Millimeter-Wave/Sub-Terahertz Ultra-Massive MIMO Transmission Technology

✍ Scribed by Zhen Gao, Ziwei Wan, Yikun Mei, Keke Ying, Kuiyu Wang

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 193
• Category: Library

✦ Synopsis

The book focuses on the advanced mmWave/Sub-terahertz ultra-massive MIMO wireless communications, which are regarded as a promising paradigm shift in future 5G beyond and even 6G. This is achieved by providing a comprehensive review of the rapidly developing field of massive MIMO communication, in-depth discussions on the impact of extremely large-scale antenna array, and detailed numerical simulation results on our proposed schemes. Several case studies are given after introducing basic communication system components, and the simulation codes are open sourced in our book, which shows the reproducibility of our models and methods and provides convenience for readers such as researchers, engineers, and graduate students in the fields of wireless communications.

✦ Table of Contents

Contents
Acronyms
1 Introduction
1.1 MmWave and THz Communications for 6G
1.2 From Massive MIMO to Ultra-massive MIMO
1.3 Prior Work
1.3.1 Channel Estimation for MmWave Massive MIMO
1.3.2 Beamforming Design for MmWave Massive MIMO
1.3.3 Massive MIMO-Aided Massive IoT Access
1.3.4 Near-Field Channel Estimation for MmWave/THz Ultra-massive MIMO
1.3.5 Fast Time-Varying Channel Estimation and Tracking
1.4 Book Organization
2 Closed-Loop Sparse Channel Estimation for MmWave Full-Dimensional MIMO Systems
2.1 Introduction
2.2 Overview of the Close-Loop Sparse Channel Estimation
2.3 Downlink CE Stage
2.3.1 Problem Formulation
2.3.2 Horizontal/Vertical AoAs Acquisition at UE
2.3.3 Design Combining Matrix at UE
2.3.4 EVD-Based Estimates for the Number of MPCs
2.4 Uplink CE Stage
2.4.1 Estimate Horizontal/Vertical AoDs and Delays at BS
2.4.2 Design Multi-beam Transmit Precoding Matrix at UE
2.5 MDU-ESPRIT Algorithm
2.6 ML-Based Parameters Pairing and Channel Gains Estimation
2.7 Performance Evaluation
2.7.1 Evaluation of CE Performance
2.7.2 Computational Complexity
2.8 Summary
3 Compressive Sensing Based Channel Estimation for MmWave Full-Dimensional Lens Antenna Array
3.1 Introduction
3.2 The Principle of Lens Antenna Array
3.3 An Overview of Compressive Sensing Theory
3.3.1 Compressive Sensing Modeling
3.3.2 Sensing Matrix
3.4 The Proposed Channel Estimation Scheme
3.4.1 System Model
3.4.2 Pilot Training Scheme Design
3.4.3 Redundant Dictionary Design
3.4.4 Pilot Design Based on CS Theory
3.4.5 Computational Complexity Analysis
3.4.6 Simulation Results
3.5 Summary
4 Hybrid Beamforming Design for MmWave Massive MIMO Systems
4.1 Introduction
4.2 Narrowband Hybrid Beamforming Design with Fully-Connected Architecture
4.2.1 System Model
4.2.2 Digital Precoder/Combiner Design
4.2.3 Analog Precoder/Combiner Design
4.2.4 Simulation Results
4.3 Wideband Hybrid Beamforming Design with Fully-Connected Architecture
4.3.1 System Model
4.3.2 Wideband Digital Precoder/Combiner Design
4.3.3 Wideband Analog Precoder/Combiner Design
4.3.4 Simulation Results
4.4 Hybrid Beamforming Design with Partially-Connected Architecture
4.4.1 System Model
4.4.2 Analog Beamforming Deign Under Fixed Partially-Connected Structure
4.4.3 Analog Beamforming Deign Under Dynamic Partially-Connected Structure
4.4.4 Simulation Results
4.5 Summary
5 Compressive Sensing Based Joint Active User Detection and Channel Estimation in MmWave XL-MIMO Systems
5.1 Introduction
5.2 System Model
5.2.1 Signal Transmission Model
5.2.2 MmWave XL-MIMO Channel Model
5.3 Joint AUD and CE Scheme Design Based on Compressed Sensing
5.3.1 Compressed Sensing Problem Description
5.3.2 Common Structured Block Sparsity for XL-MIMO Channels
5.3.3 Sensing Matrices Design
5.3.4 The Proposed Joint AUD and CE Algorithm
5.3.5 Computational Complexity Analysis
5.4 Simulation Results
5.5 Summary
6 Channel Estimation and Data Transmission for THz UM-MIMO in Space-Air-Ground Integrated Network
6.1 Introduction
6.2 CE and Data Transmission in Aeronautical Terahertz Ultra-Massive MIMO Systems
6.2.1 Triple Delay-Beam-Doppler Squint Effects
6.2.2 The Overview of Proposed CE and Tracking Scheme
6.3 System Model
6.3.1 Signal Transmission Model
6.3.2 THz UM MIMO Channel Model
6.4 Initial CE
6.4.1 Accurate Angle Estimation Based on Reconfigurable RF Selection Network
6.4.2 Accurate Doppler Shift Estimation Under Doppler-Squint Effect
6.4.3 Estimation of Path Delay and Channel Gain
6.5 Data-Aided Channel Tracking
6.6 Pilot-Aided Channel Tracking
6.7 Performance Analysis
6.7.1 CRLB of Channel Parameter Estimation
6.7.2 Computational Complexity Analysis
6.8 Numerical Experiments
6.8.1 Settings
6.8.2 Numerical Results
6.9 Summary
7 Summary and Future Directions
7.1 Summary of This Book
7.2 Future Directions
Appendix A Proof of Equation (2.7)
Appendix B
The Proof of Lemma 3.2.1
Appendix C The Proof of Theorem 3.3.2
Appendix D The Proof Theorem 3.3.3
Appendix E Derivation of Equation (6.4)
Appendix F Proof of Lemma (6.1)
Appendix References