An ML-assisted OTFS vs. OFDM adaptable modem

TL;DR

This paper introduces a deep neural network-based adaptive system that switches between OTFS and OFDM waveforms to optimize performance in high-mobility wireless channels, demonstrating significant MSE improvements.

Contribution

It proposes a novel DNN-assisted adaptive modulation scheme that dynamically switches between OTFS and OFDM based on channel conditions, enhancing communication robustness.

Findings

The adaptive scheme outperforms standalone OTFS and OFDM in simulations.

DNN classifier effectively predicts optimal waveform based on channel state.

Significant reduction in mean-squared-error achieved with the proposed method.

Abstract

The Orthogonal-Time-Frequency-Space (OTFS) signaling is known to be resilient to doubly-dispersive channels, which impacts high mobility scenarios. On the other hand, the Orthogonal-Frequency-Division-Multiplexing (OFDM) waveforms enjoy the benefits of the reuse of legacy architectures, simplicity of receiver design, and low-complexity detection. Several studies that compare the performance of OFDM and OTFS have indicated mixed outcomes due to the plethora of system parameters at play beyond high-mobility conditions. In this work, we exemplify this observation using simulations and propose a deep neural network (DNN)-based adaptation scheme to switch between using either an OTFS or OFDM signal processing chain at the transmitter and receiver for optimal mean-squared-error (MSE) performance. The DNN classifier is trained to switch between the two schemes by observing the channel condition, received SNR, and modulation format. We compare the performance of the OTFS, OFDM, and the proposed switched-waveform scheme. The simulations indicate superior performance with the proposed scheme with a well-trained DNN, thus improving the MSE performance of the communication significantly.