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Light(Science & Applications) 2025年第1期14卷 118-130页

作者： Shengguo Hu Mingyi Li Jiawen Xu Hongrui Zhang Shanghang Zhang Tie Jun Cui Philipp del Hougne Lianlin Li State Key Laboratory of Advanced Optical Communication Systems and Networks School of ElectronicsPeking UniversityBeijing100871China National Key Laboratory for Multimedia Information Processing School of Computer SciencePeking UniversityBeijing100871China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing210096China Pazhou Laboratory(Huangpu) GuangzhouGuangdong510555China Univ Rennes CNRSIETR-UMR 6164F-35000RennesFrance

Metamaterials have revolutionized wave control;in the last two decades,they evolved from passive devices via programmable devices to sensor-endowed self-adaptive devices realizing a user-specified *** deep-learning techniques play an increasingly important role in metamaterial inverse design,measurement post-processing and end-to-end optimization,their role is ultimately still limited to approximating specific mathematical relations;the metamaterial is still limited to serving as proxy of a human operator,realizing a predefined ***,we propose and experimentally prototype a paradigm shift toward a metamaterial agent(coined metaAgent)endowed with reasoning and cognitive capabilities enabling the autonomous planning and successful execution of diverse long-horizon tasks,including electromagnetic(EM)field manipulations and interactions with robots and *** recently released foundation models,metaAgent reasons in high-level natural language,acting upon diverse prompts from an evolving complex ***,metaAgent's cerebrum performs high-level task planning in natural language via a multi-agent discussion mechanism,where agents are domain experts in sensing,planning,grounding,and *** response to live environmental feedback within a real-world setting emulating an ambient-assisted living context(including human requests in natural language),our metaAgent prototype self-organizes a hierarchy of EM manipulation tasks in conjunction with commanding a *** masters foundational EM manipulation skills related to wireless communications and sensing,and it memorizes and learns from past experience based on human feedback.

关键词： agent prompt reasoning

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Edge-Driven Industrial Computing Power networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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A Review of Pseudonym Change Strategies for Location Privacy Preservation Schemes in Vehicular networks

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ACM Computing Surveys 2025年第8期57卷 1-37页

作者： Benarous, Leila Zeadally, Sherali Boudjit, Saadi Mellouk, Abdelhamid Department of Computer science Amar Telidji University of Laghouat Laghouat Algeria LISSI-Tinc-NET Laboratory University of Paris-Est Creteil Créteil France College of Communication and Information University of Kentucky Lexington United States Department of Electronic Engineering Kyung Hee University Seoul Korea Republic of Litis Lab Intelligent Transportation Systems Group University of Rouen Normandy Mont-Saint-Aignan France Lissi Laboratory Department of Networks and Telecom University Paris-Est Créteil Val de Marne Créteil France

Location privacy protection in vehicular networks has been a primary priority to ensure because of its direct impact on human physical safety. Leakage and violation of road users' location privacy may be perilous beyond simple curiosity as it may scale to cyberstalking and tracking. This may lead to road users being subjected to mental and physical distress, from blackmailing to targeted advertising, and trap planning. The results of tracking may be fatal to road users. Over the past 17 years, researchers have been actively investigating location privacy protection mechanisms for vehicular networks. Numerous research results have been published that examined the use of pseudonym change strategies, which have been considered as the most appropriate solution that maintains the tradeoff between ease of application, good protection level, and the correct functionality of the network. We evaluate, classify, and analyze pseudonym change strategies aimed at location privacy preserving solutions for vehicular networks and discuss their performance. We also review the simulation tools used for evaluating pseudonym change techniques used in location privacy preserving solutions for the vehicular environment. The results of this survey will help researchers in this field to develop more robust, efficient, and cost-effective location privacy protection schemes in the future. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Differential privacy

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Near-Far Field Three-Stage Beam Training for RIS-Assisted Wideband OFDM communications

Near-Far Field Three-Stage Beam Training for RIS-Assisted Wi...

