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⛄ 内容介绍

在多输入多输出正交频分复用(MIMO-OFDM)系统中,当发送天线数或支持用户的天线数超过接收的天线数(称为"秩亏")时,传统的多用户检测方法都有一定的局限性.将混合遗传算法引入到MIMO-OFDM系统多用户检测中解决秩亏问题.在秩亏环境下的仿真表明,基于混合遗传算法的多用户检测可实现比传统多用户检测方法更高的性能.

⛄ 部分代码

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%   秩亏情况下MIMO-OFDM系统多用户检测MMSE-GA-GSAA算法仿真   %%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%  说明  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%通信系统:通过第l个用户传输的复信号s(l)是零均值的,方差为(lou)。

%加性高斯白噪声信号n(p)也是零均值的,其方差为(lou)。

%不同接收者或用户的频域信道传递函数Hpl是相互独立的,满足零均值单位方差的高斯分布。

   %%%%%%%%%%%  GA  %%%%%%%%%%%%%%%

%GA过程:从适应度大的个体中取出Nm/2个体,用赌盘选择法(选择算子)随机选择

%Nm/2次(每次一个个体),得到Nm/2个个体组成杂交集合,将杂交集合中个体与适

%应度函数值小的个体根据杂交概率进行杂交操作(两点杂交法),然后根据变异算子

disp('===============  作图  ===============')

%%%   作图   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%    

figure(1); %源信号  

plot(source,'.');%源信号个数、0/1分布、信源

axis([1 100 -0.1 1.1]);title('Source transmitted in the Last Channel');

xlabel('Numbers of Signal');ylabel('0/1 Distribution');

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%   

figure(2);

subplot(1,2,1);%接收到的各路信号(不同SNR)

f13=1:1:Ni; %坐标

for f14=1:1:P

    plot3(real(x(f14,:)),imag(x(f14,:)),f13,'x');%hold on; 

end;

%plot3(real(x(1,:)),imag(x(1,:)),f13,'rx');hold on;

%plot3(real(x(2,:)),imag(x(2,:)),f13,'bx');hold on;

%plot3(real(x(3,:)),imag(x(3,:)),f13,'gx');hold on;

d1=real(x);  %x实部

d2=imag(x);  %x虚部

d31=min(min(d1)); 

d32=max(max(d1));

d41=min(min(d2));

d42=max(max(d2));

axis([d31 d32 d41 d42 0 Ni]);title('Received Signals of P-Antennas');

xlabel('Real');ylabel('Imaginary');zlabel('Numbers=Nm');

grid on;

figure(2);  %加噪声信号  *

subplot(1,2,2);

f13=1:1:Ni; %坐标

for f14=1:1:P

    plot3(real(x(f14,:)),imag(x(f14,:)),f13,'x');%hold on; 

end;

%plot3(real(x(1,:)),imag(x(1,:)),f13,'-rx');hold on;

%plot3(real(x(2,:)),imag(x(2,:)),f13,'-bx');hold on;

%plot3(real(x(3,:)),imag(x(3,:)),f13,'-gx');hold on;

axis([d31 d32 d41 d42 0 10]);%信号实部、信号虚部、个数

xlabel('Real');ylabel('Imaginary');zlabel('Numbers=Nm/10');

title('Received Signals of P-Antennas');%接收到的各路信号(不同SNR)

grid on;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 

figure(3);

subplot(1,2,1);

f13=1:1:Ni; %坐标

for f14=1:1:P

    plot3(real(Smmse(f14,:)),imag(Smmse(f14,:)),f13,'.');hold on; 

end

%plot3(real(Smmse(1,:)),imag(Smmse(1,:)),f13,'rx');hold on;

%plot3(real(Smmse(2,:)),imag(Smmse(2,:)),f13,'bx');hold on;

%plot3(real(Smmse(3,:)),imag(Smmse(3,:)),f13,'gx');hold on;

%plot3(real(Smmse(4,:)),imag(Smmse(4,:)),f13,'yx');hold on;

xlabel('Real');ylabel('Imaginary');zlabel('Numbers');

title('Received Signals of MMSE Algorithm');%接收到的各路信号(不同SNR)

d1=real(Smmse);  %x实部

d2=imag(Smmse);  %x虚部

d31=min(min(d1)); 

d32=max(max(d1));

d41=min(min(d2));

d42=max(max(d2));

axis([d31 d32 d41 d42 0 Ni]);grid on;

figure(3);  %加噪声信号  *

subplot(1,2,2);

f13=1:1:Ni; %坐标

for f14=1:1:P

    plot3(real(Smmse(f14,:)),imag(Smmse(f14,:)),f13,'.');hold on; 

end;

%plot3(real(Smmse(1,:)),imag(Smmse(1,:)),f13,'rx');hold on;

%plot3(real(Smmse(2,:)),imag(Smmse(2,:)),f13,'bx');hold on;

%plot3(real(Smmse(3,:)),imag(Smmse(3,:)),f13,'gx');hold on;

