Terminal and method in terminal

Abstract

The invention discloses a terminal and a method in the terminal, and the method comprises the steps: transmitting a first message which indicates at least degree distribution information, and the degree distribution information is used for coding at least one coding symbol; receiving the at least one coded symbol; wherein the degree distribution information comprises at least one of degree distribution of a repair symbol and degree distribution of a non-repair symbol, the non-repair symbol is a source symbol or an intermediate symbol, and the intermediate symbol is generated by the source symbol; the step of receiving the at least one coded symbol comprises the step of recovering a corresponding source symbol from the at least one coded symbol; the at least one encoded symbol includes at least a repair symbol. The receiving performance can be improved.

Description

Technical Field

[0001] The present application relates to a terminal in a wireless communication system and a method in the terminal, and in particular to coding. Background Art

[0002] In the future, the application scenarios of wireless communication systems will become more and more diversified, and different application scenarios will put forward different performance requirements for the system. In order to meet the different performance requirements of various application scenarios, the 3GPP (3rd Generation Partner Project) RAN (Radio Access Network) #72 plenary meeting decided to study the new radio (NR) (or Fifth Generation, 5G) technology, and the NR WI (Work Item) was passed at the 3GPP RAN #75 plenary meeting, and the standardization work on NR began.

[0003] In communication, both LTE (Long Term Evolution) and 5G NR involve accurate reception of reliable information, optimized energy efficiency, determination of information validity, flexible resource allocation, scalable system structure, efficient non-access layer information processing, low service interruption and drop rate, and support for low power consumption. This is of great significance to the normal communication between base stations and user equipment, the reasonable scheduling of resources, and the balancing of system loads. It can be said to be the cornerstone of high throughput, meeting the communication needs of various services, improving spectrum utilization, and improving service quality. It is indispensable for eMBB (ehanced Mobile BroadBand), URLLC (Ultra Reliable Low Latency Communication) and eMTC (enhanced Machine Type Communication). At the same time, in IIoT (Industrial Internet of Things), V2X (Vehicular to X), device to device, unlicensed spectrum communication, user communication quality monitoring, network planning and optimization, TN (Territial Network), dual connectivity system, wireless resource management and multi-antenna codebook selection, signaling design, neighbor management, business management, and beamforming, there are extensive demands. Information transmission methods are divided into broadcast and unicast. Both transmission methods are essential for 5G systems because they are very helpful in meeting the above requirements.

[0004] As the scenarios and complexity of the system continue to increase, higher requirements are placed on reducing interruption rates, reducing latency, enhancing reliability, enhancing system stability, business flexibility, and power saving. At the same time, compatibility between different versions of different systems also needs to be considered during system design. Summary of the invention

[0005] The researchers found that error correction coding is a very important function in wireless communications, and how to assist the encoder to use the appropriate degree distribution is a problem that needs to be solved.

[0006] In view of the above-mentioned problems, this application provides a solution.

[0007] It should be noted that, in the absence of conflict, the embodiments and features in any node of the present application can be applied to any other node. In the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other arbitrarily. At the same time, the method proposed in this application can also be used to solve other problems in communication, such as NR evolution and problems in 6G systems. The method proposed in this application is suitable for AI decoding, and is also suitable for traditional decoding methods.

[0008] As an embodiment, the interpretation of the terminology in the present application refers to the definition of the 3GPP specification protocol TS38 series.

[0009] As an example, the interpretation of the terms in the present application refers to the definitions of the TS37 series of specification protocols of 3GPP.

[0010] The present application discloses a method used in a terminal, comprising:

[0011] sending a first message, the first message indicating at least degree distribution information, the degree distribution information being used for encoding of at least one encoding symbol;

[0012] receiving the at least one coded symbol;

[0013] The degree distribution information includes at least one of the degree distribution of the repair symbol and the degree distribution of the non-repair symbol, wherein the non-repair symbol is a source symbol or an intermediate symbol, and the intermediate symbol is generated by the source symbol; the receiving of the at least one encoded symbol includes recovering the corresponding source symbol from the at least one encoded symbol; the at least one encoded symbol includes at least a repair symbol.

[0014] As an embodiment, the problem to be solved by the present application includes: how to assist the encoding end to use an appropriate degree distribution for encoding.

[0015] As an embodiment, the benefits of the above method include: better performance, more flexibility, and more suitable for AI (artificial intelligence) decoders.

[0016] Specifically, according to one aspect of the present application, recovering the corresponding source symbol from the at least one coded symbol includes recovering the corresponding source symbol from the at least one coded symbol using the degree distribution information.

[0017] Specifically, according to one aspect of the present application, the encoding of the at least one encoding symbol belongs to a fountain code.

[0018] Specifically, according to one aspect of the present application, the encoding of the at least one encoding symbol belongs to the Raptor code in the fountain code.

[0019] Specifically, according to one aspect of the present application, the degree distribution information is associated with the temperature set in the AI.

[0020] Specifically, according to one aspect of the present application, the degree distribution information includes a distribution probability of at least one degree.

[0021] Specifically, according to one aspect of the present application, compared with the theoretical degree distribution, the degree distribution information indicates that the probability of a larger degree is higher.

[0022] Specifically, according to one aspect of the present application, the degree distribution of the non-repair symbols included in the degree distribution information depends on the expectation that the first segment is successfully decoded, and the first segment includes a source symbol that at least partially generates the at least one encoded symbol.

[0023] Specifically, according to one aspect of the present application, first association information is received, wherein the first association information indicates which encoding symbols of the at least one encoding symbol the first encoding symbol is associated with in terms of content or semantics.

[0024] Specifically, according to one aspect of the present application, the terminal is an Internet of Things terminal.

[0025] Specifically, according to one aspect of the present application, the terminal is a user equipment.

[0026] Specifically, according to one aspect of the present application, the terminal is a vehicle-mounted terminal.

[0027] Specifically, according to one aspect of the present application, the terminal is a mobile phone.

[0028] The present application discloses a terminal, comprising:

[0029] The terminal includes: one or more processors and a memory;

[0030] The memory is coupled to the one or more processors, and is used to store computer program codes, where the computer program codes include computer instructions. The one or more processors call the computer instructions to enable the terminal to execute the method used in the terminal.

[0031] As an embodiment, compared with the traditional solution, this application has the following advantages:

[0032] In traditional schemes, the degree distribution is often fixed, for example, obtained based on theoretical calculations, but the premise of such theoretical calculations is idealized. For actual communication systems, it is difficult to achieve idealized conditions, and therefore it is also difficult to achieve the expected performance. The method proposed in this application helps to improve flexibility and can better adapt to current channel conditions or decoding states.

[0033] On the other hand, when AI is used for decoding, it is different from the traditional decoding method and the theoretical basis is also different. Therefore, using the degree distribution obtained by theoretical calculation for encoding will inevitably fail to achieve the ideal effect. The method proposed in this application is conducive to supporting AI decoding.

[0034] The method proposed in the present application is beneficial to improving the performance of fountain codes, such as Raptor codes.

[0035] It is beneficial to use content or semantic correlation for decoding through, for example, AI decoding, which is completely different from the assumptions of traditional coding, so adaptive changes to the encoding are required. The method proposed in this application is beneficial to supporting similar more powerful decoders. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0037] Figure 1 A schematic diagram showing sending a first message and receiving at least one coding symbol according to an embodiment of the present application is shown;

[0038] Figure 2 A schematic diagram of a network architecture according to an embodiment of the present application is shown;

[0039] Figure 3 A schematic diagram showing an embodiment of a wireless protocol architecture of a user plane and a control plane according to an embodiment of the present application;

[0040] Figure 4 A schematic diagram showing a first communication device and a second communication device according to an embodiment of the present application is shown;

[0041] Figure 5 A flow chart showing wireless signal transmission according to an embodiment of the present application is shown;

[0042] Figure 6 A schematic diagram of RAN (Radio Access Network) domain (Domain) AI / ML function deployment according to an embodiment of the present application is shown;

[0043] Figure 7A schematic diagram showing the deployment of AI / ML functions of a UE according to an embodiment of the present application is shown;

[0044] Figure 8 A flowchart based on artificial intelligence or machine learning according to an embodiment of the present application is shown;

[0045] Fig. 9 A schematic diagram of encoding according to an embodiment of the present application is shown;

[0046] Fig.10 A schematic diagram showing the use of degree distribution information for encoding according to an embodiment of the present application is shown;

[0047] Fig.11 A schematic diagram showing the use of degree distribution information for decoding according to an embodiment of the present application is shown;

[0048] Fig.12 A schematic diagram showing association between degree distribution information and temperature according to an embodiment of the present application is shown;

[0049] Fig.13 The following is a schematic diagram illustrating a processing device used in a terminal according to an embodiment of the present application. DETAILED DESCRIPTION

[0050] The technical solution of the present application will be further described in detail below in conjunction with the accompanying drawings. It should be noted that, unless there is a conflict, the embodiments in the present application and the features in the embodiments can be combined with each other arbitrarily.

[0051] Example 1

[0052] Embodiment 1 illustrates a flowchart of sending a first message and receiving at least one coded symbol according to an embodiment of the present application, as shown in the attached Figure 1 Attached Figure 1 In the figure, each box represents a step. It should be emphasized that the order of the boxes in the figure does not represent the temporal relationship between the steps represented.

[0053] In Embodiment 1, the first node in the present application sends a first message in step 101; receives at least one coding symbol in step 102;

[0054] wherein the first message indicates at least degree distribution information, and the degree distribution information is used for encoding at least one coded symbol; wherein the degree distribution information includes at least one of the degree distribution of the repair symbol and the degree distribution of the non-repair symbol, wherein the non-repair symbol is a source symbol or an intermediate symbol, and the intermediate symbol is generated by the source symbol; wherein the receiving of the at least one coded symbol includes recovering the corresponding source symbol from the at least one coded symbol; and the at least one coded symbol includes at least a repair symbol.

[0055] As an embodiment, the coding involved in this application is error correction coding.

[0056] As an embodiment, the terminal is UE (User Equipment).

[0057] As an embodiment, any parameter in the present application may be either configured by the network or generated by the terminal according to an internal algorithm, such as randomly.

[0058] As an embodiment, the values ​​of the timers in the present application are all limited and do not exceed 2560 milliseconds.

[0059] As an embodiment, the value of the timer is the running time when the timer is not interfered with.

[0060] As an example, the value of any parameter in the present application, including but not limited to the value of a timer and the value of a counter, is limited unless otherwise stated.

[0061] As a sub-embodiment of this embodiment, the upper limit of the value of any parameter in this application is 1024 times of 65536.

[0062] As a sub-embodiment of this embodiment, the upper limit of the value of any parameter in this application is 65536 or 65535.

[0063] As a sub-embodiment of this embodiment, the upper limit of the value of any parameter in this application is 1024.

[0064] As a sub-embodiment of this embodiment, the upper limit of the value of any parameter in this application is 640 or 320.

[0065] As an embodiment, the present application is directed to NR.

[0066] As an embodiment, the present application is directed to a NR evolved wireless communication network.

[0067] As an embodiment, the serving cell refers to the cell where the UE resides. Performing a cell search includes the UE searching for a suitable cell of the selected PLMN (Public Land Mobile Network) or SNPN (Stand-alone Non-Public Network), selecting the suitable cell to provide available services, and monitoring the control channel of the suitable cell. This process is defined as residing on the cell; that is, a residing cell is the serving cell of the UE relative to the UE. Residing on a cell in the RRC idle state or the RRC inactive state has the following benefits: it enables the UE to receive system messages from the PLMN or SNPN; after registration, if the UE wishes to establish an RRC connection or continue a suspended RRC connection, the UE can do so by performing initial access on the control channel of the residing cell; the network can page the UE; and the UE can receive ETWS (Earthquake and Tsunami Warning System) and CMAS (Commercial Mobile Alert System) notifications.

[0068] As an embodiment, for a UE in an RRC connected state that is not configured with CA / DC (carrier aggregation / dual connectivity), there is only one service cell including a primary cell. For a UE in an RRC connected state that is configured with CA / DC (carrier aggregation / dual connectivity), the service cell is used to indicate a set of cells including a special cell (SpCell) and all cells from the small cell. The primary cell (Primary Cell) is an MCG (Master Cell Group) cell that operates on the main frequency. The UE performs an initial connection establishment process or initiates connection reconstruction on the primary cell. For dual connection operations, the special cell refers to the PCell (Primary Cell) of the MCG or the PSCell (Primary SCGCell) of the SCG (Secondary Cell Group); if it is not a dual connection operation, the special cell refers to the PCell.

[0069] As an embodiment, the operating frequency of the SCell (Secondary Cell) is a secondary frequency.

[0070] As an embodiment, the terminal is only configured with MCG.

[0071] As an example, individual contents of an information element are referred to as fields.

[0072] As an embodiment, MR-DC (Multi-Radio Dual Connectivity) refers to dual connectivity between E-UTRA and NR nodes, or dual connectivity between two NR nodes.

[0073] As an embodiment, in MR-DC, the wireless access node that provides the control plane connection to the core network is the master node, which may be a master eNB, a master ng-eNB, or a master gNB.

[0074] As an embodiment, MCG refers to a group of service cells associated with a master node in MR-DC, including SpCells, and may also, optionally, include one or more SCells.

[0075] As an embodiment, PCell is the SpCell of MCG.

[0076] As an embodiment, the PSCell is the SpCell of the SCG.

