Which one of the following represents the output signal-to-noise ratio of a uniform quantizer? (where P denotes average power of the message signal m(t) and R denotes number of bits per sample)

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UPSC ESE Electronics & Communication 2022 Official Paper
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  1. \(\left( {\frac{{3P}}{{m_{\max }^2}}} \right){2^{2R}}\)
  2. \(\left( {\frac{{2P}}{{m_{\max }^3}}} \right){2^{3R}}\)
  3. \(\left( {\frac{{3P}}{{m_{\max }^2}}} \right){2^R}\)
  4. \(\left( {\frac{{3P}}{{m_{\max }^2}}} \right)\)

Answer (Detailed Solution Below)

Option 1 : \(\left( {\frac{{3P}}{{m_{\max }^2}}} \right){2^{2R}}\)
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Concept:

Signal-to-quantization-noise ratio (SQNR) is a widely used quality measure in analyzing digitizing schemes such as pulse-code modulation (PCM). The SQNR reflects the relationship between the maximum nominal signal strength and the quantization error (also known as quantization noise) introduced in the analog-to-digital conversion.

The SQNR formula is derived from the general signal-to-noise ratio (SNR) formula:

\(SQNR=\frac{P_{signal}}{P_{noise}}\)

Calculation:

Given the average power of the message signal

P = mmax2(t)

Noise power:

Pnoise = Δ2/12 = mmax2(t)/3L2

Signal to noise ratio:

\(\mathrm{SQNR}=\frac{P}{m_{max}^{2}(t)/3L^{2}}=\frac{3P}{m_{max}^{2}(t)}L^{2}=\frac{3P}{m_{max}^{2}(t)}L^{2}=\left ( \frac{3P}{m_{max}^{2}(t)} \right )2^{2R}[\because \,L=2^{R}]\)

 

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