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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Wang, Chengyu Wang, Luhan Lu, Zhaoming Zhou, Shuang Wu, Celimuge Wen, Xiangming Shou, Guochu Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China Meta-Networking Research Center The University of Electro-Communications 1-5-1 Chofugaoka Chofu-shi Tokyo Japan

Recently, the autonomous driving technology is at a critical phase evolving from typical, closed scenarios to largescale, open driving scenarios, which is challenged by the diversity and complexity of the driving environment. Reinforcement learning has the potential to address this challenge, but its application has been hindered by safety issues. To address the safety concern, we propose a Lagrangian framework called Knowledge-Enhanced Safe Reinforcement Learning (KESRL), which integrates prior knowledge of model-based methods into the model-free safe RL framework. Firstly, a model-based constraint based on high-order control barrier function is derived to deduce the safety criteria for the control system. Secondly, we propose a model-free constraint which estimates the distribution of accumulated safety cost and restricts the worst-case safety cost value. Furthermore, the above constraints are unified into the proposed Lagrangian framework to equip the end-to-end autonomous driving system with safety constraints. Experimental results conducted in the CARLA simulator demonstrate the effectiveness of the proposed framework KESRL in decreasing the collision rate and enhancing the control safety compared with several state-of-the-art RL methods. IEEE

关键词： Autonomous vehicles

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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

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.

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

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Deterministic Overlay networking for Edge Computing Distributed Cloud in Optical networks

Deterministic Overlay Networking for Edge Computing Distribu...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Mingyang Liu Guochu Shou Junli Xue Mengjie Guo Hongxing Li Yaqiong Liu Yihong Hu School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence

We propose a SD-TSN-based overlay network scheme to realize deterministic transmission for edge computing distributed cloud in optical network. A gateway is implemented to establish data transmission tunnels among clo...

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RIS-Aided Hotspot Capacity Enhancement for Multibeam Satellite systems

RIS-Aided Hotspot Capacity Enhancement for Multibeam Satelli...

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IEEE International Conference on Communications (ICC)

作者： Ziyuan Zheng Wenpeng Jing Zhaoming Lu Xiangming Wen Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

Precoding techniques combined with aggressive full frequency reuse (FFR) are a promising solution to meet the evergrowing capacity demand for multibeam satellite systems (MSSs). However, LOS-dominant satellite-ground propagation environments will cause strong channel correlation among the geographically clustered hotspot users (HUs), and the consequent degradation of spatial multiplexing gain severely restricts multiuser multiple-input multiple-output (MU-MIMO) capacity. In this paper, we propose a reconfigurable intelligent surface (RIS)-aided scheme for MSSs to enhance the hotspot capacity of HUs via RIS-improved spatial-multiplexed transmission. Specifically, we propose to employ a RIS in the MSS's hotspot area formed by HUs for channel decorrelation and then jointly optimize the RIS passive beamforming and satellite precoding to reap the benefits of the spatial multiplexing. In this context, we formulate a novel HUs' sum rate maximization problem, subject to the transmit power constraint, the quality-of-service constraints for non-hotspot users, and the unit-modulus constraint for the RIS. The formulated problem is non-convex, and we propose an efficient iterative algorithm based on quadratic transform, semi-definite relaxation, and alternating optimization methods to solve it. Simulation results show that in our proposed RIS-aided scheme with optimized precoding and passive beamforming, significant sum rate enhancement is achieved for HUs in the MSS, owing to channel reconfigurable capability brought by the RIS.

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Refine Reinforcement Learning for Safety Training of Autonomous Driving

Refine Reinforcement Learning for Safety Training of Autonom...

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International Conference on Intelligent Transportation

作者： Fukun Yang Zhiqun Hu Yuanming Zhang Hao Huang Guixin Wang Beijing Key Laboratory for Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The random exploration nature of reinforcement learning (RL) impedes the way to achieve human-like autonomous driving, owing to the prohibitively high safety requirement. In this paper, we propose a deep refine reinforcement learning (DR2L) approach to removing non-safety-critical actions and reconstructing critical ones, which effectively enhance the efficiency of exploration. The core is to design an action filter based on two-stage vehicle motion model to calculate the critical value of dangerous actions and reconstructe the action space by filtering out the obvious incorrect actions. Besides, we propose to use the beta distribution as the stochastic policy, which eliminates the bias of the Gaussian policy and provides faster convergence. Finally, we design spacial-temporal attention network to extract hidden information of environment as the state to enhance the performance of RL. The simulation shows that DR2L can effectively improve the safety of agent during training process. Our resutls show that the beta policy provides significantly faster convergence over the Gaussian policy when both are used with proximal policy optimization (PPO).

关键词： Training Space vehicles Filtering Stochastic processes Reinforcement learning Safety Autonomous vehicles Optimization Intelligent transportation systems convergence

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Information Bottleneck Guided Joint Source-Channel Coding with HARQ

Information Bottleneck Guided Joint Source-Channel Coding wi...

