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Simulation of a convolutional encoder and decoder

In this example we will show how to use the convolutional encoder/decoder class in it++. The Viterbi decoder uses the soft received values.

#include <itpp/itcomm.h>
using namespace itpp;
//These lines are needed for use of cout and endl
using std::cout;
using std::endl;
int main()
{
//Scalars
int constraint_length, MaxNrofErrors, Nobits, MaxIterations, p, i;
double Ec, Eb;
//Vectors
ivec generators;
vec EbN0dB, EbN0, N0, ber, trans_symbols, rec_symbols;
bvec uncoded_bits, coded_bits, decoded_bits;
//Classes
BPSK bpsk;
BERC berc;
Convolutional_Code conv_code;
AWGN_Channel channel;
/*
Set up the convolutional encoder/decoder class:
The generators are given in octal form by adding a zero in front of the numbers.
In this example we will simulate a rate 1/3 code that is listed in J. G. Proakis,
"Digital communications". The encoder has constraint length 7.
*/
generators.set_size(3, false);
generators(0) = 0133;
generators(1) = 0145;
generators(2) = 0175;
constraint_length = 7;
conv_code.set_generator_polynomials(generators, constraint_length);
//Init: Calculate some simulation specific parameters:
Ec = 1.0;
EbN0dB = linspace(-2, 6, 5);
EbN0 = inv_dB(EbN0dB);
Eb = Ec / conv_code.get_rate();
N0 = Eb * pow(EbN0, -1);
MaxNrofErrors = 100;
Nobits = 10000;
MaxIterations = 10;
ber.set_size(EbN0dB.length(), false);
ber.clear();
//Randomize the random number generators.
for (p = 0; p < EbN0dB.length(); p++) {
cout << "Now simulating point " << p + 1 << " out of " << EbN0dB.length() << endl;
berc.clear(); //Clear the bit error rate counter.
channel.set_noise(N0(p) / 2.0); //Set the noise value of the AWGN channel.
for (i = 0; i < MaxIterations; i++) {
uncoded_bits = randb(Nobits); //The uncoded bits.
coded_bits = conv_code.encode(uncoded_bits); //The convolutional encoder function.
bpsk.modulate_bits(coded_bits, trans_symbols); //The BPSK modulator.
rec_symbols = channel(trans_symbols); //The AWGN channel.
decoded_bits = conv_code.decode(rec_symbols); //The Viterbi decoder function.
berc.count(uncoded_bits, decoded_bits); //Count the errors.
ber(p) = berc.get_errorrate();
//Break the simulation on this point if sufficient number of bit errors were observed:
if (berc.get_errors() > MaxNrofErrors) {
cout << "Breaking on point " << p + 1 << " with " << berc.get_errors() << " errors." << endl;
break;
}
}
}
//Print the results:
cout << "BER = " << ber << endl;
cout << "EbN0dB = " << EbN0dB << endl;
//Exit program:
return 0;
}

When you run this program, the output will look something like this:

Now simulating point 1 out of 5
Breaking on point 1 with 3297 errors.
Now simulating point 2 out of 5
Breaking on point 2 with 781 errors.
Now simulating point 3 out of 5
Breaking on point 3 with 112 errors.
Now simulating point 4 out of 5
Now simulating point 5 out of 5
BER = [0.330858 0.0783743 0.00280983 0 0]
EbN0dB = [-2 0 2 4 6]
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