Pre-equalization Design for ISAC-OTFS Air-Ground Transmission: A Deep Learning Approach

TL;DR

This paper introduces a deep learning-based pre-equalization method for OTFS air-ground transmission that reduces receiver complexity and improves sensing accuracy by predicting and compensating for channel variations.

Contribution

It proposes a novel deep learning framework for channel prediction and pre-equalization in OTFS systems, enabling direct symbol detection without complex equalization.

Findings

Significantly reduces receiver complexity and pilot overhead.

Achieves near-optimal symbol detection performance with imperfect CSI.

Enhances sensing accuracy in high-mobility scenarios.

Abstract

Despite the strong Doppler resilience capability, orthogonal time-frequency space (OTFS) modulation suffers from high channel estimation and equalization complexity at the receiver, hindering its applicability in air-ground transmission. In this paper, we propose a pre-equalization-based integrated sensing and communications-OTFS downlink transmission framework in which the terrestrial access point executes pre-equalization using the predicted channel state information (CSI), so that the unmanned aerial vehicle can perform direct symbol detection without channel equalization. In particular, the mean square error of OTFS symbol demodulation and Cramer-Rao lower bound of sensing parameter estimation are considered, with their weighted sum utilized as the metric for optimizing the pre-equalization matrix. To address the time-varying CSI, we develop a deep learning based framework composed of channel prediction and pre-equalization. In particular, a parameter-level channel prediction module is utilized to decouple OTFS channel parameters, and a low-dimensional prediction network is leveraged to correct outdated CSI, which is then used to initialize the input of the pre-equalization module. Finally, a dual-branch residual-structured deep neural network is cascaded to execute pre-equalization. Simulation results show that the proposed channel prediction-based pre-equalization framework significantly reduces receiver complexity and pilot overhead while achieving symbol detection performance close to minimum mean square error equalization with perfect CSI under high mobility, as well as substantially improving sensing accuracy.