KR101276845B1 - Method of Low Density Parity Check Code decoding using a plurality of layers - Google Patents

Abstract

The present invention relates to a low density parity check (LDPC) decoding method, and more particularly, to a method for performing LDPC decoding using a plurality of layers.

The present invention comprises the steps of: receiving a signal corresponding to the LDPC codeword punctured at least one parity bit from the transmitting end to achieve the above object; Determining a rank for each of the plurality of layers according to the number of punctures in the received signal among at least one parity bit corresponding to each of the layers; And decoding the LDPC codeword using the plurality of layers sequentially according to the rank.

LDPC, layer, decoding, puncturing, priority, decoding rank

Description

Method of Low Density Parity Check Code decoding using a plurality of layers}

1 is a view showing an example of the structure of a mobile communication channel to which the prior art and the present invention is applied.

2 to 3 are diagrams illustrating a retransmission scheme according to the prior art and the present invention.

4 is a diagram illustrating a concept in which a model matrix is extended to a parity check matrix.

5 is an example of a diagram illustrating a conventional LDPC decoding method according to the present invention.

FIG. 6A shows an arbitrary parity check matrix H of size 4 * 8.

FIG. 6B is an example of displaying the matrix of FIG. 6A in a bipartite graph.

FIG. 7 is a block diagram illustrating an example of a parity check matrix divided into layers.

8 shows a parity check matrix having a specific 4 * 8 size through a dichotomous graph.

9 is a view for explaining a method of determining the priority of layers according to the present embodiment.

FIG. 10A is a block diagram of the dichotomy graph of FIG. 9 represented by a parity check matrix.

10B to 10F are block diagrams for explaining the present embodiment.

11 is another block diagram for explaining the present embodiment.

The present invention relates to a low density parity check (LDPC) decoding method, and more particularly, to a method for performing LDPC decoding using a plurality of layers.

1 is a view showing the structure of a mobile communication channel to which the present invention and the prior art are applied. Hereinafter, the structure of a mobile communication channel will be described with reference to FIG. 1. In order to transmit the data to be transmitted from the transmitter without loss or distortion in the radio channel, a channel coding procedure is performed. As the channel coding technique, there are various techniques such as convolutional coding, turbo coding, and LDPC coding. Data that has undergone the channel coding procedure may be transmitted as a single symbol by collecting a plurality of bits when transmitted through a wireless channel. In this case, a procedure in which several bits are mapped to one symbol is called modulation.

The modulated data is converted into a signal for multiplex transmission through a multiplexing process or a multiple access method. As the multiplexing method, various methods such as CDM, TDM, and FDM exist. The signal that has passed through the multiplexing block is changed into a structure suitable for transmission to one or more multiple antennas and transmitted to a receiver through a radio channel. Data transmitted in the course of passing through the wireless channel may experience fading and thermal noise, which may cause distortion of the data.

The modulated data is transmitted to a receiver through a wireless channel. In this process, the transmitted data may experience fading and thermal noise, which may cause distortion of the data. After receiving the distorted data, the receiving end performs a series of procedures in the reverse order. A demodulation operation of converting the data mapped to the symbol into a bit string is performed, and the distorted data is restored to the original data through a channel decoding process.

The apparatus for performing channel coding is derived from an H matrix or an H matrix which is a parity check matrix used to generate parity bits to be added to input data (Information bits or Systematic Bits). Generation Matrix Contains the G matrix. That is, the transmitting end includes an encoder for generating parity bits through the H or G matrix and the input data. The apparatus for performing channel decoding checks whether the inputted data (Systematic Bits) are properly recovered through the H matrix and the operation of the received data (distorted Systematic Bits + Parity Bits). Rerun

The modulation includes Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK), Quadrature Amplitude Modulation (16-QAM), 64-QAM, 256-QAM, and the like. For example, 16-QAM maps a sequence of data that has undergone Channel Encoding procedure to one symbol in units of 4 bits during modulation. In demodulation, 16-QAM demaps one symbol of data received through a wireless channel into four bits.

The LDPC code will be described below. The concept of the LDPC code is as follows.

