CN112953554B

CN112953554B - LDPC decoding method, system and medium based on layered confidence propagation

Info

Publication number: CN112953554B
Application number: CN202110112815.9A
Authority: CN
Inventors: 黄巍; 陈住生; 胡峰; 熊连海; 程健
Original assignee: Wuhan Mengxin Technology Co ltd
Current assignee: Wuhan Mengxin Technology Co ltd
Priority date: 2021-01-27
Filing date: 2021-01-27
Publication date: 2022-12-13
Anticipated expiration: 2041-01-27
Also published as: CN112953554A

Abstract

The invention relates to an LDPC decoding method, system and medium based on layered confidence propagation, which obtains a check matrix corresponding to a code element sequence and a received signal sequence; carrying out initial hard decision on the received signal sequence, judging whether the initial decoding is successful, if so, outputting the initial hard decision value sequence as decoding and terminating the decoding; if not, self-defining iteration serial decoding parameters, and carrying out iteration serial decoding according to the received signal sequence, the check matrix and the iteration serial decoding parameters based on a confidence coefficient vector propagation method to obtain an iteration decision value sequence after each iteration; judging whether the serial decoding is successful under the corresponding current iteration times, if so, taking the iteration decision value sequence under the current iteration times as decoding output and stopping the decoding; if not, the iterative serial decoding is continued until the decoding is successful or the maximum iterative times are reached. The invention really realizes the serial message transmission, improves the convergence speed and reduces the resource occupation.

Description

LDPC decoding method, system and medium based on layered confidence propagation

Technical Field

The invention relates to the technical field of GNSS satellite communication, in particular to an LDPC decoding method, system and medium based on layered confidence propagation.

Background

In the technical field of GNSS satellite communication, LDPC decoding is generally implemented by using an extended min-sum algorithm. The method adopts a mechanism of transmitting messages in parallel, in each iteration process, all check nodes simultaneously receive messages transmitted from the connected variable nodes, after the update is finished, the updated messages of all check nodes are used for updating the messages of all variable nodes, and finally, each variable node is judged according to judgment conditions, and the steps are repeated until the ending conditions are met.

However, although the mechanism for transmitting messages in parallel adopted by the method is simple, the performance is not optimal, a large amount of storage resources are occupied in practical application, and more iterations are often needed to realize correct LDPC decoding.

In addition, because the nature of the LDPC decoding algorithm is that message scheduling can be performed, where scheduling refers to a message update order between a variable node and a check node in a decoding process of confidence propagation, and in the extended min-sum algorithm, the update order of the nodes is not involved, so that a message scheduling policy of the confidence propagation is not fully utilized, and thus serial message transmission cannot be implemented.

Therefore, an LDPC decoding method capable of being effectively applied to confidence propagation is required, which can adopt a mechanism of serially transmitting messages, select a proper node update sequence, and calculate messages to be transmitted by using updated information in time in each iteration process until all check nodes or variable nodes are sequentially updated, thereby improving the algorithm convergence speed and reducing the storage resource occupation.

Disclosure of Invention

The technical problem to be solved by the present invention is to provide an LDPC decoding method, system and medium based on hierarchical confidence propagation, which can adopt a mechanism of serially transmitting messages, select a proper node update sequence, and calculate the messages to be transmitted by using the updated information in time during each iteration until all check nodes or variable nodes are updated in sequence, thereby improving the algorithm convergence speed and reducing the storage resource occupation.

The technical scheme for solving the technical problems is as follows:

an LDPC decoding method based on layered belief propagation comprises the following steps:

step 1: acquiring a check matrix corresponding to the code element sequence and a received signal sequence;

step 2: carrying out initial hard decision on the received signal sequence to obtain an initial hard decision value sequence; judging whether the initial decoding is successful according to the check matrix and the initial hard decision value sequence, if so, executing the step 3; if not, self-defining the iterative serial decoding parameters and then executing the step 4;

and step 3: taking the initial hard decision value sequence as decoding output and stopping decoding;

and 4, step 4: performing iterative serial decoding according to the received signal sequence, the check matrix and the iterative serial decoding parameters based on a confidence vector propagation method to obtain an iterative decision value sequence after each iteration; successively judging whether serial decoding succeeds under the corresponding current iteration times according to the check matrix and the iteration decision value sequence after each iteration respectively, if so, taking the iteration decision value sequence under the current iteration times as decoding output and stopping decoding; if not, adding 1 to the current iteration number, and continuing to perform iteration serial decoding until the decoding is successful or the current iteration number reaches the maximum iteration number in the iteration serial decoding parameters;

if each row in the check matrix corresponds to a check node and each column in the check matrix corresponds to a variable node, then, in step 4, based on a confidence vector propagation method, performing iterative serial decoding according to the received signal sequence, the check matrix, and the iterative serial decoding parameters to obtain an iterative decision value sequence after each iteration, which specifically includes the following steps:

step 41: calculating to obtain an original confidence coefficient vector sequence according to the received signal sequence;

step 42: acquiring the current iteration times, selecting a check node corresponding to the ith row in the check matrix, enabling i =0, and updating all variable nodes corresponding to the selected ith row of check nodes according to a variable node updating rule; based on the confidence coefficient vector propagation method, respectively calculating to obtain a first target confidence coefficient vector sequence which is transmitted to the connected check nodes by each updated variable node corresponding to the ith row of check nodes selected under the current iteration times according to the iteration serial decoding parameters, the check matrix and the original confidence coefficient vector sequence;

step 43: after the calculation of the first target confidence coefficient vector sequences transmitted by all the updated variable nodes corresponding to the selected ith row of check nodes is completed, updating the selected ith row of check nodes according to a check node updating rule; based on the confidence coefficient vector propagation method, according to the iterative serial decoding parameters, the check matrix and all first target confidence coefficient vector sequences received by the selected ith row of check nodes under the current iteration times, respectively calculating to obtain second target confidence coefficient vector sequences which are transmitted to the connected variable nodes by the ith row of check nodes after being updated under the current iteration times;

and step 44: making i = i +1, and judging whether i reaches the maximum row number of the check matrix, if so, executing step 45, otherwise, returning to step 42;

step 45: after all variable nodes corresponding to all check nodes and all check nodes under the current iteration number are updated, calculating to obtain an iteration decision value sequence under the current iteration number according to the original confidence coefficient vector sequence and all second target confidence coefficient vector sequences.

According to another aspect of the present invention, there is also provided an LDPC decoding system based on hierarchical confidence propagation, which is applied to the LDPC decoding method based on hierarchical confidence propagation of the present invention, and includes a signal obtaining module, an initialization decoding module, a serial decoding module, and a decoding output module;

the signal acquisition module is used for acquiring a check matrix corresponding to the code element sequence and a received signal sequence;

the initialization decoding module is used for carrying out initial hard decision on the received signal sequence to obtain an initial hard decision value sequence; judging whether the initialization decoding is successful or not according to the check matrix and the initial hard decision value sequence;

the decoding output module is used for taking the initial hard decision value sequence as decoding output and stopping decoding when the initialization decoding module judges that the initialization decoding is successful;

the serial decoding module is used for performing iterative serial decoding according to the received signal sequence, the check matrix and the iterative serial decoding parameters based on a confidence vector propagation method when the initialization decoding module judges that initialization decoding fails, so as to obtain an iterative decision value sequence after each iteration; respectively and successively judging whether the serial decoding is successful under the corresponding current iteration times according to the check matrix and the iteration decision value sequence after each iteration;

the decoding output module is further configured to, when the serial decoding module determines that serial decoding is successful under the corresponding current iteration number, take the iteration decision value sequence under the current iteration number as a decoding output and terminate decoding;

the serial decoding module is also used for adding 1 to the current iteration times and continuing to perform iterative serial decoding when the serial decoding module judges that the serial decoding fails under the corresponding current iteration times, until the decoding is successful or the current iteration times reach the maximum iteration times in the iterative serial decoding parameters;

wherein each row in the check matrix corresponds to a check node, and each column in the check matrix corresponds to a variable node, then the serial decoding module is specifically configured to:

calculating to obtain an original confidence coefficient vector sequence according to the received signal sequence;

acquiring the current iteration times, selecting a check node corresponding to the ith row in the check matrix, enabling i =0, and updating all variable nodes corresponding to the selected ith row of check nodes according to a variable node updating rule; based on the confidence coefficient vector propagation method, respectively calculating to obtain a first target confidence coefficient vector sequence which is transmitted to the connected check nodes by each updated variable node corresponding to the ith row of check nodes selected under the current iteration times according to the iteration serial decoding parameters, the check matrix and the original confidence coefficient vector sequence;