[0077] As an embodiment, in MR-DC, the control plane connection to the core network is not provided, and the radio access node that provides additional resources to the UE is a slave node. The slave node can be an en-gNB, a slave ng-eNB or a slave gNB.

[0078] As an embodiment, in MR-DC, a group of service cells associated with a slave node is a SCG (secondary cell group), including a SpCell and, optionally, one or more SCells.

[0079] As an embodiment, the SpCell is a PCell or the SpCell is a PSCell.

[0080] As an embodiment, in the RRC inactive state, DC is not used.

[0081] As an example, in the RRC inactive state, CA is typically not used.

[0082] As an embodiment, the RRC information block refers to an information block (information element) in an RRC message.

[0083] As an embodiment, SSB may be referred to as SS\PBCH, or SS block.

[0084] As an embodiment, L1 is layer 1 (Layer-1) or physical layer.

[0085] As an embodiment, L2 is layer 2 (Layer-2).

[0086] As an embodiment, the present application is directed to NR and NR evolved networks, such as 6G networks.

[0087] As an embodiment, one RRC information block may include one or more RRC information blocks.

[0088] As an embodiment, an RRC information block may not include any RRC information block, but only include at least one parameter.

[0089] As an embodiment, the radio bearer includes at least a signaling radio bearer and a data radio bearer.

[0090] As an embodiment, the radio bearer is a service or an interface of a service provided by the PDCP layer to a higher layer.

[0091] As a sub-embodiment of this embodiment, the higher layer includes one of the RRC sublayer, NAS, and SDAP layer.

[0092] As an embodiment, the signaling radio bearer is a service or an interface of services provided by PDCP to a higher layer.

[0093] As a sub-embodiment of this embodiment, the higher layer includes an RRC sublayer, at least the former in NAS.

[0094] As an embodiment, the data radio bearer is a service or an interface of services provided by PDCP to a higher layer.

[0095] As a sub-embodiment of this embodiment, the higher layer includes the SDAP layer, at least the former in NAS.

[0096] As an embodiment, after the terminal establishes an RRC connection with the network, the terminal enters an RRC connection state.

[0097] As a sub-embodiment of this embodiment, the network is a Radio Access Network (RAN).

[0098] As an embodiment, when the terminal does not establish an RRC connection with the network, the terminal is in an RRC idle state.

[0099] As a sub-embodiment of this embodiment, the network is a Radio Access Network (RAN).

[0100] As an embodiment, when the RRC connection established between the terminal and the network is suspended, the terminal enters an RRC inactive state.

[0101] As a sub-embodiment of this embodiment, the network is a Radio Access Network (RAN).

[0102] As an embodiment, different functions are supported in different RRC states.

[0103] As an embodiment, only very limited functions are supported in the non-RRC connected state.

[0104] As an embodiment, the non-RRC connected state is or includes an RRC idle state.

[0105] As an embodiment, the non-RRC connected state is or includes an RRC inactive state.

[0106] As an embodiment, the first message is a message above the AS layer.

[0107] As an embodiment, the message above the AS layer is a NAS message.

[0108] As an embodiment, the message above the AS layer is a message sent to the encoder.

[0109] As an embodiment, the message above the AS layer is an application layer message.

[0110] As an embodiment, the first message is generated by a protocol layer above the RRC sublayer.

[0111] As an embodiment, the first message is sent to the core network.

[0112] As an embodiment, the first message is generated by the PDCP sublayer.

[0113] As an embodiment, degree is a special term in the field of coding.

[0114] As an embodiment, the first message is uplink.

[0115] As an embodiment, the first message is sent to a network.

[0116] As an embodiment, the first message is sent via the Uu interface.

[0117] As an embodiment, the first message indicates at least degree distribution information including: the first message indicates an index of the degree distribution.

[0118] As an embodiment, the first message indicating the index of the degree distribution includes: indicating the index of a recommended degree distribution among a plurality of pre-configured degree distributions.

[0119] As an embodiment, the first message indicating the index of the degree distribution includes: indicating the index of a recommended degree distribution among multiple degree distributions.

[0120] As an embodiment, the index of the degree distribution indicated in the first message includes: indicating an index of a recommended degree distribution among a plurality of reported degree distributions.

[0121] As an embodiment, the first message indicating at least the degree distribution information includes: indicating a first identifier, and the first identifier has a mapping relationship with the degree distribution information.

[0122] As an embodiment, the first identifier is an identifier of an AI model.

[0123] As an embodiment, the first identifier is an identifier configured by AI.

[0124] As an embodiment, the first identifier is an identifier of a decoder.

[0125] As an embodiment, the first identifier is a profile identifier.

[0126] As an embodiment, the encoding in this application is processed in units of symbols.

[0127] As an embodiment, the source block is a block consisting of K source symbols.

[0128] As a sub-embodiment of this embodiment, the K source symbols are processed together by the encoder.

[0129] As a sub-embodiment of this embodiment, the encoder encodes one source block at a time, namely, K source symbols.

[0130] As an embodiment, the at least one encoded symbol is an encoded output of the K source symbols.

[0131] As an embodiment, the source symbol corresponding to the at least one coded symbol is the K source symbols.

[0132] As an embodiment, the source symbol refers to: the smallest data unit in the encoding process.

[0133] As an embodiment, in a source block, all source symbols are equal in size.

[0134] As an embodiment, the coding symbol refers to a symbol in a data packet.

[0135] As an embodiment, the encoding symbol includes a source symbol.

[0136] As an embodiment, the encoding symbol includes a repair symbol.

[0137] As an embodiment, repair symbols are generated by source blocks.

[0138] As an embodiment, the size of the repair symbol is equal to that of the source symbol.

[0139] As an embodiment, the encoding symbol includes the source symbol, which means that the data included in the encoding symbol is the same as the data included in the source symbol.

[0140] As an embodiment, the coding symbol includes the source symbol, which means that the bits included in the coding symbol are the same as the bits included in the source symbol.

[0141] As a sub-embodiment of this embodiment, the same includes the same order of bits.

[0142] As a sub-embodiment of this embodiment, the same includes the same value of the bit.

[0143] As an embodiment, the coding symbol includes a source symbol, which means that the coding used by the coding symbol is a system code.

[0144] As an embodiment, the encoding symbol includes the source symbol, which means that the encoding of the encoding symbol is equal to or equivalent to the product of the source symbol and the unit matrix.

[0145] As an embodiment, the encoding symbol includes the source symbol, which means that the encoding of the encoding symbol does not change the source symbol.

[0146] As an embodiment, the encoding symbol includes the source symbol, which means that the encoding of the encoding symbol is equivalent to the direct output of the source symbol.

[0147] As an embodiment, an encoding symbol is either a source symbol or a repair symbol.

[0148] As an embodiment, the coding symbol in the above embodiment is any one of the at least one coding symbol.

[0149] As an embodiment, the encoding method used by the at least one encoded symbol determines which source symbol any encoded symbol includes.

[0150] As an embodiment, the encoding method used by the at least one encoding symbol determines which repair symbol any encoding symbol is.

[0151] As an embodiment, the encoded symbol is the output of an encoder.

[0152] As an embodiment, the source symbols are input to an encoder.

[0153] As an embodiment, the intermediate symbol is the input of an encoder.

[0154] As an embodiment, the intermediate symbol is the output of another encoder that takes the source symbol as input, and the present application does not involve the other encoder.

[0155] As an embodiment, when a symbol output by the encoder is the same as a source symbol, the encoded symbol includes the source symbol.

[0156] As an embodiment, the encoding symbol includes a repair symbol, which means that the encoding symbol is a repair symbol.

[0157] As an embodiment, when the symbol output by the encoder is a repair symbol, the encoded symbol is a repair symbol.

[0158] As an embodiment, the systematic code means that all source symbols can be included in the encoding symbols.

[0159] As an embodiment, the repair symbol refers to a coding symbol sent for a source block that does not belong to a source symbol.

[0160] As an embodiment, for the source block refers to for the purpose of recovering the source block.

[0161] As an embodiment, for the source block refers to for recovering each source symbol in the source block.

[0162] As an embodiment, a coding symbol belonging to a source symbol refers to a coding symbol that includes or carries the source symbol.

[0163] As an embodiment, a coding symbol belonging to a source symbol means that the coding symbol includes the source symbol.

[0164] As an embodiment, a coding symbol that does not belong to a source symbol means that the coding symbol is different from data carried by any source symbol.

[0165] As an embodiment, a coding symbol that does not belong to a source symbol means that the coding symbol is different from the bits carried by any source symbol.

[0166] As an embodiment, the different data carried means that, for any source symbol, the encoded symbol carries at least one data that is different from the encoded symbol.

[0167] As an embodiment, the different data carried means that, for any source symbol, the encoded symbol carries at least one bit that is different from the encoded symbol.

[0168] As an embodiment, for example, one source symbol is 10010001 and the other source symbol is 10110001. If the encoding symbol is 10010001, the encoding symbol carries or includes the one source symbol; if the other encoding symbol is 11110001, the other encoding symbol is a repair symbol.

[0169] As an embodiment, the intermediate symbol refers to a symbol generated from a source symbol.

[0170] As an embodiment, the intermediate symbols are generated from the source symbols through a reverse encoding process.

[0171] As an embodiment, the repair symbol is generated from the intermediate symbol.

[0172] As an embodiment, the encoding symbols do not include intermediate symbols.

[0173] As an embodiment, the intermediate symbols are not included in the data packet.

[0174] As an embodiment, the symbol is a data unit.

[0175] As an embodiment, the size of the symbol is one or more bytes.

[0176] As an embodiment, a coding symbol group refers to a group of coding symbols sent together.

[0177] As an embodiment, the encoding symbol group is sent by the same data packet.

[0178] As an embodiment, the receiving of the at least one encoded symbol includes decoding using the degree distribution information.

[0179] As an embodiment, the decoding refers to: recovering the corresponding source symbol from the encoded symbol.

[0180] As an embodiment, recovering the corresponding source symbol from the at least one encoded symbol includes recovering the corresponding source symbol from the at least one encoded symbol using the degree distribution information.

[0181] As an embodiment, the receiving of the at least one encoded symbol includes performing radio frequency processing.

[0182] As an embodiment, the radio frequency processing includes a method.

[0183] As an embodiment, the radio frequency processing includes applying antenna parameters.

[0184] As an embodiment, the receiving of the at least one encoded symbol includes performing baseband processing.

[0185] As an embodiment, the baseband processing includes demodulation.

[0186] As an embodiment, the baseband processing includes equalization.

[0187] As an embodiment, the baseband processing includes channel estimation.

[0188] As an embodiment, the baseband processing includes channel decoding.

[0189] As an embodiment, the receiving of the at least one encoded symbol includes processing of a protocol sublayer.

[0190] As an embodiment, the processing of the protocol sublayer includes: processing by the MAC sublayer. The processing by the MAC sublayer includes extracting the MAC SDU and delivering it to the RLC layer.

[0191] As an embodiment, the processing of the protocol sublayer includes: decompression of headers.

[0192] As an embodiment, the processing of the protocol sublayer includes: decryption.

[0193] As an embodiment, the processing of the protocol sublayer includes: cascading.

[0194] As an embodiment, the receiving of the at least one encoded symbol includes recovering the corresponding source symbol from the at least one encoded symbol, including: decoding the at least one encoded symbol.

[0195] As an embodiment, the protocol layer where the encoding and decoding of the at least one coded symbol are located belongs to NAS.

[0196] As an embodiment, the protocol layer where the encoding and decoding of the at least one coded symbol is located is above AS.

[0197] As an embodiment, the corresponding source symbol includes: the at least one encoding symbol is generated by the corresponding source symbol.

[0198] As an embodiment, the corresponding source symbol includes: the at least one encoding symbol is not generated by a source symbol other than the corresponding source symbol.

[0199] As an embodiment, the corresponding source symbol includes: the at least one encoding symbol is an encoding output for the corresponding source symbol.

[0200] As an embodiment, the corresponding source symbol includes: the at least one encoding symbol is encoded for the corresponding source symbol.

[0201] As an embodiment, the at least one coding symbol includes at least a repair symbol, including: at least one coding symbol among the at least one coding symbol is a repair symbol.

[0202] As an embodiment, the at least one coding symbol includes at least a repair symbol, including: at least one coding symbol among the at least one coding symbol includes a repair symbol.

[0203] As an embodiment, the at least one coding symbol includes at least a repair symbol including: any one of the at least one coding symbol is a repair symbol.

[0204] As an embodiment, Kx coding symbols among the at least one coding symbol are source symbols, and coding symbols other than the Kx coding symbols among the at least one coding symbol are repair symbols, wherein there are Kx source symbols in total.

[0205] As an embodiment, the total number of Kx source symbols means that there are a total number of Kx source symbols to be encoded.

[0206] As an embodiment, the at least one coded symbol is composed of Kx source symbols to be encoded.

[0207] As an embodiment, Kx is a positive integer.

[0208] As an embodiment, the number of the at least one coding symbol is greater than the Kx coding symbols.

[0209] As an embodiment, one source block includes Kx source symbols.

[0210] As an embodiment, receiving the at least one coded symbol includes recovering Kx source symbols from the at least one coded symbol.

[0211] As an embodiment, recovering the corresponding source symbol from the at least one encoded symbol includes recovering the corresponding source symbol from the at least one encoded symbol using the degree distribution information.

