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

IEEE Open Journal of Circuits and Systems

Tony Chan Carusone¹  Antonio Liscidini¹  Qingnan Yu¹ 

[1] Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada;

关键词: Quantized analog (QA);    digital calibration;    adaptive linear combiner (ALC);    non-uniform quantization;    peak-to-average power ratio (PAPR);    genetic algorithms;   

DOI  :  10.1109/OJCAS.2022.3154062

来源: DOAJ

【 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-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.

【 授权许可】

Unknown