Channel estimation in RIS-assisted downlink massive MIMO: a learning-based approach

Thanh Tung Vu*, Trinh Van Chien, Canh T. Dinh, Hien-Quoc Ngo, Michalis Matthaiou

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
99 Downloads (Pure)

Abstract

For downlink massive multiple-input multiple-output (MIMO) operating in time-division duplex protocol, users can de-code the signals effectively by only utilizing the channel statistics as long as channel hardening holds. However, in a reconfigurable intelligent surface (RIS)-assisted massive MIMO system, the propagation channels may be less hardened due to the extra random fluctuations of the effective channel gains. To address this issue, we propose a learning-based method that trains a neural network to learn a mapping between the received downlink signal and the effective channel gains. The proposed method does not require any downlink pilots and statistical information of interfering users. Numerical results show that, in terms of mean-square error of the channel estimation, our proposed learning-based method outperforms the state-of-the-art methods, especially when the light-of-sight (LoS) paths are dominated by non-LoS paths with a low level of channel hardening, e.g., in the cases of small numbers of RIS elements and/or base station antennas.
Original languageEnglish
Title of host publication2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC): Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
DOIs
Publication statusPublished - 28 Jul 2022

Publication series

NameIEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
PublisherIEEE
ISSN (Print)1948-3244
ISSN (Electronic)1948-3252

Fingerprint

Dive into the research topics of 'Channel estimation in RIS-assisted downlink massive MIMO: a learning-based approach'. Together they form a unique fingerprint.

Cite this