QoS-Oriented Joint Resource and Trajectory Optimization in NOMA-Enhanced AAV-MEC Systems

Abstract

Autonomous Aerial Vehicle (AAV)-assisted Mobile Edge Computing (MEC) has received extensive attention because it provides resilient computation services for multiple Mobile Users (MUs). However, due to the increasing scale of offloaded tasks, the uncertain mobility of MUs, and the limited energy budget of AAV and MUs, it is extremely challenging to achieve satisfactory Quality-of-Service (QoS). Non-Orthogonal Multiple Access (NOMA), a promising technology to serve multiple MUs with limited communication resources, has great potential to be integrated with MEC. To this end, this paper proposes a QoS-oriented NOMA-enhanced AAV-MEC system, which aims to capture the potential gains of uplink NOMA and enable more MUs to benefit from edge computing servers in resource-constrained AAV-assisted MEC environments. This synergy reduces MUs’ uplink energy consumption but poses new challenges in resource allocation and AAV trajectory design. To address these challenges, we define a new metric called System Overhead Ratio (SOR) to reflect the system’s QoS, and then consider a joint optimization problem of resource allocation, transmission power control, and AAV trajectory design, with the goal of minimizing the SOR. Given the NP-hard nature of the optimization problem, we propose a Lyapunov and convex optimization-based Low-complexity Online Resource allocation and Trajectory optimization method (LORT) to solve it, and further analyze the convergence and complexity of LORT. Finally, extensive simulations show that the proposed method surpasses other benchmarks, reducing the SOR by approximately $10\%$-$ 25\%$ under various scenarios.