WO2022062406A1 - Packet error correction code decoding method, apparatus, storage medium, and electronic device - Google Patents

Abstract

Embodiments of the present invention provide a packet error correction code decoding method, apparatus, storage medium, and electronic device, relating to the technical field of packet error correction code communication technology; the method comprises: determining the number of iterative decoding layers according to the size of a submatrix in a check matrix, said check matrix consisting of a sequence of packet error correction codes; on the basis of a target check node in a target iterative decoding layer, performing target processing of a target variable node associated with said target check node; if all of the results of said target processing are non-establishment of all hard decisions, then performing the target processing on all other variable nodes in the target iterative decoding layer; if all of the results of said target processing are non-establishment of a hard decision, then performing target processing on the variable nodes in the other iterative decoding layers corresponding to the variable nodes in said target iterative decoding layer until the hard decision is established. The present invention addresses the problem of inefficient error correction, and thus achieves the effect of improving the efficiency of error correction.

Description

Decoding method, device, storage medium and electronic device for block error correction code technical field

Embodiments of the present invention relate to the field of communications, and in particular, to a method, device, storage medium, and electronic device for decoding a packet error correction code.

Background technique

LDPC code (Low-density parity-check code) is a block error correction code with a sparse check matrix, which maps the information sequence to the transmission sequence through a generator matrix G, where the generator matrix There are several sub-matrices in G; the performance of LDPC codes is close to the Shannon limit, and the description and implementation are simple.

At present, when decoding LDPC codes, the normalized minimum sum decoding algorithm is usually used; the algorithm is as follows:

Referring to Figure 1 and Figure 2, u0 to u3 are check nodes, and v0 to v7 are variable nodes. As can be seen from the figure, the check node u0 is connected to the variable nodes v0, v2, and v3, then the row operation is to calculate the variable node v0 from the check node. The information obtained from the test node u0, the information obtained by the variable node v0 is related to the variable nodes v2 and v3.

That is, for row u0, it is necessary to calculate the information obtained by v0 from u0, the information obtained by v2 from u0, and the information obtained by v3 from u0.

Similarly, it can be seen from the figure that the variable node v0 is connected to the check nodes u0 and u3, then the column calculation is to calculate the information obtained by the check node u0 from the variable node v0, and the information obtained by the check node u0 is related to the check node u3; For the u0 column, the information obtained by u0 from v0 and the information obtained by u3 from v0 need to be calculated separately.

When the above algorithm performs the decoding work, it needs to complete the row operation of all rows and then perform the column operation. This method leads to a slow convergence rate during decoding, that is, each decoding takes a long time, which will cause the error correction code module. The throughput rate is reduced, resulting in low error correction efficiency of the error correction code module, thereby reducing the guaranteed signal integrity.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide a method, device, storage medium and electronic device for decoding a block error correction code, so as to at least solve the problems of slow convergence speed and low error correction code module efficiency in the related art.

According to an embodiment of the present invention, a method for decoding a block error correction code is provided, including:

The number of iterative decoding layers is determined according to the size of the sub-matrix in the check matrix, wherein the check matrix is composed of a block error correction code sequence;

The target variable node associated with the target check node is subjected to target processing based on the target check node in the target iterative decoding layer, wherein the target processing includes:

The first line of operation is performed on the target check node associated with the target variable node in the target iterative decoding layer, and the first line of operation is used to calculate the difference between the target check node and the target check node. The information obtained by all variable nodes associated with the test node, wherein the information includes the log-likelihood value of the target variable node; according to the operation result of the operation in the first row, the first column is performed on the target variable node. operation, the first column operation is used to calculate the information obtained by the target variable node from the target check node; according to the operation result of the first column operation, the information of the target variable node is updated to Determine the updated target variable node; according to the operation result of the first column operation, make a hard decision on the updated target variable node; in the case that the target processing result is that the hard decision does not hold, based on the other target check nodes associated with the updated target variable node perform the target processing on the updated target variable node;

Under the circumstance that all of the target processing results are all hard decisions that fail to hold, perform the target processing on all other variable nodes in the target iterative decoding layer;

In the case that the target processing results are all hard decisions that fail to hold, the target processing is performed on the variable nodes corresponding to the variable nodes of the target iterative decoding layer in other iterative decoding layers in turn, until the hard decision is reached. Wherein, the variable nodes in the other iterative decoding layers corresponding to the variable nodes in the target iterative decoding layer include the position in the other iterative decoding layers and the position of the variable node in the target iterative decoding layer The same variable node.

In an exemplary embodiment, before performing the target processing on the variable nodes corresponding to the variable nodes of the target iterative decoding layer in other iterative decoding layers in sequence, the method further includes:

The column operation result of the variable node of the target iterative decoding layer is used as the initial value of the variable node corresponding to the variable node of the target iterative decoding layer in other iterative decoding layers.

In an exemplary embodiment, taking the column operation result of the target variable node of the target iterative decoding layer as a variable corresponding to the target variable node of the target iterative decoding layer in other iterative decoding layers The initial values of nodes include:

A column update is performed on the check nodes of the other iterative decoding layers, wherein the column update is used for the mth layer in the nth iteration to transfer information from the updated variable node of the m-1th layer to the The check node corresponding to the mth layer;

The row update is performed on the variable nodes of the other iterative decoding layers, wherein the row update is used for the mth layer in the nth iteration, and the updated information is transmitted to the mth layer by the check node of the mth layer. variable nodes in the m layer;

Update the posterior probability information on the variable nodes of the other iterative decoding layers, wherein the posterior probability information update is used for the mth layer in the nth iteration, according to the row update and the column update information, update the posterior probability information of the mth layer variable node.

