{"title":"扩频系统的纠错编码性能界","authors":"E. Chandler, G. Cooper","doi":"10.1109/MILCOM.1982.4805907","DOIUrl":null,"url":null,"abstract":"This study was motivated by a desire to minimize the required input signal-to-noise ratio at the receiver as a first step in optimizing the anti-intercept (AI) performance of a spread spectrum system. In the absence of error correction coding, specification of the message bit rate, the transmitted signal bandwidth, and the required bit error probability can yield a required signal-to-noise ratio for each type of SS system. Error correction coding can be used to reduce the required signal-to-noise ratio. When coding is employed, lower and upper bounds on the reduction of required signal-to-noise ratio can be determined in terms of the required bit error probability and code complexity. These performance bounds are evaluated as functions of the restricted code complexity.","PeriodicalId":179832,"journal":{"name":"MILCOM 1982 - IEEE Military Communications Conference - Progress in Spread Spectrum Communications","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1982-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Error Correction Coding Performance Bounds for Spread Spectrum Systems\",\"authors\":\"E. Chandler, G. Cooper\",\"doi\":\"10.1109/MILCOM.1982.4805907\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study was motivated by a desire to minimize the required input signal-to-noise ratio at the receiver as a first step in optimizing the anti-intercept (AI) performance of a spread spectrum system. In the absence of error correction coding, specification of the message bit rate, the transmitted signal bandwidth, and the required bit error probability can yield a required signal-to-noise ratio for each type of SS system. Error correction coding can be used to reduce the required signal-to-noise ratio. When coding is employed, lower and upper bounds on the reduction of required signal-to-noise ratio can be determined in terms of the required bit error probability and code complexity. These performance bounds are evaluated as functions of the restricted code complexity.\",\"PeriodicalId\":179832,\"journal\":{\"name\":\"MILCOM 1982 - IEEE Military Communications Conference - Progress in Spread Spectrum Communications\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1982-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MILCOM 1982 - IEEE Military Communications Conference - Progress in Spread Spectrum Communications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MILCOM.1982.4805907\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MILCOM 1982 - IEEE Military Communications Conference - Progress in Spread Spectrum Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MILCOM.1982.4805907","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Error Correction Coding Performance Bounds for Spread Spectrum Systems
This study was motivated by a desire to minimize the required input signal-to-noise ratio at the receiver as a first step in optimizing the anti-intercept (AI) performance of a spread spectrum system. In the absence of error correction coding, specification of the message bit rate, the transmitted signal bandwidth, and the required bit error probability can yield a required signal-to-noise ratio for each type of SS system. Error correction coding can be used to reduce the required signal-to-noise ratio. When coding is employed, lower and upper bounds on the reduction of required signal-to-noise ratio can be determined in terms of the required bit error probability and code complexity. These performance bounds are evaluated as functions of the restricted code complexity.