Next Generation Multiple Access

Highly comprehensive resource investigating how next-generation multiple access (NGMA) relates to unrestricted global connection, business requirements, and sustainable wireless networks Next Generation Multiple Access is a comprehensive, state-of-the-art, and approachable guide to the fundamentals and applications of next-generation multiple access (NGMA) schemes, guiding the future development of industries, government requirements, and military utilization of multiple access systems for wireless communication systems and providing various application scenarios to fit practical case studies. The scope and depth of this book are balanced for both beginners to advanced users. Additional references are provided for readers who wish to learn more details about certain subjects. Applications of NGMA outside of communications, including data and computing assisted by machine learning, protocol designs, and others, are also covered. Written by four leading experts in the field, Next Generation Multiple Access includes information on: Foundation and application scenarios for non-orthogonal multiple access (NOMA) systems, including modulation, detection, power allocation, and resource management NOMA's interaction with alternate applications such as satellite communication systems, terrestrial-satellite communication systems, and integrated sensing Collision resolution, compressed sensing aided massive access, latency management, deep learning enabled massive access, and energy harvesting Holographic-pattern division multiple access, over-the-air transmission, multi-dimensional multiple access, sparse signal detection, and federated meta-learning assisted resource management Next Generation Multiple Access is an essential reference for those who are interested in discovering practical solutions using NGMA technology, including researchers, engineers, and graduate students in the disciplines of information engineering, telecommunications engineering, and computer engineering. Cover Title Page Copyright Contents About the Editors List of Contributors Preface Acknowledgments Chapter 1 Next Generation Multiple Access Toward 6G 1.1 The Road to NGMA 1.2 Non‐Orthogonal Multiple Access 1.3 Massive Access 1.4 Book Outline Part I Evolution of NOMA Towards NGMA Chapter 2 Modulation Techniques for NGMA/NOMA 2.1 Introduction 2.2 Space‐Domain IM for NGMA 2.2.1 SM‐Based NOMA 2.2.1.1 Multi‐RF Schemes 2.2.1.2 Single‐RF Schemes 2.2.1.3 Recent Developments in SM‐NOMA 2.2.2 RSM‐Based NOMA 2.2.3 SM‐Aided SCMA 2.3 Frequency‐Domain IM for NGMA 2.3.1 NOMA with Frequency‐Domain IM 2.3.1.1 OFDM‐IM NOMA 2.3.1.2 DM‐OFDM NOMA 2.3.2 C‐NOMA with Frequency‐Domain IM 2.3.2.1 Broadcast Phase 2.3.2.2 Cooperative Phase 2.4 Code‐Domain IM for NGMA 2.4.1 CIM‐SCMA 2.4.2 CIM‐MC‐CDMA 2.5 Power‐Domain IM for NGMA 2.5.1 Transmission Model 2.5.1.1 Two‐User Case 2.5.1.2 Multiuser Case 2.5.2 Signal Decoding 2.5.3 Performance Analysis 2.6 Summary References Chapter 3 NOMA Transmission Design with Practical Modulations 3.1 Introduction 3.2 Fundamentals 3.2.1 Multichannel Downlink NOMA 3.2.2 Practical Modulations in NOMA 3.3 Effective Throughput Analysis 3.3.1 Effective Throughput of the Single‐User Channels 3.3.2 Effective Throughput of the Two‐User Channels 3.4 NOMA Transmission Design 3.4.1 Problem Formulation 3.4.2 Power Allocation 3.4.2.1 Power Allocation within Channels 3.4.2.2 Power Budget Allocation Among Channels 3.4.3 Joint Resource Allocation 3.5 Numerical Results 3.6 Conclusion References Chapter 4 Optimal Resource Allocation for NGMA 4.1 Introduction 4.2 Single‐Cell Single‐Carrier NOMA 4.2.1 Total Power Minimization Problem 