A Blind Recognition Method of Coding Parameters of Nonsystematic Convolutional Codes with High Bit Errors

Abstract

The invention belongs to the technical field of wireless communication, and discloses a method for blindly identifying coding parameters of non-systematic convolutional codes under high error rates, including arranging the bit streams of convolutional codes into an analysis matrix of L×l, and identifying the code length n; Construct the analysis matrix of the convolutional code bit stream according to the code length n, and identify the information bit length k; construct the analysis matrix according to the code length n of the convolutional code bit stream, and use the linear constraint relationship with the check vector to identify the check vector; for the system Convolutional code, traverse the register length m, rearrange the check sequence and perform convolution operation with the m+1-dimensional vector space, and perform Gaussian transformation to obtain the generation matrix and register length m of the systematic convolutional code; for non-systematic codes , using the check matrix to establish a system of equations, solving the equations and then screening to obtain the optimal generator matrix and register length m of the non-systematic code. The invention better overcomes the problem of blind identification of convolutional code encoding parameters under the condition of high bit errors, and can be used in the fields of intelligent communication, communication investigation, communication confrontation 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...

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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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