Deep learning-based channel estimation using Gaussian mixture distribution and expectation maximum algorithm

Li, S., Liu, Y., Sun, Y. and Cai, Y. (2023) Deep learning-based channel estimation using Gaussian mixture distribution and expectation maximum algorithm. Physical Communication, 58, 102018. (doi: 10.1016/j.phycom.2023.102018)

Abstract

In massive multiple-input multiple-output (MIMO), it is much challenging to obtain accurate channel state information (CSI) after radio frequency (RF) chain reduction due to the high dimensions. With the fast development of machine learning(ML), it is widely acknowledged that ML is an effective method to deal with channel models which are typically unknown and hard to approximate. In this paper, we use the low complexity vector approximate messaging passing (VAMP) algorithm for channel estimation, combined with a deep learning framework for soft threshold shrinkage function training. Furthermore, in order to improve the estimation accuracy of the algorithm for massive MIMO channels, an optimized threshold function is proposed. This function is based on Gaussian mixture (GM) distribution modeling, and the expectation maximum Algorithm (EM Algorithm) is used to recover the channel information in beamspace. This contraction function and deep neural network are improved on the vector approximate messaging algorithm to form a high-precision channel estimation algorithm. Simulation results validate the effectiveness of the proposed network.

• Item Type: Articles
• Additional Information: This work was supported in part by the National Natural Science Funding of China under Grant 61601414, and in part by the Central University Basic Research Fund of China under Grant CUC210B032.
• Keywords: Massive MIMO channel estimation, vector approximate message passing (VAMP), deep learning framework, gaussian mixture distribution, expectation maximum algorithm.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sun, Dr Yao

Creator Roles

Sun, Y.: Writing – review and editing

• Authors: Li, S., Liu, Y., Sun, Y., and Cai, Y.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: Physical Communication
• Publisher: Elsevier
• ISSN: 1874-4907
• ISSN (Online): 1876-3219
• Published Online: 08 February 2023
• Copyright Holders: Copyright © 2023 Elsevier B.V.
• First Published: First published in Physical Communication 58: 102018
• Publisher Policy: Reproduced in accordance with the publisher copyright policy