Encoding method, decoding method and communication apparatus

Abstract

The present application provides an encoding method, a decoding method, and a communication apparatus. An encoding end device divides an information vector m1 k into a first information vector m1 kc and a second information vector mkc+1 k , and carries out code rate configuration one on the first information vectorm1 kc to obtain a vector m´ 1 Nc ; and carries out convolution transformation on a vector m´ 1 Nc to obtain a code word vector v1 Nc of the convolution code; carries out the code rate configuration one on the code word vector v1 Nc and the second information vector mkc+1 k to obtain a message vector u1 N , and carries out polarization transformation on the message u1 N to obtain a codeword vector c1 N . A coding end device receives a symbol vector y1 N and determines a message vector uˆ 1 N ; to be estimated; according to a node position i of a decoding node uˆi, calls a continuous elimination decoding algorithm or a Fano decoding algorithm to decode a decoding node uˆ 1 N . It is apparent that in the present application, the number of nodes allowed to fall back in the decoding process is reduced, and the complexity of overall decoding can be reduced.

Description

Coding method, decoding method and communication device Technical Field

[0001] The present application relates to the field of communication technology, and in particular to an encoding method, a decoding method, and a communication device. Background Art

[0002] As a core technology for ensuring reliable information transmission, channel coding has played a vital role in wireless communication systems throughout history. Given the new requirements for ultra-reliable, low-latency communication (uRLLC) in next-generation communication systems, research on channel coding is of great significance. Polar codes are the first type of coding scheme theoretically proven to reach channel capacity. When the code length approaches infinity, they exhibit excellent decoding performance under the successive cancellation (SC) decoding algorithm. However, within the medium and short code length range, polar codes are limited in range, and their decoding performance struggles to meet the new ultra-reliability requirements.

[0003] Summary of the Invention

[0004] The present application provides an encoding method, a decoding method, and a communication device, which can achieve better decoding performance.

[0005] In the first aspect, the embodiment of the present application provides a coding method, which can be applied to a coding end device, or applied to a chip or chip module in the coding end device for execution, or applied to a module or unit that can realize all or part of the functions of the coding end device, etc. The following description is based on the coding end device as an example. In this method, the coding end device converts the information vector Divided into the first information vector and the second information vector in, Represents a vector of dimension K with elements of 0 or 1, where K is the information vector The information bit dimension of Kc is the information bit dimension to be preprocessed by convolution transformation; Perform code rate configuration 1 and obtain vector Where Nc is the convolution transformation matrix G′ c Dimension of the vector Perform convolution transformation to obtain the codeword vector of the convolution code The codeword vector and the second information vector Perform code rate configuration 2 and obtain the message vector N is the code length of the channel code; Perform polarization transformation to obtain the codeword vector

[0006] As can be seen, this coding method uses two code rate configuration processes to construct a coding scheme with better performance, which in turn facilitates better decoding performance. Furthermore, in this coding method, Kc of the K information bits undergo convolutional transformation and then polarization transformation (i.e., polar coding), while the remaining (K-Kc) information bits undergo direct polarization transformation (i.e., polar coding). This facilitates the organic combination of Finoi decoding and successive elimination decoding during decoding, reducing the number of nodes allowed to fall back during the decoding process. This not only compensates for the error propagation shortcomings of successive elimination decoding, but also reduces the decoding complexity of Finoi decoding.

[0007] Optionally, the encoding device can also send codeword vectors

[0008] In an optional implementation, the encoding end device converts the codeword vector and the second information vector Perform code rate configuration II and obtain message vector Including: According to the Gaussian approximation criterion, the reliability of the subchannels corresponding to the subscript set [1, N] of the polarization subchannel is sorted to obtain the KK with the highest reliability c +N c The subscripts of the polarization subchannels constitute the subscript set and the subscript set consisting of the subscript of the residual polarization subchannel According to the Gaussian approximation criterion, the subscript set The corresponding sub-channels are sorted by reliability to obtain the N with the lowest reliability. c The subscripts of the polarization subchannels constitute the subscript set and the subscript set consisting of the subscript of the residual polarization subchannel In the subscript set The codeword vector is transmitted on the subchannel indexed by the element in And in the subscript set The second information vector is transmitted on the polarization channel indexed by the element in And in the subscript set Transmit the frozen bits on the polarization subchannel indexed by the element in to obtain the message vector As can be seen, in this implementation, the Gaussian approximation criterion is introduced to estimate the reliability of the polarimetric subchannel, thereby determining code rate configuration II. Polarimetric subchannels with high reliability are used to directly transmit information bits, while polarimetric subchannels with certain transmission capabilities but less reliability are used to transmit codewords after convolution transformation.

[0009] In an optional implementation manner, the encoding end device encodes the first information vector Perform code rate configuration I and obtain vector Including: According to the Reed-Muller code criterion, the subscript set The corresponding sub-channels are prioritized by codeword distance characteristics to obtain a set of Kc positions with the highest priority codeword distance characteristics. will be collected and codeword set Arrange the elements in increasing order to get the vector and and confirm Elements in vector The relative position in the , obtains the subscript set of Kc polarization subchannels In the subscript set The first information vector is transmitted on the polarization subchannel indexed by the element in In the subscript set Transmit the frozen bits on the polarization subchannels with the remaining subscript indexes in , and obtain the vector It can be seen that in this implementation, the Reed-Muller code criterion is introduced to improve the distance characteristics of the coding, thereby determining code rate configuration I, and preferentially selecting the polarization subchannel with the best codeword distance characteristic for information bit transmission.

[0010] In a second aspect, the present application provides a decoding method, which can be applied to a decoding end device, or to a chip or chip module in the decoding end device, or to a module or unit that can implement all or part of the functions of the decoding end device. The following description is based on the decoding end device as an example. In this method, the decoding end device receives a symbol vector And determine the message vector to be estimated Where N is the code length of the channel code; according to the message vector Decoding node At node position i, call the continuous elimination decoding algorithm or Fano decoding algorithm to decode the node Decode, where 1∈[i,N].

[0011] It can be seen that in the serial decoding process, Fano decoding and continuous elimination decoding will be called at different decoding nodes, which not only makes up for the shortcomings of error propagation of continuous elimination decoding, but also reduces the decoding complexity of Fano decoding.

[0012] Optionally, the decoding method may further include: the decoding end device decodes and estimates the message vector Extract information and obtain information vector Where K is the information vector The information bit dimension.

[0013] In an optional implementation, the decoding end device is configured to decode the node At node position i, call the continuous elimination decoding algorithm or Fano decoding algorithm to decode the node Decoding, including: When the Fano decoding algorithm is called, the decoding node Decode; when When the continuous elimination decoding algorithm is called, the decoding node Decode; is the set of subscripts corresponding to the subchannels that transmit the convolutional codeword. Thus, in this implementation, the Fano decoding algorithm is used to recover the information bits that have undergone convolutional transformation, allowing decoding nodes to fall back during this process. Successive elimination decoding is used to recover the directly transmitted information bits, and this process does not allow decoding nodes to fall back.

[0014] In an optional implementation, the decoding end device When the Fano decoding algorithm is called, the decoding node Decoding, including: Calculate the path metric when and And compare it with the current decoding threshold T to determine the extended decoding node And according to the convolution transformation constraint relationship Sure and get in, when When, confirm Among them, B corresponds to the position set of information bits of convolution transformation, The frozen bit position corresponding to the convolution transform. Where i′ represents the decoding node that has not been expanded It can be seen that in this embodiment, Fano decoding is used to recover the information bits that have undergone convolution transformation, and the decoding nodes are allowed to fall back during the decoding tree search process.

[0015] In an optional implementation, the decoding end device When the continuous elimination decoding algorithm is called, the decoding node Decoding, including: When the likelihood ratio is conduct The judgment obtained when When direct judgment in, is the subscript set corresponding to the subchannel that directly transmits information bits, It is the subscript set corresponding to the subchannel of directly transmitting frozen bits. It can be seen that in this embodiment, continuous elimination decoding is used to recover the directly transmitted information bits, which will be directly determined based on the likelihood ratio value. Perform bit decisions and do not allow decoding nodes to fall back.

[0016] It can be seen that this decoding method organically combines Fano decoding with successive elimination decoding, reduces the number of nodes allowed to fall back during the decoding process, and can reduce the overall decoding complexity.

[0017] In a third aspect, embodiments of the present application further provide a communication device. The communication device is a coding end device, or a device of the coding end device, or a device capable of being used in conjunction with the coding end device. In one possible implementation, the communication device includes a functional module, which is implemented as a hardware circuit, or software, or a combination of a hardware circuit and software.

