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IEEE Conference on Wireless Communications and Networking

作者： Wanyu Meng Xinghe Chu Zhaoming Lu Luhan Wang Xiangming Wen Meiling Li Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Taiyuan University of Science and Technology Taiyuan China

With the development of the 5G mobile communication system, the network infrastructure is becoming more widely deployed. Ubiquitous 5G wireless signals bring new opportunities to the positioning of connected autonomous vehicles. Radio-based positioning can utilize the existing 5G network infrastructure to locate connected vehicles without additional sensor equipment installed in the vehicle. Moreover, location-related information of vehicles can be obtained from V2V communication supported by 3GPP, which provides a new way to increase positioning accuracy. In this paper, we develop a V2V communication assisted cooperative localization algorithm that exploits the location-related information in V2V communication and the multipath radio signals received by vehicles to further improve localization accuracy. A Bayesian model is derived for feature-based simultaneous localization and mapping (SLAM) according to the information exchanged between vehicles and the characteristics of the multipath components, and a cooperative belief propagation algorithm is used to locate vehicles on a factor graph. Simulation results show that this algorithm has better positioning performance than non-V2V-cooperative scenarios.

关键词： Location awareness Wireless communication Wireless sensor networks Simultaneous localization and mapping Connected vehicles 5G mobile communication Simulation

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Vehicle localization via cooperative channel mapping

arXiv

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arXiv 2021年

作者： Chu, Xinghe Lu, Zhaoming Gesbert, David Wang, Luhan Wen, Xiangming Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China Communications Systems Department EURECOM Sophia Antipolis France

This paper addresses vehicle positioning, a topic whose importance has risen dramatically in the context of future autonomous driving systems. While classical methods that use GPS and/or beacon signals from network infrastructure for triangulation tend to be sensitive to multi-paths and signal obstruction, our method exhibits robustness with respect to such phenomena. Our approach builds on the recently proposed Channel-SLAM method which first enabled leveraging of multipath so as to improve (single) vehicle positioning. Here, we propose a cooperative mapping approach which builds upon the Channel-SLAM concept, referred to here as Team Channel-SLAM. Team Channel-SLAM not only exploits the stationary nature of many reflecting objects around the vehicle, but also capitalizes on the multi-vehicle nature of road traffic. The key intuition behind our method is the exploitation for the first time of the correlation between reflectors around multiple neighboring vehicles. An algorithm is derived for reflector selection and estimation, combined with a team particle filter (TPF) so as to achieve high precision simultaneous multiple vehicle positioning. We obtain large improvement over the single-vehicle positioning scenario, with gains being already noticeable for moderate vehicle densities, such as over 40% improvement for a vehicle density as low as 4 vehicles in 132 meters’ length road. Copyright © 2021, The Authors. All rights reserved.

关键词： Reflection

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TAX-B2BREC: Multi-Stage Cascade Downstream Company Recommender system Based on Taxation Data

TAX-B2BREC: Multi-Stage Cascade Downstream Company Recommend...

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

作者： Shengmin Wang Yi Chu Zhenhao Qiao Lan Ma Hao Zhang Zehao Wang Yuanyuan Qiao Beijing Laboratory of Advanced Information Networks & Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China The Technology Research Institute Aisino Corporation Beijing China

ISBN: (纸本)9781665405836

The public crisis triggered by the COVID-19 pandemic has disastrous effects for B2B markets. With the supply chain and trade disrupted, the benefits of the company have been affected to varying degrees. In order to help companies find potential customers and recover the supply chain, we propose a multi-stage cascade downstream company recommender system based on taxation data. The proposed system can recommend potential buyers for upstream companies, which can help upstream companies find new sales channels. This system includes data processing, matching module, ranking module and system deployment. In the match module, we propose a hybrid recall algorithm to generate the candidate enterprises. In the ranking module, we use DCNV2 model to rank the candidate companies. Moreover, the multistage cascade recommendation algorithm achieves better results compared with the traditional algorithm in B2B recommender system.