In an exemplary embodiment, the performing column update on the check node of the target iterative decoding layer includes:

The column update is performed on the check node of the target iterative decoding layer by the following formula:

L ⁽ⁿ⁾ (q _ijm )=L ⁽ⁿ⁾ (Q _im-1 )-L ^(n-1) (r _ijm )

In the formula, i represents the variable node, j represents the check node; m represents the number of layers, n represents the number of iterations; L ⁽ⁿ⁾ (q _ijm ) represents the mth layer in the nth iteration, the variable node is sent to The information log-likelihood value of the check node, L ⁽ⁿ⁾ (Q _im-1 ) represents the m-1th layer in the n-th iteration, the information log-likelihood value of the variable node after updating the information , L ^(n-1) (r _ijm ) represents the mth layer in the n-1th iteration, the log-likelihood value of the information sent by the check node to the variable node.

In an exemplary embodiment, the performing row update on the variable node of the target iterative decoding layer includes:

The row update is performed on the variable nodes of the target iterative decoding layer by the following formula:

In the formula, L ⁽ⁿ⁾ (r _ijm ) represents the log-likelihood value of the information sent by the check node to the target check node corresponding to the check node at the m-th layer in the n-th iteration.

In an exemplary embodiment, the updating the posterior probability information of the mth layer variable node according to the information of row update and column update includes:

Update the posterior probability information of the m-th layer variable node according to the information of row update and column update according to the following formula:

L ⁽ⁿ⁾ (Q _im )=L ⁽ⁿ⁾ (r _ijm-1 )+L ⁽ⁿ⁾ (q _ijm )

In the formula, L ⁽ⁿ⁾ (Q _im ) represents the log-likelihood value of the information of the m-th layer in the n-th iteration after the information is updated by the variable node.

In an exemplary embodiment, before performing column update on check nodes of other iterative decoding layers, the method includes:

The initial information log-likelihood value is set, and the number of iterative decoding layers and the number of iterations are initialized to preset values, wherein the initial information log-likelihood value is obtained according to the received signal value and channel characteristics, and the The initial information log-likelihood value represents the information log-likelihood value of the variable node that has not been iterated.

According to another embodiment of the present invention, there is provided an apparatus for decoding a block error correction code, comprising:

The decoding layer grouping module is configured to determine the number of iterative decoding layers according to the size of the sub-matrix in the check matrix, wherein the check matrix is composed of a grouped error correction code sequence;

The target processing module is configured to perform target processing on the target variable node associated with the target check node based on the target check node in the target iterative decoding layer, wherein the target processing includes:

The first line of operation is performed on the target check node associated with the target variable node in the target iterative decoding layer, and the first line of operation is used to calculate the difference between the target check node and the target check node. The information obtained by all variable nodes associated with the test node; according to the operation result of the first row operation, the first column operation is performed on the target variable node, and the first column operation is used to calculate the target variable node. The information obtained from the target check node; according to the operation result of the first column operation, update the information on the target variable node to determine the updated target variable node; according to the operation result of the first column operation As a result of the operation, a hard decision is made on the updated target variable node; in the case that the target processing result is that the hard decision is not established, based on other target check nodes associated with the updated target variable node The updated target variable node performs the target processing;

a first iterative module, configured to perform the target processing on all other variable nodes in the target iterative decoding layer under the condition that the target processing results are all hard decisions that fail to hold;

The second zone module is configured to perform the target processing on the variable nodes corresponding to the variable nodes of the target iterative decoding layer in the other iterative decoding layers in turn when the target processing results are all invalid. processing until the hard decision is established; wherein, the variable nodes in the other iterative decoding layers corresponding to the variable nodes in the target iterative decoding layer include the position in the other iterative decoding layers and the target iterative decoding layer. The variable node of the code layer has the same position as the variable node.

According to yet another embodiment of the present invention, a storage medium is also provided, wherein a computer program is stored in the storage medium, wherein the computer program is configured to execute the steps in any one of the above method embodiments when running.

According to yet another embodiment of the present invention, there is also provided an electronic device comprising a memory and a processor, wherein the memory stores a computer program, the processor is configured to run the computer program to execute any of the above Steps in Method Examples.

By means of the present invention, a column operation is performed after a row operation is performed, and the iteration is stopped immediately when the hard decision of the column operation result is performed. Compared with the prior art, there is no need to perform a hard decision on all variable nodes, and the cost of a single calculation is reduced. The calculation amount also reduces the overall calculation amount. Therefore, the problems of slow convergence speed and low error correction code module efficiency in the related art can be solved, and the effect of improving the calculation error correction efficiency can be achieved.

Description of drawings

Fig. 1 is the schematic diagram representing the row operation of the prior art in the background technology of the present invention;

FIG. 2 is a schematic diagram showing the column operation of the prior art in the background technology of the present invention;

3 is a block diagram of a hardware structure of a mobile terminal of a method for decoding a packet error correction code according to an embodiment of the present invention;

4 is a flowchart of a method for decoding a block error correction code according to an embodiment of the present invention;

5 is a structural block diagram of a decoding apparatus for a block error correction code according to an embodiment of the present invention;

6 is a schematic diagram showing an iterative decoding layer in an embodiment of the present invention;

7 is a schematic diagram showing row calculation performed in a target iterative decoding layer according to an embodiment of the present invention;

8 is a schematic diagram illustrating a column operation performed in a target iterative decoding layer according to an embodiment of the present invention;

FIG. 9 is a schematic structural diagram showing a decoding circuit performing decoding in an embodiment of the present invention;

10 is a block diagram showing the structure of a decoding circuit that performs decoding in an embodiment of the present invention;

FIG. 11 is a flowchart showing a decoding process in an embodiment of the present invention.

detailed description

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings and in conjunction with the embodiments.

