Secure and Robust Communications for Cislunar Space Networks
Selen Gecgel Cetin Gunes Karabulut Kurt Angeles Vazquez-Castro
There is no doubt that the Moon has become the center of interest for
commercial and international actors. Over the past decade, the number of
planned long-term missions has increased dramatically. This makes the
establishment of cislunar space networks (CSNs) crucial to orchestrate
uninterrupted communications between the Moon and Earth. However, there are
numerous challenges, unknowns, and uncertainties associated with cislunar
communications that may pose various risks to lunar missions. In this study, we
aim to address these challenges for cislunar communications by proposing a
machine learning-based cislunar space domain awareness (SDA) capability that
enables robust and secure communications. To this end, we first propose a
detailed channel model for selected cislunar scenarios. Secondly, we propose
two types of interference that could model anomalies that occur in cislunar
space and are so far known only to a limited extent. Finally, we discuss our
cislunar SDA to work in conjunction with the spacecraft communication system.
Our proposed cislunar SDA, involving heuristic learning capabilities with
machine learning algorithms, detects interference models with over 96%
accuracy. The results demonstrate the promising performance of our cislunar SDA
approach for secure and robust cislunar communication.