[0212] As a sub-embodiment of this embodiment, the degree distribution information of the non-repair symbols in the degree distribution information is used to select non-repair symbols for generating repair symbols, and recovering the corresponding source symbols from the at least one encoded symbol is the inverse process of the above process, that is, determining which non-repair symbols are used to generate any repair symbol included in the at least one encoded symbol, and further recovering the source symbol corresponding to any repair symbol.

[0213] As a sub-embodiment of this embodiment, the degree distribution information of the repair symbol in the degree distribution information is used to determine the number of non-repair symbols used to generate the repair symbol, and recovering the corresponding source symbol from the at least one encoded symbol is the inverse process of the above process, that is, determining which non-repair symbols are used to generate any repair symbol included in the at least one encoded symbol, and further recovering the source symbol corresponding to any repair symbol.

[0214] As an embodiment, the encoding of the at least one encoding symbol belongs to a fountain code.

[0215] As an embodiment, the fountain code is also a rateless code.

[0216] As an embodiment, the fountain code includes LT code.

[0217] As an embodiment, the fountain code includes a Raptor code.

[0218] As an embodiment, the fountain code includes a RaptorQ code.

[0219] As an example, what a fountain code is is common knowledge in the art.

[0220] As an embodiment, the at least one encoding symbol is generated by a node outside the wireless access network.

[0221] As an embodiment, the at least one encoding symbol is generated by an application layer.

[0222] As an embodiment, the at least one coding symbol is generated by NAS.

[0223] As an embodiment, the degree distribution information is the output of the first AI model.

[0224] As an embodiment, the degree distribution information is an inherent characteristic of the first AI model.

[0225] As an embodiment, the terminal starts decoding using the first AI model, triggering the sending of the first message.

[0226] As an embodiment, the first message is associated with the first AI model.

[0227] As an embodiment, the first AI model is updated, triggering the sending of the first message.

[0228] As an embodiment, the activation of the first AI model triggers the sending of the first message.

[0229] As an embodiment, the degree distribution information is a result of long-term statistics.

[0230] As a sub-embodiment of this embodiment, the long-term statistics is that when different distribution information is used, the degree distribution information with better decoding performance using the first AI model is selected as the recommended degree distribution information.

[0231] As a sub-embodiment of this embodiment, the use of different distribution information is determined according to the support degree of the first AI model for temperature.

[0232] As a sub-embodiment of this embodiment, the use of different distribution information is determined according to the degree of support of the first AI model for the content and semantics of the source symbol.

[0233] As an embodiment, the degree distribution information is obtained by computer simulation.

[0234] As an embodiment, a portion of the degree distribution information is determined with the assistance of a radio access network.

[0235] As an embodiment, the degree distribution information is obtained through dynamic negative feedback fine-tuning.

[0236] As an embodiment, the degree distribution information includes a distribution probability of at least one degree.

[0237] As an embodiment, the at least one degree is or includes a degree of a non-repair symbol.

[0238] As an embodiment, the at least one degree is or includes a degree of a repair symbol.

[0239] As an embodiment, the sum of the probabilities of the degrees of non-repair symbols included in the degree distribution information is 1.

[0240] As an embodiment, the sum of probabilities of degrees of non-repair symbols included in the degree distribution information is less than 1.

[0241] As a sub-embodiment of this embodiment, the degree distribution information only includes probabilities of partial degrees for non-repair symbols.

[0242] As a sub-embodiment of this embodiment, the sum of the probabilities of the partial degrees for non-repair symbols included in the degree distribution information is equal to the sum of the probabilities of the partial degrees for non-repair symbols before the degree distribution information is sent.

[0243] As an embodiment, the sum of the probabilities of the degrees of the repair symbols included in the degree distribution information is 1.

[0244] As an embodiment, the sum of probabilities of degrees of repair symbols included in the degree distribution information is less than 1.

[0245] As a sub-embodiment of this embodiment, the degree distribution information only includes probabilities of partial degrees of the repair symbol.

[0246] As a sub-embodiment of this embodiment, the sum of probabilities of partial degrees for repair symbols included in the degree distribution information is equal to the sum of probabilities of partial degrees for non-repair symbols before the degree distribution information is sent.

[0247] As an embodiment, compared with the theoretical degree distribution, the degree distribution information indicates that the probability of a larger degree is higher.

[0248] As a sub-embodiment of this embodiment, the encoding used by the at least one encoding symbol is Raptor code.

[0249] As a sub-embodiment of this embodiment, the encoding used by the at least one encoding symbol is a variant of the Raptor code.

[0250] As an example, the theoretical distribution is the distribution given in a paper when a code was invented.

[0251] As an embodiment, the above method has the following advantages: better use of AI for decoding, better use of content or semantic relevance for decoding, and sufficient degree of decoding.

[0252] As an embodiment, the degree distribution information includes a degree distribution of non-repair symbols that depends on an expectation that a first segment is successfully decoded, wherein the first segment includes source symbols that at least partially generate the at least one encoded symbol.

[0253] As an embodiment, the expectation that the first segment is successfully decoded depends on the first AI model.

[0254] As an embodiment, the expectation that the first segment is successfully decoded depends on the second AI model.

[0255] As an embodiment, the expectation that the first segment is successfully decoded includes the correlation of the first segment with a previously received information block.

[0256] As a sub-embodiment of this embodiment, the correlation includes correlation in information processing.

[0257] As a sub-embodiment of this embodiment, the relevance includes relevance in content.

[0258] As a sub-embodiment of this embodiment, the correlation includes semantic correlation.

[0259] As an embodiment, the expectation of the first segment being successfully decoded is divided into a finite number of levels.

[0260] As a sub-embodiment of this embodiment, there is a mapping relationship between the limited number of levels and the correlation of previously received information blocks.

[0261] As an example, calculating the correlation of different symbols or information blocks is a prior art.

[0262] As an embodiment, the descriptive information of the first segment is used to determine the relevance of the first segment to a previously received information block.

[0263] As an embodiment, the description information of the first segment is successfully decoded.

[0264] As an embodiment, the decoding of the at least one coding symbol using the first AI model includes: decoding the at least one coding symbol using an information block other than the information block carried by the at least one coding symbol, or using a coding symbol other than the at least one coding symbol.

[0265] As an embodiment, the information block other than the information block carried by the at least one coding symbol is an information block that is successfully decoded before the at least one coding symbol.

[0266] As an embodiment, the coding symbols other than the at least one coding symbol are coding symbols that are successfully decoded before the at least one coding symbol.

[0267] As an embodiment, the expectation that the first fragment is successfully decoded depends on the first AI model, including: the success probability of the first AI model decoding the coded symbols after encoding the first fragment through information blocks other than the information blocks carried by the at least one coded symbol.

[0268] As an embodiment, the expectation that the first fragment is successfully decoded depends on the first AI model, including: the success probability of the first AI model decoding the coding symbols after the first fragment is encoded by the at least one coding symbol unexpected to the coding symbol.

[0269] As an embodiment, the information blocks other than the information blocks carried by the at least one coding symbol are not used to generate the at least one coding symbol.

[0270] As an embodiment, the expectation of the first segment being successfully decoded relies on an AI model: the first AI model uses the data that has been recovered from the first segment to evaluate the expectation of the first segment being successfully decoded.

[0271] As an embodiment, the using of the data that has been restored in the first fragment to evaluate the expectation of the first fragment being successfully decoded includes using the amount of the restored quantity.

[0272] As an embodiment, the use of the data that has been recovered in the first segment to evaluate the expectation of the first segment being successfully decoded includes using the recovered source symbols of the first segment as input of the AI ​​model.

[0273] As an embodiment, the distribution information is output by AI.

[0274] As an embodiment, along with the at least one coding symbol, the terminal receives degree distribution information for generating the at least one coding symbol.

[0275] As a sub-embodiment of this embodiment, accompanying the at least one encoded symbol includes receiving before the at least one encoded symbol.

[0276] As a sub-embodiment of this embodiment, the accompanying at least one encoded symbol is sent within a data packet including the at least one encoded symbol.

[0277] As a sub-embodiment of this embodiment, the accompanying the at least one encoded symbol includes sending it via a signaling radio bearer.

[0278] As an embodiment, the degree distribution information received by the terminal for generating the at least one coding symbol accompanying the at least one coding symbol is control information.

[0279] As an embodiment, the terminal receiving the at least one coded symbol includes: recovering a source symbol from the at least one coded symbol using the degree distribution information for generating the at least one coded symbol.

[0280] As an embodiment, the terminal, for example, uses AI to predict that the next content is closely related to the previously received content. For example, in a racing scene, the next frame is also a racing scene, but the scene on the street has changed. Since the car speed is very fast, the scene on the street is blurred, so the scene on the street carries less information. Then the terminal can use this content correlation to generate degree distribution information. For example, when the predicted next frame to be received is closely related to the previous frame that has been received, the probability of a large degree in the degree distribution of non-repair symbols in the degree distribution information carried by the first message is reduced, for example, according to a predefined table, or for example, the distribution probability of each degree is divided by the degree and then normalized. If the predicted next frame to be received is not closely related to the previous frame that has been received, the theoretical degree distribution can be used; and for the degree of the coded symbol in the degree distribution information of the first message, when the predicted next frame to be received is closely related to the previous frame that has been received, the probability of a large degree can be increased, for example, according to a predefined table, or the distribution probability of each degree is multiplied by the degree and then normalized.

[0281] As an embodiment, the terminal, for example, using AI, can predict part of the source symbols corresponding to the at least one coded symbol, and the degree of the non-repair symbols in the degree distribution information indicated by the first message can be increased.

[0282] As a sub-embodiment of this embodiment, for example, a larger degree is added. For example, the theoretical degree distribution does not include a distribution with a degree of 10, and the included degrees are concentrated in 1 to 3. In this case, a distribution with a degree of 10 can be added. The probability of the newly added degree can be set to the probability of a degree that is smaller than the newly added degree and exists in the theoretical degree. For example, the next degree smaller than 10 in the theoretical degree distribution is 9, that is, the theoretical degree distribution does not include a degree distribution with a degree of 10 but includes a degree distribution with a degree of 9. Then the probability of the newly added degree distribution with a degree of 10 is equal to the theoretical degree distribution with a degree of 9. Finally, the degree distribution probability is normalized.

[0283] As a sub-implementation of this embodiment, for example, the distribution probability of a larger degree is increased, and the probability of the degree distribution is multiplied by a coefficient, and this coefficient is linearly related to the degree, for example, the probability of a degree of 10 is multiplied by the coefficient 3, the probability of a degree of 5 is multiplied by 1.5, and the probability of a degree of 1 is multiplied by 0.3, and finally normalized.

[0284] As an embodiment, the terminal, for example, using AI, can predict part of the source symbols corresponding to the at least one coding symbol, and the degree of the coding symbol in the degree distribution information indicated by the first message can be increased.

[0285] As a sub-embodiment of this embodiment, for example, a larger degree is added. For example, the theoretical degree distribution does not include the distribution of degree 10, and the included degree distribution is concentrated in 1-3. In this case, the distribution of degree 10 can be added. The probability of the newly added degree can be set to the probability of the nearest degree that is smaller than the newly added degree and exists in the theoretical degree. For example, the degree closest to the degree equal to 10 in the theoretical degree distribution is 9, that is, the theoretical degree distribution does not include the degree distribution of degree 10 but includes the degree distribution of degree 9. Then the probability of the newly added degree distribution of degree 10 is equal to the theoretical degree distribution of degree 9. Finally, the degree distribution probability is normalized again.

[0286] As a sub-implementation of this embodiment, for example, the distribution probability of a larger degree is increased, and the probability of the degree distribution is multiplied by a coefficient, and this coefficient is linearly related to the degree, for example, the probability of a degree of 10 is multiplied by the coefficient 3, the probability of a degree of 5 is multiplied by 1.5, and the probability of a degree of 1 is multiplied by 0.3, and finally normalized.

[0287] As a sub-embodiment of this embodiment, the degree distribution for the coded symbol is added with the average or expected number of non-repair symbols that can be predicted according to the theoretical degree distribution. For example, if X source symbols can be predicted on average, the degree distribution of the coded symbol is added with X, and the probability of the degree distribution can remain unchanged. For example, the theoretical degree distribution gives the probability when the degree is 1, 2, and 5, and the probabilities are x1%, x2%, and x3% respectively, and X is equal to 2, then the degree distribution indicated by the degree distribution information for the coded symbol in the first message is 1+2=3, 2+2=4, 5+2=7, and the probabilities when using the above 3, 4, and 7 are x1%, x2%, and x3% respectively. The distribution with a degree of 1 can also be retained, for example, the degree distribution for the coded symbol in the degree distribution information only adds those degrees greater than 1, for example, the theoretical degree distribution gives the probability when the degree is 1, 2, and 5, and X is equal to 2, then the degree distribution indicated by the degree distribution information for the coded symbol in the first message is 1, 4, and 7. Of course, X can also be processed more conservatively, such as reducing X by an offset, such as setting X to X-1. In this example, the adjusted X=1. In the above example, the degree distribution information in the first message is the degree distribution probability when the degrees are 1, 3, and 6.

[0288] Example 2

[0289] Embodiment 2 illustrates a schematic diagram of a network architecture according to the present application, as shown in the attached Figure 2 shown.

