AI-Enhanced Non-Terrestrial Networks (NTN): The Future of Space-Based Connectivity

As global connectivity demands increase, terrestrial networks alone are no longer capable of supporting the requirements of modern communications. Non-terrestrial networks (NTNs), a term used to describe satellites, high-altitude platforms (HAPS) and unmanned aerial vehicles (UAVs), are emerging as a critical component in next-generation telecoms infrastructure.

The rise of low earth orbit (LEO) constellations, such as Starlink, OneWeb and Amazon’s Project Kuiper highlight the degree to which NTNs are rapidly becoming a primary solution for global broadband, IoT applications and emergency communications. However, managing vast numbers of satellites, optimising spectrum usage and mitigating interference all require advanced automation, which can often be a complex undertaking. Fortunately, AI is capable of tackling these challenges, by  enhancing network optimisation, enabling predictive maintenance, facilitating autonomous satellite operations and delivering efficient spectrum sharing. This article explores how AI is shaping the future of non-terrestrial networks, its key applications and the challenges ahead.

1. AI-Optimised Spectrum Allocation and Interference Mitigation

One of the biggest challenges with NTNs is efficient spectrum management. Satellites share spectrum with terrestrial networks, which can lead to congestion and interference. AI-driven solutions can minimise these issues through various mechanisms, including:

• Dynamic spectrum allocation (DSA): AI analyses real-time data, which facilitates dynamic allocation of spectrum frequencies;
• Interference prediction: Machine learning models predict interference scenarios, which allows  operators to be more proactive with the adjustment of transmissions;
• AI-enhanced beamforming: AI optimises the trajectory of satellite antenna beams to maximise throughput and prevent overlapping signals.

2. AI-Driven Predictive Maintenance for Satellites

Satellite health is critical for network longevity and reliability. AI enables operators to use predictive analytics to monitor satellite performance and preempt failures. Benefits include:

• Anomaly detection: AI helps operators to identify the early warning signs of hardware degradation or system failure;.
• Autonomous fault resolution: AI-driven onboard systems can detect and resolve possible minor technical issues to ensure operations are uninterrupted and do not require ground intervention;
• Enhanced mission planning: AI helps determine optimal maintenance schedules, which reduces operational costs.

3. AI-Powered Autonomous Satellite Operations

Traditionally, satellite operations rely on ground station instructions. AI reduces the need for human intervention and ground instruction by facilitating autonomous operation, which also reduces latency. . Some of the satellite operations capable through AI include:

• AI-driven orbital manoeuvres: Machine learning algorithms can calculate the most efficient orbital adjustments, which improves station-keeping and helps prevent collisions;
• Inter-satellite communications: AI enables satellites to communicate and reroute traffic, which improves the efficiency of  data transmission.
• Optimised power management: AI adjusts power consumption based on battery health, sunlight exposure and operational priorities.

4. AI-Enhanced Orbital Debris Tracking and Collision Avoidance

With thousands of satellites in LEO and beyond, the management of space debris is critical. AI enhances  situational awareness in space in various ways, including:

• Real-time debris detection: AI processes radar and optical data to help accurately track objects in space;
• Predictive collision avoidance: Machine learning can predict potential collisions before they occur, and avoid possible catastrophe by recommending critical evasive action.
• AI-based debris removal planning: AI can help design missions to deorbit defunct satellites and debris.

Starlink – AI-Driven Satellite Network Management

SpaceX’s Starlink satellite network leverages AI to route traffic across its LEO constellation, optimising latency and bandwidth distribution for its global user base.

ESA’s AI-Based Collision Avoidance System

The European Space Agency (ESA) employs AI for real-time orbital monitoring, ensuring satellites can locate  and avoid debris without the need for human intervention.

OneWeb’s AI-Enhanced Spectrum Management

OneWeb is deploying AI models to engage in dynamic  spectrum allocation, which prevents  interference with both terrestrial and other satellite networks, and ensures efficient use of bandwidth.

1. Computational Constraints in Space

• AI satellite models require significant computational resources, but the effectiveness of satellite hardware is often curbed by power and processing constraints. This is being partly tackled through the development of edge AI and low-power AI accelerators to enable onboard AI processing.

2. Regulatory and Compliance Challenges

• AI-powered NTN solutions need to comply with ITU (International Telecommunication Union) regulations, which requires global coordination;
• AI-powered NTN systems analyse vast amounts of data, which could raise privacy concerns.

3. Security Risks and AI-Powered Cyber Threats

• AI-driven NTN systems are vulnerable to cyber attacks, which means advanced encryption and AI-based anomaly detection will be required to maximise security;
• AI-powered cyber threats, such as adversarial attacks on AI models, will pose a risk to satellite communications.

As AI capabilities continue to evolve, NTNs will become increasingly autonomous and efficient. Some of the main future developments include:

• Quantum AI for NTN security: Quantum computing combined with AI can enhance encryption for space-based networks;
• AI-powered optical communication systems: AI can free up space for optical communications, increasing NTN data throughput rates;
• AI-enabled space-based edge computing: Future satellites will host AI-driven edge computing nodes, which will reduce reliance on terrestrial data centres.