{"title":"Joint information- and jamming-beamforming for full duplex secure communication","authors":"F. Zhu, F. Gao, M. Yao, Jian Li","doi":"10.1109/GLOCOM.2014.7037039","DOIUrl":null,"url":null,"abstract":"In this paper, we design joint information beam-forming and jamming beamforming to guarantee both transmit security and receive security for a full duplex base station (FD-BS). Specifically, we aim to maximize the secret transmit rate while constrain the secret receive rate to be greater than a predefined bound. We convert the original non-convex problem into a new sequence of subproblems where the semidefinite programming (SDP) relaxation can be applied to efficiently find the optimal solutions. We strictly prove that such a relaxation does not change the optimality for these subproblems. Then the global optimal solutions of the original non-convex problem can be obtained via a one-dimensional search. Simulation results are provided to verify the efficiency of the proposed algorithms.","PeriodicalId":6492,"journal":{"name":"2014 IEEE Global Communications Conference","volume":"83 1","pages":"1614-1618"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Global Communications Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GLOCOM.2014.7037039","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, we design joint information beam-forming and jamming beamforming to guarantee both transmit security and receive security for a full duplex base station (FD-BS). Specifically, we aim to maximize the secret transmit rate while constrain the secret receive rate to be greater than a predefined bound. We convert the original non-convex problem into a new sequence of subproblems where the semidefinite programming (SDP) relaxation can be applied to efficiently find the optimal solutions. We strictly prove that such a relaxation does not change the optimality for these subproblems. Then the global optimal solutions of the original non-convex problem can be obtained via a one-dimensional search. Simulation results are provided to verify the efficiency of the proposed algorithms.