By: John Yates
It is well known that the satellite communications industry is undergoing a major transformation. The technology is becoming accessible to everyone and is increasingly adopted by new industries and new customers. As reliable communications systems have become widely available, more data is being shared than ever before. In light of this, High Throughput Satellites (HTS) are an increasingly popular choice for satellite operators as they provide more than twenty times the data capacity of traditional satellites at a fraction of the cost per bit. To achieve such impressive data transfer rates, HTSs operate on higher frequency bands than conventional satellites.
The challenge associated with using higher frequency bands such as Ka and Q/V bands is their susceptibility to attenuation caused by rain fading. Weather has a significant influence on HTS network performance, and when coupled with the fact that HTS ground segments include dozens or even hundreds of gateways, effectively managing these networks is a complex undertaking. For the effectiveness of services, it is essential that networks are designed and adapted to manage the impact of weather events. This is an area of satcom where AI already plays a vital role.
The main advantage of HTS systems is the use of spot beams to enable frequency reuse, allowing more efficient use of the available spectrum and, combined with the use of higher frequency bands, greater capacity of the network. However, as mentioned, these higher frequencies are also subject to greater atmospheric attenuation, particularly due to rain fading. Although this problem also affects Ku band to a lesser extent, it becomes much more pronounced in frequencies at the higher end of the spectrum, such as Ka band, Q band and V band, as used by HTS and much more. advanced very high bandwidth satellite (VHTS) networks.
Rain fading occurs when there is moisture in the atmosphere, which absorbs and scatters radio waves, causing signal degradation. The higher the frequency, the more likely the signal is to be attenuated. As a result, in regions prone to precipitation or variable weather conditions, network operators must struggle to mitigate frequent signal degradation to avoid intermittent outages or reduced network performance. This vulnerability to weather interference represents a significant challenge for HTS network operators, which is exacerbated by the large number of gateways involved. Operators typically mitigate interference caused by precipitation by switching to another gateway. However, such a switchover can take several minutes and there is always a risk that the alternative gateway itself will experience adverse weather conditions and degradation.
For seamless network operation, these gateways must be managed effectively so that any instance of weather-related mitigation does not impact the customer. There are two aspects to achieving this and maximizing network and service availability. First, networks must be designed to ensure that gateways are located in locations least affected by local weather conditions. Second, network operators must be able to switch to an alternative gateway before the impact of weather conditions is felt by the customer. Through the use of AI, combined with the scalable power of cloud computing, operators can design and operate complex multi-gateway Ka-band and Q/V-band satellite networks much more efficiently.
HTS networks require a large number of gateways to support network capacity and achieve adequate coverage. They are therefore more complex to design than traditional satellite networks. It is essential that networks are designed to be cost-effective in terms of the number of gateways used, while meeting service availability requirements. This is a delicate balance to achieve. Operators must determine the number of gateways and diversity gateways needed, determine the location of gateways for best availability, and determine both the optimal gain for each gateway antenna as well as the optimal link budget/fading margin . This is where AI can really make a difference.
