Multi-Model Machine Learning Based Path Loss Estimation for Indoor 5G Signal Propagation at 12 GHz
Keywords:
5G, LOS, NLOS, path loss, machine learning, random forestAbstract
In recent years, the advent of 5th Generation (5G) wireless communication technologies has led to a boom in network service usage and access of high-quality multimedia services. In order to maintain acceptable Quality-of-Service (QoS), high-frequency 5G communication at frequencies such as 12 GHz have become commonplace. Consequently, in order to account for signal attenuation, accurate estimation of path loss considering both Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) propagation, is critical for successful implementation of such wireless communication systems. The present work therefore outlines a multi-model approach to path loss modelling and estimation using standard path loss models such as Close In (CI) and Floating Intercept (FI) models, in conjunction with machine learning (ML) models implementing Random Forest, Decision Tree and Gradient Boosting regression to accurately estimate path loss. The machine learning models employed allow for generation of accurate estimation even in case of significantly varying and noisy datasets. Of the ML models implemented for generation of path loss estimates, the Random Forest Regressor model is illustrated to offer the most accurate and stable results for the given scenario. The results obtained by the multi-model approach are appreciably close to the real-world experimental results, establishing the efficacy of the proposed methods.
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