Search Results (1,321)

This work analyzes the ergodic capacity behavior of reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) systems with a finite and arbitrary number of antennas and RIS elements under Nakagami-m fading conditions. By combining Hadamard’s determinant inequality with the Cauchy–Schwarz inequality, this work derives a dimensionally consistent closed-form upper bound on the ergodic capacity in terms of the Meijer G-function. Subsequently, it is demonstrated that at a high signal-to-noise ratio (SNR), a simplified expression for the capacity upper bound can be derived, enabling an analytical assessment of how the fading parameter influences the ergodic capacity. The study also explores the asymptotic behavior in the large-system regime, where the number of antennas or RIS elements tends to infinity. Monte Carlo (MC) simulations confirm the accuracy of the proposed bound and scaling laws. Full article

The rapid expansion of sub-6 GHz 5G and Internet of Things (IoT) networks demands highly miniaturized Multiple-Input Multiple-Output (MIMO) antennas. However, balancing extreme physical compactness with rigorous inter-port isolation introduces severe computational bottlenecks for conventional optimization algorithms. To overcome these multidimensional challenges, this paper proposes a novel Deep Surrogate Active Learning framework for the autonomous design and empirical validation of an ultra-compact dual-band MIMO antenna. By using a surrogate-assisted closed-loop strategy to reduce reliance on repeated full-wave evaluations, the methodology combined a custom-penalized Deep Neural Network with dynamic boundary reduction. After training the initial surrogate model with 440 valid full-wave responses obtained from the offline design-of-experiments (DOE) stage, the best CST-validated candidate was identified at the 83rd active learning cycle. The optimized nested-loop geometry, incorporating a partial defected ground structure (DGS), occupies an extremely confined footprint of only 1634 mm² on a Rogers RO4350B substrate (Rogers Corporation, Chandler, AZ, USA). The selected geometry provided simulated −10 dB impedance bands of 3.35–3.88 GHz and 4.34–5.05 GHz, while the complete two-port model maintained inter-port isolation better than 13.8 dB and 14.9 dB across the lower and upper target passbands, respectively. Measurements of the fabricated prototype showed the intended dual-band behavior, a maximum measured gain of 4.54 dBi, and total radiation efficiencies of approximately 51–63% across both ports at the evaluated frequencies. The simulated Envelope Correlation Coefficient (ECC) remained below 0.035 across the target passbands, supporting the suitability of the compact geometry for the investigated sub-6 GHz MIMO bands. Full article

The rapid evolution of fifth-generation (5G) and emerging sixth-generation (6G) wireless communication systems has considerably intensified the need for high data rates, ultra-low latency, massive connectivity, and intelligent network integration. To satisfy these requirements, millimeter-wave (mmWave) bands offer large available bandwidths; however, their severe propagation losses and integration constraints necessitate advanced antenna solutions. In this context, compact multi-port Multiple-Input–Multiple-Output (MIMO) antennas are a key solution for high-capacity and reliable mmWave communications. This review presents a comprehensive overview of recent antenna system technologies for 5G/6G applications, focusing on small mmWave MIMO antenna designs, performance improvement methods, advanced materials, and smart integration methods. Several antenna structures, such as microstrip patch, dielectric resonator, slot-based, and metamaterial-inspired designs, are critically discussed and compared. In addition, this review analyzes key design challenges involving miniaturization, mutual coupling reduction, bandwidth enhancement, gain improvement, radiation efficiency, and integration complexity, along with their impact on key performance metrics. The importance of advanced materials, artificial-intelligence-assisted optimization, hybrid antenna architectures, and smart integration strategies in future 5G/6G systems is also emphasized. Finally, we identified current challenges, emerging trends, and future research directions to provide useful design guidelines for researchers and engineers developing next-generation high-performance antenna systems for intelligent wireless communications. Full article

