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arXiv 2020年

作者： Liu, Yifei Chen, Chao Zhang, Xi Xie, Sihong Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Bupt Beijing China Lehigh University BethlehemPA United States

Probabilistic graphical models, such as Markov random fields (MRF), exploit dependencies among random variables to model a rich family of joint probability distributions. Sophisticated infer-ence algorithms, such as belief propagation (BP), can effectively compute the marginal posteriors. Nonetheless, it is still difficult to interpret the infer-ence outcomes for important human decision mak-ing. There is no existing method to rigorously attribute the inference outcomes to the contributing factors of the graphical models. Shapley values provide an axiomatic framework, but naively computing or even approximating the values on general graphical models is challenging and less stud-ied. We propose GraphShapley to integrate the de-composability of Shapley values, the structure of MRFs, and the iterative nature of BP inference in a principled way for fast Shapley value compu-tation, that 1) systematically enumerates the im-portant contributions to the Shapley values of the explaining variables without duplicate;2) incre-mentally compute the contributions without start-ing from scratches. We theoretically character-ize GraphShapley regarding independence, equal contribution, and additivity. On nine graphs, we demonstrate that GraphShapley provides sensible and practical explanations. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

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Deep reinforcement learning based dynamic optimization of bus timetable

arXiv

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arXiv 2021年

作者： Ai, Guanqun Zuo, Xingquan Chen, Gang Wu, Binglin School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing100876 China School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand

Bus timetable optimization is a key issue to reduce operational cost of bus companies and improve the service quality. Existing methods use exact or heuristic algorithms to optimize the timetable in an offline manner. In practice, the passenger flow may change significantly over time. Timetables determined in offline cannot adjust the departure interval to satisfy the changed passenger flow. Aiming at improving the online performance of bus timetable, we propose a Deep Reinforcement Learning based bus Timetable dynamic Optimization method (DRL-TO). In this method, the timetable optimization is considered as a sequential decision problem. A Deep Q-Network (DQN) is employed as the decision model to determine whether to dispatch a bus service during each minute of the service period. Therefore, the departure intervals of bus services are determined in real time in accordance with passenger demand. We identify several new and useful state features for the DQN, including the load factor, carrying capacity utilization rate, and the number of stranding passengers. Taking into account both the interests of the bus company and passengers, a reward function is designed, which includes the indicators of full load rate, empty load rate, passengers’ waiting time, and the number of stranding passengers. Building on an existing method for calculating the carrying capacity, we develop a new technique to enhance the matching degree at each bus station. Experiments demonstrate that compared with the timetable generated by the state-of-the-art bus timetable optimization approach based on a memetic algorithm (BTOA-MA), Genetic Algorithm (GA) and the manual method, DRL-TO can dynamically determine the departure intervals based on the real-time passenger flow, saving 8% of vehicles and reducing 17% of passengers’ waiting time on average. Copyright © 2021, The Authors. All rights reserved.

关键词： Buses

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Reinforcement Learning Based Antenna Selection in User-Centric Massive MIMO 91

Reinforcement Learning Based Antenna Selection in User-Centr...

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91st IEEE Vehicular Technology Conference, VTC Spring 2020

作者： Chai, Xinxin Gao, Hui Sun, Ji Su, Xin Lv, Tiejun Zeng, Jie Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing100876 China Institute of Telecommunications Ministry of Industry and Information Technology Beijing100804 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100091 China

ISBN: (纸本)9781728152073

In this paper, we consider a user-centric massive multiple-input multiple-output (UC-MMIMO) system, wherein the optimal antenna selection (AS) is very complicated, because of the huge number of deployed antennas. Traditional AS algorithms rely heavily on full and perfect channel state information (CSI). Thus, we propose a novel AS algorithm to achieve low-complexity and less CSI reliance for UC-MMIMO. The proposed AS algorithm consists of the selection stage and the adjustment stage. In the selection stage, antennas are selected by a reinforcement learning (RL) based algorithm in which input data are the locations of users. In the adjustment stage, an adjustment mechanism is designed to further improve the performance. Numerical results show that our algorithm achieves better performance with lower complexity compared with related traditional algorithms. © 2020 IEEE.

