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Author: search.filters.author.Jaruvitayakovit, TanunSubject: search.filters.subject.Convolutional Neural Network

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    Deep Learning Aided Robust RSRP Prediction in Cellular Networks
    (2024-01-01)
    Wongphatcharatham, Tanutsorn
    ;
    Phakphisut, Watid
    ;
    Jaruvitayakovit, Tanun
    ;
    Boonkajay, Amnart
    ;
    Huang, Jiajia
    We propose a transfer learning enhanced hybrid model for robust reference signal received power (RSRP) prediction. The hybrid model comprises an expected RSRP estimation based on transmit power, 3-D antenna gain models, path loss, and a deep learning (DL) for predicting an error from ground-truth measurement. The DL architecture consists of regression neural network (NN) and convolutional neural network (CNN). Besides cell site configuration and the long-term evolution (LTE) measurement report from user equipments (UEs), the expected RSRP and geospatial data e.g. building percentage and clutter index are considered. Since trained model may not perform well in new environment, it requires tedious work and long time to collect data at a new cell site. Therefore, we use transfer learning (TL) to apply the trained model to the other areas, which have differences in environment information and antenna configurations, by transferring the knowledge acquired from trained model. The results of the trained area show that root mean square error (RMSE) and mean absolute error (MAE) are approximately 2.92 and 2.01, respectively. For the other area, TL have improved MAE approximately 1 to 2.
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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.