Preamble Synchronization

Preamble is a signal used in network communications to synchronize transmission timing between two or more systems.

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Preamble Synchronization

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Schematics

Block Diagram of Preamble Synchronization

Code

Integer Vector Correlator

VHDL
package Correlation IS
    TYPE int_vector is ARRAY(integer RANGE <>) OF integer;
end;

library IEEE;
use IEEE.STD_LOGIC_1164.ALL;
use work.Correlation.ALL;

entity correlation4 is
    port (clk:in std_logic;
          C:out int_vector(1 to 5)
          );
end correlation4;

architecture Behavioral of correlation4 is
	constant zeros:integer:=0;
   signal x:int_vector(1 to 4);
   signal h:int_vector(1 to 2);


begin
x<=(-1,2,1,-3);
h<=(2,1);
process (clk,x,h)
	variable x1:int_vector(1 to 7);
	variable sum:integer := 0;
	variable corr:integer;
	variable a:integer:=5;
	variable m:integer range 1 to 2:=1;
	variable k:integer range 1 to 5:=1;
begin
x1:=x&zeros&zeros&zeros;
	if (rising_edge(clk)) then
		corr := h(m)*x1(m+a-1);
		sum := sum + corr;
			if (m<2) then
				m:=m+1;
			else
				m:=1;
				if(a>1) then
					a:=a-1;
				end if;
				C(k) <= sum ;
				sum:=0;
				k:=k+1;
			end if;
	end if;
end process;
end Behavioral;

Normalized Correlation Matlab Code

MATLAB
 function normC=n_crosscorr(x,y)
L=length(x)+length(y)-1;
a=L;
y1=[zeros(1,length(y)-1),y,zeros(1,length(x)-1)];
corr=0;
C=0;
csum=0;
for k=1:L
    

    
    for m=1:length(x)
 
     
     corr=conj(x(m)).*y1(m+a-1);
     csum=corr+csum;

end

a=a-1;
C(k)=csum;
csum=0;
end
normC=C./max(C(:));
end

802.11a preamble correlation

MATLAB
close all;
clear all;
clc;


short_sym=[(0.023+0.023j)  (-0.132 +0.002j)  (-0.013-0.079j)  (0.143-0.013j)...
 (0.092+0.000j)  (0.143-0.013j)  (-0.013-0.079j)      (-0.132+0.002j)...
(0.046+0.046j) (0.002-0.132j)  (-0.079-0.013j)        (-0.013+0.143j)...
 (0.000 +0.092j) (-0.013+0.143j)  (-0.079-0.013j)     (0.002 -0.132j)];%
cp=[(-0.078+0.000j)  (0.012-0.098j)  (0.092-0.106j)     (-0.092-0.115j)...
 (-0.003-0.054j) (0.075+0.074j)  (-0.127+0.021j)  (-0.122+0.017j)...
 (-0.035+0.151j)  (-0.056+0.022j ) (-0.060-0.081j)  (0.070-0.014j)...
 (0.082-0.092j)  (-0.131-0.065j)  (-0.057-0.039j)  (0.037-0.098j)...
 (0.062+0.062j)  (0.119+0.004j)  (-0.022-0.161j)  (0.059+0.015j)...
 (0.024+0.059)  (-0.137+0.047j)  (0.001+0.115j)  (0.053-0.004j)...
 (0.098+0.026j)  (-0.038+0.106j) (-0.115+0.055j)  (0.060+0.088j)...
 (0.021-0.028j)  (0.097-0.083j)  (0.040+0.111j)  (-0.005+0.120j) ]; 
long_sym=[(0.156+0.000j) (-0.005+(-0.120j))  (0.040+(-0.111j))  (0.097+0.083j)...
  (0.021+0.028j)  (0.060+(-0.088j))  (-0.115+(-0.055j))  (-0.038+(-0.106j))...
  (0.098+(-0.026j))  (0.053+0.004j) (0.001-0.115j) (-0.137-0.047j)...
  (0.024+(-0.059j))  (0.059-0.015j)  (-0.022+0.161j)  (0.119-0.004j)...
  (0.062+(-0.062j))  (0.037+0.098j)  (-0.057+0.039j)  (-0.131+0.065j)...
  (0.082+0.092j)  (0.070+0.014j)  (-0.060+0.081j)  (-0.056-0.022j)...
  (-0.035+(-0.151j))  (-0.122+(-0.017j))  (-0.127+(-0.021j))  (0.075+(-0.074j))...
  (-0.003+(0.054j))  (-0.092+0.115j)  (0.092+0.106j)  (0.012+0.098j)...
  (-0.156+0.000j)  (0.012+(-0.098j))  (0.092+(-0.106j))  (-0.092+(-0.115j))...
  (-0.003+(-0.054j))  (0.075+0.074j)  (-0.127+0.021j)  (-0.122+0.017j)...
  (-0.035+0.151j)  (-0.056+0.022j)  (-0.060-0.081j)  (0.070+(-0.014j))...
  (0.082+(-0.092j))  (-0.131+(-0.065j))  (-0.057+(-0.039j))  (0.037+(-0.098j))...
  (0.062+0.062j)  (0.119+0.004j)  (-0.022+(-0.161j))  (0.059+0.015j)...
  (0.024+0.059j)  (-0.137+0.047j)  (0.001+0.115j)  (0.053+(-0.004j))...
  (0.098+0.026j)  (-0.038+0.106j)  (-0.115+0.055j)  (0.060+0.088j)...
  (0.021+(-0.028j))  (0.097+(-0.083j))  (0.040+0.111j)  (-0.005+0.120j)];
x=[short_sym short_sym short_sym short_sym short_sym short_sym short_sym short_sym short_sym short_sym cp long_sym long_sym ];

