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EasyWiTrack: Fine-Grained Sensing for Plug-and-Play Position Tracking with Wi-Fi

EasyWiTrack: Fine-Grained Sensing for Plug-and-Play Position...

引用

IEEE Conference on Wireless Communications and networking

作者： Jiawei Jiang Zijun Han Zhaoming Lu Xiangming Wen Gaolong Jiang Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Previous work have verified the feasibility of Wi-Fi - based indoor position tracking. However, these research rely heavily on anchors' position as prior knowledge, which lowers the deployability in practical scenarios. To this end, we propose a novel system named EasyWiTrack to realize a plug-and-play, rel-ative position tracking by establishing a time-domain correlation model to associate the target path, angle information with target motion, and deduce the target's relative position compared to the previous moment for trajectory shape without any anchor position information. During the system implementation, to address the issue of error introduced by asynchronous Wi-Fi transceivers, we adopt bi-directional data acquisition for denoising and achieve millimeter-level target path length estimation. Additionally, to eliminate ambiguity in linear array angle estimation, we employ circle array and apply Multiple Signal Classification algorithm to estimate signal azimuth. Finally through extensive experimentation, we have validated that EasyWiTrack achieves a fine-grained indoor position tracking performance with 1.48cm and 1.73cm average error respectively in LoS and NLoS scenarios with commodity Wi-Fi.

关键词： Target tracking Correlation Trajectory tracking Estimation Transceivers Trajectory Arrays

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WiFineTrack: Enabling Fine-Grained Position Tracking Using Commodity WiFi

WiFineTrack: Enabling Fine-Grained Position Tracking Using C...

引用

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Gaolong Jiang Zijun Han Zhaoming Lu Xiangming Wen Jiawei Jiang Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

With the development of integrated sensing, communication and computation (ISCC), wireless sensing technology is turning a WiFi device into a special sensor. Specifically, the current research focuses on extending the inherent communication attributes of ubiquitous WiFi signals to achieve indoor position tracking. However, previous works based on geometric positioning like triangulation are limited by the number of antennas and bandwidth, and can only achieve decimeter-level positioning accuracy. To this end, we present WiFineTrack, enabling fine-grained position tracking based on Channel State Information (CSI). Instead of representing the continuous absolute positions directly, WiFineTrack first characterizes the relative motion traces by mapping the target’s movements in relation to its prior position. Then the model correlates the angle-based triangulation with the derived relative motion traces to reconstruct the absolute motion traces. Thus, the model shifts the tracking task from traditional absolute geometry to relative length variation. WiFineTrack designs an elaborate denoising framework for tracking refinement, including phase correcting based on the bidirectional transmission mechanism and accumulated error suppression based on the Extended Kalman Filter (EKF). We implement WiFineTrack using off-the-shelf WiFi devices, and empirical results show that WiFineTrack can achieve an accuracy of 10.45 cm under diverse environment conditions.

关键词： Geometry Wireless communication Wireless sensor networks Target tracking Accuracy Prototypes Turning Sensors Wireless fidelity Standards

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Trajectory Design for Multi-UAV-Enabled Wireless Powered Communication networks: A Multi-Agent DRL Approach

Trajectory Design for Multi-UAV-Enabled Wireless Powered Com...

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IEEE Conference on Wireless Communications and networking

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

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Unmanned aerial vehicles (UAVs) have emerged as a promising solution to support ground networks. In UAV-enabled wireless powered communication networks (WPCNs), the UAV provides data collection (DC) and energy transmission (ET) services for ground energy harvesting (EH) IoT devices. In view of the limited working efficiency of a single UAV, we propose a multi-UAV-enabled WPCN system. In order to further improve the efficiency of the system, we jointly optimize the data transmission performance of the network and the average harvested energy of IoT devices by designing the trajectories of the UAVs. The high mobility of UAVs causes the rapid change of channels, which leads to limitations of the centralized management scheme. Therefore, the problem is modeled as a multi-agent deep reinforcement learning (MADRL) problem. The scenario of multiple UAVs working collaboratively is formulated as a decentralized partially observable Markov decision process (Dec-POMDP). We develop a multi-agent deep deterministic policy gradient (MADDPG)-based approach to design UAVs' trajectories by adjusting the flying velocity and direction. Simulation results prove that the proposed algorithm far exceeds the benchmark schemes.