[0018] In one possible implementation, the communication device includes one or more functional units, such as a processing unit, wherein the processing unit is configured to convert the information vector Divided into the first information vector and the second information vector in, Represents a vector of dimension K with elements of 0 or 1, where K is the information vector The information bit dimension of Kc is the information bit dimension to be preprocessed by convolution transformation; Perform code rate configuration I and obtain vector Where Nc is the convolution transformation matrix G′ c Dimension of the vector Perform convolution transformation to obtain the codeword vector of the convolution code The codeword vector and the second information vector Perform code rate configuration II and obtain message vector N is the code length of the channel code; and Perform polarization transformation to obtain the codeword vector Optionally, the communication device further includes a communication unit configured to send a codeword vector

[0019] It can be seen that the encoding method adopts two bit rate configuration processes to construct a coding scheme with better performance, which is conducive to achieving better decoding performance.

[0020] In an optional embodiment, the processing unit converts the codeword vector and the second information vector Perform code rate configuration II and obtain message vector Including: According to the Gaussian approximation criterion, the reliability of the subchannels corresponding to the subscript set [1, N] of the polarization subchannel is sorted to obtain the KK with the highest reliability c +N c The subscripts of the polarization subchannels constitute the subscript set and the subscript set consisting of the subscript of the residual polarization subchannel According to the Gaussian approximation criterion, the subscript set The corresponding sub-channels are sorted by reliability to obtain the N with the lowest reliability. c The subscripts of the polarization subchannels constitute the subscript set and the subscript set consisting of the subscript of the residual polarization subchannel In the subscript set The codeword vector is transmitted on the subchannel indexed by the element in And in the subscript set The second information vector is transmitted on the polarization channel indexed by the element in And in the subscript set Transmit the frozen bits on the polarization subchannel indexed by the element in to obtain the message vector It can be seen that in this implementation, the Gaussian approximation criterion is introduced to estimate the reliability of the polarimetric subchannel, thereby determining code rate configuration II. Polarimetric subchannels with high reliability are used to directly transmit information bits, while polarimetric subchannels with certain transmission capabilities but less reliability are used to transmit codewords after convolution transformation.

[0021] In an optional embodiment, the processing unit processes the first information vector Perform code rate configuration I and obtain vector Including: According to the Reed-Muller code criterion, the subscript set The corresponding sub-channels are prioritized by codeword distance characteristics to obtain a set of Kc positions with the highest priority codeword distance characteristics. will be collected and codeword set Arrange the elements in increasing order to get the vector and and confirm Elements in vector The relative position in the , obtains the subscript set of Kc polarization subchannels In the subscript set The first information vector is transmitted on the polarization subchannel indexed by the element in In the subscript set Transmit the frozen bits on the polarization subchannels with the remaining subscript indexes in , and obtain the vector It can be seen that in this implementation, the Reed-Muller code criterion is introduced to improve the distance characteristics of the MPAC code, thereby determining code rate configuration I, and preferentially selecting the polarization subchannel with the best codeword distance characteristic for information bit transmission.

[0022] Fourthly, embodiments of the present application further provide a communication device. The communication device is a decoding device, or a device for the decoding device, or a device capable of being used in conjunction with the decoding device. In one possible implementation, the communication device includes a functional module, which is implemented as a hardware circuit, software, or a combination of hardware circuit and software.

[0023] In one possible implementation, the communication device includes one or more functional units, such as a communication unit and a processing unit, wherein the communication unit is configured to receive a symbol vector The processing unit is based on the message vector Decoding node At node position i, call the continuous elimination decoding algorithm or Fano decoding algorithm to decode the node Decode, where 1∈[i,N]. Optionally, the processing unit may also be used to estimate the message vector obtained by decoding. Extract information and obtain information vector Where K is the information vector The information bit dimension.

[0024] It can be seen that in the serial decoding process, Fano decoding and continuous elimination decoding will be called at different decoding nodes, which not only makes up for the shortcomings of error propagation of continuous elimination decoding, but also reduces the decoding complexity of Fano decoding.

[0025] In an optional implementation, the decoding end device is configured to decode the node At node position i, call the continuous elimination decoding algorithm or Fano decoding algorithm to decode the node Decoding, including: When the Fano decoding algorithm is called, the decoding node Decode; when When the continuous elimination decoding algorithm is called, the decoding node Decode; is the set of subscripts corresponding to the subchannels that transmit the convolutional codeword. Thus, in this implementation, the Fano decoding algorithm is used to recover the information bits that have undergone convolutional transformation, allowing decoding nodes to fall back during this process. Successive elimination decoding is used to recover the directly transmitted information bits, and this process does not allow decoding nodes to fall back.

[0026] In an optional implementation, the decoding end device When the Fano decoding algorithm is called, the decoding node Decoding, including: Calculate the path metric when and And compare it with the current decoding threshold T to determine the extended decoding node And according to the convolution transformation constraint relationship Sure and get in, when When, confirm Among them, B corresponds to the position set of information bits of convolution transformation, The frozen bit position corresponding to the convolution transform. Where i′ represents the decoding node that has not been expanded It can be seen that in this embodiment, Fano decoding is used to recover the information bits that have undergone convolution transformation, and the decoding nodes are allowed to fall back during the decoding tree search process.

[0027] In an optional implementation, the decoding end device When the continuous elimination decoding algorithm is called, the decoding node Decoding, including: When the likelihood ratio is conduct The judgment obtained when When direct judgment in, is the subscript set corresponding to the subchannel that directly transmits information bits, It is the subscript set corresponding to the subchannel of directly transmitting frozen bits. It can be seen that in this embodiment, continuous elimination decoding is used to recover the directly transmitted information bits, which will be directly determined based on the likelihood ratio value. Perform bit decisions and do not allow decoding nodes to fall back.

[0028] It can be seen that this decoding method organically combines Fano decoding with successive elimination decoding, reduces the number of nodes allowed to fall back during the decoding process, and can reduce the overall decoding complexity.

[0029] For the third and fourth aspects, as an example, the processing unit can be a processing unit or can be embodied as a processing circuit or a logic circuit; the transceiver unit can be an input / output interface, interface circuit, output circuit, input circuit, pin or related circuit on the chip or chip system.

[0030] During implementation, the processor can be used to perform, for example, but not limited to, baseband-related processing, and the transceiver or communication interface can be used to perform, for example, but not limited to, radio frequency transceiver. The above-mentioned devices can be provided on separate chips, or at least partially or entirely on the same chip. For example, the processor can be further divided into an analog baseband processor and a digital baseband processor. The analog baseband processor can be integrated with the transceiver (or communication interface) on the same chip, while the digital baseband processor can be provided on a separate chip. With the continuous development of integrated circuit technology, more and more devices can be integrated on the same chip. For example, a digital baseband processor can be integrated with multiple application processors (such as, but not limited to, a graphics processor, a multimedia processor, etc.) on the same chip. Such a chip can be called a system on a chip (SoC). Whether each device is provided independently on different chips or integrated on one or more chips often depends on the needs of the product design. The embodiments of the present application do not limit the implementation form of the above-mentioned devices.

[0031] In a fifth aspect, an embodiment of the present application further provides a processor for executing the method of any possible implementation of the first aspect or the second aspect, or the first aspect or the second aspect. In the process of executing these methods, the process of sending the above-mentioned signal and receiving the above-mentioned signal in the above-mentioned method can be understood as the process of outputting the above-mentioned signal by the processor, and the process of the above-mentioned signal input by the processor. When outputting the above-mentioned signal, the processor outputs the above-mentioned signal to the transceiver so that it is transmitted by the transceiver (or communication interface). After being output by the processor, the above-mentioned signal may also need to be processed otherwise before arriving at the transceiver (or communication interface). Similarly, when the processor receives the above-mentioned signal input, the transceiver (or communication interface) receives the above-mentioned signal and inputs it into the processor. Furthermore, after the transceiver (or communication interface) receives the above-mentioned signal, the above-mentioned signal may need to be processed otherwise before being input into the processor.

[0032] For the sending and receiving operations involved in the processor, unless otherwise specified, or unless they conflict with their actual functions or internal logic in the relevant descriptions, they can be more generally understood as processor output, reception, input and other operations, rather than sending and receiving operations directly performed by the RF circuit and antenna.

[0033] During implementation, the processor may be a processor specifically configured to execute these methods, or may be a processor that executes computer instructions in a memory to execute these methods, such as a general-purpose processor. The memory may be a non-transitory memory, such as a read-only memory (ROM), which may be integrated with the processor on the same chip or disposed on separate chips. The embodiments of the present application do not limit the type of memory or the configuration of the memory and the processor.

