Movable Antennas Meet Intelligent Reflecting Surface: Friends or Foes?

TL;DR

This paper investigates whether movable antennas still outperform fixed-position antennas in IRS-assisted wireless systems, analyzing conditions for performance gains and proposing optimization algorithms for multi-user scenarios.

Contribution

It provides a comprehensive analysis of the performance of movable antennas versus fixed antennas in IRS-assisted systems, including conditions affecting their relative gains and new optimization algorithms.

Findings

Performance gain of MAs diminishes with IRS passive beamforming optimization.

Conditions identified where MAs outperform FPAs in LoS channels.

Proposed algorithms effectively optimize sum-rate in multi-user IRS-assisted systems.

Abstract

Movable antenna (MA) and intelligent reflecting surface (IRS) are considered promising technologies for the next-generation wireless communication systems due to their shared channel reconfiguration capabilities. This, however, raises a fundamental question: Does the performance gain of MAs over conventional fixed-position antennas (FPAs) still exist in the presence of the IRS? To answer this question, we investigate in this paper an IRS-assisted multi-user multiple-input single-output (MISO) MA system, where a multi-MA base station (BS) transmits to multiple single-FPA users. We formulate a sum-rate maximization problem by jointly optimizing the active/passive beamforming of the BS/IRS and the MA positions within a one-dimensional transmit region, which is challenging to be optimally solved. To drive essential insights, we first study a simplified case with a single user. Then, we analyze the performance gain of MAs over FPAs in the light-of-sight (LoS) BS-IRS channel and derive the conditions under which this gain becomes more or less significant. In addition, we propose an alternating optimization (AO) algorithm to solve the signal-to-noise ratio (SNR) maximization problem in the single-user case by combining the block coordinate descent (BCD) method and the graph-based method. For the general multi-user case, our performance analysis unveils that the performance gain of MAs over FPAs diminishes with typical transmit precoding strategies at the BS under certain conditions. We also propose a high-quality suboptimal solution to the sum-rate maximization problem by applying the AO algorithm that combines the weighted minimum mean square error (WMMSE) algorithm, manifold optimization method and discrete sampling method. Numerical results validate our theoretical analyses and demonstrate that the performance gain of MAs over FPAs may be reduced if the IRS passive beamforming is optimized.