Coding matrix determination method, data processing device, chip, computer readable storage medium and computer program product

Abstract

The embodiment of the invention discloses a coding matrix determination method, a data processing device, a chip, a computer readable storage medium and a computer program product, and the method comprises the steps: determining a plurality of N-row M-column coding matrixes meeting a target constraint, the target constraint comprises a constraint for the column weight ratio of the coding matrixes, N and M are integers greater than 1, N is smaller than M, and M is greater than 1; the column weight proportion of the coding matrix comprises the proportion of the column number of each column weight in the coding matrix to the total column number of the coding matrix; determining a pattern segmentation multiple access PDMA coding matrix based on the plurality of N-row M-column coding matrixes; compared with the method of directly selecting the PDMA coding matrix from the coding matrix pool, the method can reduce the calculation amount and save the time.

Description

Technical Field

[0001] The present application relates to the field of communications, and in particular to a coding matrix determination method, a data processing device, a chip, a computer-readable storage medium, and a computer program product. Background Art

[0002] To meet the demands of dense, massive access and ultra-large capacity in future 5G wireless networks, and given limited spectrum resources, optimizing non-orthogonal multiple access (NOMA) technology is seen as an evolutionary trend and breakthrough direction following 4G orthogonal frequency division multiple access (OFDMA). Pattern division multiple access (PDMA) is a new type of NOMA technology.

[0003] When using PDMA technology, the user maps the data to be transmitted to a group of resources using a column (PDMA coding pattern) in the PDMA coding matrix to construct unequal diversity. Different users obtain different transmission diversity through the mapping resources determined by different columns in the PDMA coding matrix. At present, the selection of PDMA coding matrix is to select several columns with low correlation in the coding matrix pool. Selecting a coding matrix with low correlation columns from a large number of coding matrix pools often requires high computing time and complexity. The manuscript "R1-1811860--NOMA transmitter side signal processing" proposed a complex domain 4*96 coding matrix pool, and selected 10 columns of coding matrices from the 96-column coding matrix pool, with a total of (96) 10 There are many combinations, and traversing these combinations is a computationally intensive task. Summary of the Invention

[0004] The embodiments of the present application disclose a coding matrix determination method, a data processing device, a chip, a computer-readable storage medium, and a computer program product, which can reduce the amount of calculation and time required to determine a PDMA coding matrix.

[0005] In a first aspect, an embodiment of the present application provides a method for determining a coding matrix, the method comprising: determining a plurality of N-row, M-column coding matrices that satisfy target constraints, the target constraints comprising a constraint on a column weight ratio of the coding matrix, wherein N and M are both integers greater than 1, N is less than M, and the column weight ratio of the coding matrix comprises the ratio of the number of columns of each column weight in the coding matrix to the total number of columns in the coding matrix; and determining a PDMA coding matrix based on the plurality of N-row, M-column coding matrices. In the present application, column weight refers to the number of non-zero elements in a column of a matrix.

[0006] In an embodiment of the present application, a plurality of N-row, M-column coding matrices that meet the target constraints are first determined; then, based on the plurality of N-row, M-column coding matrices, a PDMA coding matrix is determined; compared to directly selecting the PDMA coding matrix from the coding matrix pool, the amount of calculation can be reduced and time can be saved.

[0007] In a possible implementation, the constraints on the column weight ratio of the coding matrix are that the column weight ratios of the coding matrix are C4=0.6, C3=0.3, C2=0.1, and C1=0, respectively, where C4 represents the ratio of the number of columns with a column weight of 4 in the coding matrix to the total number of columns in the coding matrix, C3 represents the ratio of the number of columns with a column weight of 3 in the coding matrix to the total number of columns in the coding matrix, C2 represents the ratio of the number of columns with a column weight of 2 in the coding matrix to the total number of columns in the coding matrix, and C1 represents the ratio of the number of columns with a column weight of 1 in the coding matrix to the total number of columns in the coding matrix.

[0008] In this implementation, by constraining the column weight ratios of the coding matrix to C4=0.6, C3=0.3, C2=0.1, and C1=0, the amount of computation can be reduced, and multiple N-row and M-column coding matrices that meet the target constraints can be reasonably diluted, thereby improving the system capacity.

[0009] In one possible implementation, the target constraint further includes a first value for the sparsity ratio of the encoding matrix, where the sparsity ratio of the encoding matrix is a ratio of the number of zero elements in the encoding matrix to the total number of elements in the encoding matrix. For example, the first value is 0.125.

[0010] In this implementation, by constraining the sparsity ratio of the coding matrix, the coding matrix that meets the target constraint can have a more reasonable sparsity characteristic and obtain lower computational complexity.

[0011] In a possible implementation, the encoding matrix includes a matrix with 4 rows and 10 columns as shown below:

[0012]

[0013] The PDMA encoding matrix includes P rows and Q columns in the encoding matrix, where P is an integer from 1 to 4, and Q is an integer from 1 to 10.

[0014] In one possible implementation, determining a pattern division multiple access (PDMA) coding matrix based on the multiple N-row, M-column coding matrices includes: determining the PDMA coding matrix based on a first coding matrix having non-diagonal element correlation lower than a first threshold value among the multiple N-row, M-column coding matrices; or determining the PDMA coding matrix based on a first coding matrix having the lowest non-diagonal element correlation among the multiple N-row, M-column coding matrices.

[0015] In this implementation, a PDMA coding matrix is determined based on a first coding matrix having low correlation between non-diagonal elements in a plurality of N-row and M-column coding matrices; thus, system capacity can be better improved.

[0016] In a possible implementation, the first encoding matrix is a matrix with 4 rows and 10 columns as shown below:

[0017]

[0018] The PDMA encoding matrix includes P rows and Q columns in the first encoding matrix, where P is an integer from 1 to 4, and Q is an integer from 1 to 10.

[0019] In a second aspect, an embodiment of the present application provides a communication method, which includes: multiplying the modulated symbol with the vector in the kth column of the PDMA coding matrix to obtain an extended transmission symbol vector, wherein the PDMA coding matrix is a matrix of N rows and M columns and satisfies the target constraints, and the target constraints include constraints on the column weight ratio of the coding matrix, N and M are both integers greater than 1, N is less than M, and the column weight ratio of the coding matrix includes the ratio of the number of columns of each column weight in the coding matrix to the total number of columns of the coding matrix, and k is an integer greater than 0; based on the extended transmission symbol vector, sending a signal.

[0020] In the embodiment of the present application, the PDMA coding matrix satisfies the target constraints, has a more reasonable sparse characteristic (ie, sparsity) and is closer to the ideal coding matrix than the existing PDMA coding matrix, and the communication system can obtain better spreading gain / diversity gain.

[0021] In a possible implementation, the column weight ratios of the PDMA coding matrix are C4=0.6, C3=0.3, C2=0.1, and C1=0, respectively, where C4 represents the ratio of the number of columns with a column weight of 4 in the PDMA coding matrix, C3 represents the ratio of the number of columns with a column weight of 3 in the PDMA coding matrix, C2 represents the ratio of the number of columns with a column weight of 2 in the PDMA coding matrix, and C1 represents the ratio of the number of columns with a column weight of 1 in the PDMA coding matrix.

[0022] In this implementation, by constraining the column weight ratios of the coding matrix to C4=0.6, C3=0.3, C2=0.1, and C1=0, the amount of computation can be reduced, and multiple N-row and M-column coding matrices that meet the target constraints can be reasonably diluted, thereby improving the system capacity.

[0023] In a possible implementation, the target constraint further includes that the sparse ratio of the encoding matrix is a first value.

[0024] In this implementation, by constraining the sparsity ratio of the coding matrix, the coding matrix that meets the target constraint can have a more reasonable sparsity characteristic and obtain lower computational complexity.

[0025] In a possible implementation, the PDMA encoding matrix includes P rows and Q columns of a matrix with 4 rows and 10 columns as shown below:

[0026]

[0027] P is an integer from 1 to 4, and Q is an integer from 1 to 10.

[0028] In this implementation, the PDMA coding matrix is a coding matrix with low correlation and reasonable sparsity, which is closer to the ideal coding matrix. The system obtains better spreading gain / diversity gain, while improving spectrum efficiency and reducing the complexity of the detection end, achieving more efficient PDMA uplink transmission.

