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A Game-Theoretic Approach for Satellites Beam Scheduling and Power Control in a Mega Hybrid Constellation Spectrum Sharing Scenario

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IEEE Internet of Things Journal 2025年

作者： Li, Wei Jia, Luliang Chen, Yingwu Chen, Quan Yan, Jungang Qi, Nan National University of Defense Technology College of Systems Engineering Changsha China Space Engineering University College of Space Information Beijing China National University of Defense Technology College of Aerospace Science and Engineering Changsha China Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing210016 China

A mega hybrid constellation of Low Earth Orbit (LEO) and Geosynchronous Orbit (GEO) satellites represents a typical architecture for future space networks. However, the emergence of such large constellations exacerbates the shortage of spectrum resources. To address this issue, this paper investigates a hierarchical optimization method for beam scheduling and power control of LEO satellites, aiming to extend the available spectrum by sharing the downlink spectrum of GEO satellites. In the upper-layer optimization, a many-to-one matching game model is constructed to achieve optimal matching of LEO satellite beams and LEO Users (LU). A distributed beam matching learning algorithm (DBMLA) is designed to find a stable matching solution for the model, with the convergence of the algorithm theoretically proven. In the lower-layer optimization, an LEO beam power level optimization game model is developed for discrete LEO beam power levels. This model is demonstrated to be an exact potential game with at least one Nash Equilibrium (NE). To solve this NE, a dynamic power allocation logarithmic learning algorithm (DPALLA) is proposed. Simulation results verify that the proposed DBMLA-DPALLA hierarchical optimization scheme effectively balances convergence time and accuracy compared to traditional independent optimization strategies. By leveraging the combined effects of the two optimization strategies, it better mitigates the co-channel interference experienced by GEO satellites and improves the average network satisfaction of the LUs. © 2025 IEEE.

关键词： Cochannel interference

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MEMS-based High-Density Ultra-Conformal μeCOG Electrode Array for Real-Time Motor Decoding 38

MEMS-based High-Density Ultra-Conformal μeCOG Electrode Arr...

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38th IEEE International Conference on Micro Electro Mechanical systems, MEMS 2025

作者： Zhou, Erda Liu, Changjiang Wang, Xiner Wei, Xiaoling Sun, Liuyang Tao, Tiger H. Zhou, Zhitao 2020 X-lab Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China School of Graduate Study University of Chinese Academy of Sciences Beijing China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Neuroxess Co. Ltd. Shanghai China Guangdong Institute of Intelligence Science and Technology Zhuhai Guangdong Hengqin China Tianqiao and Chrissy Chen Institute for Translation Research Shanghai China

ISBN: (纸本)9798331508890

In this study, we present a high-density, high-channel-count micro-electrocorticographic (μECoG) electrode array for real-time motor decoding. The 256-channel μECoG electrode arrays, based on MEMS processes, possess excellent flexibility and mechanical robustness, allowing them to conform to the cortical surface and enabling the acquisition of high-quality ECoG signals. The advanced brain-computer interface (BCI) system was applied to a labrador dog and high accurate real-time motor decoding was achieved, showing the advantages of high-resolution ECoG sampling. Our method demonstrates the potential for controlling a cursor with the ECoG signals, offering the possibility of reconstructing motor functions and synthesizing avatars. © 2025 IEEE.

关键词： Brain computer interface

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Multi-frequency terahertz Smith–Purcell radiation via momentum-mismatch-driven quasi-bound states in the continuum

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Photonics Research 2025年第3期13卷 593-603页

作者： ZI-WEN ZHANG JUAN-FENG ZHU FENG-YUAN HAN XIAO LIN CHAO-HAI DU Center for Carbon-based Electronics School of ElectronicsPeking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of ElectronicsPeking University Science Mathematicsand Technology (SMT)Singapore University of Technology and Design Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and InstrumentationZJU-Hangzhou Global Scientific and Technological Innovation CenterCollege of Information Science&Electronic EngineeringZhejiang University International Joint Innovation Center The Electromagnetics Academy at Zhejiang UniversityZhejiang University

