By: John Yates

It is well documented that the satcom industry is undergoing a major transformation. The technology is becoming accessible to all and is increasingly being adopted by fresh industries and new customers. As reliable communication 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 because they deliver more than twenty times the data capacity of traditional satellites at a fraction of the cost per bit. To achieve such impressive rates of data transfer, HTS operate at higher frequency bands than conventional satellites.

The challenge associated with utilizing higher frequency bands such as the Ka-, and Q/V-bands, is their susceptibility to attenuation caused by rain fade. Weather is hugely influential on HTS network performance, and when you add to the mix that HTS ground segments comprise tens, if not hundreds, of gateways, managing these networks effectively is a complex undertaking. For service efficiency, it is critical that networks are designed and adapted to manage the impact of weather events. This is one area of satcom where AI is already playing a critical role.

Managing Complex HTS Networks

The primary advantage of HTS systems lies in the use of spot beams to enable frequency re-use, which allows more efficient use of the available spectrum and, combined with the use of the higher frequency bands, for greater network capacity. However, as mentioned, these higher frequencies are also prone to greater atmospheric attenuation, particularly due to rain fade. While this issue also affects Ku-band to a lesser degree, 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 even more advanced Very High Throughput Satellite (VHTS) networks.

Rain fade occurs when moisture is in the atmosphere, which absorbs and scatters radio waves, leading to signal degradation. The higher the frequency, the more susceptible the signal is to attenuation. As a result, in regions prone to rainfall or variable weather conditions, network operators must contend with mitigating frequent signal degradation to avoid intermittent outages or reduced network performance. This susceptibility to weather interference presents a significant challenge for HTS network operators, which is exacerbated by the vast number of gateways involved. Operators will typically mitigate interference from precipitation by switching to an alternate gateway. However, such switching can take several minutes and there is always the risk that the alternate gateway may itself go on to experience adverse weather conditions and degradation.

For seamless network operation, these gateways need to be managed effectively so that any instances of weather-related attenuation do not impact the customer. There are two aspects to achieving this and to maximising network and service availability. First, networks need to be designed in such a way as to ensure that gateways are sited in locations least affected by local weather conditions. Second, network operators need the ability to switch to an alternative gateway before the impact of weather is felt by the customer. With 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 effectively.

AI-Enhanced Network Design

HTS networks require a large number of gateways to support the network capacity and to achieve adequate coverage, so they are more complex to design than traditional satellite networks. It’s critical that the networks are designed to be cost-effective in terms of how many gateways are employed, while at the same time meeting service availability requirements. This is a delicate balance to achieve. Operators need to determine how many gateways and diversity gateways are needed, work out where gateways should be located for best availability, and determine both the optimum gain for each gateway antenna and the optimum link budget/fade margin. This is where AI can really make a difference.