Channel Estimation for Millimeter Wave Massive MIMO Systems Using Separable Compressive Sensing
Channel estimation is a fundamental problem for downlink transmission in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. This paper proposes a channel estimation algorithm by exploiting the separable structured sparsity of mmWave massive MIMO channel. The mmWave downlink chan...
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doaj-12a54e66add249aab5124b8e538ada742021-04-05T17:38:24ZengIEEEIEEE Access2169-35362021-01-019497384974910.1109/ACCESS.2021.30693359388641Channel Estimation for Millimeter Wave Massive MIMO Systems Using Separable Compressive SensingTing Jiang0https://orcid.org/0000-0003-1305-1993Maozhong Song1https://orcid.org/0000-0001-8183-9139Xuejian Zhao2https://orcid.org/0000-0001-7006-7233Xu Liu3College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaKey Laboratory of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing, ChinaKey Laboratory of Wireless Communication of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing, ChinaChannel estimation is a fundamental problem for downlink transmission in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. This paper proposes a channel estimation algorithm by exploiting the separable structured sparsity of mmWave massive MIMO channel. The mmWave downlink channel is firstly formulated as a two dimensional (2D) separable compressive sensing (CS) model according to the sparsity structure of the channel in angle of arrivals (AoAs) and angle of departures (AoDs) domains. Then a separable compressive sampling match pursuit (SCoSaMP) algorithm is proposed to solve the separable CS recovery problem for channel estimation. Based on the separable sparsity structure of the channel, we design the precoding and combining matrices under the metric of mutual information to further improve the performance of channel estimation. Simulations demonstrate the advantages of the proposed algorithm over the traditional CS-based channel estimation methods.https://ieeexplore.ieee.org/document/9388641/Channel estimationseparable compressive sensingprecoder designmillimeter wavemassive MIMO system |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ting Jiang Maozhong Song Xuejian Zhao Xu Liu |
spellingShingle |
Ting Jiang Maozhong Song Xuejian Zhao Xu Liu Channel Estimation for Millimeter Wave Massive MIMO Systems Using Separable Compressive Sensing IEEE Access Channel estimation separable compressive sensing precoder design millimeter wave massive MIMO system |
author_facet |
Ting Jiang Maozhong Song Xuejian Zhao Xu Liu |
author_sort |
Ting Jiang |
title |
Channel Estimation for Millimeter Wave Massive MIMO Systems Using Separable Compressive Sensing |
title_short |
Channel Estimation for Millimeter Wave Massive MIMO Systems Using Separable Compressive Sensing |
title_full |
Channel Estimation for Millimeter Wave Massive MIMO Systems Using Separable Compressive Sensing |
title_fullStr |
Channel Estimation for Millimeter Wave Massive MIMO Systems Using Separable Compressive Sensing |
title_full_unstemmed |
Channel Estimation for Millimeter Wave Massive MIMO Systems Using Separable Compressive Sensing |
title_sort |
channel estimation for millimeter wave massive mimo systems using separable compressive sensing |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
Channel estimation is a fundamental problem for downlink transmission in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. This paper proposes a channel estimation algorithm by exploiting the separable structured sparsity of mmWave massive MIMO channel. The mmWave downlink channel is firstly formulated as a two dimensional (2D) separable compressive sensing (CS) model according to the sparsity structure of the channel in angle of arrivals (AoAs) and angle of departures (AoDs) domains. Then a separable compressive sampling match pursuit (SCoSaMP) algorithm is proposed to solve the separable CS recovery problem for channel estimation. Based on the separable sparsity structure of the channel, we design the precoding and combining matrices under the metric of mutual information to further improve the performance of channel estimation. Simulations demonstrate the advantages of the proposed algorithm over the traditional CS-based channel estimation methods. |
topic |
Channel estimation separable compressive sensing precoder design millimeter wave massive MIMO system |
url |
https://ieeexplore.ieee.org/document/9388641/ |
work_keys_str_mv |
AT tingjiang channelestimationformillimeterwavemassivemimosystemsusingseparablecompressivesensing AT maozhongsong channelestimationformillimeterwavemassivemimosystemsusingseparablecompressivesensing AT xuejianzhao channelestimationformillimeterwavemassivemimosystemsusingseparablecompressivesensing AT xuliu channelestimationformillimeterwavemassivemimosystemsusingseparablecompressivesensing |
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1721539122664833024 |