J. B. Abdo, Imad Sarji, I. Elhajj, A. Chehab, A. Kayssi
{"title":"Application-Aware Fast Dormancy in LTE","authors":"J. B. Abdo, Imad Sarji, I. Elhajj, A. Chehab, A. Kayssi","doi":"10.1109/AINA.2014.28","DOIUrl":null,"url":null,"abstract":"Two Radio Resource Control states have been proposed in LTE and implemented to ensure low UE power consumption and high network resource availability. Transiting between these two states optimizes network performance if tuned properly. Currently, a UE switches from the LTE_ACTIVE state to the LTE_IDLE state after a pre-configured static inactivity duration. This paper seeks to demonstrate that no static timeout is optimal for all users at all times. In addition, a user-level dynamic decision algorithm is proposed to have fine-grain user level optimization. Since achieving better efficiency is related to context awareness, we present a solution that allows the UE to auto-learn its traffic behavior. The dynamic algorithm was applied to five different user load scenarios of combined application and legacy traffic, and the results showed that we are able to attain power savings of up to 30% when compared to the fixed timeout case.","PeriodicalId":316052,"journal":{"name":"2014 IEEE 28th International Conference on Advanced Information Networking and Applications","volume":"os-58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 28th International Conference on Advanced Information Networking and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AINA.2014.28","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Two Radio Resource Control states have been proposed in LTE and implemented to ensure low UE power consumption and high network resource availability. Transiting between these two states optimizes network performance if tuned properly. Currently, a UE switches from the LTE_ACTIVE state to the LTE_IDLE state after a pre-configured static inactivity duration. This paper seeks to demonstrate that no static timeout is optimal for all users at all times. In addition, a user-level dynamic decision algorithm is proposed to have fine-grain user level optimization. Since achieving better efficiency is related to context awareness, we present a solution that allows the UE to auto-learn its traffic behavior. The dynamic algorithm was applied to five different user load scenarios of combined application and legacy traffic, and the results showed that we are able to attain power savings of up to 30% when compared to the fixed timeout case.