Towards Facilitating URLLC in UAV-enabled MEC Systems for 6G Networks

This paper jointly studies the fairness and efficient trajectory design problem for facilitating ultra-reliable and low latency communications (URLLC) in unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) systems, in the context of sixth-generation (6G) networks. In this regard, a fixed-wing UAV is equipped with an aerial server, and it is programmed to collect critical task allocation data from Internet of things (IoT) devices deployed on the ground. To prolong the operational time of the ground IoT devices, we aim to minimize the maximum energy consumption among the ground IoT devices. Furthermore, due to the non-convexity of the original problem, we use successive convex approximations (SCA) to divide the original problem into two convex sub-problems. To this end, we propose an iterative sub-optimal joint fairness and trajectory design algorithm (JFTDA), which is numerically shown to yield fair data allocation for task offloading and comparable energy consumption among all the ground IoT devices to that of different deployment scenarios. Lastly, the proposed JFTDA also yields a decoding error probability of less than $$10^{-5}$$ ensuring URLLC for the UAV-enabled MEC systems.