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COMMUNICATION SYSTEM EEEB453 Chapter 5 (Part II) DIGITAL TRANSMISSION. Intan Shafinaz Mustafa Dept of Electrical Engineering Universiti Tenaga Nasional http://metalab.uniten.edu.my/~shafinaz. PCM Quantization. - PowerPoint PPT Presentation
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COMMUNICATION SYSTEM EEEB453Chapter 5 (Part II)
DIGITAL TRANSMISSION
Intan Shafinaz MustafaDept of Electrical Engineering
Universiti Tenaga Nasionalhttp://metalab.uniten.edu.my/~shafinaz
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PCM Quantization
Quantization is a process of rounding off the amplitudes of flat-top samples to a manageable number of levels.
With quantization, the total voltage range is subdivided into smaller number of sub ranges.
Called folded binary code – mirror image
Table1 shown a PCM code with a three-bit sign magnitude together with eight possible combinations.
3
PCM Quantization
Magnitude difference between adjacent steps is called quantization interval or quantum or step size or resolution.
Resolution = the magnitude of the minimum step size or, = magnitude of Vlsb .
From the Table 1, the quantization interval = 1V The smaller the magnitude of the minimum step size, the
better (smaller) the resolution and the more accurate the quantized signal will resembles the original signal.
Quantization noise, Qn ≈ Quantization error, Qe is due to
any round-off errors (quantization) in the transmitted signal, and the error would be reproduced at the Rx.
Mathematically, Qn,e = ½ quantum = Vlsb/2
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(a) Analog input signal (b) sample pulse (c) PAM signal (d) PCM code
At t1, Vi = +2V, PCM code = 110, Qe = 0
At t2, Vi = -1V, PCM code = 001, Qe = 0
At t3, Vi = +2.6V, magnitude of the sample is rounded off to the nearest valid code, i.e +3V, PCM code = 111. The rounding off process results in a quantization error, Qe = 0.4V
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Amplitudes of the signal m(t) lie in the range (-mp.mp), which is partitioned into L intervals.
Then each magnitude, v = 2mp /L
Where L = 2n, and n = number of bits.
6
An example of 3-bit quantization.
An example of 2-bit quantization.
An example of 3-bit quantization with increased sample rate.
The quality of the PCM signal can be improved by:
i. Using more bits for PCM code
ii. Reduce the magnitude of quantum
iii. Improve the resolution
iv. Sampling at higher rate
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Dynamic Range (DR)
Define as the ratio of largest possible magnitude to the smallest possible magnitude (other than 0V) that can be decoded by DAC in the Rx.
Mathematically, =
where Vmax = max voltage magnitude, Vmin = resolution (quantum value)
Dynamic range is generally expressed in dB, therefore,
DRdB = 20log
min
max
V
VDR
resolution
Vmax
min
max
V
V
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Dynamic Range (DR)
The number of bits used for a PCM code depends of the dynamic range i.e
2n – 1 ≥ DR and for a minimum number of bits
2n – 1 = DR or 2n = DR + 1
where n = number of bits in a PCM code, excluding the sign bit
DR = absolute value of dynamic range
Then DRdB = 20log (2n – 1),
and for n > 4,
DRdB ≈ 20log (2n) ≈ 6n,
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Coding Efficiency
Coding efficiency is a numerical indication of how efficiently a PCM code is utilized.
It is the ratio of the minimum number of bits required to achieve a certain dynamic range to the actual number of PCM bits used.
Mathematically,
Coding efficiency 100
bit)sign (including bits ofnumber actual
bit)sign (including bits ofnumber minimum
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Example 3 - For a PCM system with the following parameters, Maximum analog input frequency = 4kHz Maximum decoded voltage at the Rx = 2.55V Minimum dynamic range = 46dB, determine:
a. minimum sample rateb. minimum number of bits used in the PCM codec. resolutiond. quantization errore. coding efficiency
Example 4 – In a digital PCM system, the maximum quantization error is 0.6% of the peak amplitude of the modulating signal of 10 Khz. Determine:
a. The number of quantization levelsb. The number of bits per samplec. Total number of samplesd. Total number of bits.
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Signal to Quantization Noise Ratio (SQR) Linear codes – the magnitude change between any two successive
codes is the same i.e the quantum/quantization interval is equal, thus the magnitude of the quantization errors are also equal.
Recall, maximum quantization noise, Qe = ½ quantum = Vlsb/2,
Then worst-case (minimum) voltage SQR (occurs when input signal is at its minimum amplitude) is
Maximum SQR occurs at the maximum signal amplitude, i.e
From previous example,
22min
lsb
lsb
e V
V
Q
resolutionSQR
dBSQR dB 62log20min,
65.0
3maxmax
eQ
VSQR
dBSQR dB 6.156log20max,
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Signal to Quantization Noise Ratio (SQR) From the example, even though the magnitude of the Qe remains constant, the
percentage of error decreases as the magnitude of the sample increase.Thus, SQR is not constant.
For linear PCM code i.e all quantization intervals have equal magnitude, SQR or SNR is defined as
Generally,SQRdB = 10.8 + 20 log v/q
where v = rms signal voltageq = quantization interval
or SQRdB = 6.02n + 1.76 where n = no. of bits
powernoiseonquantizatiavg
powersignalavgSQRdB log10
Rq
RvSQRdB
/12log10
2
2
12log10
2
2
q
v (assume equal R)
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Linear versus Nonlinear PCM Codes. For linear coding, accuracy of the higher amplitude analog signal is the
same as for the lower amplitude signal.
SQR for lower amplitude signal is less than the higher amplitude signal.
For voice transmission, low amplitude signals are more likely to occur than large amplitude signals.
Thus a nonlinear encoding is the solution.
With non-linear coding, the step size increases with the amplitude of the input signal.
Nonlinear encoding gives larger dynamic range.
SQR is sacrificed for higher amplitude signals to achieve more accuracy for the lower amplitude signals.
However, it is difficult to fabricate nonlinear ADC.
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SQR at lower amplitude < SQR at higher amplitude
Lower amplitude values are relatively more distorted
More of voice signal is at lower amplitude
Reduce step size at lower amplitude
More accuracy at lower amplitude
Sacrifices SQR at higher amplitude
Provides higher dynamic range