关键词： COVID-19 Pandemics Conferences Supply chains Companies Data processing Hybrid power systems

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3D-Printed Sub-Terahertz Lens for Manipulation of Deflective Quasi-Non-Diffractive OAM Waves

3D-Printed Sub-Terahertz Lens for Manipulation of Deflective...

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Asia-Pacific Conference on Microwave

作者： Xiaoyuan He Li Deng Yang Yang Beijing Laboratory of Advanced Information Networks & Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China School of Electrical and Data Engineering Tech Lab University of Technology Sydney Australia

A 3D-printed sub-terahertz lens for manipulating quasi-non-diffractive orbital angular momentum (OAM) waves is proposed in this paper. The proposed lens comprises all-dielectric elements that impose desired phase profiles on the incident waves by varying their height. Based on the concept of the Bessel beam launchers and the beam deflectors, quasi-non-diffractive OAM waves with a pre-defined deflection angle can be produced by the proposed lens. For verification, two lenses were 3D printed for producing OAM waves with a non-diffractive depth of 55.58λ c (λ c is the free-space wavelength at 140 GHz). Measured by a terahertz imaging camera, the desired quasi-non-diffractive OAM waves were observed behind the proposed lenses with expected non-diffractive performance and a large deflection angle of up to 47°.

关键词： Microwave measurement Terahertz wave imaging Three-dimensional displays Wavelength measurement Extraterrestrial measurements Cameras Orbits

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Roadside 3D Extended Object Tracking by Fusing Sparse Radar Points and Pixel Keypoints

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IEEE Sensors Journal 2025年

作者： Deng, Jiayin Hu, Zhiqun Xia, Yuxuan Lu, Zhaoming Wen, Xiangming Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing100876 China Linköping University Department of Electrical Engineering Linköping58183 Sweden

Roadside perception is a key component in intelligent transportation systems. In this paper, we present a novel three-dimensional (3D) extended object tracking (EOT) method, which simultaneously estimates the object kinematics and extent state, in roadside perception using both the radar and camera data. Because of the influence of sensor viewing angle and limited angle resolution, radar measurements from objects are sparse and non-uniformly distributed, leading to inaccuracies in object extent and position estimation. To address this problem, we present a novel spherical Gaussian function weighted Gaussian mixture model. This model assumes that radar measurements originate from a series of probabilistic weighted radar reflectors on the vehicle’s extent. Additionally, we utilize visual detection of vehicle keypoints to provide additional information on the positions of radar reflectors. Since keypoints may not always correspond to radar reflectors, we propose an elastic skeleton fusion mechanism, which constructs a virtual force to establish the relationship between the radar reflectors on the vehicle and its extent. Furthermore, to better describe the kinematic state of the vehicle and constrain its extent state, we develop a new 3D constant turn rate and velocity motion model, considering the complex 3D motion of the vehicle relative to the roadside sensor. Finally, we apply variational Bayesian approximation to the intractable measurement update step to enable recursive Bayesian estimation of the object’s state. Simulation results using Carla simulator and experimental results on the nuScenes dataset demonstrate the effectiveness and superiority of the proposed method in comparison to several state-of-the-art 3D EOT methods. © 2001-2012 IEEE.

关键词： Gaussian distribution

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Broadband Optically Transparent Absorber and Multi-Band Stealth Based on Organic Metasurface

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Chinese Physics Letters 2025年第5期42卷 93-99页

作者： Chuwen Lan Yutong Gao Zehua Gao Hongye Wang Ke Bi Ming Lei Guoan Zhao School of Information and Communication Engineering Beijing Laboratory of Advanced Information NetworksBeijing Key Laboratory of Network System Architecture and ConvergenceBeijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Information Photonics and Optical Communications School of ScienceBeijing University of Posts and TelecommunicationsBeijing 100876China School of Integrated Circuits Beijing University of Posts and TelecommunicationsBeijing 100876China Network Education College of Beijing University of Posts and Telecommunications Beijing 100876China

