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

2026-04-10

AI Summary

This work addresses the challenge of integrating synthetic aperture radar (SAR) sensing with secure communication in wideband systems under emergency or surveillance scenarios where the location of a mobile eavesdropper is unknown. The authors propose a dynamic time-division joint framework that leverages an airborne base station to estimate the ground-based eavesdropper’s position and velocity in real time via cognitive SAR along-track interferometry. These estimates are then used to adaptively optimize beamforming, artificial noise injection, and time-power allocation to maximize the worst-case secrecy rate. For the first time, deep reinforcement learning is introduced to this joint optimization problem, formulated as a Markov decision process, enabling online adaptation and generalization to unknown eavesdropping trajectories. Simulations demonstrate that the proposed method significantly outperforms baseline schemes with equal aperture or random time-slot allocation in terms of secrecy rate and effectively generalizes to unseen eavesdropper motion patterns.

Technology Category

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

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• SAR-communication integration
• secure communications
• mobile eavesdropper
• time-division joint sensing and communication
• aerial base station

Innovation

• Deep Reinforcement Learning
• Cognitive SAR
• Secure Communications
• Time-Division Joint Sensing and Communication
• Artificial Noise Jamming