关键词： Wireless communication Performance evaluation Federated learning Simulation Markov decision processes Benchmark testing Autonomous aerial vehicles

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Video-Based Multi-Stage 3D Human Mesh Reconstruction 10

Video-Based Multi-Stage 3D Human Mesh Reconstruction

引用

10th International Conference on Computer and Communications, ICCC 2024

作者： Qiu, Zhiwen Wu, Muqing Su, Qihui Yin, Chengzhang Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing China

Existing Model-based 3D human reconstruction provides a new perspective for generating realistic full-body meshes for 3D human pose estimation. However, many existing methods for reconstructing human meshes typically rely on neural networks to directly estimate the parameters of the SMPL model, subsequently utilizing the SMPL model to recover the human mesh. Despite significant progress in recent research, these methods still encounter several issues, including shape estimation overfitting, cumulative rotation errors, and rotational semantic ambiguities. Furthermore, these methods have not leveraged recent advancements in 2D and 3D pose estimation. To address these challenges, we propose a framework that decomposes the task of reconstructing 3D human meshes into three components: extracting 2D keypoints, a 2D-to-3D lifting network, and fitting the rotations of the keypoints. We decompose the rotation of the 3D keypoints into twist and swing components. In the 2D-to-3D network, we regress the 3D keypoint locations, shape parameters, and twist of the keypoints, while also introducing an optimization method for fitting the rotations. We conducted experiments on the Human3.6M dataset, achieving significant performance improvements. © 2024 IEEE.

关键词： MESH networking

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EasyCount: Crowd Counting Based on Easy Deployment Using Commodity Wi-Fi

EasyCount: Crowd Counting Based on Easy Deployment Using Com...

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IEEE Conference on Wireless Communications and networking

作者： Sida Ling Jingbo Zhao Zhaoming Lu Xiangming Wen Zhe Xiao Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Recent years have witnessed the great potential of Wi-Fi-based crowd counting technique due to its popularity and non-invasive. However, the existing schemes with high accuracy require labeled data with different numbers of people for a priori learning, which poses an obstacle to the usability of crowd counting systems. Hence in this paper, we propose EasyCount, an easy learning-free multi-environment crowd counting system that only needs one person's prior data for simple calibration. First, we model the reflection signal of an individual as a Gaussian stochastic process. Then, we propose the ratio of dynamic power to noise power (RDN), which establishes a mapping between the autocorrelation function (ACF) of Channel State Information (CSI) amplitude and the number of people. The proposed RDN metric is proportional to the number of people, thus eliminating the dependence on labeled data with different numbers of people. Finally, we design a calibration scheme to obtain the base RDN, which enables our system to overcome the impact of different environments. Extensive experiments show that EasyCount can be easily deployed in different environments, achieving an average accuracy of 93.9 % for 0–6 people counting tasks in multiple environments with only one pair of transceivers, which is comparable to the existing state-of-the-art works.

关键词： Accuracy Noise Stochastic processes Transceivers Reflection Calibration Sensors

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Human Pose Estimation Based on Multi-branch Convolutional Neural network 2

Human Pose Estimation Based on Multi-branch Convolutional Ne...

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2nd IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2022

作者： Zhang, Haochen Xia, Hailun Zhang, Rui Guo, Caili Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China

ISBN: (纸本)9781665442763

With the development of the convolutional neural network, significant progress has been made in human pose estimation. Recent human pose estimation approaches adopt a single branch network, which can only provide limited surveillance information. Aiming at the problem, we propose to adopt a multi-branch network for both 2D human pose estimation and 3D human pose estimation. Leading results on multiple datasets verify the effectiveness of our multi-branch network. © 2022 IEEE.

关键词： Convolutional neural networks

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Dynamic Beam Hopping and Resource Management Optimization Based on Deep Reinforcement Learning for Interference Avoidance

Dynamic Beam Hopping and Resource Management Optimization Ba...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Zeyuan Lv Wenpeng Jing Ziyuan Zheng Zhaoming Lu Changhao Liu Xiangming Wen Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

The rapid expansion of low-Earth orbit (LEO) constellations has greatly intensified the difficulty of mitigating interference among them. Beam hopping (BH) technology is widely considered as one of the key technologies to achieve on-demand services while avoiding interference among these constellation systems. However, the majority of existing BH schemes neglect to address the interference among separate constellations. This paper proposes a novel BH scheme to tackle interference challenges in multi-LEO constellations coexistence scenarios. In contrast to existing BH schemes that predominantly target intra-system interference mitigation, our proposed BH scheme has the capability to dynamically adjust the beam bandwidth to avoid both intra-system interference and inter-system interference. We formulate an optimization problem that maximizes throughput, guarantees delay fairness, minimizes transmission power for LEO constellations, and avoids inter-system interference. To tackle this formidable non-convex and non-linear problem, the optimization problem is interpreted as a sequential decision-making problem, modeled by a Markov Decision Process (MDP). By utilizing the Advantage Actor-Critic (A2C) algorithm, the BH pattern is dynamically designed to avoid interference between beams from multiple LEO constellations in a time-varying network environment. Simulation results demonstrate that the proposed scheme outperforms other schemes in terms of long-term throughput and delay fairness.

