CN104683072A - Parameter blind identification method of puncturing turbo code component coder - Google Patents
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Abstract
The invention relates to a parameter blind identification method of a puncturing turbo code component coder, belonging to the technical field of the code identification in a communication system. The method comprises the steps of multiplexing a puncturing turbo code information bit and a verification bit to form a puncturing convolutional code, carrying out statistics and analysis for the code length and a code word starting point of the puncturing convolutional code by virtue of a linear matrix analysis method, identifying a verification matrix of the component coder by utilizing partial Walsh-Hadamard conversion, obtaining a puncturing mode and generating a matrix further by virtue of a Gaussian elimination method, carrying out the blind identification on the parameters of the puncturing turbo code component coder, and verifying the accuracy of the identified parameters. By adopting the method, the operation complexity of identifying the component coder parameter can be alleviated, the identification efficiency and the identification reliability can be improved, and the method is applicable to the fields of the intelligent communication, information processing and the like.
Description
Technical field
The present invention relates to a kind of parameter blind identification deleting remaining turbo code component coder in digital communication system, belong to the code word recognition technology field in communication system.
Background technology
Delete remaining turbo code to apply in modern communications widely, along with the development of digital communication technology, increasing field all can produce the demand to punctured Turbo codes blind recognition technology, deletes the Disciplinary Frontiers that remaining turbo code blind recognition technology also becomes current Communication Studies.
Classical parallel cascade turbo code coder forms primarily of two recursive systematic convolutional code (RSC) component coder parallel cascades, be connected by interleaver between component coder, generally, the structure of each rsc encoder is identical, the code check using the turbo code of two RSC component coders is 1/3, an information code element is had, two verification code elements in every three bits.For improving code check, can carry out more than periodicity deletes, then obtaining final coded sequence with information code element through multiplexing to verification unit.
For the blind recognition of (the n-1)/N-shaped Punctured convolutional code based on 1/2 source convolution code, the requirement of traditional Walsh-Hadamard transfer pair memory headroom is higher, Wang Lei, Hu Yihua, Wang Yong etc. are published in " computer engineering and application " 16 phases " the deletion convolution code recognition methods based on a PWHT " literary composition for 2012 at it, operand and the excessive problem of data volume is there is deleting in the identification of convolution code check matrix for Walsh-Hadamard conversion, check matrix equation group is out of shape, part Walsh-Hadamard conversion (PWHT) is proposed, but do not provide the method being applied to and deleting remaining turbo code component coder parameter blind recognition.For overcoming defect and the deficiency of prior art existence, the present invention proposes the parameter blind identification deleting remaining turbo code component coder based on part Walsh-Hadamard conversion (PWHT), reduce the computational complexity of component coder parameter identification, improve recognition efficiency.
The mode that the present invention adopts computer software to simulate realizes the parameter identification of deleting remaining turbo code component coder, first the above-mentioned classical turbo code cataloged procedure of software simulation is used in implementation process, code word is preserved hereof, from file, reads data during identification carry out blind recognition.
Summary of the invention
In order to overcome defect and the deficiency of prior art existence, the present invention proposes a kind of parameter blind identification deleting remaining turbo code component coder that operand is little, recognition efficiency is high.
