Multi-Domain Correlation-Aided Implicit CSI Feedback Using Deep Learning

Deep learning has been introduced to improve implicit channel state information (CSI) feedback, and it significantly outperforms codebook-based feedback methods used in existing systems. This study proposes a multi-domain correlation-aided implicit CSI feedback framework that uses deep learning. This framework retains the existing implicit feedback mechanism while introducing the aid of the multi-domain correlation property of CSI matrices to the feedback process for performance improvement. First, a time correlation-aided implicit feedback framework is proposed, where the correlation among adjacent CSI matrices is exploited to improve the CSI reconstruction accuracy. Second, to utilize the correlation between the uplink and downlink channel, the uplink channel magnitude is introduced into the CSI reconstruction process at the base station. Additionally, the framework combines the aid of time and bidirectional channel correlation properties to further enhance performance. Simulation results show that, with the aid of the multi-domain correlation property, the feedback overhead can be reduced by 75% and 85% compared to approaches without correlation utilization and Type II codebook, respectively.