Reference Signal Received Power Prediction Using Convolutional Neural Network with Residual Loss

Ngenjaroendee, Thearrawit; Phakphisut, Watid; Wijitpornchai, Thongchai; Areeprayoonkij, Poonlarp; Jaruvitayakovit, Tanun; Puttarak, Nattakan

doi:10.1109/ITC-CSCC58803.2023.10212448

Reference Signal Received Power Prediction Using Convolutional Neural Network with Residual Loss

Author(s)

Ngenjaroendee, Thearrawit

Phakphisut, Watid

Wijitpornchai, Thongchai

Areeprayoonkij, Poonlarp

Jaruvitayakovit, Tanun

Puttarak, Nattakan

Date Issued

January 1, 2023

Type

Conference Paper

DOI

10.1109/ITC-CSCC58803.2023.10212448

Abstract

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.

Citation

2023 International Technical Conference on Circuits Systems Computers and Communications Itc Cscc 2023, 2023

Subjects

Convolutional Neural ...

Machine Learning

Neural Network

RSRP Prediction