Optimization of Quantized Analog Signal Processing Using Genetic Algorithms and μ-Law

被引:2
|
作者
Yu, Qingnan [1 ,2 ]
Carusone, Tony Chan [1 ]
Liscidini, Antonio [1 ]
机构
[1] Univ Toronto, Dept Elect & Comp Engn, Toronto, ON M5S 3G4, Canada
[2] Analog Devices Canada, Toronto, ON M5G 2C8, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Quantized analog (QA); digital calibration; adaptive linear combiner (ALC); non-uniform quantization; peak-to-average power ratio (PAPR); genetic algorithms; mu-law;
D O I
10.1109/OJCAS.2022.3154062
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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-noiseand-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.
引用
收藏
页码:38 / 49
页数:12
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