Resource Allocation and 3D Deployment of UAVs-Assisted MEC Network with Air-Ground Cooperation

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 7; p. 2590
• Main Authors: Huang, Jinming, Xu, Sijie, Zhang, Jun, Wu, Yi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.03.2022; MDPI
• Subjects: 3D deployment; air-ground cooperation; Algorithms; Bandwidths; Cloud computing; Communication; Cooperation; Drone aircraft; Energy consumption; Internet of Things; Investigations; mobile edge computing; Resource allocation; Servers; UAV communication; Unmanned aerial vehicles; Wireless networks; UAV communication; mobile edge computing; resource allocation; air-ground cooperation; 3D deployment
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22072590

Equipping an unmanned aerial vehicle (UAV) with a mobile edge computing (MEC) server is an interesting technique for assisting terminal devices (TDs) to complete their delay sensitive computing tasks. In this paper, we investigate a UAV-assisted MEC network with air-ground cooperation, where both UAV and ground access point (GAP) have a direct link with TDs and undertake computing tasks cooperatively. We set out to minimize the maximum delay among TDs by optimizing the resource allocation of the system and by three-dimensional (3D) deployment of UAVs. Specifically, we propose an iterative algorithm by jointly optimizing UAV-TD association, UAV horizontal location, UAV vertical location, bandwidth allocation, and task split ratio. However, the overall optimization problem will be a mixed-integer nonlinear programming (MINLP) problem, which is hard to deal with. Thus, we adopt successive convex approximation (SCA) and block coordinate descent (BCD) methods to obtain a solution. The simulation results have shown that our proposed algorithm is efficient and has a great performance compared to other benchmark schemes.