[转帖]RLS算法程序

suffer

1. %RLS_S.m
   
2. %RLS algorithm
   
3. %static status
   
4. %synchronous CDMA
   
5. %channel: White Gaussis Noise
   
6. 
   
7. function [P1,P_i_n,y,correct,E_ex] = RLS_S(b,N,K,step,yN_step,S,SNR)
   
8. 
   
9. %multiuser detection
   
10. yyN_step=(yN_step);
    
11. delta=1e-2;
    
12. P=1/delta*eye(N);%P=1/R
    
13. lamda=0.997;
    
14. for i=1:step
    
15. KK=P*yyN_step(:,i)/(lamda+yyN_step(:,i)'*P*yyN_step(:,i));
    
16. P=1/lamda*(P-KK*yyN_step(:,i)'*P);
    
17. h=P*S(1,:).';
    
18. c=h/(S(1,:)*h);
    
19. 
    
20. P1(i)=abs( (c.'*S(1,:).')^2 );
    
21. P_i_n(i)=abs( ( c.'*(yyN_step(:,i)-b(1,i)*S(1,:).') )^2 );
    
22. %SINR(i)=P1/P_i_n;
    
23. y(i)=sign(real(c.'*yyN_step(:,i)));
    
24. 
    
25. E=abs( (c.'*yyN_step(:,i))^2 );
    
26. E_min=1;
    
27. E_ex(i)=E-E_min;
    
28. end
    
29. correct=(y==b(1,:));
    
30. 
    
31. %plot(SINR);

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suffer

1. %RLS_S.m
   
2. %RLS algorithm
   
3. %static status
   
4. %synchronous CDMA
   
5. %channel: White Gaussis Noise
   
6. 
   
7. function [P1,P_i_n,y,correct,E_ex,PP] = RLS_D(b,N,K,step,yN_step,S,SNR,P)
   
8. 
   
9. %multiuser detection
   
10. lamda=0.997;
    
11. PP=P;
    
12. 
    
13. for i=1:step
    
14. KK=PP*yN_step(:,i)/(lamda+yN_step(:,i).'*PP*yN_step(:,i));
    
15. PP=1/lamda*(PP-KK*yN_step(:,i).'*PP);
    
16. h=PP*S(1,:).';
    
17. c=h/(S(1,:)*h);
    
18. 
    
19. P1(i)=(c.'*S(1,:).')^2;
    
20. P_i_n(i)=( c.'*(yN_step(:,i)-b(1,i)*S(1,:).') )^2;
    
21. %SINR(i)=P1/P_i_n;
    
22. y(i)=sign(c.'*yN_step(:,i));
    
23. 
    
24. E=(c.'*yN_step(:,i))^2;
    
25. E_min=1;
    
26. E_ex(i)=E-E_min;
    
27. end
    
28. 
    
29. correct=(y==b(1,:));
    
30. 
    
31. %plot(SINR);

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yyfei11

学习下，谢谢分享，:lol :@)