Deep Learning: Applications in Telecommunication Companies

Deep Learning^[1]For example this Review: LeCun, Y., Bengio, Y. and Hinton, G. (2015). Deep learning. Nature. doi:10.1038/nature14539. is en vogue across all businesses. Also Telecommunications companies have (re-)shifted their attention to this topic. While the recent performance increases based on Deep Learning techniques have to be seen in the context of the tasks for which the techniques have been developed for^[2]From the review in Nature referenced above: “Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on … Continue reading, the question of how these tasks / challenges relate to the telecommunications business is worthwhile.

This is a list of telco task domains^[3]Acker, O., Blockus, A. and Pötscher, F. (2013). Benefiting from data. pwc Strategy& Report. that could benefit from the strengths of machines, software and statistical inference:

• Optimizing routing and quality of service by analyzing network traffic in real time
• Analyzing call data records in real time to identify fraudulent behavior immediately
• Allowing call center reps to flexibly and profitably modify subscriber calling plans immediately
• Tailoring marketing campaigns to individual customers using location-based and social networking technologies
• Using insights into customer behavior and usage to develop new products and services

This list can be extended and will be. What is of interest here is to what extent Deep Learning approaches can be successfully applied to these domains. As each of these domains has its own characteristics we shall focus on them individually – starting with the networks, routing, traffic control, etc. domain.