Machine-Learning-Aided TDD Massive MIMO Downlink Transmission for High-Mobility Multi-Antenna Users With Partial Uplink Channel State Information

Estimation of downlink (DL) channel state information (CSI) is necessary in massive multiple-input multiple-output (MIMO) systems to enable precoding and achieve high spectral efficiency. However, CSI estimation (for both the uplink (UL) and DL) is challenging in an environment with highly-mobile users due to rapidly-varying fading. The estimation becomes even more challenging when UL CSI is incomplete due to system constraints. In this work, we combine two machine learning techniques to tackle the twofold problem of predicting upcoming DL CSI from earlier UL CSI estimates and estimating full UL CSI from its incomplete form. For the first sub-problem, we employ long short-term memory (LSTM) to capture the spatio-temporal correlation between CSI at different time instances and user positions. For the second sub-problem, we use a conditional generative adversarial network (CGAN) to estimate the full UL CSI from varying amounts of incomplete CSI. We examine the normalized mean square error performance of the proposed CGAN-LSTM method and compare the spectral efficiency of the system with what is maximally achievable with complete up-to-date CSI. Furthermore, we extend our machine learning methodology to directly estimate precoding matrices from partial CSI and similarly compare the performance with that achievable using complete up-to-date CSI.