English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

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