{"title":"小细胞通过遗传算法关闭","authors":"Yasmina El Morabit, F. Mrabti, E. Abarkan","doi":"10.1109/ATSIP.2017.8075586","DOIUrl":null,"url":null,"abstract":"The densification of small cells in heterogeneous cellular networks is one of the main approaches of 5G technology that aim to fulfill the growth of traffic demand. However, this densification leads to increase total energy consumption of the network. One way to save energy is to switch off some underutilized cells during low traffic periods. In order to address this problem, we propose dynamic switch off cell scheme based on the genetic algorithm to optimize and improve the energy efficiency by considering diverse parameters for each small cell in the decision process such as: the load traffic of the cell, load traffic of neighboring cells and the coverage provided by the multiple interfering small cells. The simulation result showed that our approach allows to save up to 10.87% more energy of total network energy consumption.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Small cell switch off using genetic algorithm\",\"authors\":\"Yasmina El Morabit, F. Mrabti, E. Abarkan\",\"doi\":\"10.1109/ATSIP.2017.8075586\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The densification of small cells in heterogeneous cellular networks is one of the main approaches of 5G technology that aim to fulfill the growth of traffic demand. However, this densification leads to increase total energy consumption of the network. One way to save energy is to switch off some underutilized cells during low traffic periods. In order to address this problem, we propose dynamic switch off cell scheme based on the genetic algorithm to optimize and improve the energy efficiency by considering diverse parameters for each small cell in the decision process such as: the load traffic of the cell, load traffic of neighboring cells and the coverage provided by the multiple interfering small cells. The simulation result showed that our approach allows to save up to 10.87% more energy of total network energy consumption.\",\"PeriodicalId\":259951,\"journal\":{\"name\":\"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ATSIP.2017.8075586\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ATSIP.2017.8075586","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The densification of small cells in heterogeneous cellular networks is one of the main approaches of 5G technology that aim to fulfill the growth of traffic demand. However, this densification leads to increase total energy consumption of the network. One way to save energy is to switch off some underutilized cells during low traffic periods. In order to address this problem, we propose dynamic switch off cell scheme based on the genetic algorithm to optimize and improve the energy efficiency by considering diverse parameters for each small cell in the decision process such as: the load traffic of the cell, load traffic of neighboring cells and the coverage provided by the multiple interfering small cells. The simulation result showed that our approach allows to save up to 10.87% more energy of total network energy consumption.