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arXiv 2021年

作者： Huang, Chen He, Ruisi Ai, Bo Molisch, Andreas F. Lau, Buon Kiong Haneda, Katsuyuki Liu, Bo Wang, Cheng-Xiang Yang, Mi Oestges, Claude Zhong, Zhangdui The Purple Mountain Laboratories Nanjing211111 China The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China The State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing Jiaotong University Beijing100044 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China Peng Cheng Laboratory Shenzhen518055 China The Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States The Department of Electrical and Information Technology Lund University Lund22100 Sweden The Department of Radio Science and Engineering Aalto University Aalto00076 Finland The School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom The Institute of Information and Communication Technologies Electronics and Applied Mathematics Universite Catholique de Louvain Louvain-la-Neuve 1348 Belgium

This two-part paper investigates the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. In Part I, we introduced AI and ML as well as provided a comprehensive survey on ML enabled channel characterization and antenna-channel optimization, and in this part (Part II) we review state-of-the-art literature on scenario identification and channel modeling here. In particular, the key ideas of ML for scenario identification and channel modeling/prediction are presented, and the widely used ML methods for propagation scenario identification and channel modeling and prediction are analyzed and compared. Based on the state-of-art, the future challenges of AI/ML-based channel data processing techniques are given as well. © 2021, CC BY-NC-ND.

关键词： Machine learning

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From concept to action: a united, holistic and One Health approach to respond to the climate change crisis

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Infectious Diseases of Poverty 2022年第1期11卷 68-73页

作者： Renhe Zhang Xu Tang Jian Liu Martin Visbeck Huadong Guo Virginia Murray Catherine Mcgillycuddy Bing Ke Gretchen Kalonji Panmao Zhai Xiaoming Shi Jiahai Lu Xiaonong Zhou Haidong Kan Qunli Han Qian Ye Yong Luo Jianmin Chen Wenjia Cai Huiling Ouyang Riyanti Djalante Alexander Baklanov Lu Ren Guy Brasseur George Fu Gao Lei Zhou Integrated Research on Disaster Risk International Centre of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health&Institute of Atmospheric Sciences Fudan UniversityShanghai200438China Science Division United Nations Environment Programme(UNEP)United Nations AvenueGigiriPO Box 30552Nairobi00100Kenya GEOMAR Helmholtz Centre for Ocean Research Kiel and Kiel University KielGermany Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences Chinese Academy of SciencesBeijingChina UK Health Security Agency LondonSE18UGUK The Administrative Center for China’s Agenda 21 Ministry of Science and Technology of ChinaBeijing100038China Institute for Disaster Management and Reconstruction Sichuan UniversityChengdu610207China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing100081China China CDC Key Laboratory of Environment and Population Health National Institute of Environmental HealthChinese Center for Disease Control and PreventionBeijing100021China One Health Center of Excellence for Research and Training School of Public HealthSun Yat-Sen UniversityGuangzhouChina National Institute of Parasitic Diseases Chinese Center for Disease Control and Prevention(Chinese Center for Tropical Diseases Research)ShanghaiChina Integrated Research On Disaster Risk-IPO BeijingChina State Key Lab of Earth Surface Processes and Resource Ecology Beijing Normal UniversityBeijing100875China Ministry of Education Key Laboratory for Earth System Modeling and Department of Earth System ScienceTsinghua UniversityBeijingChina ASEAN Secretariat Indonesia Integrated Research on Disaster Risk(IRDR)JakartaIndonesia Science and Innovation Department World Meteorological Organization(WMO)7 bisAvenue de la PaixBP2300CH-1211Geneva 2Switzerland Max Planck Institute for Meteorology HamburgGermany Chinese Center for Dis

It is unequivocal that human influence has warmed the planet,which is seriously affecting the planetary health including human *** climate change should not only be a slogan,but requires a united,holistic action and a paradigm shift from crisis response to an ambitious and integrated approach *** the urgent needs to tackle the risk connection between climate change and One Health,the four key messages and recommendations that with the intent to guide further research and to promote international cooperation to achieve a more climate-resilient world are provided.

