%% OFDM Simulation in MATLAB
% This simulation code is based on "Jordan Street's" OFDM simulation
%
% if you like to cite this code you can see citation in jordan video (if
% any) or cite our paper LMS based channel estimation papers
%
clear all
close all
clc
%% Simulation Parameters
%Moduluation method: BPSK, QPSK, 8PSK, 16QAM, 32QAM, 64QAM,
mod_method = 'QPSK';
%fft Size
nfft = 64;
n_fft = 64;
%Size of cycle prefix extension
n_cpe = 16;
snr = 20; % in dB
%number of channel taps.
n_taps = 8;
ch_est_method = 'LS';
% ch_est_method = 'none';
mod_methods = {'BPSK', 'QPSK', '8PSK', '16QAM', '32QAM', '64QAM'};
mod_order = find(ismember(mod_methods,mod_method));
%% Read the image and convert it into binary format.
im = imread('baboon.png');
im_bin = dec2bin(im(:))';
im_bin = im_bin(:);
%% Binary stream to symbols
% 1. parse binary stream into mod_order bit symbols
% 2. pads input signal to appropriate length
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);
%% symbol modulation
% 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 - Generates 6x6 constellation and removes corners
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);
%% Use IFFT to move to time domain
% pad input signal to appropriate length
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(:);
%% Add AWGN
% Calculate data power
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;
% Measure SNR
snr_meas = 10*log10(mean(abs(x_s.^2))/mean(abs(noise.^2)));
%% Apply fading channel
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('同步')
ylabel('正交')
if n_taps > 1
title(sprintf('\\bfTransmit Constellation\n\\rm%s Modulation\nMultipath Channel Taps: %d',mod_method,n_taps));
else
title(sprintf('\\bfTransmit Constellation\n\\rm%s Modulation\nMultipath Channel Taps: %d',mod_method));
end
grid on
% Recovered constellation
subplot(2,2,2);
plot(X_hat(1:500:end),'x','markersize',3);
xlim([-2 2]);
ylim([-2 2]);
xlabel('同步')
ylabel('正交')
if n_taps > 1
title(sprintf('\\bfReceived Constellation\n\\rmMeasured SNR: %.2d dB\nChannel Estimation: %s',snr_meas,ch_est_method));
else
title(sprintf('\\bfReceived Constellation\n\\rmMeasured SNR: %.2d dB',snr_meas));
end
grid on
% Original image
subplot(2,2,3);
imshow(im);
title('\bfTransmit Image');
% Recovered image
subplot(2,2,4);
imshow(rec_im);
title(sprintf('\\bfRecovered Image\n \\rmBER: %.2g',ber));
