Search Results (77)

Millimeter wave (mmWave) Massive MIMO is a cornerstone technology for sixth-generation (6G) wireless networks, providing the directional gain necessary to overcome high path loss. However, the acquisition of high-fidelity Channel State Information (CSI) and the associated beamforming overhead remain significant bottlenecks, particularly in dynamic environments with frequent blockages. In this paper, we propose a fast and robust beamforming strategy enabled by a digital twin (DT) framework. Specifically, we develop a Conditional Generative Adversarial Network (cGAN)-based DT module that serves as a high-fidelity virtual surrogate for site-specific ray-tracing. By processing environmental 3D geometry and dynamic obstacle data, the cGAN predicts real-time Beam-Power Maps (BPM) with minimal computational latency. Building upon these predictions, we introduce a Graph Neural Network (GNN)-based resource allocation agent that models the network as a spatial interference graph to perform coordination and power control. Numerical results demonstrate that our proposed DT-assisted approach significantly reduces online interaction overhead by shifting the computational burden of ray-tracing to an offline generative phase. Furthermore, the framework achieves superior sum-rate performance and link robustness under dynamic blockages compared to conventional deep learning and heuristic benchmarks. Full article

Reconfigurable intelligent surfaces (RISs) are emerging as a key enabling technology to engineer the wireless propagation environment in 5G/6G systems. This paper presents the design, characterization, and control of a high-resolution RIS targeting slowly time-varying scenarios, in which fine-grained and stable phase control is more valuable than fast reconfiguration. Starting from the OpenRIS unit-cell layout, the cell is re-optimized for single-polarization operation and continuous phase tuning through a single varactor diode, exploiting the full tuning range enabled by a high-resolution DAC infrastructure rather than multi-bit quantization. The unit cell is analyzed via full-wave 3D FEM simulation in COMSOL Multiphysics and optimized to maximize the reflection-phase excursion while limiting amplitude modulation across the 5G N78 band (3.60–3.78 GHz). The design is experimentally validated in a WR-284 waveguide fixture, exhibiting a phase excursion approaching the full ${360}^{\circ }$ near resonance, with an amplitude variation below 1 dB over the bias sweep at any given frequency, while the average reflection level decreases by about 2 dB from the center to the upper band edge. A fifth-order polynomial phase–voltage calibration feeds a lookup table driving a layered control system based on a Python HMI, a server, and STM32-driven 16-bit DACs. Experimental measurements confirm that the control chain delivers the commanded bias voltages to the addressed unit cells within measurement uncertainty; the array-level beamforming is assessed at simulation level under idealized (unit-magnitude) assumptions, while the experimental characterization of the assembled surface is left to future work. Full article

This study focuses on accelerating adaptive beamforming in the Multicarrier Wireless Internet Local Loop (McWiLL) professional radio communication system using “Korona” ring smart antennas. The work investigates algorithms for calculating complex weight coefficients in an eight-element uniform circular antenna array. The main objective is to reduce beam pattern adaptation time while maintaining interference suppression depth and robustness under multipath propagation. To achieve this, an ensemble machine learning approach based on the Random Forest algorithm is employed to approximate the optimal Minimum Variance Distortionless Response (MVDR) solution using elements of the sample covariance matrix of received signals. The training dataset is generated through McWiLL channel simulations considering mutual coupling between array elements, signal-to-noise ratio (SNR) variation, and different angles of arrival of the desired and interfering signals. The proposed method is evaluated against the classical MVDR algorithm in terms of radiation pattern null depth, robustness to phase distortions, and inference time on a Field-Programmable Gate Array (FPGA) hardware platform. Results demonstrate that the Random Forest-based approach achieves more than a fourfold reduction in computation time while forming radiation-pattern nulls of about 30–35 dB toward the interferers (versus 44–46 dB for the classical MVDR); the synthesized core uses no hardware multipliers (DSP48), and its functional equivalence to the software model is confirmed by bit-exact RTL co-simulation. The findings show promise for deployment in McWiLL base stations and other professional radio systems requiring fast, adaptive beamforming under dynamic channel conditions. Full article

