直序擴(kuò)頻通信-matlab仿真DIRECT-SEQUENCE-SPREAD-SPECTRUM-TECHNIQUES

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1、精選優(yōu)質(zhì)文檔-----傾情為你奉上 Lab2 Direct Sequence Spread Frequency Techniques 直序擴(kuò)頻通信仿真 Content Abstract-------------------------------------------------------------------------------------------3 Experiment Background----------------------------------------------------------------------3 Experiment Pro

2、cedure------------------------------------------------------------------------5 Analysis and Conclusion---------------------------------------------------------------------10 Reference --------------------------------------------------------------------------------------10 Appendix---------------

3、-------------------------------------------------------------------------12 1. Abstract The objective of this lab experiment is to learn the fundamentals of the direct sequence spread spectrum and code division multiple address techniques. To get familiar with the dir

4、ect sequence spread spectrum modulator and demodulator. And the direct sequence spread spectrum system can be shown as: Figure 1. Direct sequence spread spectrum system 2. Experiment Background 2.1 Introduction of Direct Sequence Spread Spectrum [1] In , direct-sequence spread spect

5、rum (DSSS) is a technique. As with other technologies, the transmitted signal takes up more than the information signal that is being modulated. The name 'spread spectrum' comes from the fact that the carrier signals occur over the full bandwidth (spectrum) of a device's transmitting frequency.

6、 Figure 2.1 Procedure to generate a DSSS signal 2.2 Generation of Direct Sequence Spread Spectrum To generate a spread spectrum signal one requires: 1. A modulated signal somewhere in the RF spectrum 2. A PN sequence to spread it 2.3 Features of Direct Sequence Spread Spectrum DSS

7、S has some features as following: 1. DSSS a with a continuous of (PN) symbols called "", each of which has a much shorter duration than an information . That is, each information bit is modulated by a sequence of much faster chips. Therefore, the is much higher than the signal . 2. DSSS us

8、es a structure in which the sequence of chips produced by the transmitter is known a priori by the receiver. The receiver can then use the same to counteract the effect of the PN sequence on the received signal in order to reconstruct the information signal. 2.4 Transmission of Direct Sequenc

9、e Spread Spectrum Direct-sequence spread-spectrum transmissions multiply the data being transmitted by a "noise" signal. This noise signal is a pseudorandom sequence of 1 and ?1 values, at a frequency much higher than that of the original signal, thereby spreading the energy of the original signal

10、into a much wider band. The resulting signal resembles , like an audio recording of "static". However, this noise-like signal can be used to exactly reconstruct the original data at the receiving end, by multiplying it by the same pseudorandom sequence (because 1 × 1 = 1, and ?1 × ?1 = 1). This pr

11、ocess, known as "de-spreading", mathematically constitutes a of the transmitted PN sequence with the PN sequence that the receiver believes the transmitter is using. For de-spreading to work correctly, the transmit and receive sequences must be synchronized. This requires the receiver to synchroni

12、ze its sequence with the transmitter's sequence via some sort of timing search process. However, this apparent drawback can be a significant benefit: if the sequences of multiple transmitters are synchronized with each other, the relative synchronizations the receiver must make between them can be u

13、sed to determine relative timing, which, in turn, can be used to calculate the receiver's position if the transmitters' positions are known. This is the basis for many . The resulting effect of enhancing on the channel is called . This effect can be made larger by employing a longer PN sequence

14、and more chips per bit, but physical devices used to generate the PN sequence impose practical limits on attainable processing gain. If an undesired transmitter transmits on the same channel but with a different PN sequence (or no sequence at all), the de-spreading process results in no processing

15、gain for that signal. This effect is the basis for the (CDMA) property of DSSS, which allows multiple transmitters to share the same channel within the limits of the properties of their PN sequences. As this description suggests, a plot of the transmitted waveform has a roughly bell-shaped envelo

16、pe centered on the carrier frequency, just like a normal transmission, except that the added noise causes the distribution to be much wider than that of an AM transmission. In contrast, pseudo-randomly re-tunes the carrier, instead of adding pseudo-random noise to the data, which results in a uni

17、form frequency distribution whose width is determined by the output range of the pseudo-random number generator. 3. Experiment Procedure 3.1. Generate the pseudo random numbers sequences (m sequence) with a polynomial as following The polynomial is corresponding to the LFSR of the Figure

