Coding or decoding method, and communication apparatus

Abstract

Provided in the present application are a coding method, a decoding method, and a communication apparatus. In the coding method, a JSCC scheme is used to design a base matrix of LDPCs, and a state column is introduced into a check part of the base matrix, wherein a first column of the check part is a high-column-weight state column, and a second column thereof is a column with the degree of 1; in addition, the element "1" in the column with the degree of 1 and the element "2" in the state column are located in the same row of the base matrix. The combination of the column with the degree of 1 and the state column can improve a decoding threshold value for JSCC-LDPCs, and improve the decoding performance of the JSCC-LDPCs.

Description

Coding or decoding method and communication device

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of China on June 5, 2023, with application number 202310661340.8 and application name “Encoding or decoding method and communication device”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of information source and channel coding, and more specifically, to a coding or decoding method and a communication device. Background Art

[0003] In the field of channel coding, low-density parity check code (LDPC) is the most mature and widely used channel coding scheme. LDPC codes offer performance close to the Shannon limit and possess numerous advantages. Consequently, IEEE protocols such as 802.11n, 802.11ac, and 802.11ax have proposed LDPC as the standard channel coding scheme for wireless local area networks (WLANs). The 802.11ac / ax standard currently incorporates 12 LDPC parity check matrices, with three code lengths, each supporting four code rates. The encoding device selects the appropriate parity check matrix from among the 12 based on the target code length and code rate for LDPC encoding.

[0004] However, existing communication systems, such as cellular and wireless fidelity (WiFi), are designed based on separate source channel coding (SSCC). SSCC schemes cannot compress data at the physical layer. If compressible data exists at the physical layer, it wastes resources such as bandwidth and power. Furthermore, the decoding performance of existing SSCC schemes still needs to be improved.

[0005] Summary of the Invention

[0006] The present application provides a coding or decoding method and a communication device, which can realize the function of compressing data at the physical layer and improve decoding performance.

[0007] In a first aspect, a method for encoding or decoding is provided, which is applied to a communication device, such as a network device, a terminal device, a chip, a chip system, a hardware circuit, a software module, or a combination of a hardware circuit and a software module. The method may include:

[0008] Encoding or decoding based on a check matrix;

[0009] In which, the check matrix is ​​obtained by expanding the base matrix, the base matrix includes an information part and a check part, the check part includes a state column and a column with a degree of 1, the state column includes at least one element "2", the column with a degree of 1 includes an element "1", and one element "2" of the at least one element "2" and the element "1" are located in the same row of the base matrix.

[0010] It should be understood that when the method of the first aspect is applied to the encoding side, the communication device performs encoding based on the check matrix; if applied to the decoding side, the communication device performs decoding based on the check matrix.

[0011] In this application, the design of the base matrix adopts the joint source channel coding (JSCC) scheme, which can realize the function of compressing data at the physical layer. In addition, in order to achieve compression of sparse information sources, the JSCC-LDPC obtained by encoding based on the check matrix provided by this application only transmits check bits on the channel, and all information bits are punctured before transmission. Introducing a state column in the check part of the base matrix can improve the decoding threshold and decoding performance of JSCC-LDPC.

[0012] By designing a basis matrix with a smaller dimension and then expanding the basis matrix to obtain a check matrix, the design complexity of the check matrix can be reduced.

[0013] In addition, in the state column of the base matrix, one element "2" of at least one element "2" and the element "1" are located in the same row of the base matrix, which can achieve a lower decoding threshold and greatly reduce the complexity of encoding and decoding.

[0014] On the encoding side, the method may optionally further include: the communication device (the device corresponding to the encoding side) obtaining a check matrix. Alternatively, the method may further include: the communication device obtaining an information sequence to be encoded. Furthermore, the communication device performing encoding based on the check matrix includes: the communication device encoding the information sequence to be encoded based on the check matrix to obtain coded bits. Alternatively, the method may further include: the communication device outputting the coded bits.

[0015] On the decoding side, the method may optionally further include: the communication device (the device corresponding to the decoding side) obtaining a parity check matrix. Optionally, the method may further include: the communication device obtaining a sequence to be decoded. Furthermore, the communication device performing decoding based on the parity check matrix includes: the communication device decoding the sequence to be decoded based on the parity check matrix to obtain a decoded sequence. Optionally, the method may further include: the communication device outputting the decoded sequence.

[0016] In combination with the first aspect, in certain implementations of the first aspect, the state column of the base matrix further includes an element "1", and the number of elements "2" and "1" in the state column is close to that in the state column.

[0017] Such a design can achieve a compromise between the decoding threshold and the trap set to obtain ideal decoding performance.

[0018] In combination with the first aspect, in certain implementations of the first aspect, the elements of the remaining columns of the basis matrix except for the state column and the column with degree 1 include element "2", element "1" or element "0".

[0019] In combination with the first aspect, in certain implementations of the first aspect, the elements in the last row of the remaining columns of the basis matrix except for the state column and the column with degree 1 are 0.

[0020] This implementation can significantly reduce the design complexity of the check matrix by reducing the degree of freedom in design.

[0021] In the above implementation, the elements of the state column of the basis matrix are the variable nodes corresponding to the punctured check bits.

[0022] In conjunction with the first aspect, in some implementations of the first aspect, expanding the base matrix to obtain the check matrix may include two-step expansion, where a first expansion factor corresponding to a first expansion step of the two-step expansion is 2, and the first expansion step includes:

[0023] Expanding the base matrix based on a first expansion factor to obtain a first matrix;

[0024] Among them, the element "2" in the base matrix is ​​expanded to a 2×2 all-1 matrix, the element "1" is expanded to a 2×2 unit matrix or an anti-diagonal square matrix, the element "0" is expanded to a 2×2 all-zero square matrix, and the first matrix includes the element "1" and the element "0".

[0025] In conjunction with the first aspect, in some implementations of the first aspect, a second expansion factor corresponding to the second expansion step of the two-step expansion is 32, and the second expansion step includes:

[0026] Expanding the first matrix based on the second expansion factor to obtain a check matrix;

[0027] The element "1" in the first matrix is ​​expanded into a 32×32 circulant permutation matrix P i , the element "0" in the first matrix is ​​expanded to a 32×32 all-zero square matrix, where 0≤i≤31 and i is an integer, P0 represents the unit matrix, P i Represents the square matrix obtained by cyclically shifting each column vector in P0 right by i positions.

[0028] In this implementation, the base matrix is ​​expanded in two steps using two expansion factors to obtain the parity check matrix. The values ​​of these two expansion factors are designed so that the base matrix dimensions for different signal source sparsity ranges are 10 rows and 22 columns, 12 rows and 22 columns, and 14 rows and 22 columns, respectively. This maximizes compatibility with Wi-Fi hardware and facilitates practical applications.

[0029] In conjunction with the first aspect, in certain implementations of the first aspect, the base matrix has a dimension of 5 rows and 11 columns, the state column is the first column of the verification portion, the column with degree 1 is the second column of the verification portion, and the base matrix includes matrix 1, or a matrix obtained by performing a row transformation or a column transformation on matrix 1. Matrix 1 can be described in detail in the embodiments of the specification and is not listed here.

[0030] Optionally, a basis matrix with a dimension of 5 rows and 11 columns can be applicable when the sparsity of the source belongs to the sparsity interval (0, 0.05]. In other words, when the sparsity of the source is in the sparsity interval (0, 0.05], the base matrix with a dimension of 5 rows and 11 columns given in this application can be used to encode by expanding the check matrix obtained.

[0031] In combination with the first aspect, in some implementations of the first aspect, when the sparsity of the source belongs to the sparsity interval (0, 0.05], the check matrix is ​​such as matrix 2, or the matrix obtained by row transformation and / or column transformation of matrix 2, and matrix 2 refers to the embodiment of the specification.

[0032] In conjunction with the first aspect, in certain implementations of the first aspect, the base matrix has a dimension of 6 rows and 11 columns, the state column is the first column of the verification portion, the column with a degree of 1 is the second column of the verification portion, and the base matrix includes matrix 3, or a matrix obtained by performing a row transformation or a column transformation on matrix 3. Matrix 3 can be described in the embodiments of the specification and is not listed here.

[0033] Optionally, a basis matrix with a dimension of 6 rows and 11 columns may be suitable for a case where the sparsity of the signal source falls within the sparsity interval (0.05, 0.11).

[0034] In combination with the first aspect, in some implementations of the first aspect, when the sparsity of the source belongs to the sparsity interval (0.05, 0.11], the check matrix is ​​such as matrix 4, or the matrix obtained by row transformation and / or column transformation of matrix 4, see the embodiment of the specification.

[0035] In conjunction with the first aspect, in certain implementations of the first aspect, the base matrix has a dimension of 7 rows and 11 columns, the state column is the first column of the verification portion, the column with a degree of 1 is the second column of the verification portion, and the base matrix includes matrix 5, or a matrix obtained by performing a row transformation or a column transformation on matrix 5. Matrix 5 can be described in the embodiments of the specification and is not listed here.

[0036] Optionally, a basis matrix with a dimension of 7 rows and 11 columns may be suitable for a case where the sparsity of the signal source falls within the sparsity interval (0.11, 0.3).

[0037] In combination with the first aspect, in certain implementations of the first aspect, when the sparsity of the source belongs to the sparsity interval (0.11, 0.3], the check matrix is ​​such as matrix 6, or the matrix obtained by row transformation and / or column transformation of matrix 6, and matrix 6 refers to the embodiment of the specification.

[0038] In the above-mentioned base matrix design, the state column is specifically designed to achieve a compromise between the decoding threshold and the elimination of loops and traps. The combination of degree-1 columns and the state column can achieve a lower decoding threshold and significantly reduce codec complexity.

[0039] In a second aspect, a communication device is provided, wherein the communication device has the function of implementing the method of the first aspect or any possible implementation thereof. The function can be implemented by hardware or by hardware executing corresponding software implementation. The hardware or software includes one or more units corresponding to the above-mentioned functions.

[0040] In a third aspect, the present application provides a communication device comprising at least one processor, wherein the at least one processor is coupled to at least one memory, wherein the at least one memory is used to store a computer program or instruction, and the at least one processor is used to call and run the computer program or instruction from the at least one memory, so that the communication device performs the method in the first aspect or any possible implementation thereof.

