Secure and Robust Communications for Cislunar Space Networks

TL;DR

This paper proposes a machine learning-based cislunar space domain awareness system to enhance the security and robustness of lunar communication networks, addressing key challenges and interference detection with high accuracy.

Contribution

It introduces a detailed channel model and interference detection methods for cislunar space, integrating machine learning for secure communication in lunar missions.

Findings

Interference detection accuracy exceeds 96%

Proposed channel model captures cislunar communication scenarios

Machine learning enhances security and robustness

Abstract

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.