Joint Task Offloading and Resource Allocation in AAV-Assisted MEC Networks for Disaster Rescue: A Large AI Model Enabled DRL Approach

Natural disasters often destroy critical infrastructure, such as terrestrial communication networks and transportation routes, thereby severely disrupting post-disaster rescue operations. To rapidly re-establish communication links and provide flexible computational support in disaster rescue scenarios, the integration of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) has emerged as a promising solution. Nevertheless, the highly complex and resource-constrained characteristics of disaster environments pose significant challenges for UAV-assisted computation task offloading. In this paper, we investigate the joint task offloading and resource allocation (JTORA) problem to minimize the energy consumption associated with communication and computation during task offloading. Specifically, we develop a twin-delayed deep deterministic policy gradient (TD3)-based JTORA (JTORA-TD3) algorithm, which enables the UAV to optimize decisions of task offloading and resource allocation intelligently. To further enhance the training efficiency of the JTORA-TD3 algorithm in a complex disaster rescue environment, we integrate a large AI model (LAM) into the TD3 framework. Based on the textual interaction, we propose an LAM-enabled TD3-based JTORA (JTORA-LAM4TD3) algorithm. Simulation results demonstrate that the proposed JTORA-LAM4TD3 algorithm significantly outperforms baselines. These findings confirm the effectiveness of integrating LAMs with deep reinforcement learning (DRL) for solving the decision optimization problem.