Learning to Jointly Optimize Antenna Positioning and Beamforming for Movable Antenna-Aided Systems

TL;DR

This paper introduces a deep learning framework for jointly optimizing antenna positioning and beamforming in movable antenna systems, significantly improving performance and reducing computation time.

Contribution

It presents the first end-to-end deep learning approach to jointly optimize discrete antenna positions and continuous beamforming weights under coupled constraints.

Findings

Achieves higher sum rate performance.

Reduces computation time compared to existing methods.

Effectively handles mixed discrete and continuous optimization variables.

Abstract

The recently emerged movable antenna (MA) and fluid antenna technologies offer promising solutions to enhance the spatial degrees of freedom in wireless systems by dynamically adjusting the positions of transmit or receive antennas within given regions. In this paper, we aim to address the joint optimization problem of antenna positioning and beamforming in MA-aided multi-user downlink transmission systems. This problem involves mixed discrete antenna position and continuous beamforming weight variables, along with coupled distance constraints on antenna positions, which pose significant challenges for optimization algorithm design. To overcome these challenges, we propose an end-to-end deep learning framework, consisting of a positioning model that handles the discrete variables and the coupled constraints, and a beamforming model that handles the continuous variables. Simulation results demonstrate that the proposed framework achieves superior sum rate performance, yet with much reduced computation time compared to existing methods.