Framework for Cognitive Self-Healing of Real Broadband Networks

With the growing volume of data traffic demanded by corporate, business, and retail consumers, telecommunications operators are becoming an increasingly important player in the world economy. However, the operators must prepare themselves with solutions that allow dealing with incidents more quickly or even avoid them, always focusing on maintaining an acceptable customer service level. In this context, Self Healing (SH) solutions, supported by Machine Learning (ML) mechanisms, emerge as possibilities to address this challenge. This work presents a cognitive self-healing framework for telecommunications operators. This framework encompasses self-diagnosis, analysis, and automatic actuation for failure mitigation in fiber broadband telecommunications based on Gigabit Passive Optical Network (GPON). In addition, we did an experimental evaluation using a dataset from the operators’ modems extracted from its Network Management System (NMS), bringing more reliability to our results. This work shows that using ML in telecommunication broadband networks is viable and can change how telecom operators manage and improve customer experience. We show that an intelligent model could do machine learning in telecom networks and make decisions without human intervention. Three automatic cognitive models were tested as experimental proof of concept with an average accuracy above 96%.