Deep Learning-based Reference Signal Received Power Prediction for LTE Communication System

被引：1

作者：

Ngenjaroendee, Thearrawit ^{[1
]}

Phakphisut, Watid ^{[1
]}

Wijitpornchai, Thongchai ^{[2
]}

Areeprayoonkij, Poonlarp ^{[2
]}

Jaruvitayakovit, Tanun ^{[2
]}

机构：

[1] King Mongkuts Inst Technol Ladkrabang, Sch Engn, Bangkok, Thailand

[2] Adv Wireless Network Co Ltd, Bangkok, Thailand

来源：

2022 37TH INTERNATIONAL TECHNICAL CONFERENCE ON CIRCUITS/SYSTEMS, COMPUTERS AND COMMUNICATIONS (ITC-CSCC 2022) | 2022年

关键词：

LTE wireless communication; machine learning; RSRP Prediction;

D O I：

10.1109/ITC-CSCC55581.2022.9895098

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：888 / 891

页数：4

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