[0029] In a third aspect, an embodiment of the present application provides another communication method, which includes: receiving a signal; based on a PDMA coding matrix, using a receiver based on a successive interference cancellation (SIC) algorithm or a message passing algorithm (MPA) to recover the received signal, wherein the PDMA coding matrix is a matrix with N rows and M columns and satisfies a target constraint, wherein the target constraint includes a constraint on the column weight ratio of the coding matrix, and N and M are both integers greater than 1.

[0030] In the embodiment of the present application, the PDMA coding matrix satisfies the target constraints, has a more reasonable sparse characteristic (ie, sparsity) and is closer to the ideal coding matrix than the existing PDMA coding matrix, and the communication system can obtain better spreading gain / diversity gain.

[0031] In a possible implementation, the column weight ratios of the PDMA coding matrix are C4=0.6, C3=0.3, C2=0.1, and C1=0, respectively, where C4 represents the ratio of the number of columns with a column weight of 4 in the PDMA coding matrix, C3 represents the ratio of the number of columns with a column weight of 3 in the PDMA coding matrix, C2 represents the ratio of the number of columns with a column weight of 2 in the PDMA coding matrix, and C1 represents the ratio of the number of columns with a column weight of 1 in the PDMA coding matrix.

[0032] In this implementation, by constraining the column weight ratios of the coding matrix to C4=0.6, C3=0.3, C2=0.1, and C1=0, the amount of computation can be reduced, and multiple N-row and M-column coding matrices that meet the target constraints can be reasonably diluted, thereby improving the system capacity.

[0033] In a possible implementation, the target constraint further includes that the sparse ratio of the encoding matrix is a first value.

[0034] In this implementation, by constraining the sparsity ratio of the coding matrix, the coding matrix that meets the target constraint can have a more reasonable sparsity characteristic and obtain lower computational complexity.

[0035] In a possible implementation, the PDMA encoding matrix includes P rows and Q columns of a matrix with 4 rows and 10 columns as shown below:

[0036]

[0037] P is an integer from 1 to 4, and Q is an integer from 1 to 10.

[0038] In this implementation, the PDMA coding matrix is a coding matrix with low correlation and reasonable sparsity, which is closer to the ideal coding matrix. The system obtains better spreading gain / diversity gain, while improving spectrum efficiency and reducing the complexity of the detection end, achieving more efficient PDMA uplink transmission.

[0039] In a fourth aspect, an embodiment of the present application provides a data processing device, which includes: a processing module for determining multiple N-row and M-column coding matrices that meet target constraints, wherein the target constraints include constraints on the column weight ratio of the coding matrix, N and M are both integers greater than 1, N is less than M, and the column weight ratio of the coding matrix includes the ratio of the number of columns of each column weight in the coding matrix to the total number of columns of the coding matrix; based on the multiple N-row and M-column coding matrices, a pattern division multiple access PDMA coding matrix is determined.

[0040] In a possible implementation, the constraints on the column weight ratio of the coding matrix are that the column weight ratios of the coding matrix are C4=0.6, C3=0.3, C2=0.1, and C1=0, respectively, where C4 represents the ratio of the number of columns with a column weight of 4 in the coding matrix to the total number of columns in the coding matrix, C3 represents the ratio of the number of columns with a column weight of 3 in the coding matrix to the total number of columns in the coding matrix, C2 represents the ratio of the number of columns with a column weight of 2 in the coding matrix to the total number of columns in the coding matrix, and C1 represents the ratio of the number of columns with a column weight of 1 in the coding matrix to the total number of columns in the coding matrix.

[0041] In a possible implementation, the target constraint further includes that the sparse ratio of the coding matrix is a first value, and the sparse ratio of the coding matrix is a ratio of the number of 0 elements in the coding matrix to the total number of elements in the coding matrix.

[0042] In a possible implementation, the encoding matrix includes a matrix with 4 rows and 10 columns as shown below:

[0043]

[0044] The PDMA encoding matrix includes P rows and Q columns in the encoding matrix, where P is an integer from 1 to 4, and Q is an integer from 1 to 10.

[0045] In one possible implementation, the processing module is specifically used to determine the PDMA coding matrix based on a first coding matrix in which the correlation of non-diagonal elements among the multiple N-row and M-column coding matrices is lower than a first threshold; or, the processing module is specifically used to determine the PDMA coding matrix based on a first coding matrix in which the correlation of non-diagonal elements among the multiple N-row and M-column coding matrices is the lowest.

[0046] In a possible implementation, the first encoding matrix is a matrix with 4 rows and 10 columns as shown below:

[0047]

[0048] The PDMA encoding matrix includes P rows and Q columns in the first encoding matrix, where P is an integer from 1 to 4, and Q is an integer from 1 to 10.

[0049] For the technical effects brought about by various possible implementations of the fourth aspect, reference may be made to the introduction to the technical effects of the first aspect or various possible implementations of the first aspect.

[0050] In a fifth aspect, an embodiment of the present application provides a communication device, which includes: a processing module for multiplying the modulated symbols with the vectors in the kth column of the PDMA coding matrix to obtain an extended transmission symbol vector, wherein the PDMA coding matrix is a matrix of N rows and M columns and satisfies the target constraints, and the target constraints include constraints on the column weight ratio of the coding matrix, N and M are both integers greater than 1, N is less than M, and the column weight ratio of the coding matrix includes the ratio of the number of columns of each column weight in the coding matrix to the total number of columns of the coding matrix, and k is an integer greater than 0; a transceiver module for sending a signal based on the extended transmission symbol vector.

[0051] In a possible implementation, the column weight ratios of the PDMA coding matrix are C4=0.6, C3=0.3, C2=0.1, and C1=0, respectively, where C4 represents the ratio of the number of columns with a column weight of 4 in the PDMA coding matrix, C3 represents the ratio of the number of columns with a column weight of 3 in the PDMA coding matrix, C2 represents the ratio of the number of columns with a column weight of 2 in the PDMA coding matrix, and C1 represents the ratio of the number of columns with a column weight of 1 in the PDMA coding matrix.

[0052] In a possible implementation, the target constraint further includes that the sparse ratio of the encoding matrix is a first value.

[0053] In a possible implementation, the PDMA encoding matrix includes P rows and Q columns of a matrix with 4 rows and 10 columns as shown below:

[0054]

[0055] P is an integer from 1 to 4, and Q is an integer from 1 to 10.

[0056] For the technical effects brought about by various possible implementation methods of the fifth aspect, reference may be made to the introduction to the technical effects of the second aspect or various possible implementation methods of the second aspect.

[0057] In a sixth aspect, an embodiment of the present application provides another communication device, which includes: a transceiver module for receiving a signal; a processing module for recovering the received signal based on a PDMA coding matrix and a receiver based on a serial interference cancellation SIC algorithm or a message passing algorithm MPA. The PDMA coding matrix is a matrix with N rows and M columns and satisfies the target constraints. The target constraints include constraints on the column weight ratio of the coding matrix, N and M are both integers greater than 1, and k is an integer greater than 0.

[0058] In a possible implementation, the column weight ratios of the PDMA coding matrix are C4=0.6, C3=0.3, C2=0.1, and C1=0, respectively, where C4 represents the ratio of the number of columns with a column weight of 4 in the PDMA coding matrix, C3 represents the ratio of the number of columns with a column weight of 3 in the PDMA coding matrix, C2 represents the ratio of the number of columns with a column weight of 2 in the PDMA coding matrix, and C1 represents the ratio of the number of columns with a column weight of 1 in the PDMA coding matrix.

[0059] In a possible implementation, the target constraint further includes that the sparse ratio of the encoding matrix is a first value.

[0060] In a possible implementation, the PDMA encoding matrix includes P rows and Q columns of a matrix with 4 rows and 10 columns as shown below:

[0061]

[0062] P is an integer from 1 to 4, and Q is an integer from 1 to 10.

[0063] For the technical effects brought about by various possible implementation methods of the sixth aspect, reference may be made to the introduction to the technical effects of the third aspect or various possible implementation methods of the third aspect.

