LTE Uplink Interference Inspection Using Convolutional Neural Networks

Abstract

Interference management is one of the challenging tasks in Long-Term Evolution (LTE) technologies in Telecom Networks. One of these tasks is classifying interference problems affecting Uplink (UL) channel into different types. The interference classification problem can be formulated as an image classification task by converting the signal's power spectral density to an image. Convolutional Neural Networks (CNN) proved to have great success in image classification tasks. In this paper, different CNN architectures such as (VGG, MobileNet, RESNET) are used and assessed to classify the type of interference affecting the uplink channel in LTE. CNNs are characterized by their ability to detect and describe the abnormal behavior of UL channel which provided significant improvement over traditional rule-based systems. These rule-based systems rely on extracting domain driven features and classifying the interference using manually created rules by an expert. Our study shows that CNN yields 95% accuracy with training data. The end-to-end solution was deployed in Vodafone Group on Google Cloud Platform (GCP) to serve the different Local Markets.