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Table of Contents
Intro
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
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