Search Results (263)

Light fidelity (LiFi) is progressively evolving as a highly promising communication technology because of its unique benefits, available spectrum, low implementation costs, and adaptive beamforming capabilities. Despite their advantages, existing LiFi networks remain constrained by limited data rates, coverage area, and information security in practical environments. Therefore, a high-speed, high-capacity, and secure quantum key distribution (QKD)-assisted integrated multi-wavelengths (450/532/620 nm) LiFi system using mode division multiplexing (MDM) is proposed. The results demonstrate that the proposed system achieves maximum transmission distances of 20.5–22 m and 19–22 m using different Laguerre–Gaussian (LG) and Hermite–Gaussian (HG) mode indices {[0,0], [0,10], [0,20], [0,30]}, at an aggregate data rate of 40 Gbps. Furthermore, the minimum acceptable transmitter angles of 30–90° for irradiance angles of 20–80° are required to maintain the target bit error rate (BER) of 10⁻⁹. The minimum photodetector detection areas required at transmission distances of 20–30 m are 1–2 cm² at the minimum BER limit. Moreover, the proposed system exhibits optimum performance, achieving an optical loss of −39.47 dB, −49.03 dBm received power, and 45.39 dB signal-to-noise ratio for 1–10 photons/pulse. Compared with existing studies, the proposed system demonstrates enhanced overall performance across various communication metrics. Full article

Limited snapshots and element-wise gain–phase mismatch jointly impair covariance estimation and array manifold accuracy in uniform planar arrays. This paper proposes a deep robust adaptive beamforming framework that combines statistical base-weight generation, element-wise array manifold calibration, and regularized response projection. The base-weight network extracts finite-snapshot covariance information, whereas the calibration network estimates a physically bounded element-wise complex-gain vector from covariance features and nominal direction context. Phase-aligned auxiliary supervision makes the calibration loss invariant to the unidentifiable common phase and is required only during training. The calibrated steering vectors define a closed-form minimum-distance projection that preserves the normalized base weight’s desired direction response while suppressing the calibrated interference responses. Across three training seeds, the method achieves 23.43 ± 0.06 dB output SINR and a −52.48 ± 0.09 dB average null level, improving the former by 5.64 dB and deepening the latter by 5.11 dB relative to the best-performing baseline under the main test distribution. Experiments on mismatch severity, input SNR, snapshot number, direction-of-arrival errors, controlled ablations, and computational cost characterize the performance and limitations of the method under the stated synthetic-array model. Full article

Micromachined ultrasonic transducers (MUTs) represent a notable advance in miniaturized sensing, enabling compact, low-power, and complementary metal-oxide-semiconductor (CMOS)-integrated platforms that extend ultrasonic capabilities into wearable, implantable, and edge-computing domains. The integration of artificial intelligence (AI) has introduced new approaches for signal interpretation, adaptive control, and data-driven optimization, enhancing performance in specific areas such as compressed sensing, neural beamforming, and learned image enhancement that complement conventional signal processing. Meanwhile, sensor fusion strategies that combine ultrasonic data with complementary modalities have improved robustness, contextual awareness, and diagnostic accuracy across applications ranging from industrial monitoring to clinical diagnostics. This review provides a comprehensive analysis of this active research area, systematically covering transducer hardware platforms, design methodologies, and intelligent signal processing frameworks. While traditional bulk piezoelectric transducers remain the benchmark for high-power applications, capacitive and piezoelectric micromachined variants offer superior acoustic impedance matching and monolithic CMOS compatibility essential for portable systems. We examine the evolution from deterministic analytical and numerical modeling toward AI-powered inverse design, which enables the discovery of non-intuitive, high-performance geometries beyond human intuition. Furthermore, the integration of machine learning (ML) for signal recovery, image enhancement, and multi-modal sensor fusion is discussed as a pathway to compensate for hardware constraints such as limited aperture, sparse sampling, and low signal-to-noise ratio (SNR), while pointing out that AI technology cannot overcome fundamental physical limits including acoustic attenuation, thermal noise floors, and transduction efficiency boundaries. By synthesizing recent advancements, this review demonstrates how the convergence of classical acoustic physics and data-driven intelligence is guiding the development of of intelligent ultrasonic systems. Full article

