Communication method and device

Abstract

The invention discloses a communication method and device. The method comprises the following steps: receiving a first data packet; wherein the data carried by the first data packet can be decoded by M neural network layers, M is a positive integer, a second data packet and first indication information are sent, the second data packet is obtained by decoding the data carried by the first data packet through K neural network layers, and the data carried by the second data packet can be decoded by M-K neural network layers, k is a positive integer and is less than M; the K neural network layers belong to the M neural network layers; the first indication information indicates that decoding is carried out through the K neural network layers or decoding is not carried out through the M-K neural network layers. By adopting the method, the first equipment can execute decoding of part of the neural network layer firstly, and since the first equipment has stronger neural network computing power, the decoding efficiency can be improved, and the end-to-end delay of the data packet can be ensured.

Description

Technical Field

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

[0002] The encoding and decoding method based on artificial intelligence (AI) feature stream can reduce the transmission rate of streaming media communications represented by extended reality (XR) services. The encoding and decoding method of AI feature streams currently used in industry research has a large parameter scale, and the number of parameters is generally in the millions or even tens of millions.

[0003] On the one hand, terminal devices are limited by volume and usually cannot deploy powerful computing units, so it is difficult to complete large-scale neural network calculations. On the other hand, XR services have strict end-to-end latency requirements. Generally speaking, from the time the server starts rendering the XR video frame to the time the terminal device displays the screen corresponding to the XR video frame, the latency needs to be controlled within 70ms, where the latency is decomposed into the time for terminal device decoding, which is usually around 10 to 20ms. Therefore, the computing power level of the terminal device is also difficult to complete the encoding of the AI ​​feature stream within the latency requirements, resulting in a poor user service experience. Summary of the invention

[0004] The embodiments of the present application provide a communication method and apparatus to solve the problem that the computing power of a terminal device is limited and cannot efficiently complete the encoding and decoding of an AI feature stream, thereby resulting in a poor user experience.

[0005] In a first aspect, the present application provides a communication method, which can be executed by a first device or a module (such as a chip) in the first device. For example, the first device can be an access network device or a core network element. The method includes: receiving a first data packet; wherein the data carried by the first data packet can be decoded by M neural network layers, M is a positive integer; sending a second data packet and a first indication message, wherein the second data packet is obtained by decoding the data carried by the first data packet through K neural network layers, and the data carried by the second data packet can be decoded by MK neural network layers, K is a positive integer, K<M, and the K neural network layers belong to the M neural network layers; the first indication message indicates that it has been decoded by the K neural network layers, or has not been decoded by the MK neural network layers.

[0006] By using the above method, the first device can first perform decoding of part of the neural network layer, which can reduce the dependence on the neural network computing capability of the terminal device and save the power consumption and cost of the terminal device. Compared with the terminal device, the first device has stronger neural network computing capability, which can improve the decoding efficiency, reduce the transmission delay of the data packet, and improve the user service experience.

[0007] Among them, the data carried by the first data packet can be decoded by M neural network layers, which can also be understood as, the data carried by the first data packet can be decoded by M neural network layers, or the data carried by the first data packet can be decoded by M neural network layers, or the data carried by the first data packet needs to be decoded by M neural network layers.

[0008] The data carried by the second data packet can be decoded by MK neural network layers, which can also be understood as, the data carried by the second data packet can be decoded by MK neural network layers, or the data carried by the second data packet can be decoded by MK neural network layers, or the data carried by the second data packet needs to be decoded by MK neural network layers.

[0009] In one possible design, the first data packet includes second indication information, and the second indication information indicates that the data carried by the first data packet can be decoded by a neural network. It can also be understood that the second indication information indicates that the data carried by the first data packet can be decoded by a neural network. The above design can be used to indicate the encoding and decoding method of the first data packet.

[0010] In one possible design, third indication information is received, and the third indication information indicates that the data carried by the first data packet can be decoded by the neural network. It can also be understood that the third indication information indicates that the data carried by the first data packet can be decoded by the neural network. The above design can be used to indicate the encoding and decoding method of the first data packet.

[0011] In one possible design, fourth indication information is received, and the fourth indication information indicates a mapping relationship between the M neural network layers and the N decoding subtasks, wherein each of the N decoding subtasks corresponds to at least one of the M neural network layers, and N is a positive integer; the second data packet is obtained by executing S of the N decoding subtasks on the data carried by the first data packet, and the neural network layers corresponding to the S decoding subtasks are the K neural network layers, S is a positive integer, and S<N. The above design can be used to implement the execution of some decoding subtasks.

[0012] In one possible design, the first indication information indicates that the S decoding subtasks have been completed, or that the NS decoding subtasks have not been completed, the neural network layers corresponding to the NS decoding subtasks are the MK neural network layers, and the NS decoding subtasks are the decoding subtasks of the N decoding subtasks excluding the S decoding subtasks. The above design can be used to indicate completed decoding subtasks or unfinished decoding subtasks.

[0013] In one possible design, the K neural network layers are determined based on capability information and / or channel state information of the terminal device, wherein the capability information of the terminal device is used to indicate the neural network computing capability of the terminal device.

[0014] It can also be understood that before the data carried by the first data packet is decoded through K neural network layers, the capability information and / or channel state information of the terminal device is obtained, wherein the capability information of the terminal device is used to indicate the neural network computing capability of the terminal device; the K neural network layers are determined according to the capability information and / or channel state information of the terminal device. The above design can be used to determine which neural network layers need to be decoded.

[0015] In a second aspect, the present application provides a communication method, which can be executed by a terminal device or a module (such as a chip) in the terminal device. The method includes: receiving a second data packet and a first indication information, the data carried by the second data packet can be decoded by MK neural network layers, the K neural network layers belong to M neural network layers, M and K are positive integers, K<M; the first indication information indicates that it has been decoded by the K neural network layers, or has not been decoded by the MK neural network layers; the data carried by the second data packet is decoded according to the first indication information to obtain service data.

[0016] By adopting the above method, the terminal device performs decoding of the remaining neural network layers, thereby reducing the dependence on the neural network computing power of the terminal device, saving the power consumption and cost of the terminal device, and improving the user service experience.

[0017] In one possible design, fourth indication information is received, wherein the fourth indication information indicates a mapping relationship between M neural network layers and N decoding subtasks, wherein each of the N decoding subtasks corresponds to at least one of the M neural network layers, and N is a positive integer. With the above design, the terminal device can obtain a mapping relationship between the M neural network layers and the N decoding subtasks.

[0018] In one possible design, the first indication information indicates that S decoding subtasks have been completed, and the neural network layers corresponding to the S decoding subtasks are the K neural network layers, S is a positive integer, S<N, or the first indication information indicates that NS decoding subtasks are not completed, and the neural network layers corresponding to the NS decoding subtasks are the MK neural network layers. The above design can be used to enable the terminal device to obtain information about completed decoding subtasks or unfinished decoding subtasks.

[0019] In a third aspect, the present application provides a communication device, comprising: a processing unit and a transceiver unit; the transceiver unit is used to send and receive information; the processing unit is used to receive a first data packet through the transceiver unit; wherein the data carried by the first data packet can be decoded by M neural network layers, M is a positive integer; and the second data packet and first indication information are sent through the transceiver unit, wherein the second data packet is obtained by decoding the data carried by the first data packet through K neural network layers, and the data carried by the second data packet can be decoded by MK neural network layers, K is a positive integer, K<M; the K neural network layers belong to the M neural network layers; the first indication information indicates that it has been decoded by the K neural network layers, or has not been decoded by the MK neural network layers.

