  Orthogonal time frequency space (OTFS) is a promising modulation scheme for
wireless communication in high-mobility scenarios. Recently, a reservoir
computing (RC) based approach has been introduced for online subframe-based
symbol detection in the OTFS system, where only the limited over-the-air (OTA)
pilot symbols are utilized for training. However, the previous RC-based
approach does not design the RC architecture based on the properties of the
OTFS system to fully unlock the potential of RC. This paper introduces a novel
two-dimensional RC (2D-RC) approach for online symbol detection on a subframe
basis in the OTFS system. The 2D-RC is designed to have a two-dimensional (2D)
filtering structure to equalize the 2D circular channel effect in the
delay-Doppler (DD) domain of the OTFS system. With the introduced architecture,
the 2D-RC can operate in the DD domain with only a single neural network,
unlike our previous work which requires multiple RCs to track channel
variations in the time domain. Experimental results demonstrate the advantages
of the 2D-RC approach over the previous RC-based approach and the compared
model-based methods across different modulation orders.
