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IEEE International Conference on Communications (IEEE ICC)

作者： Yan, Yingjie Wang, Ying Zhao, Junwei Ni, Wanli Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9781538674628

In industrial control system, applications perform time-critical operations based on the observations of multiple processes. We consider a request-based scenario, where a cache-enabled base station (BS) stores the most recent status observed by energy harvesting (EH) sensors, and delivers the cached status to applications upon request. Due to the time-varying nature of processes, cached status may be outdated which affects the accuracy of operations. Frequent cache update improves the status freshness but leads to high energy consumption of EH sensors. Furthermore, the freshness on the application side is simultaneously determined by multiple status, which requires joint updating of multiple sensors to improve update efficiency. Age of Information (AoI) is employed to measure the freshness of status. We adopt the maximum AoI among the responded status as age of response (AoR). A long-term average AoR minimization problem is formulated, subject to the number of wireless channels and the energy constraint of each EH sensor. The problem is challenging due to the random arrivals of requests and energy harvesting as well as the random association between requests and sensors. By introducing AoR reduction as the reward of each schedule, the problem is decomposed into a per-slot reward maximization problem, and then transformed into a knapsack problem. Then, an online correlated cache update algorithm is proposed. Numerical experiments illustrate that our solution outperforms the traditional greedy policy and achieves 16% performance gains.

关键词： Age of information energy harvesting caching update industrial control

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Adaptive Prediction-Based Beam Tracking Scheme for UAV mmWave Communications

Adaptive Prediction-Based Beam Tracking Scheme for UAV mmWav...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Zhu, Jianan Liu, Chenxi Chen, Wenyun Hu, Xiaoling Peng, Mugen Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

ISBN: (纸本)9798350370218

In this paper, we propose a prediction-based beam tracking scheme for the unmanned aerial vehicle (UAV) millimeter wave (mmWave) communication systems, where a UAV transmits to another UAV moving with relative high speed and rapidly changing angular velocity. Specifically, we first characterize the impacts of time-varying angular velocity and beam tracking frequency on the average transmission rate of our considered systems. Based on which, we obtain an analytical expression of the average transmission rate in the regime of high signal-to-noise ratio, thus enabling us to determine the optimal beam tracking frequency and predict the angle of arrival/angle of departure such that the maximum average transmission rate can be achieved. Through numerical results, we validate the effectiveness of our proposed adaptive predication-based beam tracking scheme, demonstrating how it can be robust to the rapidly changing angular velocity of the UAV, compared to various baselines. © 2023 IEEE.

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Efficient and Adaptive Human Pose Estimation on Resource-Constrained Computing Devices via Knowledge Distillation and Temporal Propagation

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IEEE INTERNET OF THINGS JOURNAL 2025年第11期12卷 17263-17280页

作者： Zhou, Xiaomao Hu, Yujiao Jia, Qingmin Xie, Renchao Future Network Res Ctr Purple Mt Labs Nanjing 211111 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Existing video-based human pose estimation (HPE) methods commonly rely on large networks to localize body joints across all frames, achieving remarkable accuracy but imposing high memory and computational demands that limit their applications on resource-constrained devices. Moreover, most models lack the capability to accommodate dynamic changes in available resources, which can negatively impact the performance of parallel tasks. To address these issues, this article proposes a novel yet effective framework for efficient and adaptive HPE on resource-constrained devices. Specifically, the proposed approach adopts the knowledge distillation (KD) strategy to train a light-weight pose estimator network, which is capable of executing rapidly with low computational cost. To further increase the overall efficiency, it exploits the temporal coherence between successive video frames and explicitly propagates body joints from previous frames rather than naively extracting them using a pose estimator. Furthermore, a prediction-based mechanism is adopted to facilitate adaptive key-frame selection, dynamically determining the optimal number of keyframes, thus enhancing the overall efficiency and adaptability. Experiments on Penn Action, Sub-JHMDB, and real-world systems demonstrate that the proposed method achieves comparative accuracy, superior efficiency, and robust flexibility in dynamic scenarios.

关键词： Pose estimation Adaptation models Predictive models Performance evaluation Computational modeling Accuracy Solid modeling Computational efficiency Three-dimensional displays Long short term memory Attention mechanism human pose estimation (HPE) knowledge distillation (KD) pose propagation resource prediction

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Semi-Swinderain: Semi-Supervised Image Deraining Network Using SWIN Transformer 48

Semi-Swinderain: Semi-Supervised Image Deraining Network Usi...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Ren, Chun Yan, Danfeng Cai, Yuanqiang Li, Yangchun Bupt State Key Laboratory of Networking and Switching Technology China Chinese Academy of Cyberspace Studies China