It should be noted that the terms "first", "second" and the like in the description and claims of the present invention and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific sequence or sequence.

The method embodiments provided in the embodiments of this application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Taking running on a mobile terminal as an example, FIG. 3 is a block diagram of a hardware structure of a mobile terminal of a method for decoding a packet error correction code according to an embodiment of the present invention. As shown in FIG. 3 , the mobile terminal may include one or more (only one is shown in FIG. 3 ) processor 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 configured to store data, wherein the above-mentioned mobile terminal may further include a transmission device 106 and an input/output device 108 configured as a communication function. Those skilled in the art can understand that the structure shown in FIG. 3 is only for illustration, and it does not limit the structure of the above-mentioned mobile terminal. For example, the mobile terminal may further include more or less components than those shown in FIG. 3 , or have a different configuration than that shown in FIG. 3 .

The memory 104 may be configured to store computer programs, for example, software programs and modules of application software, such as a computer program corresponding to a method for decoding a block error correction code in an embodiment of the present invention. The processor 102 stores the program in the memory 104 by running The computer program within the system, thereby executing various functional applications and data processing, implements the above-mentioned method. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory located remotely from the processor 102, and these remote memories may be connected to the mobile terminal through a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

Transmission means 106 are arranged to receive or transmit data via a network. The specific example of the above-mentioned network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC for short), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (Radio Frequency, RF for short) module, which is configured to communicate with the Internet in a wireless manner.

A method for decoding a block error correction code is provided in this embodiment. FIG. 4 is a flowchart according to an embodiment of the present invention. As shown in FIG. 4 , the flowchart includes the following steps:

Step S202, determining the number of iterative decoding layers according to the size of the sub-matrix in the check matrix, wherein the check matrix is composed of a block error correction code sequence;

In an optional embodiment, since there are many sub-matrices in the check matrix, determining the number of iterative decoding layers according to the size of the sub-matrix can check the sub-matrices of different layers respectively. When the check result meets the requirements, Then the check is stopped, so that it is not necessary to check all the sub-matrices, which improves the check rate; taking LDPC code as an example, for example, the check matrix is a 4*8 matrix, and the sub-matrix is a 1*1 matrix, then iterative decoding The number of code layers is 4 layers.

Step S204, performing target processing on the target variable node associated with the target check node based on the target check node in the target iterative decoding layer, wherein the target processing includes:

The first line of operation is performed on the target check node associated with the target variable node in the target iterative decoding layer, and the first line of operation is used to calculate the information obtained by the target check node from all variable nodes associated with the target check node. , where the information includes the log-likelihood value of the target variable node;

According to the operation result of the operation in the first row, a first column operation is performed on the target variable node, and the first column operation is used to calculate the information obtained by the target variable node from the target check node;

According to the operation result of the operation in the first column, the information of the target variable node is updated to determine the updated target variable node;

According to the operation result of the first column operation, make a hard decision on the updated target variable node;

In the case that the result of the target processing is that the hard decision does not hold, target processing is performed on the updated target variable node based on other target check nodes associated with the updated target variable node.

In this embodiment, taking an LDPC code as an example, if the LDPC code has only one iterative decoding layer, a sub-matrix includes multiple check nodes and multiple variable nodes, and one check node is often associated with multiple check nodes. Each variable node is associated with one variable node, and one variable node is also associated with multiple check nodes, and decoding is to decode the information of all variable nodes, so it is necessary to perform column operations on the variable nodes in turn to calculate the information of all variable nodes. .

At the same time, a variable node is associated with one or more check nodes. Therefore, when calculating the information of a variable node, it is necessary to perform row operations on the check nodes to determine the information contained in each check node.

For example, if the check node u0 is respectively associated with the variable nodes v0, v1 and v2, the information obtained by the check node u0 from the variable nodes v0, v1 and v2 is calculated respectively.

Similarly, during the column operation, since the variable node v0 is associated with the check nodes u0 and u1 respectively, the information obtained by the variable node v0 from the check nodes u0 and u1 is calculated respectively, and then the variable node is calculated according to the calculation result. Data Update.

In this embodiment, after the row operation is performed on the check node u0, the first column of the variable node v0 is operated immediately to determine the information obtained by the variable node from the check node u0. The information obtained by the verification node u1 is reduced, and the calculation efficiency is improved.

Further, when the variable node v0 is respectively associated with the check nodes u0 and u1, the information obtained by the variable node v0 from the check node u0 is used as the information of the target variable node v0', and the target variable node v0' does not contain variables at this time. The node v0 checks the information of the node u1, so when the decoding is performed, the amount of calculation is relatively small, thereby improving the calculation efficiency.

In this embodiment, the condition for the establishment of the hard decision is Hc ^T =0, where H represents the parity check matrix, c represents the code vector of the convolutional code, and t represents the vector transposition, where when Hc ^T =0 is established , then it is determined that the decoding is successful, if not, it is determined that the decoding is unsuccessful.

For example, after the target variable node is determined, the target variable node v0' is decoded, and the hard decision on the target variable node v0' is to judge whether the decoding of the target variable node v0' is successful; wherein, H represents an M For a parity check matrix with rows and N columns, c represents a row vector of length N. When Hc ^T = 0 is established, it is determined that the elements in the parity check matrix are linearly correlated, and the decoding is determined to be successful, and the iteration is terminated at this time.

