Blind identification method for non-system convolutional code coding parameters under high error codes

Abstract

The invention belongs to the technical field of wireless communication, and discloses a blind identification method for non-system convolutional code coding parameters under high error codes, which comprises the following steps: arranging convolutional code bit streams into an L * l analysis matrix, and identifying the code length n; constructing an analysis matrix for the convolutional code bit stream according to the code length n, and identifying the information bit length k; constructing an analysis matrix by using the convolutional code bit stream according to the code length n, and identifying a check vector by using a linear constraint relationship with the check vector; for the system convolutional code, traversing the length m of the register, rearranging the check sequence, carrying out convolution operation on the check sequence and an m + 1-dimensional vector space, and carrying out Gaussian transformation to obtain a generation matrix of the system convolutional code and the length m of the register; and for the non-system code, establishing an equation set by utilizing the check matrix, solving the equation set, and screening to obtain an optimal generation matrix anda register length m of the non-system code. The method well overcomes the blind recognition problem of the convolutional code coding parameters under the condition of high error codes, and can be applied to the fields of intelligent communication, communication investigation, communication countermeasure and the like.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a method for blindly identifying encoding parameters of non-systematic convolutional codes under high error codes. Background technique [0002] At present, during channel transmission of digital signals, the signal is distorted due to noise interference. In order to improve the reliability of data transmission, it is necessary to perform channel coding on the signal. As a block code, the convolutional code has a simple encoding and decoding method and good error correction performance, so it is widely used in satellite communication, deep space communication, mobile communication and other fields. In a non-cooperative scenario, there is an urgent need for a technology that only uses the acquired encoded data to quickly and accurately identify encoding parameters, so as to obtain the decoded data of the other party and better analyze the useful informat...

AI Technical Summary

Problems solved by technology

[0003] In the prior art "A Method for Blind Recognition of (n,k,m) System Convolutional Codes", under the premise of no error code, the recognition of high code rate convolutional codes can be realized by matrix analysis method, which cannot be used in Identifying Coding Parameters of Convolutional Codes in the Context of Bit Errors

In prior art 2 "A Method for Blind Recognition of Error-Tolerant Systematic Convolutional Codes", the method of comprehensive analysis of multi-segment data has a certain degree of error tolerance, and can identify systematic volumes with k / n code rates under certain bit error conditions. Product codes, but the identification of generator matrices for non-systematic convolutional codes does not mention

[0004] In summary, the existing problems in the prior art are: limited fault tolerance; there are very few studies on blind recognition methods for non-systematic convolutional codes, and the present invention solves the problem of blind recognition of non-systematic convolutional codes under high bit error conditions

[0006] The difference between systematic codes and non-systematic codes lies in the corresponding relationship between the generation matrix and the check matrix. The systematic code is a one-to-one definite relationship, while the non-systematic code is a one-to-many uncertain relationship. Therefore, the identification of non-systematic convolutional codes is more complicated. At present, there are few studies on the blind recognition of non-systematic convolutional codes. The main method is to perform multiple elementary transformations on the obtained generator matrix. The complexity of this method is High, and cannot accurately identify the generator polynomial matrix

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