The peak sidelobe level (PSL) of an orthogonal waveform set directly governs the range and azimuth ambiguities of synthetic aperture radar (SAR) images and is therefore a key figure of merit for the imaging quality of multiple-input multiple-output SAR (MIMO-SAR). Minimizing the PSL is an NP-hard problem with a non-differentiable objective, and existing approaches often suffer from high computational cost and limited scalability while achieving only suboptimal PSL suppression. We therefore propose a loss function built around the 1.5-entmax sparse transform, which is simultaneously differentiable, sparse, and adaptive. Analysis of its differentiability and derivation of its analytical gradient allow us to recast the NP-hard problem as a differentiable optimization problem that can be solved by well-established algorithms. Owing to the sparsity of the transform, the gradient is concentrated on the high-energy sidelobes, while the gradient contribution from low-energy sidelobes becomes exactly zero, removing the gradient noise contributed by low-energy sidelobes and yielding a lower PSL. To overcome the scale sensitivity of the 1.5-entmax function, the upper bound on the sidelobe magnitude is used to normalize the input, which makes the threshold adaptive and removes the need for additional loss-function hyperparameter tuning across waveform-design tasks of different sizes. To further improve computational efficiency, we combine gradient descent with a deep learning framework: the waveform phases are treated as the learnable parameters of a neural network-like model, thereby yielding a back-propagation-based optimization framework with graphics processing unit (GPU) parallelism. The algorithm is implemented for parallel execution on GPU, and the gradient is computed efficiently through the network’s automatic differentiation in conjunction with a custom analytical-gradient operator, leading to a substantial increase in computational speed. Without the need for the manual tuning of loss-function hyperparameters, the proposed algorithm achieves the lowest PSL across waveform sets of various sizes and reduces computation time by approximately two orders of magnitude in large-scale settings. Full article

Accurate channel estimation in frequency-selective multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems requires balancing pilot overhead, reconstruction accuracy, and computational cost. This paper presents a reproducible compressed sensing benchmark for sparse delay-domain channel estimation with reduced pilot observations. Its novelty is not the invention of Orthogonal Matching Pursuit (OMP), Compressive Sampling Matching Pursuit (CoSaMP), or Subspace Pursuit (SP), but the construction of a transparent and auditable evaluation protocol in which all estimators operate on the same channel realizations, sensing matrices, pilot budgets, signal-to-noise ratios (SNRs), stopping rules, and Monte Carlo trials. The framework explicitly defines the underdetermined observation model, the per-link 4 × 4 MIMO interpretation, the minimum-norm least-squares (LS) baseline, the identity-prior linear minimum mean-square error (LMMSE) baseline, the oracle-known sparsity assumption, uncertainty reporting, runtime protocol, and the mapping from delay-domain estimates to link- and subcarrier-domain quantities. OMP, CoSaMP, SP, LS, and LMMSE are evaluated for a 128-element delay dictionary, five active taps, sampling ratios from 0.10 to 0.70, SNRs from 0 to 30 dB, and 80 independent trials per operating point. The results show that sparse recovery exploits the assumed delay-domain sparsity more effectively than non-sparse baselines in the underdetermined regime, while pilot density remains a dominant factor in support identification and reconstruction error. The accompanying Python human–machine interface (HMI) produces confidence-aware metrics and publication-ready figures, enabling exact repetition of the benchmark and controlled extension to more realistic channel models. The conclusions are limited to simulation-based algorithmic evidence and define a direct pathway toward standardized-channel, software-defined radio (SDR), and measured radio-frequency (RF) validation. Full article

In millimeter-wave frequency-modulated continuous-wave (FMCW) multiple-input multiple-output synthetic-aperture radar (MIMO-SAR) imaging, platform displacement beyond the spatial Nyquist limit during a slow-time sampling interval creates aperture gaps, causing azimuth aliasing and degraded resolution. This paper proposes a spectral-prior-guided Swin TransUnet (SSTU) method for suppressing azimuth ambiguity in sparse moving-array imaging. Gaussian soft labels derived from point-scatterer positions formulate localization as heatmap regression and guide mainlobe learning. A two-dimensional fast Fourier transform (2D-FFT) layer then constructs a range–azimuth spectrum that exposes main peaks, sidelobes, and periodic grating lobes. A convolutional encoder extracts local spectral features, Swin Transformer blocks model long-range ambiguity correlations, and a U-Net-style multiscale decoder reconstructs high-resolution range–azimuth images. Simulations show that SSTU reliably recovers multiple point targets from noise and grating lobes despite substantial aperture gaps. At 60% aperture sparsity and signal-to-noise ratio (SNR) above −6 dB, it achieves a root mean square error (RMSE) below ${10}^{-2}$ and an azimuth ambiguity suppression ratio better than −30 dB, outperforming conventional methods. Measurements using a 77 GHz radar platform further demonstrate high-quality outdoor imaging of randomly distributed strong scatterers at 60% moving-aperture sparsity. 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