Improving the performance of computer networks using seamless Wi-Fi can be achieved by implementing a number of strategies and technologies. Strategies include, first of all, the optimal location of routers and access points, the use of a multi-band network or routers supporting different bands. Routers with support for beamforming technology, which directs the Wi-Fi signal directly to connected devices, allow you to improve the signal quality and data transfer speed. Increasing the performance of Wi-Fi computer networks is also provided by the use of network monitoring and management software, which allows you to monitor its performance and respond to possible problems in the network infrastructure. This is an important task, because it determines the quality and convenience of access to network resources. First of all, it allows you to achieve a high data transfer rate, which is especially important in conditions of high traffic necessary for demanding applications. Seamless Wi-Fi technologies also promote increased mobility and flexibility of users, allowing them to connect to the network in any place with a good signal without having to use wired connections. Network management becomes more efficient with automatic switching between access points and increased fault tolerance in the face of changing traffic usage scales. Quantitative results: Implementation of the Wi-Fi roaming mechanism using the IEEE 802.11 specification; Wi-Fi performance measurements obtained for various IEEE 802.11n HT20 and IEEE 802.11a client ratios; the original test environment included 50 laptops and netbooks from various manufacturers, equipped with various operating systems and wireless network adapters; seamless Wi-Fi technologies based on IEEE 802.11k, IEEE 802.11v, and IEEE 802.11r improve communication continuity during device mobility and support real-time AI-based decision making; Wi-Fi based on local communication standards (WLAN-Wireless Local Area Network). It allows data transmission speeds from 1 Mb∙s⁻¹ to 6.75 Gb∙s⁻¹. Indoors, the Wi-Fi range is 20 m, and outdoors 100 m; WiMax (Worldwide Interoperability for Microwave Access) is a built-in set of wireless broadband standards that provide a constant data rate of 1 Gb∙s⁻¹ and 100 Mb∙s⁻¹ in a cellular network; LR-WPANs (Low-Rate Wireless Personal Area Networks) are standards that are the basis for higher communication protocols, ZigBee. They offer data rates ranging from 40 kb to 250 kb∙s⁻¹. In devices with limited resources, these standards operate at 2.4 GHz at higher transmission speeds and 868/915 MHz at lower. The novelty in the article is the implementation of the Wi-Fi roaming mechanism, presentation of Wi-Fi scenarios, discussion of module generations, indication of integrated agriculture in terms of modern digitalization technologies, and characteristics of smart farming. Full article

Platform-induced synthetic aperture is an effective approach to enhancing azimuth resolution in forward-looking radar imaging. However, for small platforms such as automobiles and unmanned aerial vehicles, the large volume of echo data required under continuous sampling, combined with the presence of Doppler ambiguity, poses substantial challenges for high-resolution imaging. To address these issues, this paper proposes a forward-looking FMCW radar imaging method based on sparse sampling intervals. A uniform linear array is first employed to acquire measurements at different platform positions, and an initial range-angle image is obtained for each channel. Adaptive beamforming is then applied to impose nulls on false-alarm regions, including grating lobes and left-right ambiguity. Finally, coherent accumulation across channels yields a high-resolution range-angle image. Simulation and experimental results demonstrate that the proposed method achieves high-resolution forward-looking imaging while significantly reducing the volume of echo data. Full article

Pilot contamination attacks (PCAs) pose a great physical-layer threat to modern multiple-input–multiple-output (MIMO) systems by exploiting channel reciprocity to corrupt channel state information estimation. While existing literature acknowledges this vulnerability, precise parametric threat models that capture the transition from active channel estimation poisoning to data exploitation remain scarce. This article addresses this gap by developing a novel, physical-layer parameterized PCA framework structured as a two-stage operational attack: channel estimation poisoning and adaptive information contamination. We formulate an algorithmic attack strategy that systematically manipulates base transceiver station precoding and beamforming weights. This formulation allows us to quantify the precise degradation of the system through the lens of the confidentiality, integrity, and availability (CIA) triad, specifically mapping the adversary’s security gains against legitimate users’ signal-to-noise ratio degradation. Finally, we leverage this parametric threat model to outline a qualitative roadmap of actionable detection vectors and structural mitigation strategies. The proposed algorithmic attack serves as an evaluation benchmark and an analytical baseline for conceptualizing resilient architectures in emerging physical-layer security frameworks. Full article

