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Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization

IEEE Transactions on Cognitive communications and Networking 2025年

作者： Wang, Yanhu Afzal, Muhammad Muzammil Li, Zhengyang Zhou, Jie Feng, Chenyuan Guo, Shuaishuai Quek, Tony Q. S. Shandong University School of Control Science and Engineering Jinan250062 China EURECOM Department of Communication Systems Sophia Antipolis06410 France Singapore University of Technology and Design Singapore487372 Singapore Kyung Hee University Department of Electronic Engineering Yongin17104 Korea Republic of

Traditional base station siting (BSS) methods rely heavily on drive testing and user feedback, which are laborious and require extensive expertise in communication, networking, and optimization. As large language models (LLMs) and their associated technologies advance, particularly in the realms of prompt engineering and agent engineering, network optimization will witness a revolutionary approach. This approach entails the strategic use of well-crafted prompts to infuse human experience and knowledge into these sophisticated LLMs, and the deployment of autonomous agents as a communication bridge to seamlessly connect the machine language based LLMs with human users using natural language. Furthermore, our proposed framework incorporates retrieval-augmented generation (RAG) to enhance the system's ability to acquire domain-specific knowledge and generate solutions, thereby enabling the customization and optimization of the BSS process. This integration represents the future paradigm of artificial intelligence (AI) as a service and AI for more ease. This research first develops a novel LLM-empowered BSS optimization framework, and heuristically proposes three different potential implementations: the strategies based on Prompt-optimized LLM (PoL), LLM-empowered autonomous BSS agent (LaBa), and Cooperative multiple LLM-based autonomous BSS agents (CLaBa). Through evaluation on real-world data, the experiments demonstrate that prompt-assisted LLMs and LLM-based agents can generate more efficient and reliable network deployments, noticeably enhancing the efficiency of BSS optimization and reducing trivial manual participation. © 2015 IEEE.

关键词： Systems analysis

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Experimental demonstration of 8190-km long-haul semiconductor-laser chaos synchronization induced by digital optical communication signal

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光：科学与应用（英文版） 2025年第2期14卷 333-343页

作者： Anbang Wang Junli Wang Lin Jiang Longsheng Wang Yuncai Wang Lianshan Yan Yuwen Qin Key Laboratory of Photonic Technology for Integrated Sensing and Communication Ministry of Education of ChinaGuangdong University of TechnologyGuangzhou 510006China Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi ProvinceTaiyuan 030024China Institute of Advanced Photonics Technology School of Information EngineeringGuangdong University of TechnologyGuangzhou 510006China Guangdong Provincial Key Laboratory of Information Photonics Technology Guangdong University of TechnologyGuangzhou 510006China Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi ProvinceTaiyuan 030024China College of Physics and Optoelectronics Taiyuan University of TechnologyTaiyuan 030024China Center for Information Photonics and Communications Southwest Jiaotong UniversityChengdu 610031China Key Laboratory of Photonic Technology for Integrated Sensing and Communication Ministry of Education of ChinaGuangdong University of TechnologyGuangzhou 510006China Institute of Advanced Photonics Technology School of Information EngineeringGuangdong University of TechnologyGuangzhou 510006China Guangdong Provincial Key Laboratory of Information Photonics Technology Guangdong University of TechnologyGuangzhou 510006China

Common-signal-induced synchronization of semiconductor lasers have promising applications in physical-layer secure transmission with high speed and compatibility with the current fiber ***,we propose an ultra-long-distance laser synchronization scheme by utilizing random digital optical communication signal as the common drive *** utilizing the long-haul optical coherent communication techniques,high-fidelity fiber transmission of the digital drive can be achieved and thus ultra-long-distance synchronization is *** were implemented with distributed feedback lasers injected by a random-digital phase-modulated drive *** show that high-quality synchronization can be achieved as the drive signal rate is larger than the laser relaxation frequency and the transmission bit error ratio is below a critical *** synchronization over 8191-km fiber transmission was experimentally *** to traditional common-signal-induced synchronization using analog drive signal such as chaos,the distance is increased by 8 times,and complicated hardware devices for channel impairment compensation are no longer *** addition,the proposed method does not sacrifice communication capacity like traditional methods which need a channel to transmit analog drive *** is therefore believed that this common-digital-signal induced laser synchronization paves a way for secure backbone and submarine transmission.

