CN110730006B - LDPC code error correction method and error correction module for MCU - Google Patents
LDPC code error correction method and error correction module for MCU Download PDFInfo
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- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
- H03M13/00—Coding, decoding or code conversion, for error detection or error correction; Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes
- H03M13/03—Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words
- H03M13/05—Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words using block codes, i.e. a predetermined number of check bits joined to a predetermined number of information bits
- H03M13/11—Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words using block codes, i.e. a predetermined number of check bits joined to a predetermined number of information bits using multiple parity bits
- H03M13/1102—Codes on graphs and decoding on graphs, e.g. low-density parity check [LDPC] codes
- H03M13/1148—Structural properties of the code parity-check or generator matrix
- H03M13/1177—Regular LDPC codes with parity-check matrices wherein all rows and columns have the same row weight and column weight, respectively
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention provides an LDPC code error correction method for MCU, which is characterized in that: the method comprises the following steps: s1, constructing a check matrix H of LDPC according to set LDPC code length and code rate, converting the check matrix H to obtain a generator matrix G, and storing the check matrix H and the generator matrix G; s2, inputting an information sequence X with the length of k; based on the generation matrix G, the information sequence X is encoded to obtain a codeword Y; coding the codeword Y into codeword information and transmitting the codeword information outwards; s3, receiving codeword information to obtain a noisy codeword Y'; the noisy codeword Y' is directly decoded into codeword X using a deep learning decoding method. The method has stronger error correction capability, so that the MCU has higher reliability, and the iteration times and complexity of decoding are reduced. The invention also provides an error correction module for realizing the LDPC code error correction method.
Description
Technical Field
The invention relates to the technical field of electronic communication, in particular to an LDPC code error correction method and an LDPC code error correction module for MCU.
Background
The micro control unit (Microcontroller Unit; MCU), also called as single chip microcomputer (Single Chip Microcomputer) or single chip microcomputer, is to properly reduce the frequency and specification of the CPU (Central Process Unit; CPU), integrate the peripheral interfaces such as memory (memory), counter (Timer), USB, A/D conversion, etc., and even LCD driving circuit on a single chip to form a chip-level computer. In order to improve the reliability of communication, an error correction (ECC) module is provided inside the MCU, and a hamming code and a BCH code are used by a conventional ECC module to correct errors. With the continuous development of MCUs, people are using MCUs for a wide variety of new and more complex computing tasks; however, the hamming code and BCH code have limited error correction capability, and cannot meet more complex calculation tasks and more huge data traffic.
The low-density parity check code (Low Density Parity Check Code; LDPC) is a good code with performance approaching Shannon limit, and is an ideal scheme for replacing Hamming code and BCH code due to the advantages of strong error correction capability, low decoding complexity and the like, but the application of the LDPC code in the MCU technical field is still blank.
Disclosure of Invention
To overcome the disadvantages and shortcomings in the prior art, an object of the present invention is to provide an LDPC code error correction method for MCUs; the method has stronger error correction capability, so that the MCU has higher reliability, and the iteration times and complexity of decoding are reduced. Another object of the present invention is to provide an LDPC code error correction module that implements the above LDPC code error correction method, has strong error correction capability, high reliability, and can reduce the number of decoding iterations and complexity.
In order to achieve the above purpose, the invention is realized by the following technical scheme: an LDPC code error correction method for MCU, characterized in that: the method comprises the following steps:
s1, constructing a check matrix H of LDPC according to set LDPC code length and code rate, converting the check matrix H to obtain a generator matrix G, and storing the check matrix H and the generator matrix G;
s2, inputting an information sequence X with the length of k; based on the generation matrix G, the information sequence X is encoded to obtain a codeword Y; coding the codeword Y into codeword information and transmitting the codeword information outwards;
s3, receiving codeword information to obtain a noisy codeword Y'; the noisy codeword Y' is directly decoded into codeword X using a deep learning decoding method.
Preferably, in the step S1, the method for constructing the check matrix H is as follows: setting the LDPC code length as n, the check bit length as m and the code rate as R=k/n; and constructing a check matrix H by using a maykay construction method.
Preferably, in the step S1, the generating matrix G is obtained by converting the check matrix H, which means: the right half H of the check matrix H 2 Inversion and multiplication of the left half H of the check matrix H 1 Obtaining the right half part G of the generator matrix G 2 The method comprises the steps of carrying out a first treatment on the surface of the Generating the left half G of the matrix G 1 Set as identity matrix I of size k×k:
the size of the generator matrix G is k×n.