[0020] In one possible design, the first data packet includes second indication information, and the second indication information indicates that data carried by the first data packet can be decoded by a neural network.

[0021] In one possible design, the transceiver unit is used to receive third indication information, and the third indication information indicates that the data carried by the first data packet can be decoded by a neural network.

[0022] In one possible design, the transceiver unit is used to receive fourth indication information, and the fourth indication information indicates a mapping relationship between the M neural network layers and the N decoding subtasks, wherein each of the N decoding subtasks corresponds to at least one of the M neural network layers, and N is a positive integer; the second data packet is obtained by executing S of the N decoding subtasks on the data carried by the first data packet, and the neural network layers corresponding to the S decoding subtasks are the K neural network layers, S is a positive integer, and S<N.

[0023] In one possible design, the first indication information indicates that the S decoding subtasks have been completed, or that the NS decoding subtasks are not completed, the neural network layers corresponding to the NS decoding subtasks are the MK neural network layers, and the NS decoding subtasks are the decoding subtasks among the N decoding subtasks except the S decoding subtasks.

[0024] In one possible design, the K neural network layers are determined based on capability information and / or channel state information of the terminal device, wherein the capability information of the terminal device is used to indicate the neural network computing capability of the terminal device.

[0025] In a fourth aspect, the present application provides a communication device, comprising: a processing unit and a transceiver unit; the transceiver unit is used to receive a second data packet and a first indication information, the data carried by the second data packet can be decoded by MK neural network layers, the K neural network layers belong to M neural network layers, M and K are positive integers, K<M; the first indication information indicates that it has been decoded by the K neural network layers, or has not been decoded by the MK neural network layers; the processing unit is used to decode the data carried by the second data packet according to the first indication information to obtain business data.

[0026] In one possible design, the transceiver unit is used to receive fourth indication information, where the fourth indication information indicates a mapping relationship between M neural network layers and N decoding subtasks, wherein each of the N decoding subtasks corresponds to at least one of the M neural network layers, and N is a positive integer.

[0027] In one possible design, the first indication information indicates that S decoding subtasks have been completed, and the neural network layers corresponding to the S decoding subtasks are the K neural network layers, S is a positive integer, S<N, or the first indication information indicates that NS decoding subtasks are not completed, and the neural network layers corresponding to the NS decoding subtasks are the MK neural network layers.

[0028] In a fifth aspect, the present application provides a communication system, comprising a server, a core network network element and a terminal device; the server is used to generate a first data packet and send the first data packet to the core network network element, wherein the data carried by the first data packet can be decoded by M neural network layers, M is a positive integer; the core network network element is used to receive the first data packet from the server, and send the second data packet and a first indication information; the second data packet is obtained by decoding the data carried by the first data packet through K neural network layers, wherein the K neural network layers belong to the M neural network layers, and the data carried by the second data packet can be decoded by MK neural network layers, K is a positive integer, K<M; the first indication information indicates that it has been decoded by the K neural network layers, or has not been decoded by the MK neural network layers; the terminal device is used to receive the second data packet and the first indication information from the core network network element, and decode the data carried by the second data packet according to the first indication information to obtain service data.

[0029] In a sixth aspect, the present application provides a communication system, comprising a server, a core network network element, an access network device and a terminal device; the server is used to generate a third data packet and send the third data packet to the core network network element, wherein the data carried by the third data packet can be decoded by M neural network layers, and M is a positive integer; the core network network element is used to receive the third data packet from the server, generate a first data packet based on the third data packet, the data carried by the first data packet is the same as the data carried by the third data packet, and the data carried by the first data packet can be decoded by the M neural network layers; the access network device is used to receive the third data packet from the core network network element. A data packet, and sending the second data packet and the first indication information; the second data packet is obtained by decoding the data carried by the first data packet through K neural network layers, wherein the K neural network layers belong to the M neural network layers, and the data carried by the second data packet can be decoded by MK neural network layers, K is a positive integer, K<M; the first indication information indicates that it has been decoded by the K neural network layers, or has not been decoded by the MK neural network layers; the terminal device is used to receive the second data packet and the first indication information from the access network device, and decode the data carried by the second data packet according to the first indication information to obtain service data.

[0030] In the seventh aspect, the present application provides a communication device, which may be a first device, or a module or unit (for example, a chip, or a chip system, or a circuit) in the first device that corresponds one-to-one to the method / operation / step / action described in any one of the first to second aspects, or may be capable of being used in combination with the first device.

[0031] In an eighth aspect, the present application provides a communication device comprising at least one processing element and at least one storage element, wherein the at least one storage element is used to store programs and data, and the at least one processing element is used to read and execute the programs and data stored in the storage element, so that any method described in any one of the above aspects of the present application is implemented.

[0032] In a ninth aspect, the present application further provides a computer program, which, when executed on a computer, enables the computer to execute any of the methods described in any of the above aspects.

[0033] In the tenth aspect, the present application provides a communication device, comprising: an interface circuit and at least one processor; the interface circuit is used to provide the at least one processor with input and / or output of programs or instructions; the at least one processor is used to execute the program or instructions so that the communication device can implement any of the methods described in any of the above aspects.

[0034] In a possible manner, the communication device includes the at least one memory, and the at least one memory is used to store the program or instruction.

[0035] In an eleventh aspect, the present application provides a computer storage medium storing a software program, which, when read and executed by one or more processors, can implement any of the methods described in any of the above aspects.

[0036] In a twelfth aspect, the present application provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute any of the methods described in any of the above aspects.

[0037] In a thirteenth aspect, the present application provides a chip system, comprising at least one chip and a memory, wherein the at least one chip is used to read and execute a program stored in the memory to implement any of the methods described in any of the above aspects.

[0038] Based on the implementations provided in the above aspects, the present application can also be further combined to provide more implementations. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0040] Figure 1 A schematic diagram of the transmission process of an XR video frame is shown;

[0041] Figure 2 An architectural diagram of a communication system is shown;

[0042] Figure 3A A schematic diagram showing a system architecture 1 to which the present application may be applied;

[0043] Figure 3B A schematic diagram showing a system architecture 2 to which the present application may be applied;

[0044] Figure 3C A schematic diagram showing a system architecture 3 to which the present application may be applied;

[0045] Figure 4 A possible flow chart of a communication method provided in an embodiment of the present application is shown;

[0046] Figure 5A shows one of the schematic diagrams of data packet transmission and decoding process;

[0047] Figure 5B One of the specific flow charts of data packet transmission and decoding is shown;

[0048] Fig. 6A The second schematic diagram of the data packet transmission and decoding process is shown;

[0049] Figure 6B The second specific flow chart of data packet transmission and decoding is shown;

[0050] Figure 7 This is a schematic diagram of the structure of a communication device in this application;

[0051] Figure 8 This is a schematic diagram of the structure of another communication device in this application. DETAILED DESCRIPTION

[0052] The specific implementation of the application is described below by way of example in conjunction with the accompanying drawings in the embodiments of the application. However, the implementation of the application may also include combining these embodiments without departing from the spirit or scope of the application, such as adopting other embodiments and making structural changes. Therefore, the detailed description of the following embodiments should not be understood in a restrictive sense. The terms used in the embodiments of the application are only used to explain the specific embodiments of the application, and are not intended to limit the application.