[0290] Attached Figure 2A diagram of a network architecture 200 for 5G NR, LTE (Long-Term Evolution) and LTE-A (Long-Term Evolution Advanced) systems is illustrated. The 5G NR or LTE network architecture 200 may be referred to as 5GS (5G System) / EPS (Evolved Packet System) 200 or some other suitable terminology. The 5GS / EPS 200 may include one or more UEs (User Equipment) 201, NG-RAN (Next Generation Radio Access Network) 202, 5GC (5G Core Network) / EPC (Evolved Packet Core) 210, HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and Internet services 230. The 5GS / EPS may be interconnected with other access networks, but these entities / interfaces are not shown for simplicity. As shown, 5GS / EPS provides packet-switched services, however, those skilled in the art will readily appreciate that the various concepts presented throughout this application can be extended to networks providing circuit-switched services or other cellular networks. NG-RAN includes NR Node B (gNB) 203 and other gNBs 204. gNB 203 provides user and control plane protocol terminations toward UE 201. gNB 203 can be connected to other gNBs 204 via an Xn interface (e.g., backhaul). gNB 203 may also be referred to as a base station, a base transceiver station, a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS), an extended service set (ESS), a TRP (transmitting receiving node), or some other suitable term. gNB 203 provides an access point to 5GC / EPC 210 for UE 201. Examples of UE 201 include cellular phones, smart phones, session initiation protocol (SIP) phones, laptop computers, personal digital assistants (PDAs), satellite radios, non-terrestrial base station communications, satellite mobile communications, global positioning systems, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, drones, aircraft, narrowband Internet of Things devices, machine type communication devices, land vehicles, cars, wearable devices, or any other similar functional devices. Those skilled in the art may also refer to UE 201 as a mobile station, a subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a mobile device, a wireless device, a wireless communication device, a remote device, a mobile subscriber station, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, a user agent, a mobile client, a client, or some other suitable term.gNB203 is connected to 5GC / EPC210 via the S1 / NG interface. 5GC / EPC210 includes MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MME / AMF / SMF214, S-GW (Service Gateway) / UPF (User Plane Function) 212, and P-GW (Packet Data Network Gateway) / UPF213. MME / AMF / SMF211 is the control node that handles signaling between UE201 and 5GC / EPC210. ​​In general, MME / AMF / SMF211 provides bearer and connection management. All user IP (Internet Protocol) packets are transmitted through S-GW / UPF212, which itself is connected to P-GW / UPF213. P-GW provides UE IP address allocation and other functions. P-GW / UPF213 is connected to Internet service 230. Internet service 230 includes operator-corresponding Internet protocol services, which may specifically include Internet, intranet, IMS (IP Multimedia Subsystem, IP Multimedia Subsystem) and packet switching streaming services. When near field communication is used, the network may also include functions or nodes related to the near field communication function accordingly.

[0291] As an embodiment, the terminal in the present application is UE201.

[0292] As an embodiment, the base station of the network node in the present application is gNB203.

[0293] As an embodiment, the wireless link from the UE201 to the NR Node B is an uplink.

[0294] As an embodiment, the wireless link from the NR Node B to UE201 is a downlink.

[0295] As an embodiment, the UE 201 includes a mobile phone.

[0296] As an embodiment, the UE201 is a dedicated device or special device with communication function.

[0297] As an embodiment, the gNB203 is a micro cell base station.

[0298] As an embodiment, the gNB203 is a pico cell base station.

[0299] As an embodiment, the gNB203 is a base station used in a home network.

[0300] As an embodiment, the gNB203 is a base station used in a private network.

[0301] Example 3

[0302] Embodiment 3 shows a schematic diagram of an embodiment of a wireless protocol architecture of a user plane and a control plane according to the present application, as shown in the attached Figure 3 shown. Figure 3 is a schematic diagram illustrating an embodiment of a radio protocol architecture for a user plane 350 and a control plane 300, Figure 3The radio protocol architecture of the control plane 300 for a terminal (UE, gNB) and a network node (gNB, UE), or between two UEs is shown in three layers: Layer 1, Layer 2, and Layer 3. Layer 1 (L1 layer) is the lowest layer and implements various PHY (physical layer) signal processing functions. The L1 layer will be referred to as PHY301 in this article. Layer 2 (L2 layer) 305 is above PHY301 and is responsible for the link between the terminal and the network node and the two UEs through PHY301. The L2 layer 305 includes a MAC (Medium Access Control) sublayer 302, an RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304, which terminate at the network node. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security by encrypting data packets and provides support for inter-zone mobility of terminals between network nodes. The RLC sublayer 303 provides segmentation and reassembly of upper layer data packets, retransmission of lost data packets, and reordering of data packets to compensate for out-of-order reception due to HARQ. The MAC sublayer 302 provides multiplexing between logical and transport channels. The MAC sublayer 302 is also responsible for allocating various radio resources (e.g., resource blocks) in a cell between terminals. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control) sublayer 306 in layer 3 (L3 layer) in the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and configuring the lower layer using RRC signaling between network nodes and terminals. Nodes using sidelink communication may also include a PC5-S (PC5 Signaling Protocol) sublayer 307 responsible for processing the signaling protocol of the PC5 interface. The radio protocol architecture of the user plane 350 includes layer 1 (L1 layer) and layer 2 (L2 layer). The radio protocol architecture for terminals and network nodes in the user plane 350 is substantially the same as the corresponding layers and sublayers in the control plane 300 for the physical layer 351, the PDCP sublayer 354 in the L2 layer 355, the RLC sublayer 353 in the L2 layer 355, and the MAC sublayer 352 in the L2 layer 355, but the PDCP sublayer 354 also provides header compression for upper layer data packets to reduce radio transmission overhead.The L2 layer 355 in the user plane 350 also includes an SDAP (Service Data Adaptation Protocol) sublayer 356, which is responsible for mapping between QoS flows and data radio bearers (DRBs) to support the diversity of services. SRB can be regarded as a service or interface provided by the PDCP layer to a higher layer, such as the RRC sublayer. In the NR system, SRBs include SRB1, SRB2, and SRB3, which are used to transmit different types of control signaling. SRB is a bearer between the UE and the access network, and is used to transmit control signaling including RRC signaling between the UE and the access network. SRB1 has special significance for the UE. After each UE establishes an RRC connection, there will be SRB1 for transmitting RRC signaling. Most of the signaling is transmitted through SRB1. If SRB1 is interrupted or cannot be used, the UE must perform RRC reconstruction. SRB2 is generally only used to transmit NAS signaling or security-related signaling. The UE may not configure SRB3. Except for emergency services, the UE must establish an RRC connection with the network for subsequent communications. Although not shown, the terminal may have several upper layers above the L2 layer 355. In addition, it also includes a network layer (e.g., IP layer) terminated at the P-GW on the network side and an application layer terminated at the other end of the connection (e.g., remote UE, server, etc.). The protocol layer may also be referred to as a protocol sublayer. Attached. Figure 3 What is shown is a general protocol layer structure, and the node used in this application may lack some protocol layers. When the terminal communicates with the L2 U2N relay, an adaptation sublayer may also be used, and the adaptation sublayer is located between the PDCP sublayer and the RLC sublayer.

[0303] As an example, Figure 3 The wireless protocol architecture in is applicable to the terminal described in this application.

[0304] As an example, Figure 3 The wireless protocol architecture in is applicable to the network nodes in this application.

[0305] As an embodiment, the first message in the present application is generated by NAS.

[0306] As an embodiment, the at least one coding symbol in the present application is generated in NAS.

[0307] As an embodiment, the source symbols in the present application are generated in NAS.

[0308] As an embodiment, the intermediate symbol in the present application is generated in NAS.

[0309] As an embodiment, the repair symbol in the present application is generated in NAS.

[0310] As an embodiment, the source block in the present application is generated in NAS.

[0311] As an embodiment, the NAS includes an application layer.

[0312] As an embodiment, the first association information in the present application is generated in NAS.

[0313] Example 4

[0314] Embodiment 4 shows a schematic diagram of a first communication device and a second communication device according to an embodiment of the present application, as shown in the attached figure. Figure 4 shown. Figure 4 is a block diagram of a first communication device 450 and a second communication device 410 communicating with each other in an access network.

[0315] The first communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmit processor 468, a receive processor 456, and may optionally also include a multi-antenna transmit processor 457, a multi-antenna receive processor 458, a transmitter / receiver 454 and an antenna 452.

[0316] The second communication device 410 includes a controller / processor 475 , a memory 476 , a receiving processor 470 , a transmitting processor 416 , and may optionally also include a multi-antenna receiving processor 472 , a multi-antenna transmitting processor 471 , a transmitter / receiver 418 and an antenna 420 .

[0317] In the transmission from the second communication device 410 to the first communication device 450, at the second communication device 410, the upper layer data packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements the functionality of the L2 (Layer-2) layer. In the transmission from the second communication device 410 to the first communication device 450, the controller / processor 475 provides header compression, encryption, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocation to the first communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for the retransmission of lost packets and signaling to the first communication device 450. The transmit processor 416 and the multi-antenna transmit processor 471 implement various signal processing functions for the L1 layer (i.e., the physical layer). The transmit processor 416 implements coding and interleaving to facilitate forward error correction (FEC) at the second communication device 410, as well as mapping of signal constellations based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-phase shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The multi-antenna transmit processor 471 performs digital spatial precoding, including codebook-based precoding and non-codebook-based precoding, and beamforming processing on the coded and modulated symbols to generate one or more spatial streams. The transmit processor 416 then maps each spatial stream to a subcarrier, multiplexes it with a reference signal (e.g., a pilot) in the time domain and / or frequency domain, and then uses an inverse fast Fourier transform (IFFT) to generate a physical channel carrying a time-domain multi-carrier symbol stream. The multi-antenna transmit processor 471 then performs a transmit analog precoding / beamforming operation on the time-domain multi-carrier symbol stream. Each transmitter 418 converts the baseband multi-carrier symbol stream provided by the multi-antenna transmit processor 471 into a radio frequency stream, and then provides it to a different antenna 420.

[0318] In the transmission from the second communication device 410 to the first communication device 450, at the first communication device 450, each receiver 454 receives a signal through its corresponding antenna 452. Each receiver 454 recovers the information modulated onto the RF carrier and converts the RF stream into a baseband multi-carrier symbol stream and provides it to the receiving processor 456. The receiving processor 456 and the multi-antenna receiving processor 458 implement various signal processing functions of the L1 layer. The multi-antenna receiving processor 458 performs a receiving analog precoding / beamforming operation on the baseband multi-carrier symbol stream from the receiver 454. The receiving processor 456 uses a fast Fourier transform (FFT) to convert the baseband multi-carrier symbol stream after the receiving analog precoding / beamforming operation from the time domain to the frequency domain. In the frequency domain, the physical layer data signal and the reference signal are demultiplexed by the receiving processor 456, wherein the reference signal will be used for channel estimation, and the data signal is recovered after multi-antenna detection in the multi-antenna receiving processor 458 to any spatial stream destined for the first communication device 450. The symbols on each spatial stream are demodulated and recovered in the receiving processor 456, and soft decisions are generated. The receiving processor 456 then decodes and deinterleaves the soft decisions to recover the upper layer data and control signals transmitted by the second communication device 410 on the physical channel. The upper layer data and control signals are then provided to the controller / processor 459. The controller / processor 459 implements the functions of the L2 layer. The controller / processor 459 may be associated with a memory 460 that stores program codes and data. The memory 460 may be referred to as a computer-readable medium. In the transmission from the second communication device 410 to the first communication device 450, the controller / processor 459 provides multiplexing between transport and logical channels, packet reassembly, decryption, header decompression, and control signal processing to recover the upper layer data packets from the core network. The upper layer data packets are then provided to all protocol layers above the L2 layer. Various control signals may also be provided to L3 for L3 processing.

[0319] In the transmission from the first communication device 450 to the second communication device 410, at the first communication device 450, a data source 467 is used to provide upper layer data packets to the controller / processor 459. The data source 467 represents all protocol layers above the L2 layer. Similar to the transmission function at the second communication device 410 described in the transmission from the second communication device 410 to the first communication device 450, the controller / processor 459 implements header compression, encryption, packet segmentation and reordering, and multiplexing between logical and transport channels based on radio resource allocation, and implements L2 layer functions for user plane and control plane. The controller / processor 459 is also responsible for the retransmission of lost packets and signaling to the second communication device 410. The transmit processor 468 performs modulation mapping and channel coding processing, and the multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based precoding and non-codebook-based precoding, and beamforming processing. Then, the transmit processor 468 modulates the generated spatial stream into a multi-carrier / single-carrier symbol stream, which is then provided to different antennas 452 via the transmitter 454 after analog precoding / beamforming operations in the multi-antenna transmit processor 457. Each transmitter 454 first converts the baseband symbol stream provided by the multi-antenna transmit processor 457 into a radio frequency symbol stream, and then provides it to the antenna 452.

[0320] In the transmission from the first communication device 450 to the second communication device 410, the function at the second communication device 410 is similar to the reception function at the first communication device 450 described in the transmission from the second communication device 410 to the first communication device 450. Each receiver 418 receives a radio frequency signal through its corresponding antenna 420, converts the received radio frequency signal into a baseband signal, and provides the baseband signal to the multi-antenna reception processor 472 and the reception processor 470. The reception processor 470 and the multi-antenna reception processor 472 jointly implement the functions of the L1 layer. The controller / processor 475 implements the L2 layer functions. The controller / processor 475 can be associated with a memory 476 storing program codes and data. The memory 476 can be referred to as a computer-readable medium. In the transmission from the first communication device 450 to the second communication device 410, the controller / processor 475 provides multiplexing between transport and logical channels, packet reassembly, decryption, header decompression, control signal processing to recover the upper layer data packets from the UE 450. Upper layer packets from controller / processor 475 may be provided to the core network.

