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231103 ||| eng |
| 020 |
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|a 9789819953943
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| 100 |
1 |
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|a Gao, Zhen
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| 245 |
0 |
0 |
|a Sparse Signal Processing for Massive MIMO Communications
|h Elektronische Ressource
|c by Zhen Gao, Yikun Mei, Li Qiao
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| 250 |
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|a 1st ed. 2024
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| 260 |
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|a Singapore
|b Springer Nature Singapore
|c 2024, 2024
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| 300 |
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|a XVI, 217 p. 87 illus., 69 illus. in color
|b online resource
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| 505 |
0 |
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|a Introduction -- Massive MIMO Performance Analysis and Channel Estimation Scheme in Sparse Channels -- Channel Estimation Based on Structured Compressed Sensing Theory in FDD Massive MIMO Systems -- Channel Feedback Based on Distributed Compressed Sensing Theory in FDD Massive MIMO Systems -- Channel Estimation and Beamforming Based on Compressed Sensing Theory in mmWave Massive MIMO Systems -- Sparse Channel Estimation Based on Spectral Estimation Theory for mmWave Massive MIMO Systems -- Quasi-Optimal Signals Detection for Massive Spatial Modulation MIMO Systems Based on Structured Compressed Sensing -- Multiuser Signal Detection Based on Compressed Sensing for Massive Media Modulation MIMO Systems -- Compressed Sensing Mass Access Techniques in Medium Modulation Assisted IoT Machine Type Communication -- Time-varying Channel Estimation Based on Compressed Sensing Theory for TDS-OFDM Systems -- Summary and Prospects for Massive MIMOTechnology
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| 653 |
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|a Communications Engineering, Networks
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| 653 |
|
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|a Telecommunication
|
| 653 |
|
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|a Signal processing
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| 653 |
|
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|a Signal, Speech and Image Processing
|
| 653 |
|
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|a Microwaves, RF Engineering and Optical Communications
|
| 700 |
1 |
|
|a Mei, Yikun
|e [author]
|
| 700 |
1 |
|
|a Qiao, Li
|e [author]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b Springer
|a Springer eBooks 2005-
|
| 028 |
5 |
0 |
|a 10.1007/978-981-99-5394-3
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-981-99-5394-3?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
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|a 621.382
|
| 520 |
|
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|a 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
|