Millimeter-wave (mm-wave) near-field imaging using frequency-modulated continuous wave (FMCW) radar has emerged as a pivotal technology for high-resolution applications, including security screening, non-destructive testing, and medical diagnostics. This review evaluates the performance and evolution of key imaging algorithms, categorized into spatial-domain and frequency-domain frameworks. We analyze the delay-and-sum (DAS) beamformer for its real-time utility and the back-projection algorithm (BPA) for its baseline phase precision and robust adaptability to irregular scanning trajectories. To address the high computational demands of standard spatial-domain processing, we examine fast alternatives such as the range migration algorithm (RMA). The exact RMA leverages Fourier-domain operations and Stolt coordinate mapping to achieve optimal computational scaling on uniform grids while preserving diffraction-limited spatial resolutions. Concurrently, we evaluate fast spatial-domain approximations, including Fast Back-Projection (Fast-BPA), which introduces localized Taylor-series expansions to linearize near-field range paths within sub-apertures, accelerating voxel reconstruction times at a reduced computational cost. Furthermore, this study explores advanced modifications designed to overcome physical and operational constraints, such as motion-compensated matched filtering (MF) to eliminate the “stop-and-go” assumption in continuous scanning, and sparse multiple-input multiple-output (MIMO) configurations to mitigate aliasing in undersampled environments. Comparative analysis reveals that while spatial-domain methods (DAS/BPA) generally offer higher robustness to non-uniform aperture perturbations, frequency-domain migration pathways (RMA) maximize the computational throughput required for large-volume three-dimensional (3D) reconstructions. The findings demonstrate that achievable resolution is primarily governed by signal bandwidth and aperture synthesis, though practical performance is often limited by calibration errors and computational overhead. Collectively, these advancements validate the potential of mm-wave FMCW systems to achieve sub-millimeter 3D imaging, bridging the gap between theoretical diffraction limits and real-world indoor sensing challenges. Full article

Reliable and fast radiation pattern prediction is critical for large-scale tightly coupled linear antenna arrays. Strong mutual coupling and finite-array edge effects limit the accuracy of conventional array factor methods, while full-wave simulations become computationally prohibitive for large arrays. To address this issue, a fast and efficient radiation pattern prediction method (FERPP) is proposed. For central elements, the far-field response is obtained from a calibrated reference array and extended through position-dependent phase compensation. For edge elements, responses are extracted from independent local full-wave simulations. All element responses are assembled into a global far-field response matrix, enabling direct radiation pattern synthesis using the extended method of maximum power transmission efficiency. Simulation results obtained with a 1024-element linear microstrip patch antenna array operating at 3.5 GHz, with small inter-element spacing, demonstrate close agreement with full-wave simulations. For a broadside single-beam case, the predicted peak gain is 29.10 dBi, compared with 29.02 dBi from full-wave simulation. For a scanned beam at 30°, the predicted peak gain is 28.22 dBi, while the full-wave result is 28.99 dBi. For an equal-weight three-beam configuration at −30°, 0°, and 30°, the proposed method yields a peak gain of 23.87 dBi, compared with 24.21 dBi from full-wave simulation. In terms of computational efficiency, the proposed method requires only about 1.8% of the computational time required for a full-wave simulation. These results demonstrate that the proposed FERPP method provides a practical and efficient solution for radiation pattern prediction and beamforming analysis of large-scale tightly coupled linear antenna arrays. Full article

