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arXiv 2023年

作者： Ma, Chengzhi Yang, Xi Wang, Jintao Yang, Guanghua Zhang, Wei Ma, Shaodan The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China The Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China The School of Intelligent Systems Science and Engineering GBA and B&R International Joint Research Center for Smart Logistics Jinan University Zhuhai519070 China The School of Electrical Engineering & Telecommunications University of New South Wales Sydney Australia

Distributed Antenna systems (DASs) employ multiple antenna arrays in remote radio units to achieve highly directional transmission and provide great coverage performance for future-generation networks. However, the utilization of fully digital or hybrid active antenna arrays results in a significant increase in hardware costs and power consumption for DAS. To address these issues, integrating DAS with Reconfigurable Intelligent Surfaces (RIS) offers a viable approach to ensure coverage and transmission performance while maintaining low hardware costs and power consumption. To incorporate the merits of RIS into the DAS from practical consideration, a novel architecture of "Reconfigurable Distributed Antennas and Reflecting Surfaces (RDARS)" is proposed in this paper. Specifically, based on the design of the additional direct-through state together with the existing high-quality fronthaul link, any element of the RDARS can be dynamically programmed to connect with the base station (BS) via fibers and perform the connected mode as remote distributed antennas of the BS to receive or transmit signals. Additionally, RDARS also inherits the low-cost and low-energy-consumption benefits of fully passive RISs by default configuring the elements as passive to perform the reflection mode. As a result, RDARS encompasses both DAS and RIS as special cases, offering flexible control over the trade-off between distribution gain and reflection gain to enhance performance. To unveil the potential of such architecture, the ergodic achievable rate under the RDARS architecture is analyzed and closed-form expression with meaningful insights is derived. The theoretical analysis proves that the RDARS can achieve a higher achievable rate than both DAS and fully passive RIS with the passive beamforming gain provided by elements acting reflection mode while combating the "multiplicative fading" suffered by RISs through the connected mode performed at the RDARS. Simulation results also demonstr

关键词： Reconfigurable architectures

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TraCemop: Towards Federated Learning with Traceable Contribution Evaluation and Model Ownership Protection

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IEEE Transactions on Mobile computing 2025年

作者： Liu, Jingwei Zhou, Zihan Sun, Rong Liu, Lei Lu, Rongxing Dustdar, Schahram Niyato, Dusit Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Xidian University Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710071 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Xidian University Guangzhou Institute of Technology Guangzhou510555 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Queen's University School of Computing KingstonONK7L 2N8 Canada TU Wien Distributed Systems Group Austria UPF ICREA Barcelona Spain Nanyang Technological University School of Computer Science and Engineering Singapore

Federated Learning (FL) allows multiple clients to collaboratively train machine learning models without the need to share their local private data. As a result, it can effectively address the issue of data fragmentation. Nevertheless, insufficient evaluation of individual contributions and the lack of protections for both the intellectual property rights (IPR) of models and client privacy can greatly reduce clients' motivations in federated training. To address these challenges, this paper introduces the Traceable Contribution Evaluation and Model Ownership Protection (TraCemop) framework for federated learning, which allows each client to swiftly assess the contributions of others in each round, with integrated support for the traceability of evaluation results. To safeguard the intellectual property of models, a collective watermark is embedded in the global model. Additionally, a secure mechanism for verifying model ownership is also available in case of disputes. Security analysis indicates that TraCemop is capable of resisting data reconstruction attacks as well as various types of model copyright infringements. Finally, we evaluate the proposed framework using two commonly-used datasets, and the experimental results show a significant improvement in the efficiency of contribution evaluation compared to existing methods. Meanwhile, IPR infringement tests on TraCemop reveal that the proposed framework is resilient against malicious efforts to monopolize model ownership. © 2002-2012 IEEE.