[0041] In one example, the communication device described in the second aspect or the third aspect may be an encoding device or a decoding device.

[0042] In a fourth aspect, the present application provides a communication device comprising a communication interface and a circuit, wherein the communication interface is used to receive an information sequence to be encoded (or called bits to be encoded) and input the information sequence to be encoded into the circuit; the circuit encodes the information sequence to be encoded based on the check matrix provided in the present application to obtain encoded bits; the communication interface is also used to output the encoded bits.

[0043] Exemplarily, the communication device of the fourth aspect is a coding device.

[0044] In a fifth aspect, a communication device is provided, comprising a communication interface and a circuit, wherein the communication interface is used to receive a sequence to be decoded and input the sequence to be decoded into the circuit; the circuit decodes the sequence to be decoded based on the check matrix provided in this application to obtain a decoded sequence; and the communication interface is also used to output the decoded sequence.

[0045] Exemplarily, the communication device of the fifth aspect is a decoding device.

[0046] In a sixth aspect, the present application provides a computer-readable storage medium, in which computer instructions are stored. When the computer instructions are executed on a computer, the method in the first aspect or any possible implementation thereof is implemented.

[0047] In a seventh aspect, the present application provides a computer program product, comprising computer program code, which, when executed on a computer, enables the method in the first aspect or any possible implementation thereof to be implemented.

[0048] In an eighth aspect, the present application provides a wireless communication system, comprising a communication device as described in any one of the second to fifth aspects, such as an encoding device and / or a decoding device. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] FIG1 shows an example of a check matrix H of LDPC.

[0050] FIG2 is an example of the Tanner graph corresponding to the check matrix H.

[0051] FIG3 is a schematic diagram of a shortening operation in an LDPC encoding process in a WLAN.

[0052] FIG4 is a schematic diagram of a check matrix of the Double-LDPC scheme.

[0053] Figures 5(a) and (b) are schematic diagrams of a communication system applicable to an embodiment of the present application.

[0054] FIG6 is a schematic flowchart of a method 600 for encoding or decoding provided in this application.

[0055] 7 to 9 are schematic diagrams of the structure of the base matrix provided in this application.

[0056] FIG10 is a comparison diagram of the JSCC-EXIT graph analysis method provided in this application and the traditional JSCC-EXIT graph analysis method.

[0057] Figure 11 is a schematic diagram of the NR LDPC base matrix.

[0058] Figure 12 is a schematic diagram of the base matrices of NR LDPC and JSCC-LDPC provided in this application.

[0059] FIG13 is a schematic flow chart of the improved differential evolution method for designing a basis matrix provided in this application.

[0060] Figures 14-16 are simulation comparison diagrams of the decoding performance of JSCC-LDPC and SSCC provided in this application.

[0061] FIG17 is a schematic structural diagram of the communication device 1000 provided in this application.

[0062] FIG18 is a schematic structural diagram of the communication device 1100 provided in this application. DETAILED DESCRIPTION

[0063] The technical solution in this application will be described below with reference to the accompanying drawings.

[0064] In order to facilitate understanding of the technical solution of this application, the relevant technologies involved in the embodiments of this application are first introduced.

[0065] Existing data coding in communication systems such as cellular and wireless fidelity (WiFi) is based on the theory of separate source channel coding (SSCC). The encoder first performs source coding on the source data to be encoded. Traditional source coding (e.g., for the compression of images, video, speech, and text) is not error-resistant. Therefore, channel coding at the physical layer, combined with mechanisms such as retransmission, is required to enable error-free recovery of the transmitted bits at the decoder. Source coding is typically performed at the application layer, while channel coding is typically performed at the physical layer. However, SSCC-based schemes cannot compress data at the physical layer. If compressible data exists at the physical layer, this wastes resources such as bandwidth and power, reducing spectral efficiency. Furthermore, the optimal performance of SSCC-based schemes is based on Shannon's source-channel separation theorem, assuming infinite code length transmission without delay constraints. For finite code lengths, academic research has demonstrated that joint source channel coding (JSCC) offers superior transmission rates.

[0066] To fully utilize the sparsity of raw data such as voice, data, images, and video to improve transmission performance, JSCC is a potential solution that can simultaneously achieve source compression and channel protection. Furthermore, if there is source redundancy at the physical layer, JSCC can further exploit this redundancy to achieve both compression and protection.

[0067] Low-density parity check code (LDPC) is the most mature and widely used channel coding scheme. The check matrix of LDPC is a sparse matrix, that is, the number of non-zero elements in the matrix is ​​much smaller than the number of zero elements, or the row weight and column weight of the matrix are very small compared to the code length. In 1981, Tanner used a graph to represent the codeword. This graphical representation is now called the Tanner graph, and it was proved that the sum-product algorithm can achieve the best decoding performance in the acyclic code graph. The Tanner graph corresponds one-to-one to the check matrix and consists of two types of nodes. The first type is the variable node, which represents the codeword symbol; the second type of check node represents the check constraint relationship, and each node represents a check constraint relationship. Figure 1 shows an example of an LDPC check matrix H, and Figure 2 shows the Tanner graph corresponding to the check matrix H, {V i} represents a variable node set, {C i} represents the check node set.

[0068] The LDPC scheme used in the IEEE 802.11ac / ax standard is quasi-cyclic LDPC (QC-LDPC). QC-LDPC is a widely used structured LDPC scheme. Due to the unique structure of its parity check matrix, encoding can be implemented using a simple feedback shift register, effectively addressing the coding complexity issue of LDPC. Currently, the LDPC channel decoders used in WiFi are fixed-length. The dimensions of the base matrix used for different code rates are all [x, 24], where x is the number of rows in the base matrix and x is a positive integer. The WiFi standard uses parity check matrices for three code lengths and four code rates, for a total of 12 parity check matrices. The parity bit portion of these 12 parity check matrices has the same structure. For example, the base matrix for the LDPC code with code length N = 1944 and code rate R = 5 / 6 in 802.11ac is shown below:

[0069] It can be seen that the dimension of the base matrix is ​​4×24. Each element in the base matrix represents a square matrix of order z=N / 24, where 0 represents the unit matrix, "-" represents the full zero matrix, and the element P i represents the cyclic permutation matrix, i represents the cyclic shift value, 0≤i≤z-1, i is an integer. The following is a cyclic permutation matrix with a cyclic shift value of 1:

[0070] Existing WLAN standards (such as IEEE802.11n / ac) use orthogonal frequency division multiplexing (OFDM) technology. The LDPC encoding module needs to encode data bits and place them into an integer number of OFDM symbols. These encoded bits must also fit into an integer number of LDPC codewords. To perform these steps, the system first calculates the minimum number of OFDM symbols N required for this transmission. SYM , and further according to N SYM Calculate the total number of coded bits N that can be stored in all OFDM symbols using the current coding and modulation scheme TCB ,N TCB =N CBPs ·N SYM , where N CBPs The number of coded bits that can be stored in each OFDM symbol. Then, the system calculates the LDPC code length L used in the current transmission based on all the above calculation results. LDPC and the required number of codewords N CW For most combinations of data bit lengths to be encoded and coding modulation schemes, there are not enough data bits to fill the information bits in the LDPC codeword, and a shortening operation is required before generating the check bits.

[0071] Figure 3 is a schematic diagram of the shortening operation in the LDPC coding process in WLAN. Shortening means filling a certain number of zeros in the information bits of the codeword before the LDPC coding generates the check bits, and then deleting these zeros after the coding generates the check bits. As shown in Figure 5, step 1 is the data bits to be encoded; step 2 calculates the length L of the LDPC codeword. LDPC and the number of codewords N CW ; Step 3 shortens the data bits; Step 4 generates check bits using the information bits and the 0s to be shortened, and finally deletes the 0s to be shortened.

[0072] The LDPC channel coding in current Wi-Fi standards is designed for compressed data and therefore lacks compression capabilities. If compressible data exists at the physical layer, it will waste resources such as bandwidth and power.

[0073] The Double-LDPC scheme is a currently known JSCC scheme based on LDPC. This scheme cascades two LDPC schemes. The source LDPC scheme is responsible for source compression, while the channel LDPC scheme is responsible for channel protection. Decoding can be performed sequentially or in parallel.

[0074] Figure 4 is a schematic diagram of the check matrix of the Double-LDPC scheme. As can be seen from Figure 4, the source LDPC part of the check matrix (i.e., H SC ) and the LDPC portion of the channel (i.e., H CC ) are designed relatively independently, with clear divisions of labor between the two components. Even if improvements are proposed later, the connection between the two matrices remains relatively fixed, and the code rate allocation between the source and channel coding components significantly impacts performance and involves tradeoffs. This solution requires significant modifications to standard WiFi hardware, making it difficult to implement, and performance also needs improvement.

[0075] Based on the above technical status, this application provides a coding method that aims to provide a JSCC-LDPC check matrix, which can achieve physical layer compression and maximize the adaptability of existing WiFi hardware. In addition, compared with some existing coding schemes, decoding performance is also improved.

[0076] The technical solution provided by this application is introduced below.

[0077] The technical solution of the present application can be applied to various wireless communication systems, including but not limited to: the 5th generation (5G) system or new radio (NR) system, long term evolution (LTE) system, long term evolution-advanced (LTE-A) system, LTE frequency division duplex (FDD) system, LTE time division duplex (TDD) system, etc., and can also be applied to future communication systems, such as the sixth generation mobile communication system. In addition, it can also be applied to device to device (D2D) communication, vehicle-to-everything (V2X) communication, machine to machine (M2M) communication, machine type communication (MTC), and Internet of Things (IoT) communication system or other communication systems, etc. In addition, the technical solution of the present application can be applied to WLAN scenarios, for example, it can be applied to the 802.11 system standards of the Institute of Electrical and Electronics Engineers (IEEE), such as the 802.11a / b / g standards, 802.11n standards, 802.11ac standards, 802.11ax standards, 802.11be standards or their next-generation standards, without limitation. The above-mentioned communication systems applicable to the present application are only examples, and the communication systems applicable to the present application are not limited thereto.