을 만족하도록 부호가 구성된다는 점이다. 이 선형 부호의 일종으로서, 최근에 주목받는 LDPC 부호는 1962년 Gallager에 의하여 처음 제안되었다. 이 부호의 특징으로는 패리티 체크 행렬의 원소가 대부분 0으로 이루어지고, 0이 아닌 원소의 수는 부호 길이에 비하여 적은 수를 가지도록 하여 확률을 기반으로 한 반복적 복호가 가능한 점이다. 처음 제안된 LDPC 부호는 패리티 체크 행렬을 비체계적인(non-systematic) 형태로 정의하였고, 그것의 행과 열에 균일하게 적은 무게(weight)를 갖도록 설계되었다. The linear code can be described by the generation matrix G or parity check matrix H. The characteristic of the linear code is that for all codewords c,

The sign is constructed to satisfy. As a form of this linear code, a recent notable LDPC code was first proposed by Gallager in 1962. The characteristic of this code is that the elements of the parity check matrix are mostly 0, and the number of non-zero elements has a smaller number than the code length, so that it is possible to perform iterative decoding based on probability. The first proposed LDPC code defines the parity check matrix in a non-systematic form and is designed to have a uniformly low weight in its rows and columns.

Here, the weight means the number of 1s included in a column or a row in the matrix.

Since the density of nonzero elements on the parity check matrix H of the LDPC code is small, the decoding complexity is low. In addition, the decoding performance is also better than the existing codes, showing a good performance close to Shannon's theoretical limit. The LDPC code, however, was a hardware technology that was difficult to implement at the time and has not attracted much attention for more than 30 years. In the early 1980s, a method of iterative decoding using a graph was developed, and various algorithms were developed to actually decode the LDPC code using the graph. The sum-product algorithm can be extracted as the representative algorithm.

Hereinafter, the features of the LDPC code will be described. LDPC codes have high error correction performance, which allows for improvements in communication speed and capacity. The LDPC code may be applied to a high speed wireless LAN capable of transmitting hundreds of Mbit / s in combination with a multiple input multiple output (MIMO) system, and may be applied to high speed mobile communication having a transmission speed of 1 Mbit / s or more at 250 km / h. It can also be applied to optical communication of 40Gbits / s or more. In addition, the high error correction performance of the LDPC code may improve the transmission quality, thereby enabling quantum encrypted communication that reduces the number of retransmissions in a low quality communication path. In addition, due to the low complexity and excellent loss compensation of the LDPC code, lost packets can be easily recovered, which makes it possible to transmit content of the same quality as TV quality through the Internet and mobile communication. Due to the wide coverage and large capacity of LDPC, 10GBASE-T transmissions up to the 100m range, previously considered impossible, are possible with LDPC codes. At the same time, the transmission capacity of a single satellite transmitter in the 36MHz band can be increased by 1.3 times to 80Mbit / s.

The structured LDPC is described below.

In order to use the LDPC code includes a matrix H to use, uses a parity check matrix H is most 0 and a part 1 of an element (elemnet), the H matrix, since the size of the H matrix size by more than 10 ⁵ bits It requires a large amount of memory to represent. The structured LDPC technique is a method of representing elements of the H matrix used for LDPC encoding and decoding as sub-blocks having a constant size as shown in FIG. 2. In IEEE802.16e, the subblock is represented by one integer index to reduce the size of memory required to store the H matrix. The subblock may be various matrices, for example, a permutation matrix of a constant size.

In the structured LDPC technique, instead of storing a matrix of 1 or 0 in a specific memory, only one integer (that is, an index) needs to be stored. Can be reduced.

For example, when the size of the codeword reflected in the IEEE802.16e standard is 2304 and the code rate is 2/3, the model matrix used for LDPC encoding / decoding is shown in FIG. Same as 3. The model matrix means a parity check matrix composed of at least one subblock represented by the number of shifts described below. The model matrix may be generated by extending the parity check matrix by a method described below. Therefore, encoding and decoding by a specific model matrix is the same as encoding and decoding by a parity check matrix generated by extending from the corresponding model matrix.

As shown in FIG. 3, the structured LDPC matrix may be represented by '-1', '0' and a positive integer. When the index is '-1', it indicates a zero matrix of a specific size, and when the index is '0', it indicates an identity matrix of a specific size. In an index having a positive integer except '-1' and '0', the index represents a shift number. When a subblock is represented by an index of '1', the subblock is shifted '1' times in a specific direction in the unit matrix.

