Channel estimation using variational Bayesian learning for multi‐user mmWave MIMO systems
Abstract This paper presents a novel variational Bayesian learning‐based channel estimation scheme for hybrid pre‐coding‐employed wideband multiuser millimetre wave multiple‐input multiple‐output communication systems. We first propose a frequency variational Bayesian algorithm, which leverages comm...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-03-01
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| Series: | IET Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cmu2.12088 |
| _version_ | 1798035954721619968 |
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| author | Bo Xiao Pingmu Huang Zhipeng Lin Jie Zeng Tiejun Lv |
| author_facet | Bo Xiao Pingmu Huang Zhipeng Lin Jie Zeng Tiejun Lv |
| author_sort | Bo Xiao |
| collection | DOAJ |
| description | Abstract This paper presents a novel variational Bayesian learning‐based channel estimation scheme for hybrid pre‐coding‐employed wideband multiuser millimetre wave multiple‐input multiple‐output communication systems. We first propose a frequency variational Bayesian algorithm, which leverages common sparsity of different sub‐carriers in the frequency domain. The algorithm shares all the information of the support sets from the measurement matrices, significantly improving channel estimation accuracy. To enhance robustness of the frequency variational Bayesian algorithm, we develop a hierarchical Gaussian prior channel model, which employs an identify‐and‐reject strategy to deal with random outliers imposed by hardware impairments. A support selection frequency variational Bayesian channel estimation algorithm is also proposed, which adaptively selects support sets from the measurement matrices. As a result, the overall computational complexity can be reduced. Validated by the Bayesian Cramér‐Rao bound, simulation results show that, both frequency variational Bayesian and support selection‐frequency variational Bayesian algorithms can achieve higher channel estimation accuracy than existing methods. Furthermore, compared with frequency variational Bayesian, support selection‐frequency variational Bayesian requires significantly lower computational complexity, and hence, it is more practical for channel estimation applications. |
| first_indexed | 2024-04-11T21:05:40Z |
| format | Article |
| id | doaj.art-41561bb6a362428aa985824c3f442b98 |
| institution | Directory Open Access Journal |
| issn | 1751-8628 1751-8636 |
| language | English |
| last_indexed | 2024-04-11T21:05:40Z |
| publishDate | 2021-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Communications |
| spelling | doaj.art-41561bb6a362428aa985824c3f442b982022-12-22T04:03:21ZengWileyIET Communications1751-86281751-86362021-03-0115456657910.1049/cmu2.12088Channel estimation using variational Bayesian learning for multi‐user mmWave MIMO systemsBo Xiao0Pingmu Huang1Zhipeng Lin2Jie Zeng3Tiejun Lv4School of Information and Communication Engineering Beijing University of Posts and Telecommunications (BUPT) Beijing 100876 ChinaSchool of Information and Communication Engineering Beijing University of Posts and Telecommunications (BUPT) Beijing 100876 ChinaSchool of Information and Communication Engineering Beijing University of Posts and Telecommunications (BUPT) Beijing 100876 ChinaDepartment of Electronic Engineering Tsinghua University Beijing 100084 ChinaSchool of Information and Communication Engineering Beijing University of Posts and Telecommunications (BUPT) Beijing 100876 ChinaAbstract This paper presents a novel variational Bayesian learning‐based channel estimation scheme for hybrid pre‐coding‐employed wideband multiuser millimetre wave multiple‐input multiple‐output communication systems. We first propose a frequency variational Bayesian algorithm, which leverages common sparsity of different sub‐carriers in the frequency domain. The algorithm shares all the information of the support sets from the measurement matrices, significantly improving channel estimation accuracy. To enhance robustness of the frequency variational Bayesian algorithm, we develop a hierarchical Gaussian prior channel model, which employs an identify‐and‐reject strategy to deal with random outliers imposed by hardware impairments. A support selection frequency variational Bayesian channel estimation algorithm is also proposed, which adaptively selects support sets from the measurement matrices. As a result, the overall computational complexity can be reduced. Validated by the Bayesian Cramér‐Rao bound, simulation results show that, both frequency variational Bayesian and support selection‐frequency variational Bayesian algorithms can achieve higher channel estimation accuracy than existing methods. Furthermore, compared with frequency variational Bayesian, support selection‐frequency variational Bayesian requires significantly lower computational complexity, and hence, it is more practical for channel estimation applications.https://doi.org/10.1049/cmu2.12088AlgebraMathematical analysisCodesCommunication channel equalisation and identificationRadio links and equipmentOptimisation techniques |
| spellingShingle | Bo Xiao Pingmu Huang Zhipeng Lin Jie Zeng Tiejun Lv Channel estimation using variational Bayesian learning for multi‐user mmWave MIMO systems IET Communications Algebra Mathematical analysis Codes Communication channel equalisation and identification Radio links and equipment Optimisation techniques |
| title | Channel estimation using variational Bayesian learning for multi‐user mmWave MIMO systems |
| title_full | Channel estimation using variational Bayesian learning for multi‐user mmWave MIMO systems |
| title_fullStr | Channel estimation using variational Bayesian learning for multi‐user mmWave MIMO systems |
| title_full_unstemmed | Channel estimation using variational Bayesian learning for multi‐user mmWave MIMO systems |
| title_short | Channel estimation using variational Bayesian learning for multi‐user mmWave MIMO systems |
| title_sort | channel estimation using variational bayesian learning for multi user mmwave mimo systems |
| topic | Algebra Mathematical analysis Codes Communication channel equalisation and identification Radio links and equipment Optimisation techniques |
| url | https://doi.org/10.1049/cmu2.12088 |
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