The integration of low-Earth-orbit (LEO) satellites with unmanned aerial vehicles (UAVs) promises high-throughput and flexible wireless connectivity, yet it faces critical challenges in simultaneously guaranteeing data rates and long-term energy harvesting under mobility and imperfect channel state information (CSI). Additionally, the rate–energy trade-off imposed by simultaneous wireless information and power transfer (SWIPT) further complicates per-slot resource allocation. In this paper, we propose a Lyapunov-based scheduling framework that stabilizes UAV data and virtual energy queues while maximizing weighted throughput. The framework employs a custom inner solver combining successive convex approximation (SCA) and weighted minimum mean-square error (WMMSE) optimization to efficiently compute per-slot beamformers and power-splitting ratios. Our approach explicitly accounts for UAV mobility, Rician fading channels with Doppler, and circuit nonlinearities in energy harvesting, ensuring feasible and energy-aware SWIPT operation. A LEO satellite–UAV integrated communication system is considered, where multiple satellites provide wireless connectivity to energy-constrained UAVs operating in a dynamic three-dimensional environment. The satellites employ multi-antenna transmission, while the UAVs rely on energy harvesting mechanisms to sustain their operation. The communication links are characterized by dominant line-of-sight propagation conditions, and UAV trajectories are adaptively optimized to improve network performance and energy efficiency. Simulation results demonstrate that the proposed Lyapunov-based SCA-WMMSE framework significantly outperforms a fixed baseline approach, providing substantial improvements in signal quality, achievable data rates, and harvested energy. Moreover, the proposed method maintains stable energy management behavior and guarantees long-term energy sustainability for the UAVs. Full article

In wireless networks with severe blockage, path loss critically limits communication coverage. Reconfigurable Intelligent Surfaces (RIS) offer a promising remedy. However, the fine-grained control of massive reflecting elements incurs prohibitive computational overhead, which hinders real-time deployment. To address these challenges, this paper proposes a low-complexity scheme integrating RIS block selection with adaptive beamforming. The large-scale RIS is partitioned into multiple sub-arrays to enable block-wise phase control. By activating only those blocks with dominant channel gains, the system maximizes reflection gain while minimizing control overhead. To avoid the exponential complexity of exhaustive search, we develop a deep neural network (DNN)-based prediction architecture. By learning the mapping from channel states to optimal configurations, the DNN enables instantaneous selection of near-optimal RIS block combinations. Simulation results show that the proposed data-driven scheme achieves near-optimal bit error rate (BER) performance compared to exhaustive search. Notably, it avoids the exponential complexity growth typically associated with an increasing number of reflecting elements. The proposed mechanism extends reliable coverage range and improves link stability, offering an efficient solution for future wireless networks. Full article

To improve the interference suppression performance of the inverse beamforming algorithm, an improved inverse beamforming algorithm based on the least squares criterion is proposed in this paper. Firstly, the array manifold matrix is constructed, and its generalized inverse matrix is multiplied by the beam-domain data of interference to obtain the element-domain interference data. Subsequently, the constructed element-domain interference data is subtracted from the original element-domain data to realize interference cancellation. This paper theoretically analyzes why the traditional inverse beamforming algorithm forms nulls towards interference directions yet retains prominent residual power in the regions immediately adjacent to the nulls. The proposed algorithm adopts grid search and Bayesian optimization to realize adaptive parameter selection. The theoretical analysis proves that the new algorithm can completely eliminate residual power, adjust the width of interference nulls, and further improve the local signal-to-noise ratio (SNR). Simulation results show that compared with the conventional inverse beamforming algorithm, the proposed algorithm increases the local SNR by 2–3 dB, suppresses the in-mainlobe power by no less than 30 dB, and enables adjustable width of interference nulls. Sea trial data further verifies the effectiveness and superior performance of the proposed algorithm. 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

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 presents a novel optimal control-based beamforming framework for phased antenna arrays, targeting advanced wireless communication and radar applications, including 5G systems. Unlike conventional beamforming techniques, such as Fourier-based methods and adaptive algorithms (e.g., LMS and RLS), the proposed approach formulates the beam synthesis problem as a discrete-time optimal control problem. The antenna array is modeled using a state-space representation, and a quadratic cost function is introduced to jointly minimize the deviation from a desired radiation pattern and the excitation power. The optimal excitation weights are derived using the Linear Quadratic Regulator (LQR) framework by solving the discrete-time algebraic Riccati equation. This formulation enables an effective trade-off between sidelobe suppression, main lobe accuracy, and power efficiency. Simulation results demonstrate that the proposed method achieves a well-focused main beam, significantly reduced sidelobe levels, and improved directivity compared to conventional approaches. Furthermore, the framework offers robustness and computational efficiency, making it a promising candidate for future FPGA and embedded implementations. Overall, the proposed optimal control-based beamforming approach provides a flexible, robust, and computationally efficient solution for next-generation antenna systems in 5G, beyond-5G (B5G), and radar applications. Full article