after the calculation of the first target confidence coefficient vector sequences transmitted by all updated variable nodes corresponding to the selected ith row of check nodes is completed, updating the selected ith row of check nodes according to a check node updating rule; based on the confidence coefficient vector propagation method, respectively calculating to obtain a second target confidence coefficient vector sequence which is transmitted to the connected variable nodes by the ith row of check nodes after being updated under the current iteration times according to the iteration serial decoding parameters, the check matrix and all the first target confidence coefficient vector sequences which are received by the selected ith row of check nodes under the current iteration times;

enabling i = i +1, and judging whether i reaches the maximum row number of the check matrix;

if i does not reach the maximum line number of the check matrix, continuously updating all variable nodes corresponding to the updated ith row of check nodes according to the variable node updating rule, and respectively calculating to obtain a first target confidence coefficient vector sequence transmitted to the connected check nodes by each updated variable node corresponding to the updated ith row of check nodes under the current iteration times; updating the updated ith row of check nodes according to the check node updating rule, and respectively calculating to obtain a second target confidence coefficient vector sequence which is transmitted to the connected variable nodes by the ith row of check nodes updated again under the current iteration times; making i = i +1, and repeating the judgment until i reaches the maximum row number of the check matrix;

and if i reaches the maximum line number of the check matrix, after the variable nodes corresponding to all check nodes and all check nodes under the current iteration number are updated, calculating to obtain an iteration decision value sequence under the current iteration number according to the original confidence coefficient vector sequence and all second target confidence coefficient vector sequences.

According to another aspect of the present invention, there is provided a layered belief propagation based LDPC decoding system, comprising a processor, a memory and a computer program stored in the memory and executable on the processor, wherein the computer program is executable to implement the steps of the layered belief propagation based LDPC decoding method of the present invention.

In accordance with another aspect of the present invention, there is provided a computer storage medium comprising: at least one instruction which, when executed, implements a step in the layered belief propagation-based LDPC decoding method of the present invention.

The LDPC decoding method, system and medium based on layered confidence propagation of the invention have the advantages that: after a code element sequence is sent by a sending end, a corresponding check matrix and a received signal sequence transmitted by a channel can be obtained, the received signal sequence can be directly checked according to the check matrix, the received signal sequence is initialized and judged, whether the initialized hard judgment value sequence is correct decoding information or not can be judged according to the check matrix and the initialized hard judgment value sequence obtained by the initialized hard judgment, if so, the initialized hard judgment value sequence can be output as a decoding result, and if not, iterative serial decoding is carried out after iterative serial decoding parameters are defined; in the iterative serial decoding process, firstly, an original confidence coefficient vector sequence is obtained according to a received signal sequence, then, based on a confidence coefficient vector propagation method, in each iteration, a check node corresponding to the ith row in the check matrix is selected, i =0 is set, all variable nodes corresponding to the ith row of check nodes are updated according to a variable node update rule, in the row update process, a first target confidence coefficient vector sequence transmitted to the connected check nodes by each updated variable node is calculated, the check nodes corresponding to the row are updated according to the first target confidence coefficient vector sequence transmitted by the variable nodes connected with the check nodes and the check node update rule received by the check nodes corresponding to the row, and similarly, a second target confidence coefficient vector sequence transmitted to the connected variable nodes by the updated check nodes corresponding to the row can be obtained;

then, enabling i = i +1, and judging whether i reaches the maximum row number of the check matrix;

if i does not reach the maximum line number of the check matrix, continuing to update and calculate according to the methods of the step 42 and the step 43, making i = i +1, and repeating the judgment until i reaches the maximum line number of the check matrix;

if i reaches the maximum row number of the check matrix, updating and calculating under the current iteration times;

in each iteration process, after all variable nodes and all check nodes are updated, an iteration decision value sequence in each iteration process (namely under each current iteration number) can be obtained according to the original confidence coefficient vector sequence and all calculated second target confidence coefficient vector sequences; according to a judgment method similar to the initialization decoding, whether an iteration judgment value sequence obtained in each serial decoding process is a correct decoding result or not can be judged, if yes, the corresponding iteration judgment value sequence is used as a decoding result under the corresponding current iteration times to be output, if not, after the current iteration times are added by 1, the iterative serial decoding is continued until a correct decoding result is obtained and output or the maximum iteration times is reached, and the decoding is stopped, wherein when the maximum iteration times are reached and a correct decoding result is not obtained, the decoding is failed;

the LDPC decoding method, the system and the medium based on the hierarchical confidence propagation can obtain a proper node updating sequence by setting the variable node updating rule to update the variable nodes and setting the check node updating rule to update the check nodes in the updating process of each row, further fully utilize the message scheduling strategy of the confidence propagation to realize a mechanism of serially transmitting messages, timely utilize the updated information to calculate the messages to be transmitted in each iteration process until all the check nodes or the variable nodes are updated in sequence.

Drawings

Fig. 1 is a schematic flowchart of an LDPC decoding method based on hierarchical confidence propagation according to an embodiment of the present invention;

FIG. 2 is a schematic flow chart of step 4 according to a first embodiment of the present invention;

FIG. 3 is a flowchart illustrating the complete steps of LDPC decoding based on layered belief propagation according to a first embodiment of the present invention;

fig. 4 is a schematic structural diagram of an LDPC decoding system based on hierarchical belief propagation in a second embodiment of the present invention.

Detailed Description

The principles and features of this invention are described below in conjunction with the following drawings, which are set forth to illustrate, but are not to be construed to limit the scope of the invention.

The present invention will be described with reference to the accompanying drawings.

In an embodiment, as shown in fig. 1, an LDPC decoding method based on hierarchical belief propagation includes the following steps:

s1: acquiring a check matrix corresponding to the code element sequence and a received signal sequence;

s2: carrying out initial hard decision on the received signal sequence to obtain an initial hard decision value sequence; judging whether the initial decoding is successful according to the check matrix and the initial hard decision value sequence, if so, executing S3; if not, executing S4 after self-defining the iteration serial decoding parameters;

s3: taking the initial hard decision value sequence as decoding output and stopping decoding;

s4: based on a confidence vector propagation method, carrying out iterative serial decoding according to the received signal sequence, the check matrix and the iterative serial decoding parameters to obtain an iterative decision value sequence after each iteration; successively judging whether serial decoding succeeds under the corresponding current iteration times according to the check matrix and the iteration decision value sequence after each iteration respectively, if so, taking the iteration decision value sequence under the current iteration times as decoding output and stopping decoding; if not, adding 1 to the current iteration number, and continuing to perform iteration serial decoding until the decoding is successful or the current iteration number reaches the maximum iteration number in the iteration serial decoding parameters;

wherein each row in the check matrix corresponds to a check node, and each column in the check matrix corresponds to a variable node, as shown in fig. 2, then in S4, based on a confidence vector propagation method, performing iterative serial decoding according to the received signal sequence, the check matrix, and the iterative serial decoding parameters, to obtain an iterative decision value sequence after each iteration, specifically including the following steps:

s41: calculating to obtain an original confidence coefficient vector sequence according to the received signal sequence;

s42: acquiring the current iteration times, selecting a check node corresponding to the ith row in the check matrix, enabling i =0, and updating all variable nodes corresponding to the selected ith row of check nodes according to a variable node updating rule; based on the confidence coefficient vector propagation method, respectively calculating to obtain a first target confidence coefficient vector sequence which is transmitted to the connected check nodes by each updated variable node corresponding to the ith row of check nodes selected under the current iteration times according to the iteration serial decoding parameters, the check matrix and the original confidence coefficient vector sequence;

s43: after the calculation of the first target confidence coefficient vector sequences transmitted by all updated variable nodes corresponding to the selected ith row of check nodes is completed, updating the selected ith row of check nodes according to a check node updating rule; based on the confidence coefficient vector propagation method, respectively calculating to obtain a second target confidence coefficient vector sequence which is transmitted to the connected variable nodes by the ith row of check nodes after being updated under the current iteration times according to the iteration serial decoding parameters, the check matrix and all the first target confidence coefficient vector sequences which are received by the selected ith row of check nodes under the current iteration times;

s44: enabling i = i +1, judging whether i reaches the maximum row number of the check matrix, if so, executing S45, and if not, returning to S42;

s45: after all variable nodes corresponding to all check nodes and all check nodes under the current iteration number are updated, calculating to obtain an iteration decision value sequence under the current iteration number according to the original confidence coefficient vector sequence and all second target confidence coefficient vector sequences.

