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One-Dimensional Bilateration Localization Algorithm Based on Indoor Distributed Antenna systems

One-Dimensional Bilateration Localization Algorithm Based on...

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IEEE Conference on Wireless Communications and networking

作者： Ruoqian Hu Xinghe Chu Zhaoming Lu Luhan Wang 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

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Indoor spaces have already been accounted for a significant proportion of people's lives, and with the advent of the fifth-generation mobile communication technology (5G) era and the rise of the Internet of Things (IoT), the demand for indoor localization is further increasing. Current solutions for indoor positioning, such as Bluetooth, Wi-Fi, ultra-wideband (UWB), and pseudo-satellite technologies, have faced issues in terms of cost, accuracy, and scalability during the promotion and application process. In this regard, distributed antenna systems (DAS) have an absolute advantage in terms of indoor coverage for communication, which shows enormous potential for fusion with current indoor positioning technologies. This paper proposed a universal one-dimensional localization algorithm fusing UWB technology with the indoor DAS system, aimed at narrow indoor environments such as corridors and cable tunnels, which can achieve high-precision localization as evaluated in a real scenario of an indoor corridor in the beijing University of Posts and Telecommunications (BUPT). Additionally, our algorithm requires only two anchors, effectively reducing positioning costs while improving accuracy. The results indicate that this localization technique effectively meets the requirements defined for indoor scenarios in 3GPP 38.855, with an error of decimeter level.

关键词： Location awareness Costs Accuracy Scalability Ultra wideband antennas Telecommunications Internet of Things 3GPP Wireless fidelity Antenna arrays

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Dynamic Acceleration-Aware Risk Potential Field Car-Following Model for Heterogeneous Individuals

Dynamic Acceleration-Aware Risk Potential Field Car-Followin...

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IEEE Conference on Wireless Communications and networking

作者： Yuchen Liu Chengyu Wang Zhengrui Shi Luhan Wang Yawen Chen Sirui Li Xiangming Wen Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In the developing phase of connected autonomous driving, roads are populated with heterogeneous individuals, i.e., connected and unconnected vehicles, autonomous driving and human driving, resulting in intricate interactions and significant uncertainty in their motion trajectories. At present, the research of modeling under mixed traffic scenarios has the following challenges: 1) Traditional path planning ignores dynamic risks from the third-order vehicle state, i.e., acceleration; 2) Existing microscopic models lack the descriptions for the diverse decision-making patterns among heterogeneous individuals, which increases the uncertainty and complexity of the scene. In this paper, we propose a novel Risk Potential Field car-Following Model (RPFFM) tailored for heterogeneous individuals. It employs differentiated parameter adjustments, integrating forward propulsion with potential field repulsion to calculate vehicle acceleration. RPFFM achieves proactive and safe car-following by perceiving the change of dynamic obstacle acceleration through dynamic factors and converting it into the variation of the dynamic potential field. Experimental results based on the Next Generation Simulation (NGSIM) dataset demonstrate that the RPFFM surpasses both the Intelligent Driver Model (IDM) and Optimal Velocity Model (OVM) in terms of effectiveness, safety, and providing enhanced car-following comfort.

关键词： Adaptation models Uncertainty Microscopy Propulsion Road safety Safety Trajectory Vehicle dynamics Autonomous vehicles Vehicles

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Precise Timing Based on Software-Defined Time-Sensitive networking in Smart City

Precise Timing Based on Software-Defined Time-Sensitive Netw...

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IEEE International Conference on Communications in China (ICCC)

作者： Mufeng Zhang Guochu Shou Hongxing Li Yaqiong Liu Yihong Hu Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Communications Beijing China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Smart city infrastructure, as entities with multisensory perception and heterogeneous data fusion, presents new temporal challenges. With the increasing reliance on applications such as Cyber-Physical systems (CPS) for real-time interaction, high-frequency financial transactions, data centers, digital twins, augmented reality, and the metaverse, there is a critical need for data with accurate timestamps. This paper proposes a precise timing scheme based on Software-Defined Time-Sensitive networking (SD-TSN), utilizing a paradigm combining SDN and TSN to address unified control and management of data timestamping across diverse smart city scenarios. By employing software timestamping at time-sensitive nodes, sub-millisecond precision is achieved, while hardware timestamping achieves sub-microsecond precision. Leveraging standardized TSN networks economically and effectively fulfills the timing precision requirements for data timing needs in different scenarios.

关键词： Accuracy Smart cities Metaverse Diversity reception Data integration Software Hardware

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A UCA-Based Orbital Angular Momentum Solution for Integrated Sensing and Communication systems

A UCA-Based Orbital Angular Momentum Solution for Integrated...

