With its ability to quickly process huge datasets and identify patterns and correlations, AI, combined with the scalable power of cloud computing, is transforming HTS network design. It can be used to assimilate historic rainfall measurement datasets to determine the satellite link availability for potential gateway locations, and to identify weather correlations between individual candidate sites. With this information, operators can determine overall network availability for specific groups of gateway locations. Of course, there may well be numerous potential locations for gateways and groups of gateways so operators can apply this process to different groupings of gateway locations, to optimise the design of a network for minimum cost and maximum overall network availability.

Using AI in this way enables operators to not only select the gateway locations with the best link availability, but also to group the gateways in a way that minimizes the number of sites that are likely to be affected by the same weather system concurrently. However, designing multi-gateway networks to provide the most cost-effective and optimized network configuration is only half the battle. Managing these complex networks effectively is another major challenge.

Proactive Network Operation Enabled by AI

Once a HTS network is operational, real-time monitoring and proactive management are integral to maintain performance. For a seamless service, operators need to switch traffic to an alternative gateway before an outage occurs. And to do that, they need to be able to accurately predict an outage a number of hours before it happens so they have time to synchronize and seamlessly switch traffic between gateways. Additionally, network operators need to know with a high level of certainty that the diversity gateway to which the traffic will be switched isn’t also impacted or about to be impacted by a weather event.

AI is already making this kind of network management possible. It can be used to process historical weather data together with real-time data collected from the network to predict outages ahead of occurrence, as well as to predict weather at diversity gateways. These AI generated predictive analytics enable network operators to proactively take corrective actions, before performance is impacted, by manually or automatically rerouting traffic through an unaffected gateway. This approach minimizes service interruptions, maintaining quality of service while also optimizing network efficiency.

Looking Ahead

As the need for high throughput services increases, operators will undoubtedly continue to turn to multi-gateway HTS networks to meet demand. And as more satellites are launched, including non-geostationary satellite constellations and multi-orbit networks with inter-satellite links operating across LEO/MEO and GEO, the complexity of managing the ground stations for these networks is only going to increase. Already AI is proving to be an indispensable tool in overcoming the challenges associated with operating at high frequencies and ensuring that HTS networks remain resilient, efficient, and dependable. Indeed, we’re only just beginning to see what’s possible in terms of AI capabilities.

AI technology is constantly advancing and there’s every reason to expect it will take on an even more prominent role in satcom in the not-too-distant future. AI enables automation, improving cost and operational efficiency, as well as scalability, both of which are critical factors for the long-term success of satcom. Over time it will also likely help operators optimize network operations by detecting potentially hidden issues and dynamically adjusting resources in real time to make ground segments fully autonomous.

HTS networks constitute a significant leap forward in satellite communications, and AI is the key to unlocking their full potential, transforming the way these networks are both designed and operated to ensure optimal service reliability and efficiency.