{"title":"时延QoS约束下LTE网络的高效上行资源分配","authors":"Adnan Aijaz, M. R. Nakhai, H. Aghvami","doi":"10.1109/GLOCOM.2014.7036978","DOIUrl":null,"url":null,"abstract":"In this paper, we investigate power efficient resource allocation for the uplink of LTE networks under delay Quality-of-Service (QoS) constraints. We formulate the resource allocation problem as the minimization of sum power in the uplink under statistical delay QoS provisioning, which is complicated due to the specific constraints of SC-FDMA (uplink air interface in LTE networks). We solve the problem using Canonical duality theory. Numerical results which are obtained using the Invasive Weed Optimization algorithm, show that the proposed resource allocation algorithm not only outperforms classical algorithms in terms of power efficiency while satisfying the QoS requirements, but also performs closer to the optimal solution.","PeriodicalId":6492,"journal":{"name":"2014 IEEE Global Communications Conference","volume":"26 1","pages":"1239-1244"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Power efficient uplink resource allocation in LTE networks under delay QoS constraints\",\"authors\":\"Adnan Aijaz, M. R. Nakhai, H. Aghvami\",\"doi\":\"10.1109/GLOCOM.2014.7036978\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we investigate power efficient resource allocation for the uplink of LTE networks under delay Quality-of-Service (QoS) constraints. We formulate the resource allocation problem as the minimization of sum power in the uplink under statistical delay QoS provisioning, which is complicated due to the specific constraints of SC-FDMA (uplink air interface in LTE networks). We solve the problem using Canonical duality theory. Numerical results which are obtained using the Invasive Weed Optimization algorithm, show that the proposed resource allocation algorithm not only outperforms classical algorithms in terms of power efficiency while satisfying the QoS requirements, but also performs closer to the optimal solution.\",\"PeriodicalId\":6492,\"journal\":{\"name\":\"2014 IEEE Global Communications Conference\",\"volume\":\"26 1\",\"pages\":\"1239-1244\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Global Communications Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GLOCOM.2014.7036978\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Global Communications Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GLOCOM.2014.7036978","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Power efficient uplink resource allocation in LTE networks under delay QoS constraints
In this paper, we investigate power efficient resource allocation for the uplink of LTE networks under delay Quality-of-Service (QoS) constraints. We formulate the resource allocation problem as the minimization of sum power in the uplink under statistical delay QoS provisioning, which is complicated due to the specific constraints of SC-FDMA (uplink air interface in LTE networks). We solve the problem using Canonical duality theory. Numerical results which are obtained using the Invasive Weed Optimization algorithm, show that the proposed resource allocation algorithm not only outperforms classical algorithms in terms of power efficiency while satisfying the QoS requirements, but also performs closer to the optimal solution.