📄 mequal_estimation_bit_improve.m
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judge(7,5)=1;
judge(7,6)=1;
judge(8,7)=1;
judge(8,8)=1;
%the value of c(i)(j) is the input data of encoder that tansform state i into state j
c(1,1)=0;
c(1,2)=1;
c(2,3)=0;
c(2,4)=1;
c(3,5)=0;
c(3,6)=1;
c(4,7)=0;
c(4,8)=1;
c(5,1)=0;
c(5,2)=1;
c(6,3)=0;
c(6,4)=1;
c(7,5)=0;
c(7,6)=1;
c(8,7)=0;
c(8,8)=1;
%// the output data when state i turn into state j
d(1,1,1)=0;d(1,1,2)=0;%d(state,nextstate,output1/2th)=output
d(1,2,1)=1; d(1,2,2)=1;
d(2,3,1)=1;d(2,3,2)=1;
d(2,4,1)=0;d(2,4,2)=0;
d(3,5,1)=0;d(3,5,2)=1;
d(3,6,1)=1;d(3,6,2)=0;
d(4,7,1)=1;d(4,7,2)=0;
d(4,8,1)=0;d(4,8,2)=1;
d(5,1,1)=1;d(5,1,2)=1;
d(5,2,1)=0; d(5,2,2)=0;
d(6,3,1)=0;d(6,3,2)=0;
d(6,4,1)=1;d(6,4,2)=1;
d(7,5,1)=1;d(7,5,2)=0;
d(7,6,1)=0; d(7,6,2)=1;
d(8,7,1)=0;d(8,7,2)=1;
d(8,8,1)=1;d(8,8,2)=0;
%--------------------Training Stage-----------------------
algorithp =1;% input('输入采用何种算法更新训练权系数[0-LMS,1-RLS] algorithp =' ');
%PN = input('输入训练比特数目 PN = ');
soft_PNandData=zeros(K,length(encoderout)+2); %shift 2 bits
error = zeros(1,length(encoderout));
wrong_No=zeros(1,K);
wrong_cut=zeros(1,K);
ben = zeros(1,K); % 每个用户判决错误比特个数
ben_all = 0; % 所有用户判决错误的总比特数
mse=zeros(1,K);
abero=zeros(K,bit_extension/fs);
template_signal=zeros(K,fix(bit_extension/fs));
template_signal1=zeros(K,fix(bit_extension/fs));
template_signal2=zeros(K,fix(bit_extension/fs));
template_u1=zeros(K,fix(bit_extension/fs));
template_u2=zeros(K,fix(bit_extension/fs));
temp_counter=0;
temp_variant=zeros( buflength+2, fix(bit_extension/fs)); %由于信道译码有延迟buflength，需要存储buflength个数据用于信道估计，为衔接，从训练阶段就开始
for nn = 1: num
w = zeros(fix(bit_extension/fs),K); % 初使化用户的权值
for ppp = 1 : K % 初使化P
P(:,:,ppp) = diat*I;
end
tr1=(nn-1)*(PN/2+block)+2;
tr2=(nn-1)*(PN/2+block)+PN/2+1;
for i = tr1:tr2 % 训练比特数,每次读2比特,PN/2次。
u1 = zeros(1,fix(bit_extension/fs));
u2 = zeros(1,fix(bit_extension/fs));
for j = 1:K %u1,u2 叠加各个用户的有用信号
t = min(2*i-1,datav(j,3)-datav(j,2)+1);
for kk = (datav(j,1)-datav(j,2)+1):1:t
u1 = u1 + encoder_positive_nagetive(j,2*i-1-kk+1)*v((kk+datav(j,2)-1),:,j); % V就是窗口长度截取的数据V(1~20,180,USER)，(1 2) (2 3) (3 4)......
