{"title":"对生成一个光滑的高斯随机变量达到5个标准差","authors":"G. Muller, C. Pauw","doi":"10.1109/COMSIG.1988.49303","DOIUrl":null,"url":null,"abstract":"The generation of a Gaussian random variable for use in a low-error-rate communication simulation is discussed. The authors describe the requirements for a uniform probability density function (PDF) pseudorandom number generator used as well as different methods of obtaining a Gaussian random number for various applications. A lookup table method is presented that was designed for digital signal processing cases where it is difficult to generate a logarithmic function for analytic synthesis of the Gaussian random number. The techniques and approach used are applicable to any probability distribution (other than a Gaussian PDF) for which the inverse cumulative density function either cannot be evaluated analytically or is too computationally intensive.<<ETX>>","PeriodicalId":339020,"journal":{"name":"COMSIG 88@m_Southern African Conference on Communications and Signal Processing. Proceedings","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1988-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"On the generation of a smooth Gaussian random variable to 5 standard deviations\",\"authors\":\"G. Muller, C. Pauw\",\"doi\":\"10.1109/COMSIG.1988.49303\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The generation of a Gaussian random variable for use in a low-error-rate communication simulation is discussed. The authors describe the requirements for a uniform probability density function (PDF) pseudorandom number generator used as well as different methods of obtaining a Gaussian random number for various applications. A lookup table method is presented that was designed for digital signal processing cases where it is difficult to generate a logarithmic function for analytic synthesis of the Gaussian random number. The techniques and approach used are applicable to any probability distribution (other than a Gaussian PDF) for which the inverse cumulative density function either cannot be evaluated analytically or is too computationally intensive.<<ETX>>\",\"PeriodicalId\":339020,\"journal\":{\"name\":\"COMSIG 88@m_Southern African Conference on Communications and Signal Processing. Proceedings\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1988-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"COMSIG 88@m_Southern African Conference on Communications and Signal Processing. Proceedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COMSIG.1988.49303\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"COMSIG 88@m_Southern African Conference on Communications and Signal Processing. Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMSIG.1988.49303","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On the generation of a smooth Gaussian random variable to 5 standard deviations
The generation of a Gaussian random variable for use in a low-error-rate communication simulation is discussed. The authors describe the requirements for a uniform probability density function (PDF) pseudorandom number generator used as well as different methods of obtaining a Gaussian random number for various applications. A lookup table method is presented that was designed for digital signal processing cases where it is difficult to generate a logarithmic function for analytic synthesis of the Gaussian random number. The techniques and approach used are applicable to any probability distribution (other than a Gaussian PDF) for which the inverse cumulative density function either cannot be evaluated analytically or is too computationally intensive.<>