Machine Learning

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.