Enhancing Secure Communication: Deep Q-Learning for Location-Based Authentication

In the evolving landscape of next-generation wireless networks, ensuring secure communications in covert military operations is paramount. This paper proposes an advanced localization-based security system utilizing passive receivers and Time Difference of Arrival (TDOA) techniques to continuously authenticate the signals of a commander in dynamic operational scenarios. Our system effectively counters physical layer spoofing attacks by distinguishing between the precise locations of a legitimate entity and potential adversaries. To that end, we derive the positioning error bound (PEB) specific to TDOA systems, emphasizing the critical impact of receiver arrangement on localization accuracy. Furthermore, we introduce a novel application of deep Q-learning for the NP-hard problem of optimal placement of receivers, addressing the challenge of spatial geometry, which significantly influences localization accuracy. Through extensive testing, we demonstrate that our proposed approach notably outperforms traditional placement methods in mitigating geometry-induced errors and enhancing overall localization precision. Ultimately, this facilitates realizing and maintaining a secure zone where users can authenticate each other through localization.