Machine Learning Prediction Based CSI Acquisition for FDD Massive MIMO Downlink

In this paper, we propose a simple and efficient approach to reduce the overhead of downlink channel estimation and feedback using linear regression (LR) and support vector regression (SVR) in machine learning. Specifically, we divide the indexes of the antennas at the base station (BS) into two sets. We first use some well estimated channel samples to train a regression model, where the channel state information (CSI) corresponding to one set is used as input while the other is output. In the online channel estimation phase, only the CSI of the antennas in one set needs to be estimated and the CSI of the antennas in the other set can be predicted by inputting the estimated CSI into the trained regression model. Numerical results show the proposed approach can reduce the overhead of both downlink pilot and uplink feedback considerably and thus can improve the downlink achievable rate significantly compared with the existing schemes.