Movable Antenna for Integrating Near-field Channel Estimation and Localization

Movable antenna (MA) introduces a new degree of freedom for future wireless communication systems by enabling the adaptive adjustment of antenna positions. Its large-range movement renders wireless channels transmission into the near-field region, which brings new performance enhancement for integrated sensing and communication (ISAC). This paper proposes a novel multi-stage design framework for broadband near-field ISAC assisted by MA. The framework first divides the MA movement area into multiple subregions, and employs the Newtonized orthogonal matching pursuit algorithm (NOMP) to achieve high-precision angle estimation in each subregion. Subsequently, a method called near-field localization via subregion ray clustering (LSRC) is proposed for identifying the positions of scatterers. This method finds the coordinates of each scatterer by jointly processing the angle estimates across all subregions. Finally, according to the estimated locations of the scatterers, the near-field channel estimation (CE) is refined for improving communication performance. Simulation results demonstrate that the proposed scheme can significantly enhance MA sensing accuracy and CE, providing an efficient solution for MA-aided near-field ISAC.

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

As 6G wireless communication systems evolve toward intelligence and high reconfigurability, the limitations of traditional fixed antenna (TFA) has become increasingly prominent, with geometrically movable antenna (GMA) and electromagnetically reconfigurable antenna (ERA) emerging as key technologies to break through this bottleneck. GMA activates spatial degrees of freedom (DoF) by dynamically adjusting antenna positions, ERA regulates radiation characteristics using tunable metamaterials, thereby introducing DoF in the electromagnetic domain. However, the ``geometric-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 geometric optimization of GMA and the electromagnetic reconfiguration of ERA for the first time, propose a unified modeling framework for movable and reconfigurable antenna (MARA), investigate the channel modeling and spectral efficiency (SE) optimization for GMA, ERA, and MARA. Besides, we systematically review artificial intelligence (AI)-based solutions, focusing on analyzing the advantages of AI over traditional algorithms in high-dimensional non-convex optimization computations. This paper fills the gap in existing literature regarding the lack of a comprehensive review on the AI-driven signal processing paradigm under geometric-electromagnetic dual reconfiguration and provides theoretical support for the design and optimization of 6G wireless systems with high SE and flexibility.