CN113824450A - Decoding method, decoder and storage medium of multi-element LDPC code - Google Patents

Abstract

The application discloses a decoding method, a decoder and a storage medium of a multi-element LDPC code. The method comprises the following steps: setting the maximum number of iterations I_maxAnd with the current iteration number k equal to 0, a first confidence vector V2C is initialized_j→i(ii) a Using the first confidence vector V2C_j→iUpdate the second confidence vector C2V_i→j(ii) a According to a check matrix H_mnChecking the code word sequence; if the verification is passed, outputting a decoding result; if the verification fails, a second confidence vector C2V is utilized_i→jUpdating the first confidence vector V2C_j→iAnd making the current iteration number k equal to k +1, and making the current iteration number k less than the maximum iteration number I_maxThen execution returns according to the first confidence vector V2C_j→iUpdate the second confidence vector C2V_i→jUntil the current iteration number k is equal to the maximum iteration number I_maxTo date, wherein the first confidence vector V2C_j→iIs updated according to linear adjustment and/or nonlinear adjustmentI is more than or equal to 0 and less than m, and j is more than or equal to 0 and less than n. The method and the device can improve an EMS algorithm, improve decoding gain, adapt to different front-end systems, and have good universality and adjustable configurable performance.

Description

Decoding method, decoder and storage medium of multi-element LDPC code

Technical Field

The present application relates to the field of encoding and decoding, and in particular, to a decoding method, a decoder, and a storage medium for a multi-element LDPC code.

Background

Low Density Parity Check (LDPC) codes are block error correcting codes with sparse Check matrices, applicable to almost all channels. The performance of the LDPC code is very close to Shannon (Shannon) limit, and has the characteristics of low decoding complexity, parallel decoding capability, and the like, and thus, the LDPC code has become a research hotspot in the communication field in recent years. In the existing decoding method of the multi-element LDPC code, the decoding complexity of the Extended-Min-Sum (EMS) algorithm based on the minimum Log Likelihood Ratio (LLR) is low, the space required for storage is small, the hardware implementation is very convenient, and the method has more applications in engineering practice. However, there is some loss in decoding gain in terms of its performance.

Disclosure of Invention

The embodiment of the application mainly aims to provide a decoding method, a decoder and a storage medium of a multi-element LDPC code, which can improve an EMS algorithm, improve decoding gain, adapt to different front-end systems, and have good universality and adjustable configurable performance.

In order to achieve the above object, an embodiment of the present application provides a method for decoding a multi-element LDPC code, including: setting the maximum number of iterations I_maxAnd with the current iteration number k equal to 0, a first confidence vector V2C is initialized_j→iWherein the first confidence vector V2C_j→iIs a check matrix H_mnEach variable node VN_jTo check nodes CN_iThe confidence vector of (2);

using the first confidence vector V2C_j→iUpdate the second confidence vector C2V_i→jWherein the second confidence vector C2V_i→jFor each check node CN_iTo variable nodes VN_jThe confidence vector of (2);

according to the check matrix H_mnChecking a sequence of codewords according to the first confidence vector V2C_j→iObtaining;

if the verification is passed, outputting a decoding result;

if the verification fails, the second confidence vector C2V is utilized_i→jUpdating the first confidence vector V2C_j→iAnd making the current iteration number k equal to k +1, and making the current iteration number k less than the maximum iteration number I_maxThen execution returns according to the first confidence vector V2C_j→iUpdating the second confidence vector C2V_i→jUntil the current iteration number k is equal to the maximum iteration number I_maxTo date, wherein the first confidence vector V2C_j→iIs updated according to the linear adjustment and/or the nonlinear adjustment, i is more than or equal to 0 and less than m, and j is more than or equal to 0 and less than n.

To achieve the above object, an embodiment of the present application provides a decoder, including: the device comprises an initialization module, an updating module, a checking module, a result output module and an iteration processing module;

an initialization module arranged to set a maximum number of iterations I_maxAnd with the current iteration number k equal to 0, a first confidence vector V2C is initialized_j→iWherein the first confidence vector V2C_j→iIs a check matrix H_mnEach variable node VN_jTo check nodes CN_iThe confidence vector of (2);

an update module arranged to utilize the first confidence vector V2C_j→iUpdate the second confidence vector C2V_i→jWherein the second confidence vector C2V_i→jFor each check node CN_iTo variable nodes VN_jThe confidence vector of (2);

a check module configured to check the matrix H_mnChecking a sequence of codewords according to the first confidence vector V2C_j→iObtaining;

the result output module is set to output a decoding result if the verification is passed;

an update module further configured to utilize the second confidence vector C2V if the verification fails_i→jUpdating the first confidence vector V2C_j→iWherein the first confidence vector V2C_j→iIs updated according to a linear adjustment and/or a nonlinear adjustment mode, i is more than or equal to 0 and less than m, and j is more than or equal to 0 and less than n;

the iteration processing module is set to enable the current iteration number k to be k +1 and enable the current iteration number k to be smaller than the maximum iteration number I_maxThen, returning to make the updating module execute according to the first confidenceDegree vector V2C_j→iUpdating the second confidence vector C2V_i→jUntil the current iteration number k is equal to the maximum iteration number I_maxUntil now.

To achieve the above object, an embodiment of the present application further provides a decoder, including: a processor for implementing the method of any of the above embodiments when executing the computer program.

To achieve the above object, the present application provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the method of any of the above embodiments.