Optically transparent microwave absorbers and multi-band stealth have extensive potential applications in military defense and wireless communication fields, and thus have attracted considerable attention. So far,most related work is based on inorganic transparent conductive metasurfaces. In this paper, we proposed and experimentally demonstrated a flexible, broadband and optically transparent microwave absorber using an organic metasurface. The metasurface absorber is composed of a sandwich structure, in which electric resonances and magnetic resonances are induced resulting in broadband absorption. A spraying process was developed to prepare this metasurface absorber. Both simulations and experiment show that this metasurface has broadband microwave absorption and good optical transparency. We further found that by using a multi-layer structure, visible, radar,and infrared stealth(multi-band stealth) can be achieved simultaneously. With the advantages of excellent foldability and low cost, the proposed metasurfaces may have applications in military and wireless communication fields.

关键词： microwave absorber broadband microwave absorption organic metasurface metasurface absorber microwave absorbers inorganic transparent conductive metasurfaces military defense sandwich structure

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Hybrid model enabling highly efficient follicular segmentation in thyroid cytopathological whole slide image

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Intelligent Medicine 2021年第2期1卷 70-79页

作者： Chuang Zhu Siyan Tao Huang Chen Minzhen Li Ying Wang Jun Liu Mulan Jin Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and ConvergenceSchool of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijing 100876China Department of Pathology China-Japan Friendship HospitalBeijing 100029China Department of Pathology Beijing Chao-Yang HospitalCapital Medical UniversityBeijing 100020China

Background The prevalence of thyroid cancer is growing *** and precise diagnosis is critical in thy-roid cancer *** automatic thyroid cancer diagnostic tool can be valuable to achieve early detection and diagnostic *** the follicular areas in the sample contain useful information to the thyroid cancer diagnosis based on fine needle aspiration(FNA).This study aimed to develop a highly efficient accurate method for follicular cell areas segmentation(FCAS)of thyroid cytopathological whole slide images(WSIs).Methods A total of 96 cell samples from July 2017 to July 2018 were collected in one hospital in Beijing,***-three WSIs were selected and manually labeled,including 17 cases of papillary thyroid carci-noma sample and 26 cases of benign *** thousand and nine hundred cropped typical image patches(available on https://***/bupt-ai-cz/Hybrid-Model-Enabling-Highly-Efficient-Follicular-Segmentation)of 1024×1024 pixels from 13 large WSIs were used for patch-level model training and testing and all of the 13 large WSIs were papillary thyroid carcinoma *** testing WSIs with an average size 36,217×29,400(from 10,240×10,240 to 81,920×61,440)were used to test the effectiveness of the hybrid *** on the traditional semantic segmentation model deeplabv3,we constructed a hybrid segmentation architecture by adding a classification branch into the segmentation scheme to improve *** was used to measure the performance of the classification model;pixel accuracy(pAcc),mean accuracy(mAcc),mean intersection over union(mIoU),and frequency weighted intersection over union(fwIoU)were used to measure the performance of the segmentation model,*** Using this method,up to 93%WSI segmentation time was reduced by skipping the colloidal areas and the blank background *** average processing time of 30 WSI was 49.49 *** the patch dataset,this hybrid model might reach pAcc=98.65%,mAcc=85.60%,mI

关键词： Thyroid cancer Hybrid model Follicular cell areas segmentation Whole slide image

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Leveraging multiple legacy Wi-Fi links for human behavior sensing

arXiv

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arXiv 2021年

作者： Guo, Lingchao Lu, Zhaoming Wen, Xiangming Wang, Liming Gesbert, David Han, Zijun Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks The School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Communications Systems Department EURECOM Sophia Antipolis France

