A communication method, a communication device and a storage medium

Abstract

Embodiments of the present application disclose a communication method, a communication device and a storage medium, which are applied to the technical field of communication and used for ensuring the continuity of AI service. The method of the embodiments of the present application comprises: determining first information, the first information being used for indicating X AI data, X being a positive integer; and sending the first information and Y data units, the Y data units being used for transmitting M AI data, the Y data units carrying the X AI data, Y being a positive integer, and M being an integer greater than or equal to X. In the embodiments of the present application, since the Y data units are used for transmitting the M AI data, and X is less than or equal to M, the sending end device can send part of the M AI data, so that the receiving end device can provide the AI service according to the X AI data, thereby ensuring the continuity of the AI service and solving the problem of service interruption caused by insufficient air interface resources.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a communication method, communication device and storage medium. Background Technology

[0002] To address the vision of future intelligent and inclusive access, intelligence will further evolve at the wireless network architecture level. Artificial intelligence (AI) will be more deeply integrated with wireless networks to achieve inherent intelligence within the network, as well as the intelligence of terminals.

[0003] Currently, AI services provided by wireless networks include model training and data processing. Model training includes, for example, joint training or distributed learning, where model parameters are transmitted between nodes via the wireless network. Data processing, such as deploying neural network models in a wireless network and managing their lifecycle, involves a significant amount of data acquisition. This requires transmitting the acquired or processed datasets between nodes, including terminals, via communication links for tasks such as inference and training.

[0004] However, due to limited air interface resources, issues such as data transmission interruptions may occur, which in turn affect service continuity. Summary of the Invention

[0005] This application provides a communication method, communication device, and storage medium that prioritizes the transmission of a portion of AI data to ensure the continuity of AI services and solves the problem of service interruption caused by insufficient air interface resources.

[0006] The first aspect of this application provides a communication method. Optionally, the subject executing the method can be a transmitting device, which can be a network device, a component or device applied to the network device (e.g., a processor, circuit, chip, or chip system), or a logic module or software capable of implementing all or part of the functions of the network device (e.g., a central unit (CU), a distributed unit (DU), or a radio unit (RU)). The transmitting device can also be a terminal device, a component or device applied to the terminal device (e.g., a processor, circuit, chip, or chip system), or a circuit or chip in the terminal device responsible for communication functions (e.g., a modem chip, also known as a baseband chip, or a system-on-a-chip (SoC) chip containing a modem core, or a system-in-package (SIP) chip). In this method, the sending device determines first information, which is used to indicate X AI data, where X is a positive integer; the sending device sends the first information and Y data units to the receiving device, where the Y data units are used to transmit M AI data, and the Y data units carry X AI data, where Y is a positive integer and M is an integer greater than or equal to X.

[0007] Based on the first aspect of this application, since Y data units are used to transmit M AI data, and X is less than or equal to M, the transmitting device can send a portion of the M AI data so that the receiving device can provide AI services based on the X AI data, thereby ensuring the continuity of AI services and solving the problem of service interruption caused by insufficient air interface resources.

[0008] Based on the first aspect of this application, in some possible implementations, the data unit is a transport block or a service data unit. N is used to indicate that each of the Y data units is used to transmit N AI data units.

[0009] In this embodiment, AI data is indicated at the granularity of transport blocks or service data units, thereby reducing the transmission latency of AI data.

[0010] Based on the first aspect of this application, in some possible implementations, the Y data units include a first data unit, X AI data and first information are carried in the first data unit, the first data unit is the first data unit among the Y data units, and X is less than or equal to N.

[0011] In this embodiment of the application, since the first information is carried in the first data unit, the receiving device can determine the X AI data carried in the first data unit based on the first information carried in the first data unit, thereby reducing the transmission delay of AI data.

[0012] Based on the first aspect of this application, in some possible implementations, Y is a positive integer greater than 2, Y data units include a first data unit and a second data unit, first information is carried in the first data unit, X AI data are carried in the second data unit, the first data unit is the i-th data unit among the Y data units, the second data unit is the (i+1)-th data unit among the Y data units, i is a positive integer less than Y-1, and X is less than or equal to N.

[0013] In this embodiment of the application, the transmitting device indicates the X AI data carried in the next data unit by using the d information in the previous data unit, thereby enabling the receiving device to release the idle resources in the next data unit according to the first information.

