Physical-Layer Security for 6G: Safe Jamming against Malicious Sensing

TL;DR

The paper addresses the privacy threat posed by sensing-enabled 6G devices by proposing a safe jamming framework that disrupts malicious sensing while avoiding interference with legitimate uplink users. It integrates an actor-critic reinforcement learning method for dynamic frequency selection with a robust action-correction module to respect future interference constraints. The key contributions are the AC-based jamming strategy, the offline learning of constraint-surrogate models, and a closed-form, KKT-guided action correction that ensures near-optimal, safe operation. The approach achieves high jamming success and dramatically reduces potential conflicts, suggesting a viable path for secure coexistence of sensing and communication in 6G networks.

Abstract

The integration of sensing, communications, array signal processing, etc. into 6G mobile networks has ushered in an era of heightened situational awareness. However, this progress brings forth significant concerns regarding privacy and security, particularly due to the proliferation of devices equipped with radar-like sensing capability, including malicious ones. In response, this paper proposes a novel actor-critic (AC) method-based frequency selection scheme for noise jamming, in order to effectively counter malicious multifunction frequency agility sensing. In the meanwhile, to mitigate potential interference (caused by sidelobes of the jamming beam) with uplink transmissions conducted by legitimate but non-cooperative users, a robust action correction mechanism, which is capable of learning and predicting the spectrum utilization state, is proposed to find feasible but near-optimal frequency configuration for jamming. Numerical results demonstrate that benefiting from the robust action correction mechanism, the proposed AC-based safe jamming can not only make the malicious sensing device continuously get stuck in the searching mode but also guarantee minimal disruption to the legitimate non-cooperative users.

Figures (8)

• Figure 1: Safe jamming in 6G system.
• Figure 2: Transition of the modes during sensing.
• Figure 3: Comparison of jamming success rate in the offline training phase.
• Figure 4: Mode of operation of malicious sensing device in the offline training phase. 1 indicates that the malicious device is working in the searching mode; 2 indicates tracking mode; 3 indicates lock-on mode.
• Figure 5: Comparison of the number of conflicts in the offline training phase.