卷积码欧几里得算法盲识别代码

以下是一个简单的卷积码欧几里得算法盲识别的 Python 代码实现：

import numpy as np

def euclid_dist(a, b):
    """计算欧几里得距离"""
    return np.sqrt(np.sum(np.square(a - b)))

def blind_convolutional_decoding(y, r):
    """卷积码欧几里得算法盲识别"""
    n = len(y)
    distances = [0] * (2 ** r)
    for i in range(2 ** r):
        c = np.array([int(x) for x in np.binary_repr(i, width=r)])
        s = np.zeros(n)
        for j in range(r):
            s[j::r] = c[j]
        distances[i] = euclid_dist(y, s)
    return np.argmin(distances)

其中，`y` 是接收到的信号序列，`r` 是卷积码的约束长度。函数 `euclid_dist` 计算两个向量之间的欧几里得距离，函数 `blind_convolutional_decoding` 利用欧几里得算法进行盲识别，返回可能的卷积码的生成多项式的编号。

相关问题

卷积码欧几里得算法盲识别matlab代码

卷积码欧几里得算法盲识别的 Matlab 代码如下：

function [decodedBits] = BlindConvolutionalDecoding(receivedBits, constraintLength, generatorPolynomials)
% Function to perform blind convolutional decoding on the received bits
% receivedBits - the received bits
% constraintLength - constraint length of the convolutional encoder
% generatorPolynomials - generator polynomials of the convolutional encoder

% Calculate the number of states in the convolutional encoder
numStates = 2^(constraintLength-1);

% Generate the state transition table for the convolutional encoder
stateTable = GenerateStateTable(constraintLength);

% Generate the trellis for the convolutional encoder
trellis = poly2trellis(constraintLength, generatorPolynomials);

% Initialize the distances and survivor paths for each state
distances = Inf(numStates, 1);
survivors = zeros(numStates, constraintLength);

% Initialize the distances for the initial state
distances(1) = 0;

% Loop through the received bits
for i = 1:length(receivedBits)
    % Calculate the distances for each possible transition
    for j = 1:numStates
        % Calculate the current state
        currentState = j-1;
        
        % Calculate the input bit for the current state
        if currentState == 0
            inputBit = 0;
        else
            inputBit = receivedBits(i);
        end
        
        % Calculate the output bits for the current state and input bit
        outputBits = convenc([currentState, inputBit], trellis);
        
        % Calculate the next state for each possible output bit
        for k = 1:length(outputBits)/2
            % Calculate the next state for the current output bit
            nextState = stateTable(j, outputBits(2*k-1)*2+outputBits(2*k));
            
            % Calculate the distance for the current transition
            distance = distances(j) + (receivedBits(i)~=outputBits(2*k-1)) + (receivedBits(i+1)~=outputBits(2*k));
            
            % Update the distances and survivor paths if the distance is lower
            if distance < distances(nextState+1)
                distances(nextState+1) = distance;
                survivors(nextState+1, :) = [survivors(j, 2:end), outputBits(2*k-1)];
            end
        end
    end
end

% Find the state with the minimum distance
[minDistance, minState] = min(distances);

% Extract the decoded bits from the survivor path of the minimum distance state
decodedBits = survivors(minState, :);

% Remove the initial zeros from the decoded bits
decodedBits = decodedBits(constraintLength:end);
end

function [stateTable] = GenerateStateTable(constraintLength)
% Function to generate the state transition table for a convolutional encoder
% constraintLength - constraint length of the convolutional encoder

% Calculate the number of states in the convolutional encoder
numStates = 2^(constraintLength-1);

% Initialize the state transition table
stateTable = zeros(numStates, 4);

% Loop through each state
for i = 1:numStates
    % Convert the state to binary and pad with zeros
    state = dec2bin(i-1, constraintLength-1);
    state = ['0', state];
    
    % Calculate the output bits and next state for each possible input bit
    for j = 0:1
        inputBit = num2str(j);
        
        % Calculate the output bits for the current state and input bit
        outputBits = convenc([bin2dec(state), j], poly2trellis(constraintLength, [1 1 1]));
        
        % Convert the output bits to binary and pad with zeros
        outputBits = dec2bin(outputBits, 2);
        outputBits = reshape(outputBits.', 1, []);
        
        % Convert the next state to decimal
        nextState = bin2dec(state(2:end));
        nextState = bitshift(nextState, 1);
        nextState = nextState + j;
        
        % Update the state transition table
        stateTable(i, j*2+1:j*2+2) = outputBits;
        stateTable(i, j*2+3:j*2+4) = nextState;
    end
end
end

其中，`receivedBits` 是接收到的码字，`constraintLength` 是卷积码的约束长度，`generatorPolynomials` 是卷积码的生成多项式。函数首先生成卷积码的状态转移表和状态图，然后利用欧几里得算法进行盲识别，最后提取出解码后的比特流。

卷积码盲识别知道校验序列和码率，如何得到生成多项式

卷积码盲识别是指在不知道原始数据的情况下，通过观测经过卷积编码后的码序列，来确定卷积码的结构和参数。其中包括了卷积码的生成多项式。

在已知卷积码的校验序列和码率的情况下，我们可以进行卷积译码，利用Viterbi算法来解码，然后通过比对解码输出和观测码序列，来确定卷积码的生成多项式。具体的步骤如下：

1. 对于已知的码率和校验序列，构建一个与之相对应的卷积译码器。

2. 将观测到的经过卷积编码的码序列输入到卷积译码器中，进行卷积译码。

3. 利用Viterbi算法对卷积码进行译码，得到解码输出。

4. 将解码输出与观测码序列进行比对，确定哪些比特位是校验序列。

5. 根据已知的码率和校验序列，计算出校验序列对应的状态转移矩阵。

6. 利用校验序列的状态转移矩阵，反推出卷积码的生成多项式。

需要注意的是，在卷积码盲识别过程中，可能会存在多种可能的卷积码结构和参数，因此需要进行进一步的判决和筛选，来确定最终的卷积码结构和参数。