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Secrecy outage analysis for underlay cognitive radio networks over correlated channels

Science China(information Sciences) 2017年第2期60卷 107-115页

作者： Jiliang ZHANG Hui ZHAO Gaofeng PAN Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing School of Electronic and Information Engineering Southwest University

This paper investigates the secrecy outage performance of underlay cognitive radio networks, in which a source in a secondary system transmits its confidential information to a legitimate destination in the presence of an eavesdropper. Specifically, the main(the source-to-destination) and eavesdropping(the sourceto-eavesdropper) channels are assumed to be correlated. Moreover, it is also assumed that the main channel and the channel from the source to the primary user's receiver are correlated. Tight closed-form analytical expression for secrecy outage probability and the closed-form analytical expression for the probability of non-zero secrecy capacity are derived and validated by simulation results when the interference temperature limit is comparably large.

关键词： cognitive radio networks correlated fading channels probability of non-zero secrecy capacity secrecy outage probability

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Enhancing the Robustness of Random Boolean Networks by Epigenetic Regulation

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第1期22卷 261-270页

作者： Junxiu Liu Jufang Dai Qiang Fu Min Su Yuling Luo Sheng Qin Su Yang Lingxi Ma Guangxi Key Lab of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Nonlinear Circuits and Optical Communications Guangxi Normal University Guilin China Department of Computer Science Swansea University Swansea U.K.

Random Boolean Network (RBN) is a type of regulatory network in which the nodes have Boolean values representing their states. The robustness of RBNs against perturbations is a crucial characteristic, and there has been a growing interest in enhancing the network's robustness. In this study, a biologically inspired epigenetic regulation method is proposed to enhance the robustness of the RBNs. A frequency encoding method based on pulse counting is employed to encode the node states within a sliding time window, thereby improving the form of epigenetic regulation. To verify the performance of this method, an antifragility indicator is adopted to measure the robustness of RBNs and yeast cell networks at different scales. The experimental results demonstrate that the networks with epigenetic regulation exhibit excellent robustness, even in the presence of large-scale networks and severe perturbations. This approach provides a new perspective and idea for designing robust RBNs and discrete networks.

关键词： Epigenetics Robustness Regulation Perturbation methods Complexity theory DNA Gene expression Evolution (biology) Encoding Computational biology

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Task Offloading in Dynamic Energy Splitting STAR-RIS Assisted NOMA-MEC Systems with Decomposition Based Multi-Agent DRL

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IEEE Transactions on Vehicular Technology 2025年

作者： Lu, Baoshan Fang, Junli Hong, Xumin Shi, Jianghong Guilin University of Electronic Technology Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory Guilin541004 China Guangxi Normal University Key Laboratory of Nonlinear Circuits and Optical Communications Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining & Security Guilin541004 China Xiamen University Department of Informatics and Communication Engineering Xiamen361000 China

This paper explores a system that integrates non-orthogonal multiple access (NOMA) with mobile edge computing (MEC), supported by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in energy splitting (ES) mode. We aim to address the challenge of minimizing energy consumption over the long-term by modeling it as a non-convex optimization problem. To simplify the resolution of this complex issue, we introduce a decomposition approach using a multi-agent deep reinforcement learning (DRL) technique. Specifically, we decompose the problem into the sub-problems of computational resource and power allocation, and phase shifting, amplitude coefficient, offloading ratio, and time optimization. Based on theoretical derivations, we determine the optimal computational resource and power allocation for the given phase shifting, amplitude coefficient, offloading ratio, and time. Then, we propose a multi-agent twin delayed deep deterministic policy gradient (TD3) algorithm to jointly optimize phase shifting, amplitude coefficient, offloading ratio, and time for each time slot. Simulation outcomes demonstrate that the decomposition-based multi-agent TD3 (DB-MATD3) algorithm achieves faster convergence compared to conventional centralized DRL approaches. Compared with other existing methods, the proposed task offloading strategy, which employs the ES-based STAR-RIS, achieves energy savings of 28.7% to 40.8%. © 2025 IEEE.

关键词： Convex optimization

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A Fully Distributed Approach to Optimal Energy Scheduling of Users and Generators Considering a Novel Combined Neurodynamic Algorithm in Smart Grid

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1325-1335页

作者： Chentao Xu Xing He the Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information EngineeringSouthwest UniversityChongqing 400715China

A fully distributed microgrid system model is presented in this *** the user side,two types of load and plug-in electric vehicles are considered to schedule energy for more *** charging and discharging states of the electric vehicles are represented by the zero-one variables with more *** solve the nonconvex optimization problem of the users,a novel neurodynamic algorithm which combines the neural network algorithm with the differential evolution algorithm is designed and its convergence speed is faster.A distributed algorithm with a new approach to deal with the inequality constraints is used to solve the convex optimization problem of the generators which can protect their *** results and comparative experiments show that the model and algorithms are effective.

关键词： Differential evolution algorithm distributed algorithm electric vehicle neural network zero-one variable.

