Deep learning based Channel Estimation and Beamforming in Movable Antenna Systems

TL;DR

This paper introduces a deep learning framework for channel estimation and beamforming in movable antenna systems, enhancing accuracy and performance through innovative neural network designs and joint optimization strategies.

Contribution

It presents a novel two-stage channel estimation method and a Transformer-based joint optimization network for movable antenna systems, improving accuracy and efficiency over existing methods.

Findings

Achieves superior channel estimation accuracy.

Enhances beamforming performance.

Demonstrates robustness under various conditions.

Abstract

Movable antenna (MA) has emerged as a promising technology for future wireless systems. Compared with traditional fixed-position antennas, MA improves system performance by antenna movement to optimize channel conditions. For multiuser wideband MA systems, this paper proposes deep learning-based framework integrating channel estimation (CE), antenna position optimization, and beamforming, with a clear workflow and enhanced efficiency. Specifically, to obtain accurate channel state information (CSI), we design a two-stage CE mechanism: first reconstructing the channel matrix from limited measurements via compressive sensing, then introducing a Swin-Transformer-based denoising network to refine CE accuracy for subsequent optimization. Building on this, we address the joint optimization challenge by proposing a Transformer-based network that intelligently maps CSI sequences of candidate positions to optimal MA positions while combining a model-driven weighted minimum mean square error (WMMSE) beamforming approach to achieve better performance. Simulation results demonstrate that the proposed methods achieve superior performance compared with existing counterparts under various conditions. The codes about this work are available at https://github.com/ZiweiWan/Code-4-DL-MA-CE-BF.