[0053] In recent years, with the continuous development of the fifth generation (5G) communication system, data transmission latency has been continuously reduced and the transmission capacity has been increasing. 5G communication systems have gradually infiltrated some multimedia services with strong real-time requirements and large data capacity requirements, such as video transmission, cloud gaming (CG) and XR, among which XR includes virtual reality (VR) and augmented reality (AR).

[0054] With the rapid increase in communication transmission rates, real-time video transmission services have gradually become one of the core services in the current network. With the continuous advancement and improvement of extended reality technology, related industries have also been booming. Today, XR technology has entered various fields closely related to people's production and life, such as education, entertainment, military, medical care, environmental protection, transportation, and public health. Compared with traditional video services, XR has the advantages of multiple perspectives and strong interactivity, providing users with a new visual experience.

[0055] The core idea of ​​the encoding and decoding method based on AI feature stream is to solve the expression and transmission of information meaning at the feature stream level (semantic level), and partially or completely pre-position the understanding of the meaning of information to the sending end, thereby reducing the transmission volume and reducing the demand for transmission bandwidth. In the current XR business transmission architecture, the media encoding and decoding functions of the source are performed by the cloud server and the terminal device respectively, and the network is only responsible for the transmission function. The communication architecture using the encoding and decoding method of AI feature stream is shown in 1.

[0056] like Figure 1 As shown, the server encodes the business data of the XR video frame through AI feature stream encoding (or AI feature stream encoding neural network) to assemble multiple Internet protocol (IP) packets, such as 50 IP packets, and then transmits them through the fixed network / core network to the radio access network (RAN), that is, the base station side, and then transmits them to the terminal device through the wireless air interface. The terminal device decodes the encoded data in the IP packet through AI feature stream decoding (or AI feature stream decoding neural network) to restore the business data of the XR video frame and display it through the display device. Table 1 shows the types of neural network layers in the AI ​​feature stream encoding and decoding neural network as well as the related functions and characteristics.

[0057] Table 1

[0058]

[0059]

[0060] It should be noted that the encoding and decoding method involved in the embodiments of the present application refers to encoding and decoding through a neural network, wherein the encoding neural network can be used to encode the business data to obtain the encoded data, and the decoding neural network can be used to decode the encoded data to obtain the business data. The encoding and decoding methods involved in the embodiments of the present application include but are not limited to the encoding and decoding methods of the AI ​​feature stream, and the following content is only described by taking the AI ​​feature stream encoding method as an example.

[0061] Exemplarily, the encoding neural network can be referred to as the encoding network or editor. Exemplarily, the encoding neural network can be an AI feature stream encoding neural network, or an AI encoder, etc., which is not limited in this application. Generally, the encoding neural network includes multiple neural network layers. Exemplarily, the front end of the encoding neural network is usually a convolutional layer and a pooling layer, the purpose of which is to extract the feature information of the data frame and reduce the redundancy between the business data carried by the data frame. The back end is usually a fully connected layer and an activation layer, wherein the fully connected layer is used to further classify the extracted feature information. The role of the activation layer is to introduce nonlinearity, and through nonlinearity, the neural network approximates any function, thereby solving complex real-world problems.

[0062] The decoding neural network can be referred to as a decoding network or a decoder. Exemplarily, the decoding neural network can be an AI feature stream decoding neural network, or an AI decoder, etc., which is not limited in this application. Generally, the decoding neural network includes multiple neural network layers. The function of the decoding neural network is opposite to that of the encoding neural network. The structure of the decoding neural network is symmetrical to that of the encoding neural network. The front end is the fully connected layer and the activation layer, and the back end is the convolution layer and the pooling layer. Generally speaking, the closer to the end of the decoder, the greater the amount of data generated.

[0063] It should be noted that the number of neural network layers included in the encoding neural network and the number of neural network layers included in the decoding neural network may be the same or different, and this application does not limit this.

[0064] The embodiments of the present application can be applied to various communication systems, such as: long term evolution (LTE) system, LTE frequency division duplex (FDD) system, LTE time division duplex (TDD), fifth generation (5G) system or new radio (NR), or to future communication systems or other similar communication systems.

[0065] like Figure 2The figure shows an architecture diagram of a 5G communication system developed by the 3rd Generation Partnership Project (3GPP) standard. Figure 2 The 5G network architecture shown may include terminal equipment, access network (AN) equipment and core network elements. The terminal equipment accesses the data network (DN) through the access network equipment and the core network elements.

[0066] The access network device may be a radio access network (RAN) device. For example: a base station, an evolved NodeB (eNodeB), a transmission reception point (TRP), a next generation NodeB (gNB) in a 5G mobile communication system, a next generation base station in a sixth generation (6G) mobile communication system, a base station in a future mobile communication system, or an access node in a wireless fidelity (WiFi) system, etc.; it may also be a module or unit that completes part of the functions of a base station, for example, a centralized unit (CU) or a distributed unit (DU). The radio access network device may be a macro base station, a micro base station or an indoor station, a relay node or a donor node, etc. The embodiments of the present application do not limit the specific technology and specific device form adopted by the access network device.

[0067] Terminal devices can be user equipment (UE), mobile stations, mobile terminals, etc. Terminal devices can be widely used in various scenarios, such as device-to-device (D2D), vehicle to everything (V2X) communication, machine-type communication (MTC), Internet of Things (IOT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grid, smart furniture, smart office, smart wearable, smart transportation, smart city, etc. Terminal devices can be mobile phones, tablet computers, computers with wireless transceiver functions, wearable devices, vehicles, urban air vehicles (such as drones, helicopters, etc.), ships, robots, robotic arms, smart home devices, etc.

[0068] The core network elements include user plane function (UPF) elements, access and mobility management function (AMF) elements, session management function (SMF) elements, network exposure function (NEF) elements, network function repository function (NRF) elements, unified data management (UDM) elements, policy control function (PCF) elements, application function (AF) elements, etc. Among them, UPF elements are user plane elements, and the above-mentioned other elements are control plane elements.

[0069] The interfaces between the control plane network elements can be service-oriented interfaces (such as Figure 2 As shown), it can also be a point-to-point interface, which is not limited in this application. Figure 2 Take this as an example to illustrate.

[0070] The following is a brief introduction to some core network equipment:

[0071] 1. SMF network element, referred to as SMF, is mainly used for session management, IP address allocation and management of terminal devices, selection of manageable user equipment plane functions, policy control, or termination points of charging function interfaces, and downlink data notification. Nsmf is a service-based interface provided by SMF, through which SMF can communicate with other network functions.

[0072] 2. AMF network element, referred to as AMF, is mainly used for mobility management and access management. Namf is a service-based interface provided by AMF. AMF can communicate with other network functions through Namf.

[0073] 3. UDM network element, referred to as UDM, is used to process user identification, contract signing, access authentication, registration, or mobility management. Nudm is a service-based interface provided by UDM, and UDM can communicate with other network functions through Nudm.

[0074] 4. UPF network element, referred to as UPF, is used for packet routing and forwarding, or quality of service (QoS) processing of user plane data.

[0075] 5. NEF network element, referred to as NEF, is used to expose the services and capabilities of 3GPP network functions to AF, and also allows AF to provide information to 3GPP network functions.

[0076] 6. PCF network element, referred to as PCF, is used for policy management of charging policies and QoS policies.