关键词： communication semiconductor synchronization signal digital chaos laser demonstration experimental induced

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Polarized Modulation for RIS-Aided Vehicular communication Networks

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IEEE Internet of Things Journal 2025年

作者： Shang, Pingping Xie, Longhui Li, Jun Feng, Li Hai, Han Xu, Binbin Li, Jie Yu, Ling Hubei University of Technology School of Electrical and Electronic Engineering Wuhan430068 China Hubei University of Technology Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Wuhan430068 China Guangzhou University Research Center of Intelligent Communication Engineering School of Electronics and Communication Engineering Guangzhou510006 China Key Laboratory of On-Chip Communication and Sensor Chip of Guangdong Higher Education Institutes Guangzhou510006 China Donghua University School of Information Science and Technology Shanghai201620 China Maritime Communication Research Institute Wuhan430068 China Guangdong Provincial Hospital of Chinese Medicine Guangzhou510120 China State Key Laboratory of Traditional Chinese Medicine Syndrome Guangzhou510120 China

The reconfigurable intelligent surface (RIS) stands as an emergent technique poised to revolutionize future vehicular communication. In order to facilitate the application of RIS in vehicular communication, this paper proposes two effective RIS-aided schemes tailored for polarization modulation (PM) aiming to achieve remarkable spectral efficiency. Specifically, Scheme I proposes a novel index modulation (IM) aided PM-RIS framework that innovatively encodes information across three distinct dimensions, the indices of dual-polarized antennas (DP-AEs), PM symbols and the reflecting surface indices of the RIS. Scheme II employs a deep neural network method to analyze the channel state information (CSI) for transmit antenna selection, that enhances the reliability and interference immunity in dynamic conditions while reducing computational complexity. Furthermore, a stepwise estimation (SE) method is introduced at the detection stage, which significantly reduces the complexity associated with information decoding. The simulation results highlight the superiority of the proposed system. Compared with traditional PM systems, it demonstrates enhanced performance in terms of spectral efficiency, channel stability, and bit error rate (BER). © 2014 IEEE.

关键词： Signal modulation

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Unsupervised Feature Enrichment and Fidelity Preservation Learning Framework for Skeleton-based Action Recognition

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Li, Chuankun Li, Shuai Gao, Yanbo Gao, Xingyu Chen, Ping Li, Jian Li, Wanqing North University of China State Key Laboratory of Dynamic Testing Technology School of Information and Communication Engineering Taiyuan030051 China Shandong University School of Control Science and Engineering School of Software Jinan250100 China Chinese Academy of Sciences Institute of Microelectronics Beijing100029 China University of Wollongong Advanced Multimedia Research Lab WollongongNSW2522 Australia

Unsupervised skeleton-based action recognition has achieved remarkable progress recently. Existing unsupervised learning methods suffer from severe overfitting problem, and thus small networks are used, significantly reducing the representation capability. To address this problem, the overfitting mechanism behind the unsupervised learning for skeleton-based action recognition is first investigated. It is observed that skeleton is already a relatively high-level and low-dimension feature, but not in the same manifold as the features for action recognition. Simply applying the existing unsupervised learning method tends to produce features that discriminate the different samples rather than action classes, resulting in the overfitting problem. To address this problem, this paper proposes an Unsupervised spatial-temporal Feature Enrichment and Fidelity Preservation (U-FEFP) learning framework to generate rich distributed features that contain all the information of a skeleton sample. A spatial-temporal feature transformation subnetwork is developed using channel-wise topology refinement graph convolutional block and graph convolutional gated recurrent unit block as the basic feature extraction network. The unsupervised Bootstrap Your Own Latent-based learning is utilized to generate rich distributed features, and the unsupervised pretext task-based learning is employed to preserve the information contained in the skeleton. The two unsupervised learning ways are collaborated as U-FEFP to produce robust and discriminative representations. Experimental results on four widely used benchmarks, namely NTU-RGB+D-60, PKU-MMD, NTU-RGB+D-120 and UAV-Human dataset, demonstrate that the proposed U-FEFP obtains the best result compared with the state-of-the-art unsupervised learning methods. © 1991-2012 IEEE.

关键词： Convolution

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A Novel Consecutive Artificial Noise Secrecy Enhancing Scheme for RIS Aided MIMO System With Imperfect CSI

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IEEE Transactions on Vehicular technology 2025年

作者： Shang, Pingping Tao, Zhuofan Xie, Zhonghui Huang, Min Sui, He Li, Jun Hai, Han Hubei University of Technology School of Electrical and Electronic Engineering Wuhan430068 China Hubei University of Technology Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Wuhan430068 China Civil Aviation University of China College of Aeronautical Engineering Tianjin300399 China Guangzhou University School of Electronics and Communication Engineering Guangzhou510006 China Donghua University College of Information Science and Technology Shanghai201620 China

Reconfigurable intelligent surface (RIS) is a technology with great promise, which is widely used in wireless networks to improve the coverage and enhance the performance. However, security concerns pose a major challenge for wireless networks, especially given inevitable errors in channel estimation. This paper proposes a high spectrum efficiency security scheme using generalized spatial modulation (GSM) and space-time block coding (STBC) for scenarios with imperfect channel state information (CSI). In this scheme, GSM uses RIS block indices as unique identifiers, ideal for secure scenarios. It operates by activating different RIS blocks over two time slots to reflect unmodulated carrier signals, while STBC is designed based on the phase shifts of the RIS. To ensure secure communication, a low-complexity continuous artificial noise (CAN) is transmitted with STBC signals to interfere with eavesdroppers and prevent them from decoding the signals. The ergodic secrecy rate is deduced, and the bit error rate (BER) is validated through experiments. The simulation results indicate that the proposed solution can remarkably improves the security performance of the RIS-assisted wireless communication systems. © 1967-2012 IEEE.

