dsss.zip_dsss_zip

clc close all clear all R = 20; % The length of the bit stream. S = 21; % Bit duration(each bit is represented with % S samples of the waveform used in signaling. bit_stream = round(rand(1,R)); % A Randomley Generated Bit Stream. PAM = zeros(1,S*R); % Predefinition of the Base-band PAM signal. % Generating the PAM signal using Polar Non-Return-to-Zero signaling. Every % S samples correspond to one bit. for i = 1:R if bit_stream(1,i)== 0 PAM(1+(i-1)*S : i*S) = -1; else PAM(1+(i-1)*S : i*S) = 1; end end subplot(3,2,1) plot(PAM); axis([-1 S*R+10 -1.2 1.2]); title('The Original Base-Band PAM Signal generated using Polar Non-Return-to-Zero Signaling'); % As we know, The Fourier transform of the autocorrelation function results % to the Power Spectral Density function. z = abs(fft(xcorr(PAM))); % PSD of the PAM signal. subplot(3,2,2); plot(z); axis([0 840 0 1.1*max(z)]); title('PSD of the PAM signal'); % Generating the pseudo random SEQUENCE for spreading. pn_seq = round(rand(1,60)); % Generating the pseudo random SIGNAL for spreading. t = 0:2*pi/6:2*pi; % Creating 7 samples for one cosine c = sin(t); carrier=[]; pn_sig_txr1 = zeros(1,S*R); % Pseudo Noise signal in the trx. for i = 1:60 if pn_seq(1,i)==0 pn_sig_trx1(1+(i-1)*7:i*7) = -1; else pn_sig_trx1(1+(i-1)*7:i*7) = 1; end carrier=[carrier c]; end K = round(rand(1,1)*420); pn_sig_trx = [pn_sig_trx1(K:end) pn_sig_trx1(1:K-1)]; % Spreading of sequence spreaded_sig = PAM .* pn_sig_trx; subplot(3,2,3); plot(spreaded_sig); axis([-1 S*R+10 -1.2 1.2]); title('Spreaded signal'); z = abs(fft(xcorr(spreaded_sig))); % PSD of the spreaded signal. subplot(3,2,4); plot(z); axis([0 840 0 1.1*max(z)]); title('PSD of the spreaded signal'); % BPSK Modulation of the spreaded signal bpsk_sig = spreaded_sig .* carrier; % Modulating the signal subplot(3,2,5); plot(bpsk_sig) axis([-1 100 -1.2 1.2]); % for visual purpuses, just first 100 samples are plotted. title('BPSK Modulated Signal(first 100 samples)'); % Plotting the PSD of DSSS signal. y = abs(fft(xcorr(bpsk_sig))); subplot(3,2,6) plot(y) xlabel('Frequency(Hz)') ylabel('Power Density') title('PowerSpectralDensity(Watts/Hz)') %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Demodulation and Despreading of Received Signal. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %========================================================================== % Demodulation of the Recevded Signal % %========================================================================== integrand = bpsk_sig .* carrier; % The Integrand in the Receiver Correlator. demod_sig = zeros(1,S*R); % Predefining the Demodulated Signal. for i = 1:60 if sum(integrand(1+(i-1)*7 : 7*i))>=0 demod_sig(1+(i-1)*7 : 7*i) = 1; else demod_sig(1+(i-1)*7 : 7*i) = -1; end end %========================================================================== %Despreading the Demodulated signal with TIME-ASYNCHRONIZATION considration % Using a Paralle-Search strategy. % %========================================================================== P = zeros(1,60); % Stores the power of the LowPass-filtered despreaded signal. % Paralle-Search strategy...Looking for the maximum-power lowpass-filtered % signal disp('Parallel Search strategy started...') b_r = zeros(60,R); % recovered bit stream corresponding to each hyp_sig signal. BER = zeros(1,60); % Bit Error Rate vector. for i = 1:60 % Creating different delayed versions of the PN-signal at receiver.(with an offset % asynchronization value of 3 that can be changed within the range[0,7-1]). pn_sig_rcx = [pn_sig_trx1(7*(i-1)+3:end) pn_sig_trx1(1:7*(i-1)+2)]; % despreaded signal using pn_sig_trx[n-7(i-1)+3]: % delayed version of the PN-signal in the transmitter. hyp_sig = demod_sig .* pn_sig_rcx; b_r(i,:) = hyp_sig(1:S:end); BER(i) = sum(abs(bit_stream - b_r(i,:)))/R; % attempt to calculate PSD using selected delayed version of PN-signal. % z = 0.5+0.5*xcorr(despread_sig); z = abs(fft(xcorr(hyp_sig))); % Collecting the LowPass power of the resulted despreaded signal. P(i) = sum(z(1:30)); % Plot some of the results. if mod(i-1,12) == 0 figure subplot(2,1,1) plot(hyp_sig) %titel('Hypothesized despreaded signal') axis([-1 S*R+10 -1.2 1.2]); % title(['Despreaded data using a delay value of'],in2str(i),['samples in PN-signal']); %Power Spectrum of Despreaded data subplot(2,1,2) plot(z) axis([0 840 0 1.1*max(z)]) xlabel('Frequency(Hz)') ylabel('Power Density') title('Corresponding Power Spectral Density') end end D = find(P == max(P));% The true delay value of the PN-signal at receiver % is the one which results in the the maximum low-pass energy. s = demod_sig .* [pn_sig_trx1(7*(D-1)+3:end) pn_sig_trx1(1:7*(D-1)+2)]; ss = abs(fft(xcorr(s))); % PSD of the maximum lowpass energy signal. figure subplot(2,1,1); plot(s); axis([-1 S*R+10 -1.2 1.2]); title('Maximum lowpass energy signal'); subplot(2,1,2); plot(ss) axis([0 840 0 1.1*max(ss)]) xlabel('Frequency(Hz)') ylabel('Power Density') title('Corresponding Power Spectral Density') figure; % colormap summer bar(BER) colormap(summer) %stem(BER,'MarkerFaceColor','red','Marker','square'); axis([0 61 0.5*min(BER) 1.2*max(BER)]); title('Bit Error Reate for different delay values.')