y=[short_sym zeros(1,304)];

z=[long_sym zeros(1,256)];

short_corr=n_crosscorr(x,y);

figure(1);
stem(short_corr);
title('short preamble ');
axis([300 600 0 1])
 

long_corr=n_crosscorr(x,z);

figure(2); 
stem(long_corr); 
title ('long preamble');
axis([300 600 0 1])

Preamble Present Decision Statistics

MATLAB
clear all;close all;clc; 
onalti=[0.046+0.046i -0.1324+0.0023i -0.0135-0.0785i 0.1428-0.0127i 0.092 0.1428-0.0127i -0.0135-0.0785i -0.1324+0.0023i...
    0.046+0.046i 0.0023-0.1324i -0.0785-0.0135i -0.0127+0.1428i 0.092i -0.0127+0.1428i -0.0785-0.0135i 0.0023-0.1324i];%short 


otuziki=[(-0.078+0.000j)  (0.012-0.098j)  (0.092-0.106j)     (-0.092-0.115j)...
 (-0.003-0.054j) (0.075+0.074j)  (-0.127+0.021j)  (-0.122+0.017j)...
 (-0.035+0.151j)  (-0.056+0.022j ) (-0.060-0.081j)  (0.070-0.014j)...
 (0.082-0.092j)  (-0.131-0.065j)  (-0.057-0.039j)  (0.037-0.098j)...
 (0.062+0.062j)  (0.119+0.004j)  (-0.022-0.161j)  (0.059+0.015j)...
 (0.024+0.059)  (-0.137+0.047j)  (0.001+0.115j)  (0.053-0.004j)...
 (0.098+0.026j)  (-0.038+0.106j) (-0.115+0.055j)  (0.060+0.088j)...
 (0.021-0.028j)  (0.097-0.083j)  (0.040+0.111j)  (-0.005+0.120j) ]; 
altmisdort=[(0.156+0.000j) (-0.005+(-0.120j))  (0.040+(-0.111j))  (0.097+0.083j)...
  (0.021+0.028j)  (0.060+(-0.088j))  (-0.115+(-0.055j))  (-0.038+(-0.106j))...
  (0.098+(-0.026j))  (0.053+0.004j) (0.001-0.115j) (-0.137-0.047j)...
  (0.024+(-0.059j))  (0.059-0.015j)  (-0.022+0.161j)  (0.119-0.004j)...
  (0.062+(-0.062j))  (0.037+0.098j)  (-0.057+0.039j)  (-0.131+0.065j)...
  (0.082+0.092j)  (0.070+0.014j)  (-0.060+0.081j)  (-0.056-0.022j)...
  (-0.035+(-0.151j))  (-0.122+(-0.017j))  (-0.127+(-0.021j))  (0.075+(-0.074j))...
  (-0.003+(0.054j))  (-0.092+0.115j)  (0.092+0.106j)  (0.012+0.098j)...
  (-0.156+0.000j)  (0.012+(-0.098j))  (0.092+(-0.106j))  (-0.092+(-0.115j))...
  (-0.003+(-0.054j))  (0.075+0.074j)  (-0.127+0.021j)  (-0.122+0.017j)...
  (-0.035+0.151j)  (-0.056+0.022j)  (-0.060-0.081j)  (0.070+(-0.014j))...
  (0.082+(-0.092j))  (-0.131+(-0.065j))  (-0.057+(-0.039j))  (0.037+(-0.098j))...