Preferably, in the step S2, based on the generator matrix G, the information sequence X is multiplied by the generator matrix G to obtain a codeword Y with a length n:
Y＝X×G。
preferably, in the step S3, the noisy codeword Y' is directly decoded into the codeword X by using a deep learning decoding method, which means that: comprises the following sub-steps:
s31, constructing a deep learning model: setting the dimension of an input layer, the number of hidden layers, the number of neurons of each layer of network and the number of neurons of an output layer of the model by adopting a DNN model, adding an activation function at the output part of the hidden layer, and setting a loss function of the model;
s32, training a model: constructing a data set, wherein the data set is a plurality of groups of noisy code words Y', and the labels are information sequences X of original code words; dividing the data set into a training set and a verification set according to a set proportion; inputting the training set into a DNN model, updating parameters of the model through a back propagation algorithm to enable the loss function to be converged, stopping training when the accuracy of the verification set and the loss function tend to be stable, and storing the model;
s33, taking the stored model as an error correction model, and decoding the noisy codeword Y' into a codeword X after passing through the error correction model.
An LDPC code error correction module for implementing the above LDPC code error correction method for MCUs, characterized in that: comprising the following steps:
the LDPC matrix storage module is used for constructing a check matrix H of LDPC according to the set LDPC code length and code rate, converting the check matrix H to obtain a generation matrix G, and storing the check matrix H and the generation matrix G;
the LDPC coding module is used for coding the information sequence X with the length of k based on the generation matrix G to obtain a codeword Y;
and an LDPC decoding module for receiving the noisy codeword Y 'and directly decoding the noisy codeword Y' into codeword X using a deep learning decoding method.
Preferably, in the LDPC matrix storage module, the LDPC code length is set to n, the check bit length is set to m, and the code rate is set to r=k/n; and constructing a check matrix H by using a maykay construction method.
Preferably, in the LDPC matrix storage module, the generating matrix G is obtained by converting the check matrix H, which means: the right half H of the check matrix H 2 Inversion and multiplication of the left half H of the check matrix H 1 Obtaining the right half part G of the generator matrix G 2 The method comprises the steps of carrying out a first treatment on the surface of the Generating the left half G of the matrix G 1 Set as identity matrix I of size k×k:
the size of the generator matrix G is k×n.
Preferably, in the LDPC coding module, based on the generator matrix G, the information sequence X is multiplied by the generator matrix G to obtain a codeword Y with a length n:
Y＝X×G。
compared with the prior art, the invention has the following advantages and beneficial effects:
1. compared with the traditional mode of adopting Hamming codes and BCH codes, the invention uses LDPC codes as error control codes, has stronger error correction capability under the condition of the same code length and code rate, and ensures that MCU has higher reliability;
2. the invention uses the deep neural network to decode, compared with the traditional BP iterative decoding, the performance of LDPC is further improved, and the iterative times and complexity of decoding are reduced.
Drawings
FIG. 1 is a flowchart of an LDPC code error correction method for an MCU of the present invention;
FIG. 2 is a block diagram of an LDPC code error correction module for an MCU according to the present invention;
FIG. 3 is a deep neural network block diagram of an LDPC code in an embodiment;
FIG. 4 is a graph of performance comparison of LDPC codes with conventional BCH codes in an embodiment.
Detailed Description
The invention is described in further detail below with reference to the drawings and the detailed description.
Example 1
The flow of the LDPC code error correction method for MCU of the present embodiment is shown in FIG. 1, comprising the following steps:
s1, constructing a check matrix H of LDPC according to the set LDPC code length and code rate, converting the check matrix H to obtain a generator matrix G, and storing the check matrix H and the generator matrix G.
Specifically, the LDPC code length is set to be n, the length of the information sequence X to be input is k, the check bit length is m, and the code rate is R=k/n; and constructing a check matrix H by using a maykay construction method.
The generation matrix G is obtained by converting the check matrix H, which means that: the right half H of the check matrix H 2 Inversion and multiplication of the left half H of the check matrix H 1 Obtaining the right half part G of the generator matrix G 2 The method comprises the steps of carrying out a first treatment on the surface of the Generating the left half G of the matrix G 1 Set as identity matrix I of size k×k:
the size of the generator matrix G is k×n.
S2, inputting an information sequence X with the length of k; based on the generation matrix G, the information sequence X is encoded to obtain a codeword Y; codeword Y is organized into codeword information and transmitted outwardly.
Specifically, based on the generator matrix G, the information sequence X is multiplied by the generator matrix G to obtain a codeword Y of length n:
Y＝X×G。
s3, receiving codeword information to obtain a noisy codeword Y'; the noisy codeword Y' is directly decoded into codeword X using a deep learning decoding method.
