Deep Reinforcement Learning for Cognitive Time-Division Joint SAR and Secure Communications

TL;DR

This paper introduces a deep reinforcement learning-based joint SAR and secure communication framework for aerial systems, optimizing secrecy and resource allocation in dynamic adversary scenarios.

Contribution

It develops a novel TD-JSARC framework that combines cognitive SAR, adaptive beamforming, and DRL to enhance security in aerial communications.

Findings

The proposed DRL approach outperforms baseline schemes in secrecy rate.

The method effectively tracks eavesdropper movement patterns.

It generalizes well to unseen eavesdropper behaviors.

Abstract

Synthetic aperture radar (SAR) imaging can be exploited to enhance wireless communication performance through high-precision environmental awareness. However, integrating sensing and communication functionalities in such wideband systems remains challenging, motivating the development of a joint SAR and communication (JSARC) framework. We propose a dynamic time-division JSARC (TD-JSARC) framework for secure aerial communications that is relevant for critical scenarios, such as surveillance or post-disaster communication, where conventional localization of mobile adversaries often fails. In particular, we consider a secure downlink communication scenario where an aerial base station (ABS) serves a ground user (UE) in the presence of a ground-moving eavesdropper. To detect and track the eavesdropper, the ABS uses cognitive SAR along-track interferometry (ATI) to estimate its position and velocity. Based on these estimates, the ABS applies adaptive beamforming and artificial-noise jamming to enhance secrecy. To this end, we jointly optimize the time and power allocation to maximize the worst-case secrecy rate, while satisfying both SAR and communication constraints. Using the estimated eavesdropper trajectory, we formulate the problem as a Markov decision process (MDP) and solve it via deep reinforcement learning (DRL). Simulation results show that the proposed learning-based approach outperforms both learning and non-learning baseline schemes employing equal-aperture and random time allocation. The proposed method also generalizes well to previously unseen eavesdropper motion patterns.