Bound states in the continuum (BICs) have gained considerable attention for their ability to strengthen light–matter interactions,enabling applications in lasing,sensing,and *** properties also show great promise for intensifying free-electron ***,researchers realized momentum-mismatch-driven quasi-BICs in compound grating *** category of quasi-BICs exhibits high Q factors over a broad frequency *** this paper,we explore the possibility of achieving multi-frequency terahertz Smith–Purcell radiation empowered by momentum-mismatch-driven quasi-BICs in silicon compound grating *** leveraging the low-loss properties of silicon in the terahertz range,quasi-BICs are achieved through guided-mode resonance,delivering exceptionally high Q factors over a broad frequency *** broadband nature of these quasi-BICs enables efficient energy extraction from electron beams across varying voltages,while their multimode characteristics support simultaneous interactions with multiple modes,further boosting radiation *** findings demonstrate significant enhancement of free-electron radiation at multiple frequencies,addressing the limitations of narrowband methods and high-loss metallic *** integrating broadband performance with the advantages of low-loss dielectric platforms,this work advances the development of compact,tunable terahertz free-electron radiation sources and provides valuable insights into optimizing quasi-BIC systems for practical applications.

关键词： Light matter interactions Lithium niobate waveguides Nonlinear frequency conversion Q factor Quantum light sources Waveguide modes

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Minimizing Interference in Robotic Rehabilitation Via Asymmetric Stiffness Force Fields

IEEE Open Journal of the Industrial Electronics Society

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IEEE Open Journal of the Industrial Electronics Society 2025年 6卷 840-850页

作者： Kato, Yasuhiro Sakaino, Sho Tsuji, Toshiaki University of Tokyo Center for Education and Research in Information Science and Technology 4-6-1 Komaba Meguro-ku Tokyo Japan University of Tsukuba Faculty of Engineering Information and Systems 1-1-1 Tennodai Ibaraki Tsukuba Japan Saitama University Graduate School of Science and Engineering 255 Shimo-Okubo Sakura-ku Saitama Japan

This paper introduces a novel approach for guiding human arm movement in the context of robotic rehabilitation. We propose upper-limb movement guidance using a force field based on an asymmetric stiffness matrix. By introducing asymmetry in stiffness design, the proposed force field can deflect arm movement toward the target direction of a reaching movement while minimizing impeding effects. We hypothesize that this method can guide a human in the desired direction without interfering with their voluntary movement. To evaluate the performance of the human arm guidance technique, we conducted upper-limb reaching experiments using a 2-degree-of-freedom (DoF) robot arm with ten healthy volunteers. The experimental results revealed that the proposed approach demonstrated a similar reduction in movement error compared to the conventional stiffness approach. Moreover, participants exhibited higher movement activeness, and robotic interference with human movement was lower. The proposed approach may improve movement guidance based on stiffness control by enabling the robot to guide without inhibiting voluntary movement. © 2020 IEEE.

关键词： Robotic arms

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A Method of Detecting Port Bay Numbers and Optimizing Effects Under Various Weather Scenarios

A Method of Detecting Port Bay Numbers and Optimizing Effect...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Jiazheng Li Lei Ao Kai Sheng Yoshiki Yamaguchi Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Japan Key Laboratory of Collaborative Intelligence Systems Ministry of Education School of Electronic Engineering Xidian University Xi'an Shaanxi China Academy of Advanced Interdisciplinary Research Xidian University Xi'an Shaanxi China Institute of Systems and Information Engineering University of Tsukuba Japan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Ports are transportation hubs located along coasts, rivers, lakes, and reservoirs, with water and land transport facilities that allow ships to enter, exit, and berth safely. The port bay numbers are located on the land side of the port and are rectangular frames of specific sizes designated by port management, containing unique codes. These codes identify where containers are stacked under various weather conditions. Therefore, the ability to quickly and accurately recognize bay numbers in all weather conditions has become a significant issue affecting work efficiency, especially in rainy ports. However, collecting images of bay numbers during rainy days is not permitted due to safety restrictions. In this article, we first generate images of rainy scenes based on images collected in sunny weather. We then compare two detection algorithms and select the one with a smaller decrease in detection metrics but fewer model parameters. Finally, we incorporate an attention mechanism into the chosen algorithm to optimize and enhance target detection performance across various weather scenarios.

