[Paper Review] On the Use of AI for Satellite Communications

Miguel Ángel Vázquez, Pol Henarejos+7 more|arXiv (Cornell University)|Jul 15, 2020

Satellite Communication Systems5 references4 citations

TL;DR

This paper explores the integration of artificial intelligence (AI) into satellite communication (SatCom) systems to automate human-dependent operations. It identifies key AI tools—such as machine learning and signal processing techniques—for enhancing system autonomy, and outlines long-term AI-driven advancements in SatCom efficiency and reliability.

ABSTRACT

This document presents an initial approach to the investigation and development of artificial intelligence (AI) mechanisms in satellite communication (SatCom) systems. We first introduce the nowadays SatCom operations which are strongly dependent on the human intervention. Along with those use cases, we present an initial way of automatizing some of those tasks and we show the key AI tools capable of dealing with those challenges. Finally, the long term AI developments in the SatCom sector is discussed.

Motivation & Objective

• To address the heavy reliance on human intervention in current satellite communication operations.
• To identify and evaluate AI mechanisms capable of automating critical SatCom tasks.
• To propose a roadmap for long-term AI development in the satellite communications sector.
• To bridge the gap between traditional SatCom operations and intelligent, adaptive systems using AI.
• To position AI as a transformative force in improving scalability, reliability, and performance of future SatCom networks.

Proposed method

• Analyzes existing SatCom operations that require manual oversight and intervention.
• Identifies candidate AI techniques—particularly in machine learning and signal processing—for automating these tasks.
• Evaluates the feasibility of applying AI to real-time signal processing, resource allocation, and anomaly detection in SatCom.
• Proposes a framework for integrating AI into the operational lifecycle of satellite systems.
• Reviews current AI tools such as supervised and unsupervised learning, reinforcement learning, and deep neural networks for SatCom applications.
• Discusses system-level integration challenges, including latency, reliability, and real-time processing constraints.

Experimental results

Research questions

• RQ1 Which SatCom operations are currently dependent on human intervention, and how can AI reduce this dependency?
• RQ2 What specific AI techniques are most suitable for automating key satellite communication tasks?
• RQ3 How can AI be integrated into existing SatCom architectures without compromising reliability or performance?
• RQ4 What are the long-term prospects for AI-driven evolution in satellite communication systems?
• RQ5 What technical and operational challenges must be overcome to enable scalable AI deployment in SatCom?

Key findings

• AI mechanisms can significantly reduce human involvement in routine SatCom operations, improving system responsiveness and scalability.
• Machine learning and signal processing techniques are viable for automating tasks such as interference mitigation and link adaptation.
• The paper identifies reinforcement learning and deep neural networks as promising approaches for dynamic resource allocation in satellite networks.
• Long-term AI integration is expected to enhance network resilience, adaptability, and energy efficiency in future SatCom systems.
• Key challenges include ensuring real-time performance, system reliability, and secure deployment of AI models in space environments.
• The study establishes a foundation for future research by outlining a structured approach to AI adoption in satellite communications.