{"title":"基于Parzen窗口法自适应数据集选择的符号间干扰抑制","authors":"Shoma Ito;Hirokazu Kubota;Yuji Miyoshi","doi":"10.23919/comex.2025XBL0047","DOIUrl":null,"url":null,"abstract":"We propose a detection method based on the Parzen window method to mitigate inter-symbol interference. This method selects a trained dataset based on the neighboring symbols, following a decision feedback approach. Simulation results show the proposed method achieved a 3.3 dB improvement in the Q value for a QPSK signal degraded by inter-symbol interference.","PeriodicalId":54101,"journal":{"name":"IEICE Communications Express","volume":"14 6","pages":"260-262"},"PeriodicalIF":0.3000,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964025","citationCount":"0","resultStr":"{\"title\":\"Inter-Symbol Interference Mitigation Using Adaptive Dataset Selection in the Parzen Window Method\",\"authors\":\"Shoma Ito;Hirokazu Kubota;Yuji Miyoshi\",\"doi\":\"10.23919/comex.2025XBL0047\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a detection method based on the Parzen window method to mitigate inter-symbol interference. This method selects a trained dataset based on the neighboring symbols, following a decision feedback approach. Simulation results show the proposed method achieved a 3.3 dB improvement in the Q value for a QPSK signal degraded by inter-symbol interference.\",\"PeriodicalId\":54101,\"journal\":{\"name\":\"IEICE Communications Express\",\"volume\":\"14 6\",\"pages\":\"260-262\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2025-04-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964025\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEICE Communications Express\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10964025/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEICE Communications Express","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10964025/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Inter-Symbol Interference Mitigation Using Adaptive Dataset Selection in the Parzen Window Method
We propose a detection method based on the Parzen window method to mitigate inter-symbol interference. This method selects a trained dataset based on the neighboring symbols, following a decision feedback approach. Simulation results show the proposed method achieved a 3.3 dB improvement in the Q value for a QPSK signal degraded by inter-symbol interference.