Robust WMMSE Precoder With Deep Learning Design for Massive MIMO

• Published in: IEEE transactions on communications Vol. 71; no. 7; pp. 3963 - 3976
• Main Authors: Shi, Junchao, Lu, An-An, Zhong, Wen, Gao, Xiqi, Li, Geoffrey Ye
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Antennas; Channel estimation; Channel models; Deep learning; deep learning design; Downlink; Ergodic processes; imperfect CSI; Instructional design; Lower bounds; Massive MIMO; MIMO communication; neural network; Neural networks; Precoding; Robust WMMSE precoder; Robustness; Sums
• ISSN: 0090-6778; 1558-0857
• DOI: 10.1109/TCOMM.2023.3269849

In this paper, we investigate the downlink robust precoding with imperfect channel state information (CSI) for massive multiple-input-multiple-output (MIMO) communications. With the estimated channel and channel error statistics, the general design of the robust precoder is to maximize the ergodic sum rate subject to the total transmit power constraint. To make the problem more tractable, we find a lower bound of the ergodic sum rate and propose the robust weighted minimum mean-squared-error (WMMSE) precoder to maximize the bound. We characterize the structure of the precoding vectors by low-dimensional parameters, which are learned directly from the available CSI through a neural network. As such, the precoding vectors can be immediately computed without iterations. To extend the deep learning design to multi-antennas users, we present a flexible approach that allows the various antenna configurations at the user side to be handled. Simulation results show that the deep learning design can significantly reduce the computational complexity compared with the existing precoder designs while achieving near optimal performance.