FURI | Spring 2025

Machine Learning-based Channel Prediction for Wireless Communication Efficiency

This research project explores the use of machine learning techniques to predict wireless channel characteristics between two users. Channel estimation is essential for wireless communication systems but is resource-intensive and time-consuming. This study leverages machine learning models, including Random Forest and Support Vector Classifier (SVC), to predict complex channel parameters from a dataset characterizing wireless channels. By training and evaluating these models, the research aims to improve prediction accuracy, reducing the need for real-time estimation. Enhanced channel prediction will increase communication reliability, reduce latency, and improve overall system efficiency, contributing to more adaptive and responsive wireless networks.