Deep Learning-based Reference Signal Received Power Prediction for LTE Communication System

T Ngenjaroendee, W Phakphisut… - … on Circuits/Systems …, 2022 - ieeexplore.ieee.org
T Ngenjaroendee, W Phakphisut, T Wijitpornchai, P Areeprayoonkij, T Jaruvitayakovit
2022 37th International Technical Conference on Circuits/Systems …, 2022ieeexplore.ieee.org
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 …
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.
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