[0064] In a seventh aspect, embodiments of the present application provide another data processing device, comprising one or more processors configured to process data and / or signaling to implement the method of any one of the first to third aspects described above. The data processing device may be a communication device.

[0065] Optionally, the data processing device further includes a memory storing a computer program or instructions. When the computer program or instructions are executed by the processor, the data processing device performs the method of any one of the first to third aspects described above. Exemplarily, the data processing device may be a chip, the processor may be a processing unit in the chip, and the memory may be a random access memory or cache in the chip.

[0066] In the embodiment of the present application, during the execution of the above method, the process of sending information (or signal) in the above method can be understood as the process of outputting information based on the computer program or instructions of the processor. When outputting information, the processor outputs the information to the transceiver so that it can be transmitted by the transceiver. After being output by the processor, the information may undergo other processing before reaching the transceiver. Similarly, when the processor receives input information, the transceiver receives the information and inputs it into the processor. Furthermore, after the transceiver receives the information, the information may undergo other processing before being input into the processor.

[0067] For operations such as sending and / or receiving involved by the processor, unless otherwise specified, or unless they conflict with their actual functions or internal logic in the relevant description, they can be generally understood as computer programs or instruction outputs based on the processor.

[0068] During implementation, the processor may be a processor specifically configured to execute these methods, or may be a processor that executes computer programs or instructions in a memory to execute these methods, such as a general-purpose processor. For example, the processor may also be configured to execute a program stored in a memory. When the program is executed, the data processing apparatus performs the method described in the first aspect or any possible implementation of the first aspect.

[0069] In a possible implementation, the memory is located outside the data processing device. In a possible implementation, the memory is located inside the data processing device.

[0070] In a possible implementation, the processor and the memory may also be integrated into one device, that is, the processor and the memory may also be integrated together.

[0071] In a possible implementation, the data processing device further includes a transceiver, and the transceiver is used to receive signals or send signals.

[0072] In an eighth aspect, the present application provides another data processing device, which includes a processing circuit and an interface circuit, wherein the interface circuit is used to acquire data or output data; the processing circuit is used to execute the method of any one of the first to third aspects above.

[0073] In a ninth aspect, the present application provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions, which, when executed, enable the computer to execute the method of any one of the first to third aspects mentioned above.

[0074] In a tenth aspect, the present application provides a computer program product, which includes a computer program, and the computer program includes program instructions, which, when executed, enable the computer to execute the method of any one of the first to third aspects above.

[0075] In the eleventh aspect, the present application provides a chip comprising a communication interface and a processor; the communication interface is used for transmitting and receiving signals of the chip; the processor is used for executing computer programs or instructions so that a data processing device including the chip executes a method as described in any one of the first to third aspects above.

[0076] In a twelfth aspect, the present application provides a communication system, which includes the communication device as described in the fifth aspect and the communication device as described in the sixth aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0078] Figure 1 An example of a PDMA uplink application provided in an embodiment of the present application;

[0079] Figure 2 An example of a data transmission flow chart of a sending end provided in an embodiment of the present application;

[0080] Figure 3 A flow chart of a method for determining a PDMA encoding matrix provided in an embodiment of the present application;

[0081] Figure 4 A flow chart of another method for determining a PDMA encoding matrix provided in an embodiment of the present application;

[0082] Figure 5 A flow chart of another method for determining a PDMA encoding matrix provided in an embodiment of the present application;

[0083] Figure 6 A flow chart of a communication method provided in an embodiment of the present application;

[0084] Figure 7 A schematic structural diagram of a data processing device 700 provided in an embodiment of the present application;

[0085] Figure 8 A schematic diagram of the structure of a terminal device provided in an embodiment of the present application;

[0086] Figure 9 A simplified schematic diagram of a base station structure is shown. DETAILED DESCRIPTION

[0087] The terms "first" and "second" in the specification, claims and drawings of the present application are only used to distinguish different objects, rather than to describe a specific order. It will be understood that the various digital numbers involved in the embodiments of the present application are only for the convenience of description and are not used to limit the scope of the embodiments of the present application. The size of the sequence number of each of the above processes does not mean the order of execution, and the order of execution of each process should be determined by its function and inherent logic. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0088] References to "embodiments" herein mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It will be understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0089] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application and the appended claims, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear contrary indication in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The term "multiple" used in the present application refers to two or more. In the textual description of the present application, the character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0090] It is understood that in each embodiment of the present application, "A corresponds to B" means that there is a corresponding relationship between A and B, and B can be determined according to A. However, it should also be understood that determining (or generating) B according to (or based on) A does not mean that B is determined (or generated) only according to (or based on) A, and B can also be determined (or generated) according to (or based on) A and / or other information.

[0091] It should be understood that, in this application, indication includes direct indication (also known as explicit indication) and implicit indication. Direct indication of information A refers to including information A; implicit indication of information A refers to indicating information A through the correspondence between information A and information B and the direct indication of information B. The correspondence between information A and information B can be predefined, pre-stored, pre-burned, or pre-configured.

[0092] It should be understood that, in this application, information C is used to determine information D, which includes both information D being determined solely based on information C and information D being determined based on information C and other information. Furthermore, information C can also be used to determine information D indirectly, for example, where information D is determined based on information E, and information E is determined based on information C.

[0093] In addition, in each embodiment of the present application, "network element A sends information A to network element B" can be understood as the destination end of the information A or the intermediate network element in the transmission path between the destination end and the network element B, which may include directly or indirectly sending information to network element B. "Network element B receives information A from network element A" can be understood as the source end of the information A or the intermediate network element in the transmission path between the source end and the network element A, which may include directly or indirectly receiving information from network element A. The information may be processed as necessary between the source end and the destination end of the information transmission, such as format changes, but the destination end can understand the valid information from the source end. Similar expressions in this application can be understood similarly and will not be elaborated here.

[0094] To facilitate understanding of the solutions of the present application, the following first introduces the terms and technical solutions involved in the embodiments of the present application.

[0095] Non-Orthogonal Multiple Access (NOMA): To meet the multiple access requirements of 5G and future communication systems, various uplink NOMA schemes have been proposed by industry and academia. NOMA technology can accommodate multiple users within the same orthogonal time-frequency resource block, allowing the number of user accesses in a wireless communication system to exceed the number of orthogonal resource blocks. NOMA technology not only increases the number of served users through multi-user superposition and improves the system's spectrum utilization through overloading, but also effectively reduces system latency through scheduling-free operation. Therefore, the NOMA scheme is considered one of the key candidate technologies for 5G. Typical NOMA technologies include sparse code multiple access (SCMA), multi-user shared access (MUSA), resource spread multiple access (RSMA), pattern division multiple access (PDMA), interleave-grid multiple access (IGMA), interleave division multiple access (IDMA), non-orthogonal coded multiple access (NCMA), etc. The technical solution provided in this application mainly involves PDMA technology, so other NOMA solutions will not be introduced later.

[0096] Pattern Division Multiple Access (PDMA): PDMA technology utilizes a design concept of joint optimization between the transmitter and receiver. At the transmitter, information from multiple users is encoded with unequal diversity and mapped onto time, frequency, and spatial resources. Different PDMA coding patterns are used to distinguish users with overlapping resources. At the receiver, based on the characteristic structure of the PDMA coding pattern, methods such as Successive Interference Cancellation (SIC) or the Message Passing Algorithm (MPA) are used to detect and receive multi-user signals. This enables non-orthogonal uplink and downlink transmission, approaching the capacity limit of multi-user channels. Figure 1 This is an example of a PDMA uplink application provided in the embodiment of the present application. Figure 1As shown, three users multiplex two resources for non-orthogonal transmission, wherein user 1 occupies resource 1 and resource 2 for uplink transmission, user 2 occupies resource 1 for uplink transmission, and user 3 occupies resource 2 for uplink transmission.

[0097] A PDMA coding pattern can be defined by a binary vector whose length is equal to the number of resource units in a resource group. A vector element value of "1" indicates that the user's data is mapped to that resource unit, and a vector element value of "0" indicates that the user's data is not mapped to that resource unit. Each column in the PDMA coding pattern matrix (hereinafter referred to as the PDMA coding matrix or coding matrix) is a PDMA coding pattern. Unless otherwise specified, the coding matrix or PDMA coding matrix can be referred to as the PDMA coding matrix.