The application provides a decoding method, a decoder and a storage medium of a multi-element LDPC code, which update a first confidence coefficient vector V2C by a linear adjustment and/or a non-linear adjustment mode_j→i. Thus, the linear adjustment method and the non-linear adjustment method can achieve good performance improvement, and the decoding gain is improved by 0.1dB to 0.2 dB. Therefore, the performance of the traditional minimum expansion and decoding algorithm can be improved, and the decoder adopts dual configuration of linear and nonlinear processing, can be adapted to different front-end systems to deal with different ADC capabilities of different systems, and has good universality and adjustable configurable performance.

With regard to the above embodiments and other aspects of the present application and implementations thereof, further description is provided in the accompanying drawings description, detailed description and claims.

Drawings

Fig. 1 is a flowchart illustrating a decoding method of a multi-element LDPC code according to an embodiment;

FIG. 2 is a block diagram illustrating an embodiment of determining a third confidence vector based on linear adjustment and/or non-linear adjustment

A flow diagram of the method;

FIG. 3 is a diagram illustrating simulation results of decoding according to an embodiment;

fig. 4 is a schematic structural diagram of a decoder according to an embodiment;

fig. 5 is a schematic structural diagram of another decoder according to an embodiment.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for the convenience of description of the present application, and have no peculiar meaning by themselves. Thus, "module", "component" or "unit" may be used mixedly.

The LDPC code is a grouped error correcting code with a sparse check matrix, the performance of the LDPC code is very close to Shannon (Shannon) limit, and the LDPC code has the characteristics of simple description and implementation, easy theoretical analysis and research, simple decoding, parallel operation implementation, suitability for hardware implementation and the like, and has become a research hotspot in the communication field in recent years. Current research on LDPC codes focuses mainly on two aspects: one is an irregular binary LDPC code, and when the code length is more than 10000, the performance of the code is very close to the Shannon limit; the other is a multi-element LDPC code with medium and short code length. The performance of the multi-element LDPC code is better than that of the binary LDPC code, and the LDPC code with excellent performance is usually an irregular multi-element LDPC code.

Due to the fact that the implementation is relatively simple and the performance is more excellent than that of the error correcting code which is mainstream before, the binary LDPC code is widely applied in practice, such as wire transmission and fifth generation wireless communication systems. It is undeniable that the multi-element LDPC code is a more excellent code, which has the following advantages over the binary LDPC code:

(1) under the condition that the code length is relatively short, the error code performance of the multi-element LDPC code is better: binary LDPC codes usually require very long code lengths (usually over 10000 bits) when reaching shannon limit performance, while multi-element LDPC codes can construct codes with very excellent performance of medium-long code lengths and even short code lengths. This makes the multiple LDPC code more valuable for engineering practice.

(2) Better error correction performance: belief Propagation (BP) decoding algorithm (a message passing algorithm which makes inference on a graph model) is equivalent to maximum a posteriori probability decoding in an acyclic graph, while the existence of a large number of small rings in an acyclic graph can seriously deteriorate the performance of the Belief Propagation decoding algorithm, and the elimination of the small rings is important for the correct convergence of iterative decoding. Since the multiple LDPC code combines a plurality of bits into a multiple symbol, the potential of eliminating a small ring in an LDPC code Tanner graph (representing an LDPC check matrix) is provided, and better error correction performance can be obtained.

(3) The burst error resistance is strong: errors generated in an actual channel are often burst errors or both burst errors and random errors, for example, in a wireless channel and a network channel, errors generated by the channel are often burst, a binary LDPC code does not have a strong capability of resisting burst errors, and a simultaneous Reed-Solomon (RS) code or BCH (Bose-Ray Chaudhuri-Hocquenghem) code concatenation is often required in an actual system. The multiple LDPC code is superior to the binary LDPC code in burst error resistance because it can combine multiple burst bit errors into fewer multiple symbol errors.

(4) Being suitable for high-rate transmission: the multi-element LDPC code is designed based on high-order finite field, so that the multi-element LDPC code is very suitable to be combined with a high-order modulation scheme and a multi-antenna system, thereby providing higher data transmission rate and spectral efficiency. By adopting the multi-element LDPC code, an efficient single/multi-layer coding modulation system can be designed.

In conclusion, the multi-element LDPC code has higher application value and application prospect. At present, the practical application value of the multi-element LDPC code is also explored, such as the combination with a high-order modulation system, the application in a multiple-in multiple-out (MIMO) channel, the application in satellite communication and deep space communication, and the like.

For LDPC decoding, the performance of LDPC code with extra-long code length in the multi-system GF (q) domain is close to Shannon limit, wherein q is the original length of the transferred confidence coefficient vector; meanwhile, the error code performance of the LDPC code with the medium and short code length is improved along with the increase of q, and the cost is that the decoding complexity is increased.

The decoding algorithm of the existing multivariate LDPC code mainly comprisesThe algorithm comprises a BP decoding algorithm, a BP algorithm based on Fast Fourier Transform (FFT) and an EMS algorithm, wherein the complexity of the former two algorithms is high, and particularly the BP decoding algorithm. The BP decoding algorithm based on the graph model is the most basic decoding algorithm of the multi-element LDPC code. The message that the largest difference between the BP decoder of a multi-element LDPC code and the BP decoder of a binary LDPC code is passed between its nodes is q probability metrics or q-1 log-likelihood ratio metrics. Therefore, the computation complexity of each check node is exponential to the value of q, which results in a considerable amount of computation for decoding when the order q of the finite field is large. So, the BP decoder can only be applied in situations where q is not very large. The calculation complexity of the check node can be reduced to O (qlog) by adopting the BP algorithm based on FFT₂q), although the complexity of the algorithm is greatly reduced, the algorithm uses exponential operation and multiplication operation of real numbers, which is not beneficial to hardware implementation.

