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Intelligent and Secure Underwater Acoustic Communication Networks

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Intelligent and Secure Underwater Acoustic Communication Networks Michigan Technological University Digital Commons @ Michigan Tech Dissertations, Master's Theses and Master's Reports 2018 Intelligent and Secure Underwater Acoustic Communication Networks Chaofeng Wang Michigan Technological University, [email protected] Copyright 2018 Chaofeng Wang Recommended Citation Wang, Chaofeng, "Intelligent and Secure Underwater Acoustic Communication Networks", Open Access Dissertation, Michigan Technological University, 2018. https://doi.org/10.37099/mtu.dc.etdr/694 Follow this and additional works at: https://digitalcommons.mtu.edu/etdr Part of the Artificial Intelligence and Robotics Commons, Signal Processing Commons, and the Systems and Communications Commons INTELLIGENT AND SECURE UNDERWATER ACOUSTIC COMMUNICATION NETWORKS By Chaofeng Wang ADISSERTATION Submitted in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY In Electrical Engineering MICHIGAN TECHNOLOGICAL UNIVERSITY 2018 2018 Chaofeng Wang This dissertation has been approved in partial fulfillment of the requirements for the Degree of DOCTOR OF PHILOSOPHY in Electrical Engineering. Department of Electrical and Computer Engineering Dissertation Advisor: Dr. Zhaohui Wang Committee Member: Dr. Daniel R. Fuhrmann Committee Member: Dr. Min Song Committee Member: Dr. Ossama Abdelkhalik Department Chair: Dr. Daniel R. Fuhrmann Dedication IdedicatethisdissertationtoXiangjunWang,myfather;Ying Huang, my mother; Qiao Xiao, my wife; and Huisha Wang, my elder sister. Contents List of Figures ................................. xiii List of Tables .................................. xix Preface ...................................... xxi Acknowledgments ............................... xxiii Abstract ..................................... xxv 1Introduction................................. 1 1.1 BackgroundandChallenges ...................... 1 1.2 Contributions .............................. 5 2Reinforcementlearning-basedAdaptiveTransmissioninUnder- water Acoustic Channels ......................... 9 2.1 Introduction............................... 10 2.1.1 Background ........................... 10 2.1.2 Existing Works in Terrestrial Radio Networks . 11 2.1.3 Existing Works in Underwater Acoustic Networks . 12 vii 2.1.4 OurWork ............................ 14 2.2 SystemModelandProblemFormulation . 16 2.2.1 System Description . 16 2.2.2 Underwater Acoustic Channel Model . 17 2.2.3 Evolution of the Data Queue . 20 2.2.4 Problem Formulation for Optimal Transmission . 21 2.3 Reinforcement Learning-based Adaptive Transmission . .22 2.3.1 Model-based RL for Adaptive transmission . 22 2.3.2 An Overview of the Proposed Algorithm for Online Adaptive Transmission .......................... 25 2.4 Monte Carlo Sampling for Online Approximation . 27 2.4.1 Value Function Approximation . 27 2.4.1.1 State-action Tree Construction . 30 2.4.1.2 Value Function Calculation . 32 2.4.2 Computational Complexity . 33 2.5 Recursive Estimation of Unknown Channel Model Parameters . .34 2.5.1 Approximation for Recursive Operation . 35 2.5.2 Recursive Model and Channel State Estimation . 38 2.6 Algorithm Evaluation . 40 2.6.1 Experiment Description . 41 2.6.2 Emulation Setup and Performance Metric . 43 viii 2.6.3 GeneralResults......................... 45 2.6.3.1 SPACE08 . 48 2.6.3.2 KW-NOV14 . 51 2.6.4 Performance of the Proposed Algorithm with Different System Setups . 54 2.6.4.1 Performance with DifferentDataArrivalRates . 55 2.6.4.2 Performance with Different Numbers of Child System State Samples and Actions To Be Explored in Online Approximation .................... 57 2.6.4.3 Performance with Different Depths of Monte Carlo Planning ....................... 57 2.7 Summary ................................ 58 3ReinforcementLearning-basedAdaptiveTrajectoryPlanning for AUVs in Under-ice Environments ................... 61 3.1 Introduction............................... 62 3.1.1 Existing Studies in Terrestrial Robotic Networks . 63 3.1.2 Existing Studies in Underwater AUV Networks . 65 3.1.3 OurWork ............................ 67 3.2 SystemModelandProblemFormulation . 69 3.2.1 System Description . 70 3.2.2 Constraints on Sampling Trajectories . 71 ix 3.2.2.1 Kinematics Constraint . 72 3.2.2.2 Communication Range Constraint . 72 3.2.2.3 Sensing Area Constraint . 73 3.2.3 Modeling Real Trajectories of AUVs . 73 3.2.4 Gaussian Process Regression for Field Estimation . 74 3.2.5 Problem Formulation for Optimal Trajectory Planning . 76 3.2.5.1 Cost Function . 77 3.3 Reinforcement Learning-based Adaptive Trajectory Planning . ... 79 3.3.1 DDPG Basics and Design . 80 3.3.2 Training for Actions Under Constraints (3.1) to (3.4) . 82 3.3.3 Online Learning for Trajectories Planning with Unknown Field Hyper-parameters........................ 