Sparse Signal Processing for Massive MIMO Communications

Zhen Gao; Yikun Mei; Li Qia

doi:10.1007/978-981-99-5394-3

Sparse Signal Processing for Massive MIMO Communications

Zhen Gao, Yikun Mei, Li Qia

Advanced Research Institute of Multidisciplinary Science

Beijing Institute of Technology

Research output: Book/Report › Book › peer-review

2 Citations (Scopus)

Abstract

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.

Original language	English
Publisher	Springer Nature
Number of pages	217
ISBN (Electronic)	9789819953943
ISBN (Print)	9789819953936
DOIs	https://doi.org/10.1007/978-981-99-5394-3
Publication status	Published - 1 Jan 2023

Keywords

Channel estimation
Channel feedback
Compressive sensing
Eamforming
Massive MIMO
Millimeter wave
Orthogonal frequency division multiplexing
Sparse signal processing
Structured sparsity
Wireless channel

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10.1007/978-981-99-5394-3

Cite this

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title = "Sparse Signal Processing for Massive MIMO Communications",

abstract = "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.",

keywords = "Channel estimation, Channel feedback, Compressive sensing, Eamforming, Massive MIMO, Millimeter wave, Orthogonal frequency division multiplexing, Sparse signal processing, Structured sparsity, Wireless channel",

author = "Zhen Gao and Yikun Mei and Li Qia",

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AB - 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.

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KW - Channel feedback

KW - Compressive sensing

KW - Eamforming

KW - Massive MIMO

KW - Millimeter wave

KW - Orthogonal frequency division multiplexing

KW - Sparse signal processing

KW - Structured sparsity

KW - Wireless channel

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BT - Sparse Signal Processing for Massive MIMO Communications

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ER -

Sparse Signal Processing for Massive MIMO Communications

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Keywords

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