To address the stringent requirements of 6G industrial Internet of Things (IoT) and ultra-dense networks on spectral efficiency, hardware cost, and transmission reliability, this paper investigates waveguide index modulation based on the pinching-antenna system (PASS), a promising flexible multiple-input multiple-output (MIMO) architecture featuring large-scale reconfigurability and robust line-of-sight (LoS) link establishment. A two-stage sparse transmission framework is proposed, where a Simulated Annealing-based Constrained Discrete Optimization (SA-CDO) algorithm is first employed to optimize pinching-antenna (PA) positions and construct a near-orthogonal equivalent channel dictionary for inter-waveguide interference suppression. Subsequently, an Orthogonal Least Squares-based Constellation-Constrained (OLS-CC) detector is developed to jointly recover active waveguide indices and modulation symbols with low computational complexity. Monte Carlo simulations demonstrate that the proposed scheme consistently outperforms conventional antenna index modulation under both LoS and Rician fading channels across the entire SNR range. The SA-CDO optimization significantly reduces the bit error rate (BER), while the OLS-CC detector further improves sparse recovery accuracy and reduces the detection complexity from exponential to polynomial order. These results provide valuable insights for the design of highly reliable 6G IoT communication systems. Full article

Reliable communication infrastructure is a fundamental component of Intelligent Transport Systems (ITSs), particularly in scenarios involving maritime corridors and emergency traffic management. In locations where optical fiber deployment is geographically constrained, unmanned aerial vehicle (UAV)-assisted free-space optical (FSO) relay links provide a flexible and rapidly deployable alternative. However, atmospheric attenuation, turbulence-induced fading, and wind-induced UAV misalignment can severely degrade link reliability and disrupt real-time transport data streams. This study proposes a payload-efficient multiple-input multiple-output free-space optical (MIMO-FSO) relay architecture based on a multi-output/single-input (MOSI) uplink and a single-output/multi-input (SOMI) downlink. Here, MOSI denotes multiple ground-based transmit apertures directed toward a single UAV receiving aperture, whereas SOMI denotes one UAV transmitting aperture serving multiple ground-based receiving apertures. Unlike conventional symmetric UAV-assisted MIMO-FSO relays that may duplicate diversity hardware on the aerial node, the proposed design shifts the parallel optical branches to the ground stations and keeps only one optical receiver and one optical transmitter on board the UAV. Under the adopted 4 × 4 comparison assumption, this reduces the UAV-side optical branch count from eight to two, corresponding to a 75% branch-count reduction proxy. System performance is evaluated over a 1.54 km relay link. The analytical framework describes Beer–Lambert attenuation, log-normal/gamma–gamma turbulence, and statistical pointing errors; in the OptiSystem implementation, their combined effects are represented by equivalent aggregate losses of 25 dB/km for atmospheric absorption/scattering and 25.5 dB/km for turbulence- and pointing-related degradation. Comparative simulations for SISO, 2 × 2, and 4 × 4 configurations show that the proposed 4 × 4 architecture increases the Q-factor from 8.38 to 18.25 and changes the OptiSystem-reported minimum BER from 2.73 × 10⁻¹⁷ to 9.95 × 10⁻⁷⁵. Because a finite simulation cannot statistically validate error probabilities of this magnitude through raw error counting, values far below 10⁻¹² are interpreted primarily as comparative indicators of receiver decision margin. The findings provide simulation-based evidence that the proposed architecture is a scalable candidate for resilient optical wireless backhaul in smart transport corridors under adverse propagation conditions. Full article