[0078] Referring to Figure 5, (a) and (b) of Figure 5 are schematic diagrams of a communication system applicable to an embodiment of the present application. The communication system includes an encoding end and a decoding end. The encoding end is not limited to one or more, and the decoding end is not limited to one or more. Exemplarily, one of the encoding end and the decoding end can be an access point (AP) in a WLAN scenario, and the other can be a station (STA). The embodiment of the present application can be applicable to wireless communication between AP and STA. Optionally, it can include communication scenarios such as AP communicating with a single STA and AP communicating with multiple STAs. Communication between AP and multiple STAs can also include downlink transmission of signals sent by AP to one or more STAs, and uplink transmission of signals sent by one or more STAs to AP. Figure 5 (a) shows a scenario in which an AP communicates with a single STA. Figure 5 (b) shows a scenario in which an AP communicates with multiple STAs. The encoding end in each embodiment below can be the STA in the uplink transmission shown in Figure 5, or it can be the AP in the downlink transmission. In addition, the technical solution of the present application can also be applied to similar non-standard wireless transmission systems without limitation.

[0079] The encoding device and decoding device in the embodiments of the present application can be terminal devices, including various communication kits with wireless communication functions (the communication kits can include, for example, antennas, power supply templates, cables, and WiFi modules), handheld devices, vehicle-mounted devices, or other processing devices connected to a wireless modem, and can specifically refer to user equipment (UE), user, access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent, user device, wireless modem, machine type communication device, or other processing devices connected to a wireless modem. It can also be a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a terminal in self-driving, a terminal in remote medical care, a terminal in a smart grid, a terminal in transportation safety, a terminal in a smart city, a terminal in a smart home, or a terminal device in a future communication network. Of course, the terminal device in this application may also refer to a chip, modem, system on a chip (SoC) or a communication platform that may include a radio frequency (RF) part, which is mainly responsible for the relevant communication functions in the device.

[0080] The encoding device and decoding device in the embodiments of the present application may also be a network device, which may include but is not limited to: a next-generation base station (gNodeB, gNB) in a fifth-generation (5G) communication system, a base station in a sixth-generation (6G) mobile communication system, a base station in a future mobile communication system, an access point (AP) in a wireless fidelity (WiFi) system, a wireless relay node, a wireless backhaul node, a transmission point (TP) or a transmission reception point (TRP), an evolved node B (eNB) in a long-term evolution (LTE) system, a network device in a non-terrestrial network (NTN) communication system, etc. The network device may also be one or a group (i.e., multiple) antenna panels of a base station. In addition, the network device may also be a network node constituting a gNB or TP, such as a baseband unit (BBU), a centralized unit (CU), a distributed unit (DU), or a radio unit (RU). Alternatively, the network device may also be a device-to-device (D2D) communication system, a machine-to-machine (M2M) communication system, an Internet of Things (IoT), an Internet of Vehicles communication system, or a device that performs network-side functions in other communication systems, without limitation.

[0081] In the embodiments of the present application, the device for implementing a terminal function may be a terminal, or a device capable of supporting the terminal in implementing the function, such as a chip system or chip, which may be installed in the terminal. Furthermore, the device for implementing a network device function may be a network device, or a device capable of supporting the network device in implementing the function, such as a chip system or chip, which may be installed in the network device. Alternatively, the chip system may consist of a chip alone, or may include a chip and other discrete components.

[0082] Figure 6 is a schematic flowchart of the encoding / decoding method 600 in the wireless system provided by the present application, wherein: step 610 can be performed by the encoding device, or by an apparatus installed in the encoding device (such as a chip, a chip system or a circuit, etc.); step 650 can be performed by the decoding device, or by an apparatus installed in the decoding device (such as a chip, a chip system or a circuit, etc.).

[0083] The following uses an encoding device as an example to illustrate the encoding side method.

[0084] 610. The encoding device performs encoding based on a check matrix.

[0085] The check matrix provided in this application is obtained by expanding the base matrix.

[0086] Specifically, the basis matrix includes an information portion and a check portion. The check portion includes a state column and a degree-1 column. The state column includes at least one element "2," and the degree-1 column includes an element "1." An element "2" of the at least one element "2" and an element "1" are located in the same row of the basis matrix. In other words, the element "1" in the degree-1 column is connected to an element "2" in the state column.

[0087] Furthermore, the state column also includes element "1," and the number of elements "2" and "1" in the state column is similar. The element "1" in the degree 1 column is connected to the element "2" in the state column, but not to the element "1" or to the same row in the basis matrix.

[0088] It should be noted that the elements of the state column in this application are variable nodes corresponding to the check bits that will be punctured during transmission. During the research process of this application solution, it was found that introducing the state column in the check part of the base matrix can further improve the decoding threshold.

[0089] Optionally, the elements of the remaining columns of the basis matrix, excluding the state column and the column with degree 1, include the element "2", the element "1", or the element "0". That is, the value of the element at any position in the remaining columns of the basis matrix, excluding the state column and the column with degree 1, can be designed to be 2, 1, or 0.

[0090] Optionally, as an implementation manner, the last row of the remaining columns in the basis matrix except the state column and the column with degree 1 is an element "0".

[0091] Alternatively, in various embodiments of the present application, the element "x" may also mean that the value of the element is "x". For example, the column with a degree of 1 includes element 1, which means that the column with a degree of 1 includes one element, and the value of the element is 1. The status column includes at least one element "2", which means that the status column includes at least one element, and the value of the at least one element is 2.

[0092] Optionally, expanding the base matrix to obtain the check matrix may include two-step expansion.

[0093] Specifically, the first step of the two-step expansion corresponds to the first expansion factor, and the second step corresponds to the second expansion factor. The expansion process can be: expanding the base matrix based on the first expansion factor to obtain the first matrix; expanding the first matrix based on the second expansion factor to obtain the check matrix. Based on the above description, it can be seen that, in general, the base matrix includes three elements, namely element "2", element "1" and element "0". If the first expansion factor is 2, then in the first expansion step, the element "2" in the base matrix is ​​expanded to a 2×2 all-1 matrix, the element "1" is expanded to a 2×2 unit matrix or an anti-diagonal square matrix, and the element "0" is expanded to a 2×2 all-zero square matrix.

[0094] After the first expansion step, the base matrix is ​​expanded into the first matrix, which includes two elements, element "1" and element "0". The second expansion factor is 32, so in the second expansion step, the element "1" in the first matrix is ​​expanded into a 32×32 circulant permutation matrix P i , the element "0" in the first matrix is ​​expanded to a 32×32 all-zero square matrix, where 0≤i≤31 and i is an integer, P0 represents the unit matrix, P i Represents the square matrix obtained by cyclically shifting each column vector in P0 right by i positions.

[0095] Optionally, for different information source sparsities, this application provides several different dimensions of the basis matrix.

[0096] (1) The dimension of the basis matrix is ​​5 rows and 11 columns.

[0097] One of the columns of the check portion of the basis matrix is ​​a state column, one of the columns of the check portion is a degree-1 column, the state column includes at least one element "2", the degree-1 column includes an element "1", and one element "2" of the at least one element "2" and the element "1" are located in the same row of the basis matrix. Optionally, the elements of the remaining columns of the basis matrix other than the state column and the degree-1 column are elements "2", elements "1", or elements "0". As an example, the last row of the remaining columns of the basis matrix other than the state column and the degree-1 column contains the element "0".

[0098] As mentioned above, the number of elements "2" and "1" in the status column is similar. For example, the status column is [1 1 1 2 2] T , it can be seen that the state column includes 5 elements in total, the number of elements "2" and "1" accounts for 2 and 3 respectively, which is close to half. In addition, the column with degree 1 is [0 0 0 0 1]T It can be seen that the element "1" in the degree 1 column and an element "2" in the state column are located in the same row in the basis matrix.

[0099] Based on the above design ideas, in one case, the dimension of the basis matrix is ​​5 rows and 11 columns. For example, the state column is the first column of the verification part, and the column with degree 1 is the second column of the verification part. The design of the basis matrix can be shown in Figure 7.

[0100] As an example, the present application provides a basic matrix such as Matrix 1, or a matrix obtained by performing row transformation and column transformation on Matrix 1.

[0101] Matrix 1

[0102] Based on matrix 1 and the above two-step expansion process, the check matrix obtained by expanding the base matrix can be specifically matrix 2. In addition, the check matrix can also be a matrix obtained by performing row transformation and / or column transformation on matrix 2.

[0103] Matrix 2

[0104] The element "-1" in the matrix 2 represents a z×z all-zero matrix, the element "0" represents a z×z unit matrix, and the element P i represents a z×z cyclic permutation matrix, i represents the cyclic shift value, 0≤i≤z-1, i is an integer, and z represents the second extension factor. As an example, the second extension factor z=32. Furthermore, the element "-1" in the parity check matrix can be replaced with other symbols, for example, the symbol "-" represents an all-zero matrix. This description is also applicable to other parity check matrices and will not be repeated below.

[0105] Optionally, the above matrix 1 or matrix 2 can be used for encoding with a target code rate R=1.5.

[0106] In addition, due to the large dimension of the check matrix, a cyclic shift permutation matrix is ​​generally used to represent the check matrix, and the two are equivalent. For example, each element in matrix 2 corresponds to a cyclic permutation matrix or an all-zero matrix or a unit matrix with a dimension of 32×32. After matrix 2 is expanded, the corresponding matrix is ​​the check matrix. Therefore, the dimension of the check matrix can be the dimension of the matrix after matrix 2 is expanded. However, it can also be said that matrix 2 is a check matrix. Therefore, it can also be said that the dimension of matrix 2 is the dimension of the check matrix. These alternative expressions known to those skilled in the art should not impose any limitations on the check matrix in the embodiments of the present application. This will not be repeated in other embodiments below.

[0107] (2) The dimension of the basis matrix is ​​6 rows and 11 columns.

[0108] One of the columns of the check portion of the basis matrix is ​​a state column, one of the columns of the check portion is a degree 1 column, the state column includes at least one element "2", the degree 1 column includes an element "1", and one element "2" of the at least one element "2" and the element "1" are located in the same row of the basis matrix. Optionally, the elements of the remaining columns of the basis matrix other than the state column and the degree 1 column are elements "2", elements "1", or elements "0". As an example, the last row of the remaining columns of the basis matrix other than the state column and the degree 1 column contains the element "0".

