{"title":"基于人工神经网络的微蜂窝覆盖预测","authors":"A. Neskovic, N. Neskovic, D. Paunovic","doi":"10.1109/NEUREL.2002.1057997","DOIUrl":null,"url":null,"abstract":"A new microcell prediction model for mobile phone environment is presented in this paper. The model is based on the principles of popular feedforward neural networks. Utilising a new artificial neural network model some important disadvantages of both deterministic and empirical models can be overcome. In order to build the model, extensive electric field level measurements (in 900 MHz frequency band) were carried out in the city of Belgrade, for two different test transmitter locations. The comparison between the data obtained by the proposed electric field level prediction model and the independent measurement sets, have shown that the proposed model is accurate (on the order of the local mean measurements uncertainty) and reliable. At the same time, the algorithm is suitable for computer implementation, simple and fast.","PeriodicalId":347066,"journal":{"name":"6th Seminar on Neural Network Applications in Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Microcell coverage prediction using artificial neural networks\",\"authors\":\"A. Neskovic, N. Neskovic, D. Paunovic\",\"doi\":\"10.1109/NEUREL.2002.1057997\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new microcell prediction model for mobile phone environment is presented in this paper. The model is based on the principles of popular feedforward neural networks. Utilising a new artificial neural network model some important disadvantages of both deterministic and empirical models can be overcome. In order to build the model, extensive electric field level measurements (in 900 MHz frequency band) were carried out in the city of Belgrade, for two different test transmitter locations. The comparison between the data obtained by the proposed electric field level prediction model and the independent measurement sets, have shown that the proposed model is accurate (on the order of the local mean measurements uncertainty) and reliable. At the same time, the algorithm is suitable for computer implementation, simple and fast.\",\"PeriodicalId\":347066,\"journal\":{\"name\":\"6th Seminar on Neural Network Applications in Electrical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"6th Seminar on Neural Network Applications in Electrical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NEUREL.2002.1057997\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"6th Seminar on Neural Network Applications in Electrical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NEUREL.2002.1057997","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Microcell coverage prediction using artificial neural networks
A new microcell prediction model for mobile phone environment is presented in this paper. The model is based on the principles of popular feedforward neural networks. Utilising a new artificial neural network model some important disadvantages of both deterministic and empirical models can be overcome. In order to build the model, extensive electric field level measurements (in 900 MHz frequency band) were carried out in the city of Belgrade, for two different test transmitter locations. The comparison between the data obtained by the proposed electric field level prediction model and the independent measurement sets, have shown that the proposed model is accurate (on the order of the local mean measurements uncertainty) and reliable. At the same time, the algorithm is suitable for computer implementation, simple and fast.