This paper introduces a sociotropy-inspired game-theoretic framework for distributed multiple-input multiple-output (MIMO) beamforming systems, where each antenna element is modeled as a strategic agent that adapts its beamforming parameters by balancing individual transmission performance with coordinated interaction among neighboring antennas. The resulting distributed beamforming problem is formulated as an exact potential game, enabling a unified analysis of cooperative antenna behavior under per-antenna power constraints. A complete mathematical formulation is developed, including the derivation of the utility and potential functions, the associated KKT stationarity conditions, and distributed projected gradient dynamics for beamforming adaptation. In addition, a graph-based multi-agent coordination mechanism is introduced to incorporate structured information exchange among antennas through similarity-driven message passing. Numerical simulations compare the proposed sociotropic strategy against both a selfish non-cooperative baseline and a graph-regularized multi-agent learning approach. Results demonstrate that sociotropic coordination improves interference management, convergence stability, and robustness under dynamic channel conditions, while maintaining lower computational complexity than learning-based coordination methods. Finally, the proposed distributed sociotropic beamforming framework is evaluated against classical maximum ratio transmission (MRT), zero-forcing (ZF), and regularised zero-forcing (RZF) beamforming schemes under identical time-varying channel dynamics. Results demonstrate that while conventional baselines exhibit performance saturation under channel fluctuations, the proposed method achieves continuous adaptation and improved sum-rate evolution over time. Full article

This paper investigates the stabilization of unstable third-order Multiple-Input Multiple-Output (MIMO) systems whose interaction structure is described by a doubly stochastic combined matrix, also known as the Relative Gain Array (RGA). Starting from systems with negative Niederlinski index, we derive necessary and sufficient conditions under which stability can be recovered through diagonal perturbations while preserving the doubly stochastic structure of the combined matrix. By exploiting the canonical representation of matrices associated with a prescribed combined matrix and the invariance properties under diagonal equivalence, the problem is reduced to a structured parametric form that allows a complete algebraic characterization. Special attention is given to perturbations involving the (1, 1) entry and one additional diagonal entry, leading to explicit bounds on the perturbation parameters that guarantee stabilization. The results extend previous papers on diagonal perturbations of combined matrices and provide a constructive method for stabilizing MIMO systems without altering their interaction pattern. Numerical examples illustrate the applicability of the proposed approach. Full article

Maritime accident analysis increasingly uses machine learning to support safety management, but many existing studies focus on single-output prediction, such as accident-occurrence probability, severity class, near-miss frequency, or one specific consequence. This study proposes a data-driven decision-support framework based on a Multi-Input Multi-Output Artificial Neural Network (MIMO-ANN) for the simultaneous prediction of multiple maritime accident consequences. A dataset of 582 recorded accident cases is constructed by integrating SafePASS project records with consequence, severity, and structural-damage information from the literature. The dataset includes 15 input variables covering ship characteristics, operational context, environmental conditions, accident type, and geographical zone and 15 consequence outputs covering structural damage, casualties, emergency-response indicators, total loss, and secondary consequence/escalation mechanisms. The ANN is trained using the Scaled Conjugate Gradient (SCG) algorithm and evaluated under different network configurations and data-partitioning strategies. The best-performing model uses 30 hidden neurons with a 60/20/20 split, achieving a correlation coefficient (R) equal to 0.9249 and a mean squared error (MSE) equal to 0.0240 for testing, and a R equal to 0.9278 and a MSE equal to 0.0231 for validation. Ten-fold cross-validation further confirms internal predictive stability, with mean testing R equal to 0.8803 ± 0.0827 and MSE equal to 0.0445 ± 0.0478. Permutation-based sensitivity analysis shows that accident type, zone, flag, natural light, environment, and visibility are key drivers of predicted consequences, whereas vessel-specific parameters have a secondary, context-dependent influence. The framework should be interpreted as predicting the relative likelihood, severity, or magnitude of accident consequences in recorded or scenario-defined accident cases, not the probability of accident occurrence. Future work should address dataset imbalance, include near-miss and nonserious records, incorporate richer AIS and metocean data, integrate exposure data, and validate the framework using independent accident datasets. Full article