4.2.2 Sum‐Rate Maximization Problem 4.2.3 Energy‐Efficiency Maximization Problem 4.2.4 Key Features and Implementation Issues 4.2.4.1 CSI Insensitivity 4.2.4.2 Rate Fairness 4.3 Single‐Cell Multicarrier NOMA 4.3.1 Total Power Minimization Problem 4.3.2 Sum‐Rate Maximization Problem 4.3.3 Energy‐Efficiency Maximization Problem 4.3.4 Key Features and Implementation Issues 4.4 Multi‐cell NOMA with Single‐Cell Processing 4.4.1 Dynamic Decoding Order 4.4.1.1 Optimal JSPA for Total Power Minimization Problem 4.4.1.2 Optimal JSPA for Sum‐Rate Maximization Problem 4.4.1.3 Optimal JSPA for EE Maximization Problem 4.4.2 Static Decoding Order 4.4.2.1 Optimal FRPA for Total Power Minimization Problem 4.4.2.2 Optimal FRPA for Sum‐Rate Maximization Problem 4.4.2.3 Optimal FRPA for EE Maximization Problem 4.4.2.4 Optimal JRPA for Total Power Minimization Problem 4.4.2.5 Suboptimal JRPA for Sum‐Rate Maximization Problem 4.4.2.6 Suboptimal JRPA for EE Maximization Problem 4.5 Numerical Results 4.5.1 Approximated Optimal Powers 4.5.2 SC‐NOMA versus FDMA–NOMA versus FDMA 4.5.3 Multi‐cell NOMA: JSPA versus JRPA versus FRPA 4.6 Conclusions Acknowledgments References Chapter 5 Cooperative NOMA 5.1 Introduction 5.2 System Model for D2MD‐CNOMA 5.2.1 System Configuration 5.2.2 Channel Model 5.3 Adaptive Aggregate Transmission 5.3.1 First Phase 5.3.2 Second Phase 5.4 Performance Analysis 5.4.1 Outage Probability 5.4.2 Ergodic Sum Capacity 5.5 Numerical Results and Discussion 5.5.1 Outage Probability 5.5.2 Ergodic Sum Capacity 5.A.1 Proof of Theorem 5.1 References Chapter 6 Multi‐scale‐NOMA: An Effective Support to Future Communication–Positioning Integration System 6.1 Introduction 6.2 Positioning in Cellular Networks 6.3 MS‐NOMA Architecture 6.4 Interference Analysis 6.4.1 Single‐Cell Network 6.4.1.1 Interference of Positioning to Communication 6.4.1.2 Interference of Communication to Positioning 6.4.2 Multicell Networks 6.4.2.1 Interference of Positioning to Communication 6.4.2.2 Interference of Communication to Positioning 6.5 Resource Allocation 6.5.1 The Constraints 6.5.1.1 The BER Threshold Under QoS Constraint 6.5.1.2 The Total Power Limitation 6.5.1.3 The Elimination of Near‐Far Effect 6.5.2 The Proposed Joint Power Allocation Model 6.5.3 The Positioning–Communication Joint Power Allocation Scheme 6.5.4 Remarks 6.6 Performance Evaluation 6.6.1 Communication Performance 6.6.2 Ranging Performance 6.6.3 Resource Consumption of Positioning 6.6.3.1 Achievable Positioning Measurement Frequency 6.6.3.2 The Resource Element Consumption 6.6.3.3 The Power Consumption 6.6.4 Positioning Performance 6.6.4.1 Comparison by Using CP4A and the Traditional Method 6.6.4.2 Comparision Between MS‐NOMA and PRS References Chapter 7 NOMA‐Aware Wireless Content Caching Networks 7.1 Introduction 7.2 System Model 7.2.1 System Description 7.2.2 Content Request Model 7.2.3 Random System State 7.2.4 System Latency Under Each Random State 7.2.5 System's Average Latency 7.3 Algorithm Design 7.3.1 User Pairing and Power Control Optimization 7.3.2 Cache Placement 7.3.3 Recommendation Algorithm 7.3.4 Joint Optimization Algorithm and Property Analysis 7.4 Numerical Simulation 7.4.1 Convergence Performance 7.4.2 System's Average Latency 7.4.3 Cache Hit Ratio 7.5 Conclusion References Chapter 8 NOMA Empowered Multi‐Access Edge Computing and Edge Intelligence 8.1 Introduction 8.2 Literature Review 8.3 System Model and Formulation 8.3.1 Modeling of Two‐Sided Dual Offloading 8.3.2 Overall Latency Minimization 8.4 Algorithms for Optimal Offloading 8.5 Numerical Results 8.6 Conclusion Acknowledgments References Chapter 9 Exploiting Non‐orthogonal Multiple Access in