matlab_计算信号的高阶累积量和二阶矩 8PSK_16QAM_64QAM_盲检测

clear all clc; nSamp = 8; %矩形脉冲的取样点数 numSymb = 1000; %每种SNR下的传输的符号数 M=8; %QPSK的符号类型数 SNR=0:30; %SNR的范围 grayencod=[0 1 2 3 6 7 4 5]; %Gray编码格式 ss=ones(100,31);%先定义一个矩阵，防止报错 for jj=1:1000 %独立重复实验的次数 for ii=1:length(SNR) msg=randsrc(1,numSymb,[0:7]); %产生发送符号 msg_gr=grayencod(msg+1); %进行Gray编码影射 msg_tx=pskmod(msg_gr,M); %QPSK调制 msg_tx=rectpulse(msg_tx,nSamp); %矩形脉冲成形 msg_rx=awgn(msg_tx,SNR(ii),'measured'); %通过AWGN信道 % msg_rx_down = intdump(msg_rx,nSamp); %匹配滤波相干解调 % msg_gr_demod = pskdemod(msg_rx_down,M); %QPSK解调 % [dummy graydecod] = sort(grayencod); graydecod = graydecod - 1; % msg_demod = graydecod(msg_gr_demod+1); %Gray编码逆映射 % [errorBit BER(ii)] = biterr(msg, msg_demod, log2(M)); %计算BER % [errorSym SER(ii)] = symerr(msg, msg_demod); %计算SER d(ii)=mean(abs(msg_rx)); e(ii)=mean((abs(msg_rx)).^4)-(abs(mean(msg_rx.^2))).^2-2*(mean((abs(msg_rx)).^2)).^2; %c42的值 ss(jj,ii)=e(ii);%多次重复实验结果写得的矩阵，横轴是信噪比，纵轴是每次独立实验的结果 f(ii)=abs(mean(msg_rx.^4)-3*(mean(msg_rx.^2).^2));%c40的绝对值 j(ii)=abs(f(ii)/e(ii));%特征值f1 % k(ii)=mean((abs(msg_rx)).^6)-9*mean((abs(msg_rx)).^4)*mean((abs(msg_rx)).^2)+9*mean((abs(msg_rx)).^2)*(abs(mean(msg_rx.^2)).^2)+12*mean((abs(msg_rx)).^2);%c63 k(ii)=mean((abs(msg_rx)).^6)-9*e(ii)*mean(abs(msg_rx).^2)-6*(mean(abs(msg_rx).^2).^3);%c63 l(ii)=abs(k(ii).^2)/abs(e(ii).^3);%特征值f2 end end plot(SNR,f,'-ko',SNR,e,'-k*',SNR,0,'-k+',SNR,-1,'-k^'); hold on; legend('c40绝对值','c42的值','c40理论值','c42理论值') title('8PSK高阶累积量') % plot(SNR,e); figure plot(SNR,l,'-k*'); title('f参数')