Uplink Sparse Channel Estimation for Hybrid Millimeter Wave Massive MIMO Systems by UTAMP-SBL
The compressive sensing (CS)-based sparse channel estimator is recognized as the most effective solution to the excessive pilot overhead in massive MIMO systems. However, due to the complex signal processing in the wireless communication systems, the measurement matrix in the CS-based channel estima...
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doaj-53d721eb1a0c48619aee2190485521372021-07-23T14:05:38ZengMDPI AGSensors1424-82202021-07-01214760476010.3390/s21144760Uplink Sparse Channel Estimation for Hybrid Millimeter Wave Massive MIMO Systems by UTAMP-SBLShuai Hou0Yafeng Wang1Chao Li2School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaThe compressive sensing (CS)-based sparse channel estimator is recognized as the most effective solution to the excessive pilot overhead in massive MIMO systems. However, due to the complex signal processing in the wireless communication systems, the measurement matrix in the CS-based channel estimation is sometimes “unfriendly” to the channel recovery. To overcome this problem, in this paper, the state-of-the-art sparse Bayesian learning using approximate message passing with unitary transformation (UTAMP-SBL), which is robust to various measurement matrices, is leveraged to address the multi-user uplink channel estimation for hybrid architecture millimeter wave massive MIMO systems. Specifically, the sparsity of channels in the angular domain is exploited to reduce the pilot overhead. Simulation results demonstrate that the UTAMP-SBL is able to achieve effective performance improvement than other competitors with low pilot overhead.https://www.mdpi.com/1424-8220/21/14/4760millimeter-wave massive MIMOchannel estimationsparse Bayesian learningcompressive sensing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shuai Hou Yafeng Wang Chao Li |
spellingShingle |
Shuai Hou Yafeng Wang Chao Li Uplink Sparse Channel Estimation for Hybrid Millimeter Wave Massive MIMO Systems by UTAMP-SBL Sensors millimeter-wave massive MIMO channel estimation sparse Bayesian learning compressive sensing |
author_facet |
Shuai Hou Yafeng Wang Chao Li |
author_sort |
Shuai Hou |
title |
Uplink Sparse Channel Estimation for Hybrid Millimeter Wave Massive MIMO Systems by UTAMP-SBL |
title_short |
Uplink Sparse Channel Estimation for Hybrid Millimeter Wave Massive MIMO Systems by UTAMP-SBL |
title_full |
Uplink Sparse Channel Estimation for Hybrid Millimeter Wave Massive MIMO Systems by UTAMP-SBL |
title_fullStr |
Uplink Sparse Channel Estimation for Hybrid Millimeter Wave Massive MIMO Systems by UTAMP-SBL |
title_full_unstemmed |
Uplink Sparse Channel Estimation for Hybrid Millimeter Wave Massive MIMO Systems by UTAMP-SBL |
title_sort |
uplink sparse channel estimation for hybrid millimeter wave massive mimo systems by utamp-sbl |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-07-01 |
description |
The compressive sensing (CS)-based sparse channel estimator is recognized as the most effective solution to the excessive pilot overhead in massive MIMO systems. However, due to the complex signal processing in the wireless communication systems, the measurement matrix in the CS-based channel estimation is sometimes “unfriendly” to the channel recovery. To overcome this problem, in this paper, the state-of-the-art sparse Bayesian learning using approximate message passing with unitary transformation (UTAMP-SBL), which is robust to various measurement matrices, is leveraged to address the multi-user uplink channel estimation for hybrid architecture millimeter wave massive MIMO systems. Specifically, the sparsity of channels in the angular domain is exploited to reduce the pilot overhead. Simulation results demonstrate that the UTAMP-SBL is able to achieve effective performance improvement than other competitors with low pilot overhead. |
topic |
millimeter-wave massive MIMO channel estimation sparse Bayesian learning compressive sensing |
url |
https://www.mdpi.com/1424-8220/21/14/4760 |
work_keys_str_mv |
AT shuaihou uplinksparsechannelestimationforhybridmillimeterwavemassivemimosystemsbyutampsbl AT yafengwang uplinksparsechannelestimationforhybridmillimeterwavemassivemimosystemsbyutampsbl AT chaoli uplinksparsechannelestimationforhybridmillimeterwavemassivemimosystemsbyutampsbl |
_version_ |
1721285980378365952 |