Metin Ozturk, J. Nadas, P. V. Klaine, S. Hussain, M. Imran
{"title":"基于集群的无人机基站定位增强网络容量","authors":"Metin Ozturk, J. Nadas, P. V. Klaine, S. Hussain, M. Imran","doi":"10.1109/AECT47998.2020.9194188","DOIUrl":null,"url":null,"abstract":"Unmanned aerial vehicles (UAVs) are expected to be deployed in a variety of applications in future mobile networks due to several advantages they bring over the deployment of ground base stations. However, despite the recent interest in UAVs in mobile networks, some issues still remain, such as determining the placement of multiple UAVs in different scenarios. In this paper we propose a solution to determine the optimal 3D position of multiple UAVs in a capacity enhancement use-case, or in other words, when the ground network cannot cope with the user traffic demand. For this scenario, real data from the city of Milan, provided by Telecom Italia is utilized to simulate an event. Based on that, a solution based on k-means, a machine learning technique, to position multiple UAVs is proposed and it is compared with two other baseline methods. Results demonstrate that the proposed solution is able to significantly outperform other methods in terms of users covered and quality of service.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Clustering Based UAV Base Station Positioning for Enhanced Network Capacity\",\"authors\":\"Metin Ozturk, J. Nadas, P. V. Klaine, S. Hussain, M. Imran\",\"doi\":\"10.1109/AECT47998.2020.9194188\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Unmanned aerial vehicles (UAVs) are expected to be deployed in a variety of applications in future mobile networks due to several advantages they bring over the deployment of ground base stations. However, despite the recent interest in UAVs in mobile networks, some issues still remain, such as determining the placement of multiple UAVs in different scenarios. In this paper we propose a solution to determine the optimal 3D position of multiple UAVs in a capacity enhancement use-case, or in other words, when the ground network cannot cope with the user traffic demand. For this scenario, real data from the city of Milan, provided by Telecom Italia is utilized to simulate an event. Based on that, a solution based on k-means, a machine learning technique, to position multiple UAVs is proposed and it is compared with two other baseline methods. Results demonstrate that the proposed solution is able to significantly outperform other methods in terms of users covered and quality of service.\",\"PeriodicalId\":331415,\"journal\":{\"name\":\"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AECT47998.2020.9194188\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AECT47998.2020.9194188","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Clustering Based UAV Base Station Positioning for Enhanced Network Capacity
Unmanned aerial vehicles (UAVs) are expected to be deployed in a variety of applications in future mobile networks due to several advantages they bring over the deployment of ground base stations. However, despite the recent interest in UAVs in mobile networks, some issues still remain, such as determining the placement of multiple UAVs in different scenarios. In this paper we propose a solution to determine the optimal 3D position of multiple UAVs in a capacity enhancement use-case, or in other words, when the ground network cannot cope with the user traffic demand. For this scenario, real data from the city of Milan, provided by Telecom Italia is utilized to simulate an event. Based on that, a solution based on k-means, a machine learning technique, to position multiple UAVs is proposed and it is compared with two other baseline methods. Results demonstrate that the proposed solution is able to significantly outperform other methods in terms of users covered and quality of service.