[0034] In a sixth aspect, embodiments of the present application further provide a communication device, comprising: a processor configured to invoke a computer program stored in a memory to cause the communication device to implement the method of the first or second aspect, or any possible implementation of the first or second aspect. Optionally, the communication device further comprises a memory, and the processor and the memory are coupled.

[0035] In a seventh aspect, the present application further provides a communication system, comprising at least one first network device that performs the method of the first aspect or any optional implementation method of the first aspect, and at least one second network device that performs the method of the second aspect or any possible implementation method of the second aspect. In another possible design, the system may further include other devices that interact with the first network device and / or the second network device in the solution provided in the embodiments of the present application.

[0036] As can be seen, this communication system performs convolution transformations on only a portion of the information bits during encoding and organically combines Fano decoding with successive elimination decoding during decoding, reducing the number of nodes allowed to fall back during the decoding process and thus lowering the overall decoding complexity. This communication system can achieve lower decoding complexity and, under complexity constraints, achieve superior decoding performance and decoding complexity. The low complexity advantage of this communication system is even more pronounced when the code length is longer (e.g., code length of 512 or greater).

[0037] In an eighth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is run, the method of any one of the above-mentioned first to second aspects or any optional implementation method of any one of the aspects is executed.

[0038] In the ninth aspect, the present application also provides a computer program product comprising instructions, the computer program product comprising: computer program code, which, when the computer program code is run, enables the method of any one of the above-mentioned first to second aspects or any optional implementation method of any one of the aspects to be executed.

[0039] In a tenth aspect, the present application provides a chip system, which includes a processor and an interface, the interface being used to obtain a program or instruction, and the processor being used to call the program or instruction to implement the method of any aspect of the first to second aspects or any optional implementation method of any aspect. In one possible design, the chip system also includes a memory, which is used to store program instructions and data necessary for the terminal. The chip system can be composed of a chip or can include a chip and other discrete devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] FIG1 is a schematic diagram of a communication system;

[0041] FIG2 is a butterfly diagram for iterative calculation of LLR values ​​in SC decoding;

[0042] FIG3 is a schematic diagram of SC decoding of a polar code with N=4 on a decoding binary tree;

[0043] FIG4 is a flow chart of encoding and decoding of a PAC code;

[0044] Figure 5 is a Fano decoding estimation vector with N=4 Binary tree graph of ;

[0045] FIG6 is a schematic diagram of an encoding method provided in an embodiment of the present application;

[0046] FIG7 is a schematic diagram of the construction process of the subscript set in the code rate configuration of the MPAC code;

[0047] FIG8 is a schematic diagram of a decoding method provided in an embodiment of the present application;

[0048] FIG9 is a schematic diagram showing a comparison of frame error rate and normalized complexity performance of different coding schemes with N=128, K=64 provided in an embodiment of the present application;

[0049] FIG10 is a schematic diagram showing a comparison of FER and normalized complexity performance of different coding schemes with N=512, K=256 provided in an embodiment of the present application;

[0050] FIG11 is a schematic structural diagram of a communication device provided in an embodiment of the present application;

[0051] FIG12 is a schematic structural diagram of another communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] In the embodiments of this application, " / " can indicate that the associated objects are in an "or" relationship. For example, A / B can mean A or B. "And / or" can be used to describe the existence of three relationships between associated objects. For example, "A and / or B" can mean: A exists alone, A and B exists simultaneously, or B exists alone. A and B can be singular or plural. To facilitate the description of the technical solutions of the embodiments of this application, the words "first" and "second" may be used in the embodiments of this application to distinguish between technical features with the same or similar functions. The words "first" and "second" do not limit the number or order of execution, and the words "first" and "second" do not necessarily mean different. In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. The use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for easier understanding.

[0053] The present application provides a communication system, which includes an encoding end device and a decoding end device. The encoding end device only performs convolution transformation on some information bits during encoding, and the decoding end device organically combines Fano decoding with continuous elimination decoding during decoding, thereby reducing the number of nodes allowed to fall back during the decoding process and reducing the overall decoding complexity. The communication system can achieve lower decoding complexity and, under complexity constraints, achieve better decoding performance and decoding complexity. The low complexity advantage brought by the communication system is more obvious when the length is longer (such as a code length of 512 or more).

[0054] The present application can be applied to the communication system shown in Figure 1. For example, the communication system includes a network device and a terminal. Figure 1 only describes one network device and multiple terminals (such as terminal 1-terminal 6) as an example. The present application does not limit the number of network devices and terminals. For example, the network device can perform some or all operations of the encoding end device, and the terminal can perform some or all operations of the decoding end device; for another example, the terminal can perform some or all operations of the encoding end device, and the network device can perform some or all operations of the decoding end device; for another example, the network device can perform some or all operations of the encoding end device and the decoding end device, and the terminal can perform some or all operations of the encoding end device and the decoding end device. The embodiments of the present application do not limit this.

[0055] The embodiments of the present application can be applied to communication systems of various radio access technologies (RAT), for example, narrowband Internet of Things (NB-IoT) systems, long term evolution (LTE) communication systems, 5G (or new radio (NR)) communication systems, and transition systems between LTE communication systems and 5G communication systems. The transition system can also be called a 4.5G communication system, and of course it can also be a future communication system, such as the sixth generation (6G) or even the seventh generation (7G) system. The embodiments of the present application can also be applied to non-terrestrial networks (NTN), vehicle to everything (V2X), long term evolution-vehicle (LTE-V), vehicle to vehicle (V2V), machine type communications (MTC), Internet of Things (IoT), long term evolution-machine to machine (LTE-machine to machine, LTE-M), machine to machine (M2M), or future mobile communication systems. The embodiments of the present application can be applied to ultra-reliable low-latency communications (uRLLC) scenarios, such as telemedicine, industrial automation, drones and self-driving cars. In addition, the embodiments of the present application can effectively reduce the decoding complexity and can also be applied to massive machine type of communication (mMTC) scenarios with high demand for low-power devices, such as smart cities, smart transportation and other fields.

[0056] Optionally, the network device is an access network (AN) device, such as a base station, that the terminal uses to access the mobile communication system wirelessly. The network device may also refer to a device that communicates with the terminal device over an air interface. The network device may include an evolved NodeB (eNodeB or eNB) transmission reception point (TRP) in a long term evolution (LTE) system or long term evolution-advanced (LTE-A), a next generation NodeB (gNB) in a fifth generation (5G) mobile communication system, an access network device in an open radio access network (O-RAN or open RAN), a next generation base station in a sixth generation (6G) mobile communication system, or a base station in a future mobile communication system, or an access node in a wireless fidelity (WiFi) system, etc.; or, the network device may be a relay station, a vehicle-mounted device, a future evolved public land mobile network (PLMN) device, a device in a device-to-device (D2D) network, a device in an M2M network, a device in an IoT network, or a network device in a public land mobile network (PLMN), etc.

[0057] Optionally, taking a base station as an example of a network device, the base station can communicate with the terminal device, or communicate with the terminal device through a relay station. The terminal can communicate with multiple base stations in different access technologies. The network device can be a module or unit that completes part of the functions of the base station, for example, it can be a centralized unit (CU), a distributed unit (DU), a centralized unit control plane (CU control plane, CU-CP) module, or a centralized unit user plane (CU user plane, CU-UP) module. Multiple DUs can be centrally controlled by one CU. CU and DU can be divided according to the protocol layer functions of the wireless network they possess, for example, the functions of the packet data convergence protocol (PDCP) layer and above protocol layers are set in the CU, and the functions of the protocol layers below the PDCP, such as the radio link control (RLC) layer and the medium access control (MAC) layer, are set in the DU. It should be noted that this division of the protocol layer is only an example, and it can also be divided at other protocol layers. The radio frequency device can be remote and not placed in the DU, or it can be integrated in the DU, or partly remote and partly integrated in the DU, and the embodiments of the present application do not impose any restrictions. In addition, in some embodiments, the control plane (CP) and user plane (UP) of the CU can also be separated and divided into different entities for implementation, namely the control plane CU entity (CU-CP entity) and the user plane CU entity (CU-UP entity). In this network architecture, the signaling generated by the CU can be sent to the terminal device through the DU, or the signaling generated by the terminal device can be sent to the CU through the DU. The DU can directly encapsulate the signaling through the protocol layer and pass it to the terminal device or CU without parsing it. In this network architecture, the CU is divided into a network device on the radio access network (RAN) side. In addition, the CU can also be divided as a network device on the core network (CN) side, and this application does not impose any restrictions on this. Optionally, the access network device can be a server, etc. For example, the network device in the vehicle V2X technology can be a road side unit (RSU). Optionally, the network device may also be various devices constituting an access node, such as an active antenna unit (AAU), a baseband unit (BBU), and the like.