The LDPC (Low Density Parity Check Code) decoding is an error correction Code decoding method closest to the shannon limit, and a typical decoding method thereof is mainly a belief propagation decoding method, that is, a decoding method based on a belief vector.

In the LDPC decoding method based on hierarchical confidence propagation according to this embodiment, variable nodes are updated by setting a variable node update rule, and check nodes are updated by setting a check node update rule in each row update process, so that a proper node update order can be obtained, and further, a message scheduling policy of confidence propagation is fully utilized to implement a mechanism of serially transmitting messages.

It should be noted that, a sequence signal generated through LDPC encoding, that is, a navigation message, is included in a symbol sequence transmitted by a transmitting end, where the sequence signal includes multiple transmission symbols, and each transmission symbol includes multiple transmission information bits; therefore, the received signal sequence is obtained to include the same number of received symbols as the number of transmitted symbols (each received symbol includes a set of symbol information), and each received symbol includes the same number of symbol information as the number of transmitted information bits. Correspondingly, the number of initial hard decision vectors in the initial hard decision value sequence obtained subsequently is the same as the number of received symbols.

Preferably, in S1, obtaining a received signal sequence corresponding to the symbol sequence is specifically implemented as:

modulating the code element sequence and then transmitting the modulated code element sequence through a channel to obtain the received signal sequence;

the expression of the code element sequence is specifically as follows:

c＝(c ₀ ,c ₁ ,...c _j ,...,c _n-1 ),c _j ∈GF(q),q＝2 ^r ,0≤j＜n；

wherein c is the symbol sequence, c ₀ ,c ₁ ,...c _j ,...,c _n-1 Are all transmitted symbols in said sequence of symbols, c _n-m ,c _n-m+1 ,...,c _n-1 All the check code elements in the code element sequence are check code elements, n is the length of the code element sequence, m is the length of the check code element sequence, GF (q) represents a finite field, q represents the number of all possible values of the sending code elements in the code element sequence, and r is the bit width occupied by the sending code elements in the code element sequence;

the expression of the check matrix is specifically as follows:

h _i,j ∈GF(q),0≤i＜m,0≤j＜n；

wherein H is the check matrix, H _i,j The element of the ith row and the jth column in the check matrix H; in the check matrix H, there are m check nodes CN _i And n variable nodes VN _j Then the check matrix H is compared with the check node CN _i The collection of the columns of all the variable nodes connected is the check node CN _i Corresponding column sequence number set, and variable node VN in the check matrix H _j The collection of the rows to which all the check nodes connected with each other belong is a variable node VN _j Corresponding line sequence number sets;

check node CN _i Corresponding column sequence number set and variable node VN _j The expressions of the corresponding line sequence number sets are respectively:

wherein N is _i For checking node CN _i Corresponding set of column sequence numbers, M _j For variable nodes VN _j Corresponding set of line sequence numbers, h _i,j Not equal to 0 represents a check node CN _i And variable node VN _j Connecting;

the expression of the received signal sequence is specifically:

wherein y isThe received signal sequence, y ₀ ,y ₁ ,…,y _j ,…,y _n-1 Are all received symbols in said received signal sequence, MOD (c) stands for modulating said symbol sequence, n _noise For noise signal sequences in the received signal sequence, y _j,0 ,y _j,1 ,…,y _j,b ,…,y _j,r-1 Is the symbol information in the jth received symbol of the received signal sequence.

The code element sequence is a signal sequence of a sending signal of a sending end after being subjected to LDPC coding, a receiving signal sequence is obtained through modulation and channel transmission, and a subsequent receiving end calculates an initial hard decision value sequence or an iteration decision value sequence so as to realize decoding.

Each row in the check matrix represents a check node and each column represents a variable node, so that there are m check nodes CN _i And n variable nodes VN _j When a certain element h in the check matrix _i,j Not equal to 0 (i.e. non-zero element) represents the check node CN _i And variable node VN _j Connected so that any check node CN in the check matrix H is connected to _i The set of columns to which all the variable nodes connected belong is taken as a set, namely a check node CN _i Corresponding column sequence number set and any variable node VN in check matrix H _j The set of rows to which all the check nodes connected belong serves as the other set, namely the variable nodes VN _j The corresponding row sequence number set can help to obtain proper node updating times subsequently, and then node updating is carried out according to a proper node updating sequence, a message scheduling strategy of confidence coefficient propagation is fully utilized, a mechanism of serially transmitting messages is realized, in each iteration process, for any node, information transmitted by the node which is updated first in the node updating sequence is utilized to calculate the message to be transmitted by the node, and then all check nodes or variable nodes are updated in sequence, all updated information is transmitted for the next iteration, the convergence speed is effectively improved, and the resource occupation is effectively reduced; the method overcomes the defect that the traditional decoding method can only utilize the information in the last iteration process but can not utilize the section updated previously in the current iterationThe disadvantage of the information transmitted by the points.

Specifically, taking B1C of the beidou satellite No. three as an example, the check matrix of the GF (64) -LDPC code used in the subframe 2 is H (100, 200), that is, m =100, n =200, the H matrix has 100 check nodes, 200 variable nodes, the row weight dr =4, and the column weight dc =2; at a transmitting end, a code word sequence c comprises n =200 transmission symbols, each transmission symbol has r =6 transmission symbol information, and a jth transmission symbol c _j May be represented by 0 to 63 (000000 to 111111) and is an element of a finite field or Galois field GF (64); then at the receiving end, the corresponding received signal sequence is y, the jth original received symbol y of the received signal sequence is _j In which there are 6 symbol information y _j,b ，y _j Is represented by (y) _j,0 ,y _j,1 ,…,y _j,r-1 ) Where r =6.

Preferably, in S2, judging whether decoding is successful according to the check matrix and the initial hard decision value sequence, specifically including the following steps:

s21: calculating according to the check matrix and the initial hard decision value sequence to obtain an initial check sum;

the first formula for calculating the initial checksum is:

wherein v is _initial In order to be able to perform said initial checksum,

for the initial sequence of hard decision values, H ^T Is a transpose of the check matrix;

s22: substituting the initial checksum serving as a target checksum into a decoding success criterion, judging whether the target checksum meets the decoding success criterion, if so, judging that the initial decoding is successful, and executing S3; if not, judging that the initial decoding is unsuccessful, and executing S4;

the decoding success criterion is specifically as follows: v =0; wherein v is the target checksum;

in S2, the iterative serial decoding parameters include a maximum number of iterations and an initial hierarchical confidence vector that each check node transmits to a connected variable node, and each initial hierarchical confidence vector is a zero vector.

Whether the initial decoding is correctly decoded can be effectively judged by calculating the initial check sum and combining the decoding success criterion, so that on one hand, when the initial decoding is correct, the corresponding decoding result can be simply and directly output; on the other hand, when the initialization decoding is incorrect, a data basis is provided for the subsequent iteration serial decoding; the iterative serial decoding parameters are customized, so that termination conditions and initialization conditions of iterative serial decoding are conveniently provided, and the iterative serial decoding is conveniently and smoothly carried out; the initial hierarchical confidence vectors transmitted to the connected variable nodes by each check node in the iterative serial decoding parameters are all set as zero vectors, so that the information transmitted to the connected check nodes by the updated variable nodes in each iterative process can be sequentially calculated, the check nodes can be updated again according to the transmitted information, and the iterative serial decoding can be carried out smoothly.

Specifically, in this embodiment, the expression of the initial hard decision value sequence is specifically:

wherein the content of the first and second substances,

for the initial sequence of hard decision values,

are the initial hard decision symbols in the sequence of initial hard decision values.

Preferably, S41 specifically comprises the following steps:

s411: selecting any one receiving symbol in the receiving signal sequence, and acquiring an information amplitude value corresponding to each symbol information in the selected receiving symbol one to one;

s412: acquiring a finite field element sequence according to the finite field, selecting one finite field element in the finite field element sequence optionally, and calculating to obtain the log-likelihood ratio of the selected receiving symbol under the selected finite field element according to the selected finite field element and all the information amplitude values corresponding to the selected receiving symbol;

s413: traversing each finite field element in the finite field element sequence to obtain a log-likelihood ratio of the selected receiving symbol under each finite field element;

s414: obtaining an original confidence vector corresponding to the selected receiving symbol according to all the finite field elements and all the log-likelihood ratios of the selected receiving symbol;

s415: traversing each receiving symbol in the receiving signal sequence to obtain an original confidence coefficient vector corresponding to each receiving symbol one by one, and obtaining the original confidence coefficient vector sequence according to all the original confidence coefficient vectors.