ISBN: (纸本)9781728163277

Currently, single image deraining lacks paired rain/clean images in real world and most studies use synthetic data. Real rain image deraining is still a challenge. To solve this problem, we propose a semi-supervised image deraining network using Swin Transformer, which can both use features of synthetic data and real data to get a better result. Specifically, the network is divided into supervised branch and unsupervised branch. Supervised and unsupervised branches are trained using synthetic data and real data, respectively. The network architecture is based on Swin Transformer, which adds a self-supervised memory module between encoder and decoder to store rain information. In the unsupervised branch, contrastive loss is added to ensure restored real rain image in features space is close to clear image, away from real rain image. In addition, we propose a real rain dataset RealRain11k. Experiments show our method has better result in real rain image deraining. The source code and RealRain11k are available at https://***/imissrc/Semi-SwinDerain. © 2023 IEEE.

关键词： Contrastive Loss Deraining Memory Semi-suprivised Swin Transformer

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Flow Scheduling and Network Updates in Time-Sensitive Software-Defined Networks 4

Flow Scheduling and Network Updates in Time-Sensitive Softwa...

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4th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2023

作者： Nie, Tianzheng Yao, Haipeng Wang, Xiaolong Zhu, Lin State Key Laboratory of Networking and Switching Technology BUPT China The Faculty of Information Technology Beijing University of Technology China

ISBN: (纸本)9781510666412

With the further deepening and upgrading of digitization and informatization of industrial enterprises, the industrial network needs higher flexibility and reliability. As a comprehensive, unified, and standard new industrial Ethernet technology, Time-sensitive network technology makes industrial network technology and industrial ecology more open and dynamic. Time-sensitive Software-defined network introduces the concept of software-defined network based on time-sensitive network, which makes the whole network more real-time and flexible. With the emergence of dynamic applications, time-sensitive networks also need to be updated in real-time. The old update mechanism cannot ensure that the new and old flow is separate from one another, resulting in network congestion. In this paper, a new scheduling update scheme is proposed, which eliminates the conflict between the old and new traffic by separating the old scheduling scheme from the new. The experimental results on two different network topologies show that the update time of the proposed scheduling scheme is shortened by 5.68% compared with the traditional scheduling scheme. The network resource utilization of the two network topologies decreased by 3.3% and 10.02%, respectively. © 2023 SPIE.

关键词： Network topology

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Large Doppler Frequency Offset Estimation for LEO Satellite Communication System based on 5G TRS

Large Doppler Frequency Offset Estimation for LEO Satellite ...

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International Conference on Satellite Computing

作者： Li, Lihua Huang, Jiping Chang, Kexin Zhou, Wenhui Beijing Univ Posts & Telecommun Beijing State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9798350305883;9798350305890

In order to solve the problem of large Doppler frequency offset (DFO) in 5G Non-Terrestrial Networks (NTN) based low Earth orbiting (LEO) satellite scenario, three DFO estimation algorithms of different complexity based on Doppler rate calculations are proposed Firstly, TRS conjugate correlation estimator is proposed, and then least squares estimator is proposed on the basis of conjugate correlation. Then, by constructing weights, a weighted least square estimator is proposed. Finally, the DFO can be estimated according to the initial frequency offset and Doppler rate estimation. Compared with the existing algorithms, simulation results demonstrate the proposed three algorithms slightly sacrifice the complexity to improve the estimation accuracy of the Doppler rate, and has good frequency offset estimation performance under the condition of high dynamic frequency offset and low signal to noise ratio. The differences in complexity and performance of the proposed three algorithms are analyzed.

关键词： 5G LEO satellite communication Doppler frequency offset Doppler rate

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Spherical Convolution-based Saliency Detection for FoV Prediction in 360-degree Video Streaming 19

Spherical Convolution-based Saliency Detection for FoV Predi...

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19th IEEE International Wireless Communications and Mobile Computing (IEEE IWCMC)

作者： Peng, Shuai Hu, Jialu Li, Zitong Xiao, Han Yang, Shujie Xu, Changqiao Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9798350333398

Field of view (FoV) prediction is a crucial issue in 360 degrees video streaming, which is the basis for selectively transmitting panoramic videos to reduce bandwidth. The saliency feature is a very important part of FoV prediction. The saliency area identifies a user's region of interest (RoI) and reflects the user's viewing behavior preference. The regular convolutional neural network (CNN) cannot effectively extract the spatial representation of panoramic video content because significant geometric distortion will be introduced after panoramic video projection, especially in polar regions. In this paper, we propose a depth neural network model based on spherical convolution, which can learn the spatial features of the 360 degrees videos by encoding the distortion invariance into the architecture of CNNs. A series of experiments on the public 360 degrees video saliency dataset show the proposed model outperforms the existing saliency models. Finally, we embed the proposed saliency network into a popular FoV prediction framework and propose a complete FoV prediction framework for 360 degrees video streaming.

