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Weighted sum-rate maximization for phase quantized IRS-Assisted rate-splitting multiple access systems

PHYSICAL COMMUNICATION 2025年 68卷

作者： Han, Qingyu Wang, Kehao Li, Kuiliang Zhang, Zhiheng Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Hubei Peoples R China

The rapid advancement of the next generation of mobile communication technologies has set increasingly stringent demands on transmission quality, capacity, and rate. In this paper, we discuss the weighted sum rate (WSR) using intelligent reflecting surface (IRS) assisted rate-splitting multiple access (RSMA) for downlink multiuser multiple-input single-output (MISO) systems with consideration of phase quantization of IRS. The aim is to propose a scheme to obtain the maximum WSR by optimizing the equivalent beamforming matrix, quantized IRS phase shift matrix, and the common rate splitting scheme. To this end, an equivalent optimization scheme is designed on the basis of weighted minimum mean square error (WMMSE) algorithm, difference of convex sets (DCS) theory, Lagrange penalty and Taylor approximation, where the non-convex mixed continuous and discrete optimization problem is converted to three continuous convex optimization sub-problems and then worked out iteratively with the alternating optimization algorithm. We compare the maximum WSR achievable by quantized IRS-aided 1-layer-RSMA with three other multiple access (MA) technologies: dirty paper coding (DPC), non-orthogonal multiple access (NOMA), and multiuser linear precoding (MULP) under different signalto-noise ratio (SNR) scenarios. It is also found that the proposed method provides an improvement in WSR with the increase of the number of base station antennas, IRS reflective elements and phase quantization bits.

关键词： alternating optimization algorithm Intelligent reflecting surface Phase shift matrix Rate-splitting multiple access Weighted sum rate

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Fuzzy K-means clustering with reconstructed information

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年第1期16卷 43-53页

作者： Huang, Honglan Shi, Wei Yang, Fangjie Feng, Yanghe Zhang, Longfei Liang, Xingxing Shi, Jun Cheng, Guangquan Huang, Jincai Liu, Zhong Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China 93789 Unit Peoples Liberat Army Beijing Peoples R China

Clustering techniques play a pivotal role in unveiling the inherent structure of unlabeled data. When dealing with overlapping clusters, traditional hard clustering methods encounter challenges. As a representative of soft clustering methods, Fuzzy K-Means (FKM) enables data points to be assigned different degrees of membership to multiple clusters, offering a solution to this problem. However, when dealing with high-dimensional data, the performance of FKM is often affected by redundant features and noise. To address this limitation, this paper introduces a Fuzzy K-Means Clustering with Reconstructed Information (FKMRI) method. This method combines the reconstruction term with a cluster weight variable to effectively capture the true nature of data structure, thereby enhancing the clustering capability of FKM in high-dimensional spaces. We theoretically analyze the convergence of the FKMRI algorithm and prove its time complexity to be O(c+P(c))nd2+cnd\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O\left( (c+P(c)) n d<^>2+c n d\right)$$\end{document}. Finally, we evaluate the performance of FKMRI on standard benchmark datasets including Yale-32x32, Yale-64x64, ORL-32x32, and ORL-64x64. The results demonstrate that, in comparison to five current state-of-the-art algorithms (K-Means, FKM, Kernel-km, RSFKM, DFKM), FKMRI exhibits an average improvement of over 18% in terms of accuracy rate (ACC) and normalized mutual information (NMI). These findings convincingly validate the effectiveness and efficiency of the proposed algorithm in handling high-dimensional data clustering, providing valuable support for related research fields.

关键词： Fuzzy K-means Reconstructed information High-dimensional data alternating optimization algorithm

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Enhancing Secrecy Capacity in Integrated Sensing and Communication Systems Using Pilot-Based Codebook Artificial Noise

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IEEE COMMUNICATIONS LETTERS 2025年第3期29卷 522-526页

作者： Gu, Yebo Qu, Haozhen Shen, Tao Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650032 Peoples R China

This letter investigates the use of pilot-based codebook artificial noise (PCAN) in Integrated Sensing and Communication (ISAC) systems to enhance physical layer security. ISAC technology integrates communication and sensing through shared resources, emphasizing the need for secure strategies. PCAN allows the receiver to extract information from the pilot codebook, effectively removing interference and increasing secrecy capacity. The non-convex secrecy capacity optimization problem is addressed by decomposing it into simpler subproblems. A semidefinite relaxation (SDR) and alternating optimization algorithm is proposed to maximize secrecy capacity while maintaining sensing accuracy. Simulation results show significant improvements, particularly at low and moderate Signal-to-Noise Ratio (SNR), with consistent gains at high SNRs. This work underscores the role of optimization techniques in enhancing secure communication and balancing secrecy with sensing performance in ISAC systems.

