An Efficient Machine Learning-Based Channel Prediction Technique for OFDM Sub-Bands

Abstract

The acquisition of accurate channel state information (CSI) is of utmost importance since it provides performance improvement for wireless communication systems. However, acquiring accurate CSI, which can be done through channel estimation or channel prediction, is an intricate task due to the complexity of the time-varying and frequency selectivity of the wireless environment. To this end, we propose an efficient machine learning (ML)-based technique for channel prediction in orthogonal frequency-division multiplexing (OFDM) sub-bands. The novelty of the proposed approach lies in the training of channel fading samples used to estimate future channel behavior in selective fading.