Movable Antennas-Enabled Two-User Multicasting: Do We Really Need Alternating Optimization for Minimum Rate Maximization?

TL;DR

This paper proposes a novel method for optimizing movable antenna positions and transmit beamforming in a two-user multicasting scenario, achieving the same performance as traditional methods but with lower complexity and new insights.

Contribution

It demonstrates that antenna positions and beamforming can be optimized separately, eliminating the need for alternating optimization in this setting.

Findings

Optimal antenna positions can be found via successive convex approximation.

Closed-form transmit beamforming can be derived after position optimization.

The proposed method reduces computational complexity while maintaining performance.

Abstract

Movable antenna (MA) technology, which can reconfigure wireless channels by flexibly moving antenna positions in a specified region, has great potential for improving communication performance. In this paper, we consider a new setup of MAs-enabled multicasting, where we adopt a simple setting in which a linear MA array-enabled source (${\rm{S}}$) transmits a common message to two single-antenna users ${\rm{U}}_1$ and ${\rm{U}}_2$. We aim to maximize the minimum rate among these two users, by jointly optimizing the transmit beamforming and antenna positions at ${\rm{S}}$. Instead of utilizing the widely-used alternating optimization (AO) approach, we reveal, with rigorous proof, that the above two variables can be optimized separately: i) the optimal antenna positions can be firstly determined via the successive convex approximation technique, based on the rule of maximizing the correlation between ${\rm{S}}$-${\rm{U}}_1$ and ${\rm{S}}$-${\rm{U}}_2$ channels; ii) afterwards, the optimal closed-form transmit beamforming can be derived via simple arguments. Compared to AO, this new approach yields the same performance but reduces the computational complexities significantly. Moreover, it can provide insightful conclusions which are not possible with AO.