viterbi.m

RS编解码的Matlab源代码

function viterbi_out= viterbi(symbols)
%用于BCM-8PSK的Vterbi译码,其中BCM的三个分组内码为(8,1,8)(8,7,2)(8,8,1)

%symbols=[5.2 7.3 0.7 7.2 1.6 2.7 1.2 3.2];
for frame=0:(length(symbols)/8-1)
    mmm=frame*8+1;
    nnn=frame*8+8;
    sym= symbols(mmm:nnn);
%以下为一帧8个符号的译码
%--------------------------------------------------------------------------    
paths=zeros(4,8);                             %用于记录4条路径的输出
old_paths=zeros(4,8); %%%%%%%%%%%%%%%%%%%%%%%
cum_metrics = zeros(4, 2);                    %用于保存累计度量值
for state=1:4                                 %对于step=1情况
    switch state
        case 1
             [min_metrics,sym_out]=compare(sym(1),0,4); 
             %分别对各状态进入两条平行求分支度量,小的值放在min_metrics中,对应分支的输出值放sym_out中输出
        case 2
             [min_metrics,sym_out]=compare(sym(1),2,6);  
        case 3
             [min_metrics,sym_out]=compare(sym(1),1,5);  
        case 4
             [min_metrics,sym_out]=compare(sym(1),3,7);       
    end    
    cum_metrics(state,1)=min_metrics;            %对于step=1情况,累积度量值为min_metrics
    paths(state,1)=sym_out;                      %对于step=1情况,保存sym_out1
end
old_paths=paths;%%%%%%%%%%%%%%%%%%%%%%%

for step=2:7                                                    %这里从step=2开始到length(sym)-1=7
   for state=1:2                                                  %网格图分上下两层,对于上层两状态Ae,Ao,每个状态两组平行共4个分支进入
       [min_metrics1,sym_out1]=compare(sym(step),0,4);            %第一组平行分支,其输出分别为0和4,分别计算和输入符号的距离
       temp_metrics1=cum_metrics(state,1)+min_metrics1;             %和第一组对应的前一状态(Ae)的累积度量值相加
       [min_metrics2,sym_out2]=compare(sym(step),2,6);            %第二组平行分支,其输出分别为2和6,分别计算和输入符号的距离
       temp_metrics2=cum_metrics((3-state),1)+min_metrics2;           %和第二组对应的前一状态(Ao)的累积度量值相加
       if (temp_metrics1<=temp_metrics2)                          %比较
           cum_metrics(state,2)=temp_metrics1;                      %小的值保存
           paths(state,step)=sym_out1;                            %输出值保存
       else
           cum_metrics(state,2)=temp_metrics2;
           paths(state,:)=old_paths((3-state),:);
           paths(state,step)=sym_out2;
       end
   end       
   for state=3:4                                                  %对下层情况处理算法相同
       [min_metrics3,sym_out3]=compare(sym(step),1,5);         
       temp_metrics3=cum_metrics(state,1)+min_metrics3;
       [min_metrics4,sym_out4]=compare(sym(step),3,7);
       temp_metrics4=cum_metrics((7-state),1)+min_metrics4;
       if (temp_metrics3<=temp_metrics4)
           cum_metrics(state,2)=temp_metrics3;
           paths(state,step)=sym_out3;
       else
           cum_metrics(state,2)=temp_metrics4;
           paths(state,:)=old_paths((7-state),:);%%%%%%%%%%%%%%%%%%%%%%%%这里有错,如果在上面state=3时最优路径从4状态过来,那么
           paths(state,step)=sym_out4;
       end
   end   
   cum_metrics(:,1)=cum_metrics(:,2);
   old_paths=paths;%%%%%%%%%%%%%%%%%%%%%%%
end


for state=1:4      
    switch state
        case 1
             [min_metrics,sym_out]=compare(sym(8),0,4);  %对于step=1情况
             %分别对各状态进入两条平行求分支度量,小的值放在min_metrics中,对应分支的输出值放sym_out中输出
        case 2
             [min_metrics,sym_out]=compare(sym(8),2,6);  
        case 3
             [min_metrics,sym_out]=compare(sym(8),1,5);  
        case 4
             [min_metrics,sym_out]=compare(sym(8),3,7);       
    end    
    cum_metrics(state,2)=cum_metrics(state,1)+min_metrics;              %对于step=1情况,累积度量值为min_metrics
    paths(state,8)=sym_out;                      %对于step=1情况,保存sym_out1
end


min_metrics=1e6;
for state=1:4
    if min_metrics>cum_metrics(state,2)
       min_metrics=cum_metrics(state,2);
       temp_viterbi_out=paths(state,:);
       a=state;%%%%%%%%%%
    end
end
%--------------------------------------------------------------------------
%以上为一帧8个符号的译码
   if frame==0
      viterbi_out= temp_viterbi_out;
   else
   viterbi_out=[viterbi_out temp_viterbi_out];
   end
end