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Optical Fiber Communication (OFC) Conference

作者： Xiaodong Zhang Guochu Shou Junli Xue Hongxing Li Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

We propose a time synchronization modeling and error compensation method for Software-Defined TSN. Experiments verify that the combination method of POE and NNPID can effectively improve time synchronization performan... 详细信息

ISBN: (纸本)9781557524669

关键词： Error compensation Artificial neural networks Optical fiber communication Synchronization

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Cost-effective Delay-bounded Topology Construction of Time-Sensitive networking for Industrial Internet Applications

Cost-effective Delay-bounded Topology Construction of Time-S...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Hao Ma Guochu Shou Junli Xue Yaqiong Liu Yihong Hu Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

We propose a cost-effective delay-bounded topology construction method that expands local network via preferential attachment with delay constraint. The experiments show that the method is effective, and the node degr... 详细信息

关键词： network topology Asia Optical fiber networks Topology Delays Internet Photonics

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Precise Timing over Beyond 5G networks for Intelligent Transport in the Smart City

Precise Timing over Beyond 5G Networks for Intelligent Trans...

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

作者： Hongxing Li Guochu Shou Yaqiong Liu Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China 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)9798350362244

ISBN: (纸本)9798350362251

The need for precise timing is increasing with the development of the smart city, and intelligent transport is a key application that often relies on the distribution of accurate timing. Existing time synchronization solutions cannot adequately meet the precise timing needs for practical applications in intelligent transportation. Global Navigation Satellite systems (GNSS) signals, for instance, can be obstructed, weakened, or deflected in urban canyon environments. While certain sectors like telecommunications and smart grids have successfully adopted IEEE 1588 for high-precision time synchronization, its dependence on standalone networks and limited application within local area networks poses challenges when catering to the wide-ranging and mobile demands of intelligent transportation applications. This paper categorizes the time synchronization requirements of intelligent transport and proposes a time synchronization scheme over converged networks, which separates time transmission and precision compensation based on software-defined networking (SDN) principles, and makes it possible to provide precise timing for intelligent transport devices, including on-board units (OBUs) and roadside units (RSUs), over wired and wireless communication networks. Experimental results indicate that the proposed scheme achieves a relative time error of less than 1.2 microseconds over the converged networks.

关键词： Wireless communication Global navigation satellite system Accuracy Smart cities Transportation Telecommunications Smart grids Synchronization Software defined networking Local area networks

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Generation of Multi-Channel Co-axial Quasi-Perfect Vortex Electromagnetic Waves with Equal Divergence Radii Based on Aperture-Shared Metasurface

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IEEE Transactions on Antennas and Propagation 2025年

作者： Deng, Li Yang, Kaiqiao Yin, Kaiyue Chen, Jiawei Qu, Meijun Feng, Botao Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing100876 China Southeast University Institute of Electromagnetic Space State Key Laboratory of Millimeter Wave Nanjing211189 China Communication University of China School of Information and Communication Engineering Beijing Beijing100024 China Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China

Vortex electromagnetic (EM) waves hold significant promise for next-generation wireless communication applications. However, their practical deployment is hindered by varying divergence characteristics across vortex modes with different mode numbers. To overcome this challenge and enable true spatial multiplexing of orbital angular momentum (OAM) waves, a dual-polarized shared-aperture metasurface is proposed. By leveraging both polarization and OAM multiplexing, quasi-perfect vortex (QPV) EM waves with co-axial beams and equal divergence radii are successfully generated at microwave frequencies. Initially, the properties of quasi-perfect vortex waves in the microwave band are analyzed, followed by the design of a dual-polarized metasurface unit. This unit integrates parasitic damping-coupled patches based on the traditional Jerusalem cross structure, providing polarization-independent control and approximately 360° phase-tuning capability. Furthermore, simulations validate the effectiveness of the dual-polarized single-mode metasurface design. Based on this, the concept is extended to a 4-channel multiplexing metasurface, operating at 15 GHz and capable of generating QPV waves. Finally, experimental results from the fabricated metasurface demonstrate the generation of QPV waves with OAM mode numbers 0, +2, +1, and -1, achieving simulated and measured OAM mode purities of 0.835 and 0.70, respectively. The experimental data closely aligns with simulations, confirming the effectiveness of the design. This study offers new insights into generating and applying perfect vortex EM waves, paving the way for high-capacity near-field wireless communication systems and advanced control of novel EM physical dimensions. © 1963-2012 IEEE.

