In June 2019 the GSMA announced the four mobile network operators, STC, Telenor, TELUS and Turkcell, who were selected as challenge leaders in the first GSMA Global AI Challenge. The Challenge investigated three specific areas: connectivity in rural areas, mobile energy efficiency and enhanced services in urban areas.
The Challenge comprised an intensive five-day hackathon and is part of Turing’s Data Study Group (DSG). It is focused on raising the profile of AI as a key enabler in the mobile industry and the transformational opportunities it provides, while also exploring how science, society and the economy might benefit.
The DSGs took place in September 2019 with initial results available at MWC Los Angeles 2019.
Different mobile users have different bandwidth needs. Bandwidth availability usually depends on the user density in the targeted area. Reserving bandwidth is usually not desired in mobile networks as it is considered a waste of network resources. This DSG explored how mobile network operators can make available the necessary bandwidth to specific urban or rural areas, which lag behind either because of high population density or lack of necessary infrastructure.
TELUS regularly conducts surveys with its customers to gather feedback and identify service improvement opportunities to prioritize investments in a way that reflects their customers’ needs. This DSG focused on understanding how the network and the customer experience (whilst using the network) influence the results of this survey. More specifically, TELUS wanted to understand: (i) how accurately the customer’s experience of reliability on their network can be predicted, and (ii) what are the main drivers of network performance to influence their customer’s rating of their experience.
Mobile networks waste energy by keeping too many radio-cells turned on when demand is low during off-peak. This challenge was about automating next-day power saving schemes for each individual cell tower in a country, based on current load and expected demand profile in the area. The solution should optimise power saved while avoiding negative impact on the user’s network experience. Access the post-Challenge report here.
In urban areas jammers cause interruption on mobile 3G and 4G communication networks which leads to severe service quality deterioration. This situation causes customer complaints and time and labour costs during detection studies. Based on network data including some network service quality indicators and jammer geographical location, this DSG investigated methods for real-time detection of jammer presence, identification of jammer type and its location.
Further information can be found on the data study group via the Turing Institute website.