Joint Placement and Resource Allocation for UAV-Assisted Mobile Edge Computing Networks with URLLC

Joint Placement and Resource Allocation for UAV-Assisted Mobile Edge Computing Networks with URLLC

ZHANG Pengyu1, XIE Lifeng1, LIU Xinwei2

(1. School of Information Engineering, Guangdong University of Technology, Guangzhou, Guangdong 510006, China;
2. Future Network of Intelligence Institute and School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China )

Abstract: This paper investigates an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) network with ultra-reliable and low-latency communications (URLLC), in which a UAV acts as an aerial edge server to collect information from a set of sensors and send the processed data (e.g., command signals) to the corresponding actuators. In particular, we focus on the round-trip URLLC from the sensors to the UAV and to the actuators in the network. By considering the finite block-length codes, our objective is to minimize the maximum end-to-end packet error rate (PER) of these sensor-actuator pairs, by jointly optimizing the UAV’s placement location and transmitting power allocation, as well as the users’ block-length allocation, subject to the UAV’s sum transmitting power constraint and the total block-length constraint. Although the maximum-PER minimization problem is non-convex and difficult to be optimally solved, we obtain a high-quality solution to this problem by using the technique of alternating optimization. Numerical results show that our proposed design achieves significant performance gains over other benchmark schemes without the joint optimization.
Keywords: UAV; MEC; URLLC; placement optimization; resource allocation