[0058] Optionally, the network device may be a macro base station (also known as a large station), a micro base station or an indoor station (also known as a small station), or a relay node or a donor node. This application does not limit the specific technology and specific device form adopted by the access network device. Optionally, the communication device used to implement the function of the network device may be a network device, or a device that can support the network device to implement the function, such as a chip system, which may be installed in the network device.

[0059] Optionally, a terminal, also known as a terminal device (terminal), user equipment (UE), mobile station (MS), mobile terminal (MT), etc., is a device with wireless transceiver functions that can send signals to network devices or receive signals from network devices. Terminal devices may include user equipment (UE), sometimes also called terminal, access station, UE station, remote station, wireless communication equipment, or user device, etc. The terminal device is used to connect people, objects, machines, etc., and can be widely used in various scenarios, such as but not limited to the following scenarios: cellular communication, D2D, V2X, M2M / MTC, IoT, virtual reality (VR), augmented reality (AR), industrial control, self driving, remote medical, smart grid, smart furniture, smart office, smart wear, smart transportation, smart city, drones, robots and other scenarios. For example, the terminal can be a mobile phone, a tablet computer, a computer with wireless transceiver functions, a VR terminal, an AR terminal, a wireless terminal in industrial control, a wireless terminal in unmanned driving, a smart speaker in an IoT network, a wireless terminal device in telemedicine, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, or a wireless terminal device in a smart home, etc. As an example and not a limitation, the terminal can also be a wearable device, which can also be called a wearable smart device or a smart wearable device, etc., which is a general term for wearable devices that are intelligently designed and developed using wearable technology for daily wear, such as glasses, gloves, watches, clothing, and shoes. The various terminals described above, if located on a vehicle (for example, placed in or installed in a vehicle), can be considered as vehicle-mounted terminal devices, which are also called on-board units (OBUs). The terminal can also be a vehicle-mounted module, vehicle-mounted module, vehicle-mounted component, vehicle-mounted chip or vehicle-mounted unit that is built into the vehicle as one or more components or units. The vehicle can implement the method described in the embodiments of the present application through the built-in vehicle-mounted module, vehicle-mounted module, vehicle-mounted component, vehicle-mounted chip or vehicle-mounted unit.

[0060] The network equipment and / or terminal equipment can be fixed or movable. The network equipment and / or terminal equipment can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; or can be deployed on the water surface; or can be deployed on aircraft, balloons and artificial satellites in the air. This application does not limit the environment / scenario in which the network equipment and terminal equipment are located. The network equipment and terminal equipment can be deployed in the same or different environments / scenarios, for example, the network equipment and terminal equipment are deployed on land at the same time; or, the network equipment is deployed on land and the terminal equipment is deployed on water surface, etc., and no further examples are given.

[0061] As a core technology for ensuring reliable information transmission, channel coding has played a vital role in wireless communication systems throughout history. Given the new requirements for ultra-reliable low-latency communication (uRLLC) in next-generation communication systems, research on short-code-length channel coding is of great significance. Several channel coding methods are introduced below:

[0062] 1. Polar code

[0063] Polar codes are the first coding scheme theoretically proven to reach channel capacity. When the code length approaches infinity, they exhibit excellent decoding performance using the Successive Cancellation (SC) decoding algorithm. However, within the short to medium code length range, polar codes are limited in range, and their decoding performance struggles to meet the new requirements for ultra-high reliability. By improving both the coding structure and the decoding scheme, the decoding performance of short to medium polar codes can be improved.

[0064] The encoding and decoding processes of polar codes are described below.

[0065] (1) Polar code encoding process

[0066] Assume that the code length of the polar code is N, which can be any power of 2, that is, N = 2 n , the information dimension of the information vector is K, then for an (N,K) polar code, its polarization coding process can be expressed as:

[0067] in, is the codeword vector c1, c2, ..., c after polarization coding of the (N, K) polar code N , that is, the codeword vector

[0068] is the information vector The message vector (u1,u2,…,u N ), the message vector Among them, the information vector Represents a set of binary sequences whose elements are binary and whose length is N. P is the generator matrix, and its construction process can be expressed as:

[0069] in, is the bit-flip matrix, Represents a set of binary, N×N dimensional matrices; is the polar kernel matrix, Expressed as a Kronecker product operation, is the matrix obtained by performing n Kronecker product operations on the polar kernel matrix F. Represents a set of binary sequences whose elements are binary and length is N.

[0070] Among them, the message vector By information vector During the code rate configuration process, the subscript set [1, N] of the polarization subchannel is divided into information sets based on different criteria, such as Gaussian Approximation (GA) criterion, Reed-Muller (RM) criterion, RM-polar criterion, etc. (satisfy Represents a collection of information ), and frozen sets (satisfy Represents a collection of information The number of elements in ). Among them, the information set The indexed polarization subchannel is used to transmit information bits, denoted as Frozen Collection The indexed polarization sub-channel is used to transmit the frozen bit. In this application, the default frozen bit is 0, which is recorded as Therefore, the message vector By information vector The process of bitrate configuration can be expressed as:

[0071] and

[0072] The RM criterion supports a limited number of K values ​​(or message sizes). For example, it can only support discrete values ​​of K, such as K = {1, 6, 16, 26, 31}. When K is outside the range supported by the RM criterion, such as K = 10, the RM-polar criterion first selects K = 6 message bits based on the RM criterion. Then, based on the reliability criterion, it selects the four most reliable positions between the RM-supported positions of 6 and 16, ultimately arriving at the message bit positions of K = 10.

[0073] (2) Polar code decoding process

[0074] Assume that the modulation scheme used in the communication system is binary phase shift keying (BPSK) modulation. After transmission through the channel, the symbol received by the receiver is The message vector to be estimated is In the successive cancellation (SCL) decoding process, the message vector to be estimated middle The estimation of will depend on its decision logarithm likelihood ratio (LLR) value. Represents a set of real number sequences whose elements are real numbers and length is N. Among them, The LLR value is Obtained by the following formula (3):

[0075] in, is the channel conditional state transition probability. Figure 2 is a butterfly diagram for the iterative calculation of LLR values ​​in SC decoding, which is the SC process for depolarization transformation. There are n+1 columns in the butterfly diagram, each with N LLR value calculation units. Based on the recursive principle of polar code polarization channels, the LLR value of a polar code of length N can be decomposed into the LLR values ​​of two sub-polar codes of length N / 2. This can be recursively decomposed from the left end of the butterfly diagram to the right, and after log2N decompositions, it can be decomposed into N sub-polar codes of length 1. Where n = log2N.

[0076] Figure 2 takes the polar code with N=4 as an example. For the rightmost subcode with length 1 in Figure 2, their LLR values ​​are as follows: to It can be directly observed from the channel and is called the channel LLR value

[0077] In Figure 2, the solid arrow represents f +Calculation, the dotted arrow represents f - Calculation. The LLR iterative calculation is performed from the rightmost end of the butterfly diagram to the left, and each calculation unit will be activated and calculate the LLR value in the increasing order of the number in its upper right corner. Optionally, the calculation function f used in this iterative calculation process + 、f - , defined as follows:

[0078] Based on the above formulas (1) and (2), the calculation process of the LLR value can be expressed as a layer-by-layer recursive form:

[0079] in, and Respectively Subvectors indexed by odd numbers (odd, o) and even numbers (even, e). Optionally, LLR calculation (the above f + Calculate or f - The number of times the code is called can be used to measure the complexity of decoding, and the SC decoding complexity of the polar code with a code length of N can be determined to be O(N log2N). After being recursively calculated on the butterfly graph, the decoding end device can perform SC decoding bit decision. For the frozen set bits, that is, The verdict is For the information set bits, that is, The following judgment is made:

[0080] Optionally, describe the SC decoding on the decoding binary tree, assuming exist The posterior probability (APP) of the decoding is uniformly distributed on the The LLR value can be determined by it Calculation yields:

[0081] On this basis, SC decoding can be represented on a decoding binary tree. For a polar code of length N, the number of node layers in the decoding binary tree is N+1, and the number of leaf nodes is 2. N During the SC decoding process, it starts from the root node of the binary tree and proceeds layer by layer. When , we can get Calculate the APP values ​​corresponding to its child nodes, that is, and Each time, only the child nodes with larger APP values ​​are selected for expansion. When the decoding path is expanded to one of the leaf nodes, the decoding ends and a complete decoding path is obtained.

