RAN Datasets Must Evolve to meet AI/ML Objectives Using standard datasets for Artificial Intelligence (AI) and Machine Learning (ML) analysis of Next Generation Radio Access Networks (RAN) will not solve many of the critical RAN capacity and coverage issues or automated intelligence goals due to their complex objectives. Legacy signaling datasets can no longer simply be correlated to resolve performance issues and service objectives for these advanced 4G/5G networks.

Communications Service Providers (CSPs) know that the only path forward to next-generation AI/ML automation intelligence framework is to apply stateful analysis of raw RAN datasets and all critical emerging network variables prior to feeding AI/ML frameworks. And this path requires a journey of discovery to analyze data at the source.

With 30+ years of experience working with data at NETSCOUT, we know data secrets and we are helping CSPs succeed with their next generation 4G/5G

Ruth Brown, Principal Analyst – Mobile Networks, Heavy Reading

October 11, 2022

1 Hr View
The Secret Is Out: 5G Success with Intelligent Automation

Date: Nov 29, 2022

Duration: 1 Hr

About the Author(s)

Ruth Brown

Principal Analyst – Mobile Networks, Heavy Reading

Ruth covers mobile network research for Heavy Reading. Key coverage areas include system architecture, core infrastructure and services, and supporting cloud technologies. Prior to joining Heavy Reading, Ruth worked in mobile and fixed network research and design for BT for over 20 years. Her research interests have included convergence, mobile QoS, network slicing, private networks, cloud native mobile core technology and automation. She has filed more than 40 patents on both real world applications and enhancements to mobile core networks. Ruth is an advocate for women in engineering.

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