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7th International Conference on Computer and Communications, ICCC 2021

作者： Yu, Haoze Shou, Guochu Zhang, Xiaodong Hu, Ledong Liu, Yaqiong Hu, Yihong Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665409506

Time-sensitive networking featured with precise time synchronization, is significant in modern industrial systems for real-time guarantee. However, some industrial scenarios, such as smart grids and 5G fronthaul networks, have much higher requirements on time synchronization. This paper proposes a method to improve the synchronization performance of TSN with a neural network model. A time error compensation algorithm including timestamp data processing, neural network training, and error compensation is designed. Comparative experiment results show that this algorithm improves the stability and accuracy of time synchronization. © 2021 IEEE.

关键词： Synchronization

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Multi-domain Time Synchronization Model and Performance Evaluation in TSN 7

Multi-domain Time Synchronization Model and Performance Eval...

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7th International Conference on Computer and Communications, ICCC 2021

作者： Hu, Ledong Shou, Guochu Zhang, Xiaodong Liu, Yaqiong Hu, Yihong Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing China

ISBN: (纸本)9781665409506

Time-Sensitive networking (TSN) has precise time synchronization capabilities and can provide reliable time-deterministic services for industrial communications and other fields. IEEE 802.1AS is used as the time synchronization protocol in TSN and has support for multiple gPTP domains, which further expands the application range of TSN and enhances the synchronization capabilities of TSN. Industrial automation and other use cases also have multi-domain time synchronization requirements of TSN, but there is still a lack of research in this field. Therefore, this paper studies the multi-domain synchronization mechanism in TSN and proposes the multi-domain time synchronization model according to actual application requirements. Based on the multi-domain model, this paper analyzes the time errors in multi-domain time synchronization, introduces the SDN paradigm, and proposes a software-defined time compensation method to compensate the time errors centrally. Further, the time synchronization experiment environment is built to analyze synchronization performance. The results verify the effectiveness of the multi-domain time synchronization model, demonstrating that the multi-domain TSN network can provide high-precision time synchronization services. © 2021 IEEE.

关键词： Errors

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Research of Epidemic Dynamics Model Considering Individual Movements and Urban Areas 7

Research of Epidemic Dynamics Model Considering Individual M...

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7th IEEE International Conference on network Intelligence and Digital Content, IC-NIDC 2021

作者： Pan, Boxu Yang, Jie Wang, Yu Wang, Zehao Zhu, Yuangeng Zhang, Zhiqi Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing100876 China Aisino Corporation Beijing100195 China

ISBN: (纸本)9781665405829

With the rapid spread of COVID-19, hundreds of millions of people worldwide have been infected. In order to cope with the epidemic, experts from various countries have carried out a lot of research works. Most of these works chose to use the traditional SEIR model, but the traditional model doesn't consider the individual's movement in the city. Based on the transmission characteristics of COVID-19, this paper optimized the traditional SEIR model by combining the in-depth mining and processed multiple data, such as the real epidemic data published by some official organizations, as well as data with certain credibility obtained from reference papers, journals or newspapers. Compared with the traditional SEIR model, the proposed model takes into account the impact of individuals' movement and the division of urban functional areas. The outcomes can play a certain role in the prediction and analysis of the spread of the epidemic in cities with regular individuals' movements and functions of urban areas. © 2021 IEEE.

关键词： COVID-19

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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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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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