[0109] For example, the status column is [1 2 1 2 1 2] T , the state column includes 6 elements, the number of elements "2" and "1" are 3 and 3 respectively, each accounting for half. In addition, the column with degree 1 is [0 0 0 0 0 1] T . An element "1" in the degree 1 column and an element "2" in the state column are located in the same row in the basis matrix.

[0110] Based on the above design ideas, in one case, the dimension of the basis matrix is ​​6 rows and 11 columns. For example, the state column is the first column of the check part, and the column with degree 1 is the second column of the check part. The design of the basis matrix can be shown in Figure 8.

[0111] As an example, the present application provides a base matrix such as Matrix 3, or a matrix obtained by performing row transformation and / or column transformation on Matrix 3.

[0112] Matrix 3

[0113] Based on matrix 3 and the above two-step expansion process, the check matrix obtained by expanding the base matrix can be specifically matrix 4, or a matrix obtained by performing row transformation and / or column transformation on matrix 4, without limitation.

[0114] Matrix 4

[0115] The element "-1" in the matrix 4 represents a z×z all-zero matrix, the element "0" represents a z×z unit matrix, and the element P i represents a z×z cyclic permutation matrix, i represents a cyclic shift value, 0≤i≤z-1, i is an integer, and z represents the second extension factor. As an example, the second extension factor z=32.

[0116] Optionally, the above matrix 3 or matrix 4 can be used for encoding with a target code rate R=1.

[0117] (3) The dimension of the basis matrix is ​​7 rows and 11 columns.

[0118] One of the columns of the check portion is a state column, one of the columns of the check portion is a degree 1 column, the state column includes at least one element "2", the degree 1 column includes an element "1", and one element "2" of the at least one element "2" and the element "1" are located in the same row of the basis matrix. Optionally, the element at any position in the remaining columns of the basis matrix other than the state column and the degree 1 column is an element "2", an element "1", or an element "0". As an example, the last row of the remaining columns of the basis matrix other than the state column and the degree 1 column contains an element "0".

[0119] For example, the status column is [1 1 1 1 2 2 2] T , the state column includes 7 elements in total, the number of elements "1" and "2" accounts for 4 and 3 respectively, which is close to half. In addition, the column with degree 1 is [0 0 0 0 0 0 1] T . An element "1" in the degree 1 column and an element "2" in the state column are located in the same row in the basis matrix.

[0120] Based on the above design ideas, in one possible case, the dimension of the basis matrix is ​​7 rows and 11 columns. For example, the state column is the first column of the check part, and the column with degree 1 is the second column of the check part. The design of the basis matrix can be shown in Figure 9.

[0121] As an example, the present application provides a basic matrix such as matrix 5, or a matrix obtained by performing row transformation and / or column transformation on matrix 5.

[0122] Matrix 5

[0123] Based on matrix 5 and the above two-step expansion process, the check matrix obtained by expanding matrix 5 can be specifically matrix 6, or a matrix obtained by performing row transformation and / or column transformation on matrix 6, without limitation.

[0124] Matrix 6

[0125] The element "-1" in the matrix 6 represents a z×z all-zero matrix, the element "0" represents a z×z unit matrix, and the element P i represents a z×z cyclic permutation matrix, i represents a cyclic shift value, 0≤i≤z-1, i is an integer, and z represents the second extension factor. As an example, the second extension factor z=32.

[0126] Optionally, the above matrix 5 or matrix 6 can be used for encoding with a target code rate R=2 / 3.

[0127] The above-mentioned basis matrices of different dimensions can be applied to encoding under different source sparsities. As an example, when the source sparsity belongs to the sparsity interval (0, 0.05], matrix 1 can be used as the basis matrix, or matrix 2 can be used as the check matrix for encoding. In this case, the target coding rate can be R = 1.5. When the source sparsity belongs to the sparsity interval (0.05, 0.11], matrix 3 can be used as the basis matrix, or matrix 4 can be used as the check matrix for encoding. In this case, the target coding rate can be R = 1. When the source sparsity belongs to the sparsity interval (0.11, 0.3], matrix 5 can be used as the basis matrix, or matrix 6 can be used as the check matrix for encoding. In this case, the target coding rate can be R = 2 / 3.

[0128] It should be understood that since the check matrix is ​​usually obtained by expanding the base matrix, the above embodiment gives an example of the base matrix, and the base matrix is ​​expanded in two steps to obtain the check matrix of JSCC-LDPC. Optionally, the check matrix can also be designed directly. In this case, the embodiments of the present application provide check matrices such as the above-mentioned matrix 2, matrix 4 or matrix 6, or matrices obtained by row transformation and / or column transformation of these matrices, without limitation.

[0129] Optionally, the encoding method may further include steps 620 to 630 .

[0130] 620. The encoding device obtains an information sequence to be encoded (or referred to as information bits).

[0131] The encoding device performs encoding based on the check matrix, including encoding the information sequence to be encoded based on the check matrix to obtain encoding bits.

[0132] 630. The encoding device outputs the encoded bits.

[0133] Optionally, the encoding method may further include step 640 .

[0134] 640. The encoding device obtains a check matrix.

[0135] The encoding device can obtain the check matrix in a variety of ways. For example, the encoding device reads the check matrix from a memory; or a processing module (e.g., a processor, circuit, chip, or chip system) of the encoding device reads the base matrix from the memory and determines (or generates) the check matrix based on the base matrix. Alternatively, the memory can be integrated with the processing module or physically exist separately, without limitation.

[0136] The above is the method on the encoding side.

[0137] The decoding side method is described below using a decoding device as an example.

[0138] 650. The decoding device performs decoding based on the check matrix.

[0139] It should be understood that the check matrix used by the decoding device for decoding in step 650 is the same as the check matrix used by the encoding device for encoding.

[0140] Optionally, the decoding method may further include steps 660 to 670 .

[0141] 660. The decoding device obtains a sequence to be decoded.

[0142] For example, the decoding device receives the sequence to be decoded from the encoding device via an air interface.

[0143] Exemplarily, the decoding device performs decoding based on a check matrix, including: the decoding device decodes the sequence to be decoded based on the check matrix to obtain a decoded sequence.

[0144] 670. The decoding device outputs the decoded sequence.

[0145] Optionally, the decoding method may further include step 680 .

[0146] 680. The decoding device obtains a check matrix.

[0147] It should be understood that step 680 may be located before step 650. There are various possible ways for the decoding device to obtain the check matrix. For example, the decoding device receives the check matrix encoded by the encoding device. Alternatively, the decoding device reads the check matrix for current decoding from a plurality of pre-stored check matrices based on an instruction (e.g., an index) from the encoding device. Alternatively, the decoding device reads a base matrix from a memory and generates a check matrix based on the base matrix, etc., without limitation.

[0148] It should be noted that the serial numbers of each step in Figure 6 are only used to distinguish different steps in order to clearly describe the technical solution. The size of the serial numbers of each step does not mean the order of execution. The execution order of each step should be determined by the internal logic of the encoding method / decoding method.

[0149] The above describes the base matrix or check matrix of the JSCC-LDPC scheme given in this application.

[0150] It is understood that the design of a base matrix or check matrix takes into account many factors. The above embodiments provide the check matrix and base matrix of the JSCC-LDPC provided by this application. A method for designing a check matrix and base matrix for JSCC-LDPC is provided below. The check matrix designed based on the method provided by this application can improve decoding performance while achieving the compression function of the physical layer.

[0151] In summary, the specific design process of the JSCC-LDPC check matrix for WLAN in this application mainly includes two steps:

[0152] The first step is the design of the base model diagram.

[0153] In the design of the base model graph, this application first designs a calculation method for the JSCC decoding threshold (hereinafter referred to as the decoding threshold). It is known that the decoding threshold is a theoretical standard for judging the quality of the JSCC base model graph. By analyzing the calculation method of the decoding threshold, a calculation method that can obtain an accurate decoding threshold is adopted. Furthermore, based on the basic structure and constraints that the base model graph provided by this application should meet, and combined with the judgment criteria of the decoding threshold, a specific base model graph is designed for the source sparsity p and the target bit rate.

[0154] The second step is the expansion of the base model diagram.

[0155] The expansion of the base model graph refers to the use of a circulant permutation matrix (CPM) and an all-zero square matrix to expand the designed base matrix into the final check matrix. It should be noted that the traditional JSCC coding has the problem of difficult to reduce the error floor. The error floor of traditional channel coding is mainly dominated by the trap set. This application takes into account the difference between the impact of the trap set of JSCC-LDPC on decoding performance and the impact of traditional channel coding on decoding performance. Therefore, in the second step of the base model graph expansion, the trap set of the code in the JSCC scenario is analyzed to perform a trap set performance evaluation on the candidate check matrix obtained by the expansion to determine the final check matrix of JSCC-LDPC.

[0156] The above two steps can be roughly summarized as the following design process:

[0157] Step 1: Design of the base model graph (or base matrix), including the following aspects:

[0158] 1.1. Calculation method of decoding threshold, used to judge the quality of base mode graph or degree distribution;

[0159] 1.2. The basic structure and constraints that the base model diagram needs to comply with;

[0160] 1.3. Construction of the base model graph: Based on the decoding threshold (calculated using the improved method for calculating the decoding threshold provided in this application), the basic structure of the base model graph and the constraints, a specific base model graph is constructed.

[0161] Step 2: Expansion of the base model diagram mainly involves the following aspects:

[0162] 2.1. Trap set analysis: a criterion for judging decoding performance, especially the error floor area;

[0163] 2.2. Extension factor design: Expand the designed base mode graph into the parity check matrix of JSCC-LDPC;

[0164] 2.3. Trap set evaluation: Quickly locate the dominant trap set to determine the final check matrix.

[0165] The above aspects are introduced in detail below.

[0166] 1.1. Calculation method of decoding threshold.

[0167] This application uses an improved JSCC-extrinsic information transfer chart (EXIT) chart analysis method for calculating the decoding threshold. It should be understood that the improved JSCC-EXIT is different from the traditional JSCC-EXIT chart analysis method.

