Efficient and reliable indoor radio propagation modeling tools are inextricably intertwined with the design and operation of next-generation wireless networks. Machine learning (ML)-based radio propagation models can be trained with simulated or real-world data to provide accurate estimates of the wireless channel characteristics in a computationally efficient way. However, most of the existing research on ML-based propagation models focuses on outdoor propagation modeling, while indoor data-driven propagation models remain site-specific with limited scalability. In our work, we present an efficient and credible ML-based radio propagation modeling framework for indoor environments. Specifically, we demonstrate how a convolutional encoder-decoder can be trained to replicate the results of a ray-tracer, by encoding physics-based information of an indoor environment, such as the permittivity of the walls, and decoding it as the path-loss (PL) heatmap for an environment of interest. Our model is trained over multiple indoor geometries and frequency bands, and it can eventually predict the PL for unknown indoor geometries and frequency bands within a few milliseconds We present this approach for the estimation of the PL of communication devices operating at various sub-6 GHz frequency bands comparing the predicted PL heatmaps to both simulated and measured data.
About Stefanos Bakirtzis
Stefanos Bakirtzis received the Diploma degree in electrical and computer engineering from the National Technical University of Athens, Athens, Greece, in 2017, and the M.A.Sc. degree in electrical and computer engineering from the University of Toronto, Toronto, ON, Canada, in 2020. He is currently pursuing the Ph.D. degree with the Department of Computer Science and Technology, University of Cambridge, Cambridge, U.K. He is currently working on the Big Data Analytics for Radio Access Networks (BANYAN) Project as a member of Ranplan Wireless with the University of Cambridge. His research interests include wireless communication systems, network optimization, wireless channel modeling, machine learning and artificial intelligence, computational modeling, and stochastic uncertainty quantification. Mr. Bakirtzis has received the Onassis Foundation Scholarship and the Marie Skłodowska-Curie Actions-Innovative Training Networks (MSCA-ITN) Fellowship.
This event will be conducted in English