关键词： YOLO Measurement Codes Attention mechanisms Seaports Reservoirs Safety Rivers Marine vehicles Meteorology

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Low Probability of Intercept Signal Design for MIMO Integrated Sensing and Communication systems

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IEEE Transactions on Communications 2025年

作者： Shi, Qiao Wang, Yujie Zhou, Zhengchun Cui, Guolong Fan, Pingzhi The School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China The School of Mathematics Southwest Jiaotong University Chengdu611756 China The School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China The Key Lab of Information Coding and Transmission Southwest Jiaotong University Chengdu611756 China

In this paper, aimed to protect the transmitted waveform from being intercepted by advanced electronic support measure systems, a low probability of intercept (LPI) signal design, taking into account the transmitted waveform and precoder of communication users, is developed for multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) systems. The considered system simultaneously senses multiple targets and communicates with multiple users in the presence of various interferences. Specifically, based on the analysis of cyclic spectrum, the desired LPI performance is achieved by minimizing the cyclic frequency sidelobe level. Moreover, sensing and communication requirements are satisfied by respectively maximizing the minimum signal-to-interference-plus-noise ratio (SINR) of targets and constraining the received SINR lower bound of each user. Energy budget and peak-to-average power ratio constraints are introduced to meet the practical resources limits, while similarity constraint is constructed to further ensure the target sensing. Next, in order to solve the formulated nonconvex problem, an efficient iterative algorithm is proposed based on the idea of successive convex approximation (SCA). Finally, simulation results demonstrate the effectiveness of our proposed scheme and display the trade-off among LPI, sensing and communication performance. © 1972-2012 IEEE. All rights reserved.

关键词： Budget control

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Toward Democratized Generative AI in Next-Generation Mobile Edge Networks

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IEEE Network 2025年

作者： Zhang, Ruichen He, Jiayi Luo, Xiaofeng Niyato, Dusit Kang, Jiawen Xiong, Zehui Li, Yonghui Sikdar, Biplab Nanyang Technological University College of Computing and Data Science Singapore Guangdong University of Technology School of Automation Guangzhou510006 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore University of Sydney School of Electrical and Information Engineering SydneyNSW2006 Australia National University of Singapore College of Design and Engineering Department of Electrical and Computer Engineering Singapore

The rapid development of generative artificial intelligence (AI) technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we propose a model-centric framework for democratizing generative AI deployment on mobile and edge networks. First, we comprehensively review key compact model strategies, such as quantization, model pruning, and knowledge distillation, and present key performance metrics to optimize generative AI for mobile deployment. Next, we provide a focused review of mobile and edge networks, emphasizing the specific challenges and requirements of these environments. We further conduct a case study demonstrating the effectiveness of these strategies by deploying LLMs on real mobile edge devices. Experimental results highlight the practicality of democratized LLMs, with significant improvements in generalization accuracy, hallucination rate, accessibility, and resource consumption. Finally, we discuss potential research directions to further advance the deployment of generative AI in resource-constrained environments. © 1986-2012 IEEE.

关键词： Generative adversarial networks

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Enhancing Mobile Edge Computing customer reviews analysis with Ensemble Sparse Support Vector L1 Regularization Based Crossover Discrete Mycorrhized Algorithm

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Biomedical Signal Processing and Control 2025年 109卷

作者： S., Vinodh Kumar S., Bharath Babu Vellingiri, J. Ramani, D. Roja Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Tamil Nadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Engineering New Horizon College of Engineering Karnataka Bangalore India

Mobile Edge Computing has emerged as a transformative technology, enhancing the efficiency of internet community platforms by enabling real-time data processing and analysis at the network's edge. These platforms facilitate the exchange of user opinions and ideas, providing valuable insights into user attitudes and preferences. Despite advancements in Mobile Edge Computing, challenges persist in effectively categorizing customer reviews due to latency, data scarcity, and overfitting issues in computational models. This study integrates natural language processing techniques with edge computing infrastructure to analyze reviews closer to their source, thereby minimizing latency and improving overall performance. To enhance the detection accuracy in Mobile Edge customer reviews, this paper proposes an innovative approach called the Ensemble Sparse Support Vector L1 Regularization-based Crossover Discrete Mycorrhized Algorithm. Pre-processing steps, such as tokenization, lemmatization, and stemming, are employed to improve data quality. Feature extraction is performed using a stacked autoencoder, which incorporates multiple layers to address data scarcity issues. To optimize the performance of the Sparse Support Vector L1 Regularization, a novel Crossover Discrete Mycorrhized Optimization Algorithm is introduced, mitigating overfitting and improving classification accuracy. The proposed approach is validated using datasets such as the Mobile Recommendation System Dataset, Mobile Positioning Dataset, Mobile Edge Distance Analysis Dataset, International Phone Checker API, and Financial Fraud Detection Dataset. Experimental results demonstrate superior performance, achieving 98.52% accuracy, 98.39% precision, 98.28% recall, and 98.21% F1-score in aspect category detection. The proposed method addresses critical gaps in latency reduction and data processing accuracy in MEC environments by significantly improving reliability and efficiency in customer review analysis. These

