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 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](https://event.on24.com/wcc/r/4052086/EF07E69B7670903B7DEC67A23A5CCFBD))

Co-sponsored by: IEEE North Jersey Section

Speaker(s): Prof. Anding Zhu ,

Agenda:
Please click the link for registration for attending the webinar: ([Register now](https://event.on24.com/wcc/r/4052086/EF07E69B7670903B7DEC67A23A5CCFBD))

Virtual: https://events.vtools.ieee.org/m/342940