Secure Communication in MIMOME Movable-Antenna Systems with Statistical Eavesdropper CSI

2026-01-21

AI Summary

This work addresses the challenge of incomplete eavesdropper channel state information—specifically, only statistical properties and line-of-sight components are known—in MIMOME systems. To maximize the ergodic secrecy rate, the paper proposes a joint optimization of transmit precoding and movable antenna positions. Leveraging random matrix theory, a deterministic equivalent expression for the ergodic secrecy rate is derived, circumventing the need for Monte Carlo simulations. An alternating optimization framework is developed, wherein antenna placement is optimized via a novel surrogate function method assisted by AMSGrad, overcoming limitations of conventional Majorization-Minimization algorithms. The proposed approach achieves fast convergence and significantly enhances physical-layer security, while also uncovering the underlying influence of spatial channel statistics on secrecy performance.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

This paper investigates the potential of movable antennas (MAs) to enhance physical layer security within a multiple-input multiple-output multiple-antenna eavesdropper (MIMOME) system. We consider a practical scenario where the transmitter operates with imperfect eavesdropper channel state information (ECSI), knowing only the instantaneous line-of-sight (LoS) component and the statistical properties of non-line-of-sight (NLoS) component. To rigorously quantify secrecy performance under the ECSI uncertainty, we adopt the ergodic secrecy rate (ESR) as the metric. Since deriving an exact analytical expression for the ESR is intractable, we leverage random matrix theory to derive a deterministic equivalent. This avoids heavy Monte Carlo simulations and also provides explicit insights into the effects of channel spatial statistics on secrecy performance. Building upon the deterministic equivalent, we formulate a joint maximization problem for the transmit precoding matrix and the antenna positions at the legitimate transmitter. To tackle the non-convexity of this optimization problem, we develop a comprehensive alternating optimization framework. Specifically, the precoding matrix is optimized via a majorization-minimization (MM) algorithm, where the gradient is computed by solving an implicit fixed-point equation. For the antenna position optimization, the complexity of the objective function prevents the construction of standard MM surrogate. To this end, we further propose a novel AMSGrad-based surrogate function that relies solely on gradient information. We provide a rigorous theoretical proof that guarantees the convergence of this proposed algorithm despite relaxing the strict majorization conditions.

Problem

• Movable Antennas
• Physical Layer Security
• MIMOME
• Eavesdropper CSI
• Ergodic Secrecy Rate

Innovation

• movable antennas
• physical layer security
• ergodic secrecy rate
• alternating optimization
• AMSGrad-based surrogate