[0321] As an embodiment, the first communication device 450 apparatus includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used together with the at least one processor, and the first communication device 450 apparatus at least: sends a first message, the first message indicates at least degree distribution information, the degree distribution information is used for encoding at least one coding symbol; receives the at least one coding symbol; wherein the degree distribution information includes at least one of the degree distribution of the repair symbol and the degree distribution of the non-repair symbol, wherein the non-repair symbol is a source symbol or an intermediate symbol, and the intermediate symbol is generated by the source symbol; the receiving of the at least one coding symbol includes recovering the corresponding source symbol from the at least one coding symbol; the at least one coding symbol includes at least a repair symbol.

[0322] As an embodiment, the first communication device 450 includes: a memory storing a computer-readable instruction program, wherein the computer-readable instruction program generates actions when executed by at least one processor, and the actions include: sending a first message, wherein the first message indicates at least degree distribution information, and the degree distribution information is used for encoding at least one coding symbol; receiving the at least one coding symbol; wherein the degree distribution information includes at least one of the degree distribution of the repair symbol and the degree distribution of the non-repair symbol, wherein the non-repair symbol is a source symbol or an intermediate symbol, and the intermediate symbol is generated by the source symbol; the receiving of the at least one coding symbol includes recovering the corresponding source symbol from the at least one coding symbol; the at least one coding symbol includes at least a repair symbol.

[0323] As an embodiment, the first communication device 450 corresponds to the terminal in this application.

[0324] As an embodiment, the second communication device 410 corresponds to the network node in this application.

[0325] As an embodiment, the first communication device 450 is a UE.

[0326] As an embodiment, the first communication device 450 is a mobile phone.

[0327] As an embodiment, the second communication device 410 is a relay.

[0328] As an embodiment, the second communication device 410 is an L2 U2N relay.

[0329] As an embodiment, the second communication device 410 is a base station.

[0330] As an embodiment, the receiver 454 (including the antenna 452 ), the receiving processor 456 and the controller / processor 459 are used to receive the first association information in the present application.

[0331] As an embodiment, the receiver 454 (including the antenna 452), the receiving processor 456 and the controller / processor 459 are used to receive the at least one coded symbol in the present application.

[0332] As an embodiment, the transmitter 454 (including the antenna 452), the transmit processor 468 and the controller / processor 459 are used to send the first message in the present application.

[0333] Example 5

[0334] Embodiment 5 illustrates a wireless signal transmission flow chart according to an embodiment of the present application, as shown in the attached Figure 5 Attached Figure 5 In the example, U01 corresponds to the terminal of the present application. It is particularly noted that the order in this example does not limit the signal transmission order and implementation order in the present application, and the steps in F51 are optional.

[0335] for Terminal U01 , receiving first association information in step S5101; sending a first message in step S5102; receiving at least one coding symbol in step S5103.

[0336] for Network Node U02 , sending first association information in step S5201; receiving a first message in step S5202; sending at least one coding symbol in step S5202.

[0337] In embodiment 5, the first message indicates at least degree distribution information, and the degree distribution information is used for encoding at least one encoded symbol; wherein the degree distribution information includes at least one of the degree distribution of the repair symbol and the degree distribution of the non-repair symbol, wherein the non-repair symbol is a source symbol or an intermediate symbol, and the intermediate symbol is generated by the source symbol; the receiving of the at least one encoded symbol includes recovering the corresponding source symbol from the at least one encoded symbol; the at least one encoded symbol includes at least a repair symbol.

[0338] As an embodiment, the network node U02 includes a service cell of the terminal U01 or a node corresponding to the service cell.

[0339] As an embodiment, the network node U02 includes a base station.

[0340] As an embodiment, the network node U02 includes a wireless access network device.

[0341] As an embodiment, step S5101 precedes step S5102.

[0342] As an embodiment, step S5102 precedes step S5103.

[0343] As an embodiment, the first association information indicates which encoding symbols of the at least one encoding symbol the first encoding symbol is associated with in terms of content or semantics.

[0344] As an embodiment, the first coding symbol is any coding symbol among the at least one coding symbol.

[0345] As an embodiment, the first association information indicates which coding symbols of the at least one coding symbol the first coding symbol is associated with in terms of content or semantics, which means that the first association information indicates which coding symbols of the at least one coding symbol the first coding symbol is associated with in terms of content or semantics other than the first coding symbol.

[0346] As an embodiment, the at least one coding symbol includes multiple coding symbols.

[0347] As an embodiment, for example, the first association information indicates that the first coding symbol is associated with the second coding symbol in the at least one coding symbol in content or semantics, and the second coding symbol has been decoded, then the AI ​​model is used to take the second coding symbol as input, combined with other decoded source symbols in the first coding symbol to infer the first coding symbol, thereby further inferring other coding symbols.

[0348] As an embodiment, by utilizing the first association information, taking the second coding symbol as the initial input of the first coding symbol during decoding, and then inferring or correcting the initial input based on the generation relationship between the first coding symbol and the corresponding source symbol, a better decoding effect can be obtained.

[0349] As an embodiment, the terminal U01 receives second association information.

[0350] As an embodiment, the network node U02 sends the second association information.

[0351] As an embodiment, the second association information indicates the content or semantic association between the first source symbol and the second source symbol, and the first source symbol and the second source symbol are both source symbols for generating the at least one encoding symbol.

[0352] As an embodiment, when the second source symbol has been decoded, the terminal U01 can use the AI ​​model to take the second source symbol as input to infer the first source symbol.

[0353] As an embodiment, the advantage of the above method is that it can avoid decoding traps, that is, avoid the situation in which the decoding process cannot proceed because some source symbols cannot be decoded during belief propagation decoding.

[0354] As an embodiment, using the AI ​​method in Embodiments 6, 7, and 8, another source symbol can be inferred from a related source symbol.

[0355] As an embodiment, by utilizing the second association information, taking the second source symbol as the initial value of the first source symbol during decoding, and then inferring or correcting the initial value based on the relationship between the first source symbol and other source symbols and encoding symbols, a better decoding effect can be obtained, especially in scenarios where all decoding is not required to be correct.

[0356] As an embodiment, the relationship between the source symbols and the coding symbols is determined by a coding matrix.

[0357] As an embodiment, the relationship between the source symbol and the encoding symbol is determined by degree distribution information.

[0358] As an embodiment, the terminal U01 sends recommended coding parameters to the network node U02 multiple times, and any one of the coding parameters sent multiple times includes information related to degree distribution.

[0359] As an embodiment, the multiple sending of the recommended encoding parameters includes periodically sending the recommended encoding parameters.

[0360] As an embodiment, the sending of the recommended encoding parameters multiple times does not mean sending the same encoding parameters repeatedly.

[0361] As an embodiment, the degree distribution information included in the recommended encoding parameters sent multiple times depends on video content.

[0362] As an embodiment, the degree distribution information included in the recommended encoding parameters sent multiple times depends on the first AI model.

[0363] As an embodiment, the degree distribution information belongs to parameters included in one of the encoding parameters sent multiple times.

[0364] As an embodiment, the above method has the following advantages: the degree distribution information can be dynamically adjusted according to the decoding situation to achieve a better decoding effect.

[0365] As an embodiment, the encoding of the at least one encoding symbol utilizes the degree distribution information carried by the first message.

[0366] As an embodiment, the at least one encoding symbol belongs to a data packet.

[0367] As an embodiment, the at least one encoding symbol belongs to multiple data packets.

[0368] As an embodiment, the at least one encoding symbol belongs to an output of a single encoding.

[0369] As an embodiment, the at least one encoding symbol belongs to an output of encoding for a source block.

[0370] As an embodiment, the at least one encoding needs to be decoded together.

[0371] As an embodiment, the at least one encoding belongs to the same PDU set.

[0372] As an embodiment, the PDU set carries information of a unit of the application layer.

[0373] Example 6

[0374] Embodiment 6 illustrates a schematic diagram of RAN (Radio Access Network) domain (Domain) AI / ML function deployment according to an embodiment of the present application, as shown in the attached Figure 6 The gNB in ​​Example 8 may be replaced by a network device such as an eNB or a 6G base station.

[0375] AI / ML related functions include ML training function (also called AI training, or AI / ML training), ML testing function, ML reasoning (also called AI reasoning, or AI / ML reasoning) function, etc. ML training function, ML testing function, and ML reasoning function can be deployed independently or co-located. The deployment of AI / ML related functions can be achieved through software, such as downloading and / or running executable files; it can also be achieved through a combination of software and hardware, such as accelerating specific computing units through hardware to increase computing speed or save power consumption.

[0376] For ML training functions, they can be deployed in a cross-domain management system or a domain-specific management system; the domain-specific management system is used to manage the RAN domain or the CN (Core Network) domain. For example, the ML training function for MDA (Management Data Analytics) can be deployed in MDAF (MDA function); ML training for network data analysis can be deployed in NWDAF (Network Data Analytics Function), that is, the ML training function is MTLF (Model Training logical function).

[0377] The ML reasoning function can also be deployed in a cross-domain management system or a domain-specific management system; for example, the ML reasoning function is MDAF, or the ML reasoning function is AnLF (Analytics logical function) located in NWDAF.

[0378] Similarly, ML testing capabilities can also be deployed in cross-domain management systems, or domain-specific management systems.

[0379] In Example 6, the RAN domain ML training function 1402 is located in the RAN domain management function 1403; and the ML reasoning function is located in the base station, that is, the AI / ML reasoning function 1404 is located in gNB1405, the AI / ML reasoning function 1406 is located in gNB1407, and so on.

[0380] Attached Figure 6 In the example, the management of the ML inference functions of multiple base stations is completed by the RAN domain management function 1403, that is, data interaction with the RAN domain MnS (Management Service) consumer / cross-domain management 1401 (see attached Figure 6 ).

[0381] Optionally, the management of the ML reasoning function may also be performed by the base station itself, that is, each base station may independently interact with the RAN domain MnS consumer / cross-domain management 1401 for data.

[0382] It should be noted that Example 6 is merely a non-restrictive implementation method; optionally, the ML training function of the RAN domain may also be deployed in the base station; or optionally, some base stations deploy the ML reasoning function and the ML training function of the RAN domain, while some base stations only deploy the ML reasoning function.

[0383] As an embodiment, a gNB (or base station) in Embodiment 6 is the second node of the present application.

[0384] As an embodiment, the second processor includes an attached Figure 6 An AL / ML reasoning function, namely 1404 or 1406.

[0385] As an example, Figure 6 An AL / ML inference function performs ML training based on the target precoding vector; the target reference signal is different from the first reference signal; any reference signal among the multiple reference signals except the first reference signal is associated with a precoding vector, wherein the target precoding vector is associated with the target reference signal.

[0386] As an embodiment, the generation or sending of the first report uses an attached Figure 6 at least one function in .

[0387] As an embodiment, the execution of the first measurement configuration uses an additional Figure 6 at least one function in .

[0388] As an embodiment, the execution of the first measurement configuration depends on the attached Figure 6 at least one function in .

[0389] As an embodiment, the first report depends on training, and the first measurement configuration does not depend on training, or the first report does not depend on training, and the first measurement configuration depends on training.

[0390] Example 7

[0391] Embodiment 7 illustrates a schematic diagram of the AI / ML function deployment of a UE according to an embodiment of the present application, as shown in the attached Figure 7 shown.

[0392] Attached Figure 7 The RAN domain ML training functionality 1505 is optional.

[0393] The UE function 1504 is deployed in the first node of the present application, and the UE function 1504 includes an AI / ML reasoning function 1506; the AI / ML reasoning function 1506 uses an ML model (also called an AI model) for reasoning; an ML model is usually trained before being used for AI / ML reasoning.

[0394] As an embodiment, the UE function 1504 includes a RAN domain ML training function 1505, and the RAN domain ML training function 1505 runs the training data through the ML model to derive relevant losses, and adjusts the parameters of the ML model based on the calculated losses; the ML training includes at least one of ML initial training, ML re-training, and reinforcement learning.

[0395] The above embodiments can reduce the complexity of the base station, or save air interface resources caused by reporting training data; however, the above embodiments place high demands on the processing capability of the UE side.

[0396] Optionally, the UE function 1504 also includes a CN domain ML training function ( Figure 7 not included).

[0397] Optionally, the UE function 1504 also includes an AI / ML deployment function: Figure 7 Not included, used to load ML models and data.

[0398] As an embodiment, the first node indicates whether the ML training function is supported (RAN domain or CN domain) through capability reporting, and the capability reporting is RRC signaling, or NAS (Non-Access Stratum) signaling.

[0399] As an embodiment, the ML model and related metadata are loaded by the first node from a network device or a remote server.

[0400] Optionally, the UE function 1504 is a MnS (Management Service) producer, providing data to the CN domain MnF (Management Function) 1501, and / or the RAN domain MnF 1502, and / or the cross-domain management system 1503 for management or analysis (as shown by the double arrow 1507).