The decoding complexity can be further reduced based on the log domain FFT-BP algorithm, the BP algorithm is simplified based on the EMS algorithm based on the log likelihood ratio, the complexity is reduced more, the space required to be stored is reduced a lot, the hardware implementation is very convenient, the application is more in the engineering practice, but in the performance aspect, certain loss is caused due to the simplification.

In order to solve the above problems, embodiments of the present application provide a decoding method, a decoder, and a storage medium for a multi-element LDPC code, which can improve an EMS algorithm, increase decoding gain, adapt to different front-end systems, and have good versatility and adjustable configurability.

The terms "system" and "network" are often used interchangeably in this application. The following embodiments of the present application may be implemented individually, or in combination with each other, and the embodiments of the present application are not limited specifically.

Hereinafter, a decoding method, a decoder, and a storage medium of a multi-element LDPC code, and technical effects thereof will be described.

The method provided by the embodiment is based on an EMS algorithm. In EMS Algorithm, a confidence vector of the transfer (as mentioned in the examples below)To the first confidence vector V2C_j→iAnd a second confidence vector C2V_i→j) Is reduced from q finite field elements to n_mA finite field element (n)_mQ), i.e. q is the first confidence vector delivered V2C_j→iAnd a second confidence vector C2V_i→jOf original length n_mIs the first confidence vector V2C of the transfer_j→iAnd a second confidence vector C2V_i→jThe actual length of (c). That is, only n with the smallest value of the log-likelihood ratio (hereinafter, abbreviated as LLR value) is retained in the confidence vector (i.e., the confidence is the highest)_mA finite field element.

The embodiment provides a decoding method of a multi-element LDPC code, which comprises the following steps:

setting the maximum number of iterations I_maxAnd with the current iteration number k equal to 0, a first confidence vector V2C is initialized_j→iWherein the first confidence vector V2C_j→iIs a check matrix H_mnEach variable node VN_jTo check nodes CN_iThe confidence vector of (2);

using the first confidence vector V2C_j→iUpdate the second confidence vector C2V_i→jWherein the second confidence vector C2V_i→jFor each check node CN_iTo variable nodes VN_jThe confidence vector of (2);

according to a check matrix H_mnChecking the sequence of code words according to a first confidence vector V2C_j→iObtaining;

if the verification is passed, outputting a decoding result;

if the verification fails, a second confidence vector C2V is utilized_i→jUpdating the first confidence vector V2C_j→iAnd making the current iteration number k equal to k +1, and making the current iteration number k less than the maximum iteration number I_maxThen execution returns according to the first confidence vector V2C_j→iUpdate the second confidence vector C2V_i→jUntil the current iteration number k is equal to the maximum iteration number I_maxTo date, wherein the first confidence vector V2C_j→iIs updated according to the linear adjustment and/or the nonlinear adjustment, i is more than or equal to 0 and less than m, and j is more than or equal to 0 and less than n.

Specifically, referring to fig. 1, fig. 1 shows a schematic flow chart of a decoding method of a multi-element LDPC code provided in an embodiment, where the method provided in this embodiment is applicable to a decoder, and the method includes the following steps.

S110, setting the maximum iteration number I_maxAnd let the current iteration number k equal to 0.

The code word sequence c ═ c (c) of multi-element LDPC generated by coder₀,c₁,…,c_n-1) After modulation, the transmission is processed to obtain a receiving sequence y (y) at a receiving end (e.g., a decoder)₀,y₁,…,y_n-1) Wherein, y_j＝(y_j,0,y_j,1,…,y_j,r-1) Is a code character number c_jCorresponding channel reception symbol information, c_j∈GF(q),q＝2^rJ is more than or equal to 0 and less than n. For a check matrix H of size mxn_mn，H_mnEach element h in (1)_i,jAre all elements in GF (q). Check matrix H_mnEach row in the system corresponds to a check node CN_iEach column corresponding to a variable node VN_j。

Check matrix H using multivariate LDPC code_mnThe received sequence y can be checked directly. Before checking, the maximum iteration number I needs to be set_maxAnd let the current iteration number k equal to 0. Maximum number of iterations I_maxThe value of (a) can be set according to actual needs, such as 50 times, 100 times and the like.

S120, initializing each variable node VN_jTo check nodes CN_iFirst confidence vector V2C_j→i。

If h_i,jNot equal to 0, the node CN is checked_iAnd variable node VN_jAre connected with each other and can transmit information with each other. By variable nodes VN_jTo connected check nodes CN_iIs denoted as V2C_j→iBy check nodes CN_iTo connected variable nodes VN_jIs denoted as C2V_i→jWherein i is more than or equal to 0 and less than m, and j is more than or equal to 0 and less than n.

Specifically, a first confidence vector V2C is initialized_j→iThe method of (3) may comprise: the decoder is based on the received symbol vector y_jComputing a confidence vector L for a received sequence_j(ii) a Using confidence vectors L according to variable node initialization rules_jInitializing a first confidence vector V2C_j→i。

It will be appreciated that the first confidence vector V2C_j→iIs a node comprising variables VN_jTo neighboring check nodes CN_iIs a set of vectors.

