Towards a throughput-optimal routing algorithm for data collection on satellite networks

Abstract

To perform Earth observation, more and more satellites are equipped with high-resolution sensors like hyperspectral imagers, which generate data at tens of Gbps. Researchers aim to maximize the volume of data available for use on the ground in near-real time. To achieve this goal, many solutions have recently been proposed to perform data collection through satellite networks. Most of these solutions have so far been focusing on either source rate control and load balancing, or heuristic routing. However, optimizing routing together with resources allocation is critical for improving delivery performance. In this paper, we challenge the fact that transmission paths have to be built under high link variability with limited resources, and develop a throughput-optimal solution based on the utility maximization framework. For delay performance, we embed a distance factor into the objective of the framework and derive a geographic-location-aware backpressure algorithm. Further, we exploit transmission opportunities missed by backpressure-type algorithms to accelerate data transfer. The simulation results show that our algorithms are able to deliver large volumes of data in time, and scale well in all scenarios tested.

Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy

“…The bias is learned through GNN, which seeks to minimize end-to-end delay. Chen et al [29] developed a throughput-optimal scheme utilizing the utility maximization framework for data collection through satellite networks. The shortest path is determined by the distance between adjacent nodes and sink nodes.…”