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 …

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 …

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 …

Wireless propagation modeling is crucial for designing 5G networks and deploying base
stations. Traditional models are constrained by different propagation environments, and …

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 article, artificial neural networks are applied to data measured on a wireless indoor
mobile communications scenario for 768 MHz transmission. Three multilayer perceptron …

This paper proposes a method to predict received power in indoor environments
deterministically, which can learn a prediction model from small amount of measurement …

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 …

In this letter, an environmental features (EFs) extraction model is proposed for estimating
reference signal received power (RSRP) accurately. Firstly, 18-D measured data is …

Accurate received power prediction is important to wireless network planning and
optimization, and appropriate channel modeling approach is highly demanded. The existing …