S130, utilizing the first confidence coefficient vector V2C_j→iUpdating each check node CN_iTo variable nodes VN_jSecond confidence vector C2V_i→j。

Specifically, the second confidence vector C2V is updated_i→jThe method of (3) may comprise: the decoder updates the rule according to the check node using the first confidence vector V2C_j→iUpdate the second confidence vector C2V_i→j。

For each check node CN_iReceiving the first confidence vector V2C transmitted by all the variable nodes connected with the variable nodes_j→iCalculating a second confidence vector C2V_i→j：

S140, according to the check matrix H_mnAnd checking the code word sequence.

Wherein the codeword sequence is based on the first confidence vector V2C_j→iAnd (4) obtaining the product.

Specifically, the method for checking the codeword sequence may include:

step 1: according to the first confidence vector V2C_j→iObtaining a sequence of codewords

Step 2: according to a check matrix H_mnCalculating a checksum

And step 3: it is determined whether the checksum is 0.

And S150, if the check is passed, outputting a decoding result.

Referring to the description in step S140, if the checksum S is equal to 0, it indicates that the decoding is successful (i.e., the checksum passes), and the codeword sequence is decoded

Output as a correct decoding result; otherwise, the following step S160 is continuously executed.

S160, if the verification fails, utilizing a second confidence coefficient vector C2V_i→jUpdating the first confidence vector V2C_j→i。

Wherein the first confidence vector V2C is updated_j→iThe method of (3) may comprise: the decoder updates the rule according to the variable node using the second confidence vector C2V_i→jUpdating the first confidence vector V2C_j→i。

Illustratively, the first confidence vector V2C is updated_j→iThe method can be realized by the following three steps:

step 1: if the current iteration number k is not equal to 0, determining a third confidence coefficient vector according to a linear adjustment and/or nonlinear adjustment mode

Wherein the third confidence vector

Is the second confidence vector C2V_i→jQ-n of_mA finite field element, q being a first confidence vector of propagation V2C_j→iAnd a second confidence vector C2V_i→jOf original length n_mIs the first confidence vector V2C of the transfer_j→iAnd a second confidence vector C2V_i→jThe actual length of (c).

Q-n as mentioned above_mOne finite field element is represented by a second confidence vector C2V_i→jDiscarding (also called clipping) q-n_mA finite field element, i.e. the q-n_mOne finite field element does not participate in the first confidence vector V2C_j→iThe update process of (1).

Step 2: according to the third confidence coefficient vector

Computing a first confidence vector V2C_j→i。

Wherein, assume C2V_f→jIs a connected check node CN_fDelivery to variable node VN_jA set of confidence vectors of size n_m(n_mQ). Using VN_jAll confidence vectors received C2V_f→j(f is belonged to M, f is not equal to i), and VN is calculated_jIs transmitted to CN_iFirst confidence vector V2C_j→i。

h_i,jIs a check matrix H_mnThe elements (A) and (B) in (B),

is h_i,jIs inverse (i.e. the

)，C2V_f→jFor checking node CN_fDelivery to variable node VN_jSet of confidence vectors of size n_m，

Is to use each confidence vector C2V_i→jAdding the values of the log-likelihood ratios of the same elements, L_jIn order to receive the confidence vector for the sequence,

the elements in the confidence coefficient vector are arranged in ascending order and the top n is selected_mA finite field element, and the first n_mThe elements of the finite field are different from each other, Rs_j→iIs front n_mA vector of finite field elements, R_j→iIs front n_mAnd the value vector of the log-likelihood ratio corresponding to each finite field element.

And step 3: determining a first confidence vector V2C_j→iMinimum value LLR of middle log likelihood ratio_minAnd the first confidence vector V2C_j→iSubtracting LLR from the value of the log-likelihood ratio of each element_min。

For step 1, the conventional method calculates the third confidence vector

The following formula is usually adopted:

offset is offset, and is greater than or equal to 0.

From the principle of LLR, the average amplitude of the LLR will tend to increase (towards higher SNR) or decrease (towards lower SNR) with the SNR, and then the addition of the fixed offset constitutes

The aggregate approach may bring performance loss (0.1-0.2dB unequal), so the fixed offset cannot give the same decoding performance throughout the whole operating snr interval, where the details have a significant impact on the performance of the minimum-sum algorithm. Therefore, the present application proposes a method for determining a third confidence vector according to a linear adjustment and/or a non-linear adjustment

The method can improve the EMS algorithm, improve the decoding gain and adapt to the EMS algorithmThe system is matched with different front-end systems, and has good universality and adjustable and configurable performance.

Specifically, referring to fig. 2, fig. 2 illustrates an embodiment of determining a third confidence vector according to a linear adjustment and/or a non-linear adjustment

A schematic flow diagram of a method comprising the following steps.

S210, determining a linear adjustment factor_linearAnd a nonlinear adjustment factor_non-linear。

S220, according to the linear adjustment factor_linearAdjusting the second confidence vector C2V_i→jAnd the maximum confidence vector in, and the factor according to non-linearity_non-linearAdjusting the offset to obtain an adjusted second confidence vector C2V_i→jThe maximum confidence coefficient vector and the adjusted offset in the process of the second confidence coefficient calculation are used for obtaining a third confidence coefficient vector

Wherein the third confidence vector

The following formula is used for calculation:

wherein, max (C2V)_i→j) Is a second confidence vector C2V_i→jThe largest confidence vector in, F_offset(C2V_i→j) Is an offset.