4 is a diagram illustrating a method of expressing a matrix according to the above-described index, that is, a shift number. When a specific parity check matrix is structured and expressed as a 4 * 4 matrix (that is, a sub block), if the specific sub block is represented by an index of '3', the sub block becomes the matrix of FIG. 4.

Hereinafter, the LDPC encoding method will be described.

In the general LDPC encoding method, a generation matrix G is derived from an LDPC parity check matrix H to encode an information bit. In order to derive the generation matrix G, the inspection matrix H is configured in the form of [P ^T : I] through a Gaussian Reduction method. When the number of information bits is k and the size of an encoded codeword is n, the P matrix is a matrix in which the number of rows is k and the number of columns is nk, and I is K is the identity matrix whose k column size is k.

The generation matrix G becomes a [I: P] matrix when the check matrix H is expressed as [P ^T : I]. If the encoded information bits of k-bit size are represented in a matrix, the number of rows is 1 and the number of columns is k. In this case, the code word c is described as follows.

In the above formula, x represents a systematic part and xP represents a parity part.

Hereinafter, the codeword generated by the above-described LDPC encoding method is referred to as an 'LDPC codeword'. The LDPC codeword includes an information bit ('x' in Equation 1) corresponding to information to be transmitted to a receiving end and a parity bit generated by a parity check matrix.

)을 이용하여, 상기 수학식 1에서 H^T을 곱하면, 하기 수 학식 2 같은 수학식을 얻을 수 있다. 하기 수학식 2에 부합하는 패리티 비트를 정보 비트(x) 뒤에 추가하여 코드워드 c를 얻을 수 있다. On the other hand, in the case of encoding by the Gaussian Reduction method as described above, a large amount of calculation is performed, and a method of directly encoding the H matrix without inducing the G matrix by designing the shape of the H matrix into a special structure. Use That is, H ^{T in the} form of a transpose of the G matrix and the H matrix. The product of 0 (that is,

By multiplying H ^T by Equation 1, Equation 2 can be obtained. A codeword c may be obtained by adding a parity bit corresponding to Equation 2 after the information bit x.

Hereinafter, the LDPC decoding method will be described.

5 is an example of a diagram illustrating a conventional LDPC decoding method according to the present invention.

The coded data in the communication system includes noise in the process of passing through the wireless channel of FIG. 1, and the receiving end decodes the data through the procedure of FIG. The decoded block of the receiving end to obtain the information bits from the noise to the encoded code word (c) adding the received signal (c ') (x), find using the cH ^T = 0 in nature. That is, when the received codeword is c ', the value of c'H ^T is calculated, and if the result is 0, k bits located at the front of c' are determined as the information bits x.

FIG. 6A shows an arbitrary parity check matrix H of size 4 * 8. The arbitrary parity check matrix as shown in FIG. 6 may be represented by a bipartite graph composed of a check node check node and a variable node.

FIG. 6B is an example of displaying the matrix of FIG. 6A in a bipartite graph. Referring to the connection relationship between each Check Node Unit (CNU) and Variable Node Unit (VNU) shown in FIG. 6B, it can be seen that FIG. 6B represents FIG. 6A. For convenience of explanation, the following description will be made using a dichotomous graph.

A process of decoding by applying an algorithm on a dichotomy graph may be classified into three processes as follows.

1. Update the probability value from a check node to a variable node (S502 in FIG. 5)

2. Update the probability value from the variable node to the check node (S503 in Fig. 5)

3. Decoding Value Determination through Probability of Variable Node (S504 in FIG. 5)

In order to perform the operations S502 to S504, an initialization step (S501 of FIG. 5) in which a probability value received from a channel is input must be performed first.

After performing step S502, if a probability value from the variable node to a check node is updated, step S504 is performed.

In addition, the decoding value is determined using the probability value updated through the steps S502 and S503. The LDPC decoding process determines the value c 'as a decoded value when the decoding value c' determined in step S504 satisfies the check expression c'H ^T = 0. If the inspection equation is not satisfied, the steps S502 and S504 are repeated as many times as the predetermined number of times.

As the steps S502 to S504 are repeated, the reliability of the probability value between the check node and the variable node is increased, and as a result, it is closer to the true value of the codeword to be obtained.

