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Subject-independent human pose image construction with commodity Wi-Fi

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arXiv 2020年

作者： Zhou, Shuang Guo, Lingchao Lu, Zhaoming Wen, Xiangming Zheng, Wei Wang, Yiming Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

Recently, commodity Wi-Fi devices have been shown to be able to construct human pose images, i.e., human skeletons, as fine-grained as cameras. Existing papers achieve good results when constructing the images of subjects who are in the prior training samples. However, the performance drops when it comes to new subjects, i.e., the subjects who are not in the training samples. This paper focuses on solving the subject-generalization problem in human pose image construction. To this end, we define the subject as the domain. Then we design a Domain-Independent Neural network (DINN) to extract subject-independent features and convert them into fine-grained human pose images. We also propose a novel training method to train the DINN and it has no re-training overhead comparing with the domain-adversarial approach. We build a prototype system and experimental results demonstrate that our system can construct fine-grained human pose images of new subjects with commodity Wi-Fi in both the visible and through-wall scenarios, which shows the effectiveness and the subject-generalization ability of our model. Copyright © 2020, The Authors. All rights reserved.

关键词： Sampling

Polarization-space based interference alignment for cognitive heterogeneous cellular network 13th

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Polarization-space based interference alignment for cognitiv...

13th EAI International Conference on Cognitive Radio Oriented Wireless networks, CROWNCOM 2018

作者： Gao, Xiaofang Guo, Caili Chen, Shuo Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9783030054892

In underlay cognitive heterogeneous cellular network (CHCN), small cells can transmit their signals as long as the interference to macro cell is below a threshold. Consider a two-layer CHCN with polarized MIMO small cells, a novel polarization-space based interference alignment scheme is proposed. The cross-tier interference between macro cell and small cells is addressed by two given algorithms with different purposes. Orthogonal projection based polarization-space interference alignment (OP-PSIA) for ensuring the minimum effect to macro cell and interference constrained polarization-space interference alignment (IC-PSIA) for maximizing the performances of small cells if permitted. The co-tier interference between small cells are reduced by a minimum total mean squared error (MMSE) algorithm. Then we give specific solutions for two algorithms both including orthogonal projection processing and analytically iterative calculations. Simulation results show the improvement of two algorithms in BER performance of small cells while ensuring the protection of macro cell and keeping maximum overall sum rate. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Polarization

Queue-sensing-based CSAT: A downlink transmission scheme for LTE-M in unlicensed spectrum

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Queue-sensing-based CSAT: A downlink transmission scheme for...

2019 International Conference on Advances in Computer Technology, Information Science and Communications, CTISC 2019

作者： Gong, Zelin Wang, Ying Luo, Tao Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789897583575

A shortage of licensed spectrum is a major hindrance to the capacity of Long Term Evolution for Metro (LTE-M) to carry urban rail transit services at present. Inspired by LTE-Unlicensed (LTE-U) offloading some of data traffic transmitted in licensed spectrum by accessing the unlicensed 5GHz frequency band, we propose a queue-sensing-based Carrier Sensing Adaptive Transmission (CSAT) scheme for LTE-M in unlicensed spectrum based on LTE-U. The scheme calculates the minimum amount of data that should be transmitted during current transmission duration by queue length at the beginning of each LTE-U cycle, according to which, LTE-U adjusts its duty cycle to meet the requirement of LTE-M service. This paper focuses on the downlink transmission scheme for LTE-M in unlicensed spectrum, and verifies its performance by simulation, which tells that Queue-sensing-based CSAT will not only provide a reliable transmission for LTE-M services, but also maximize the fairness of channel occupancy in unlicensed spectrum. © 2019 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Long Term Evolution (LTE)

From point to space: 3D moving human pose estimation using commodity WiFi

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arXiv 2020年

作者： Wang, Yiming Guo, Lingchao Lu, Zhaoming Wen, Xiangming Zhou, Shuang Meng, Wanyu School of Information and Communication Engineering Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing100876 China

In this paper, we present Wi-Mose, the first 3D moving human pose estimation system using commodity WiFi. Previous WiFi-based works have achieved 2D and 3D pose estimation. These solutions either capture poses from one perspective or construct poses of people who are at a fixed point, preventing their wide adoption in daily scenarios. To reconstruct 3D poses of people who move throughout the space rather than a fixed point, we fuse the amplitude and phase into Channel State Information (CSI) images which can provide both pose and position information. Besides, we design a neural network to extract features that are only associated with poses from CSI images and then convert the features into key-point coordinates. Experimental results show that Wi-Mose can localize key-point with 29.7mm and 37.8mm Procrustes analysis Mean Per Joint Position Error (P-MPJPE) in the Line of Sight (LoS) and Non-Line of Sight (NLoS) scenarios, respectively, achieving higher performance than the state-of-the-art method. The results indicate that Wi-Mose can capture high-precision 3D human poses throughout the space. Copyright © 2020, The Authors. All rights reserved.

关键词： Channel state information

CentiTrack: Towards centimeter-level passive gesture tracking with commodity WiFi

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arXiv 2020年

作者： Han, Zijun Lu, Zhaoming Wen, Xiangming Zheng, Wei Zhao, Jingbo Guo, Lingchao School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks China

Gesture awareness plays a crucial role in promoting human-computer interface. Previous works either depend on customized hardware or need a priori learning of wireless signal patterns, facing downsides in terms of the privacy concern, availability and reliability. In this paper, we propose CentiTrack, the first centimeter-level passive gesture tracking system that works with only three commodity WiFi devices, without any extra hardware modifications or wearable sensors. To this end, we first identify the Channel State Information (CSI) measurement error sources in the physical layer process, and then denoise CSI by the complex ratio between adjacent antennas. Principal Component Analysis (PCA) is further adopted to separate the reflected signals from noises. Benchmark experiments are conducted to verify that the phase changes of denoised CSI are proportional to the length changes of dynamic path reflected off the hand. In addition, we adopt the Multiple Signal Classification (MUSIC) algorithm to estimate the Angle-of-Arrivals (AoAs) of dynamic paths, and then locate the initial position of hands with triangulation. We also propose a novel static componnets elimination algorithm for tracking correction by eliminating the components unrelated to motion. A prototype of CentiTrack is fully realized and evaluated in various real scenarios. Extensive experiments show that CentiTrack is superior in terms of tracking accuracy, sensing range and device cost, compared with the state-of-the-arts. © 2020, CC-BY.

