I have a new preprint available for my submission to the 2019 IEEE International Workshop on Signal Processing Systems in Nanjing, China. The paper is titled “Design and Implementation of a Neural Network Based Predistorter for Enhanced Mobile Broadband” and is available here.
In this paper, I use a neural network (NN) to implement digital predistortion (DPD) to correct for power amplifier (PA) nonlinearities. The main contributions are:
A novel training method where we learn the NN DPD by first modeling the PA with a NN and backpropagating through the PA NN model to update the DPD NN weights.
Almost everything I’ve done while at Rice has been in Matlab. It’s very popular in academia, but that’s been threatened by Python for a while with most Data Science applications almost exclusively using it now. The biggest strengths I can think of for Matlab are their toolboxes and the fact that most of the previous code that I’ve written so far was written in Matlab.
However, I’m beginning to realize I may not always have access to a Matlab license.