The decoding of a conventional LDPC code is mainly performed by repeating a process of increasing reliability by updating probability values between check nodes and variable nodes on a dichotomous graph, which is another representation of a parity check matrix. In the decoding method using a dichotomy graph, which is another representation of the parity check matrix, the decoding value is determined through the updated probability value, and thus the process of updating the probability value that determines the decoding value directly affects the performance of the decoder. Get mad.

The process of updating the reliability, that is, the probability value, may be divided into steps S502 and S503. When updating a specific node according to steps S502 and S503, the node is updated using a probability value located in the same column or the same row as the node to be updated on the parity check matrix. More specifically, the updated node performs its own update using a probability value other than its own value, but performs its own update using the probability values located in the same column or the same row as described above. In this case, it is common to have higher reliability as more probability values are updated.

In the conventional LDPC decoding method, that is, the conventional Belief Propagation (BP) algorithm, a method of calculating reliability reliability on the same row or column using a probability value that has undergone the same degree of update process is used.

Proposed to improve such a conventional BP algorithm is layered decoding. The layered decoding is an LDPC decoding method for updating a probability value from a variable node to an inspection node using the updated value when there is already an updated value in the same column.

More specifically, the layered decoding is a method of repeatedly decoding a row of a parity check matrix in units of layers.

The layered decoding may also be applied to structured LDPC codes. That is, when encoding / decoding is performed using a general parity check matrix, one row becomes one layer, and when a structured parity code is used, one subblock becomes one layer. Can be.

In other words, when the layered decoding is used for a structured LDPC code, one layer includes at least one row. In this case, the number of rows included in one layer preferably corresponds to the size of the sub block used in the structured LDPC code. For example, when the size of the sub block is z * z, it is preferable that the number of rows included in one layer is z.

FIG. 7 is a block diagram illustrating an example of a parity check matrix divided into layers. As shown, sub-blocks in the same position in the row direction form one layer.

The layered decoding updates reliability by using probability values that have all been updated in the same row in reliability update of the parity check matrix H in the same row. That is, like the BP algorithm described above, the probability value update between the check node and the variable node is performed on a dichotomous graph.

However, in the process of updating the probability value from the variable node to the check node, that is, updating the probability value of the column of the parity check matrix, the column of the parity check matrix H is updated using the previously updated value. There is a characteristic of updating the probability value.

As a result, layered decoding is a method of decoding a received signal using each layer sequentially in the parity check matrix composed of several layers.

In the case of performing an operation on one layer to update the probability value, and performing an operation for updating the probability value on the next layer, the result calculated in the already updated layer is obtained from the next layer. By using in the operation of, a more reliable decoding method can be provided.

Specific advantages of layered decoding will be described through a dichotomy graph.

8 shows a parity check matrix having a specific 4 * 8 size through a dichotomous graph. In FIG. 8, there are eight variable nodes, four check nodes, and each check node forms one layer.

In order to perform the operation of the first layer, channel input information for the first, third, and sixth variable nodes, and extrinsic information transmitted to the variable node through an update that has already been performed are needed.

After updating the first layer, update the second layer. At this time, the information of the third variable node of the information on the variable nodes 2, 3, 7 is already updated by the operation on the first layer.

When the layered decoding technique is compared with the BP algorithm, the BP algorithm performs eight operations on the check node through two iterative decodings until the information of the first variable node is transferred to the seventh variable node. However, the layered decoding technique performs two check node operations through two iterative decodings.

In summary, the layered decoding technique may achieve the same performance as the BP algorithm through a small number of iterative decoding.

The layered decoding technique is represented by Equation 3 below.

는 변수 노드에서 검사 노드로 전달되는 정보이고,

는 검사 노드에서 변수 노드로 전달되는 정보를 나타내고 있다. 그리고,

함수는

를 의미한다.In the above formula, m and j are the indices of test node and variable node, respectively.

Is the information passed from the variable node to the inspection node,

Represents the information passed from the check node to the variable node. And,

The function is

.

It is proposed to improve the problems of the prior art of the present invention, and an object of the present invention is to propose an LDPC decoding method which proposes an improved performance when some information of a variable node is not transmitted.

Another object of the present invention is to propose a layered decoding technique that operates efficiently when a part of a variable node is punctured.