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Some characteristics of logistic map over the finite field

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Science China(information Sciences) 2019年第3期62卷 199-201页

作者： Bo YANG Xiaofeng LIAO Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University

Dear editor,Chaotic maps have good characteristics, which include randomness, sensitivity to the initial value,and unpredictability. Hence, there have been many attempts to discuss the dynamic behavior of chaotic maps... 详细信息

关键词： Some characteristics of logistic map over the finite field

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Physical-layer secrecy outage of spectrum sharing CR systems over fading channels

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Science China(information Sciences) 2016年第10期59卷 112-120页

作者： Hequn LIU Hui ZHAO Hong JIANG Chaoqing TANG Gaofeng PAN Tingting LI Yunfei CHEN Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University School of Mathematics and Statistics Southwest University School of Engineering University of Warwick

In this paper, we investigate the physical-layer secrecy outage performance of underlay spectrum sharing systems over Rayleigh and log-normal fading channels in the presence of one eavesdropper. In particular, the secondary transmitter sends data to the legitimate receiver under the constraints of the interference temperature at the primary receiver, while suffering the wiretap from the eavesdropper. Closed-form and approximated expressions are derived for the secrecy outage probability over Rayleigh and log-normal fading channels,respectively. The accuracy of our performance analysis is verified by simulation results.

关键词： spectrum sharing cognitive radio secrecy outage probability Rayleigh fading channels log-normal fading channels

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Robust variable normalization least mean p-power algorithm

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Science China(information Sciences) 2020年第9期63卷 294-302页

作者： Kui XIONG Yu ZHANG Shiyuan WANG College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Faculty of Computer and Information Science Southwest University

Dear editor,Adaptive filtering algorithms have been widely applied in system identification, channel equalization, and echo cancellation over the past decades [1–3]. Generally, adaptive filtering algorithms can be ge... 详细信息

关键词： Least mean p-power algorithm variable normalization sigmoid function steady-state performance analysis noise robustness

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Prox-DBRO-VR: A Unified Analysis on Byzantine-Resilient Decentralized Stochastic Composite Optimization With Variance Reduction and Nonasymptotic Convergence Rates

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年

作者： Hu, Jinhui Chen, Guo Li, Huaqing Guo, Xiaoyu Ran, Liang Huang, Tingwen Central South University School of Automation Hunan Changsha 410083 China City University of Hong Kong Department of Mechanical Engineering Hong Kong University of New South Wales School of Electrical Engineering and Telecommunications Sydney 2052 NSW Australia Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing 400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen 518055 China

Decentralized stochastic gradient algorithms efficiently solve large-scale finite-sum optimization problems when all agents in the network are reliable. However, most of these algorithms are not resilient to adverse conditions, such as malfunctioning agents, software bugs, and cyber attacks. This article aims to handle a class of general composite optimization problems over multiagent systems (MASs) in the presence of an unknown number of Byzantine agents. Building on a resilient aggregation mechanism and the proximal-gradient mapping method, a Byzantine-resilient decentralized stochastic proximal-gradient algorithmic framework is proposed, dubbed Prox-DBRO-VR, which achieves an optimization and control goal using only local computations and communications. To asymptotically reduce the noise variance arising from local gradient estimation and accelerate the convergence, we incorporate two localized variance-reduced (VR) techniques (SAGA and LSVRG) into Prox-DBRO-VR to design Prox-DBRO-SAGA and Prox-DBRO-LSVRG. By analyzing the contraction relationships among the gradient-learning error, resilient consensus condition, and convergence error in a unified theoretical framework, it is proved that both Prox-DBRO-SAGA and Prox-DBRO-LSVRG, with a well-designed constant (resp., decaying) step-size, converge linearly (resp., sublinearly) inside an error ball around the optimal solution to the original problem under standard assumptions. A tradeoff between convergence accuracy and Byzantine resilience in both linear and sublinear cases is also characterized. In numerical experiments, the effectiveness and practicability of the proposed algorithms are manifested via resolving a decentralized sparse machine learning problem under various Byzantine attacks. © 2013 IEEE.

关键词： Byzantine-resilient algorithms composite optimization decentralized stochastic optimization system security variance reduction

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Bayesian random Fourier filters for Gaussian noises

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Science China(information Sciences) 2018年第12期61卷 214-216页

作者： Shiyuan WANG Wanli WANG Shukai DUAN Lidan WANG Chi K.TSE College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University

Dear editor,Signal processing algorithms are generally used to identify an unknown system, and can be classified as batch algorithms and online learning algorithms(OLAs). Batch algorithms are applied to offline scenar... 详细信息

关键词： Bayesian random Fourier filters for Gaussian noises

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Kernel Least Mean Square with Tracking 36

Kernel Least Mean Square with Tracking

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第36届中国控制会议

作者： Wanli Wang Shiyuan Wang Guobing Qian Bo Yang College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing

ISBN: (纸本)9781538629185

Based on the Gaussian process(GP) regression,the relationship between the input-output pair can be modeled by the sum of the Gaussian noise and an unknown latent function evaluated on an input(LFEI).The acquisition of LFEI is thus essentially *** this paper,a novel algorithm called kernel least mean square with tracking(KLMS-T) is proposed to estimate *** on the existing kernel least mean square(KLMS) that is a simple and efficient kernel adaptive filtering algorithm,the proposed KLMS-T utilizes the framework of Bayesian inference to estimate LFEI from the function-space *** show that compared with LMS and KLMS,KLMS-T achieves better filtering performance in terms of the convergence rate and the steady state mean square error.

关键词： Gaussian process latent function kernel adaptive filter mean square error

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