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2025 IEEE Wireless Communications and networking Conference, WCNC 2025

作者： Li, Hongxing Li, Haotian Gao, Qianhan Shou, Guochu Liu, Yaqiong Guo, Zhigang Hu, Yihong State Key Laboratory of Networking and Switching Technology China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing China

ISBN: (纸本)9798350368369

The demand for precise timing continues to grow with the rapid development of smart cities, where intelligent transportation emerges as a critical application heavily dependent on accurate time distribution. Deterministic communication is anticipated to be a defining feature of nextgeneration mobile networks (6G), enabling end-to-end timecritical applications, and unlocking new possibilities for achieving end-to-end precise time synchronization as well. To cope with the challenge of delivering precise time synchronization across broader areas at a lower cost, this paper proposes a method to achieve end-to-end precise time synchronization by leveraging the deterministic characteristics of communication networks. Specially, the proposed approach adopts the Software-Defined networking (SDN) paradigm to implement deterministic networking for the seamless integration of Time-Sensitive networking (TSN) and cellular networks. Furthermore, to mitigate the impact of wireless channel uncertainties on synchronization accuracy, we propose a network delay measurement mechanism based on the principle of time redundancy, designed to reduce end-to-end network delay variation. Experimental results validate the effectiveness of the proposed method, demonstrating its capability to constrain the end-to-end relative time error of wired and wireless converged networks to the microsecond scale, without necessitating modifications to the existing network infrastructure. © 2025 IEEE.

关键词： Mobile telecommunication systems

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Demonstration of Time Redundancy Mechanism for Deterministic Communications over Hybrid Networks

Demonstration of Time Redundancy Mechanism for Deterministic...

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OptoElectronics and Communications Conference, OECC

作者： Hongxing Li Guochu Shou State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

We propose a time redundancy mechanism to enhance the determinism of 5G TSN hybrid networks, and experimentally demonstrate the most cost-effective enhancement occurs when frame is replicated four times with 42% maxim... 详细信息

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

关键词： 5G mobile communication Redundancy Jitter

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Digital Twin-Empowered Task Assignment in Aerial MEC Network: A Resource Coalition Cooperation Approach with Generative Model

arXiv

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arXiv 2024年

作者： Tang, Xin Chen, Qian Yu, Rong Li, Xiaohuan Guangxi University Key Laboratory of Intelligent Networking and Scenario System School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Nanning530001 China School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guilin541004 China School of Automation Guangdong University of Technology Guangzhou510006 China

To meet the demands for ubiquitous communication and temporary edge computing in 6G networks, aerial mobile edge computing (MEC) networks have been envisioned as a new paradigm. However, dynamic user requests pose challenges for task assignment strategies. Most of the existing research assumes that the strategy is deployed on ground base station (GBS) or unmanned aerial vehicle (UAV), which will be ineffective in an environment lacking infrastructure and continuous energy supply. Moreover, the resource mutual exclusion problem of dynamic task assignment has not been effectively solved. Toward this end, we introduce the digital twin (DT) into the aerial MEC network to study the resource coalition cooperation approach with the generative model (GM), which provides a preliminary coalition structure for the coalition game. Specifically, we propose a novel network framework that is composed of an application plane, a physical plane, and a virtual plane. After that, the task assignment problem is simplified to convex optimization programming with linear constraints. And then, we also propose a resource coalition cooperation approach that is based on a transferable utility (TU) coalition game to obtain an approximate optimal solution. Extensive analysis and numerical results confirm the effectiveness of our proposed approach in terms of energy consumption and utilization of resources. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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Towards Expanding Precise Timing for Collaborative Its with Deterministic Communications in 6G

Towards Expanding Precise Timing for Collaborative Its with ...