In this embodiment, if the hard decision of the column operation of the first check node associated with the target variable node does not hold, the row operation and column operation are respectively performed on the second check node associated with the target variable node. ;

For example, when the variable node v0 is associated with the check nodes u0 and u1 respectively, in the case that the hard decision on the column operation of u0 and v0 does not hold, the information obtained by the variable node v0 from the check node u1 also needs to be calculated, so It is necessary to make sure that the row operation is performed on the check node u1 to check the information contained in the node u1, and then the information of the check node u1 is transferred to the variable node v0; since the row operation of the check node u1 is completed, the Perform column operation, because column operation only calculates the information obtained by the variable node v0 from the second check node u1, and does not need to calculate the information obtained by the variable node v0 from other check nodes (such as u0), thus reducing the amount of calculation and improving the operation. effectiveness.

After the column operation between u1 and v0 is completed, the result of the column operation between u1 and v0 and the operation result between u1 and v0 are used as initial information to update the variable node v0, so that the variable node v0 is determined as The target variable node v0' to be decoded.

Then a hard decision is made on the updated variable node v0'. When Hc ^T =0 is established, it is determined that the decoding is successful. If not, the above actions are repeated for the third check node (eg v2) associated with the target variable node. , until the decoding of the target variable node is successful.

Step S206, in the case that the target processing results are all hard decisions and none of the hard decisions are established, perform target processing on all other variable nodes in the target iterative decoding layer.

For example, in the case where the target iterative decoding layer has multiple variable nodes v0-v7, the variable node v0 completes the row and column operations of all check nodes (such as u0-u1) associated with it and completes the variable node v0. After all the information of v0 is updated, the hard decision of the variable node v0 is still not established, then the target processing is performed on the variable nodes v1-v7 in sequence until the hard decision is established or the update of all variable nodes is completed.

Step S208, in the case where the target processing result is that the hard decision is not established, the target processing is performed on the variable nodes corresponding to the variable nodes of the target iterative decoding layer in other iterative decoding layers in turn, until the hard decision is established; wherein, The variable nodes in the other iterative decoding layers corresponding to the variable nodes of the target iterative decoding layer include variable nodes in the other iterative decoding layers whose positions are the same as the positions of the variable nodes of the target iterative decoding layer.

In this embodiment, when there are multiple layers of iterative decoding layers and none of the hard decisions of the first layer hold, information iteration between the iterative decoding layers is performed.

Taking the iterative decoding layer of the LDPC code as an example, after completing the information obtained by a variable node of the first layer from all check nodes with the variable node, decoding and hard decision processing are performed on the variable node, When the decoding is unsuccessful, the information obtained by the variable node from all check nodes associated with the variable node is used as the initial value of the corresponding variable node of the second layer, and then the corresponding variable node of the second layer is used. The variable nodes of , perform column operations and row operations, and so on, until decoding is successful.

For example, in the first layer, the variable node v0 is respectively associated with the check nodes u0 and u1, then after calculating the information of the variable node v0 from the check nodes u0 and u1, update the variable node v0 to obtain the target variable node v0', then perform decoding and hard decision on the target variable node v0', in the case of unsuccessful decoding, take the information of the target variable node v0' as the initial information of the variable node v02 of the second layer, wherein, the variable node v02 corresponds to the variable node v0, and in the second layer, the variable node v02 is respectively associated with the check node u12, the check node u22 and the check node u32, and then performs row operations on the check node u12, and then the variable Node v02 performs the column operation, and then updates and decodes the variable node v02. If the decoding is successful, the iterative operation is stopped. Otherwise, the iterative calculation of the iterative decoding layer is continued, and the above actions are repeated until the decoding is successful.

Through the above steps, since only the information of the variable node and a single check node needs to be performed when performing column operations on the variable nodes, the amount of calculation is relatively small, which solves the problem of long time-consuming decoding and slow decoding convergence speed. The decoding efficiency of the error correction module.

Wherein, the execution subject of the above steps may be a base station, a terminal, etc., but is not limited thereto.

In an optional embodiment, before the target processing is performed on the variable nodes corresponding to the variable nodes of the target iterative decoding layer in other iterative decoding layers in sequence, the method further includes:

Step S2080, taking the column operation result of the variable node of the target iterative decoding layer as the initial value of the variable node corresponding to the variable node of the target iterative decoding layer in other iterative decoding layers.

In this embodiment, taking the iterative decoding layer of the LDPC code as an example, after completing the information obtained by a variable node of the first layer from all check nodes associated with the variable node, the variable node is decoded. code and hard decision processing, when the decoding is unsuccessful, the information obtained by the variable node from all check nodes associated with the variable node is used as the initial value of the corresponding variable node of the second layer, and then the second The variable node corresponding to the variable node of the layer performs column operation and row operation, and so on, until the decoding is successful.

For example, in the first layer, the variable node v0 is respectively associated with the check nodes u0 and u1, then after calculating the information of the variable node v0 from the check nodes u0 and u1, update the variable node v0 to obtain the target variable node v0', then perform decoding and hard decision on the target variable node v0', in the case of unsuccessful decoding, take the information of the target variable node v0' as the initial information of the variable node v02 of the second layer, wherein, the variable node v02 corresponds to the variable node v0, and in the second layer, the variable node v02 is respectively associated with the check node u12, the check node u22 and the check node u32, and then performs row operations on the check node u12, and then the variable Node v02 performs the column operation, and then updates and decodes the variable node v02. If the decoding is successful, the iterative operation is stopped. Otherwise, the iterative calculation of the iterative decoding layer is continued, and the above actions are repeated until the decoding is successful.

Wherein, in an optional embodiment, the column operation result of the target variable node of the target iterative decoding layer is used as the initial value of the variable node corresponding to the target variable node of the target iterative decoding layer in other iterative decoding layers. Values include:

Step S2084, perform column update on the check nodes of other iterative decoding layers, wherein the column update represents the mth layer in the nth iteration, and the information is transferred from the updated variable node of the m-1th layer to the mth layer. The check node corresponding to the layer;

During the information transfer of different iterative decoding layers, since the information of the variable node is related to the information of the check node, the information of the corresponding check node needs to be determined before the update transfer of the variable node.

