{"title":"Energy efficient transmission in MIMO interference channels with QoS constraints","authors":"Yang Yang, M. Pesavento","doi":"10.1109/CAMSAP.2017.8313062","DOIUrl":null,"url":null,"abstract":"In this paper, we consider the energy efficiency maximization problem in MIMO interference channels where all users have a guaranteed minimum transmission rate. To solve this optimization problem with a nonconcave objective function and a nonconvex constraint set, we extend the recently developed successive pseudoconvex approximation framework and propose a novel iterative algorithm that has the following advantages: 1) fast convergence, as the structure of the original optimization problem is preserved as much as possible in the approximate problem solved in each iteration, 2) efficient implementation, as each approximate problem is natural for parallel computation and its solution has a closed-form expression, and 3) guaranteed convergence to a Karush-Kuhn-Tucker (KKT) point.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAMSAP.2017.8313062","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
In this paper, we consider the energy efficiency maximization problem in MIMO interference channels where all users have a guaranteed minimum transmission rate. To solve this optimization problem with a nonconcave objective function and a nonconvex constraint set, we extend the recently developed successive pseudoconvex approximation framework and propose a novel iterative algorithm that has the following advantages: 1) fast convergence, as the structure of the original optimization problem is preserved as much as possible in the approximate problem solved in each iteration, 2) efficient implementation, as each approximate problem is natural for parallel computation and its solution has a closed-form expression, and 3) guaranteed convergence to a Karush-Kuhn-Tucker (KKT) point.