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Trustworthy DNN partition for blockchain-enabled digital twin in wireless IIoT networks

Science China(Information Sciences) 2024年第11期67卷 373-374页

作者： Xiumei DENG Jun LI Long SHI Kang WEI Ming DING Yumeng SHAO Wen CHEN Shi JIN Pillar of Information Systems Technology and Design Singapore University of Technology and Design National Mobile Communications Research Laboratory Southeast University School of Electronic and Optical Engineering Nanjing University of Science and Technology Department of Computing Hong Kong Polytechnic University Data61 CSIRO Department of Electronics Engineering Shanghai Jiao Tong University

The integration of artificial intelligence(AI) and digital twin(DT) technology has revolutionized the industrial Internet of Things(IIoT), enabling advanced automation and intelligent manufacturing [1]. Through sophisticated interactions between physical entities and their virtual counterparts,AI-driven DTs facilitate performance monitoring, analysis,simulation, and optimization of physical assets, enabling predictive maintenance and informed decision-making [2].

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Toward Extra Large-Scale MIMO: New Channel Properties and Low-Cost designs

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IEEE Internet of Things Journal 2023年第16期10卷 14569-14594页

作者： Han, Yu Jin, Shi Matthaiou, Michail Quek, Tony Q. S. Wen, Chao-Kai Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Queen's University Belfast Centre for Wireless Innovation BelfastBT3 9DT United Kingdom Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines487372 Singapore Kyung Hee University Department of Electronic Engineering Yongin17104 Korea Republic of National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan

Extra large-scale multiple-input-multiple-output (MIMO) has been recognized as one of the potential development directions of massive MIMO. By employing even more antennas than massive MIMO in the fifth-generation era, extra large-scale MIMO can further exploit the spatial domain resources and enable ultra-high data rates, low latency communications as well as emerging applications, such as sensing and localization, in sixth-generation mobile communication systems. However, with the increase of the size of the antenna array, and the decrease of the distance between a user and the array, new channel properties, that did not manifest in conventional massive MIMO, start to kick in. Most importantly, existing research strategies pertaining to massive MIMO cannot be directly applied or simply extended to fit the extra large-scale MIMO case. Moreover, increasing the number of antennas will inevitably boost the total cost, which refers to not only the high hardware cost, but also the burden of vast processing and computations as well as the substantial training overhead. In this article, we make a survey on the state-of-the-art on the new channel properties of and low-cost designs for extra large-scale MIMO systems. Particularly, we pursue a mathematical analysis to explain why the new features appear and illustrate how they affect the system model. Furthermore, we summarize and compare the low-cost designs from various perspectives and give our suggestions from a practical deployment point of view. © 2014 IEEE.

关键词： Antenna arrays

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Hierarchical Intelligence Enabled Joint RAN Slicing and MAC Scheduling for SLA Guarantee

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IEEE Transactions on Communications 2024年

作者： Jia, Yi Zhang, Cheng Li, Nan Huang, Yongming Quek, Tony Q. S. Southeast University National Mobile Communication Research Laboratory Frontiers Science Center for Mobile Information Communication and Security School of Infomation Science and Engineering Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Department of Wireless and Device Technology Research China Mobile Research Institute Beijing China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore

As a key technology in beyond 5G and future 6G communications, RAN slicing can realize differentiated service level agreement (SLA) guarantees. In this paper, we investigate the multi-slice multi-user RAN slicing. A dynamic bandwidth allocation scheme for RAN slicing is proposed based on hierarchical intelligence, where bandwidth pre-allocation and hyper-parameter tuning for MAC layer schedulers are jointly optimized to maximize the system utility, i.e., the weighted sum of spectrum efficiency (SE) and SLA satisfaction ratio (SSR) of different slices. The problem is formulated as a twin-time scale Markov decision process (MDP), where the bandwidth pre-allocation and the scheduler parameter tuning are performed on a long-term scale (e.g., seconds) and a short-term scale (e.g., 100 ms), respectively, for which we propose a hierarchical twin-time scale Dueling deep Q learning network (TTS-DDQN) algorithm. A new reward-clipping mechanism is proposed to get a better trade-off between stabilized training and higher system utility. In order to improve the robustness to time-varying traffic patterns and non-stationary dynamic environments, we further propose a traffic-aware module for more efficient sampling of the experience pool, and a variational adversarial inverse reinforcement learning (VAIRL) module for reward automation design. Extensive simulations show that the traffic-aware TTS-DDQN in stationary scenarios and the VAIRL module embedded TTS-DDQN in non-stationary scenarios outperform existing typical DQN-based algorithms, hard slicing and non-slicing, etc. © 1972-2012 IEEE.

关键词： Bandwidth

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Service Anycast Forwarding for Software Defined Computing Power Network

Service Anycast Forwarding for Software Defined Computing Po...

