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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhang, Hongyi Wu, Muqing Zhao, Min Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing China

ISBN: (纸本)9781665450515

As one of the most important tasks in the computer vision, vehicle re-identification aims to retrieve and identify the same vehicle under different surveillance cameras, which plays a key role in urban road traffic safety and intelligent traffic management system. However, the large intra-class difference and high inter-class similarity are still main challenges, as well as the diversity in lighting conditions, camera's shooting angle, and occlusion degrees. In order to further improve the average accuracy and algorithm performance, this paper proposes a vehicle re-identification algorithm based on multi-feature fusion and non-local operation. We embed non-local operation into the ResNet50 network, and employ feature slicing and reorganization to obtain multiple feature branches. Besides, learning rate warm-up and cosine annealing scheduler are also used. The experimental results show that our proposed method achieves higher accuracy on two commonly used datasets VeRi-776 and VehicleID. © 2022 IEEE.

关键词： Vehicles

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Positioning and Dynamic Tracking under 3GPP Cellular network on GNURadio Testbed

Positioning and Dynamic Tracking under 3GPP Cellular Network...

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2023 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Chang, Shunyu Lu, Zhaoming Chu, Xinghe Zhang, Di Wang, Luhan Wen, Xiangming Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665494274

This paper addresses high precision 5G positioning and dynamic tracking, which has risen increasingly attention from industry and academia recently. The recent works show that the position of user equipment (UE) could be estimated based on reference signals (e.g. PRS) in 5G on real testbed. However, these works failed to exploit the positional information behind the consecutive nature of users' moving, which could enhance the positioning accuracy by exploiting dynamic tracking architectures. In this paper, we extract the angle of arrival (AoA) and time of arrival (ToA) under 3GPP physical layer protocol, and then introduce a dynamic tracking method to improve the accuracy of AoA and ToA estimation. The performance of the proposed method was evaluated on GNURadio. The results show that the AoA and ToA could be precisely extracted in the real propagation channel on the premise of guaranteeing the communication performance. And in the case of the same bandwidth and number of antennas, compared with traditional base station (BS) tracking method, our dynamic tracking could reduce tracking error by 35.11% during the 115s tracking process. © 2023 IEEE.

关键词： Testbeds

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Optimization of SVM and ANN Based on GAPA and Its Application in Short-Term Load Forecasting 6

Optimization of SVM and ANN Based on GAPA and Its Applicatio...

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6th IEEE International Conference on Energy Internet, ICEI 2022

作者： Zhang, Jingyi Wang, Yueting Jing, Wenpeng Lu, Zhaoming Wen, Xiangming Liu, Yong Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing China

ISBN: (纸本)9781665493277

Widespread employment of renewable energy such as wind and solar pushes power grids to move towards comprehensive data and predictive analysis. At present, a large number of researches have been conducted especially on machine learning methods to achieve load forecast. However, premature convergence and redundant iteration are two major defects of existing machine learning-based load forecasting methods, resulting in poor prediction effect and high time consumption. In this paper, a novel combined intelligent optimization algorithm based on genetic algorithm (GA), artificial fish swarm algorithm (AFSA) and particle swarm optimization (PSO) is proposed for optimizing machine learning-based load forecasting models. By replacing GA's mutation process with AFSA operator and PSO operator, the proposed algorithm named GA-AFSA-PSO Algorithm (GAPA) enhances both global search ability and local search ability, leading to its high prediction accuracy and fast convergence speed. To validate its effectiveness, GAPA is applied to the optimization of support vector machine (SVM) and artificial neural network (ANN) to predict one-day ahead load data. Moreover, two different sets of comparative tests are carried out to confirm the advantages of GAPA. The simulation results illustrate that, compared with GA, AFSA, PSO, AFSA-GA and GA-PSO, GAPA brings forth advancement in prediction accuracy, convergence rate and global search ability. © 2022 IEEE.

