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Dataprocessing.m
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Dataprocessing.m
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%close all
close all
clear all
load('/home/labuser/payload_reconstruction/BPSK/payload_1.mat')
% load('/home/labuser/payload_reconstruction/test_dataset/microwave/BPSK/payload_MC.mat')
%
%
% dataset1.save_pilots = [pilots(1:10,:);pilots(1:40,:);pilots];
%
%
data_ind = [2:7 9:21 23:27 39:43 45:57 59:64];
x = 1:48;
x_p = 1:40;
error_table = zeros(48,1);
error_table_64 = zeros(64,1);
error_table_t = zeros(40,1);
fft_pilot_sum = zeros(64,40);
CSI_threshold = 0;
Reorganized_CSI_sum = zeros(1,48);
count=0;
% snr = cell(5000,1);
% for i = 1:5000
% payload_syms_mat = data_set.Constallation{i,1};
% pilots = data_set.Pilots{i,1};
% tx_syms_mat = data_set.Txpayload{i,1};
% evm_mat = abs(payload_syms_mat - tx_syms_mat).^2;
% aevms = mean(evm_mat(:));
% snr{i,1} = 10*log10(1./aevms);
% end
% dataset1.save_pilots = [pilots(1:10,:);pilots(1:40,:);pilots];
% dataset1.save_payload = [payload_syms_mat(1:10*48,:);payload_syms_mat(1:40*48,:);payload_syms_mat];
% dataset1.save_CSI = data_set.CSI{1,1};
% data_set.SNR = snr;
%% This part is for finding pattern
for i = 1:5000
error_mat = zeros(1920,1);
CSI = data_set.CSI{i,1};
Pilot = data_set.Pilots{i,1};
Tx_data = data_set.Tx_dec{i,1};
Rx_data = data_set.Rx_dec{i,1};
[a,b] = find(Tx_data ~= Rx_data);
error_sub = mod(a,48);
error_t = floor(a/48);
for j = 1:48
error_table(j,1) = length(find(error_sub == j));
error_table_64(data_ind(1,j),1) = length(find(error_sub == j));
end
for k = 1:40
error_table_t(k,1) = length(find(error_t == k-1));
end
error_mat(a,1)= 1;
error_mat_reshape = reshape(error_mat,48,40);
angleCSI = rad2deg(angle(CSI));
phase_diff = zeros(48,1);
phase_diff(48,1) = angleCSI(1,48);
for j = 2:48
phase_diff(j-1,1) = abs(angleCSI(j)-angleCSI(j-1));
end
flip_loc = find(phase_diff>300);
if isempty(flip_loc)
count = count +1;
continue
end
reorganized_csi = circshift(angleCSI,-(max(flip_loc)));
figure(i)
plot(x,reorganized_csi);
% figure(i+200)
% plot(x,abs(phase_diff));
%hold on
%plot(x,abs(phase_diff));
Reorganized_CSI_sum = Reorganized_CSI_sum + reorganized_csi;
valid_phase_diff = find(phase_diff<3);
CSI_threshold = CSI_threshold + mean(phase_diff(valid_phase_diff));
fft_pilot = fft(Pilot.',64);
fft_pilot_sum = fft_pilot + fft_pilot_sum;
cwt_csi = cwt(abs(CSI));
%figure(i+1)
%plot(x_p,angle(Pilot)+10);
%imagesc(abs(fft_pilot(data_ind,:))/max(max(abs(fft_pilot(data_ind,:)))))
%legend('1-12','13-24','25-36','37-48')
%hold on
%bar(x_p,error_table_t);
%hold on
%barh(1:64,error_table_64)
%figure(2*i)
% plot(x,angle(CSI));
%figure(i+2)
% imagesc(error_mat_reshape);
%figure(i+3)
%imagesc(abs(cwt_csi(:,:,2)));
end
fft_pilot_mean = fft_pilot_sum/(5000-count);
CSI_threshold_mean = CSI_threshold/(5000-count);