关键词： Near field communication

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Multimodal Semantic Communication for Generative Audio-Driven Video Conferencing

arXiv

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arXiv 2024年

作者： Tong, Haonan Li, Haopeng Du, Hongyang Yang, Zhaohui Yin, Changchuan Niyato, Dusit Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China Singapore

This paper studies an efficient multimodal data communication scheme for video conferencing. In our considered system, a speaker gives a talk to the audiences, with talking head video and audio being transmitted. Since the speaker does not frequently change posture and high-fidelity transmission of audio (speech and music) is required, redundant visual video data exists and can be removed by generating the video from the audio. To this end, we propose a wave-to-video (Wav2Vid) system, an efficient video transmission framework that reduces transmitted data by generating talking head video from audio. In particular, full-duration audio and short-duration video data are synchronously transmitted through a wireless channel, with neural networks (NNs) extracting and encoding audio and video semantics. The receiver then combines the decoded audio and video data, as well as uses a generative adversarial network (GAN) based model to generate the lip movement videos of the speaker. Simulation results show that the proposed Wav2Vid system can reduce the amount of transmitted data by up to 83% while maintaining the perceptual quality of the generated conferencing video. © 2024, CC BY-NC-SA.

关键词： Video conferencing

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Consistency-based Semi-supervised Learning Framework for Power Line Insulators Detection

Consistency-based Semi-supervised Learning Framework for Pow...

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2022 International Conference on Electrical, Power and Grid systems, ICEPGS 2022

作者： Yao, Xu Lei, Longwu Zheng, Zhou Mo, Wenhao Wu, Han Li, Shufang Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Haidian District Beijing100876 China State Grid Fujian Electric Power Research Institute Fujian Province Fuzhou350007 China China Electric Power Research Institute or2 Haidian District Beijing100192 China

It is commonly believed that fast and accurate object detection is the prerequisite for power line insulators automatic fault diagnosis. While the insulators detection task requires a huge number of labeled images to guarantee its performance, placing bounding boxes for every object in each image is time-consuming and requires professional knowledge. To alleviate this problem, we propose CASD, a two-stage semi-supervised learning framework for power line insulators detection based on consistency regularization and data augmentation. Firstly, we train a teacher model on labeled images, then generate pseudo labels for unlabeled images using the trained model, and the confidence-based thresholding is introduced to control the quality of pseudo labels. In the second stage, we apply strong data augmentation to unlabeled images and use both the labeled and unlabeled images to train the CASD. Consistency regularization is introduced in the calculation of unsupervised loss to improve the accuracy and robustness of the model. Finally, we compare CASD with supervised learning, data augmentation and self-training methods on insulators aerial image dataset. The results show that the proposed method performs better than all the three baseline methods, and the detection accuracy of CASD is far better than the supervised learning method in the case of less available images. The comparison results further prove the efficacy of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Multi-modal Data based Semi-Supervised Learning for Vehicle Positioning

arXiv

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arXiv 2024年

作者： Huan, Ouwen Yang, Yang Luo, Tao Chen, Mingzhe Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

In this paper, a multi-modal data based semisupervised learning (SSL) framework that jointly use channel state information (CSI) data and RGB images for vehicle positioning is designed. In particular, an outdoor positioning system where the vehicle locations are determined by a base station (BS) is considered. The BS equipped with several cameras can collect a large amount of unlabeled CSI data and a small number of labeled CSI data of vehicles, and the images taken by cameras. Although the collected images contain partial information of vehicles (i.e. azimuth angles of vehicles), the relationship between the unlabeled CSI data and its azimuth angle, and the distances between the BS and the vehicles captured by images are both unknown. Therefore, the images cannot be directly used as the labels of unlabeled CSI data to train a positioning model. To exploit unlabeled CSI data and images, a SSL framework that consists of a pretraining stage and a downstream training stage is proposed. In the pretraining stage, the azimuth angles obtained from the images are considered as the labels of unlabeled CSI data to pretrain the positioning model. In the downstream training stage, a small sized labeled dataset in which the accurate vehicle positions are considered as labels is used to retrain the model. Simulation results show that the proposed method can reduce the positioning error by up to 30% compared to a baseline where the model is not pretrained. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Work in Progress: Exploration of Research-study Dual Cycle Mode for Simulation Practice Teaching

Work in Progress: Exploration of Research-study Dual Cycle M...