关键词： Climate change One Health International coordination and cooperation Risk-oriented recommendations

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Time-varying massive MIMO channel estimation: Capturing, reconstruction and restoration

arXiv

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arXiv 2019年

作者： Li, Muye Zhang, Shun Zhao, Nan Zhang, Weile Wang, Xianbin State Key Laboratory of Integrated Services Networks Xidian University Xian710071 China School of Information and Communication Engineering Dalian University of Technology Dalian116024 China MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Engineering Western University LondonON Canada

On the time-varying channel estimation, the traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to unacceptable overhead in massive MIMO systems. To tackle this problem, in this paper, we propose a novel scheme for the DL channel tracking. First, with the help of virtual channel representation (VCR), we build a dynamic uplink (UL) massive MIMO channel model with the consideration of off-grid refinement. Then, a coordinate-wise maximization based expectation maximization (EM) algorithm is adopted for capturing the model parameters, including the spatial signatures, the time-correlation factors, the off-grid bias, the channel power, and the noise power. Thanks to the angle reciprocity, the spatial signatures, timecorrelation factors and off-grid bias of the DL channel model can be reconstructed with the knowledge of UL ones. However, the other two kinds of model parameters are closely related with the carrier frequency, which cannot be perfectly inferred from the UL ones. Instead of relearning the DL model parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with the partially prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： MIMO systems

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Fog-assisted multi-user swipt networks: Local computing or offloading

arXiv

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arXiv 2019年

作者： Zheng, Haina Xiong, Ke Fan, Pingyi Zhong, Zhangdui Letaief, Khaled Ben School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100044 China National Laboratory for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Lab of Rail Traffic Control and Safety Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China Clear Water Bay Hong Kong

This paper investigates a fog computing-assisted multi-user simultaneous wireless information and power transfer (SWIPT) network, where multiple sensors with power splitting (PS) receiver architectures receive information and harvest energy from a hybrid access point (HAP), and then process the received data by using local computing mode or fog offloading mode. For such a system, an optimization problem is formulated to minimize the sensors' required energy while guaranteeing their required information transmissions and processing rates by jointly optimizing the multi-user scheduling, the time assignment, the sensors' transmit powers and the PS ratios. Since the problem is a mixed integer programming (MIP) problem and cannot be solved with existing solution methods, we solve it by applying problem decomposition, variable substitutions and theoretical analysis. For a scheduled sensor, the closed-form and semi-closedform solutions to achieve its minimal required energy are derived, and then an efficient multi-user scheduling scheme is presented, which can achieve the suboptimal user scheduling with low computational complexity. Numerical results demonstrate our obtained theoretical results, which show that for each sensor, when it is located close to the HAP or the fog server (FS), the fog offloading mode is the better choice;otherwise, the local computing mode should be selected. The system performances in a frame-by-frame manner are also simulated, which show that using the energy stored in the batteries and that harvested from the signals transmitted by previous scheduled sensors can further decrease the total required energy of the sensors. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy harvesting

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Blind Decoding for a Multi-cell Massive Antenna Array Uplink System

Blind Decoding for a Multi-cell Massive Antenna Array Uplink...

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International Conference on information Systems and Computer Aided Education (ICISCAE)

作者： Han Yu L. Shen B.B. Yan The State Key Lab of Information Control Technology in Communication System of No.36 Research Institute China Electronic Technology Corporation JiaXing Zhejiang The School of Communications Engineering Hangzhou Dianzi University Hangzhou Zhejiang

ISBN: (纸本)9781538657393;9781538657386

In a multi-cell massive array multiple input multiple output (MIMO) uplink system, the pilot sequences reused in neighboring cells causes pilot contamination and decoding performance to degrade. In this paper, a blind decoding method based on independent component analysis (ICA) is proposed for a multi-cell massive antenna array system. The proposed blind decoding method uses ICA to separate the received signals (from in-cell and neighboring cells) and estimated channels, without using pilot sequence. Thus, pilot contamination is avoided and decoding performance will be optimized. Each user's angle-of-arrival (AOA) is estimated for recognizing the desired user signals and overcoming the uncertainty of signals separated by ICA. The analytical performance results of the blind decoding method are derived. Numerical results show that the proposed scheme has a better performance compared to MMSE decoding and blind decoding method based on singular value decomposition (SVD).

