Reinforcement Learning for Satellite Communications: From LEO to Deep Space Operations

Abstract

The National Aeronautics and Space Administration (NASA) is in the midst of defining and developing the future space and ground architecture for the coming decades to return science and exploration discovery data back to investigators on Earth. Optimizing the data return from these missions requires planning, design, standards, and operations coordinated from formulation and development throughout the mission. The use of automation enhanced by cognition and machine learning are potential methods for optimizing data return, reducing costs of operations, and helping manage the complexity of the automated systems. In this article, we discuss the potential role of machine learning in the linkto- link aspect of the communication systems. An experiment using NASA's Space Communication and Navigation Testbed onboard the International Space Station and the ground station located at NASA John H. Glenn Research Center demonstrates for the first time the benefits and challenges of applying machine learning to space links in the actual flight environment. The experiment used machine learning decisions to configure a space link from the ISS-based testbed to the ground station to achieve multiple objectives related to data throughput, bandwidth, and power. Aspects of the specific neural-network-based reinforcement learning algorithm formation and on-orbit testing are discussed.