Technical solution of the present invention is as follows:
A kind of parameter blind identification deleting remaining turbo code component coder, remaining turbo code information bit will be deleted and the first bit check bit multiplex constructs Punctured convolutional code, by the code length of linear matrix analytic approach statistical analysis Punctured convolutional code, code word starting point, recycling part Walsh-Hadamard conversion identifies the check matrix of component coder, obtained deleting complementary modul formula and generator matrix by Gaussian elimination method further, thus realize the parameter blind recognition deleting remaining turbo code component coder, and obtain confidence level by Viterbi decoding, to verify the accuracy identified, the method concrete steps are as follows:
1) from deleting remaining turbo code starting point, the check digit sequence choosing its information sequence and component coder RSC1 is multiplexing, structure Punctured convolutional code sequence, what obtain 1/2 code check more than deleting for 1/3 code check turbo code deletes remaining turbo code, if former turbo code sequence is expressed as x
1a
1b
1x
2a
2b
2x
3a
3b
3x
4a
4b
4then delete remaining rear turbo code sequence and be expressed as x
1a
1x
2b
2x
3a
3x
4b
4, structure Punctured convolutional code is x
1a
1x
2x
3a
3x
4, wherein x
1, x
2, x
3represent the signal bit sequence do not exported in the same time; a
1, a
2, a
3represent the RSC1 check digit sequence do not exported in the same time; b
1, b
2, b
3represent the RSC2 check digit sequence do not exported in the same time;
2) by Punctured convolutional code Sequence composition p × q matrix, require q > N, p > q, wherein N presentation code constraint degree, get the scope of determining train value q, change q obtains different matrix, calculate these ranks of matrix respectively, only leave and take the matrix that rank of matrix is not equal to columns, then elementary transformation is carried out to these matrixes unitization, write down the matrix train value that in unitization rear left, angular unit battle array dimension is equal, greatest common divisor is got to retention train value, thus obtains Punctured convolutional code code length n;
3) Punctured convolutional code sequence is re-constructed matrix, columns is step 2) middle certain train value N ' retained, wherein N ' the multiple that is code length n, get the smaller value retained in train value, get matrix line number and be greater than columns, code sequential shift is obtained n-1 different matrix, together with code sequence not shift matrix obtain n different matrix altogether, n is code length, elementary transformation is carried out to these matrixes unitization, write down the upper left corner unit matrix dimension of unitization rear matrix respectively, displacement when dimension is minimum and Punctured convolutional code starting point;
4) identify check polynomial matrix, concrete identification step is as follows:
A) set up coefficient matrix R (D) by the Punctured convolutional code sequence constructed, verification polynomial matrix is expressed as H (D)=[h
0(D), h
1(D) ... h
n-1(D)]
t, wherein, h
i(D)=h
0i+ h
1id+h
2id
2... h
did
d, i ∈ [0, n-1], d=max{deg h
i(D) }, wherein symbol deg represents it is the function asking polynomial number of times, by R (D) × H (D)
t=0 to be write as the form of equation group as follows:
Due to h
n-1(D) containing constant term h in
0 (n-1)=1, the constant term part in formula (1) is moved on the right of equation, obtains following formula:
B) by the coefficient matrix R'(D in formula (2)) be divided into two parts, matrix length is N+1, width is respectively R1 and R2, wherein R1 and R2's and be (d+1) × n-1, (usual R2 value is between 16 to 24), then first, second coefficient matrix dimension is respectively (N+1) × R1, (N+1) × R2;
C) set the binary vector iTemp that a length is R1, size is from 0 to 2
r1circulate, the row vector mould two of each fixing iTemp and the first coefficient matrix is added rear negate, and often row obtains a binary number 0 or 1, finally obtains the binary vector of a N+1 dimension, be expressed as P, P is added with the value of the vectorial corresponding row on the right of formula (2) equal sign;
D) again statistic is carried out to the second coefficient matrix, the row vector dimension of the second coefficient matrix be 1*R2, R2 arbitrarily ' 0 ' and ' 1 ' combination as state, totally 2
r2-1 state, each row vector is one of them state, and on the right of formula (2) equal sign, the value of vectorial corresponding row is as the output of this state, and the output valve of equal state adds up, and non-existent State-output value is 0, obtains one (2
r2-1) × 1 dimension state vector;
E) Walsh-Hadamard conversion is carried out to the above results, the solution being greater than confidence level is found to be converted to binary vector again, be expressed as Q, then iTemp vector now combined with binary vector Q the check polynomial H (D) just obtaining us and require;
5) set the generator polynomial matrix exponent number of source convolution code, traversal deletes complementary modul formula, builds system of linear equations Gp (D) H (D)
t=0, wherein G
p(D) be generator matrix, H (D)
tfor the transposition of check matrix H (D), solved unique untrivialo solution of this equation group by Gaussian elimination method, thus determine the generator polynomial of source convolution code and delete complementary modul formula;
6) utilize the component coder parameter identifying and obtain, Punctured convolutional code is obtained confidence level by Viterbi decoding, the accuracy of checking identification parameter.
The present invention will delete the identification being converted to Punctured convolutional code of remaining turbo code component coder parameter preferably, on the basis of linear matrix-analysis method identification code length, starting point, the creationary part WH algorithm by improvement is used for the identification of component coder check matrix, and identification obtains deleting complementary modul formula and generator matrix further, the parameter blind recognition of remaining turbo code component coder is deleted in final realization, and obtain confidence level by Viterbi decoding, the accuracy that checking identifies.Present invention reduces the computational complexity of component coder parameter identification, improve recognition efficiency and reliability.
Embodiment
Below in conjunction with embodiment, the invention will be further described, but be not limited thereto.
