Digital Predistortion of Wireless Transmitters Using Machine Learning

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IEEE North Jersey MTT/AP Chapter Co-Sponsors the MTT-S Technical Webinar

Digital pre-distortion (DPD) has been widely adopted to keep RF power amplifiers operating with high efficiency without losing linearity in the exiting 4G systems. It is expected that DPD will continue to be deployed in 5G systems. However, due to shifting from the single antenna to the multiple-input multiple-output (MIMO) phased array and continuously increased signal bandwidth, system designers face significant challenges in managing power consumption and meeting the linearity requirement of wireless transmitters. In this talk, we will discuss the recent advances in DPD development that can resolve some of the issues in linearizing 5G MIMO systems using machine learning, including DPD system architectures, model order reduction, fast model adaptation, and power reduction techniques.

Please click the link for registration for attending the webinar: (Register now)


  Date and Time

  Location

  Hosts

  Registration


  • Date: 10 Jan 2023
  • Time: 12:00 PM to 01:00 PM
  • All times are (GMT-05:00) US/Eastern
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  • Contact Event Hosts

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

  • Co-sponsored by IEEE North Jersey Section

  Speakers

Prof. Anding Zhu Prof. Anding Zhu of University College Dublin, Ireland

Topic:

Digital Predistortion of Wireless Transmitters Using Machine Learning

Digital pre-distortion (DPD) has been widely adopted to keep RF power amplifiers operating with high efficiency without losing linearity in the existing 4G systems. It is expected that DPD will continue to be deployed in 5G systems. However, due to shifting from the single antenna to the multiple-input multiple-output (MIMO) phased array and continuously increased signal bandwidth, system designers face significant challenges in managing power consumption and meeting the linearity requirement of wireless transmitters. In this talk, we will discuss the recent advances in DPD development that can resolve some of the issues in linearizing 5G MIMO systems using machine learning, including DPD system architectures, model order reduction, fast model adaptation, and power reduction techniques.

Biography:

Anding Zhu received a Ph.D. degree in electronic engineering from University College Dublin (UCD), Dublin, Ireland, in 2004. He is currently a Professor at the School of Electrical and Electronic Engineering at UCD. His research interests include high-frequency nonlinear system modeling and device characterization techniques, high-efficiency power amplifier design, wireless transmitter architectures, and nonlinear system identification algorithms. He has published more than 200 peer-reviewed journal and conference articles.

Prof. Zhu is an IEEE Fellow. He served as the Secretary of the Administrative Committee (AdCom) of the IEEE Microwave Theory and Technology Society (MTT-S) in 2018. He has been an Elected Member of MTT-S AdCom since 2019 and a Member of the IEEE Future Directions Committee since 2000. Prof. Zhu served as a Track Editor of IEEE Transactions on Microwave Theory and Techniques in 2020-2022 and he was a recipient of the 2021 IEEE MTT-S Microwave Prize.

Address:University College Dublin, , Dublin, Ireland




Agenda

Please click the link for registration for attending the webinar:  (Register now)