Mobile Edge Computing for AAV-enabled Internet of Vehicles with NOMA: Delay Optimization and Performance Analysis

Autonomous aerial vehicles (AAVs) can effectively eliminate communication blind zones and establish line-of-sight links with ground vehicles by leveraging their flexible deployment capabilities. Motivated by the above, this paper employs an AAV as a mobile edge computing (MEC) server to provide task offloading services, based on which the non-orthogonal multiple access (NOMA) technology is used in AAV-enabled Internet of Vehicles (IoV). To reduce the MEC offloading delay, we propose a NOMA-enhanced MEC framework for AAV-enabled IoV. More explicitly, we formulate a total offloading delay minimization problem by optimizing the transmit power and the AAV position. To tackle the non-convex problem, we decouple it into two sub-problems: power allocation and AAV position optimization. Specifically, the power allocation is optimized via the successive convex optimization algorithm, and the AAV position is adjusted using the improved particle swarm optimization-genetic algorithm (PSO-GA). Then, we propose an iterative optimization algorithm to alternately iterate these two processes to find the optimal solution. Next, we analyze the achievable offloading probability of the NOMA-MEC scheme compared with the OMAMEC scheme and derive its asymptotic expressions under high signal-to-noise ratio (SNR) conditions. Finally, simulation results indicate that the proposed scheme outperforms existing methods in reducing total offloading delay while validating the accuracy of performance analysis.