Energy-Efficient UAV Deployment and Computation Offloading in Space-Air-Ground Integrated Networks

In the domain of computation offloading from mobile devices, Space-Air-Ground Integrated Networks (SAGIN) emerge as a potent paradigm, leveraging unmanned aerial vehicles (UAVs) and low earth orbit (LEO) satellites as computational resource providers. One prevalent strategy to reducing energy consumption within SAGIN systems is to jointly optimize UAV deployment and computation offloading. However, given the variable nature of mobile device positioning and computational requisites, the deployment and offloading must be continually recalibrated. This joint optimization faces a significant challenge of combinatorial explosion. Addressing the difficulties of real-time computation tasks necessitates efficacious methodologies for devising joint optimization schemes. In this paper, we introduce a convex optimization-based algorithm to minimzie the weighted total energy consumption within the SAGIN framework by jointly optimizing UAV deployment and computation offloading. The algorithm decomposes the original problem into a set of sub-problems: UAV deployment, ground device (GD) access, and computation offloading, employing the Block Coordinate Descent (BCD) method. By alternately addressing these sub-problems, the algorithm derives a near-optimal solution efficiently. Simulation results demonstrate that our approach can generate a joint optimization solution in a few seconds and diminish the weighted total energy consumption than other classic methods by 1.5%$\sim$10.97%.