[0168] Figure 10 is a comparison diagram of the improved JSCC-EXIT graph analysis method provided by this application and the traditional JSCC-EXIT graph analysis method. As shown in Figure 10, the traditional JSCC-EXIT analysis approximates the biased signal source and the transmission channel as a single white Gaussian noise (AWGN) channel. However, during the research process of this application, it was found that this will lead to inaccurate calculation of the mutual information of the JSCC-EXIT graph. Therefore, this application proposes an improved JSCC-EXIT graph analysis method, which processes the BSC channel and transmission channel that are equivalent to the biased signal source separately, so that the mutual information of the iterative decoding process can be tracked more accurately, and thus the decoding threshold can be obtained more accurately. Among them, the decoding threshold of the base mode graph refers to the minimum signal-to-noise ratio (SNR) or Eb / N0 required for the LDPC code set corresponding to the base mode graph to be successfully decoded when the code length is infinite. That is, the smaller the decoding threshold, the closer the LDPC code set corresponding to the base mode graph is to the channel capacity when the code length tends to infinite, and the better the performance.

[0169] 1.2. The basic structure and constraints of the base model diagram.

[0170] In traditional JSCC-LDPC coding, such as NR LDPC for channel coding, all coded bits except those corresponding to the state nodes are transmitted through the channel, and the optimization of the base model graph (or base matrix) is almost entirely focused on the information portion of the base matrix, while the structure of the check portion of the base matrix is ​​relatively fixed. The state nodes mentioned in NR LDPC refer to the variable nodes corresponding to the punctured information bits in the channel coding scenario.

[0171] Figure 11 is a schematic diagram of the NR LDPC base matrix.

[0172] As shown in Figure 11, since the check portion of the base model graph (or base matrix) is fixed, optimization of the base model graph in the channel coding case primarily focuses on the information portion of the base matrix. For channel coding, fixing the check portion of the base matrix does not affect overall performance, so all coded bits (excluding those corresponding to state nodes) pass through the same channel.

[0173] In order to achieve compression of sparse information sources, the JSCC-LDPC proposed in this application only transmits check bits on the channel, and all information bits are punctured before transmission. Therefore, both the source and channel parts should be optimized.

[0174] In summary, this application proposes a basic structure and four constraints for the basis matrix of JSCC-LDPC. For the convenience of description, the basic structure and four conditions are respectively denoted as S1 and C1~C4.

[0175] S1: Introducing state nodes in the check part of the base matrix can improve the decoding threshold of JSCC and the actual performance of JSCC-LDPC.

[0176] It should be noted that the base matrices of the JSCC-LDPC and NR LDPC algorithms provided in this application are different. Figure 12 shows the base matrices of NR LDPC and the JSCC-LDPC algorithm provided in this application. As shown in Figure 12, in the NR LDPC base matrix, the state nodes are located in the information portion, while in the JSCC-LDPC base matrix, the state nodes are located in the check portion.

[0177] C1: For the base matrix constructed in this application, the square matrix corresponding to the check part after expansion is full rank to ensure that the source bits are encoded without bit rate loss. Specifically, the even numbers in the base matrix can be set to zero and the odd numbers can be set to 1, thereby ensuring that the resulting binary square matrix is ​​full rank under binary operations. It should be noted that the even numbers in the base matrix specifically refer to the elements "0" and "2", and the odd numbers in the base matrix specifically refer to the element "1".

[0178] C2: Controls the maximum row weight and maximum column weight in the basis matrix to control the density of 1 in the basis matrix to ensure the performance of the basis matrix under medium and short code lengths.

[0179] C3: Controls the number of variable nodes with degree 2 to ensure that the minimum distance of the constructed basis matrix increases linearly with the code length, thereby better controlling the performance of the error floor.

[0180] C4: It is necessary to store multiple basis matrices with better decoding thresholds for further optimization, so as to determine the final basis matrix from the multiple basis matrices with better decoding thresholds. The multiple basis matrices with better decoding thresholds can also become candidate basis matrices.

[0181] This constraint is designed because, for short codes, the decoding threshold cannot fully determine the ultimate performance of the actual code, as the complex subgraph structure in the factor graph of short codes can seriously affect decoding performance. Therefore, storing multiple basis matrices with good thresholds can better select basis matrices with excellent performance for medium and short code lengths.

[0182] Based on the above introduction, the method for constructing a basis matrix provided in this application can be summarized as the following process: based on the basic structure and constraints of the basis matrix provided in this application, multiple candidate basis matrices are obtained; based on the multiple candidate basis matrices, the basis matrix is ​​determined (i.e., corresponding to the above-mentioned constraint C4).

[0183] Among them, the basic structure of the basis matrix (i.e. S1) includes:

[0184] The base matrix includes an information part and a check part. The check part includes a state column. The state nodes included in the state column are variable nodes corresponding to the check bits punctured during transmission.

[0185] The constraints on the basis matrix include:

[0186] The check part of the basis matrix, after expansion, satisfies the full rank (i.e., C1) under the modulo-2 operation;

[0187] The maximum row weight and maximum column weight of the basis matrix meet the first setting condition, which is set based on the performance of the waterfall region and error flat region of JSCC-LDPC (i.e., C2);

[0188] The number of variable nodes of degree 2 of the base matrix meets the second setting condition, which is set based on the performance of the error floor region of JSCC-LDPC (ie, C3).

[0189] Based on the above description, compared to traditional channel coding LDPC, the JSCC-LDPC for WLAN in this application needs to meet more structural characteristics and constraints. Therefore, the check part and information part of the base matrix have different designs from the traditional base matrix. When designing a specific base matrix, the temporary or final base matrix generated in each design step needs to meet the above constraints C1 to C3 while conforming to the basic structure described in S1 above, and the generation of the base matrix needs to meet constraint C4.

[0190] 1.3. Construction of basis matrix.

[0191] Specifically, a differential evolution-based method can be used to iteratively obtain multiple candidate basis matrices, wherein the differential evolution method includes using the above-mentioned improved JSCC-EXIT graph analysis method to calculate the decoding threshold, and the temporarily generated basis matrix or candidate basis matrix must satisfy the basic structure S1 and constraints C1 to C3.

[0192] Figure 13 is a schematic flow chart of the improved differential evolution method provided in this application for designing a base matrix. As shown in Figure 13, the dimension of the base matrix is ​​determined according to the sparsity p of the information bits and the target code rate. Based on the dimension of the determined base matrix, M base matrices are randomly generated, and the M base matrices all satisfy the basic structure S1. Determine whether the M base matrices all satisfy the constraints C1 to C3. If not, regenerate M base matrices until each of the generated M base matrices satisfies the basic structure S1 and the constraints C1 to C3. Afterwards, the improved differential evolution method is used to iterate the M base matrices, and the improved JSCC-EXIT graph analysis method is used to calculate the decoding threshold during the iteration process. Further determine whether all the base matrices generated by the iteration meet the constraints C1 to C3. It should be understood that although the M randomly generated basis matrices satisfy constraints C1-C3, the basis matrices obtained through the iterative differential evolution process do not necessarily satisfy constraints C1-C3. Therefore, it is necessary to ensure that each basis matrix generated by the iteration still satisfies constraints C1-C3. Otherwise, the improved differential evolution method is iterated until each basis matrix obtained through the iterative improvement satisfies constraints C1-C3. If the improved differential evolution algorithm has not converged or has not reached the maximum number of iterations, the iteration is continued. When the improved differential evolution algorithm converges or reaches the maximum number of iterations, the X basis matrices generated by the iteration are output, referred to herein as X candidate basis matrices. The basis matrix for JSCC-LDPC can be determined from these X candidate basis matrices.

[0193] Before designing the specific basis matrix of JSCC-LDPC, this application divides the source sparsity into several typical intervals, namely (0, 0.05], (0.05, 0.11], (0.11, 0.3] and (0.3, 0.5]. These four sparsity intervals can cover all different source sparsities. Therefore, this application only needs to design basis matrices with source sparsities of 0.05, 0.11 and 0.30 respectively. The basis matrices corresponding to different source sparsities can be expanded to obtain the corresponding check matrices. In practical applications, the corresponding check matrix is ​​selected according to the current actual source sparsity p1. The corresponding relationship can be as follows:

[0194] When p1∈(0,0.05], select the check matrix corresponding to p1=0.05;

[0195] When p1∈(0.05,0.11], select the check matrix corresponding to p1=0.11;

[0196] When p1∈(0.11,0.3], select the check matrix corresponding to p1=0.30;

[0197] When p1∈(0.3,0.5], the traditional check matrix for equal probability sources is selected.

[0198] To adapt to the hardware in existing WiFi standards, while considering the source sparsity and target bit rate design, this application designs basis matrices with source sparsities of 0.05, 0.11, and 0.30, respectively. The ideas are as follows:

[0199] The base matrix includes an information part and a check part. The check part includes a state column, which is also called a puncturing column. In addition, during the research process of the present application, it was found that the combination of the state node and the node with degree 1 can better adapt to the hybrid automatic repeat request (HARQ). Therefore, the check part also includes a column with degree 1. The state column includes at least one element "2", the column with degree 1 includes the element "1", and the element "1" in the column with degree 1 and one element "2" in the at least one element "2" in the state column are located in the same row in the base matrix. In addition, the value of the last element of the remaining columns in the base matrix except the state column and the column with degree 1 can all be 0. Exemplarily, for different source sparsities, the base matrix structure of JSCC-LDPC provided in this application is shown in Figures 7 to 9.

[0200] It should be noted that the base matrix structures shown in Figures 7 to 9 are examples only. The state column can optionally be any column in the check part, and the degree-1 column can be any column in the check part. Figures 7 to 9 use the state column as the first column in the check part, and the degree-1 column as the second column in the check part as an example. The value of the element in the last row of the remaining columns in the base matrix, excluding the state column and the degree-1 column, can be 0. Furthermore, the element "x" in the base matrix represents the part to be designed, and the value of "x" can be 0, 1, or 2. It can be seen that the dimensions of the base matrix vary for different source sparsities, but the structure of the base matrix is ​​similar. When subsequently expanding the base matrix, this application uses a two-step expansion method to obtain the JSCC-LDPC check matrix. The first expansion step corresponds to the first expansion factor, which is 2, to eliminate the element "2" in the base matrix. Furthermore, the expanded check part of the base matrix must meet the full rank condition under modulo-2 operations, and the information portion of the base matrix is ​​completely punctured during transmission.

