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RIS-Aided MIMO Communications: Angle-Dependent Amplitude-Phase Response Model and Capacity Analysis

IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第2期14卷 290-294页

作者： Xu, Bilian Zhou, Ting Gao, Feifei Xu, Tianheng Hu, Honglin Shanghai Univ Sch Microelect Shanghai 200444 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Chinese Acad Sci Shanghai Adv Res Inst Shanghai 201210 Peoples R China

Most phase-shift models for reconfigurable intelligent surfaces (RIS) rely on ideal models of the reflection coefficient, i.e., assuming full-reflection amplitude and constant reflection coefficient for any incident angle. Since electromagnetic waves incident on RIS from varying directions exhibit different responses, ideal phase-shift models may overestimate the capacity gain of the RIS-aided multiple-input-multiple-output (MIMO) communications system. With varactor diode-based RIS, this letter performs full-wave simulation to verify the impact of incident angle on its reflection coefficient. Specifically, we first propose an angle-dependent amplitude-phase response model. The proposed model is then applied to a RIS-aided MIMO communications system, where the capacity maximization problem is formulated and solved via joint RIS reflection coefficients and transmit covariance matrix optimization. Simulation results quantify the capacity gain achieved by RIS with varying incident angles.

关键词： Reflection coefficient Reflection Varactors MIMO communication Optimization Mathematical models Capacitance linear programming Electromagnetic scattering Reconfigurable intelligent surface full-wave simulation incident angle-dependent characteristics system capacity multiple-input-multiple-output multiple-input-multiple-output

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Rethinking Distributed Average Consensus for Wireless Networks: A Low-Cost Approach to Broadcast Probability Optimization

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IEEE INTERNET OF THINGS JOURNAL 2025年第5期12卷 6050-6053页

作者： Jiang, Miao Li, Yiqing Wu, Tuo Yuen, Chau Al-Dhahir, Naofal Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Univ Texas Dallas Dept Elect & Comp Engn Richardson TX 75080 USA

This letter rethinks the probabilistic broadcast gossip scheme to achieve fast distributed average consensus in wireless networks. The consensus attainment in this scheme is heavily influenced by the broadcast probability of each node, which directly affects the convergence rate. To reduce communication costs for achieving consensus, we formulate an optimization problem to determine the optimal broadcast probability for each node. This problem involves a challenging nonconvex spectral radius term in the objective function. To address this challenge, we introduce an enhanced majorization-minimization-based approach that leverages a novel surrogate function to effectively upper bound the spectral radius function. Simulation results show that the proposed method provides substantial performance improvements over existing heuristic methods for broadcast probability optimization.

关键词： Optimization Wireless networks Convergence Internet of Things Costs Wireless sensor networks Probabilistic logic linear programming Broadcasting Vectors Fast distributed linear average majorization-minimization (MM) spectral radius optimization wireless consensus

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Optimal Space-Variant Anisotropic Tikhonov Regularization for Full Waveform Inversion of Sparse Data

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Gholami, Ali Gazzola, Silvia Polish Acad Sci Inst Geophys PL-01452 Warsaw Poland Univ Pisa Dept Math I-56122 Pisa Italy

Full waveform inversion (FWI) is a challenging, ill-posed nonlinear inverse problem that requires robust regularization techniques to stabilize the solution and yield geologically meaningful results, especially when dealing with sparse data. Standard Tikhonov regularization, though commonly used in FWI, applies uniform smoothing that often leads to oversmoothing of key geological features, as it fails to account for the underlying structural complexity of the subsurface. To overcome this limitation, we propose an FWI algorithm enhanced by a novel Tikhonov regularization technique involving a parametric regularizer, which is automatically optimized to apply directional space-variant smoothing. Specifically, the parameters defining the regularizer (orientation and anisotropy) are treated as additional unknowns in the objective function, allowing the algorithm to estimate them simultaneously with the model. We introduce an efficient numerical implementation for FWI with the proposed space-variant regularization. Numerical tests on sparse data demonstrate the proposed method's effectiveness and robustness in reconstructing models with complex structures, significantly improving the inversion results compared with the standard Tikhonov regularization.

