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35 DATA RATE LIMITS
35 DATA RATE LIMITS
A very important consideration in data communications
A very important consideration in data communications
is how fast we can send data, in bits per second, over a
is how fast we can send data, in bits per second, over a
channel. Data rate depends on three factors:
channel. Data rate depends on three factors:
The bandwidth available
The bandwidth available
The level of the signals we use
The level of the signals we use
. The quality of the channel (the level of noise) . The quality of the channel (the level of noise)
Noiseless Channel: Nyquist Bit Rate
Noisy Channel: Shannon Capacity
Using Both Limits
Topics discussed in this section:
Topics discussed in this section:
Increasing the levels of a signal
increases the probability of an error
occurring, in other words it reduces the
reliability of the system. Why??
Note
Nyquist Theorem
Nyquist gives the upper bound for the
bit rate of a transmission system by
calculating the bit rate directly
from the number of bits in a symbol
(or signal levels) and the bandwidth
of the system (assuming 2 symbols/per
cycle and first harmonic).
Nyquist theorem states that for a
noiseless channel:
C = 2 B log
2
2
n
C= capacity in bps
B = bandwidth in Hz
Does the Nyquist theorem bit rate agree with the
intuitive bit rate described in baseband transmission?
Solution
They match when we have only two levels. We said, in
baseband transmission, the bit rate is 2 times the
bandwidth if we use only the first harmonic in the worst
case. However, the Nyquist formula is more general than
what we derived intuitively; it can be applied to baseband
transmission and modulation. Also, it can be applied
when we have two or more levels of signals.
Example 3.
Consider the same noiseless channel transmitting a
signal with four signal levels (for each level, we send 2
bits). The maximum bit rate can be calculated as
Example 3.
We need to send 265 kbps over a noiseless channel with
a bandwidth of 20 kHz. How many signal levels do we
need?
Solution
We can use the Nyquist formula as shown:
Example 3.
Since this result is not a power of 2, we need to either
increase the number of levels or reduce the bit rate. If we
have 128 levels, the bit rate is 280 kbps. If we have 64
levels, the bit rate is 240 kbps.
Consider an extremely noisy channel in which the value
of the signal-to-noise ratio is almost zero. In other
words, the noise is so strong that the signal is faint. For
this channel the capacity C is calculated as
Example 3.
This means that the capacity of this channel is zero
regardless of the bandwidth. In other words, we cannot
receive any data through this channel.
We can calculate the theoretical highest bit rate of a
regular telephone line. A telephone line normally has a
bandwidth of 3000. The signal-to-noise ratio is usually
3162. For this channel the capacity is calculated as
Example 3.
This means that the highest bit rate for a telephone line
is 34.860 kbps. If we want to send data faster than this,
we can either increase the bandwidth of the line or
improve the signal-to-noise ratio.
For practical purposes, when the SNR is very high, we
can assume that SNR + 1 is almost the same as SNR. In
these cases, the theoretical channel capacity can be
simplified to
Example 3.
For example, we can calculate the theoretical capacity of
the previous example as
We have a channel with a 1-MHz bandwidth. The SNR
for this channel is 63. What are the appropriate bit rate
and signal level?
Solution
First, we use the Shannon formula to find the upper
limit.
Example 3.
The Shannon capacity gives us the
upper limit; the Nyquist formula tells us
how many signal levels we need.
Note
