Broadband Communications, Computing, and Control for Ubiquitous Intelligence

Description

This book reports on the latest advances from both industry and academia on ubiquitous intelligence and how it is enabled by 5G/6G communication technologies. The authors cover network protocol and architecture design, machine learning and artificial intelligence, coordinated control and digital twins technologies, and security and privacy enhancement for ubiquitous intelligence. The authors include recent studies of performance analysis and enhancement of the Internet of Things, cyber-physical systems, edge computing, and cyber twins, all of which provide importance guidance and theoretical tools for developing future ubiquitous intelligence. The content of the book will be of interest to students, educators, and researchers in academia, industry, and research laboratories.Provides comprehensive coverage of enabling communications, computing, and control technologies for ubiquitous intelligence;Presents a novel paradigm of ubiquitous intelligence powered by broadband communications, computing, and control;Includes a review of 5G/6G communication technologies, network protocol and architecture design, and ubiquitous computing. 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 References 4 Utility-Based Dynamic Resource Allocation in IEEE 802.11ax Networks: A Genetic Algorithm Approach 4.1 Introduction 4.2 Related Works 4.3 Background on OFDMA and RU Allocation in IEEE 802.11ax 4.4 System Model 4.5 Utility-Based Dynamic Resource Allocation Scheme 4.5.1 Optimal Resource Allocation Problem Formulation 4.5.2 Genetic Algorithm 4.6 Simulation Results 4.6.1 UDRA vs. Exhaustive Search 4.6.2 Network-Wise Throughputs and Fairness Indexes 4.7 Conclusion References 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 6.2.1 Unicast Routing Algorithms 6.2.2 Broadcast Routing Algorithms 6.2.3 Geocast Routing Algorithms 6.2.4 Related Work in Routing Algorithms 6.3 Machine Learning-Based Routing Algorithm for IoV with Mobility Prediction 6.3.1 Network Model 6.3.2 Statistical Mobility Model 6.3.2.1 Inter-Arrival Time Distribution 6.3.2.2 Inter-Vehicle Spacing Distribution 6.3.3 Channel Model 6.3.4 ANN Model 6.4 Performance Evaluation 6.5 Conclusion References 7 Teaching from Home: Computer and Communication Network Perspectives 7.1 Introduction 7.2 Related Work 7.3 Network Technologies Involved 7.3.1 Host Computers 7.3.1.1 Desktop, Laptop, or Tablet? 7.3.1.2 Windows, Mac OS, or Linux? 7.3.1.3 Other Necessary Peripherals 7.3.2 Home Networks 7.3.2.1 Ethernet Structured Wiring 7.3.2.2 No-New-Wires Home Backbone 7.3.2.3 Wireless Home Network 7.3.3 Internet Access 7.3.3.1 Fiber, Cellular, or Satellite? 7.3.3.2 Telephone Service Providers 7.3.3.3 Television Service Providers 7.4 Improvement for Online Teaching 7.4.1 WiFi Interference Avoidance 7.4.1.1 A Better (Al)located WiFi AP 7.4.1.2 Wired Interconnected WiFi APs 7.4.1.3 Wireless Interconnected WiFi APs 7.4.2 WAN Reliability Augmentation 7.4.2.1 DSL vs. Cable Modem 7.4.2.2 Primary vs. Backup 7.4.2.3 Load Balancing 7.4.3 Recommendations on Teaching from Home 7.5 Further Discussion 7.6 Conclusions References Part II Caching, Computing, and Control for Ubiquitous Intelligence 8 State Transition Field: A New Framework for Mobile Dynamic Caching 8.1 Introduction 8.2 State Transition Field 8.2.1 Content Request and Replacement 8.2.2 Cache State 8.2.3 State and Content Caching Probabilities 8.2.4 General Cache State Transition Model 8.2.5 State Transition Field 8.2.6 Discussions on the Steady State and the Convergence 8.3 State Transition Field with Time-Varying Content Popularity 8.3.1 General Replacement Model 8.3.2 Instantaneous STF: The General Case 8.3.3 Impact of STF on Instantaneous Cache Hit Probability 8.4 Dynamic Probabilistic Caching with Time-Varying Content Popularity 8.4.1 The Content Replacement Markov Chain 8.4.2 Generating the State Transition Matrix 8.4.3 Discussion on Scalability 8.5 Numerical Results 8.5.1 State Transition Field with Time-Invariant Content Popularity 8.5.2 State Transition Field with Time-Varying Content Popularity 8.5.3 Dynamic Probabilistic Caching with Time-Varying Content Popularity 8.6 Summary References 9 Deep Reinforcement