Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems
Compressed sensing (CS) has great potential in channel estimation for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. To solve such a CS-based channel estimation problem, three categories of algorithms, namely convex relaxation algorithms, greedy iteration algorithms...
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doaj-8be4afee089840d69b7309d66e0649ad2020-11-25T01:55:20ZengMDPI AGApplied Sciences2076-34172019-10-01920434610.3390/app9204346app9204346Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO SystemsXingbo Lu0Weiwei Yang1Yueming Cai2Xinrong Guan3College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaCollege of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaCollege of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaCollege of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaCompressed sensing (CS) has great potential in channel estimation for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. To solve such a CS-based channel estimation problem, three categories of algorithms, namely convex relaxation algorithms, greedy iteration algorithms and Bayesian inference algorithms, are widely used. In this paper, with a unified massive MIMO framework, comprehensive comparisons among three categories of algorithms are presented in the perspective of the estimated accuracy, which is affected by the received signal-to-noise ratio (SNR), the number of resolvable paths, angular quantization error, the number of pilot symbols and hardware impairments. Specifically, it shows that convex relation algorithms achieve the best estimation accuracy at the high SNR range and it is mainly affected by the received SNR and transmitter’s hardware impairments. At the low SNR range, greedy iteration algorithms outperform others and the estimated accuracy is then limited by the angle quantization error. Furthermore, a tradeoff between the estimated error and complexity is achieved in Bayesian inference algorithms, a;though its estimated error is sensitive to the number of available pilot symbols.https://www.mdpi.com/2076-3417/9/20/4346millimeter wavemassive mimochannel estimationcompressed sensing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xingbo Lu Weiwei Yang Yueming Cai Xinrong Guan |
spellingShingle |
Xingbo Lu Weiwei Yang Yueming Cai Xinrong Guan Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems Applied Sciences millimeter wave massive mimo channel estimation compressed sensing |
author_facet |
Xingbo Lu Weiwei Yang Yueming Cai Xinrong Guan |
author_sort |
Xingbo Lu |
title |
Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems |
title_short |
Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems |
title_full |
Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems |
title_fullStr |
Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems |
title_full_unstemmed |
Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems |
title_sort |
comparison of cs-based channel estimation for millimeter wave massive mimo systems |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2019-10-01 |
description |
Compressed sensing (CS) has great potential in channel estimation for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. To solve such a CS-based channel estimation problem, three categories of algorithms, namely convex relaxation algorithms, greedy iteration algorithms and Bayesian inference algorithms, are widely used. In this paper, with a unified massive MIMO framework, comprehensive comparisons among three categories of algorithms are presented in the perspective of the estimated accuracy, which is affected by the received signal-to-noise ratio (SNR), the number of resolvable paths, angular quantization error, the number of pilot symbols and hardware impairments. Specifically, it shows that convex relation algorithms achieve the best estimation accuracy at the high SNR range and it is mainly affected by the received SNR and transmitter’s hardware impairments. At the low SNR range, greedy iteration algorithms outperform others and the estimated accuracy is then limited by the angle quantization error. Furthermore, a tradeoff between the estimated error and complexity is achieved in Bayesian inference algorithms, a;though its estimated error is sensitive to the number of available pilot symbols. |
topic |
millimeter wave massive mimo channel estimation compressed sensing |
url |
https://www.mdpi.com/2076-3417/9/20/4346 |
work_keys_str_mv |
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1724983815854620672 |