Assuming that the noise mean of an additive white Gaussian noise channel is 0 and the variance is sigma ² Because the confidence vector corresponding to each corrected received symbol consists of finite field elements and corresponding log-likelihood ratios thereof, and the finite field element sequence comprises a plurality of finite field elements, for any received symbol, the one-to-one corresponding log-likelihood ratio of the selected received symbol under each finite field element needs to be calculated in the finite field element sequence, and finally the confidence vector corresponding to the selected received symbol is obtained according to all the finite field elements and all the log-likelihood ratios; based on the symbol information in the received symbols, the calculated log-likelihood ratio is more accurate, the accuracy of the confidence coefficient vector and the LDPC decoding performance are improved, and therefore the error rate after decoding is reduced.

In particular, for any chosen received symbol y _j Calculating the selected received symbol y _j In the selected finite field element x _t The following specific formula of log-likelihood ratio is:

wherein the content of the first and second substances,

for selected received symbols y _j In the selected finite field element x _t Log likelihood ratio of (x) _t,0 ,x _t,1 ,...x _t,b ,...x _t,r-1 Are all selected finite field elements x _t The number of bits of the element in (b),

for selected received symbols y _j Symbol information y in (1) _j,b Corresponding hard decision bit, XOR is XOR operation, sigma ² Is the noise variance;

when the selected received symbol y is calculated _j In the selected finite field element x _t Traversing each finite field element in the finite field element sequence to obtain the log-likelihood ratio of the selected receiving symbol under each finite field element, and then obtaining the selected receiving symbol y according to all the log-likelihood ratios of all the finite field elements and the selected receiving symbol _j The corresponding original confidence vector containing q elements is L _j The method comprises the following steps:

L _j ＝(x,LLR(x))；

the original confidence vectors of all the received symbols form an original confidence vector sequence; through the original confidence coefficient vector, the updated variable node is convenient to be transmitted to the first layered confidence coefficient vector of the connected check node in each iteration process of subsequent calculation, and the first layered confidence coefficient vector is convenient to be screened, ordered and the like subsequently according to the numerical value of the log likelihood ratio, so that the more accurate updated variable node VN is obtained _j And transmitting the first target confidence vector sequence to the connected check nodes to realize the ordered updating of the variable nodes and provide a data basis for the subsequent realization of the ordered updating of the check nodes, the hierarchical confidence propagation and the calculation of the confidence vectors.

Preferably, S42 specifically comprises the following steps:

s421: selecting a check node corresponding to the ith row in the check matrix, enabling i =0, and for any variable node VN corresponding to the selected ith row of check nodes _j To change the variable node VN _j The corresponding row sequence number sets are sorted in ascending order to obtain variable nodes VN _j A corresponding variable node update sequence;

s422: obtaining the current iteration number itr, and obtaining the current iteration number itr and the variable node VN _j Obtaining the corresponding variable node updating sequence and the variable node VN under the sliding iteration times _j Propagation confidence vectors transmitted by all connected inspection nodes; and according to variable node VN _j Corresponding variable node update sequence, according to variable node VN _j All propagation confidence vectors received are to the variable node VN _j Updating is carried out;

s423: according to the original confidence coefficient vector sequence and variable nodes VN _j Corresponding variable node update sequence and variable node VN _j Calculating all the received propagation confidence coefficient vectors to obtain the updated variable node VN under the current iteration times _j A first hierarchical confidence vector transmitted to each connected check node;

calculating the updated variable node VN under the current iteration number _j To connected check nodes CN _i The second formula for the first hierarchical confidence vector of (1) is:

where, itr is the current iteration number, V2C _itr,j→i For variable nodes VN updated at current iteration number _j To connected check nodes CN _i First hierarchical confidence vector of f _s For variable nodes VN _j The serial number of the s-th row in the corresponding variable node updating sequence, dc is the column weight of the check matrix, itr' is the number of sliding iterations depending on the current iteration number and the variable node updating sequence, C2V _{itr′,fs→j} For the number of sliding iterations, with the variable node VN _j Connected check nodes

To variable node VN _j Propagated confidence vector of propagation, L _j The original confidence coefficient vector corresponding to the jth received symbol in the original confidence coefficient vector sequence is obtained;

s424: the variable node VN updated at the current iteration number is determined according to the log-likelihood ratio value under the finite field element _j The elements in each transmitted first hierarchical confidence vector are respectively arranged in an ascending order, and the elements are respectively intercepted from the front end of each arranged first hierarchical confidence vector according to the preset element quantity, so that first screening confidence vectors corresponding to each first hierarchical confidence vector one to one are obtained;

calculating the updated variable node VN under the current iteration number _j To connected check nodes CN _i The third formula of the first screening confidence vector corresponding to the first hierarchical confidence vector is:

wherein, TV2C _itr,j→i For variable nodes VN updated at the current number of iterations _j To connected check nodes CN _i First hierarchical confidence vector V2C _itr,j→i A corresponding first screening confidence vector;

represents the first hierarchical confidence vector V2C _itr,j→i The elements in (2) are arranged in ascending order according to the magnitude of the log likelihood ratio value under the finite field elementAnd intercepting the ranked first hierarchical confidence vector V2C _itr,j→i Front end n _m Operation of individual elements;

s425: respectively obtaining the minimum log-likelihood ratio of elements in each first screening confidence coefficient vector, and respectively obtaining first target confidence coefficient vectors corresponding to each first screening confidence coefficient vector one to one according to each minimum log-likelihood ratio; and obtaining the variable node VN updated under the current iteration times according to all the first target confidence coefficient vectors _j A first target confidence vector sequence transmitted to the connected check nodes;

s426: and traversing each variable node corresponding to the ith row of check nodes selected from the check matrix to obtain a first target confidence coefficient vector sequence transmitted to the connected check nodes by each updated variable node corresponding to the ith row of check nodes selected under the current iteration times.

In S421, the line sequence number sets corresponding to any variable node are arranged in ascending order, so that each element in any variable node is updated according to the sequence of line numbers from small to large, that is, the appropriate variable node update sequence of the variable node is obtained, and update and message transmission are performed according to the appropriate variable node update sequence;

meanwhile, since the update of any variable node needs to apply the information of all check nodes connected with the variable node, if the check node connected with the variable node is updated in the update process of the variable node, the updated information needs to be applied, and if the update is not performed, the updated information is used according to the information or initialization information in the last iteration (according to the initialization information when the current iteration number is 1); after each element in any variable node is arranged according to the sequence of line numbers from small to large, hierarchical confidence vector propagation can be realized according to the ordered variable node updating sequence, and based on theoretical knowledge of confidence propagation, data in the process of calculating a first hierarchical confidence vector propagated from the updated variable node to the self-connected check node can be conveniently obtained, wherein the data is specifically the propagation confidence vector transmitted by each check node connected with the variable node under the number of sliding iterations;

e.g. variable nodes VN _j When variable node VN _j The s-th line sequence number f in the corresponding variable node update sequence _s When the number of the nodes is larger than i, the variable nodes are connected with the VN according to the variable node updating sequence under the current iteration number _j One of the connected check nodes

To variable node VN _j If the propagated confidence vector is not updated, then VN is determined at the variable node _j In the process of updating and confidence coefficient propagation, the test nodes connected in the last iteration process need to be detected

The transmitted propagation confidence coefficient vector is that the number of sliding iterations is reduced by 1 from the current number of iterations; when variable node VN _j The s-th line sequence number f in the corresponding variable node update sequence _s When the number of the variable nodes is less than i, the variable nodes are connected with the VN under the current iteration number according to the variable node updating sequence _j Connected check nodes

To variable nodes VN _j If the transmitted propagation confidence coefficient vector is updated, the propagation confidence coefficient vector needs to be checked according to the connected check nodes in the current iteration process