Embodiment:
The embodiment of the present invention is as follows: a kind of parameter blind identification deleting remaining turbo code component coder, remaining turbo code information bit will be deleted and the first bit check bit multiplex constructs Punctured convolutional code, by the code length of linear matrix analytic approach statistical analysis Punctured convolutional code, code word starting point, recycling part Walsh-Hadamard conversion identifies the check matrix of component coder, obtained deleting complementary modul formula and generator matrix by Gaussian elimination method further, thus realize the parameter blind recognition deleting remaining turbo code component coder, and obtain confidence level by Viterbi decoding, to verify the accuracy identified, the method concrete steps are as follows:
1) from deleting remaining turbo code starting point, the check digit sequence choosing its information sequence and component coder RSC1 is multiplexing, structure Punctured convolutional code sequence, what obtain 1/2 code check more than deleting for 1/3 code check turbo code deletes remaining turbo code, if former turbo code sequence is expressed as x
1a
1b
1x
2a
2b
2x
3a
3b
3x
4a
4b
4then delete remaining rear turbo code sequence and be expressed as x
1a
1x
2b
2x
3a
3x
4b
4, structure Punctured convolutional code is x
1a
1x
2x
3a
3x
4, wherein x
1, x
2, x
3represent the signal bit sequence do not exported in the same time; a
1, a
2, a
3represent the RSC1 check digit sequence do not exported in the same time; b
1, b
2, b
3represent the RSC2 check digit sequence do not exported in the same time;
2) by Punctured convolutional code Sequence composition p × q matrix, require q > N, p > q, wherein N presentation code constraint degree, get the scope of determining train value q, change q obtains different matrix, calculate these ranks of matrix respectively, only leave and take the matrix that rank of matrix is not equal to columns, then elementary transformation is carried out to these matrixes unitization, write down the matrix train value that in unitization rear left, angular unit battle array dimension is equal, greatest common divisor is got to retention train value, thus obtains Punctured convolutional code code length n;
3) Punctured convolutional code sequence is re-constructed matrix, columns is step 2) middle certain train value N ' retained, wherein N ' the multiple that is code length n, get the smaller value retained in train value, get matrix line number and be greater than columns, code sequential shift is obtained n-1 different matrix, together with code sequence not shift matrix obtain n different matrix altogether, n is code length, elementary transformation is carried out to these matrixes unitization, write down the upper left corner unit matrix dimension of unitization rear matrix respectively, displacement when dimension is minimum and Punctured convolutional code starting point;
4) identify check polynomial matrix, concrete identification step is as follows:
A) set up coefficient matrix R (D) by the Punctured convolutional code sequence constructed, verification polynomial matrix is expressed as H (D)=[h
0(D), h
1(D) ... h
n-1(D)]
t, wherein, h
i(D)=h
0i+ h
1id+h
2id
2... h
did
d, i ∈ [0, n-1], d=max{deg h
i(D) }, wherein symbol deg represents it is the function asking polynomial number of times, by R (D) × H (D)
t=0 to be write as the form of equation group as follows:
Due to h
n-1(D) containing constant term h in
0 (n-1)=1, the constant term part in formula (1) is moved on the right of equation, obtains following formula:
B) by the coefficient matrix R'(D in formula (2)) be divided into two parts, matrix length is N+1, width is respectively R1 and R2, wherein R1 and R2's and be (d+1) × n-1, (usual R2 value is between 16 to 24), then first, second coefficient matrix dimension is respectively (N+1) × R1, (N+1) × R2;
C) set the binary vector iTemp that a length is R1, size is from 0 to 2
r1circulate, the row vector mould two of each fixing iTemp and the first coefficient matrix is added rear negate, and often row obtains a binary number 0 or 1, finally obtains the binary vector of a N+1 dimension, be expressed as P, P is added with the value of the vectorial corresponding row on the right of formula (2) equal sign;
D) again statistic is carried out to the second coefficient matrix, the row vector dimension of the second coefficient matrix be 1*R2, R2 arbitrarily ' 0 ' and ' 1 ' combination as state, totally 2
r2-1 state, each row vector is one of them state, and on the right of formula (2) equal sign, the value of vectorial corresponding row is as the output of this state, and the output valve of equal state adds up, and non-existent State-output value is 0, obtains one (2
r2-1) × 1 dimension state vector;
E) Walsh-Hadamard conversion is carried out to the above results, the solution being greater than confidence level is found to be converted to binary vector again, be expressed as Q, then iTemp vector now combined with binary vector Q the check polynomial H (D) just obtaining us and require;
5) set the generator polynomial matrix exponent number of source convolution code, traversal deletes complementary modul formula, builds system of linear equations Gp (D) H (D)
t=0, wherein G
p(D) be generator matrix, H (D)
tfor the transposition of check matrix H (D), solved unique untrivialo solution of this equation group by Gaussian elimination method, thus determine the generator polynomial of source convolution code and delete complementary modul formula;
6) utilize the component coder parameter identifying and obtain, Punctured convolutional code is obtained confidence level by Viterbi decoding, the accuracy of checking identification parameter.
