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Robust and Passive Motion Detection with COTS WiFi Devices

Tsinghua Science and technology 2017年第4期22卷 345-359页

作者： Hai Zhu Fu Xiao Lijuan Sun Xiaohui Xie Panlong Yang Ruchuan Wang the College of Computer Nanjing University of Posts and Telecommunications the Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education and College of Computer Nanjing University of Posts and Telecommunications the College of Computer Science and Technology University of Science and Technology of China

Device-free Passive（DfP） detection has received increasing attention for its ability to support various pervasive applications. Instead of relying on variable Received Signal Strength（RSS）, most recent studies rely on finer-grained Channel State Information（CSI）. However, existing methods have some limitations, in that they are effective only in the Line-Of-Sight（LOS） or for more than one moving individual. In this paper, we analyze the human motion effect on CSI and propose a novel scheme for Robust Passive Motion Detection（R-PMD）. Since traditional low-pass filtering has a number of limitations with respect to data denoising, we adopt a novel Principal Component Analysis（PCA）-based filtering technique to capture the representative signals of human motion and extract the variance profile as the sensitive metric for human detection. In addition, existing schemes simply aggregate CSI values over all the antennas in MIMO systems. Instead, we investigate the sensing quality of each antenna and aggregate the best combination of antennas to achieve more accurate and robust detection. The R-PMD prototype uses off-the-shelf WiFi devices and the experimental results demonstrate that R-PMD achieves an average detection rate of 96.33% with a false alarm rate of 3.67%.

关键词： device-free passive detection Received Signal Strength（RSS） channel state information MIMO

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Secure beamformer design for cognitive satellite terrestrial networks 4

Secure beamformer design for cognitive satellite terrestrial...

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4th IEEE International Conference on Computer and communications, ICCC 2018

作者： Yin, Chunyan Lin, Zhi Tao, Xiangshuai Ouyang, Jian Lin, Min Panagopoulos, Athanasios D. Ministry of Education Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing China College of Communications Engineering Army Engineering University of PLA Nanjing China Nanjing Institute of Telecommunication Technology Nanjing China School of Electrical and Computer Engineering National Technical University of Athens Zografou15780 Greece

ISBN: (纸本)9781538683392

This paper proposes a beamforming (BF) scheme based on multi-objective optimization (MOO) for a cognitive satellite-terrestrial network (CSTN), where the eavesdroppers (Eves) attempt to intercept the private message conveyed to an intended user. By considering that the maximization of achievable secrecy rate (ASR) and the minimization of transmit power can not be achieved simultanesouly, we adopt artificial noise (AN) and apply the weighted Tchebycheff approach to formulate a multi-object optimization problem. Then, by introducing several auxiliary variables, applying semidefinite programming (SDP) and means of Taylor approximation, the original nonconvex MOO problem is transformed into a convex one with a series of linear constraints, and the BF weight vector and AN vector can be obtained. Finally, simulation results are given to illustrate the tradeoff between the conflicting objectives and the superiority of the proposed algorithm in comparison with the commonly used schemes. © 2018 IEEE.

关键词： Beamforming

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Energy Efficiency Optimization Based on Context Awareness in Wearable Computing

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Procedia Computer Science 2018年 129卷 291-297页

作者： Feng Tian Yongan Guo Wei Liu Dapeng Li Zhen Yang Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications) Ministry of Education Nanjing 210003 China National Mobile Communications Research Laboratory Southeast University Nanjing 210096 China

The motion capture system of the wireless wearable device can achieve identification and classification of human motion through various classifiers, but wireless data transmission consumes most of the energy of the sensor node. An adaptive wireless transmission control algorithm based on context awareness is proposed realize energy efficiency optimization based on single classifier and multiple classifiers fusion. Firstly, adaptive wireless transmission control method based on fixed single classifier can effectively improve the overall energy efficiency under the limit of classification accuracy. Secondly, adaptive transmission control method of multi-classifier fusion is proposed with adjusting decision threshold based on various classifiers and their characteristics. The experimental results validate our proposed algorithm.

关键词： Wearable computing energy efficiency adaptive transmission control context awareness multiple classifiers fusion

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A Deep Learning Framework with Spatial-Temporal Attention Mechanism for Cellular Traffic Prediction

A Deep Learning Framework with Spatial-Temporal Attention Me...