[0082] As shown in Figure 3, the schematic diagram of SC decoding of polar code with N=4 on the decoding binary tree is shown in Figure 3. Starting from the root node of the binary tree and proceeding layer by layer, at each layer When , we can get Calculate the APP values ​​corresponding to its child nodes, and only select and expand the child nodes with larger APP values ​​each time. For example, the value marked next to each child node is the APP value of the child node. The information set The final decoded estimate is

[0083] While polar codes, assisted by SC decoding, have been theoretically proven to reach channel capacity and exhibit excellent decoding performance for long code lengths, within the medium and short code length range, the overall decoding performance struggles to meet the ultra-reliability requirements of communication systems due to the limited minimum Hamming distance of polar codes and the error propagation characteristics of SC decoding.

[0084] 2. Cyclic Redundancy Check (CRC)-polar code

[0085] CRC-polar codes concatenate CRC codes with polar codes to improve the distance characteristics of polar codes. A relatively mature decoding algorithm for CRC-polar codes is the Successive Cancellation List (SCL) decoding algorithm. This algorithm retains multiple SC decoding paths during the decoding process and, in the final stage, uses CRC to assist in screening the decoding paths in the list, thereby achieving better decoding performance.

[0086] However, the SCL decoding algorithm needs to calculate and store the decoding paths in the list. Generally, the larger the list is, the better the SCL decoding performance will be, but its computational complexity and storage cost will also increase.

[0087] 3. Polarization-Adjusted Convolutional (PAC) Code

[0088] PAC codes are a novel concatenated coding scheme and a competitive medium-short code length coding scheme. By concatenating convolutional coding and polar coding, assisted by the Fano decoding algorithm, this scheme can achieve a performance upper bound close to the finite code length of the Normal Approximation (NA). During PAC code decoding, the Fano decoding algorithm retains only a single decoding path and finds the most likely decoding path by performing forward and backward judgments at decoding nodes. As a result, this decoding algorithm offers excellent decoding performance and efficient storage.

[0089] However, when the channel conditions are poor, Fano decoding will have the problems of high decoding complexity and uncontrollable delay, which limits the further application of PAC codes.

[0090] The following is a brief introduction to the encoding and decoding process of the PAC code. As shown in Figure 4, Figure 4 is the encoding and decoding flow chart of the PAC code. The encoding process of the PAC code can be considered as adding a convolution transformation with a code rate of 1 to the code rate configuration and polarization transformation of the polar code. For a (N, K) PAC code, the information vector First, the vector is obtained through code rate configuration Specifically, the GA criterion or RM criterion of the polar code rate configuration can be used to obtain PAC codes with different performances. After the convolution transformation, the vector vector The PAC codeword will be further obtained through polarization transformation Vector after convolution transformation It can be considered that the transmission is performed directly on the polarization sub-channel of the polar code.

[0091] Suppose the generating polynomial of the convolution transform is g(x)=g0+g1x+…+g m x m , whose constraint length is m+1. The convolution transform code rate is 1, and its generator matrix G c It can be expressed as an upper triangular Toeplitz matrix:

[0092] Therefore, the vector after convolution transformation will satisfy Further polarization transformation can obtain the codeword vector of the PAC code

[0093] For the decoding process of PAC code, as shown in Figure 4, the received symbol vector The decoding end device needs to The process of depolarization transformation of SC decoding is performed to obtain the polarization transformation input to be estimated The decision LLR value of 1≤i≤N. Then, the decoding end device uses each The decision LLR value of the bit and the constraint relationship of the convolution transformation can be determined by performing tree searches of different strategies on the decoding binary tree. And the convolution transformation input vector to be estimated Finally, the information vector can be extracted

[0094] The Fano decoding algorithm was adopted when PAC codes were first proposed, enabling maximum likelihood (ML) decoding performance for PAC codes. It is a decoding method with excellent decoding performance and efficient storage. Compared to SC decoding, which uses the same single decoding path, Fano decoding introduces the concept of path metric (reliability). Based on the reliability of the current decoding path, Fano can determine whether to continue expanding nodes forward or retracting nodes backward, thereby obtaining the most reliable decoding path. The specific calculation of the path metric is:

[0095] in, The initial value is 0, For each polarization channel The cutoff rate can be determined by its Bhattacharyya parameter Calculated, that is:

[0096] For AWGN channels, Pap smear parameters The calculation can be simplified to in, is the noise variance of each polarization channel, which can be obtained by using the GA method. In the GA method, the mean of the LLR values ​​in the recursive process will satisfy

[0097] Among them, the initial value of the iteration The function δ(·) is defined as

[0098] It is worth noting that the path metric The larger the value, the corresponding estimated path To better describe the Fano decoding process, we can let and Decoding path (Path Metrics ) Expand branches separately and The paths corresponding to the larger and smaller values ​​of the obtained path metrics are and

[0099] In the Fano decoding process, in order to measure whether the reliability of the current decoding path is worth expanding, two new decoding parameters need to be introduced, namely the decoding threshold T and the step size Δ. The reliability of the current path will be determined by comparing it with T, and T will be dynamically adjusted by Δ during the decoding process. Assume that the i-th decoding node is currently being judged. The judgment of Fano decoding forward extension and backward fallback is as follows:

[0100] (1) If Then expand forward to

[0101] (2) If Then fall back to the root node or fall back to the node that satisfies and Nodes that have not been expanded The process will then be The node continues decoding.

[0102] During the Fano decoding process, the decoding threshold T will be adjusted by the step size Δ according to different situations:

[0103] (1) During the forward expansion process, if the path For the first time to be expanded and meet Then order

[0104] (2) During the rollback process, if the path Make If it holds, let T = T-Δ;

[0105] (3) During the rollback process, if the rollback reaches the root node of the binary tree, let T = T-Δ.

[0106] When the Fano decoding path extends to a leaf node, the decoding ends and the decoder obtains the complete decoding path.

[0107] For example, FIG5 is a Fano decoding estimation vector for N=4 Binary tree diagram, where ① to ⑦ in Figure 5 are the order of node access. When the path metric corresponding to the child node in the next step is less than the preset threshold, it falls back to another branch of the previous step to ensure that the cumulative metric value of the final path is the largest. For example, the information set The decoding threshold T = 0, the step size Δ = 4, and the final decoding estimate is

[0108] In the PAC code decoding process, the Fano decoding tree search process will be combined with the mapping constraints of the convolution transform. Indicates that the shift register corresponding to the convolution change is in the input vector The initial state is all zero. Therefore, the convolution transform outputs bits u i Can be Then, for the estimated bits obtained by searching the Fano decoding tree, According to the register status updated in the previous decoding process Bit The mapping relationship And the only certainty. For the frozen position have Bit The mapping relationship will also be Determine the path metric in the subsequent Fano decoding process can be calculated sequentially.

[0109] While Fano decoding offers excellent performance, approaching the ML decoding performance of PAC codes, its decoding complexity and latency can become uncontrollable under poor channel conditions. In implementations, an upper limit on the decoding computational complexity is often set, and decoding is terminated when the complexity reaches this limit. This compromises Fano decoding performance and makes it difficult to meet the high decoding performance requirements of communication systems. These shortcomings are further exacerbated when Fano decoding is applied to PAC codes of length 512 or greater.

[0110] For example, Table 1 shows the proportion of PAC codes with parameters (128, 64) and (512, 256) that can output complete decoding paths under the RM construction criterion, subject to a limited decoding complexity upper bound η (η is the normalized complexity value, corresponding to the number of LLR calculations Φ = ηN log2N). The table shows that when the signal-to-noise ratio (SNR) is low, the Fano decoding algorithm is more likely to linger in the decoding binary tree, failing to output a complete decoding result, resulting in a loss of decoding performance. This phenomenon is exacerbated with increasing code length, severely impacting the application of the Fano decoding algorithm for PAC codes with lengths of 512 or longer. The increase in Fano decoding complexity is primarily due to the significant increase in the number of node fallbacks and adjustments to the decoding threshold T during the decoding process when channel conditions are poor. Consequently, the decoding path will linger between decoding nodes in the decoding binary tree, preventing the algorithm from obtaining a complete path and, consequently, from outputting a complete decoding result.

[0111] Table 1 Proportion of complete paths generated by different PAC codes under the complexity upper limit η (%)

[0112] The embodiment of the present application proposes a coding method, which adopts the idea of ​​partial convolution transformation and can be called modified PAC (MPAC) code. The embodiment of the present application also provides a decoding method, which organically combines Fano decoding with SC decoding, and the decoding method can be called hybrid Fano-successive elimination (HFSC) decoding method. The coding method provided by the embodiment of the present application reduces the number of nodes allowed to perform node fallback during the decoding process, and can effectively reduce the decoding complexity. Compared with the above-mentioned coding and decoding schemes, for example, the embodiment of the present application applies Fano decoding to PAC code, SCL decoding to CRC-polar code and SC decoding to polar code, it can achieve better advantages in terms of the compromise between decoding performance and complexity, and its hardware implementation is friendly, and it has great application potential in practical applications.

