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Table of Contents
Intro
Preface
Contents
Contributors
1 Tribute to Professor Jon W. Mark
Personal Stories
Greeting Messages from Alumni
Part I Broadband Communications for Ubiquitous Connectivity
2 Network Slicing for 5G Networks and Beyond
2.1 Introduction to 5G Communication Networks
2.2 Network Slicing
2.2.1 Network Slicing in 5G Wireless Networks
2.2.1.1 Dynamic Radio Resource Slicing Framework
2.2.2 Network Slicing in 5G Core Networks
2.2.2.1 Joint Computing and Transmission Resource Slicing
2.2.3 AI-Assisted Network Slicing in Beyond 5G Networks
2.2.3.1 Beyond 5G Networks
2.2.3.2 AI-Assisted Network Slicing
2.3 Case Study
2.4 Conclusion
References
3 Responsive Regulation of Dynamic UAV Communication Networks Based on Deep Reinforcement Learning
3.1 Introduction
3.2 Related Works
3.3 System Model and Problem Formulation
3.3.1 Network Environment
3.3.2 Spectrum Access
3.3.3 Energy-Related Considerations
3.3.4 Problem Formulation
3.4 Preliminaries
3.5 Learning Algorithm Design for Proactive Self-Regulation Strategy
3.5.1 State Space
3.5.1.1 Case of UAV Quit
3.5.1.2 Case of UAV Join-In
3.5.2 Action Definition
3.5.3 Reward Function Design
3.5.4 State Transition Definition
3.5.4.1 Case of UAV Quit
3.5.4.2 Case of UAV Join-In
3.5.5 Training Tune-Ups
3.5.5.1 Tune-Ups for Neural Network Training
3.5.5.2 Tune-Ups for RL Training
3.5.6 Parallel Computing
3.6 Proactive Self-Regulation with Dynamic User Distribution
3.7 Numerical Results
3.7.1 Simulation Setup
3.7.2 Simulation Results
3.7.2.1 Case Without UAV or User Dynamics
3.7.2.2 Case of UAV Quit
3.7.2.3 Case of UAV Join-In
3.7.2.4 Case of UAV and User Dynamics
3.8 Conclusions
5 Intelligentized Radio Access Network for Joint Optimization of User Association and Power Allocation
5.1 Introduction
5.2 Related Work
5.3 Main Contribution
5.4 System Model
5.5 Problem Formulation
5.6 DQL Framework
5.6.1 DQN
5.6.2 Design the DQN
5.6.2.1 Actions
5.6.2.2 Reward
5.7 Results and Discussions
5.7.1 Training and Testing Results
5.7.2 UE Performance
5.7.3 Robustness
5.7.4 Scalability
5.7.5 Closer Look at DQN
5.8 Summary
References
6 Routing Algorithms for Heterogeneous Vehicular Networks
6.1 Introduction
6.2 Background
Preface
Contents
Contributors
1 Tribute to Professor Jon W. Mark
Personal Stories
Greeting Messages from Alumni
Part I Broadband Communications for Ubiquitous Connectivity
2 Network Slicing for 5G Networks and Beyond
2.1 Introduction to 5G Communication Networks
2.2 Network Slicing
2.2.1 Network Slicing in 5G Wireless Networks
2.2.1.1 Dynamic Radio Resource Slicing Framework
2.2.2 Network Slicing in 5G Core Networks
2.2.2.1 Joint Computing and Transmission Resource Slicing
2.2.3 AI-Assisted Network Slicing in Beyond 5G Networks
2.2.3.1 Beyond 5G Networks
2.2.3.2 AI-Assisted Network Slicing
2.3 Case Study
2.4 Conclusion
References
3 Responsive Regulation of Dynamic UAV Communication Networks Based on Deep Reinforcement Learning
3.1 Introduction
3.2 Related Works
3.3 System Model and Problem Formulation
3.3.1 Network Environment
3.3.2 Spectrum Access
3.3.3 Energy-Related Considerations
3.3.4 Problem Formulation
3.4 Preliminaries
3.5 Learning Algorithm Design for Proactive Self-Regulation Strategy
3.5.1 State Space
3.5.1.1 Case of UAV Quit
3.5.1.2 Case of UAV Join-In
3.5.2 Action Definition
3.5.3 Reward Function Design
3.5.4 State Transition Definition
3.5.4.1 Case of UAV Quit
3.5.4.2 Case of UAV Join-In
3.5.5 Training Tune-Ups
3.5.5.1 Tune-Ups for Neural Network Training
3.5.5.2 Tune-Ups for RL Training
3.5.6 Parallel Computing
3.6 Proactive Self-Regulation with Dynamic User Distribution
3.7 Numerical Results
3.7.1 Simulation Setup
3.7.2 Simulation Results
3.7.2.1 Case Without UAV or User Dynamics
3.7.2.2 Case of UAV Quit
3.7.2.3 Case of UAV Join-In
3.7.2.4 Case of UAV and User Dynamics
3.8 Conclusions
5 Intelligentized Radio Access Network for Joint Optimization of User Association and Power Allocation
5.1 Introduction
5.2 Related Work
5.3 Main Contribution
5.4 System Model
5.5 Problem Formulation
5.6 DQL Framework
5.6.1 DQN
5.6.2 Design the DQN
5.6.2.1 Actions
5.6.2.2 Reward
5.7 Results and Discussions
5.7.1 Training and Testing Results
5.7.2 UE Performance
5.7.3 Robustness
5.7.4 Scalability
5.7.5 Closer Look at DQN
5.8 Summary
References
6 Routing Algorithms for Heterogeneous Vehicular Networks
6.1 Introduction
6.2 Background