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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhang, Mengxin Qiu, Xiaofeng Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing China

ISBN: (纸本)9781665450515

Deep neural networks (DNNs) are playing an increasingly significant role in the modern world. However, they are weak to adversarial samples that are generated by adding specially crafted perturbations. Most defenses against adversarial samples focused on refining the DNN models, which often sacrifice the performance and computational cost of models on benign samples. In this paper, we propose a manifold distance detection method to distinguish legitimate samples and adversarial samples by measuring the different distances on the manifold. The manifold distance detection method neither modifies the protected models nor requires knowledge of the process for generating adversarial samples. Inspired by the effectiveness of the manifold distance detection, we demonstrated a well-designed orchestrated defense strategy, named Manifold Distance Judge (MDJ), which selects the best image processing method that will effectively expand the manifold distance between legitimate and adversarial samples, and thus, enhances the performance of the following manifold distance detection method. Tests on the ImageNet dataset, the MDJ is effective against the most adversarial samples under white-box, gray-box, and black-box attack scenarios. We show empirically that the orchestration strategy MDJ is significantly better than Feature Squeezing on the recall rate. Meanwhile, MDJ achieves high detection rates against CW attack and DI-FGSM attack. © 2022 IEEE.

关键词： Neural network models

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Positioning and Dynamic Tracking under 3GPP Cellular network on GNURadio Testbed

Positioning and Dynamic Tracking under 3GPP Cellular Network...

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2023 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Chang, Shunyu Lu, Zhaoming Chu, Xinghe Zhang, Di Wang, Luhan Wen, Xiangming Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665494274

This paper addresses high precision 5G positioning and dynamic tracking, which has risen increasingly attention from industry and academia recently. The recent works show that the position of user equipment (UE) could be estimated based on reference signals (e.g. PRS) in 5G on real testbed. However, these works failed to exploit the positional information behind the consecutive nature of users' moving, which could enhance the positioning accuracy by exploiting dynamic tracking architectures. In this paper, we extract the angle of arrival (AoA) and time of arrival (ToA) under 3GPP physical layer protocol, and then introduce a dynamic tracking method to improve the accuracy of AoA and ToA estimation. The performance of the proposed method was evaluated on GNURadio. The results show that the AoA and ToA could be precisely extracted in the real propagation channel on the premise of guaranteeing the communication performance. And in the case of the same bandwidth and number of antennas, compared with traditional base station (BS) tracking method, our dynamic tracking could reduce tracking error by 35.11% during the 115s tracking process. © 2023 IEEE.

关键词： Testbeds

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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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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Liu, Mingyang Shou, Guochu Xue, Junli Guo, Mengjie Li, Hongxing Liu, Yaqiong Hu, Yihong School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Beijing Laboratory of Advanced Information Networks China Beijing Key Laboratory of Network System Architecture and Convergence China

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... 详细信息

ISBN: (纸本)9798350312614

关键词： distributed cloud edge computing software-defined networking time-sensitive networking

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Time Error Bound of Deterministic Transmission in Cross-Domain Time-Sensitive networking

Time Error Bound of Deterministic Transmission in Cross-Doma...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical Communications, IPOC 2024

作者： Ma, Hao Li, Hongxing Chen, Li Zhao, Dong Zhao, Xiaoyu Xia, Kai Shou, Guochu Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China

Based on coupled network theory, a bounded time error model for network determinism is proposed. The limit of traffic scheduling performance affected by time synchronization error in cross-domain time-sensitive networ... 详细信息

ISBN: (纸本)9798350379266

关键词： time synchronization Time-Sensitive networking traffic scheduling

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Adaptive Bitrate Video Semantic Communication over Wireless networks 15

Adaptive Bitrate Video Semantic Communication over Wireless ...

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15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Gong, Wentao Tong, Haonan Wang, Sihua Yang, Zhaohui He, Xinxin Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Zhejiang University College of Information Science and Electronic Engineering Hangzhou China

ISBN: (纸本)9798350324662

This paper investigates the adaptive bitrate (ABR) video semantic communication over wireless networks. In the considered model, video sensing devices must transmit video semantic information to an edge server, to facilitate ubiquitous video sensing services such as road environment monitoring at the edge server in autonomous driving scenario. However, due to the varying wireless network conditions, it is challenging to guarantee both low transmission delay and high semantic accuracy at the same time if devices continuously transmit a fixed bitrate video semantic information. To address this challenge, we develop an adaptive bitrate video semantic communication (ABRVSC) system, in which devices adaptively adjust the bitrate of video semantic information according to network conditions. Specifically, we first define the quality of experience (QoE) for video semantic communication. Subsequently, a swin transformer-based semantic codec is proposed to extract semantic information with considering the influence of QoE. Then, we propose an Actor-Critic based ABR algorithm for the semantic codec to enhance the robustness of the proposed ABRVSC scheme against network variations. Simulation results demonstrate that at low bitrates, the mean intersection over union (MIoU) of the proposed ABRVSC scheme is nearly twice that of the traditional scheme. Moreover, the proposed ABRVSC scheme, which increases the QoE in video semantic communication by 36.57%, exhibits more robustness against network variations compared to both the fixed bitrate schemes and traditional ABR schemes. © 2023 IEEE.

