Collapsed VBI-DP Based Structured Sparse Channel Estimation Algorithm for Massive MIMO-OFDM
Massive multiple input multiple output (MIMO) technology significantly improves the capacity of wireless communication systems by deploying hundreds of antennas at the base station. However, the large scale of the array implies higher computational complexity and pilot overhead when implementing cha...
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doaj-f4b476a4382f41818c314b1aa3cbbb752021-03-29T22:26:19ZengIEEEIEEE Access2169-35362019-01-017166651667410.1109/ACCESS.2019.28961258629904Collapsed VBI-DP Based Structured Sparse Channel Estimation Algorithm for Massive MIMO-OFDMXinhua Lu0https://orcid.org/0000-0002-2338-7020Carles Navarro Manchon1Zhongyong Wang2https://orcid.org/0000-0003-0870-0303School of Information Engineering, Zhengzhou University, Zhengzhou, ChinaDepartment of Electronic Systems, Aalborg University, Aalborg, DenmarkSchool of Information Engineering, Zhengzhou University, Zhengzhou, ChinaMassive multiple input multiple output (MIMO) technology significantly improves the capacity of wireless communication systems by deploying hundreds of antennas at the base station. However, the large scale of the array implies higher computational complexity and pilot overhead when implementing channel estimation in the uplink. Utilizing the sparse channel structure is a promising approach to improve the channel estimation performance while circumventing such problems. In this paper, we investigate the detailed physical structure in the delay-spatial domain of uplink channels in massive MIMO-orthogonal frequency division multiplexing (MIMO-OFDM) systems and construct a hierarchical probabilistic model based on Dirichlet process (DP) prior to match the channel's structural sparse features. Based on the model, we derive a structured sparse channel estimation algorithm by implementing collapsed variational Bayesian inference (CVBI). The simulation results demonstrate that the proposed CVBI-DP algorithm can improve channel estimation performance significantly compared with the state-of-the-art methods for massive MIMO-OFDM, without increasing the computational complexity and pilot overhead.https://ieeexplore.ieee.org/document/8629904/Massive MIMOstructured sparse channelDirichlet processcollapsed variational Bayesian inference |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xinhua Lu Carles Navarro Manchon Zhongyong Wang |
spellingShingle |
Xinhua Lu Carles Navarro Manchon Zhongyong Wang Collapsed VBI-DP Based Structured Sparse Channel Estimation Algorithm for Massive MIMO-OFDM IEEE Access Massive MIMO structured sparse channel Dirichlet process collapsed variational Bayesian inference |
author_facet |
Xinhua Lu Carles Navarro Manchon Zhongyong Wang |
author_sort |
Xinhua Lu |
title |
Collapsed VBI-DP Based Structured Sparse Channel Estimation Algorithm for Massive MIMO-OFDM |
title_short |
Collapsed VBI-DP Based Structured Sparse Channel Estimation Algorithm for Massive MIMO-OFDM |
title_full |
Collapsed VBI-DP Based Structured Sparse Channel Estimation Algorithm for Massive MIMO-OFDM |
title_fullStr |
Collapsed VBI-DP Based Structured Sparse Channel Estimation Algorithm for Massive MIMO-OFDM |
title_full_unstemmed |
Collapsed VBI-DP Based Structured Sparse Channel Estimation Algorithm for Massive MIMO-OFDM |
title_sort |
collapsed vbi-dp based structured sparse channel estimation algorithm for massive mimo-ofdm |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Massive multiple input multiple output (MIMO) technology significantly improves the capacity of wireless communication systems by deploying hundreds of antennas at the base station. However, the large scale of the array implies higher computational complexity and pilot overhead when implementing channel estimation in the uplink. Utilizing the sparse channel structure is a promising approach to improve the channel estimation performance while circumventing such problems. In this paper, we investigate the detailed physical structure in the delay-spatial domain of uplink channels in massive MIMO-orthogonal frequency division multiplexing (MIMO-OFDM) systems and construct a hierarchical probabilistic model based on Dirichlet process (DP) prior to match the channel's structural sparse features. Based on the model, we derive a structured sparse channel estimation algorithm by implementing collapsed variational Bayesian inference (CVBI). The simulation results demonstrate that the proposed CVBI-DP algorithm can improve channel estimation performance significantly compared with the state-of-the-art methods for massive MIMO-OFDM, without increasing the computational complexity and pilot overhead. |
topic |
Massive MIMO structured sparse channel Dirichlet process collapsed variational Bayesian inference |
url |
https://ieeexplore.ieee.org/document/8629904/ |
work_keys_str_mv |
AT xinhualu collapsedvbidpbasedstructuredsparsechannelestimationalgorithmformassivemimoofdm AT carlesnavarromanchon collapsedvbidpbasedstructuredsparsechannelestimationalgorithmformassivemimoofdm AT zhongyongwang collapsedvbidpbasedstructuredsparsechannelestimationalgorithmformassivemimoofdm |
_version_ |
1724191692710477824 |