AI Signal Processing Paradigm for Movable Antenna: From Spatial Position Optimization to Electromagnetic Reconfigurability

TL;DR

This paper introduces a unified framework for movable and reconfigurable antennas (MARA) that combines spatial and electromagnetic reconfiguration, addressing high-dimensional optimization challenges in 6G wireless systems.

Contribution

It proposes a comprehensive modeling framework for MARA, investigates channel modeling and spectral efficiency optimization, and reviews AI-based solutions for complex signal processing tasks.

Findings

Unified MARA framework for joint spatial-electromagnetic reconfiguration

Enhanced spectral efficiency through integrated optimization

AI solutions outperform traditional algorithms in high-dimensional problems

Abstract

As 6G wireless communication systems evolve toward intelligence, high reconfigurability, and space-air-ground integration \cite{liu2025toward, liu2024near}, the limitations of traditional fixed antenna (TFA) have become increasingly prominent. As a remedy, spatially movable antenna (SMA) and electromagnetically reconfigurable antenna (ERA) have respectively emerged as key technologies to break through this bottleneck. SMA activates spatial degree of freedom (DoF) by dynamically adjusting antenna positions, ERA regulates radiation characteristics using tunable metamaterials, thereby introducing DoF in the electromagnetic domain. However, the ``spatial-electromagnetic dual reconfiguration" paradigm formed by their integration poses severe challenges of high-dimensional hybrid optimization to signal processing. To address this issue, we integrate the spatial optimization of SMA and the electromagnetic reconfiguration of ERA, propose a unified modeling framework termed movable and reconfigurable antenna (MARA) and investigate the channel modeling and spectral efficiency (SE) optimization for MARA. Besides, we systematically review artificial intelligence (AI)-based solutions, focusing on analyzing the advantages of AI over traditional algorithms in solving high-dimensional non-convex optimization problems. This paper fills the gap in existing literature regarding the lack of a comprehensive review on the AI-driven signal processing paradigm under spatial-electromagnetic dual reconfiguration and provides theoretical guidance for the design and optimization of 6G wireless systems with advanced MARA.