[0098] PDMA constructs unequal diversity by mapping each user's data to be transmitted onto a set of resources using a specific mapping pattern (i.e., PDMA coding pattern). In other words, PDMA constructs unequal diversity by mapping each user's data to be transmitted onto the resources corresponding to a column of the PDMA coding pattern matrix, where a row of the PDMA coding pattern matrix corresponds to a data layer. Different users obtain different transmit diversity through the mapping resources determined by the PDMA coding pattern. PDMA constructs unequal diversity by mapping each user's data to be transmitted onto a set of resources using a specific coding pattern, where the PDMA coding pattern defines the mapping rules from data to resources, such as specifically defining how many resources the data is mapped to and which resources it is mapped to. The number of resources determines the transmit diversity of the data. See Figure 1 , the diversity degree of user 1 is 1, and the diversity degrees of user 2 and user 3 are both 1.

[0099] On the basis of unequal diversity, structured optimization is used to ensure the reasonable sparsity of the coding matrix. Drawing on the idea of sparse coding in low-density spread spectrum codes, a part of "0" elements is inserted into the spread spectrum codewords of the PDMA coding pattern, which can make the PDMA coding pattern sparse, thereby effectively reducing the complexity of the receiver and realizing SIC or MPA. Furthermore, by replacing some of the "1" in the PDMA spread spectrum codewords with complex values, the expansion of the traditional sparse coding matrix to the complex domain coding matrix can be realized. Specifically: the value "1" in the PDMA coding pattern matrix is replaced by a complex number reflecting power scaling and phase shift to form an extended PDMA pattern coding matrix, wherein the power scaling and phase shift factors can be incorporated into the PDMA pattern coding matrix. At the same time, although such sequences are short, they have low correlation and can significantly improve system performance compared to traditional sparse coding matrices. An example of a traditional 4*10 PDMA pattern coding matrix is as follows:

[0100]

[0101] where j and (-j) are complex numbers reflecting power scaling and phase shift.

[0102] In this application, the overload ratio of the number of users (i.e., terminal devices) in NOMA to the resources used is defined as K / N, where K is the number of users and the number of columns of the PDMA coding pattern matrix; N is the number of resources used and the number of rows of the PDMA coding pattern matrix. In other words, the ratio of the number of columns to the number of rows of the PDMA coding pattern matrix is the overload ratio. In practical applications, various forms of PDMA coding pattern matrices can be designed to achieve a given overload ratio. For example, a PDMA coding matrix that meets the following conditions Both can realize multi-user coding pattern mapping:

[0103]

[0104] Diversity is N Diversity is N-1 Diversity is 1

[0105]

[0106]

[0107]

[0108] in, Represents the PDMA encoding matrix K columns are selected to form a PDMA coding pattern matrix for K users, where N, M, and K are all integers greater than 1, K is less than or equal to M, N is less than M, and N is less than K. Table 1 shows an example of a traditional PDMA image coding matrix.

[0109] Table 1

[0110]

[0111] Data transmission process at the transmitting end (such as a terminal device): At the transmitting end, user signals can be selectively applied or used in combination in the code domain, power domain, time domain, frequency domain or spatial domain. PDMA designs non-orthogonal patterns for superposition transmission by maximizing diversity and minimizing correlation between coding matrices. The data transmission process using PDMA technology includes: each access terminal first uses a column (spread sequence) in the PDMA coding pattern matrix to spread the data symbols after digital amplitude and phase modulation. During the spreading process, each modulated data symbol (for example, a constellation point symbol modulated by Quadrature Amplitude Modulation (QAM)) is multiplied with each symbol of the spread sequence (i.e., a column in the PDMA coding pattern matrix), and finally each modulated data symbol will be spread into a symbol sequence with the same length as the spread sequence used. For example, the modulated symbol x of the kth user (a terminal device) is k The extended vector g corresponding to the kth column of the PDMA coding pattern matrix k Multiply them one by one to get the expanded transmission symbol vector v k (represents a symbol sequence), the process is expressed as:

[0112]

[0113] Among them, g k is the spread vector (i.e., spread sequence) corresponding to the kth column of the PDMA coding pattern matrix. The spread symbol sequences of all access terminals can be transmitted on the same time-frequency resources. The base station receives the combined signal of the spread signals from all access terminals. The base station can use the SIC algorithm or MPA technology to detect different user signals.

[0114] Figure 2 This is an example of a data transmission flow chart provided by the transmitter in the embodiment of the present application. Figure 2 As shown in FIG, the data transmission (uplink transmission) process at the transmitting end includes: channel coding, digital modulation, PDMA coding and channel transmission. Figure 2 The kth user in ( Figure 2 As an example, the data stream of the kth user undergoes channel coding and digital modulation to obtain the modulated symbol vector x k ; x k Multiply it by the kth column of the PDMA encoding matrix one by one to get the transmitted symbol vector v k , k=1,2,3,…,K, K is an integer greater than 1. Since channel coding, digital modulation and channel transmission are relatively mature technologies in this field, they will not be described in detail in this application.

[0115] According to the high load of 5G mobile Internet of Things and mobile Internet applications and the requirement for low complexity at the receiving end, it is necessary to design a PDMA coding pattern matrix that meets the complex domain multi-code and reasonable sparse characteristics. At the same time, the PDMA coding pattern matrix is required to have low correlation, unequal diversity and good single-user peak to average power ratio (PAPR) characteristics. The selection of traditional coding matrices is to select a few columns with low correlation from the coding matrix pool. Selecting a coding matrix with low correlation columns from a large number of coding matrix pools often requires high computing time and complexity. According to the complex domain 4*96 coding matrix pool proposed in the manuscript "R1-1811860--NOMA transmitter side signal processing", the coding pattern matrix shown in formula (1) is 10 columns selected based on the low correlation between coding matrices, but the correlation of the coding matrix is still too high. The total number of coding matrices selected from the 96-column coding matrix pool is (96) 10 To solve the problem that directly selecting a PDMA encoding matrix from the encoding matrix pool requires a lot of computing time and is highly complex, this application provides a method for determining the PDMA encoding matrix, which can reduce the amount of calculation and save time.

[0116] When multiple users use the PDMA coding pattern matrix for uplink transmission, in order to obtain excellent access performance, the PDMA coding pattern matrix used must ensure good cross-correlation characteristics. To this end, the present application designs a low-correlation coding matrix based on the complex domain and structured sparse characteristics, while ensuring low computational complexity, which can efficiently improve the system transmission performance, to solve the problem of massive access of 5G and greatly improve system performance, thereby improving spectrum efficiency to achieve efficient communication. Specifically, the embodiment of the present application designs a PDMA coding pattern matrix with low correlation and reasonable sparsity based on the complex domain multivariate code (for example, an overload ratio of 250%), which can achieve efficient uplink PDMA transmission. The PDMA coding pattern matrix designed in the present application has the following advantages: 1) The complex domain multivariate code is suitable for multi-user shared access solutions without scheduling, and uses a complex domain extension sequence to perform expansion processing on the transmitted data symbols, which is very suitable for low-cost, low-power 5G massive connections. 2) The real and imaginary parts of the initial extended sequence (corresponding to the columns in the PDMA coding pattern matrix before optimization) are both taken from the set of P real numbers, where P is an integer greater than or equal to 2. To reduce the correlation of the PDMA coding pattern matrix, the replacement matrix R is used to structurally optimize the initial extended sequence to obtain a reasonable sparsity characteristic and correlation (e.g., optimal correlation). For example, when the maximum overload ratio is 250%, the PDMA coding pattern matrix obtained has 6 sparse columns and 4 non-sparse columns (i.e., 6 sparse extended columns and 4 non-sparse extended columns), which has a reasonable sparsity characteristic and can achieve efficient data transmission. 3) When multiple users perform uplink transmission, each user is mapped to a column of the PDMA coding pattern matrix, and a row of the PDMA coding pattern matrix corresponds to a data layer. The PDMA coding pattern matrix is optimized in a structured manner to ensure that the PDMA coding pattern matrix has reasonable sparsity. At the same time, the higher the transmission diversity order, the higher the reliability.

[0117] The following introduces the PDMA coding pattern matrix design scheme provided by this application.

