ML-based RAN application raises spectral efficiency says Capgemini
Machine learning (ML) inference enables real time analytics in 5G services. Capgemini’s Project Marconi intelligently boosts subscriber quality of experience with real-time predictive analytics. It conforms to Open Radio Access Network (O-RAN) guidelines and is believed to be the industry’s first artificial intelligence / machine learning (AI / ML) -based radio network application for a 5G medium access control (MAC) scheduler.
Capgemini has based Project Marconi on Intel architecture designed to increase the amount of traffic each cell can handle. It allows operators to serve more subscribers and launch 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: “We gathered and utilised over 1Tbyte of data and conducted countless test runs with NetAnticipate5G to fine-tune the predictive analytics . . . 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.”
Cristina Rodriguez, vice president of Intel’s wireless access network division, added: “Our 3rd Gen Intel Xeon Scalable processors with built-in AI acceleration provide high performance for deep learning on the Net Anticipate 5G platform.” The two companies collaborated to deliver fast inference data to enhance the Open-Source ML libraries to create a RAN that can predict and quickly react to subscriber coverage requirements.
Unveiled at MWC21, the AI powered analytics uses NetAnticipate5G and RATIO O-RAN. It forecasts and assigns the appropriate modulation and coding scheme values for signal transmission by forecasting of the user signal quality and mobility patterns accurately. RAN can therefore intelligently schedule MAC resources to achieve up to 40 per cent more accurate MCS prediction and yield to 15 per cent better spectrum efficiency in the case studies and testing. According to Capgemini, it delivers faster data speeds, better and more consistent quality of experience (QoE) to subscribers and robust coverage for use cases that rely on low latency connectivity such as robotics-based manufacturing and vehicle-to-everything (V2X).
