Minimizing Energy Consumption in H-NOMA Based UAV-Assisted MEC Network

This letter investigates a hybrid non-orthogonal multiple access (H-NOMA) based Unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) network. The goal is to reduce the offloading energy consumption in data-intensive and time-critical smart devices (SDs). To achieve this, we proposed latency-based clustering and low-complexity resource allocation algorithms. Instead of traditional clustering methods, we have introduced a latency condition where H-NOMA surpasses both NOMA and orthogonal multiple access (OMA) in effectively clustering the users. Furthermore, we derived and analyzed a closed-form resource allocation solution and compared it with OMA and NOMA. In addition, the simulation results indicate that the system’s performance utilizing the proposed algorithms outperforms NOMA and OMA approaches, whether with heuristic clustering or without clustering, thereby emphasizing its undeniable superiority.