Towards FDD Massive MIMO: Downlink Channel Covariance Matrix Estimation Using Conditional Generative Adversarial Networks

Abstract

Estimating or predicting the downlink channel state information (CSI) is extremely important for practical implementation of frequency division duplex (FDD) massive MIMO. Estimation of downlink CSI from uplink CSI using second order channel statistics, namely the channel covariance matrix (CCM), is a promising approach. However, published work so far has rarely applied machine learning techniques to solve this problem using CCMs, most probably due to the unavailability of a direct mapping function or parametric model for supervised learning to convert from uplink to downlink CCMs. In this paper, we develop a conditional generative adversarial network (CGAN) method for uplink-to-downlink CCM conversion. To apply the CGAN-based method, we convert the uplink and downlink CCMs to images and use image translation techniques for CGANs. The normalized mean square error performance of the proposed CGAN is evaluated for several antenna array sizes and with both perfect and imperfect knowledge of the CCMs. Our results demonstrate performance improvement over existing algorithms.