In an optional embodiment, the calculation formula for column update of the check node of the target iterative decoding layer includes:

L ⁽ⁿ⁾ (q _ijm )=L ⁽ⁿ⁾ (Q _im-1 )-L ^(n-1) (r _ijm )

In the formula, i represents the variable node, j represents the check node; m represents the number of layers, n represents the number of iterations; L ⁽ⁿ⁾ (q _ijm ) represents the mth layer in the nth iteration, and the variable node i is sent to the check node. The log-likelihood value of the information of the test node j, L ⁽ⁿ⁾ (Q _im-1 ) represents the log-likelihood value of the information after updating the information of the variable node i in the m-1th layer in the nth iteration, L ^{( n-1)} (r _ijm ) represents the log-likelihood value of the information sent by the check node j to the variable node i at the mth layer in the n-1th iteration.

The above formula expresses the information passed from the updated variable node of the m-1th layer to the check node of the mth layer in the mth layer in the nth iteration; in the calculation of the mth layer in the nth iteration, by the When the updated variable node i of the m-1 layer transmits the information to the check node j of the m-th layer, it is necessary to first determine L ⁽ⁿ⁾ (q _ijm ), that is, in the calculation of the n-th iteration, the check node of the m-th layer is When j sends the log-likelihood value L(r) of the information to the variable node i, it needs to calculate L ⁽ⁿ⁾ (q _ijm ) before it can be calculated, so the information L ^(n-1) (r _ijm ) of the previous iteration is used. As L(r), and the information log-likelihood value L(Q) of the variable node i of the mth layer in the nth iteration after updating the information requires the log-likelihood of the information sent by the variable node i to the check node j. The natural value L(r) can only be obtained by adding the log-likelihood value L(q) of the information sent by the check node j to the variable node i, so L ⁽ⁿ⁾ (Q _im-1 ) of the m-1 layer is used as This time, L(Q) in L(q)=L(Q)-L(r) is calculated.

Step S2086, performing row update on the variable nodes of other iterative decoding layers, wherein the row update represents the mth layer in the nth iteration, and the check node of the mth layer transmits the updated information to the mth layer. the variable node;

After the update of the check nodes of the current layer is completed, row operations are subsequently performed on the variable nodes of the current layer to complete the update of the variable nodes.

In an optional embodiment, the calculation formula for row update of the variable node of the target iterative decoding layer includes:

Mode

, L ⁽ⁿ⁾ (r _ijm ) represents the log-likelihood value of the information sent by the check node to the target check node corresponding to the check node at the m-th layer in the n-th iteration; α is the normalized factor, in an optional embodiment, in order to reduce the hardware implementation complexity and maintain good decoding performance, α is usually set to 0.75.

The above formula represents the information transmitted to the _mth layer variable node after the mth layer and the mth layer check node in the ^nth iteration; , the value of sign(L ⁽ⁿ⁾ (q _ijm )) can be positive or negative. If the value of sign(L ⁽ⁿ⁾ (q _ijm )) is positive, the variable can be determined The element type of the node, if it is a negative value, it means that the element type of the variable node is another value; for example, taking the LDPC code as an example, since the LDPC code is a binary code, its elements only include 0 and 1, that is, the variable node The value of is 0 or 1; assuming that the probability that the element is 0 in the last iteration is 0.7, then in this iteration, the probability of getting 0 is 0.7 + positive value sign(L ⁽ⁿ⁾ (q _ijm ) ) or 0.7+ negative sign(L ⁽ⁿ⁾ (q _ijm )), so when sign(L ⁽ⁿ⁾ (q _ijm )) is positive, the probability that the element is 0 will become larger, sign(L When ⁽ⁿ⁾ (q _ijm )) is a negative value, the probability of the element being 0 will become smaller, so that the element type of the variable node can be determined. 0 or 1.

Step S2088, update the posterior probability information of the variable nodes of other iterative decoding layers, that is, at the mth layer in the nth iteration, update the posterior probability of the mth layer variable node according to the information of row update and column update information.

In an optional embodiment, according to the information of row update and column update, the calculation formula for updating the posterior probability information of the mth layer variable node includes:

L ⁽ⁿ⁾ (Q _im )=L ⁽ⁿ⁾ (r _ijm-1 )+L ⁽ⁿ⁾ (q _ijm )

In the formula, L ⁽ⁿ⁾ (Q _im ) represents the information log-likelihood value of the m-th layer in the n-th iteration, after the variable node completes the information update, where the information log-likelihood value is used to correct the information data ; In the formula, L ⁽ⁿ⁾ (q _ijm-1 ) is used instead of L ⁽ⁿ⁾ (q _ijm ), which can make the calculation faster, and can save computing resources during hardware implementation. Because, when calculating the m layer for n iterations, as long as L ⁽ⁿ⁾ (q _ijm ) of the current layer is calculated, L ⁽ⁿ⁾ (r _ijm-1 ) of the m-1 layer can be used for the variable node of the m-th layer. Calculate the posterior probability information of the variable node, so that you do not have to wait for the L ⁽ⁿ⁾ (r _ijm ) information of this layer to calculate the posterior probability information of the variable node. As for the L ⁽ⁿ⁾ (r _ijm ) obtained by this layer ) information is then used when performing the iterative calculation of the m+1th layer.

It should be noted that, in the m layer, after the information update of the variable node is completed, the hard decision and decoding of the variable node is performed. If the hard decision Hc ^T =0 is established, the decoding is correct and the iteration is terminated; otherwise, the variable The information of the node is passed to the next layer, that is, m=m+1 in the above formula, and column update and row update and row operation and column operation are performed.

