H-NOMA-Based Offloading Energy Minimization in UAV-Enabled MEC Networks

Abstract

In this letter, we explore the hybrid Non-orthogonal multiple access (H-NOMA) premised Unmanned aerial vehicle (UAV)-Enabled mobile edge computing (MEC) network. Our research emphasizes the importance of adept clustering techniques to meet the high demands of data-intensive and time-sensitive users. Instead of conventional single-stage clustering, we introduce a novel 2-tier clustering mechanism. We assort users based on channel gains and cluster sizes in the initial phase. Then, these users undergo a second clustering based on their latency demands. This layered approach enables more efficient resource allocation tailored to individual user needs. Furthermore, we derive and analyze a formula for resource assignment, ensuring a more streamlined and efficient process. We compare our findings with pure NOMA, and orthogonal multiplexing access (OMA) benchmarks for clearer understanding. Simulations show the superior performance of the H-NOMA using our proposed methodologies.