Movable-Antenna Position Optimization for Physical-Layer Security via Discrete Sampling

TL;DR

This paper introduces a discrete sampling approach for optimizing movable antenna positions in secure wireless communication systems, significantly improving secrecy rates with efficient algorithms.

Contribution

It transforms continuous antenna position optimization into a discrete problem and proposes novel algorithms for optimal and suboptimal solutions.

Findings

Proposed algorithms outperform baseline schemes in secrecy rate.

Discrete sampling simplifies the optimization process.

High-quality solutions achieved with reduced complexity.

Abstract

Fluid antennas (FAs) and mobile antennas (MAs) are innovative technologies in wireless communications that are able to proactively improve channel conditions by dynamically adjusting the transmit/receive antenna positions within a given spatial region. In this paper, we investigate an MA-enhanced multiple-input single-output (MISO) secure communication system, aiming to maximize the secrecy rate by jointly optimizing the positions of multiple MAs. Instead of continuously searching for the optimal MA positions as in prior works, we propose to discretize the transmit region into multiple sampling points, thereby converting the continuous antenna position optimization into a discrete sampling point selection problem. However, this point selection problem is combinatory and thus difficult to be optimally solved. To tackle this challenge, we ingeniously transform this combinatory problem into a recursive path selection problem in graph theory and propose a partial enumeration algorithm to obtain its optimal solution without the need for high-complexity exhaustive search. To further reduce the complexity, a linear-time sequential update algorithm is also proposed to obtain a high-quality suboptimal solution. Numerical results show that our proposed algorithms yield much higher secrecy rates as compared to the conventional FPA and other baseline schemes.