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IEEE Conference on Wireless communications and Networking

作者： Zhichao Cheng Shupei Zhang Shu Fu Boya Di School of Electronics Engineering and Computer Science Peking University Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Department of Microelectronics and Communication Engineering Chongqing University Chongqing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Large-scale reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for signal strength enhancement and coverage extension in 6G networks. However, as the antenna scale and the bandwidth increase, large-scale RIS-assisted wideband orthogonal frequency division multiplexing (OFDM) communication systems face new challenges due to the near-field range expansion and the beam split effect, complicating the acquisition of channel state information (CSI). To tackle these challenges, in this paper, we present a wideband beam training scheme by utilizing the beam split effect to bypass the CSI acquisition. Specifically, by analyzing the beam split effect in RIS-assisted OFDM communication systems, we propose a beam-split-aware codebook capable of covering both the near and far fields. The coverage of codewords in the proposed codebook is expanded benefiting from the beam split effect, leading to fewer codewords compared to conventional narrow-band codebooks. Utilizing such a codebook, a three-stage beam training mechanism is performed to obtain the optimal codeword with low time overhead, thereby facilitating the beamforming. Simulation results demonstrate that the proposed scheme outperforms existing codebook-based beam training schemes in terms of training overhead and sum rate in the hybrid near-far field.

关键词： Training Upper bound Array signal processing OFDM Simulation Reconfigurable intelligent surfaces Broadband antennas Wideband Faces Channel state information

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Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular networks with Double Dynamics

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IEEE Transactions on communications 2025年

作者： Yang, Zonghui Gao, Shijian Cheng, Xiang Yang, Liuqing Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Internet of Things Thrust Guangzhou511400 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

Integrated sensing and communication (ISAC) technology is vital for vehicular networks, yet the time-varying communication channels and rapid movement of targets present significant challenges for real-time precoding design. Traditional optimization-based methods are computationally complex and strongly depend on perfect prior information, which is often unavailable in double-dynamic scenarios. In this paper, we propose a synesthesia of machine (SoM)-enhanced precoding paradigm that leverages modalities such as positioning and initial channel information to adapt to these dynamics. Utilizing a deep reinforcement learning (DRL) framework, our approach pushes ISAC performance boundaries. We also introduce a parameter-shared actor-critic architecture to accelerate training in complex state and action spaces. Extensive experiments validate the superiority of our method over existing approaches. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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Hybrid Near-field and Far-field Localization with Holographic MIMO

arXiv

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arXiv 2025年

作者： Cao, Mengyuan Zhang, Haobo Eldar, Yonina C. Zhang, Hongliang State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China Department of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

Due to its ability to precisely control wireless beams, holographic multiple-input multiple-output (HMIMO) is expected to be a promising solution to achieve high-accuracy localization. However, as the scale of HMIMO increases to improve beam control capability, the corresponding near-field (NF) region expands, indicating that users may exist in both NF and far-field (FF) regions with different electromagnetic transmission characteristics. As a result, existing methods for pure NF or FF localization are no longer applicable. We consider a hybrid NF and FF localization scenario in this paper, where a base station (BS) locates multiple users in both NF and FF regions with the aid of a reconfigurable intelligent surface (RIS), which is a low-cost implementation of HMIMO. In such a scenario, it is difficult to locate the users and optimize the RIS phase shifts because whether the location of the user is in the NF or FF region is unknown, and the channels of different users are coupled. To tackle this challenge, we propose a RIS-enabled localization method that searches the users in both NF and FF regions and tackles the coupling issue by jointly estimating all user locations. We derive the localization error bound by considering the channel coupling and propose an RIS phase shift optimization algorithm that minimizes the derived bound. Simulations show the effectiveness of the proposed method and demonstrate the performance gain compared to pure NF and FF techniques. Copyright © 2025, The Authors. All rights reserved.

