Anti-Jamming Communication Framework for AAV-Based Defense Operations to Control AI-Enabled Attacks

Nowadays, unmanned aerial vehicles (UAVs) have become essential assets for defense operations, providing efficient and precise communication and surveillance capabilities in complex and hostile environments. Artificial intelligence (AI)-enabled jamming poses a critical threat to the security and reliability of UAV communication networks. To address this, we propose an offline-learning anti-jamming framework that leverages advanced algorithms, including deep reinforcement learning (DRL) and decision transformers (DT), to enhance UAV performance in intelligent jamming environments. This work adopts an offline, cloud-based training paradigm for anti-jamming, which learns from historical decision trajectories to handle uncertain and unseen jamming patterns and enable reliable decision making. The proposed framework constrains cumulative reward loss within a predefined bound while improving training stability and reducing the time complexity of DRL-based decision model training. The proposed framework exhibits greater performance of UAVs in controlling AI-enabled jams by showing remarkable results for the performance metrics such as Bit Error Rate (BER), Signal-to-Noise Ratio (SNR), Packet Delivery Ratio (PDR), Jamming Detection Time (JDT), and latency (delay in response). The extensive experimental outcomes illustrate the efficiency of the proposed framework in complex defense operations and uncertain environments for mitigating AI-enabled attacks by counteracting the jamming attempts.