Learning to Equalize OTFS

TL;DR

This paper introduces a neural network-based online equalization method for OTFS that adapts to channel variations without explicit CSI, outperforming traditional methods especially in high Doppler scenarios.

Contribution

It presents a reservoir computing neural network framework for OTFS equalization that operates in real-time within each frame, improving robustness and performance.

Findings

Lower bit error rate in low SNR regimes

Effective handling of channel variations without explicit CSI

Better complexity-performance tradeoff compared to OFDM neural methods

Abstract

Orthogonal Time Frequency Space (OTFS) is a novel framework that processes modulation symbols via a time-independent channel characterized by the delay-Doppler domain. The conventional waveform, orthogonal frequency division multiplexing (OFDM), requires tracking frequency selective fading channels over the time, whereas OTFS benefits from full time-frequency diversity by leveraging appropriate equalization techniques. In this paper, we consider a neural network-based supervised learning framework for OTFS equalization. Learning of the introduced neural network is conducted in each OTFS frame fulfilling an online learning framework: the training and testing datasets are within the same OTFS-frame over the air. Utilizing reservoir computing, a special recurrent neural network, the resulting one-shot online learning is sufficiently flexible to cope with channel variations among different OTFS frames (e.g., due to the link/rank adaptation and user scheduling in cellular networks). The proposed method does not require explicit channel state information (CSI) and simulation results demonstrate a lower bit error rate (BER) than conventional equalization methods in the low signal-to-noise (SNR) regime under large Doppler spreads. When compared with its neural network-based counterparts for OFDM, the introduced approach for OTFS will lead to a better tradeoff between the processing complexity and the equalization performance.