[0201] (1) For the JSCC-LDPC basis matrix with source sparsity p1 = 0.05, the basis matrix dimension is [5, 11], i.e., 5 rows and 11 columns. The basis matrix satisfies the following characteristics:

[0202] a) One of the columns in the check part is the status column. For example, this column is the first column in the check part, specifically [1 1 1 2 2] T ,The specific selection of this column is to achieve a compromise between the ,decoding threshold and trap set / ring elimination;

[0203] b) One of the columns of the check part is a column of degree 1. For example, the column is the second column of the check part matrix, and specifically can be [0 0 0 0 1] T This design, combined with the aforementioned state column, can achieve an extremely low decoding threshold and significantly reduce the complexity of encoding and decoding. It should be noted that the 1 in this column can be located anywhere in the column, and the 1 in this column must be connected to the 2 in the state column (not connected to 1), or in other words, be in the same row in the basis matrix;

[0204] c) Except for the first two columns of the check part, the last row of the remaining part of the basis matrix is ​​set to all 0s;

[0205] Although such a design reduces the degree of design freedom, that is, some sacrifices are made in terms of decoding threshold or performance, the complexity, especially the complexity of the encoding end, is lower.

[0206] According to the above-mentioned constraint C2, in order to optimize the decoding performance of the constructed basis matrix under medium and short codes, it is necessary to constrain the density of the basis matrix, that is, to control the maximum row weight and column weight. For p1=0.05, this application provides six constraints, respectively expressed as C3R8, C3R9, C4R8, C4R9, C5R8 and C5R9 (some examples of the first setting condition, i.e. C2). Among them, the constraint C3R8 indicates that the maximum column weight containing x in the basis matrix does not exceed 3, and the maximum row weight of the entire basis matrix does not exceed 8. The other constraints can be derived from this and will not be explained one by one.

[0207] The following are the three basis matrices with the minimum decoding threshold under the above six constraints, where Thr represents the decoding threshold of the basis matrix provided in this application at the corresponding code rate. The decoding threshold refers to the minimum Eb / N0 (Eb represents the average energy of a binary bit, and N0 represents the noise power spectral density) required for successful decoding of the JSCC-LDPC corresponding to the basis matrix when the code length is infinite, and Gap refers to the distance (expressed in dB) between the decoding threshold and the Eb / N0 corresponding to the channel capacity of the corresponding code rate. The meanings of these parameters also apply to other embodiments below and will not be repeated.

[0208] In addition, the three basis matrices with the smallest decoding threshold under these constraints (as some examples of candidate basis matrices) can be used as candidate basis matrices of JSCC-LDPC to expand and obtain the final check matrix.

[0209] C3R8:

[0210] C4R8:

[0211] C5R8:

[0212] C3R9:

[0213] C4R9:

[0214] C5R9:

[0215] These basis matrices given above are actually examples of candidate basis matrices in this application. For a candidate basis matrix, it can be expanded in two steps to obtain a check matrix, and in the expansion process, a basis matrix can be expanded to obtain multiple check matrices (which can be considered as candidate check matrices). Therefore, based on multiple candidate basis matrices, multiple candidate check matrices will be obtained. Next, by performing performance evaluation on the candidate check matrix, the candidate check matrix whose decoding performance meets the requirements (e.g., optimal decoding performance) is selected as the final check matrix. As an example, assuming that the check matrix a of the JSCC-LDPC finally determined is obtained based on the candidate basis matrix b expansion, the candidate basis matrix b is the basis matrix provided in this application, and the check matrix a is the check matrix provided in this application.

[0216] For example, the basis matrix corresponding to p1=0.05 may be the following matrix 1, or a matrix obtained by performing row transformation and / or column transformation on the matrix 1:

[0217] Matrix 1

[0218] It should be noted that in order to obtain a check matrix with ideal performance, a large number of candidate basis matrices are used to determine the final check matrix. Due to limited space, the above embodiment only provides three basis matrices with the smallest decoding thresholds as candidate basis matrices for each of the six constraints. Of course, candidate basis matrices can also be provided for constraints other than the above six constraints, and a larger number of basis matrices can be provided as candidate basis matrices for each constraint. For example, for each constraint, the top 100 basis matrices in ascending order of decoding thresholds are provided as candidate basis matrices, without limitation.

[0219] Based on a candidate basis matrix, the first step is to expand the candidate basis matrix by an expansion factor of z = 2 (i.e., the first expansion factor) to obtain a first matrix. Here, the element "2" in the candidate basis matrix can be expanded into a 2×2 matrix of all ones, while the element "1" can be expanded into a 2×2 identity matrix or a 2×2 anti-diagonal matrix. The second step is to expand the first matrix by an expansion factor of z = 32 (i.e., an example of the second expansion factor) to obtain a candidate check matrix. Next, a trap set analysis can be performed on the candidate check matrix to evaluate its decoding performance.

[0220] By evaluating the decoding performance of multiple candidate check matrices, a check matrix for JSCC-LDPC can be selected from the multiple candidate check matrices. For example, the above matrix 1 is expanded to obtain the check matrix corresponding to p1=0.05 provided in this application. The check matrix can be specifically such as matrix 2, or a matrix obtained by row transformation and / or column transformation of matrix 2. Matrix 2 can refer to the above embodiment and will not be listed again.

[0221] (2) For the JSCC-LDPC basis matrix with source sparsity p1 = 0.11, the dimension of the basis matrix is ​​[6, 11], i.e., 6 rows and 11 columns. The main characteristics of the basis matrix are described as follows:

[0222] a) One of the columns in the check part is the status column. For example, this column is the first column in the check part, specifically [1 2 1 2 1 2] T ,The specific selection of this column is to achieve a compromise between the ,decoding threshold and trap set / ring elimination;

[0223] b) One of the columns of the check part is a column of degree 1. Exemplarily, this column is the second column of the check part matrix, and specifically can be [0 0 0 0 0 1] T This design, combined with the aforementioned state column, can achieve an extremely low decoding threshold and significantly reduce the complexity of encoding and decoding. Similarly, the 1 in this column can be located anywhere in the column, and the 1 in this column must be connected to the 2 in the state column, or in the same row in the matrix;

[0224] c) Except for the first two columns of the check part, the last row of the remaining part of the basis matrix can be set to all zeros.

[0225] According to the constraint C2, in order to optimize the decoding performance of the constructed base matrix under medium and short codes, it is necessary to further constrain the density of the base matrix, that is, to control the maximum row weight and column weight.

[0226] For p1 = 0.11, this application provides six constraints, denoted as C3R6, C3R7, C4R6, C4R7, C5R7, and C5R8. C3R6 indicates that the maximum column weight containing "x" in the basis matrix does not exceed 3, and the maximum row weight of the entire basis matrix does not exceed 6. The remaining constraints are similar to C3R6 and will not be described one by one.

[0227] The following are three basis matrices with the smallest decoding thresholds under the above six constraints.

[0228] C3R6:

[0229] C4R6:

[0230] C5R7:

[0231] C3R7:

[0232] C4R7:

[0233] C5R8:

[0234] For example, the base matrix corresponding to p1=0.11 provided in this application is the following matrix 3, or a matrix obtained by performing row transformation and / or column transformation on matrix 3:

[0235] Matrix 3

[0236] Based on the base matrix, the first step is to expand the base matrix with an expansion factor z = 2 to obtain a first matrix, wherein the element "2" in the base matrix can be expanded to a 2×2 all-1 matrix, and the element "1" can be expanded to a 2×2 unit matrix or a 2×2 anti-diagonal matrix; the second step is to expand the first matrix with an expansion factor z = 32 (i.e., an example of the second expansion factor) to obtain a check matrix.

[0237] The matrix obtained by expanding matrix 3 is the check matrix corresponding to p1=0.11 provided in this application, which can be matrix 4, or a matrix obtained by row transformation and / or column transformation of matrix 4. Matrix 4 can refer to the above embodiment and will not be listed again.

[0238] (3) For the JSCC-LDPC basis matrix with source sparsity p1 = 0.30, the dimension of the basis matrix is ​​[7, 11], i.e., 7 rows and 11 columns. The main characteristics of the basis matrix are described as follows:

[0239] a) One of the columns of the check matrix is ​​the state column. Exemplarily, this column is the first column of the check matrix, specifically [1 1 1 1 2 2 2] T ,The specific selection of this column is to achieve a compromise between the ,decoding threshold and trap set / ring elimination;

[0240] b) One of the columns of the check matrix has a degree of 1. Exemplarily, the column is the second column of the check matrix, and specifically can be [0 0 0 0 0 0 1] T This design, combined with the aforementioned state column, can achieve an extremely low decoding threshold and significantly reduce the complexity of encoding and decoding. The 1 in this column can be located anywhere in the column, and the 1 in this column must be connected to the 2 in the state column, or in the same row in the matrix;

[0241] c) Except for the first two columns of the check matrix, the last row of the remaining part of the basis matrix is ​​all set to 0.

[0242] According to the constraint C2, in order to optimize the decoding performance of the constructed basis matrix under medium and short codes, it is necessary to further constrain the density of the basis matrix. For p1 = 0.30, this application provides eight constraints, denoted as C3R6, C3R7, C4R6, C4R7, C4R8, C5R7, C5R8, and C5R9. The following are the three basis matrices with the smallest decoding thresholds under these eight constraints.

[0243] C3R6:

[0244] C4R6:

[0245] C4R8:

[0246] C5R8:

[0247] C3R7:

[0248] C4R7:

[0249] C5R7:

[0250] C5R9:

[0251] Illustratively, the base matrix corresponding to p1=0.30 given in this application is the following matrix 5, or a matrix obtained by performing row transformation and / or column transformation on matrix 5.

[0252] Matrix 5

[0253] Based on this base matrix (i.e., matrix 5), the first step is to expand the base matrix with an expansion factor z = 2 (i.e., the first expansion factor) to obtain a first matrix. Here, the element "2" in the base matrix can be expanded into a 2×2 all-one matrix, and the element "1" can be expanded into a 2×2 identity matrix or a 2×2 anti-diagonal matrix. The second step is to expand the first matrix with an expansion factor z = 32 (i.e., an example of the second expansion factor) to obtain a candidate check matrix.