[0014] Based on the first aspect of this application, in some possible implementations, the first information is bit information of N bits, the N bits correspond to N AI data, and X bits of the N bits are 1, or X bits of the N bits are 0.

[0015] In this embodiment of the application, X AI data are indicated by bit information, thereby clarifying the quantity or specific AI data, so that the receiving device can obtain X AI data according to the first information.

[0016] Based on the first aspect of this application, in some possible implementations, the Y data units include a first data unit, first information is carried in the first data unit, the Y data units are data units of the same transmission cycle, and X AI data are carried in one or more of the Y data units.

[0017] In this embodiment of the application, since the Y data units share the same transmission cycle, the transmission delay is reduced.

[0018] Based on the first aspect of this application, in some possible implementations, the first information is bit information of M bits, the M bits correspond to M AI data, and X bits of the M bits are 1, or X bits of the M bits are 0.

[0019] In this embodiment of the application, X AI data are indicated by bit information, thereby clarifying the quantity or specific AI data, so that the receiving device can obtain X AI data according to the first information.

[0020] Based on the first aspect of this application, in some possible implementations, the first information is carried in signaling, and X AI data are carried in one or more data units among Y data units.

[0021] In this embodiment of the application, first information is transmitted via signaling, enabling the sending device to transmit AI data at the medium access control (MAC) layer.

[0022] Media access control (MAC) assigns X AI data points the same priority, and the first information is also used to indicate the priority of the X AI data points.

[0023] In this embodiment, by defining the priority of X AI data, the AI ​​data with higher priority can be transmitted first, thereby ensuring the continuity of AI services.

[0024] Based on the first aspect of this application, in some possible implementations, the transmitting device may also receive second information, which is used to indicate the index of X data, the priority of X data, and one or more items in the set corresponding to X data, and the second information is used to determine the first information.

[0025] In this embodiment of the application, the sending device obtains information about X AIs through the second information, and after determining the first information based on the second information, the receiving device can determine which AI data is being carried based on the first information.

[0026] A second aspect of this application provides a communication method. Optionally, the executing entity of this method can be a receiving device, which can be a network device, a component or device applied to a network device (e.g., a processor, circuit, chip, or chip system), or a logic module or software (e.g., CU, DU, or RU) capable of implementing all or part of the functions of the network device. The receiving device can also be a terminal device, a component or device applied to a terminal device (e.g., a processor, circuit, chip, or chip system), or a circuit or chip in the terminal device responsible for communication functions (e.g., a modem chip, or a SoC chip or SIP chip containing a modem core). In this method, the receiving device receives first information and Y data units. The Y data units are used to transmit M AI data, each carrying X AI data, where X and Y are positive integers, and M is an integer greater than or equal to X. The receiving device obtains X AI data from the Y data units according to the first information.

[0027] Based on the second aspect of this application, since the receiving device can obtain X AI data from Y data units according to the first information, the receiving device can release the idle resources in the Y data units, so that these idle resources can be used for the transmission of other services.

[0028] Based on the second aspect of this application, in some possible implementations, the data unit is a transport block or a service data unit. N is used to indicate that each of the Y data units is used to transmit N AI data units.

[0029] Based on the second aspect of this application, in some possible implementations, the Y data units include a first data unit, X AI data and first information are carried in the first data unit, the first data unit is the first data unit among the Y data units, and X is less than or equal to N.

[0030] Based on the second aspect of this application, in some possible implementations, Y is a positive integer greater than 2, Y data units include a first data unit and a second data unit, first information is carried in the first data unit, X AI data are carried in the second data unit, the first data unit is the i-th data unit among the Y data units, the second data unit is the (i+1)-th data unit among the Y data units, i is a positive integer less than Y-1, and X is less than or equal to N.

[0031] Based on the second aspect of this application, in some possible implementations, the first information is bit information of N bits, where the N bits correspond to N AI data, and X bits of the N bits are 1, or X bits of the N bits are 0.

[0032] Based on the second aspect of this application, in some possible implementations, the Y data units are data units of the same transmission cycle, and the X AI data are carried in one or more data units among the Y data units.

[0033] Based on the second aspect of this application, in some possible implementations, the first information is bit information of M bits, the M bits correspond to M AI data, and X bits of the M bits are 1, or X bits of the M bits are 0.

