1. An Overview of Digital Communication and Transmission
Chapter 4
By
Donavon M. Norwood
CS286
Dr. Moh
07/21/2009

2. Introduction - 4.1
The equipment that is used to convert the information bearing signal into a suitable form
for transmission over the communication channel and then back into a form that is
comprehensible to the end user is called the transmitter and receiver. The transmitter of
a radio system consists of:

Source Encoder – converts the data stream into a form that is more resistant to
degradations in the communication channel.

Modulator – takes a sine wave at a required frequency and modifies the signal
characteristics.

RF Section – generates a signal of sufficient power at a required frequency. It
contains a power amplifier, a local oscillator and a up converter.

Antenna – converts the electrical signal into a wave propagating in free space.
We can look at the encoder and modulator as one subsystem that maps data presented to
it by the user interface onto the RF carrier for processing, amplification, and transmission
by the RF section. The demodulator and decoder does the inverse by taking the received
RF signal and inverse mapping the signal back to the data stream for transmission. 2

3. Figure 4.1 - Overview of a communication system
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4. Figure 4.2 - Structure of the transmitter for a radio system
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5. Baseband Systems - 4.2
Information from a source can either be analog, textual or digital data.
Formatting involves sampling, equalization, and encoding which makes the
message compatible with digital processing. Transmit formatting transforms
source information into digital symbols. When data compression is used in
addition to formatting, the process is called source coding.
Figure 4.3 - Formatting, transmission, and reception of baseband signals
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6. Messages, Characters and Symbols - 4.3
In the process of digital transmission the characters are encoded first into a
sequence of bits which is called a bit stream or baseband signal. Groups of
b
b bits form a finite symbol set or word M =2 of such symbols. This is also
known as a M-ary system. The value of b and M is important for the initial design
of any digital communication system. When b = 1, the system is called a binary
system, the size of the symbol set M is 2, and the modulator uses two different
waveforms to represent the binary '1' and the binary '0' (see Figure 4.4 Binary
and quatenary systems). The symbol rate and the bit rate in this case are the
same. When b = 2, the system is called quantentary or 4-ary (M=4) system. At
each symbol time, the modulator uses one of the four different waveforms that
represent the symbol.
Figure 4.4 Binary and quatenary systems
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7. Sampling Process - 4.4
The first process in digital transmission is sampling, which converts the
analog information into a digital format which is called a discrete pulse-
amplitude-modulated waveform. The sampling process is usually
described in time domain, which is the operation that is basic to digital signal
processing and digital communication. The sampling process can be
implemented in several ways with the most popular being the sample-and-
hold operation. In this operation a switch and storage mechanism form a
sequence of samples of the continuous input waveform, and the output of
this is called pulse amplitude modulation (PAM), because the the
successive output intervals are described as a successive sequence of
pulses with amplitudes derived from the input waveform samples. The wave
of a PAM waveform can be retrieved from a low pass filter if the sampling
rate is chosen properly. The ideal form of sampling is called instantaneous
sampling.
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8. Figure 4.5 Sampling Process
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9. Aliasing - 4.4.1
Aliasing refers to the phenomenon of a high-frequency component in the spectrum of
the signal seemingly taking the identity of a lower frequency in the spectrum of its
sampled version. In figure 4.6, it shows the part of the spectrum that is aliased due to
under-sampling. The aliased spectral components represent ambigous data that can
be retrieved only under special conditions. In general the ambiguity is not resolved
and ambigous data appears in the frequency band between  f s− f m  and f m ,
where f s is the maximum frequency and f m is the sampling rate.
Figure 4.6 Sampled signal spectrum
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10. Aliasing – 4.4.1 (Continued)
In figure 4.7 we use the higher sampling rate f 's to eliminate the aliasing by
separating the spectral replicas.
Figure 4.7 Higher sampling rate to eliminate aliasing
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11. Aliasing – 4.4.1 (Continued)
Figure 4.8 and 4.9 show two ways to eliminate aliasing using anti-aliasing filters.
The analog signal is prefiltered so that the new maximum frequency f mis less
than or equal to f s / 2. Thus there are no aliasing components seen in figure 4.8
'
since f s2 f m .
Figure 4.8 Pre-filtering to eliminate aliasing
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12. Aliasing – 4.4.1 (Continued)
Figure 4.9 Post filter to eliminate aliasing portion of the spectrum
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13. Quantization - 4.4.2
In figure 4.10 each pulse is expressed as a level from a finite number of
predetermined levels; each level can be represented by a symbol from a finite
alphabet. The pulses in figure 4.10 are called quantized samples. When
sample values are quantized to a finite set, this format can interface with a
digital system. After quantization the analog waveform can still be recovered,