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IEEE Conference on Wireless Communications and networking

作者： Yihong Zheng Shumin Wang Xinghe Chu Wei Zheng Zhiqun Hu Zhaoming Lu Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In the sixth generation (6G) Internet of Things (IoT), integrated sensing and communication (ISAC) emerges as a key technology, which is expected to significantly enhance the perception capabilities and spectrum efficiency of base stations (BSs). It holds potential for applications in unmanned aerial vehicle (UAV) monitoring, vehicle positioning, and crowd detection. However, developing an integrated waveform that efficiently conserves spectrum resources while maintaining lower complexity remains challenging. This paper designs an ISAC system that utilizes orbital angular momentum (OAM) waves generated by a uniform circular array (UCA), which enhances communication and sensing capabilities by allocating distinct modes. This paper proposes a multimode multiplexing communication scheme based on a single UCA and an OAM-circular reception method (OAM-CRM) for the two-dimensional direction-of-arrival (DOA) estimation of targets, encompassing both azimuth and elevation angles. Additionally, an OAM-different modes (OAMDM) algorithm is designed to optimize communication and sensing performance across various mode sets. Simulation results verify the effectiveness of the OAM ISAC system and demonstrate the superior performance of the proposed algorithms compared to conventional methods.

关键词： Multiplexing Orbital calculations Direction-of-arrival estimation Accuracy Simulation Estimation Integrated sensing and communication Approximation algorithms Autonomous aerial vehicles Internet of Things

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Clock Drift and Time Error Compensation with Neural network for Time Synchronization in TSN

Clock Drift and Time Error Compensation with Neural Network ...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Yiheng Yang Chenlong Yao Guochu Shou Yaqiong Liu Zhifu Li Hongxing Li 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

TSN implements time synchronization through IEEE 802.1AS protocol, and the performance of time synchronization is related to the frequency accuracy and stability of each TSN clock. However, the problems of clock drift and timestamp granularity error (TSGE) will degrade the network time synchronization performance. This paper analyzes the causes of time errors caused by network time synchronization, and puts forward the overall scheme to improve the performance of time synchronization. An intelligent compensation algorithm for clock drift is designed, which includes measuring node clock correlation data, training neural network, using neural network to make prediction and compensating time stamp. Experimental results show that the proposed algorithm is stable and accurate in time synchronization.

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Continuous Trajectory Tracking Across Sensors at Intersection using Dynamic Target Set within the Overlapping Area

Continuous Trajectory Tracking Across Sensors at Intersectio...

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International Conference on Intelligent Transportation

作者： Xianning Cheng Jiayin Deng Zhiqun Hu Zhaoming Lu Xiangming Wen Beijing Key Laboratory for Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

Across-sensor tracking is an important technology to realize global perception in the field of next-generation intelligent transportation. However, identifying targets within an overlapping area and merging trajectories from different sensors remain challenging. Previous methods require high labor costs to manually identify an overlapping area and neglect the problem of noise and occlusion when data present inconsistency. To address these issues, this paper proposes a novel across-sensor tracking method at intersection. The proposed method adaptively identifies the target set for the overlapping area based on the Gaussian product of target states from two sensors and fuses trajectories based on Arithmetic Average algorithm. Additionally, the paper improves the birth model of the Poisson Multi-Bernoulli Mixture filter in order to handle complex intersection situations. Extensive simulation and real-world experiments evaluate the method's effectiveness, and its superior performance compared to previous methods.

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EM-Mamba: An Edge-Mix Enhanced Long-Range Sequential Modeling Mamba for Kidney Segmentation in CT Scans 10

EM-Mamba: An Edge-Mix Enhanced Long-Range Sequential Modelin...

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10th IEEE Smart World Congress, SWC 2024

作者： Feng, Shaowei Li, Zheng Zheng, Mengmeng Yang, Yang Wang, Xin Guo, Caili Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing100876 China China Telecom Digital Intelligence Technology Co. Ltd Beijing100089 China Capital Medical University Beijing Friendship Hospital Department of Urology Beijing100050 China Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing100876 China

ISBN: (纸本)9798331520861

Manual delineation of the kidney region in CT scans is a laborious process. Although existing models have achieved remarkable success in the realm of automated segmentation, they still encounter challenges in effectively modeling both the global and local information. To address this, we introduce Edge-Mix enhanced Mamba (EM-Mamba) for kidney segmentation, which is designed to capture global and local information from multi-scales. EM-Mamba leverages SegMamba as its backbone, utilizing Mamba's efficiency in extracting long-range dependencies. To further enhance the model's integration of local and global information, we introduce the Edge-Mix module, which captures edge information from the shallow layers of the network during the encoding phase and merges it with multi-scale features during the decoding phase. Additionally, a Multi-Scale Convolution (MSC) module is designed to enhance the capability of extracting effective information from 3D medical images, achieving a fusion of rich local and global information across multiple scales. The innovation of the EM-Mamba model lies in its integration of detailed and global information, as well as its robust feature extraction ability in 3D medical image analysis, offering a new solution for precise kidney segmentation. The experimental results show that our framework achieving a Dice score of 9 1. 9 5 % on the KiTS21 dataset and 86.35 % Dice score on our proprietary dataset called Kidney Parenchyma Segmentation (KPS). © 2024 IEEE.

