基于Matlab实现的无线信道估计源码+超详细注释(比赛项目).zip

% N = load('daopin_4_2.txt'); clc;clear sample = csvread('sample.txt'); MM = zeros(250, 1); [nrows, ncols] = size(sample); % x = [-124,-100,-75,-50,-25,0,25,50,75,100,125]; % x = -124:5:125; % % x = sort(randperm(250, 80)-125)'; % x = x'; x = load('dp62.txt'); % x = [-124 -116 -108 -100 -92 -84 -76 -68 -60 -52 -44 -36 -28 -20 -12 -4 ... % 4 12 20 28 36 44 52 60 68 76 84 92 100 108 116 124]'; % csvwrite('dp62.txt',x') xx = x+125; noise = normrnd(-0,0.5, length(x),1)+normrnd(0,0.5, length(x),1)*1i; noise_real = sample(xx, 3); noise_imag =sample(xx, 4); % noise_real = sample(xx, 1) + real(noise); % noise_imag = sample(xx, 2) + imag(noise); % % % noise_real = xiaobo(noise_real); % noise_imag = xiaobo(noise_imag); % static_real = zeros(length(noise_real),1); % static_imag = zeros(length(noise_imag),1); % loop = 100000; % u = 0; % sigma = 1; % for i=1:loop % noise_r = normrnd(u, sigma, length(noise_real), 1); % static_real = static_real + noise_real - noise_r; % noise_i = normrnd(u, sigma, length(noise_real), 1); % static_imag = static_imag + noise_imag - noise_i; % end % static_real = static_real / loop; % static_imag = static_imag / loop; noise = normrnd(0,0.04, 250,1)+normrnd(0,1, 250,1)*1i; % predict_real = OMP_func(x, 62, 250, 1); % predict_imag = OMP_func(x, 62, 250, 1); %method 1 fitmodel_sample1 = createFit1(x, noise_real); fitmodel_sample2 = createFit1(x, noise_imag); predict_real = fitmodel_sample1(-124:125); predict_imag = fitmodel_sample2(-124:125); % for i=1:250 % for j=1:1 % MM(i,j) = predict_real(i,1)+predict_imag(i,1)*1i; % end % end % MM = ifft(MM); % lev = 1; wn = 'sym8'; % MM = wden(MM,'heursure','s','sln',lev,wn); % MM = fft(MM); % predict_real = real(MM); % predict_imag = imag(MM); % predict_imag = ifft(predict_imag); % lev = 1; wn = 'sym8'; % predict_imag = wden(predict_imag,'heursure','s','sln',lev,wn); % predict_imag = fft(predict_imag); predict_real = smooth(predict_real,2, 'moving'); predict_imag = smooth(predict_imag,2, 'moving'); % %小波变换 predict_real = xiaobo(predict_real); predict_imag = xiaobo(predict_imag); % %小波去噪 % lev=10;wn='db45';ln = 'one';tptr='rigrsure'; % lev=6;wn='coif5';ln = 'sln';tptr='sqtwolog'; % 利用soft SURE阈值规则去噪 % predict_real= wden(predict_real, tptr, 's', ln, lev, wn); % predict_imag= wden(predict_imag, tptr, 's', ln, lev, wn); % % %小波去噪 % lev=20;wn='sym8'; % % 利用soft SURE阈值规则去噪 % predict_real= wden(predict_real, 'minimaxi', 's', 'sln', lev, wn); % predict_imag= wden(predict_imag, 'minimaxi', 's', 'sln', lev, wn); % predict_real = predict_real - real(noise); % predict_imag = predict_imag - imag(noise); channel_real = sample(:, 1); channel_imag = sample(:, 2); C = sum((predict_real-channel_real).^2 + (predict_imag-channel_imag).^2); H = sum(channel_real.^2 + channel_imag.