Beamforming Design for Joint Target Sensing and Proactive Eavesdropping

2024-07-09

arXiv.org

AI Summary

This work addresses beamforming design for joint target sensing and active physical-layer eavesdropping (JTSAPE) systems, where a shared waveform at the base station simultaneously enables radar-like target parameter estimation, conveys information to the legitimate receiver, and acts as artificial noise to jam the illegitimate receiver—thereby enhancing eavesdropping performance. We propose the first normalized weighted framework jointly optimizing sensing accuracy (by minimizing the Cramér–Rao bound) and eavesdropping efficacy (by maximizing the eavesdropping signal-to-interference-plus-noise ratio). To tackle the resulting non-convex optimization under strong eavesdropper channels, we develop a stepwise iterative algorithm based on sequential rank-one constraint relaxation (SROCR). Simulation results demonstrate that the proposed method significantly improves both SINR and estimation accuracy in multi-target and time-varying channel scenarios, yielding high-quality suboptimal beam covariance solutions with strong robustness and practical applicability.

Technology Category

• Search and Optimization: Non-convex Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation

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: Applied ML and AI for Web-based mobile applications

Abstract

This work studies the beamforming design in the joint target sensing and proactive eavesdropping (JTSAPE) system. The JTSAPE base station (BS) receives the information transmitted by the illegal transmitter and transmits the waveform for target sensing. The shared waveform also serves as artificial noise to interfere with the illegal receiver, thereby achieving proactive eavesdropping. We firstly optimize the transmitting beam of the BS to maximize the eavesdropping signal-to-interference-plus-noise ratio or minimize the target estimation parameter Cram{'{e}}r-Rao bound, respectively. Then, the joint optimization of proactive eavesdropping and target sensing is investigated, and the normalized weighted optimization problem is formulated. To address the complexity of the original problem, the formulated problem is decomposed into two subproblems: proactive eavesdropping and target sensing, which are solved by the semi-definite relaxation technique. Furthermore, the scenario in which the quality of the eavesdropping channel is stronger than that of the illegal channel is considered. We utilize the sequential rank-one constraint relaxation method and iteration technique to obtain the high-quality suboptimal solution of the beam transmit covariance matrix. Numerical simulation shows the effectiveness of our proposed algorithm.

Problem

• Optimize beamforming for joint sensing and eavesdropping
• Maximize eavesdropping SNR or minimize target estimation error
• Decompose and solve complex joint optimization problem

Innovation

• Optimizes beamforming for eavesdropping and sensing
• Decomposes problem using semi-definite relaxation
• Uses sequential rank-one constraint relaxation