Joint power-trajectory-scheduling optimization in a mobile UAV-enabled network via alternating iteration

• Published in: China communications Vol. 19; no. 1; pp. 136 - 152
• Main Authors: Qi, Xiaohan, Yuan, Minxin, Zhang, Qinyu, Yang, Zhihua
• Format: Journal Article
• Language: English
• Published: China Institute of Communications 01.01.2022; Peng Cheng Laboratory,Shenzhen 518052,China; Communications Engineering Research Center,Harbin Institute of Technology,Shen zhen 518055,China%Communications Engineering Research Center,Harbin Institute of Technology,Shen zhen 518055,China
• Subjects: Autonomous aerial vehicles; Base stations; D2D; Device-to-device communication; Energy efficiency; Interference; joint optimization; Throughput; Trajectory; UAV MEC network; joint optimiza-tion; UAV MEC network; D2D; energy efficiency
• ISSN: 1673-5447
• DOI: 10.23919/JCC.2022.01.011

This work focuses on an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system based on device-to-device (D2D) communication. In this system, the UAV exhibits caching, computing and relaying capabilities to periodically provide specific service to cellular users and D2D receiver nodes in the appointed time slot. Besides, the D2D transmitter can provide additional caching services to D2D receiver to reduce the pressure of the UAV. Note that communication between multi-type nodes is mutually restricted and different links share spectrum resources. To achieve an improved balance between different types of node, we aim to maximize the overall energy efficiency while satisfying the quality-of-service requirements of the cellular nodes. To address this problem, we propose an alternating iteration algorithm to jointly optimize the scheduling strategies of the user, transmitting power of the UAV and D2D-TX nodes, and UAV trajectory. The successive convex approximation, penalty function, and Dinkelbach method are employed to transform the original problem into a group of solvable subproblems and the convergence of the method is proved. Simulation results show that the proposed scheme performs better than other benchmark algorithms, particularly in terms of balancing the tradeoff between minimizing UAV energy consumption and maximizing throughput.