First machine learning-based RAN application

July 01, 2021 // By Jean-Pierre Joosting
First machine learning-based RAN application
Machine Learning inference enables real time analytics for faster insight and action, while boosting spectral efficiency by 15%.

Capgemini has announced a breakthrough radio network application giving mobile operators a significant advantage to monetize 5G services faster. Entitled “Project Marconi”, the application conforms to O-RAN (Open Radio Access Network) guidelines to maximize spectrum efficiency. It intelligently boosts subscriber quality of experience (QoE) with real-time predictive analytics. Project Marconi is the industry’s first Artificial Intelligence, Machine Learning (AI/ML) based radio network application for the 5G Medium Access Control (MAC) scheduler. It is optimized with Intel AI software and 3rd Gen Intel Xeon Scalable processors.

Network providers globally have invested heavily in spectrum and are looking for ways to develop and gain 5G services faster. According to the Global Mobile Suppliers Association, the total value of spectrum auctions reached over $27 billion in 2020. Capgemini’s application on Intel Architecture increases the amount of traffic each cell can handle. Te machine learning application allows operators to serve more subscribers and deliver an outstanding experience, while launching new Industry 4.0 services such as enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (URLLC) use cases.

Walid Negm, Chief Research and Innovation Officer at Capgemini Engineering said: “Our teams worked closely with Intel to create a truly innovative solution that can really move the needle for operators. We gathered and utilized over one terabyte of data and conducted countless test runs with NetAnticipate5G to fine-tune the predictive analytics to meet diverse operator requirements. In short, machine learning can be deployed for intelligent decision-making on the RAN without any additional hardware requirement. This makes it cost efficient in the short run and future proof in the long run as we move into Cloud Native RAN implementations.”

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