Multiple access technology towards ubiquitous networks : overview and efficient designs / / Neng Ye [and three others]

• Record Nr.: UNINA9910627256603321
• Autore: Ye Neng
• Pubbl/distr/stampa: Gateway East, Singapore : , : Springer, , [2023]; ©2023
• ISBN: 981-19-4025-8
• Descrizione fisica: 1 online resource (194 pages)
• Disciplina: 621.38456
• Soggetti: Mobile communication systems; Multiple access protocols (Computer network protocols)
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Contents -- Acronyms -- 1 Introduction -- 1.1 Background -- 1.2 Evolution of Multiple Access Technology -- 1.3 Signal Construction for Multiple Access Technology -- 1.4 AI-Enhanced Multiple Access Technology -- 1.5 Organization -- References -- 2 Multiple Access Towards 5G and Beyond -- 2.1 Introduction -- 2.2 Typical Multiple Access Technologies -- 2.2.1 Bit-Level Non-orthogonal Multiple Access -- 2.2.2 Symbol-Level Non-orthogonal Multiple Access -- 2.2.3 Multi-user Detection Technologies -- 2.3 Grant-Free Multiple Access for mMTC -- 2.3.1 Motivation -- 2.3.2 Grant-Free Process -- 2.3.3 Typical Grant-Free Multiple Access Technologies -- 2.3.4 Detection Techniques -- 2.4 Implementation Issues -- 2.4.1 Scheduling-Based Multiple Access -- 2.4.2 Grant-Free Multiple Access -- 2.5 Conclusions -- References -- 3 Multiple Access Towards Non-terrestrial Networks -- 3.1 Introduction -- 3.2 Overview on Non-terrestrial IoT -- 3.2.1 Satellite IoT -- 3.2.2 UAV IoT -- 3.3 Physical Layer Technologies of Satellite IoT -- 3.3.1 Wireless Access Technologies -- 3.3.2 High-Efficacy Resource Allocation -- 3.3.3 Large Dynamic Channel -- 3.3.4 MmWave Transmission System -- 3.3.5 Other Enabling Technologies -- 3.4 Non-physical Layer Technologies of Satellite IoT -- 3.4.1 High-Efficacy Protocol -- 3.4.2 Ubiquitous Network Architecture -- 3.4.3 Other Enabling Technologies -- 3.5 Multiple Access Technologies of UAV IoT -- 3.5.1 Flexible Deployment ; and Route Planning -- 3.5.2 Low Power Consumption Design -- 3.5.3 Collision Resolution Design -- 3.5.4 Large Dynamic Channel -- 3.5.5 Other Enabling Technologies -- 3.6 Conclusions -- References -- 4 Constellation Design Technique for Multiple Access -- 4.1 Introduction -- 4.2 System Model and Problem Formulation -- 4.3 Constellation Rotation Method -- 4.3.1 Problem Transformation.; 4.3.2 Variational Approximation Method -- 4.4 Analysis and Discussions -- 4.4.1 Achievable Capacity with SIC Receiver -- 4.4.2 Analysis on Infinite Number of Receiving Antenna -- 4.5 Simulation Results and Conclusions -- References -- 5 Rate-Adaptive Design for Multiple Access -- 5.1 Introduction -- 5.1.1 Related Work and Motivation -- 5.1.2 Contributions -- 5.2 System Model -- 5.3 Rate-Adaptive Multiple Access -- 5.3.1 Rate-Splitting Principle -- 5.3.2 RAMA for Grant-Free Transmission -- 5.3.3 Implementation Issues -- 5.4 Performance Analysis of Conv-GF and RAMA -- 5.4.1 Outage Performance Analysis of Grant-Free Access -- 5.4.2 Outage Performance Analysis of RAMA -- 5.4.3 Comparisons -- 5.5 RAMA Amenable Constellations -- 5.5.1 Overlapping Method -- 5.5.2 Bundling Method -- 5.6 Simulation Results -- 5.6.1 Ideal Settings -- 5.6.2 Realistic Settings -- 5.7 Conclusions -- References -- 6 Artificial Intelligence-Enhanced Multiple Access -- 6.1 Introduction -- 6.1.1 Related Work and Motivation -- 6.1.2 Contributions -- 6.2 System Model and Problem Formulation -- 6.2.1 Uplink NOMA System Model -- 6.2.2 Problem Formulation -- 6.3 DeepNOMA: An End-to-End DL Framework for NOMA Based on Multi-task Learning -- 6.3.1 Deep Multi-task Learning -- 6.3.2 Network Structure of DeepNOMA -- 6.3.3 Multi-task Balancing Technique -- 6.3.4 Training Algorithm -- 6.4 DeepMAS: Model-Based MAS Mapping Network Design -- 6.4.1 Model-Based Transmitter Design -- 6.4.2 Parameter Initialization -- 6.5 DeepMUD: Interference Cancellation-Based MUD Network Design -- 6.5.1 Interference Cancellation for Multiple Access Channel -- 6.5.2 ICNN: Interference Cancellation-Enabled DNN -- 6.5.3 DeepMUD Based on ICNN -- 6.5.4 Training DeepMUD over Fading Channel -- 6.6 Simulation Results -- 6.6.1 Network Training Performance -- 6.6.2 Design Examples of DeepMAS.; 6.6.3 Performance Evaluation of DeepNOMA -- 6.7 Conclusions -- References -- 7 Deep Learning-Aided High-Throughput Multiple Access -- 7.1 Introduction -- 7.2 System Model and Problem Formulation -- 7.3 Deep Learning-Aided Grant-Free NOMA -- 7.3.1 Deep VAE for Grant-Free NOMA -- 7.3.2 Encoding Network -- 7.3.3 Decoding Network -- 7.4 Multi-loss Based Network Training Algorithm -- 7.4.1 Dataset Organization with Random User Activation -- 7.4.2 Multi-loss Function Design -- 7.4.3 Overall Algorithm -- 7.5 Simulation Results -- 7.5.1 Network Training Results and Design Examples -- 7.5.2 Detection Accuracy Analysis -- 7.6 Conclusions -- References -- 8 Summary and Outlook -- 8.1 Summary -- 8.2 Future Directions.