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.
Hybrid MIMO communications, analog matrices, channel estimation, training optimization
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
12 August 2020
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), , [9095239].
(doi:10.1109/TWC.2020.2993694).
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.
Text
hyb-tai-opt-d
- Accepted Manuscript
More information
Accepted/In Press date: 6 May 2020
Published date: 12 August 2020
Additional Information:
Funding Information:
Manuscript received August 29, 2019; revised February 15, 2020; accepted May 3, 2020. Date of publication May 18, 2020; date of current version August 12, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61722104, Grant 61671058, and Grant 61620106001, and in part by the Ericsson. The work of Lajos Hanzo was supported in part by the Engineering and Physical Sciences Research Council projects EP/N004558/1, EP/P034284/1, EP/P034284/1, and EP/P003990/1 (COALESCE), in part by the Royal Society’s Global Challenges Research Fund Grant, and in part by the European Research Council’s Advanced Fellow Grant QuantCom. The associate editor coordinating the review of this article and approving it for publication was I. Guvenc. (Corresponding author: Chengwen Xing.) Chengwen Xing, Dekang Liu, and Shiqi Gong are with the School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China (e-mail: xingchengwen@gmail.com; dkliu17@126.com; gsqyx@163.com).
Publisher Copyright:
© 2002-2012 IEEE.
Keywords:
Hybrid MIMO communications, analog matrices, channel estimation, training optimization
Identifiers
Local EPrints ID: 440786
URI: http://eprints.soton.ac.uk/id/eprint/440786
ISSN: 1536-1276
PURE UUID: 81dbb2af-bac2-47c8-ba11-7423884e8bd7
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Date deposited: 18 May 2020 16:34
Last modified: 18 Mar 2024 02:36
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Contributors
Author:
Chengwen Xing
Author:
Dekang Liu
Author:
Shiqi Gong
Author:
Wei Xu
Author:
Sheng Chen
Author:
Lajos Hanzo
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