Data-driven resource allocation for ensuring remote data collection timeliness in integrated ground-air-space networks

Abstract

Ensuring remote data collection timeliness without terrestrial network infrastructure support is a huge challenge. The exploration of addressing this challenge with the aid of opportunistic unmanned aerial vehicles (UAVs) and satellites has received extensive attention. In this paper, we address a data-driven resource allocation problem, which aims to ensure data collection timeliness, minimize communication resource waste, and maximize data collection amount under the UAVs’ opportunistic access mode and satellites’ random access mode. However, due to UAVs’ dynamic behaviors, time-varying data collection missions, real-time matching demand between ground nodes and UAVs, and free competition of UAV-satellite access resources, it will be difficult to achieve the above goal if it is considered as a global optimization problem. Thus, we construct three problems in turn that collectively describe the requirements of above goal, and then reformulate the first two problems as the Markov decision process models and take deep reinforcement learning tools to get the corresponding solutions, respectively. Next, the solution to the third problem is approximated by alternately invoking the algorithms of the first two problems. Finally, our simulation results are compared with those of other benchmark schemes from different perspectives, and the effectiveness and superiority of the presented solutions are verified.