{"title":"利用遗传算法进行频谱调制频谱编码波形设计","authors":"T. W. Beard, M. Temple, J. O. Miller, R. Mills","doi":"10.1109/WDDC.2007.4339423","DOIUrl":null,"url":null,"abstract":"A genetic algorithm (GA) is used to design Spectrally Modulated, Spectrally Encoded (SMSE) waveforms while characterizing the impact of parametric variation on coexistence. As recently proposed, the SMSE framework supports cognition-based, software defined radio (SDR) applications and is well-suited for coexistence analysis. For initial proof-of-concept, two SMSE waveform parameters (number of carriers and carrier bandwidth) are optimized in a coexistent scenario to characterize SMSE impact on Direct Sequence Spread Spectrum (DSSS) bit error performance. Given optimization via GA techniques have been successfully applied in many engineering fields, as well as operations research, they are viable candidates for robust SMSE waveform design. As demonstrated, the analytic SMSE framework is well-suited for parametric optimization via GA techniques.","PeriodicalId":142822,"journal":{"name":"2007 International Waveform Diversity and Design Conference","volume":"221 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Using genetic algorithms for Spectrally Modulated Spectrally Encoded waveform design\",\"authors\":\"T. W. Beard, M. Temple, J. O. Miller, R. Mills\",\"doi\":\"10.1109/WDDC.2007.4339423\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A genetic algorithm (GA) is used to design Spectrally Modulated, Spectrally Encoded (SMSE) waveforms while characterizing the impact of parametric variation on coexistence. As recently proposed, the SMSE framework supports cognition-based, software defined radio (SDR) applications and is well-suited for coexistence analysis. For initial proof-of-concept, two SMSE waveform parameters (number of carriers and carrier bandwidth) are optimized in a coexistent scenario to characterize SMSE impact on Direct Sequence Spread Spectrum (DSSS) bit error performance. Given optimization via GA techniques have been successfully applied in many engineering fields, as well as operations research, they are viable candidates for robust SMSE waveform design. As demonstrated, the analytic SMSE framework is well-suited for parametric optimization via GA techniques.\",\"PeriodicalId\":142822,\"journal\":{\"name\":\"2007 International Waveform Diversity and Design Conference\",\"volume\":\"221 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 International Waveform Diversity and Design Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WDDC.2007.4339423\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 International Waveform Diversity and Design Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WDDC.2007.4339423","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using genetic algorithms for Spectrally Modulated Spectrally Encoded waveform design
A genetic algorithm (GA) is used to design Spectrally Modulated, Spectrally Encoded (SMSE) waveforms while characterizing the impact of parametric variation on coexistence. As recently proposed, the SMSE framework supports cognition-based, software defined radio (SDR) applications and is well-suited for coexistence analysis. For initial proof-of-concept, two SMSE waveform parameters (number of carriers and carrier bandwidth) are optimized in a coexistent scenario to characterize SMSE impact on Direct Sequence Spread Spectrum (DSSS) bit error performance. Given optimization via GA techniques have been successfully applied in many engineering fields, as well as operations research, they are viable candidates for robust SMSE waveform design. As demonstrated, the analytic SMSE framework is well-suited for parametric optimization via GA techniques.