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Author: search.filters.author.Wijitpornchai, ThongchaiSubject: search.filters.subject.RSRP Prediction

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    Reference Signal Received Power Prediction Using Convolutional Neural Network with Residual Loss
    (2023-01-01)
    Ngenjaroendee, Thearrawit
    ;
    Phakphisut, Watid
    ;
    Wijitpornchai, Thongchai
    ;
    Areeprayoonkij, Poonlarp
    ;
    Jaruvitayakovit, Tanun
    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 loss and geospatial data are used in the learning process of convolutional neural network (CNN). We also use the site configuration and three-dimensional antenna pattern. Thus, the neural network and convolutional neural network are proposed to construct deep learning to predict the reference signal received power (RSRP) in Bangkok, Thailand. The results show that residual loss can improve the efficiency of prediction.
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    Deep Learning-based Reference Signal Received Power Prediction for LTE Communication System
    (2022-01-01)
    Ngenjaroendee, Thearrawit
    ;
    Phakphisut, Watid
    ;
    Wijitpornchai, Thongchai
    ;
    Areeprayoonkij, Poonlarp
    ;
    Jaruvitayakovit, Tanun
    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.