Specifically, the use of the deep learning decoding method to directly decode the noisy codeword Y' into codeword X means: comprises the following sub-steps:
s31, constructing a deep learning model: setting the dimension of an input layer, the number of hidden layers, the number of neurons of each layer of network and the number of neurons of an output layer of the model by adopting a DNN model, adding an activation function at the output part of the hidden layer, and setting a loss function of the model;
s32, training a model: constructing a data set, wherein the data set is a plurality of groups of noisy code words Y', and the labels are information sequences X of original code words; dividing the data set into a training set and a verification set according to a set proportion; inputting the training set into a DNN model, updating parameters of the model through a back propagation algorithm to enable the loss function to be converged, stopping training when the accuracy of the verification set and the loss function tend to be stable, and storing the model;
s33, taking the stored model as an error correction model, and decoding the noisy codeword Y' into a codeword X after passing through the error correction model.
Compared with the traditional mode of adopting Hamming codes and BCH codes, the invention uses LDPC codes as error control codes, has stronger error correction capability under the condition of the same code length and code rate, and ensures that MCU has higher reliability; the invention uses the deep neural network to decode, compared with the traditional BP iterative decoding, the performance of LDPC is further improved, and the iterative times and complexity of decoding are reduced.
In order to implement the above LDPC code error correction method, this embodiment provides an LDPC code error correction module, whose structure is shown in fig. 2, including:
the LDPC matrix storage module is used for constructing a check matrix H of LDPC according to the set LDPC code length and code rate, converting the check matrix H to obtain a generation matrix G, and storing the check matrix H and the generation matrix G;
the LDPC coding module is used for coding the information sequence X with the length of k based on the generation matrix G to obtain a codeword Y;
and an LDPC decoding module for receiving the noisy codeword Y 'and directly decoding the noisy codeword Y' into codeword X using a deep learning decoding method.
Specifically, in the LDPC matrix storage module, the LDPC code length is set to n, the check bit length is set to m, and the code rate is set to r=k/n; and constructing a check matrix H by using a maykay construction method.
In the LDPC matrix storage module, the generation matrix G is obtained by converting the check matrix H, which means that: the right half H of the check matrix H 2 Inversion and multiplication of the left half H of the check matrix H 1 Obtaining the right half part G of the generator matrix G 2 The method comprises the steps of carrying out a first treatment on the surface of the Generating the left half G of the matrix G 1 Set as identity matrix I of size k×k:
the size of the generator matrix G is k×n.
In the LDPC encoding module, based on a generator matrix G, the information sequence X is multiplied by the generator matrix G to obtain a codeword Y with a length of n:
Y＝X×G。
example two
The present embodiment is described by taking an LDPC code with a code rate of 7/15 as an example. An LDPC code error correction method for an MCU, comprising the steps of:
s1, setting the LDPC code length n as 15 bits, the length k of an information sequence X to be input as 7 bits, the check bit length m as 8 bits and the code rate R=7/15; and constructing a check matrix H of the LDPC by using a maykay construction method according to the code length and the code rate, wherein the dimension is 8 multiplied by 15.
The check matrix H is converted into a corresponding generator matrix G, the generator matrix G being dimensioned 7 x 15.
S2, multiplying the information sequence X with the length of 7 before encoding by the LDPC code generation matrix G to obtain a codeword Y with the length of 15 after encoding; codeword Y is organized into codeword information and transmitted outwardly.
S3, receiving codeword information to obtain a noisy codeword Y'; codeword Y' is directly decoded into codeword X using a deep learning decoding method. The method comprises the following steps:
s31, constructing a deep learning model: and a DNN model is adopted, the dimension of an input layer of the model is set to be 15, the number of hidden layers is 3, the number of neurons of each layer of network is respectively 2048, 1024 and 1024, the number of neurons of an output layer is 128, and the model structure is shown in figure 3. Adding an activation function as a ReLU function to an output part of the hidden layer, and setting a loss function of a model as cross entropy;
s32, training a model: constructing a data set, wherein the data set is a plurality of groups of noisy code words Y', and the labels are information sequences X of original code words; dividing the data set into a training set and a verification set according to the proportion of 7:3; inputting the training set into a DNN model, updating parameters of the model through a back propagation algorithm to enable the loss function to be converged, stopping training when the accuracy of the verification set and the loss function tend to be stable, and storing the model;
s33, taking the stored model as an error correction model, decoding the noisy codeword Y' into a codeword X after passing through the error correction model, and finishing decoding.
The above examples are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above examples, and any other changes, modifications, substitutions, combinations, and simplifications that do not depart from the spirit and principle of the present invention should be made in the equivalent manner, and the embodiments are included in the protection scope of the present invention.
