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.

翻译：信道状态信息（CSI）的精确获取对于提升无线通信系统的性能至关重要。然而，由于无线环境的时变性和频率选择性的复杂性，通过信道估计或信道预测来获取精确的CSI是一项艰巨的任务。为此，我们提出了一种基于机器学习（ML）的高效技术，用于正交频分复用（OFDM）子带中的信道预测。该方法的新颖之处在于利用信道衰落样本进行训练，以估计选择性衰落中未来的信道行为。