[0077] It is understandable that the core network network elements may also include other network elements, which is not limited in this application. The above network elements are examples of one implementation method, and this application does not exclude the existence of network elements or devices with the above network element functions in 6G or newer wireless communication systems with other names or other forms. The above network elements or functions may be network elements in hardware devices, software functions running on dedicated hardware, or virtualized functions instantiated on a platform (for example, a cloud platform). As a possible implementation method, the above network elements or functions may be implemented by one device, or by multiple devices, or may be a functional module within a device, which is not specifically limited in this embodiment of the present application.

[0078] The following describes a system architecture that may be applied to the present application. It should be understood that the following architectures are only examples, and the names of specific network element nodes are also only examples.

[0079] System architecture 1: Server-Network-UE architecture

[0080] like Figure 3A As shown, in system architecture 1, the server can transmit data with the UE through the network. For example, the server can implement video source encoding and decoding, rendering, etc. The network can specifically include but is not limited to the following devices: DN (such as fixed network), core network element (such as UPF), access network equipment. UE can be head-mounted XR glasses, video player, holographic projector and other equipment.

[0081] System architecture 2: UE-network-UE architecture

[0082] like Figure 3B As shown, in system architecture 2, UE1 can perform data transmission with UE2 through the network. The network may specifically include devices that can refer to architecture 1.

[0083] System Architecture 3: WiFi Scenario

[0084] like Figure 3C As shown, in system architecture 3, the server can transmit data with the UE through a fixed network, a WiFi router, or an access point (AP) or a set-top box.

[0085] Based on the above system architecture and the above related technical introduction, a possible communication method is provided in the embodiment of the present application, and the execution subjects of each communication method are introduced by taking the first device and the terminal device as examples. Figure 3A and Figure 3B The access network device or core network element in the embodiment, or the first device may be the aforementioned Figure 3C The terminal device can be the fixed network, WiFi router, AP or set-top box. FIG. 3A to FIG. 3C Any UE shown. In addition, it should be understood that the first device can also be replaced by a communication device having the function of the first device or a chip, unit or module inside the communication device having the function of the first device. The terminal device can also be replaced by a communication device having the function of a terminal device or a chip, unit or module inside the communication device having the function of a terminal device.

[0086] Figure 4 A possible flow chart of a communication method provided in an embodiment of the present application is shown, and the method includes:

[0087] Step 400: A first device receives a first data packet.

[0088] Exemplarily, the data carried by the first data packet can be decoded by M neural network layers, which can also be understood as the data carried by the first data packet can be decoded by M neural network layers. Wherein, M is a positive integer. Exemplarily, the data carried by the first data packet is the data after passing through the encoding neural network, and the data carried by the first data packet can be completely decoded through all the neural network layers in the decoding neural network to obtain the data before encoding, that is, the business data. Wherein, the decoding neural network includes M neural network layers. The number of neural network layers included in the encoding neural network may be equal to M or may not be equal to M, and this application does not limit this.

[0089] It is understandable that the number of neural network layers included in the decoding neural networks of different services may be different. For example, the decoding neural network of service 1 has 8 layers, and the decoding neural network of service 2 has 9 layers. Exemplarily, if the first data packet is a data packet of the first service, then before the first device receives the first data packet, the first device may receive configuration information indicating that the decoding neural network of the first service includes M neural network layers.

[0090] In a possible implementation, if the first device is a core network element, the first data packet may come from the server, or if the first device is an access network device, the first data packet may come from the core network element. For details, please refer to the following Figure 5A and Figure 5B ,as well as Fig. 6A and the embodiment shown in FIG. B .

[0091] The second data packet is obtained by decoding the data carried by the first data packet through K neural network layers, that is, the first device decodes the data carried by the first data packet through K neural network layers to obtain the second data packet.

[0092] Among them, the data carried by the second data packet can be decoded by MK neural network layers, which can also be understood as the data carried by the second data packet can be decoded by MK neural network layers, K is a positive integer, K<M. K neural network layers belong to M neural network layers. In other words, the first device can perform partial neural network layer decoding on the data carried by the first data packet to obtain the second data packet. Therefore, the complete decoding of the data carried by the second data packet can also be decoded by MK neural network layers, that is to say, the complete decoding of the data carried by the second data packet also needs to be decoded by MK neural network layers.

[0093] Exemplarily, the K neural network layers are the first K neural network layers among the M neural network layers. For example, if M=9, K=3, the first device performs decoding of the first 3 neural network layers among the 9 neural network layers on the data carried by the first data packet.

[0094] In addition, as a possible implementation method, the first device may also perform decoding of all neural network layers on the data carried by the first data packet, that is, K=M, or it can be understood as completely decoding the data carried by the first data packet, which is not limited in this application.

[0095] Exemplarily, before the first device performs decoding, the first device may also determine that the data carried by the first data packet can or is capable of being decoded by the neural network in the following manner.

[0096] Method 1: The first data packet may include second indication information, and the second indication information indicates that the data carried by the first data packet can or can be decoded by a neural network. It can also be understood that the second indication information indicates that the data carried by the first data packet can be decoded by a neural network. The neural network here may refer to a decoding neural network. Alternatively, the second indication information may indicate a decoding method for the data carried by the first data packet, such as AI feature stream decoding. Using the above method 1, the first data packet may carry the second indication information so that the first device knows that the first data packet can or can be decoded by a neural network.

[0097] Mode 2: The first device may also receive third indication information, and the third indication information indicates that the data carried by the first data packet can or can be decoded by the neural network. It can also be understood that the third indication information indicates that the data carried by the first data packet can be decoded by the neural network. Using the above method, the sender of the first data packet can directly notify the first device through signaling that the data carried by the data packet can or can be decoded by the neural network.

[0098] In addition, in a possible implementation, before the first device performs decoding, the first device may obtain capability information and / or channel state information of the terminal device. For example, the terminal device sends the capability information and / or channel state information of the terminal device to the first device. Then, the first device determines K neural network layers based on the capability information and / or channel state information of the terminal device, and determines the value of K. That is, the K neural network layers are determined based on the capability information and / or channel state information of the terminal device. It can also be understood that the first device can determine whether it is necessary to perform decoding of some neural network layers based on the capability information and / or channel state information of the terminal device, and when it is determined that decoding of some neural network layers needs to be performed, which specific neural network layers need to be decoded.

[0099] Among them, the capability information of the terminal device is used to indicate the neural network computing capability of the terminal device, and the neural network computing capability of the terminal device can also be referred to as the computing power of the terminal device, or the computing power level, or the floating-point computing capability, etc. For example, the capability information of the terminal device includes at least one of the number of floating-point operations per second (FLOPS), the main frequency of the chip, the memory size, or the time to run certain predefined computing tasks. For example, the channel state information of the terminal device may include the channel state information (CSI) reported by the terminal device, or at least one of the average service rates detected by the quality of service (QoS) flow. It is understandable that the specific content included in the capability information of the above-mentioned terminal device or the specific content included in the channel state information of the terminal device is only for example and is not limited to the present application. In addition, the capability information of the terminal device or the channel state information of the terminal device may also include other content.

[0100] Exemplarily, if the neural network computing capability of the terminal device is relatively poor, the first device can bear more decoding work at this time, that is, the value of K can be relatively large. If the neural network computing capability of the terminal is acceptable, but the channel state information indicates that the channel transmission condition between the first device and the terminal device is relatively poor, the first device can bear less decoding work at this time, that is, the value of K can be relatively small, and the terminal device is more responsible for decoding. Since the closer to the end of the decoding neural network, the greater the amount of data generated, the first device bears less decoding work at this time, and the amount of data transmitted over the air interface can also be reduced, thereby reducing transmission delays. If the neural network computing capability of the terminal device is relatively good, the first device may not bear the decoding work, for example, K may also be equal to 0.

