Ch4 1 Data communication and networking by neha g. kurale

4-2 ANALOG-TO-DIGITAL CONVERSION
A digital signal is superior to an analog signal because
it is more robust to noise and can easily be recovered,
corrected and amplified. For this reason, the tendency
today is to change an analog signal to digital data. In
this section we describe two techniques, pulse code
modulation and delta modulation.
 Pulse Code Modulation (PCM)
 Delta Modulation (DM)
Topics discussed in this section:

PCM
 PCM consists of three steps to digitize an
analog signal:
1. Sampling
2. Quantization
3. Binary encoding
 Before we sample, we have to filter the
signal to limit the maximum frequency of
the signal as it affects the sampling rate.
 Filtering should ensure that we do not
distort the signal, ie remove high frequency
components that affect the signal shape.

Figure 4.21 Components of PCM encoder

Sampling
 Analog signal is sampled every TS secs.
 Ts is referred to as the sampling interval.
 fs = 1/Ts is called the sampling rate or
sampling frequency.
 There are 3 sampling methods:
 Ideal - an impulse at each sampling instant
 Natural - a pulse of short width with varying
amplitude
 Flattop - sample and hold, like natural but with
single amplitude value
 The process is referred to as pulse amplitude
modulation PAM and the outcome is a signal
with analog (non integer) values

Figure 4.22 Three different sampling methods for PCM

According to the Nyquist theorem, the
sampling rate must be
at least 2 times the highest frequency
contained in the signal.
Note

Figure 4.23 Nyquist sampling rate for low-pass and bandpass signals

For an intuitive example of the Nyquist theorem, let us
sample a simple sine wave at three sampling rates: fs = 4f
(2 times the Nyquist rate), fs = 2f (Nyquist rate), and
fs = f (one-half the Nyquist rate). Figure 4.24 shows the
sampling and the subsequent recovery of the signal.
It can be seen that sampling at the Nyquist rate can create
a good approximation of the original sine wave (part a).
Oversampling in part b can also create the same
approximation, but it is redundant and unnecessary.
Sampling below the Nyquist rate (part c) does not produce
a signal that looks like the original sine wave.
Example 4.6

Figure 4.24 Recovery of a sampled sine wave for different sampling rates

Consider the revolution of a hand of a clock. The second
hand of a clock has a period of 60 s. According to the
Nyquist theorem, we need to sample the hand every 30 s
(Ts = T or fs = 2f ). In Figure 4.25a, the sample points, in
order, are 12, 6, 12, 6, 12, and 6. The receiver of the
samples cannot tell if the clock is moving forward or
backward. In part b, we sample at double the Nyquist rate
(every 15 s). The sample points are 12, 3, 6, 9, and 12.
The clock is moving forward. In part c, we sample below
the Nyquist rate (Ts = T or fs = f ). The sample points are
12, 9, 6, 3, and 12. Although the clock is moving forward,
the receiver thinks that the clock is moving backward.
Example 4.7

Figure 4.25 Sampling of a clock with only one hand

An example related to Example 4.7 is the seemingly
backward rotation of the wheels of a forward-moving car
in a movie. This can be explained by under-sampling. A
movie is filmed at 24 frames per second. If a wheel is
rotating more than 12 times per second, the under-
sampling creates the impression of a backward rotation.
Example 4.8

Telephone companies digitize voice by assuming a
maximum frequency of 4000 Hz. The sampling rate
therefore is 8000 samples per second.
Example 4.9

A complex low-pass signal has a bandwidth of 200 kHz.
What is the minimum sampling rate for this signal?
Solution
The bandwidth of a low-pass signal is between 0 and f,
where f is the maximum frequency in the signal.
Therefore, we can sample this signal at 2 times the
highest frequency (200 kHz). The sampling rate is
therefore 400,000 samples per second.
Example 4.10

A complex bandpass signal has a bandwidth of 200 kHz.
What is the minimum sampling rate for this signal?
Solution
We cannot find the minimum sampling rate in this case
because we do not know where the bandwidth starts or
ends. We do not know the maximum frequency in the
signal.
Example 4.11

Quantization
 Sampling results in a series of pulses of
varying amplitude values ranging between
two limits: a min and a max.
 The amplitude values are infinite between the
two limits.
 We need to map the infinite amplitude values
onto a finite set of known values.
 This is achieved by dividing the distance
between min and max into L zones, each of
height 
 = (max - min)/L

Quantization Levels
 The midpoint of each zone is assigned a
value from 0 to L-1 (resulting in L
values)
 Each sample falling in a zone is then
approximated to the value of the
midpoint.

