基于博弈论的频谱分配的matlab程序.doc

是认知无线电场景下，基于博弈论的频谱分配的matlab程序-Cognitive radio scenario, based on game theory matlab spectrum allocation processclc clear all B=15; BER=10(-4); K=1.5/(logm(0.2/BER); SNR=8,10; k=log2(1+K.*SNR); r1=10; r2=12; x=0; y=1; z=1; % b=0,0; % c=x+y*(sum(b); % p=r.*k.*b-b.*c; a(1)=0.001; for i=1:300 a(2)=0.001; for j=1:300 eigenvalue(1)=1-10/3*(2*a(1)*k(1)-a(1)*k(2)+2*a(2)*k(2)-a(2)*k(1)+10/3*(a(1)*(k(2)-2*k(1)*a(2)*(k(1)-2*k(2)+(2*a(2)*k(2)-a(2)*k(1)-2*a(1)*k(1)+a(1)*k(2)2)(0.5); eigenvalue(2)=1-10/3*(2*a(1)*k(1)-a(1)*k(2)+2*a(2)*k(2)-a(2)*k(1)-10/3*(a(1)*(k(2)-2*k(1)*a(2)*(k(1)-2*k(2)+(2*a(2)*k(2)-a(2)*k(1)-2*a(1)*k(1)+a(1)*k(2)2)(0.5); if (eigenvalue(1)-1)&(eigenvalue(2)-1) a(2)=a(2)+0.002; end end if a(2)0 a(2)=a(2)-0.002; else a(2)=0; end f2(i)=a(2); f1(i)=a(1); a(1)=a(1)+0.002; end figure(1) plot(f1,f2,-r) axis(0,0.25,0,0.25) hold on B=15; BER=10(-4); K=1.5/(logm(0.2/BER); SNR=10,10; k=log2(1+K.*SNR); r1=10; r2=12; x=0; y=1; z=1; % b=0,0; % c=x+y*(sum(b); % p=r.*k.*b-b.*c; a(1)=0.001; for i=1:300 a(2)=0.001; for j=1:300 eigenvalue(1)=1-10/3*(2*a(1)*k(1)-a(1)*k(2)+2*a(2)*k(2)-a(2)*k(1)+10/3*(a(1)*(k(2)-2*k(1)*a(2)*(k(1)-2*k(2)+(2*a(2)*k(2)-a(2)*k(1)-2*a(1)*k(1)+a(1)*k(2)2)(0.5); eigenvalue(2)=1-10/3*(2*a(1)*k(1)-a(1)*k(2)+2*a(2)*k(2)-a(2)*k(1)-10/3*(a(1)*(k(2)-2*k(1)*a(2)*(k(1)-2*k(2)+(2*a(2)*k(2)-a(2)*k(1)-2*a(1)*k(1)+a(1)*k(2)2)(0.5); if (eigenvalue(1)-1)&(eigenvalue(2)-1) a(2)=a(2)+0.002; end end if a(2)0 a(2)=a(2)-0.002; else a(2)=0; end f2(i)=a(2); f1(i)=a(1); a(1)=a(1)+0.002; end figure(1) plot(f1,f2,-.*b) axis(0,0.25,0,0.25) B=15; BER=10(-4); K=1.5/(logm(0.2/BER); SNR=10,8; k=log2(1+K.*SNR); r1=10; r2=12; x=0; y=1; z=1; % b=0,0; % c=x+y*(sum(b); % p=r.*k.*b-b.*c; a(1)=0.001; for i=1:300 a(2)=0.001; for j=1:300 eigenvalue(1)=1-10/3*(2*a(1)*k(1)-a(1)*k(2)+2*a(2)*k(2)-a(2)*k(1)+10/3*(a(1)*(k(2)-2*k(1)*a(2)*(k(1)-2*k(2)+(2*a(2)*k(2)-a(2)*k(1)-2*a(1)*k(1)+a(1)*k(2)2)(0.5); eigenvalue(2)=1-10/3*(2*a(1)*k(1)-a(1)*k(2)+2*a(2)*k(2)-a(2)*k(1)-10/3*(a(1)*(k(2)-2*k(1)*a(2)*(k(1)-2*k(2)+(2*a(2)*k(2)-a(2)*k(1)-2*a(1)*k(1)+a(1)*k(2)2)(0.5); if (eigenvalue(1)-1)&(eigenvalue(2)-1) a(2)=a(2)+0.002; end end if a(2)0 a(2)=a(2)-0.002; else a(2)=0; end f2(i)=a(2); f1(i)=a(1); a(1)=a(1)+0.002; end figure(1) plot(f1,f2,-g) axis(0,0.25,0,0.25) B=15; BER=10(-4); K=1.5/(logm(0.2/BER); SNR=7,7; k=log2(1+K.*SNR); r1=10; r2=12; x=0; y=1; z=1; % b=0,0; % c=x+y*(sum(b); % p=r.*k.*b-b.*c; a(1)=0.001; for i=1:300 a(2)=0.001; for j=1:300 eigenvalue(1)=1-10/3*(2*a(1)*k(1)-a(1)*k(2)+2*a(2)*k(2)-a(2)*k(1)+10/3*(a(1)*(k(2)-2*k(1)*a(2)*(k(1)-2*k(2)+(2*a(2)*k(2)-a(2)*k(1)-2*a(1)*k(1)+a(1)*k(2)2)(0.5); eigenvalue(2)=1-10/3*(2*a(1)*k(1)-a(1)*k(2)+2*a(2)*k(2)-a(2)*k(1)-10/3*(a(1)*(k(2)-2*k(1)*a(2)*(k(1)-2*k(2)+(2*a(2)*k(2)-a(2)*k(1)-2*a(1)*k(1)+a(1)*k(2)2)(0.5); if (eigenvalue(1)-1)&(eigenvalue(2)-1) a(2)=a(2)+0.002; end end if a(2)0 a(2)=a(2)-0.002; else a(2)=0; end f2(i)=a(2); f1(i)=a(1); a(1)=a(1)+0.002; end figure(1) plot(f1,f2,-.y) axis(0,0.25,0,0.25) B=15; BER=10(-4); K=1.5/(logm(0.2/BER); SNR=8,8; k=log2(1+K.*SNR); r1=10; r2=12; x=0; y=1; z=1; % b=0,0; % c=x+y*(sum(b); % p=r.*k.*b-b.*c; a(1)=0.001; for i=1:300 a(2)=0.001; for j=1:300 eigenvalue(1)=1-10/3*(2*a(1)*k(1)-a(1)*k(2)+2*a(2)*k(2)-a(2)*k(1)+10/3*(a(1)*(k(2)-2*k(1)*a(2)*(k(1)-2*k(2)+(2*a(2)*k(2)-a(2)*k(1)-2*a(1)*k(1)+a(1)*k(2)2)(0.5); eigenvalue(2)=1-10/3*(2*a(1)*k(1)-a(1)*k(2)+2*a(2)*k(2)-a(2)*k(1)-10/3*(a(1)*(k(2)-2*k(1)*a(2)*(k(1)-2*k(2)+(2*a(2)*k(2)-a(2)*k(1)-2*a(1)*k(1)+a(1)*k(2)2)(0.5); if (eigenvalue(1)-1)&(eigenvalue(2)-1) a(2)=a(2)+0.002; end end if a(2)0 a(2)=a(2)-0.002; else a(2)=0; end f2(i)