关键词： Antenna arrays Decoding Channel estimation Covariance matrices Partial transmit sequences Base stations

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Investigating Correlation of Rough Surface Diffuse Scattering in Frequency Domain

Investigating Correlation of Rough Surface Diffuse Scatterin...

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European Conference on Antennas and Propagation, EuCAP

作者： Yang MIAO Mingming Gan Claude Oestges Katsuyuki Haneda Ke Guan Andrés Alayón Glazunov Wei Wang Yi Gong Dept. of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Universite catholique de Louvain Louvain-la-Neuve BE Institute of Information and Communication Technologies Electronics and Applied Mathematics (ICTM) Universit Catholique de Louvain Louvain-la-Neuve Belgium Dept. of Electronics and Nanoengineering Aalto University Espoo Finland State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Dept. of Electrical Engineering University of Twente Enschede Netherlands School of Information Engineering Chang’an University Xi’an China

This paper investigates numerically the correlation function in the frequency domain due to diffuse scattering originated from rough surfaces. The scattered fields are computed by means of physical optics methods. The irregularities of the rough surface are modeled as a correlated Gaussian process. The correlation of the channel transfer function comprising only diffuse scattering components is analyzed. In the analysis, radio propagation channels comprising orthogonally oriented dipoles at the link ends are considered while assuming different Gaussian roughness profiles. This study offers a useful characterization of the channel behavior in a multipath-rich environment due to diffuse scattering. The presented results are of particular interest to wireless systems based on orthogonal frequency-division multiplexing.

关键词： Rough surfaces Surface roughness Correlation Optical surface waves Scattering Surface impedance Frequency-domain analysis

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2D Structured Turbo Compressed Sensing for Channel Estimation in OTFS Systems

2D Structured Turbo Compressed Sensing for Channel Estimatio...

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International Conference on communication Systems

作者： Mingchen Zhang Fanggang Wang Xiaojun Yuan Lei Chen Center for Intelligent Networking and Commun National Key Lab. of Sci. and Tech. on Commun University of Electronic Sci. and Tech. of China Chengdu China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China School of Information Science and Technology ShanghaiTech University Shanghai China

ISBN: (纸本)9781538678657;9781538678640

High-speed mobility is a challenging scenario in wireless communications since it involves fast-varying channels, in which orthogonal frequency-division multiplexing (OFDM) could degrade significantly without accurate Doppler compensation. Recently, a modulation called orthogonal time frequency space (OTFS) was proposed for the specific scenario. The advantages are two-fold, i.e., the orthogonality in the time-frequency domain and the clustered sparsity of channel information in the delay-Doppler domain. In this paper, we propose a two-dimensional structured Turbo compressed sensing (CS) algorithm based on our previous work on Turbo CS for fast time-varying channel estimation. Markov random field is applied to model the clustered sparse prior of the channel in the two-dimensional delay-Doppler domain. In contrast to the conventional Turbo CS, a two-dimensional structured minimum mean-square error (MMSE) estimator is designed by message passing over a Markov random field. The convergence and the MSE performance of the proposed algorithm is evaluated, and the simulation results validate that the proposed algorithm outperforms the existing algorithms.

