Resource Allocation and Trajectory Design for MISO UAV-Assisted MEC Networks

• Published in: IEEE transactions on vehicular technology Vol. 71; no. 5; pp. 4933 - 4948
• Main Authors: Liu, Boyang, Wan, Yiyao, Zhou, Fuhui, Wu, Qihui, Hu, Rose Qingyang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Autonomous aerial vehicles; Beamforming; Central processing units; CPUs; Edge computing; Energy consumption; Iterative algorithms; Iterative methods; Mobile computing; Mobile edge computing (MEC); multiple input single output (MISO); Optimization; Relays; Resource allocation; Resource management; Servers; Task analysis; Trajectory; unmanned aerial vehicle (UAV); Unmanned aerial vehicles
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2022.3140833

Mobile Edge Computing (MEC) is a promising technology in the next generation network, which provides computing services for user equipments (UEs) to reduce the task delay and prolong the usage time of UEs. To address the deficiency of poor channel quality caused by multipath and blockages in traditional MEC networks, a multiple input single output (MISO) UAV-assisted MEC network is studied. A system energy consumption minimization problem is formulated by jointly optimizing the the UAV's beamforming vectors, the UAV's central processing unit (CPU) frequency, the UAV's trajectory, the UEs' transmission power and the UEs' CPU frequency subject to the constraints on the task, the UAV's trajectory, and the UEs' computation tasks. A three-stage iterative algorithm is proposed to solve the challenging non-convex problem. The closed-form expressions for the optimal UAV CPU frequency and the transmission power of UEs are derived. Simulation results show that the proposed algorithm is superior to the benchmark schemes in terms of energy consumption, and the convergence performance is guaranteed.