关键词： Smoothing methods Anisotropic Adaptation models Optimization linear programming Geology Numerical models Inverse problems Data models Standards Full waveform inversion (FWI) local anisotropic regularization sparse data Tikhonov regularization

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CSI-Free Position Optimization for Movable Antenna Communication Systems: A Derivative-Free Optimization Approach

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第1期14卷 53-57页

作者： Zeng, Xianlong Fang, Jun Wang, Bin Ning, Boyu Li, Hongbin Univ Elect Sci & Technol China Natl Key Lab Wireless Commun Chengdu 611731 Peoples R China Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA

Movable antenna (MA) is a new technology which leverages local movement of antennas to improve channel qualities and enhance the communication performance. Nevertheless, to fully realize the potential of MA systems, complete channel state information (CSI) between the transmitter-MA and the receiver-MA is required, which involves estimating a large number of channel parameters and incurs an excessive amount of training overhead. To address this challenge, in this letter, we propose a CSI-free MA position optimization method. The basic idea is to treat position optimization as a derivative-free problem involving an unknown objective function and calculate the gradient of the unknown objective function using zeroth-order (ZO) gradient approximation techniques. Simulation results show that the proposed ZO-based method, through adaptively adjusting the position of the MA, can achieve a favorable signal-to-noise-ratio (SNR) using a smaller number of position measurements than the CSI-based approach. Such a merit makes the proposed algorithm more adaptable to fast-changing propagation channels.

关键词： Optimization Signal to noise ratio Receiving antennas Vectors Channel estimation Position measurement Sensors Optimization methods linear programming Convergence Movable antenna CSI-free position optimization

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Outlier-Resilient Model Fitting via Percentile Losses: Methods for General and Convex Residuals

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 931-935页

作者： Domingos, Joao Xavier, Joao Inst Super Tecn Inst Sistemas & Robot P-1049001 Lisbon Portugal

We consider the problem of robustly fitting a model to data that includes outliers by formulating a percentile optimization problem. This problem is non-smooth and non-convex, hence hard to solve. We derive properties that the minimizers of such problems must satisfy. These properties lead to methods that solve the percentile formulation both for general residuals and for convex residuals. The methods fit the model to subsets of the data, and then extract the solution of the percentile formulation from these partial fits. As illustrative simulations show, such methods endure higher outlier percentages, when compared with standard robust estimates. Additionally, the derived properties provide a broader and alternative theoretical validation for existing robust methods, whose validity was previously limited to specific forms of the residuals.

关键词： Vectors Fitting Data models Optimization linear programming Training Reactive power Indexes Estimation Data mining Least median squares least quantile regression outliers percentile optimization robust methods subset sampling

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Self-Weighted Multi-View Fuzzy Clustering With Multiple Graph Learning

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1585-1589页

作者： Liu, Chaodie Chang, Cheng Nie, Feiping Henan Normal Univ Sch Comp & Informat Engn Xinxiang 453007 Peoples R China Henan Normal Univ Key Lab Artificial Intelligence & Personalized Lea Xinxiang 453007 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect IOPEN Xian 710072 Peoples R China Minist Ind & Informat Technol Key Lab Intelligent Interact & Applicat Xian 710072 Peoples R China

Graph-based multi-view clustering has garnered considerable attention owing to its effectiveness. Nevertheless, despite the promising performance achieved by previous studies, several limitations remain to be addressed. Most graph-based models employ a two-stage strategy involving relaxation and discretization to derive clustering results, which may lead to deviation from the original problem. Moreover, graph-based methods do not adequately address the challenges of overlapping clusters or ambiguous cluster membership. Additionally, assigning appropriate weights based on the importance of each view is crucial. To address these problems, we propose a self-weighted multi-view fuzzy clustering algorithm that incorporates multiple graph learning. Specifically, we automatically allocate weights corresponding to each view to construct a fused similarity graph matrix. Subsequently, we approximate it as the scaled product of fuzzy membership matrices to directly derive clustering assignments. An iterative optimization algorithm is designed for solving the proposed model. Experiment evaluations conducted on benchmark datasets illustrate that the proposed method outperforms several leading multi-view clustering approaches.

关键词： Clustering algorithms Data mining Benchmark testing linear programming Computational complexity Training Signal processing algorithms Optimization Flowering plants Vectors Fuzzy clustering graph learning multi-view clustering self-weighted

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Joint Admission Control and Power Minimization in IRS-Assisted Networks

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IEEE COMMUNICATIONS LETTERS 2025年第3期29卷 512-516页

作者： Xiong, Weijie Lin, Jingran Xiao, Zhiling Li, Qiang Zhang, Yuhan Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Lab Electromagnet Space Cognit & Intelligent Contr Beijing 100083 Peoples R China Tianfu Jiangxi Lab Chengdu 641419 Sichuan Peoples R China

Joint admission control and power minimization are critical challenges in intelligent reflecting surface (IRS)-assisted networks. Traditional methods often rely on $ l_{1} $ -norm approximations and alternating optimization (AO) techniques, which suffer from high computational complexity and lack robust convergence guarantees. To address these limitations, we propose a sigmoid-based approximation of the $ l_{0} $ -norm AC indicator, enabling a more efficient and tractable reformulation of the problem. Additionally, we introduce a penalty dual decomposition (PDD) algorithm to jointly optimize beamforming and admission control, ensuring convergence to a stationary solution. This approach reduces computational complexity and supports distributed implementation. Moreover, it outperforms existing methods by achieving lower power consumption, accommodating more users, and reducing computational time.