关键词： Federated learning

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Diffusion-based Dynamic Contract for Federated AI Agent Construction in Mobile Metaverses

arXiv

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arXiv 2025年

作者： Wen, Jinbo Kang, Jiawen Zhang, Yang Zhong, Yue Niyato, Dusit Xu, Jie Tang, Jianhang Yuen, Chau College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China School of Automation Guangdong University of Technology China College of Computing and Data Science Nanyang Technological University Singapore Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen China State Key Laboratory of Public Big Data Guizhou University China School of Electrical and Electronics Engineering Nanyang Technological University Singapore

Mobile metaverses have attracted significant attention from both academia and industry, which are envisioned as the next-generation Internet, providing users with immersive and ubiquitous metaverse services through mobile devices. Driven by Large Language Models (LLMs) and Vision-Language Models (VLMs), Artificial Intelligence (AI) agents hold the potential to empower the creation, maintenance, and evolution of mobile metaverses. Currently, AI agents are primarily constructed using cloud-based LLMs and VLMs. However, several challenges hinder their effective implementation, including high service latency and potential sensitive data leakage during perception and processing. In this paper, we develop an edge-cloud collaboration-based federated AI agent construction framework in mobile metaverses. Specifically, Edge Servers (ESs), acting as agent infrastructures, collaboratively create agent modules in a distributed manner. The cloud server then integrates these modules into AI agents and deploys them at the edge, thereby enabling low-latency AI agent services for users. Considering that ESs may exhibit dynamic levels of willingness to participate in federated AI agent construction, we design a two-period dynamic contract model to continuously motivate ESs to participate in agent module creation, effectively addressing the dynamic information asymmetry between the cloud server and the ESs. Furthermore, we propose an Enhanced Diffusion Model-based Soft Actor-Critic (EDMSAC) algorithm to efficiently generate optimal dynamic contracts, in which dynamic structured pruning is applied to DM-based actor networks to enhance denoising efficiency and policy learning performance. Extensive simulations demonstrate the effectiveness and superiority of the EDMSAC algorithm and the proposed contract model. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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An Efficient Implementation for Bayesian Manifold Regularization Method

An Efficient Implementation for Bayesian Manifold Regulariza...

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IEEE Conference on Decision and Control

作者： Junpeng Zhang Yue Ju Bo Wahlberg Biqiang Mu Tianshi Chen School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Instituite of Technology Stockholm Sweden Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Beijing China

When applying the Bayesian manifold regularization method to function estimation problem with manifold constraints, the direct implementation has computational complexity $\mathcal{O}(N^{3})$ , where $N$ is the number of input-output data measurements. This becomes particularly costly when $N$ is large. In this paper, we propose a more efficient implementation based on the Kalman filter and smoother using a state-space model realization of the underlying Gaussian process. Moreover, we explore the sequentially semi-separable structure of the Laplacian matrix and the posterior covariance matrix. Our proposed implementation has computational complexity $\mathcal{O}(N)$ and thus can be applied to large data problems. We exemplify the effectiveness of our proposed implementation through numerical simulations.

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Constrained Optimization with Decision-Dependent Distributions

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IEEE Transactions on Automatic Control 2025年

作者： Wang, Zifan Liu, Changxin Parisini, Thomas Zavlanos, Michael M. Johansson, Karl H. KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures StockholmSE-10044 Sweden East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Italy Duke University Department of Mechanical Engineering and Materials Science DurhamNC United States

In this paper we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization problems with decision-dependent distributions has assumed either the absence of constraints or fixed constraints. This work considers a more general setting where the constraints can also dynamically adjust in response to changes in the decision variables. Specifically, we consider linear constraints and analyze the effect of decision-dependent distributions in both the objective function and constraints. Firstly, we establish a sufficient condition for the existence of a constrained equilibrium point, at which the distributions remain invariant under retraining. Moreover, we propose and analyze two algorithms: repeated constrained optimization and repeated dual ascent. For each algorithm, we provide sufficient conditions for convergence to the constrained equilibrium point. Furthermore, we explore the relationship between the equilibrium point and the optimal point for the constrained decision-dependent optimization problem. Notably, our results encompass previous findings as special cases when the constraints remain fixed. To show the effectiveness of our theoretical analysis, we provide numerical experiments on both a market problem and a dynamic pricing problem for parking based on real-world data. © 1963-2012 IEEE.