Quantization Zones
 Assume we have a voltage signal with
amplitutes Vmin=-20V and Vmax=+20V.
 We want to use L=8 quantization levels.
 Zone width = (20 - -20)/8 = 5
 The 8 zones are: -20 to -15, -15 to -10,
-10 to -5, -5 to 0, 0 to +5, +5 to +10,
+10 to +15, +15 to +20
 The midpoints are: -17.5, -12.5, -7.5, -
2.5, 2.5, 7.5, 12.5, 17.5

Assigning Codes to Zones
 Each zone is then assigned a binary code.
 The number of bits required to encode the
zones, or the number of bits per sample as it
is commonly referred to, is obtained as
follows:
nb = log2 L
 Given our example, nb = 3
 The 8 zone (or level) codes are therefore:
000, 001, 010, 011, 100, 101, 110, and 111
 Assigning codes to zones:
 000 will refer to zone -20 to -15
 001 to zone -15 to -10, etc.

Figure 4.26 Quantization and encoding of a sampled signal

Quantization Error
 When a signal is quantized, we introduce an
error - the coded signal is an approximation
of the actual amplitude value.
 The difference between actual and coded
value (midpoint) is referred to as the
quantization error.
 The more zones, the smaller  which results
in smaller errors.
 BUT, the more zones the more bits required
to encode the samples -> higher bit rate

Quantization Error and SNQR
 Signals with lower amplitude values will suffer
more from quantization error as the error
range: /2, is fixed for all signal levels.
 Non linear quantization is used to alleviate
this problem. Goal is to keep SNQR fixed for
all sample values.
 Two approaches:
 The quantization levels follow a logarithmic curve.
Smaller ’s at lower amplitudes and larger’s at
higher amplitudes.
 Companding: The sample values are compressed
at the sender into logarithmic zones, and then
expanded at the receiver. The zones are fixed in
height.

Bit rate and bandwidth
requirements of PCM
 The bit rate of a PCM signal can be calculated form
the number of bits per sample x the sampling rate
Bit rate = nb x fs
 The bandwidth required to transmit this signal
depends on the type of line encoding used. Refer to
previous section for discussion and formulas.
 A digitized signal will always need more bandwidth
than the original analog signal. Price we pay for
robustness and other features of digital transmission.

We want to digitize the human voice. What is the bit rate,
assuming 8 bits per sample?
Solution
The human voice normally contains frequencies from 0
to 4000 Hz. So the sampling rate and bit rate are
calculated as follows:
Example 4.14

PCM Decoder
 To recover an analog signal from a digitized
signal we follow the following steps:
 We use a hold circuit that holds the amplitude
value of a pulse till the next pulse arrives.
 We pass this signal through a low pass filter with a
cutoff frequency that is equal to the highest
frequency in the pre-sampled signal.
 The higher the value of L, the less distorted a
signal is recovered.

Figure 4.27 Components of a PCM decoder

We have a low-pass analog signal of 4 kHz. If we send the
analog signal, we need a channel with a minimum
bandwidth of 4 kHz. If we digitize the signal and send 8
bits per sample, we need a channel with a minimum
bandwidth of 8 × 4 kHz = 32 kHz.
Example 4.15

Delta Modulation
 This scheme sends only the difference
between pulses, if the pulse at time tn+1 is
higher in amplitude value than the pulse at
time tn, then a single bit, say a “1”, is used to
indicate the positive value.
 If the pulse is lower in value, resulting in a
negative value, a “0” is used.
 This scheme works well for small changes in
signal values between samples.
 If changes in amplitude are large, this will
result in large errors.

Figure 4.28 The process of delta modulation

Figure 4.29 Delta modulation components

Figure 4.30 Delta demodulation components

Delta PCM (DPCM)
 Instead of using one bit to indicate positive
and negative differences, we can use more
bits -> quantization of the difference.
 Each bit code is used to represent the value
of the difference.
 The more bits the more levels -> the higher
the accuracy.

4-3 TRANSMISSION MODES
The transmission of binary data across a link can be
accomplished in either parallel or serial mode. In
parallel mode, multiple bits are sent with each clock
tick. In serial mode, 1 bit is sent with each clock tick.
While there is only one way to send parallel data, there
are three subclasses of serial transmission:
asynchronous, synchronous, and isochronous.
 Parallel Transmission
 Serial Transmission
Topics discussed in this section:

Figure 4.31 Data transmission and modes

Figure 4.32 Parallel transmission

Figure 4.33 Serial transmission

In asynchronous transmission, we send
1 start bit (0) at the beginning and 1 or
more stop bits (1s) at the end of each
byte. There may be a gap between
each byte.
Note

Asynchronous here means
“asynchronous at the byte level,”
but the bits are still synchronized;
their durations are the same.
Note

Figure 4.34 Asynchronous transmission

In synchronous transmission, we send
bits one after another without start or
stop bits or gaps. It is the responsibility
of the receiver to group the bits. The bits
are usually sent as bytes and many
bytes are grouped in a frame. A frame is
identified with a start and an end byte.
Note

Figure 4.35 Synchronous transmission

Isochronous
 In isochronous transmission we cannot
have uneven gaps between frames.
 Transmission of bits is fixed with equal
gaps.

Ch4 1 Data communication and networking by neha g. kurale

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Ch4 1 Data communication and networking by neha g. kurale