Artificial intelligence (AI) is rapidly integrating into a wide range of industries, and satellite communications is no exception. With the emergence of more complicated networks, increased demand for connectivity and the need to manage orbital resources, AI is becoming an important tool for optimizing operations and improving the performance of satellite systems.

In space communications, AI comprises the use of machine learning algorithms, neural networks and big data analysis to automate the management of satellite systems, improve their reliability and optimize the use of orbit and frequency resources. AI enables satellites to autonomously analyse huge amounts of data, identify anomalies in system operation, and efficiently manage resources to ensure their sustainable use and uninterrupted connectivity.

Expanding the Role of AI in Satellite Communication Systems

AI is gradually being introduced into satellite systems, expanding their capabilities in control and monitoring. However, AI is used not only for data analysis, but also for automating processes and increasing the overall level of autonomy of satellites. In the future, AI will be able to automatically adjust operating parameters of satellites to improve their performance, minimising the need for operational intervention from Earth. However, the implementation of such solutions requires thorough testing and accumulation of significant amounts of data to ensure the reliability and security of their operation.

The efficiency of AI in satellite systems largely depends on the quality and volume of data on which it is trained. As experience is gained in operating satellites with AI elements, their algorithms become more accurate and efficient. It is important to keep in mind that data collected from one satellite may not always be applicable to another, as each satellite may have unique design features and operating conditions. Therefore, the use of AI is especially efficient in constellations where satellites are built according to a single standard and can exchange accumulated data.

The use of AI in the satellite industry is expected to further expand in the coming years. Experiments are already underway to use AI for telemetry analysis, satellite constellation management, space debris monitoring, and intelligent spectrum allocation, making AI an important area of development for the industry. These applications will be described in more detail below.

AI in Telemetry Analysis and Anomaly Detection

One of the key and most promising areas of AI application is telemetry analysis and fault detection. Modern satellite monitoring methods are often based on pre-set thresholds, which, if exceeded, require operator intervention. However, such methods are not always efficient because they do not take into account hidden trends that may indicate future problems.

Machine learning models trained on historical data are able to identify subtle patterns in telemetry data streams that indicate possible future system degradation that would not be apparent through standard data analysis. This approach allows for early detection of potential faults and their prevention, significantly reducing the risk of satellite system failures. In the future, such technologies will not only automatically diagnose faults, but also provide recommendations for adjusting the satellite operation without the need for prompt intervention of personnel.

This is especially important for long-term space missions where maintenance is impossible. The European Space Agency's Open Source Satellite (OPS-SAT) project, which aims to assess the feasibility of widespread deployment of AI to analyse satellite telemetry, demonstrated the potential of AI in this area: a satellite itself analysed its telemetry and took corrective actions when deviations were detected. Minimizing human intervention and response time makes such AI systems a critical element in increasing the reliability of satellite missions. Another example of the successful use of AI for handling telemetry data is the Advanced Intelligent Monitoring System (AIMS), implemented by the operator of the meteorological satellite constellation – the US National Oceanic and Atmospheric Administration (NOAA). This AI-powered system is used to monitor Geostationary Operational Environmental Satellite (GOES-R) satellites and analyses approximately 1,800 telemetry parameters from each vehicle in real time, accurately identifying abnormalities and failures in an extremely short time.

Autonomous Operation of Satellite Constellations Using AI

Effective control of satellite constellations requires constant redistribution of resources, orbit adjustments and coordination between satellites to ensure consistent coverage and communication quality. Unlike traditional manual control, which requires significant resources and time to analyse data, AI can automatically coordinate the movement of satellites, calculating their optimal position relative to each other in real time, taking into account external factors such as weather conditions and interference. Such systems are particularly useful in controlling large satellite constellations, where traditional control methods require significant time and human interventions.

One of the key areas of research is also the use of AI to automatically distribute inter-satellite transmission traffic depending on the current demand for communications, which allows improving the quality of user service and minimizing the energy consumption of satellites.