关键词： Genetic algorithms

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Perception-Enhanced Multitask Multimodal Semantic Communication for UAV-Assisted Integrated Sensing and Communication system

arXiv

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arXiv 2025年

作者： Guo, Ziji Tong, Haonan Zhang, Zhilong Liu, Danpu Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing100876 China

Recent advances in integrated sensing and communication (ISAC) unmanned aerial vehicles (UAVs) have enabled their widespread deployment in critical applications such as emergency management. This paper investigates the challenge of efficient multitask multimodal data communication in UAV-assisted ISAC systems, in the considered system model, hyperspectral (HSI) and LiDAR data are collected by UAV-mounted sensors for both target classification and data reconstruction at the terrestrial BS. The limited channel capacity and complex environmental conditions pose significant challenges to effective air-to-ground communication. To tackle this issue, we propose a perception-enhanced multitask multimodal semantic communication (PE-MMSC) system that strategically leverages the onboard computational and sensing capabilities of UAVs. In particular, we first propose a robust multimodal feature fusion method that adaptively combines HSI and LiDAR semantics while considering channel noise and task requirements. Then the method introduces a perception-enhanced (PE) module incorporating attention mechanisms to perform coarse classification on UAV side, thereby optimizing the attention-based multimodal fusion and transmission. Experimental results demonstrate that the proposed PE-MMSC system achieves 5%–10% higher target classification accuracy compared to conventional systems without PE module, while maintaining comparable data reconstruction quality with acceptable computational overheads. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk management

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MIMO Radar Aided Beam Selection in Millimeter Wave V2I Communications 8

MIMO Radar Aided Beam Selection in Millimeter Wave V2I Commu...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Li, Dianang He, Xinxin Lv, Jie Wang, Tao Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781665450515

The multiple-in-multiple-out (MIMO) radar is deployed to assist the millimeter wave (mmWave) communication with low beam training overhead in vehicle-to-infrastructure (V2I), which has attracted increasing attention. This paper proposes a structured weighted radar aided beam selection. To be specific, since the spatial information provided by radar involves inevitable inaccuracy due to the angle deviation, we calculate the probability distribution of the codewords based on the deviation angle and use it for designing the training codebook. Furthermore, beam selection is formulated as prior weighted sparse signal recovery problem, where the prior structured-weight for communication is determined by the spatial congruence with radar channel, and the difference between two frequency bands is derived to correct the weight. The resulting prior structured weight is further deployed to the orthogonal matching pursuit algorithm to enhance its performance. Simulation results demonstrate that the proposed method provides the efficient and reliable beam selection performance of the mobility mmWave channel with fewer training overhead. © 2022 IEEE.

关键词： Millimeter waves

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Semantic-driven Computation Offloading and Resource Allocation for UAV-assisted Monitoring system in Vehicular networks 48

Semantic-driven Computation Offloading and Resource Allocati...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Sun, Xin Chen, Jiujiu Guo, Caili Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing 100876 China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

In the vehicular networks monitoring scenario where the unmanned aerial vehicle (UAV) assists the vehicle data collection and the edge-cloud server cooperates to complete the visual intelligent task (e.g. object detection), a large amount of video data needs to be transmitted. For task-oriented communication systems, traditional resource allocation schemes mainly focus on the network performance or user experience of video transmission, without considering the impact of semantic content on task performance. In this paper, we propose a semantic-driven computation offloading and resource allocation scheme, namely semantic-driven CO&RA. In the offloading decision, we propose a convolutional neural network (CNN) segmentation scheme, which makes full use of computing resources and better completes intelligent tasks. At the same time, facing the complex resource allocation problem, we design a multi-agent deep Q-network (DQN) algorithm. Last, the experimental results show that our proposed scheme has more advantages in the optimization of multiple objectives of latency, energy consumption and task performance. © 2022 IEEE.

关键词： Semantics

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A Novel Channel Estimation Method for RIS Assisted Wireless Communication systems

A Novel Channel Estimation Method for RIS Assisted Wireless ...

