Joint AoI-Aware and Trajectory Optimization for NOMA-Based and UAV-Assisted IoV Networks

Integrating mobile edge computing (MEC) into the Internet of Vehicles (IoV) allows IoV devices with limited computation capabilities and energy to offload their computation-intensive tasks to the network edge, thereby providing high-quality service to vehicles. Due to the flexibility of unmanned aerial vehicles (UAVs) and the capability of non-orthogonal multiple access (NOMA) to support massive connectivity, a NOMA-based and UAV-assisted MEC framework offers flexible and high-performance computing services for vehicles in large-scale access networks. This paper introduces a novel joint optimization framework that incorporates Age of Information (AoI) awareness to enhance task freshness in IoV networks. By leveraging the AoI metric, the optimization problem is formulated to simultaneously minimize total energy consumption and maximize the number of completed tasks. The proposed solution achieves these objectives by jointly optimizing vehicle wireless transmission power using the NOMA communication technique, task offloading decisions, and the UAV's trajectory planning and computing frequency allocation across time slots. Given the non-convex nature of the problem within a hybrid action space, we employ the Twin Delayed Deep Deterministic (TD3) policy gradient algorithm to address it. Experimental results indicate that the TD3 policy gradient algorithm is effective in solving the proposed optimization framework, particularly in terms of convergence speed and the system reward.