Transfer Learning and Domain Adaptation in Telecommunications

Abstract

Transfer learning (TL) and domain adaptation (DA) are well-known approaches in the AI literature that can be used to address the fundamental challenge of data scarcity. TL and DA propose ways to reuse previously trained models to compensate for missing data or the absence of labeled information, which often comes at the cost of manual work. The potential of TL and DA has only recently started to demonstrate its worth in the telecommunication area as mobile networks are undergoing significant changes to become AI-native. As such, an AI-native network is one that allows for swapping a classical implementation of a network function with one that is data-driven and powered by an AI algorithm. In this book chapter, we present our experiences and findings while working with TL and DA to build AI-native networks. More specifically, we share our findings when applying TL and DA in network management use cases for service performance, end-to-end latency, and block call rate prediction. Via these use cases, and to overcome limitations with TL and DA, we introduce enhancements such as source selection, unsupervised domain adaptation, and transfer learning in distributed intelligence. These enhancements can benefit the general AI community and other industries.