Few-Shot Learning and Self-Training for eNodeB Log Analysis for Service-Level Assurance in LTE Networks

Shogo Aoki,Kohei Shiomoto,Chin Lam Eng

doi:10.1109/tnsm.2020.3032156

Abstract

With the increasing network topology complexity and continuous evolution of the new wireless technology, it is challenging to address the network service outage with traditional methods. In the long-term evolution (LTE) networks, a large number of base stations called eNodeBs are deployed to cover the entire service areas spanning various kinds of geographical regions. Each eNodeB generates a large number of key performance indicators (KPIs). Hundreds of thousands of eNodeBs are typically deployed to cover a nation-wide service area. Operators need to handle hundreds of millions of KPIs to cover the areas. It is impractical to handle manually such a huge amount of KPI data, and automation of data processing is therefore desired. To improve network operation efficiency, a suitable machine learning technique is used to learn and classify individual eNodeBs into different states based on multiple performance metrics during a specific time window. However, an issue with supervised learning requires a large amount of labeled dataset, which takes costly human-labor and time to annotate data. To mitigate the cost and time issues, we propose a method based on few-shot learning that uses Prototypical Networks algorithm to complement the eNodeB states analysis. Using a dataset from a live LTE network that consists of thousand of eNodeB, our experiment results show that the proposed technique provides high performance while using a low number of labeled data.

Highlights

I N THE long-term evolution (LTE) networks, the radio communication services to the mobile users are provided by base stations called eNodeBs
By using trace data from eNodeBs deployed in production networks, we evaluate the proposed few-shot learning method that leverages Prototypical Network
We focus on three types of neural networks: multilayer perceptron (MLP) [13], two-dimensional convolutional neural networks (2D-Convolutional neural network (CNN)) [16] and one-dimensional convolutional neural networks (1D-CNN)

Summary

Introduction

I N THE long-term evolution (LTE) networks, the radio communication services to the mobile users are provided by base stations called eNodeBs. A large number of eNodeBs are deployed to cover the entire service areas spanning various kinds of geographical regions. The state of the areas covered by eNodeBs can be diverse; radio channel condition, user mobility, and traffic load condition varies from area to area. Each eNodeB generates a large number of key Manuscript received March 31, 2020; revised July 30, 2020 and October 12, 2020; accepted October 15, 2020. Date of publication October 20, 2020; date of current version December 9, 2020. The associate editor coordinating the review of this article and approving it for publication was N.

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