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International Conference on Computer and Communications (ICCC)

作者： Mingze Sun Tao Wang Nuocheng Yang Changchuan Yin Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Channel estimation (CE) can be challenging in reconfigurable intelligent surface (RIS)-assisted wireless communication systems. On the one hand, the dimensionality of the cascaded base station-RIS-user equipment (BS-RIS-UE) channel is exceedingly large. On the other hand, the currently available RIS hardware cannot arbitrarily switch between the active (reflection with configurable phase) and deactivate (absorption) modes, thereby resulting in a perpetual coupling between the BS-RIS-UE channel and BS-UE channel. To address the challenges above, we introduce a novel CE method that leverages the spatial modulation capabilities of RIS. The proposed CE strategy is bifurcated into two distinct phases: the BS-UE CE and the RIS-UE CE. In the BS-UE CE phase, the RIS is strategically configured to steer the incoming signals from the UEs away from the BS. This maneuver significantly suppresses the signal through the cascaded BS-RIS-UE channel, allowing it to be effectively disregarded. Consequently, this facilitates a separate BS-UE CE process using the conventional least squares (LS) algorithm. Armed with the estimated BS-UE channels, the RIS-UE channels can then be readily estimated. Simulation results show that, compared to the existing CE schemes, the proposed CE method can improve the estimation accuracy and reduce the computational complexity.

关键词： Wireless communication Hands Absorption Channel estimation Estimation Modulation Switches Reconfigurable intelligent surfaces Hardware Computational complexity

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Energy Efficient UAV Trajectory Design for Hovering-Flying Data Collection

Energy Efficient UAV Trajectory Design for Hovering-Flying D...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Chen, Zijing Guo, Yijun Hao, Jianjun Du, Yu Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence China Beijing Language and Culture University China

ISBN: (纸本)9781665459754

In this paper, we consider a UAV-aided data collection network where the UAV provides communication service for sensor nodes (SNs) which are distributed randomly on the ground with hovering-flying mode. We aim at minimizing the total energy consumption for the UAV to collect data from SNs, including both hovering energy and flying energy, by jointly optimizing the number of hovering points and the UAV trajectory. We use block coordinate decent (BCD) and successive convex optimization (SCO) techniques by decomposing it into two sub-problems to solve the original non-convex problem, which are alternately solved until the algorithm converges. Numerical results show that the proposed solution saves the consumed energy effectively. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Joint Optimization of Latency and Energy Consumption for Mobile Edge Computing Based Proximity Detection in Road networks

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China Communications 2022年第4期19卷 274-290页

作者： Tongyu Zhao Yaqiong Liu Guochu Shou Xinwei Yao School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and ConvergenceBeijing 100876China School of Computer Science and Technology Zhejiang University of TechnologyChina

In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene,which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing(MEC)into a constrained multiobjective optimization problem(CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.

关键词： proximity detection mobile edge computing road networks constrained multiobjective optimization

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A multipath-assisted localization algorithm under 5G-based opensource testbed 9

A multipath-assisted localization algorithm under 5G-based o...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Lu, Yuye Zheng, Wei Chu, Xinghe Lu, Zhaoming Hu, Ruoqian Li, Meiling Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Taiyuan University of Science and Technology Taiyuan China

ISBN: (纸本)9798350317251

Indoor localization services are playing an important role in a variety of emerging industries. Traditional positioning methods suffer from insufficient accuracy or high deployment costs in indoor environments, and with the rapid development of cellular mobile communication systems, positioning under cellular networks has been more widely used due to its low cost and easy deployment. In the indoor environment, the number of available base stations is limited, and positioning by direct radius information provided by only one base station suffers from insufficient accuracy. In this paper, we designed a method for indoor localization based on cellular networks, which considering the localization information carried by the multi-path, to verify the performance of this algorithm in practical scene. We implement this algorithm in a 5G-based testbed based on software-defined radio (SDR) technologies. The results show that our algorithm can meet the requirements of indoor cases defined in 3GPP 38.855. © 2023 IEEE.

关键词： Testbeds

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