关键词： Pilot-based codebook artificial noise integrated sensing and communication physical layer security physical layer security semidefinite relaxation semidefinite relaxation alternating optimization algorithm alternating optimization algorithm secrecy capacity secrecy capacity secrecy capacity

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Physical layer security in double RIS-aided WPCN systems based on non-cooperative game

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COMPUTER NETWORKS 2025年 259卷

作者： Liu, Zhixin Cao, Haiyang Su, Jiawei Yuan, Yazhou Guan, Xinping Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Peoples R China Hebei Key Lab Ind Comp Control Engn Qinhuangdao 066004 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

Physical layer security issue is investigated in Wireless Power Transfer Communication Network (WPCN) system, and the double Reconfigurable Intelligent Surface (RIS) are introduced to assist the system to improve communication security and reduce transmission power. The system operates in two phases: wireless energy transfer (WET) and wireless information transfer (WIT). First, the WET phase is conducted to address the issue of insufficient transmitter (TS) energy in the WIT phase, followed by utilizing RIS assistance during the WIT phase to achieve secure transmission at the physical layer. In the WIT phase, the presence of eavesdroppers and interference among multiple Internet of Things (IoT) devices, along with obstructed direct links from TS to IoT devices, reduce the system's security rate. Especially, we introduce double RIS deployed above TS and IoT devices, respectively. Compared to a single RIS, double RISs not only improves the communication system's coverage and data transmission rate but also enhances system security and efficiency. In the WET phase, since TS and power station (PS) belong to different service providers, we propose an energy trading and secure communication scheme assisted by double RIS based on Stackelberg game theory. The leader's strategy problem in this scheme is non-convex, hence decomposed into two subproblems. The first subproblem is independently solved using alternating optimization (AO) algorithm. The second subproblem is tackled with a block coordinate descent (BCD) based iterative algorithm, employing successive convex approximation (SCA) to solve each block-level subproblem. Finally, the numerical results demonstrate that the proposed scheme enhances the system's security rate, reduces the time required for wireless energy transfer (WET), and thereby improves overall system efficiency.

关键词： Wireless powered communication network Intelligent reflecting surface Stackelberg game alternating optimization algorithm Block coordinate descent Successive convex approximation

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Hybrid Intelligent Reflecting Surface and Cell-free Massive MIMO-Aided Over-the-Air Computation for Digital Twin 44

Hybrid Intelligent Reflecting Surface and Cell-free Massive ...

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44th IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Di, Jiaying Zhang, Ni Wan, Junjie Li, Sen Wang, Kunlun East China Normal Univ Sch Commun & Elect Engn Shanghai 200241 Peoples R China Guangxi Key Lab Digital Infrastruct Nanning Peoples R China

ISBN: (纸本)9798350354720;9798350354713

This paper investigates the over-the-air computation (AirComp) problem in a hybrid intelligent reflecting surface (IRS) and cell-free massive multiple-input multiple-output (CF-mMIMO) assisted digital twin system, where multiple users offload their data to access points (APs) and central processing unit (CPU) via the IRS for data aggregation. We formulate a joint beamforming design, IRS phase shift optimization, and power allocation problem to minimize the mean squared error (MSE) of data aggregation. We solve the resultant non-convex optimization problem in three steps. First, we transform the original problem into two sub-problems. Then, we exploit a convex optimization framework to respectively determine the beamforming design, IRS phase shift optimization, and power allocation. Last, we propose an alternating optimization algorithm for finding the jointly optimized results. The simulation results demonstrate the effectiveness of the proposed scheme as compared with other benchmark schemes.

