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7th International Conference on Wireless communications, Networking and Applications, WCNA 2023

作者： Tang, Tao Zhao, Runhui Wen, Hong Feng, Xuewei Shi, Weihong Peng, Yulin School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China Sichuan Intelligent IoT Communication Technology Engineering Research Center UESTC Chengdu China

ISBN: (纸本)9789819624089

Unmanned Aerial Vehicles (UAV) face challenges from advanced interference technologies, making them susceptible to malicious node attacks, data interception, and tampering. Traditional anti-interference decisions have limitations, as they cannot adaptively adjust to changes in interference signals. Moreover, anti-interference communication models based on Deep Reinforcement Learning (DRL) require prolonged interactions with the environment, demanding high requirements for anti-interference environments. This paper investigates an offline anti-interference decision based on Decision-Transformers, which can quickly and stably acquire practical anti-interference decision models. Simulation experiments have verified the effectiveness of this algorithm in making anti-interference decisions under AWGN and fading channel conditions. Furthermore, this offline approach can achieve the expected reward targets with fewer training iterations. © The Author(s) 2025.

关键词： Deep reinforcement learning

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Robust Multi-agent Federated Reinforcement Learning for Task Offloading 7th

Robust Multi-agent Federated Reinforcement Learning for Task...

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7th International Conference on Wireless communications, Networking and Applications, WCNA 2023

作者： Yan, Dibao Wang, Yongfeng Hou, Wenjing Song, Huanhuan Wen, Hong Ma, Wendi Sun, Fan School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China Sichuan Intelligent IoT Communication Technology Engineering Research Center UESTC Chengdu China

ISBN: (纸本)9789819624089

With the proliferation of various mobile smart devices, edge computing and computational offloading technologies have emerged as pivotal support mechanisms, enhancing the service quality of these devices. To facilitate the learning of task offloading strategies across diverse and complex scenarios, this study introduces a federated learning strategy algorithm based on multi-agent deep reinforcement learning. This algorithm aims to aggregate the training strategies of multiple edge computing devices, thereby enabling the synthesis of superior task offloading strategies tailored to a wide array of environments while also providing safeguards against malicious nodes. This paper specifically examines the reward inversion attack, illustrating the algorithm’s capability to identify and counteract such malicious threats effectively. Experimental results validate that the proposed algorithm not only robustly defends against these attacks but also adeptly learns the task offloading strategies pertinent to each node. © The Author(s) 2025.

关键词： Federated learning

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Fast Heatsink Topology Optimization for RF Power Amplifier Chip Heat Dissipation Enhancement

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IEEE Transactions on Components, Packaging and Manufacturing technology 2025年

作者： Xiang, Huazhi Fu, Jialong Shang, Yaocheng Inserra, Daniele Guo, Rui Wen, Guangjun University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Sichuan Provincial Engineering Research Center of Communication Technology for Intelligent IoT China

This article proposes a fast topology optimization algorithm methodology for the design of heatsinks for RF power amplifier circuits based on a thermal resistance network model. This methodology can achieve heatsinks lightweight design while keeping the temperature within an allowable range. The Solid Isotropic Materials with Punishments (SIMP) method is applied within the design area of the heatsink, while the impact of the RF power amplifier circuit on heat dissipation is represented by a thermal resistance network model. The main advantage of this methodology is that it avoids the use of conventionally employed finite element method (FEM) thermal simulation of the whole chip/heat dissipation structure, which requires very long simulation time and high computational effort. On the contrary, the thermal resistance network calculation method offers a very fast and sufficiently accurate temperature distribution analysis tool, speeding up the heatsink structure design when employed within the optimization routine. Compared with a traditional heatsink, the volume (mass) of the heatsink optimized through the aforementioned method has been reduced of more than 20% while satisfying the highest temperature requirements. © 2025 IEEE. All rights reserved.

关键词： Shape optimization

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Design of Orthogonal Polyphase Codes for MIMO Radar Using an Enhanced Salp Swarm Algorithm 1

Design of Orthogonal Polyphase Codes for MIMO Radar Using an...

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1st International Conference on intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Pang, Yu Zhao, Runhui Wen, Hong Wang, Yongfeng Feng, Xuewei Tang, Tao Zhao, Ziang Ma, Wendi School of Aeronautics and Astronautics University of Elec.Science and Tech. of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Lab. of Sichuan Province UESTC 611731 China Intelligent IoT Communication Technology Engineering Research Center of Sichuan Province UESTC 611731 China

ISBN: (纸本)9781643685779

This paper presents the design of highly orthogonal waveforms focusing on minimizing autocorrelation sidelobes and cross-correlation peaks. An enhanced Salp Swarm Algorithm is proposed to improve global search capabilities through refined population initialization and leader position updates. The feasibility and benefits of this approach are demonstrated through practical implementation. © 2025 The Authors.

关键词： MIMO radar

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Adaptive Differentially Private Robust Federated Learning Against Backdoor Attacks 1

Adaptive Differentially Private Robust Federated Learning Ag...

