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

#AI-ML #6G #CSI
Share

IEEE North Jersey Section Co-Sponsors the TALK: "Towards an AI-native Air Interface in 6G: Machine Learning-based Channel State Information (CSI) Feedback Enhancement"

 

 

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.

 

 

 

 

 

 


  Date and Time

  Location

  Hosts

  Registration


  • Date: 19 Feb 2025
  • Time: 11:00 AM to 12:00 PM
  • All times are (GMT-05:00) US/Eastern
  • Add_To_Calendar_icon Add Event to Calendar
If you are not a robot, please complete the ReCAPTCHA to display virtual attendance info.
  • Contact Event Hosts

  • Ajay Poddar (akpoddar@ieee.org), Edip Niver (edip.niver@njit.edu), Durga Mishra (dmisra@njit.edu), (Anisha Apte (anisha_apte@ieee.org)

     

     

     

     

     

     

     

     

  • Co-sponsored by IEEE North Jersey Section

  Speakers

Andreas Roessler of Rohde & Schwarz

Topic:

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

In this webinar, we will explore advancements in machine learning (ML)-based enhancement of channel state information (CSI) feedback, 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 enhancing CSI feedback.

- The fundamentals of CSI reference signal (CSI-RS) configuration and parameterization in 5G NR, and how these elements integrate 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.

 

 

 

 

 

Biography:

Andreas Roessler is 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 the Rohde & Schwarz test and measurement division. By carefully following industry trends and the standardization process for cellular communication standards, 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.

 

 

 

 

 

 

 

Address:Rohde & Schwarz, , Germany

Francesco Rossetto of Rohde & Schwarz

Topic:

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

In this webinar, we will discuss advancements in machine learning (ML) for enhancing channel state information (CSI) feedback, a key use case in 3GPP Releases 18 and 19. Our focus will be on an AI/ML framework for 5G Advanced, examining techniques for compressing and predicting CSI to improve spectral efficiency and reduce feedback overhead. Participants will learn how ML is transforming the air interface in preparation for future 6G networks. Topics include:

- AI/ML-assisted air interface use cases in 3GPP Releases 18 and 19, focusing on CSI feedback enhancement.

- The basics of CSI reference signal (CSI-RS) configuration in 5G NR and its integration into ML-based feedback frameworks.

- Benefits of ML-based CSI feedback in dense and dynamic network environments.

- The importance of testing and measurement instruments in validating and assessing ML-based CSI feedback enhancements.

 

Biography:

Dr. Francesco Rossetto is a senior staff member of the Corporate R&D team at Rohde & Schwarz, headquartered in Munich, Germany. His current professional interests converge at the intersection of mobile communication standardization, signal processing, and artificial intelligence. In his role, he investigates the impact of AI/ML-enhanced signal processing on current and future test and measurement products and standardization activities. Additionally, he develops early proof-of-concept T&M solutions. Dr. Rossetto has contributed to developing Rohde & Schwarz's mobile communication signaling test platforms for over a decade. Before joining Rohde & Schwarz, he worked at the German Aerospace Center (DLR), focusing on signal processing for satellite communications. He holds a PhD in Communication Engineering from the University of Padua, Italy.

 

 

 

 

Address:Germany