Taking advantage of the rich information provided by Wi-Fi measurement setups, Wi-Fi-based human behavior sensing leveraging Channel State Information (CSI) measurements has received a lot of research attention in recent years. The CSI-based human sensing algorithms typically either rely on an explicit channel propagation model or, more recently, adopt machine learning so as to robustify feature extraction. In most related work, the considered CSI is extracted from a single dedicated Access Point (AP) communication setup. In this paper, we consider a more realistic setting where a legacy network of multiple APs is already deployed for communications purposes and leveraged for sensing benefits using machine learning. The use of legacy network presents challenges and opportunities as many Wi-Fi links can present with richer yet unequally useful data sets. In order to break the curse of dimensionality associated with training over a too large dimensional CSI, we propose a link selection mechanism based on Reinforcement Learning (RL) which allows for dimension reduction while preserving the data that is most relevant for human behavior sensing. The method is based on a sequential state decision-making process in which the CSI is modeled as a part of the state. From actual experiment results, our method is shown to perform better than state-of-the-art approaches in a scenario with multiple available Wi-Fi links. Copyright © 2021, The Authors. All rights reserved.

关键词： Channel state information

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A Joint Transmit and Receive Design for Dimmable High Speed MC MU VLC systems

A Joint Transmit and Receive Design for Dimmable High Speed ...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Yujie Yang Yang Yang Hailun Xia Caili Guo Mahdi Chehimi Walid Saad Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of. Posts and Telecommunications Beijing China Bradley Department of Electrical and Computer Engineering Wireless@VT Group Virginia Tech Blacksburg VA USA

ISBN: (纸本)9781665435413

In multi-cell multi-user multiple-input multiple-output (MC MU-MIMO) visible light communication (VLC) systems, each user is equipped with multiple closely-placed photodiodes (PDs) with similar channel gains, leading to severe inter-cell interference and intra-cell interference. To address this problem, this paper proposes a hybrid dimming (HD) scheme with MIMO VLC transceiver design, which jointly optimizes transmit and receive antenna selection (TRAS), cell clustering and precoding (TRASP-HD) for MC MU-MIMO VLC systems. In this scheme, a sum-rate maximization problem under the dimming level and illumination uniformity is formulated and solved by being divided into two sub-problems. In particular, The first sub-problem is on TRAS and cell formation based on the criterion of sum-rate maximization under the illumination uniformity constraint. With the same goal, the second sub-problem is on optimizing the precoding matrices of each cell. Finally, these two sub-problems are iteratively solved to obtain a convergent solution. Simulation results verify that in a typical indoor scenario, the mean bandwidth efficiency of TRASP-HD scheme is 2.36 bit/s/Hz higher than the conventional MC MU-MIMO system.

关键词： Spectral efficiency Precoding Simulation Lighting Receiving antennas Transceivers MIMO

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Learning multiscale correlations for human motion prediction

arXiv

引用

arXiv 2021年

作者： Zhou, Honghong Guo, Caili Zhang, Hao Wang, Yanjun Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing China China Telecom Dict Application Capability Center China

In spite of the great progress in human motion prediction, it is still a challenging task to predict those aperiodic and complicated motions. We believe that capturing the correlations among human body components is the key to understand the human motion. In this paper, we propose a novel multiscale graph convolution network (MGCN) to address this problem. Firstly, we design an adaptive multiscale interactional encoding module (MIEM) which is composed of two sub modules: scale transformation module (STM) and scale interaction module (SIM) to learn the human body correlations. Secondly, we apply a coarse-to-fine decoding strategy to decode the motions sequentially. We evaluate our approach on two standard benchmark datasets for human motion prediction: Human3.6M and CMU motion capture dataset. The experiments show that the proposed approach achieves the state-of-the-art performance for both short-term and long-term prediction especially in those complicated action category. We make codes publicly available at https://***/zhouhongh/MGCN. © 2021, CC BY-NC-ND.

关键词： Motion estimation

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