Artificial Intelligence implementation of onboard flexible payload and adaptive beamforming using commercial off-the-shelf devices

TL;DR

This paper explores using commercial off-the-shelf AI and machine learning techniques to enhance onboard satellite payload flexibility and adaptive beamforming, improving signal quality and throughput in high-throughput satellite systems.

Contribution

It introduces a novel approach of implementing AI-driven flexible payload and beamforming on COTS devices for space applications, addressing onboard processing constraints.

Findings

Machine learning models improve signal quality

Enhanced spectral efficiency achieved

Increased throughput over traditional methods

Abstract

Very High Throughput satellites typically provide multibeam coverage, however, a common problem is that there can be a mismatch between the capacity of each beam and the traffic demand: some beams may fall short, while others exceed the requirements. This challenge can be addressed by integrating machine learning with flexible payload and adaptive beamforming techniques. These methods allow for dynamic allocation of payload resources based on real-time capacity needs. As artificial intelligence advances, its ability to automate tasks, enhance efficiency, and increase precision is proving invaluable, especially in satellite communications, where traditional optimization methods are often computationally intensive. AI-driven solutions offer faster, more effective ways to handle complex satellite communication tasks. Artificial intelligence in space has more constraints than other fields, considering the radiation effects, the spaceship power capabilities, mass, and area. Current onboard processing uses legacy space-certified general-purpose processors, costly application-specific integrated circuits, or field-programmable gate arrays subjected to a highly stringent certification process. The increased performance demands of onboard processors to satisfy the accelerated data rates and autonomy requirements have rendered current space-graded processors obsolete. This work is focused on transforming the satellite payload using artificial intelligence and machine learning methodologies over available commercial off-the-shelf chips for onboard processing. The objectives include validating artificial intelligence-driven scenarios, focusing on flexible payload and adaptive beamforming as machine learning models onboard. Results show that machine learning models significantly improve signal quality, spectral efficiency, and throughput compared to conventional payload.