A Derivative-Free Position Optimization Approach for Movable Antenna Multi-User Communication Systems

TL;DR

This paper introduces a derivative-free optimization method for movable antenna positioning in multi-user MISO systems, improving efficiency without explicit channel state information estimation.

Contribution

It proposes a novel zeroth-order gradient approximation technique for position optimization, bypassing the need for explicit CSI estimation in movable antenna systems.

Findings

Achieves higher sample efficiency than CSI-based methods.

Converges to optimal or stationary solutions.

Effective in scenarios with many multipath components or limited pilots.

Abstract

Movable antennas (MAs) have emerged as a disruptive technology in wireless communications for enhancing spatial degrees of freedom through continuous antenna repositioning within predefined regions, thereby creating favorable channel propagation conditions. In this paper, we study the problem of position optimization for MA-enabled multi-user MISO systems, where a base station (BS), equipped with multiple MAs, communicates with multiple users each equipped with a single fixed-position antenna (FPA). To circumvent the difficulty of acquiring the channel state information (CSI) from the transmitter to the receiver over the entire movable region, we propose a derivative-free approach for MA position optimization. The basic idea is to treat position optimization as a closed-box optimization problem and calculate the gradient of the unknown objective function using zeroth-order (ZO) gradient approximation techniques. Specifically, the proposed method does not need to explicitly estimate the global CSI. Instead, it adaptively refines its next movement based on previous measurements such that it eventually converges to an optimum or stationary solution. Simulation results show that the proposed derivative-free approach is able to achieve higher sample and computational efficiencies than the CSI estimation-based position optimization approach, particularly for challenging scenarios where the number of multi-path components (MPCs) is large or the number of pilot signals is limited.