{"title":"Non-Coherent Multiple-Symbol Detection for Diffusive Molecular Communications","authors":"V. Jamali, N. Farsad, R. Schober, A. Goldsmith","doi":"10.1145/2967446.2967466","DOIUrl":null,"url":null,"abstract":"Most of the available works on molecular communication (MC) assume that the channel state information (CSI) is perfectly known at the receiver for data detection. In contrast, in this paper, we study non-coherent multiple-symbol detection schemes which do not require knowledge of the CSI. In particular, we derive the optimal maximum likelihood (ML) multiple-symbol (MLMS) detector. Moreover, we propose an approximated detection metric and a sub-optimal detector to cope with the high complexity of the optimal MLMS detector. Numerical results reveal the effectiveness of the proposed optimal and suboptimal detection schemes with respect to a baseline scheme which assumes perfect CSI knowledge, particularly when the number of observations used for detection is sufficiently large.","PeriodicalId":281609,"journal":{"name":"Proceedings of the 3rd ACM International Conference on Nanoscale Computing and Communication","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 3rd ACM International Conference on Nanoscale Computing and Communication","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2967446.2967466","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21
Abstract
Most of the available works on molecular communication (MC) assume that the channel state information (CSI) is perfectly known at the receiver for data detection. In contrast, in this paper, we study non-coherent multiple-symbol detection schemes which do not require knowledge of the CSI. In particular, we derive the optimal maximum likelihood (ML) multiple-symbol (MLMS) detector. Moreover, we propose an approximated detection metric and a sub-optimal detector to cope with the high complexity of the optimal MLMS detector. Numerical results reveal the effectiveness of the proposed optimal and suboptimal detection schemes with respect to a baseline scheme which assumes perfect CSI knowledge, particularly when the number of observations used for detection is sufficiently large.