考虑概率干扰的瑞利信道编码M-FSK的最大似然译码

I. Zhilin
{"title":"考虑概率干扰的瑞利信道编码M-FSK的最大似然译码","authors":"I. Zhilin","doi":"10.1109/BlackSeaCom.2019.8812847","DOIUrl":null,"url":null,"abstract":"This work focuses on the analysis of the achievable performance of coded FSK transmitted over memoryless Rayleigh channel with AWGN and probabilistic additive white Gaussian interference.The soft-output maximum-likelihood (ML) decoder for this channel is derived. It is implemented in the form of a simulation model and is used for assessing performance gap for goodness-of-fit criteria decoders known for their good performance in channels with strong interference.","PeriodicalId":359145,"journal":{"name":"2019 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On the Maximum Likelihood Decoding of the coded M-FSK in Rayleigh Channel with Probabilistic Interference\",\"authors\":\"I. Zhilin\",\"doi\":\"10.1109/BlackSeaCom.2019.8812847\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work focuses on the analysis of the achievable performance of coded FSK transmitted over memoryless Rayleigh channel with AWGN and probabilistic additive white Gaussian interference.The soft-output maximum-likelihood (ML) decoder for this channel is derived. It is implemented in the form of a simulation model and is used for assessing performance gap for goodness-of-fit criteria decoders known for their good performance in channels with strong interference.\",\"PeriodicalId\":359145,\"journal\":{\"name\":\"2019 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BlackSeaCom.2019.8812847\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BlackSeaCom.2019.8812847","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文重点分析了在无记忆瑞利信道中,加性高斯白干扰和AWGN下编码FSK传输的可实现性能。推导了该信道的软输出最大似然(ML)解码器。它以仿真模型的形式实现,用于评估拟合优度标准解码器的性能差距,该解码器以其在强干扰信道中的良好性能而闻名。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the Maximum Likelihood Decoding of the coded M-FSK in Rayleigh Channel with Probabilistic Interference
This work focuses on the analysis of the achievable performance of coded FSK transmitted over memoryless Rayleigh channel with AWGN and probabilistic additive white Gaussian interference.The soft-output maximum-likelihood (ML) decoder for this channel is derived. It is implemented in the form of a simulation model and is used for assessing performance gap for goodness-of-fit criteria decoders known for their good performance in channels with strong interference.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信