  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 a limited number of
over-the-air (OTA) pilot symbols are utilized for training. However, this
approach does not leverage the domain knowledge specific to the OTFS system to
fully unlock the potential of RC. This paper introduces a novel two-dimensional
RC (2D-RC) method that incorporates the domain knowledge of the OTFS system
into the design for symbol detection in an online subframe-based manner.
Specifically, as the channel interaction in the delay-Doppler (DD) domain is a
two-dimensional (2D) circular operation, the 2D-RC is designed to have the 2D
circular padding procedure and the 2D filtering structure to embed this
knowledge. With the introduced architecture, 2D-RC can operate in the DD domain
with only a single neural network, instead of necessitating multiple RCs to
track channel variations in the time domain as in previous work. Numerical
experiments demonstrate the advantages of the 2D-RC approach over the previous
RC-based approach and compared model-based methods across different OTFS system
variants and modulation orders.