86 3.4 Algorithm Evaluation . 87 3.5 Summary ................................ 92 4SignalAlignmentforSecureUnderwaterCoordinatedMultipoint Transmissions ................................ 95 4.1 Introduction............................... 96 4.1.1 Physical-layer Security in Terrestrial Radio Networks . 98 4.1.2 Underwater Acoustic Network Security . 99 4.1.3 OurWork ............................ 101 4.2 System Model for Coordinated Multipoint Transmissions . 104 x 4.3 Receiver Processing at the Eavesdropper . 108 4.4 Signal Alignment for Transmission Secrecy . 111 4.4.1 Signal Alignment with Eavesdropper’s Location Information 112 4.4.2 Optimization Problem Solver . 115 4.4.2.1 Synchronous Signal Alignment . 115 4.4.2.2 Quasi-synchronous Signal Alignment . 117 4.4.3 Signal Alignment without Eavesdropper’s Location . 120 4.5 Secrecy Capacity in AWGN Channels . 121 4.6 Simulation Results . 126 4.6.1 BLERPerformance....................... 128 4.6.2 Sensitivity Analysis . 131 4.6.3 SecrecyCapacityandSecureDOF . 131 4.6.4 ACaseStudy.......................... 133 4.7 EmulatedExperimentResults . 135 4.8 Summary ................................ 139 5Conclusions................................. 141 ADetailedDerivationandExtensionforChapter2 ......... 145 A.1 Reformulation of Optimization Problem (4.21) . 145 A.2 ExtensiontoGeneralScenarios . 147 A.3 DerivationofSecureDegreesofFreedom . 149 xi BPermissionLetters............................. 153 B.1 PermissionLetterforChapter2 . 153 B.2 PermissionLetterforChapter4 . 154 References .................................... 157 xii List of Figures 1.1 An example of an underwater acoustic communication network. The stationary sensor nodes, AUVs, and surface buoys can communicate with each other using acoustic links. Some stationary nodes and the buoys are connected to a control center via cables and high-rate radio links, respectively. 2 2.1 Epoch structure at the transmitter and the receiver. The transmission parameters, including the transmission power, the modulation size and the channel coding rate, could vary from epoch to epoch. 17 2.2 An illustration of the state-action tree for online planning, with the tree depth D = 3. There are 4 actions in the action space .Atdepth A d, No = 3 system state samples are drawn based on the action and the system state at depth (d 1). N = 2 actions and 1 child system state − a nodearefurtherexploredateachdepth. 28 2.3 Estimated parameters µ, σ, m in two experiments. In KW-NOV14, { } the estimated σ’s are on the order of 10−3..............41 xiii 2.4 The performance of fixed-mode transmissions. The number nextto each mode is the average cost calculated based on the cost function in (2.24). .................................. 46 2.5 SPACE08: The mean of the channel lognormal shadowing and imme- diate collected costs by different schemes. 49 2.6 SPACE08: The mean of the channel lognormal shadowing and selected actions in differentschemes. ...................... 49 2.7 SPACE08: Comparison between the mean (¯µ, σ¯, m¯ ) of the estimated channel belief state and the true channel state (µ, σ, m), and the NRMSE. ................................ 50 2.8 KW-NOV14: The mean of the channel lognormal shadowing and im- mediate collected costs by different schemes. 52 2.9 KW-NOV14: The mean of the channel lognormal shadowing and se- lected actions in differentschemes. .................. 52 2.10 KW-NOV14: Comparison between the mean (¯µ, σ¯, m¯ ) of the esti- mated channel belief state and the true channel state (µ, σ, m), and theNRMSE................................ 53 2.11 Normalized difference with respect to the genie-aided method with different data arrival rates with No =3,Na =3,andD =5. 55 xiv 2.12 Normalized difference with respect to the genie-aided method with different Monte Carlo planning parameters. rg = 20 kb/epoch in SPACE08, and rg =6kb/epochinKW-NOV14. 56 (a) With Na =3andD =5..................... 56 (b) With No =3andD =5..................... 56 (c) With No =3andNa =3..................... 56 3.1 An illustration of a system layout with 3 AUVs and 4 APs. 67 3.2 Epoch structure for water parameter field reconstruction using AUVs. 71 3.3 An example of the forward structure of actor network. .. 81 3.4 An example of the forward structure of critic network. .81 3.5 The true field and the estimated fields obtained by the three schemes. 91 (a) Truefield ............................. 91 (b) EstimatedfieldbyScheme1................... 91 (c) EstimatedfieldbyScheme2................... 91 (d) EstimatedfieldbyScheme3................... 91 3.6 Trajectories of 4 AUVs obtained by the three schemes, where the black squares and the black circles indicate the positions of 4 APs and the communication ranges of the APs, respectively. The black circles also are the initial deployment locations of the 4 AUVs.
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