of gateways, as well as planning for link availability and weather resilience, involved significant manual analysis and conservative assumptions. It’s also worth noting here that when assessing the impact of weather, conventional approaches to ground network design do not always capture the impact of recent climate change. To address this, it’s essential that recent years’ weather data is included to ensure that the climate change impact is taken into account. Utilising AI tools with advanced algorithms, operators can now analyse historical rainfall as well as more recent weather data to determine link availability for potential gateway locations quickly. The same AI-based approach can also be used to identify correlations between different sites to help determine how likely they are to be affected by the same weather system. Operators can use these types of AI algorithms to run simulations and model various groupings of gateway sites to find the combinations that offer the highest availability at the lowest cost.

However, choosing the right number of gateways and where to put them is only part of the challenge. There’s a constant trade-off between service availability and the cost of infrastructure. Operators must decide how many diversity gateways are required, determine optimal antenna gain, and calculate the right link budgets and fade margins. AI helps here, too, enabling operators to reduce CapEx. By selecting the right number of gateways, with locations that complement each other and reduce the chance of simultaneous weather outages, operators can build in resilience and ensure the desired throughput is reached, all without adding unnecessary diversity gateways that increase complexity and cost. The difference between needing, let’s say, 22 antennas or 26 antennas, could well be in excess of several million dollars, so if AI tools can help operators to make these savings, that is going to make a huge difference to the industry.

Network Management Use Case: Maritime

Of course, designing a cost-effective, multi-gateway network is only half the job. Managing that network, with all its moving parts, brings about its own set of challenges, some of which AI can certainly help operators to overcome. 

Let’s take, for instance, the maritime market, specifically the autonomous service vessel segment. These vessels are often operated for offshore surveys, geophysical and geotechnical exploration, or to carry out inspections. 

They are equipped with a wealth of sensors and technology for data acquisition, from acoustic sensors to sampling systems to video streaming. They can also contain remote operations centres that allow management and analysis of data. These autonomous types of vessels are also utilised for construction support, for the building of wind farms, for example. 

Connectivity plays a critical role on board these vessels, as the amount of technological equipment on board and the sheer volume of data gleaned means that constant, uninterrupted connectivity is essential. Connectivity facilitates rapid decision-making and ensures that data can be sent directly to shore. Where there is no fixed communications infrastructure, such as subsea fibre, satellite is often the only connectivity option available; therefore, it is essential that it is kept up and always running. 

The weather at sea is both harsh and unpredictable, and given the high amount of data traffic that is transported by survey vessels to and from a remote marine site, high-frequency, high-performance satellite links must be used. As previously explained, these sit in the high and very high frequency bands; therefore, the use of an AI tool can predict adverse weather changes using real-time weather data, enabling mitigating action to be taken in advance of the outage. This kind of intelligent network management allows for a proactive approach to service continuity. By anticipating weather disruptions and automatically or manually rerouting traffic through unaffected terminals, operators can maintain quality of service and minimise interruptions. The result is a more resilient and efficient network, capable of supporting the demands of modern satellite operations across every orbit.

Defining AI’s Role

The role of AI in satellite ground networks will undoubtedly become better defined as it is increasingly used across the ground segment. It is already emerging as an important tool for monitoring and automation for ground segment operations. Ultimately, building and managing reliable ground networks at the scale required for every orbit requires far more than just infrastructure. It demands intelligent and proactive orchestration, and AI is fast becoming an indispensable tool in making that possible.