Deep learning for fast channel estimation in millimeter-wave MIMO systems

doi:10.23919/JSEE.2022.000126

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (6): 1088-1095.doi: 10.23919/JSEE.2022.000126

• ELECTRONICS TECHNOLOGY • Previous Articles

Deep learning for fast channel estimation in millimeter-wave MIMO systems

Siting LYU¹^,²(), Xiaohui LI¹^,²^,*(), Tao FAN¹^,²(), Jiawen LIU¹^,²(), Mingli SHI¹^,²()

¹ School of Telecommunication Engineering, Xidian University, Xi’an 710071, China
² State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China

Received:2021-10-15 Accepted:2022-06-07 Online:2022-12-18 Published:2022-12-24
Contact: Xiaohui LI E-mail:stlv_0202@stu.xidian.edu.cn;xhli@mail.xidian.edu.cn;601391627@qq.com;316631694@qq.com;1392571846@qq.com
About author:
LYU Siting was born in 1998. She received her B.S. degree in communication engineering from Yunnan University, Kunming, Yunnan, China, in 2019. She is currently pursuing her Ph.D. degree with the State Key Laboratory on Integrated Services Network, Xidian University. Her research interests include wireless communication, deep learning, and millimeter wave beamforming. E-mail: stlv_0202@stu.xidian.edu.cn

LI Xiaohui was born in 1972. In 1994, she graduated from Xidian University with her B.S. degree in communication engineering. In 1994, she stayed at school to work and has been engaged in scientific research in the field of wireless communication. She received her M.S. degree in engineering in 2000 from Xidian University. She received her Ph.D. degree in engineering in 2007. She is a professor in Xidian University. Her research interests are broadband wireless communication and wireless resource management. E-mail: xhli@mail.xidian.edu.cn

FAN Tao was born in 1994. He received his B.S. degree in Xidian University, Xi ’an, Shaanxi, China, in 2016. He is currently pursuing his Ph.D. degree with the State Key Laboratory on Integrated Services Network, Xidian University. His research interests include wireless communication, non-orthogonal multiple access and millimeter wave channel estimation. E-mail: 601391627@qq.com

LIU Jiawen was born in 1995. He received his B.S. degree in Harbin Engineering University, Harbin, Heilongjiang, China, in 2018. He is currently pursuing his Ph.D. degree in communication engineering with the State Key Laboratory on Integrated Services Network in Xidian University, Xi’an, Shaanxi, China. His research interests include wireless communication, surface wave communication, and millimeter wave beamforming. E-mail: 316631694@qq.com

SHI Mingli was born in 1997. He received his B.S. degree in Xidian University, Xi’an, Shaanxi, China, in 2019. He is currently pursuing his Ph.D. degree with the State Key Laboratory on Integrated Services Network, Xidian University. His research interests include wireless communication, millimeter wave communication, and intelligent reconfigurable surface. E-mail: 1392571846@qq.com
Supported by:
This work was supported by the National Key R&D Program of China (2018YFB1802004) and 111 Project (B08038)

Abstract

Abstract:

Channel estimation has been considered as a key issue in the millimeter-wave (mmWave) massive multi-input multi-output (MIMO) communication systems, which becomes more challenging with a large number of antennas. In this paper, we propose a deep learning (DL)-based fast channel estimation method for mmWave massive MIMO systems. The proposed method can directly and effectively estimate channel state information (CSI) from received data without performing pilot signals estimate in advance, which simplifies the estimation process. Specifically, we develop a convolutional neural network (CNN)-based channel estimation network for the case of dimensional mismatch of input and output data, subsequently denoted as channel (H) neural network (HNN). It can quickly estimate the channel information by learning the inherent characteristics of the received data and the relationship between the received data and the channel, while the dimension of the received data is much smaller than the channel matrix. Simulation results show that the proposed HNN can gain better channel estimation accuracy compared with existing schemes.

Key words: millimeter-wave (mmWave), channel estimation, deep learning (DL), dimensional mismatch, channel state information (CSI)

Siting LYU, Xiaohui LI, Tao FAN, Jiawen LIU, Mingli SHI. Deep learning for fast channel estimation in millimeter-wave MIMO systems[J]. Journal of Systems Engineering and Electronics, 2022, 33(6): 1088-1095.

Figures/Tables 6

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$l$	Conv1	Conv2	Conv3	DeConv1	DeConv2	DeConv3	FC1	FC2
$K_l^{(h)},K_l^{(w)}$	3, 3	3, 3	3, 3	1, 1	1, 1	1, 1	−	−
$M_l^{(h)},M_l^{(w)}$	64, 4	64, 4	64, 4	64, 4	64, 4	64, 4	−	−
${C_{l - 1} } , {C_l}$	1, 8	8, 32	32, 64	64, 64	64, 32	32, 8	−	−
${N_{l - 1} } , {N_l}$	−	−	−	−	−	−	2048, 4096	4096, 8192

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Deep learning for fast channel estimation in millimeter-wave MIMO systems

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