Summary of the Invention

The present invention comprises the steps of: receiving a signal corresponding to the LDPC codeword punctured at least one parity bit from the transmitting end to achieve the above object; Determining a rank for each of the plurality of layers according to the number of punctures in the received signal among at least one parity bit corresponding to each of the layers; And decoding the LDPC codeword using the plurality of layers sequentially according to the rank.

In addition, the present invention includes the steps of receiving a signal corresponding to the LDPC codeword punctured bits corresponding to at least one variable node from the transmitting end; Determining a rank for each of the plurality of layers according to the number of punctured variable nodes among variable nodes used for updating at least one check node corresponding to each of the plurality of layers; And decoding the LDPC codeword using the plurality of layers sequentially according to the rank.

One embodiment of the invention

The objects and features of the present invention will be further embodied by one embodiment of the present invention described below. Hereinafter, an embodiment of the present invention will be described with reference to the accompanying drawings.

The present embodiment relates to a method of decoding an LDPC coded codeword and is based on the above-described layered decoding technique. In addition, the codeword to be decoded according to the present embodiment is preferably a codeword to which a puncturing technique is applied.

Hereinafter, a puncturing technique applied to LDPC encoding will be described.

In order to support various code rates using a specific number of parity check matrices, a technique of not transmitting specific bits may be used. That is, the puncturing technique is used to design a code capable of a variable code rate having a high code rate at a low code rate.

Bits not punctured and transmitted are preferably parity bits corresponding to the parity portion of the parity check matrix. The parity check matrix is divided into an information word part corresponding to information to be transmitted and a parity part corresponding to a parity bit additionally generated according to the LDPC encoding method. It can be seen that an information word part exists in one part of FIG. 7 and a parity part exists in the other part.

Although the puncturing for the parity bits is performed, normal decoding is possible using the remaining parity bits and information bits, so that the puncturing for the parity bits is performed to support a desired code rate.

Hereinafter, a method of preventing degradation of LDPC decoding performance when a puncturing technique is applied will be described.

In terms of a receiver having a decoder, when a probability value, that is, a Log Likelihood Ratio (LLR) value is calculated through an operation on a check node, the channel information of the punctured node becomes '0' (LLR value). . That is, the probability that the decoding value is determined by the '0' bit or the decoding value is determined by the '1' bit is the same. When the LDPC decoding process is performed in this state, the LLR value transferred from the check node to the variable node becomes '0' again. This may cause performance deterioration during decoding.

Meanwhile, the information is transmitted to the punctured node by using the information on the variable node that is not punctured. This process should be done with minimal iterative decoding to reduce the performance degradation of the code.

In the case of a layered decoding technique, a probability value is updated in units of layers. In addition, there is a priority for determining which of the plurality of layers to update first.

Referring to FIG. 7, a probability value update may be performed on layer 1 (Layer_1) among eight layers, and then a probability value update may be performed on layer 2 (Layer_2). In this case, since the probability value is updated for layer 2 using the already updated layer 1, the reliability of decoding is further improved. In addition, the probability value may be updated for layer 2 (Layer_2), and the probability value may be updated for layer 3 (Layer_3). In this case, since the probability value is updated for layer 3 using the already updated layer 2, the reliability of decoding is further improved.

This embodiment proposes a layered decoding technique that prioritizes layers according to the punctured bits when an LDPC codeword in which some bits are punctured is received.

Hereinafter, a method of determining the priority of a layer according to the punctured bits will be described with reference to FIG. 9. 9 is a view for explaining a method of determining the priority of layers according to the present embodiment.

In a layered decoding technique, a layer updated for the first time has a great effect on updating probability values of other layers. That is, it is preferable that the layer used for decoding for the first time is a layer independent of the punctured bits.

That is, if there are inspection nodes that are not connected to the punctured variable nodes, those inspection nodes are updated first.

In FIG. 9, the test node 5 is connected to the variable nodes 1, 5, 8, and 10, but the variable nodes 1, 5, 8, and 10 are not punctured. Therefore, the layer corresponding to check node 5 is first determined as the layer used for decoding. That is, the probability value is first updated.

In addition, in the case of the check node connected to the punctured variable node, the smaller the number of the punctured variable nodes to be connected, the higher the priority and the first layer to be used for decoding. In other words, the probability value is updated first.

