Dimension-deficient channel estimation of hybrid beamforming based on compressive sensing

Xiao, Yu, Wang, Yafeng, and Xiang, Wei (2019) Dimension-deficient channel estimation of hybrid beamforming based on compressive sensing. IEEE Access, 7. pp. 13791-13798.

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Due to high hardware costs for digital beamforming, hybrid beamforming (HBF) is widely employed in millimeter-wave (mmWave) communications systems. However, the number of radio frequency chains in the analog part of HBF is far less than that of antennas, which causes a serious dimension-deficient problem. In order to overcome this problem, this paper proposes a compressive sensing algorithm using an adaptive overcomplete dictionary to estimate the sparse channel in the HBF-based mmWave system. The algorithm adaptively generates the dictionary by using the received signal to accurately reconstruct the mmWave channel. The simulation results are presented to demonstrate that the proposed algorithm outperforms its traditional counterparts in terms of the normalized mean square error and the spectral efficiency.

Item ID: 57332
Item Type: Article (Research - C1)
ISSN: 2169-3536
Keywords: sparse channel estimation, hybrid beamforming, adaptive overcomplete dictionary, compressive sensing
Copyright Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Funders: National Key Technology R&D (NKTRD) Program of China
Projects and Grants: NKTRD grant 2017ZX03001012-003
Date Deposited: 06 Mar 2019 07:44
FoR Codes: 40 ENGINEERING > 4006 Communications engineering > 400608 Wireless communication systems and technologies (incl. microwave and millimetrewave) @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8901 Communication Networks and Services > 890103 Mobile Data Networks and Services @ 100%
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