关键词： 360-degree video streaming saliency detection viewport prediction spherical convolution

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Exploration on 2D DOA Estimation of Linear Array Motion:Uniform Linear Motion

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China Communications 2023年第11期20卷 78-95页

作者： Jianhong Chu Zhi Zhang Yu Guo School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

Generally,due to the limitation of the dimension of the array aperture,linear arrays cannot achieve two-dimensional(2D)direction of arrival(DOA)*** the emergence of array motion provides a chance for *** this paper,a generalized motion scheme and a novel method of 2D DOA estimation are proposed by exploring the linear array *** be specific,the linear arrays are controlled to move along an arbitrary direction at a constant velocity and snap per fixed time *** the received signals are processed to synthesize the comprehensive observation vector for an extended 2D virtual ***,since most of 2D DOA estimation methods are not universal to our proposed motion scheme and the reduced-dimensional(RD)method fails to handle the case of the coupled parameters,a decoupled reduced-complexity multiple signals classification(DRC MUSIC)algorithm is designed *** results demonstrate that:a)our proposed scheme can achieve underdetermined 2D DOA estimation just by the linear arrays;b)our designed DRC MUSIC algorithm has the good properties of high accuracy and low complexity;c)our proposed motion scheme with the DRC method has better universality in the motion direction.

关键词： 2D DOA estimation linear array motion reduced complexity synthetic aperture

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Data-Efficient Adaptive Global Pruning for Convolutional Neural Networks in Edge Computing

Data-Efficient Adaptive Global Pruning for Convolutional Neu...

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IEEE International Conference on Communications (IEEE ICC)

作者： Gao, Zhipeng Sun, Shan Mo, Zijia Rui, Lanlan Yang, Yang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9781538674628

Deep convolutional neural networks are hindered from empowering resource-constrained devices due to their demanding computational and storage resources. Structured pruning effectively removes the redundant components from neural networks and obtains compact models. Previous pruning methods usually evaluate the importance of filters from a layer-wise perspective, which is deprived of global guidance. We propose an adaptive pruning algorithm based on relevance scores to evaluate the contribution of each channel by calculating its relevance score from the back-propagation of the neural network's output. Our method identifies and removes channels with low contribution from a global perspective. Unlike previous methods that manually set the pruning rate for each pruning iteration, our method adaptively adjusts the pruning rate. In addition, our method performs satisfactorily with limited data for one-shot pruning in the absence of fine-tuning. The ability to obtain compact models through one-shot pruning with limited data is ideally suited for edge computing scenarios. We validate the effectiveness of our method with multiple combinations of convolutional neural networks and datasets. Our approach outperforms existing pruning methods in scenarios with limited data.

关键词： Edge computing convolutional neural networks structured pruning data-efficient model compression

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Cooperation-Based Joint Active and Passive Sensing With Asynchronous Transceivers for Perceptive Mobile Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第10期23卷 15627-15641页

作者： Jiang, Wangjun Wei, Zhiqing Yang, Shaoshi Feng, Zhiyong Zhang, Ping Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China Minist Educ Key Lab Universal Wireless Commun Beijing 100876 Peoples R China State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Perceptive mobile network (PMN) is an emerging concept for next-generation wireless networks capable of conducting integrated sensing and communication (ISAC). A major challenge for realizing high performance sensing in PMNs is how to deal with spatially separated asynchronous transceivers. Asynchronicity results in timing offsets (TOs) and carrier frequency offsets (CFOs), which further cause ambiguity in ranging and velocity sensing. Most existing algorithms mitigate TOs and CFOs based on the line-of-sight (LOS) propagation path between sensing transceivers. However, LOS paths may not exist in realistic scenarios. In this paper, we propose a cooperation based joint active and passive sensing scheme for the non-LOS (NLOS) scenarios having asynchronous transceivers. This scheme relies on the cross-correlation cooperative sensing (CCCS) algorithm, which regards active sensing as a reference and mitigates TOs and CFOs by correlating active and passive sensing information. Another major challenge for realizing high performance sensing in PMNs is how to realize high accuracy angle-of-arrival (AoA) estimation with low complexity. Correspondingly, we propose a low complexity AoA algorithm based on cooperative sensing, which comprises coarse AoA estimation and fine AoA estimation. Analytical and numerical simulation results verify the performance advantages of the proposed CCCS algorithm and the low complexity AoA estimation algorithm.

关键词： Sensors Estimation Synchronization Accuracy Complexity theory Antenna arrays Wireless communication Joint active and passive sensing cooperative sensing integrated sensing and communication (ISAC) angle-of-arrival (AoA) timing offset (TO) carrier frequency offset (CFO)

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