but not precisely; improved reconstruction fidelity of the analog waveform can be
achieved by increasing the number of quantization levels.
Figure 4.10 Flat-top quantization
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14. Uniform Quantization – 4.4.4
We consider a uniform quantization process in figure 4.11. With the quantizer
input having zero mean, and the quantizer being symmetric, the quantizer ouput
and quantization error will have a zero mean.
Figure 4.11 Uniform quantization
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15. Voice Communication - 4.5
Voice communication has very low speech volumes that predominate: 50% of
the time, the voltage characterizing detected speech energy is less than ¼ of
the rms value of the voltage. Large amplitude are rare, in which only 15% of the
time does the voltage exceed the rms value. Uniform quantization would be
wasteful for speech signals. In a system that uses equally spaced quantization
levels, the quantization noise is the same for all signal magnitudes because the
noise depends on the step size of quantization. Nonuniform quantization can
provide better quantization of the weak signals versus uniform quantization, and
also coarse quantization of the strong signal. The nonuniform quantization is
used to make SNR a constant for all signals within the input range Nonuniform
quantization is achieved by first disorting the original signal with a logarithmic
compression, and then using a uniform quantizer. A device called a expander
at the receiver to for compression. The whole process of compression is called
companding. There are two compression algorithms used today: −law
and A-law.
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16. −law Compression characteristic used in North America
[1∣input∣]
∣output∣=log log {1}
input ,output are the normalized input / output voltages respectively
=a positve constant
=0 represents uniform quantization ,=255is used North America
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17. Pulse Amplitude Modulation (PAM) - 4.6
Pulse Amplitude Modulation (PAM) is a process that represents a continuous
analog signal with a series of discrete analog pulses in which amplitude of the
information signal at a given time is encoded as a binary number. Pulse
Amplitude Modulation (PAM) is now rarely used and has been replaced by
Pulse Code Modulation (PCM). Two operations involved in the Pulse
Amplitude Modulation (PAM) signal are:
1. Sampling of the message s(t) every T seconds, where f =1/T is selected
s s s
according to the sampling theorem.
2. Lengthening the duration of each sample obtained to some constant T.
These operations are referred together as sample and hold. The reason for
increasing the duration of each sample is to avoid the use of excessive
bandwidth, since bandwith is proportional to pulse duration.
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18. Figure 4.12 Rectangular pulse and its spectrum
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19. Pulse Amplitude Modulation (PAM) – 4.6 (Continued)
Using a flat-top sampling of an analog signal with a sample-and-hold circuit such
that the sample has the same amplitude for its whole duration introduces
amplitude distortion as well as delay. The disortion caused by PAM to
transmit a signal called the aperture effect. The disortion can be corrected by
using a equalizer.
Figure 4.13 An equalizer application
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20. Pulse Code Modulation (PCM) - 4.7
Pulse Code Modulation (PCM) is a digital scheme for transmitting analog data.
It converts an analog signal into digital form. Using PCM it is possible to digitize
all forms of analog data, including full motion video, voice, music, telemetry, etc.
To obtain a PCM signal from an analog signal at the source (transmitter) of a
communication circuit, the analog signal is sampled at regular time intervals.
The sample rate is several times the maximum frequency of the analog signal.
The amplitude of the analog signal is rounded off to the nearest of several
specific, predetermined levels (quantization). The number of levels is always a
power of 2. The output of PCM is a series of binary numbers, each represented
by some power of 2 bits. At the destination of the communication circuit, the
PCM converts the binary numbers back into pulses having the same quantum
levels as those in the modulator. These pulses are then further processed to
restore the original analog waveform. When PCM is applied to a binary symbol,
the resulting binary wave form what is known as pulse code modulation
waveform. When PCM is applied to a non binary number, the resulting wave is
called M-ary pulse modulation waveform.
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21. Modulation - 4.9
Baseband signals are generated at low rates, therefore these signals are
modulated onto a radio frequency carrier for transmission. Baseband signal
s(t) is complex and can be represented mathematically as st =a t  e j t  ,
where a(t) is the sampling rate and t  is the phase. The modulation can be
classified as linear modulation or nonlinear modulation. A modulation
process is linear when a t cos t  and a t sin t  are linearly related to the
message information signal. Examples of linear modulation are amplitude
modulation in which the modulating signal signal affects only the amplitude
of the modulated signal, and phase modulation is where the modulated
signal affects only the phase of the modulated signal.
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22. Figure 4.16 Functional block diagram of a generic modulator
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23. Thank You
References:
Wireless Communication and Networking, Vijay K. Garg
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