Integrated Sensing and Communications 9.1 Introduction 9.2 Developing Trends and Fundamental Models of ISAC 9.2.1 ISAC: From Orthogonality to Non‐orthogonality 9.2.2 Downlink ISAC 9.2.3 Uplink ISAC 9.3 Novel NOMA Designs in Downlink and Uplink ISAC 9.3.1 NOMA‐Empowered Downlink ISAC Design 9.3.2 Semi‐NOMA‐Based Uplink ISAC Design 9.4 Case Study: System Model and Problem Formulation 9.4.1 System Model 9.4.1.1 Communication Model 9.4.1.2 Sensing Model 9.4.2 Problem Formulation 9.5 Case Study: Proposed Solutions 9.6 Case Study: Numerical Results 9.6.1 Convergence of Algorithm 9.1 9.6.2 Baseline 9.6.3 Transmit Beampattern 9.7 Conclusions References Part II Massive Access for NGMA Chapter 10 Capacity of Many‐Access Channels 10.1 Introduction 10.2 The Many‐Access Channel Model 10.3 Capacity of the MnAC 10.3.1 The Equal‐Power Case 10.3.2 Heterogeneous Powers and Fading 10.4 Energy Efficiency of the MnAC 10.4.1 Minimum Energy per Bit for Given PUPE 10.4.2 Capacity per Unit‐Energy Under Different Error Criteria 10.5 Discussion and Open Problems 10.5.1 Scaling Regime 10.5.2 Some Practical Issues Acknowledgments References Chapter 11 Random Access Techniques for Machine‐Type Communication 11.1 Fundamentals of Random Access 11.1.1 Coordinated Versus Uncoordinated Transmissions 11.1.2 Random Access Techniques 11.1.2.1 ALOHA Protocols 11.1.2.2 CSMA 11.1.3 Re‐transmission Strategies 11.2 A Game Theoretic View 11.2.1 A Model 11.2.2 Fictitious Play 11.3 Random Access Protocols for MTC 11.3.1 4‐Step Random Access 11.3.2 2‐Step Random Access 11.3.3 Analysis of 2‐Step Random Access 11.3.4 Fast Retrial 11.4 Variants of 2‐Step Random Access 11.4.1 2‐Step Random Access with MIMO 11.4.2 Sequential Transmission of Multiple Preambles 11.4.3 Simultaneous Transmission of Multiple Preambles 11.4.4 Preambles for Exploration 11.5 Application of NOMA to Random Access 11.5.1 Power‐Domain NOMA 11.5.2 S‐ALOHA with NOMA 11.5.3 A Generalization with Multiple Channels 11.5.4 NOMA‐ALOHA Game 11.6 Low‐Latency Access for MTC 11.6.1 Long Propagation Delay 11.6.2 Repetition Diversity 11.6.3 Channel Coding‐Based Random Access References Chapter 12 Grant‐Free Random Access via Compressed Sensing: Algorithm and Performance 12.1 Introduction 12.2 Joint Device Detection, Channel Estimation, and Data Decoding with Collision Resolution for MIMO Massive Unsourced Random Access 12.2.1 System Model and Encoding Scheme 12.2.1.1 System Model 12.2.1.2 Encoding Scheme 12.2.2 Collision Resolution Protocol 12.2.3 Decoding Scheme 12.2.3.1 Joint DAD‐CE Algorithm 12.2.3.2 MIMO‐LDPC‐SIC Decoder 12.2.4 Experimental Results 12.3 Exploiting Angular Domain Sparsity for Grant‐Free Random Access: A Hybrid AMP Approach 12.3.1 Sparse Modeling of Massive Access 12.3.2 Recovery Algorithm 12.3.2.1 Application to Unsourced Random Access 12.3.3 Experimental Results 12.4 LEO Satellite‐Enabled Grant‐Free Random Access 12.4.1 System Model 12.4.1.1 Channel Model 12.4.1.2 Signal Modulation 12.4.1.3 Problem Formulation 12.4.2 Pattern Coupled SBL Framework 12.4.2.1 The Pattern‐Coupled Hierarchical Prior 12.4.2.2 SBL Framework 12.4.3 Experimental Results 12.5 Concluding Remarks Acknowledgments References Chapter 13 Algorithm Unrolling for Massive Connectivity in IoT Networks 13.1 Introduction 13.1.1 Massive Random Access 13.1.2 Sparse Signal Processing 13.1.2.1 Bayesian Methods 13.1.2.2 Optimization‐Based Methods 13.1.2.3 Deep Learning‐Based Methods 13.2 System Model 13.3 Learned Iterative Shrinkage Thresholding Algorithm for Massive Connectivity 13.3.1 Problem Formulation 13.3.2 Unrolled Neural Networks 13.3.2.1 LISTA‐GS 13.3.2.2 LISTA‐GSCP 13.3.2.3 ALISTA‐GS 13.3.3 Convergence Analysis 13.3.3.1 “Good” Parameters for Learning 13.4 Learned Proximal Operator Methods for Massive Connectivity 13.4.1 Problem Formulation 13.4.2 Unrolled Neural Networks 13.4.2.1 LPOM‐GS 13.4.2.2 LPOMCP‐GS 13.4.2.3 ALPOM‐GS 13.5 Training and Testing Strategies 13.6 Simulation Results 13.7 Conclusions References Chapter 14 Grant‐Free Massive Random Access: Joint Activity Detection, Channel Estimation, and Data Decoding 14.1 Introduction 14.2 System Model 14.3 Joint Estimation via a Turbo Receiver 14.3.1 Overview of the Turbo Receiver 14.3.2 The Joint Estimator 14.3.3 The Channel Decoder 14.4 A Low‐Complexity Side Information‐Aided Receiver 14.4.1 Overview of the SI‐Aided Receiver 14.4.2 The Sequential Estimator and the Channel Decoder 14.4.3 The Side Information 14.5 Simulation Results 14.5.1 Simulation Setting and Baseline Schemes 14.5.2 Results 14.6 Summary References Chapter 15 Joint User Activity Detection, Channel Estimation, and Signal Detection for Grant‐Free Massive Connectivity 15.1 Introduction 15.2 Receiver Design for Synchronous Massive Connectivity 15.2.1 System Model 15.2.1.1 Synchronous Uplink Transmission 15.2.1.2 Problem Formulation 15.2.2 Proposed JUICESD Algorithm 15.2.2.1 JUICESD Algorithm Structure 15.2.2.2 SMD Operation 15.2.2.3 CSCE Operation 15.2.2.4 Overall Algorithm and Complexity Analysis 15.2.3 Numerical Results 15.2.4 Summary 15.3 Receiver Design for Asynchronous Massive Connectivity 15.3.1 System Model 15.3.1.1 Asynchronous Uplink Transmission 15.3.1.2 Problem Formulation 15.3.2 Extended Probability Model and Factor Graph Construction 15.3.2.1 Auxiliary Variables 15.3.2.2 Extended Probability Model Construction 15.3.2.3 Factor Graph Construction 15.3.3 Proposed TAMP Algorithm 15.3.3.1 Structure of Bayesian Receiver 15.3.3.2 Channel and Signal Decomposition Module 15.3.3.3 Delay Learning Module 15.3.3.4 Packet Recovery 15.3.3.5 Overall Algorithm and Complexity Analysis 15.3.4 Numerical Results 15.3.5 Summary 15.4 Conclusion References Chapter 16 Grant‐Free Random Access via Covariance‐Based Approach 16.1 Introduction 16.2 Device Activity Detection in Single‐Cell Massive MIMO 16.2.1 System Model and Problem Formulation 16.2.2 Phase Transition Analysis 16.2.3 Coordinate Descent Algorithms 16.2.3.1 Coordinate Descent Algorithm 16.2.3.2 Active Set Coordinate Descent Algorithm 16.2.4 Performance Evaluation 16.3 Device Activity Detection in Multi‐Cell Massive MIMO 16.3.1 System Model and Problem Formulation 16.3.2 Phase Transition Analysis 16.3.3 Coordinate Descent Algorithms 16.3.4 Performance Evaluation 16.4 Practical Issues and Extensions 16.4.1 Joint Device Data and Activity Detection 16.4.2 Device Activity Detection in Asynchronous Systems 16.5 Conclusions References Chapter 17 Deep Learning‐Enabled Massive Access 17.1 Introduction 17.1.1 Existing Work 17.1.2 Main Contribution 17.1.3 Notation 17.2 System Model 17.3 Model‐Driven Channel Estimation 17.3.1 GROUP LASSO‐Based Channel Estimation 17.3.2 AMP‐Based Channel Estimation 17.4 Model‐Driven Activity Detection 17.4.1 Covariance‐Based Activity Detection 17.4.1.1 MAP‐Based Activity Detection 17.5 Auto‐Encoder‐Based Pilot Design 17.6 Numerical Results 17.6.1 Channel Estimation 17.6.2 Device Activity Detection 17.7 Conclusion References Chapter 18 Massive Unsourced Random Access 18.1 Introduction 18.2 URA with Single‐Antenna Base Station 18.2.1 System Model and Problem Formulation 18.2.2 Algorithmic Solutions 18.2.3 Slotted Transmission Framework 18.2.3.1 Decoding 18.2.3.2 CS Decoding 18.2.3.3 Tree Decoding 18.2.4 Sparse Kronecker Product (SKP) Coding 18.2.5 Numerical Discussion 18.3 URA with Multi‐Antenna Base Station 18.3.1 System Model 18.3.2 Algorithmic