An Efficient Machine Learning-based Channel Prediction Technique for
  OFDM Sub-Bands
Pedro E. G. Silva Jules M. Moualeu Pedro H. Nardelli Rausley A. A. de Souza
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.