The transmitted propagation confidence coefficient vector is that the number of sliding iterations is the same as the current number of iterations; in the above process, at each current iteration time, the information transmitted by the node which has been updated earlier in the current iteration needs to participate in the updating process of the node which is updated later in the current iteration; therefore, the variable node update sequence in S421 can facilitate updating nodes according to a proper update sequence on the one hand, and can facilitate obtaining the propagation confidence vector under the number of sliding iterations in S422 on the other hand, so as to realize that the node updated earlier in the current iteration is updated laterThe new node propagates the information, thereby facilitating S423 to calculate an accurate first hierarchical confidence vector; compared with the traditional decoding method based on confidence coefficient vector propagation, the updated information can be fully utilized in each iteration, the serial message scheduling strategy is fully realized, the serial decoding is really realized, and the decoding efficiency is improved by improving the convergence speed;

in the second formula in S423,

the addition operation (including sigma and +) in the method is a basic operation of variable nodes, and the specific process is that each confidence coefficient vector is used

The LLR values (log likelihood ratio values) of the same elements are added, and the sum is calculated

And L _j The LLR values of the same elements are added, and the operation result V2C _itr,j→i The elements in (1) are arranged in ascending order according to the size of the symbol value of the finite field element;

in the third formula in S424,

represents the first hierarchical confidence vector V2C _itr,j→i The elements in (2) are arranged in ascending order according to the magnitude of the log-likelihood ratio value under the finite field element, and the arranged first hierarchical confidence vector V2C is intercepted _itr,j→i Front end n _m Operation of individual elements, this truncated n _m Each element is a finite field element, and n is _m The individual finite field elements are different from each other;

in S425, when each first screening confidence vector TV2C is obtained _itr,j→i Then, find each TV2C _itr,j→i Wherein the minimum value of LLR is LLR _min And each first screening confidence vector TV2C _itr,j→i Respectively subtracting LLR from the elements in (1) _min After that, namelyGet each first screening confidence vector TV2C _itr,j→i A one-to-one corresponding first target confidence vector, specifically TV2C' _itr,j→i 。

Preferably, S43 specifically comprises the following steps:

s431: for the selected ith row check node CN _i According to check node CN _i The corresponding column sequence number set obtains a check node CN _i A corresponding check node update sequence; and according to the check node CN _i Updating sequence of corresponding check node according to all the check nodes CN _i First target confidence vector pair check node CN passed by connected updated variable node _i Updating is carried out;

s432: according to the check matrix and the check node CN _i Corresponding check node update sequence and check node CN _i Calculating all the received first target confidence coefficient vectors to obtain updated check nodes CN under the current iteration times _i A second hierarchical confidence vector that is passed to each connected variable node;

calculating the updated check node CN under the current iteration times _i The fourth formula of the second hierarchical confidence vector passed to each connected updated variable node is:

wherein, TC2V _itr,i→j For check node CN updated under current iteration number _i A second hierarchical confidence vector, gamma being a check node CN, passed to each successive updated variable node _i The corresponding check node updates the sequence number in the sequence and meets the condition that gamma is not equal to j; h is _i,γ Is an element of the ith row and the gamma column in the check matrix H, TV2C' _itr,γ→i Is a and check node CN _i Connected updated variable node VN of _γ The first target confidence vector that is passed on,

is an element h _i,j Of (2), TV2C' _itr,γ→i ·h _i,γ Is TV2C' _itr,γ→i Q elements of (2), and TV2C' _itr,γ→i Q finite field elements of (1) are respectively associated with h _i,γ Carrying out finite field multiplication;

s433: for the check node CN updated under the current iteration number _i The second hierarchical confidence vectors transmitted to each connected updated variable node are respectively processed to obtain second target confidence vectors corresponding to each second hierarchical confidence vector one to one; and obtaining the updated check node CN under the current iteration number according to all the second target confidence coefficient vectors _i Transmitting the second target confidence coefficient vector sequence to the connected updated variable node;

calculating the updated check node CN under the current iteration times _i The fifth formula of the second target confidence vector corresponding to the second hierarchical confidence vector transferred to each connected updated variable node is:

C2V _itr,i→j ＝[(TC2V _itr,i→j ) _α ] _ext ；

wherein, C2V _itr,i→j For check nodes CN updated under current iteration times _i Transmitting the second target confidence coefficient vector corresponding to the second hierarchical confidence coefficient vector of each connected updated variable node; (.) _α Represents the second hierarchical confidence vector TC2V _itr,i→j In n _m The values of the log likelihood ratios of the elements are multiplied by a normalization factor alpha respectively; [. For] _ext Representative Capture (TC 2V) _itr,i→j ) _α The maximum log-likelihood ratio of the medium element is expanded according to the maximum log-likelihood ratio and a preset offset to obtain q-n _m After element, multiplying n by normalization factor alpha _m Element and extended q-n _m The elements are operated according to the ascending order of the symbol values of the elements.

At the selected ith row check node CN _i In the update process ofThe information passed by the application to all updated variable nodes connected thereto, i.e. the first target confidence vector TV2C' _itr,γ→i (ii) a Thus, in S431, CN is checked for any check node _i Can also be based on the check node CN _i Acquiring a check node updating sequence by corresponding column sequence number sets, thereby acquiring a proper check node updating sequence and realizing hierarchical confidence propagation of check nodes; in S432, the check matrix and all the received first target confidence vectors are combined to update the check nodes, and then the updated check node CN is calculated _i The second hierarchical confidence coefficient vector is transmitted to each connected variable node, so that the subsequent processing process is facilitated, and a second target confidence coefficient vector is obtained; in S433, when the updated check node CN is calculated _i Each corresponding second hierarchical confidence vector TC2V _itr,i→j Then, each second hierarchical confidence vector is respectively processed appropriately to obtain a second target confidence vector C2V corresponding to each second hierarchical confidence vector one to one _itr,i→j And thus facilitates the confidence vector C2V of all second targets _itr,i→j A second target confidence vector sequence is constructed.

In the fourth formula of S432, Σ (TV 2C' _itr,γ→i ·h _i,γ ) The summation operation (sigma) in the process is the basic operation of the check node, and the specific process is that for two n _m Long confidence vectors (each containing n) _m A finite field element and n _m Respective LLR values), performing finite field addition on finite field elements from different confidence degree vectors to obtain candidate elements, performing real number addition on the respective LLR values to obtain LLR values of the candidate elements, arranging the addition results in ascending order according to the LLR values of all the candidate elements, and intercepting front end n _m Each element being the result of the operation.

Specifically, in S433, TC2V is calculated for each second hierarchical confidence vector _itr,i→j The treatment carried out at the time is:

at any second hierarchical confidence vector TC2V _itr,i→j In (1), n is _m LLR values of each element are respectivelyMultiplying the normalization factor alpha to perform normalization processing; then n after normalization _m Obtaining the maximum value from LLR values of each element as LLR _max And presetting a preset offset as LLR _set Extend q-n _m A finite field element, and q-n _m LLR values of all finite field elements are LLRs _max +LLR _set (ii) a Finally, n multiplied by the normalization factor alpha _m Individual element sum homologation LLR _max +LLR _set Q-n of _m The elements are arranged in an ascending order according to the size of the symbol value of the finite field element to obtain a final second target confidence coefficient vector C2V _itr,i→j 。

The normalization factor is introduced to correct the LLR, so that the decoding performance can be improved, and the decoding accuracy is effectively improved under the condition of the same signal-to-noise ratio; meanwhile, the process can ensure that an accurate second target confidence coefficient vector and a second target confidence coefficient vector sequence are obtained, and an accurate data basis is provided for the judgment of each subsequent current iteration, so that the accuracy of iterative serial decoding is improved conveniently, and the efficiency in the iterative serial decoding is obviously improved.

Preferably, S45 specifically comprises the steps of:

s451: after all variable nodes corresponding to all check nodes and all check nodes are updated under the current iteration number, each updated variable node is judged once, and any updated variable node VN _j According to the original confidence coefficient vector sequence and the updated variable node VN _j Calculating the received second target confidence coefficient vectors transmitted by all the connected updated check nodes to obtain the updated variable node VN _j An iteration decision value under the current iteration number;

calculating an updated variable node VN _j The sixth formula of the iteration decision value at the current iteration number is:

wherein, the first and the second end of the pipe are connected with each other,

for updated variable nodes VN _j The iteration decision value argmin (-) under the current iteration times is an independent variable function taking the minimum value;

s452: traversing each updated variable node in the check matrix, and calculating to obtain an iteration decision value of each updated variable node under the current iteration times; obtaining an iteration decision value sequence under the current iteration times according to all iteration decision values;

in S4, successively determining whether serial decoding succeeds under the corresponding current iteration number according to the check matrix and the iteration decision value sequence after each iteration, specifically including the following steps:

s46: for the current iteration number itr, calculating to obtain a real-time checksum under the current iteration number itr according to the check matrix and the iteration decision value sequence under the current iteration number itr;

s47: substituting the real-time checksum under the current iteration number itr as the target checksum into the decoding success criterion, judging whether the target checksum meets the decoding success criterion, if so, judging that the serial decoding succeeds under the current iteration number itr, and outputting an iteration decision value sequence under the current iteration number itr as a decoding output and stopping the decoding; if not, judging that the serial decoding fails to decode under the current iteration number itr, adding 1 to the current iteration number itr, and continuing to perform the iterative serial decoding until the decoding is successful or the current iteration number itr reaches the maximum iteration number in the iterative serial decoding parameters.

