# fixed.realQuantizationNoiseStandardDeviation

Estimate standard deviation of quantization noise of real-valued signal

Since R2021b

## Description

example

noiseStandardDeviation = fixed.realQuantizationNoiseStandardDeviation(precisionBits) returns an estimate of the quantization noise standard deviation of a real-valued signal with a quantization level q=2-precisionBits, where precisionBits is the required number of bits of precision.

## Examples

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Quantizing a real signal to $p$ bits of precision can be modeled as a linear system that adds normally distributed noise with a standard deviation of ${ϛ}_{\text{noise}}=\frac{{2}^{-p}}{\sqrt{12}}$ [1,2].

Compute the theoretical quantization noise standard deviation with $p$ bits of precision using the fixed.realQuantizationNoiseStandardDeviation function.

p = 14;
theoreticalQuantizationNoiseStandardDeviation = fixed.realQuantizationNoiseStandardDeviation(p);

The returned value is ${ϛ}_{\text{noise}}=\frac{{2}^{-p}}{\sqrt{12}}$.

Create a real signal with $n$ samples.

rng('default');
n = 1e6;
x = rand(1,n);

Quantize the signal with $p$ bits of precision.

wordLength = 16;
x_quantized = quantizenumeric(x,1,wordLength,p);

Compute the quantization noise by taking the difference between the quantized signal and the original signal.

quantizationNoise = x_quantized - x;

Compute the measured quantization noise standard deviation.

measuredQuantizationNoiseStandardDeviation = std(quantizationNoise)
measuredQuantizationNoiseStandardDeviation = 1.7607e-05

Compare the actual quantization noise standard deviation to the theoretical and see that they are close for large values of $n$.

theoreticalQuantizationNoiseStandardDeviation
theoreticalQuantizationNoiseStandardDeviation = 1.7619e-05

References

1. Bernard Widrow. “A Study of Rough Amplitude Quantization by Means of Nyquist Sampling Theory”. In: IRE Transactions on Circuit Theory 3.4 (Dec. 1956), pp. 266–276.

2. Bernard Widrow and István Kollár. Quantization Noise – Roundoff Error in Digital Computation, Signal Processing, Control, and Communications. Cambridge, UK: Cambridge University Press, 2008.

## Input Arguments

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Required number of bits of precision, specified as a positive integer-valued scalar.

Data Types: double

## Output Arguments

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Noise standard deviation, returned as a scalar.

## Tips

fixed.realQuantizationNoiseStandardDeviation is used in these functions.

## Algorithms

The variance of a real-valued error sequence e(k) with quantization level q=2-precisionBits [1][2] is

${\sigma }_{q}^{2}=\frac{1}{q}{\int }_{-q/2}^{q/2}{e}^{2}de=\frac{{q}^{2}}{12}=\frac{{2}^{-2precisionBits}}{12}.$

The standard deviation of a real error sequence e(k) is

${\sigma }_{q}=\frac{{2}^{-precisionBits}}{\sqrt{12}}.$

## References

[1] Widrow, Bernard. "A Study of Rough Amplitude Quantization by Means of Nyquist Sampling Theory." IRE Transactions on Circuit Theory 3, no.4 (December 1956): 266-276.

[2] Widrow, Bernard, and Kollár, István. Quantization Noise – Roundoff Error in Digital Computation, Signal Processing, Control, and Communications. Cambridge, UK: Cambridge University Press, 2008.

## Version History

Introduced in R2021b