Showing papers by "Teruo Onishi published in 2023"

Large-Area Monitoring of Radiofrequency Electromagnetic Field Exposure Levels from Mobile Phone Base Stations and Broadcast Transmission Towers by Car-Mounted Measurements around Tokyo

Abstract: Car-mounted measurements of radiofrequency electromagnetic exposure levels were carried out in a large area around Tokyo. Prior to the electric field (E-field) measurements using a car, the effect of the car body was evaluated in an anechoic chamber. The measurements between May 2021 and February 2022 were carried out within a radius of 100 km centering on Nihonbashi, Tokyo, with a measurement distance of about 13,800 km. The measurement results were averaged in the reference area mesh (1 km2). It was found that the E-field strengths of FM/TV frequency bands are lower than that of mobile phone base stations. It was also found that the E-field strength of only the 5G frequency band is approximately 20–30 dB lower than that of all mobile phone systems. However, note that it is possible to depend on the data traffic of 5G. The E-field strength of all bands is higher in Tokyo than in other prefectures. Additionally, repeated measurements were carried out to investigate the reproducibility of the measured E-field. The standard deviation is less than 3 dB along the same route, and a similar tendency of E-field strength by the car to the time-averaged results of spot measurements in the past was confirmed. Finally, the relationship of E-field strength with population density was investigated. It was found that the E-field strength from mobile phone base stations has a positive relationship with population density.

A Generalizable Indoor Propagation Model Based on Graph Neural Networks

Abstract: A surrogate model that “learns” the physics of radio wave propagation is indispensable for the efficient optimization of communication network coverages and comprehensive electromagnetic field (EMF) exposure assessments. The capability of a model to predict reasonable outputs given an input that is beyond the data with which the model is trained, namely, “generalizability,” is a fundamental challenge and a key factor for its practical deployment. In this article, by leveraging the concept of graph neural networks (GNNs), a prediction model for indoor propagation that is “generalized” to not only transmitter (Tx) positions but also new geometries is presented. We demonstrate that a geometry and a Tx antenna can be modeled as a graph with all necessary information being included, and a GNN can acquire the knowledge of propagation physics through “learning” from these graphs. We further show that the model can be generalized to new geometry shapes, beyond the shape (square) for model training. We provide useful information on how to obtain an acceptable accuracy for different scenarios. We also discuss the potential solutions to further improve the model’s capability.