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IEEE Conference on Wireless Communications and networking

作者： Hongxing Li Haotian Li Qianhan Gao Guochu Shou Yaqiong Liu Zhigang Guo Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

关键词： 6G mobile communication Wireless communication Cellular networks Uncertainty Accuracy Measurement uncertainty Transportation Delays Synchronization Software defined networking

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Precise Timing over Beyond 5G Networks for Intelligent Transport in the Smart City

Precise Timing over Beyond 5G Networks for Intelligent Trans...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Hongxing Li Guochu Shou Yaqiong Liu Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

The need for precise timing is increasing with the development of the smart city, and intelligent transport is a key application that often relies on the distribution of accurate timing. Existing time synchronization solutions cannot adequately meet the precise timing needs for practical applications in intelligent transportation. Global Navigation Satellite systems (GNSS) signals, for instance, can be obstructed, weakened, or deflected in urban canyon environments. While certain sectors like telecommunications and smart grids have successfully adopted IEEE 1588 for high-precision time synchronization, its dependence on standalone networks and limited application within local area networks poses challenges when catering to the wide-ranging and mobile demands of intelligent transportation applications. This paper categorizes the time synchronization requirements of intelligent transport and proposes a time synchronization scheme over converged networks, which separates time transmission and precision compensation based on software-defined networking (SDN) principles, and makes it possible to provide precise timing for intelligent transport devices, including on-board units (OBUs) and roadside units (RSUs), over wired and wireless communication networks. Experimental results indicate that the proposed scheme achieves a relative time error of less than 1.2 microseconds over the converged networks.

关键词： Wireless communication Global navigation satellite system Accuracy Smart cities Transportation Telecommunications Smart grids Synchronization Software defined networking Local area networks

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DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach

arXiv

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arXiv 2024年

作者： Tang, Xin Chen, Qian Weng, Wenjie Liao, Binhan Wang, Jiacheng Cao, Xianbin Li, Xiaohuan Guangxi University Key Laboratory of Intelligent Networking and Scenario System School of Information and Communication Guilin University of Electronic Technology Guilin541004 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guilin541004 China National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Guangxi Nanning530001 China College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Electronic and Information Engineering The Key Laboratory of Advanced Technology of Near Space Information System Ministry of Industry and Information Technology of China Beihang University Beijing100191 China

Unmanned Aerial Vehicles (UAVs) possess high mobility and flexible deployment capabilities, prompting the development of UAVs for various application scenarios within the Internet of Things (IoT). The unique capabilities of UAVs give rise to increasingly critical and complex tasks in uncertain and potentially harsh environments. The substantial amount of data generated from these applications necessitates processing and analysis through deep neural networks (DNNs). However, UAVs encounter challenges due to their limited computing resources when managing DNN models. This paper presents a joint approach that combines multiple-agent reinforcement learning (MARL) and generative diffusion models (GDM) for assigning DNN tasks to a UAV swarm, aimed at reducing latency from task capture to result output. To address these challenges, we first consider the task size of the target area to be inspected and the shortest flying path as optimization constraints, employing a greedy algorithm to resolve the subproblem with a focus on minimizing the UAV’s flying path and the overall system cost. In the second stage, we introduce a novel DNN task assignment algorithm, termed GDM-MADDPG, which utilizes the reverse denoising process of GDM to replace the actor network in multi-agent deep deterministic policy gradient (MADDPG). This approach generates specific DNN task assignment actions based on agents’ observations in a dynamic environment. Simulation results indicate that our algorithm performs favorably compared to benchmarks in terms of path planning, Age of Information (AoI), energy consumption, and task load balancing. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

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DQ-RM: Deep Reinforcement Learning-based Route Mutation Scheme for Multimedia Services 16

DQ-RM: Deep Reinforcement Learning-based Route Mutation Sche...

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16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020

作者： Zhang, Tao Xu, Changqiao Zhang, Bingchi Kuang, Xiaohui Wang, Yue Yang, Shujie Muntean, Gabriel-Miro State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of International Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Science and Technology on Information System Security Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Performance Engineering Lab. School of Electronic Engineering Dublin City University Dublin Ireland

ISBN: (纸本)9781728131290

Increasingly growing various multimedia services (e.g., interactive live video and so on) have brought tremendous pressure on existing static defense techniques. To cope with inherent drawback of static defense techniques, Network Moving Target Defense (NMTD) such as route mutation (RM) was proposed. What's more, applying reinforcement learning (RL) into RM has been proved feasible in our previous work. But two main problems still need to be considered in this combination of RL with RM: 1) It lacks the consideration of multiple flows situation. 2) With the state-action space grow larger, current solution can't handle efficiently. In this paper, we propose a deep Q-learning method for RM (DQ-RM) to solve above two problems. Firstly, benefited from the satisfiability module theory, we formalize RM space considering single flow and multiple flows concurrently. Then we further propose a deep reinforcement learning-based RM scheme based on our previous work, which is suitable for large-scale state-action space. Finally, extensive experimental results highlight the improvement of DQ-RM in defense performance and convergence speed compared to the representative solution. © 2020 IEEE.