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IEEE Globecom Workshops

作者： Yun Gao Xin Wei Liang Zhou Haibing Lv College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China National Engineering Research Center for Communication and Information Technology (NUPT) Nanjing China Key Laboratory of Broadband Wireless Communications and Sensor Network Technology Ministry of Education (NUPT) Nanjing China Wireline Product Planning Dept Wireline Product Operation ZTE Corporation Nanjing China

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

Currently, traffic prediction in cellular communication has become an important way to relieve traffic congestion, and then guarantee users' quality of experience (QoE) in multimedia service. However, when concerning traffic prediction in the context of big data, traditional deep learning models only have limited memory lengths and thus do not satisfy high prediction accuracy demand. In this paper, we design a new deep learningbased framework, which is built on our proposed novel spatialtemporal attention mechanism, to further increase the prediction accuracy of cellular traffic resource. Specifically, we firstly design a big data hierarchical architecture with four levels to extract meaningful information. Then, considering that cellular traffic resource from base stations characterizes spatial-temporal dependencies, we integrate conventional temporal attention mechanism for target area with that from spatial neighbor areas, proposing a novel spatial-temporal attention mechanism. Finally, we utilize this spatial-temporal attention mechanism based LSTM (STaLSTMs) to predict cellular traffic resource from base stations. Experimental results demonstrate that our proposed framework has better performances in cellular traffic prediction than other competing models. Importantly, in some application scenarios, this framework can also maintain high accuracy but only with 60% data.

关键词： Big Data Machine learning Data models Predictive models Base stations Quality of experience Time series analysis

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A Node Localization Algorithm in WSNs Based on Iterative Estimation

A Node Localization Algorithm in WSNs Based on Iterative Est...

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IEEE Information technology and Mechatronics Engineering Conference (ITOEC)

作者： Rui Jiang Qian Wu Youyun Xu Broadband Wireless Communication and Sensor Network Technology Key Lab of Ministry of Education Nanjing University of Posts and Telecommunications Jiangsu China

ISBN: (纸本)9781538653746

According to the application of range-free measurement for wireless sensor networks (WSNs), a novel localization algorithm of nodes in based on iterative centroid estimation is proposed in the paper. First, we derive the centroid of the plane enclosed by the current connected beacon nodes as well as the received signal strength (RSSI) between target and beacon nodes. Then, we replace the farthest beacon node from the target node with the centroid node we derive in order to narrow the area of the plane enclosed by the connected beacon nodes and improve the location accuracy through multiple iterations. Judging from the simulation results, the proposed algorithm presents decent results of all indications and is thus eligible for localization in WSNs.

关键词： wireless sensor networks Measurement errors Signal to noise ratio Mathematical model Classification algorithms Estimation Approximation algorithms

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A network Adaptive Fragmentation Algorithm for P2P-Based CDN 9

A Network Adaptive Fragmentation Algorithm for P2P-Based CDN

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2017 9th IEEE International Conference on Communication Software and networks (ICCSN 2017)

作者： Yunyi Zheng Jianxin Chen Yi Cheng Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781509038237;9781509038220

P2 P-based content request has been widely used because of its numerous advantages, such as flexibility, load distribution, efficiency and so on. However, there are still some problems to be solved such as service stability, adaptation and so on. The fragmentation methods used in existing P2 P systems are generally fixed block-size method or fixed duration method,which have significantly different performances in different network conditions and cannot adjust to the change of network conditions. In this paper, a novel method is presented to provide adaptive adjustment of fragmentation size for enhancing the performance and service stability. To calculate the adaptive size of fragmentation, several factors are taken into consideration including transmission rate, packet loss rate, file distributions and different peers. The simulation results show that the presented method performs better than the other methods in most network conditions.

关键词： P2P fragmentation adaptive fixed block size fixed duration transmission rate packet loss rate file distributions different peers

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Deep Reinforcement Learning for UAV Navigation Through Massive MIMO Technique

arXiv

引用

arXiv 2019年

作者： Huang, Hongji Yang, Yuchun Wang, Hong Ding, Zhiguo Sari, Hikmet Adachi, Fumiyuki Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China Sequans Communications Colombes92700 France Jilin University Changchun130012 China School of Electrical and Electronic Engineering University of Manchester ManchesterM13 9PL United Kingdom Tohoku University Sendai980-8577 Japan

Unmanned aerial vehicles (UAVs) technique has been recognized as a promising solution in future wireless connectivity from the sky, and UAV navigation is one of the most significant open research problems, which has attracted wide interest in the research community. However, the current UAV navigation schemes are unable to capture the UAV motion and select the best UAV-ground links in real-time, and these weaknesses overwhelm the UAV navigation performance. To tackle these fundamental limitations, in this paper, we merge the state-of-the-art deep reinforcement learning with the UAV navigation through massive multiple input multiple output (MIMO) technique. To be specific, we carefully design a deep Q-network (DQN) for optimizing the UAV navigation by selecting the optimal policy, and then we propose a learning mechanism for processing the DQN. The DQN is trained so that the agent is capable of making decisions based on the received signal strengths for navigating the UAVs with the aid of the powerful Q-learning. Simulation results are provided to corroborate the superiority of the proposed schemes in terms of the coverage and convergence compared with that of other schemes. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Influence of oceanic turbulence on propagation of Airy vortex beam carrying orbital angular momentum

arXiv

引用

arXiv 2018年

作者： Wang, Xinguang Yang, Zhen Zhao, Shengmei Nanjing210003 China Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education NUPT Nanjing210003 China

