Analysis And Research Of Channel Coding Parameter Under Specific Conditions

Channel coding parameter reverse analysis is the process of using the received sequences to obtain the encoding parameters.Such technology has basic significance in the field of intelligent communications,signal acquisition,information confrontation and other fields.In real communi--cation,the data received by non partners is often accompanied by special situations such as interleaving,cepstrum and phase ambiguity and these special situations will bring difficulties to the analysis of coding parameter.In order to further adapt to the actual environment needs,the thesis analyzes the recognition of coding type,LDPC code and convolutional code under these specific conditions.In order to solve the problem that random interleaving code-words are difficult to recognize under noisy conditions,this paper presents an algorithm based on searching for small weight vectors for interweaving and coding type identification.First,selecting some code-words randomly and converting them to a dual matrix.Next,using the small weight vectors search algorithm to obtain some valid check vectors with the eliminating operation.Then,according to the principle of LDPC decoding,performing similar decoding operation for original code-words and iterating with the previous steps to obtain most of check vectors.Finally,the existence of interweaving and coding type can be recognized through mean span and dispersion of check vectors.The proposed method overcomes the limitations of the existing methods which can not be applied to random interleaving code-words.Simulations are conducted based on convolutional codes of 1/2 code rate and(15,11)Hamming codes.The results show that the interleaving code-words can still be recognized effectively under the bit error rate of 0.006.A strong fault tolerant algorithm was proposed for recognition of LDPC codes without a candidate set to solve the problem that the parity-check matrix of LDPC codes is hard to be reconstructed.First,the codes are sorted by reliability to construct data matrix and part of the parity-check vectors can be obtained by using Gaussian-Jordan column elimination operation.Then,codes with errors need to be eliminated and some codes need to be replaced to obtain most of the parity-check vectors.Finally,by using the parity-check vectors,some codes with high reliability can be decoded to obtain all parity-check vectors with the repetition of previous procedures.Compared to existing recognition algorithms,the proposed method can implement blind identification of LDPC codes under higher BER,having a relatively low complexity and can be better applied to the actual environment.In the simulation experiments which mainly use(576,288)LDPC codes,the recognition rate of this method is above 90% under BER of 0.0029.It is a little difficult to recognize convolutional codes with different code rates when cepstrum,phase ambiguity and bit errors exist.A strong fault tolerance algorithm is proposed in this paper to solve this problem.This paper takes(133,171)convolutional codes for example under the QPSK modulation mode.Firstly,the transformation relationship between symbol information and bit soft information is derived.Then,check vectors under various conditions are obtained by using the check vector solution algorithm proposed in this paper.Finally,performance of check vectors is analyzed by using three methods which include Walsh--Hadamard transform(WHT),log-likelihood ratio(LLR)and likelihood difference(LD).Simulation results show that for(133,171)convolutional codes,we can identify different code rates,degree phase ambiguity and cepstrum effectively at low snr by using check vectors after combining the three methods.Moreover,the algorithm reduces the analysis range from 56 to 2 or 4,has low computational complexity and it can meet the needs of the actual environment.