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

Weidong Mei, Xin Wei, Yijie Liu, Boyu Ning, Zhi Chen

TL;DR

The paper tackles maximizing secrecy rate in an MA-enhanced MISO system by discretizing the transmitter region into sampling points and formulating a discrete MA position selection problem. It converts the optimization to a graph path problem and provides a graph-based partial enumeration method for optimality along with a linear-time sequential update for efficient suboptimal solutions. The optimal beamformer for fixed MA positions is obtained via a generalized Rayleigh quotient leading to Rs = log2 λmax(AB,AE), and the MA positions are constrained by zone indices and minimum spacing. Numerical results confirm large secrecy-rate gains over FPAs and baselines, with moderate sampling resolution (around M ≥ 36) sufficing to approach continuous-search performance, and the sequential update offering near-optimal performance with linear-time 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.

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

TL;DR

The paper tackles maximizing secrecy rate in an MA-enhanced MISO system by discretizing the transmitter region into sampling points and formulating a discrete MA position selection problem. It converts the optimization to a graph path problem and provides a graph-based partial enumeration method for optimality along with a linear-time sequential update for efficient suboptimal solutions. The optimal beamformer for fixed MA positions is obtained via a generalized Rayleigh quotient leading to Rs = log2 λmax(AB,AE), and the MA positions are constrained by zone indices and minimum spacing. Numerical results confirm large secrecy-rate gains over FPAs and baselines, with moderate sampling resolution (around M ≥ 36) sufficing to approach continuous-search performance, and the sequential update offering near-optimal performance with linear-time 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.
Paper Structure (9 sections, 15 equations, 4 figures, 2 algorithms)

This paper contains 9 sections, 15 equations, 4 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. System Model
  4. Problem Formulation
  5. Proposed Algorithms for (P2)
  6. Optimal Solution by Partial Enumeration
  7. Suboptimal Solution by Sequential Update
  8. Numerical Results
  9. Conclusion

Figures (4)

  • Figure 1: MA-enhanced secure communication system with discrete sampling.
  • Figure 2: Secrecy rate versus the number of sampling points.
  • Figure 3: Secrecy rate versus the length of antenna arrays.
  • Figure 4: Secrecy rate versus the number of transmit MAs.

Theorems & Definitions (1)

  • Definition 1