Learning for Mobile EdgeComputing Systems 9.1 Introduction 9.2 Overview of Deep Reinforcement Learning 9.2.1 DRL Problem Formulation 9.2.2 Determine the Optimal Policy with Deep Learning 9.2.3 Existing DRL Algorithms 9.3 Case Study: Deep Q-Learning for Task Offloading in MEC 9.3.1 System Model 9.3.1.1 Task Model 9.3.1.2 Task Offloading Decision 9.3.1.3 Local Processing Model 9.3.1.4 Edge Node Offloading Model 9.3.2 Task Offloading Problem 9.3.2.1 State 9.3.2.2 Action 9.3.2.3 Cost 9.3.2.4 Problem Formulation 9.3.3 Deep Q-Learning-Based Algorithm 9.3.3.1 Neural Network 9.3.3.2 Algorithm Design 9.3.4 Performance Evaluation 9.3.4.1 Algorithm Convergence 9.3.4.2 Method Comparison 9.4 Challenges and Future Directions 9.5 Conclusion References 10 Mobile Computation Offloading with Hard TaskCompletion Times 10.1 Introduction 10.2 Continuous Offloading 10.2.1 System Description and Problem Formulation 10.2.1.1 Local Execution 10.2.1.2 Remote Execution 10.2.2 Markovian Channel and the Time-Dilated Absorbing Markov Model 10.2.3 Offline Bound 10.2.4 OnOpt (Online Optimal) Algorithm 10.3 Multi-part Offloading 10.3.1 Problem Formulation 10.3.2 Offline Bound 10.3.3 The Time-Dilated Absorbing Markov Model 10.3.4 Optimal Algorithm for K-Part Offloading 10.4 Numerical Results 10.4.1 Simulation Set 1 10.4.2 Simulation Set 2 10.5 Summary References 11 Online Incentive Mechanism Design in Edge Computing 11.1 Introduction 11.2 Mechanism Design and Auction 11.3 Primal–Dual-Based Online Incentive Mechanism 11.3.1 Primal–Dual-Based Method for Linear Systems 11.3.2 Primal–Dual-Based Method for Nonlinear Systems 11.4 Application of Primal–Dual Online Incentive Mechanism Design in Edge Computing 11.4.1 System Model Descriptions 11.4.1.1 System Model 11.4.1.2 Problem Formulation 11.4.2 The Design of OMAP 11.4.2.1 Problem Reformulation 11.4.2.2 OMAP 11.4.3 Performance Analyses 11.4.4 Numerical Simulations 11.5 Summary References 12 Collaborative Deep Neural Network Inference via Mobile Edge Computing 12.1 Introduction 12.2 Background 12.2.1 DNN Inference 12.2.2 Mobile Edge Computing 12.2.3 Machine Learning 12.3 Collaborative DNN Inference via Device-Edge Orchestration 12.3.1 Collaborative DNN Inference Framework 12.3.2 Service Delay and Accuracy Analysis of Collaborative DNN Inference 12.3.2.1 Inference Delay Analysis 12.3.2.2 Inference Accuracy Analysis 12.3.3 Joint Sampling Rate Selection and Resource Allocation Problem 12.3.3.1 Constrained Markov Decision Process 12.3.4 Deep RL-Based Solution 12.3.4.1 Markov Decision Process Transformation (Step 1) 12.3.4.2 Optimization Subroutine for Resource Allocation (Step 3) 12.3.4.3 Deep RL-Based Algorithm (Step 2) 12.4 Performance Evaluation 12.4.1 Experiment Setup 12.4.2 Convergence Performance 12.4.3 Impact of Task Arrival Rate 12.4.4 Impact of Optimization Subroutine 12.5 Conclusion References 13 Automated Data-Driven System for Compliance Monitoring 13.1 Introduction 13.1.1 Radio Spectrum Management 13.1.2 Spectrum Monitoring for Compliance 13.1.3 Chapter Contributions and Organization 13.2 Automated Data-driven System 13.3 Data Sources 13.3.1 Spectrum Measurements 13.3.2 Spectrum Management Records 13.4 Signal Identification 13.4.1 Mode Analysis 13.4.2 Mode-Sensor Matching 13.4.3 License-Measurements Association 13.5 Violation Identification 13.5.1 Detecting Violations 13.5.2 Characterizing Violations 13.5.2.1 Confidence Indicators 13.5.2.2 Behavioral Indicators 13.5.2.3 Extent Indicators 13.5.2.4 Impact Indicators 13.5.3 Prioritizing Violations 13.6 Summary References 14 AI Driven User Authentication 14.1 Introduction 14.2 Facial Recognition 14.2.1 Overview 14.2.2 Facial Recognition Using EigenFace Algorithm 14.2.3 Facial Recognition Using CNN 14.3 Implementation 14.3.1 Mobile Authenticator 14.3.2 Supporting Cloud Backend 14.4 Conclusion References 15 Control and Communication Coordination for Industrial Digital Twins of Sintering Process 15.1 Introduction 15.2 Control–Communication Coordination Architecture for Industrial Digital Twins 15.3 Sintering Production Line 15.4 Deterministic Communication Based on Time-Sensitive Networking 15.5 Intelligent Modeling for Sintering Process 15.6 Digital Twins Coordination of the Sintering Process 15.7 Summary References Index