An Efficient Machine Learning-based Channel Prediction Technique for OFDM Sub-Bands
This addresses channel prediction for wireless communication systems, but appears incremental as it builds on existing ML methods for a known bottleneck.
The paper tackles the problem of acquiring accurate channel state information in wireless communication systems by proposing an efficient machine learning-based technique for channel prediction in OFDM sub-bands, but no concrete results or numbers are provided.
The acquisition of accurate channel state information (CSI) is of utmost importance since it provides performance improvement of 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 behaviour in selective fading.