Energy-Efficient Multi-UAV-Enabled MEC Systems over Space-Air-Ground Integrated Networks

TL;DR

This paper proposes an energy-efficient framework for multi-UAV-enabled MEC systems within SAGIN, optimizing associations, trajectories, and resource allocations to enhance performance in next-generation networks.

Contribution

It introduces a novel joint optimization approach for UAV-assisted MEC over SAGIN, addressing energy efficiency through an alternating optimization algorithm.

Findings

The proposed algorithm outperforms benchmark methods in simulations.

Joint optimization significantly improves energy efficiency.

The framework effectively supports latency-sensitive tasks in SAGIN-MEC.

Abstract

With the development of artificial intelligence integrated next-generation communication networks, mobile users (MUs) are increasingly demanding the efficient processing of computation-intensive and latency-sensitive tasks. However, existing mobile computing networks struggle to support the rapidly growing computational needs of the MUs. Fortunately, space-air-ground integrated network (SAGIN) supported mobile edge computing (MEC) is regarded as an effective solution, offering the MUs multi-tier and efficient computing services. In this paper, we consider an SAGIN supported MEC system, where a low Earth orbit satellite and multiple unmanned aerial vehicles (UAVs) are dispatched to provide computing services for MUs. An energy efficiency maximization problem is formulated, with the joint optimization of the MU-UAV association, the UAV trajectory, the task offloading decision, the computing frequency, and the transmission power control. Since the problem is non-convex, we decompose it into four subproblems, and propose an alternating optimization based algorithm to solve it. Simulation results confirm that the proposed algorithm outperforms the benchmarks.