CsiNet-Plus Model with Truncation and Noise on CSI Feedback
Feng Liu, Xuecheng He, Conggai Li, Yanli Xu · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · 2019
For the frequency-division-duplex (FDD)-based massive multiple-input multiple-output (MIMO) systems, channel state information (CSI) feedback plays a critical role. Although deep learning has been used to compress the CSI feedback, some issues like truncation and noise still need further investigation. Facing these practical concerns, we propose an improved model (called CsiNet-Plus), which includes a truncation process and a channel noise process. Simulation results demonstrate that the CsiNet-Plus outperforms the existing CsiNet. The performance interchangeability between truncated decimal digits and the signal-to-noise-ratio helps support flexible configuration.