{"title":"一种用于无线网络QoS增强的链路自适应方法","authors":"S. Ci, H. Sharif, A. Young","doi":"10.1109/LCN.2001.990810","DOIUrl":null,"url":null,"abstract":"We propose a new link adaptation approach for QoS enhancement in wireless networks. We utilize the Kalman filter, which predicts the local optimal frame size based on the channel quality varying based on the derived channel model. We have also presented the simulations and experimental results. The results show that the performance of proposed predictor is much better than other prediction methods like the moving average.","PeriodicalId":213526,"journal":{"name":"Proceedings LCN 2001. 26th Annual IEEE Conference on Local Computer Networks","volume":"76 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A link adaptation approach for QoS enhancement in wireless networks\",\"authors\":\"S. Ci, H. Sharif, A. Young\",\"doi\":\"10.1109/LCN.2001.990810\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a new link adaptation approach for QoS enhancement in wireless networks. We utilize the Kalman filter, which predicts the local optimal frame size based on the channel quality varying based on the derived channel model. We have also presented the simulations and experimental results. The results show that the performance of proposed predictor is much better than other prediction methods like the moving average.\",\"PeriodicalId\":213526,\"journal\":{\"name\":\"Proceedings LCN 2001. 26th Annual IEEE Conference on Local Computer Networks\",\"volume\":\"76 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings LCN 2001. 26th Annual IEEE Conference on Local Computer Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/LCN.2001.990810\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings LCN 2001. 26th Annual IEEE Conference on Local Computer Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LCN.2001.990810","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A link adaptation approach for QoS enhancement in wireless networks
We propose a new link adaptation approach for QoS enhancement in wireless networks. We utilize the Kalman filter, which predicts the local optimal frame size based on the channel quality varying based on the derived channel model. We have also presented the simulations and experimental results. The results show that the performance of proposed predictor is much better than other prediction methods like the moving average.
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