关键词： Data reduction

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The Impact Analysis of Delays in Asynchronous Federated Learning with Data Heterogeneity for Edge Intelligence

arXiv

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arXiv 2025年

作者： Hao, Ziruo Cui, Zhenhua Yang, Tao Hu, Bo Wu, Xiaofeng Feng, Hui Department of Electronic Engineering School of Information Science and Technology Fudan University Shanghai200438 China Shanghai Institute of Intelligent Electronics and Systems Shanghai200433 China

Federated learning (FL) has provided a new methodology for coordinating a group of clients to train a machine learning model collaboratively, bringing an efficient paradigm in edge intelligence. Despite its promise, FL faces several critical challenges in practical applications involving edge devices, such as data heterogeneity and delays stemming from communication and computation constraints. This paper examines the impact of unknown causes of delay on training performance in an Asynchronous Federated Learning (AFL) system with data heterogeneity. Initially, an asynchronous error definition is proposed, based on which the solely adverse impact of data heterogeneity is theoretically analyzed within the traditional Synchronous Federated Learning (SFL) framework. Furthermore, Asynchronous Updates with Delayed Gradients (AUDG), a conventional AFL scheme, is discussed. Investigation into AUDG reveals that the negative influence of data heterogeneity is correlated with delays, while a shorter average delay from a specific client does not consistently enhance training performance. In order to compensate for the scenarios where AUDG are not adapted, Pseudo-synchronous Updates by Reusing Delayed Gradients (PSURDG) is proposed, and its theoretical convergence is analyzed. In both AUDG and PSURDG, only a random set of clients successfully transmits their updated results to the central server in each iteration. The critical difference between them lies in whether the delayed information is reused. Finally, both schemes are validated and compared through theoretical analysis and simulations, demonstrating more intuitively that discarding outdated information due to time delays is not always the best approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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CLAF-IoT: Context-Aware LLMs-Enhanced Authentication Framework for Internet of Things

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IEEE Internet of Things Journal 2025年

作者： Rehman, Abdul Awan, Kamran Ahmad Hassan, Mahmood Ul Shaikh, Asadullah Alqazzaz, Ali Cengiz, Korhan Department of Information Technology University of Haripur Hattar Road Khyber Pakhtunkhwa Haripur22620 Pakistan Najran University Department of Computer Skills Deanship of Preparatory Year Najran61441 Saudi Arabia Najran University College of Computer Science and Information Systems Department of Information Systems Najran61441 Saudi Arabia College of Computer Science and Information Systems Najran61441 Saudi Arabia College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Prince Mohammad Bin Fahd University Department of Electrical Engineering Al Khobar31952 Saudi Arabia

The significant increase in the number of Internet of Things (IoT) devices in various domains requires robust and adaptive authentication mechanisms. Existing methods often fail to address the dynamic and heterogeneous nature of the IoT ecosystem, resulting in significant security vulnerabilities. This paper presents a context-aware LLM-enhanced authentication framework (CLAF-IoT) that dynamically adjusts authentication protocols based on real-time environmental and user-specific contexts. Using the advanced contextual understanding and generation capabilities of Large Language Models (LLMs), the proposed framework enhances both security and usability in highly dynamic IoT environments. Key components include environmental context sensing, user behavior analysis, adaptive authentication protocols, real-time threat detection, and federated learning integration for continuous improvement and privacy preservation. Experimental evaluations demonstrate that CLAF-IoT achieves higher authentication accuracy in different scenarios, 11.11% false acceptance rate and 9.09% false rejection rate. © 2014 IEEE.

关键词： Federated learning

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