关键词： Over-the-air computation (AirComp) intelligent reflecting surface (IRS) cell-free massive multiple-input multipleoutput (CF-mMIMO) alternating optimization algorithm digital twin

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RIS-assisted secure UAV communications with resource allocation and cooperative jamming

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IET COMMUNICATIONS 2022年第13期16卷 1582-1592页

作者： Guo, Jichang Yu, Lisu Chen, Zhiqiong Yao, Yuanzhi Wang, Zhen Wang, Zhenghai Zhao, Qingmin Nanchang Univ Sch Informat Engn Nanchang 330031 Jiangxi Peoples R China

Unmanned aerial vehicles (UAVs) are widely used in wireless communication networks due to their rapid deployment and high mobility. However, in practical scenarios, the existence of obstacles and eavesdroppers will seriously interfere with the communication quality of the UAV network and produce a security risk. Thus, this paper combines reconfigurable intelligent surface (RIS) technology with UAVs to build a secure UAV communication network. Normally, a rotary-wing UAV (labeled as UAV-S) acting as a base station sends information signals to a legitimate user on the ground with RIS equipment. However, there is a passive eavesdropper on the ground who can steal the information. Therefore, a friendly UAV jammer (labeled as UAV-J) with a fixed location is introduced to send jamming signals to confuse the eavesdropper. The goal of this paper is to maximize the average secrecy rate of the communication network by jointly optimizing the flight trajectory, transmit power of the UAV-S and UAV-J, and phase shifter of the RIS. Since the constructed problem is highly nonconvex, an alternating optimization algorithm based on successive convex approximation techniques is proposed to solve the problem. Simulation results show that the proposed algorithm can achieve a higher secrecy rate in comparison with other schemes.

关键词： RIS-assisted secure UAV communications UAV-J autonomous aerial vehicles wireless communication networks reconfigurable intelligent surface information signals legitimate user jamming signals resource allocation base station intelligent materials optimisation phase shifter passive eavesdropper unmanned aerial vehicles successive convex approximation rotary-wing UAV telecommunication security high mobility RIS equipment communication quality friendly UAV jammer alternating optimization algorithm cooperative communication security risk jamming transmit power cooperative jamming UAV network UAV-S flight trajectory secure UAV communication network

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Multiuser beamforming optimization for IRS-aided systems with discrete phase shifts

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IET COMMUNICATIONS 2022年第13期16卷 1523-1530页

作者： Shi, Mingli Li, Xiaohui Fan, Tao Liu, Jiawen Lv, Siting Xidian Univ State Key Lab Integrated Serv Network Xian 710071 Shaanxi Peoples R China

Here, the beamforming design for multiuser multiple-input and multiple-output (MIMO) downlink transmission aided by an intelligent reflecting surface (IRS) with discrete phase shifts is studied. The authors deduce a minimum signal-to-interference-plus-noise-ratio (SINR) maximization fairness problem with the transmit beamformer, the receive beamformer and IRS phase shifts as optimization variables, which is generally NP-hard. Hence, an alternating optimization algorithm based on the gradient extrapolated majorization-minimization (GEMM) approach is employed to solve the above problem. Specifically, when the transmit beamformer and reflective phase shift are fixed, the authors give an optimal closed expression for the receive beamformer. Then, the receive beamformer is fixed, and the minimum SINR maximization problem at the transmitter is transformed into a power minimization problem. Finally, under the zero-forcing (ZF) transmitter beamformer, the GEMM algorithm is used to optimize the reflective phase shift. Simulation results show that by comparing to existing methods, the proposed GEMM algorithm can almost achieve the same performance with lower complexity.

关键词： minimum SINR maximization problem beamforming design transmitter beamformer transmit beamformer extrapolation reflective phase shift multiuser multiple-input multiple-output downlink transmission IRS phase shifts MIMO communication discrete phase shifts receive beamformer IRS-aided systems gradient extrapolated majorization-minimization approach array signal processing multiuser multiple-input minimum signal-to-interference-plus-noise-ratio maximization fairness problem power minimization problem GEMM algorithm minimisation alternating optimization algorithm multi-access systems optimization variables multiuser beamforming optimization intelligent reflecting surface

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Using IRS to Improve the Secrecy Rate of Millimeter Wave Communication System 97

Using IRS to Improve the Secrecy Rate of Millimeter Wave Com...