关键词： Signal to noise ratio Interference Optimization Convergence Minimization Admission control Computational complexity Approximation algorithms linear programming Array signal processing Power minimization admission control IRS non-convex optimization penalty dual decomposition

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Manifold Optimization for Distributed Phased-MIMO Radar Broad Beampattern Design

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1520-1524页

作者： Geng, Xueyin Wang, Jun Yang, Bin Li, Zihao Sun, Jinping Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China

This letter investigates the low-variance broad beampattern design method in distributed phased multiple-input multiple-output (phased-MIMO) radar. The constant modulus constraint across multiple subarrays results in a low-rank and nonconvex objective function, which is traditionally addressed by reformulating it into a solvable semidefinite program through convex relaxation. In contrast, we propose a Riemannian manifold-based method to directly address the low-rank problem without relaxation. The low-variance broad beampattern design is first transformed into an unconstrained quadratic form on a complex constant modulus manifold. Then, a Riemannian conjugate gradient descent (RCGD)-based optimization is proposed to solve the nonconvex objective function by deriving the gradient descent direction and adaptive step size. Numerical simulations demonstrate the superior performance in terms of computation speed and accuracy compared to the conventional methods.

关键词： Manifolds Vectors Radar antennas Training Signal processing algorithms Signal generators Search problems Optimization methods linear programming Data mining Phased-MIMO radar Riemannian manifold optimization broad beampattern low variance

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Hierarchical Functionality Prioritization in Multicast ISAC: Optimal Admission Control and Discrete-Phase Beamforming

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IEEE COMMUNICATIONS LETTERS 2025年第1期29卷 180-184页

作者： Abanto-Leon, Luis F. Maghsudi, Setareh Ruhr Univ Bochum Fac Elect Engn & Informat Technol ETIT D-44801 Bochum Germany

We investigate the joint admission control and discrete-phase multicast beamforming design for integrated sensing and commmunications (ISAC) systems, where sensing and communications functionalities have different hierarchies. Specifically, the ISAC system first allocates resources to the higher-hierarchy functionality and opportunistically uses the remaining resources to support the lower-hierarchy one. This resource allocation problem is a nonconvex mixed-integer nonlinear program (MINLP). We propose an exact mixed-integer linear program (MILP) reformulation, leading to a globally optimal solution. In addition, we implemented three baselines for comparison, which our proposed method outperforms by more than 39%.

关键词： Array signal processing Signal to noise ratio Resource management Uncertainty Vectors Unicast Receiving antennas linear programming Integrated sensing and communications multicast beamforming discrete phases admission control discrete phases admission control

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Harmonic-Tuned Power Amplifier Using Artificial Intelligence-Assisted Topology Generation Algorithm

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IEEE MICROWAVE AND WIRELESS TECHNOLOGY LETTERS 2025年第5期35卷 561-564页

作者： Shu, Pei Wen Zhou, Xinyu Shama, Tushar Zhou, Liheng Chan, Wing Shing City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Hong Kong Polytech Univ Dept Elect & Elect Engn Hong Kong Peoples R China Renesas Elect Corp San Diego CA 92014 USA Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China

Numerical optimization techniques are widely used in microwave circuits but are limited to fixed topologies, constraining their achievable performance and suffering from suboptimal designs. This letter presents a novel topological algorithm that can automatically synthesize practical circuit layouts that can satisfy prescribed specifications without preselecting the topology. To achieve this goal, the proposed method integrates a pixelated circuit generation strategy with the differential evolution algorithm (DEA). A new objective function is also developed to match the fundamental impedances while controlling harmonic responses. Good agreement between simulation and measurements for a wideband high-efficiency power amplifier (PA) validates the proposed algorithm.

关键词： Power amplifiers Circuits Optimization Layout Image quality Vectors Topology Frequency measurement Current measurement linear programming Differential evolution algorithm (DEA) harmonic control high efficiency pixelated circuits topological optimization wideband power amplifier (PA)

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