基于BP神经网络的数字调制识别MATLAB代码

clear all; close all; echo off; fc=20000; %载波频率 fs=40000; %采样速率 k=2; code_size=15*round(k*fs/fc); %信息码元长度 t0=5.5; %信号长度 Ns=256; %采样点个数 fd=200; %符号速率 ts=1/fs; %采样周期 M=64; %码元个数 ti=1/fd; %码元间隔 N=ti/ts; t=[0:ts:t0]; select=menu('调制方式','2ASK','2FSK','2PSK','4ASK','4FSK','4PSK'); switch select case 1 % 2ASK signal x=randi(1,M); m=sin(2*pi*fc*t); y=ones(1,M*N); for i=1:M for j=1:N y((i-1)*N+j)=x(i)*m(j); end end T=zeros(6,50); T(1,1:50)=1; case 2 %2FSK signal x=randi(1,M); m1=sin(2*pi*fc*t); m2=sin(2*pi*2*fc*t); y=zeros(1,M*N); for i=1:M if x(i)==1 for j=1:N y((i-1)*N+j)=x(i)*m1(j); end elseif x(i)==0 for j=1:N y((i-1)*N+j)=(1-x(i))*m2(j); end end end T=zeros(6,50); T(2,1:50)=1; case 3 %2PSK signal, x=randi(1,M); m1=sin(2*pi*fc*t); m2=sin(2*pi*fc*t+pi); y=zeros(1,M*N); for i=1:M if x(i)==1 for j=1:N y((i-1)*N+j)=x(i)*m1(j); end elseif x(i)==0 for j=1:N y((i-1)*N+j)=(1-x(i))*m2(j); end end end T=zeros(6,50); T(3,1:50)=1; case 4 % 4ASK signal x=randi(1,M,4); m=sin(2*pi*fc*t); y=ones(1,M*N); for i=1:M if x(i)==0 for j=1:N y((i-1)*N+j)=x(i)*m(j); end elseif x(i)==1 for j=1:N y((i-1)*N+j)=x(i)*m(j); end elseif x(i)==2 for j=1:N y((i-1)*N+j)=x(i)*m(j); end elseif x(i)==3 for j=1:N y((i-1)*N+j)=x(i)*m(j); end end end T=zeros(6,50); T(4,1:50)=1; case 5 % 4FSK signal x=randi(1,M,4); m1=sin(2*pi*fc*t); m2=sin(2*pi*2*fc*t); m3=sin(2*pi*3*fc*t); m4=sin(2*pi*4*fc*t); y=zeros(1,M*N); for i=1:M if x(i)==0 for j=1:N y((i-1)*N+j)=(1-x(i))*m1(j); end elseif x(i)==1 for j=1:N y((i-1)*N+j)=x(i)*m2(j); end elseif x(i)==2 for j=1:N y((i-1)*N+j)=(x(i)-1)*m3(j); end elseif x(i)==3 for j=1:N y((i-1)*N+j)=(x(i)-2)*m4(j); end end end plot(y) T=zeros(6,50); T(5,1:50)=1; case 6 %4PSK signal x=randi(1,M,4); m1=sin(2*pi*fc*t); m2=sin(2*pi*fc*t+pi/2); m3=sin(2*pi*fc*t+pi); m4=sin(2*pi*fc*t+3*pi/2); y=zeros(1,M*N); for i=1:M if x(i)==0 for j=1:N y((i-1)*N+j)=(1-x(i))*m1(j); end elseif x(i)==1 for j=1:N y((i-1)*N+j)=x(i)*m2(j); end elseif x(i)==2 for j=1:N y((i-1)*N+j)=(x(i)-1)*m3(j); end elseif x(i)==3 for j=1:N y((i-1)*N+j)=(x(i)-2)*m4(j); end end end T=zeros(6,50); T(6,1:50)=1; end SNR=20; %定义信噪比，单位DB sigpow=mean(abs(y).^2); %power of input signal noisefac=10^(-SNR/10); noise=randn(1,size(y,2)); noise=noise*(sqrt(sigpow*noisefac)/sqrt(mean(noise.^2))); %产生所需的高斯噪声 ynoise=noise+y; %加噪后的信号 for n=1:1:50 m=n*Ns; x=(n-1)*Ns; for i=x+1:m %提取信号段 y0(i)=ynoise(i); end Y=fft(y0); %调制信号的傅立叶变换 y1=hilbert(y0); %实信号的解析式 z=abs(y0); %实信号的瞬时幅度 phase=angle(y1); %实信号的瞬时相位 add=0; %求Rmax for i=x+1:m add=add+z(i); end ma=add/Ns; %瞬时幅度的平均值 y2=z./ma ; %幅度比,即为文献中的an(i) y3=y2-1; %归一化瞬时幅度 y4=max(abs(y3)); y2n=y3./y4; % 即为文献中的acn(i) s=fft(y2n); R=abs(s); Rmax=max((R)/Ns).^2; %零中心归一化瞬时幅度的谱密度的最大值 Xcn=0; Ycn=0; for i=x+1:m Xcn=Xcn+y2n(i).