18、3.1, where denotes a connection. Figure 3.1 Linear feedback shift register As the polynomial shows, we can get the LFSR in this experiment with 14 orders (n=14). Figure 3.2 n=15 LFSR As Figure 3.2 shows, the feedback output has a relationship with the registers. Hence, we can get the l

19、ongest m sequence as. In this experiment, I take the message data rate as 1bit/s, which means Tb=1. Here, the input sequence is initialized as (1 0 1 1 0 1 1 1 0 1 1 0 0). I take the PN sequence data rate as Tc=1/64bit/s, because the spreading gain is 64, in another word, Tb/Tc=64. And I can get the

20、 waveform of M sequences and message (input data) as follows: Figure 3.3 Input message data waveform Figure 3.4 M sequences waveform In this experiment, in fact, both message data and m sequences are single polar codes. In Matlab we use a simple function to change them into double polar codes

21、, which are easy to produce the BPSK signal (phase reversing). 3.2. Generate a spreading signal d(t), and the producing formula as following: Here we can get the waveform of d(t) comparing to b(t) and c(t). Figure 3.5 c(t) (red) and d(t) (green) Because b(t) equals 1 at the interval (

22、0,1) and -1 at the interval (1,2), the d(t) is reversed at the interval (1,2) as Figure 3.5 shows. Now, I have get the spreading sequences d(t) with the data rate as 64bits/s. 3.3. Modulate the spreading sequences d(t) to be the BPSK signal By using the formula of BPSK, I can get the BPSK sig

23、nal of spreading sequences d(t). In this experiment, I take the carrier frequency as 128Hz. I can get the BPSK waveform as Figure 3.6. Figure 3.6 BPSK of d(t) At the same time, I should keep a PN sequence signal that has the same length as the spreading signal d(t) to keep the synchronization

24、 between the transmitter and receiver. 3.4. In an AWGN channel to transmit the BPSK signal. First of all, I get the AWGN signal as follow Figure 3.7 AWGN signal We can find that the AWGN signal has large number of harmonics and it’s power spectral density is uniform and it’s amplitude dist

25、ribution obeys the Gauss distribution. In our transmitting channel, I add the noise to the BPSK signal and I take the SNR equals 10. Then I get the signal as shown in Figure 3.8. Figure 3.8 BPSK signal adding AWGN 3.4. Recover our message data b(t) Firstly, I use two synchronized circuit

26、to despread and demodulate the receiving signal. Then I use a matching filter to take the value at the time Tb. Figure 3.9 Matching filter (1) Figure 3.10 Matching filter (2) As we can see, because of the effect of AWGN, in each Tb time interval, the matching filter output has some dif

27、ferences. However, I can still get the nearly maximum and minimum value at the time points Tb+n*Tb (N=1,2,3,..,N and N equals the length of the message data). Secondly, I do the judgment for maximum and minimum value. Obviously, the y(Tb) get the maximum value, the recovery bit will be 1 and the la

28、st time will be Tb, to be inverse, the recovery bit will be -1. Here I can find that the output signal in our receiver is the same as that in transmitter. Figure 3.11 Output signal on the receiver 4. Analysis and Conclusion 4.1. Spectrum Spread Digital communication has played a

29、more important role than analog communication. In digital communication, the fastest speed of data transmission is seen to be the bandwidth of digital channel, which is also called the capacity of the channel. We can know that the larger the capacity, the stronger the ability of anti-interference, b

30、ecause of the Shannon theorem: B is the bandwidth of frequency spectrum. Obviously, when I enlarge the capacity, the bandwidth will be wider. This is also the reason I use the high baud rate m sequence to spread the message data bandwidth. 4.2. Matching Filter In this experiment, the matchi

31、ng filter is achieved by using a simple function in Matlab which is ‘xcorr’. This is a function used to calculate the cross-correlation of two sequences. As we know, in fact, matching filter is just like an autocorrelation calculating function. 4.3. The advantages of m sequence (double polar)

32、The characteristic of autocorrelation of m sequence is very good. I can know it from the following result. I can find that when the N is very large which is just like that in this experiment, . This is very good for the multichannel processing. In CDMA system, I take orthogonal code to encode d

