Performance Analysis of XL-MIMO with Rotary and Movable Antennas for High-speed Railway

TL;DR

This paper develops an analytical framework for configuring rotary and movable antennas in XL-MIMO systems for high-speed railway communications, optimizing antenna spacing and rotation to improve system performance under dynamic conditions.

Contribution

It introduces a novel optimization framework for ROMA-based XL-MIMO in high-speed scenarios, including real-time localization, channel orthogonality analysis, and differential evolution-based rotation angle optimization.

Findings

Optimized antenna spacing enhances spatial multiplexing.

Rotation angle optimization improves array gain.

Numerical results validate the proposed framework.

Abstract

The rotary and movable antennas (ROMA) technology is efficient in enhancing wireless network capacity by adjusting both the antenna spacing and three-dimensional (3D) rotation of antenna surfaces, based on the spatial distribution of users and channel statistics. Applying ROMA to high-speed rail (HSR) wireless communications can significantly improve system performance in terms of array gain and spatial multiplexing. However, the rapidly changing channel conditions in HSR scenarios present challenges for ROMA configuration. In this correspondence, we propose a analytical framework for configuring ROMA-based extremely large-scale multiple-input-multiple-output (XL-MIMO) system in HSR scenarios based on spatial correlation. First, we develop a localization model based on a mobility-aware near-field beam training algorithm to determine the real-time position of the train relay antennas. Next, we derive the expression for channel orthogonality and antenna spacing based on the spatial correlation matrix, and obtain the optimal antenna spacing when the transceiver panels are aligned in parallel. Moreover, we propose an optimization algorithm for the rotation angle of the transceiver panels, leveraging the differential evolution method, to determine the optimal angle. Finally, numerical results are provided to validate the computational results and optimization algorithm.