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IEEE Conference on Wireless Communications and networking

作者： Shixu Dong Jingbo Zhao Zhaoming Lu J. Andrew Zhang Tianpu Yang Jiayin Deng Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China School of Electrical and Data Engineering University of Technology Sydney Sydney Australia China Mobile Group Design Institute CO. Ltd Beijing China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In indoor integrated sensing and communication (ISAC) systems, angle of arrival (AoA) estimation for moving targets via reflected signals plays an important role in passive sensing. It is a challenging task in the presence of multiple static paths which cause significant interference to AoA estimation for dynamic paths. In this paper, we propose a subspace algorithm that achieves accurate AoA estimation for multiple targets using static communication transceivers in general indoor scenarios. We first use a complex vector to model static paths as a merged static path. Then we design a conditional pseudo-likelihood probability density function (PDF) based on the projection matrix of the signal subspace to bridge the signal subspace with the merged static path and AoAs of dynamic paths. We propose to utilize the Particle Filter to track merged static path and AoAs of dynamic paths via signal subspace. Thereby, the interference caused by static path signals can be eliminated and the accurate AoA estimation results are obtained. Simulation results are provided and validate the proposed algorithm.

关键词： Target tracking Accuracy Heuristic algorithms Simulation Estimation Interference Probability density function

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Task-Oriented Multi-user Semantic Communication based on Deep Over-the-Air Computation

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IEEE Transactions on Vehicular Technology 2025年

作者： Wei, Shuangying Feng, Chunyan Guo, Caili Zhang, Biling Chen, Jiujiu 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

The presence of large-scale heterogeneous IoT devices and AI-based applications has brought significant transmission and computation pressure on the wireless communication system. In this paper, we propose a novel multi-user semantic communication framework based on deep over-the-air computation called SC-DAC to tackle this challenge. By integrating communication and computation through a deep over-the-air computation (AirComp) encoder, SC-DAC is able to utilize the superposition of multiple access channels, facilitating multi-user information fusion during transmission and eliminating the need for multi-user detection at the receiver. Further, to achieve the optimal task results, the semantic encoders, decoders, and AirComp encoder are jointly optimized, where stochastic gradient descent (SGD) algorithm and deep double-Q network (DDQN) are employed to optimize the encoder/decoder parameters. The simulation results demonstrate that as the number of users increased, the performance of the system remains relatively stable and shows potential in resource utilization. What's more, when applied to the AI-based tasks such as multi-user multimodal action recognition and semantic segmentation, SC-DAC enhances the accuracy of action recognition by 26% compared with traditional methods and by about 30%-50% compared with other multi-user semantic communication methods under conditions of low signal-to-noise ratio (SNR), while it enhances the accuracy of semantic segmentation by 0.22 to 0.32 compared to other methods © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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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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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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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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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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Exploration of Simulation Teaching Mode for Intelligent Information network Course

Exploration of Simulation Teaching Mode for Intelligent Info...

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2021 International Conference on networking and network Applications, NaNA 2021

作者： He, Yue Li, Hui Li, Yiming Wu, Qiang Ji, Yuefeng Gu, Rentao Fan, Shaobin Beijing Laboratory of Advanced Information Networks Beijing100876 China BUPT Beijing Key Laboratory of Network System Architecture and Convergence Beijing100876 China BUPT School of Information and Communication Engineering Beijing100876 China China Unicom China

ISBN: (纸本)9781665441582

With the rapid development of information technology, the society's demand for information technology talents has become particularly high;how colleges and universities devote themselves to cultivating professional information technology talents is an important direction at present. However, the teacher's evaluation method stays at the exam-oriented education. In the university education, in order to help students with basic professional knowledge to learn more professional knowledge and skills, and to help teachers understand the characteristics of students more objectively, this article discusses how to improve the existing teaching environment and simulation teaching mode. A simulation teaching platform for the "Intelligent Information network"course is designed with multiple modules for complex network construction. Through the online network simulation teaching platform and the feedback obtained from the platform's use, different students' different problems can be found objectively, and the combination of theory and engineering, the combination of new and old knowledge, and the simplicity of complex software can be realized. © 2021 IEEE.

关键词： Students

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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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RIS-Enhanced LEO Satellite Communication: Joint Passive Beamforming and Orientation Optimization

RIS-Enhanced LEO Satellite Communication: Joint Passive Beam...

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IEEE Globecom Workshops

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

ISBN: (纸本)9781665459761

Low Earth orbit (LEO) satellite communication (SatCom) systems have emerged as a key enabler to provide ubiquitous connectivity in the upcoming generation networks. Yet the conflict between power-limited link budget and long-distance path loss restricts the universal application of LEO SatCom. Fortunately, the reconfigurable intelligent surface (RIS) technique can be an efficient solution to address this issue. In this paper, we incorporate a RIS into the LEO SatCom system to enhance its downlink transmission by introducing a controllable reflection link. A joint RIS passive beamforming and orientation optimization problem is formulated to maximize average throughput at the ground terminal (GT) during the service. The formulated non-convex problem has the unit-modulus constraint on the phase shifters and the piecewise RIS radiation coefficient function related to the predictable satellite trajectory. A two-stage optimization framework is proposed to equivalently solve two sequential subproblems. Specifically, for the passive beamforming subproblem, a closed-form solution is derived at first in compliance with the phase alignment principle. Then, an iterative algorithm is proposed to approximately solve the orientation optimization subproblem. Simulation results show the substantial performance gains on link budget and throughput achieved by the proposed RIS-assisted LEO SatCom scheme.

关键词： Satellites Array signal processing Simulation Low earth orbit satellites Performance gain Throughput Downlink

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A New Paradigm of User-Centric Wireless Communication Driven by Large Language Models

arXiv

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arXiv 2025年

作者： Ding, Kuiyuan Guo, Caili Yang, Yang Hu, Wuxia Eldar, Yonina C. Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Beijing Laboratory of Advanced Information Net works School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

The next generation of wireless communications seeks to deeply integrate artificial intelligence (AI) with user-centric communication networks, with the goal of developing AI-native networks that more accurately address user requirements. The rapid development of large language models (LLMs) offers significant potential in realizing these goals. However, existing efforts that leverage LLMs for wireless communication often overlook the considerable gap between human natural language and the intricacies of real-world communication systems, thus failing to fully exploit the capabilities of LLMs. To address this gap, we propose a novel LLM-driven paradigm for wireless communication that innovatively incorporates the nature language to structured query language (NL2SQL) tool. Specifically, in this paradigm, user personal requirements is the primary focus. Upon receiving a user request, LLMs first analyze the user intent in terms of relevant communication metrics and system parameters. Subsequently, a structured query language (SQL) statement is generated to retrieve the specific parameter values from a high-performance real-time database. We further utilize LLMs to formulate and solve an optimization problem based on the user request and the retrieved parameters. The solution to this optimization problem then drives adjustments in the communication system to fulfill the user’s requirements. To validate the feasibility of the proposed paradigm, we present a prototype system. In this prototype, we consider user-request centric semantic communication (URC-SC) system in which a dynamic semantic representation network at the physical layer adapts its encoding depth to meet user requirements, such as improved data transmission quality or reduced data transmission latency. Additionally, two LLMs are employed to analyze user requests and generate SQL statements, respectively. Simulation results demonstrate the effectiveness of the prototype in personalizing and addressing user r

关键词： Structured Query Language

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