*y2n(i); Ycn=Ycn+abs(y2n(i)); end Xcnav=Xcn/Ns; Ycnav=(Ycn/Ns).*(Ycn/Ns); deltaaa=sqrt(Xcnav-Ycnav); %零中心归一化瞬时幅度绝对值得标准偏差 if phase(2+x)-phase(1+x)>pi %修正相位序列 Ck(1+x)=-2*pi; elseif phase(1+x)-phase(2+x)>pi Ck(1+x)=2*pi; else Ck(1+x)=0; end for i=x+2:m-1 if phase(i+1)-phase(i)>pi Ck(i)=Ck(i-1)-2*pi; elseif phase(i)-phase(i+1)>pi Ck(i)=Ck(i-1)+2*pi; else Ck(i)=Ck(i-1); end end if -phase(m)>pi Ck(m)=Ck(m-1)-2*pi; elseif phase(m)>pi Ck(m)=Ck(m-1)+2*pi; else Ck(m)=Ck(m-1); end phase1=phase+Ck ; %去相位卷叠后的相位序列 phasenl=phase1-2*pi*fc*i/fs; %非线性相位 at=1; %判决门限电平 a=0; %求取零中心非弱信号段瞬时相位非线性分量绝对值的标准偏差和零中心非弱信号段瞬时相位非线性分量的标准偏差 b=0; d=0; c=0; for i=x+1:m if y2(i)>at c=c+1; phasesquare(i)=phasenl(i).*phasenl(i); a=a+phasesquare(i); phaseabs(i)=abs(phasenl(i)); b=b+phaseabs(i); d=d+phasenl(i) ; end end a1=a/c; b1=(b/c).*(b/c); d1=(d/c).*(d/c); deltaap=sqrt(a1-b1); %零中心非弱信号段瞬时相位非线性分量绝对值的标准偏差 deltadp=sqrt(a1-d1); %零中心非弱信号段瞬时相位非线性分量的标准偏差 freqN(i)=phase1(i)-phase1(i-1); for i=x+1:m if i>at c=c+1; freqNsquare(i)=freqN(i).*freqN(i); a=a+freqNsquare(i); b=b+freqN(i); end end a1=a/c; b1=(b/c)^2; deltaaf=sqrt(a1-b1); %零中心归一化非弱信号段瞬时频率绝对值得标准偏差 Pi=rand(5,50); P0=rand(5,50); Pi(5*n-4)=Rmax; Pi(5*n-3)=deltaap; Pi(5*n-2)=deltadp; Pi(5*n-1)=deltaaa; Pi(5*n)=deltaaf; end %采用BP网络 %NEWCF--生成一个新的前向神经网络 %TRAIN--对网络进行训练 % 定义训练样本 %Pi为输入矢量 %T为目标矢量 %创建一个新的前向神经网络　 net=newff(minmax(Pi),[5,15,6],{'tansig','purelin','logsig'},'traingda'); %设置训练参数 net.trainParam.show=50; net.trainParam.lr=1; net.trainParam.mc=0.5; net.trainParam.epochs=2000; net.trainParam.goal=1e-3; %调用TRAINGDM算法训练网络 [net,tr]=train(net,Pi,T); %对网络进行仿真 A=sim(net,Pi); A E=T-A; T1=zeros(6,50); T1(1,1:50)=1; E1=0; for c=1:1:300 Eout=(T1(c)-A(c))^2; E1=Eout+E1 ; end T2=zeros(6,50); T2(2,1:50)=1; E2=0; for c=1:1:300 Eout=(T2(c)-A(c))^2; E2=Eout+E2; end T3=zeros(6,50); T3(3,1:50)=1; E3=0; for c=1:1:300 Eout=(T3(c)-A(c))^2; E3=Eout+E3; end T4=zeros(6,50); T4(4,1:50)=1; E4=0; for c=1:1:300 Eout=(T4(c)-A(c))^2; E4=Eout+E4; end T5=zeros(6,50); T5(5,1:50)=1; E5=0; for c=1:1:300 Eout=(T5(c)-A(c))^2; E5=Eout+E5; end T6=zeros(6,50); T6(6,1:50)=1; E6=0; for c=1:1:300 Eout=(T6(c)-A(c))^2; E6=Eout+E6; end E0=0; if (E1>E2) E0=E2; else E0=E1; end if (E3<E0) E0=E3; end if (E4<E0) E0=E4; end if (E5<E0) E0=E5; end if (E6<E0) E0=E6; end E0; if (E0==E1) type=menu('输入信号是','2ASK信号'); end if (E0==E2) type=menu('输入信号是','2FSK信号'); end if(E0==E3) type=menu('输入信号是','BPSK信号'); end if(E0==E4) type=menu('输入信号是','4ASK信号'); end if(E0==E5) type=menu('输入信号是','4FSK信号'); end if(E0==E6) type=menu('输入信号是','QPSK信号'); end %计算正确识别率sita sita=0; j=0; for nn=1:1:50 Ee=(E(6*nn-5)^2)+(E(6*nn-4)^2)+(E(6*nn-3)^2)+(E(6*nn-2)^2)+(E(6*nn-1)^2)+(E(6*nn)^2); if Ee<0.01 j=j+1; end end sita=j/50; type=menu('正确识别率为:',sita*100,'％');