Deep Reinforcement Learning Based Resource Allocation in Multi-UAV-Aided MEC Networks

• Published in: IEEE transactions on communications Vol. 71; no. 1; p. 1
• Main Authors: Chen, Jingxuan, Cao, Xianbin, Yang, Peng, Xiao, Meng, Ren, Siqiao, Zhao, Zhongliang, Wu, Dapeng Oliver
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computational geometry; Convexity; Deep learning; DRL; Edge computing; Energy consumption; Machine learning; Mathematical programming; MEC; Mixed integer; Mobile computing; movement control; Network latency; Power control; Resource allocation; Series (mathematics); UAV; Unmanned aerial vehicles
• DOI: 10.1109/TCOMM.2022.3226193
• ISSN: 0090-6778

Resource allocation for mobile edge computing (MEC) in unmanned aerial vehicle (UAV) networks has been a popular research issue. Different from existing works, this paper considers a multi-UAV-aided uplink communication scenario and investigates a resource allocation problem of minimizing the total system latency and the energy consumption, subject to constraints on transmit power of mobile users (MUs), system latency caused by transmission and computation. The problem is confirmed to be a challenging time-series mixed-integer non-convex programming problem, and we propose a joint UAV Movement control, MU Association and MU Power control (UMAP) algorithm to solve it effectively, where three sub-problems are optimized iteratively. Specifically, UAV movement and MU association are optimized utilizing deep reinforcement learning (DRL) to decrease the energy consumption and system latency. Next, a closed-form solution of the MU transmit power is derived. Finally, simulation results show that the UMAP algorithm can significantly decrease the system latency and energy consumption and increase the coverage rate compared with benchmark algorithms.