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

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 µ-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-anddistortion 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.

A Reconfigurable Power-Efficient Quantized Analog RF Front-End With Smart Calibration

“…First, the overall SNR for a given power will increase in the QA circuit when compared to a single (regular) amplifier as explained in [14]. Another benefit of the QA approach is reducing the impact of mismatches and nonlinearities by adjusting the individual voltage offsets between each slices (i.e., pre-distorting the voltage offsets) as shown in [15]. Lastly, the DR of the QA receiver can be reconfigured based on the value of the voltage offsets between each slice as demonstrated in the QA prototype from [16].…”

“…In contrast, the proposed QA receiver kept the same distribution for all scenarios, evaluated through an optimization algorithm. The strategy was to find the proper offset for each slice by performing an agile calibration of the mismatch impairments and a correction of distortion of the overall RX, as theoretically suggested in [15]. Hence, the generation of the DC offset was carefully designed to be capable of a more flexible choice of the offsets compared to [16].…”