Latency-Optimal Resource Allocation for UAV-Aided LEO Communication

This paper investigates the utilization of unmanned aerial vehicles (UAVs) to offer communication support to ground users in remote areas where terrestrial networks are unavailable. We assume these UAVs are equipped with transceivers, allowing them to establish connections with low-earth orbit (LEO) satellites. Serving as relay nodes, these UAVs are responsible for forwarding data packets received from ground users to LEO satellites with buffers with allocated channel resources. For resource allocation, we introduce a latency-optimized approach that aims to equalize the queue lengths of the UAVs as a convex optimization problem. However, this approach is not only myopic, as it centers on minimizing the maximum of the one-step-ahead queue lengths, but also relies on statistics related to arrival rates of data packets from ground users and LEO channel conditions. To address these issues, we also employ reinforcement learning for resource allocation. This strategy capitalizes on the quasi-periodic behaviors exhibited by channel conditions to the serving LEO satellite, which can be learned over time. Through simulations, we validate the effectiveness of this strategy. Furthermore, to analyze the proposed resource allocation approach, which equalizes the queue lengths, we examine the effective bandwidth with equalized quality-of-service (QoS) exponents to reflect the equalized queue lengths. This analysis reveals that the behavior of the proposed approach can be accurately predicted using the QoS exponent.