关键词： Space-time block coding (STBC)

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Domain Adaptation-based Radio Frequency Fingerprint Identification for Industrial Cyber-Physical Systems

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IEEE Network 2025年

作者： Wang, Yatong Cao, Bin Wen, Zhongyi Yan, Mu Song, Changqing Beijing Institute of Technology School of Information and Electronics Beijing100081 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Technology Beijing100191 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100191 China

With the widespread adoption of cloud and fog platforms, security issues facing Industrial Cyber-Physical Systems (ICPSs) are increasingly prominent. Radio Frequency Fingerprinting (RFFI) emerges as a promising physical layer security technique for user authentication, effectively enhancing ICPS security over wireless networks by exploiting inherenthardware imperfections in transmitters. However, as technologies evolve and user environments become denser and more complex, existing RFFI techniques encounter significant domain shift challenges, leading to performance degradation. Therefore, developing domain-adaptive RFFI techniques is crucial for the effectiveness of ICPSs. In this article, we first provide a comprehensive review of the RFFI framework, emphasizing its applications in ICPSs and the key processes involved. Next, we focus on the domain shift issues faced by RFFI in ICPSs and present a comprehensive survey of domain adaptation methods. To tackle these challenges, we propose a novel feature-alignment based domain adaptation (FADA) framework for RFFI, which is designed to mitigate the performance degradation caused bycross-domain shifts by aligning the feature representations of the source and target domain. Finally, we discuss the inherent challenges and future directions for developing domain adaptation based RFFI to enhance the security of ICPSs. © 2025 IEEE. All rights reserved.

关键词： Radio frequency identification (RFID)

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Multi-Modal Multi-Kernel Graph Learning for Autism Prediction and Biomarker Discovery

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 842-854页

作者： Jin Liu Junbin Mao Hanhe Lin Hulin Kuang Shirui Pan Xusheng Wu Shan Xie Fei Liu Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China School of Science and Engineering University of Dundee Dundee U.K. School of Information and Communication Technology Griffith University Gold Coast QLD Australia Shenzhen Health Development Research and Data Management Center Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology.

关键词： Diseases Autism Random access memory Data mining Kernel Electronic mail Training Neuroimaging Graph convolutional networks Bioinformatics

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Research of the test generation algorithm based on search state dominance for combinational circuit

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Journal of Harbin Institute of technology(New Series) 2006年第1期13卷 62-64页

作者：吴丽华俞红娟王轸马怀俭 College of Measure-control Technology & Communication Engineering Harbin University of Science and Technology Harbin 150040China

On the basis of EST (Equivalent STate hashing) algorithm, this paper researches a kind of test generation algorithm based on search state dominance for combinational circuit. According to the dominance relation of the E-frontier (evaluation frontier), we can prove that this algorithm can terminate unnecessary searching step of test pattern earlier than the EST algorithm through some examples, so this algorithm can reduce the time of test generation. The test patterns calculated can detect faults given through simulation.

关键词： E-frontier test generation combinational circuit

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Sensing With Random communication Signals

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IEEE Network 2025年

作者： Lu, Shihang Liu, Fan Xiong, Yifeng Du, Zhen Cui, Yuanhao Li, Shuangyang Yuan, Weijie Yang, Jie Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing210044 China Technical University of Berlin Faculty of Electrical Engineering and Computer Science Berlin10587 Germany Shenzhen518055 China Frontiers Science Center for Mobile Information Communication and Security China Southeast University MOE Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing211189 China

communication-centric Integrated Sensing and communication (ISAC) has been recognized as a promising methodology to implement wireless sensing functionality over existing network architectures, due to its cost-effectiveness and backward compatibility to legacy cellular systems. However, the inherent randomness of the communication signal may incur huge fluctuations in sensing capabilities, leading to unfavorable detection and estimation performance. To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality. Specifically, we commence by discussing the fundamentals of sensing with random communication signals, including the performance metrics and optimal ranging waveforms. Building on these concepts, we then present a general framework for random ISAC signal transmission, followed by an in-depth exploration of time-domain pulse shaping, frequency-domain constellation shaping, and spatial-domain precoding methods. We provide a comprehensive overview of each of these topics, including models, results, and design guidelines. Finally, we conclude this article by identifying several promising research directions for random ISAC signal transmission. © 2025 IEEE.

关键词： Mobile telecommunication systems

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Sensing With Random communication Signals

arXiv

引用

arXiv 2025年

作者： Lu, Shihang Liu, Fan Xiong, Yifeng Du, Zhen Cui, Yuanhao Li, Shuangyang Yuan, Weijie Yang, Jie Jin, Shi National Mobile Communications Research Laboratory Southeast University Nanjing210096 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin10587 Germany Southern University of Science and Technology Shenzhen518055 China Frontiers Science Center for Mobile Information Communication and Security and also The Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing211189 China

关键词： Signaling

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