In this webinar, we will explore advancements in machine learning (ML)-based channel state information (CSI) feedback enhancement, which serves as a critical pilot use case in 3GPP Releases 18 and 19. Our focus will be on defining an AI/ML framework for 5G Advanced. We will examine AI-driven techniques for compressing and predicting CSI, highlighting their impact on improving spectral efficiency and reducing feedback overhead. Participants will gain a comprehensive understanding of how ML is transforming the air interface and laying the groundwork for future 6G networks. The following topics will be discussed: – AI/ML-assisted air interface pilot use cases in 3GPP Releases 18 and 19, with an emphasis on CSI feedback enhancement. – The fundamentals of CSI reference signal (CSI-RS) configuration and parameterization in 5G NR, and how it integrates into ML-advanced feedback frameworks. – The advantages of ML-based CSI feedback in addressing challenges within dense and dynamic network environments. – The role of testing and measurement instruments in validating the functionality of ML-based CSI feedback enhancement and assessing its performance. Co-sponsored by: IEEE North Jersey Section Speaker(s): Andreas Roessler , Francesco Rossetto, Virtual: https://events.vtools.ieee.org/m/468080