[0118] This application designs a PDMA coding pattern matrix based on the complex domain to ensure the system transmission performance of the uplink PDMA technology. The complex domain coding matrix designed in this application has a complex extension sequence in each column, and the real and imaginary parts of all elements are derived from the generating set P, which is a set of P real numbers, where P is an integer greater than or equal to 2.

[0119] If P is an odd number, the range of the P-element real number set is [-(P-1) / 2, (P-1) / 2], which is a set consisting of P integers; or a set consisting of P real numbers obtained by multiplying P integers in the range [-(P-1) / 2, (P-1) / 2] by corresponding energy normalization coefficients.

[0120] If P is an even number, the P-element real number set is a set consisting of P odd numbers in the range of [-(P-1), P(-1); or a set consisting of M real numbers obtained by multiplying P odd numbers in the range of [-(P-1), P(-1) by corresponding energy normalization coefficients. In this application, the value of the real number P can be 2 or 3.

[0121] In the present application, a possible implementation of determining the PDMA coding pattern matrix is as follows: first, a pseudo-random first sequence and second sequence are obtained based on the above-mentioned generating set P; then, the ath element of the first sequence is used as the real part, the bth element of the second sequence is used as the imaginary part, and a combination of real elements or imaginary elements is used as the tth element of the initial extended sequence, where a = 1, 2, ..., P, b = 1, 2, ..., P, and t = 1, 2, ..., 5; the non-zero elements in the initial extended sequence are taken to obtain a replacement sequence R, and according to the idea of structured optimization and the correlation constraint rule, the replacement sequence R is used to optimize the traditional coding matrix, and finally the PDMA coding pattern matrix is obtained.

[0122] In this embodiment, P = 3, meaning that all elements in the first and second sequences are taken from the 3-ary real number set {-1, 0, 1}. For the two real number sequences, the first and second sequences, a 5-ary initial extended sequence {-j, j, 0, -1, 1} is obtained. Each element of the replacement sequence R {-j, j, -1, 1} is used to perform structural optimization on the traditional coding matrix, selecting the coding matrix with the best correlation to achieve optimal coding matrix performance.

[0123] Assume that the receiving vector at the receiving end satisfies the following formula:

[0124]

[0125] Among them, Y is the received signal at the receiving end, h k is the received power, let D=diag([h1,h2,…,h K ]), assuming that the receiving power of each user is equal, that is, h1=h2=…=h K =h. g k ∈C N is the codeword of user k, x k ∈C is the user's sent symbol, with a mean of 0 and a variance of 1, i.e. w∈C N is a complex Gaussian vector, and w~C N (0,σ 2 I). In addition, G=[g1,g2,…,g K ] is the PDMA coding pattern matrix,

[0126] According to Shannon's channel capacity formula, the following formula can be obtained:

[0127]

[0128] Among them, C sum Represents the channel capacity corresponding to the signals of K users received by the receiver. According to the Welch boundary definition, for any K and N, when the number of users K is greater than the number of subcarriers N, the ideal coding matrix generated is a set of codewords that conforms to the Welch boundary principle. When the correlation between coding matrices reaches the minimum value, G T G is closer to the unit matrix. According to the channel capacity formula, the smaller the correlation between the coding matrices, the larger the channel capacity. Therefore, we can get the calculation formula for the correlation between the coding matrices of K users:

[0129]

[0130] According to the above formula (8), the correlation of the off-diagonal elements of each PDMA coding pattern matrix is calculated and compared, and the PDMA coding pattern matrix corresponding to the minimum correlation can be obtained. The performance of the PDMA coding pattern matrix after structured optimization will be improved. According to the above formula (8), the correlation of the off-diagonal elements of each PDMA coding pattern matrix is calculated and compared, and an example of a PDMA coding pattern matrix is shown in the following formula (9). Referring to formula (9), when the user overload ratio is 250%, 6 columns of sparse extended sequences and 4 columns of non-sparse extended sequences are obtained, which have reasonable sparse characteristics. For the convenience of representation, the PDMA coding pattern matrix designed in this scheme is denoted as The traditional coding matrix is denoted as The PDMA coding pattern matrix designed in this scheme The sparse ratio is S = 0.125 (the calculation formula is m and n represent the number of rows and columns of the matrix respectively), Refers to the number of 0 elements in the PDMA coding pattern matrix. For example, the column weight ratios of the PDMA coding pattern matrix designed in this scheme are: C4 = 0.6, C3 = 0.3, C2 = 0.1, C1 = 0 (the calculation formula is i represents column weight, C i Represents the column weight ratio, W i represents the number of columns with column weight i in this matrix, U is the total number of users), and column weight is the number of non-zero elements in a column.

[0131]

[0132] Through correlation calculation, it can be concluded that the PDMA coding pattern matrix designed in this application can improve system capacity, see the following formula (10):

[0133]

[0134] By comparing with the traditional coding matrix, the PDMA coding pattern matrix G designed in this application is a PDMA coding pattern matrix with better performance than the traditional PDMA coding pattern matrix G, and the correlation between coding matrices is smaller, which can better improve the system capacity.

[0135] The following combination Figure 3 The present invention introduces the method flow for determining the PDMA coding matrix provided by the present application.

[0136] Figure 3 A flow chart of a method for determining a PDMA coding matrix provided in an embodiment of the present application. Figure 3 As shown, the method includes:

[0137] 301. A data processing device determines a plurality of N-row and M-column encoding matrices that meet target constraints.

[0138] The data processing device can be a terminal device such as a laptop computer, a desktop computer, a tablet computer, a mobile phone, a PDA, a computing device, etc.; it can also be a server, such as a cloud server. This application does not specifically limit the specific structure of the execution subject of the method provided in the embodiment of the present application, as long as it can determine the PDMA encoding matrix according to the method provided in the embodiment of the present application by running a program that records the code of the method provided in the embodiment of the present application. The above-mentioned target constraints include constraints on the column weight ratio of the encoding matrix, N and M are both integers greater than 1, N is less than M, and the column weight ratio of the encoding matrix includes the ratio of the number of columns of each column weight in the encoding matrix to the total number of columns of the encoding matrix.

[0139] In one possible implementation, the aforementioned constraints on the column weight ratio of the coding matrix are as follows: C4 = 0.6, C3 = 0.3, C2 = 0.1, and C1 = 0, where C4 represents the ratio of the number of columns with a column weight of 4 to the total number of columns in the coding matrix, C3 represents the ratio of the number of columns with a column weight of 3 to the total number of columns in the coding matrix, C2 represents the ratio of the number of columns with a column weight of 2 to the total number of columns in the coding matrix, and C1 represents the ratio of the number of columns with a column weight of 1 to the total number of columns in the coding matrix. In this implementation, by constraining the column weight ratios of the coding matrix to C4 = 0.6, C3 = 0.3, C2 = 0.1, and C1 = 0, the computational effort can be reduced while ensuring that multiple N-row, M-column coding matrices that meet the target constraints have a reasonable dilution, thereby improving system capacity.

[0140] In one possible implementation, the target constraint further includes a first value for the sparsity ratio of the encoding matrix, where the sparsity ratio of the encoding matrix is the ratio of the number of zero elements in the encoding matrix to the total number of elements in the encoding matrix. For example, the first value is 0.1, 0.125, 0.15, etc., and is not limited in this embodiment of the present application. In this implementation, by constraining the sparsity ratio of the encoding matrix, the encoding matrix that meets the target constraint can have a more reasonable sparsity characteristic.

[0141] 302. The data processing device determines a PDMA coding matrix based on the multiple N-row and M-column coding matrices.

[0142] In a possible implementation, the encoding matrix (i.e., one of the multiple encoding matrices with N rows and M columns) includes P rows and Q columns in a matrix with 4 rows and 10 columns as shown below:

[0143]

[0144] The PDMA encoding matrix includes P rows and Q columns in the encoding matrix, where P is an integer from 1 to 4, and Q is an integer from 1 to 10. This application does not limit the method of determining the PDMA encoding matrix based on the above multiple N rows and M columns encoding matrices.

