Energy-Efficient Resource Allocation in Multi-UAV-Assisted Two-Stage Edge Computing for Beyond 5G Networks

Unmanned aerial vehicle (UAV)-assisted multi-access edge computing (MEC) has become one promising solution for energy-constrained devices to run the applications with high computation demand and stringent delay requirement in beyond 5G era. In this work, we study a multi-UAV-assisted two-stage MEC system which UAVs provide relaying services mobile devices. Due limited resources, each UAV executes only portion of offloaded tasks from its associated MDs first stage. Hence, second stage, relays portions terrestrial base station (TBS) rich resources enough handle all relayed it. regard, formulate joint task offloading, communication resource allocation problem minimize energy consumption by considering tolerable latency tasks. The formulated is mixed-integer non-convex NP hard. To solve optimization problem, apply Block Successive Upper-bound Minimization (BSUM) method guarantees obtain stationary points objective function. Finally, extensive evaluation results are conducted show superior performance our proposed framework.