To address robust polarization-domain adaptive anti-jamming for dual-polarized radars with limited secondary data and time-varying interference, this paper proposes a covariance-reliability-driven MVDR framework based on forgetting-factor covariance estimation and adaptive diagonal loading. The forgetting-factor recursion assigns larger weights to recent jammer-plus-noise snapshots to track nonstationary interference, while the adaptive loading coefficient is jointly controlled by sample deficiency and covariance condition-number degradation to improve inversion stability. Unlike many robust adaptive beamforming methods that require steering-vector uncertainty sets, mismatch distributions, or subspace information, the proposed method relies only on secondary data and a small set of scalar design parameters. Simulation results based on a synthetic dual-polarized array model show that the proposed method achieves competitive output SINR, effective jammer suppression, and improved robustness to moderate DOA and polarization mismatch under limited-snapshot and time-varying interference conditions. Complexity analysis indicates that the proposed method has the same dominant computational order as standard covariance-based MVDR beamforming, apart from condition-number evaluation. The present validation is simulation-based, and further verification using measured polarimetric radar data, realistic propagation models, or hardware experiments is still required. Full article

This paper presents a cascaded neural network framework for phase-only beamforming under covariance matrix mismatch. The proposed architecture combines a denoising autoencoder (DAE) with a residual network (ResNet) to address performance degradation caused by finite-snapshot covariance estimation errors and signal-of-interest contamination. The DAE reconstructs an ideal covariance representation from mismatched covariance inputs and provides compact covariance features for subsequent phase prediction. The ResNet then maps the denoised covariance features to phase-only excitation vectors, thereby avoiding repeated online optimization. Unlike conventional robust adaptive beamforming methods that rely on explicit uncertainty modeling or iterative covariance reconstruction, the proposed framework separates covariance feature denoising from phase excitation emulation in a data-driven manner. Numerical results demonstrate that the cascaded network improves covariance-mismatch tolerance and achieves competitive output SINR performance under limited-snapshot and noisy covariance-estimation conditions. Full article

Modern hands-free and wearable communication devices employ multiple microphones and loudspeakers, leading to the joint presence of acoustic echo, background noise, and desired speech signals. While acoustic echo cancellation (AEC) and beamforming are commonly combined to address this challenge, existing architectures face a trade-off between computational complexity, stability, and adaptability. In particular, adaptive beamforming approaches require repeated estimation and inversion of covariance matrices, incurring high computational cost and introducing potential sensitivity to time-varying conditions. Conversely, fixed beamformers reduce online complexity and improve stability, but their performance can degrade when the acoustic scene differs from the calibration condition. In this work, we investigate low-complexity AEC–beamforming architectures that combine fixed minimum-variance distortionless response (MVDR) beamforming with adaptive AEC. Since the ordering of these stages yields two inequivalent architectures, we evaluate two configurations: AEC followed by beamforming (AEC-BF) and beamforming followed by AEC (BF-AEC). To reduce dependence on a single head pose in wearable devices, we use an offline orientation-averaged calibration strategy in which the undesired-signal covariance matrix and, when required, the relative echo transfer functions (RETFs) are estimated from calibration measurements averaged across multiple head orientations. The proposed methods are evaluated using real-device recordings from a six-microphone wearable device. The results show a clear architectural asymmetry: the fixed BF-AEC configuration achieves the highest average echo return loss enhancement (ERLE) and perceptual evaluation of speech quality (PESQ), with substantially lower online complexity than the fully adaptive baseline, whereas the fixed AEC-BF configuration provides a higher signal-to-distortion ratio (SDR) in the evaluated experiment. Additional calibration experiments show that orientation-averaged RETF calibration provides partial generalization across the measured head orientations, but also that the RETFs are not fully orientation-invariant. Overall, the results indicate that fixed BF-AEC provides a favorable trade-off between echo suppression, stability, and online complexity under the evaluated real-recording conditions. Full article