[0113] The following describes the embodiments of the present application in conjunction with the accompanying drawings.

[0114] Please refer to Figure 6, which is a schematic diagram of an encoding method provided by an embodiment of the present application. As shown in Figure 6, in this encoding method, the information bits use a partial convolution transformation process and include two bit rate configuration processes. Specifically, the encoding method may include but is not limited to the following steps:

[0115] (1) The encoding end device converts the information vector Divided into the first information vector and the second information vector in, Represents a vector of dimension K with elements of 0 or 1, where K is the information vector The information bit dimension of Kc is the information bit dimension to be preprocessed by convolution transformation;

[0116] (2) For the first information vector Perform code rate configuration 1 and obtain vector Where Nc is the convolution transformation matrix Dimensions;

[0117] (3) Vector Perform convolution transformation to obtain the codeword vector of the convolution code

[0118] (4) The codeword vector and the second information vector Perform code rate configuration 2 and obtain the message vector N is the code length of the channel coding;

[0119] (5) Message Perform polarization transformation to obtain the codeword vector

[0120] Optionally, the encoding device can also send codeword vectors Among them, the information vector First information vector The second information vector vector Codeword vector Message Vector Codeword vector

[0121] As mentioned above, for an (N, K) MPAC code, the convolved parameters (N c ,K c ) definition, these parameters need to satisfy KK c +N c ≤N, so it can also be defined as (N,K)-(N c ,K c ) MPAC code. The codeword of the MPAC code can be represented by Get. Optional, convolution parameters (N c ,K c ) are greater than zero.

[0122] As can be seen, in this coding method, Kc of the K information bits participate in the convolution transformation and then undergo polarization transformation (i.e., polar coding), while the remaining (K-Kc) information bits directly undergo polarization transformation (i.e., polar coding). Two code rate configuration processes are used to construct a coding scheme with better performance, which in turn facilitates better decoding performance.

[0123] In an optional implementation, the encoding end device converts the codeword vector and the second information vector Perform code rate configuration II and obtain message vector include:

[0124] (1) According to the Gaussian approximation criterion, the subchannels corresponding to the subscript set [1, N] of the polarization subchannel are sorted by reliability to obtain the KK with the highest reliability. c +N c The subscripts of the polarization subchannels constitute the subscript set and the subscript set consisting of the subscript of the residual polarization subchannel

[0125] Specifically, Represents the mean LLR value estimated by the GA method, that is, The larger the value, the more polarization channels Therefore, the polarization channel can be based on The reliability of the order is sorted by the size, that is, GA sorting. On this basis, define r(i) = b(i-1), where b(i-1) represents the number of 1s in the binary expression of i-1. Thus, the RM-polar (RMP) ordered pair (i1, i2) can be defined RMP , which satisfies r(i1)>r(i2) or r(i1)=r(i2),

[0126] FIG7 is a schematic diagram of the construction process of the subscript set in the code rate configuration of the MPAC code. As shown in FIG7 , in the construction process of the subscript set, first, the reliability ranking of the polarization subchannel (j1, j2, …, j N ) GA ,satisfy On this basis, we get the KK with the highest reliability c +N c The polarization sub-channel subscripts form a set:

[0127] The subscript set composed of the subscripts of the residual polarization subchannel The corresponding sub-channel will transmit frozen bits to ensure reliable information transmission.

[0128] (2) According to the Gaussian approximation criterion, the subscript set The corresponding sub-channels are sorted by reliability to obtain the N with the lowest reliability. c The subscripts of the polarization subchannels constitute the subscript set and the subscript set consisting of the subscript of the residual polarization subchannel in,

[0129] (3) In the subscript set The codeword vector is transmitted on the subchannel indexed by the element in And in the subscript set The second information vector is transmitted on the polarization channel indexed by the element in And in the subscript set Transmit the frozen bits on the polarization subchannel indexed by the element in to obtain the message vector

[0130] It can be seen that in this implementation, the Gaussian approximation criterion is introduced to estimate the reliability of the polarimetric subchannel, thereby determining code rate configuration II. Polarimetric subchannels with high reliability are used to directly transmit information bits, while polarimetric subchannels with certain transmission capabilities but less reliability are used to transmit codewords after convolution transformation.

[0131] In an optional implementation manner, the encoding end device encodes the first information vector Perform code rate configuration I and obtain vector Including: According to the RM criterion, the subscript set The corresponding sub-channels are prioritized by codeword distance characteristics to obtain a set of Kc positions with the highest priority codeword distance characteristics. will be collected and codeword set Arrange the elements in increasing order to get the vector and and confirm Elements in vector The relative position in the , obtains the subscript set of Kc polarization subchannels In the subscript set The first information vector is transmitted on the polarization subchannel indexed by the element in In the subscript set Transmit the frozen bits on the polarization subchannels with the remaining subscript indexes in , and obtain the vector

[0132] It can be seen that for the code rate configuration I process, the subscript set The corresponding sub-channels are reordered using the RM-polar criterion to obtain satisfy Ordered pair relationship. Therefore, select the K with the highest priority under the RM-polar criterion c positions, forming a set Set the subscript and Arrange the elements in increasing order to get the vector and In order to characterize the process of bit rate configuration I, it is necessary to further determine Elements in vector The relative position in the result is the subscript set It can be seen that after the subscript sets of rate configuration II and rate configuration I are constructed according to the above method, the parameters are defined as (N, K)-(N c ,K c The construction and encoding process of the MPAC code will be uniquely determined. The code rate configuration I process will be determined, the subscript set and collection The code rate configuration II process will be jointly determined.

[0133] It can be seen that in this embodiment, the RM-polar criterion is introduced, and the generator matrix G corresponding to the polarization transformation is preferably selected. P The polarization subchannel with the highest codeword weight is used to transmit information bits. If the codeword weights are the same, the polarization subchannel with higher reliability is selected to improve the distance characteristics of the MPAC code. This determines code rate configuration I, and prioritizes the polarization subchannel with the highest codeword distance characteristics for information bit transmission.

[0134] The design of code rate configuration I and code rate configuration II affects the construction and coding process of MPAC code. Code rate configuration II selects the reliability criterion of polarization sub-channel based on GA estimation, so that the polarization sub-channel with high reliability can be selected for information bit Direct transmission of the MPAC code is adopted; while rate configuration I adopts the RM-polar criterion that prioritizes the codeword distance characteristic to improve the decoding performance of the constructed MPAC code.

[0135] The decoding method provided in the embodiment of the present application can be called HFSC decoding. HFSC decoding is a serial process that is performed node by node on the decoding binary tree, and finally a complete decoding path output is obtained. During the decoding process, the SC process will be applied to the depolarization transformation to obtain the polarization transformation input bit. The decision LLR value On this basis, different decoding nodes will adopt different decoding strategies, that is, SC decoding and Fano decoding will be called according to the node position i.

[0136] The decoding method may include but is not limited to the following steps:

[0137] (1) The decoding end device receives the symbol vector And determine the message vector to be estimated Where N is the code length of the channel coding;

[0138] (2) According to the decoding node At node position i, call the continuous elimination decoding algorithm or Fano decoding algorithm to decode the node Decode; where 1∈[i,N].

[0139] Please refer to FIG8, which is a schematic diagram of a decoding method provided by an embodiment of the present application. As shown in FIG8, when When the Fano decoding algorithm is called, the decoding node Decode; when When the continuous elimination decoding algorithm is called, the decoding node Decode; is the set of subscripts corresponding to the subchannels that transmit the convolutionally transformed codeword. For details, see the relevant content of the encoding method described in Figure 6 and will not be detailed here. As can be seen, in this implementation, the Fano decoding algorithm is used to recover the information bits that have undergone convolutional transformation, allowing decoding nodes to fall back during this process. Successive elimination decoding is used to recover the directly transmitted information bits, and this process does not allow decoding nodes to fall back.

[0140] Optionally, the decoding device When the Fano decoding algorithm is called, the decoding node Decoding, including: Calculate the path metric when and And compare it with the current decoding threshold T to determine the extended decoding node And according to the convolution transformation constraint relationship Sure and get in, when When, confirm Among them, B corresponds to the position set of information bits of convolution transformation, For details on the frozen bit positions corresponding to the convolution transformation, please refer to the relevant content in the encoding method described in FIG6 , which will not be described in detail here.

[0141] If i′≤i, it means that node rollback is possible, and the judgment result Will be returned to the SC process to update the path metric value Through the mapping relationship It can be determined And feed back to the SC process to update the path metric value

[0142] It can be seen that in this embodiment, Fano decoding is used to recover the information bits that have undergone convolution transformation, and the decoding nodes are allowed to fall back during the decoding tree search process.