Claims (1)
1. delete the parameter blind identification of remaining turbo code component coder for one kind, remaining turbo code information bit will be deleted and the first bit check bit multiplex constructs Punctured convolutional code, by the code length of linear matrix analytic approach statistical analysis Punctured convolutional code, code word starting point, recycling part Walsh-Hadamard conversion identifies the check matrix of component coder, obtained deleting complementary modul formula and generator matrix by Gaussian elimination method further, thus realize the parameter blind recognition deleting remaining turbo code component coder, and obtain confidence level by Viterbi decoding, to verify the accuracy identified, the method concrete steps are as follows:
1) from deleting remaining turbo code starting point, the check digit sequence choosing its information sequence and component coder RSC1 is multiplexing, structure Punctured convolutional code sequence, what obtain 1/2 code check more than deleting for 1/3 code check turbo code deletes remaining turbo code, if former turbo code sequence is expressed as x
1a
1b
1x
2a
2b
2x
3a
3b
3x
4a
4b
4then delete remaining rear turbo code sequence and be expressed as x
1a
1x
2b
2x
3a
3x
4b
4, structure Punctured convolutional code is x
1a
1x
2x
3a
3x
4, wherein x
1, x
2, x
3represent the signal bit sequence do not exported in the same time; a
1, a
2, a
3represent the RSC1 check digit sequence do not exported in the same time; b
1, b
2, b
3represent the RSC2 check digit sequence do not exported in the same time;
2) by Punctured convolutional code Sequence composition p × q matrix, require q > N, p > q, wherein N presentation code constraint degree, get the scope of determining train value q, change q obtains different matrix, calculate these ranks of matrix respectively, only leave and take the matrix that rank of matrix is not equal to columns, then elementary transformation is carried out to these matrixes unitization, write down the matrix train value that in unitization rear left, angular unit battle array dimension is equal, greatest common divisor is got to retention train value, thus obtains Punctured convolutional code code length n;
3) Punctured convolutional code sequence is re-constructed matrix, columns is step 2) middle certain train value N ' retained, wherein N ' the multiple that is code length n, get the smaller value retained in train value, get matrix line number and be greater than columns, code sequential shift is obtained n-1 different matrix, together with code sequence not shift matrix obtain n different matrix altogether, n is code length, elementary transformation is carried out to these matrixes unitization, write down the upper left corner unit matrix dimension of unitization rear matrix respectively, displacement when dimension is minimum and Punctured convolutional code starting point;
4) identify check polynomial matrix, concrete identification step is as follows:
A) set up coefficient matrix R (D) by the Punctured convolutional code sequence constructed, verification polynomial matrix is expressed as H (D)=[h
0(D), h
1(D) ... h
n-1(D)]
t, wherein, h
i(D)=h
0i+ h
1id+h
2id
2... h
did
d, i ∈ [0, n-1], d=max{degh
i(D) }, wherein symbol deg represents it is the function asking polynomial number of times, by R (D) × H (D)
t=0 to be write as the form of equation group as follows:
Due to h
n-1(D) containing constant term h in
0 (n-1)=1, the constant term part in formula (1) is moved on the right of equation, obtains following formula:
B) by the coefficient matrix R'(D in formula (2)) be divided into two parts, matrix length is N+1, width is respectively R1 and R2, wherein R1 and R2's and be (d+1) × n-1, (usual R2 value is between 16 to 24), then first, second coefficient matrix dimension is respectively (N+1) × R1, (N+1) × R2;
C) set the binary vector iTemp that a length is R1, size is from 0 to 2
r1circulate, the row vector mould two of each fixing iTemp and the first coefficient matrix is added rear negate, and often row obtains a binary number 0 or 1, finally obtains the binary vector of a N+1 dimension, be expressed as P, P is added with the value of the vectorial corresponding row on the right of formula (2) equal sign;
D) again statistic is carried out to the second coefficient matrix, the row vector dimension of the second coefficient matrix be 1*R2, R2 arbitrarily ' 0 ' and ' 1 ' combination as state, totally 2
r2-1 state, each row vector is one of them state, and on the right of formula (2) equal sign, the value of vectorial corresponding row is as the output of this state, and the output valve of equal state adds up, and non-existent State-output value is 0, obtains one (2
r2-1) × 1 dimension state vector;
E) Walsh-Hadamard conversion is carried out to the above results, the solution being greater than confidence level is found to be converted to binary vector again, be expressed as Q, then iTemp vector now combined with binary vector Q the check polynomial H (D) just obtaining us and require;
5) set the generator polynomial matrix exponent number of source convolution code, traversal deletes complementary modul formula, builds system of linear equations Gp (D) H (D)
t=0, wherein G
p(D) be generator matrix, H (D)
tfor the transposition of check matrix H (D), solved unique untrivialo solution of this equation group by Gaussian elimination method, thus determine the generator polynomial of source convolution code and delete complementary modul formula;
6) utilize the component coder parameter identifying and obtain, Punctured convolutional code is obtained confidence level by Viterbi decoding, the accuracy of checking identification parameter.
