Artificial Noise Aided Physical Layer Security for Near-Field MIMO with Fluid Antenna Systems

2025-12-02

AI Summary

To address the limited physical-layer security (PLS) performance in near-field MIMO systems—caused by insufficient beam focusing under non-massive antenna arrays—this paper proposes an artificial-noise (AN)-aided secure transmission scheme for fluid antenna systems (FAS). The method innovatively integrates AN into the near-field FAS architecture and jointly optimizes beamforming and port selection. Specifically, it introduces a row-energy-based pruning-and-refitting rule for efficient active port selection, and employs generalized spectral water-filling to jointly design fully digital beamforming and AN power allocation. Further, it combines block coordinate descent with a hardware-friendly hybrid beamforming structure, injecting AN in the baseband without requiring additional RF chains. Simulation results demonstrate that the proposed scheme significantly improves secrecy rate—particularly for medium-scale arrays—effectively compensating for near-field focusing deficiencies and substantially enhancing PLS performance.

Technology Category

• Machine Learning: Hardware-aware ML
• Multiagent Systems: Mechanism Design
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

With the evolution of wireless systems toward large-scale arrays and high-frequency reconfigurable architectures, fluid antenna systems (FAS) operating in the near-field (NF) regime provide new degrees of freedom (DoF) for physical layer security (PLS). This paper proposes an artificial-noise (AN)-aided PLS scheme for NF fluid-antenna multiple-input multiple-output (FA-MIMO) systems, with joint beamforming (BF) and AN design for both compact and large arrays. An alternating-optimization (AO) framework addresses the sparsity-constrained non-convex design by splitting it into a continuous BF/AN joint-design subproblem and a discrete FAS port-selection subproblem. Closed-form fully digital BF/AN solutions are obtained via a generalized spectral water-filling procedure within a block coordinate descent (BCD) surrogate and realized by a hardware-efficient hybrid beamforming (HBF) architecture that embeds AN in the baseband without extra radio-frequency (RF) chains. For FAS port selection, a row-energy based prune--refit rule, aligned with Karush--Kuhn--Tucker (KKT) conditions of a group-sparsity surrogate, enables efficient active-port determination. Simulation results confirm that the proposed design exploits the geometry and position-domain DoF of FAS and significantly improves secrecy performance, particularly for non-extremely-large arrays where NF beam focusing alone is inadequate.

Problem

• Enhances physical layer security in near-field MIMO with fluid antennas
• Optimizes joint beamforming and artificial noise for compact/large arrays
• Efficiently selects active ports in fluid antenna systems via sparse design

Innovation

• Alternating-optimization framework for joint beamforming and artificial noise design
• Hardware-efficient hybrid beamforming embedding artificial noise without extra RF chains
• Row-energy based prune-refit rule for efficient fluid antenna port selection