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arXiv 2022年

作者： Wang, Wen Ni, Wanli Tian, Hui Yang, Zhaohui Huang, Chongwen Wong, Kai-Kit The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China The Department of Electronic and Electrical Engineering University College London London United Kingdom Zhejiang Provincial Key Lab of Information Processing Communication and Networking Zhejiang University Hangzhou China

This paper investigates the use of the reconfigurable dual-functional surface to guarantee the full-space secure transmission in non-orthogonal multiple access (NOMA) networks. In the presence of eavesdroppers, the downlink communication from the base station to the legitimate users is safeguarded by the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), where three practical operating protocols, namely energy splitting (ES), mode selection (MS), and time splitting (TS), are studied. The joint optimization of power allocation, active and passive beamforming is investigated to maximize the secrecy energy efficiency (SEE), taking into account the imperfect channel state information (CSI) of all channels. For ES, by approximating the semi-infinite constraints with the S-procedure and general sign-definiteness, the problem is solved by an alternating optimization framework. Besides, the proposed algorithm is extended to the MS protocol by solving a mixed-integer non-convex problem. While for TS, a two-layer iterative method is proposed. Simulation results show that: 1) The proposed STAR-RIS assisted NOMA networks are able to provide up to 33.6% higher SEE than conventional RIS counterparts;2) TS and ES protocols are generally preferable for low and high power domain, respectively;3) The accuracy of CSI estimation and the bit resolution power consumption are crucial to reap the SEE benefits offered by STAR-RIS. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy efficiency

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Fairness-Aware Incentive Mechanism for Multi-Server Federated Learning in Edge-Enabled Wireless Networks with Differential Privacy

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IEEE Transactions on Mobile Computing 2025年

作者： Yang, Yu Peng, Kai Wang, Shangguang Xu, Xiaolong Xiao, Peiyun Leung, Victor C.M Huaqiao University College of Engineering Quanzhou362021 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanjing University of Information Science and Technology School of Software Nanjing China Nanjing University State Key Lab. for Novel Software Technology Nanjing210093 China Shenzhen University College of the Computer Science and Software Engineering Shenzhen518060 China University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

As a distributed machine learning method, federated learning (FL) can collaboratively train a global model with multiple devices without sharing the original data, thus protecting certain privacy. However, due to the strong heterogeneity of edge nodes (ENs) participating in FL, the quality of data uploaded to the parameter server (PS) varies significantly. Without an appropriate incentive mechanism, low-quality contributors may receive disproportionately high rewards, while high-quality contributors may lack sufficient motivation, leading to inefficient participation and suboptimal global model performance. Consequently, it is critical to develop an effective incentive mechanism to promote fairness for the FL process. To address the issues of existing FL incentive mechanisms lacking privacy protection performance analysis, we propose a fairness-aware incentive mechanism for multi-server FL in edge-enabled wireless differential privacy (DP) networks. Specifically, the wireless channel noise is used to provide DP protection for the local model gradients uploaded by ENs. Next, the interaction between the PSs and ENs is modeled as a Stackelberg game. Furthermore, we solve the Stackelberg game process using backward induction and theoretically propose optimal strategies for both the PSs and ENs. Finally, extensive numerical simulations using real datasets demonstrate the superior performance of our theoretical analysis of the proposed scheme. © 2002-2012 IEEE.

关键词： Federated learning

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A Trajectory Prediction Based Intelligent Handover Control Method in UAV Cellular Networks

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China Communications 2019年第1期16卷 1-14页

作者： Bo Hu Hanzhang Yang Lei Wang Shanzhi Chen State Key Lab of Networking and Switching Technology Beijing University of Posts and Telecommunications State Key Lab of Wireless Mobile Communication China Academy of Telecommunication Technology

The airborne base station(ABS) can provide wireless coverage to the ground in unmanned aerial vehicle(UAV) cellular *** mobile users move among adjacent ABSs,the measurement information reported by a single mobile user is used to trigger the handover *** handover mechanism lacks the consideration of movement state of mobile users and the location relationship between mobile users,which may lead to handover misjudgments and even communication *** this paper,we propose an intelligent handover control method in UAV cellular ***,we introduce a deep learning model to predict the user *** prediction model learns the movement behavior of mobile users from the measurement information and analyzes the positional relations between mobile users such as avoiding collision and accommodating fellow ***,we propose a handover decision method,which can calculate the users' corresponding receiving power based on the predicted location and the characteristic of air-to-ground channel,to make handover decisions ***,we use realistic data sets with thousands of non-linear trajectories to verify the basic functions and performance of our proposed intelligent handover *** simulation results show that the handover success rate of the proposed method is 8% higher than existing methods.