A digital predistortion (DPD) scheme for concurrent multi-beam transmission in fully digital multiple-input, multiple-output (MIMO) systems, using Fast Fourier Transform (FFT) beamforming and so-called virtual-array processing, is proposed. In a MIMO array with nonlinear power amplifiers (PAs), transmitting multiple beams concurrently yields intermodulation products that end up in both user and non-user directions. In the setting with few users in a large array, the array dimension will typically be much larger than the number of generated intermodulation products. At the same time, linearization per PA is excessively costly for large arrays. This work shows that it is instead possible to linearize the system by producing predistorted user beams, and non-user intermodulation products, through DPD processing in a virtual array of a much smaller dimension than the physical array. Theoretical derivations and simulation examples show how this approach can lead to manyfold reductions in DPD complexity. Full article

Integrated sensing and communications (ISAC) requires tight coordination between spatial signal design and multiple-access strategies to balance communication throughput and sensing accuracy under shared spectral and hardware constraints. However, existing ISAC frameworks with rate-splitting multiple access (RSMA) typically rely on fixed antenna arrays and decoupled optimization, which fundamentally limit their ability to adapt to fast channel variations and dynamic sensing requirements. This paper introduces a fluid antenna-enabled RSMA-assisted ISAC architecture, in which movable antenna ports are exploited as a new spatial degree of freedom to enhance adaptability in both communication and sensing operations. Fluid antenna systems (FAS) are deployed at both the base station and user terminals, allowing dynamic port selection that reshapes the effective channel and sensing beampattern in real time. We formulate a joint sum-rate maximization problem subject to explicit sensing-quality constraints, capturing the coupled impact of antenna port selection, RSMA rate allocation, and multi-beam transmit design. The proposed framework maximizes the communication sum-rate while ensuring that the sensing functionality satisfies a predefined sensing quality constraint. This constraint-based ISAC formulation guarantees that sufficient sensing power is directed toward the target while optimizing communication performance. The resulting optimization involves strongly coupled discrete and continuous decision variables, rendering conventional optimization methods ineffective. To address this challenge, a hierarchical deep reinforcement learning (HDRL) framework is developed, where an upper-layer deep Q-network (DQN) determines discrete antenna port selection and a lower-layer twin delayed deep deterministic policy gradient (TD3) algorithm optimizes continuous beamforming and rate-splitting parameters. Numerical results demonstrate that the proposed approach significantly improves system performance, achieving higher communication sum-rate while satisfying sensing requirements under dynamic propagation conditions. Full article

Distributed beamforming in UAV swarms requires fast and accurate carrier-phase alignment under sparse connectivity and propagation-induced phase bias. This paper proposes a physics-aware decentralized synchronization framework for quasi-static UAV swarm beamforming by integrating momentum-accelerated Metropolis–Hastings consensus with position-aided phase pre-compensation. To preserve phase evolution on the circular manifold, a sinusoidal coupling law is adopted, while the momentum term improves convergence in sparse random geometric graphs. A propagation model is further established to characterize how geometric separation and ranging uncertainty translate into residual phase error and coherent power loss. Under small-signal conditions, local stability is analyzed, and Monte Carlo simulations are conducted to evaluate convergence, synchronization accuracy, robustness, and beam-focusing performance. Results show that, at 2.4 GHz with low-centimeter ranging uncertainty, the proposed method achieves sub-wavelength synchronization accuracy while providing an effective balance among convergence speed, accuracy, and complexity. Compared with standard Metropolis–Hastings, fixed-weight, and other accelerated consensus methods, the proposed scheme converges faster over most sparse topologies. Although its steady-state accuracy is slightly lower than that of filter-based predictive methods such as KF-DFPC in some cases, those schemes incur higher implementation and computational overhead. Therefore, from the perspectives of decentralized realization and practical deployment, the proposed method is more suitable for lightweight phase synchronization in distributed UAV swarms. Full article

