数字调制识别分类 PSK+QPSK+8QAM+16QAM+32QAM+64QAM实现加性高斯白噪声信道下数字调制识别分类附Matlab源码

clear all;
clc;

%%参数设置
snr_mini = 5;               %信噪比最小值
snr_max = 20;               %信噪比最大值
TxSampleRate = 32e9;        %信号的码元速率
TxLinewidth = 0;            %发射信号的载波线宽
TxCarrierRate = 0;          %发射信号的载波频率
DataSymbolNumber = 10000;   %数据点的个数
ClassifySetNumber = 100;    %独立仿真的次数
printJ = 5;                 %需要输出观察的调制方式，0为不输出
printXingZuo = 1;           %是否需要打印星座图，0为不打印

% 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 = snr_mini:snr_max;  %SNR信噪比的设置，单位dB
classify_correct_ratio = zeros(length(snr), 1);
for snrIndex= 1:length(snr)
if(j==printJ) fprintf('\n--------------- snr = %d ------------\n',snr(snrIndex)); end
classify_correct = 0;

for i = 1:ClassifySetNumber
Tx.SampleRate = TxSampleRate; %symbol Rate，信号的码元速率，可以自行定义
Tx.Linewidth = TxLinewidth;%发射信号的载波的线宽，一般与信号的相位噪声有关
Tx.Carrier = DataSymbolNumber;%发射信号的载波频率
M = 2^System.BitPerSymbol;
%%信号生成


%subplot(1,7,snrIndex);%绘制原始噪声
%plot(Rx.Signal,'.');
%plot(CMAOUT,'.');
end
classify_correct_ratio(snrIndex) = classify_correct/ClassifySetNumber*100;
end
%%绘制图形
figure(1);subplot(2, 3, j);
plot(snr, classify_correct_ratio, '-b.');
axis([snr_mini snr_max 0 110]);
ylabel('识别正确率/%');
xlabel('信噪比/dB');
if(j == 1)
    title('PSK调制方式识别');
elseif(j == 2)
    title('QPSK调制方式识别');
elseif(j == 3)
    title('8QAM调制方式识别');
elseif(j == 4)
    title('16QAM调制方式识别');
elseif(j == 5)
    title('32QAM调制方式识别');
else
    title('64QAM调制方式识别');
end

if(printXingZuo==1)
figure(2);subplot(2, 3, j);
plot(real(CMAOUT),imag(CMAOUT),'.'); 
if(j == 1)
    title('PSK调制方式星座图');
elseif(j == 2)
    title('QPSK调制方式星座图');
elseif(j == 3)
    title('8QAM调制方式星座图');
elseif(j == 4)
    title('16QAM调制方式星座图');
elseif(j == 5)
    title('32QAM调制方式星座图');
else
    title('64QAM调制方式星座图');
end
end

end