Learning in Time-Frequency Domain for Fractional Delay-Doppler Channel Estimation in OTFS

In this letter, we propose a learning-based approach for estimation of fractional delay-Doppler (DD) channel in orthogonal time frequency space (OTFS) systems. A key novelty in the proposed approach is that learning is done in the time-frequency (TF) domain for DD domain channel estimation. Learning in the TF domain is motivated by the fact that the range of values in the TF channel matrix is favorable for training as opposed to the large swing of values in the DD channel matrix which is not favourable for training. A key beneficial outcome of the proposed approach is its low complexity along with very good performance. Specifically, it drastically reduces the complexity of the computation of a constituent DD parameter matrix (CDDPM) in a state-of-the-art algorithm. Simulation results show that the proposed TF learning-based algorithm achieves almost the same performance as that of the state-of-the-art algorithm, while being drastically less complex making it practically appealing.