关键词： UAV airborne base station handover control trajectory prediction deep learning

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Deep Reinforcement Learning for Energy-Efficient Beamforming Design in Cell-Free Networks

Deep Reinforcement Learning for Energy-Efficient Beamforming...

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IEEE Wireless Communications and networking Conference Workshops (WCNCW)

作者： Weilai Li Wanli Ni Hui Tian Meihui Hua State Key Lab. of Networking and Switching Technology Beijing Univ. of Posts and Telecommun. Beijing China

Cell-free network is considered as a promising architecture for satisfying more demands of future wireless networks, where distributed access points coordinate with an edge cloud processor to jointly provide service to a smaller number of user equipments in a compact area. In this paper, the problem of uplink beamforming design is investigated for maximizing the long-term energy efficiency (EE) with the aid of deep reinforcement learning (DRL) in the cell-free network. Firstly, based on the minimum mean square error channel estimation and exploiting successive interference cancellation for signal detection, the expression of signal to interference plus noise ratio (SINR) is derived. Secondly, according to the formulation of SINR, we define the long-term EE, which is a function of beam-forming matrix. Thirdly, to address the dynamic beamforming design with continuous state and action space, a DRL-enabled beamforming design is proposed based on deep deterministic policy gradient (DDPG) algorithm by taking the advantage of its double-network architecture. Finally, the results of simulation indicate that the DDPG-based beamforming design is capable of converging to the optimal EE performance. Furthermore, the influence of hyper-parameters on the EE performance of the DDPG-based beamforming design is investigated, and it is demonstrated that an appropriate discount factor and hidden layers size can facilitate the EE performance.

关键词： Array signal processing Heuristic algorithms Conferences Wireless networks Channel estimation Reinforcement learning Energy efficiency

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Multi-Functional RIS: An Integration of Reflection, Amplification, and Energy Harvesting

Multi-Functional RIS: An Integration of Reflection, Amplific...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Wen Wang Wanli Ni Hui Tian Naofal Al-Dhahir State Key Lab. of Networking and Switching Technology Beijing Univ. of Posts and Telecommun. Beijing China Department of Electrical and Computer Engineering The University of Texas at Dallas Richardson TX USA

ISBN: (纸本)9781665435413

This paper proposes a novel concept of multi-functional reconfigurable intelligent surfaces (MF-RISs). Different from conventional single-functional RISs (SF-RISs) that only reflect signals, the proposed MF-RIS simultaneously supports multiple functionalities, namely, reflection, amplification, and energy harvesting. Specifically, by harvesting energy from incident signals, MF-RIS is able to simultaneously reflect and amplify signals without an external power supply, which is beneficial for overcoming the double-fading attenuation in a flexible manner. A new operation protocol of MF-RIS is presented, and then a sum rate (SR) maximization problem is formulated for an MF-RIS aided multi-user network. Next, an efficient iterative algorithm is proposed to solve this non-convex problem. Furthermore, through theoretical analysis, we determine the number of reflection elements required for MF-RISs to outperform self-sustainable RISs. Finally, our numerical results show that: 1) MF-RISs are able to provide up to 81.1% higher SR than the self-sustainable RISs. 2) Unlike the SF-RIS, which prefers to be deployed near the transmitter or receiver, MF-RISs should be deployed closer to the transmitter for better performance.

关键词： Wireless communication Protocols Transmitters Power supplies Receivers Reconfigurable intelligent surfaces Attenuation

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Safeguarding NOMA Networks via Reconfigurable Dual-Functional Surface Under Imperfect CSI

Safeguarding NOMA Networks via Reconfigurable Dual-Functiona...

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作者： Wang, Wen Ni, Wanli Tian, Hui Yang, Zhaohui Huang, Chongwen Wong, Kai-Kit Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Zhejiang University College of Information Science and Electronic Engineering Zhejiang Hangzhou310027 China Zhejiang Provincial Key Lab of Information Processing Hangzhou Zhejiang310007 China Hangzhou Zhejiang310027 China University College London Department of Electronic and Electrical Engineering LondonWC1E 6BT United Kingdom

关键词： Energy efficiency

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A Graph Routing Algorithm Enhancing Wireless Sensor Networks Lifetime

A Graph Routing Algorithm Enhancing Wireless Sensor Networks...

