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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Machine learning (ML) is an important component for enabling automation inRadio Access Networks (RANs). The work on applying ML for RAN has been underdevelopment for many years and is now also drawing attention in 3GPP andOpen-RAN standardization fora. A key component of multiple features, alsohighlighted in the recent 3GPP specification work, is the use of mobility,traffic and radio channel prediction. These types of predictions form theintelligence enablers to leverage the potentials for ML for RAN, both forcurrent and future wireless networks. This paper provides an overview withevaluation results of current applications that utilize such intelligenceenablers, we then discuss how those enablers likely will be a cornerstone foremerging 6G use cases such as wireless energy transmission.

Mobility, traffic and radio channel prediction: 5G and beyond applications