Webinar: Towards an AI-native air interface in 6G
5G-Advanced: Machine Learning-based channel state information feedback enhancement
29 January – 4pm CET; 3pm GMT; 10am Eastern
About the Webinar
Machine Learning (ML) is transforming the air interface and building the foundation for future 6G networks. There is a critical pilot use case in 3GPP Releases 18 and 19: advancements in machine learning (ML)-based channel state information (CSI) feedback enhancement.
ML-based CSI is aimed at defining an AI/ML framework for 5G Advanced. In this interactive webinar, we examine AI-driven techniques for CSI compression and prediction, emphasising their impact on improving spectral efficiency and reducing feedback overhead.
You will learn about:
- AI/ML-assisted air interface pilot use cases in 3GPP Releases 18 and 19, with a focus on CSI feedback enhancement.
- Fundamentals of CSI-RS configuration and parameterization in 5G NR and its integration into ML-advanced feedback frameworks.
- Advantages of ML-based CSI feedback in addressing challenges in dense and dynamic network environments.
- The role of test and measurement instruments in validating ML-based CSI feedback enhancement functionality and assessing its performance.
Speakers
Andreas Roessler is working as a Technology Manager for Rohde & Schwarz, a premium supplier of test and measurement solutions to the wireless industry, headquartered in Munich, Germany. As a technology manager, he focuses on 3GPP’s 5G New Radio (NR) standard and advancing 6G research topics.
His responsibilities include strategic marketing and product portfolio development for the entire value chain offered by Rohde & Schwarz test and measurement division. By following industry trends and the standardisation process for cellular communication standards very carefully, he gained more than 15 years of experience in the mobile industry and wireless technologies. He holds an MSc in electrical engineering with a focus on wireless communication.