33、ifferent users’ message data. However, the characteristics of cross-correlation and autocorrelation of orthogonal code is bad for the multichannel systems. In order to improve this phenomenon, I should multiple m sequences to the orthogonal codes. APPENDIX Some Parts of Matlab Codes I. DSSS

34、 (main function) clear; %the PAM input digital sequence and the first input of PN producer p=[1 0 1 1 0 1 1 1 0 1 1 0 0];%get 0 1 0 0 1 pn_in=[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0];%get 0 0 0 0 0 0 0 0 0 0 0 %the period of input signal Tb=1; %the period of PN sequence signal Tc=1/256; %produce

35、 the PN sequence pn_sq=PN_Producer(pn_in); pn_dou=zero_double(pn_sq); p_dou=zero_double(p); %square wave %[x1,y1]=square_wave(p_dou,Tb); %[a2,b2]=size(x1); %[x2,y2]=square_wave(pn_dou,Tc); %plot(x1,y1); %get the signal after spreading frequcecy operation b_dou=PN_Signal(p_dou,pn_dou,Tb,

36、Tc); %the synchronization of PN p_syn=PN_Syn(p_dou,pn_dou,Tb,Tc); %the carry signal period T_carry=1/512; %get the carry signal by PSK wc=2*pi*1/T_carry; [psks,tpsk]=PSK_Producer(b_dou,wc,Tc); [a1,b1]=size(tpsk); fs=b1-1; N=400; FSpectrum(psks,fs,N); %plot(tpsk,psks); %axis([0 1.6 -2 2]

37、); %grid on; %add the noise to the PSK signal by AWGN in_signal=awgn(psks,10);%the SNR is 20 %fs=b1-1; %N=400; %FSpectrum(in_signal,fs,N); %plot(tpsk,in_signal); %the recovery signal signal_corr=Signal_Recover(in_signal,tpsk,p_syn,wc,Tc,Tb); II. PN_Producer (produce the m-sequence) fu

38、nction y=PN_Producer(x) m_sq(1).a=x; m_sq(2).a=x; m_sq(1).c=[1 0 0 0 0 1 0 1 1 1 0 0 0 1 0 1 ]; %get1 0 0 0 0 1 0 1 1 1 0 0 0 1 0 1 get1 1 0 0 1 [l_min,l_max]=size(m_sq(1).c); n=2; %the first digit of m-sequence pn_out(1)=m_sq(2).a(1); %the m-sequence producer for shifting first time m_

39、sq(2).a(l_max)=m_sq(1).c(2)*m_sq(1).a(l_max-1); for i=1:1:(l_max-2) if m_sq(2).a(l_max)==(m_sq(1).c(2+i)*m_sq(1).a(l_max-i-1)) m_sq(2).a(l_max)=0; else m_sq(2).a(l_max)=1; end end for j=0:1:(l_max-2) m_sq(2).a(1+j)=m_sq(2).a(2+j); end for j1=1:1:(l_max-

40、1) m_sq(3).a(j1)=m_sq(2).a(j1); end pn_out(n)=m_sq(2).a(1); n=n+1; %to check whether the m-sequence becomes back to the original sequence if isequal(m_sq(3).a,m_sq(1).a) pn_check=1; else pn_check=0; end %the whole shifting operation and produce the PN while pn_check==0 m_sq

41、(2).a(l_max)=m_sq(1).c(2)*m_sq(2).a(l_max-1); for i=1:1:(l_max-2) if m_sq(2).a(l_max)==(m_sq(1).c(2+i)*m_sq(2).a(l_max-i-1)) m_sq(2).a(l_max)=0; else m_sq(2).a(l_max)=1; end end for j=0:1:(l_max-2) m_sq(2).a(1+j)=m_sq(2).a

42、(2+j); end for j1=1:1:(l_max-1) m_sq(3).a(j1)=m_sq(2).a(j1); end pn_out(n)=m_sq(3).a(1); if isequal(m_sq(3).a,m_sq(1).a) pn_check=1; else pn_check=0; end n=n+1; end %return the PN code for i4=1:1:(n-2) y(i4)=pn_out(i4);

