Artificial Intelligence Satellite Telecommunication Testbed using Commercial Off-The-Shelf Chipsets

TL;DR

This paper presents the development and preliminary evaluation of an AI-enabled satellite communication testbed utilizing commercial off-the-shelf chips to enhance onboard processing and optimize various SATCOM functions.

Contribution

It introduces a novel testbed architecture that leverages COTS AI chips for satellite payloads, demonstrating improved signal quality and spectral efficiency over traditional methods.

Findings

ML models improve signal quality

Enhanced spectral efficiency and throughput

Validation of AI-driven SATCOM scenarios

Abstract

The Artificial Intelligence Satellite Telecommunications Testbed (AISTT), part of the ESA project SPAICE, is focused on the transformation of the satellite payload by using artificial intelligence (AI) and machine learning (ML) methodologies over available commercial off-the-shelf (COTS) AI-capable chips for onboard processing. The objectives include validating artificial intelligence-driven SATCOM scenarios such as interference detection, spectrum sharing, radio resource management, decoding, and beamforming. The study highlights hardware selection and payload architecture. Preliminary results show that ML models significantly improve signal quality, spectral efficiency, and throughput compared to conventional payload. Moreover, the testbed aims to evaluate the performance and the use of AI-capable COTS chips in onboard SATCOM contexts.