Conventional multiple-input multiple-output (MIMO) systems rely on static antenna placement. To exploit additional spatial degrees of freedom, the fluid antenna (FA) concept has emerged as a promising solution for improving data rates and diversity performance. Most existing FA studies assume Rayleigh fading, whereas analytical characterization under Nakagami-m fading is more challenging. This article investigates the ergodic capacity of FA-assisted MIMO systems over Nakagami-m fading channels. By applying majorization theory, upper and lower bounds on the ergodic capacity are derived. High signal-to-noise ratio (SNR) approximations are then obtained to clarify the role of the fading parameter and the number of propagation paths. The large-system behavior is also studied, and Monte Carlo simulations are used to assess the tightness of the proposed bounds. The results show that the upper bound closely tracks the simulated capacity, while the lower bound remains useful mainly in the low-SNR regime. Full article

Optical wireless communication (OWC) has emerged as a complementary technology to conventional radio frequency (RF)-based communication systems, particularly in scenarios requiring low electromagnetic interference, enhanced security, and efficient spectrum utilization. Within various OWC approaches, optical camera communication (OCC) has attracted increasing attention due to its ability to utilize commercially available image sensors as receivers. This paper presents a 2D multiple-input–multiple-output (MIMO) OCC system based on light-emitting diode (LED) arrays for reliable communication in mobile environments. The proposed system employs on–off keying (OOK) modulation, which supports both rolling shutter and global shutter cameras. To improve decoding reliability under mobility conditions, a deep learning-based decoding model is introduced to enhance LED state detection compared with conventional zero-crossing approaches. In addition, a sequence number-based synchronization is implemented to compensate for frame rate variation and packet missing in a real-time environment. Besides that, by applying YOLOv13 for light source detection and tracking, we can achieve 98% accuracy at 3 m/s velocity. Experimental results show reliable communication performance at transmission distances of up to 22 m under various mobility conditions. Furthermore, the proposed system is validated through real-time environmental data transmission using temperature and humidity sensors with 20 links. The results indicate that the proposed scheme provides stable and reliable OCC performance for mobility Internet of Things (IoT) applications. Full article

This paper presents the design, fabrication, and experimental validation of a compact four-port metasurface for tri-band X-band multiple-input multiple-output (MIMO) applications operating at 8.75 GHz, 9.75 GHz, and 10.5 GHz. The proposed structure employs a scalable unit-cell configuration to enhance radiation performance while maintaining a compact footprint. A four-port feeding mechanism is integrated to support MIMO operation with improved channel diversity and reduced mutual coupling. The metasurface is realized using a $32\times 32$ unit-cell array, where increasing the number of unit cells significantly improves gain due to enhanced aperture efficiency. The fabricated prototype is experimentally characterized, and the measured S-parameters demonstrate good impedance matching at the three operating frequencies, with acceptable agreement between simulation and measurement results. In addition, reduced mutual coupling between ports confirms effective MIMO performance across the three bands. Radiation characteristics are evaluated through both 2D and 3D patterns. The radiation patterns were measured for a single port at frequencies where the reflection coefficient shows optimal performance, specifically at 8.75 GHz, 9.75 GHz, and 10.5 GHz. At these frequencies, the antenna exhibits well-defined main lobes with symmetrical radiation characteristics, indicating stable radiation behavior across the operating band. The realized gain exceeds 12 dBi at all three frequencies, with a peak gain of approximately 13 dB, along with satisfactory directivity and radiation efficiency. The results confirm that array scaling is an effective approach for gain enhancement without significantly increasing system complexity. In addition, the proposed MIMO metasurface achieves excellent diversity performance with ECC values below 0.04, DG values close to 10 dB, balanced MEG characteristics, and CCL values below 4 bits/s/Hz. The obtained results confirm that the proposed metasurface is a promising candidate for compact high-performance X-band MIMO systems for radar and advanced wireless communication applications. Full article