Reorganized_CSI_mean = Reorganized_CSI_sum/(5000-count);
load('/home/labuser/payload_reconstruction/BPSK_full/payload_1.mat')
error_table_intf = zeros(48,1);
error_table_64_intf = zeros(64,1);
error_table_t_intf = zeros(40,1);
corrected_find_error = zeros(5000,1);
corrected_find_error_CSI = zeros(5000,1);
wrong_find_error = zeros(5000,1);
wrong_find_error_CSI = zeros(5000,1);
Possible_find_error_CSI = zeros(5000,1);
FalseNegative_find_error_CSI = zeros(5000,1);
for i = 1:5000
error_mat_intf = zeros(1920,1);
decision_map = zeros(48,40);
CSI_intf = data_set.CSI{i,1};
Pilot_intf = data_set.Pilots{i,1};
Tx_data_intf = data_set.Tx_dec{i,1};
Rx_data_intf = data_set.Rx_dec{i,1};
[a_intf,b1_intf] = find(Tx_data_intf ~= Rx_data_intf);
error_sub_intf = mod(a_intf,48);
error_t_intf = floor(a_intf/48);
for j = 1:48
error_table_intf(j,1) = length(find(error_sub_intf == j));
error_table_64_intf(data_ind(1,j),1) = length(find(error_sub_intf == j));
end
for k = 1:40
error_table_t_intf(k,1) = length(find(error_t_intf == k-1));
end
error_mat_intf(a_intf,1)= 1;
error_mat_reshape_intf = reshape(error_mat_intf,48,40);
angleCSI_intf = rad2deg(angle(CSI_intf));
phase_diff_intf = zeros(48,1);
phase_diff_intf(48,1) = angleCSI_intf(1,48);
for j = 2:48
phase_diff_intf(j-1,1) = abs(angleCSI_intf(j)-angleCSI_intf(j-1));
end
flip_loc_intf = find(phase_diff_intf>300);
CSI_intefered = phase_diff_intf;
CSI_indicator = find(CSI_intefered>(CSI_threshold_mean*2));
%
% bar(x,error_table_intf);
% hold on
% plot(x,abs(phase_diff_intf));
%
fft_pilot_intf = fft(Pilot_intf.',64);
pilot_diff = angle(fft_pilot_intf) - angle(fft_pilot_mean);
phase_diff_pilot_intf = pilot_diff(data_ind,:);
if isempty(flip_loc_intf)
continue
end
reorganized_csi_intf = circshift(angleCSI_intf,-(max(flip_loc_intf)));
reorganized_pilot_intf = circshift(phase_diff_pilot_intf,-(max(flip_loc_intf)));
% figure(i)
% plot(x,circshift(angleCSI_intf,-(max(flip_loc_intf))));
csi_diff = deg2rad(reorganized_csi_intf).' - deg2rad(Reorganized_CSI_mean).';
phase_diff = -0.2*reorganized_pilot_intf +0.8*csi_diff;
Tx_constellation = data_set.Txpayload{i,1};
Rx_constellation = data_set.Constallation{i,1};
constellation_diff = circshift(reshape(Tx_constellation,48,40),-(max(flip_loc_intf)),1) - circshift(reshape(Rx_constellation,48,40),-(max(flip_loc_intf)),1);
[a_intf1,b1_intf1] = find(circshift(reshape(Tx_data_intf,48,40),-(max(flip_loc_intf)),1) ~= circshift(reshape(Rx_data_intf,48,40),-(max(flip_loc_intf)),1));
error_constellation = zeros(48,40);
error_constellation_compute = zeros(48,40);
error_constellation(a_intf1,b1_intf1) = abs(angle(constellation_diff(a_intf1,b1_intf1)));
error_constellation_compute(a_intf1,b1_intf1) = abs(phase_diff(a_intf1,b1_intf1));
phase_subtract = reshape(abs(error_constellation_compute -error_constellation),[],1);
distribution = find(phase_subtract~=0);