%plot3(real(Smmse(4,:)),imag(Smmse(4,:)),f13,'yx');hold on;

xlabel('Real');ylabel('Imaginary');zlabel('Numbers');

title('Received Signals of MMSE Algorithm');%接收到的各路信号(不同SNR)

axis([d31 d32 d41 d42 Ni/10-1 Ni/10+1]);grid on;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

m11=1:1:Nm;%GA算法第一代种群

figure (4);subplot(2,1,1);plot(m11,AFc,'-x');axis([0 100 0 0.2]);

ylabel('Values of AF'); 

title('Adaptive Function(AF) of The 1st GA Generation');grid on;

figure (4);subplot(2,1,2);plot(m11,OFcz,'-o');axis([0 100 0 0.2]);

xlabel('Numbers of Individuals in a GA Population');ylabel('Values of OF');

title('Object Function(OF) of The 1st GA Generation');grid on;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

m11=1:1:Nm;

figure (5);%GA算法第Ng代种群

subplot(2,1,1);plot(m11,AF,'-x');axis([0 100 0 20]);ylabel('Values of AF'); 

title('Adaptive Function(AF) of The Ng-st GA Generation');grid on;

figure (5);subplot(2,1,2);plot(m11,OFz,'-o');axis([0 100 0 20]);

xlabel('Numbers of Individuals in a GA Population');ylabel('Values of OF'); 

title('Object Function(OF) of The Ng-st GA Generation');grid on;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

m12=1:1:Nm/2;

figure (6); %ph:存活概率

subplot(2,1,1);plot(m12,ph,'x');ylabel('Probability Values'); 

title('Survived Probability of Hybrid Assembly');grid on;

figure (6);%plj:累积概率

subplot(2,1,2);plot(m12,plj,'.');axis([0 Nm/2 0 1]);grid on;

xlabel('Numbers of Selected Individuals');ylabel('Probability Values');

title('Accumulated Probability of Hybrid Assembly');

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

m11=1:1:Nm;

figure (7);%%%%%%%%%%%%

subplot(2,1,1);plot(m11,AFx,'-x');axis([0 100 0 60]);

ylabel('Values of AF'); %适应度函数值 AF

title('Adaptive Function(AF) of The New GA Generation');grid on;

figure (7);%%%%%%%%%%%%

subplot(2,1,2);plot(m11,OFxz,'-o');axis([0 100 0 60]);

xlabel('Numbers of Individuals in a GA Population'); %遗传种群个体数 

ylabel('Values of OF'); %目标函数值 OF

title('Object Function(OF) of The New GA Generation');grid on;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%    

figure(8);%%%%%%%%%%%%

plot(pjs,'x');title('Meteopolis Formula');

xlabel('Numbers of Signal');ylabel('Probability Distribution');

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 

figure(9);subplot(2,1,1);hold on; snr=1:1:10; plot(snr,ave1,'-o');hold on;

xlabel('SNR/dB');ylabel('BER');title('SNR-BER in a GA Generation');

figure(9); snr=1:1:10; subplot(2,1,2);plot(snr,ave1,'-o');hold on;

xlabel('SNR/dB');ylabel('BER');

title('SNR-BER of GA Operation in Once Generation');plot(snr,ave2,'-r*');

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%  

figure(10); subplot(2,1,1); snr=1:1:10; plot(snr,ave0,'-o');hold on;

xlabel('SNR/dB');ylabel('BER');title('SNR-BER of MMSE and GA Algorithm');

plot(snr,ave3,'-r*');legend('MMSE','GA');hold on;      

figure(10);subplot(2,1,2);snr=1:1:10; plot(snr,ave3,'-r*');hold on; 

xlabel('SNR/dB');ylabel('BER');title('SNR-BER of GA and GSAA Algorithm');

plot(snr,ave4,'-k^');legend('GA','GSAA');hold on; 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 

%figure(11); snr=1:1:10; plot(snr,ave0,'-o');hold on;

%plot(snr,ave3,'-r*');hold on;xlabel('SNR/dB');ylabel('BER');

%title('SNR-BER of MMSE/GA/GSAA Algorithm');

%plot(snr,ave4,'-k^');legend('MMSE','GA','GSAA');hold on; 


figure(11); %%%%%%%%%%%%

snr=1:1:10; plot(snr,ave0,'-o');hold on;

%plot(snr,ave3,'-r*');hold on;

xlabel('SNR/dB');ylabel('BER');

title('SNR-BER of MMSE/HGA(GSAA) Algorithm');

plot(snr,ave4,'-k^');legend('MMSE','HGA');hold on; 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

figure(12); snr=1:1:10; plot(snr,yp0,'-o');hold on;

%plot(snr,yp3,'-r*');hold on;

xlabel('SNR/dB');ylabel('BER');

title('SNR-BER of MMSE/HGA(GSAA) Algorithm');

plot(snr,yp4,'-k^');legend('MMSE','HGA');hold on; grid on

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

figure(13);%%%%%%%%%%%%

m22=1:1:Ng;rx=mean(rfs);hold on;plot(m22,rx,'-o'); %相关系数

title('Correlation Coefficient of Genetic Operators');grid on;

xlabel('Times of Genetic Generation');ylabel('Correlation Coefficient');

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

disp('===============  figure(12)  ===============')

toc

disp('--------------------------------------------')

disp('=============  HGA程序运行结束  =============')

disp('--------------------------------------------')

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

⛄ 运行结果

【OFDM仿真】基于秩亏情况下遗传算法和粒子群算法优化MIMO-OFDM系统多用户检测附matlab代码_d3

⛄ 参考文献

[1]杨光达, 胡封晔, 侯金凤. 基于混合遗传算法的MIMO-OFDM系统多用户检测研究[C]// 中国电子学会第十六届信息论学术年会. 0.

⛄ 完整代码

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