Referring to the example of FIG. 9, the check node 4 is connected to the punctured variable node 9. That is, since the check node 4 is connected to one punctured variable node, it is given a priority after the check node 5.

In the example of FIG. 9, check node 2 and check node 3 are connected with the same number of punctured variable nodes. In this case, higher priority can be given to check node 2 or check node 3.

When the number of punctured variable nodes connected are the same, a method of prioritizing inspection nodes may be variously determined. For example, the priority of inspection nodes may be determined according to the number of variable nodes that are not punctured. For example, a higher priority may be given to a check node having a large number of unpunctured variable nodes or a check node having a small number of unpunctured variable nodes.

In addition, priority may be given according to a rule already determined by the communication system. For example, a higher index may be assigned to a smaller index or higher priority may be assigned to a larger index according to an index identifying a check node.

In addition, various methods are possible. For example, higher or lower priority may be given according to the structure of the layer corresponding to the check node 2 and the structure of the layer corresponding to the check node 3. For example, priority may be given according to the number or position of components having weight included in each layer. As another example, higher or lower priority may be given according to the size or arrangement of the number of shifts included in each layer.

If the priority of each inspection node is determined based on the above-described method, priority is given to inspection node 5-> inspection node 4-> inspection node 2-> inspection node 3-> inspection node 1. . When one layer is mapped to one inspection node, layer 5 is first used for decoding, and then layers are used according to priority.

FIG. 10A is a block diagram of the dichotomy graph of FIG. 9 represented by a parity check matrix. Since the relationship between the check node and the variable node is determined by a nonzero component (in other words, a weighted component) of the parity check matrix, FIG. 10A shows only a weighted component. That is, parts not separately indicated in FIG. 10A have a value of '0'.

When the 6, 7, 9 variable nodes are punctured, it means that the parity bits 6, 7, and 9 are not transmitted on FIG. 10A. That is, the LDPC codeword consists of 10 bits, the first 5 bits of which are referred to as information bits 1 to 5, and the remaining 5 bits may be referred to as sixth to tenth parity bits. In this case, the sixth, seventh, and ninth parity bits are not transmitted.

First, referring to FIG. 10B, in the case of an operation for updating the fifth layer corresponding to the fifth inspection node, parity bits 6, 7, and 9 may not be participated.

In other words, the weighted components in layer 5 are in rows 1, 5, 8, and 10. That is, the information bits or parity bits corresponding to the weighted component in the fifth layer are only the first information bit, the fifth information bit, the eighth parity bit, and the tenth parity bit.

In the case of layer 5, the parity bits corresponding to the component having the weight of the layer are only parity bits 8 and 10 parity bits. Accordingly, it can be seen that the parity bit corresponding to the layer is not punctured. Therefore, layer 5 is preferably used for decoding first.

10C, it can be seen that the parity bit 9 participates in the operation of updating the layer 4 corresponding to the check node 4. That is, parity bit 9 is required to use layer 4 for decoding.

Meanwhile, parity bits corresponding to layer 4 are parity bits 9 and 10, and it can be seen that parity bit 9 is punctured among them.

If you compare layer 4 and layer 5, the parity bits corresponding to layer 5 are not punctured, so they are assigned the highest priority, and only one parity bit corresponding to layer 4 is punctured. Assigned.

10D to 10F, it can be seen how many bits of the parity bits corresponding to layers 3 to 1 are punctured.

That is, two parity bits corresponding to layer 3 are punctured (parity bits 6 and 7), and two parity bits corresponding to layer 2 are punctured (parity bits 6 and 9). Three parity bits (parity bits 6, 7, and 9) corresponding to layer 1 were punctured.

Looking at the number of punctured parity bits, it can be seen that layer 3 and layer 2 are given higher priority than layer 1, and layer 1 is given the lowest priority.

As described above, the present embodiment determines the priority of the layer according to the number of punctured bits among the bits necessary for updating a specific layer.

10A to 10F have described the layered decoding technique based on the parity check matrix.

Meanwhile, the above-described layered decoding technique may be applied to a structured LDPC code as it is. That is, the example of FIG. 10 is a case where one row of the parity check matrix is one layer, and this example is applicable to a case where a plurality of rows are defined as one layer.