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1st International Conference on intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Yao, Ruixiang Wen, Hong Hou, Wenjing Ma, Wendi Sun, Fan Zhao, Ziang School of Aeronautics and Astronautics University of Elec.Science and Tech. of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Lab. of Sichuan Province UESTC 611731 China Intelligent IoT Communication Technology Engineering Research Center of Sichuan Province UESTC 611731 China

ISBN: (纸本)9781643685779

In federated learning systems, malicious attackers can manipulate training datasets by injecting backdoor triggers to achieve data poisoning. Addressing this vulnerability, this paper proposes a new defense method, the adaptive differential privacy stochastic gradient descent (ADP-SGD) algorithm, which increases the Euclidean distance between malicious and benign updates to enhance aggregation efficiency. Experiments were conducted on typical datasets, and the results show that combining ADP-SGD with the m-Krum aggregation algorithm can effectively defend against backdoor attacks in complex attack environments. While ensuring the accuracy of the main task, it significantly reduces the success rate of backdoor attacks, especially under the condition of unknown proportions of malicious participants, proving to be effective and robust. © 2025 The Authors.

关键词： Federated learning

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Adaptive communication Spectrum Sensing Algorithm Based on Energy Detection 7th

Adaptive Communication Spectrum Sensing Algorithm Based on E...

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7th International Conference on Wireless communications, Networking and Applications, WCNA 2023

作者： Shi, Weihong Tang, Tao Zhao, Runhui Wen, Hong Feng, Xuewei School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China Sichuan Intelligent loT Communication Technology Engineering Research Center UESTC Chengdu China

ISBN: (纸本)9789819624089

Based on the traditional energy detection algorithm, an adaptive communication spectrum sensing algorithm based on energy detection is proposed for signals whose noise power is difficult to be estimated accurately. The algorithm takes into account the problem of large error in calculating the energy detection judgment threshold by noise power when the estimated value of noise power is inaccurate, and is able to perform adaptive communication spectrum sensing for signals with less a priori information and noise power that cannot be estimated accurately. Simulation results show that the adaptive communication spectrum sensing algorithm based on energy detection can effectively improve the performance of spectrum sensing for signals, and the algorithm can effectively detect and sense signals under the condition of reasonable setting of judgment coefficients. © The Author(s) 2025.

关键词： Adaptive Cognitive radio Energy detection Spectrum sensing

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Frequency Division Multiple Access Extension Of Standard UHF RFID Systems For Multiple Tags Inventory With Successive Interference Cancellation

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IEEE Internet of Things Journal 2025年

作者： Huang, Zihan Wen, Ruiming Meng, Jie Inserra, Daniele Su, Jingfang Guo, Rui Kuang, Pengju Li, Gang Wen, Guangjun University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Sichuan Provincial Engineering Research Center of Communication Technology for Intelligent IoT China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China

As the use of ultra-high frequency (UHF) radio-frequency identification (RFID) increases in various fields requiring the rapid identification of a large number of tags, research has shifted towards improving the access capacity of RFID systems. This paper proposes a frequency division multiple access (FDMA) extension of conventional time division multiple access (TDMA) RFID systems by modulating tag signals to different Miller-subcarrier frequencies, achieving a theoretical throughput limit value of 0.9135 average successfully read tags per slot, which is 2.48 times that of the usually referred dynamic frame-slotted Aloha algorithm. The power spectral density (PSD) of Miller-modulated subcarrier (MMS) sequences is derived to uncover the tag interference caused by the modulation signal sidelobe power within each subchannel. After that, a successive interference cancellation (SIC) scheme is employed in a four-tag signal reception situation, enhancing the simultaneous tag replies interference suppression and achieving a small performance deterioration if compared with a conventional TDMA system. An experimental analysis is also described to show the feasibility of the proposed FDMA extension of TDMA-based UHF RFID systems. © 2014 IEEE.

关键词： Radio frequency identification (RFID)

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Wireless Security Enhancement Framework Based on AI and RIS in Industrial iot Networks

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IEEE Network 2025年

作者： Zhao, Runhui Song, Huanhuan Wen, Hong Chen, Zhiwei Hou, Wenjing Feng, Xuewei University of Electronic Science and Technology of China School of Aeronautics and Astronautics Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province China Sichuan Provincial Engineering Research Center of Com-munication Technology for Intelligent IoT China

Reconfigurable intelligent Surface (RIS) technology, closely integrated with Artificial Intelligence (AI) as one of the Physical Layer (PL) technology approaches, is considered an ef-fective solution for enhancing security in industrial Internet of Things (Iiot) networks. Integrating RIS, AI, and heterogeneous Iiot networks presents a promising approach to improving the se-curity of wireless communications in Iiot. This paper discusses new AI-based Physical Layer Authentication (PLA) approaches and how they can be integrated with RIS to enable identity authen-tication and malicious node detection, aiming to provide light-weight isolation in Iiot terminals. An anti-jamming and secure communication framework for Iiot is proposed, which leverages Deep Reinforcement Learning (DRL) and RIS to defend against malicious jamming and eavesdropping targeting legitimate users' channels. Additionally, a foundational framework that employs Quantum Reinforcement Learning (QRL) is presented to improve RIS-assisted secure communication. Furthermore, the threats posed by Generative AI (GAI) to RIS-assisted Iiot communica-tion networks and possible defense schemes are provided. Finally, the paper concludes by discussing research opportunities and fu-ture challenges. © 1986-2012 IEEE.

