Recently, in our previous work [3], we have proposed the deep learning-based reference
signal received power (RSRP) prediction for LTE communication system. However, in this …

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 …

In this paper, LTE measurement reports collected from user equipments are used to
generate the residual loss, which can represent the loss value of each grid. The residual …

This paper presents a new methodology for emulating the LTE modem power consumption,
emitted SAR and RF signal strength when transmitting an LTE signal. The inputs of the …

As a reliable communication medium, the wireless radio channel determines the quality and
performance of wireless communication systems. It is imperative for operators to accurately …

We report a method for proactively predicting received power using GRU (Gated Recurrent
Unit), which is one of RNN (Recurrent Neural Network) as deep learning. One of the input …

This paper presents a proactive prediction method for received power distribution using
GRU (Gated Recurrent Unit), which is one type of RNN (Recurrent Neural Network), as deep …

In recent years, with the rapid development of wireless communication technology, the
diversification of communication business scenarios has made the mass production of low …

In this paper, we propose a measurement-based spectrum database using model classifier.
In the radio propagation, path loss is the fundamental factor to recognize the coverage area …

Building an accurate and efficient wireless propagation model is important for predicting
radio wave propagation signals in the target area of the communication area. Different from …