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

The recent O-RAN specifications promote the evolution of RAN architecture byfunction disaggregation, adoption of open interfaces, and instantiation of ahierarchical closed-loop control architecture managed by RAN IntelligentControllers (RICs) entities. This paves the road to novel data-driven networkmanagement approaches based on programmable logic. Aided by ArtificialIntelligence (AI) and Machine Learning (ML), novel solutions targetingtraditionally unsolved RAN management issues can be devised. Nevertheless, theadoption of such smart and autonomous systems is limited by the currentinability of human operators to understand the decision process of such AI/MLsolutions, affecting their trust in such novel tools. eXplainable AI (XAI) aimsat solving this issue, enabling human users to better understand andeffectively manage the emerging generation of artificially intelligent schemes,reducing the human-to-machine barrier. In this survey, we provide a summary ofthe XAI methods and metrics before studying their deployment over the O-RANAlliance RAN architecture along with its main building blocks. We then presentvarious use-cases and discuss the automation of XAI pipelines for O-RAN as wellas the underlying security aspects. We also review some projects/standards thattackle this area. Finally, we identify different challenges and researchdirections that may arise from the heavy adoption of AI/ML decision entities inthis context, focusing on how XAI can help to interpret, understand, andimprove trust in O-RAN operational networks.

A Survey on Explainable AI for 6G O-RAN: Architecture, Use Cases, Challenges and Research Directions