CSI Classification for 5G via Deep Learning
Ankur Vora, Pierre-Xavier Thomas, Rong Chen, Kyoung‐Don Kang · 2019
5G communication requires continuous exchanges of channel state information (CSI) between the base station and user equipment (UE) to adjust the physical layer parameters. CSI classification in a noisy environment is challenging, since CSI can get corrupted. To address this problem, we apply a convolutional neural network (CNN) to classify several key CSI parameters. In a simulation study, our CNN method classifies the CSI parameters with accuracy ranging between 84-98%, which is approximately 24-38% higher than the 3GPP recommendations for UEs.