[0145] In one possible implementation, the data processing device determines the PDMA coding matrix based on the first coding matrix in which the correlation of non-diagonal elements in the coding matrices of the above-mentioned multiple N rows and M columns is lower than the first threshold. The first threshold can be set according to actual needs and is not limited here. Exemplarily, the data processing device determines the first coding matrix in which the correlation of non-diagonal elements in the coding matrices of the above-mentioned multiple N rows and M columns is lower than the first threshold as the PDMA coding matrix; wherein the first coding matrix is any one (or any one) coding matrix in which the correlation of non-diagonal elements in the coding matrices of the above-mentioned multiple N rows and M columns is lower than the first threshold. Exemplarily, the data processing device first determines the first coding matrix in which the correlation of non-diagonal elements in the coding matrices of the above-mentioned multiple N rows and M columns is lower than the first threshold; then, P rows and Q columns in the first coding matrix are used as the PDMA coding matrix, wherein the first coding matrix is any one coding matrix in which the correlation of non-diagonal elements in the coding matrices of the above-mentioned multiple N rows and M columns is lower than the first threshold, P is less than or equal to N, and Q is less than M.

[0146] In one possible implementation, the data processing device determines the PDMA encoding matrix based on the first encoding matrix with the lowest correlation among the multiple N-row, M-column encoding matrices; the encoding matrix with the lowest correlation can be obtained here. Exemplarily, the data processing device determines the first encoding matrix with the lowest correlation among the multiple N-row, M-column encoding matrices as the PDMA encoding matrix. Exemplarily, the data processing device first determines the first encoding matrix with the lowest correlation among the multiple N-row, M-column encoding matrices; then, P rows and Q columns in the first encoding matrix are used as the PDMA encoding matrix, where P is less than or equal to N and Q is less than M.

[0147] In a possible implementation, the first encoding matrix is a matrix with 4 rows and 10 columns as shown below:

[0148]

[0149] The PDMA coding matrix includes P rows and Q columns in the first coding matrix, where P is an integer from 1 to 4, and Q is an integer from 1 to 10.

[0150] In an embodiment of the present application, a plurality of N-row, M-column coding matrices that meet the target constraints are first determined; then, based on the plurality of N-row, M-column coding matrices, a PDMA coding matrix is determined; compared to directly selecting the PDMA coding matrix from the coding matrix pool, the amount of calculation can be reduced and time can be saved.

[0151] Figure 4 A flowchart of another method for determining a PDMA coding matrix provided in an embodiment of the present application. Figure 4 The method flow is Figure 3 A possible implementation of the method described is as follows. Figure 4 As shown, the method includes:

[0152] 401. The data processing device determines (screens out) a plurality of N-row and M-column encoding matrices that meet target constraints from an encoding matrix pool.

[0153] For example, the coding matrix pool may be a coding matrix pool of a complex domain (N*96). The above-mentioned multiple N-row and M-column coding matrices may be coding matrices of N rows and M columns in the above-mentioned coding matrix pool that all meet the target constraints, or may be coding matrices of N rows and M columns that partially meet the target constraints. Step 401 may be Figure 3 Specific implementation of step 301 in the example. For example, the encoding matrix pool is a complex domain (N*96) encoding matrix pool, in which M columns are selected to form the encoding matrix, with a total of (96) M combinations; the data processing device can traverse the (96) MThe data processing device may also use other algorithms to determine (screen out) multiple N-row, M-column encoding matrices that meet the target constraints from the encoding matrix pool, which is not limited in this embodiment of the present application.

[0154] 402. The data processing device determines the correlation of non-diagonal elements of each coding matrix in the plurality of N rows and M columns coding matrices.

[0155] In a possible implementation, the data processing device uses the above formula (8) to calculate the correlation of the non-diagonal elements of each coding matrix in the above multiple N-row and M-column coding matrices.

[0156] 403. The data processing device determines a first coding matrix having the lowest correlation between non-diagonal elements among the plurality of N-row and M-column coding matrices as a PDMA coding matrix.

[0157] In an embodiment of the present application, a plurality of N-row and M-column coding matrices that meet the target constraints are first determined from a coding matrix pool; then, based on the plurality of N-row and M-column coding matrices, a PDMA coding matrix is determined; compared to directly selecting a PDMA coding matrix from the coding matrix pool, the amount of calculation can be reduced and time can be saved.

[0158] Figure 5 A flowchart of another method for determining a PDMA coding matrix provided in an embodiment of the present application. Figure 5 The method flow is Figure 3 A possible implementation of the method described is as follows. Figure 5 As shown, the method includes:

[0159] 501. A data processing device obtains an initial extended sequence.

[0160] In one possible implementation, a pseudo-random first and second sequences are generated based on the generator set P. Then, the ath element of the first sequence is used as the real part, the bth element of the second sequence is used as the imaginary part, and a combination of real and imaginary elements is used as the tth element of the initial extended sequence, where a = 1, 2, ..., P, b = 1, 2, ..., P, and t = 1, 2, ..., 5. In this embodiment, P = 3, meaning that all elements in the first and second sequences are taken from the 3-ary real number set {-1, 0, 1}.

[0161] 502. The data processing device obtains non-zero elements in the initial extended sequence to obtain a replacement sequence.

[0162] Exemplarily, the initial extension sequence is {-j, j, 0, -1, 1}, and the replacement sequence is {-j, j, -1, 1}.

[0163] 503. The data processing device obtains a plurality of second coding matrices with N rows and M columns based on the replacement sequence and the traditional coding matrix.

[0164] For example, a traditional encoding matrix only contains element 1 and element 0, see Table 1 above.

[0165] In one possible implementation, the data processing device replaces one or more non-zero elements in a conventional coding matrix (N rows and M columns) with one or more elements of a replacement sequence, resulting in multiple different second coding matrices with N rows and M columns. Based on the concept of structured optimization and correlation constraints, the data processing device can use the replacement sequence R to optimize the conventional coding matrix, ultimately obtaining a PDMA coding pattern matrix. For example, the data processing device uses each element of the replacement sequence R = {-j, j, -1, 1} to perform structured optimization on the conventional coding matrix, selecting the coding matrix with the best correlation to achieve optimal coding matrix performance.

[0166] 504. The data processing device determines a plurality of N-row and M-column encoding matrices among the plurality of N-row and M-column second encoding matrices that meet the target constraint.

[0167] The above target constraints include constraints on the column weight ratio of the encoding matrix, where N and M are both integers greater than 1, N is less than M, and the column weight ratio of the encoding matrix includes the ratio of the number of columns of each column weight in the encoding matrix to the total number of columns in the encoding matrix.

[0168] 505. The data processing device determines a PDMA coding matrix based on the plurality of N-row and M-column coding matrices.

[0169] Step 505 can refer to Figure 3 Step 302 in .

[0170] In an embodiment of the present application, multiple N-row, M-column encoding matrices that meet the target constraint are determined among the multiple N-row, M-column second encoding matrices. Then, a PDMA encoding matrix is determined based on the multiple N-row, M-column encoding matrices. Compared with directly selecting the PDMA encoding matrix from the encoding matrix pool, the amount of calculation can be reduced and time can be saved.

[0171] Figure 6 A flow chart of a communication method provided in an embodiment of the present application. Figure 6 The process of using the PDMA coding matrix provided by this application to transmit data is described. Figure 6 As shown, the method includes:

[0172] 601. The transmitter multiplies the modulated symbols by the vectors in the kth column of the PDMA coding matrix respectively to obtain an expanded transmission symbol vector.

[0173] The PDMA coding matrix is a matrix with N rows and M columns and satisfies target constraints. The target constraints include constraints on the column weight ratio of the coding matrix. N and M are both integers greater than 1, and N is less than M. The column weight ratio of the coding matrix includes the ratio of the number of columns of each column weight in the coding matrix to the total number of columns in the coding matrix, and k is an integer greater than 0. Step 601 is an example of a transmitter (e.g., the kth user) obtaining an extended transmit symbol vector. Step 601 can be replaced by: K transmitters (users) multiply the modulated symbols by vectors in different columns of the PDMA coding matrix to obtain extended transmit symbol vectors. For example, the K transmitting ends include the 1st to the Kth users. The 1st user multiplies the modulated symbols with the vectors in the 1st column of the PDMA coding matrix to obtain the extended transmission symbol vector #1; the 2nd user multiplies the modulated symbols with the vectors in the 2nd column of the PDMA coding matrix to obtain the extended transmission symbol vector #2; and so on. The Kth user multiplies the modulated symbols with the vectors in the Kth column of the PDMA coding matrix to obtain the extended transmission symbol vector #K.