11 is a model matrix consisting of subblocks of a specific size. The model matrix of FIG. 11 may be generated by being extended to a parity check matrix having a specific size by the method described with reference to FIG. 4.

As described above, a plurality of inspection nodes may be defined as one layer. In the case of the example of Figure 11, divided into five layers, each layer includes a number of check nodes corresponding to the size of the sub-block.

The LDPC codeword received in FIG. 11 is composed of {V1, V2, ... V10}, and each of V1 to V10 includes a specific number of bits.

The method of performing layered decoding by using the model matrix as shown in FIG. 11 is the same as that described above. That is, the number of punctured bits is determined among the plurality of bits participating in the operation for decoding each layer. In this case, layered decoding is performed by giving higher priority to the layer where the punctured bits are least used.

In order for the receiving end to know the priority of the layers correctly, it must know which bits are punctured. The bits to be punctured may be determined according to a predetermined rule, and may be notified to the receiving end according to separate control information. The control information may include information of bits to be punctured, or may indicate a specific pattern to inform information of bits to be punctured.

The specific numerical values used above are merely for explaining an example of the present invention, and the present invention is not limited to the specific numerical values described above.

It will be apparent to those skilled in the art that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. Accordingly, the above detailed description should not be construed as limiting in all aspects and should be considered as illustrative. The scope of the invention should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent scope of the invention are included in the scope of the invention.

Hereinafter, the effect of the present invention will be described.

Layered decoding, which is a new decoding technique for LDPC codes, has the advantage of fast convergence speed, but performance degradation due to codes such as puncturing cannot be reduced. The present invention is a layered decoding scheme suitable for LDPC codes using the puncturing technique, and has a faster convergence rate because more information can be transmitted through fewer iterative decoding processes than conventional sequential layered decoding.

Claims (9)

In the method of performing LDPC decoding using a plurality of layers consisting of at least one row of the parity check matrix, Receiving a signal from a transmitting end corresponding to an LDPC codeword in which at least one parity bit is punctured; Determining a rank for each of the plurality of layers according to the number of punctures in the received signal among at least one parity bit corresponding to each of the plurality of layers; And Decoding the LDPC codeword using the plurality of layers sequentially according to the rank Consists of A method of performing LDPC decoding using a plurality of layers. The method of claim 1, At least one parity bit corresponding to each of the plurality of layers is At least one parity bit corresponding to a weighted component of each of the plurality of layers Characterized by A method of performing LDPC decoding using a plurality of layers. The method of claim 1, Determining the rank, Assigning the highest rank among the plurality of layers to a layer having the least number of perforations in the received signal among at least one parity bit corresponding to each of the plurality of layers Characterized by A method of performing LDPC decoding using a plurality of layers. The method of claim 3, Determining the rank, In the case of layers having the same number of perforations in the received signal, determining the rank of the layers according to the priority of each layer preset in the communication system. Characterized in that A method of performing LDPC decoding using a plurality of layers. The method of claim 3, Determining the rank, In the case of layers having the same number of punctures in the received signal, determining the rank of the layers according to the number of variable nodes that are not punctured. Characterized in that A method of performing LDPC decoding using a plurality of layers. The method of claim 1, The parity check matrix is Generated by extending from a model matrix of sub-blocks corresponding to a particular size unit matrix Characterized by A method of performing LDPC decoding using a plurality of layers. The method of claim 6, The sub-block corresponds to a matrix shifted in a specific direction by a unit matrix of a specific size Characterized by A method of performing LDPC decoding using a plurality of layers. The method of claim 1, Performing the decryption, Determining a decoding value using the plurality of layers; And Determining whether the determined decoding value is a true value using a check equation; Further comprising A method of performing LDPC decoding using a plurality of layers. In the method of performing LDPC decoding using a plurality of layers consisting of at least one row of the parity check matrix, Receiving a signal corresponding to an LDPC codeword from which a bit corresponding to at least one variable node is punctured; Determining a rank for each of the plurality of layers according to the number of variable nodes punctured in the received signal among variable nodes used for updating at least one check node corresponding to each of the plurality of layers; And Decoding the LDPC codeword using the plurality of layers sequentially according to the rank Consists of Parity A method of performing LDPC decoding using a plurality of layers.

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