For number of elements n_mConfidence vector set of individuals

Extraction of

Two confidence vectors in a set

And

its superscript indicates that the element is at the second confidence level vector C2V_i→jIndex position in (2). To find the relative difference

Due to the fact that

The sets are sorted from small to large according to the confidence coefficient values, so

Is the minimum value, the relative difference is always greater than zero, and then the likelihood offset (positive value) is dynamically calculated by the natural logarithm | ln (·) |. Corresponding offset F_offset(C2V_i→j) The following formula is used for calculation:

wherein C2V_f→jIs a connected check node CN_fDelivery to variable node VN_jA set of confidence vectors of size n_m(ii) a Optionally, as elements of a decrement

Using the last element

For the linear adjustment method: with reference to the normalization principle, a linear adjustment factor is introduced_linearParticipate in

Assignment processing of collections, i.e. to

The maximum value in the set is linearly amplified. By combining a nonlinear real-time adjustment method, the method can be obtained

In one embodiment, the linear adjustment factor_linearIs greater than or equal to 1; nonlinear adjustment factor_non-linearIs greater than or equal to 0.

Linear adjustment factor_linearIs chosen in view of the implementation application, and takes the value of

Where N is a finite number of sets of non-zero positive integers. The specific value is related to a multi-element LDPC check matrix applied in practice and can be obtained through simulation; the processing process is conveniently realized by adopting a shift addition mode, so that compared with the traditional EMS algorithm, excessive complexity increase is not caused; due to the linear characteristic of the algorithm, the performance of the system is relatively stable and uniform in the whole working range, and meanwhile, the performance of an analog-to-digital converter (ADC) of a front-end system is not excessively required.

Nonlinear adjustment factor_non-linearThe method is mainly used for controlling the enabling of the nonlinear part, and the typical value is {0,1 }. Nonlinear adjustment factor_non-linearThe value of (a) may also be other non-zero positive real numbers, which are used to adjust the correction weight of the nonlinear part, and this application does not specifically limit this.

In one embodiment, the linear adjustment factor is used as the linear adjustment factor_linearValue of 1 and nonlinear adjustment factor_non-linearWhen the value of (1) is 1, the linear adjustment is closed, and the nonlinear adjustment is opened;

when the non-linear adjustment factor is_non-linearWhen the value of (1) is 0, the non-linear adjustment is closed, and the linear adjustment is opened.

In addition, by adopting a nonlinear adjustment method, dynamic offset operation can be carried out in real time according to the received LLR, and performance change caused by using offset in different signal-to-noise ratio intervals is avoided. The calculation of the non-linear processing ln (-) can be done in the form of a look-up table.

According to the scheme provided by the application, no matter the linear adjustment method or the nonlinear adjustment method is adopted, good performance improvement is achieved, the decoding gain is improved by 0.1dB to 0.2dB, and the nonlinear adjustment method has better performance in a high signal-to-noise ratio range. Under some unstable transmission scenes, by means of flexible adjustable and configurable characteristics, various modes of linear adjustment, nonlinear adjustment and linear and nonlinear mixed adjustment can be realized.

Compared with the prior art, the performance of the traditional minimum expansion and decoding algorithm can be improved, and the decoder adopts the double configuration of linear and nonlinear processing, can also adapt to different front-end systems to deal with different ADC capabilities of different systems, and has good universality and adjustable configurable performance.

S170, enabling the current iteration number k to be k +1, and judging whether the current iteration number k is smaller than the maximum iteration number I_max。

If the checksum s ≠ 0, it indicates a codeword sequence

Not correctly decoded information, the iterative decoding needs to be continued. At the moment, adding one to the current iteration times, and judging whether the current iteration times k are less than the maximum iteration times I_max。

S180, if the current iteration number k is equal to the maximum iteration number I_maxThen decoding is terminated and decoding failure is declared.

The current iteration number k is equal to the maximum iteration number I_maxIf so, the iterative decoding can not be continued, the decoding is terminated and the decoding failure is declared.

S190, if the current iteration number k is smaller than the maximum iteration number I_maxThen, the process returns to step S130.

Illustratively, the decoding method of the multivariate LDPC code provided by the present application is described with reference to an LDPC code on GF (64) adopted in the beidou system.

In the Beidou system, the B-CNAV2 navigation message is coded by 64-system LDPC (96,48), each code character number of the B-CNAV2 navigation message is also composed of 6 bits, and the B-CNAV2 navigation message is defined by a primitive polynomial p (x) which is 1+ x⁶Finite field GF (2)⁶). The mapping of the multi-system symbols and the binary bits adopts a vector representation method, and the high order is in front. The information length is k-48 codeword symbols, i.e., 288 bits. The check matrix is a sparse matrix H of 48 × 96_48,96The primitive polynomial is defined as p (x) 1+ x⁶Finite field GF (2)⁶) The first 48 × 48 part corresponds to information symbols, the second 48 × 48 part corresponds to check symbols, and the code rate is 1/2.

First, log-likelihood ratio (LLR) values used by a decoder are obtained from received symbols obtained by a receiver. Let the Noise mean of an Additive White Gaussian Noise (AWGN) channel be 0 and the variance be σ². According to the received symbol vector y corresponding to each code character number_jComputing a confidence vector L for a received sequence_j. All q finite field elements x ∈ GF (q) and the corresponding log-likelihood ratios LLR (x) form a confidence vector L_jWherein L is_jThe first element 0 ≦ l < q is composed of the first finite field symbol x and its LLR value. Confidence vector L for a received sequence_jThe log-likelihood ratio of the mid-finite field symbol x is:

wherein r is log₂(q)，

Is the order probability P (y) in GF (q)_j| x) the largest finite field element, i.e. directly on the received symbol y_j＝(y_j，0,y_j，1,...,y_j，r-1) And carrying out hard decision bit by bit to obtain elements. Finite field element x and

the corresponding bit sequences are x ═ x (x) respectively₀,x₁,...,x_r-1) And

confidence vector L of transmission in extended min-sum decoding algorithm_jIs reduced from q finite field elements to n_mA finite field element (n)_mQ). I.e. only the n with the minimum LLR value (i.e. the highest confidence) is kept in the confidence vector_mA finite field element. In the present embodiment, n_m＝16。