关键词： Actor-Critic adaptive bitrate Semantic communication video semantic segmentation

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TSN Disciplined Oscillators for Time Synchronization in Distributed networks

TSN Disciplined Oscillators for Time Synchronization in Dist...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical Communications, IPOC 2024

作者： Li, Zhifu Shou, Guochu Zhang, Xiaodong Chen, Li Zhao, Dong Zhao, Xiaoyu Xia, Kai Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China

This paper proposes a TSNDO scheme utilizing time-sensitive networking to discipline clocks, providing precise time for distributed networks. The experimental results demonstrate that TSNDO achieves the time accuracy ... 详细信息

ISBN: (纸本)9798350379266

关键词： Clock distribution networks

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Joint Optimization of Latency and Energy Consumption for Mobile Edge Computing Based Proximity Detection in Road networks

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China Communications 2022年第4期19卷 274-290页

作者： Tongyu Zhao Yaqiong Liu Guochu Shou Xinwei Yao School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and ConvergenceBeijing 100876China School of Computer Science and Technology Zhejiang University of TechnologyChina

In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene,which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing(MEC)into a constrained multiobjective optimization problem(CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.

关键词： proximity detection mobile edge computing road networks constrained multiobjective optimization

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Tasks-oriented channel optimization and resource allocation in vehicular networks: a hierarchical reinforcement learning based approach

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IEEE Transactions on Vehicular Technology 2024年第5期74卷 7624-7636页

作者： Lin, Runtao Guo, Caili Zhang, Biling Chen, Jiujiu Li, Hanyang Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Nanchang Hangkong University Key Laboratory of Processing and Pattern Recognition of Jiangxi Province School of Information Engineering Nanchang330063 China China Mobile Communication Co. Ltd Wireless and Terminal Technology Research Institute Beijing100053 China

The number of devices, data volume, and pictures and videos in the vehicular networks have all increased significantly as a result of the continued development of 6G technology, putting an enormous burden on the edge communication resources. To solve this problem, a good resource allocation scheme is necessary. Existing resource allocation algorithms often pursue accurate transmission and ignore time-varying channels. In order to further obtain real and reasonable resource allocation results, this paper introduces semantic communication. It then decomposes that problem to further establish a hierarchical framework. The problem is addressed via a task-oriented channel optimization and resource allocation scheme based on a hierarchical reinforcement learning (TCRS-HRL) algorithm. First, a channel optimization and resource allocation problem under an imperfect channel state is developed, and it is divided into two separate sub problems for optimization. Second, the resource allocation problem at the edge is addressed with the introduction of hierarchical reinforcement learning and the TCRSHRL algorithm. The ideal channel state and resource allocation approach are found by constructing the state space, action space, and environmental feedback for each aspect individually. Finally, simulations have demonstrated the superiority and performance benefits of the proposed algorithm compared to current methods, showing that the implementation of TCRS-HRL can result in an accuracy improvement of roughly 8%. © 1967-2012 IEEE.

关键词： Reinforcement learning

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A multipath-assisted localization algorithm under 5G-based opensource testbed 9

A multipath-assisted localization algorithm under 5G-based o...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Lu, Yuye Zheng, Wei Chu, Xinghe Lu, Zhaoming Hu, Ruoqian Li, Meiling Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Taiyuan University of Science and Technology Taiyuan China

ISBN: (纸本)9798350317251

Indoor localization services are playing an important role in a variety of emerging industries. Traditional positioning methods suffer from insufficient accuracy or high deployment costs in indoor environments, and with the rapid development of cellular mobile communication systems, positioning under cellular networks has been more widely used due to its low cost and easy deployment. In the indoor environment, the number of available base stations is limited, and positioning by direct radius information provided by only one base station suffers from insufficient accuracy. In this paper, we designed a method for indoor localization based on cellular networks, which considering the localization information carried by the multi-path, to verify the performance of this algorithm in practical scene. We implement this algorithm in a 5G-based testbed based on software-defined radio (SDR) technologies. The results show that our algorithm can meet the requirements of indoor cases defined in 3GPP 38.855. © 2023 IEEE.

关键词： Testbeds

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Efficient Data Collection Scheme based on Information Entropy for Vehicular Crowdsensing

Efficient Data Collection Scheme based on Information Entrop...

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2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022

作者： Liu, Chuanwen Wang, Luhan Wen, Xiangming Liu, Luning Zheng, Wei Lu, Zhaoming Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing China

ISBN: (数字)9781665426718

ISBN: (纸本)9781665426718

Vehicular crowdsensing system plays an important role in monitoring the dynamic characteristics of real environment with the assistance of mobile edge computing (MEC). However, connected vehicles (CVs) continuously generate a huge amount of sensed data, which causes severe data redundancy and considerable communication overhead. To tackle the above challenge, we propose an efficient data collection scheme for vehicular crowdsensing to mitigate data redundancy and communication overhead while improving data quality. Particularly, we design a sensed data preprocessing mechanism to reduce data volume and guarantee data quality. Besides, a grid selection algorithm is proposed to select the regions with abundant information by taking advantage of information entropy theory. Then we design an online collecting parameter adjustment algorithm, which adjusts collecting parameters according to the result of grid selection algorithm and current data redundancy, thus communication overhead can be reduced. Finally, extensive simulations are conducted to evaluate the performance of proposed scheme. The simulation results demonstrate that our proposed scheme can significantly reduce communication overhead and data redundancy while meeting the data quality requirement of vehicular crowdsensing. © 2022 IEEE.

关键词： Data acquisition

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