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IEEE World Engineering Education Conference (EDUNINE)

作者： Hui Li Qiang Wu Zhiqun Gu Rentao Gu Yuefeng Ji Beijing Laboratory of Advanced Information Networks 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 School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

Traditional information course simulation teaching is often based on specific simulation software to simulate and verify some theoretical models in the course knowledge points. Under the constraints of fixed topics and methods, the limited teaching content leads to the limitation of students' innovation ability and practical ability. In view of the above problems, the present paper undertakes the progress of the dual center teaching model, with the help of an extensible online simulation platform, to design the simulation problems of related artificial intelligence algorithms for the actual engineering problems in the communication network, forming a simulation teaching mode of research and learning double cycle through which students simulate cases and apply these algorithms to innovation and entrepreneurship practice.

关键词： Technological innovation Entrepreneurship Learning (artificial intelligence) Software Communication networks Engineering education

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A Deep Learning-Based Algorithm for Energy and Performance Optimization of Computational Offloading in Mobile Edge Computing

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Khan, Israr Raza, Salman Rehman, Waheed Ur Khan, Razaullah Nahida, Kiran Tao, Xiaofeng National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science National Textile University Faisalabad Pakistan Department of Computer Science University of Peshawar Peshawar Pakistan Department of Computer Science University of Engineering and Technology Mardan Pakistan Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications China

Mobile edge computing (MEC) has produced incredible outcomes in the context of computationally intensive mobile applications by offloading computation to a neighboring server to limit the energy usage of user equipment (UE). However, choosing a pool of application components to offload in addition to the volume of data transfer along with the latency in communication is an intricate issue. In this article, we introduce a novel energy-efficient offloading scheme based on deep neural networks. The proposed scheme trains an intelligent decision-making model that picks a robust pool of application components. The selection is based on factors such as the remaining UE battery power, network conditions, the volume of data transfer, required energy by the application components, postponements in communication, and computational load. We have designed the cost function taking all the mentioned factors, get the cost for all conceivable combinations of component offloading decisions, pick the robust decisions over an extensive dataset, and train a deep neural network as a substitute for the exhaustive computations associated. Model outcomes illustrate that our proposed scheme is proficient in the context of accuracy, root mean square error (RMSE), mean absolute error (MAE), and energy usage of UE. © 2023 Israr Khan et al.

关键词： Data transfer

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Image Coding and Compression Method Based on Semantics of Pixels for Semantic Communication

Image Coding and Compression Method Based on Semantics of Pi...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Qizheng Sun Caili Guo Rui Tang Chuanhong Liu Yufeng Zhang Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing China Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing China China Telecom Dict Application Capability Center Beijing University of Posts and Telecommunications

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Semantic image transmission is promising for both image reconstruction or downstream task execution. However, it is still challenging to simultaneously achieve pleasing visual clarity and superior downstream tasks. To tackle this issue, we propose a semantic-preserving image coding and compression method based on the semantics of pixels (SP). Specifically, we first project the semantic importance onto pixels, which is quantified by a gradient-based mechanism. Then, we design an SP-based loss function to train the image coding and compression network. This loss function not only preserves the semantic information to boost the performance of intelligent tasks but also preserves the pixel information to achieve pleasing visual effects. Experimental results confirm improvements in both aspects of performance. Especially for intelligent task performance, the proposed method achieves up to 22.22 % and 78.40 % performance gain ratio compared with the state of art deep learning-based and the traditional method, respectively.

关键词： Training Fading channels Image coding Image communication Semantics Performance gain Visual effects Generative adversarial networks Encoding Image reconstruction

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