end
t = min(2*i,datav(j,3)-datav(j,2)+1);
for kk = (datav(j,1)-datav(j,2)+1):1:t
u2 = u2 + encoder_positive_nagetive(j,2*i-kk+1)*v((kk+datav(j,2)-1),:,j);
end
end %end users
if(open==1) % 考虑高斯噪声
u1 = u1 + noise;
tempnoise1 = noise(fix(90/fs)+1:fix(bit_extension/fs));
tempnoise2 = normrnd(MU,SIGMA);
noise = tempnoise2;
noise(1:round((across-1)*90/fs)) = tempnoise1;
u2 = u2 + noise;
tempnoise1 = noise(fix(90/fs)+1:fix(bit_extension/fs));
tempnoise2 = normrnd(MU,SIGMA);
noise = tempnoise2;
noise(1:round((across-1)*90/fs)) = tempnoise1;
end
% plot(u)
%此处加入针对多用户的模板估计算法=======================================
for j = 1:K %u1,u2
template_signal(j,:)=template_signal(j,:)+u1*encoder_positive_nagetive(j,2*i-1);
template_signal(j,:)=template_signal(j,:)+u2*encoder_positive_nagetive(j,2*i);
end
%针对多用户的模板估计算法结束
%adjust u1,u2
% if i>tr2
template_temp=template_signal/( (i-1)*2 );%模版
for j=1:K
template_u1(j,:) = template_temp(j,:).*u1;%相关
template_u2(j,:) = template_temp(j,:).*u2;
end
% else
% for j=1:K
% template_u1(j,:) = u1;%接收信号直接用于训练
% template_u2(j,:) = u2;
% end
%
% end
%adjust u1,u2 end
%缓存60个u1,u2值，目前无用，译码时才有用---------------------
for mm= 1 : buflength/2
temp_variant( 2*mm-1,:) = temp_variant( 2*(mm+1)-1,:);
temp_variant( 2*mm,:) = temp_variant( 2*(mm+1),:);
end
temp_variant( buflength+1,:) = u1;
temp_variant( buflength+2,:) = u2;
%------------------------------------------------------
if algorithp==1 %input('输入采用何种算法更新判决权系数[0-LMS,1-RLS,2-LMS_modify] algorithd =' ');
for jj = 1:K
pai(jj,:) = template_u1(jj,:) * P(:,:,jj); % for all users u1
k(:,jj) = pai(jj,:)'/( nmda+pai(jj,:) * template_u1(jj,:)' ); % for all users
soft_PNandData(jj,2*i-1) = template_u1(jj,:) * w(:,jj);
w(:,jj) = w(:,jj) + (encoder_positive_nagetive(jj,2*i-1) - soft_PNandData(jj,2*i-1))*k(:,jj); % for different users ,RLS
P(:,:,jj) = (P(:,:,jj)-k(:,jj)*pai(jj,:))/nmda; % for all users u2
end
for jj = 1:K
pai(jj,:) = template_u2(jj,:) * P(:,:,jj);% for all users
k(:,jj) = pai(jj,:)'/( nmda+pai(jj,:) * template_u2(jj,:)' ); % for all users
soft_PNandData(jj,2*i)=template_u2(jj,:)*w(:,jj);
w(:,jj) = w(:,jj) + (encoder_positive_nagetive(jj,2*i) - soft_PNandData(jj,2*i))*k(:,jj); % for different users ,RLS
P(:,:,jj) = (P(:,:,jj)-k(:,jj)*pai(jj,:))/nmda; % for all users u2
end
elseif algorithp==0 % input('输入采用何种算法更新判决权系数[0-LMS,1-RLS,2-LMS_modify] algorithd = ');
if(i<PN/2)
mlms = 0.0032;
else
mlms = 0.0005;
end
for jj = 1:K %LMS算法 FPGA中用这种算法实现比较好
soft_PNandData(jj,2*i-1)=template_u1(jj,:)*w(:,jj);% for different users ,LMS
