Advanced Mathematical Modeling of Machine Learning and Artificial Intelligent Addressing Satellite Transponder Distortions

Abstract

This paper describes innovative frameworks and associated mathematical models using Machine Learning and Artificial Intelligent (ML-AI) technology to address signal distortions caused by the satellite transponder (TXDER) and related operational conditions. The operating conditions include unknown Input Power Back-Off (IPBO) and unknown TXDER operating temperature due to satellite exposure to the space environment. The paper also presents and discusses an End-to-End Satellite System and Mathematical Model (E2E-SSM2) that can be used for generating training data and demonstrating of the proposed ML-AI frameworks.