[0174] The transmitting end may be a terminal device. In the embodiments of the present application, the terminal equipment may also be referred to as user equipment (UE), access terminal, subscriber unit, user station, mobile station, mobile station (MS), remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent, or user device.

[0175] The terminal device may be a device that provides wireless communication functions, such as a handheld device or a vehicle-mounted device with a wireless connection function. At present, some examples of terminal devices are: mobile phones, cellular phones, smart phones, tablet computers, laptop computers, PDAs, mobile internet devices (MIDs), wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, cellular phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), handheld devices with wireless communication capabilities, computing devices or other processing devices connected to wireless modems, wearable devices, machine type communications (MTCs), and other similar terminal devices. Communication, MTC) terminal, terminal equipment in a 5G network, or terminal equipment in a future evolved public land mobile communication network (public land mobile network, PLMN), etc., and the embodiments of the present application are not limited to this.

[0176] In a possible implementation, the column weight ratios of the above-mentioned PDMA coding matrix are C4=0.6, C3=0.3, C2=0.1, and C1=0, respectively, where C4 represents the ratio of the number of columns with a column weight of 4 in the above-mentioned PDMA coding matrix, C3 represents the ratio of the number of columns with a column weight of 3 in the above-mentioned PDMA coding matrix, C2 represents the ratio of the number of columns with a column weight of 2 in the above-mentioned PDMA coding matrix, and C1 represents the ratio of the number of columns with a column weight of 1 in the above-mentioned PDMA coding matrix.

[0177] In this implementation, by constraining the column weight ratios of the coding matrix to C4=0.6, C3=0.3, C2=0.1, and C1=0, the amount of computation can be reduced, and multiple N-row and M-column coding matrices that meet the target constraints can be reasonably diluted, thereby improving the system capacity.

[0178] In a possible implementation, the target constraint further includes that the sparse ratio of the encoding matrix is a first value.

[0179] In this implementation, by constraining the sparsity ratio of the coding matrix, the coding matrix that meets the target constraint can have a more reasonable sparsity characteristic and obtain lower computational complexity.

[0180] In a possible implementation, the PDMA encoding matrix includes P rows and Q columns of a matrix with 4 rows and 10 columns as shown below:

[0181]

[0182] The above-mentioned P is an integer from 1 to 4, and the above-mentioned Q is an integer from 1 to 10.

[0183] In this implementation, the PDMA coding matrix is a coding matrix with low correlation and reasonable sparsity, which is closer to the ideal coding matrix. The system obtains better spreading gain / diversity gain, while improving spectrum efficiency and reducing the complexity of the detection end, achieving more efficient PDMA uplink transmission.

[0184] 602. The transmitting end sends a signal to the receiving end based on the expanded transmitting symbol vector.

[0185] The receiving end receives signals from one or more transmitting ends. For example, the receiving end receives signals from K transmitting ends.

[0186] The network device in the embodiments of the present application may be a device for communicating with a terminal device, and may also be referred to as an access network device or a radio access network device. For example, the network device may be a base station. The network device in the embodiments of the present application may refer to a radio access network (RAN) node (or device) that connects a terminal device to a wireless network. The term "base station" may broadly cover the following names or be replaced with the following names, such as: RAN node, NodeB, evolved NodeB (eNB), next generation NodeB (gNB), base station controller (BSC), base transceiver station (BTS), home base station (e.g., home evolved NodeB, or home NodeB, HNB), baseband unit (BBU), wireless relay node, wireless backhaul node, relay station, transmitting and receiving point (TRP), IAB node, transmitting point (TP), master station, auxiliary station, multi-standard radio (motor slide retainer, MSR) node, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (central unit), etc. unit (CU), distributed unit (DU), radio unit (RU), positioning node, one or a group (including multiple antenna panels) of antenna panels of a base station in a 5G system, etc.

[0187] 603. The receiving end recovers the received signal by using a receiver based on the SIC algorithm or the MPA based on the PDMA coding matrix.

[0188] The PDMA encoding matrix is a matrix with N rows and M columns and satisfies target constraints. The target constraints include constraints on the column weight ratio of the encoding matrix. N and M are both integers greater than 1, and k is an integer greater than 0.

[0189] In the embodiment of the present application, the PDMA coding matrix satisfies the target constraints, has a more reasonable sparse characteristic (ie, sparsity) and is closer to the ideal coding matrix than the existing PDMA coding matrix, and the communication system can obtain better spreading gain / diversity gain.

[0190] The following, in conjunction with the accompanying drawings, describes the structure of a data processing device capable of implementing the coding matrix determination method provided in the embodiments of the present application, as well as the structure of a communication device capable of implementing the communication method provided in the embodiments of the present application. The following only briefly describes the data processing device and the communication device. For implementation details of the scheme, please refer to the description of the method embodiment above and will not be repeated here.

[0191] Figure 7 A structural diagram of a data processing device 700 provided in an embodiment of the present application. The data processing device 700 can implement the functions or steps implemented by the data processing device in each of the above-mentioned method embodiments. The data processing device may include a processing module 710. In one possible implementation, it may also include a storage unit, which can be used to store instructions (code or program) and / or data. The processing module 710 can be coupled with the storage unit. For example, the processing module 710 can read the instructions (code or program) and / or data in the storage unit to implement the corresponding method. The above-mentioned units can be set independently or partially or fully integrated. The data processing device 700 may also include a transceiver module 720 (optional). The transceiver module 720 may include a sending module and a receiving module. The sending module may be a transmitter, and the receiving module may be a receiver. The entity corresponding to the transceiver module 720 may be a transceiver circuit, such as a transceiver or a communication interface.

[0192] In some possible implementations, the data processing device 700 can implement the behaviors and functions of the data processing device in the above method embodiments. For example, the data processing device 700 can be a terminal device, or a component (such as a chip or circuit) used in the terminal device. The processing module 710 can be used to execute Figures 3 to 5 All operations except the sending and receiving operations performed by the data processing device in the embodiment.

[0193] Figure 8 This is a schematic diagram of the structure of a terminal device provided in an embodiment of the present application. Figure 8 As shown, the terminal device 80 includes a processor 801, a memory 802, a communication interface 803 and an input / output device 804; the processor 801, the memory 802 and the communication interface 803 are interconnected via a bus. Figure 8 The terminal device in the embodiment may be the data processing device in the foregoing embodiment.

[0194] Memory 802 includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or compact disc read-only memory (CDROM), and is used for related instructions and data. Communication interface 803 is used to receive and send data. Input and output devices 804 may include input devices such as a keyboard, mouse, and touch screen, as well as output devices such as a display and screen. A user can input instructions to the terminal device through the input device, such as inputting at least one correction operation or cutout operation for the initial foreground image. The output device can display the application interface of the image processing software and other content.

[0195] The processor 801 may be one or more central processing units (CPUs). In the case where the processor 801 is a CPU, the CPU may be a single-core CPU or a multi-core CPU. The steps performed by the data processing device or the sending end in the above embodiment may be based on the Figure 8 The structure of the terminal device shown.

[0196] The present application also provides a device 900, which can be a network device (such as a base station) or a chip. The device 900 can be used to perform the above Figure 6 The operations performed by the receiving end in the embodiment shown are shown.

[0197] When the apparatus 900 is a network device, it is, for example, a base station. Figure 9 A simplified schematic diagram of a base station structure is shown. The base station includes parts 910, 920, and 930.

[0198] Part 910 is mainly used for baseband processing, base station control, etc.; Part 910 is usually the control center of the base station, which can usually be called a processor, used to control the base station to perform the processing operations of the base station in the above method embodiment.

[0199] Part 920 is mainly used to store computer program code and data.

