Maximizing Energy Efficiency in UAV-Assisted NOMA-MEC Networks

Mobile-edge computing (MEC) is a key technology to enable multitasking and low-latency user experiences for 5G Internet of Things (IoT) devices. The nonorthogonal multiple access (NOMA) technology is used in this context to enable large-scale connectivity and improve spectrum efficiency, with the unmanned aerial vehicle (UAV) serving as both computing units and relays for mobile users (MUs). Energy efficiency (EE) remains challenging given the limited energy available to the UAV and MUs. In this article, a UAV-assisted NOMA-MEC communication network architecture is studied to maximize the EE of the total system by jointly optimizing the user's communication scheduling, resource allocation, and UAV flight trajectory. Among them, the resource allocation problem can further be divided into the transmit power optimization problem and the task computation allocation problem, whereby the corresponding time slot scheduling is obtained. The objective function is a nonconvex mixed-integer nonlinear fractional programming (MINLFP) problem, which is too complex to solve directly. Therefore, it is decomposed into more manageable subproblems and solved iteratively. Fractional problems are solved using the Dinkelbach method, which transforms their original subproblems into convex forms with methods such as successive convex approximation (SCA). Simulation results demonstrate the convergence of our proposed algorithm and its significant advantage over existing strategies in terms of EE.