1. Setting the maximum number of iterations I_maxLet the current iteration number k be 0, 50.

2. From the received symbol vector y_jComputing a confidence vector L for a received sequence_j(ii) a Using confidence vectors L according to variable node initialization rules_jInitializing a first confidence vector V2C_j→i。

3. Using the first confidence vector V2C_j→iUpdate the second confidence vector C2V_i→j。

4. According to a check matrix H_mnChecking the sequence of code words according to a first confidence vector V2C_j→iAnd (4) obtaining the product.

5. And if the check is passed, outputting a decoding result.

6. If the verification fails, a second confidence vector C2V is utilized_i→jUpdating the first confidence vector V2C_j→i。

7. Setting the current iteration number k to k +1, and judging whether the current iteration number k is less than the maximum iteration number I_max。

8. If the current iteration number k is equal to the maximum iteration number I_maxThen decoding is terminated and decoding failure is declared.

9. If the current iteration number k is less than the maximum iteration number I_maxThen the step 3 is executed.

For 6 above, the first confidence vector V2C is updated_j→iThe step (b) may comprise:

suppose C2V_f→jIs a connected check node CN_fDelivery to variable node VN_jA set of confidence vectors of size n_m(n_mQ). Using VN_jAll confidence vectors received C2V_f→j(f is belonged to M, f is not equal to i), and VN is calculated_jIs transmitted to CN_iConfidence vector V2C_j→i。

Wherein h is_i,jIs a check matrix H_mnThe elements (A) and (B) in (B),

is h_i,jIs inverse (i.e. the

)，C2V_f→jFor checking node CN_fDelivery to variable node VN_jSet of confidence vectors of size n_m，

Is to use each confidence vector C2V_i→jAdding the values of the log-likelihood ratios of the same elements, L_jIn order to receive the confidence vector for the sequence,

the elements in the confidence coefficient vector are arranged in ascending order and the top n is selected_mA finite field element, and the first n_mThe elements of the finite field are different from each other, Rs_j→iIs front n_mA vector of finite field elements, R_j→iIs front n_mAnd the value vector of the log-likelihood ratio corresponding to each finite field element.

According to linear adjustment and/orDetermining a third confidence vector in a non-linearly adjusted manner

Wherein, max (C2V)_i→j) Is a confidence vector C2V_i→jThe largest confidence vector in, F_offset(C2V_i→j) In order to be offset in the amount of the offset,

for the linear adjustment mode: factor_linear＝1.25；factor_non-linear＝0。

I.e. the third confidence vector

For the non-linear adjustment mode: factor_linear＝1；factor_non-linear＝1；k＝15。

I.e. the third confidence vector

At each confidence vector V2C_j→iAfter the calculation is completed, the confidence vector V2C is determined_j→iMinimum value LLR of middle log likelihood ratio_minAnd the confidence vector V2C_j→iSubtracting LLR from the value of the log-likelihood ratio of each element_min。

After the decoding process is performed by the above method, the simulation result is shown in fig. 3. Fig. 3 is a diagram illustrating a decoding simulation result according to an embodiment. It can be seen that the improved gain of more than 0.1dB is obtained by either the linear adjustment method or the nonlinear adjustment method, and meanwhile, the performance curve of the nonlinear mode in the high signal-to-noise ratio interval has better slope performance, and the increased complexity is within an acceptable range.

The embodiment of the application provides a decoding method of a multi-element LDPC code, which comprises the following steps: setting the maximum number of iterations I_maxAnd with the current iteration number k equal to 0, a first confidence vector V2C is initialized_j→iWherein the first confidence vector V2C_j→iIs a check matrix H_mnEach variable node VN_jTo check nodes CN_iThe confidence vector of (2); using the first confidence vector V2C_j→iUpdate the second confidence vector C2V_i→jWherein the second confidence vector C2V_i→jFor each check node CN_iTo variable nodes VN_jThe confidence vector of (2); according to a check matrix H_mnChecking the sequence of code words according to a first confidence vector V2C_j→iObtaining; if the verification is passed, outputting a decoding result; if the verification fails, a second confidence vector C2V is utilized_i→jUpdating the first confidence vector V2C_j→iAnd making the current iteration number k equal to k +1, and making the current iteration number k less than the maximum iteration number I_maxThen execution returns according to the first confidence vector V2C_j→iUpdate the second confidence vector C2V_i→jUntil the current iteration number k is equal to the maximum iteration number I_maxTo date, wherein the first confidence vector V2C_j→iIs updated according to the linear adjustment and/or the nonlinear adjustment, i is more than or equal to 0 and less than m, and j is more than or equal to 0 and less than n. The method and the device can improve an EMS algorithm, improve decoding gain, adapt to different front-end systems, and have good universality and adjustable configurable performance.

Fig. 4 is a schematic structural diagram of a decoder according to an embodiment, and as shown in fig. 4, the decoder includes: the device comprises an initialization module 10, an updating module 11, a checking module 12, a result output module 13 and an iteration processing module 14.