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IEEE Conference on Wireless Communications and Networking

作者： Wenzheng Wang Renchao Xie Qinqin Tang Tao Huang Tianjiao Chen Zehui Xiong State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China China Mobile Research Institute Pillar of Information Systems Technology and Design Singapore University of Technology and Design

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With the rise of the computing power network (CPN), which integrate edge computing, cloud computing, and network infrastructure, replicated computing services are increasingly distributed to meet user demands for location-independent, reliable, and low latency services. Service anycast forwarding coordinates distributed service instances by binding them to a unified identifier and dynamically routing requests to the optimal instance. However, challenges such as varying user demand distribution, network complexity, and service instance heterogeneity complicate balanced service forwarding. To address these, we propose an SDN-based service anycast forwarding mechanism for CPN (SA-CPN). In the data plane, a cyclic forwarding queue efficiently maps weighted strategies and selects instances for each service request, improving policy performance. In the control plane, an optimal transport model balances network and computation latency based on service instance capabilities. We further design an optimal transport-based service anycast forwarding algorithm (OTSAF) using Sinkhorn iterations. Our implementation of SA-CPN in a real system shows that OTSAF consistently outperforms four baseline methods across various performance metrics.

关键词： Computational modeling Software algorithms Distribution networks Routing Software Complexity theory Reliability Software defined networking Low latency communication Edge computing

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CaRCS: Joint Optimization of Computing-aware Routing and Collaborative Scheduling in Computing Power Networks

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IEEE Network 2025年

作者： Feng, Li Xie, Renchao Tang, Qinqin Huang, Tao Xiong, Zehui Chen, Tianjiao Zhang, Ran Tan, Sha Fang, Zeru Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Purple Mountain Laboratories Nanjing211111 China Singapore University of Technology and Design Singapore Pillar of Information Systems Technology and Design 639798 Singapore China Mobile Research Institute Beijing100053 China

With the rapid advancement of technology, intelligent applications increasingly require higher computing power, driving the evolution of the end-edge-cloud Collaborative Scheduling (CS) paradigm. However, this paradigm primarily focuses on vertical collaboration, neglecting horizontal coordination among ubiquitous heterogeneous computing resources, leading to imbalanced resource utilization. Fortunately, the Computing Power Network (CPN) has been introduced to facilitate ubiquitous resource coordination through pervasive networks. The core issue in CPN is the CS of network-wide computing tasks, yet most existing research has largely concentrated on offloading decisions while critical aspects of network routing remain underexplored. Therefore, this paper studies the optimization of joint routing and scheduling within large-scale CPNs. Initially, we develop a CS framework that accommodates the return of task computing results. We then incorporate computing information into the routing domain and introduce a CS mechanism based on Compute-aware Routing (CaR), which narrows the scope of routing exchanges and collaborative scheduling. Ultimately, we present a Deep Reinforcement Learning (DRL)-based algorithm to optimize the long-term CS problems based on balanced CaR, enhancing the success rate of computing tasks and ensuring balanced utilization of computing and network resources. The effectiveness of our proposed mechanism and algorithm is confirmed through simulation experiments. © 2025 IEEE.

关键词： Scheduling algorithms

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Joint Placement and Beamforming design in UAV Enabled Multi-Stage ISAC System

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IEEE Transactions on Communications 2025年

作者： Xu, Linlin Zhu, Qi Xia, Wenchao Wang, Zhongbin Quek, Tony Q. S. Zhu, Hongbo Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Wireless Communications Nanjing210003 China ZTE Corporation State Key Laboratory of Mobile Network and Mobile Multimedia Technology Wireless Computing Research Institute Shenzhen518055 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore Yonsei University Yonsei Frontier Laboratory Seoul03722 Korea Republic of

In this paper, we propose an unmanned aerial vehicle (UAV) enabled multi-stage integrated sensing and communications (ISAC) system, where a multi-antenna equipped UAV performs location sensing for a target whose location is initially unknown, while serves the communication users simultaneously, with the aid of an existing receive access point (RAP). By fusing the measurement results of the UAV and RAP, the location of the target is estimated. To improve the location sensing accuracy, we propose a multi-stage location sensing scheme. Specifically, in the first stage, in the absence of prior knowledge about the target’s location, the UAV fixes at the initial location and adjusts the beamformer to perform wide beam sensing to probe the target. In the following stages, with the previous coarse estimation result of the target’s location, the UAV performs narrow beam sensing by jointly adjusting the placement and also transmit beamformer. Besides, the quality of service requirements of the users are guaranteed in all stages. Accordingly, optimization problems are formulated for the first and following stages, respectively. By involving the semidefinite relaxation technique and then solving a quadratic semidefinite programming problem, the solution in the first stage is obtained. In the following stages, we jointly apply the alternating optimization, successive convex approximation, trust region, and also Dinkelbach’s methods to address the intricate coupling between the UAV placement and beamformer. Finally, numerical results demonstrate the effectiveness of the proposed algorithms. © 1972-2012 IEEE.

