Convolutional code blind identification method based on soft information Gaussian elimination algorithm

Abstract

The invention discloses a convolutional code blind identification method based on a soft information Gaussian elimination algorithm. The method comprises the following steps: receiving a convolutional code soft information sequence; generating a soft credibility matrix; carrying out elimination by using a soft information Gaussian elimination algorithm; updating the soft credibility matrix according to an updating criterion; and setting a proper judgment threshold value to detect the verification vector. According to the method, a soft information Gaussian elimination algorithm is utilized, so the problem of loss of related channel information in the prior art is solved, and the retention of related channel error statistical characteristic information is realized; according to the updating criterion adopted by the invention, the problem of continuous propagation of error bits in the prior art is solved, so that the channel transmission reliability is improved; according to the method, the appropriate judgment threshold is adopted, the problem that judgment errors exist on the check vector in the prior art is solved, and accurate judgment on the check vector is achieved.

Description

technical field [0001] The invention belongs to the technical field of basic electronic circuits, and further relates to a blind recognition method of a convolutional code based on a soft information Gaussian elimination algorithm in the technical field of coding. The invention can be used to identify convolutional codes under the condition of unknown coding information. Background technique [0002] Convolutional code is a channel coding method with good error correction ability, which has been widely used in recent years. The convolutional code is usually expressed in the form of (n, k, m), that is, k information bits are coded into n bits through an encoder with a constraint length of m. In a non-cooperative communication scenario, the encoding parameters of the convolutional code used in the communication process are unknown to the receiver. Therefore, the blind recognition technology of convolutional codes can identify coding parameters from the received data stream w...

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Problems solved by technology

[0005] The purpose of the present invention is to address the deficiencies in the above-mentioned prior art, and propose a blind recognition method for convolutional codes based on the soft information Gaussian elimination algorithm, which is used to solve the loss of channel error statistical characteristic information and channel transmission reliability in the prior art. Low and there is a problem of judgment error for the check vector with large weight

The present invention adopts column replacement and row replacement operations on the soft credibility matrix to generate a matrix of elements to be eliminated whose principal elements have relatively high credibility values, and updates the matrix of elements to be eliminated according to an update criterion based on the soft credibility matrix, The elements in the element matrix to be eliminated in the present invention are only related to the elements in the soft credibility matrix, and have nothing to do with the elements containing errors in the element matrix to be eliminated, so as to solve the problem of low reliability of channel transmission in the prior art

In the present invention, by deriving the decision threshold formula related to the weight of the check vector, the present invention selects a suitable decision threshold for different check vector weights, realizes accurate judgment of a check vector with a large weight, and solves the problem of the prior art that has a large weight. There is a problem of judgment error in the check vector of

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