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Communications System
Lecture 3
Formatting and transmission of baseband signal
Lecture 22
Encode
Transmit
Pulse
modulateSample Quantize
Demodulate/
Detect
Channel
Receive
Low-pass
filter
Decode
Pulse
waveformsBit stream
Format
Format
Digital info.
Textual
info.
Analog
info.
Textual
info.
Analog
info.
Digital info.
source
sink
Format analog signals
Lecture 23
 To transform an analog waveform into a form
that is compatible with a digital communication
system, the following steps are taken:
1. Sampling
2. Quantization and encoding
3. Baseband transmission
Sampling
Lecture 24
Time domain Frequency domain
)()()( txtxtxs   )()()( fXfXfXs  
|)(| fX
)(tx
|)(| fX
|)(| fXs
)(txs
)(tx
Aliasing effect
Lecture 25
LP filter
Nyquist rate
aliasing
Sampling theorem
Lecture 26
 Sampling theorem: A band limited signal
with no spectral components beyond , can be
uniquely determined by values sampled at uniform
intervals of
The sampling rate, is
called Nyquist rate.
Sampling
process
Analog
signal
Pulse amplitude
modulated (PAM) signal
Quantization
Lecture 27
 Amplitude quantizing: Mapping samples of a continuous
amplitude waveform to a finite set of amplitudes.
In
Out
Quantized
values
Average quantization noise power
Signal peak power
Signal power to average
quantization noise power
Encoding (PCM)
Lecture 28
 A uniform linear quantizer is called Pulse Code
Modulation (PCM).
 Pulse code modulation (PCM): Encoding the quantized
signals into a digital word (PCM word or codeword).
 Each quantized sample is digitally encoded into an l bits codeword
where L in the number of quantization levels and
Quantization example
Lecture 29
t
Ts: sampling time
x(nTs): sampled values
xq(nTs): quantized values
boundaries
Quant. levels
111 3.1867
110 2.2762
101 1.3657
100 0.4552
011 -0.4552
010 -1.3657
001 -2.2762
000 -3.1867
PCM
codeword 110 110 111 110 100 010 011 100 100 011 PCM sequence
amplitude
x(t)
Quantization error
Lecture 210
 Quantizing error: The difference between the input and
output of a quantizer
)()(ˆ)( txtxte 
+
)(tx )(ˆ tx
)()(ˆ
)(
txtx
te


AGC
x
)(xqy 
Qauntizer
Process of quantizing noise
)(tx )(ˆ tx
)(te
Model of quantizing noise
Quantization error …
Lecture 211
 Quantizing error:
 Granular or linear errors happen for inputs within the dynamic
range of quantizer
 Saturation errors happen for inputs outside the dynamic range
of quantizer
 Saturation errors are larger than linear errors
 Saturation errors can be avoided by proper tuning of AGC
Uniform and non-uniform quant.
Lecture 212
 Uniform (linear) quantizing:
 No assumption about amplitude statistics of the input.
 Not using the user-related specifications
 Robust to small changes in input statistic by not finely tuned to a
specific set of input parameters
 Simple implementation
 Application of linear quantizer:
 Signal processing, graphic and display applications, process control
applications
 Non-uniform quantizing:
 Using the input statistics to tune quantizer parameters
 Larger SNR than uniform quantizing with same number of levels
 Non-uniform intervals in the dynamic range with same quantization
noise variance
 Application of non-uniform quantizer:
 Commonly used for speech
Non-uniform quantization
Lecture 213
 It is achieved by uniformly quantizing the “compressed” signal.
 At the receiver, an inverse compression characteristic, called
“expansion” is employed to avoid signal distortion.
compression+expansion companding
)(ty)(tx )(ˆ ty )(ˆ tx
x
)(xCy  xˆ
yˆ
Compress Quantize
Channel
Expand
Transmitter Receiver
Statistics of speech amplitudes
Lecture 214
 In speech, weak signals are more frequent than strong ones.
 Using equal step sizes (uniform quantizer) gives low for weak
signals and high for strong signals.
 Adjusting the step size of the quantizer by taking into account the speech statistics
improves the SNR for the input range.
0.0
1.0
0.5
1.0 2.0 3.0
Normalized magnitude of speech signal
Probabilitydensityfunction
qN
S






qN
S






Baseband transmission
Lecture 215
 To transmit information through physical channels,
PCM sequences (code words) are transformed to
pulses (waveforms).
 Each waveform carries a symbol from a set of size M.
 Each transmit symbol represents bits of
the PCM words.
 PCM waveforms (line codes) are used for binary
symbols (M=2).
 M-ary pulse modulation are used for non-binary
symbols (M>2).
Mk 2log
PCM waveforms
Lecture 216
 PCM waveforms category:
 Phase encoded
 Multilevel binary
 Nonreturn-to-zero (NRZ)
 Return-to-zero (RZ)
1 0 1 1 0
0 T 2T 3T 4T 5T
+V
-V
+V
0
+V
0
-V
1 0 1 1 0
0 T 2T 3T 4T 5T
+V
-V
+V
-V
+V
0
-V
NRZ-L
Unipolar-RZ
Bipolar-RZ
Manchester
Miller
Dicode NRZ
PCM waveforms …
Lecture 217
 Criteria for comparing and selecting PCM
waveforms:
 Spectral characteristics (power spectral density and
bandwidth efficiency)
 Bit synchronization capability
 Error detection capability
 Interference and noise immunity
 Implementation cost and complexity

