Near-field Physical Layer Security: Robust Beamforming under Location Uncertainty

2026-01-20

AI Summary

This work addresses the performance degradation of conventional robust beamforming in near-field communications caused by uncertainty in eavesdropper locations. To tackle this issue, a two-stage robust beamforming approach is proposed that explicitly accounts for the amplification effect of near-field angular errors. By modeling positional uncertainty in the polar coordinate domain and leveraging sector-based subregion partitioning combined with first-order Taylor approximation, the method formulates an optimization framework based on linear matrix inequalities (LMIs). This framework maximizes the sum rate of legitimate users while enforcing a worst-case constraint on the eavesdropping rate. Simulation results demonstrate that the proposed scheme achieves a superior trade-off between secrecy rate and robustness, significantly outperforming existing benchmark methods.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

In this paper, we study robust beamforming design for near-field physical-layer-security (PLS) systems, where a base station (BS) equipped with an extremely large-scale array (XL-array) serves multiple near-field legitimate users (Bobs) in the presence of multiple near-field eavesdroppers (Eves). Unlike existing works that mostly assume perfect channel state information (CSI) or location information of Eves, we consider a more practical and challenging scenario, where the locations of Bobs are perfectly known, while only imperfect location information of Eves is available at the BS. We first formulate a robust optimization problem to maximize the sum-rate of Bobs while guaranteeing a worst-case limit on the eavesdropping rate under location uncertainty. By transforming Cartesian position errors into the polar domain, we reveal an important near-field angular-error amplification effect: for the same location error, the closer the Eve, the larger the angle error, severely degrading the performance of conventional robust beamforming methods based on imperfect channel state information. To address this issue, we first establish the conditions for which the first-order Taylor approximation of the near-field channel steering vector under location uncertainty is largely accurate. Then, we propose a two-stage robust beamforming method, which first partitions the uncertainty region into multiple fan-shaped sub-regions, followed by the second stage to formulate and solve a refined linear-matrix-inequality (LMI)-based robust beamforming optimization problem. In addition, the proposed method is further extended to scenarios with multiple Bobs and multiple Eves. Finally, numerical results validate that the proposed method achieves a superior trade-off between rate performance and secrecy robustness, hence significantly outperforming existing benchmarks under Eve location uncertainty.

Problem

• near-field
• physical-layer security
• location uncertainty
• eavesdropping
• beamforming

Innovation

• near-field beamforming
• physical-layer security
• location uncertainty
• angular-error amplification
• robust optimization