With Rytov approximation theory, we derive the analytic expression of de- tection probability of Airy vortex beam carrying orbital angular momentum (OAM) through an anisotropic weak oceanic turbulence. We investigate the influences of turbulence parameters and beam parameters on the propagation properties of Airy-OAM beam. The numerical simulation results show that the anisotropic oceanic turbulence with a lower dissipation rate of temperature vari- ance, smaller ratio of temperature and salinity contributions to the refractive index spectrum, higher dissipation rate of kinetic energy per unit mass of fluid, bigger inner scale factor, larger anisotropic coefficient causes the larger detection probability of Airy-OAM beam. Moreover, the Airy-OAM beam with a smaller topological charge, larger main ring radius and longer wavelength, has strong resistance to oceanic turbulent interference. Additionally, the detection proba- bility decreases with the increase of receiving aperture size. In comparison with Laguerre-Gaussian-OAM beam, Airy-OAM beam has more anti-interference to turbulence when its topological charge is larger than 5 due to its non-diffraction and self-healing characteristics. The results are useful for underwater optical communication link using Airy-OAM beam. Copyright © 2018, The Authors. All rights reserved.

关键词： Anisotropy

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Outage Performance of Satellite-Aerial-Terrestrial network

Outage Performance of Satellite-Aerial-Terrestrial Network

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IEEE Globecom Workshops

作者： Qingquan Huang Min Lin Jun-Bo Wang Wei-Ping Zhu Communications Engineering College Army Engineering University of PLA Nanjing China The 63rd Research Institute National University of Defense Technology Nanjing China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Electrical and Computer Engineering Concordia University Montreal Canada School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

In this paper, we investigate the performance of an amplify-and-forward satellite-aerial-terrestrial network (SATN) with a multi-antenna unmanned aerial vehicle (UAV) relay, where the satellite-UAV link undergoes correlated Shadowed-Rician fading while the UAV-terrestrial links experience correlated Rician fading, respectively. Specifically, we first formulate a constrained optimization problem in terms of maximizing the system capacity to obtain the beamforming (BF) weight vectors at the UAV. By assuming that perfect channel state information (CSI) is unavailable, we design statistical CSI-aided BF scheme to enhance the system performance. Then, with the help of probability theory, we obtain the closed-form expression of outage probability for the considered SATN. Simulation results are provided to validate the effectiveness of our proposed BF scheme and the correctness of the derived expressions.

关键词： Satellites Unmanned aerial vehicles Relays Antenna arrays Signal to noise ratio wireless communication Optimization

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An Economy-mode Framework for Task Offloading in Fog Computing networks

An Economy-mode Framework for Task Offloading in Fog Computi...

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International Conference on wireless communications and Signal Processing (WCSP)

作者： Beibei Wang Fei Shen Xujie Li Fei Qin Feng Yan Siyuan Zhou College of computer and information Hohai University Nanjing China Shanghai Research Center for Wireless Communications Shanghai China Key Lab for Wireless Sensor network and Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China The School of Electronic and Electrical Communication Engineering University of Chinese Academy of Sciences Beijing China National Mobile Communications Research Laboratory Southeast University Nanjing China

In the fog network, by offtoading the computing tasks to the fog nodes (FNs) located at the edge of the network, the task processing delay of the terminal nodes (TNs) can be reduced significantly. However, not all the FNs are willing to help TNs for free. Therefore, an economy-mode framework for task offtoading in the fog network is required to motivate the FNs. In this paper, we formulate the task offtoading problem based on Stackelberg game. The TN with computing tasks acts as the leader and its strategy is the optimal unit computing price. Each FN with idle resources acts as the follower and its strategy is the sharable optimal proportion of computing capability. Firstly, the leader predicts the followers' strategies and provides the unit computing price. Next, given the price, the followers will do exactly what the leader wishes, which is the system optimal point. By maximizing the utility functions of both the TN and FNs, a win-win solution is obtained. The numerical simulations indicate the importance of our proposed economy-mode framework for task offtoading.

关键词： Task analysis Delays Games Energy consumption Edge computing Computational modeling wireless communication

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