关键词： Channel state information

Exploiting Visual Semantic Reasoning for Video-Text Retrieval

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arXiv 2020年

作者： Feng, Zerun Zeng, Zhimin Guo, Caili 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

Video retrieval is a challenging research topic bridging the vision and language areas and has attracted broad attention in recent years. Previous works have been devoted to representing videos by directly encoding from frame-level features. In fact, videos consist of various and abundant semantic relations to which existing methods pay less attention. To address this issue, we propose a Visual Semantic Enhanced Reasoning network (ViSERN) to exploit reasoning between frame regions. Specifically, we consider frame regions as vertices and construct a fully-connected semantic correlation graph. Then, we perform reasoning by novel random walk rule-based graph convolutional networks to generate region features involved with semantic relations. With the benefit of reasoning, semantic interactions between regions are considered, while the impact of redundancy is suppressed. Finally, the region features are aggregated to form frame-level features for further encoding to measure video-text similarity. Extensive experiments on two public benchmark datasets validate the effectiveness of our method by achieving state-ofthe-art performance due to the powerful semantic reasoning. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal encoding

C-RAN Enabled Seamless Mobility Mechanism in Autonomous Driving

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C-RAN Enabled Seamless Mobility Mechanism in Autonomous Driv...

General Assembly and Scientific Symposium, URSI

作者： Jian Zhang Luhan Wang Wei Zheng Xiangming Wen Beijing Advanced Innovation Center for Future Internet Technology Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9789463968003

ISBN: (纸本)9781728156903

Connected autonomous driving has already been a new paradigm of driving for safety, efficiency and mobility. Connected autonomous driving can overcome the perceptual limitations of single vehicle, which has a high demand for high speed and seamless network connection. With the superiority of C-RAN, autonomous driving network can effectively support a vast number of vehicles communications and flexible multi-RRH cooperative transmission to improve throughput. However, due to the high mobility of vehicle, the problem of long latency or even link break caused by frequent hard handover still exists. In this context, based on cooperative transmission and the idea of multiple connections, we studied seamless mobility mechanism in C-RAN enabled autonomous driving. In this paper, we propose a user-centric dynamic cooperative cluster updating scheme with the purpose of guaranteeing the seamless of the vehicle during handover process due to mobility. Simulation results demonstrate that the proposed scheme can effectively solve the interruption latency or link interruption problem caused by handover. And compared with the non-cooperative case, the proposed scheme is superior in throughput gain.

关键词： Handover Throughput Autonomous vehicles Delays 5G mobile communication Vehicle dynamics Signal to noise ratio

When healthcare meets off-the-shelf wifi: A non-wearable and low-Costs approach for in-home monitoring

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arXiv 2020年

作者： Guo, Lingchao Lu, Zhaoming Zhou, Shuang Wen, Xiangming He, Zhihong Beijing Laboratory of Advanced Information Networks China Beijing Key Laboratory of Network System Architecture and Convergence China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

As elderly population grows, social and health care begin to face validation challenges, in-home monitoring is becoming a focus for professionals in the field. Governments urgently need to improve the quality of healthcare services at lower costs while ensuring the comfort and independence of the elderly. This work presents an in-home monitoring approach based on off-the-shelf WiFi, which is low-costs, non-wearable and makes all-round daily healthcare information available to caregivers. The proposed approach can capture fine-grained human pose figures even through a wall and track detailed respiration status simultaneously by off-the-shelf WiFi devices. Based on them, behavioral data, physiological data and the derived information (e.g., abnormal events and underlying diseases), of the elderly could be seen by caregivers directly. We design a series of signal processing methods and a neural network to capture human pose figures and extract respiration status curves from WiFi Channel State Information (CSI). Extensive experiments are conducted and according to the results, off-the-shelf WiFi devices are capable of capturing fine-grained human pose figures, similar to cameras, even through a wall and track accurate respiration status, thus demonstrating the effectiveness and feasibility of our approach for in-home monitoring. Copyright © 2020, The Authors. All rights reserved.

关键词： Channel state information

A Compact Dual-Polarized Differentially-Fed Antenna with High Gain for 5G Microcells

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A Compact Dual-Polarized Differentially-Fed Antenna with Hig...

IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Haoming Li Wenhua Cao Botao Feng Li Deng College of Electronics and Information Engineering Shenzhen University Shenzhen China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728190457

ISBN: (纸本)9781728190464

A compact dual-polarized differentially-fed antenna with high gain is proposed for 5G microcells. The antenna mainly consists of two parts: a radiating element with metasurface and a differentially-fed network. The metasurface with four square cells is etched on the top layer, while the radiating octagonal patch etched with four fan-shaped slots is placed on the second layer. The profile of the antenna is 0.125 λ 0 (where λ 0 is the starting wavelength). Simulation results show that the overlapped bandwidths are 3.30-3.61 GHz and 4.72-5.07 GHz, and their corresponding gains vary from 8.30 to 9.65 dBi.

关键词： 5G mobile communication Simulation Conferences Microcell networks Metasurfaces Information and communication technology Antennas