For each current iteration number, after all variable nodes and check nodes are updated, each updated variable node is judged once, and any updated variable node VN _j Calculating an iteration decision value according to all second target confidence vectors and original confidence vector sequences which are received by the updated variable node and transmitted by the updated check node connected with the updated variable node; when each update is calculatedAfter the iteration decision values of the variable nodes are obtained, an iteration decision value sequence under the current iteration times is obtained according to the iteration decision values of all the variable nodes; during verification, similarly to the initialization decoding judgment method, the real-time checksum corresponding to the current iteration number is calculated and substituted into the decoding correctness criterion, so that whether the serial decoding is successful or not under the current iteration number can be judged.

Specifically, when it is determined that the serial decoding fails to decode under the current iteration count, if the current iteration count reaches the maximum iteration count, decoding failure information is output to declare that the decoding fails.

The complete flow of the LDPC decoding method in this embodiment is shown in fig. 3, and the complete flow is briefly described as follows:

initialization: setting the maximum number of iterations itr _max The current iteration number, itr =0. According to each received symbol y _j Make initial hard decision and calculate initial checksum

If v = v _initial =0, the initial decision value sequence is then repeated

Outputting as decoding and terminating the decoding; otherwise, calculating a confidence vector L _j (0 ≦ j < n), let itr =1, i =0;

step 1: for each variable node VN _j (j∈N _i ) Calculating a first hierarchical confidence vector V2C according to a variable node update rule _itr,j→i And a first target confidence vector TV2C' _itr,j→i ；

Step 2: to check node CN _i For j ∈ N _i Calculating a second hierarchical confidence vector TC2V according to the check node updating rule _itr,i→j And a second target confidence vector C2V _itr,i→j . Let i = i +1, if i = m, execute step 3; otherwise, executing the step 1;

and 3, step 3: computing a sequence of iterative decision values

And real-time checksum

If v = v _itr =0, then the sequence of decision values will be iterated

Outputting as decoding and terminating the decoding; otherwise, executing the step 4;

and 4, step 4: let itr = itr +1, i =0. If r>itr _max If yes, terminating the decoding and declaring the decoding failure; otherwise, turning to the step 1.

In the traditional minimum sum expansion algorithm, the updated information of the check node cannot be immediately transmitted in each iteration process, so that the minimum sum expansion algorithm can only be applied to the next iteration; by the LDPC decoding method based on hierarchical confidence propagation, the updated message of a certain check node in each iteration process can be immediately transmitted, and the updated message can be applied to the updating of the message of the connected variable node in the iteration, the convergence characteristic of the message is improved by the dependency relationship, the decoding complexity is not increased, but the iteration times required by algorithm convergence are reduced by half compared with the traditional minimum expansion and algorithm; and each iteration is sequentially processed according to a proper node updating sequence, and the check matrix is H (100, 200), namely m =100, n =200, the H matrix has 100 check nodes, 200 variable nodes, the row weight dr =4 and the column weight dc =2, so that the consumed storage resource is reduced by 1/4 compared with the traditional minimum sum of expansion algorithm.

An embodiment two, as shown in fig. 4, is an LDPC decoding system based on hierarchical belief propagation, applied to the LDPC decoding method based on hierarchical belief propagation of embodiment one, including a signal obtaining module, an initialization decoding module, a serial decoding module, and a decoding output module;

the serial decoding module is used for carrying out iterative serial decoding according to the received signal sequence, the check matrix and the iterative serial decoding parameters based on a confidence coefficient vector propagation method when the initialization decoding module judges that initialization decoding fails, so as to obtain an iterative decision value sequence after each iteration; respectively and successively judging whether the serial decoding is successful under the corresponding current iteration times according to the check matrix and the iteration decision value sequence after each iteration;

the serial decoding module is further configured to add 1 to the current iteration number when the serial decoding module itself determines that the serial decoding fails under the corresponding current iteration number, and continue the iterative serial decoding until the decoding is successful or the current iteration number reaches the maximum iteration number in the iterative serial decoding parameters;

acquiring the current iteration times, selecting a check node corresponding to the ith row in the check matrix, enabling i =0, and updating all variable nodes corresponding to the selected ith row of check nodes according to a variable node updating rule; based on the confidence coefficient vector propagation method, respectively calculating to obtain a first target confidence coefficient vector sequence which is transmitted to connected check nodes by each updated variable node corresponding to the ith row of check nodes selected under the current iteration times according to the iteration serial decoding parameters, the check matrix and the original confidence coefficient vector sequence;

after the calculation of the first target confidence coefficient vector sequences transmitted by all updated variable nodes corresponding to the selected ith row of check nodes is completed, updating the selected ith row of check nodes according to a check node updating rule; based on the confidence coefficient vector propagation method, according to the iterative serial decoding parameters, the check matrix and all first target confidence coefficient vector sequences received by the selected ith row of check nodes under the current iteration times, respectively calculating to obtain second target confidence coefficient vector sequences which are transmitted to the connected variable nodes by the ith row of check nodes after being updated under the current iteration times;

In the LDPC decoding system based on hierarchical confidence propagation of the embodiment, variable nodes are updated by setting variable node update rules, and check nodes are updated by setting check node update rules in each row of update process, so that a proper node update sequence can be obtained, further, a message scheduling strategy of confidence propagation is fully utilized, and a mechanism for serially transmitting messages is realized.

Details of the embodiment are not described in detail in the first embodiment and the specific descriptions in fig. 1 to 3, which are not repeated herein.

Third embodiment, based on the first embodiment and the second embodiment, the present embodiment further discloses an LDPC decoding system based on hierarchical belief propagation, including a processor, a memory, and a computer program stored in the memory and executable on the processor, where the computer program implements the specific steps of S1 to S4 when running.

Through a computer program stored on a memory and running on a processor, a message scheduling strategy of confidence coefficient propagation is fully utilized, a mechanism of serially transmitting messages is realized, in each iteration process, the messages to be transmitted are calculated by utilizing the updated information in time until all check nodes or variable nodes are updated in sequence, compared with the traditional decoding method (such as the minimum extension method), the decoding complexity is not increased, but the iteration frequency required by algorithm convergence can be effectively reduced by half, each iteration is sequentially processed according to the sequence, the consumed storage resources are greatly reduced, the algorithm convergence speed is obviously improved, and the storage resource occupation is greatly reduced.

The present embodiment also provides a computer storage medium, where at least one instruction is stored on the computer storage medium, and when executed, the instruction implements the specific steps of S1 to S4.

By executing a computer storage medium containing at least one instruction, a message scheduling strategy of confidence coefficient propagation is fully utilized, a mechanism of serially transmitting messages is realized, in each iteration process, updated information is timely utilized to calculate the messages to be transmitted until all check nodes or variable nodes are updated in sequence, compared with the traditional decoding method (such as the minimum extension method), the decoding complexity is not increased, but the iteration frequency required by algorithm convergence can be effectively reduced by half, each iteration is processed in sequence, the consumed storage resources are greatly reduced, the algorithm convergence speed is remarkably improved, and the storage resource occupation is greatly reduced.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.

