Investigation of the Relevance of Parameters Influencing DL User Throughput

In the rapidly advancing domain of wireless communication networks, the delivery of high-speed data services has entered a heightened phase of competition. As the demand for uninterrupted user connectivity experiences continual growth, the precise prediction of downlink user throughput emerges as a pivotal challenge within the field of network optimization. In this research endeavor, we harness the capabilities of cutting-edge machine learning techniques to discern the most pertinent and impactful Key Performance Indicators (KPIs) governing the evolution of downlink throughput. Our overarching objective is to construct a pioneering predictive model with the acumen to accurately estimate throughput, grounded in these indispensable KPIs.