[0101] In addition, the first device may also obtain parameters such as the power level or temperature of the terminal device. For example, when the terminal device cannot maintain a high neural network computing capability due to insufficient power, the first device may undertake more decoding work, that is, the value of K may be larger. It is understandable that the first device may determine the value of K based on a variety of factors, or the value of K may be configured in advance, and this application does not limit this.

[0102] Step 410: The first device sends a second data packet and first indication information.

[0103] Exemplarily, the first device sends the second data packet and the first indication information to the terminal device, and correspondingly, the terminal device receives the second data packet and the first indication information.

[0104] The first indication information indicates that the decoding has been completed through K neural network layers, or that the decoding has not been completed through MK neural network layers. In other words, the first indication information may indicate a neural network layer that has completed decoding, or a neural network layer that has not completed decoding.

[0105] For example, if M=9 and K=3, the first indication information may indicate that it has been decoded through the first three of the nine neural network layers, or that it has not been decoded through the last six of the nine neural network layers.

[0106] It is understandable that the capability information and / or channel status information of the terminal device may also be updated. The first device can update the value of K and the first indication information in combination with the updated capability information and / or channel status information of the terminal device.

[0107] For example, assuming that M=9, if the neural network computing capability of the terminal is acceptable, but the channel state information indicates that the channel transmission condition between the first device and the terminal device is poor, the first device can determine that the value of K is 2, and the first device performs decoding of the first two of the nine neural network layers on the received data packet. At this time, the first indication information indicates that the decoding has passed the first two of the nine neural network layers. If after a period of time, the second device obtains updated channel state information, and the updated channel state information indicates that the channel transmission condition between the first device and the terminal device has improved, the first device can adjust the value of K. For example, if the value of K is 4, the first device performs decoding of the first four of the nine neural network layers on the received data packet, and simultaneously updates the first indication information. The updated first indication information indicates that the decoding has passed the first four of the nine neural network layers.

[0108] In addition, in a possible implementation, the first device may also receive fourth indication information, wherein the fourth indication information indicates a mapping relationship between M neural network layers and N decoding subtasks, wherein each of the N decoding subtasks corresponds to at least one of the M neural network layers, and N is a positive integer. Exemplarily, a decoding subtask may also be referred to as a subtask, an atomic task, or a decoding atomic task, etc. The specific meaning of the mapping relationship between M neural network layers and N decoding subtasks may be understood as defining a mapping relationship between a subtask that needs to be actually calculated or run and a decoding neural network layer. This application is not limited to this. The N decoding subtasks are different, and the neural network layers included in different decoding subtasks are not repeated. The number of neural network layers included in different decoding subtasks may be the same or different. The mapping relationship between M neural network layers and N decoding subtasks may be determined by the server and notified to the first device.

[0109] For example, if M=9, N=3, the mapping relationship between 9 neural network layers and 3 decoding subtasks can be shown in Table 2 below.

[0110] Table 2

[0111] Decoding subtask Decoding Neural Networks 1 Floor 1 to 3 2 Floor 4 to 6 3 7th to 9th floors

[0112] Further, the first device may decode the data carried by the first data packet through K neural network layers according to the fourth indication information. Exemplarily, the first device may perform S decoding subtasks out of N decoding subtasks on the data carried by the first data packet, and the neural network layers corresponding to the S decoding subtasks are K neural network layers, where S is a positive integer and S<N.

[0113] Exemplarily, S decoding subtasks among N decoding subtasks may be understood as the first S decoding subtasks among the N decoding subtasks.

[0114] Correspondingly, the first indication information indicates that S decoding subtasks have been completed, or NS decoding subtasks have not been completed, the neural network layers corresponding to the NS decoding subtasks are MK neural network layers, and the NS decoding subtasks are the decoding subtasks among the N decoding subtasks except the S decoding subtasks.

[0115] For example, the first device can perform the first decoding subtask of the three decoding subtasks on the data carried by the first data packet according to the above Table 2, that is, decode through the first three neural network layers to obtain the second data packet, and send the second data packet and the first indication information to the terminal device, wherein the first indication information indicates that the first decoding subtask has been completed, or that the second decoding subtask and the third decoding subtask are not completed.

[0116] Step 420: The terminal device decodes the data carried by the second data packet according to the first indication information to obtain service data.

[0117] In one possible implementation, the terminal device determines which neural network layers still need to be decoded for the second data packet based on the first indication information, and completes the decoding of these neural network layers to obtain service data, or the service delay may also be considered. When the service delay is long, the terminal device may no longer continue to decode the data. Exemplarily, the first indication information indicates that it has been decoded through K neural network layers, or has not been decoded through MK neural network layers. Then the terminal device determines that the data carried by the second data packet can also be decoded through MK neural network layers based on the first indication information, that is, the terminal device determines that the data carried by the second data packet needs to be decoded through MK neural network layers based on the first indication information, and then the terminal device decodes the data carried by the second data packet through MK neural network layers to obtain service data. Therefore, the first device and the terminal device can jointly complete the decoding of the data carried by the first data packet, wherein the first device performs decoding of K neural network layers, and the terminal device performs decoding of MK neural network layers.

[0118] In addition, in a possible implementation, the terminal device may also receive fourth indication information, and then the terminal device may decode the data carried by the second data packet according to the fourth indication information and the first indication information.

[0119] For example, in combination with Table 2 above, the first device sends a second data packet and a first indication message to the terminal device, wherein the first indication message indicates that the first decoding subtask has been completed, or that the second decoding subtask and the third decoding subtask have not been completed. At this time, the terminal device can determine that the data carried by the second data packet still needs to execute the second decoding subtask and the third decoding subtask based on the fourth indication message (such as Table 2) and the first indication message, that is, the data carried by the second data packet can or can be decoded through the 4th to 9th neural network layers, and then the data carried by the second data packet is decoded through the 4th to 9th neural network layers.

[0120] Using the above method, the first device can first execute decoding of part of the neural network layer, and the terminal device executes decoding of the remaining neural network layers, thereby reducing the dependence on the neural network computing capability of the terminal device and saving the power consumption and cost of the terminal device. Compared with the terminal device, the first device has stronger neural network computing capability, which can improve the decoding efficiency, reduce the transmission delay of the data packet, and improve the user service experience.

[0121] The following is a specific embodiment of the invention. Figure 4 The method embodiment shown further illustrates:

[0122] like Figure 5A and Figure 5B The figure shows one of the specific flow charts of data packet transmission and decoding.

[0123] S501: The server generates a first data packet.

[0124] Exemplarily, the server encodes the data of the first service through an encoding neural network to generate a first data packet, wherein the data carried by the first data packet can or can be decoded by M neural network layers, M is a positive integer, and the first data packet is a data packet of the first service.