Claims

1. An LDPC decoding method based on layered belief propagation is characterized by comprising the following steps:

step 2: carrying out initial hard decision on the received signal sequence to obtain an initial hard decision value sequence; judging whether the initialization decoding is successful according to the check matrix and the initial hard decision value sequence, if so, executing the step 3; if not, self-defining the iteration serial decoding parameters and then executing the step 4;

and 4, step 4: performing iterative serial decoding according to the received signal sequence, the check matrix and the iterative serial decoding parameters based on a confidence vector propagation method to obtain an iterative decision value sequence after each iteration; respectively and successively judging whether the serial decoding succeeds under the corresponding current iteration times according to the check matrix and the iteration judgment value sequence after each iteration, if so, taking the iteration judgment value sequence under the current iteration times as decoding output and stopping decoding; if not, adding 1 to the current iteration number, and continuing to perform iteration serial decoding until the decoding is successful or the current iteration number reaches the maximum iteration number in the iteration serial decoding parameters;

step 42: acquiring the current iteration times, selecting a check node corresponding to the ith row in the check matrix, enabling i =0, and updating all variable nodes corresponding to the selected ith row of check nodes according to a variable node updating rule; based on the confidence coefficient vector propagation method, respectively calculating to obtain a first target confidence coefficient vector sequence which is transmitted to connected check nodes by each updated variable node corresponding to the ith row of check nodes selected under the current iteration times according to the iteration serial decoding parameters, the check matrix and the original confidence coefficient vector sequence;

step 44: making i = i +1, and judging whether i reaches the maximum row number of the check matrix, if so, executing step 45, and if not, returning to step 42;

step 45: after all variable nodes corresponding to all check nodes and all check nodes under the current iteration number are updated, calculating to obtain an iteration decision value sequence under the current iteration number according to the original confidence coefficient vector sequence and all second target confidence coefficient vector sequences;

the step 41 specifically includes the following steps:

step 411: selecting any one receiving symbol in the receiving signal sequence, and acquiring an information amplitude value corresponding to each symbol information in the selected receiving symbol one by one;

step 412: acquiring a finite field element sequence according to a finite field, optionally selecting one finite field element in the finite field element sequence, and calculating to obtain the log-likelihood ratio of the selected receiving symbol under the selected finite field element according to the selected finite field element and all information amplitude values corresponding to the selected receiving symbol;

step 413: traversing each finite field element in the finite field element sequence to obtain a log-likelihood ratio of the selected receiving symbol under each finite field element;

step 414: obtaining an original confidence vector corresponding to the selected receiving symbol according to all the finite field elements and all the log-likelihood ratios of the selected receiving symbol;

step 415: traversing each received symbol in the received signal sequence to obtain an original confidence coefficient vector corresponding to each received symbol one by one, and obtaining the original confidence coefficient vector sequence according to all the original confidence coefficient vectors;

the step 42 specifically includes the following steps:

step 421: selecting a check node corresponding to the ith row in the check matrix, enabling i =0, and for any variable node VN corresponding to the selected ith row of check nodes _j To change the variable node VN _j Corresponding toThe row sequence number set is sorted in an ascending order to obtain a variable node VN _j Updating the sequence of the corresponding variable nodes;

step 422: obtaining the current iteration number itr, and obtaining the variable node VN according to the current iteration number itr _j Obtaining the corresponding variable node updating sequence and the variable node VN under the sliding iteration times _j Propagation confidence vectors transmitted by all connected inspection nodes; and according to variable node VN _j Corresponding variable node update sequence according to variable node VN _j All propagation confidence vectors received are to the variable node VN _j Updating is carried out;

the step 43 specifically includes the following steps:

step 431: for the selected ith row, checking node CN _i According to check nodes CN _i The corresponding column sequence number set obtains a check node CN _i Updating a sequence of corresponding check nodes; and according to the check node CN _i Updating sequence of corresponding check node according to all the check nodes CN _i First target confidence vector pair check node CN passed by connected updated variable node _i And (4) updating.

2. The LDPC decoding method based on layered belief propagation according to claim 1, wherein in the step 1, a received signal sequence corresponding to the symbol sequence is obtained, and is specifically implemented as follows:

the expression of the code element sequence is specifically as follows:

c＝(c ₀ ,c ₁ ,...c _j ,...,c _n-1 ),c _j ∈GF(q),q＝2 ^r ,0≤j＜n；

wherein c is the symbol sequence, c ₀ ,c ₁ ,...c _j ,...,c _n-1 Are all transmitted symbols in said sequence of symbols, c _n-m ,c _n-m+1 ,...,c _n-1 All are check code elements in the code element sequence, n is the length of the code element sequence, and m is the check code elementThe length of the sequence, GF (q) represents a finite field, q represents the number of all possible values of the sending code element in the code element sequence, and r is the bit width occupied by the sending code element in the code element sequence;

the expression of the check matrix is specifically as follows:

wherein H is the check matrix, H _i,j The element of the ith row and the jth column in the check matrix H; in the check matrix H, there are m check nodes CN _i And n variable nodes VN _j Then the check matrix H is compared with the check node CN _i The collection of the columns of all the variable nodes connected is a check node CN _i Corresponding column sequence number set, and variable node VN in the check matrix H _j The collection of the rows to which all the check nodes connected with each other belong is a variable node VN _j A corresponding line sequence number set;

the expression of the received signal sequence is specifically:

wherein y is the received signal sequence, y ₀ ,y ₁ ,...y _j ,...,y _n-1 Are received symbols in the received signal sequence, MOD (c) represents the modulation of the symbol sequence, n _noise For noise signal sequences in said received signal sequence, y _j,0 ,y _j,1 ,…,y _j,b ,…,y _j,r-1 Is the symbol information in the jth received symbol of the received signal sequence.

3. The LDPC decoding method based on hierarchical confidence propagation according to claim 2, wherein in the step 2, whether decoding is successful is judged according to the check matrix and the initial hard decision value sequence, and specifically includes the following steps:

step 21: calculating according to the check matrix and the initial hard decision value sequence to obtain an initial check sum;

the first formula for calculating the initial checksum is:

wherein v is _initial In order to be able to perform said initial checksum,

for said initial sequence of hard decision values, H ^T Is a transpose of the check matrix;

step 22: substituting the initial checksum serving as a target checksum into a decoding success criterion, judging whether the target checksum meets the decoding success criterion, if so, judging that the initial decoding is successful, and executing the step 3; if not, judging that the initialization decoding is unsuccessful, and executing the step 4;

the decoding success criterion is specifically as follows: v = v _initial =0; wherein v is the target checksum;

in step 2, the iterative serial decoding parameters include a maximum number of iterations and initial hierarchical confidence vectors that are transmitted to the connected variable nodes by each check node, and each initial hierarchical confidence vector is a zero vector.

4. The layered belief propagation-based LDPC decoding method of claim 1, wherein the step 42 further comprises the steps of:

step 423: according to the original confidence coefficient vector sequence and variable nodes VN _j Corresponding variable node update sequence and variable node VN _j Calculating all the received propagation confidence coefficient vectors to obtain the updated variable node VN under the current iteration times _j A first hierarchical confidence vector transmitted to each connected check node;

calculating the updated variable node VN under the current iteration number _j To connected check nodes CN _i The second formula for the first hierarchical confidence vector of (a) is:

where, itr is the current iteration number, V2C _itr,j→i For variable nodes VN updated at current iteration number _j To connected check nodes CN _i First hierarchical confidence vector of f _s For variable nodes VN _j The serial number of the s-th row in the corresponding variable node update sequence, dc is the column weight of the check matrix, itr' is the number of sliding iterations depending on the current number of iterations and the variable node update sequence,

for the number of sliding iterations, with variable node VN _j Connected check nodes CN _fs To variable node VN _j Propagated confidence vector, L, of the transfer _j For the original confidence vector sequenceM of the original confidence vector corresponding to the jth received symbol _j For variable nodes VN _j Corresponding line sequence number sets;

step 424: according to the value of the log likelihood ratio under the finite field element, the variable node VN updated under the current iteration number _j The elements in each transmitted first hierarchical confidence vector are respectively arranged in an ascending order, and according to the number of preset elements, the elements are respectively intercepted from the front end of each arranged first hierarchical confidence vector, so that first screening confidence vectors corresponding to each first hierarchical confidence vector one to one are obtained;

wherein, TV2C _itr,j→i For variable nodes VN updated at current iteration number _j To connected check nodes CN _i First hierarchical confidence vector V2C _itr,j→i A corresponding first screening confidence vector;

represents the first hierarchical confidence vector V2C _itr,j→i The elements in (1) are arranged in ascending order according to the magnitude of the log-likelihood ratio value under the finite field element, and the arranged first hierarchical confidence vector V2C is intercepted _itr,j→i Front end n _m Operation of individual elements;

step 425: respectively obtaining the minimum log-likelihood ratio of elements in each first screening confidence coefficient vector, and respectively obtaining first target confidence coefficient vectors corresponding to each first screening confidence coefficient vector one to one according to each minimum log-likelihood ratio; and obtaining the updated variable under the current iteration times according to all the first target confidence coefficient vectorsNode VN _j A first target confidence vector sequence transmitted to the connected check nodes;

step 426: and traversing each variable node corresponding to the ith row of check nodes selected from the check matrix to obtain a first target confidence coefficient vector sequence which is transmitted to the connected check nodes by each updated variable node corresponding to the ith row of check nodes selected under the current iteration times.

