Description
Nonlinear Signal Processing for 6G Systems and Beyond provides a unified, systems-level perspective on next-generation physical layer (PHY) processing approaches for future, nonorthogonal wireless networks capable of simultaneously supporting a large number of information streams and users within the same spectrum. This guide explains precisely how advanced nonlinear processing methods can reshape multiple-input-multiple-output (MIMO) and nonorthogonal multiple access (NOMA) performance, unlock higher-capacity 6G-and-beyond architectures, and overcome the fundamental limitations of linear techniques in dense, traffic-intensive environments. It establishes the technical foundation required for designing systems that meet future connectivity demands.
The book progresses step-by-step from understanding the limitations of linear signal processing to recognizing how nonlinear detection, vector precoding, and massively parallel algorithms can fundamentally influence how future wireless networks are architected. The analysis highlights how channel behavior, spatial structure, and interference patterns shape system limits, and details how nonlinear signal processing opens new pathways for gains in throughput, latency, and power efficiency. Dedicated sections explore massively parallel architectures and structured search strategies, demonstrating how these techniques translate into practical designs suitable for large-scale deployment.
This authoritative resource equips wireless engineers, PHY-layer designers, telecom R&D teams, and advanced students with the implementation-focused knowledge needed to develop cutting-edge communication systems. Readers gain the tools to design PHY-layer solutions that scale effectively with dense deployments and heavy traffic loads, ensuring first-pass success in future wireless connectivity. The book delivers fast, practical guidance for meeting the emerging performance demands of next-generation wireless communications.
Table Of Contents
Introduction – The Emergence of Non-Orthogonal Transmissions and the Detection Problem
1.1 Introduction
1.2 The Paradigm Shift Towards Non-Orthogonal Signal Transmissions
1.3 The Role of MIMO Systems
1.4 Linear Receiver Processing for MU-MIMO Uplink
1.5 Emerging Other Non-Orthogonal Transmissions
1.6 Additional Applications and Validations of Non-Linear Processing
1.7 Overview of the Next Chapters
Appendix 1: Principles and Limitations of Linear Receiver Processing
Maximum Likelihood Receiver Processing for Non-Orthogonal Signal Transmissions
2.1 Introduction
2.2 Maximum Likelihood (ML) Detection Problem
2.3 Tree Search-Based Non-Linear Detection Algorithms
2.4 Non-Tree Search-Based Non-Linear Detection Algorithms
2.5 Chapter Summary
Hard Massively Parallelizable Non-Linear Processing – The MultiSphere Approach
3.1 Introduction
3.2 Motivation for Massively Parallel Processing
3.3 MultiSphere Massively Parallelizable Non-Linear Processing Framework
3.4 MultiSphere’s Preprocessing: SD Tree Partitioning
3.5 MultiSphere’s Sphere Detection
3.6 Chapter Summary
The Soft Detection Problem
4.1 Introduction
4.2 The Soft Detection Principles
4.3 Exercising Soft Information and Sphere Decoders
4.4 Exercising Soft Information from Massively Parallel Non-Linear Processing
4.5 The Application of Soft Information in the MIMO Detection Problem
4.6 Chapter Summary
Iterative Detection and Decoding
5.1 Introduction
5.2 The Principles of Iterative Detection and Decoding
5.3 Performing Iterative Detection and Decoding with Sphere Decoders
5.4 Performing Iterative Detection and Decoding with Iterative MPNL
5.5 Chapter Summary
MPNL vs Deep Learning-Based MIMO Detection
6.1 Introduction
6.2 DL-Based MIMO Detection
6.3 MIMO Detection Preliminaries
6.4 Overview and Evolution of DL-Based Detection Algorithms
6.5 Evaluation and Performance Indicators
6.6 Discussion and Way Forward
6.7 Chapter Summary
Appendix 6: Complexity Derivations of DL Detectors
Quantum Annealing as an Alternative Computational Approach for MIMO Detection
7.1 Introduction
7.2 The Need for Alternative Computation Paradigms
7.3 Binary Quadratic Models: Ising and QUBO
7.4 Quantum Annealing
7.5 Quantum Annealing-Based MIMO Detection
7.6 QA-Based MIMO Detection Capabilities and Challenges
7.7 Performance Evaluation Results
7.8 Outlook and Other Computational Methods
7.9 Chapter Summary
Appendix 7: Derivations of Ising and QUBO Formulations
Conclusions and Future Directions
8.1 Conclusions
8.2 Future Directions