[0125] In addition, before the first service starts, for example, before S501, the server can determine that the decoding neural network of the first service includes M neural network layers, and determine the set of decoding subtasks based on the neural network layers included in the decoding neural network of the first service, that is, determine the mapping relationship between the M neural network layer decodings and the N decoding subtasks. Further, the server can notify the core network device and the terminal device of the mapping relationship between the M neural network layer decodings and the N decoding subtasks. For example, the server can notify the core network network element of the mapping relationship between the M neural network layer decodings and the N decoding subtasks through the N33 interface, and the core network network element can notify the terminal device of the mapping relationship between the M neural network layer decodings and the N decoding subtasks through non-access stratum (NAS) signaling.

[0126] For example, the decoding neural network of the first service includes 9 neural network layers, and the server determines 3 decoding subtasks based on the 9 neural network layers. The specific mapping relationship can be shown in Table 2.

[0127] S502: The server sends a first data packet to a core network element. Correspondingly, the core network element receives the first data packet from the server.

[0128] Exemplarily, the server may carry the second indication information through the first data packet, that is, add the second indication information to the first data packet. Furthermore, the core network element may determine that the received first data packet can or can be decoded using a neural network based on the second indication information.

[0129] For example, the server may add the second indication information to a real-time transport protocol (RTP) header of the first data packet.

[0130] S503: The core network element decodes the data carried by the first data packet through K neural network layers to obtain a second data packet.

[0131] Exemplarily, after determining that the received first data packet can or is capable of neural network decoding, and before decoding the data carried by the first data packet, the core network element can determine which neural network layers need to be decoded based on the obtained terminal device capability information and / or channel status, for example, determine which decoding subtasks of N decoding subtasks to execute. For details, please refer to the relevant description in the above step 410, which will not be repeated here.

[0132] S504: The core network element sends the second data packet and the first indication information to the terminal device. Correspondingly, the terminal device receives the second data packet and the first indication information from the core network element.

[0133] The data carried by the second data packet can or can be decoded by MK neural network layers, K is a positive integer, K<M, and the first indication information indicates that it has been decoded by K neural network layers, or has not been decoded by MK neural network layers.

[0134] Exemplarily, the core network element may carry the first indication information via NAS signaling.

[0135] S505: The terminal device decodes the data carried by the second data packet according to the first indication information to obtain service data.

[0136] Exemplarily, the terminal device determines the neural network layer that has not completed decoding based on the first indication information, decodes the data carried by the second data packet, and obtains business data.

[0137] By adopting the above method, the core network element can first perform decoding of part of the neural network layer, and the terminal device performs decoding of the remaining neural network layers, thereby reducing the dependence on the neural network computing power of the terminal device and saving the power consumption and cost of the terminal device. Compared with the terminal device, the core network element has stronger neural network computing power, which can improve the decoding efficiency, reduce the transmission delay of the data packet, and improve the user service experience.

[0138] like Fig. 6A and Figure 6B The figure shows one of the specific flow charts of data packet transmission and decoding.

[0139] S601: The server generates a third data packet.

[0140] Exemplarily, the server encodes the data of the first service through an encoding neural network to generate a third data packet, wherein the data carried by the third data packet can or can be decoded by M neural network layers, M is a positive integer, and the third data packet is the data packet of the first service.

[0141] In addition, before the first service starts, for example, before S601, the server can determine that the decoding neural network of the first service includes M neural network layers, and determine the set of decoding subtasks based on the neural network layers included in the decoding neural network of the first service, that is, determine the mapping relationship between the decoding of the M neural network layers and the N decoding subtasks. Further, the server can notify the access network device and the terminal device of the mapping relationship between the decoding of the M neural network layers and the N decoding subtasks. For example, the server can notify the core network element of the mapping relationship between the decoding of the M neural network layers and the N decoding subtasks through the N33 interface, and the core network element can notify the access network device of the mapping relationship between the decoding of the M neural network layers and the N decoding subtasks through the general packet radiosystem tunneling protocol-control plane (GTP-C) signaling. The core network element can notify the terminal device of the mapping relationship between the decoding of the M neural network layers and the N decoding subtasks through NAS signaling, or the access network device can notify the terminal device of the mapping relationship between the decoding of the M neural network layers and the N decoding subtasks through the media access control control element (MAC CE).

[0142] For example, the decoding neural network of the first service includes 9 neural network layers, and the server determines 3 decoding subtasks based on the 9 neural network layers. The specific mapping relationship is shown in Table 2.

[0143] S602: The server sends a third data packet to the core network element. Correspondingly, the core network element receives the third data packet from the server.

[0144] Exemplarily, the server may carry the second indication information through a third data packet, that is, add the second indication information to the first data packet. Furthermore, the core network element may determine that the received third data packet may or can be decoded using a neural network based on the second indication information.

[0145] For example, the server may add the second indication information to the RTP header of the third data packet.

[0146] S603: The core network element generates a first data packet based on the third data packet. The data carried by the first data packet is the same as the data carried by the third data packet. The data carried by the first data packet can be or can be decoded by M neural network layers.

[0147] For example, the core network element may encapsulate the third data packet using a general packet radio system tunneling protocol-user plane (GTP-U) to obtain the first data packet. The GTP header of the first data packet carries second indication information, and the second indication information indicates that the data carried by the first data packet can be or can be decoded by the neural network.

[0148] Alternatively, other core network elements (e.g., SMF) may send a notification message to the access network device, where the notification message includes the second indication information. In this case, the core network element (e.g., UPF) may not need the GTP header to carry the second indication information. The second indication information indicates that the data carried by the first data packet may or can be decoded by the neural network. For example, the core network element (e.g., SMF) may add the second indication information to the QoS description information (profile).

[0149] S604: The core network element sends a first data packet to the access network device. Correspondingly, the access network device receives the first data packet from the core network element.

[0150] S605: The access network device decodes the data carried by the first data packet through K neural network layers to obtain a second data packet.

[0151] Exemplarily, after determining that the received first data packet can or is capable of neural network decoding, and before decoding the data carried by the first data packet, the access network device can determine which neural network layers need to be decoded based on the obtained capability information and / or channel status of the terminal device, for example, determine which decoding subtasks of N decoding subtasks to execute. For details, please refer to the relevant description in the above step 410, which will not be repeated here.

[0152] S606: The access network device sends the second data packet and the first indication information. Correspondingly, the terminal device receives the second data packet and the first indication information from the access network device.

[0153] The data carried by the second data packet can or can be decoded by MK neural network layers, K is a positive integer, K<M; the first indication information indicates that it has been decoded by K neural network layers, or has not been decoded by MK neural network layers.

[0154] For example, the first indication information may be carried by MAC CE.

[0155] S607: The terminal device decodes the data carried by the second data packet according to the first indication information to obtain service data.

[0156] Exemplarily, the terminal device determines the neural network layer that has not completed decoding based on the first indication information, decodes the data carried by the second data packet, and obtains business data.

[0157] By adopting the above method, the access network device can first perform decoding of part of the neural network layers, and the terminal device can perform decoding of the remaining neural network layers, thereby reducing the dependence on the neural network computing power of the terminal device and saving the power consumption and cost of the terminal device. Compared with the terminal device, the access network device has stronger neural network computing power, which can improve the decoding efficiency, reduce the transmission delay of the data packet, and improve the user service experience.

[0158] It is understandable that, in order to implement the functions in the above embodiments, the terminal device and the first device include hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and method steps of each example described in the embodiments disclosed in this application, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application scenario and design constraints of the technical solution.

[0159] Figure 7 and Figure 8 The schematic diagram of the structure of possible communication devices provided by the embodiments of the present application. These communication devices can be used to implement the functions of the terminal device or the first device in the above method embodiments, and thus can also achieve the beneficial effects possessed by the above method embodiments.

