Enhancing Indoor mmWave Communication With ML-Based Propagation Models

With the advancement of 5G and emerging wireless communication technologies, accurate modeling of wave propagation in indoor environments has become increasingly crucial. This study focuses on demonstrating how machine learning (ML) techniques can be applied to predict path loss within the millimeter wave (mmWave) spectrum in a specific indoor environment. We address high-frequency challenges like path loss and complex building layouts that impact signal propagation. We employ various ML models, including Artificial Neural Networks (ANNs), hybrid models integrating linear regression, ANNs, and Gaussian Processes, and Extreme Gradient Boosting (XGBoost), to predict and analyze the propagation loss in a controlled indoor setting. The models were trained and validated using data collected from a comprehensive measurement campaign at 28 GHz, which involved high precision radio equipment in a complex indoor environment. Our results demonstrate that while traditional models provide a baseline for understanding path loss, advanced ML models, particularly hybrid approaches, significantly enhance prediction accuracy and provide a deeper understanding of indoor propagation dynamics within this specific environment. The study highlights the potential of ML in overcoming the limitations of empirical models and showcases methodologies that can be adapted for similar indoor scenarios. This research advances our understanding of mmWave propagation indoors and sets a framework for utilizing ML in telecommunication system design and optimization in specific environments.