Solutions 18.3.3 Covariance‐Based Compressed Sensing 18.3.4 Clustering‐Based Method 18.3.5 Tensor‐Based Modulation 18.3.6 Bilinear Methods 18.3.6.1 Bilinear Vector Approximate Message Passing 18.3.7 Numerical Discussion References Part III Other Advanced Emerging MA Techniques for NGMA Chapter 19 Holographic‐Pattern Division Multiple Access 19.1 Overview of HDMA 19.1.1 RHS Basics 19.1.2 Principle of HDMA 19.1.2.1 Holographic Pattern Construction 19.1.2.2 HDMA Transmission Model 19.2 System Model 19.2.1 Scenario Description 19.2.2 Channel Model 19.3 Multiuser Holographic Beamforming 19.4 Holographic Pattern Design 19.5 Performance Analysis and Evaluation 19.5.1 Relation Between Sum Rate and Capacity 19.5.2 Performance Evaluation 19.6 Summary References Chapter 20 Over‐the‐Air Computation 20.1 Introduction 20.1.1 Notations 20.2 AirComp Fundamentals 20.3 Power Control for AirComp 20.3.1 Static Channels 20.3.2 Fading Channels 20.3.3 Effect of Imperfect CSI 20.4 Beamforming for AirComp 20.4.1 SIMO AirComp 20.4.2 MIMO AriComp 20.5 Extension 20.5.1 Multicell AirComp Networks 20.5.2 Intelligent Reflecting Surface (IRS)‐Assisted AirComp 20.5.3 Unmanned Aerial Vehicle (UAV)‐Enabled AirComp 20.5.4 Over‐the‐Air FEEL (Air‐FEEL) 20.6 Conclusion References Chapter 21 Multi‐Dimensional Multiple Access for 6G: Efficient Radio Resource Utilization and Value‐Oriented Service Provisioning 21.1 Introduction 21.1.1 Difficulties of Existing Multiple Access Techniques 21.1.1.1 Lack of Diverse and Individualized Service Provisioning Capabilities 21.1.1.2 Lack of Flexibility and Adaptability in Coping with Heterogeneous Network Scenarios 21.1.1.3 Lack of Opportunistic Resource Orchestration and Utilization Capabilities 21.1.2 Embracing 6G with Multi‐Dimensional Multiple Access 21.2 Principle of MDMA 21.2.1 Core Concepts and Mechanisms of Achieving MDMA 21.2.2 Enabling Blocks of Individualized Service Provisioning in MDMA 21.3 Value‐Oriented Operation of MDMA 21.3.1 Value‐Oriented Operation Paradigm 21.3.1.1 Individual Level 21.3.1.2 System Level 21.3.2 Individual Level Value Realization: User‐Centric Perspective 21.3.3 System Level Value Realization: Network Operator Perspective 21.4 Multi‐Dimensional Resource Utilization in Value‐Oriented MDMA 21.4.1 User Coalition Formation Approach 21.4.1.1 Preferences Over Matchings 21.4.2 Real‐Time Multi‐Dimensional Resource Allocation 21.4.2.1 Lagrange Dual Decomposition Method 21.5 Numerical Results and Analysis 21.5.1 Performance Evaluation of Value‐Oriented Paradigm in MDMA 21.5.2 Performance Comparison of Value‐Oriented MDMA and State‐of‐the‐Art MA Schemes 21.6 Conclusion References Chapter 22 Efficient Federated Meta‐Learning Over Multi‐Access Wireless Networks 22.1 Introduction 22.2 Related Work 22.3 Preliminaries and Assumptions 22.3.1 Federated Meta‐Learning Problem 22.3.2 Standard Algorithm 22.3.3 Assumptions 22.4 Nonuniform Federated Meta‐Learning 22.4.1 Device Contribution Quantification 22.4.2 Device Selection 22.5 Federated Meta‐Learning Over Wireless Networks 22.5.1 System Model 22.5.1.1 Computation Model 22.5.1.2 Communication Model 22.5.2 Problem Formulation 22.5.3 A Joint Device Selection and Resource Allocation Algorithm 22.5.3.1 Solution to (SP1) 22.5.3.2 Solution to (SP2) 22.6 Extension to First‐Order Approximations 22.7 Simulation 22.7.1 Datasets and Models 22.7.2 Baselines 22.7.3 Implementation 22.7.4 Experimental Results 22.7.4.1 Convergence Speed 22.7.4.2 Effect of Local Update Steps 22.7.4.3 Performance of URAL in Wireless Networks 22.7.4.4 Effect of Resource Blocks 22.7.4.5 Effect of Channel Quality 22.7.4.6 Effect of Weight Parameters 22.8 Conclusion References Index EULA