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| CN106411328A (en) * | 2016-10-28 | 2017-02-15 | 华南理工大学 | Soft-bit-based blind identification method for Turbo code interleaver |
| CN107370566A (en) * | 2017-07-28 | 2017-11-21 | 西安电子科技大学 | A kind of punctured Turbo codes blind-identification method under the conditions of error code |
| CN107566091A (en) * | 2017-10-24 | 2018-01-09 | 重庆电子工程职业学院 | A kind of blind recognition of convolutional code method for taking the dark information of Generalized Coupled into account |
| CN110535478A (en) * | 2019-09-27 | 2019-12-03 | 电子科技大学 | Dual input class Turbo code closed set recognition methods in a kind of DVB-RCS2 agreement |
| CN110690907A (en) * | 2019-09-27 | 2020-01-14 | 电子科技大学 | Known branch information turbo code deletion mode estimation method |
| CN111510164A (en) * | 2020-05-14 | 2020-08-07 | 中国人民解放军海军航空大学 | Turbo code component encoder identification method and system |
| CN116566404A (en) * | 2023-07-11 | 2023-08-08 | 北京谷数科技股份有限公司 | Method and device for determining interleaving mapping relation of punctured Turbo codes |
| CN118074728A (en) * | 2024-04-18 | 2024-05-24 | 北京邮电大学 | Recognition method of Turbo code puncturing mode |
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| CN106411328A (en) * | 2016-10-28 | 2017-02-15 | 华南理工大学 | Soft-bit-based blind identification method for Turbo code interleaver |
| CN107370566A (en) * | 2017-07-28 | 2017-11-21 | 西安电子科技大学 | A kind of punctured Turbo codes blind-identification method under the conditions of error code |
| CN107370566B (en) * | 2017-07-28 | 2020-07-14 | 西安电子科技大学 | Punctured Turbo code blind identification method under error code condition |
| CN107566091A (en) * | 2017-10-24 | 2018-01-09 | 重庆电子工程职业学院 | A kind of blind recognition of convolutional code method for taking the dark information of Generalized Coupled into account |
| CN110535478A (en) * | 2019-09-27 | 2019-12-03 | 电子科技大学 | Dual input class Turbo code closed set recognition methods in a kind of DVB-RCS2 agreement |
| CN110690907A (en) * | 2019-09-27 | 2020-01-14 | 电子科技大学 | Known branch information turbo code deletion mode estimation method |
| CN110690907B (en) * | 2019-09-27 | 2023-04-25 | 电子科技大学 | Method for estimating deletion mode of known branch information turbo code |
| CN111510164A (en) * | 2020-05-14 | 2020-08-07 | 中国人民解放军海军航空大学 | Turbo code component encoder identification method and system |
| CN116566404A (en) * | 2023-07-11 | 2023-08-08 | 北京谷数科技股份有限公司 | Method and device for determining interleaving mapping relation of punctured Turbo codes |
| CN116566404B (en) * | 2023-07-11 | 2023-09-19 | 北京谷数科技股份有限公司 | Method and device for determining interleaving mapping relation of punctured Turbo codes |
| CN118074728A (en) * | 2024-04-18 | 2024-05-24 | 北京邮电大学 | Recognition method of Turbo code puncturing mode |
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