Sensing-Then-ISAC: A Distance-Constrained Safe Reinforcement Learning for UAV Secure Communications

Abstract

Integrated sensing and communication (ISAC) technology, when deployed on unmanned aerial vehicles (UAVs), enables aerial base stations to simultaneously provide wireless connectivity to ground users and perform environmental sensing through echo signal analysis. However, the broadcast nature of wireless transmission, combined with the line-of-sight (LoS) propagation characteristics of UAVs, increases the risk of passive eavesdropping on transmitted signals during ISAC missions. This paper investigates the joint trajectory design and power allocation (JTDPA) problem for UAV-enabled ISAC systems in environments with multiple mobile ground users and potential eavesdroppers. The proposed approach formulates the optimization problem as a constrained Markov decision process (CMDP), aiming to balance communication rate, secrecy rate, and energy consumption. To address the limitations of existing secure trajectory designs, such as unnecessary energy expenditure and overly conservative avoidance actions, we propose a two-stage (TS) strategy that incorporates the safe twin delayed deep deterministic policy gradient (Safe-TD3) algorithm, referred to as TS-SafeTD3. In the first stage (sensing stage), the UAV navigates toward a user-centric location without communication to enhance initial coverage efficiency, while satisfying the minimum-distance safety constraints with respect to potential eavesdroppers.In the second stage (ISAC stage), Safe-TD3 is employed to jointly optimize both trajectory and power allocation under the same safety constraints to maximize the weighted secrecy rate. Simulation results indicate that the proposed algorithm improves the weighted secrecy rate and energy efficiency under various operational conditions, while maintaining a low violation probability of the safety constraints.