[0254] The matrix obtained based on matrix 5 is the check matrix corresponding to p1=0.30 given in this application, which can be matrix 6, or a matrix obtained by row transformation and / or column transformation of matrix 6. Matrix 6 can refer to the above embodiment and is not repeated here.

[0255] Optionally, in each embodiment, the positions of the information portion and the check portion of the base matrix or the check matrix can be interchanged. For example, in the base matrix, the information portion is on the left and the check portion is on the right; or, the check portion is on the left and the information portion is on the right, without limitation.

[0256] The decoding performance of the JSCC-LDPC provided by this application under the source sparsity p1 is given below in conjunction with Figures 14 to 16, and compared with the performance of SSCC. Among them, SSCC is a traditional encoding and decoding scheme with separate source coding and channel coding. Here, the source coding uses classic traditional arithmetic coding for source coding (lossy), while the channel coding uses the NR LDPC code in the 5G standard, and the maximum number of iterations is set to 20 times; in JSCC, p1 is the source sparsity, which represents the probability of "1" appearing in each source bit.

[0257] Figures 14 to 16 show the block error rate (BLER) and signal-to-noise ratio (SNR) curves. It can be seen that compared with the traditional SSCC solution, the technical solution of the present application has the lowest BLER at the same SNR; in other words, at the same BLER, the technical solution of the present application has the lowest SNR requirement.

[0258] According to the simulation results of Figures 14 to 16, it can be seen that the JSCC-LDPC provided in this application can achieve error control performance that is better than the traditional SSCC scheme under various typical p1 conditions, so the decoding performance is better than the traditional SSCC scheme.

[0259] Specifically, in FIG14 , “Ra=1.5, JSCC-LDPC” represents the JSCC scheme of the present application: a binary biased signal source N with a length of 384 bits S , encoded as a codeword N with a length of 256 bits C , the code rate is 1.5, and the matrix 2 given in this application is used as the check matrix. "Ra=1.5,SSCC(NR LDPC,Rcc=0.78)" represents the SSCC scheme, and the length of the binary biased signal source N is 384 bits. S First, it is compressed into 200 bits by arithmetic coding (arithmetic coding is a source coding, source coding, SC), and then NR LDPC coding is used to obtain 256 bits of N C , the code rate of the channel coding part is 0.78, and the overall code rate is the same as the JSCC scheme, which is 1.5. "Ra=1.5,SSCC(NR LDPC,Rcc=0.81)" represents the SSCC scheme, with a 384-bit binary biased signal source N S It is first compressed to 208 bits through arithmetic coding, and then NR LDPC coding is performed to obtain 256 bits of N CThe code rate of the channel coding part is 0.81, and the overall code rate is the same as the JSCC solution, which is 1.5. Obviously, it can be seen that compared with the traditional SSCC solution, the technical solution of this application has the lowest BLER at the same SNR; in other words, at the same BLER, the technical solution of this application has the lowest SNR requirement.

[0260] In Figure 15, "Ra = 1.0, JSCC-LDPC" represents the JSCC scheme of this application, where Ra = 1.0 indicates a code rate of 1.0 and the use of matrix 4, provided in this application, as the parity check matrix. Clearly, compared to the traditional SSCC scheme, the technical scheme of this application achieves the lowest BLER at the same SNR; in other words, at the same BLER, the technical scheme of this application has the lowest SNR requirement.

[0261] In Figure 16, "Ra = 0.67, JSCC-LDPC" represents the JSCC scheme of this application, where Ra = 0.67 indicates a code rate of 0.67 (i.e., 2 / 3) and the use of matrix 6 provided in this application as the parity check matrix. Clearly, compared to the traditional SSCC scheme, the technical scheme of this application achieves the lowest BLER at the same SNR; in other words, at the same BLER, the technical scheme of this application has the lowest SNR requirement.

[0262] Based on the description of the simulation parameters and simulation results of FIG14 , those skilled in the art can understand that the simulation data and simulation results of FIG15 and FIG16 are similar, and no further details will be given.

[0263] The above describes in detail the encoding method provided in the embodiment of the present application. The following introduces the communication device provided in the present application.

[0264] Referring to FIG. 17 , the present application provides a communication device 1000 .

[0265] The communication device 1000 may be a coding device, or a communication device applied to a coding device capable of implementing the method performed by the above-mentioned coding device, such as a chip, a chip system or a circuit.

[0266] Alternatively, the communication device 1000 may be a decoding device, or a communication device applied to a decoding device and capable of implementing the method performed by the above-mentioned decoding device, such as a chip, a chip system or a circuit.

[0267] As shown in FIG17 , the communication device 1000 includes a processing module 1001 , which may also be referred to as a processor, a processing board, a processing unit, or a processing device.

[0268] When the communication device 1000 is an encoding device, or a communication device applied to an encoding device and capable of implementing the method performed by the encoding device, the processing module 1001 is configured to perform encoding based on a parity check matrix. The details of the parity check matrix refer to the description of the parity check matrix in the method described above and are not further described here. When the communication device 1000 is a decoding device, or a communication device applied to a decoding device and capable of implementing the method performed by the decoding device, the processing module 1001 is configured to perform decoding based on a parity check matrix. The details of the parity check matrix refer to the description of the parity check matrix in the method described above and are not further described here.

[0269] Optionally, the communication device 1000 further includes a communication module 1002, which may also be referred to as a transceiver module, transceiver, transceiver, or transceiver device. When the communication device 1000 is an encoding device, or a communication device applied to an encoding device and capable of implementing the method performed by the encoding device, the communication module 1002 may be used to implement the transceiver function of the encoding device in the embodiments described in Figures 5 to 13; when the communication device 1000 is a decoding device, or a communication device applied to a decoding device and capable of implementing the method performed by the decoding device, the communication module 1002 may be used to implement the transceiver function of the decoding device in the embodiments described in Figures 5 to 13.

[0270] In addition, it should be noted that the aforementioned communication module and / or processing module can be implemented by a virtual module, for example, the processing module can be implemented by a software functional unit or a virtual device, and the communication module can be implemented by a software function or a virtual device. Alternatively, the processing module or the communication module can also be implemented by a physical device, for example, if the device is implemented using a chip / circuit (such as an integrated circuit or a logic circuit, etc.). The communication module can be an input / output circuit and / or a communication interface, performing input operations (corresponding to the aforementioned receiving operations) and output operations (corresponding to the aforementioned sending operations); the processing module is an integrated processor or microprocessor or circuit (such as an integrated circuit, a logic circuit, etc.).

[0271] The division of modules in this application is illustrative and represents only a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the examples of this application may be integrated into a single processor, exist physically as separate modules, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in either hardware or software functional modules.

[0272] In addition, referring to FIG18 , the present application further provides a communication device 1100. Optionally, the communication device 1100 may be a chip or a chip system. Optionally, in the present application, the chip system may be composed of a chip or may include a chip and other discrete devices.

[0273] The communication device 1100 includes at least one processor 1110, which implements the functions of the encoding device or the decoding device described in the above examples.

[0274] Optionally, the processor 1110 is coupled to a memory, which may be located within the communication device, integrated with the processor, or external to the communication device. For example, the communication device 1100 may further include at least one memory 1120. The memory 1120 stores the computer programs, instructions, or data necessary to implement any of the above-described method embodiments; the processor 1110 may execute the computer programs, instructions, or data stored in the memory 1120 to perform the method of any of the above-described embodiments.

[0275] Optionally, the communication device 1100 may further include a communication interface 1130, through which the communication device 1100 may exchange information with other devices. Exemplarily, the communication interface 1130 may be a transceiver, a circuit, a bus, a module, a pin, or another type of communication interface. When the communication device 1100 is a chip-type device or circuit, the communication interface 1130 in the device 1100 may also be an input / output circuit that may input information (or receive information) and output information (or send information). The processor 1110 may be an integrated processor, microprocessor, integrated circuit, or logic circuit, etc., and the processor may determine output information based on input information.

[0276] Coupling in this application refers to an indirect coupling or communication connection between devices, units, or modules, which can be electrical, mechanical, or other forms, and is used for information exchange between devices, units, or modules. Processor 1110 may operate in conjunction with memory 1120 and communication interface 1130. This application does not limit the specific connection medium between the processor 1110, memory 1120, and communication interface 1130.

[0277] Optionally, as shown in FIG18 , the processor 1110, the memory 1120, and the communication interface 1130 are interconnected via a bus 1140. Optionally, the bus may include an address bus, a data bus, a control bus, and other types of buses. Furthermore, for ease of illustration, FIG18 shows one bus 1140, but this does not mean that there is only one bus or only one type of bus.

[0278] In this application, a processor may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and may implement or execute the methods, steps, and logic block diagrams disclosed in this application. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in this application may be directly executed by a hardware processor, or by a combination of hardware and software modules within the processor.

[0279] The memory may be a non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), or a volatile memory, such as a random-access memory (RAM). The memory is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory in this application may also be a circuit or any other device that can perform a storage function, for storing program instructions and / or data.

[0280] Since the communication device 1100 provided in this example can be applied to an encoding device to implement a method executed by the encoding device, or the communication device 1100 can be applied to a decoding device to implement a method executed by the decoding device, the technical effects achieved by the device embodiment can be referred to the description of the above method embodiment and will not be repeated here.

[0281] Based on the above examples, the present application provides a communication system, including the encoding device and decoding device in the above method embodiment.

[0282] The technical solutions provided in this application can be implemented in whole or in part through software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in this application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal device, an access network device, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium.

[0283] In this application, under the premise of no logical contradiction, the examples can reference each other, for example, the methods and / or terms between method embodiments can reference each other, for example, the functions and / or terms between device embodiments can reference each other, for example, the functions and / or terms between device examples and method examples can reference each other.

[0284] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0285] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0286] The multiple (items) involved in the embodiments of the present application refer to two (items) or more than two (items). "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, it should be understood that although the terms first, second, etc. may be used to describe each object in this application, these objects should not be limited to these terms. These terms are only used to distinguish each object from each other.

