Machine learning in telecoms

Machine learning has a lot to offer telecoms networks, but what are the implications and areas that operators and vendors will need to address if it is to realise its potential? By Sam Fenwick

There are a huge number of possible use-cases for machine learning and AI in the telecoms industry, from underpinning the natural language processing which underpins the chatbots that answer billing queries to optimising the placement of engineers and their equipment and handling the distribution of virtualised resources.

It is also being used to help identify coverage holes – Dean Bubley, founder and director at Disruptive Analysis, recalls being shown by one operator how it had used “image recognition to look at areas of the country where there are dwellings or buildings and then matched that to what their network was telling them in terms of usage, and from that they could infer where they had coverage gaps, perhaps due to trees or topography”.

If we drop down to a more granular perspective, according to Derek Long, head of telecoms and mobile at Cambridge Consultants, “there is a significant amount of work being done to characterise the electromagnetic environment and therefore [to] be able to use channels that have lower interference, thereby improving quality of service”. He adds that it is also being used to underpin the working of phased arrays and beam steering.