonstrate that the proposed method significantly outperforms state-of-the-art neural models such as HA02, ReEsNet, and InterpResNet across a wide range of SNR levels and IRS element sizes in terms of the Normalized Mean Squared Error (NMSE). Compared to existing solutions, our method achieves better estimation accuracy with improved efficiency, making it suitable for practical deployment in IRS-aided systems. Full article

This paper presents the design and performance evaluation of a compact four-element coplanar waveguide (CPW)-fed antenna array operating in the 3.3–3.6 GHz frequency band. The proposed antenna is tailored for sub-6 GHz 5G New Radio (NR) applications, specifically aligning with the n77/n78 bands widely adopted for mid-band 5G deployment. The CPW feeding technique enables low-profile integration and ease of fabrication, while the multi-element configuration supports enhanced gain and spatial diversity. Both simulated and measured results demonstrate good impedance matching (|S₁₁| < −10 dB), stable radiation patterns, and inter-element isolation suitable for MIMO operation. The design offers a promising solution for compact 5G antenna systems and can be extended to future wireless communication platforms requiring high efficiency and compact form factors. Full article

The increasing demand for real-time, energy-efficient, and interference-resilient communication in smart healthcare environments has intensified interest in Biomedical Internet of Things (Bio-IoT) systems. However, ensuring reliable wireless connectivity for wearable and implantable biomedical sensors remains a challenge due to mobility, latency sensitivity, power constraints, and multi-user interference. This paper addresses these issues by proposing a reconfigurable massive multiple-input multiple-output (MIMO) antenna architecture, incorporating hybrid analog–digital beamforming and adaptive signal processing. The methodology combines conventional algorithms—such as Least Mean Square (LMS), Zero-Forcing (ZF), and Minimum Variance Distortionless Response (MVDR)—with a novel mobility-aware beamforming scheme. System-level simulations under realistic channel models (Rayleigh, Rician, 3GPP UMa) evaluate signal-to-interference-plus-noise ratio (SINR), bit error rate (BER), energy efficiency, outage probability, and fairness index across varying user loads and mobility scenarios. Results show that the proposed hybrid beamforming system consistently outperforms benchmarks, achieving up to 35% higher throughput, a 65% reduction in packet drop rate, and sub-10 ms latency even under high-mobility conditions. Beam pattern analysis confirms robust nulling of interference and dynamic lobe steering. This architecture is well-suited for next-generation Bio-IoT deployments in smart hospitals, enabling secure, adaptive, and power-aware connectivity for critical healthcare monitoring applications. Full article