[0143] Optionally, the decoding device When the continuous elimination decoding algorithm is called, the decoding node Decoding, including: When the likelihood ratio is conduct The judgment obtained Optionally, update the path metric So that Fano decoding can proceed. When direct judgment Optionally, further update the path metric

[0144] in, is the subscript set corresponding to the subchannel that directly transmits information bits, It is the subscript set corresponding to the subchannel that directly transmits the frozen bits. For details, please refer to the relevant content in the encoding method described in Figure 6, which will not be described in detail here.

[0145] It can be seen that in this embodiment, the continuous elimination decoding is used to recover the directly transmitted information bits, which will be directly based on the decision likelihood ratio value. Perform bit decisions and do not allow decoding nodes to fall back.

[0146] (3) Estimation of the message vector obtained by decoding Extract information and obtain information vector Combined with the When calling the Fano decoding algorithm to obtain Get decoding estimate Where K is the information vector The information bit dimension.

[0147] It can be seen that in the serial decoding process, Fano decoding and continuous elimination decoding will be called at different decoding nodes. The organic combination of Fano decoding and continuous elimination decoding reduces the number of nodes allowed to fall back during the decoding process, which not only makes up for the shortcomings of error propagation of continuous elimination decoding, but also reduces the decoding complexity of Fano decoding.

[0148] In the simulation experiment, the communication system adopts additive white gaussian noise (AWGN) channel and BPSK modulation as the modulation scheme. The generator polynomial of the convolution transform used by the MPAC code is g(x)=1+x 2 +x 3 +x 5 +x 6 , the GA criterion of the construction process will use the signal-to-noise ratio E s / N0=0.0dB. In the HFSC decoding process, b The path metric is calculated under / N0=1.5dB, and the decoding step size is set to Δ=2. For PAC code, RM structure and the same convolution transform generator polynomial g(x) are used, and its Fano decoding parameters are the same as the above HFSC configuration. For polar code and CRC-polar code, GA is used in E s / N0=0.0dB, where the CRC generating polynomial is g c (x) = 1 + x + x 2 +x 8 In the experiment, a decoding complexity upper limit η (η is the normalized complexity, corresponding to the number of LLR calculations Φ = ηN log2N) is set for HFSC decoding and Fano decoding. In this way, the complexity constraint is simulated, and the normalized complexity is used as the complexity metric for different decoding schemes for comparison.

[0149] Under the above simulation conditions, FIG9 is a schematic diagram showing the performance comparison of the frame error rate (FER) and normalized complexity (NC) of different coding schemes with N=128 and K=64 provided in the embodiment of the present application. c ,K c ) are the performance of MPAC codes with K=96,48,64,32,32, respectively, under HFSC decoding (η=128). By comparison, it can be found that under fixed K c / N c In the case of c, the decoding performance of MPAC codes will be improved with a slight complexity penalty. When compared with PAC codes, the MPAC code scheme with parameters (128,64)-(96,48) can achieve a slight advantage in both decoding performance and complexity.

[0150] Table 2 shows the proportion of complete and correct decoding paths that can be generated under the decoding complexity upper limit η = 128. It can be seen that the (128, 64)-(96, 48) MPAC code scheme is more likely to produce a correct decoding path than the PAC code scheme, and accordingly, the SNR is also higher. This explains the performance advantage of MPAC codes and demonstrates that this improved scheme can reduce decoding complexity.

[0151] Table 2 Statistics on the proportion of correct and complete decoding paths generated by MPAC and PAC code decoding when N = 128, K = 64, η = 128

[0152] Figure 10 is a diagram showing a comparison of the FER and normalized complexity performance of different coding schemes for N = 512, K = 256, provided by an embodiment of the present application. The (512, 256)-(128, 64) MPAC coding scheme significantly outperforms the PAC and Fano decoding schemes in both aspects.

[0153] Table 3 also shows the proportion of correct and complete paths that can be generated by the two schemes when the complexity upper limit η is limited to 1024. This confirms that the scheme to which the embodiment of the present application belongs can more significantly improve the performance of the PAC code and Fano decoding scheme when the decoding complexity is limited when the code length is longer. In addition, compared with the current mainstream CRC-polar code and SCL decoding scheme, the MPAC encoding and decoding scheme proposed in the embodiment of the present application can achieve decoding performance comparable to that of SCL when the list L=8, and its decoding complexity will show a lower advantage when the SNR is high, and the proposed scheme has efficient storage and more friendly hardware implementation. Overall, the MPAC code and its HFSC decoding improvement scheme proposed in the embodiment of the present application can have more advantages in the compromise between decoding performance and complexity compared to the other encoding and decoding schemes mentioned above.

[0154] Table 3 Statistics on the proportion of correct and complete paths generated by MPAC and PAC code decoding when N = 512, K = 256, η = 1024

[0155] As can be seen, Figures 9 and 10 demonstrate that the embodiments of the present application can achieve a better trade-off between decoding performance and complexity. While achieving similar or better decoding performance, the embodiments of the present application also achieve lower average decoding complexity. Furthermore, the HFSC decoding of the embodiments of the present application is memory-efficient and hardware-friendly, requiring no list sorting operations.

[0156] In the embodiments provided above, the communication method solutions provided in the embodiments of the present application are described from the perspectives of each network element or device itself, and from the perspective of the interaction between each network element or device. It is understood that, in order to implement the above functions, each network element and device, such as the aforementioned application authentication and key management anchor function network element, application function network element, and UE, includes hardware structures and / or software units corresponding to the execution of each function. Optionally, in the above method embodiments, the information sent by the UE to the application authentication and key management anchor function network element may be sent to the access and mobility management function network element, the authentication service function network element, or the network capability exposure function network element. Those skilled in the art will readily appreciate that, in combination with the various exemplary units and algorithm steps described in the embodiments disclosed in this application, the present application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0157] Please refer to Figure 11, which is a schematic diagram of the structure of a communication device provided in an embodiment of the present application. The communication device shown in Figure 11 includes a transceiver module 1101 and a processing module 1102.

[0158] In one embodiment, in one design, the communication device is an encoding end device or a related device in the encoding end device:

[0159] Exemplarily, the processing module 1102 is used to convert the information vector Divided into the first information vector and the second information vector in, Represents a vector of dimension K with elements of 0 or 1, where K is the information vector The information bit dimension of Kc is the information bit dimension to be preprocessed by convolution transformation; Perform code rate configuration I and obtain vector Where Nc is the convolution transformation matrix G′ c Dimension of the vector Perform convolution transformation to obtain the codeword vector of the convolution code The codeword vector and the second information vector Perform code rate configuration II and obtain message vector N is the code length of the channel code; and Perform polarization transformation to obtain the codeword vector Optionally, the transceiver module 1101 is used to send the codeword vector

[0160] Optionally, when the communication device is a coding end device or a related device of the coding end device, it is used to implement the functions of the coding end device in the embodiments shown in Figures 6 to 10 and optional implementation methods.

[0161] In one design, the communication device is a decoding end device or a related device in the decoding end device:

[0162] Exemplarily, the transceiver module 1101 is configured to receive a symbol vector The processing module 1102 is used to decode the node At node position i, call the continuous elimination decoding algorithm or Fano decoding algorithm to decode the node Optionally, the processing module 1102 is further configured to decode the message vector obtained by estimating Extract information and obtain information vector Where K is the information vector The information bit dimension.

[0163] Optionally, when the communication device is a decoding end device, it is used to implement the functions of the decoding end device in the embodiments shown in Figures 6 to 10 and the optional implementation methods.

[0164] Please refer to Figure 12, which is a schematic diagram of the structure of another communication device provided in an embodiment of the present application. The communication device shown in Figure 12 includes at least one processor 1201, a memory 1202, and optionally, a transceiver 1203. The specific connection medium between the above-mentioned processor 1201 and the memory 1202 is not limited in the embodiment of the present application. In Figure 12, the memory 1202 and the processor 1201 are connected via a bus 1204 as an example. The bus 1204 is represented by a bold line in the figure. The connection method between other components is only for schematic illustration and is not limited. The bus 1204 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one bold line is used in Figure 12, but it does not mean that there is only one bus or one type of bus.

[0165] The processor 1201 can have a data transceiver function and can communicate with other devices. In the device shown in Figure 12, an independent data transceiver module, such as transceiver 1203, can also be set up for sending and receiving data; when the processor 1201 communicates with other devices, data can be transmitted through the transceiver 1203.

[0166] In one example, when the encoding end device adopts the form shown in Figure 12, the processor 1201 in Figure 12 can call the computer execution instructions stored in the memory 1202 to enable the encoding end device to execute the method executed by the encoding end device in the embodiments shown in Figures 6 to 10.

