{"title":"Optimum and joint-suboptimum maximum-likelihood detection for FH/BFSK in multitone jamming","authors":"Yi-Chen Chen","doi":"10.1109/CCNC.2006.1593159","DOIUrl":null,"url":null,"abstract":"Without knowledge of jamming state indication and the resultant signal-plus-jammer tone as required in [1], we derive the optimum maximum-likelihood (ML) non-coherent detector for FH/BFSK systems in multitone jamming and additive white Gaussian noise. Due to the huge complexity of the optimum ML detector, we further propose a novel complexity-reduced scheme which is called joint-suboptimum maximum-likelihood (JSML) detector. Simulation results show the superiority of the proposed detectors over other existing diversity combining approaches. The analytical performance of the JSML detector with single diversity is also provided and verified by simulation results. In moderate and high SNR conditions, the JSML detector performs slightly worse than the optimum ML detector while it achieves much complexity reduction.","PeriodicalId":194551,"journal":{"name":"CCNC 2006. 2006 3rd IEEE Consumer Communications and Networking Conference, 2006.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CCNC 2006. 2006 3rd IEEE Consumer Communications and Networking Conference, 2006.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCNC.2006.1593159","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Without knowledge of jamming state indication and the resultant signal-plus-jammer tone as required in [1], we derive the optimum maximum-likelihood (ML) non-coherent detector for FH/BFSK systems in multitone jamming and additive white Gaussian noise. Due to the huge complexity of the optimum ML detector, we further propose a novel complexity-reduced scheme which is called joint-suboptimum maximum-likelihood (JSML) detector. Simulation results show the superiority of the proposed detectors over other existing diversity combining approaches. The analytical performance of the JSML detector with single diversity is also provided and verified by simulation results. In moderate and high SNR conditions, the JSML detector performs slightly worse than the optimum ML detector while it achieves much complexity reduction.