If m exceeds the set number of H matrix layers and the correct decoding result is not obtained, the next iteration is performed, that is, n=n+1, m=1 in the above formula, and this cycle operates until the decoding is correct or the maximum iteration is reached frequency.

Step S2082, in an optional embodiment, before performing column update on the check node of the target iterative decoding layer, including:

The initial log-likelihood value of information is set, and the number of iterative decoding layers, that is, the number of iterations, is initialized to a preset value.

In an optional embodiment, the initialization formula is:

L ⁽⁰⁾ (Q _i )=L(P _i )=y _i ; m=1, n=1, in the formula, the initial information likelihood value is y _i , the difference between y _i and the sequence obtained after channel demodulation information, and the number of layers n and the number of iterations n are both set to 1.

From the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is better implementation. Based on this understanding, the technical solutions of the present invention can be embodied in the form of software products in essence or the parts that make contributions to the prior art, and the computer software products are stored in a storage medium (such as ROM/RAM, magnetic disk, CD-ROM), including several instructions to make a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) execute the methods described in the various embodiments of the present invention.

This embodiment also provides an apparatus for decoding a block error correction code, which is used to implement the above-mentioned embodiments and preferred implementations, and what has been described will not be repeated. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, implementations in hardware, or a combination of software and hardware, are also possible and contemplated.

FIG. 5 is a structural block diagram of an apparatus for decoding a block error correction code according to an embodiment of the present invention. As shown in FIG. 5 , the apparatus includes:

The decoding layer grouping module 52 is configured to determine the number of iterative decoding layers according to the size of the sub-matrix in the check matrix, wherein the check matrix is composed of a grouped error correction code sequence;

The target processing module 54 is configured to perform target processing on the target variable node associated with the target check node based on the target check node in the target iterative decoding layer, wherein the target processing includes:

The first line of operation is performed on the target check node associated with the target variable node in the target iterative decoding layer, and the first line of operation is used to calculate the information obtained by the target check node from all variable nodes associated with the target check node. ; According to the operation result of the first row operation, perform the first column operation on the target variable node, and the first column operation is used to calculate the information obtained by the target variable node from the target check node; According to the operation result of the first column operation, to The information of the target variable node is updated to determine the updated target variable node; according to the operation result of the first column of operations, a hard decision is made on the updated target variable node; Other target check nodes associated with the updated target variable node perform target processing on the updated target variable node;

The first iterative module 56 is configured to perform target processing on all other variable nodes in the target iterative decoding layer under the condition that the result of the target processing is all the hard decisions are not established;

The second iterative module 58 is configured to perform target processing on the variable nodes corresponding to the variable nodes of the target iterative decoding layer in the other iterative decoding layers in turn when the target processing results are all unsatisfactory hard decisions, until the hard decision is reached. The variable nodes in other iterative decoding layers corresponding to the variable nodes in the target iterative decoding layer include variable nodes in other iterative decoding layers whose positions are the same as those of the variable nodes in the target iterative decoding layer.

In an optional embodiment, the device further includes:

The information transmission unit 580 is configured to take the column operation result of the variable node of the target iterative decoding layer as the initial value of the variable node corresponding to the variable node of the target iterative decoding layer in other iterative decoding layers.

In an optional embodiment, the second iteration module 58 includes:

The column update unit 584 is configured to perform column update on the check nodes of other iterative decoding layers, wherein the column update represents the mth layer in the nth iteration, and the information is updated by the variable node of the m-1th layer. Pass it to the check node corresponding to the mth layer;

The row update unit 586 is configured to perform row update on the variable nodes of other iterative decoding layers, wherein the row update represents the mth layer in the nth iteration, and the check node of the mth layer transmits the updated information to variable nodes in the mth layer;

After the update of the check node of the current layer is completed, the row operation is performed on the variable node of the current layer in turn to complete the update of the variable node;

The posterior updating unit 588 is configured to update the posterior probability information of the variable nodes of other iterative decoding layers, that is, at the mth layer in the nth iteration, update the mth layer variable according to the information of row update and column update The posterior probability information of the node.

In an optional embodiment, the second iteration module 58 further includes:

The reset unit 582 is configured to set the initial log-likelihood value of information, and initialize the number of iterative decoding layers, that is, the number of iterations, to a preset value.

It should be noted that the above modules can be implemented by software or hardware, and the latter can be implemented in the following ways, but not limited to this: the above modules are all located in the same processor; or, the above modules can be combined in any combination The forms are located in different processors.

The present invention will be described below in conjunction with specific examples:

As shown in Figures 6 to 8, when the iterative decoding layer includes z1-z4 (corresponding to Figure 6), in the target iterative decoding layer z1, the check node includes u0-u3, the variable node includes v0-v6, The variable node v0 is associated with the check node u0, and the check node u0 is associated with the variable nodes v0, v2 and v3 (corresponding to FIG. 7 and FIG. 8 ).

Before the column calculation of the variable node v0, the row calculation of the check node u0 is performed first, and then the node update of the variable node is performed according to the column calculation result of the variable node v0, and the hard judgment is made on the column calculation result of the variable node v0. If the hard decision result is invalid or the update of the variable node v0 is completed, the row calculation is performed on the check node associated with the variable node v1, and then the column calculation is performed on the variable node, and the above actions are repeated until all the variable nodes are updated.

Then, when the hard decisions of the variable nodes included in the target iterative decoding layer z1 are not established, the information of the variable nodes included in the target iterative decoding layer z1 is corresponding to the target iterative decoding layer z2. The initial information of the check node corresponding to the code layer z1, and then perform row calculation and column calculation on the check node and variable node of the target iterative decoding layer z2, and repeat the above actions until the hard decision is established.

