AoI Minimization for Air–Ground Integrated Sensing and Communication Networks With Jamming Attack

Air-ground integrated sensing and communication (AG-ISAC) networks utilize unmanned aerial vehicles (UAVs) equipped with ISAC units as aerial platforms in next-generation wireless systems, which can sense ground status information to the base stations (BSs) for further decision-making. Given that effective decisions rely on timely sensed data, the Age of Information (AoI) serves as a metric that precisely reflects the timeliness and effectiveness of the sensing and communication (S&C) process. However, recent research has paid little attention to AoI minimization in AG-ISAC networks, particularly in the presence of malicious attackers attempting to interfere S&C operations. To address this research gap, this paper considers AG-ISAC networks with S&C jamming attacks, a mission ISAC UAV senses tasks and transmits the sensed data to the BS, while a mobile UAV attacker sends jamming signals to degrade S&C performance and increase AoI. To solve this, we jointly design the trajectory and S&C transmit power of the mission UAV to mitigate S&C performance degradation to minimize the AoI. Nonetheless, the NP-hard and dynamic long-term nature of the formulated problem poses significant challenges. To tackle this troublesome problem, we develop an efficient solution using the deep reinforcement learning (DRL) technique. Specifically, we propose a twin-delayed deep deterministic policy gradient (TD3)-based trajectory design and power control algorithm (TD3-TP). In addition to this, we incorporate a reward-shaping mechanism to speed up the convergence of the algorithm. Simulation results demonstrate our algorithm achieves the fastest training speed and minimum AoI under attacks relative to four benchmark methods.