UAV-Aided Vehicular Short-Packet Communication and Edge Computing System under Time-Varying Channel

• Published in: IEEE transactions on vehicular technology Vol. 72; no. 5; pp. 1 - 13
• Main Authors: Shen, Shuai, Yang, Kun, Wang, Kezhi, Zhang, Guopeng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Autonomous aerial vehicles; Communications systems; Edge computing; Gaussian process; Internet of Things; Lower bounds; Markov processes; Mobile computing; Multi-access edge computing; Optimization; Packet transmission; Reliability; Resource management; Roadsides; short-packet communication; Signal to noise ratio; Task analysis; time-varying channel; Time-varying channels; unmanned aerial vehicle; Unmanned aerial vehicles; vehicular network
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2022.3232841

In this paper, a novel UAV-aided vehicular edge computing (VEC) network is proposed, where the vehicle and on-board UAV provide multi-access edge computing (MEC) service for the roadside Internet of Things (IoT) devices. In this system, considering the time-varying channel, we derive the lower bound of signal-to-noise ratio (SNR) based on the first-order Gauss-Markov process. Then, with the short-packet transmission, we maximize the total amount of computation by jointly optimizing the communication scheduling, the trajectories of the vehicle and on-board UAV, and the computing resource, subject to the mobility, connection and computation constraints. The formulated optimization problem is a mix-integer non-convex problem. To efficiently solve it, we propose an alternative algorithm based on the Lagrangian dual decomposition and successive convex approximation technique. Extensive simulation results are provided to show the performance gain of the proposed algorithm.