  (0.062+0.062j)  (0.119+0.004j)  (-0.022+(-0.161j))  (0.059+0.015j)...
  (0.024+0.059j)  (-0.137+0.047j)  (0.001+0.115j)  (0.053+(-0.004j))...
  (0.098+0.026j)  (-0.038+0.106j)  (-0.115+0.055j)  (0.060+0.088j)...
  (0.021+(-0.028j))  (0.097+(-0.083j))  (0.040+0.111j)  (-0.005+0.120j)];
preamble=[onalti onalti onalti onalti onalti onalti onalti onalti onalti onalti otuziki altmisdort altmisdort ];


sample=100;
st=0.11;%0.11
a1=0;
std_signal=std(onalti);
for c=1:48
     
   for t=1:sample
       
       nois = st/(sqrt(2)).*((wgn(1,16,0))+(i*wgn(1,16,0)));
%       y1=0;
%       x1=0;
%       payda1=0;
%       payda2=0;
      
      x1=onalti;
       y1=nois+onalti;
       payda1=0.0413;
              payda2=sum((abs(y1)).^2)^2;
              
             % std_signal=std((y1));
          
                  
      a1(t)=abs((y1)*(x1')).^2;
      
     a2(t)=abs((y1)*(y1')).^2;
 
     



   end
   var1(c)=var(a1./payda1);
     var2(c)=var(a2./payda2);
  k=sum(a1)/payda1;
  m=sum(a2)/payda2;
  n=sum(var1)/payda1;
  p=sum(var2)/payda2;
corrMatlab1(c)=k/sample;
varcorrMatlab1(c)=var(a1)/(sample*payda1);

corrMatlab2(c)=m/sample;
varcorrMatlab2(c)=var(a2)/(sample*payda2);
 crossteori(c)=1;
  ototeori(c)=(std_signal^4)/(((std_signal^2)+(st^2))^2);

 st=st-0.0025;%st=st+0.005 st-0.0001
 
end
  stem(corrMatlab1);
  hold on
stem(crossteori);
  hold on
   stem(corrMatlab2);
  hold on
 stem(ototeori);
legend('CrossSim','CrossTeori','OtoSim','OtoTeori');
 
 st2=0.030;
  hs=0;


  nois=st2/sqrt(2).*((wgn(1,320,0))+(i*wgn(1,320,0)));
 y2=preamble+nois;

 hs=abs(xcorr(onalti,y2))./sqrt(0.0413);

  figure(2)
  
  plot(hs);
  figure(3)
  stem(var1);
  set(gca,'yscale','log');

  hold on
   stem(var2);
set(gca,'yscale','log');


 

Only Noise Present Decision Statistics

MATLAB
No preview (download only).

Credits

Yıldız Bilgin

Yıldız Bilgin

1 project • 1 follower
nazlıcan güneş

nazlıcan güneş

1 project • 0 followers
Hasan Kanık

Hasan Kanık

1 project • 1 follower
Mehmet Şerefoğlu

Mehmet Şerefoğlu

0 projects • 0 followers
Thanks to Orhan Gazi.

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