Existing satellite operators have already achieved tangible results in this area due to artificial intelligence. For example, machine learning technologies for dynamic beam steering (beam switching) have significantly improved communication performance. In one study, a deep learning-based algorithm reduced data transmission latency by more than 50% and increased network throughput by 11% compared to conventional methods. An example of the successful implementation of AI in constellation control is SpaceX's Starlink network. Intelligent algorithms in this system continuously optimise satellite movements, improving broadband connection quality, reducing latency and ensuring continuous coverage. By analysing real-time data on user traffic and network load, AI improves efficiency and enables flexible adaptation of satellite operations.

AI in Space Debris Tracking and Collision Avoidance

There are currently more than 35,000 trackable objects in near-Earth orbit, including spent rocket stages, satellite fragments and other debris. The estimated number of untraceable objects smaller than 10 cm is around 130 million. These objects pose a significant threat to currently operating satellites and future missions, as even small particles can cause critical damage to satellites. Therefore, more and more attention is being paid to collision monitoring and avoidance systems, and AI plays an important role in this process.

To overcome this problem, AI, by analysing huge amounts of data coming from ground-based radars and satellite sensors, is able to predict possible trajectories of debris, compare them with the trajectories of satellites, and automatically calculate optimal evasive manoeuvres. Most current systems require human intervention when a manoeuvre is needed, but in the future, satellites with built-in AI algorithms will be able to independently make decisions about orbital adjustments depending on the level of threat. The development of such systems will also reduce the number of false alarms and save the fuel consumption required for manoeuvring.

One of the leaders in this field is LeoLabs, which has developed advanced technologies for tracking space debris. Its AI-based systems can analyse object trajectories in real time, automatically identifying potential threats and promptly notifying operators of the need to correct the orbit. An autonomous collision avoidance system powered by onboard AI is also used by SpaceX. Meanwhile, each Starlink satellite independently monitors potential approaches and, when necessary, turns on engines to avoid them without an intervention of operators. According to the company, during six-month operation of the system, Starlink’s constellation satellites performed about 50 thousand preventive evasive manoeuvres. A similar system called Smart Collision Avoidance is currently being developed by the European company Thales Alenia Space in collaboration with the startup Delfox.

Intelligent Spectrum and Earth Station Management

The limited frequency spectrum requires efficient management of communication resources between satellites and earth stations. Conventional spectrum management methods are based on static channel allocation, which is not always effective under variable load conditions. By analysing the current load on frequency channels, AI identifies areas of potential overload and predicts future load, which allows for more efficient resource allocation. Modern algorithms are able not only to adjust spectrum allocation, but also to suggest more efficient frequency use strategies to minimise interference and improve communication quality. Such systems can adapt in real time, reducing the likelihood of interference and improving connection quality, making them a useful tool for frequency planning and satellite earth station management purposes.

The use of AI in spectrum and infrastructure management significantly improves the overall capacity and reliability of satellite networks. Operators are already seeing reductions in congestion and connection failures where machine learning models are used to manage spectrum resources. Kratos Defense & Security Solutions has developed intelligent earth stations that use AI to automate frequency management and data routing. Such systems minimise network congestion and ensure stable connections even under conditions of high frequency spectrum congestion.

Conclusion

AI is certainly changing the satellite industry by increasing automation, optimising spectrum allocation, and making systems more resilient. The introduction of AI in telemetry processing, constellation management, space debris tracking, and ground station operations makes satellite communications more adaptive to growing demand.

As technology advances, AI will play an increasingly important role in mission planning, autonomous navigation of satellites, and even deep space exploration. Satellites are being developed that are capable of making independent decisions under conditions of long delays in communication with Earth. Such technologies are already being tested for landing on planets and tracking asteroids.

However, a number of challenges remains. Automation reduces the workload on staff, but it also raises the question of responsibility for decisions made. It is also important to consider cybersecurity and regulatory compliance. International cooperation in this area will play a key role in developing standards for regulating and ensuring the safety of the use of AI in space.