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Binary Digital Communications 631<br />

FIGURE 12.16<br />

Model of binary data communications system<br />

In practice the signal waveforms transmitted over the channel are<br />

corrupted by additive noise and other types of channel distortions that<br />

ultimately limit the performance of the communications system. As a<br />

measure of performance, we normally use the average probability of error,<br />

which is often called the bit error rate.<br />

12.7.1 PROJECT 12.7: BINARY DATA COMMUNICATIONS SYSTEM<br />

The purpose of this project is to investigate the performance of a binary<br />

data communications system on an additive noise channel by means of<br />

simulation. The basic configuration of the system to be simulated is shown<br />

in Figure 12.16. Five MATLAB functions are required.<br />

1. A binary data generator module that generates a sequence of independent<br />

binary digits with equal probability.<br />

2. Amodulator module that maps a binary digit 1 into a sequence of<br />

M consecutive +1’s, and maps a binary digit 0 into a sequence of M<br />

consecutive −1’s. Thus the M consecutive +1’s represent a sampled<br />

version of the rectangular pulse.<br />

3. A noise generator that generates a sequence of uniformly distributed<br />

numbers over the interval (−a, a). Each noise sample is added to a<br />

corresponding signal sample.<br />

4. A demodulator module that sums the M successive outputs of the<br />

noise corrupted sequence +1’s or −1’s received from the channel. We<br />

assume that the demodulator is time synchronized so that it knows the<br />

beginning and end of each waveform.<br />

5. A detector and error-counting module. The detector compares the output<br />

of the modulator with zero and decides in favor of 1 if the output<br />

is greater than zero and in favor of zero if the output is less than zero.<br />

If the output of the detector does not agree with the transmitted bit<br />

from the transmitter, an error is counted by the counter. The error rate<br />

depends on the ratio (called signal-to-noise ratio) of the size of M to<br />

the additive noise power, which is P n = a 2 /3.<br />

The measured error rate can be plotted for different signal-to-noise<br />

ratios, either by changing M and keeping P n fixed or vice versa.<br />

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