An initialization module 10 arranged to set a maximum number of iterations I_maxAnd with the current iteration number k equal to 0, a first confidence vector V2C is initialized_j→iWherein the first confidence vector V2C_j→iIs a check matrix H_mnEach variable of (1)Node VN_jTo check nodes CN_iThe confidence vector of (2);

an update module 11 arranged to use the first confidence vector V2C_j→iUpdate the second confidence vector C2V_i→jWherein the second confidence vector C2V_i→jFor each check node CN_iTo variable nodes VN_jThe confidence vector of (2);

a check module 12 arranged to check the matrix H_mnChecking the sequence of code words according to a first confidence vector V2C_j→iObtaining;

a result output module 13 configured to output a decoding result if the verification passes;

the update module 11 is further configured to utilize a second confidence vector C2V if the verification fails_i→jUpdating the first confidence vector V2C_j→iWherein the first confidence vector V2C_j→iIs updated according to a linear adjustment and/or a nonlinear adjustment mode, i is more than or equal to 0 and less than m, and j is more than or equal to 0 and less than n;

the iteration processing module 14 is configured to, if the check fails, make the current iteration number k equal to k +1, and make the current iteration number k less than the maximum iteration number I_maxReturning to having the update module 10 execute a vector based on the first confidence level V2C_j→iUpdate the second confidence vector C2V_i→jUntil the current iteration number k is equal to the maximum iteration number I_maxUntil now.

The decoder provided in this embodiment is a decoding method for implementing the multi-element LDPC code of the above embodiments, and the implementation principle and technical effect of the decoder provided in this embodiment are similar, and are not described herein again.

In an embodiment, the updating module 11 is configured to determine the third confidence vector according to a linear adjustment and/or a non-linear adjustment manner if the current iteration number k ≠ 0

Wherein the third confidence vector

Is the second confidence vector C2V_i→jQ-n of_mA finite field element, q being a first confidence vector of propagation V2C_j→iAnd a second confidence vector C2V_i→jOf original length n_mIs the first confidence vector V2C of the transfer_j→iAnd a second confidence vector C2V_i→jThe actual length of (d); according to the third confidence coefficient vector

Computing a first confidence vector V2C_j→i(ii) a Determining a first confidence vector V2C_j→iMinimum value LLR of middle log likelihood ratio_minAnd the first confidence vector V2C_j→iSubtracting LLR from the value of the log-likelihood ratio of each element_min。

In an embodiment, the update module 11 is configured to determine a linear adjustment factor_linearAnd a nonlinear adjustment factor_non-linear(ii) a According to the linear adjustment factor_linearAdjusting the second confidence vector C2V_i→jAnd the maximum confidence vector in, and the factor according to non-linearity_non-linearAdjusting the offset to obtain an adjusted second confidence vector C2V_i→jThe maximum confidence coefficient vector and the adjusted offset in the process of the second confidence coefficient calculation are used for obtaining a third confidence coefficient vector

In one embodiment, the third confidence vector

Equal to linear adjustment factor_linearAnd a second confidence vector C2V_i→jThe product of the largest confidence vector in (1) and the nonlinear adjustment factor_non-linearAnd the sum of the products of the offsets.

In one embodiment, the offset F_offset(C2V_i→j) The following formula is used for calculation:

wherein the upper landmark indicates that the element is at the second confidence vector C2V_i→jIndex position in (2).

In one embodiment, the linear adjustment factor_linearIs greater than or equal to 1; nonlinear adjustment factor_non-linearIs greater than or equal to 0.

In one embodiment, the linear adjustment factor_linearIs taken as

Nonlinear adjustment factor_non-linearIs {0,1 }; where N is a finite number of sets of non-zero positive integers.

In one embodiment, the linear adjustment factor is used as the linear adjustment factor_linearValue of 1 and nonlinear adjustment factor_non-linearWhen the value of (1) is 1, the linear adjustment is closed, and the nonlinear adjustment is opened;

when the non-linear adjustment factor is_non-linearWhen the value of (1) is 0, the nonlinear adjustment is turned off, and the linear adjustment is turned on.

An embodiment of the present application further provides a decoder, including: a processor for implementing a method as provided in any of the embodiments of the present application when executing a computer program. Fig. 5 is a schematic structural diagram of another decoder according to an embodiment, as shown in fig. 5, the decoder includes a processor 60, a memory 61, and a communication interface 62; the number of processors 60 in the decoder may be one or more, and one processor 60 is taken as an example in fig. 5; the processor 60, the memory 61 and the communication interface 62 in the decoder may be connected by a bus or other means, and fig. 5 illustrates the connection by the bus as an example. A bus represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures.

The memory 61, as a computer-readable storage medium, may be configured to store software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the methods in the embodiments of the present application. The processor 60 executes at least one functional application of the decoder and data processing by executing the software program, instructions and modules stored in the memory 61, so as to implement the decoding method of the multi-element LDPC code.

The memory 61 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the decoder, and the like. Further, the memory 61 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 61 may include memory located remotely from the processor 60, which may be connected to the transcoder through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The communication interface 62 may be configured for the reception and transmission of data.

Embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method provided in any of the embodiments of the present application.

The computer storage media of the embodiments of the present application may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. Computer-readable storage media include (a non-exhaustive list): an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an erasable programmable Read-Only Memory (EPROM), a flash Memory, an optical fiber, a portable Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, Radio Frequency (RF), etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, Ruby, Go, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of Network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

It will be clear to a person skilled in the art that the term user terminal covers any suitable type of wireless user equipment, such as a mobile phone, a portable data processing device, a portable web browser or a car mounted mobile station.