关键词： MIMO systems

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Base station network traffic prediction approach based on LMA-DeepAR

arXiv

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arXiv 2021年

作者： Zhang, Jiachen Zuo, Xingquan Xu, Mingying Han, Jing Zhang, Baisheng Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing Uinversity of Posts and Telecommunications Beijing China Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing Uinversity of Posts and Telecommunications Beijing China Network Interface Virtualization Shanghai System Design Deptment Zhongxing Telecommunication Equipment Corporation Shanghai China

Accurate network traffic prediction of base station cell is very vital for the expansion and reduction of wireless devices in base station cell. The burst and uncertainty of base station cell network traffic makes the network traffic nonlinear and non-stationary, which brings challenges to the long-term prediction of network traffic. In this paper, the traffic model LMA-DeepAR for base station network is established based on DeepAR. Acordding to the distribution characteristics of network traffic, this paper proposes an artificial feature sequence calculation method based on local moving average (LMA). The feature sequence is input into DeepAR as covariant, which makes the statistical characteristics of network traffic near a period of time in the past be considered when updating parameters, and the interference of non-stationary network traffic on model training will be reduced. Experimental results show that the proposed prediction approach (LMA-DeepAR) outperforms other methods in the overall long-term prediction performance and stability of multi cell network traffic. Copyright © 2021, The Authors. All rights reserved.

关键词： Base stations

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Automatic detection of breast nodule in the ultrasound images using CNN

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The Journal of China Universities of Posts and Telecommunications 2019年第2期26卷 9-16页

作者： Pang Hao Bu Yunyun Wang Cong Xiao Hui School of Software Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of EducationBeijing 100876China Department of Ultrasound Beijing Friendship HospitalCapital Medical University China Electronic Data Service Company Limited

Breast cancer is the most common cancer among women *** is widely used as a harmless test for early breast cancer *** ultrasound network(USNet) model is *** is an improved object detection model specifically for breast nodule detection on ultrasound *** improved the backbone network,optimized the generation of feature maps,and adjusted the loss ***,USNet trained with real clinical *** evaluation results show that the trained model has strong nodule detection *** mean average precision(mAP) value can reach 0.734 *** nodule detection rate is 95.11%,and the in situ cancer detection rate is 79.65%.At the same time,detection speed can reach 27.3 frame per second(FPS),and the video data can be processed in real time.

关键词： convolutional neural network object detection ultrasound breast nodule

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Dynamic Multichannel Access for 5G and Beyond with Fast Time-Varying Channel

Dynamic Multichannel Access for 5G and Beyond with Fast Time...

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IEEE International Conference on Communications (ICC)

作者： Shaoyang Wang Tiejun Lv Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

In 5G and beyond wireless communication systems, providing satisfactory service to users in high-mobility scenarios becomes very essential. Unstable service supply caused by frequent handover under high-mobility compromises the service experience of users. Moreover, the demanding requirements for processing delays and non-immediate information processing errors are particularly prominent. This paper proposes a novel learning-based approach to solving the dynamic multichannel access (DMCA) problem under fast time-varying channel arising from high-mobility. Specifically, we first propose the subjective experience-based quality of service, and formulate the corresponding optimization problem based on the designed access criterion. Invoking our proposed prediction-based deep deterministic policy gradient algorithm and incremental learning-based online channel prediction model, a novel DMCA scheme, which combines the recurrent neural network with deep reinforcement learning, is proposed. Corroborated by experiments built in real channel data, the performance of proposed learning-based DMCA scheme approaches that derived from the exhaustive search method when making a decision at each time-slot, and is superior to the exhaustive search method when making a decision for every few time-slots. Furthermore, our scheme significantly reduces processing delays and effectively alleviates errors caused by the non-immediacy of information acquisition and processing.

关键词： Delays Quality of service Time-varying channels 5G mobile communication Predictive models Wireless communication Optimization

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BiTrust: Hybrid Trust Management for Secure Data Transmission in LEO Satellite Networks

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IEEE Internet of Things Journal 2025年

作者： Wei, Xinghai Yuan, Jie Hu, Runshan Wang, Haiguang Liu, Xiang Liu, Dongxiao Wang, Xingwu Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing100876 China Huawei Technologies Co. Ltd Beijing100095 China Huawei International Shield Lab Changi Business Park 486035 Singapore Pengcheng Laboratory Shenzhen518083 China

