Multiagent UAV-Aided URLLC Mobile Edge Computing Systems: A Joint Communication and Computation Optimization Approach

In this article, we consider a multiagent unmanned aerial vehicle (UAV)-aided system employing mobile edge computing (MEC) servers to satisfy the requirement of ultrareliable low latency communications (URLLCs) in intelligent autonomous transport applications. Our MEC architecture aims to guarantee quality-of-service (QoS) by investigating task offloading and caching implemented in the nearby UAVs. To enhance system performance, we propose to minimize the network energy consumption by jointly optimizing communication and computation parameters. This includes decisions on task offloading, edge caching policies, uplink transmission power, and the processing rates of users. Given the nonconvex nature and high computational complexity of this optimization problem, an alternating optimization algorithm is proposed, where the three subproblems of caching, offloading, and power allocation are solved in an alternating manner. Our simulation results demonstrate the efficacy of the proposed method, showcasing significant reductions in user energy consumption and optimal resource allocation. This work serves as an initial exploration of the transformative potential of cutting-edge technologies, such as UAVs, URLLC, and MEC, in shaping the future landscape of intelligent autonomous transport systems.