Adaptive beamforming (ABF) can improve the signal-to-interference-plus-noise ratio (SINR) of radar systems through the suppression of interference and by maintaining the desired signal. However, unavoidable array defects will cause significant performance degradation in real scenarios because of sensor position error. To address this challenge, an effective ABF based on the flamingo search algorithm (FSA) is established, referred to as FSABF. The strong global search capability and fast convergence of FSA are exploited to optimize the beamforming weights. As a result, the main lobe is accurately directed toward the target, while deep nulls are imposed in interference directions, thereby significantly improving the output SINR. In addition, an early-stopping strategy is introduced to optimize the iteration process. The resulting beamformer, namely, FSABFE, maintains excellent beamforming performance while reducing computational overhead to $11.90\%$ of that of FSABF, thereby significantly enhancing overall efficiency. This advantage makes the proposed approach more suitable for practical radar applications in situations featuring limited computational resources. The simulation results show that the proposed FSABF and FSABFE achieve robust beam control under sensor position error. Full article

We propose a low-complexity hybrid beamforming method for massive Multiple-Input Multiple-Output (MIMO) systems that is robust to Channel State Information (CSI) estimation errors. These errors stem from hardware impairments, pilot contamination, limited training, and fast fading, causing spectral-efficiency loss. However, existing hybrid beamforming solutions typically either assume near-perfect CSI or rely on greedy/black-box designs without an explicit mechanism to regularize the error-distorted singular modes, leaving a gap in unified, low-complexity, and theoretically grounded robustness. We unfold the Alternating Direction Method of Multipliers (ADMM) into a trainable Deep Learning (DL) network, termed DL-ADMM, to jointly optimize Radio-Frequency (RF) and baseband precoders and combiners. In DL-ADMM, the ADMM update mappings are learned (layer-wise parameters and projections) to amortize the joint RF/baseband optimization, whereas Regularized Singular Value Decomposition (RSVD) acts as an analytical regularizer that reshapes the observed channel’s singular values to suppress noise amplification under imperfect CSI. RSVD is integrated to stabilize singular modes and curb noise amplification, yielding a unified and scalable design. For ${\sigma }_e^2=0.1$, the proposed DL-ADMM-Reg achieves approximately 8–11 bits/s/Hz higher spectral efficiency than Orthogonal Matching Pursuit (OMP) at Signal-to-Noise Ratio (SNR) $=20$–40 dB, while remaining within <1 bit/s/Hz of the digital-optimal benchmark across both $(N_t,N_r)=(32,32)$ and $(64,64)$ settings. Simulations confirm higher spectral efficiency and robustness than OMP and Adaptive Phase Shifters (APSs). Full article

Physical-layer security offers low probability of interception (LPI) in wireless communication systems. While prior methods such as the directional beamforming and secrecy coding schemes require knowledge of the eavesdropper (Eve)’s channel, passive eavesdropping limits their practicality. Artificial additive noise and artificial fast fading (AFF) schemes address the issue by degrading detection ability of a potential Eve without knowing its channel information. In particular, AFF achieves LPI by effectively shortening the coherence time of Eve’s channel using a random precoder while keeping the legitimate receiver (Bob)’s channel deterministic. In this paper, we propose a novel AFF design for spatial multiplexing multi-input multi-output (MIMO) amplify-and-forward (AF) relay systems. First, we formulate an optimization problem to achieve minimum mean squared error (MMSE) of Bob’s signals while guaranteeing LPI conditions from Eve, which is generally non-convex. To tackle the non-convexity of the problem, we apply a convex set approximation technique and thereby derive a simple closed-form design. Finally, we evaluated the performance of both Bob and Eve via computer simulations to demonstrate the effectiveness of our proposed design. Full article