[0034] Based on the second aspect of this application, in some possible implementations, the first information is carried in signaling, and X AI data are carried in one or more data units among Y data units.

[0035] Based on the second aspect of this application, in some possible implementations, X AI data points have the same priority, and the first information is also used to indicate the priority of the X AI data points.

[0036] Based on the second aspect of this application, in some possible implementations, the receiving device may further send second information, which is used to indicate the index of X data, the priority of X data, and one or more items in the set corresponding to X data, and the second information is used to determine the first information.

[0037] A third aspect of this application provides a communication device that performs the functions described in the first aspect. For example, the communication device includes modules, units, or means corresponding to the operations involved in the first aspect. These modules, units, or means can be implemented in software, hardware, or a combination of software and hardware. The communication device includes:

[0038] The processing module is used to determine the first information, which indicates X AI data points, where X is a positive integer;

[0039] The interface module is used to send the first information and Y data units. The Y data units are used to transmit M AI data. The Y data units carry X AI data, where Y is a positive integer and M is an integer greater than or equal to X.

[0040] Based on a third aspect of this application, in some possible implementations, the data unit is a transport block or a service data unit. N is used to indicate that each of the Y data units is used to transmit N AI data units.

[0041] Based on the third aspect of this application, in some possible implementations, the Y data units include a first data unit, X AI data and first information are carried in the first data unit, the first data unit is the first data unit among the Y data units, and X is less than or equal to N.

[0042] Based on the third aspect of this application, in some possible implementations, Y is a positive integer greater than 2, Y data units include a first data unit and a second data unit, first information is carried in the first data unit, X AI data are carried in the second data unit, the first data unit is the i-th data unit among the Y data units, the second data unit is the (i+1)-th data unit among the Y data units, i is a positive integer less than Y-1, and X is less than or equal to N.

[0043] Based on the third aspect of this application, in some possible implementations, the first information is bit information of N bits, the N bits correspond to N AI data, and X bits of the N bits are 1, or X bits of the N bits are 0.

[0044] Based on the third aspect of this application, in some possible implementations, the Y data units include a first data unit, the first information is carried in the first data unit, the Y data units are data units of the same transmission cycle, and X AI data are carried in one or more of the Y data units.

[0045] Based on the third aspect of this application, in some possible implementations, the first information is bit information of M bits, the M bits correspond to M AI data, and X bits of the M bits are 1, or X bits of the M bits are 0.

[0046] Based on the third aspect of this application, in some possible implementations, the first information is carried in signaling, and X AI data are carried in one or more data units among Y data units.

[0047] Based on a third aspect of this application, in some possible implementations, the characteristic is that X AI data points have the same priority, and the first information is also used to indicate the priority of the X AI data points.

[0048] Based on the third aspect of this application, in some possible implementations, the interface module is further configured to receive second information, which indicates one or more of the indexes of X data, the priorities of X data, and the sets corresponding to X data, and the second information is used to determine the first information.

[0049] A fourth aspect of this application provides a communication device that performs the functions described in the second aspect above. For example, the communication device includes modules, units, or means corresponding to the operations involved in the second aspect. These modules, units, or means can be implemented in software, hardware, or a combination of software and hardware. The communication device includes:

[0050] The interface module is used to receive the first information and Y data units. The Y data units are used to transmit M AI data. Each Y data unit carries X AI data, where X and Y are positive integers and M is an integer greater than or equal to X.

[0051] The processing module is used to obtain X AI data from Y data units based on the first information.

[0052] Based on the fourth aspect of this application, in some possible implementations, the data unit is a transport block or a service data unit. N is used to indicate that each of the Y data units is used to transmit N AI data units.

[0053] Based on the fourth aspect of this application, in some possible implementations, the Y data units include a first data unit, X AI data and first information are carried in the first data unit, the first data unit is the first data unit among the Y data units, and X is less than or equal to N.

[0054] Based on the fourth aspect of this application, in some possible implementations, Y is a positive integer greater than 2, Y data units include a first data unit and a second data unit, first information is carried in the first data unit, X AI data are carried in the second data unit, the first data unit is the i-th data unit among the Y data units, the second data unit is the (i+1)-th data unit among the Y data units, i is a positive integer less than Y-1, and X is less than or equal to N.

