MACHINE LEARNING FOR CHANNEL ESTIMATION: EXPLORING TSETLIN MACHINES IN WIRELESS COMMUNICATION SYSTEMS

Abstract

Tsetlin machines offer a captivating approach to machine learning by balancing interpretability with performance in pattern recognition. Such regulation-based models learn from data by processing individual features through building rules that combine them using logic operators. Unlike complex models with opaque inner workings, Tsetlin machines achieve interpretability through their reliance on well-understood propositional logic. The motivation behind Tsetlin machines arises from the desire to overpass the gap between high-performance models and interpretability. While powerful models excel at tasks, their internal decision-making processes can be difficult to comprehend. Tsetlin machines strive to offer precise results while providing insights into the reasoning behind their classifications. This exploration bifurcates into various aspects of Tsetlin machines, including how multiple rules learn unanimously without a central controller, how different feedback mechanisms (Recognize, Erase, Reject) shape the understanding of target classes, and the impact of hyperparameters like Forget Value and Memorize Value on the learning process. In general, Tsetlin machines present a paramount tool for tasks where interpretability is crucial alongside performance. They offer an alternative to black-box models, allowing users to assimilate the reasoning behind classifications by effectively utilizing logical rules and distributed learning, Tsetlin machines provide a promising approach for various pattern recognition applications.