[0200] Part 930 is primarily used for receiving and transmitting radio frequency signals and converting radio frequency signals into baseband signals. Part 930 can generally be referred to as a transceiver module, transceiver, transceiver circuit, or transceiver. The transceiver module in part 930, which can also be referred to as a transceiver or transceiver, includes an antenna 933 and a radio frequency circuit (not shown in the figure), wherein the radio frequency circuit is primarily used for radio frequency processing. Optionally, the device used to implement the receiving function in part 930 can be considered a receiver, and the device used to implement the transmitting function can be considered a transmitter, that is, part 930 includes a receiver 932 and a transmitter 931. A receiver can also be referred to as a receiving module, a receiver, or a receiving circuit, and a transmitter can be referred to as a transmitting module, a transmitter, or a transmitting circuit.

[0201] Sections 910 and 920 may include one or more boards, each of which may include one or more processors and one or more memories. The processor is used to read and execute programs in the memory to implement baseband processing functions and control the base station. If multiple boards are present, the boards may be interconnected to enhance processing capabilities. As an optional implementation, multiple boards may share one or more processors, multiple boards may share one or more memories, or multiple boards may simultaneously share one or more processors.

[0202] For example, in one implementation, the transceiver module of section 930 is used to execute Figure 6 In the embodiment shown, the receiving end performs the related sending and receiving processes. The processor in part 910 is used to perform Figure 6 The embodiment shown is related to the processing performed by the receiving end.

[0203] It should be understood that Figure 9 This is only an example and not a limitation. The network device including the processor, memory and transceiver may not rely on Figure 9 The structure shown.

[0204] When device 900 is a chip, the chip includes a transceiver, a memory, and a processor. The transceiver can be an input / output circuit or a communication interface; the processor can be a processor, microprocessor, or integrated circuit integrated on the chip. The sending operation at the receiving end in the above method embodiment can be understood as the chip's output, and the receiving operation at the receiving end in the above method embodiment can be understood as the chip's input.

[0205] The present application also provides a computer-readable storage medium storing a computer program or instructions. When the computer program or instructions are executed on a computer, the computer executes the method of the above-described embodiment. For example, when the computer program is executed by a computer, the computer can implement the method performed by the data processing device, the transmitting end, or the receiving end in the above-described method embodiment.

[0206] The present application also provides a computer program product, which includes a computer program or instructions. When the computer program or instructions are run on a computer, the method in the above embodiment is executed.

[0207] The present application also provides a communication system, comprising the above-mentioned transmitting end and the above-mentioned receiving end.

[0208] The present application also provides a chip, which includes: a communication interface and a processor; the communication interface is used for sending and receiving signals of the above-mentioned chip; the processor is used to execute computer programs or instructions so that the data processing device or communication device including the above-mentioned chip executes the method in the above-mentioned embodiment.

[0209] An embodiment of the present application also provides a chip device, including a processor, for calling a computer program or computer instruction stored in the memory so that the processor executes the method provided in the embodiment shown in the above-mentioned data processing device or communication device.

[0210] Optionally, the processor is coupled to the memory via an interface.

[0211] Optionally, the chip device further includes a memory, in which a computer program or computer instructions are stored.

[0212] The processor mentioned in any of the above may be a general-purpose central processing unit, a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the method provided in any of the embodiments shown in the terminal device. The memory mentioned in any of the above may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM), etc.

[0213] Those skilled in the art can clearly understand that, for the sake of convenience and brevity of description, the explanation of the relevant contents and beneficial effects of any of the above-mentioned devices can refer to the corresponding method embodiments provided above, and will not be repeated here.

[0214] 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 above units is only 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 an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0215] The units described above 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 according to actual needs to achieve the purpose of the solution of this embodiment.

[0216] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0217] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the part that essentially contributes to the technical solution of the present application or all or 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 computer programs or instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the above-mentioned methods of each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks or optical disks.

[0218] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

[0219] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are performed in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user device, or other programmable device. The computer program or instructions may be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions may be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium may be any available medium that can be accessed 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, such as a floppy disk, hard disk, or magnetic tape; an optical medium, such as a digital video disk; or a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or nonvolatile storage medium, or may include both volatile and nonvolatile types of storage media.

[0220] In the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

Claims

1. A method for determining a coding matrix, characterized in that: include: Determine a plurality of N-row and M-column encoding matrices that satisfy target constraints, wherein the target constraints include constraints on a column weight ratio of the encoding matrix, where N and M are both integers greater than 1, N is less than M, and the column weight ratio of the encoding matrix includes a ratio of the number of columns of each column weight in the encoding matrix to the total number of columns in the encoding matrix; Based on the multiple N-row and M-column coding matrices, a pattern division multiple access (PDMA) coding matrix is determined.

2. The method according to claim 1, characterized in that The constraints on the column weight ratio of the coding matrix are that the column weight ratios of the coding matrix are C4=0.6, C3=0.3, C2=0.1, and C1=0, respectively, where C4 represents the ratio of the number of columns with a column weight of 4 in the coding matrix to the total number of columns in the coding matrix, C3 represents the ratio of the number of columns with a column weight of 3 in the coding matrix to the total number of columns in the coding matrix, C2 represents the ratio of the number of columns with a column weight of 2 in the coding matrix to the total number of columns in the coding matrix, and C1 represents the ratio of the number of columns with a column weight of 1 in the coding matrix to the total number of columns in the coding matrix.

3. The method according to claim 1 or 2, characterized in that The target constraint also includes that the sparse ratio of the coding matrix is a first value, and the sparse ratio of the coding matrix is the ratio of the number of 0 elements in the coding matrix to the total number of elements in the coding matrix.

4. The method according to any one of claims 1 to 3, characterized in that The determining of a pattern division multiple access (PDMA) coding matrix based on the plurality of N-row and M-column coding matrices includes: Determine the PDMA encoding matrix based on a first encoding matrix in which the correlation of non-diagonal elements among the plurality of N-row and M-column encoding matrices is lower than a first threshold; or The PDMA encoding matrix is determined based on a first encoding matrix having the lowest correlation between non-diagonal elements among the multiple encoding matrices with N rows and M columns.

5. The method according to any one of claims 1 to 3, characterized in that The encoding matrix includes a matrix with 4 rows and 10 columns as shown below: The PDMA encoding matrix includes P rows and Q columns in the encoding matrix, where P is an integer from 1 to 4, and Q is an integer from 1 to 10.

6. A data processing device, characterized in that: include: A processing module is configured to determine a plurality of N-row and M-column coding matrices that meet target constraints, wherein the target constraints include a constraint on a column weight ratio of the coding matrix, where N and M are both integers greater than 1, N is less than M, and the column weight ratio of the coding matrix includes a ratio of the number of columns of each column weight in the coding matrix to the total number of columns in the coding matrix; and determine a pattern division multiple access (PDMA) coding matrix based on the plurality of N-row and M-column coding matrices.

7. The device according to claim 6, characterized in that The constraints on the column weight ratio of the coding matrix are that the column weight ratios of the coding matrix are C4=0.6, C3=0.3, C2=0.1, and C1=0, respectively, where C4 represents the ratio of the number of columns with a column weight of 4 in the coding matrix to the total number of columns in the coding matrix, C3 represents the ratio of the number of columns with a column weight of 3 in the coding matrix to the total number of columns in the coding matrix, C2 represents the ratio of the number of columns with a column weight of 2 in the coding matrix to the total number of columns in the coding matrix, and C1 represents the ratio of the number of columns with a column weight of 1 in the coding matrix to the total number of columns in the coding matrix.

8. The device according to claim 6 or 7, characterized in that The target constraint also includes that the sparse ratio of the coding matrix is a first value, and the sparse ratio of the coding matrix is the ratio of the number of 0 elements in the coding matrix to the total number of elements in the coding matrix.

9. The device according to any one of claims 6 to 8, characterized in that The processing module is specifically configured to determine the PDMA coding matrix based on a first coding matrix in which the correlation of non-diagonal elements in the plurality of N-row and M-column coding matrices is lower than a first threshold; or The processing module is specifically configured to determine the PDMA coding matrix based on a first coding matrix having the lowest correlation between non-diagonal elements among the multiple coding matrices with N rows and M columns.

10. The device according to any one of claims 6 to 8, characterized in that The encoding matrix includes a matrix with 4 rows and 10 columns as shown below: The PDMA encoding matrix includes P rows and Q columns in the encoding matrix, where P is an integer from 1 to 4, and Q is an integer from 1 to 10.

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