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146 lines
4.1 KiB
C++
146 lines
4.1 KiB
C++
/*
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Regression Test for the short BCH code encoder and soft decoder
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Copyright 2020 Ahmet Inan <inan@aicodix.de>
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*/
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#include <iostream>
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#include <random>
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#include <cmath>
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#include <cassert>
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#include <algorithm>
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#include <functional>
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#include "short_bch_code_decoder.hh"
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#include "short_bch_code_encoder.hh"
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template<typename TYPE>
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int popcnt(TYPE x)
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{
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int cnt = 0;
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while (x) {
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++cnt;
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x &= x-1;
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}
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return cnt;
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}
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#if 1
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// Perfect binary Golay code using x^11+x^9+x^7+x^6+x^5+x+1
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const int LOOPS = 100000;
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const float QEF_SNR = 3.0;
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const int CODE_LEN = 23;
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const int DATA_LEN = 12;
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const int RADIUS_T = 3;
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const int GEN_POLY = 0b101011100011;
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#endif
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#if 0
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// Perfect binary Golay code using x^11+x^10+x^6+x^5+x^4+x^2+1
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const int LOOPS = 100000;
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const float QEF_SNR = 3.0;
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const int CODE_LEN = 23;
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const int DATA_LEN = 12;
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const int RADIUS_T = 3;
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const int GEN_POLY = 0b110001110101;
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#endif
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#if 0
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// NASA INTRO BCH(15, 5) T=3
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const int LOOPS = 100000;
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const float QEF_SNR = 3.0;
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const int CODE_LEN = 15;
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const int DATA_LEN = 5;
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const int RADIUS_T = 3;
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const int GEN_POLY = 0b10100110111;
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#endif
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#if 0
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// BCH(31, 16) T=3
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const int LOOPS = 50000;
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const float QEF_SNR = 6.5;
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const int CODE_LEN = 31;
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const int DATA_LEN = 16;
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const int RADIUS_T = 3;
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const int GEN_POLY = 0b1000111110101111;
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#endif
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int main()
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{
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CODE::ShortBCHCodeEncoder<CODE_LEN, DATA_LEN, GEN_POLY> encode;
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CODE::ShortBCHCodeDecoder<CODE_LEN, DATA_LEN, GEN_POLY, RADIUS_T> decode;
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std::random_device rd;
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std::default_random_engine generator(rd());
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typedef std::uniform_int_distribution<int> uniform;
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typedef std::normal_distribution<float> normal;
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float min_SNR = 20;
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for (float SNR = -10; SNR <= 10; SNR += 0.1) {
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//float mean_signal = 0;
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float sigma_signal = 1;
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float mean_noise = 0;
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float sigma_noise = std::sqrt(sigma_signal * sigma_signal / (2 * std::pow(10, SNR / 10)));
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auto data = std::bind(uniform(0, (1 << DATA_LEN) - 1), generator);
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auto awgn = std::bind(normal(mean_noise, sigma_noise), generator);
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int awgn_errors = 0;
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int quantization_erasures = 0;
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int uncorrected_errors = 0;
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int decoder_errors = 0;
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for (int loop = 0; loop < LOOPS; ++loop) {
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int8_t code[CODE_LEN], orig[CODE_LEN], noisy[CODE_LEN];
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float symb[CODE_LEN];
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int dat = data();
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int enc = encode(code, dat);
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for (int i = 0; i < CODE_LEN; ++i)
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orig[i] = code[i];
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for (int i = 0; i < CODE_LEN; ++i)
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symb[i] = code[i];
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for (int i = 0; i < CODE_LEN; ++i)
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symb[i] += awgn();
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// $LLR=log(\frac{p(x=+1|y)}{p(x=-1|y)})$
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// $p(x|\mu,\sigma)=\frac{1}{\sqrt{2\pi}\sigma}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}$
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float DIST = 2; // BPSK
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float fact = DIST / (sigma_noise * sigma_noise);
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for (int i = 0; i < CODE_LEN; ++i)
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code[i] = std::min<float>(std::max<float>(std::nearbyint(fact * symb[i]), -128), 127);
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for (int i = 0; i < CODE_LEN; ++i)
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noisy[i] = code[i];
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int dec = decode(code);
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for (int i = 0; i < CODE_LEN; ++i)
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awgn_errors += noisy[i] * orig[i] < 0;
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for (int i = 0; i < CODE_LEN; ++i)
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quantization_erasures += !noisy[i];
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uncorrected_errors += dec < 0 ? CODE_LEN : popcnt(enc^dec);
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for (int i = 0; dec >= 0 && i < CODE_LEN; ++i)
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decoder_errors += ((enc^dec)&(1<<i)) && orig[i] * noisy[i] > 0;
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}
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float bit_error_rate = (float)uncorrected_errors / (float)(CODE_LEN * LOOPS);
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if (bit_error_rate < 0.0001)
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min_SNR = std::min(min_SNR, SNR);
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if (0) {
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std::cerr << SNR << " Es/N0 => AWGN with standard deviation of " << sigma_noise << " and mean " << mean_noise << std::endl;
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std::cerr << awgn_errors << " errors caused by AWGN." << std::endl;
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std::cerr << quantization_erasures << " erasures caused by quantization." << std::endl;
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std::cerr << decoder_errors << " errors caused by decoder." << std::endl;
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std::cerr << uncorrected_errors << " errors uncorrected." << std::endl;
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std::cerr << bit_error_rate << " bit error rate." << std::endl;
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} else {
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std::cout << SNR << " " << bit_error_rate << std::endl;
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}
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}
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std::cerr << "QEF at: " << min_SNR << " SNR" << std::endl;
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assert(min_SNR < QEF_SNR);
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std::cerr << "Soft BCH regression test passed!" << std::endl;
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return 0;
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}
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