关键词： Authentication

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Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet

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Computers in Biology and Medicine 2025年 185卷 109494-109494页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Yi, Ding Atwereboannah, Abena A. Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Bakanina Kissanga, Grace-Mercure Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering & Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center Chengdu University of Technology Sichuan Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Life Science University of Electronic Science and Technology of China Chengdu China

Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung cancers. Early and accurate diagnosis is essential for successful treatment, prompting the rise of Computer-Aided Diagnosis Systems as reliable and cost-effective tools. Histopathology, renowned for its precision in cancer imaging, has become pivotal in the diagnostic landscape of breast, colon, and lung cancers. However, while deep learning models have been widely explored in this domain, they often face challenges in generalizing to diverse clinical settings and in efficiently capturing both local and global feature representations, particularly for multi-class tasks. This underscores the need for models that can reduce biases, improve diagnostic accuracy, and minimize error susceptibility in cancer classification tasks. To this end, we introduce ResoMergeNet (RMN), an advanced deep-learning model designed for both multi-class and binary cancer classification using histopathological images of breast, colon, and lung. ResoMergeNet integrates the Resboost mechanism which enhances feature representation, and the ConvmergeNet mechanism which optimizes feature extraction, leading to improved diagnostic accuracy. Comparative evaluations against state-of-the-art models show ResoMergeNet's superior performance. Validated on the LC-25000 and BreakHis (400× and 40× magnifications) datasets, ResoMergeNet demonstrates outstanding performance, achieving perfect scores of 100 % in accuracy, sensitivity, precision, and F1 score for binary classification. For multi-class classification with five classes from the LC25000 dataset, it maintains an impressive 99.96 % across all performance metrics. When applied to the BreakHis dataset, ResoMergeNet achieved 99.87 % accuracy, 99.75 % sensitivity, 99.78 % precision, and 99.77 % F1 score at 400× magnification. At 40× magnification, it still delivered robust results with 98.85 % accuracy,

关键词： Lung cancer

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6G autonomous radio access network empowered by artificial intelligence and network digital twin

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Frontiers of Information technology & Electronic engineering 2025年第2期26卷 161-213页

作者： Guangyi LIU Juan DENG Yanhong ZHU Na LI Boxiao HAN Shoufeng WANG Hua RUI Jingyu WANG Jianhua ZHANG Ying CUI Yingping CUI Yang YANG Yan ZHANG Jiangzhou WANG Ye OUYANG Xiaozhou YE Tao CHEN Rongpeng LI Yongdong ZHU Yuanyuan ZHANG Li YANG Sen BIAN Wanfei SUN Qingbi ZHENG Zhou TONG Huimin ZHANG Zecai SHAO Jiajun WU Mancong KANG China Mobile Research Institute Beijing 100053China ZGC Institute of Ubiquitous-X Innovation and Applications Beijing 100080China AsiaInfo Technologies(China)Inc. Beijing 100193China ZTE Corporation Shenzhen 518057China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China IoT Thrust and Research Center for Digital World with Intelligent Things The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511455China Department of Informatics University of OsloOslo 0316Norway School of Engineering and Digital Arts University of KentCanterbury CT27NZUK MediaTek(Beijing)Inc. Beijing 100015China College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310058China Zhejiang Lab Hangzhou 311500China CICT Mobile Communication Technology Co. Ltd.Beijing 100083China

The sixth-generation(6G)mobile network implements the social vision of digital twins and ubiquitous *** to the fifth-generation(5G)mobile network that focuses only on communications,6G mobile networks must natively support new capabilities such as sensing,computing,artificial intelligence(AI),big data,and security while facilitating Everything as a *** 5G mobile network deployment has demonstrated that network automation and intelligence can simplify network operation and maintenance(O&M),the addition of external functionalities has resulted in low service efficiency and high operational *** this study,a technology framework for a 6G autonomous radio access network(RAN)is proposed to achieve a high-level network autonomy that embraces the design of native cloud,native AI,and network digital twin(NDT).First,a service-based architecture is proposed to re-architect the protocol stack of RAN,which flexibly orchestrates the services and functions on demand as well as customizes them into cloud-native ***,a native AI framework is structured to provide AI support for the diverse use cases of network O&M by orchestrating communications,AI models,data,and computing power demanded by AI use ***,a digital twin network is developed as a virtual environment for the training,pre-validation,and tuning of AI algorithms and neural networks,avoiding possible unexpected losses of the network O&M caused by AI *** combination of native AI and NDT can facilitate network autonomy by building closed-loop management and optimization for RAN.

关键词： 6G Network autonomy Native artificial intelligence Network digital twin Service-based radio access network

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