关键词： Edge-Mix Mamba Medical image segmentation Total Kidney Volume

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Attention Interactive Graph Convolutional network for Skeleton-Based Human Interaction Recognition

Attention Interactive Graph Convolutional Network for Skelet...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Gao, Feng Xia, Hailun Tang, Zhihao Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing100876 China Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing100876 China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Skeleton-based Human interaction action recognition is a great challenge in the field of human activity analysis. The existing graph convolutional networks (GCNs) based recognition methods ignore the spatial-temporal interactive features between two persons. Therefore, we propose Attention Inter-active Graph Convolutional network(AIGCN) to explore hu-man interaction. First, we design an Interactive Attention En-coding GCN(IAE-GCN) module to extract interactive spatial structures, reflecting their influence on each other by regarding joint position encoding as semantic information. Second, we propose Interactive-Attention Mask TCN (IAM-TCN) that extracts temporal interactive features, representing temporal attentional excitation signal among the different joints of dif-ferent people along the time. Finally, we verify the effectiveness of our method on classic human interaction datasets SBU and interactive action sub-datasets of NTU-RGB+D and NTU-RGB+D 120, and experiment shows that our models achieve state-of-the-art performance. © 2022 IEEE.

关键词： Convolution

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Image Segmentation Semantic Communication over Internet of Vehicles

Image Segmentation Semantic Communication over Internet of V...

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IEEE Conference on Wireless Communications and networking

作者： Qiang Pan Haonan Tong Jie Lv Tao Luo Zhilong Zhang Changchuan Yin Jianfeng Li Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

In this paper, the problem of semantic-based efficient image transmission is studied over the Internet of Vehicles (IoV). In the considered model, a vehicle shares massive amount of visual data perceived by its visual sensors to assist other vehicles in making driving decisions. However, it is hard to maintain a high reliable visual data transmission due to the limited spectrum resources. To tackle this problem, a semantic communication approach is introduced to reduce the transmission data amount while ensuring the semantic-level accuracy. Particularly, an image segmentation semantic communication (ISSC) system is proposed, which can extract the semantic features from the perceived images and transmit the features to the receiving vehicle that reconstructs the image segmentations. The ISSC system consists of an encoder and a decoder at the transmitter and the receiver, respectively. To accurately extract the image semantic features, the ISSC system encoder employs a Swin Transformer based multi-scale semantic feature extractor. Then, to resist the wireless noise and reconstruct the image segmentation, a semantic feature decoder and a reconstructor are designed at the receiver. Simulation results show that the proposed ISSC system can reconstruct the image segmentation accurately with a high compression ratio, and can achieve robust transmission performance against channel noise, especially at the low signal-to-noise ratio (SNR). In terms of mean Intersection over Union (mIoU), the ISSC system can achieve an increase by 75%, compared to the baselines using traditional coding methods.

关键词： Wireless communication Image segmentation Visualization Wireless sensor networks Image communication Semantics Feature extraction

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Beam Tracking Based on Multi-Point Radar Sensing and DNN Prediction

Beam Tracking Based on Multi-Point Radar Sensing and DNN Pre...

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

作者： Kean Chen Danpu Liu Zhilong Zhang Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

Millimeter-Wave (mmWave) communication based on massive MIMO and beamforming has become a hotspot owing to its advantage on capacity. However, a rapidly varying mm Wave channel in high mobility scenario will lead to frequent beam switching and high signaling overhead. Integrated Sensing and Communication (ISAC) gives a new idea to the above challenges by using radar sensing to assist beam management. Nevertheless, most of the sensing -assisted beam tracking are limited to the scenario where Line-of-Sight (LOS) paths exist, and fail when a blockage occurs. To address this issue, a beam tracking scheme based on multi-point radar sensing and Deep Neural network (DNN) is proposed in this paper, where the sensing information from multiple radars are aggregated and a DNN is utilized to predict the beam direction by learning the channel nonlinear correlation in a specific environment. The simulation results indicate that the proposed scheme operates effectively in blockage scenario, and surpasses the baselines in the field of spectral efficiency.

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