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

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Mobile edge computing (MEC) enhances network capabilities by bringing computation closer to data sources.
• Unmanned Aerial Vehicles (UAVs) offer flexible deployment for wireless networks, improving coverage and connectivity.
• Delay-sensitive computing tasks in terminal devices (TDs) require efficient resource management and low latency.

Purpose of the Study:

• To minimize the maximum delay experienced by terminal devices (TDs) in a UAV-assisted MEC network.
• To optimize the three-dimensional (3D) deployment and resource allocation for UAVs and ground access points (GAPs) in cooperative networks.
• To develop an efficient algorithm for joint optimization of UAV-TD association, UAV positioning, bandwidth, and task splitting.

Main Methods:

• Formulated the problem as a mixed-integer nonlinear programming (MINLP) problem.
• Employed successive convex approximation (SCA) and block coordinate descent (BCD) methods for optimization.
• Proposed an iterative algorithm to jointly optimize UAV-TD association, UAV 3D location, bandwidth allocation, and task split ratio.

Main Results:

• The proposed iterative algorithm efficiently solves the complex optimization problem.
• The UAV-assisted MEC network with air-ground cooperation demonstrates significant performance improvements.
• Simulation results show superior performance compared to existing benchmark schemes.

Conclusions:

• The developed algorithm effectively minimizes maximum delay in UAV-assisted MEC networks.
• Air-ground cooperation enhances the efficiency of MEC task offloading for terminal devices.
• The 3D deployment optimization of UAVs is crucial for improving network performance and user experience.