关键词： Optimization algorithms

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Physics-informed Inverse Design of Multi-bit Programmable Metasurfaces

arXiv

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arXiv 2024年

作者： Xu, Yucheng Yang, Jia-Qi Fan, Kebin Wang, Sheng Wu, Jingbo Zhang, Caihong Zhan, De-Chuan Padilla, Willie J. Jin, Biaobing Chen, Jian Wu, Peiheng Key Laboratory of Optoelectronic Devices and Systems with Extreme Performances of MOE School of Electronic Science and Engineering Nanjing University Nanjing210023 China Purple Mountain Laboratories Nanjing211111 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Electrical and Computer Engineering Duke University Box 90291 DurhamNC27708 United States

Emerging reconfigurable metasurfaces offer various possibilities in programmatically manipulating electromagnetic waves across spatial, spectral, and temporal domains, showcasing great potential for enhancing terahertz applications. However, they are hindered by limited tunability, particularly evident in relatively small phase tuning over 270o, due to the design constraints with time-intensive forward design methodologies. Here, we demonstrate a multi-bit programmable metasurface capable of terahertz beam steering, facilitated by a developed physics-informed inverse design (PIID) approach. Through integrating a modified coupled mode theory (MCMT) into residual neural networks, our PIID algorithm not only significantly increases the design accuracy compared to conventional neural networks but also elucidates the intricate physical relations between the geometry and the modes. Without decreasing the reflection intensity, our method achieves the enhanced phase tuning as large as 300o. Additionally, we experimentally validate the inverse designed programmable beam steering metasurface, which is adaptable across 1-bit, 2-bit, and tri-state coding schemes, yielding a deflection angle up to 68o and broadened steering coverage. Our demonstration provides a promising pathway for rapidly exploring advanced metasurface devices, with potentially great impact on communication and imaging technologies. © 2024, CC0.

关键词： Tuning

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Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments

arXiv

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arXiv 2024年

作者： Wang, Jiacheng Du, Hongyang Niyato, Dusit Xiong, Zehui Kang, Jiawen Ai, Bo Han, Zhu Kim, Dong In School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore China Ministry of Education Guangzhou510006 China State Key Laboratory of Rail Traffic Control and Safety Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Groundbreaking applications such as ChatGPT have heightened research interest in generative artificial intelligence (GAI). Essentially, GAI excels not only in content generation but also signal processing, offering support for wireless sensing. Hence, we introduce a novel GAI-assisted human flow detection system (G-HFD). Rigorously, G-HFD first uses the channel state information (CSI) to estimate the velocity and acceleration of propagation path length change of the human induced reflection (HIR). Then, given the strong inference ability of the diffusion model, we propose a unified weighted conditional diffusion model (UW-CDM) to denoise the estimation results, enabling detection of the number of targets. Next, we use the CSI obtained by a uniform linear array with wavelength spacing to estimate the HIR’s time of flight and direction of arrival (DoA). In this process, UW-CDM solves the problem of ambiguous DoA spectrum, ensuring accurate DoA estimation. Finally, through clustering, G-HFD determines the number of subflows and the number of targets in each subflow, i.e., the subflow size. The evaluation based on practical downlink communication signals shows G-HFD’s accuracy of subflow size detection can reach 91%. This validates its effectiveness and underscores the significant potential of GAI in the context of wireless sensing. © 2024, CC BY.

关键词： Artificial intelligence

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An Artificial Intelligence Solution for Energy Management in Smart Grid

An Artificial Intelligence Solution for Energy Management in...