关键词： Beamforming

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Physical Layer Security for UAV Communications:A Comprehensive Survey

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China Communications 2022年第9期19卷 77-115页

作者： Jue Wang Xuanxuan Wang Ruifeng Gao Chengleyang Lei Wei Feng Ning Ge Shi Jin Tony Q.S.Quek School of Information Science and Technology Nantong UniversityNantong 226109China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China China Academy of Electronics and Information Technology Beijing 100041China School of Transportation and Civil Engineering Nantong UniversityNantong 226109China National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China Information Systems Technology and Design Pillar Singapore University of Technology and DesignSingapore 487372Singapore Nantong Research Institute for Advanced Communication Technologies Nantong 226109China

Due to its high mobility and flexible deployment,unmanned aerial vehicle(UAV)is drawing unprecedented interest in both military and civil applications to enable agile and ubiquitous *** operating in an open environment,UAV communications benefit from dominant line-of-sight links;however,this on the other hand renders the communications more vulnerable to malicious ***,physical layer security(PLS)has been introduced to UAV systems as an important complement to the conventional cryptography-based *** this paper,a comprehensive survey on the current achievements of UAV-PLS is *** first introduce the basic concepts including typical static/-mobile UAV deployment scenarios,the unique air-toground channel and aerial nodes distribution models,as well as various roles that a UAV may act when PLS is ***,we start by reviewing the secrecy performance analysis and enhancing techniques for statically deployed UAV systems,and extend the discussion to the more general scenario where the UAVs’mobility is further *** both cases,respectively,we summarize the commonly adopted methodologies,then describe important works in the litera ture in ***,potential research directions and challenges are discussed to provide an outlook for future works in the area of UAV-PLS.

关键词： physical layer security UAV communications static/mobile UAV deployment air-to-ground channel trajectory optimization

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Joint design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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Robust design of IRS-Aided Multi-Group Multicast System With Imperfect CSI

Robust Design of IRS-Aided Multi-Group Multicast System With...

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作者： Jiang, Weiheng Xiong, Peiyun Nie, Jiangtian Ding, Zhiguo Pan, Cunhua Xiong, Zehui Chongqing University School of Microelectronics and Communication Engineering Chongqing400044 China Nanyang Technological University School of Computer Science and Engineering Jurong West639798 Singapore Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates The University of Manchester Department of Electrical and Electronic Engineering ManchesterM13 9PL United Kingdom Southeast University National Mobile Communications Research Laboratory Nanjing211189 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Tampines487372 Singapore

In this paper, the robust design for the intelligent reflective surface (IRS) assisted wireless multi-group multicast system is considered, in which two optimization design problems under two different channel state information (CSI) error models are separately discussed, i.e., the fairness-based problems and the quality-of-service (QoS)-based problems for both the bounded CSI error model and the statistical CSI error model. In order to deal with the non-convex constraints of the considered problems, i.e., bounded CSI error based constraint and statistical CSI error based constraint, S-procedure is adopted to convert the non-convex SINR constraint with bounded CSI error into linear matrix inequalities (LMIs), and the Bernstein-type inequality is utilized to transform the outage probability constraint with statistical CSI error into a second-order cone (SOC) constraint and linear inequalities. Following that, two efficient algorithms based on alternate optimization (AO) are proposed to solve the fairness problems and QoS problems, wherein the semi-definite programming (SDP), penalty convex-concave procedure (CCP) and semi-definite relaxation (SDR) are utilized. Furthermore, we analyze the complexity of the proposed algorithms. Finally, some numerical simulation results are presented to verify the effectiveness of the proposed algorithms, and the impacts of the CSI error and the discrete precision of IRS reflection phase shift on the system performance are analyzed, which provides some insights for the IRS deployment and system robust design. © 2002-2012 IEEE.

关键词： Beamforming

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Federated Learning over Coupled Graphs

arXiv

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arXiv 2023年

作者： Lei, Runze Wang, Pinghui Zhao, Junzhou Lan, Lin Tao, Jing Deng, Chao Feng, Junlan Wang, Xidian Guan, Xiaohong The MOE Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University P.O. Box 1088 No. 28 Xianning West Road Shaanxi Xi’an710049 China China Mobile Research Institute China China Mobile Group Design Institute China The Center for Intelligent and Networked Systems Tsinghua National Lab for Information Science and Technology Tsinghua University Beijing100084 China

Graphs are widely used to represent the relations among entities. When one owns the complete data, an entire graph can be easily built, therefore performing analysis on the graph is straightforward. However, in many scenarios, it is impractical to centralize the data due to data privacy concerns. An organization or party only keeps a part of the whole graph data, i.e., graph data is isolated from different parties. Recently, Federated Learning (FL) has been proposed to solve the data isolation issue, mainly for Euclidean data. It is still a challenge to apply FL on graph data because graphs contain topological information which is notorious for its non-IID nature and is hard to partition. In this work, we propose a novel FL framework for graph data, FedCog, to efficiently handle coupled graphs that are a kind of distributed graph data, but widely exist in a variety of real-world applications such as mobile carriers’ communication networks and banks’ transaction networks. We theoretically prove the correctness and security of FedCog. Experimental results demonstrate that our method FedCog significantly outperforms traditional FL methods on graphs. Remarkably, our FedCog improves the accuracy of node classification tasks by up to 14.7%. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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