Danupol Chomsuay, W. Phakphisut, Thongchai Wijitpornchai, Poonlarp Areeprayoonkij, Tanun Jaruvitayakovit, N. Puttarak
{"title":"基于深度学习的LTE通信系统参考信号接收功率预测改进","authors":"Danupol Chomsuay, W. Phakphisut, Thongchai Wijitpornchai, Poonlarp Areeprayoonkij, Tanun Jaruvitayakovit, N. Puttarak","doi":"10.1109/ITC-CSCC58803.2023.10212575","DOIUrl":null,"url":null,"abstract":"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 work, the output of DNN is path loss error instead of RSRP. Moreover, our deep neural network is improved by increasing the number of features, such as 3-D antenna gain, digital elevation model (DEM). The path loss model is also developed by using the clustering technique. The results show that the dominant prediction testing, non-dominant prediction testing can provide the RMSE around 3.7 dB, and 4.9 dB, respectively.","PeriodicalId":220939,"journal":{"name":"2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improvement of Deep Learning-Based Reference Signal Received Power Prediction for LTE Communication System\",\"authors\":\"Danupol Chomsuay, W. Phakphisut, Thongchai Wijitpornchai, Poonlarp Areeprayoonkij, Tanun Jaruvitayakovit, N. Puttarak\",\"doi\":\"10.1109/ITC-CSCC58803.2023.10212575\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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 work, the output of DNN is path loss error instead of RSRP. Moreover, our deep neural network is improved by increasing the number of features, such as 3-D antenna gain, digital elevation model (DEM). The path loss model is also developed by using the clustering technique. The results show that the dominant prediction testing, non-dominant prediction testing can provide the RMSE around 3.7 dB, and 4.9 dB, respectively.\",\"PeriodicalId\":220939,\"journal\":{\"name\":\"2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITC-CSCC58803.2023.10212575\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITC-CSCC58803.2023.10212575","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improvement of Deep Learning-Based Reference Signal Received Power Prediction for LTE Communication System
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 work, the output of DNN is path loss error instead of RSRP. Moreover, our deep neural network is improved by increasing the number of features, such as 3-D antenna gain, digital elevation model (DEM). The path loss model is also developed by using the clustering technique. The results show that the dominant prediction testing, non-dominant prediction testing can provide the RMSE around 3.7 dB, and 4.9 dB, respectively.