Towards an AI-Native Air Interface in 6G: Machine Learning-Based Channel State Information (CSI) Feedback Enhancement

Industry Webinar

Towards an AI-Native Air Interface in 6G: Machine Learning-Based Channel State Information (CSI) Feedback Enhancement

Andreas Roessler Technology Manager, Rohde & Schwarz

ABOUT

Andreas Roessler is working as a Technology Manager for Rohde & Schwarz, 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. By following industry trends and the standardization 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.

Learn how ML-based CSI feedback is transforming 5G Advanced and laying the groundwork for 6G. Watch this webinar to explore AI-driven techniques for enhanced spectral efficiency!

Webinar Overview

In this webinar, Rohde & Schwarz technology expert Andreas Roessler explores the advancements in machine learning (ML)-based channel state information (CSI) feedback enhancement, a critical pilot use case in 3GPP Releases 18 and 19. ML-based CSI is aimed at defining an AI/ML framework for 5G Advanced. He examines AI-driven techniques for CSI compression and prediction, emphasizing their impact on improving spectral efficiency and reducing feedback overhead. Participants will gain a thorough understanding of how ML is transforming the air interface and building the foundation for future 6G networks.

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.

AI / Neural Networks

Consumer Electronics

Digital Signal Processing

Engineering Consulting

Industrial Automation

Security / Identification

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