Sparse Signal Processing for Massive MIMO Communications

✍ Scribed by Zhen Gao, Yikun Mei, Li Qiao

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 227
• Category: Library

✦ Synopsis

The book focuses on utilizing sparse signal processing techniques in designing massive MIMO communication systems. As the number of antennas has been increasing rapidly for years, extremely high-dimensional channel matrix and massive user access urge for algorithms with much higher efficiency. This book provides in-depth discussions on compressive sensing techniques and simulates the performance on wireless systems. The easy-to-understand instructions with detailed simulations and open-sourced codes provide 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 Compressive Sensing Theory
1.2 Massive MIMO Systems
1.2.1 Massive MIMO Schemes
1.2.2 Massive SM-MIMO Schemes
1.3 Prior Work
1.4 Book Organization
References
2 Subspace-Based Super-Resolution Sparse Channel Estimation in MIMO-OFDM Systems
2.1 Introduction
2.2 Sparse MIMO Channel Model
2.2.1 Channel Sparsity
2.2.2 Spatial Correlation
2.2.3 Temporal Correlation
2.3 Sparse MIMO-OFDM CE
2.3.1 Pilot Pattern
2.3.2 Super-Resolution CE
2.3.3 Discussion on Pilot Overhead
2.4 Simulation Results
2.5 Summary
References
3 Compressive Sensing Sparse Channel Estimation in FDD Massive MIMO Systems
3.1 Introduction
3.2 Spatio-Temporal Common Sparsity of Delay-Domain
3.3 Proposed SCS-Based Spatio-Temporal Joint Channel Estimation Scheme
3.3.1 Non-orthogonal Pilot Scheme at the BS
3.3.2 SCS-Based CE at the User
3.3.3 Space-Time Adaptive Pilot Scheme
3.3.4 CE in Multi-Cell Massive MIMO
3.4 Performance Analysis
3.4.1 Non-Orthogonal Pilot Design Under the Framework of CS Theory
3.4.2 Convergence Analysis of Proposed ASSP Algorithm
3.4.3 Computational Complexity of ASSP Algorithm
3.5 Simulation Results
3.6 Summary
References
4 Compressive Sensing CSI Acquisition and Feedback in FDD Massive MIMO Systems
4.1 Introduction
4.2 System Model
4.2.1 Massive MIMO in the Downlink
4.2.2 Massive MIMO Channels in Virtual Angular Domain
4.2.3 Temporal Correlation of Wireless Channels
4.2.4 Challenges of CE and Feedback
4.3 Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback Scheme
4.3.1 Non-orthogonal Pilot for Downlink CE
4.3.2 CS Based Adaptive CSI Acquisition Scheme
4.3.3 Proposed DSAMP Algorithm for CE
4.3.4 Closed-Loop Channel Tracking with Adaptive Pilot Design
4.4 Performance Analysis
4.4.1 Non-orthogonal Pilot Design for CS Based Adaptive CSI Acquisition
4.4.2 Time Slot Overhead for CS Based Adaptive CSI Acquisition
4.4.3 Frequency-Domain Placement of Pilot Signals
4.4.4 Performance Analysis of Proposed DSAMP Algorithm
4.4.5 Performance Bound of CE
4.4.6 Adaptive Pilot Design and Required Time Slot Overhead for Closed-Loop Channel Tracking
4.4.7 Selection of Thresholds for Algorithms 4.1 and 4.2
4.5 Simulation Results
4.6 Summary
References
5 Compressive Sensing Sparse Channel Estimation in Broadband Millimeter-Wave Massive MIMO Systems
5.1 Introduction
5.2 System Model
5.3 DCS-Based CE Scheme
5.3.1 UL Pilot Training
5.3.2 DCS-Based CE
5.3.3 Pilot Design According to DCS Theory
5.4 Simulation Results
5.5 Summary
References
6 Subspace-Based Super-Resolution Sparse Channel Estimation in Millimeter-Wave Massive MIMO Systems
6.1 Introduction
6.2 Subspace-Based Super-Resolution Sparse Channel Estimation in Narrowband Millimeter-Wave Massive MIMO Systems
6.2.1 System Model
6.2.2 Proposed 2D Unitary Esprit Based Super-Resolution Channel Estimation Scheme
6.2.3 Simulation Results
6.3 Subspace-Based Super-Resolution Sparse Channel Estimation in Wideband Millimeter-Wave Massive MIMO Systems
6.3.1 Downlink CE Stage
6.3.2 UL Channel Estimation Stage
6.3.3 MDU-ESPRIT Algorithm
6.3.4 ML Pairing and Path Gains Estimation
6.3.5 Performance Evaluation
6.4 Summary
References
7 Compressive Sensing Single-User Signal Detection in Massive MIMO Systems with Spatial Modulation
7.1 Introduction
7.2 System Model
7.3 SCS-Based Signal Detector for Massive SM-MIMO
7.3.1 Transmitter Design
7.3.2 SCS-Based Signal Detector at the Receiver
7.4 Performance Analysis
7.4.1 Superiority of SCS-Based Signal Detectors
7.4.2 Benefits from SM Signal Interleaving
7.4.3 Computational Complexity
7.5 Simulation Results
7.6 Summary
References
8 Compressive Sensing Multi-User Detection in Massive MIMO Systems with Spatial Modulation
8.1 Introduction
8.2 System Model
8.2.1 Multi-User Spatial Modulation Scheme for Massive MIMO Systems
8.2.2 Uplink Transmission
8.3 Multi-User Detection for Massive MIMO Systems with Spatial Modulation
8.3.1 Transmitter Design at the Users
8.3.2 SCS-Based MUD at the BS
8.3.3 Computational Complexity
8.4 Simulation Results
8.5 Summary
References
9 Compressive Sensing Massive IoT Access in Massive MIMO Systems with Media Modulation
9.1 Introduction
9.2 System Model
9.2.1 Media Modulation Aided mMTC
9.2.2 Transmission Model
9.3 CS-Based Massive Access Scheme
9.3.1 The StrOMP Algorithm for AUD
9.3.2 SIC-SSP Algorithm for Data Detection
9.3.3 Computational Complexity
9.4 Simulation Results
9.5 Summary
References
10 Sparse Channel Estimation in TDS-OFDM Systems
10.1 Introduction
10.2 System Model
10.3 PA-IHT Based Channel Estimation
10.3.1 The Proposed PA-IHT Based CE Method
10.3.2 Convergence Properties
10.3.3 Computational Complexity
10.4 Simulation Results
10.5 Summary
References
Correction to: Sparse Signal Processing for Massive MIMO Communications
Correction to: Z. Gao et al., Sparse Signal Processing for Massive MIMO Communications, https://doi.org/10.1007/978-981-99-5394-3
Appendix A Proof of Theorem 3.1
Appendix B Proof of (A.2)
Appendix C Proof of (A.3)
Appendix D Derivation of Eq. (6.7摥映數爠eflinkeq:FWtilde16.76)