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Training optimization for hybrid MIMO communication systems

Training optimization for hybrid MIMO communication systems
Training optimization for hybrid MIMO communication systems
Channel estimation is conceived for hybrid multiple-input multiple-output (MIMO) communication systems. Both mean square error minimization and mutual information maximization are used as our performance metrics and a pair of low-complexity channel estimation schemes are proposed. In each scheme, the training sequence and the analog matrices of the transmitter and receiver are jointly optimized. We commence by designing the optimal training sequences and analog matrices for the first scheme. Upon relying on the resultant optimal structures, the training optimization problems are substantially simplified and the nonconvexity resulting from the analog matrices can be overcome. In the second scheme, the channel estimation and data transmission share the same analog matrices, which beneficially reduces the overhead of optimizing the associated analog matrices. Therefore, a composite channel matrix is estimated instead of the true channel matrix. By exploiting the statistical optimization framework advocated, the analog matrices can be designed independently of the training sequence. Based on the resultant analog matrices, the training sequence can then be efficiently designed according to diverse channel statistics and performance metrics. Finally, we conclude by quantifying the performance benefits of the proposed estimation schemes.
1536-1276
5473-5487
Xing, Chengwen
2477f24d-3711-47b1-b6b4-80e2672a48d1
Liu, Dekang
9947a21c-de50-40b8-9853-7a5d86979243
Gong, Shiqi
56c61a3c-ffb4-4f08-a817-9cd4d073c6ad
Xu, Wei
cef87ede-4edd-4bde-a9f8-a4c63e18c418
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Xing, Chengwen
2477f24d-3711-47b1-b6b4-80e2672a48d1
Liu, Dekang
9947a21c-de50-40b8-9853-7a5d86979243
Gong, Shiqi
56c61a3c-ffb4-4f08-a817-9cd4d073c6ad
Xu, Wei
cef87ede-4edd-4bde-a9f8-a4c63e18c418
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Xing, Chengwen, Liu, Dekang, Gong, Shiqi, Xu, Wei, Chen, Sheng and Hanzo, Lajos (2020) Training optimization for hybrid MIMO communication systems. IEEE Transactions on Wireless Communications, 19 (8), 5473-5487.

Record type: Article

Abstract

Channel estimation is conceived for hybrid multiple-input multiple-output (MIMO) communication systems. Both mean square error minimization and mutual information maximization are used as our performance metrics and a pair of low-complexity channel estimation schemes are proposed. In each scheme, the training sequence and the analog matrices of the transmitter and receiver are jointly optimized. We commence by designing the optimal training sequences and analog matrices for the first scheme. Upon relying on the resultant optimal structures, the training optimization problems are substantially simplified and the nonconvexity resulting from the analog matrices can be overcome. In the second scheme, the channel estimation and data transmission share the same analog matrices, which beneficially reduces the overhead of optimizing the associated analog matrices. Therefore, a composite channel matrix is estimated instead of the true channel matrix. By exploiting the statistical optimization framework advocated, the analog matrices can be designed independently of the training sequence. Based on the resultant analog matrices, the training sequence can then be efficiently designed according to diverse channel statistics and performance metrics. Finally, we conclude by quantifying the performance benefits of the proposed estimation schemes.

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hyb-tai-opt-d - Accepted Manuscript
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TWC2020-Aug
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More information

Accepted/In Press date: 6 May 2020
Published date: 12 August 2020

Identifiers

Local EPrints ID: 440786
URI: http://eprints.soton.ac.uk/id/eprint/440786
ISSN: 1536-1276
PURE UUID: 81dbb2af-bac2-47c8-ba11-7423884e8bd7
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 18 May 2020 16:34
Last modified: 07 Oct 2020 01:33

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Contributors

Author: Chengwen Xing
Author: Dekang Liu
Author: Shiqi Gong
Author: Wei Xu
Author: Sheng Chen
Author: Lajos Hanzo ORCID iD

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