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Deep Learning-based Reference Signal Received Power Prediction for LTE Communication System
Author(s)
Ngenjaroendee, Thearrawit
Wijitpornchai, Thongchai
Areeprayoonkij, Poonlarp
Jaruvitayakovit, Tanun
Date Issued
January 1, 2022
Type
Conference Paper
Abstract
A highly accurate prediction of radio signal power is crucial for planning the coverage of mobile networks. Currently, a path loss model is most widely used to predict the radio signal. However, the path loss models commonly provide an over-or under-estimation of the signal power. In this paper, we present the reference signal received power (RSRP) prediction using a deep learning. To evaluate the performance of our prediction system, we use the empirical data in Bangkok metropolitan area. Especially, the empirical data comprise 2 million measurements per day for deep learning. The root mean square error (RMSE) value of our prediction is approximately 3.91 dB.
Citation
Itc Cscc 2022 37th International Technical Conference on Circuits Systems Computers and Communications, 888-891, 2022