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Space-Air-Ground Computing (SAGC), International Conference on

作者： Wang Yuxin Nie Gaofeng Tian Hui State Key Lab. of Networking and Switching Technology Beijing Univ. of Posts and Telecommun. Beijing China

ISBN: (纸本)9781665400121

Industrial wireless sensor networks provide wireless communication for devices and sensors. With the limited resources and interference and changes of network conditions, reliability is particularly required in industrial environments. Based on centralized network management, graph routing, which uses redundant paths to ensure reliable transmission, has arisen. In order to enhance latency, lifetime and reliability performance metrics of network, this paper proposes a graph routing algorithm Q-learning Graph Routing Lifetime Enhanced (QGRLE). Routing graph is reconstructed periodically and latency, lifetime and reliability metrics in network are optimized dynamically. Power resources are fully utilized with the consideration of residual energy of nodes. Simulation results show the effectiveness of the proposed algorithms in latency and lifetime performance.

关键词： Measurement Wireless communication Wireless sensor networks Q-learning Heuristic algorithms Simulation Routing

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An NFT-based Distributed Auction Mechanism for Multi-Resource Trading in Computing Power Network

An NFT-based Distributed Auction Mechanism for Multi-Resourc...

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International Conference on Computer and Communications (ICCC)

作者： Lu Han Renchao Xie Yuzheng Ren F. Richard Yu Tao Huang State Key Lab. of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing P.R. China Depart. of Systems and Computer Eng. Carleton University Ottawa ON Canada

As a recently proposed network architecture, the computing power network (CPN) combines the ability of end, edge, cloud computing, and transmission network to realize the flexible and efficient scheduling and transaction of ubiquitous multi-resources, such as computing, cache, communications, and intelligent models. However, in CPN, multi-resources are deployed in a distributed manner. So, efficient, reliable and distributed resource transaction solutions are sought. Therefore, in this paper, we propose a non-fungible token (NFT)-based resource transaction scheme for CPN. We use NFT to tokenize and describe multi-resources by metadata to enable applications to network multi-resources efficiently and ensure transaction se-curity. Also, we formulate the trading utility of sellers and buyers and present a trusted trading process. To improve the system efficiency by increasing the matching success rate and simplify the complexity of combinatorial resources matching, we design the decision and pricing policy by distributed double auction mechanism. Simulation results demonstrate the effectiveness of the proposed scheme.

关键词： Processor scheduling Computational modeling Simulation Data security Smart contracts Pricing Metadata

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Task Scheduling for ICN-Based Computing First Network: A Deep Reinforcement Learning Approach

Task Scheduling for ICN-Based Computing First Network: A Dee...

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International Conference on Computer and Communications (ICCC)

作者： Zhuang Zou Renchao Xie Yuzheng Ren F. Richard Yu Tao Huang State Key Lab. of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing P.R. China Depart. of Systems and Computer Eng. Carleton University Ottawa ON Canada

The computing first network (CFN) combines heterogeneous computing force information with network information and improves resource utilization and task execution efficiency through resource perception, service positioning, and task scheduling. However, since the heterogeneous computing force is difficult to express and perceive, and it is distributed on each node of the edge network, it is difficult to schedule tasks uniformly. Therefore, this paper proposes a CFN architecture based on information-centric network (ICN), which uses the naming mechanism of ICN to characterize the computing force and tasks, and the caching mechanism and routing and forwarding mechanism to improve the computing efficiency in the network. Under this network architecture, we study the scheduling problem of multi-user tasks in a period, model it as a multi-objective optimization model, and transform it into a Markov decision process (MDP) to enable control nodes to prioritize tasks and then schedule them to computing nodes for execution, which takes into account the task queue status and node resources. To solve the proposed problem, we use the deep reinforcement learning algorithm to approximate the optimal task scheduling solution. Finally, extensive simulation experiments are conducted to validate the effectiveness and superiority of the proposed scheme.

关键词： Deep learning Schedules Processor scheduling Computational modeling Force Reinforcement learning Transforms

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An Accurate IEEE 802.11 Packet Delay Model for Single-hop UAV Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Wei, Yayun Zhou, Zihao Tang, Jie Zhao, Nan Qin, Xiaoqi Wong, Kai-Kit South China University of Technology School of Electronic and Information Engineering Guangzhou510641 China Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of

An accurate packet delay model for the IEEE 802.11 single-hop UAV networks is proposed. Unlike most existing models that assume a fully connected network, this study considers a more practical UAV network with partial connectivity along with a realistic reception model. A closed-form expression for average packet delay is derived based on Markov chain model. The impact of network size, packet generation rate, and initial contention window size on average packet delay is evaluated. Simulation results validate the accuracy of the proposed analytical model. © 1967-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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