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Slides2 The Communication System midterm Slides

  • 2. Formatting and transmission of baseband signal Lecture 22 Encode Transmit Pulse modulateSample Quantize Demodulate/ Detect Channel Receive Low-pass filter Decode Pulse waveformsBit stream Format Format Digital info. Textual info. Analog info. Textual info. Analog info. Digital info. source sink
  • 3. Format analog signals Lecture 23  To transform an analog waveform into a form that is compatible with a digital communication system, the following steps are taken: 1. Sampling 2. Quantization and encoding 3. Baseband transmission
  • 4. Sampling Lecture 24 Time domain Frequency domain )()()( txtxtxs   )()()( fXfXfXs   |)(| fX )(tx |)(| fX |)(| fXs )(txs )(tx
  • 5. Aliasing effect Lecture 25 LP filter Nyquist rate aliasing
  • 6. Sampling theorem Lecture 26  Sampling theorem: A band limited signal with no spectral components beyond , can be uniquely determined by values sampled at uniform intervals of The sampling rate, is called Nyquist rate. Sampling process Analog signal Pulse amplitude modulated (PAM) signal
  • 7. Quantization Lecture 27  Amplitude quantizing: Mapping samples of a continuous amplitude waveform to a finite set of amplitudes. In Out Quantized values Average quantization noise power Signal peak power Signal power to average quantization noise power
  • 8. Encoding (PCM) Lecture 28  A uniform linear quantizer is called Pulse Code Modulation (PCM).  Pulse code modulation (PCM): Encoding the quantized signals into a digital word (PCM word or codeword).  Each quantized sample is digitally encoded into an l bits codeword where L in the number of quantization levels and
  • 9. Quantization example Lecture 29 t Ts: sampling time x(nTs): sampled values xq(nTs): quantized values boundaries Quant. levels 111 3.1867 110 2.2762 101 1.3657 100 0.4552 011 -0.4552 010 -1.3657 001 -2.2762 000 -3.1867 PCM codeword 110 110 111 110 100 010 011 100 100 011 PCM sequence amplitude x(t)
  • 10. Quantization error Lecture 210  Quantizing error: The difference between the input and output of a quantizer )()(ˆ)( txtxte  + )(tx )(ˆ tx )()(ˆ )( txtx te   AGC x )(xqy  Qauntizer Process of quantizing noise )(tx )(ˆ tx )(te Model of quantizing noise
  • 11. Quantization error … Lecture 211  Quantizing error:  Granular or linear errors happen for inputs within the dynamic range of quantizer  Saturation errors happen for inputs outside the dynamic range of quantizer  Saturation errors are larger than linear errors  Saturation errors can be avoided by proper tuning of AGC
  • 12. Uniform and non-uniform quant. Lecture 212  Uniform (linear) quantizing:  No assumption about amplitude statistics of the input.  Not using the user-related specifications  Robust to small changes in input statistic by not finely tuned to a specific set of input parameters  Simple implementation  Application of linear quantizer:  Signal processing, graphic and display applications, process control applications  Non-uniform quantizing:  Using the input statistics to tune quantizer parameters  Larger SNR than uniform quantizing with same number of levels  Non-uniform intervals in the dynamic range with same quantization noise variance  Application of non-uniform quantizer:  Commonly used for speech
  • 13. Non-uniform quantization Lecture 213  It is achieved by uniformly quantizing the “compressed” signal.  At the receiver, an inverse compression characteristic, called “expansion” is employed to avoid signal distortion. compression+expansion companding )(ty)(tx )(ˆ ty )(ˆ tx x )(xCy  xˆ yˆ Compress Quantize Channel Expand Transmitter Receiver
  • 14. Statistics of speech amplitudes Lecture 214  In speech, weak signals are more frequent than strong ones.  Using equal step sizes (uniform quantizer) gives low for weak signals and high for strong signals.  Adjusting the step size of the quantizer by taking into account the speech statistics improves the SNR for the input range. 0.0 1.0 0.5 1.0 2.0 3.0 Normalized magnitude of speech signal Probabilitydensityfunction qN S       qN S      
  • 15. Baseband transmission Lecture 215  To transmit information through physical channels, PCM sequences (code words) are transformed to pulses (waveforms).  Each waveform carries a symbol from a set of size M.  Each transmit symbol represents bits of the PCM words.  PCM waveforms (line codes) are used for binary symbols (M=2).  M-ary pulse modulation are used for non-binary symbols (M>2). Mk 2log
  • 16. PCM waveforms Lecture 216  PCM waveforms category:  Phase encoded  Multilevel binary  Nonreturn-to-zero (NRZ)  Return-to-zero (RZ) 1 0 1 1 0 0 T 2T 3T 4T 5T +V -V +V 0 +V 0 -V 1 0 1 1 0 0 T 2T 3T 4T 5T +V -V +V -V +V 0 -V NRZ-L Unipolar-RZ Bipolar-RZ Manchester Miller Dicode NRZ
  • 17. PCM waveforms … Lecture 217  Criteria for comparing and selecting PCM waveforms:  Spectral characteristics (power spectral density and bandwidth efficiency)  Bit synchronization capability  Error detection capability  Interference and noise immunity  Implementation cost and complexity