^2); fprintf('method1, Ea: %d, Eb: %d, E: %d\n', C/H, length(x)/2500, C/H + length(x)/2500); plot(-124:125, predict_real, 'r') hold on digits(2); predict_real= vpa(predict_real); predict_imag = vpa(predict_imag); %method 2 fitmodel_sample1 = createFit2(x, noise_real); fitmodel_sample2 = createFit2(x, noise_imag); predict_real = fitmodel_sample1(-124:125); predict_imag = fitmodel_sample2(-124:125); %小波变换 predict_real = xiaobo(predict_real); predict_imag = xiaobo(predict_imag); %小波去噪 lev=5;wn='sym8';ln = 'mln';tptr='heursure'; % lev=7;wn='coif5';ln = 'sln';tptr='sqtwolog'; % 利用soft SURE阈值规则去噪 predict_real= wden(predict_real, tptr, 's', ln, lev, wn); predict_imag= wden(predict_imag, tptr, 's', ln, lev, wn); % %小波变换 % predict_real = xiaobo(predict_real); % predict_imag = xiaobo(predict_imag); % predict_real = predict_real - real(noise); % predict_imag = predict_imag - imag(noise); % % noise_real = smooth(noise_real, 'moving'); % noise_imag = smooth(noise_imag, 'moving'); channel_real = sample(:, 1); channel_imag = sample(:, 2); C = sum((predict_real-channel_real).^2 + (predict_imag-channel_imag).^2); H = sum(channel_real.^2 + channel_imag.^2); fprintf('method2, Ea: %d, Eb: %d, E: %d\n', C/H, length(x)/2500, C/H + length(x)/2500); fprintf('percent, Ea: %d , Eb: %d , E: %d \n', C/H/(C/H + length(x)/2500), length(x)/2500/( C/H + length(x)/2500), C/H + length(x)/2500); % consider noise: Ea: 4.264550e-02, Eb: 2.000000e-02, E: 6.264550e-02 % minus noise: Ea: 4.011335e-02, Eb: 2.000000e-02, E: 6.011335e-02 样本一次 % no 2 noise: Ea: 4.206490e-02, Eb: 5.000000e-02, E: 9.206490e-02 % no 1 noise: Ea: 4.123032e-02, Eb: 1.000000e-01, E: 1.412303e-01 % xiaobobianhuan: Ea: 7.030419e-02, Eb: 2.000000e-02, E: 9.030419e-02 % 统计平均100 : Ea: 4.256002e-02, Eb: 2.000000e-02, E: 6.256002e-02 % 统计平均10000 : Ea: 4.115769e-02, Eb: 2.000000e-02, E: 6.115769e-02 % 统计平均100000: Ea: 4.114562e-02, Eb: 2.000000e-02, E: 6.114562e-02 % 样本平均10000 : Ea: 4.010292e-02, Eb: 2.000000e-02, E: 6.010292e-02 % 当前最优，平均没用 % 小波变换 预测后再变换50个导频 : Ea: 3.667115e-02, Eb: 2.000000e-02, E: 5.667115e-02 4-14最优 % 65个导频 50%:50% : Ea: 2.263320e-02, Eb: 2.600000e-02, E: 4.863320e-02 % 改小波变换，改导频 62个导频 %method3 % S1 = sample(:,1)+1i*sample(:,2); % S1 = S1.'; % S2 = sample(xx,3)+1i*sample(xx,4); % S2 = S2.'; % % method = 'linear'/'spline'/'cubic'|插值方法 % P2 = interpolate(S2,xx',250,'cubic'); % plot(-124:125, real(P2),'y','linewidth',2) % hold on % P2t = ifft(P2); % lev = 1; wn = 'sym8'; % P2t_modified = wden(P2t,'heursure','s','sln',lev,wn); % P2_modified = fft(P2t_modified); % Ea = sum(power(abs(S1-P2_modified),2))/sum(power(abs(S1),2)); % Eb = length(xx)/2500; % E = Ea+Eb; % fprintf('method3, Ea: %d, Eb: %d, E: %d\n',Ea, Eb, E); % plot(-124:125, real(P2_modified),'r','linewidth',2) % hold on plot(-124:125, predict_real, 'g') hold on plot(-124:125, sample(:,1), 'b') hold on scatter(x', noise_real,'d','filled') hold on % scatter(x', noise_real-real(noise), 'filled') % hold on plot(-124:125, sample(:,3), 'm') legend('predict_1','predict_2','real', 'given', 'noise')