关键词： Reinforcement learning

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DQ-RM: Deep Reinforcement Learning-based Route Mutation Scheme for Multimedia Services

DQ-RM: Deep Reinforcement Learning-based Route Mutation Sche...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Tao Zhang Changqiao Xu Bingchi Zhang Xiaohui Kuang Yue Wang Shujie Yang Gabriel-Miro Muntean State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of International Beijing University of Posts and Telecommunications Beijing China State key Laboratory of Science and Technology on Information System Security Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Performance Engineering Lab. School of Electronic Engineering Dublin City University Dublin Ireland

ISBN: (数字)9781728131290

ISBN: (纸本)9781728131306

关键词： Routing Streaming media Machine learning Frequency modulation Computer crime Convergence Quality of service

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SDN-based optimization model of virtual machine live migration over layer 2 networks 2nd

SDN-based optimization model of virtual machine live migrati...

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2nd International Conference on Computer, Communication and Computational Sciences, IC4S 2017

作者： Cui, Hongyan Zhang, Bo Chen, Yuyang Yu, Tao Xia, Zongguo Liu, Yunjie State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Beijing Laboratory of Advanced Information Networks Beijing China Key Laboratory of Network System Architecture and Convergence Beijing China Institute of Network Science and Cyberspace Tsinghua University Tsinghua China School Official University of Massachusetts Boston Boston United States

ISBN: (纸本)9789811303432

Virtualization can provide significant benefits for service user by cutting the cost as well as enabling easy access. However, because the MAC table capacity of each host is limited, the traditional layer 2 networks have been unable to meet the needs of the numbers of virtual machines in the center. VXLAN has become an important layer 2 over layer 3 network transmission tunneling technology, a good solution to the data center internal virtual machine isolation and cross-data center transmission problems, but VXLAN does not propose a complete solution for virtual machine migration in the data center. Therefore, we design and implement the network model based on SDN, which optimizes the current VXLAN technology. This model separates the network plane from migration control plane, which can simplify the process of VXLAN and the complex network in data center. Moreover, we propose a detailed process for virtual machine migration supported by OpenFlow protocol. Some design in our model can really reduce the complexity of VXLAN technology and make it convenient for the users of virtual machine. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Virtual machine

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Near‐Infrared Emission from Tin–Lead (Sn–Pb) Alloyed Perovskite Quantum Dots by Sodium Doping

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Angewandte Chemie 2020年第22期132卷

作者： Feng Liu Junke Jiang Yaohong Zhang Chao Ding Taro Toyoda Shuzi Hayase Ruixiang Wang Shuxia Tao Qing Shen Faculty of Informatics and Engineering The University of Electro-Communications 1-5-1 Chofugaoka Tokyo 182-8585 Japan Department of Applied Physics Eindhoven University of Technology Eindhoven 5600 MB The Netherlands Info-Powered Energy System Research Center The University of Electro-Communications 1-5-1 Chofugaoka Tokyo 182-8585 Japan Beijing Engineering Research Centre of Sustainable Energy and Buildings Beijing University of Civil Engineering and Architecture 15 Yongyuan Road Beijing 102616 China

Phase‐stable CsSn x Pb 1− x I 3 perovskite quantum dots (QDs) hold great promise for optoelectronic applications owing to their strong response in the near‐infrared region. Unfortunately, optimal utilization of their potential is limited by the severe photoluminescence (PL) quenching, leading to extremely low quantum yields (QYs) of approximately 0.3 %. The ultra‐low sodium (Na) doping presented herein is found to be effective in improving PL QYs of these alloyed QDs without alerting their favourable electronic structure. X‐ray photoelectron spectroscopy (XPS) studies suggest the formation of a stronger chemical interaction between I − and Sn 2+ ions upon Na doping, which potentially helps to stabilize Sn 2+ and suppresses the formation of I vacancy defects. The optimized PL QY of the Na‐doped QDs reaches up to around 28 %, almost two orders of magnitude enhancement compared with the pristine one.

关键词： doping nanoparticles near-infrared emission perovskite quantum dots tin–lead perovskites

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