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Reinforcement learning for maritime communications / Liang Xiao, Helin Yang, Weihua Zhuang, Minghui Min.

This book demonstrates that the reliable and secure communication performance of maritime communications can be significantly improved by using intelligent reflecting surface (IRS) aided communication, privacy-aware Internet of Things (IoT) communications, intelligent resource management and locatio...

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Bibliographic Details
Online Access: Full Text (via Springer)
Main Authors: Xiao, Liang, 1980- (Author), Yang, Helin (Author), Zhuang, Weihua (Author), Min, Minghui (Author)
Format: Electronic eBook
Language:English
Published: Cham : Springer, [2023]
Series:Wireless networks (Springer (Firm))
Subjects:
Boats and boating > Electronic equipment.
Radio in navigation.
Wireless communication systems.
Table of Contents:
  • Intro
  • Preface
  • Contents
  • 1 Introduction
  • 1.1 Maritime Communications
  • 1.1.1 Related Works
  • 1.1.2 Challenges
  • 1.1.3 Secure Maritime Communications
  • 1.1.4 Reliable Maritime Communications
  • 1.2 Motivation and Objective
  • 1.2.1 Learning-Based Secure Maritime Communications
  • 1.2.2 Learning-Based Reliable Maritime Communications
  • 1.3 Integrated Space-Air-Ground-Ocean Communication Networks
  • 1.3.1 AI-Enabled Intelligent Space-Air-Ground-Ocean Communication Networks
  • 1.3.2 AI Techniques for Maritime Communication Networks
  • 1.4 Major Contributions and Structural Arrangements
  • References
  • 2 Learning-Based Intelligent Reflecting Surface-Aided Secure Maritime Communications
  • 2.1 Related Work
  • 2.2 System Model and Problem Formulation
  • 2.2.1 System Model
  • 2.2.2 Problem Formulation
  • 2.3 Problem Transformation Based on RL
  • 2.4 Deep PDS-PER Learning-Based Secure Beamforming
  • 2.4.1 Proposed Deep PDS-PER Learning
  • 2.4.2 Secure Beamforming Based on Proposed Deep PDS-PER Learning
  • 2.4.3 Computational Complexity Analysis
  • 2.4.4 Implementation Details of DRL
  • 2.5 Simulation Results and Analysis
  • 2.6 Conclusion
  • References
  • 3 Learning-Based Privacy-Aware Maritime IoT Communications
  • 3.1 Introduction
  • 3.1.1 Mobile Edge Computing
  • 3.1.2 Energy Harvesting
  • 3.1.3 Privacy in MEC IoTs
  • 3.2 Related Work
  • 3.3 System Model
  • 3.4 Privacy in MEC
  • 3.4.1 Privacy Issues in MEC
  • 3.4.2 Location and Usage Pattern Privacy Protection
  • 3.5 Learning-Based Privacy-Aware Offloading with Energy Harvesting
  • 3.5.1 Privacy-Aware Offloading
  • 3.5.2 Performance Analysis
  • 3.6 Simulation Results
  • 3.7 Conclusion
  • References
  • 4 Learning-Based Resource Management for Maritime Communications
  • 4.1 Reinforcement Learning Principle
  • 4.2 Related Work
  • 4.3 System Model and Problem Formulation
  • 4.3.1 Network Requirements
  • 4.3.2 Problem Formulation
  • 4.4 Problem Transformation
  • 4.5 Distributed Cooperative Multi-agent RL-Based Massive Access
  • 4.5.1 Training Stage of Multi-agent RL for Massive Access
  • 4.5.2 Distributed Cooperative Implementation of Multi-agent RL for Massive Access
  • 4.5.3 Computational Complexity Analysis
  • 4.6 Simulation Results and Analysis
  • 4.6.1 Convergence Comparisons
  • 4.6.2 Performance Comparisons Under Different Thresholds of Reliability and Latency
  • 4.7 Intelligent Transmission Scheduling in Maritime Communications
  • 4.8 Conclusion
  • References
  • 5 Learning-Based Maritime Location Privacy Protection
  • 5.1 Introduction
  • 5.1.1 Maritime Location-Based Services and Location Privacy
  • 5.1.2 Inference Attacks
  • 5.1.3 Location Privacy Protection
  • 5.2 Related Work
  • 5.3 System Model
  • 5.3.1 Network Model
  • 5.3.2 Attack Model
  • 5.3.3 Privacy Protection Problem
  • 5.4 Semantic Location Privacy Protection

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