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MINISTRY OF EDUCATION AND TRAINING HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY NGUYEN HONG ANH LOCATION-AWARE MULTIPATH-BASED CHANNEL PREDICTION FOR NEXT GENERATION WIRELESS COMMUNICATION SYSTEMS DOCTORAL DISSERTATION OF TELECOMMUNICATIONS ENGINEERING Hanoi−2022 MINISTRY OF EDUCATION AND TRAINING HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY NGUYEN HONG ANH LOCATION-AWARE MULTIPATH-BASED CHANNEL PREDICTION FOR NEXT GENERATION WIRELESS COMMUNICATION SYSTEMS Major: Telecommunication Engineering Code: 9520208 DOCTORAL DISSERTATION OF TELECOMMUNICATIONS ENGINEERING SUPERVISORS: 1.Assoc Prof Nguyen Van Khang 2.Assoc Prof Klaus Witrisal Hanoi−2022 DECLARATION OF AUTHORSHIP I declare that I have authored this thesis independently, that I have not used other than the declared sources/resources, and that I have explicitly indicated all material which have been quoted either literally or by content from the sources used Hanoi, / / 2022 PhD Student Nguyen Hong Anh SUPERVISORS Assoc.Prof Nguyen Van Khang i ACKNOWLEDGEMENT This dissertation was written during my doctoral course at School of Electronics and Telecommunications (SET), Hanoi University of Science and Technology (HUST) I was also received tremendous supports from the Signal Processing and Speech Com- munication Laboratory (SPSC), Graz University of Technology (TUGraz), Austria I am so grateful for all people who always support and encourage me for completing this study First, I would like to express my sincere gratitude to my advisors, Assoc Prof Klaus Witrisal and Assoc Prof Nguyen Van Khang, for their effective guidance, their patience, continuous support and encouragement, and their immense knowledge I would like to thank all members of SPSC, TUGraz They have been very kind and supportive during my visits to Graz They helped me a lot with their deep understand- ing of the group’s topics and researches I also would like to thank all my colleagues in SET, HUST They have always helped me with the research process and given helpful advice for me to overcome my own difficulties During my Ph.D course, I have received many supports from the Management Board of School of Electronics and Telecommunications Thanks to my employer, HUST for all necessary support and encouragement during my Ph.D journey I am also grateful to Vietnam’s Program 911, for their generous financial support Last but not least, I would like to thank OeAD and SPSC for giving funds for my research visits to Graz Special thanks to my family and relatives for their never-ending support and sacri- fice Hanoi, 2022 Ph.D Student ii CONTENTS DECLARATION OF AUTHORSHIP i ACKNOWLEDGEMENT .ii CONTENTS .vi SYMBOLS vi SYMBOLS ix LIST OF TABLES xiii LIST OF FIGURES xiv CHAPTER INTRODUCTION AND MOTIVATION 1.1 Literatur e review 1.1.1 Locationawareness in mmWave beamforming 1.1.2 Locationawareness in vehicular communications 1.1.3.Location-awareness in adaptive mobile communications, scheduling and routing 1.1.4 Channel quality metric (CQM) 1.2 Challeng es and motivations 1.3 Purposes and objectives 1.4 Research hypotheses 1.4.1 Towards a site-specific radio propagation modeling 1.4.2 Towards a large-scale predicting of radio channel statistics .7 1.4.3 Towards a side information-aided single-anchor multipathbased localization 1.5 Contribut ions and outline CHAPTER SIGNAL AND SYSTEM MODELS 2.1 System and channel model ii 2.1.1 Introduct ion about channel model 2.1.2 Represent ation of reflectors using virtual anchors (VAs) 10 2.1.3 Floor plan/environment information for location-aware