[0055] Based on the fourth aspect of this application, in some possible implementations, the first information is bit information of N bits, the N bits correspond to N AI data, and X bits of the N bits are 1, or X bits of the N bits are 0.

[0056] Based on the fourth aspect of this application, in some possible implementations, the Y data units are data units of the same transmission cycle, and the X AI data are carried in one or more data units among the Y data units.

[0057] Based on the fourth aspect of this application, in some possible implementations, the first information is bit information of M bits, the M bits correspond to M AI data, and X bits of the M bits are 1, or X bits of the M bits are 0.

[0058] Based on the fourth aspect of this application, in some possible implementations, the first information is carried in signaling, and X AI data are carried in one or more data units among Y data units.

[0059] Based on the fourth aspect of this application, in some possible implementations, X AI data points have the same priority, and the first information is also used to indicate the priority of the X AI data points.

[0060] Based on the fourth aspect of this application, in some possible implementations, the interface module is further configured to send second information, which indicates one or more of the indexes of X data, the priorities of X data, and the sets corresponding to X data, and the second information is used to determine the first information.

[0061] A fifth aspect of this application provides a communication device, which may be a transmitting end device or a receiving end device, or a component applied to the transmitting end device or the receiving end device (e.g., a processor, circuit, chip, or chip system), or a logic module or software (e.g., CU, DU, or RU) capable of implementing all or part of the functions of the transmitting end device or the receiving end device. The communication device includes:

[0062] A processor for executing a program that causes the communication device to perform the method as described in the first or second aspect of the foregoing and any possible implementation thereof.

[0063] Optionally, the communication device further includes a memory, and the processor is coupled to the memory; the memory is used to store programs.

[0064] The sixth aspect of this application provides a chip or chip system including at least one processor and a communication interface, the communication interface and at least one processor being interconnected via a line, the at least one processor being used to run computer programs or instructions to perform the communication method described in any of the possible implementations of the first or second aspect.

[0065] The communication interface in the chip can be an input / output interface, pins, or circuits.

[0066] In one possible implementation, the chip or chip system described above in this application further includes at least one memory storing instructions. The memory can be an internal storage unit of the chip, such as a register or cache, or it can be a storage unit of the chip itself, such as a read-only memory or random access memory.

[0067] The seventh aspect of this application provides a communication system, including a communication device that performs the first aspect and any possible implementation thereof, and a communication device that performs the second aspect and any possible implementation thereof.

[0068] An eighth aspect of this application provides a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the method described in the first aspect above, or cause the computer to perform the method described in the second aspect above.

[0069] The ninth aspect of this application provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the method described in the first aspect above, or cause the computer to perform the method described in the second aspect above. Attached Figure Description

[0070] Figures 1a to 1cA schematic diagram of the communication system provided in this application;

[0071] Figures 2a to 2g This is a schematic diagram of the AI ​​processing involved in this application;

[0072] Figure 3 This is one possible application scenario of the communication method in the embodiments of this application;

[0073] Figures 4 to 9 Some possible implementations of the communication method provided in this application;

[0074] Figures 10 to 13 A schematic diagram of the communication device provided in this application. Detailed Implementation

[0075] This application provides a communication method, communication device, and storage medium that can ensure the continuity of AI services by prioritizing the transmission of a portion of AI data, thus solving the problem of service interruption caused by insufficient air interface resources.

[0076] The embodiments of this application will now be described with reference to the accompanying drawings. Those skilled in the art will recognize that, with technological advancements and the emergence of new scenarios, the technical solutions provided in the embodiments of this application are equally applicable to similar technical problems.

[0077] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0078] References to "one embodiment" or "some embodiments" as described in this application mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0079] In the description of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. "And / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can represent: a, b, c; a and b; a and c; b and c; or a and b and c. Where a, b, and c can be single or multiple.

[0080] In this application, unless otherwise specified, the same or similar parts between the various embodiments can be referred to each other. In the various embodiments of this application, and in the various methods / designs / implementations within each embodiment, unless otherwise specified or logically conflicting, the terminology and / or descriptions between different embodiments and between the various methods / designs / implementations within each embodiment are consistent and can be mutually referenced. The technical features in different embodiments and the various methods / designs / implementations within each embodiment can be combined to form new embodiments, methods, or implementations based on their inherent logical relationships. The following descriptions of the embodiments of this application do not constitute a limitation on the scope of protection of this application.