关键词： Simulation Heuristic algorithms Prevention and mitigation Satellite broadcasting Interference Bandwidth Throughput Delays Resource management Optimization

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Mask guided attention for fetal standard plane recognition

Mask guided attention for fetal standard plane recognition

引用

2022 International Conference on Computer Application and Information Security, ICCAIS 2022

作者： Li, Ruijie Guo, Caili Xu, Hong Li, Zheng 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 Department of Ultrasound Diagnosis First Medical Center PLA General Hospital Beijing China

ISBN: (纸本)9781510663459

At present, ultrasound is the most widely used in prenatal diagnosis. The accurate acquisition of fetal standard plane is very important in prenatal diagnosis. However, the recognition of fetal standard planes (standard versus non-standard) is a challenging task, mainly because of the need to simultaneously recognize several key anatomical structures. In this paper, we propose a novel method termed mask guided attention (MGA) to integrate global and diagnostic region cues to recognize the fetal standard plane, imitating the sonographer's diagnostic mode. In addition, to further mine key anatomical structures, an innovative attention mining (AM) module is designed to improve the MGA. Utilizing the clinical dataset, we demonstrate that MGA significantly improve the performance of fetal standard plane recognition tasks. © 2023 SPIE.

关键词： Computer aided diagnosis

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Pattern-Independent Human Gait Identification with Commodity WiFi

Pattern-Independent Human Gait Identification with Commodity...

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IEEE Conference on Wireless Communications and networking

作者： Zhe Xiao Shuang Zhou Xiangming Wen Sida Ling Xuan Yang Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Recent years have witnessed the significant advancement of WiFi-based human gait identification. However, existing works require the human subjects to walk with a standard gait pattern, i.e., the normal walking speed and free limb swings. This significantly impedes the wide adoption of these technologies since humans walk with diverse gait patterns in reality. To this end, we present a Pattern-Independent Authentication system (PIAS), the first system that enables human identification across different gait patterns using commodity WiFi. First, we extract Doppler spectrograms as human gait signatures and standardize them to narrow the distribution discrepancies between different gait patterns. Then, we design a Class-level Unsupervised Domain Adaptive network (CUDAN) to extract human identity features in the source and target domains while minimizing the feature domain discrepancies at the class-level to achieve pattern-independent human gait identification. Extensive experiments are conducted on different gait patterns and the results demonstrate that our system can achieve the highest performance compared to the baseline methods, demonstrating its effectiveness in human identification across diverse gait patterns.

关键词： Legged locomotion Time-frequency analysis Authentication Feature extraction Doppler effect Noise measurement Task analysis

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Edge and Terminal Cooperation Enabled LLM Deployment Optimization in Wireless network

Edge and Terminal Cooperation Enabled LLM Deployment Optimiz...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Wentao Zhao Wenpeng Jing Zhaoming Lu Xiangming Wen Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Recent years have witnessed the remarkable emer-gence of large language models (LLMs), demonstrating exceptional capabilities in handling complex tasks such as natural language processing, computer vision, and speech processing. However, the prevalent deployment of LLMs on cloud servers poses challenges in ensuring user data privacy and minimizing computation and transmission delay. Moreover, LLM needs to read all the parameters to generate a token for each inference and then splices the original input with the generated token as a new input to generate the next token. To address these concerns, we propose a novel LLM deployment framework leveraging edge and terminal cooperation. Our proposed framework involves deploying LLMs on both edge server and terminal device, where the LLM model on the terminal generates tokens serially, while the model on the edge generates tokens in parallel. This edge-terminal cooperative and serial and parallel combination framework can effectively reduce the computation delay of token generation. To further optimize the performance of the proposed framework, we formulate an optimization problem aimed at minimizing average delay and energy consumption. Considering its integer programming property, we employ a branch and bound algorithm to efficiently solve this problem. Extensive simulation results provide compelling evidence of the effectiveness of our LLM deployment optimization algorithm. Under diverse channel conditions and terminal device computing capabilities, our proposed scheme consistently outperforms other baseline algorithms in terms of delay and energy consumption. These findings underscore the potential of our approach in facilitating the practical deployment of LLMs on edge and terminal.

关键词： Performance evaluation Energy consumption Computational modeling Wireless networks Simulation Delays Decoding Servers Speech processing Optimization

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