As shown in FIGS. 9 to 11, FIG. 9 is a decoding circuit of the present invention, FIG. 10 is a structural block diagram of the decoding circuit, and FIG. 11 is a flowchart of the decoding process performed by the decoding circuit; referring to FIG. 10, the decoding circuit include:

The first storage module 92 is configured to store the information log-likelihood values of the variable nodes of the target iterative decoding layer; wherein, the information log-likelihood values of each variable node of the target iterative decoding layer are stored in the first storage module 92 in the V2C_ii unit;

The second storage module 94 is configured to store the symbol, the minimum value, the second minimum value, and the minimum value position of the variable node information log-likelihood value in each row of each layer; wherein, when the position of the target variable node and the minimum value position When the same, the log-likelihood value of the variable node information in each row of each layer takes the next smallest value, otherwise takes the smallest value;

Optionally, the sign (sign), the minimum value (min1), the next minimum value (min2), and the minimum value position (min1_id) of the variable node information LLR value in each row of each layer are stored in the second storage module 94. in the C2V_ii unit.

The first calculation module 96 is set to calculate the log-likelihood value of the information after the update of the variable node;

The cyclic shifter 98 is set to exchange the position of the log-likelihood value of information;

The variable node updating module 1000 is configured to update the log-likelihood value of the information sent by the variable node stored in the second storage module 94 to the check node, and update the variable stored in the second storage module 94 after the update. The log-likelihood value of the information sent by the node to the check node is stored in the second storage module 94;

The second calculation module 1002 is configured to calculate the log-likelihood value of the information sent to the corresponding variable node after the update of the check node of the target iterative decoding layer;

The third calculation module 1004 is configured to feed back the position of the minimum value of the log-likelihood value of the information stored in the second storage module 94 to the cyclic shifter 98, and perform cyclic numerical difference calculation.

Referring to FIG. 11, when performing the decoding action, the following actions are performed in sequence:

Step S1002, calculating the updated LLR value of the variable node, and the hardware formula for performing the calculation is:

LLR_update=V2C_ii+C2V_ii_new_before_layer

Step S1004, use the cyclic shifter 98 (QSN) to carry out the LLR information position exchange, and the hardware formula that performs the calculation is:

LLR_shift=QSN(LLR)

Step S1006, update the information LLR value that the variable node in the V2C_ii unit passes to the check node, and then store the value in the V2C_ii unit, and the hardware formula for the calculation is:

V2C_update=LLR_shift-C2V_ii_old_last_iter

Step S1008, calculate the information LLR value transmitted to the current layer variable node after the current layer check node is updated, and the hardware formula for performing the calculation is:

C2V_update=sign_sort(V2C_update);

Step S1010, the position of the minimum value of the LLR value in the C2V_ii unit is fed back to the cyclic shifter 98 to calculate the cyclic value difference.

An embodiment of the present invention also provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, wherein the computer program is configured to execute the steps in any of the above method embodiments when running.

In an exemplary embodiment, the above-mentioned computer-readable storage medium may include, but is not limited to, a USB flash drive, a read-only memory (Read-Only Memory, referred to as ROM for short), and a random access memory (Random Access Memory, referred to as RAM for short) , mobile hard disk, magnetic disk or CD-ROM and other media that can store computer programs.

An embodiment of the present invention also provides an electronic device, comprising a memory and a processor, where a computer program is stored in the memory, and the processor is configured to run an arithmetic computer program to execute the steps in any of the above method embodiments.

In an exemplary embodiment, the above-mentioned electronic device may further include a transmission device and an input-output device, wherein the transmission device is connected to the above-mentioned processor, and the input-output device is connected to the above-mentioned processor.

For specific examples in this embodiment, reference may be made to the examples described in the foregoing embodiments and exemplary implementation manners, and details are not described herein again in this embodiment.

Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computing device, and they can be centralized on a single computing device or distributed in a network composed of multiple computing devices On the other hand, they can be implemented in program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, can be performed in a different order than shown here. Or the described steps, or they are respectively made into individual integrated circuit modules, or a plurality of modules or steps in them are made into a single integrated circuit module to realize. As such, the present invention is not limited to any particular combination of hardware and software.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention shall be included within the protection scope of the present invention.

Claims (10)

1. A method for decoding a block error correction code, comprising:

   The number of iterative decoding layers is determined according to the size of the sub-matrix in the check matrix, wherein the check matrix is composed of a block error correction code sequence;

   Target processing is performed on the target variable node associated with the target check node based on the target check node in the target iterative decoding layer, wherein the target processing includes:

   The first line of operation is performed on the target check node associated with the target variable node in the target iterative decoding layer, and the first line of operation is used to calculate the difference between the target check node and the target check node. The information obtained by all variable nodes associated with the test node, wherein the information includes the log-likelihood value of the target variable node; according to the operation result of the operation in the first row, the first column is performed on the target variable node. operation, the first column operation is used to calculate the information obtained by the target variable node from the target check node; according to the operation result of the first column operation, the information of the target variable node is updated to Determine the updated target variable node; according to the operation result of the first column operation, make a hard decision on the updated target variable node; in the case that the target processing result is that the hard decision does not hold, based on the other target check nodes associated with the updated target variable node perform the target processing on the updated target variable node;

   Under the circumstance that the result of the target processing is all the hard decisions are not established, the target processing is performed on all other variable nodes in the target iterative decoding layer;