[0081] This application can be applied to long-term evolution (LTE) systems, new radio (NR) systems, or future communication systems beyond the fifth generation (5G) communication system. The communication system includes at least one network device and / or at least one terminal device.

[0082] Please see Figure 1a , Figure 1a This is a schematic diagram of one possible, non-limiting system. For example... Figure 1a As shown, the communication system 10 includes a radio access network (RAN) 100 and a core network (CN) 200. RAN 100 includes at least one RAN node (e.g., ...). Figure 1a 110a and 110b (collectively referred to as 110) and at least one terminal (such as Figure 1a RAN 100, denoted as RAN 120a-120j, is collectively referred to as RAN 120. RAN 100 may also include other RAN nodes, such as wireless relay equipment and / or wireless backhaul equipment. Figure 1a(Not shown in the image). Terminal 120 is connected to RAN node 110 wirelessly. RAN node 110 is connected to core network 200 wirelessly or via wired connection. The core network equipment in core network 200 and RAN node 110 in RAN 100 can be different physical devices, or they can be the same physical device integrating core network logical functions and radio access network logical functions.

[0083] RAN 100 can be a cellular system related to the 3rd Generation Partnership Project (3GPP), such as a 4G, 5G, or future mobile communication system. RAN 100 can also be an open-radio access network (ORAN), a cloud-radio access network (CRAN), or a wireless fidelity (WiFi) system. RAN 100 can also be a communication system that integrates two or more of the above systems.

[0084] RAN node 110, sometimes also referred to as access network equipment, RAN entity, or access node, constitutes part of the communication system and is used to help terminals achieve wireless access. Multiple RAN nodes 110 in communication system 10 can be of the same type or different types. In some scenarios, the roles of RAN node 110 and terminal 120 are relative, for example... Figure 1a Network element 120i can be a helicopter or a drone, and it can be configured as a mobile base station. For terminals 120j that access RAN 100 through network element 120i, network element 120i is a base station; however, for base station 110a, network element 120i is a terminal. RAN node 110 and terminal 120 are sometimes referred to as communication devices, for example... Figure 1a Network elements 110a and 110b can be understood as communication devices with base station functions, while network elements 120a-120j can be understood as communication devices with terminal functions.

[0085] In one possible scenario, access network equipment includes, but is not limited to: evolved Node B (eNodeB), radio network controller (RNC), Node B (NB), base station (BS), base station controller (BSC), base transceiver station (BTS), home base station (e.g., home evolved NodeB, or home Node B, HNB), baseband unit (BBU), access point (AP) in wireless fidelity (WIFI) system, macro base station, micro base station, wireless relay node, donor node, radio controller in CRAN scenario, wireless backhaul node, transmission point (TP), or transmission and reception point (TRP), etc., and can also be access network equipment in 5G mobile communication system. For example, a next-generation NodeB (gNB), TRP, or TP in an NR system; or one or a group of antenna panels (including multiple antenna panels) in a base station in a 5G mobile communication system; or, access network equipment can also be network nodes constituting a gNB or transmission point. Examples include centralized units (CU), distributed units (DU), centralized unit control planes (CU-CP), centralized unit user planes (CU-UP), or radio units (RU), etc. CUs and DUs can be separate or included in the same network element, such as a BBU. RUs can be included in radio equipment or radio units, such as in a remote radio unit (RRU), an active antenna unit (AAU), or a remote radio head (RRH). Alternatively, access network equipment can also be servers, wearable devices, vehicles, or in-vehicle equipment, etc. For example, the access network equipment in V2X technology can be a roadside unit (RSU). It should be understood that the aforementioned TRP can be a device or module located on the network side of the aforementioned communication system and having corresponding communication functions.The TRP typically contains communication modules, circuits, or chips that perform the corresponding communication functions. The TRP can also be configured with program instructions for the corresponding communication functions.

[0086] It should be noted that CU (or CU-CP and CU-UP), DU, or RU may have different names in different systems, but those skilled in the art will understand their meaning. For example, in an open radio access network (ORAN) system, CU can also be called an open centralized unit (O-CU) or an open CU, DU can also be called an open-distributed unit (O-DU), CU-CP can also be called an open-centralized unit control plane (O-CU-CP), CU-UP can also be called an open-centralized unit user plane (O-CU-UP), and RU can also be called an open radio unit (O-RU). This application does not impose any specific limitations. Any of the units CU, CU-CP, CU-UP, DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software and hardware modules.

