@inproceedings{60e88eeb592d4704a4e1cfe670287266,
title = "Direct Learning Neural Network Digital Predistortion Using Backpropagation Through a Memory Power Amplifier Model",
abstract = "This work presents a new direct learning for a neural network based digital predistortion linearizer using backpropagation through a memory power amplifier. The learning method is compared with indirect learning among other methods. The proposed learning methods are evaluated on a wideband CMOS power amplifier using a 160 MHz 802.11ax WiFi signal with an 11.5 dB peak-to-average power ratio and a center frequency of 1 GHz at 8.2 dBm average transmit power. The direct learning using memory backpropagation shows superior performance to other proposed learning methods, achieving a correction of 10.8 dB to the error vector magnitude to -39 dB EVM.",
keywords = "digital predistortion, direct learning architecture, indirect learning architecture, neural networks, power amplifiers, wireless communication",
author = "Erez Loebl and Nimrod Ginzberg and Emanuel Cohen",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE/MTT-S International Microwave Symposium, IMS 2023 ; Conference date: 11-06-2023 Through 16-06-2023",
year = "2023",
doi = "10.1109/IMS37964.2023.10187912",
language = "الإنجليزيّة",
series = "IEEE MTT-S International Microwave Symposium Digest",
pages = "791--794",
booktitle = "2023 IEEE/MTT-S International Microwave Symposium, IMS 2023",
}