[0401] Optionally, the UE function 1504 is an MnS consumer, which loads data from the CN domain MnF (Management Function) 1501, and / or the RAN domain MnF 1502, and / or the cross-domain management system 1503 for AI / ML related management, such as management data requests, ML model activation, and / or ML training, etc. (as shown by the double arrow 1507).

[0402] As an embodiment, the first channel information in the present application is obtained through reasoning by the AI / ML reasoning function 1506 .

[0403] As an embodiment, the RAN domain ML training function 1505 performs ML training according to a target precoding vector; wherein the target reference signal is different from the first reference signal; any reference signal among the multiple reference signals except the first reference signal is associated with a precoding vector, wherein the target precoding vector is associated with the target reference signal.

[0404] As an embodiment, the first processor includes an attached Figure 7 An AL / ML reasoning function 1506 in.

[0405] As an embodiment, the ML model is based on a neural network.

[0406] As an embodiment, the ML model is based on CNN (Conventional Neural Networks).

[0407] As an embodiment, the ML model is based on the Transformer architecture.

[0408] As an embodiment, the generation or sending of the first report uses an attached Figure 7 at least one function in .

[0409] As an embodiment, the execution of the first measurement configuration uses an additional Figure 7 at least one function in .

[0410] As an embodiment, the execution of the first measurement configuration depends on the attached Figure 7 at least one function in .

[0411] As an embodiment, the at least one function is the function in 1504.

[0412] As an embodiment, the at least one function is the function in 1505.

[0413] As an embodiment, the at least one function is the function in 1506.

[0414] As an embodiment, the first report depends on training, and the first measurement configuration does not depend on training, or the first report does not depend on training, and the first measurement configuration depends on training.

[0415] Example 8

[0416] Embodiment 8 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of the present application, as shown in the attached Figure 8 shown.

[0417] Attached Figure 8 Including a third operation, a fourth operation, a fifth operation, a sixth operation and a seventh operation. In Example 11, the third operation and the fourth operation belong to the first stage, the fifth operation belongs to the second stage, the sixth operation belongs to the third stage, and the seventh operation belongs to the fourth stage. Figure 8 In the diagram, the lines with arrows indicate the sequence of the processes.

[0418] As an embodiment, the third operation includes AI / ML training, the fourth operation includes AI / ML testing, the fifth operation includes AI / ML emulation, the sixth operation includes AI / ML entity loading, and the seventh operation includes AI / ML inference.

[0419] As an embodiment, the first phase includes a training phase, the second phase includes an emulation phase, the third phase includes a deployment phase, and the fourth phase includes an emulation phase.

[0420] As an embodiment, the first stage includes AI / ML model training.

[0421] As an embodiment, the first stage includes AI / ML model training and AI / ML testing.

[0422] As an embodiment, the AI / ML model training includes initial training and re-training of one or a group of AI / ML entities.

[0423] As an embodiment, the AI / ML model training relies on training data.

[0424] As an embodiment, the AI / ML model training includes AI / ML entity validation.

[0425] As an embodiment, the AI / ML entity verification is used to evaluate the performance of the AI / ML entity.

[0426] As an embodiment, the AI / ML entity verification relies on verification data.

[0427] As an example, if the result of AI / ML entity verification does not meet expectations, the AI / ML model will be retrained.

[0428] As an embodiment, the AI / ML testing includes testing the verified AI / ML entity to estimate the performance of the trained AI / ML model.

[0429] As an example, if the result of the AI / ML test meets expectations, the AI / ML entity proceeds to the next stage; otherwise the AI / ML model will be retrained.

[0430] As an embodiment, the AI / ML testing relies on test data.

[0431] As an embodiment, the second stage includes AI / ML simulation, which performs inference of AI / ML entities in a simulation environment.

[0432] As an embodiment, the AI / ML simulation estimates the performance of AI / ML entity reasoning in a simulation environment before using the AI / ML entity.

[0433] As an embodiment, the second stage is optional.

[0434] As an embodiment, the third stage includes AI / ML entity loading, and the AI / ML entity loading is to obtain the trained AI / ML entity to obtain the desired AI / ML reasoning function.

[0435] As an embodiment, the third stage is optional.

[0436] As an example, when the training function and the inference function are co-located, the third stage is no longer required.

[0437] As an embodiment, the fourth stage includes AI / ML reasoning.

[0438] As an embodiment, the generation or sending of the first report uses an attached Figure 8 At least one operation in .

[0439] As an embodiment, the first report uses reasoning.

[0440] As a sub-embodiment of this embodiment, at least one or all of the measurement results included in the first report are obtained by inference.

[0441] As an embodiment, the execution of the first measurement configuration uses an additional Figure 8 At least one operation in .

[0442] As an embodiment, the execution of the first measurement configuration depends on the attached Figure 8 At least one operation in .

[0443] As an embodiment, the first report depends on training, and the first measurement configuration does not depend on training, or the first report does not depend on training, and the first measurement configuration depends on training.

[0444] Example 9

[0445] Embodiment 9 illustrates a schematic diagram of encoding according to an embodiment of the present application, as shown in the attached Fig. 9 shown.

[0446] Attached Fig. 9 The encoding process and encoding principle are shown.

[0447] As an embodiment, a source symbol, an intermediate symbol, a repair symbol, and an encoded symbol are equal in size.

[0448] As an embodiment, any encoding symbol includes 1 bit.

[0449] As an embodiment, any encoding symbol includes 4 bits.

[0450] As an embodiment, any coded symbol includes 8 bits.

[0451] As an embodiment, any coded symbol includes 16 bits.

[0452] As an embodiment, any coded symbol includes 64 bits.

[0453] As an embodiment, any coded symbol includes 256 bits.

[0454] As an embodiment, any coded symbol includes 1024 bits.

[0455] As an embodiment, the bits included in any encoding symbol are configurable.

[0456] As an embodiment, any coded symbol includes a finite number of bits.

[0457] As an embodiment, the degree of a source symbol refers to how many encoding symbols the source symbol is used to generate.

[0458] As an embodiment, the degree distribution of the source symbol refers to the probability distribution of the source symbol generating the coding symbol in Z1, wherein Z1 is a set of positive integers, wherein the maximum integer included in Z1 is not greater than the total number of coding symbols. For example, Z1 = [1, 2], the degree distribution of the source symbol is 70% for generating one coding symbol and 30% for generating two coding symbols.

[0459] As an embodiment, the degree of the intermediate symbol refers to how many encoding symbols the intermediate symbol is used to generate.

[0460] As an embodiment, the degree distribution of the intermediate symbol refers to the probability distribution of the intermediate symbol generating the coding symbol in Z2, wherein Z2 is a set of positive integers, wherein the maximum integer included in Z2 is not greater than the total number of coding symbols. For example, Z2 = [1, 2], the degree distribution of the intermediate symbol is 70% for generating one coding symbol and 30% for generating two coding symbols.

[0461] As an embodiment, the degree of a coding symbol refers to how many non-repair symbols the coding symbol is generated from.

[0462] As an embodiment, the degree distribution of a coding symbol refers to the probability that the coding symbol is generated by a non-repair symbol in Z3, wherein Z3 is a set of positive integers, wherein Z3 is not greater than the number of non-repair symbols. For example, Z3 = [1, 2], the degree distribution of a coding symbol is that the probability of being generated by one non-repair symbol is 60%, and the probability of being generated by two non-repair symbols is 40%.

[0463] As an embodiment, the at least one coding block adopts linear coding.

[0464] As an example, Fig. 9 b1, b2, b3, b4, and b5 are source symbols respectively; the method proposed in this application does not limit the number of source symbols, that is, the method proposed in this application supports encoding of less than 5 source symbols and also supports encoding of more than 5 source symbols; Fig. 9In which c1, c2, c3, c4, c5, and c6 are coding symbols respectively; the method proposed in the present application does not limit the number of coding symbols, that is, the method proposed in the present application supports the output of less than 6 coding symbols and also supports the output of more than 6 coding symbols. At the same time, those skilled in the art should understand that the number of coding symbols outputted is usually more than the number of source symbols.

[0465] As a sub-embodiment of this embodiment, the encoding symbol is generated by the source symbol.

[0466] As a sub-embodiment of this embodiment, for any source symbol, the line connecting it with the coding symbol indicates which coding symbols the source symbol is used to generate. For example, b1 is connected with c1 and c2, indicating that b1 is used to generate c1 and also used to generate c2.

[0467] As a sub-embodiment of this embodiment, for any coding symbol, the line connecting it with the source symbol indicates which source symbols the coding symbol is generated from. For example, c2 is connected with b1 and b2, indicating that c2 is generated from b1 and b2.

[0468] As a sub-embodiment of the present invention, Fig. 9 The connection between the source symbol and the coded symbol is equivalent to the coding matrix. Fig. 9 The connections in are schematic.

[0469] As an embodiment, the principle of encoding the source symbol to generate the encoded symbol is as shown in the attached Fig. 9 As shown, any coded symbol is generated from one or several source symbols.

[0470] As an embodiment, the arbitrary coded symbol is generated from one or more source symbols, including being generated using fountain coding.

[0471] As an embodiment, the arbitrary coded symbol is generated from one or more source symbols, including being generated using Raptor coding.

[0472] As an embodiment, the generation of any one of the coding symbols from one or several source symbols includes: performing XOR operation on the source symbols used to generate the any one of the coding symbols, or performing addition in the binary domain to obtain the any one of the coding symbols.

[0473] As an embodiment, the number of source symbols from which any one coded symbol is generated is determined by the degree distribution information.

[0474] As an embodiment, the number of source symbols from which any one coding symbol is generated is determined by a theoretical generation method.

[0475] As an embodiment, the number of source symbols used to generate any one coded symbol may be fixed.

[0476] As an embodiment, the number of source symbols from which any one coded symbol is generated may be generated according to a predefined pseudo-random sequence.

[0477] As an embodiment, the source symbols from which any one of the encoded symbols is generated are determined by the degree distribution information.

[0478] As an embodiment, the source symbols from which any one of the encoding symbols is generated are determined by a theoretical generation method.

[0479] As an embodiment, the source symbols from which any one of the coded symbols is generated may be generated according to a predefined pseudo-random sequence.

[0480] As an example, Fig. 9 b1, b2, b3, b4, and b5 are source symbols respectively; the method proposed in this application does not limit the number of source symbols, that is, the method proposed in this application supports encoding of less than 5 source symbols and also supports encoding of more than 5 source symbols; Fig. 9 In which c1, c2, c3, c4, c5, and c6 are intermediate symbols respectively; the method proposed in the present application does not limit the number of intermediate symbols, that is, the method proposed in the present application supports the output of encodings with less than 6 intermediate symbols, and also supports the output of more than 6 intermediate symbols. At the same time, those skilled in the art should understand that the number of intermediate symbols output is usually not less than the number of source symbols.

[0481] As a sub-embodiment of this embodiment, the intermediate symbol is generated from the source symbol.

[0482] As a sub-embodiment of this embodiment, for any source symbol, the connection line with the intermediate symbol indicates which intermediate symbols the arbitrary source symbol is used to generate. For example, b1 is connected with c1 and c2, indicating that b1 is used to generate c1 and also used to generate c2.

[0483] As a sub-embodiment of this embodiment, for any intermediate symbol, the connection line with the source symbol indicates which source symbols the intermediate symbol is generated from. For example, c2 is connected with b1 and b2, indicating that c2 is generated from b1 and b2.

[0484] As a sub-embodiment of this embodiment, Fig. 9 The connection between the source symbol and the intermediate symbol is equivalent to the encoding matrix. Fig. 9 The connections in are schematic.

[0485] As a sub-embodiment of this embodiment, the source symbol may be encoded into an intermediate symbol using LDCP (Low Density Parity Check Code) encoding.

[0486] As a sub-embodiment of this embodiment, BCH coding may be used to encode source symbols into intermediate symbols.

[0487] As a sub-embodiment of this embodiment, RS coding may be used to encode source symbols into intermediate symbols.

[0488] As an example, Fig. 9 b1, b2, b3, b4, and b5 are intermediate symbols respectively; the method proposed in this application does not limit the number of intermediate symbols, that is, the method proposed in this application supports encoding of less than 5 intermediate symbols and also supports encoding of more than 5 intermediate symbols; Fig. 9 In which c1, c2, c3, c4, c5, and c6 are coding symbols respectively; the method proposed in the present application does not limit the number of coding symbols, that is, the method proposed in the present application supports the output of less than 6 coding symbols, and also supports the output of more than 6 coding symbols. At the same time, those skilled in the art should understand that the number of coding symbols outputted is usually more than the number of intermediate symbols.

[0489] As a sub-embodiment of this embodiment, the encoding symbol is generated by an intermediate symbol.

[0490] As a sub-embodiment of this embodiment, for any intermediate symbol, the line connecting it with the coding symbol indicates which coding symbols the intermediate symbol is used to generate. For example, b1 is connected with c1 and c2, indicating that b1 is used to generate c1 and also used to generate c2.

[0491] As a sub-embodiment of this embodiment, for any coding symbol, the connection line with the intermediate symbols indicates which intermediate symbols the coding symbol is generated from. For example, c2 is connected with b1 and b2, indicating that c2 is generated from b1 and b2.

[0492] As an embodiment, the arbitrary coded symbol is generated from one or several intermediate symbols, including being generated using fountain coding.

[0493] As an embodiment, the arbitrary coded symbol is generated from one or several intermediate symbols including being generated using Raptor coding.

