{"title":"一种新的无线局域网无线资源管理性能评价指标","authors":"H. Halabian, Mike Skof, Afshin Sahabi","doi":"10.1109/VTCFall.2016.7880863","DOIUrl":null,"url":null,"abstract":"In this paper, we propose Access Point Channel Capacity (ACC) as a Key Performance Indicator (KPI) for Radio Resource Management (RRM) algorithms in 802.11 Wireless Local Area Networks (WLANs). Introducing such a KPI is important especially for performance evaluation of 802.11 WLAN Self Organizing Networks (SON). ACC is a low complexity KPI that provides accurate per-AP estimate of the MAC layer potential throughput in the WLAN. The advantage of the proposed metric is that it can be derived directly from network statistics collected periodically from the access points. ACC is a vendor agnostic KPI since the required statistics are widely supported by all AP vendors and also TR-069 data model.","PeriodicalId":6484,"journal":{"name":"2016 IEEE 84th Vehicular Technology Conference (VTC-Fall)","volume":"21 1","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A New Performance Evaluation Metric for Radio Resource Management in Wireless Local Area Networks\",\"authors\":\"H. Halabian, Mike Skof, Afshin Sahabi\",\"doi\":\"10.1109/VTCFall.2016.7880863\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose Access Point Channel Capacity (ACC) as a Key Performance Indicator (KPI) for Radio Resource Management (RRM) algorithms in 802.11 Wireless Local Area Networks (WLANs). Introducing such a KPI is important especially for performance evaluation of 802.11 WLAN Self Organizing Networks (SON). ACC is a low complexity KPI that provides accurate per-AP estimate of the MAC layer potential throughput in the WLAN. The advantage of the proposed metric is that it can be derived directly from network statistics collected periodically from the access points. ACC is a vendor agnostic KPI since the required statistics are widely supported by all AP vendors and also TR-069 data model.\",\"PeriodicalId\":6484,\"journal\":{\"name\":\"2016 IEEE 84th Vehicular Technology Conference (VTC-Fall)\",\"volume\":\"21 1\",\"pages\":\"1-5\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 84th Vehicular Technology Conference (VTC-Fall)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VTCFall.2016.7880863\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 84th Vehicular Technology Conference (VTC-Fall)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VTCFall.2016.7880863","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A New Performance Evaluation Metric for Radio Resource Management in Wireless Local Area Networks
In this paper, we propose Access Point Channel Capacity (ACC) as a Key Performance Indicator (KPI) for Radio Resource Management (RRM) algorithms in 802.11 Wireless Local Area Networks (WLANs). Introducing such a KPI is important especially for performance evaluation of 802.11 WLAN Self Organizing Networks (SON). ACC is a low complexity KPI that provides accurate per-AP estimate of the MAC layer potential throughput in the WLAN. The advantage of the proposed metric is that it can be derived directly from network statistics collected periodically from the access points. ACC is a vendor agnostic KPI since the required statistics are widely supported by all AP vendors and also TR-069 data model.