加性高斯白噪声信道（AWGN）下的digital调制格式识别分类

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%%--------------------------------------------------------------------------%题目：加性高斯白噪声信道（AWGN）下的digital调制格式识别分类%实现思路：函数对信号添加了高斯白噪声，并进行带限处理，模拟信道的作用%输入:%输出：不同调制格式下识别率关于SNR的函数%%--------------------------------------------------------------------------clear all;close all;clc;% signal generation;如果想要进行100组独立的测试，可以建立100次循环，产生100组独立的数据for j = 1:6  % bit per symbol: 1. PSK; 2. QPSK; 3.8QAM; 4. 16QAM; 5. 32QAM; 6.64QAM;System.BitPerSymbol = j;snr = -5:20;  %SNR信噪比的设置，单位dBcorrect_rate = zeros(length(snr), 1); %识别正确率for snrIndex= 1:length(snr)    correct = 0;    for i =1:100     %进行100组独立测试                temp = randi([1 6], 1, 1);         %测试随机模式下的识别正确率，不测随机模式时请注释掉        System.BitPerSymbol = temp(1,1);   %测试随机模式下的识别正确率，不测随机模式时请注释掉                Tx.SampleRate = 32e9; %symbol Rate，信号的码元速率，可以自行定义        Tx.Linewidth = 0;%发射信号的载波的线宽，一般与信号的相位噪声有关，大小可自行设置，这里暂时设置为0        Tx.Carrier = 0;%发射信号的载波频率，可自行设置，这里暂设为0        M = 2^System.BitPerSymbol;        numberOfData = 10000;                %测试不同数据点在随机模式下的识别正确率，不测时请注释掉                if(j == 1)            numberOfData = 10000;        elseif(j == 2)            numberOfData = 1000;        elseif(j == 3)            numberOfData = 100;        end                Tx.DataSymbol = randi([0 M-1],1,numberOfData);%每一次随机产生的数据量，这里暂时设为数据点个数为10000个%数据的不同调制方式产生：这里把2^3（8QAM）的形式单独拿出来设置，是为了实现最优的星型8QAM星座图        if M ~= 8;            h = modem.qammod('M', M, 'SymbolOrder', 'Gray');            Tx.DataConstel = modulate(h,Tx.DataSymbol);        else            tmp = Tx.DataSymbol;            tmp2  = zeros(1,length(Tx.DataSymbol));            for kk = 1:length(Tx.DataSymbol)                switch tmp(kk)                    case 0                        tmp2(kk) = 1 + 1i;                    case 1                        tmp2(kk) = -1 + 1i;                    case 2                        tmp2(kk) = -1 - 1i;                    case 3                        tmp2(kk) = 1 - 1i;                    case 4                        tmp2(kk) = 1+sqrt(3);                    case 5                        tmp2(kk) = 0 + 1i .* (1+sqrt(3));                    case 6                        tmp2(kk) = 0 - 1i .* (1+sqrt(3));                    case 7                        tmp2(kk) = -1-sqrt(3);                end            end            Tx.DataConstel = tmp2;            clear tmp tmp2;        end        Tx.Signal = Tx.DataConstel;        %数据的载波加载，考虑到相位噪声等        N = length(Tx.Signal);        dt = 1/Tx.SampleRate;        t = dt*(0:N-1);        Phase1 = [0, cumsum(normrnd(0,sqrt(2*pi*Tx.Linewidth/(Tx.SampleRate)), 1, N-1))];        carrier1 = exp(1i*(2*pi*t*Tx.Carrier + Phase1));        Tx.Signal = Tx.Signal.*carrier1;        Rx.Signal = awgn(Tx.Signal,snr(snrIndex),'measured');%数据在AWGN信道下的接收        CMAOUT = Rx.Signal;        %normalization接收信号功率归一化        CMAOUT=CMAOUT/sqrt(mean(abs(CMAOUT).^2));               s = Rx.Signal;        signalPower = mean(abs(s).^2);        noisePower = signalPower/(10^(snr(snrIndex)/10));        C20 = mean(s.^2);        C21 = mean(abs(s).^2);        C21 = C21-noisePower;        C40 = mean(s.^4)-3*C20^2;        C41=mean((s.^3).*conj(s))-3*C20*C21;        C42=mean(abs(s).^4)-abs(C20)^2-2*C21^2;        C40_p=C40/C21^2;                %根据C40_p的取值范围来判断哪种调制格式        if(abs(C40_p) >= 1.5)            number = 1;        elseif(abs(C40_p) >= 0.9 && abs(C40_p) < 1.1)            number = 2;        elseif(abs(C40_p) >= 1.1 && abs(C40_p) < 1.3)            number = 3;        elseif(abs(C40_p) >= 0.65 && abs(C40_p) < 0.9)            number = 4;        elseif(abs(C40_p)<=0.4)            number = 5;        elseif(abs(C40_p) > 0.4 && abs(C40_p) < 0.65)            number = 6;        end        if(number == System.BitPerSymbol)            correct = correct + 1;        end    end    correct_rate(snrIndex) = correct; endfigure(j);if(j == 1)    plot(snr, correct_rate, 'r.-');%测试不同数据点时随机模式下的识别正确率，不测时请注释掉   hold onelseif(j == 2)     plot(snr, correct_rate, 'gv-');elseif(j == 3)     plot(snr, correct_rate, 'r*-');   elseif(j == 4)     plot(snr, correct_rate, 'r.-');elseif(j == 5)     plot(snr, correct_rate, 'gv-');elseif(j == 6)     plot(snr, correct_rate, 'r*-');     hold offend axis([-5 20 0 110]);xlabel('dB');ylabel('识别正确率');%测试不同数据点时随机模式下的识别正确率，不测时请注释掉legend('N=10000','N=1000','N=100');title('随机模式识别时不同数据点数量的识别正确率对比')if(j == 1)    title('PSK识别正确率');elseif(j == 2)    title('QPSK识别正确率');elseif(j == 3)    title('8QAM识别正确率');elseif(j == 4)    title('16QAM识别正确率');elseif(j == 5)    title('32QAM识别正确率');elseif(j == 6)    title('64QAM识别正确率');end%{plot(real(Rx.Signal),imag(Rx.Signal),'.');if(j == 1)    title('PSK星座图');elseif(j == 2)    title('QPSK星座图');elseif(j == 3)    title('8QAM星座图');elseif(j == 4)    title('16QAM星座图');elseif(j == 5)    title('32QAM星座图');elseif(j == 6)    title('64QAM星座图');endxlabel('实部');ylabel('虚部');%}title('随机模式识别时的识别正确率');%测试随机模式下的识别正确率，不测随机模式时请注释掉end
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