Neural Network-based Two-Dimensional Filtering for OTFS Symbol Detection

TL;DR

This paper proposes a novel two-dimensional reservoir computing approach for OTFS symbol detection, leveraging the delay-Doppler domain to improve online detection performance in high-mobility wireless scenarios.

Contribution

It introduces a 2D-RC architecture with a 2D filtering structure tailored for OTFS, enabling efficient single-network operation in the delay-Doppler domain for online symbol detection.

Findings

Outperforms previous RC-based methods in various modulation scenarios.

Effectively equalizes 2D circular channel effects in the delay-Doppler domain.

Reduces complexity by using a single neural network for channel tracking.

Abstract

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.