Claims (2)
1. An LDPC code error correction method for MCU, characterized in that: the method comprises the following steps:
s1, constructing a check matrix H of LDPC according to set LDPC code length and code rate, converting the check matrix H to obtain a generator matrix G, and storing the check matrix H and the generator matrix G;
s2, inputting an information sequence X with the length of k; based on the generation matrix G, the information sequence X is encoded to obtain a codeword Y; coding the codeword Y into codeword information and transmitting the codeword information outwards;
s3, receiving codeword information to obtain a noisy codeword Y'; directly decoding the noisy codeword Y' into codeword X using a deep learning decoding method;
in the step S1, the method for constructing the check matrix H includes: setting the LDPC code length as n, the check bit length as m and the code rate as R=k/n; constructing a check matrix H by using a maykay construction method;
in the step S1, the check matrix H is converted to obtain a generation matrix G, which isFinger means: the right half H of the check matrix H 2 Inversion and multiplication of the left half H of the check matrix H 1 Obtaining the right half part G of the generator matrix G 2 The method comprises the steps of carrying out a first treatment on the surface of the Generating the left half G of the matrix G 1 Set as identity matrix I of size k×k:
wherein the size of the generation matrix G is k multiplied by n;
in the step S2, based on the generator matrix G, the information sequence X is multiplied by the generator matrix G to obtain a codeword Y with a length n:
Y＝X×G；
in the step S3, the noisy codeword Y' is directly decoded into the codeword X by using a deep learning decoding method, which means that: comprises the following sub-steps:
s31, constructing a deep learning model: setting the dimension of an input layer, the number of hidden layers, the number of neurons of each layer of network and the number of neurons of an output layer of the model by adopting a DNN model, adding an activation function at the output part of the hidden layer, and setting a loss function of the model;
s32, training a model: constructing a data set, wherein the data set is a plurality of groups of noisy code words Y', and the labels are information sequences X of original code words; dividing the data set into a training set and a verification set according to a set proportion; inputting the training set into a DNN model, updating parameters of the model through a back propagation algorithm to enable the loss function to be converged, stopping training when the accuracy of the verification set and the loss function tend to be stable, and storing the model;
s33, taking the stored model as an error correction model, and decoding the noisy codeword Y' into a codeword X after passing through the error correction model.
2. An LDPC code error correction module implementing the LDPC code error correction method for MCUs of claim 1, characterized by: comprising the following steps:
the LDPC matrix storage module is used for constructing a check matrix H of LDPC according to the set LDPC code length and code rate, converting the check matrix H to obtain a generation matrix G, and storing the check matrix H and the generation matrix G;
the LDPC coding module is used for coding the information sequence X with the length of k based on the generation matrix G to obtain a codeword Y;
and an LDPC decoding module for receiving the noisy codeword Y 'and directly decoding the noisy codeword Y' into codeword X using a deep learning decoding method;
in the LDPC matrix storage module, the LDPC code length is set to be n, the check bit length is set to be m, and the code rate is set to be R=k/n; constructing a check matrix H by using a maykay construction method;
in the LDPC matrix storage module, the generation matrix G is obtained by converting the check matrix H, which means that: the right half H of the check matrix H 2 Inversion and multiplication of the left half H of the check matrix H 1 Obtaining the right half part G of the generator matrix G 2 The method comprises the steps of carrying out a first treatment on the surface of the Generating the left half G of the matrix G 1 Set as identity matrix I of size k×k:
wherein the size of the generation matrix G is k multiplied by n;
in the LDPC encoding module, based on a generator matrix G, the information sequence X is multiplied by the generator matrix G to obtain a codeword Y with a length of n:
Y＝X×G；
in the LDPC decoding module, the noisy codeword Y' is directly decoded into codeword X by using a deep learning decoding method, which means that: comprises the following sub-steps:
s31, constructing a deep learning model: setting the dimension of an input layer, the number of hidden layers, the number of neurons of each layer of network and the number of neurons of an output layer of the model by adopting a DNN model, adding an activation function at the output part of the hidden layer, and setting a loss function of the model;
s32, training a model: constructing a data set, wherein the data set is a plurality of groups of noisy code words Y', and the labels are information sequences X of original code words; dividing the data set into a training set and a verification set according to a set proportion; inputting the training set into a DNN model, updating parameters of the model through a back propagation algorithm to enable the loss function to be converged, stopping training when the accuracy of the verification set and the loss function tend to be stable, and storing the model;
s33, taking the stored model as an error correction model, and decoding the noisy codeword Y' into a codeword X after passing through the error correction model.
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