Next Generation Multiple Access

Name: Next Generation Multiple Access
Brand: Wiley
Price: 115.99 EUR
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Yuanwei Liu Liang Liu Zhiguo Ding Xuemin Shen(Editor)

Wiley (Publisher)

1st Edition

Published on 9. January 2024

624 pages

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978-1-394-18050-9 (ISBN)

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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.

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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
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Next Generation Multiple Access

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