{"title":"Optimization of Quantized Analog Signal Processing Using Genetic Algorithms and μ-Law","authors":"Qingnan Yu;Tony Chan Carusone;Antonio Liscidini","doi":"10.1109/OJCAS.2022.3154062","DOIUrl":null,"url":null,"abstract":"Digital mismatch calibration for quantized analog (QA) signal processing is proposed for the first time. Since the proposed calibration mechanism does not require uniform QA slicer levels, non-uniform quantization can be applied to improve the system performance. We propose two methods utilizing the genetic algorithm and \n<inline-formula> <tex-math>$\\mu $ </tex-math></inline-formula>\n-law to find non-uniform slicer levels offering superior performance compared to uniform levels. Simulations show that for a QA amplifier consisting of 32 slices, the signal-to-noise-and-distortion ratio (SNDR) under a multitone input can be doubled by adjusting only the quantization levels while maintaining the same structure and same power, compared to uniform quantization levels that provide 54 dB of SNDR.","PeriodicalId":93442,"journal":{"name":"IEEE open journal of circuits and systems","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9721424","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE open journal of circuits and systems","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/9721424/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 2
Abstract
Digital mismatch calibration for quantized analog (QA) signal processing is proposed for the first time. Since the proposed calibration mechanism does not require uniform QA slicer levels, non-uniform quantization can be applied to improve the system performance. We propose two methods utilizing the genetic algorithm and
$\mu $
-law to find non-uniform slicer levels offering superior performance compared to uniform levels. Simulations show that for a QA amplifier consisting of 32 slices, the signal-to-noise-and-distortion ratio (SNDR) under a multitone input can be doubled by adjusting only the quantization levels while maintaining the same structure and same power, compared to uniform quantization levels that provide 54 dB of SNDR.
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