43、 end end III. PN_Signal (generate the signal after spreading spectrum) function y=PN_Signal(p,pn_in,Tb,Tc) N=fix(Tb/Tc); [a,b]=size(p); [a1,b1]=size(pn_in); c=fix(b*N/b1) pn_original=pn_in; if c>0 for j=0:1:c pn_in=horzcat(pn_in,pn_original); end else pn_

44、in=pn_in; end s1=1; e1=N; for i=1:1:b if p(i)==1 for m=s1:1:e1 y(m)=pn_in(m); s1=1+N*i; e1=N+N*i; end else for m=s1:1:e1 y(m)=p(i)*pn_in(m); s1=1+N*i; e1=N+N*i; end end end end

45、 IV. PSK_Producer (generate the BPSK signal) function [y,z]=PSK_Producer(x,wc,Tb) [a,b]=size(x); %sampling the PSK signal and produce them the N is 1:2 for i=1:1:b start1=(i-1)*Tb; end1=i*Tb;%Tb is the period of PN sequence Tcs(i).s=linspace(start1,end1,50); if x(i)==

46、1 c_sq(i).s=sin(wc*Tcs(i).s); else c_sq(i).s=(-1)*sin(wc*Tcs(i).s); end end y=c_sq(1).s; z=Tcs(1).s; for j=1:1:(b-1) y=horzcat(y,c_sq(j+1).s); z=horzcat(z,Tcs(j+1).s); end end V. PN_Syn (generate the synchronized PN sequence) function y=PN_S

47、yn(p,pn_in,Tb,Tc) N=fix(Tb/Tc); [a,b]=size(p); [a1,b1]=size(pn_in); c=fix(b*N/b1); pn_original=pn_in; if c>0 for j=0:1:c pn_in=horzcat(pn_in,pn_original); end else pn_in=pn_in; end s1=1; e1=N; for i=1:1:b for m=s1:1:e1 y(m)=pn_in(m);

48、 s1=1+N*i; e1=N+N*i; end end end VI. Signal_Recover (receiver) function y=Signal_Recover(x,tpsk,pn_syn,wc,Tb,Tc) m_signal=sin(wc*tpsk); [t_sqr,pn_syn_sqr]=square_wave(pn_syn,Tb); c_psk=x.*pn_syn_sqr; N=fix(Tc/Tb); [a2,b2]=size(tpsk); t_gap=b2/50/N; m

49、id=50*N; for i2=1:1:mid t_corr_mid(i2)=tpsk(i2); end [t_corr,t_corr_mid]=corr_x(t_corr_mid,Tb,N); for k=1:1:t_gap for k2=1:1:(50*N) k3=k2+(k-1)*50*N; in_psk(k).s(k2)=c_psk(k3); in_sin(k).s(k2)=m_signal(k3); end ym=xcorr(in_psk(k).s,in_sin(k).s);

50、 %[t_corr,t_corr_mid]=corr_x(t_corr_mid,Tb,N); if ym(mid)>0 y(k)=1; else y(k)=0; end if k==2 plot(t_corr,ym,'g*:'); axis([0 2 -2000 2000]); hold on; grid on; %elseif k==3 %plot(t_corr,ym,'mx:'); %axis([0 12.8 -2000 2000]);

51、%hold on; %grid on; else disp('nice'); end end end VII. square_wave (generate the square wave signal) function [fss,sqrs]=square_wave(p,g) %square wave sampling frequency fs(1).s=linspace(0,g,50); [a,b]=size(p); %discrete ones in different time point for i=1:1:(b-

52、1) if p(i)==1 sqr_p(i).s=ones(1,50); else sqr_p(i).s=(-1)*ones(1,50); end fs(i+1).s=linspace(0+g*i,g+g*i,50); end %the last time interval if p(b)==1 sqr_p(b).s=ones(1,50); else sqr_p(b).s=(-1)*ones(1,50); end fss=fs(1).s; sqrs=sqr_p(1).s;

53、 for i2=1:1:(b-1) fss=horzcat(fss,fs(i2+1).s); sqrs=horzcat(sqrs,sqr_p(i2+1).s); end end VIII. zero_double (cover the single polar signal to the double polar signal) function y=zero_double(x) [a,b]=size(x); for i=1:1:b if x(i)==0 y(i)=-1; else y(i)=x(i); end end end 專心---專注---專業(yè)