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Deep User Modeling for Content-based Event Recommendation in Event-based Social Networks

Deep User Modeling for Content-based Event Recommendation in...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and communications Societies

作者： Zhibo Wang Yongquan Zhang Honglong Chen Zhetao Li Feng Xia Jiangsu Key Lab. of Big Data Security & Intelligent Processing NJUPT P. R. China School of Cyber Science and Engineering Wuhan University P. R. China College of Information and Control Engineering China University of Petroleum P. R. China College of Information Engineering Xiangtan University P. R. China School of Software Dalian University of Technology P. R. China

ISBN: (纸本)9781538641293

Event-based social networks (EBSNs) are the newly emerging social platforms for users to publish events online and attract others to attend events offline. The content information of events plays an important role in event recommendation. However, the content-based approaches in existing event recommender systems cannot fully represent the preference of each user on events since most of them focus on exploiting the content information from events' perspective, and the bag-of-words model, commonly used by them, can only capture word frequency but ignore word orders and sentence structure. In this paper, we shift the focus from events' perspective to users' perspective, and propose a Deep User Modeling framework for Event Recommendation (DUMER) to characterize the preference of users by exploiting the contextual information of events that users have attended. Specifically, we utilize convolutional neural network (CNN) with word embedding to deeply capture the contextual information of a user's interested events and build up a user latent model for each user. We then incorporate the user latent model into probabilistic matrix factorization (PMF) model to enhance the recommendation accuracy. We conduct experiments on the real-world dataset crawled from a typical EBSN, ***, and the experimental results show that DUMER outperforms the compared benchmarks.

关键词： Recommender systems Machine learning Social network services Feature extraction Context modeling Conferences Probabilistic logic

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A cloud-based taxi trace mining framework for smart city

A cloud-based taxi trace mining framework for smart city

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作者： Liu, Jin Yu, Xiao Xu, Zheng Choo, Kim-Kwang Raymond Hong, Liang Cui, Xiaohui State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China Third Research Institute of Ministry of Public Security Shanghai China Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX United States School of Information Technology and Mathematical Science University of South Australia AdelaideSA Australia School of Information Management Wuhan University Wuhan China International School of Software Wuhan University Wuhan China

As a well-known field of big data applications, smart city takes advantage of massive data analysis to achieve efficient management and sustainable development in the current worldwide urbanization process. An important problem in smart city is how to discover frequent trajectory sequence pattern and cluster trajectory. To solve this problem, this paper proposes a cloud-based taxi trajectory pattern mining and trajectory clustering framework for smart city. Our work mainly includes (1) preprocessing raw Global Positioning System trace by calling the Baidu API Geocoding;(2) proposing a distributed trajectory pattern mining (DTPM) algorithm based on Spark;and (3) proposing a distributed trajectory clustering (DTC) algorithm based on Spark. The proposed DTPM algorithm and DTC algorithm can overcome the high input/output overhead and communication overhead by adopting in-memory computation. In addition, the proposed DTPM algorithm can avoid generating redundant local trajectory patterns to significantly improve the overall performance. The proposed DTC algorithm can enhance the performance of trajectory similarity computation by transforming the trajectory similarity calculation into AND and OR operators. Experimental results indicate that DTPM algorithm and DTC algorithm can significantly improve the overall performance and scalab.lity of trajectory pattern mining and trajectory clustering on massive taxi trace data. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Clustering algorithms

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INTELLIGENT information NETWORK security AND MANAGEMENT

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China communications 2016年第7期13卷 3-6页

作者： Yixian Yang Zhongshan Zhang Wenjing Li Information Security Center(ISC) Beijing University of Posts and Telecommunications(BUPT) National Engineering Laboratory for Disaster Backup and Recovery the School of Computer and Communication Engineering in the University of Science and Technology Beijing(USTB) State Key Lab of Networking and Switching Technologies Network Management Research Center at BUPT National Innovation Research Group of China

Intelligent information Network security and Management(IINSM)is an emerging direction in future Internet architecture research,which has gained significant tractions among academia and *** the rapid development of modern society,intelligent information network with the computer as the core technology is widely used in every aspect of social ***

关键词： DCT INTELLIGENT information NETWORK security AND MANAGEMENT Simulation DWT DCNN

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