[0087] Optionally, for network elements in the ORAN system, each network element can implement the protocol layer functions shown in Table 1 below.

[0088] Table 1

[0089]

[0090] It should be noted that in the ORAN system, the access network equipment in this application can be one or more network elements listed in Table 1 above.

[0091] The architecture of the CU and DU of the access network equipment is described below. An access network equipment includes at least one CU and at least one DU. Optionally, the access network equipment may also include at least one RU.

[0092] The following description uses an access network device consisting of one CU and one DU as an example. The CU has some core network functions and can include CU-CP and CU-UP. The CU and DU can be configured according to the protocol layer functions of the wireless network they implement. For example, the CU may be configured to implement the functions of the Packet Data Convergence Protocol (PDCP) layer and above (e.g., RRC and / or SDAP layers). The DU may be configured to implement the functions of protocol layers below the PDCP layer (e.g., RLC, MAC, and / or physical (PHY) layers). Alternatively, the CU may be configured to implement the functions of protocol layers above the PDCP layer (e.g., RRC and / or SDAP layers), and the DU may be configured to implement the functions of protocol layers below the PDCP layer (e.g., RLC, MAC, and / or PHY layers).

[0093] When a CU includes CU-CP and CU-UP, CU-CP is used to implement the control plane functions of the CU, and CU-UP is used to implement the user plane functions of the CU. For example, when a CU is configured to implement the functions of the PDCP layer, RRC layer, and SDAP layer, CU-CP is used to implement the RRC layer functions and the control plane functions of the PDCP layer, and CU-UP is used to implement the SDAP layer functions and the user plane functions of the PDCP layer.

[0094] The CU-CP can interact with network elements in the core network used to implement control plane functions. These network elements can be access and mobility function (AMF) network elements, such as the AMF in a 5G system. The AMF is responsible for mobility management in the mobile network, such as terminal device location updates, terminal device registration with the network, and terminal device handover.

[0095] CU-UP can interact with network elements in the core network used to implement user plane functions. These network elements, such as the user plane function (UPF) in a 5G system, are responsible for forwarding and receiving data in terminal devices.

[0096] The above CU and DU configurations are merely examples; the functions of the CU and DU can be configured as needed. For instance, the CU or DU can be configured to have more protocol layer functions, or only some protocol layer processing functions. For example, some RLC layer functions and protocol layer functions above the RLC layer can be placed in the CU, while the remaining RLC layer functions and protocol layer functions below the RLC layer can be placed in the DU. Furthermore, the functions of the CU or DU can be divided according to service type or other system requirements. For example, based on latency, functions that require low latency can be placed in the DU, while functions that do not require low latency can be placed in the CU.

[0097] DU and RU can cooperate to implement the functions of the PHY layer. A DU can be connected to one or more RUs. The functions of DU and RU can be configured in various ways depending on the design. For example, a DU can be configured to implement baseband functions, and an RU can be configured to implement mid-RF functions. Another example is that a DU can be configured to implement higher-level functions in the PHY layer, and an RU can be configured to implement lower-level functions in the PHY layer, or to implement both lower-level and RF functions. Higher-level functions in the physical layer can include a portion of the physical layer's functions that are closer to the MAC layer, while lower-level functions in the physical layer can include another portion of the physical layer's functions that are closer to the mid-RF side.

[0098] It should be noted that the access network equipment can be a device or apparatus with a chip, or a device or apparatus with integrated circuits, or a chip, chip system, module, or control unit in the aforementioned device or apparatus; this application does not impose any specific limitation. It should also be noted that in this application, the term "access network equipment" can refer to the access network equipment itself, or to the chip, functional module, or integrated circuit within the access network equipment that performs the method provided in this application; this application does not impose any specific limitation.

[0099] In another possible scenario, multiple RAN nodes collaborate to assist the terminal in achieving wireless access, with each RAN node performing a portion of the base station's functions. For example, RAN nodes can be central units (CUs), distributed units (DUs), CU-CPs, CU-UPs, or radio units (RUs). CUs and DUs can be configured separately or included in the same network element, such as a baseband unit (BBU). RUs can be included in radio equipment or radio units, such as remote radio units (RRUs), active antenna units (AAUs), or remote radio heads (RRHs).