[0167] In one example, when the decoding end device adopts the form shown in Figure 12, the processor 1201 in Figure 12 can call the computer execution instructions stored in the memory 1202 to enable the decoding end device to execute the method executed by the decoding end device in the embodiments shown in Figures 6 to 10.

[0168] An embodiment of the present application further provides a communication system, which may include the encoding end device and the decoding end device in Figures 6 to 10. For details, please refer to the method embodiments described above.

[0169] The solutions described in this application can be implemented in various ways. For example, these techniques can be implemented in hardware, software, or a combination of hardware. For hardware implementation, the processing module used to execute these techniques at a communication device (e.g., a base station, a terminal, a network entity, or a chip) can be implemented in one or more general-purpose processors, digital signal processors (DSPs), digital signal processing devices, application-specific integrated circuits (ASICs), programmable logic devices, field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination thereof. The general-purpose processor can be a microprocessor, and optionally, the general-purpose processor can also be any conventional processor, controller, microcontroller, or state machine. The processor can also be implemented by a combination of computing devices, such as a digital signal processor and a microprocessor, multiple microprocessors, one or more microprocessors combined with a digital signal processor core, or any other similar configuration.

[0170] It is understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0171] The present application also provides a computer-readable medium having instructions stored thereon, which implement the functions of any of the above method embodiments when executed by a computer.

[0172] The present application also provides a computer program product, which implements the functions of any of the above method embodiments when executed by a computer.

[0173] In the above embodiments, all or part of the embodiments may be implemented by 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 instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0174] It is understandable that some optional features in the embodiments of the present application may, in certain scenarios, be implemented independently of other features, such as the solution on which they are currently based, to solve corresponding technical problems and achieve corresponding effects. They may also be combined with other features in certain scenarios as needed. Accordingly, the devices provided in the embodiments of the present application may also implement these features or functions accordingly, which will not be described in detail here.

[0175] Those skilled in the art will also appreciate that the various illustrative logical blocks and steps listed in the embodiments of the present application can be implemented by electronic hardware, computer software, or a combination of both. Whether such functions are implemented by hardware or software depends on the specific application and the design requirements of the entire system. Those skilled in the art may use various methods to implement the described functions for corresponding applications, but such implementation should not be construed as exceeding the scope of protection of the embodiments of the present application.

[0176] It will be understood that the “embodiment” mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, the various embodiments in the entire specification do not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It will be understood that in the various embodiments of the present application, the size of the sequence numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0177] It can be understood that in this application, "when", "if" and "if" all mean that the device will perform corresponding processing under certain objective circumstances, and do not limit the time, nor do they require the device to make judgments when it is implemented, nor do they mean that there are other limitations.

[0178] In this application, elements expressed in the singular are intended to mean "one or more" rather than "one and only one" unless otherwise specified. In this application, unless otherwise specified, "at least one" is intended to mean "one or more" and "a plurality" is intended to mean "two or more."

[0179] Additionally, the terms "system" and "network" are often used interchangeably. The term "and / or" is simply a description of an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. A can be singular or plural, and B can be singular or plural.

[0180] The predefined in this application may be understood as defined, predefined, stored, pre-stored, pre-negotiated, pre-configured, solidified, or pre-burned.

[0181] Those skilled in the art will appreciate that, for the sake of convenience and brevity of description, the specific working processes of the above-described systems, devices, and units may refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0182] The same or similar parts between the various embodiments in this application can refer to each other. In the various embodiments in this application, and the various implementation methods / implementation methods / implementation methods in each embodiment, if there is no special explanation and logical conflict, the terms and / or descriptions between different embodiments and the various implementation methods / implementation methods / implementation methods in each embodiment are consistent and can be referenced to each other. The technical features in different embodiments and the various implementation methods / implementation methods / implementation methods in each embodiment can be combined to form new embodiments, implementation methods, implementation methods, or implementation methods according to their inherent logical relationships. The above-described implementation methods of this application do not constitute a limitation on the scope of protection of this application.

[0183] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A coding method, characterized in that: The method comprises: The information vector Divided into the first information vector and the second information vector in, represents a vector with dimension K and elements of 0 or 1, where K is the information vector The information bit dimension Kc is the information bit dimension to be preprocessed by convolution transformation; The first information vector Perform code rate configuration I and obtain vector Wherein, Nc is the convolution transformation matrix G′ c Dimensions; For the vector Perform convolution transformation to obtain the codeword vector of the convolution code The codeword vector and the second information vector Perform code rate configuration II and obtain the message vector N is the code length of channel coding; Regarding the message Perform polarization transformation to obtain the codeword vector 2. The method according to claim 1, characterized in that The codeword vector and the second information vector Perform code rate configuration II and obtain the message vector include: According to the Gaussian approximation criterion, the subchannels corresponding to the subscript set [1, N] of the polarization subchannels are sorted by reliability to obtain the KK with the highest reliability. c +N c The subscripts of the polarization subchannels constitute the subscript set and the subscript set consisting of the subscript of the residual polarization subchannel According to the Gaussian approximation criterion, the subscript set The corresponding sub-channels are sorted by reliability to obtain the N with the lowest reliability. c The subscripts of the polarization subchannels constitute the subscript set and the subscript set consisting of the subscript of the residual polarization subchannel In the subscript set The codeword vector is transmitted on the subchannel indexed by the element in And in the subscript set The second information vector is transmitted on the polarization subchannel indexed by the element in And in the subscript set Transmit the frozen bit on the polarization subchannel indexed by the element in to obtain the message vector 3. The method according to claim 2, characterized in that The first information vector Perform code rate configuration I and obtain vector include: According to the RM criterion, the subscript set The corresponding sub-channels are prioritized by codeword distance characteristics to obtain a set of Kc positions with the highest priority codeword distance characteristics. The collection and the codeword set Arrange the elements in increasing order to get the vector and and determine the The elements in the vector The relative position in the channel is used to obtain the subscript set of Kc polarization subchannels. In the subscript set The first information vector is transmitted on the polarization subchannel indexed by the element in In the subscript set Transmit the frozen bits on the polarization subchannels with the remaining subscript indexes in , and obtain the vector 4. A decoding method, characterized in that: The method comprises: Receive symbol vector And determine the message vector to be estimated Wherein, N is the code length of channel coding; According to the message vector Decoding Node The node position i is called by the continuous elimination decoding algorithm or the Fano decoding algorithm to decode the node Decode, where 1∈[i,N].

5. The method according to claim 4, characterized in that The decoding node The node position i is called by the continuous elimination decoding algorithm or the Fano decoding algorithm to decode the node Decoding, including: when When the Fano decoding algorithm is called, the decoding node Decoding is performed; when When the continuous elimination decoding algorithm is called, the decoding node Decoding is performed; Among them, the is the subscript set corresponding to the subchannel that transmits the codeword after convolution transformation.

6. The method according to claim 5, characterized in that When When the Fano decoding algorithm is called, the decoding node Decoding, including: when Calculate the path metric when and And compare it with the current decoding threshold T to determine the extended decoding node And according to the convolution transformation constraint relationship Sure and get in, Where i′ represents the decoding node that has not been expanded. location; when When Wherein, B corresponds to the position set of information bits of the convolution transformation, The frozen bit positions corresponding to the convolution transform.

7. The method according to claim 5, characterized in that When When the continuous elimination decoding algorithm is called, the decoding node Decoding, including: when When the likelihood ratio is conduct The judgment obtained when When direct judgment Among them, the is the subscript set corresponding to the subchannel that directly transmits information bits, It is the subscript set corresponding to the subchannel that directly transmits the frozen bits.

8. A communication system, characterized in that: The system comprises: An apparatus for performing the method of any one of claims 1 to 3; and At least one device for carrying out the method according to any one of claims 4 to 7.

9. A communication device, characterized in that: The method comprises one or more functional units, wherein the one or more functional units are used to execute the method according to any one of claims 1 to 3, or to execute the method according to any one of claims 4 to 7.

10. A communication device, characterized in that: The device comprises a processor, wherein the processor calls a computer program stored in a memory so as to enable the communication device to implement the method according to any one of claims 1 to 3, or implement the method according to any one of claims 4 to 7.

11. A communication device, characterized in that: The invention comprises a processor and an interface circuit, wherein the interface circuit is used to receive signals from other communication devices other than the communication device and transmit them to the processor or send signals from the processor to other communication devices other than the communication device, and the processor is used to implement the method according to any one of claims 1 to 3 through a logic circuit or executes code instructions, or is used to implement the method according to any one of claims 4 to 7.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 3 is implemented, or the method according to any one of claims 4 to 7 is implemented.

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