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97th IEEE Vehicular Technology Conference (VTC-Spring)

作者： Song, Kunpeng Ma, Fangshu Chen, Zexian Liu, Sen Shang, Yong Cheng, Yuxin Peking Univ Dept Elect Beijing Peoples R China

ISBN: (纸本)9798350311143

With the development of 6G, millimeter wave communication has received extensive attention. Due to the characteristics of wireless transmission, information secrecy transmission is facing significant challenges. This paper uses the physical layer security (PLS) to explore the information secrecy transmission. Specifically, we use Intelligent Reflecting Surface (IRS) to control the wireless propagation environment and improve the secrecy rate of millimeter wave communication. The active beamforming matrix of the base station and the passive beamforming matrix of the IRS are optimized to achieve the maximum secrecy rate. We deduce the closed form solution of active beamforming and the approximate optimal solution of passive beamforming. An alternating optimization (AO) algorithm is applied to solve the non-convex optimization problem. Simulation results verify the convergence and effectiveness of the algorithm, which can obtain nearly twice the secrecy rate gain of the benchmark algorithm.

关键词： Millimeter wave communication physical layer security intelligent reflecting surface alternating optimization algorithm

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Exploiting Active-IRS by Maximizing Throughput in Wireless Powered Communication Networks 19th

Exploiting Active-IRS by Maximizing Throughput in Wireless P...

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Hameed, Iqra Koo, Insoo Univ Ulsan Dept Elect Elect & Comp Engn Ulsan 44610 South Korea

ISBN: (纸本)9789819947416;9789819947423

Wireless powered communication networks (WPCNs) are a promising technology for low-power wireless communication. Intelligent reflecting surfaces (IRSs) have recently emerged as a potential solution for enhancing the performance of WPCNs. This paper presents a comparative study of active IRSs with passive IRSs for maximizing the throughput in WPCNs. We propose an alternating optimization (AO)-based a near-optimal solution for maximizing the throughput in WPCNs by exploiting the active capabilities of the IRS. Our proposed algorithm is based on a joint optimization of the time allocation and the reflection coefficients and phase shift matrices of the active IRS. We evaluate the performance of the proposed algorithm and compare it with a passive IRS-assisted system. Simulation results demonstrate that the proposed algorithm achieves a higher throughput compared to the passive IRS-assisted system. Our findings highlight the potential of active IRSs for enhancing the performance of WPCNs and provide insights into the design of efficient and reliable wireless communication systems.

关键词： Active intelligent reflecting surfaces alternating optimization algorithm Successive convex approximation

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Generalization Bounds and algorithms for Learning to Communicate Over Additive Noise Channels

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IEEE TRANSACTIONS ON INFORMATION THEORY 2022年第3期68卷 1886-1921页

作者： Weinberger, Nir Technion Israel Inst Technol Andrew & Erna Viterbi Fac Elect & Comp Engn IL-3200004 Haifa Israel

An additive noise channel is considered, in which the distribution of the noise is nonparametric and unknown. The problem of learning encoders and decoders based on noise samples is considered. For uncoded communication systems, the problem of choosing a codebook and possibly also a generalized minimal distance decoder (which is parameterized by a covariance matrix) is addressed. High probability generalization bounds for the error probability loss function, as well as for a hinge-type surrogate loss function are provided. A stochastic-gradient based alternating-minimization algorithm for the latter loss function is proposed. In addition, a Gibbs-based algorithm that gradually expurgates an initial codebook from codewords in order to obtain a smaller codebook with improved error probability is proposed, and bounds on its average empirical error and generalization error, as well as a high probability generalization bound, are stated. Various experiments demonstrate the performance of the proposed algorithms. For coded systems, the problem of maximizing the mutual information between the input and the output with respect to the input distribution is addressed, and uniform convergence bounds for two different classes of input distributions are obtained.

关键词： Decoding Entropy Loss measurement Convergence Error probability Standards optimization Additive noise channels alternating optimization algorithm entropy estimation expurgation generalization bounds hinge loss Gibbs algorithm minimal distance decoding mismatch decoding statistical learning stochastic gradient descent

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