convolution_dec.m

通信中常用的卷积码信道译码源码程序

function outputs = convolution_dec(Data_received,Trellis,Lut,alpha_channel);

%------------------------------------------------------------------------------
% Decode binary 1-D data vector with binary trellis (hard inputs).
%
% Format:
% -------
%
% outputs = convolution_dec(Data_received,LData,Trellis,Lut,alpha_channel);
% 
% Author: MVe
% Date:   05.07.2002
%------------------------------------------------------------------------------

%% -- Initialisations -- %%

% Length of the received symbol/data vector
LData = length(Data_received);

% Extract most used variables from struct 'Trellis'
m = Trellis.m;
n = Trellis.n;
k = Trellis.k;
number_of_states = Trellis.StNr;
init_path_bin = reshape([Trellis.Init_path_bin{:,1}],m*n,number_of_states).';

% Number of output bits during init-state.
init_outputs = m*n;

% De-interleave data
Data_received(alpha_channel) = Data_received;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% -- Calculate metrics for the time instances 1,2,...,m (init state) -- %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Keep track of distance metrics at different states.
Metrics_dist = zeros(number_of_states,1);

%% -- Calculate metrics during init-state. -- %%

for i1 = 1:number_of_states
  % Hard decisions (hamming metrics)
  if ~Trellis.SoftDecF
    Metrics_dist(i1,1) = length(find(init_path_bin(i1,:) ...
                                     - Data_received(1:init_outputs)));
  end % if Trellis.SoftDecF
end % for i1 = number_of_states

%% -- Some parameters for steady-state trellis -- %%

% Length of the rest of the data in "symbols"
LData_to_Decode = (LData-init_outputs)/n;

% Aux variables.
% Format: 
% Rows = states
% Column = output with inputs 1,2,...,2^k
%
Metrics_dist_tmp = zeros(number_of_states,2^k);
trellis_state_path_tmp = cell(number_of_states,2^k);

% Precalculated variables (look in 'trellis_init.m' for more information)
state_ptr = Trellis.Steady_state_ptr;
state_prev_ptr = Trellis.Steady_state_prev_ptr;
weigths_bin = Trellis.Steady_weigths_bin;
trellis_state_path = Trellis.Init_state_path(:,1);

%% ----------------------Trellis search-----------------------------
% 1. State transitions caused by input sequences
% 2. Outputs of these transitions.
% 3. Distance-metrics between received sequence and transitions.
% 4. Discard transitions to each state with higher distance-metrics.
% 5. New transitions from surviving paths.
%% -----------------------------------------------------------------

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% -- Steady-state trellis calculations begin (hard decisions) -- %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

for i1 = 1:LData_to_Decode  
  
  state_cnt = ones(number_of_states,1);
  
  % Pointer for data vector
  Data_ptr = n*m + [(i1-1)*n+1:i1*n];
  
  % Calculate metrics-distances between paths and received sequence.
  % Metrices are calculated so that first all metrics caused
  % transition by sequence 00...00 are calculated, next all metrics
  % caused transition by sequence 00...01, and so on. 
  % 
  for i2=1:number_of_states*2^k
    
    % Check if this state has already some metrics calculated
    if ~isempty(find(state_ptr(1:i2-1)==state_ptr(i2)))
      state_cnt(state_ptr(i2)) = state_cnt(state_ptr(i2)) + 1;
    end % if ~isempty(find(state_ptr(1:i2-1)==state_ptr(i2)))
    
    %% -- METRICS begins -- %%
    
    % -- Hard decisions -- %
    
    % Calculate new metrics (hamming)
    Metrics_dist_tmp(state_ptr(i2),state_cnt(state_ptr(i2))) = ...
        Metrics_dist(state_prev_ptr(i2)) + ...
        length(find((weigths_bin(i2,:) - Data_received(Data_ptr))));
    
    %% -- METRICS ends -- %%
    
    % Keep track of the paths that have lead to current states.
    trellis_state_path_tmp{state_ptr(i2),state_cnt(state_ptr(i2))} = ...
        [trellis_state_path{(state_prev_ptr(i2)),1},state_ptr(i2)]; 
    
  end % for i2=1:number_of_states*2^k     
  
  % Find the survivors
  [Metrics_dist, survivor_ptr] = min(Metrics_dist_tmp,[],2);
  % Surviving paths
  for i2 = 1:number_of_states
    trellis_state_path(i2,1) = trellis_state_path_tmp(i2,survivor_ptr(i2));
  end % for i2 = 1:number_of_states
  
end % for i1 = 1:LData_to_Decode  

% Surviving state path
[dummy, decode_ptr] = min(Metrics_dist,[],1);
final_state_path = trellis_state_path{decode_ptr,:};

% Convert state path to info bit sequence
out_tmp = cell(1,length(final_state_path)-1);
for i1 = 1:length(out_tmp)
  out_tmp(i1) = ...
      Lut.InfBits(final_state_path(i1),final_state_path(i1+1));
end % for i1 = 1:2:length(Data_received)

outputs = [out_tmp{:}];