Non-contact heart rate estimation technology based on frequency-modulated continuous wave (FMCW) radar has garnered extensive attention in single-target scenarios, yet it remains underexplored in multi-target environments. Accurate discrimination of multiple targets and precise estimation of their heart rates constitute key challenges in the multi-target domain. To address these issues, we propose a novel scheme for multi-target heart rate estimation. First, a high-precision distance-bin selection (HDBS) method is proposed for target localization in the range domain. Next, multiple-input multiple-output (MIMO) array processing is combined with the Root-multiple signal classification (Root-MUSIC) algorithm for angular domain estimation, enabling accurate discrimination of multiple targets. Subsequently, we propose an efficient method for interference suppression and vital sign extraction that cascades variational mode decomposition (VMD), local mean decomposition (LMD), and wavelet thresholding (WT) termed as VLW, which enables high-quality heartbeat signal extraction. Finally, to achieve high-precision and super-resolution heart rate estimation with low computational burden, an improved fast iterative interpolated beamforming (FIIB) algorithm is proposed. Specifically, by leveraging the conjugate symmetry of real-valued signals, the improved FIIB algorithm reduces the execution time by approximately 60% compared to the standard version. In addition, the proposed scheme provides sufficient signal-to-noise ratio (SNR) gain through low-complexity accumulation in both distance and angle estimation. Six experimental scenarios are designed, incorporating densely arranged targets and front-back occlusion, and extensive experiments are conducted. Results show this scheme effectively discriminates multiple targets in all tested scenarios with a mean absolute error (MAE) below 2.6 beats per minute (bpm), demonstrating its viability as a robust multi-target heart rate estimation scheme in various engineering fields. Full article

Elevation Digital Beamforming (DBF) technology is key to achieving high-resolution wide-swath (HRWS) imaging in spaceborne Synthetic Aperture Radar (SAR) systems. However, multi-channel DBF-SAR systems face a prominent conflict between the need for real-time channel error calibration and the constraints of limited on-board hardware resources. To address this bottleneck, this paper proposes a real-time channel error calibration method based on Fast Fourier Transform (FFT) pulse compression and introduces a “calibration-operation” dual-mode control with a parameter-persistence architecture. This scheme decouples high-complexity computations by confining them to the system initialization phase, enabling on-board, real-time, closed-loop compensation for multi-channel signals with low resource overhead. Test results from a high-performance Field-Programmable Gate Array (FPGA) platform demonstrate that the system achieves high-precision compensation for inter-channel amplitude, phase, and time-delay errors. In the 4-channel system validation, the DBF synthesized signal-to-noise ratio (SNR) improved by 5.93 dB, reaching a final SNR of 44.26 dB. This performance approaches the theoretical ideal gain and significantly enhances the coherent integration gain of multi-channel signals. This research fully validates the feasibility of on-board, real-time calibration with low resource consumption, providing key technical support for the engineering robustness and efficient data processing of new-generation SAR systems. Full article

Massive Multiple-Input Multiple-Output (MIMO) systems with hundreds or thousands of antenna elements are fundamental to next-generation wireless networks, promising unprecedented spectral efficiency through spatial multiplexing and beamforming. However, the computational burden of channel state information (CSI) acquisition and processing scales dramatically with array size, creating a critical bottleneck for practical deployments. While previous works demonstrated that Fast Fourier Transform (FFT)-based beamspace processing can exploit the inherent angular sparsity of wireless channels to compress CSI feedback, the digital implementation requires intensive computations that become prohibitive for ultra-large arrays. This paper presents an analog alternative using Butler matrices—passive beamforming networks that realize the Discrete Fourier Transform in hardware—combined with RF switching circuits to select only dominant angular components. We provide a comprehensive analysis of Butler matrix architectures for arrays up to 32 × 32 elements, characterizing insertion losses across different technologies (microstrip, substrate-integrated waveguide, and waveguide) and operating frequencies (10–30 GHz). The proposed system incorporates parallel power sensing with Winner-Take-All circuits for sub-microsecond beam selection, drastically reducing the number of active RF chains. Full-wave simulations and capacity evaluations at 12 and 30 GHz demonstrate that the Butler-based approach achieves comparable performance to FFT methods while offering significant advantages in power consumption and processing latency. For a 256 × 256 array, FFT computation requires 0.36 ms compared to near-instantaneous analog processing, making Butler matrices particularly attractive for real-time massive MIMO systems. These findings establish Butler matrix front-ends as a practical pathway toward scalable, energy-efficient beamspace processing in 6G networks. 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