[0160] like Figure 7 As shown, the communication device 700 includes a processing unit 710 and a transceiver unit 720. The communication device 700 is used to implement the terminal device or the first device in the above method embodiment.

[0161] When the communication device 700 is used to implement the above Figure 4 The functions of the first device in the method embodiment shown are:

[0162] The transceiver unit 720 is used to send and receive information;

[0163] The processing unit 710 is used to receive a first data packet through the transceiver unit 720; wherein the data carried by the first data packet can be decoded by M neural network layers, M is a positive integer; and send the second data packet and the first indication information through the transceiver unit 720, wherein the second data packet is obtained by decoding the data carried by the first data packet through K neural network layers, and the data carried by the second data packet can be decoded by MK neural network layers, K is a positive integer, K<M; the K neural network layers belong to the M neural network layers; the first indication information indicates that it has been decoded by the K neural network layers, or has not been decoded by the MK neural network layers.

[0164] In one possible design, the first data packet includes second indication information, and the second indication information indicates that data carried by the first data packet can be decoded by a neural network.

[0165] In one possible design, the transceiver unit 720 is used to receive third indication information, and the third indication information indicates that the data carried by the first data packet can be decoded by a neural network.

[0166] In one possible design, the transceiver unit 720 is used to receive fourth indication information, wherein the fourth indication information indicates a mapping relationship between the M neural network layers and the N decoding subtasks, wherein each of the N decoding subtasks corresponds to at least one of the M neural network layers, and N is a positive integer; the second data packet is obtained by executing S of the N decoding subtasks on the data carried by the first data packet, and the neural network layers corresponding to the S decoding subtasks are the K neural network layers, S is a positive integer, and S<N.

[0167] In one possible design, the first indication information indicates that the S decoding subtasks have been completed, or that the NS decoding subtasks are not completed, the neural network layers corresponding to the NS decoding subtasks are the MK neural network layers, and the NS decoding subtasks are the decoding subtasks among the N decoding subtasks except the S decoding subtasks.

[0168] In one possible design, the K neural network layers are determined based on capability information and / or channel state information of the terminal device, wherein the capability information of the terminal device is used to indicate the neural network computing capability of the terminal device.

[0169] When the communication device 700 is used to implement the above Figure 4 The functions of the terminal device in the method embodiment shown are:

[0170] The transceiver unit 720 is configured to receive a second data packet and first indication information, wherein the data carried by the second data packet can be decoded by MK neural network layers, the K neural network layers belong to M neural network layers, M and K are positive integers, K<M; and the first indication information indicates that the data has been decoded by the K neural network layers or has not been decoded by the MK neural network layers;

[0171] The processing unit 710 is used to decode the data carried by the second data packet according to the first indication information to obtain business data.

[0172] In one possible design, the transceiver unit 720 is used to receive fourth indication information, where the fourth indication information indicates a mapping relationship between M neural network layers and N decoding subtasks, wherein each of the N decoding subtasks corresponds to at least one neural network layer of the M neural network layers, and N is a positive integer.

[0173] In one possible design, the first indication information indicates that S decoding subtasks have been completed, and the neural network layers corresponding to the S decoding subtasks are the K neural network layers, S is a positive integer, S<N, or the first indication information indicates that NS decoding subtasks are not completed, and the neural network layers corresponding to the NS decoding subtasks are the MK neural network layers.

[0174] A more detailed description of the processing unit 710 and the transceiver unit 720 can be directly obtained by referring to the relevant description in the above method embodiment, which will not be repeated here.

[0175] like Figure 8 As shown, the communication device 800 includes a processor 810 and an interface circuit 820. The processor 810 and the interface circuit 820 are coupled to each other. It is understood that the interface circuit 820 can be a transceiver or an input-output interface. Optionally, the communication device 800 may also include a memory 830 for storing instructions executed by the processor 810 or storing input data required by the processor 810 to execute instructions or storing data generated after the processor 810 executes instructions.

[0176] When the communication device 800 is used to implement Figure 4When the method is shown, the processor 810 is used to implement the function of the above-mentioned processing unit 710, and the interface circuit 820 is used to implement the function of the above-mentioned transceiver unit 720.

[0177] It is understandable that the processor in the embodiments of the present application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0178] In the present application, another example of a device is provided, the notification device includes at least one processor and at least one memory, the at least one processor is coupled to the at least one memory, the at least one memory is used to store instructions, when the instructions are executed by the at least one processor, the communication device executes the method in the above embodiment. Take the communication device including a processor and a memory as an example, Figure 8 As shown, the communication device 800 includes a processor 810 and a memory 830. The processor 810 and the memory 830 are coupled, and the memory 830 stores instructions. When the instructions stored in the memory 830 are executed by the processor 810, the communication device 800 executes the method executed by the terminal device or the first device in the above embodiment.

[0179] The method steps in the embodiments of the present application can be implemented in hardware or in software instructions that can be executed by a processor. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, register, hard disk, mobile hard disk, CD-ROM or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. The storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in the above-mentioned terminal device or the first device. The processor and the storage medium can also be present in the terminal device or the first device as discrete components.

[0180] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instruction is loaded and executed on a computer, the process or function described in the embodiment of the present application is executed in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user device or other programmable device. The computer program or instruction may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer program or instruction may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server, data center, etc. that integrates one or more available media. The available medium may be a magnetic medium, for example, a floppy disk, a hard disk, a tape; it may also be an optical medium, for example, a digital video disc; it may also be a semiconductor medium, for example, a solid-state hard disk. The computer-readable storage medium may be a volatile or nonvolatile storage medium, or may include both volatile and nonvolatile types of storage media.

[0181] In the various embodiments of the present application, unless otherwise specified or provided for in any logical conflict, the terms and / or descriptions between the different embodiments are consistent and may be referenced to each other, and the technical features in the different embodiments may be combined to form new embodiments according to their inherent logical relationships.

[0182] In the present application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. In the text description of the present application, the character " / " generally indicates that the previous and next associated objects are in an "or" relationship; in the formula of the present application, the character " / " indicates that the previous and next associated objects are in a "division" relationship. "Including at least one of A, B and C" can mean: including A; including B; including C; including A and B; including A and C; including B and C; including A, B and C.

[0183] It is understood that the various numbers involved in the embodiments of the present application are only for the convenience of description and are not used to limit the scope of the embodiments of the present application. The size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic.

Claims

1. A communication method, It is characterized in that The method includes: Receive a first data packet; wherein the data carried by the first data packet can be decoded by M neural network layers, where M is a positive integer; A second data packet and a first indication message are sent, wherein the second data packet is obtained by decoding the data carried by the first data packet through K neural network layers, and the data carried by the second data packet can be decoded by MK neural network layers, K is a positive integer, K<M; the K neural network layers belong to the M neural network layers, and the first indication information indicates that the data has been decoded by the K neural network layers, or has not been decoded by the MK neural network layers.

2. The method according to claim 1, It is characterized in that The first data packet includes second indication information, and the second indication information indicates that the data carried by the first data packet can be decoded by a neural network.

3. The method according to claim 1, It is characterized in that Also includes: Receive third indication information, where the third indication information indicates that data carried by the first data packet can be decoded by a neural network.