Due to characteristics such as the openness and exposure of inter-satellite links, Low-Earth Orbit (LEO) satellite networks are subject to heightened vulnerability to malicious attacks compared to ground-based networks. Given various security risks, implementing trust management in LEO satellite networks becomes imperative. However, existing trust management schemes tailored for this scenario are vulnerable to a spectrum of attacks, resulting in low detection rates and poor network performance. To address the above issues, we propose BiTrust, a hybrid trust management scheme for secure data transmission in LEO satellite networks. BiTrust introduces two distinct types of trust: state trust and behavior trust. State trust leverages remote attestation technology to verify the authenticity of node identities and the integrity of their functions, ensuring that nodes consistently disseminate reliable behavior trust announcements to safeguard the trust plane. Behavior trust, on the other hand, utilizes a trust model to quantify the real time data forwarding behavior characteristics of nodes, thereby maintaining the reliability of the data plane. To precisely quantify behavior trust, we design a trust model based on multi-path trust propagation. By integrating an unstable penalty term, the proposed trust model can effectively defend against dynamic dropping misbehavior. Besides, to facilitate the deployment of BiTrust in a distributed manner, we introduce a two-hop rely message mechanism and a query-answer mechanism to support the construction of behavior trust. Experimental results confirm the efficacy of BiTrust, demonstrating a significant improvement of up to 64.3% in data transmission rate under highly untrusted environments while maintaining affordable overhead. © 2014 IEEE.

关键词： Risk management

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Energy-Efficient and distributed Resource Allocation for WPT Enabled Multicell Massive MIMO-NOMA Networks

Energy-Efficient and Distributed Resource Allocation for WPT...

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

作者： Zhongyu Wang Tiejun Lv Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728144900

ISBN: (纸本)9781728144917

This paper investigates the energy-efficient resource allocation problem in multicell massive multiple input and multiple output (MIMO) non-orthogonal multiple access (NOMA) networks with wireless power transfer (WPT). In considered networks, Internet-of-Things (IoT) devices who have data packets to transmit in the uplink are charged by the wireless power harvested from the multi-antenna base stations (BSs) in the downlink. To adequately allocate the time of energy harvesting and data transmission, a time division protocol is proposed to divide downlink WPT and uplink wireless information transmission (WIT) time into two separate time slots. Aiming to obtain the optimal energy efficiency (EE) performance, we propose a joint power, time, subcarrier and antenna selection (AS) allocation scheme. An EE maximization problem is formulated to effectively determine the optimal resource allocation strategies. As the problem is nonconvex, we first invoke the nonlinear programming method to convert the problem into a convex optimization problem, and then solve it by taking an alternating direction method of multipliers (ADMM)-based distributed resource allocation algorithm. Simulation results demonstrate performance enhancement of the proposed algorithm over the benchmark schemes.

关键词： Resource management NOMA MIMO communication Uplink Wireless communication Antenna arrays

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A Theoretical Framework for Value Co-creation Analysis of Ecological Interaction between 6G and Its Enabling Technologies 24

A Theoretical Framework for Value Co-creation Analysis of Ec...

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Proceedings of the 2024 10th International Conference on Communication and Information Processing

作者： Jiao Zhang Xuyu Zhao Fengshuai Du Xinyue Yan Tianjian Yang Haiyan Yan Jiayin Qi School of Economics and Management Beijing University of Posts and Telecommunications Beijing Beijing China China Mobile Research Institute Beijing Beijing China Scienze Politiche e delle relazioni internazionali University of Palermo Palermo Italy and Huangpu Research School of Guangzhou University Guangzhou University Guangzhou Guangdong China School of Management Fudan University Shanghai Shanghai China School of Management Shanghai University of International Business and Economics Shanghai Shanghai China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou Guangdong China and Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing Beijing China

ISBN: (纸本)9798400717444

As demand for communication increases in terms of rate, reliability and security, 6G networks will provide more extreme communication experiences and richer communication services. It is therefore crucial that 6G enabling technologies are developed in order to meet the performance requirements of 6G networks. This study firstly analyses the core elements and development of the 6G ecosystem, and secondly reviews the ecological interactions of 6G and its enabling technologies. Notably, this study, based on the resource orchestration theory, has explored the three-phase evolution and value co-creation of the ecological interactions of 6G and its enabling technologies and provides a summary of the challenges for future development. The study is expected to provide theoretical support for the future system planning and evolutionary development of 6G ecology and its enabling technology ecology.

关键词： 6G ecology

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Research and Implementation of Trusted Data Collection Technology in Network Community 6

Research and Implementation of Trusted Data Collection Techn...

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6th Annual 2018 International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2018

作者： Wu, Xu Duan, Sishu Xie, Xiaqing Key Laboratory of Trustworthy Distributed Computing and Service BUPT China School of Cyberspace Security BUPT China Beijing University of Posts and Telecommunications Library China

This paper attempts to research and implement the data collection technology and trusted mechanism in network communities, and explore how to improve the reliability and credibility of the data collection process from multiple levels, providing reliable data sources for data analysis. © Published under licence by IOP Publishing Ltd.

关键词： Data acquisition

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