Machine learning-enabled radio wave propagation modeling for complex indoor environments

#communication #indoor-environments #machine-learning #metasurfaces #antennas #microwaves

Modeling radio wave propagation in complex indoor environments remains a significant challenge due to the dynamic nature of reflections, diffractions, scattering, and multipath effects. Traditional propagation models, including empirical and deterministic approaches, often fall short in capturing these phenomena with the precision and efficiency required for emerging communication technologies, such as 5G and beyond. In this talk, a cutting-edge, machine learning-enabled framework designed to enhance the accuracy and efficiency of propagation model will be presented. This approach utilizes large-scale datasets derived from realistic simulations, together with advanced machine learning techniques, to train models capable of adapting to the complexities of diverse environments, offering a significant improvement in terms of flexibility, prediction accuracy, and computational efficiency. By demonstrating what is possible in indoor radio wave propagation modeling, this presentation aims to provide a forward-looking perspective on how machine learning-driven innovations will revolutionize wireless network design, paving the way for more resilient, adaptive, and high-performance communication systems.