5. The layered belief propagation-based LDPC decoding method of claim 4, wherein the step 43 further comprises the steps of:

step 432: according to the check matrix and the check node CN _i Corresponding check node update sequence and check node CN _i Calculating all the received first target confidence coefficient vectors to obtain updated check nodes CN under the current iteration times _i Transmitting the second hierarchical confidence vector to each connected variable node;

wherein, TC2V _itr,i→j For check node CN updated under current iteration number _i A second hierarchical confidence vector, gamma for the check node CN, passed to each successive updated variable node _i The corresponding check node updates the sequence number in the sequence, and meets the condition that gamma is not equal to j; h is _i,γ Is an element of the ith row and the gamma column in the check matrix H, TV2C' _itr,γ→i Is a and check node CN _i Connected updated variable node VN of _γ The first target confidence vector that is passed on,

is an element h _i,j Of (2), TV2C' _itr,γ→i ·h _i,γ Is TV2C' _itr,γ→i Q elements of (2), and TV2C' _itr,γ→i Q finite field elements of (a) are respectively associated with h _i,γ Performing operations on finite field multiplication, N _i For checking node CN _i A corresponding column sequence number set;

step 433: for the check node CN updated under the current iteration number _i The second hierarchical confidence vectors transmitted to each connected updated variable node are respectively processed to obtain second target confidence vectors corresponding to each second hierarchical confidence vector one to one; and obtaining the updated check node CN under the current iteration times according to all the second target confidence coefficient vectors _i Transmitting the second target confidence coefficient vector sequence to the connected updated variable node;

C2V _itr,i→j ＝[(TC2V _itr,i→j ) _α ] _ext ；

wherein, C2V _itr,i→j For check nodes CN updated under current iteration times _i Transmitting the second target confidence coefficient vector corresponding to the second hierarchical confidence coefficient vector of each connected updated variable node; (.) _α Represents the second hierarchical confidence vector TC2V _itr,i→j In n _m The values of the log likelihood ratios of the elements are multiplied by a normalization factor alpha respectively; [. The] _ext Representative Capture (TC 2V) _itr,i→j ) _α The maximum log likelihood ratio of the medium elements is expanded according to the maximum log likelihood ratio and a preset offset to obtain q-n _m After element, multiplying n by normalization factor alpha _m Element and extended q-n _m The elements are operated in ascending order according to the symbol value of the elements.

6. The LDPC decoding method based on layered belief propagation of claim 5, wherein the step 45 specifically comprises the steps of:

step 451: after all variable nodes corresponding to all check nodes and all check nodes are updated under the current iteration number, each updated variable node is judged once, and any updated variable node VN _j According to the original confidence coefficient vector sequence and the updated variable node VN _j Calculating the received second target confidence vectors transmitted by all the connected updated check nodes to obtain the updated variable node VN _j An iteration decision value under the current iteration number;

computing an updated variable node VN _j The sixth formula of the iteration decision value at the current iteration number is:

wherein the content of the first and second substances,

for updated variable nodes VN _j The iteration decision value argmin (-) under the current iteration times is an independent variable function for taking the minimum value;

step 452: traversing each updated variable node in the check matrix, and calculating to obtain an iteration decision value of each updated variable node under the current iteration times; obtaining an iteration decision value sequence under the current iteration times according to all iteration decision values;

in the step 4, whether serial decoding succeeds under the corresponding current iteration times is successively judged according to the check matrix and the iteration decision value sequence after each iteration, which specifically includes the following steps:

step 46: for the current iteration number itr, calculating to obtain a real-time check sum under the current iteration number itr according to the check matrix and the iteration decision value sequence under the current iteration number itr;

step 47: substituting the real-time checksum under the current iteration number itr as a target checksum into a decoding success criterion, judging whether the target checksum meets the decoding success criterion, if so, judging that the serial decoding succeeds under the current iteration number itr, and outputting an iteration decision value sequence under the current iteration number itr as a decoding output and terminating the decoding; if not, judging that the serial decoding fails to decode under the current iteration number itr, adding 1 to the current iteration number itr, and continuing to perform the iterative serial decoding until the decoding is successful or the current iteration number itr reaches the maximum iteration number in the iterative serial decoding parameters.

7. An LDPC decoding system based on layered belief propagation is applied to the LDPC decoding method based on layered belief propagation according to any one of claims 1 to 6, and comprises a signal acquisition module, an initialization decoding module, a serial decoding module and a decoding output module;

the signal acquisition module is used for receiving a signal sequence by a check matrix corresponding to the code element sequence;

the serial decoding module is used for carrying out iterative serial decoding according to the received signal sequence, the check matrix and the iterative serial decoding parameters based on a confidence coefficient vector propagation method when the initialization decoding module judges that initialization decoding fails, so as to obtain an iterative decision value sequence after each iteration; respectively and successively judging whether serial decoding succeeds under the corresponding current iteration times according to the check matrix and the iteration judgment value sequence after each iteration;

if i does not reach the maximum line number of the check matrix, continuously updating all variable nodes corresponding to the updated ith row of check nodes according to the variable node updating rule, and respectively calculating to obtain a first target confidence coefficient vector sequence which is transmitted to the connected check nodes by each updated variable node corresponding to the updated ith row of check nodes under the current iteration times; updating the updated ith row of check nodes according to the check node updating rule, and respectively calculating to obtain a second target confidence coefficient vector sequence which is transmitted to the connected variable nodes by the ith row of check nodes updated again under the current iteration times; making i = i +1, and repeating the judgment until i reaches the maximum row number of the check matrix;

if i reaches the maximum line number of the check matrix, after the variable nodes corresponding to all check nodes and all check nodes under the current iteration number are updated, calculating to obtain an iteration decision value sequence under the current iteration number according to the original confidence coefficient vector sequence and all second target confidence coefficient vector sequences;

the serial coding module is also to:

selecting any one receiving symbol in the receiving signal sequence, and acquiring an information amplitude value corresponding to each symbol information in the selected receiving symbol one to one;

acquiring a finite field element sequence according to a finite field, selecting one finite field element in the finite field element sequence optionally, and calculating to obtain a log-likelihood ratio of the selected receiving symbol under the selected finite field element according to the selected finite field element and all information amplitudes corresponding to the selected receiving symbol;

traversing each finite field element in the finite field element sequence to obtain a log-likelihood ratio of the selected receiving symbol under each finite field element;

obtaining an original confidence vector corresponding to the selected receiving symbol according to all finite field elements and all log likelihood ratios of the selected receiving symbol;

traversing each received symbol in the received signal sequence to obtain an original confidence coefficient vector corresponding to each received symbol one by one, and obtaining the original confidence coefficient vector sequence according to all the original confidence coefficient vectors;

the serial coding module is also to:

selecting a check node corresponding to the ith row in the check matrix, enabling i =0, and for any variable node VN corresponding to the selected ith row of check nodes _j To change the variable node VN _j The corresponding row sequence number sets are sorted in ascending order to obtain variable nodes VN _j A corresponding variable node update sequence;

obtaining the current iteration number itr, and obtaining the current iteration number itr and the variable node VN _j Obtaining the corresponding variable node updating sequence and the variable node VN under the sliding iteration times _j Propagation confidence vectors transmitted by all connected inspection nodes; and according to variable node VN _j Corresponding variable node update sequence according to variable node VN _j All propagation confidence vectors received are to the variable node VN _j Updating is carried out;

the serial coding module is also to:

for the selected ith row check node CN _i According to check node CN _i The corresponding column sequence number set obtains the check node CN _i Updating a sequence of corresponding check nodes; and according to the check node CN _i Updating sequence of corresponding check node according to all the check nodes CN _i First target confidence vector pair check node CN transferred by connected updated variable node _i And (4) updating.

8. A layered belief propagation based LDPC decoding system comprising a processor, a memory and a computer program stored in the memory and executable on the processor, the computer program when executed implementing the method steps of any of claims 1 to 6.

9. A computer storage medium, the computer storage medium comprising: at least one instruction which when executed performs the method steps of any one of claims 1 to 6.