[0100] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an ORAN system, CU can also be called O-CU (open CU), DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. For ease of description, this application uses CU, CU-CP, CU-UP, DU, and RU as examples. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software and hardware modules.

[0101] A terminal can be a device or module that accesses the aforementioned communication system and has corresponding communication functions. A terminal can also be called a terminal device, user equipment (UE), mobile station, mobile terminal, etc. Terminals 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 grids, smart homes, smart offices, smart wearables, intelligent transportation, smart cities, etc. A terminal can be a mobile phone, tablet computer, computer with wireless transceiver capabilities, wearable device, vehicle, drone, helicopter, airplane, ship, robot, robotic arm, smart home device, transportation vehicle with wireless communication capabilities, communication module, etc. The embodiments of this application do not limit the device form of the terminal. A terminal typically contains a communication module, circuit, or chip that performs the corresponding communication functions. The terminal can also be configured with program instructions for performing the corresponding communication functions. In this application, the terminal can be a complete terminal device, or a component, functional module, chip, chip module, etc. in the terminal device that implements the solution in this application.

[0102] by Figure 1a Taking the communication system shown as an example, in addition to performing communication-related services, different devices (including network devices and terminal devices, and / or terminal devices and terminal devices) may also perform AI-related services.

[0103] like Figure 1b As shown, taking a network device as a base station as an example, a base station can perform communication-related services and AI-related services with one or more terminal devices, and different terminal devices can also perform communication-related services and AI-related services.

[0104] like Figure 1c As shown, taking terminal devices including TVs and mobile phones as an example, TVs and mobile phones can also perform communication-related services and AI-related services.

[0105] The technical solution provided in this application can be applied to wireless communication systems (e.g.) Figure 1a , Figure 1b or Figure 1cThe system shown, for example, the communication system provided in this application, can incorporate artificial intelligence (AI) network elements to implement some or all AI-related operations. AI network elements can also be called AI nodes, AI devices, AI entities, AI modules, AI models, or AI units, etc. The AI ​​network element can be built into the network elements of the communication system. For example, the AI ​​network element can be an AI module built into: access network equipment, core network equipment, cloud server, or operation, administration, and maintenance (OAM) to implement AI-related functions. The OAM can act as the network management system for the core network equipment and / or the access network equipment. Alternatively, the AI ​​network element can also be a network element independently set up in the communication system. Optionally, the terminal or its built-in chip can also include an AI entity to implement AI-related functions.

[0106] Optionally, in communication systems, AI application cases may include, but are not limited to: channel state information (CSI) feedback enhancement, beam management enhancement, positioning accuracy enhancement, network energy saving, load balancing, and mobility optimization. Examples are given below.

[0107] 1. Enhanced CSI feedback

[0108] Channel quality information (CSI) is the channel attribute of a communication link, reported by the terminal device to the network device. By reporting this information, the terminal device can select an appropriate modulation and coding scheme (MCS) to adapt to changing wireless channels. For example, the terminal device might perform channel estimation based on the received channel state information-reference signal (CSI-RS) and then feed back the CSI-RS to the network device. This information serves as input to the network device's model, enabling AI model training. Applying AI to CSI feedback enhancement can reduce overhead, improve accuracy, and enhance predictive capabilities.

[0109] CSI-RS feedback enhancement may include at least one sub-function, such as: CSI compression, CSI prediction, and CSI-RS configuration signaling reduction. CSI compression may further include CSI compression in at least one domain: spatial, time, and frequency.

[0110] 2. Enhanced Beam Management

[0111] Enhanced beam management primarily aims to discover the strongest transmit / receive beam pairs. AI-based sparse beam prediction can improve accuracy. This can be achieved through both network-side and terminal-side AI sparse beam prediction, based on AI training and inference. Taking terminal-side AI sparse beam prediction as an example, the pre-trained AI model on the terminal device can be provided by the network or pre-stored on the terminal device. During training, the network device scans all possible beams and then reports the transmit beam pattern to the terminal device. Once training is complete, the network device only needs to scan a small subset of beams, and the terminal device then feeds back the inference results. AI-based beam management can achieve beam prediction in, for example, the temporal and / or spatial domains, reducing overhead and latency and improving beam selection accuracy.

