Machine Learning

Neural-Network DPD via Backpropagation through a Neural-Network Model of the PA

New Preprint Available. Neural Networks are taking over DPD!

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.

Enabling a “Use-or-Share” Framework for PAL--GAA Sharing in CBRS Networks via Reinforcement Learning

By implementing reinforcement learning-aided listen-before-talk (LBT) schemes over a citizens broadband radio service (CBRS) network, we increase the spatial reuse at secondary nodes while minimizing the interference footprint on higher-tier nodes. …

DySPAN 2018 Paper Accepted

I just got notified that our submission to DySPAN 2018, Opportunistic Channel Access Using Reinforcement Learning in Tiered CBRS Networks, was accepted. Matthew Tonnemacher from SMU and Samsung Research America led this paper which focuses on using machine learning to help overcome the hidden terminal problem in the emerging CBRS band. Machine learning has been getting extensive attention throughout the world over the last few years. Much of the buzz surrounds the achievements in classification tasks such as image recognition or the ability to outperform humans in complicated games such Go.