DRL-Based Antenna Position Optimization For MA-Assisted OTFS System Under Imperfect CSI

TL;DR

This paper integrates movable antenna technology with OTFS systems, using deep reinforcement learning to optimize antenna positions based on estimated CSI, significantly improving channel gain and mitigating deep fading under imperfect CSI conditions.

Contribution

It introduces a DRL-based approach for antenna position optimization in MA-assisted OTFS systems, leveraging a novel sparse Bayesian learning method for better CSI estimation.

Findings

SBLVI method enhances channel estimation accuracy.

DRL-based position optimization yields higher channel gains.

Proposed system outperforms fixed-position antennas.

Abstract

In this paper, we introduce movable antenna (MA) technology into orthogonal time frequency space (OTFS) systems to enable wavelength-level antenna position optimization under imperfect channel state information (CSI), thereby mitigating deep fading. To accurately acquire CSI, we develop a sparse Bayesian learning method with variational inference (SBLVI) method. Based on estimated CSI, we formulate an MA position optimization problem with the objective of maximizing channel gain. Due to the highly non-convex character of the problem, we further develop a deep reinforcement learning (DRL) strategy to intelligently optimize MA positions. Simulation results show that the proposed SBLVI method significantly improves channel estimation accuracy over benchmark methods, and MA position optimization based on estimated CSI achieves substantially higher channel gains than the fixed-position antenna (FPA), demonstrating the effectiveness of the proposed MA-assisted OTFS system.