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Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

作者： Ferdaws Ben Naceur Sana Toumi Chokri Ben Salah Mohamed Ali Mahjoub LATIS- Laboratory of Advanced Technology and Intelligent Systems National School of Engineers of Sousse University of Sousse Sousse Tunisia Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse Tunisia Department of Electrical Engineering Laboratory of Automation Electrical Systems and Environment (LASEE) National School of Engineers University of Monastir Monastir Tunisia Institut de Recherche Dupuy de Lôme (UMR CNRS 60 27 IRDL) Brest France Laboratory of Automation Electrical Systems and Environment (LASEE) ISSAT Sousse University of Sousse Sousse Tunisia

This paper deals with an intelligent decision-making strategy of energy management for a smart grid within an element storage in order to reduce the system cost and improve its reliability. Indeed, the integration of distributed energy storage devices allows the customers to store energy and reuse at a later time or delayed some appliances operation according to a time-varying price signal. The adopted strategy is based on an artificial intelligence control algorithm “fuzzy logic”. The main objective is to determine the appropriate source (grid or battery) used for meeting the consumer. The decision solution shows the system reliability where the switches are synchronized. Switches draw electricity from the grid for both load and battery when price is low and in contrary when electricity price is high, we find a variation's price correspondance with the switch drawing electricity from the battery. The obtained results are verified carrying out Matlab simulation using real data such as the electricity price (Price (t)) and the battery state of charge (SOC (t)).

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THE INTERPLAY BETWEEN PHYSICAL ACTIVITY, PROTEIN CONSUMPTION, AND SLEEP QUALITY IN MUSCLE PROTEIN SYNTHESIS

arXiv

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arXiv 2024年

作者： Devkota, Ayush Gautam, Manakamana Dhakal, Uttam Devkota, Suman Gupta, Gaurav Kumar Nepal, Ujjwal Dhuru, Amey Dinesh Singh, Aniket Kumar Department of Applied Science and Chemical Engineering Pulchowk Engineering College Lalitpur Nepal School of Health and Allied Sciences Pokhara University Nepal Department of Electrical and Computer Engineering Youngstown State University OH United States Department of Computing and Information Systems Youngstown State University OH United States Department of Computer Science and Information Systems Youngstown State University OH United States Maharishi International University FairfieldIA United States State University of New York at Buffalo BuffaloNY United States

This systematic review examines the synergistic and individual influences of resistance exercise, dietary protein supplementation, and sleep/recovery on muscle protein synthesis (MPS). Electronic databases such as Scopus, Google Scholar, and Web of Science were extensively used. Studies were selected based on relevance to the criteria and were ensured to be directly applicable to the objectives. Research indicates that a protein dose of 20 to 25 grams maximally stimulates MPS post-resistance training. It is observed that physically frail individuals aged 76 to 92 and middle-aged adults aged 62 to 74 have lower mixed muscle protein synthetic rates than individuals aged 20 to 32. High-whey protein and leucine-enriched supplements enhance MPS more efficiently than standard dairy products in older adults engaged in resistance programs. Similarly, protein intake before sleep boosts overnight MPS rates, which helps prevent muscle loss associated with sleep debt, exercise-induced damage, and muscle-wasting conditions like sarcopenia and cachexia. Resistance exercise is a functional intervention to achieve muscular adaptation and improve function. Future research should focus on variables such as fluctuating fitness levels, age groups, genetics, and lifestyle factors to generate more accurate and beneficial results. © 2024, CC BY.

关键词： Sleep research

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IRS Channel Estimation in Cell-free MIMO Network: A Coalition Formation Guided Federated Learning Approach

IRS Channel Estimation in Cell-free MIMO Network: A Coalitio...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Liu, Haoxuan Qi, Nan Li, Xiaojie Boulogeorgos, Alexandros A. A. Tsiftsis, Theodoros A. Xiao, Ming Röning, Juha Nanjing University of Aeronautics and Astronautics Nanjing China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China School of Electrical Engineering KTH Royal Institute of Technology Stockholm Sweden University of Oulu Biomimetics and Intelligent Systems Group Finland

ISBN: (纸本)9798350368369

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy. © 2025 IEEE.

关键词： Deep reinforcement learning

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