关键词： communication channels (information theory)

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WiFi-Diffusion: Achieving Fine-Grained WiFi Radio Map Estimation with Ultra-Low Sampling Rate by Diffusion Models

arXiv

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arXiv 2025年

作者： Liu, Zhiyuan Zhang, Shuhang Liu, Qingyu Zhang, Hongliang Song, Lingyang School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China Pengcheng Laboratory Shenzhen518055 China Department of Electronics Peking University Beijing100871 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China

Fine-grained radio map presents communication parameters of interest, e.g., received signal strength, at every point across a large geographical region. It can be leveraged to improve the efficiency of spectrum utilization for a large area, particularly critical for the unlicensed WiFi spectrum. The problem of fine-grained radio map estimation is to utilize radio samples collected by sparsely distributed sensors to infer the map. This problem is challenging due to the ultra-low sampling rate, where the number of available samples is far less than the fine-grained resolution required for radio map estimation. We propose WiFi-Diffusion – a novel generative framework for achieving fine-grained WiFi radio map estimation using diffusion models. WiFi-Diffusion employs the creative power of generative AI to address the ultra-low sampling rate challenge and consists of three blocks: 1) a boost block, using prior information such as the layout of obstacles to optimize the diffusion model;2) a generation block, leveraging the diffusion model to generate a candidate set of radio maps;and 3) an election block, utilizing the radio propagation model as a guide to find the best radio map from the candidate set. Extensive simulations demonstrate that 1) the fine-grained radio map generated by WiFi-Diffusion is ten times better than those produced by state-of-the-art (SOTA) when they use the same ultra-low sampling rate;and 2) WiFi-Diffusion achieves comparable fine-grained radio map quality with only one-fifth of the sampling rate required by SOTA. Copyright © 2025, The Authors. All rights reserved.

关键词： Wi-Fi

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LLM4WM: Adapting LLM for Wireless Multi-Tasking

arXiv

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arXiv 2025年

作者： Liu, Xuanyu Gao, Shijian Liu, Boxun Cheng, Xiang Yang, Liuqing State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Guangzhou511400 China Guangzhou511400 China Department of Electronic and Computer Engineering The Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong

The wireless channel is fundamental to communication, encompassing numerous tasks collectively referred to as channel-associated tasks. These tasks can leverage joint learning based on channel characteristics to share representations and enhance system design. To capitalize on this advantage, LLM4WM is proposed—a large language model (LLM) multi-task fine-tuning framework specifically tailored for channel-associated tasks. This framework utilizes a Mixture of Experts with Low-Rank Adaptation (MoE-LoRA) approach for multi-task fine-tuning, enabling the transfer of the pre-trained LLM’s general knowledge to these tasks. Given the unique characteristics of wireless channel data, preprocessing modules, adapter modules, and multi-task output layers are designed to align the channel data with the LLM’s semantic feature space. Experiments on a channel-associated multi-task dataset demonstrate that LLM4WM outperforms existing methodologies in both full-sample and few-shot evaluations, owing to its robust multi-task joint modeling and transfer learning capabilities. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Lightweight Task-Oriented Semantic communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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Manipulating the emission spectrum of active materials by using photonic-crystal microcavity

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Journal of Optics (United Kingdom) 2025年第5期27卷

作者： Khushik, Muhammad Hanif Ahmed Khan Bano, Shahzadi Jiang, Chun School of Electronic and Communication Engineering Quanzhou University of Information Engineering Quanzhou362000 China Department of Electronics Engineering Technology Benazir Bhutto Shaheed University of Technology and Skill Development Khairpur Mirs Sindh66020 Pakistan School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai200240 China

Tailoring the emission spectra of active ions has received significant attention from researchers in the last few decades due to its applicability in narrow-line width and low-threshold lasers as well as in broadband photonic devices. One- to three-dimensional photonic crystals, photonic crystal microcavities, metamaterials, and plasmonic materials have been used to manipulate the emission spectra of optically active materials. In this paper, one-dimensional microcavities are used to provide a dielectric environment for the manipulation of emission spectra of luminescent ions, and finite-difference time-domain -based simulations are carried out to observe the behavior of luminescent ions placed in one-dimensional microcavities. The numerical results show that the emission spectrum of the active material can be tailored as a broader emission spectrum, a narrower emission spectrum, and a spectrum with multiple peaks, depending on the local density of states of the microcavity. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.

关键词： Optical emission spectroscopy

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