Deep Learning-based Channel State Information Prediction with Incomplete History

Outdated and inaccurate channel state information (CSI) prevents the transmitter from adapting to current channel conditions and impairs reliable communication. This paper proposes a deep learning-based methodology for real-time prediction of future CSI, which enables it to be fed back in advance and mitigates the effect of the feedback and processing delay. Predicting the future CSI at the user equipment may also help the gNodeB (gNB) send reference signals less frequently and may help reduce the reference signal transmission overhead. Our approach utilizes Long Short-Term Memory (LSTM) type recurrent neural networks (RNN) to predict the CSI from past channel estimations. However, the knowledge of past channel gains is assumed to be limited, and only partial channel data is available. We propose a new algorithm that utilizes LSTMs and remedies incomplete channel history problem, which we denote by RNN-I. We evaluate the performance of the proposed framework by comparing it with two conventional techniques: persistence and auto-regressive (AR) model-based prediction methods. The results show that the proposed RNN-I algorithm performs well under strong channel mismatch scenarios, while AR methods diverge and can not keep up with fast varying channels if the mismatch in the channel characteristics used in training and inference is significant.