pd = fitdist(phase_subtract(distribution),'Normal');
x_values = 0:0.1:6;
y = pdf(pd,x_values);
figure(i+1)
plot(x_values,smooth(y));
title('PDF of phase difference')
txt = ['Probablity=',num2str(trapz(x_values(1:17),y(1:17)))];
text(4,0.3,txt)
%figure(i+1)
%plot(x_p,angle(Pilot)+10);
difference_mat = abs(fft_pilot_mean(data_ind,:))/max(max(abs(fft_pilot_mean(data_ind,:))))-abs(fft_pilot_intf(data_ind,:))/max(max(abs(fft_pilot_intf(data_ind,:))));
decision_map(difference_mat>0.10) = 2;
error_difference_mapping = decision_map - error_mat_reshape_intf;
corrected_find_error(i,1) = length(find(error_difference_mapping ==1))/length(a_intf);
wrong_find_error(i,1) = length(find(error_difference_mapping == 2))/1920;
decision_map_withCSI = decision_map;
decision_map_withCSI(CSI_indicator,:) = decision_map_withCSI(CSI_indicator,:)+2;
error_difference_mapping_CSI = decision_map_withCSI - error_mat_reshape_intf;
corrected_find_error_CSI(i,1) = length(find(error_difference_mapping_CSI ==3))/length(a_intf);
wrong_find_error_CSI(i,1) = length(find(error_difference_mapping_CSI ==4))/1920;
Possible_find_error_CSI(i,1) = (length(find(error_difference_mapping_CSI ==2)) + length(find(error_difference_mapping_CSI ==1)))/1920;
FalseNegative_find_error_CSI(i,1) = length(find(error_difference_mapping_CSI ==-1))/length(a_intf);
%imagesc(abs(fft_pilot_intf(data_ind,:))/max(max(abs(fft_pilot_intf(data_ind,:)))))
%legend('1-12','13-24','25-36','37-48')
%hold on
%bar(x_p,error_table_t);
%hold on
%barh(1:64,error_table_64)
%figure(2*i)
% plot(x,angle(CSI));
if corrected_find_error_CSI(i,1) >0.60
figure(i+2)
subplot(1,4,1)
imagesc(error_mat_reshape_intf);
title('Constellation error','FontSize',24);
subplot(1,4,2)
imagesc(difference_mat);
title('Pilot difference','FontSize',24);
subplot(1,4,3)
imagesc(decision_map);
title('Error map from Pilot','FontSize',24);
subplot(1,4,4)
imagesc(error_difference_mapping_CSI);
title('Error map from Pilot and CSI','FontSize',24);
end
%figure(i+3)
%imagesc(abs(cwt_csi(:,:,2)));
end
valid_Error = rmmissing(corrected_find_error);
valid_Error_Removed = find(valid_Error~=0);
valid_Error_mean = mean(valid_Error(valid_Error_Removed));
valid_Error_CSI = rmmissing(corrected_find_error_CSI);
valid_Error_Removed_CSI = find(valid_Error_CSI~=0);
valid_Error_mean_CSI = mean(valid_Error_CSI(valid_Error_Removed_CSI));
wrong_find_error_mean = mean(wrong_find_error);
wrong_find_error_CSI_mean = mean(wrong_find_error_CSI);
Possible_find_error_CSI_mean = mean(Possible_find_error_CSI);
FalseNegative_CSI = rmmissing(FalseNegative_find_error_CSI);
FalseNegative_CSI_mean = mean(FalseNegative_CSI);
result = Possible_find_error_CSI_mean + valid_Error_mean_CSI;
% difference_mat = abs(fft_pilot(data_ind,:))/max(max(abs(fft_pilot(data_ind,:))))-abs(fft_pilot_intf(data_ind,:))/max(max(abs(fft_pilot_intf(data_ind,:))));
% figure(10000)
% imagesc(difference_mat)