Abstract
In this article, we present a novel recurrent neural network (RNN)-based behavioral model to linearize radio frequency (RF) power amplifiers (PAs) under wideband excitations. Based on the lightweight Just Another NETwork (JANET) unit, we propose a new neural network structure that is especially suitable for modeling the complex behavior of the RF PAs. A novel signal preprocessing technique is developed to model the complex interaction between amplitude information and phase information in the digital predistortion (DPD) model. By integrating the preprocessing stage into the RNN model, the complete phase-gated JANET (PG-JANET) model can provide enhanced linearization performance with lower complexity compared to the existing models.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Microwave Theory and Techniques
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
