Movable Antenna Enhanced Multi-Region Beam Coverage: A Multi-Notch-Filter-Inspired Design

TL;DR

This paper introduces a novel movable antenna design inspired by multi-notch filters to optimize multi-region beam coverage, significantly improving performance over fixed antennas with lower complexity.

Contribution

It proposes a new joint optimization algorithm for antenna position and beamforming based on multi-notch filter principles, enhancing multi-region coverage in wireless systems.

Findings

Outperforms fixed-position antennas in coverage and gain.

Achieves comparable results to more complex algorithms with lower complexity.

Effectively avoids local optima using Gibbs sampling.

Abstract

Movable antenna (MA) has emerged as a promising technology to enhance wireless communication performance by exploiting the new degree of freedom (DoF) via antenna position optimization. In this letter, we investigate the MA-enhanced wide beam coverage over multiple subregions in the spatial domain. Specifically, we aim to maximize the minimum beam gain over the desired subregions by jointly optimizing the transmit beamforming and antenna position vector (APV). Although this problem is non-convex, we propose an efficient algorithm to solve it by leveraging the similarity between the considered multi-region coverage and classical multi-notch filter (MNF) design. In particular, we construct a spatial MNF-based transmit beamforming vector by assuming a continuous amplitude and phase-shift profile within the antenna movement region. Based on this continuous profile, we propose a sequential update algorithm to select an optimal subset of MA positions for multi-region coverage, jointly with a Gibbs sampling (GS) procedure to avoid undesired local optimum. Numerical results show that our proposed algorithm can significantly outperform conventional fixed position antennas (FPAs) and achieve a comparable performance to the alternating optimization (AO) algorithm with dramatically lower complexity.