   In the case that the target processing results are all hard decisions that fail to hold, the target processing is performed on the variable nodes corresponding to the variable nodes of the target iterative decoding layer in other iterative decoding layers in turn, until the hard decision is reached. Wherein, the variable nodes in the other iterative decoding layers corresponding to the variable nodes in the target iterative decoding layer include the position in the other iterative decoding layers and the position of the variable node in the target iterative decoding layer The same variable node.
2. The method according to claim 1, wherein before performing the target processing on the variable nodes corresponding to the variable nodes of the target iterative decoding layer in the other iterative decoding layers in sequence, the method further comprises: :

   The column operation result of the variable node of the target iterative decoding layer is used as the initial value of the variable node corresponding to the variable node of the target iterative decoding layer in other iterative decoding layers.
3. The method according to claim 2, wherein the column operation result corresponding to the target variable node of the target iterative decoding layer is regarded as the target variable node of the target iterative decoding layer in other iterative decoding layers. The initial values of the corresponding variable nodes include:

   A column update is performed on the check nodes of the other iterative decoding layers, wherein the column update is used for the mth layer in the nth iteration to transfer information from the updated variable node of the m-1th layer to the The check node corresponding to the mth layer;

   The row update is performed on the variable nodes of the other iterative decoding layers, wherein the row update is used for the mth layer in the nth iteration, and the updated information is transmitted to the mth layer by the check node of the mth layer. variable nodes in the m layer;

   Update the posterior probability information on the variable nodes of the other iterative decoding layers, wherein the posterior probability information update is used for the mth layer in the nth iteration, according to the row update and the column update information, update the posterior probability information of the mth layer variable node.
4. The method according to claim 3, wherein the performing column update on the check node of the target iterative decoding layer comprises:

   The column update is performed on the check node of the target iterative decoding layer by the following formula:

   L ⁽ⁿ⁾ (q _ijm )=L ⁽ⁿ⁾ (Q _im-1 )-L ^(n-1) (r _ijm )

   In the formula, i represents the variable node, j represents the check node; m represents the number of layers, n represents the number of iterations; L ⁽ⁿ⁾ (q _ijm ) represents the mth layer in the nth iteration, the variable node is sent to The information log-likelihood value of the check node, L ⁽ⁿ⁾ (Q _im-1 ) represents the m-1th layer in the n-th iteration, the information log-likelihood value of the variable node after updating the information , L ^(n-1) (r _ijm ) represents the mth layer in the n-1th iteration, the log-likelihood value of the information sent by the check node to the variable node.
5. The method according to claim 4, wherein the row update of the variable node of the target iterative decoding layer comprises:

   The row update is performed on the variable nodes of the target iterative decoding layer by the following formula:

   

   In the formula, L ⁽ⁿ⁾ (r _ijm ) represents the mth layer in the nth iteration, the log-likelihood value of the information sent by the check node to the target check node corresponding to the check node, which is expressed in At the mth layer in the nth iteration, L ⁽ⁿ⁾ (q _i'jm ) the log-likelihood values of information sent by other variable nodes to the check node.
6. The method according to claim 5, wherein the updating of the posterior probability information on the variable nodes of the other iterative decoding layers comprises:

   Update the posterior probability information of the m-th layer variable node according to the information of row update and column update according to the following formula:

   L ⁽ⁿ⁾ (Q _im )=L ⁽ⁿ⁾ (r _ijm-1 )+L ⁽ⁿ⁾ (q _ijm )

   In the formula, L ⁽ⁿ⁾ (Q _im ) represents the log-likelihood value of the information of the m-th layer in the n-th iteration after the information is updated by the variable node.
7. The method according to claim 3, wherein before performing column update on check nodes of other iterative decoding layers, the method comprises:

   The initial information log-likelihood value is set, and the number of iterative decoding layers and the number of iterations are initialized to preset values, wherein the initial information log-likelihood value is obtained according to the received signal value and channel characteristics, and the The initial information log-likelihood value represents the information log-likelihood value of the variable node that has not been iterated.
8. A block error correction code decoding device,

   The decoding layer grouping module is configured to determine the number of iterative decoding layers according to the size of the sub-matrix in the check matrix, wherein the check matrix is composed of a grouped error correction code sequence;

   The target processing module is configured to perform target processing on the target variable node associated with the target check node based on the target check node in the target iterative decoding layer, wherein the target processing includes:

   The first line of operation is performed on the target check node associated with the target variable node in the target iterative decoding layer, and the first line of operation is used to calculate the difference between the target check node and the target check node. The information obtained by all variable nodes associated with the test node; according to the operation result of the first row operation, the first column operation is performed on the target variable node, and the first column operation is used to calculate the target variable node. The information obtained from the target check node; according to the operation result of the first column operation, update the information on the target variable node to determine the updated target variable node; according to the operation result of the first column operation As a result of the operation, a hard decision is made on the updated target variable node; in the case that the target processing result is that the hard decision does not hold, the updated target variable node is based on other target check nodes associated with the target variable node. The updated target variable node performs the target processing;

   a first iterative module, configured to perform the target processing on all other variable nodes in the target iterative decoding layer under the condition that the target processing results are all hard decisions that fail to hold;

   The second zone module is configured to perform the target processing on the variable nodes corresponding to the variable nodes of the target iterative decoding layer in the other iterative decoding layers in turn when the target processing results are all invalid. processing until the hard decision is established; wherein, the variable nodes in the other iterative decoding layers corresponding to the variable nodes in the target iterative decoding layer include the position in the other iterative decoding layers and the target iterative decoding layer. The variable node of the code layer has the same position as the variable node.
9. A storage medium in which a computer program is stored, wherein the computer program is configured to execute the method of any one of claims 1 to 7 when run.
10. An electronic device comprising a memory and a processor with a computer program stored in the memory, the processor being arranged to run the computer program to perform the method of any one of claims 1 to 7.

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