Broadband communications, computing, and control for ubiquitous intelligence / / Lin Cai, Brian L. Mark, and Jianping Pan, editors

• Pubblicazione: Cham, Switzerland : , : Springer International Publishing, , [2022]; ©2022
• Descrizione fisica: 1 online resource (353 pages)
• Disciplina: 621.384
• Soggetto topico: Wireless communication systems - Automatic control; Wireless communication systems
• Persona (resp. second.): CaiLin; MarkBrian L.; PanJianping
• Nota di contenuto: 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 -- 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.
• Titolo autorizzato: Broadband communications, computing, and control for ubiquitous intelligence
• ISBN: 3-030-98064-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996485669803316
• Lo trovi qui: Univ. di Salerno
• Serie: Wireless Networks