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IEEE Conference on Wireless Communications and networking

作者： Haoxuan Zhang Lunan Sun Caili Guo Yang Yang Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Deep joint source-channel coding (JSCC) with hybrid automatic repeat request (HARQ) for image transmission has attracted increasing attention due to its flexibility and high efficiency. Existing researches mainly focus on minimizing the distortion of mutiple retransmissions while ignoring the redundancy in retransmitted signal and such redundancy may lead bandwidth waste and reconstruction quality degradation. In this paper, we propose an information bottleneck (IB) guided deep JSCC with HARQ system (HARQ-IBJSC), which aims at improving the reconstruction quality by compressing the redundancy in retransmitted signal. In particular, we first design a new IB objective for deep JSCC with HARQ system, which simultaneously reduces redundancy in the retransmitted signal and minimizes image transmission distortion. Since the mutual information terms in the designed IB objective is intractable, we then derive a differentiable lower bound on the IB objective and use the bound as the loss function of HARQ-IBJSC. Experimental results show that the proposed HARQ-IBJSC system can increase PSNR by up to 1 dB.

关键词： Lower bound Image coding Image communication Redundancy Lead Distortion Encoding Robustness Image reconstruction Mutual information

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Slice-Based Service Function Chain Embedding with Multiple Factors

Slice-Based Service Function Chain Embedding with Multiple F...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Li, Hang Kong, Zixuan Xiang, Wan Wen, Xiangming Lu, Zhaoming Wang, Luhan Chen, Yawen Jing, Wenpeng Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665423908

network slicing is a promising technology to provide various services on a common physical network infrastructure. A network slice is a logical virtual network composed of a group of service function chains (SFCs) and the network operator performs SFC embedding (SFCE) to create and orchestrate network slices. Compared to conventional SFCE, implementing slice-based SFCE (SBSFCE) faces some new challenges like diversified requirements of SFCs, inter-slice and intra-slice VNF sharing, and slice priority-based admission control due to the introduction of slice dimension. In this paper, we study the SBSFCE problem jointly considering the above challenges. We formulate the problem and devise a dynamic programming-based heuristic, SBSEA to solve it. The simulation results demonstrate that SBSEA outperforms the existing solution and achieves near-optimal performance. © 2021 IEEE.

关键词： Heuristic algorithms

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CHRT:Clustering-Based Hybrid Re-Routing system for Traffic Congestion Avoidance

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China Communications 2021年第7期18卷 86-102页

作者： Jie Huo Xiangming Wen Luning Liu Luhan Wang Meiling Li Zhaoming Lu Beijing Laboratory of Advanced Information Networks and Beijing Key Laboratory of Network System Architecture and ConvergenceSchool of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijing 100876China Witcomm Open Source Communication Research Institute Beijing 100088China Taiyuan University of Science and Technology Taiyuan 030024China

Re-routing system has become an important technology to improve traffic *** traditional re-routing schemes do not consider the dynamic characteristics of urban traffic,making the planned routes unable to cope with the changing traf-fic *** on real-time traffic information,it is challenging to dynamically re-route connected vehicles to alleviate traffic ***,how to obtain global traffic information while reducing communication costs and improving travel efficiency poses a challenge to the re-routing *** deal with these challenges,this paper proposes CHRT,a clustering-based hybrid re-routing system for traffic congestion *** develops a multi-layer hybrid *** central server accesses the global view of traffic,and the distributed part is composed of vehicles divided into clusters to reduce latency and communication ***,a clustering-based priority mechanism is proposed,which sets priorities for clusters based on realtime traffic information to avoid secondary ***,to plan the optimal routes for vehicles while alleviating global traffic congestion,this paper presents a multi-metric re-routing *** extensive simulations based on the SUMO traffic simulator,CHRT reduces vehicle traveling time,fuel consumption,and CO2 emissions compared to other *** addition,CHRT globally alleviates traffic congestion and improves traffic efficiency.

关键词： traffic congestion dynamic re-routing intelligent transportation system(ITS) real-time traffic information VANET

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

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IEEE Transactions on Mobile Computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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RIS-Assisted Coverage Extension for LEO Satellite Communication in Blockage Scenarios

RIS-Assisted Coverage Extension for LEO Satellite Communicat...

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

作者： Ning Cao Ziyuan Zheng Wenpeng Jing Zhaoming Lu Xiangming Wen Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

Low Earth Orbit (LEO) satellite communication (SatCom) is considered a promising solution to supplement terrestrial networks. However, line-of-sight (LoS) links between the satellite and terrestrial terminals are possibly blocked by objects on Earth (e.g., mountains, tall buildings, etc.), leading to communication interruptions. This paper introduces Reconfigurable Intelligent Surface (RIS) into the LEO SatCom system to extend satellite coverage in blockage scenarios. Specifically, in contrast to the signal transmission model under the far-field assumption in usual studies, we present a model combining the near-field and far-field of the RIS, which can characterize the operational differences of each reflecting element more accurately. Next, a user-received power maximization problem is formulated and we solve it by jointly optimizing the RIS phase shifts and orientation. Simulation results demonstrate the effectiveness of the proposed optimization algorithm. Remarkably, it is shown that the scheme based on the proposed algorithm can assist the satellite in extending its coverage and providing higher power than that before blockage occurs compared to the baseline schemes.

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