1.完整项目描述和程序获取
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2.部分仿真图预览
3.算法概述
移动视频图像传输，广泛用于公安指挥车、交通事故勘探车、消防武警现场指挥车和海关、油田、矿山、水利、电力、金融、海事，以及其它的紧急、应急指挥系统，主要作用是将现场的实时图像传输回指挥中心，使指挥中心的指挥决策人员如身临其境，提高决策的准确性和及时性，提高工作效率。下面就移动视频图像传输采用公网和专用技术两种情况作相关介绍。
4.部分源码
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sym_rem = mod(mod_order-mod(length(im_bin),mod_order),mod_order);
padding = repmat('0',sym_rem,1);
im_bin_padded = [im_bin;padding];
cons_data = reshape(im_bin_padded,mod_order,length(im_bin_padded)/mod_order)';
cons_sym_id = bin2dec(cons_data);
% BPSK
if mod_order == 1
mod_ind = 2^(mod_order-1);
n = 0:pi/mod_ind:2*pi-pi/mod_ind;
in_phase = cos(n);
quadrature = sin(n);
symbol_book = (in_phase + quadrature*1i);
end
% Phase shift keying about unit circle
if mod_order == 2 || mod_order == 3
mod_ind = 2^(mod_order-1);
n = 0:pi/mod_ind:2*pi-pi/mod_ind;
in_phase = cos(n+pi/4);
quadrature = sin(n+pi/4);
symbol_book = (in_phase + quadrature*1i);
end
%16QAM, 64QAM
if mod_order == 4 || mod_order == 6
mod_ind = sqrt(2^mod_order);
%n = 0:pi/mod_ind:2*pi-pi/mod_ind;
in_phase = repmat(linspace(-1,1,mod_ind),mod_ind,1);
quadrature = repmat(linspace(-1,1,mod_ind)',1,mod_ind);
symbol_book = (in_phase(:) + quadrature(:)*1i);
end
%32QAM
if mod_order == 5
mod_ind = 6;
%n = 0:pi/mod_ind:2*pi-pi/mod_ind;
in_phase = repmat(linspace(-1,1,mod_ind),mod_ind,1);
quadrature = repmat(linspace(-1,1,mod_ind)',1,mod_ind);
symbol_book = (in_phase(:) + quadrature(:)*1i);
symbol_book = symbol_book([2:5 7:30 32:35]); %corners are removed
end
%modulate data according to the symbol_book
X = symbol_book(cons_sym_id+1);
fft_rem = mod(n_fft-mod(length(X),n_fft),n_fft);
X_padded = [X;zeros(fft_rem,1)];
X_blocks = reshape(X_padded,nfft,length(X_padded)/nfft);
x = ifft(X_blocks);
%Add cyclic prefix entension and shift from parallel to serial
x_cpe = [x(end-n_cpe+1:end,:);x];
x_s = x_cpe(:);
data_pwr = mean(abs(x_s.^2));
% Add noise to the channel
noise_pwr = data_pwr/10^(snr/10);
noise = normrnd(0,sqrt(noise_pwr/2),size(x_s))+normrnd(0,sqrt(noise_pwr/2),size(x_s))*1i;
x_s_noise = x_s + noise;
snr_meas = 10*log10(mean(abs(x_s.^2))/mean(abs(noise.^2)));
g = exp(-(0:n_taps-1));
g = g/norm(g);
x_s_noise_fading = conv(x_s_noise,g,'same');
%% Use FFT to move to frequency domain
% Remove cyclic prefix extension and shift from serial to parallel
x_p = reshape(x_s_noise_fading,nfft+n_cpe,length(x_s_noise_fading)/(nfft+n_cpe));
x_p_cpr = x_p(n_cpe+1:end,:);
% Move to frequency domain
X_hat_blocks = fft(x_p_cpr);
%% Estimate channels
if n_taps > 1
switch(ch_est_method)
case 'none'
case 'LS'
G = X_hat_blocks(:,1)./X_blocks(:,1);
X_hat_blocks = X_hat_blocks./repmat(G,1,size(X_hat_blocks,2));
end
end
%% Symbol demodulation
% remove fft padding
X_hat = X_hat_blocks(:);
X_hat = X_hat(1:end-fft_rem);
%Recover data from modulated symbols
A=[real(symbol_book) imag(symbol_book)];
if (size(A,2)>2)
A=[real(symbol_book)' imag(symbol_book)'];
end
rec_syms = knnsearch(A,[real(X_hat) imag(X_hat)])-1;
%Parse to binary stream to remove symbol padding
rec_syms_cons = dec2bin(rec_syms);
rec_im_bin = reshape(rec_syms_cons',numel(rec_syms_cons),1);
rec_im_bin = rec_im_bin(1:end-sym_rem);
ber = sum(abs(rec_im_bin-im_bin))/length(im_bin);
%% recover image
% rec_im = reshape(rec_im_bin,9,numel(rec_im_bin)/8);
rec_im = reshape(rec_im_bin,8,numel(rec_im_bin)/8);
rec_im = uint8(bin2dec(rec_im'));
rec_im = reshape(rec_im,size(im));
%% generate plots
% transmit constellation
subplot(2,2,1);
plot(X,'x','linewidth',2,'markersize',10);
xlim([-2 2]);
ylim([-2 2]);
xlabel('In phase')
ylabel('Qudrature')
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A367