applications 12 2.1.4 Hybrid geometric/stochastic signal model 12 2.2 Channel quality metric (CQM) .14 2.2.1 SMC amplitude 14 2.2.2 Signal-tointerference-plus-noise ratio (SINR) .15 2.2.3 Channel Capacity 15 2.2.4 Position error bound (PEB) 18 2.3 Discussio n 19 2.3.1 Energy capture 19 2.3.2 Contributi on of individual SMCs in the overall channel capacity 20 2.4 Chapter conclusions 24 CHAPTER GAUSSIAN PROCESS REGRESSION FOR SMC AMPLITUDES 26 3.1 Related Work 26 3.2 SMC propagation model 27 3.3 GP Modeling (GPM) of the SMC Amplitudes 28 3.4 GPR 28 3.4.1 GP Model 29 3.4.2 Prediction 29 3.4.3 Learning 30 3.4.4 Evaluate i the quality of prediction 31 3.5 Experime nt and result 32 3.5.1 Experime nt 32 3.5.2 Measure ment pre-processing 32 3.5.3 GPR of SMC Amplitudes 33 3.5.4 GPR of SMC Phases 38 3.6 Chapter conclusions 39 CHAPTER RADIO ENVIRONMENT MAP FOR SITE- SPECIFIC PROPAGATION MODELING 42 4.1 Introducti on 42 4.2 Radio environment map (REM) using Gaussian Process regression (GPR) 42 4.3 SMC amplitudes 43 4.4 SINR 46 4.5 Position error bound 46 4.6 Chapter conclusions 48 CHAPTER APPLICATION OF GPR - ENABLED REM TO ROBUST POSITIONING 53 5.1 Related work 53 5.2 Problem formulation 55 5.3 Proposed algorithm 55 5.4 Result 57 5.5 Chapter conclusions v 60 PUBLICATIONS 63 BIBLIOGRAPHY 64 APPENDICES 76 A Description of channel measurement campaigns 76 A.1 Measurement campaign 76 A.2 Measurement campaign 77 B Variance of νk 83 C Predicted Variance 84 v ABBREVIATIONS No Abbreviatio Meaning n ACF AutoCorrelation Function ADC Analog-to-Digital Converter AOA Angle-Of-Arrival AOD Angle-Of-Departure AWGN Additive White Gaussian Noise BER Bit Error Rate BF Beam Forming BS Base Station CDF Cumulative Distribution Function 10 CIR Channel Impulse Response 11 CRLB Cramer Rao Lower Bound 12 CQM Channel Quality Metric 13 CSI Channel State Information 14 CTF Channel Transfer Function 15 DM Diffuse Multipath 16 DMC Diffuse Multipath Component 17 EC Energy Capture 18 ECC European Communications Committee 19 EFIM Equivalent Fisher Information Matrix 20 EPB East Plaster Board 21 FCC Federal Communications Commission 22 FFT Fast Fourier Transform 23 FIM Fisher Information Matrix 24 GNSS Global Navigation Satellite System 25 GP Gaussian Process 26 GPM Gaussian Process Model 27 GPR Gaussian Process Regression 28 GPS Global Positioning System 29 GSCM Geometry-based Stochastic Channel Model 30 IFFT Inverse Fast Fourier Transform v 31 IoT Internet-of-Thing 32 LHF LikelyHood Function 33 LIDAR Light Detection And Ranging 34 LLHF Log LikeliHood Function 35 M2M Machine-to-Machine 36 MAC Media Access Control 37 MIMO Multiple Input Multiple Output 38 MINT Multipath-assisted Indoor Navigation and Tracking 39 ML Maximum Likelihood 40 MMSE Minimum Mean Square Error 41 MPC MultiPath Component 42 MRC Maximal Ratio Combining 43 MSLL Mean Square Log Loss 44 NLOS Non-Line-Of-Sight 45 NGW North Glass Window 46 LOS Line-Of-Sight 47 OFDM Orthogonal Frequency Division Multiplexing 48 PA Physical Anchor 49 PAM Pulse Amplitude Modulation 50 PDF Probability Distribution Function 51 PDP Power Delay Profile 52 PHY PHYsical Layer Protocol 53 PEB Position Error Bound 54 QAM Quadrature Amplitude Modulation 55 REM Radio Environment Map 56 RF Radio Frequency 57 RFID Radio Frequency IDentification 58 RRC Root Raised Cosine 59 RSS Received Signal Strength 60 RX Receiver 61 RV Random Variable 62 SALMA 63 SEP Single-Anchor Localization system using Multipath Assistance Symbol Error Probability 64 SIMO Single-Input-Multiple-Output 65 SINR Signal-to-Interference-plus-Noise Ratio v
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