Electromagnetic radiation estimation at the ground plane near fifth-generation base stations in China by using machine learning method

Abstract

A novel method based on machine learning is proposed to estimate the electromagnetic radiation level at the ground plane near fifth-generation (5G) base stations. The machine learning model was trained using data from various 5G base stations, enabling it to estimate the electric field intensity at any arbitrary radiation point when the base station provides service to different numbers of 5G terminals which are in different service modes. The inputs required for the model include the transmit power of the antenna, the antenna gain, the distance between the 5G base station and 5G terminals, terminal service modes, the number of 5G terminals and the environmental complexity around the 5G base station. Experimental results demonstrate the feasibility and effectiveness of the estimation method, with the mean absolute percentage error of the machine learning model being approximately 5.98%. This level of accuracy showcases the reliability of the approach. Moreover, the proposed method offers low costs when compared with on-site measurements. The estimated results can be utilised to reduce test costs and provide valuable guidance for optimal site selection, thereby facilitating radio wave coverage or electromagnetic radiation regulation of 5G base stations.