{"title":"多任务网络上的变步长离散余弦变换扩散LMS","authors":"Ali Al-Mohammedi, Mohamed Deriche","doi":"10.1109/ICFSP.2018.8552066","DOIUrl":null,"url":null,"abstract":"In this paper, new variable step-size transform domain (VSSTD) algorithm is developed for Diffusion Least Mean Square (DLMS) multi-task networks with system identification application over Wireless Sensor Networks (WSNs). Our main contributions are the theoretical derivations of convergence analysis of Discrete Cosine Transform DLMS (DCTDLMS) algorithm considering adaptive combiners. Our simulations showed performance improvement compared to the traditional DLMS.","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Variable Step-Size Discrete Cosine Transform Diffusion LMS over Multi-task Networks\",\"authors\":\"Ali Al-Mohammedi, Mohamed Deriche\",\"doi\":\"10.1109/ICFSP.2018.8552066\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, new variable step-size transform domain (VSSTD) algorithm is developed for Diffusion Least Mean Square (DLMS) multi-task networks with system identification application over Wireless Sensor Networks (WSNs). Our main contributions are the theoretical derivations of convergence analysis of Discrete Cosine Transform DLMS (DCTDLMS) algorithm considering adaptive combiners. Our simulations showed performance improvement compared to the traditional DLMS.\",\"PeriodicalId\":355222,\"journal\":{\"name\":\"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICFSP.2018.8552066\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICFSP.2018.8552066","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Variable Step-Size Discrete Cosine Transform Diffusion LMS over Multi-task Networks
In this paper, new variable step-size transform domain (VSSTD) algorithm is developed for Diffusion Least Mean Square (DLMS) multi-task networks with system identification application over Wireless Sensor Networks (WSNs). Our main contributions are the theoretical derivations of convergence analysis of Discrete Cosine Transform DLMS (DCTDLMS) algorithm considering adaptive combiners. Our simulations showed performance improvement compared to the traditional DLMS.
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