基于BP神经网络的调制方式识别算法MATLAB仿真,识别不同SNR下的MFSK和MPSK，含仿真操作录像

clc; clear; close all; warning off; addpath 'func\' %全局变量 parameters; SNR0 = inf; N0 = 50000; y_2FSK = zeros(1,N0); y_4FSK = zeros(1,N0); y_2PSK = zeros(1,N0); y_4PSK = zeros(1,N0); %2FSK y_2FSK = func_2FSK(N0); %4FSK y_4FSK = func_4FSK(N0); %BPSK y_2PSK = func_2PSK(N0); %QPSK y_4PSK = func_4PSK(N0); %调制识别 y_2FSKn = func_add_noise(y_2FSK,SNR0); y_4FSKn = func_add_noise(y_4FSK,SNR0); y_2PSKn = func_add_noise(y_2PSK,SNR0); y_4PSKn = func_add_noise(y_4PSK,SNR0); %首先进行FSK和PSK两种模式的区分 Ns = 2048; %用x进行功率谱估计 [p1,f1] = func_power(y_2FSKn,Ns); [p2,f2] = func_power(y_4FSKn,Ns); [p3,f3] = func_power(y_2PSKn,Ns); [p4,f4] = func_power(y_4PSKn,Ns); len1 = func_fsk_psk_check(p1); len2 = func_fsk_psk_check(p2); len3 = func_fsk_psk_check(p3); len4 = func_fsk_psk_check(p4); %根据参数获得FSK和PSK区分参数 Level= (mean([len1,len2]) - mean([len3,len4]))/2; %分别提取FSK和PSK的不同调制方式的特征参数 char1 = real(func_para_check(y_2FSKn,N0)); char2 = real(func_para_check(y_4FSKn,N0)); char3 = real(func_para_check(y_2PSKn,N0)); char4 = real(func_para_check(y_4PSKn,N0)); %通过GRNN神经网络进行训练 char = [char1;char2]'; T = [1;2]'; net_fsk = newff(char,T,5); net_fsk = train(net_fsk,char,T); char = [char3;char4]'; T = [1;2]'; net_psk = newff(char,T,5); net_psk = train(net_psk,char,T); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %加载信号进行测试 %通过大量的循环测试，计算正确率 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% zql = 0; %运行的时候，尽量将下面的两个参数指标设置大点，这样结果才精确 MTKL = 100; SNRS = [-5:0.5:5]; Bers = zeros(length(SNRS),1); for jj = 1:length(SNRS) for i = 1:MTKL SNRS(jj) i rng(i); %长度 N = N0; %SNR SNR = SNRS(jj); %2FSK y_2FSK = func_2FSK(N); %4FSK y_4FSK = func_4FSK(N); %BPSK y_2PSK = func_2PSK(N); %QPSK y_4PSK = func_4PSK(N); %设置单独的一种调制信号 tmps = [4,4,4,4];%4PSK if tmps(1) == 1 datas = y_2FSK; end if tmps(1) == 2 datas = y_4FSK; end if tmps(1) == 3 datas = y_2PSK; end if tmps(1) == 4 datas = y_4PSK; end datas = func_multipath(datas); data = func_add_noise(datas,SNR); [p,f] = func_power(data,Ns); len = func_fsk_psk_check(p); flag = 0; %首先进行FSK和PSK两种模式的区分 if len >= Level%为FSK模式 %根据识别参数进行调制类型的辨识 char = real(func_para_check(data,length(data))); T = round(net_fsk(char')); if T == 1 flag = 1; end if T == 2 flag = 2; end else%为PSK模式 %根据识别参数进行调制类型的辨识 char = real(func_para_check(data,length(data))); T = round(net_psk(char')); if T == 1 flag = 3; end if T == 2 flag = 4; end end if flag == tmps(1) zql = zql + 1; end end %识别正确率 Bers(jj) = zql/MTKL; zql = 0; end R = 100*mean(Bers,2); figure; plot(SNRS,R,'b-o','linewidth',2); grid on xlabel('snr'); ylabel('调制识别率'); axis([min(SNRS)-1,max(SNRS)+1,0,110]); save r4bp.mat SNRS R