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3rd IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2020

作者： Cai, Tingxiao Fan, Wenmin Feng, Jing Hong, Weijun China Electronics Standardization Institute Beijing China Beijing Laboratory of Advanced Information Network 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

ISBN: (纸本)9781728190457

Electronic registration identification (ERI) of the motor vehicle is an important application of passive radio frequency identification (RFID) in the field of intelligent transportation which has been put into use in some countries. ERI is in the pilot research stage now in China, but the release of the standard is accelerating its promotion. The performance evaluation of car-related RFID products is often accomplished by test and trial method currently. The test results are seriously affected by irrelevant environmental factors, making it impossible to carry out scientific repeatable experiments. In this paper, the communication channel model of ERI system in the highway traffic scene is modeled based on the ray tracing method. In order to accurately establish the model, we conducted a simulation analysis on the influence of factors such as antenna pattern, polarization mode, ground material, and environmental noise on the received signal power and tag reading distance. Files generated based on the model in this article can be loaded onto the channel simulator, so as to build an ERI test system with reproducible test results which has important application value. © 2020 IEEE.

关键词： Ray tracing

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Channel Model for RFID based Electronic Registration Identification of the motor vehicle in Practical Open Transportation Scenario 3

Channel Model for RFID based Electronic Registration Identif...

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3rd IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2020

作者： Feng, Jing Fan, Wenmin Hong, Weijun Cai, Tingxiao Shi, Chunteng Zhou, Jitianbai China Electronics Standardization Institute Beijing China Beijing Laboratory of Advanced Information Network 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

ISBN: (纸本)9781728190457

Current researches on communication channel of electronic registration identification of the motor vehicle (ERI) are mostly based on the single lane two channel model, which is not consistent with the multi-lane multi-car scene in the practical application. In this paper, a channel model combined with deterministic and stochastic component is firstly proposed for ERI. The theoretical formula of the model is derived and simulated. The influence of Doppler shift on each transmission path in the channel model and the effect of adjacent lane reflection path on channel fading is analyzed in detail. The work of this paper is with great significance for evaluation of ERI system in practical scenario. © 2020 IEEE.

关键词： Stochastic systems

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Sequence Elimination Algorithm for Multi-Server Multi-User Mobile Edge Computing Offloading

Sequence Elimination Algorithm for Multi-Server Multi-User M...

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IEEE International Conference on network Infrastructure and Digital Content (IC-NIDC)

作者： Yi Chu Ruixiang Li Fang Wang Junlong Ren Yuanyuan Qiao Jie Yang Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing China The Technology Research Institute Aisino Corporation Beijing China

ISBN: (纸本)9781665405836

Edge computing is a new paradigm of distributed computing, in which computing offloading attracts wide attention from scholars. Existing work mainly focuses on single-server and single-user, and generally only optimizes energy consumption or execution time respectively. However, optimizing one may lead to the decline in another. In order to remedy this defect, we propose sequence elimination algorithm based on genetic algorithm. The algorithm can optimize energy consumption or execution time respectively in the scenario of multi-user and multi-server. The simulation results show that the sequence elimination algorithm can effectively reduce the energy consumption and execution time in the edge computing offloading problem.

关键词： Energy consumption Multi-access edge computing Simulation Conferences Distributed computing Genetic algorithms

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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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IEEE Globecom Workshops

作者： Hang Li Zixuan Kong Wan Xiang Xiangming Wen Zhaoming Lu Luhan Wang Yawen Chen Wenpeng Jing 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)9781665423915

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.

关键词： Service function chaining Simulation network slicing Heuristic algorithms Conferences Admission control Faces

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Fusing Bluetooth with Pedestrian Dead Reckoning: A Floor Plan-Assisted Positioning Approach

arXiv

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arXiv 2025年

作者： Pan, Wenxuan Yang, Yang Chen, Mingzhe Wei, Dong Guo, Caili Mao, Shiwen Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering the Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Wireless Engineering Research and Education Center Auburn University AuburnAL36849 United States

Floor plans can provide valuable prior information that helps enhance the accuracy of indoor positioning systems. However, existing research typically faces challenges in efficiently leveraging floor plan information and applying it to complex indoor layouts. To fully exploit information from floor plans for positioning, we propose a floor plan-assisted fusion positioning algorithm (FP-BP) using Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR). In the considered system, a user holding a smartphone walks through a positioning area with BLE beacons installed on the ceiling, and can locate himself in real time. In particular, FP-BP consists of two phases. In the offline phase, FP-BP programmatically extracts map features from a stylized floor plan based on their binary masks, and constructs a mapping function to identify the corresponding map feature of any given position on the map. In the online phase, FP-BP continuously computes BLE positions and PDR results from BLE signals and smartphone sensors, where a novel grid-based maximum likelihood estimation (GML) algorithm is introduced to enhance BLE positioning. Then, a particle filter is used to fuse them and obtain an initial estimate. Finally, FP-BP performs post-position correction to obtain the final position based on its specific map feature. Experimental results show that FP-BP can achieve a real-time mean positioning accuracy of 1.19 m, representing an improvement of over 28% compared to existing floor plan-fused baseline algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Photomapping

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Enhanced User Interest and Expertise Modeling for Expert Recommendation

Enhanced User Interest and Expertise Modeling for Expert Rec...

