Deep learning based Channel Estimation and Beamforming in Movable Antenna Systems

2026-02-08

AI Summary

This work proposes an end-to-end deep learning framework to address the challenge of jointly optimizing channel estimation, antenna placement, and beamforming in multi-user broadband mobile antenna systems. The proposed method integrates a two-stage channel estimation process with a Transformer-based architecture, introducing the Swin Transformer for the first time to perform channel denoising. Furthermore, it intelligently combines the Transformer with the weighted minimum mean square error (WMMSE) algorithm to enable cooperative optimization of antenna positions and beamforming vectors. Experimental results demonstrate that the proposed approach significantly outperforms existing methods across various scenarios, achieving notable improvements in both channel estimation accuracy and overall system performance.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Optimization
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Channel Estimation
• Beamforming
• Movable Antenna
• Deep Learning
• Multiuser Wideband Systems

Innovation

• Movable Antenna
• Deep Learning
• Channel Estimation
• Beamforming
• Swin Transformer