[0494] As an embodiment, the generation of any one of the coding symbols from one or several intermediate symbols includes: performing XOR operation on the intermediate symbols used to generate the any one of the coding symbols, or performing addition in the binary domain to obtain the any one of the coding symbols.

[0495] As an embodiment, the number of intermediate symbols from which any one coding symbol is generated is determined by the degree distribution information.

[0496] As an embodiment, the number of intermediate symbols from which any one coding symbol is generated is determined by a theoretical generation method.

[0497] As an embodiment, the number of intermediate symbols from which any one encoding symbol is generated may be fixed.

[0498] As an embodiment, the number of intermediate symbols from which any one coded symbol is generated may be generated according to a predefined pseudo-random sequence.

[0499] As an embodiment, the intermediate symbols from which any one of the encoding symbols is generated are determined by the degree distribution information.

[0500] As an embodiment, the intermediate symbols from which any one of the encoding symbols is generated are determined by a theoretical generation method.

[0501] As an embodiment, the intermediate symbols from which any one of the coded symbols is generated may be generated according to a predefined pseudo-random sequence.

[0502] As an example, those skilled in the art should understand how to use LDCP encoding.

[0503] As an example, those skilled in the art should understand how to use BCH encoding.

[0504] As an example, those skilled in the art should understand how to use RS encoding.

[0505] As an embodiment, whether the encoding symbol is generated from the source symbol or the intermediate symbol depends on the encoding scheme.

[0506] As a sub-embodiment of this embodiment, when a certain encoding scheme is adopted, it is determined whether to generate from source symbols or from intermediate symbols.

[0507] As a sub-embodiment of this embodiment, when the Raptor code is used, the encoding symbols are generated from the intermediate symbols.

[0508] As a sub-embodiment of this embodiment, when the Raptor code is used, it is necessary to generate intermediate symbols from source symbols, and then generate encoding symbols from the intermediate symbols.

[0509] As a sub-embodiment of this embodiment, when LT code is adopted, the encoding symbols are generated from the source symbols.

[0510] Example 10

[0511] Embodiment 10 illustrates a schematic diagram of using degree distribution information for encoding according to an embodiment of the present application, as shown in the attached figure. Fig.10 shown.

[0512] Attached Fig.10 The method of how to use degree distribution information for encoding is shown.

[0513] Attached Fig.10 The example given is that c1, c2, …, cj, …, ck are generated from b1, b2, …, bi, …, bm.

[0514] As an example, Fig.10 b1, b2, ..., bi, ..., bm in the above code represent m source symbols, wherein m is a positive integer; the method proposed in the present application does not limit the number of source symbols, that is, the method proposed in the present application supports encoding of less than m source symbols and also supports encoding of more than m source symbols; Fig.10 In the above, c1, c2, ..., cj, ..., ck are k coding symbols respectively, wherein k is a positive integer; the method proposed in the present application does not limit the number of coding symbols, that is, the method proposed in the present application supports the output of coding with less than k coding symbols, and also supports the output of more than k coding symbols. At the same time, those skilled in the art should understand that the number of coding symbols outputted is usually more than the number of source symbols.

[0515] As a sub-embodiment of this embodiment, the m depends on the size of the source block.

[0516] As a sub-embodiment of this embodiment, the k depends on the coding scheme and the preset coding rate.

[0517] As a sub-embodiment of this embodiment, the degree distribution of the source symbol in the degree distribution information is used to control the probability of being selected when generating the coding symbol.

[0518] As a sub-embodiment of this embodiment, for any coding symbol that needs to be generated by R source symbols, among the m source symbols, the source symbol with a larger degree distribution has a greater probability of being selected.

[0519] As a sub-embodiment of this embodiment, for any coded symbol that needs to be generated by R source symbols, for any source symbol bi among the m source symbols, the probability of being selected is Di / ∑(Dx), where Di is the degree of the arbitrary source symbol bi, and ∑(Dx) is the sum of the degrees of each source symbol among the m source symbols.

[0520] As a sub-embodiment of this embodiment, a predefined sequence, such as a pseudo-random sequence, is used to select R source symbols from m source symbols to generate encoding symbols, and the degree distribution among the m source symbols is the input of the predefined sequence.

[0521] As a sub-embodiment of this embodiment, a shift register is used to select R source symbols from m source symbols to generate encoding symbols, and the degree distribution of the m source symbols is the input of the shift register.

[0522] As a sub-embodiment of this embodiment, a shift register is used to select R source symbols from m source symbols to generate coding symbols, and the degree distribution of the m source symbols controls the structure of the shift register.

[0523] As a sub-embodiment of this embodiment, the degree distribution of the m source symbols is arranged to form an integer interval, and a random number generated by a random number generator belongs to the integer interval to determine which of the m source symbols is selected. For example, the degrees of b1, b2, and b3 are 2, 1, and 3 respectively, then the integer interval includes [1, 2], [3], [4, 5, 6]. When the random number between 1 and 6 generated by the random number generator belongs to [1, 2], [3], [4, 5, 6], it can be determined which corresponding source symbol is selected. When a coding symbol requires R source symbols to generate, R different random numbers can be generated in sequence, or different R random numbers can be generated simultaneously, and then the source symbol to be selected is determined by the above method.

[0524] As a sub-embodiment of this embodiment, the degree of any one of the k coded symbols is that the any one coded symbol is generated by several source symbols.

[0525] As an embodiment, the degree distribution information includes the degree of any coding symbol among the at least one coding symbol.

[0526] As an embodiment, the degree distribution information includes the degree of any one of the at least one coding symbol that is a repair symbol.

[0527] As an embodiment, the degree of the coding symbol in the at least one coding symbol not included in the degree distribution information is 1.

[0528] As an embodiment, the degree distribution includes the degree of each encoded symbol.

[0529] As an embodiment, the degree distribution includes the degree of each repair symbol.

[0530] As an example, Fig.10b1, b2, ..., bi, ..., bm in the above code represent m intermediate symbols, wherein m is a positive integer; the method proposed in the present application does not limit the number of intermediate symbols, that is, the method proposed in the present application supports encoding of less than m intermediate symbols and also supports encoding of more than m intermediate symbols; Fig.10 In the above, c1, c2, ..., cj, ..., ck are k coding symbols respectively, wherein k is a positive integer; the method proposed in the present application does not limit the number of coding symbols, that is, the method proposed in the present application supports the output of coding with less than k coding symbols, and also supports the output of more than k coding symbols. At the same time, those skilled in the art should understand that the number of coding symbols outputted is usually more than the number of intermediate symbols.

[0531] As a sub-embodiment of this embodiment, the m depends on the size of the intermediate block.

[0532] As a sub-embodiment of this embodiment, the k depends on the coding scheme and the preset coding rate.

[0533] As a sub-embodiment of this embodiment, the degree distribution of the intermediate symbol in the degree distribution information is used to control the probability of being selected when generating the coding symbol.

[0534] As a sub-embodiment of this embodiment, for any coding symbol that needs to be generated by R intermediate symbols, among the m intermediate symbols, the intermediate symbol with a larger degree distribution has a greater probability of being selected.

[0535] As a sub-embodiment of this embodiment, for any coding symbol that needs to be generated by R intermediate symbols, for any intermediate symbol bi among the m intermediate symbols, the probability of being selected is Di / ∑(Dx), where Di is the degree of the arbitrary intermediate symbol bi, and ∑(Dx) is the sum of the degrees of each intermediate symbol among the m intermediate symbols.

[0536] As a sub-embodiment of this embodiment, a predefined sequence, such as a pseudo-random sequence, is used to select R intermediate symbols from m intermediate symbols to generate encoding symbols, and the degree distribution among the m intermediate symbols is the input of the predefined sequence.

[0537] As a sub-embodiment of this embodiment, a shift register is used to select R intermediate symbols from m intermediate symbols to generate encoding symbols, and the degree distribution of the m intermediate symbols is the input of the shift register.

[0538] As a sub-embodiment of this embodiment, a shift register is used to select R intermediate symbols from m intermediate symbols to generate encoding symbols, and the degree distribution of the m intermediate symbols controls the structure of the shift register.

[0539] As a sub-embodiment of this embodiment, the degree distribution of the m intermediate symbols is arranged to form an integer interval, and a random number generated by a random number generator belongs to the integer interval to determine which of the m intermediate symbols is selected. For example, the degrees of b1, b2, and b3 are 2, 1, and 3 respectively, then the integer interval includes [1, 2], [3], [4, 5, 6]. When the random number between 1 and 6 generated by the random number generator belongs to [1, 2], [3], [4, 5, 6], it can be determined which corresponding intermediate symbol is selected. When a coded symbol requires R intermediate symbols to be generated, R different random numbers can be generated in sequence, or different R random numbers can be generated simultaneously, and then the intermediate symbol to be selected is determined by the above method.

[0540] As a sub-embodiment of this embodiment, the degree of any coding symbol among the k coding symbols is that the any coding symbol is generated by several intermediate symbols.

[0541] As an embodiment, the degree distribution information includes the degree of any coding symbol among the at least one coding symbol.

[0542] As an embodiment, the degree distribution information includes the degree of any one of the at least one coding symbol that is a repair symbol.

[0543] As an embodiment, the degree of the coding symbol in the at least one coding symbol not included in the degree distribution information is 1.

[0544] As an embodiment, the degree distribution includes the degree of each encoded symbol.

[0545] As an embodiment, the degree distribution includes the degree of each repair symbol.

[0546] Embodiment 11

[0547] Embodiment 11 illustrates a schematic diagram of using degree distribution information for decoding according to an embodiment of the present application, as shown in the attached figure. Fig.11 shown.

[0548] Attached Fig.11 The method of how to use the degree distribution information for decoding is shown.

[0549] Attached Fig.10 The example given is that c1, c2, …, cj, …, ck recovers b1, b2, …, bi, …, bm.

[0550] As an embodiment, the degree distribution information includes at least degree information.

[0551] As an embodiment, the degree distribution information includes at least the distribution of degrees.

[0552] As an embodiment, decoding is the inverse process of encoding.

[0553] As an embodiment, the decoder can implement the inverse operation of the encoder through at least one of a predefined generation matrix, a coding function, a sequence, a random number generator, and a shift register.

[0554] As an embodiment, through at least one of the predefined generation matrix, encoding function, sequence, random number generator, and shift register, the decoder can grasp the encoding process of the encoder, that is, how each encoding symbol is generated.

[0555] As an embodiment, when it is known how each coding symbol is generated, the decoder can recover the non-repair symbol used to generate the coding symbol from the coding symbol using the existing decoding method.

[0556] As an embodiment, the existing decoding method is, for example, a decoding method for linear block codes.

[0557] As an embodiment, the meaning of understanding how each encoding symbol is generated includes understanding the pseudo-random number generator used by the encoder, that is, for the decoder, the output of the pseudo-random number generator is deterministic rather than random.

[0558] As an example, Fig.11 b1, b2, ..., bi, ..., bm in the above code represent m source symbols, wherein m is a positive integer; the method proposed in the present application does not limit the number of source symbols, that is, the method proposed in the present application supports encoding of less than m source symbols and also supports encoding of more than m source symbols; Fig.11 In the above, c1, c2, ..., cj, ..., ck are k coding symbols respectively, wherein k is a positive integer; the method proposed in the present application does not limit the number of coding symbols, that is, the method proposed in the present application supports the output of coding with less than k coding symbols, and also supports the output of more than k coding symbols. At the same time, those skilled in the art should understand that the number of coding symbols outputted is usually more than the number of source symbols.

[0559] As a sub-embodiment of this embodiment, the m depends on the size of the source block.

[0560] As a sub-embodiment of this embodiment, the k depends on the coding scheme and the preset coding rate.

[0561] As a sub-embodiment of this embodiment, the encoder indicates at least one of the m and the k to the terminal.

[0562] As a sub-embodiment of this embodiment, the degree distribution of the source symbols in the degree distribution information is used to determine which source symbols are selected when generating the encoding symbols.

[0563] As a sub-embodiment of this embodiment, for any coding symbol that needs to be generated by R source symbols, among the m source symbols, the source symbol with a larger degree distribution has a greater probability of being selected.

[0564] As a sub-embodiment of this embodiment, for any coded symbol that needs to be generated by R source symbols, for any source symbol bi among the m source symbols, the probability of being selected is Di / ∑(Dx), where Di is the degree of the arbitrary source symbol bi, and ∑(Dx) is the sum of the degrees of each source symbol among the m source symbols.

[0565] As a sub-embodiment of this embodiment, the terminal and the encoder use the same sequence and the degree distribution information to determine which source symbols any encoded symbol is generated from.

[0566] As a sub-embodiment of this embodiment, the same sequence includes a pseudo-random sequence.

[0567] As a sub-embodiment of this embodiment, the degree distribution is an input or initial value of the predefined sequence.

[0568] As a sub-embodiment of this embodiment, the terminal and the encoder use the same shift register and the degree distribution information to determine which source symbols any encoded symbol is generated from.

[0569] As a sub-embodiment of this embodiment, the shift register is used to determine R source symbols for generating any encoded symbol from m source symbols, and the degree distribution of the m source symbols is the input of the shift register.

[0570] As a sub-embodiment of this embodiment, the degree distribution controls the structure of the shift register.

