{"title":"多用户干扰系统的全局直流优化","authors":"Yang Xu, T. Le-Ngoc","doi":"10.1109/CAMSAP.2007.4498002","DOIUrl":null,"url":null,"abstract":"The weighted sum capacity of a Gaussian interference system is a nonconvex function of transmit power allocation vector of all users in the system. This paper shows that, by representing its objective function as a difference of two convex functions (d.c.), the non-convex optimization problem can be converted into an equivalent d.c. global optimization problem, which can be solved efficiently by various developed d.c. algorithms. In particular, a modified prismatic branch -and-bound algorithm that only requires solving a sequence of linear programming sub-problems, is introduced to find the global optimum. Simulation results in wireless flat-fading channel show that the proposed global d.c. optimization formulation outperforms considerably the local optimization methods in terms of achievable ergodic sum-rate capacity.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Global D.C. Optimization for Multi-User Interference Systems\",\"authors\":\"Yang Xu, T. Le-Ngoc\",\"doi\":\"10.1109/CAMSAP.2007.4498002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The weighted sum capacity of a Gaussian interference system is a nonconvex function of transmit power allocation vector of all users in the system. This paper shows that, by representing its objective function as a difference of two convex functions (d.c.), the non-convex optimization problem can be converted into an equivalent d.c. global optimization problem, which can be solved efficiently by various developed d.c. algorithms. In particular, a modified prismatic branch -and-bound algorithm that only requires solving a sequence of linear programming sub-problems, is introduced to find the global optimum. Simulation results in wireless flat-fading channel show that the proposed global d.c. optimization formulation outperforms considerably the local optimization methods in terms of achievable ergodic sum-rate capacity.\",\"PeriodicalId\":220687,\"journal\":{\"name\":\"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CAMSAP.2007.4498002\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAMSAP.2007.4498002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Global D.C. Optimization for Multi-User Interference Systems
The weighted sum capacity of a Gaussian interference system is a nonconvex function of transmit power allocation vector of all users in the system. This paper shows that, by representing its objective function as a difference of two convex functions (d.c.), the non-convex optimization problem can be converted into an equivalent d.c. global optimization problem, which can be solved efficiently by various developed d.c. algorithms. In particular, a modified prismatic branch -and-bound algorithm that only requires solving a sequence of linear programming sub-problems, is introduced to find the global optimum. Simulation results in wireless flat-fading channel show that the proposed global d.c. optimization formulation outperforms considerably the local optimization methods in terms of achievable ergodic sum-rate capacity.