[0287] The term "comprising" and any variations thereof mentioned in the embodiments of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices. In addition, words such as "exemplarily" or "for example" are used to indicate examples, illustrations or descriptions. Any method or design described in this application as "exemplarily" or "for example" should not be interpreted as being more preferred or more advantageous than other methods or designs. Specifically, the use of words such as "exemplarily" or "for example" is intended to illustrate related technical solutions or concepts in a concrete manner.

[0288] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0289] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0290] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0291] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method of encoding or decoding in a wireless system, It is characterized in that include: Encoding or decoding based on a check matrix; The check matrix is ​​obtained by expanding a base matrix, the base matrix includes an information part and a check part, the check part includes a state column and a column with a degree of 1, the state column includes at least one element "2", the column with a degree of 1 includes an element "1", and an element "2" in the at least one element "2" and the element "1" are located in the same row of the base matrix.

2. According to the method of claim 1, the status column also includes an element "1", and the number of elements "2" and element "1" in the status column is close to that of the elements "1" in the status column.

3. The method according to claim 1 or 2, It is characterized in that Elements of the remaining columns of the base matrix except the state column and the column of degree 1 include element "2", element "1" or element "0".

4. The method according to any one of claims 1 to 3, It is characterized in that The last row of the remaining columns of the base matrix except the state column and the column with degree 1 is an element "0".

5. The method according to any one of claims 1 to 4, It is characterized in that The elements of the state column are variable nodes corresponding to the punctured check bits.

6. The method according to any one of claims 1 to 5, It is characterized in that The expansion includes two-step expansion, the first expansion factor corresponding to the first expansion of the two-step expansion is 2, and the first expansion includes: The base matrix is ​​expanded based on the first expansion factor to obtain a first matrix, wherein the element "2" in the base matrix is ​​expanded to a 2×2 all-1 matrix, the element "1" is expanded to a 2×2 unit matrix or an anti-diagonal square matrix, and the element "0" is expanded to a 2×2 all-zero square matrix.

7. The method according to claim 6, It is characterized in that The second expansion factor corresponding to the second expansion step of the two-step expansion is 32, and the second expansion step includes: The first matrix is ​​expanded based on the second expansion factor to obtain the check matrix, and the element "1" in the first matrix is ​​expanded to a 32×32 circulant permutation matrix P i , the element "0" in the first matrix is ​​expanded to a 32×32 all-zero square matrix, where 0≤i≤31 and i is an integer, P 0 represents the unit matrix, P i Indicates P 0 The square matrix is ​​obtained by cyclically shifting each column vector in right i positions.

8. The method according to any one of claims 1 to 7, It is characterized in that The base matrix includes the following matrix 1, or a matrix obtained by performing row transformation or column transformation on matrix 1; Matrix 1 9. The method according to any one of claims 1 to 8, It is characterized in that The check matrix includes the following matrix 2, or a matrix obtained by performing row transformation or column transformation on the matrix 2: Matrix 2 The element "-1" in the matrix 2 represents a z×z all-zero matrix, the element "0" represents a z×z unit matrix, and the element P i represents a z×z cyclic permutation matrix, i represents a cyclic shift value, 0≤i≤z-1, i is an integer, and z represents the second expansion factor.

10. The method according to claim 8 or 9, It is characterized in that The sparsity of the basis matrix corresponding to the source belongs to the sparsity interval (0, 0.05].

11. The method according to any one of claims 1 to 7, It is characterized in that The base matrix includes the following matrix 3, or a matrix obtained by performing row transformation or column transformation on the matrix 3: Matrix 3 12. The method according to any one of claims 1 to 7, 11, It is characterized in that The check matrix includes the following matrix 4, or a matrix obtained by performing row transformation or column transformation on the matrix 4: Matrix 4 The element "-1" in the matrix 4 represents a z×z all-zero matrix, the element "0" represents a z×z unit matrix, and the element P i represents a z×z cyclic permutation matrix, i represents a cyclic shift value, 0≤i≤z-1, i is an integer, and z represents the second expansion factor.

13. The method according to claim 11 or 12, It is characterized in that The sparsity of the basis matrix corresponding to the source belongs to the sparsity interval (0.05, 0.11].

14. The method according to any one of claims 1 to 7, It is characterized in that The base matrix includes the following matrix 5, or a matrix obtained by performing row transformation or column transformation on the matrix 5: Matrix 5 15. The method according to any one of claims 1 to 7, 14, It is characterized in that The check matrix includes the following matrix 6, or a matrix obtained by performing row transformation or column transformation on the matrix 6: Matrix 6 The element "-1" in the matrix 6 represents a z×z all-zero matrix, the element "0" represents a z×z unit matrix, and the element P i represents a z×z cyclic permutation matrix, i represents a cyclic shift value, 0≤i≤z-1, i is an integer, and z represents the second expansion factor.

16. The method according to claim 14 or 15, It is characterized in that The sparsity of the basis matrix corresponding to the source belongs to the sparsity interval (0.11, 0.3].

17. A communication device in a wireless system, It is characterized in that include: A processing module, used for encoding based on a check matrix; The check matrix is ​​obtained by expanding a base matrix, the base matrix includes an information part and a check part, the check part includes a state column and a column with a degree of 1, the state column includes at least one element "2", the column with a degree of 1 includes an element "1", and an element "2" in the at least one element "2" and the element "1" are located in the same row of the base matrix.

18. The communication device according to claim 17, wherein the status column further comprises an element "1", and the number of elements "2" and element "1" in the status column is close to that of the elements "1" in the status column.

19. The communication device according to claim 17 or 18, It is characterized in that Elements of the remaining columns of the base matrix except the state column and the column of degree 1 include element "2", element "1" or element "0".

20. The communication device according to any one of claims 17 to 19, It is characterized in that The last row of the remaining columns of the base matrix except the state column and the column with degree 1 is an element "0".

21. The communication device according to any one of claims 17 to 20, It is characterized in that The elements of the state column are variable nodes corresponding to the punctured check bits.

22. The communication device according to any one of claims 17 to 21, It is characterized in that The expansion includes two-step expansion, the first expansion factor corresponding to the first expansion of the two-step expansion is 2, and the first expansion includes: The base matrix is ​​expanded based on the first expansion factor to obtain a first matrix, wherein the element "2" in the base matrix is ​​expanded to a 2×2 all-1 matrix, the element "1" is expanded to a 2×2 unit matrix or an anti-diagonal square matrix, and the element "0" is expanded to a 2×2 all-zero square matrix.

23. The communication device according to claim 22, It is characterized in that The second expansion factor corresponding to the second step expansion is 32, and the second step expansion includes: The first matrix is ​​expanded based on the second expansion factor to obtain the check matrix, and the element "1" in the first matrix is ​​expanded to a 32×32 circulant permutation matrix P i , the element "0" in the first matrix is ​​expanded to a 32×32 all-zero square matrix, where 0≤i≤31 and i is an integer, P 0 represents the unit matrix, P i Indicates P 0 The square matrix is ​​obtained by cyclically shifting each column vector in right i positions.

24. A communication device in a wireless system, It is characterized in that include: A processing module, used for decoding based on a check matrix; The check matrix is ​​obtained by expanding a base matrix, the base matrix includes an information part and a check part, the check part includes a state column and a column with a degree of 1, the state column includes at least one element "2", the column with a degree of 1 includes an element "1", and an element "2" in the at least one element "2" and the element "1" are located in the same row of the base matrix.

25. The communication device according to claim 24, wherein the status column further comprises an element "1", and the number of elements "2" and elements "1" in the status column is close to that of the elements "1" in the status column.

26. The communication device according to claim 24 or 25, It is characterized in that Elements of the remaining columns of the base matrix except the state column and the column of degree 1 include element "2", element "1" or element "0".

27. The communication device according to any one of claims 24 to 26, It is characterized in that The last row of the remaining columns of the base matrix except the state column and the column with degree 1 is an element "0".

28. The communication device according to any one of claims 24 to 27, It is characterized in that The elements of the state column are variable nodes corresponding to the punctured check bits.

29. The communication device according to any one of claims 24 to 28, It is characterized in that The expansion includes two-step expansion, the first expansion factor corresponding to the first expansion of the two-step expansion is 2, and the first expansion includes: The base matrix is ​​expanded based on the first expansion factor to obtain a first matrix, wherein the element "2" in the base matrix is ​​expanded to a 2×2 all-1 matrix, the element "1" is expanded to a 2×2 unit matrix or an anti-diagonal square matrix, and the element "0" is expanded to a 2×2 all-zero square matrix.

30. The communication device according to claim 29, It is characterized in that The second expansion factor corresponding to the second step expansion is 32, and the second step expansion includes: The first matrix is ​​expanded based on the second expansion factor to obtain the check matrix, and the element "1" in the first matrix is ​​expanded to a 32×32 circulant permutation matrix P i , the element "0" in the first matrix is ​​expanded to a 32×32 all-zero square matrix, where 0≤i≤31 and i is an integer, P 0 represents the unit matrix, P i Indicates P 0 The square matrix is ​​obtained by cyclically shifting each column vector in right i positions.

31. A communication device, It is characterized in that It comprises a communication interface and a circuit, wherein the communication interface is used to decode an information sequence to be encoded and input the information sequence to be encoded into the circuit; the circuit is used to execute the method as described in any one of claims 1 to 16, encode the information sequence to be encoded based on a check matrix to obtain coded bits; the communication interface is also used to output the coded bits.

32. A communication device, It is characterized in that It comprises a communication interface and a circuit, wherein the communication interface is used to decode a sequence to be decoded and input the sequence to be decoded into the circuit; the circuit is used to execute the method according to any one of claims 1 to 16, decode the bits to be decoded based on a check matrix to obtain a decoded sequence; the communication interface is also used to output the decoded sequence.

33. A computer readable storage medium, It is characterized in that The computer-readable storage medium stores computer instructions. When the computer instructions are executed on a computer, the method according to any one of claims 1 to 16 is implemented.

34. A computer program product, It is characterized in that The computer program product comprises a computer program code, and when the computer program code is run on a computer, the method according to any one of claims 1 to 16 is implemented.

Images