Research On Identification Technology Of Complex Channel Coding

The combination of channel coding technology and interleaving technology is the main scheme to realize reliable data transmission in the modern communication system.In the field of non-cooperative communication such as intelligence analysis and electronic investigation,as well as the field of intelligent communication,it is necessary to use the demodulated data to identify the interleaving parameters and channel coding parameters for the signal receiver,and then the original information sequence can be recovered by decoding.In this thesis,the identification of complex channel coding with interleaving is studied.The main research objects are the blind identification of random interleaving relationships when random interleaver and convolutional code are cascaded and the blind identification of interleaving relationships of Turbo code.The main tasks can be summarized into four aspects:(1)An error elimination algorithm for linear block code and convolutional code is proposed.The algorithm constructs an error-free data matrix by eliminating the data frames with errors in the received data.Using the error-free data matrix for subsequent coding parameter identification can reduce the difficulty and complexity of parameter identification.Under the given simulation conditions,the error rate that the algorithm adapts to can reach about 1% for convolutional code with a frame length of 100,and that for(63,24)BCH code can reach about 1.4%.(2)A blind identification algorithm of random interleaving relationships for convolutional code based on partial check equation graph isomorphism theory is proposed.Firstly,two isomorphic graphs of partial check equations before and after interleaving are established.Then,the partial random interleaving relationships are identified by comparing the mapping relationships between graph labels.Finally,the remaining random interleaving relationships are identified orderly with the check vector.When the interleaving depth is 1000,the bit error rate that the algorithm can adapt is about 0.17%.Compared with the existing algorithm,it improves the anti-error performance by 13%,reduces the time complexity by 90% and reduces the amount of data required by 12%.(3)A blind identification algorithm of random interleaving relationships for convolutional code based on the linear constraint relationship is proposed.The algorithm uses the error-free data matrix constructed by the error elimination algorithm to identify the interleaving relationships.After constructing the error-free data matrix,the random interleaving relationships between the constraint groups of convolutional code are identified according to the linear constraint relationship of convolutional code.Then,the random interleaving relationships in the constraint groups of convolutional code are determined by using the check relationships that convolutional code must satisfy.When the interleaving depth is 1000,the bit error rate that the algorithm can adapt is about 0.2%.Compared with the existing algorithm,it improves the anti-error performance by 25% and reduces the time complexity by 80%(4)A blind identification algorithm of interleaving relationships for Turbo code based on check decision is proposed.After constructing the error-free data matrix,the algorithm uses the coding principle to identify the initial random interleaving relationships.Then,the algorithm recursively searches the column vector satisfying the check relationship of the recursive systematic convolutional code to identify the remaining random interleaving relationships.Under the given simulation conditions,the bit error rate that the algorithm can adapt is about 0.12% when the interleaving depth is500,and the algorithm can quickly and effectively identify the interleaving relationships of Turbo codes.