w(:,jj) = w(:,jj) + mlms*(encoder_positive_nagetive(jj,2*i-1) - soft_PNandData(jj,2*i-1))*template_u1(jj,:)';
soft_PNandData(jj,2*i)=template_u2(jj,:)*w(:,jj);
w(:,jj) = w(:,jj) + mlms*(encoder_positive_nagetive(jj,2*i) - soft_PNandData(jj,2*i))*template_u2(jj,:)';
end
end %algorithm,某2个比特i,i+1
%-----------------画平均误差曲线图----------------
temp1 = 0;
temp2 = 0;
for jj = 1:K %K=1只是单个用户
temp1 = temp1 + abs(encoder_positive_nagetive(jj,2*i-1) -soft_PNandData(jj,2*i-1));
temp2 = temp2 + abs(encoder_positive_nagetive(jj,2*i) - soft_PNandData(jj,2*i));
end
error(2*i-3) = temp1/K;% 第i个比特平均训练误差
error(2*i-2) = temp2/K; %此处应该有个关于i的for循环
%---------------------------------------------------
%--------------compute aa(i)--------------------------------
for jj=1:K %jj start
x(jj,i-1)=soft_PNandData(jj,2*i-1);
y(jj,i-1)=soft_PNandData(jj,2*i);
var1=computevariance(x); %调用计算变量函数
if (var1<= 0.01)
var1=0.01;
end
var2=computevariance(y); %调用计算变量函数
if (var2<=0.01)
var2=0.01;
end
Lc1(jj)=2/var1; %公式里的两个常数
Lc2(jj)=2/var2;
factor=0;
for m=1:nstates
a_temp(m)=0;
for fm=1:nstates
if (judge(fm,m)==0)
gama_with_info=0; %r等于0
else
x_up=(2*d(fm,m,1)-1); %*(0.1746);
x_down=(2*d(fm,m,2)-1);%*(0.1746);
gama_with_info=exp(0.5*(x_up*Lc1(jj)*soft_PNandData(jj,2*i-1)+Lc2(jj)* soft_PNandData(jj,2*i)*x_down)); %求出r值
if (gama_with_info==inf)
gama_with_info=300;
elseif(gama_with_info>300)
gama_with_info=300;
end
end
a_temp(m)=a_temp(m)+gama_with_info*aa(jj,i-1-(nn-1)*(PN/2+block),fm); %a(i-1)
if (a_temp(m)==inf)
a_temp(m)=300;
end
end
factor=factor+a_temp(m);
end
for ii=1:nstates
aa(jj,i-(nn-1)*(PN/2+block),ii)=a_temp(ii)/factor; %求得a(i)
end
end %jj end multiuser,compute aa(i)
end %i PN
%此处加入针对多用户的模板估计算法
template_signal2=template_signal;
template_signal_average=template_signal/( (tr2-tr1+1)*2 );
template_signal1=template_signal_average;%作为判决阶段的初始模版
%针对多用户的模板估计算法结束
% for i=1:K
% figure(i);
% subplot(2,1,1)
% stem(1:180,v( datav(i,2) , 1:180 , i ),'.');
% title('Actual signal');
% axis([1 180 -1.5 1.5]);
% subplot(2,1,2) %draw the estimated signal
% stem( 1:180,template_signal_average(i,:), '.' );
% title('Estimated signal template');
% axis([1 180 -1.5 1.5]);
% end
%end%nn
vv=v;
%------------------Decision-Direct Stage-----------------
algorithd =1;% input('输入采用何种算法更新判决权系数[0-LMS,1-RLS, 2-LMS_modify] algorithd =‘ ');
% Sign_Data=zeros(K,length(encoderout)-PN+2+10);%+10以防溢出
% Sign_Data(1:K,3:length(encoderout)-PN+2)=b(1:K,PN+1:length(encoderout));
% soft_in = zeros(K,length(encoderout)-PN+2);%卷积码软输入，前面空2比特
soft_out = zeros(K,2*block+2); %卷积码软输出，前面空2比特
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