4. The method according to any one of claims 1 to 3, It is characterized in that Also includes: Receive fourth indication information, where the fourth indication information indicates a mapping relationship between the M neural network layers and the N decoding subtasks, wherein each decoding subtask in the N decoding subtasks corresponds to at least one neural network layer in the M neural network layers, and N is a positive integer; The second data packet is obtained by executing S decoding subtasks among the N decoding subtasks on the data carried by the first data packet, and the neural network layers corresponding to the S decoding subtasks are the K neural network layers, S is a positive integer, and S<N.

5. The method according to claim 4, It is characterized in that The first indication information indicates that the S decoding subtasks have been completed, or that the NS decoding subtasks have not been completed, the neural network layers corresponding to the NS decoding subtasks are the MK neural network layers, and the NS decoding subtasks are the decoding subtasks among the N decoding subtasks excluding the S decoding subtasks.

6. The method according to any one of claims 1 to 5, It is characterized in that The K neural network layers are determined based on capability information and / or channel state information of the terminal device, wherein the capability information of the terminal device is used to indicate the neural network computing capability of the terminal device.

7. A communication method, It is characterized in that The method includes: receiving a second data packet and first indication information, wherein the data carried by the second data packet can be decoded by MK neural network layers, the K neural network layers belong to M neural network layers, the M and K are positive integers, K<M; and the first indication information indicates that the data has been decoded by the K neural network layers or has not been decoded by the MK neural network layers; The data carried by the second data packet is decoded according to the first indication information to obtain service data.

8. The method according to claim 7, It is characterized in that Also includes: Receive fourth indication information, wherein the fourth indication information indicates a mapping relationship between the M neural network layers and the N decoding subtasks, wherein each decoding subtask of the N decoding subtasks corresponds to at least one neural network layer of the M neural network layers, and N is a positive integer.

9. The method according to claim 8, It is characterized in that The first indication information indicates that S decoding subtasks have been completed, and the neural network layers corresponding to the S decoding subtasks are the K neural network layers, S is a positive integer, S<N, or the first indication information indicates that NS decoding subtasks are not completed, and the neural network layers corresponding to the NS decoding subtasks are the MK neural network layers.

10. A communication device, It is characterized in that The device comprises a processing unit and a transceiver unit; The transceiver unit is used to send and receive information; The processing unit is used to receive a first data packet through the transceiver unit; wherein the data carried by the first data packet can be decoded by M neural network layers, M is a positive integer; and send the second data packet and first indication information through the transceiver unit, wherein the second data packet is obtained by decoding the data carried by the first data packet through K neural network layers, and the data carried by the second data packet can be decoded by MK neural network layers, K is a positive integer, K<M; the K neural network layers belong to the M neural network layers, and the first indication information indicates that it has been decoded by the K neural network layers, or has not been decoded by the MK neural network layers.

11. The device according to claim 10, It is characterized in that The first data packet includes second indication information, and the second indication information indicates that the data carried by the first data packet can be decoded by a neural network.

12. The device according to claim 10, It is characterized in that The transceiver unit is used to receive third indication information, and the third indication information indicates that the data carried by the first data packet can be decoded by the neural network.

13. The device according to any one of claims 10 to 12, It is characterized in that The transceiver unit is used to receive fourth indication information, where the fourth indication information indicates a mapping relationship between the M neural network layers and the N decoding subtasks, wherein each decoding subtask in the N decoding subtasks corresponds to at least one neural network layer in the M neural network layers, and N is a positive integer; The second data packet is obtained by executing S decoding subtasks among the N decoding subtasks on the data carried by the first data packet, and the neural network layers corresponding to the S decoding subtasks are the K neural network layers, S is a positive integer, and S<N.

14. The device according to claim 13, It is characterized in that The first indication information indicates that the S decoding subtasks have been completed, or that the NS decoding subtasks have not been completed, the neural network layers corresponding to the NS decoding subtasks are the MK neural network layers, and the NS decoding subtasks are the decoding subtasks among the N decoding subtasks excluding the S decoding subtasks.

15. The device according to any one of claims 10 to 14, It is characterized in that The K neural network layers are determined based on capability information and / or channel state information of the terminal device, wherein the capability information of the terminal device is used to indicate the neural network computing capability of the terminal device.

16. A communication device, It is characterized in that The device includes: A transceiver unit, configured to receive a second data packet and first indication information, wherein the data carried by the second data packet can be decoded by MK neural network layers, the K neural network layers belong to M neural network layers, the M and K are positive integers, K<M; and the first indication information indicates that the data has been decoded by the K neural network layers or has not been decoded by the MK neural network layers; A processing unit is used to decode the data carried by the second data packet according to the first indication information to obtain business data.

17. The device according to claim 16, It is characterized in that The transceiver unit is used to receive fourth indication information, where the fourth indication information indicates a mapping relationship between M neural network layers and N decoding subtasks, wherein each decoding subtask of the N decoding subtasks corresponds to at least one neural network layer of the M neural network layers, and N is a positive integer.

18. The device according to claim 17, It is characterized in that The first indication information indicates that S decoding subtasks have been completed, and the neural network layers corresponding to the S decoding subtasks are the K neural network layers, S is a positive integer, S<N, or the first indication information indicates that NS decoding subtasks are not completed, and the neural network layers corresponding to the NS decoding subtasks are the MK neural network layers.

19. A communication system, It is characterized in that The system includes a server, a core network element and a terminal device; The server is configured to generate a first data packet and send the first data packet to the core network element, wherein the data carried by the first data packet can be decoded by M neural network layers, where M is a positive integer; The core network element is used to receive the first data packet from the server, and send the second data packet and first indication information; the second data packet is obtained by decoding the data carried by the first data packet through K neural network layers, wherein the K neural network layers belong to the M neural network layers, and the data carried by the second data packet can be decoded by MK neural network layers, K is a positive integer, K<M; the first indication information indicates that it has been decoded by the K neural network layers, or has not been decoded by the MK neural network layers; The terminal device is used to receive the second data packet and the first indication information from the core network element, and decode the data carried by the second data packet according to the first indication information to obtain service data.

20. A communication system, It is characterized in that The system includes a server, a core network element, an access network device and a terminal device; The server is configured to generate a third data packet and send the third data packet to the core network element, wherein the data carried by the third data packet can be decoded by M neural network layers, where M is a positive integer; The core network element is configured to receive the third data packet from the server, generate a first data packet based on the third data packet, the data carried by the first data packet is the same as the data carried by the third data packet, and the data carried by the first data packet can be decoded by the M neural network layers; The access network device is used to receive the first data packet from the core network network element, and send the second data packet and first indication information; the second data packet is obtained by decoding the data carried by the first data packet through K neural network layers, wherein the K neural network layers belong to the M neural network layers, and the data carried by the second data packet can be decoded by MK neural network layers, K is a positive integer, K<M; the first indication information indicates that it has been decoded by the K neural network layers, or has not been decoded by the MK neural network layers; The terminal device is used to receive the second data packet and the first indication information from the access network device, and decode the data carried by the second data packet according to the first indication information to obtain service data.

21. A communication device, It is characterized in that The communication device comprises at least one processor; the at least one processor is configured to execute the method according to any one of claims 1 to 9.

22. A computer-readable storage medium, It is characterized in that The computer-readable storage medium includes a program, and when the program is run on a device, the device is caused to perform the method according to any one of claims 1 to 9.

23. A computer program product, It is characterized in that The computer program product comprises a program or instructions, and when the program or instructions are executed by a device, the device is caused to perform the method according to any one of claims 1 to 9.

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