[0571] As a sub-embodiment of this embodiment, the degree distribution of the m source symbols is arranged to form an integer interval, and a random number generated by a random number generator belongs to the integer interval to determine which of the m source symbols is selected. For example, the degrees of b1, b2, and b3 are 2, 1, and 3 respectively, then the integer interval includes [1, 2], [3], [4, 5, 6]. When the random number between 1 and 6 generated by the random number generator belongs to [1, 2], [3], [4, 5, 6], it can be determined which corresponding source symbol is selected. When a coding symbol requires R source symbols to generate, R different random numbers can be generated in sequence, or different R random numbers can be generated simultaneously, and then the source symbol to be selected is determined by the above method.

[0572] As a sub-embodiment of this embodiment, the terminal and the encoder use the same pseudo-random generator to generate the above-mentioned pseudo-random numbers.

[0573] As an embodiment, the degree distribution information includes the degree of any coding symbol among the at least one coding symbol.

[0574] As an embodiment, the degree distribution information includes the degree of any one of the at least one coding symbol that is a repair symbol.

[0575] As an embodiment, the degree of the coding symbol in the at least one coding symbol not included in the degree distribution information is 1.

[0576] As an embodiment, the degree distribution includes the degree of each encoded symbol.

[0577] As an embodiment, the degree distribution includes the degree of each repair symbol.

[0578] As an example, Fig.11 b1, b2, ..., bi, ..., bm in the above code represent m intermediate symbols, wherein m is a positive integer; the method proposed in the present application does not limit the number of intermediate symbols, that is, the method proposed in the present application supports encoding of less than m intermediate symbols and also supports encoding of more than m intermediate symbols; Fig.11 In the above, c1, c2, ..., cj, ..., ck are k coding symbols respectively, wherein k is a positive integer; the method proposed in the present application does not limit the number of coding symbols, that is, the method proposed in the present application supports the output of coding with less than k coding symbols, and also supports the output of more than k coding symbols. At the same time, those skilled in the art should understand that the number of coding symbols outputted is usually more than the number of intermediate symbols.

[0579] As a sub-embodiment of this embodiment, the m depends on the size of the source block.

[0580] As a sub-embodiment of this embodiment, the k depends on the coding scheme and the preset coding rate.

[0581] As a sub-embodiment of this embodiment, the encoder indicates at least one of the m and the k to the terminal.

[0582] As a sub-embodiment of this embodiment, the degree distribution of the intermediate symbols in the degree distribution information is used to determine which intermediate symbols are selected when generating the encoding symbol.

[0583] As a sub-embodiment of this embodiment, for any coding symbol that needs to be generated by R intermediate symbols, among the m intermediate symbols, the intermediate symbol with a larger degree distribution has a greater probability of being selected.

[0584] As a sub-embodiment of this embodiment, for any coding symbol that needs to be generated by R intermediate symbols, for any intermediate symbol bi among the m intermediate symbols, the probability of being selected is Di / ∑(Dx), where Di is the degree of the arbitrary intermediate symbol bi, and ∑(Dx) is the sum of the degrees of each intermediate symbol among the m intermediate symbols.

[0585] As a sub-embodiment of this embodiment, the terminal and the encoder use the same sequence and the degree distribution information to determine which intermediate symbols any encoded symbol is generated from.

[0586] As a sub-embodiment of this embodiment, the same sequence includes a pseudo-random sequence.

[0587] As a sub-embodiment of this embodiment, the degree distribution is an input or initial value of the predefined sequence.

[0588] As a sub-embodiment of this embodiment, the terminal and the encoder use the same shift register and the degree distribution information to determine which intermediate symbols any encoded symbol is generated from.

[0589] As a sub-embodiment of this embodiment, the shift register is used to determine R intermediate symbols for generating any encoded symbol from m intermediate symbols, and the degree distribution of the m intermediate symbols is the input of the shift register.

[0590] As a sub-embodiment of this embodiment, the degree distribution controls the structure of the shift register.

[0591] As a sub-embodiment of this embodiment, the degree distribution of the m intermediate symbols is arranged to form an integer interval, and a random number generated by a random number generator belongs to the integer interval to determine which of the m intermediate symbols is selected. For example, the degrees of b1, b2, and b3 are 2, 1, and 3 respectively, then the integer interval includes [1, 2], [3], [4, 5, 6]. When the random number between 1 and 6 generated by the random number generator belongs to [1, 2], [3], [4, 5, 6], it can be determined which corresponding intermediate symbol is selected. When a coded symbol requires R intermediate symbols to be generated, R different random numbers can be generated in sequence, or different R random numbers can be generated simultaneously, and then the intermediate symbol to be selected is determined by the above method.

[0592] As a sub-embodiment of this embodiment, the terminal and the encoder use the same pseudo-random generator to generate the above-mentioned pseudo-random numbers.

[0593] As an embodiment, the degree distribution information includes the degree of any coding symbol among the at least one coding symbol.

[0594] As an embodiment, the degree distribution information includes the degree of any one of the at least one coding symbol that is a repair symbol.

[0595] As an embodiment, the degree of the coding symbol in the at least one coding symbol not included in the degree distribution information is 1.

[0596] As an embodiment, the degree distribution includes the degree of each encoded symbol.

[0597] As an embodiment, the degree distribution includes the degree of each repair symbol.

[0598] Example 12

[0599] Embodiment 12 illustrates a schematic diagram of associating degree distribution information with temperature according to an embodiment of the present application, as shown in the attached figure. Fig.12 shown.

[0600] As an example, what is the temperature of AI is common knowledge in the art.

[0601] As an example, the temperature of the AI ​​is used to measure uncertainty.

[0602] As an embodiment, the temperature is used for AI-based decoding.

[0603] As an embodiment, the higher the degree of the source symbol in the degree distribution information, the lower the temperature of the associated AI.

[0604] As an embodiment, the lower the degree of the source symbol in the degree distribution information, the higher the temperature of the associated AI.

[0605] As an embodiment, the higher the degree of the middle symbol in the degree distribution information, the lower the temperature of the associated AI.

[0606] As an embodiment, the lower the degree of the middle symbol in the degree distribution information, the higher the temperature of the associated AI.

[0607] As an embodiment, the higher the degree of the coding symbol in the degree distribution information, the higher the temperature of the associated AI.

[0608] As an embodiment, the lower the degree of the coding symbol in the degree distribution information, the lower the temperature of the associated AI.

[0609] As an embodiment, the AI ​​decoding process is a process of continuously lowering the temperature.

[0610] As an embodiment, the AI ​​decoding process is a process of continuously reducing the temperature of a source block or a source symbol.

[0611] As an embodiment, the association in this embodiment includes mapping.

[0612] As an embodiment, when the temperature ranges from 0 to 1, the distribution probability in the degree distribution information is mapped to the temperature.

[0613] As an embodiment, when the temperature ranges from 0 to 1, the ratio of the distribution in the degree distribution information to the maximum degree is mapped to the temperature.

[0614] As an embodiment, when the temperature ranges from 0 to 1, the ratio of the sum of the distribution and the degree in the degree distribution information is mapped to the temperature.

[0615] Example 13

[0616] Embodiment 13 illustrates a structural block diagram of a processing device used in a terminal according to an embodiment of the present application; Fig.13 As shown in the attached Fig.13 In the embodiment, the processing device 1300 in the terminal includes a first receiver 1301, a first transmitter 1302 and a first processor 1303.

[0617] In embodiment 13, the terminal includes: one or more processors and a memory;

[0618] The memory is coupled to the one or more processors, and the memory is used to store computer program codes, wherein the computer program codes include computer instructions, and the one or more processors call the computer instructions to cause the terminal to execute at least:

[0619] sending a first message, the first message indicating at least degree distribution information, the degree distribution information being used for encoding of at least one encoding symbol;

[0620] receiving the at least one coded symbol;

[0621] The degree distribution information includes at least one of the degree distribution of the repair symbol and the degree distribution of the non-repair symbol, wherein the non-repair symbol is a source symbol or an intermediate symbol, and the intermediate symbol is generated by the source symbol; the receiving of the at least one encoded symbol includes recovering the corresponding source symbol from the at least one encoded symbol; the at least one encoded symbol includes at least a repair symbol.

[0622] As an embodiment, recovering the corresponding source symbol from the at least one encoded symbol includes recovering the corresponding source symbol from the at least one encoded symbol using the degree distribution information.

[0623] As an embodiment, the encoding of the at least one encoding symbol belongs to a fountain code.

[0624] As an embodiment, the encoding of the at least one encoding symbol belongs to the Raptor code in the fountain code.

[0625] As an embodiment, the degree distribution information is associated with the temperature set in the AI.

[0626] As an embodiment, the degree distribution information includes a distribution probability of at least one degree.

[0627] As an embodiment, compared with the theoretical degree distribution, the degree distribution information indicates that the probability of a larger degree is higher.

[0628] As an embodiment, the degree distribution information includes a degree distribution of non-repair symbols that depends on an expectation that a first segment is successfully decoded, wherein the first segment includes source symbols that at least partially generate the at least one encoded symbol.

[0629] As an embodiment, the first receiver 1301 receives first association information, where the first association information indicates which encoding symbols of the at least one encoding symbol the first encoding symbol is associated with in terms of content or semantics.

[0630] As an embodiment, the terminal is a user equipment (UE).

[0631] As an embodiment, the terminal is a mobile phone.

[0632] As an embodiment, the terminal is a communication device supporting low latency.

[0633] As an embodiment, the terminal is an industrial communication device.

[0634] As an embodiment, the terminal is an Internet of Things terminal or an industrial Internet of Things terminal.

[0635] As an embodiment, the first receiver 1301 includes at least one of the antenna 452, receiver 454, receiving processor 456, multi-antenna receiving processor 458, controller / processor 459, memory 460, or data source 467 in Example 4.

[0636] As an embodiment, the first transmitter 1302 includes at least one of the antenna 452, transmitter 454, transmit processor 468, multi-antenna transmit processor 457, controller / processor 459, memory 460, or data source 467 in Embodiment 4.

[0637] A person of ordinary skill in the art can understand that all or part of the steps in the above method can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a hard disk or an optical disk. Optionally, all or part of the steps in the above embodiment can also be implemented using one or more integrated circuits. Accordingly, each module unit in the above embodiment can be implemented in the form of hardware or in the form of a software function module, and the present application is not limited to any specific form of software and hardware combination. The user equipment, terminal and UE in the present application include but are not limited to drones, communication modules on drones, remote-controlled aircraft, aircraft, small aircraft, mobile phones, tablet computers, notebooks, vehicle-mounted communication equipment, wireless sensors, Internet cards, Internet of Things terminals, RFID terminals, NB-IoT terminals, MTC (Machine Type Communication, machine type communication) terminals, eMTC (enhancedMTC, enhanced MTC) terminals, data cards, Internet cards, vehicle-mounted communication equipment, low-cost mobile phones, low-cost tablet computers, satellite communication equipment, ship communication equipment, NTN user equipment and other wireless communication equipment. The base stations or system equipment in this application include but are not limited to macrocell base stations, microcell base stations, home base stations, relay base stations, gNB (NR Node B) NR Node B, TRP (Transmitter Receiver Point), NTN base stations, satellite equipment, flight platform equipment and other wireless communication equipment.

[0638] The present invention may be implemented in other specified forms without departing from its core or essential features. Therefore, the embodiments disclosed herein should be considered as illustrative rather than restrictive in any way. The scope of the invention is determined by the appended claims rather than the preceding description, and all modifications within their equivalent meanings and regions are considered to be included therein.

Claims

1. A method in a terminal, wherein: include: sending a first message, the first message indicating at least degree distribution information, the degree distribution information being used for encoding of at least one encoding symbol; receiving the at least one coded symbol; The degree distribution information includes at least one of the degree distribution of the repair symbol and the degree distribution of the non-repair symbol, wherein the non-repair symbol is a source symbol or an intermediate symbol, and the intermediate symbol is generated by the source symbol; the receiving of the at least one encoded symbol includes recovering the corresponding source symbol from the at least one encoded symbol; the at least one encoded symbol includes at least a repair symbol.

2. The method in the terminal according to claim 1, characterized in that: The recovering the corresponding source symbol from the at least one encoded symbol comprises recovering the corresponding source symbol from the at least one encoded symbol using the degree distribution information.

3. The method in the terminal according to claim 1 or 2, characterized in that: The encoding of the at least one encoding symbol belongs to a fountain code.

4. The method in the terminal according to claim 3, characterized in that: The encoding of the at least one encoding symbol belongs to a Raptor code in a fountain code.

5. The method in a terminal according to any one of claims 1 to 4, characterized in that: The degree distribution information is associated with the temperature set in the AI.

6. The method in a terminal according to any one of claims 1 to 5, characterized in that: The degree distribution information includes a distribution probability of at least one degree.

7. The method in a terminal according to any one of claims 1 to 6, characterized in that: Compared to the theoretical degree distribution, the degree distribution information indicates that the probability of a larger degree is higher.

8. The method in a terminal according to any one of claims 1 to 7, characterized in that: The degree distribution information includes a degree distribution of non-repair symbols dependent on a desire to successfully decode a first segment including source symbols that at least partially generated the at least one encoded symbol.

9. The method in a terminal according to any one of claims 1 to 8, characterized in that: include: First association information is received, the first association information indicating which encoding symbols of the at least one encoding symbol the first encoding symbol is content- or semantically-associated with.

10. A terminal, wherein: include: one or more processors and memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, where the computer program codes include computer instructions. The one or more processors call the computer instructions to enable the terminal to execute the method according to any one of claims 1 to 9.

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