Energy and Latency Efficient Joint Communication and Computation Optimization in a Multi-UAV Assisted MEC Network

• Published in: IEEE transactions on wireless communications Vol. 23; no. 3; p. 1
• Main Authors: Pervez, Farhan, Sultana, Ajmery, Yang, Cungang, Zhao, Lian
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Autonomous aerial vehicles; Central processing units; Computation offloading; Cost function; CPU computation resource optimization; CPUs; Decision theory; Edge computing; Iterative methods; Mobile computing; mobile edge computing (MEC); Network latency; Optimization; Power management; Radio equipment; Resource management; Segments; server selection decision; Servers; Task analysis; task computation; task offloading; Trajectories; Trajectory; transmission power optimization; UAV trajectory control; Unmanned aerial vehicles; Unmanned aerial vehicles (UAVs); Wireless communication
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2023.3291692

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent strategy where a UAV equipped with an MEC server is deployed to serve terminal devices. This paper considers a multi-UAV assisted network in which multiple UAVs and a terrestrial base station (BS) are deployed to provide MEC services to mobile users. The objective is to minimize an energy and latency-based cost function by jointly optimizing task offloading and MEC server selection decision, transmission power, UAV trajectory, and CPU frequency allocation. An alternating iterative approach based on the block descent method is proposed to solve this problem. In the first layer, task offloading and server selection decision subproblem is solved using a game theoretic approach. The second layer handles offloading and downloading transmission power allocations by utilizing a simplistic geometric waterfilling (GWF) technique, and the UAV trajectory by successive convex approximation (SCA). Whereas, the third layer solves the computation resource subproblem by performing CPU frequency allocation using a gradient descent method. The proposed method uses a segment-by-segment approach, which divides the entire UAV flight trajectory into shorter timeframe segments to reduce the computation time. Simulation results are presented to show that the proposed approach outperforms various benchmark schemes.