In general, the various embodiments of the application may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. For example, some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device, although the application is not limited thereto.

Embodiments of the application may be implemented by a data processor of a mobile device executing computer program instructions, for example in a processor entity, or by hardware, or by a combination of software and hardware. The computer program instructions may be assembly instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages.

Any logic flow block diagrams in the figures of this application may represent program steps, or may represent interconnected logic circuits, modules, and functions, or may represent a combination of program steps and logic circuits, modules, and functions. The computer program may be stored on a memory. The memory may be of any type suitable to the local technical environment and may be implemented using any suitable data storage technology, such as, but not limited to, Read Only Memory (ROM), Random Access Memory (RAM), optical storage devices and systems (digital versatile disks, DVDs, or CD discs), etc. The computer readable medium may include a non-transitory storage medium. The data processor may be of any type suitable to the local technical environment, such as but not limited to general purpose computers, special purpose computers, microprocessors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Programmable logic devices (FGPAs), and processors based on a multi-core processor architecture.

The preferred embodiments of the present application have been described above with reference to the accompanying drawings, and are not intended to limit the scope of the claims of the application accordingly. Any modifications, equivalents and improvements which may occur to those skilled in the art without departing from the scope and spirit of the present application are intended to be within the scope of the claims of the present application.

Claims (10)

1. A decoding method of a multi-element LDPC code is characterized by comprising the following steps:

setting the maximum number of iterations I_maxAnd with the current iteration number k equal to 0, a first confidence vector V2C is initialized_j→iWherein the first confidence vector V2C_j→iIs a check matrix H_mnEach variable node VN_jTo check nodes CN_iThe confidence vector of (2);

using the first confidence vector V2C_j→iUpdate the second confidence vector C2V_i→jWherein the second confidence vector C2V_i→jFor each check node CN_iTo variable nodes VN_jThe confidence vector of (2);

according to the check matrix H_mnChecking a sequence of codewords according to the first confidence vector V2C_j→iObtaining;

if the verification is passed, outputting a decoding result;

if the verification fails, the second confidence vector C2V is utilized_i→jUpdating the first confidence vector V2C_j→iAnd making the current iteration number k equal to k +1, and making the current iteration number k less than the maximum iteration number I_maxThen execution returns according to the first confidence vector V2C_j→iUpdating the second confidence vector C2V_i→jUntil the current stackThe generation number k is equal to the maximum iteration number I_maxTo date, wherein the first confidence vector V2C_j→iIs updated according to the linear adjustment and/or the nonlinear adjustment, i is more than or equal to 0 and less than m, and j is more than or equal to 0 and less than n.

2. The method of claim 1, wherein said updating said first confidence vector V2C_j→iThe method comprises the following steps:

if the current iteration number k is not equal to 0, determining a third confidence coefficient vector according to a linear adjustment and/or nonlinear adjustment mode

Wherein the third confidence vector

Is the second confidence vector C2V_i→jQ-n of_mA finite field element, q being a first confidence vector of propagation V2C_j→iAnd a second confidence vector C2V_i→jOf original length n_mIs the first confidence vector V2C of the transfer_j→iAnd a second confidence vector C2V_i→jThe actual length of (d);

according to the third confidence coefficient vector

Computing the first confidence vector V2C_j→i；

Determining the first confidence vector V2C_j→iMinimum value LLR of middle log likelihood ratio_minAnd the first confidence vector V2C_j→iSubtracting LLR from the value of the log-likelihood ratio of each element_min。

3. The method according to claim 2, wherein the third confidence vector is determined according to a linear adjustment and/or a non-linear adjustment

The method comprises the following steps:

determining a Linear adjustment factor_linearAnd a nonlinear adjustment factor_non-linear；

According to the linear adjustment factor_linearAdjusting the second confidence vector C2V_i→jAnd according to the non-linear adjustment factor, and_non-linearadjusting the offset to obtain the adjusted second confidence vector C2V_i→jThe third confidence coefficient vector is obtained by the maximum confidence coefficient vector in the confidence coefficient vectors and the adjusted offset

4. The method of claim 3, wherein the third confidence vector

Is equal to the linear adjustment factor_linearWith the second confidence vector C2V_i→jAnd the product of the largest confidence vector in and the nonlinear adjustment factor_non-linearAnd the sum of products of the offsets.

5. Method according to claim 4, characterized in that said offset F_offset(C2V_i→j) The following formula is used for calculation:

wherein the upper landmark represents an element at the second confidence vector C2V_i→jIndex position in (2).

6. Method according to claim 3 or 4, characterized in that the linear adjustment factorfactor_linearIs greater than or equal to 1; the nonlinear adjustment factor_non-linearIs greater than or equal to 0.

7. The method of claim 6, wherein the linear adjustment factor is_linearIs taken as

The nonlinear adjustment factor_non-linearIs {0,1 }; where N is a finite number of sets of non-zero positive integers.

8. The method of claim 6, wherein the linear adjustment factor is set when the linear adjustment factor is set_linearIs 1 and the nonlinear adjustment factor is_non-linearWhen the value of (1) is 1, the linear adjustment is closed, and the nonlinear adjustment is opened;

when the nonlinear adjustment factor is_non-linearWhen the value of (1) is 0, the nonlinear adjustment is turned off, and the linear adjustment is turned on.

9. A decoder, comprising: a processor for implementing a method of decoding a multivariate LDPC code as claimed in any one of claims 1-8 when executing a computer program.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements a method for decoding a multi-element LDPC code according to any one of claims 1 to 8.

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