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International Conference on Pattern Recognition

作者： Tongze He Caili Guo Yunfei Chu Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Bejing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing

The rapid development of Community Question Answering (CQA) satisfies users' request for professional and personal knowledge. In CQA, one key issue is to recommend users with high expertise and willingness to answer the given questions, namely expert recommendation. However, most of existing methods for expert recommendation ignore some key information, such as time information and historical feedback information, degrading the performance. On the one hand, users' interest are changing over time. It is biased if we don't consider the dynamics. On the other hand, feedback information is critical to estimate users' expertise. To solve these problems, we propose a unified framework for expert recommendation to exploit user interest and expertise more precisely. Considering the inconsistency between them, we propose to learn their embeddings separately. We leverage Long Short-Term Memory (LSTM) to model user's short-term interest and combine it with long-term interest. The user expertise is learned by the designed user expertise network, which explicitly models feedback on users' historical behavior. The extensive experiments on a large-scale dataset from a realworld CQA site demonstrate the superior performance of our method than state-of-the-art solutions to the problem.

关键词： Knowledge discovery Pattern recognition

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A Survey on Indoor Visible Light Positioning systems: Fundamentals, Applications, and Challenges

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IEEE Communications Surveys and Tutorials 2024年

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering and Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China The University of British Columbia Faculty of Applied Science School of Engineering KelownaBCV1V 1V7 Canada 518172 Hong Kong

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. © 1998-2012 IEEE.

关键词： Resource allocation

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Content-driven Joint Resource Allocation Based on Vehicle-edge Synergy in Vehicular networks

Content-driven Joint Resource Allocation Based on Vehicle-ed...

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IEEE Wireless Communications and networking Conference Workshops (WCNCW)

作者： Xu Zhu Fangfang Liu Zhimin Zeng Caili Guo Jiujiu Chen Beijing Laboratory of Advanced Information Networks 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)9781728195070;9781728195087

With the rapid growth of computer vision applications, a large amount of video data in vehicular networks are used for content analysis. As a representative technology in the field of artificial intelligence, deep learning has been widely used in video content understanding. However, deep learning models are usually accompanied by huge amount of calculation, which put great pressure on traditional wireless communication resource and Mobile Edge Computing (MEC) server. To solve this problem, in this paper, we first propose a DNN model segmentation strategy, and then propose a content-driven joint resource allocation scheme based on vehicle-edge synergy to maximize the accuracy of video content understanding under the constraints of communication and computing resources. Due to the real-time nature of resource allocation and the variability of the environment in vehicular networks, we design a Multi-agent Distributed Q-Learning algorithm to solve such multi-constrained nonlinear programming problems. Finally, the simulation results show that our proposed vehicle-edge synergy based joint resource allocation scheme has better video content understanding performance and system delay.

关键词： Deep learning Computational modeling Simulation Conferences Streaming media Programming Real-time systems

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Digital Over-the-Air Federated Learning in Multi-Antenna systems

arXiv

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arXiv 2023年

作者： Wang, Sihua Chen, Mingzhe Shen, Cong Yin, Changchuan Brinton, Christopher G. The Beijing Laboratory of Advanced Information Network The Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Charles L. Brown Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA United States The School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp) is studied. In particular, a MIMO system is considered in which edge devices transmit their local FL models (trained using their locally collected data) to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices. Due to the limited bandwidth in a wireless network, AirComp is adopted to enable efficient wireless data aggregation. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To tackle this challenge, we propose a modified federated averaging (FedAvg) algorithm that combines digital modulation with AirComp to mitigate wireless fading while ensuring the communication efficiency. This is achieved by a joint transmit and receive beamforming design, which is formulated as an optimization problem to dynamically adjust the beamforming matrices based on current FL model parameters so as to minimize the transmitting error and ensure the FL performance. To achieve this goal, we first analytically characterize how the beamforming matrices affect the performance of the FedAvg in different iterations. Based on this relationship, an artificial neural network (ANN) is used to estimate the local FL models of all devices and adjust the beamforming matrices at the PS for future model transmission. The algorithmic advantages and improved performance of the proposed methodologies are demonstrated through extensive numerical experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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A Scalable Computation Offloading Scheme for MEC Based on Graph Neural networks

A Scalable Computation Offloading Scheme for MEC Based on Gr...

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IEEE Globecom Workshops

作者： Tuan Wu Wenpeng Jing Xiangming Wen Zhaoming Lu Shuyue Zhao 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

ISBN: (纸本)9781665423915

Recently, computation offloading has become a promising technology to support computation-intensive applications on resource-limited mobile devices (MDs). However, existing offloading schemes suffer from poor generalization and scalability to different applications, since they mainly consider one or a few specific task dependency topologies of the application. Different from the existing schemes, this paper introduces a novel neural network, i.e., graph neural network (GNN), to adapt to the diversity of the applications. Therefore, we propose a GNN and deep reinforcement learning (DRL)-based scheme that optimizes offloading to minimize the long-term average application completion time and MD’s energy consumption. Specifically, considering the variation of the wireless network, a fine-grained task-level scheduling mechanism is presented. In addition, a scalable GNN-enabled preprocessing network is designed to process the state of the various applications and the wireless network. Furthermore, we obtain the optimal scheduling policy via training based on policy gradient algorithm under the stochastic application arrival of the unlimited inter arrival time. Extensive simulations demonstrate the effectiveness and superiority of the proposed scheme under different average inter arrival times. Specifically, the proposed scheme can lower at least 172% of the application completion time and 45% of the energy consumption of the MD compared with other baseline schemes.

关键词： Training Performance evaluation Energy consumption Wireless networks Scalability Optimal scheduling Reinforcement learning

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