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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Main Authors: Shuai Hou, Yafeng Wang, Chao Li
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/21/14/4760
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spelling 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
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