[0112] Beam management enhancements may include at least one sub-function, such as: beam scan matrix prediction and optimal beam prediction.

[0113] 3. Enhanced positioning accuracy

[0114] In line-of-sight (LOS) or non-line-of-sight (NLOS) scenarios, AI-based positioning can improve positioning accuracy with a smaller number of TRP antennas. Positioning enhancement can include at least one sub-function, such as: positioning enhancement based on access network devices, positioning enhancement based on positioning management function network elements, and positioning enhancement based on terminal devices.

[0115] 4. Network energy saving

[0116] Network energy conservation can be achieved through cell activation / deactivation, load reduction, coverage improvement, or other RAN setting adjustments. AI technology can be used to optimize energy-saving decisions by leveraging data collected within the RAN network. AI algorithms can predict energy efficiency and load status for the next cycle, which can be used to assist in cell activation / deactivation decisions to save energy. Based on the predicted load, the system can dynamically configure energy-saving strategies to maintain a balance between system performance and energy efficiency, and reduce energy consumption.

[0117] 5. Load balancing

[0118] Load balancing can distribute the load evenly between cells and across different areas within a cell, or transfer some traffic from congested cells, or offload users across a single cell, carrier, or access standard, thereby improving network performance. Using AI models to enhance load balancing performance—such as inputting various measurements and feedback from terminal devices and network nodes, as well as historical data—can provide a higher quality user experience and increase system capacity.

[0119] 6. Mobility Management

[0120] Mobility management is a solution that ensures service continuity for mobile devices by minimizing dropped calls, radio link failures (RLFs), unnecessary handovers, and ping-pong effects. AI can enhance mobility management by, for example, reducing the probability of unexpected events, predicting device location / mobility / performance, and routing traffic.

[0121] It should be understood that the definitions of the above technical terms are merely illustrative. For example, as technology continues to develop, the scope of the above definitions may also change, and the embodiments of this application are not intended to limit the scope.

[0122] For example, an AI function may include multiple AI sub-functions.

[0123] Alternatively, AI application cases are also referred to as AI application scenarios or AI functions.

[0124] As described above regarding AI application examples, AI can be widely used to improve network performance in areas such as CSI feedback enhancement, beam management, positioning accuracy enhancement, energy saving, mobility enhancement, and load balancing. AI models can typically be deployed on the network side and / or the terminal device side. The training of AI models relies on the collection of training data, which can come from measurements and feedback from the terminal devices.

[0125] The following is a brief introduction to the concepts that may be involved in this application.

[0126] AI can endow machines with human-like intelligence, for example, allowing them to use computer hardware and software to simulate certain intelligent human behaviors. To achieve artificial intelligence, machine learning methods can be employed. In machine learning, machines learn (or train) a model using training data. This model represents the mapping between inputs and outputs. The learned model can be used for reasoning (or prediction), that is, it can be used to predict the output corresponding to a given input. This output can also be called the reasoning result (or prediction result).

[0127] Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Unsupervised learning can also be called learning without supervision.

[0128] Supervised learning, based on collected sample values ​​and labels, uses machine learning algorithms to learn the mapping relationship between sample values ​​and labels, and then expresses this learned mapping relationship using an AI model. The process of training the machine learning model is the process of learning this mapping relationship. During training, sample values ​​are input into the model to obtain the model's predicted values, and the model parameters are optimized by calculating the error between the model's predicted values ​​and the sample labels (ideal values). After the mapping relationship is learned, it can be used to predict new sample labels. The mapping relationship learned in supervised learning can include linear or non-linear mappings. Based on the type of label, the learning task can be divided into classification tasks and regression tasks.

[0129] Unsupervised learning relies on collected sample values ​​to discover inherent patterns within the samples themselves. One type of unsupervised learning algorithm uses the samples themselves as supervisory signals, meaning the model learns the mapping relationship from sample to sample; this is called self-supervised learning. During training, model parameters are optimized by calculating the error between the model's predictions and the samples themselves. Self-supervised learning can be used for signal compression and decompression recovery applications; common algorithms include autoencoders and generative adversarial networks.

[0130] Reinforcement learning, unlike supervised learning, is a type of algorithm that learns problem-solving strategies through interaction with the environment. Unlike supervised and unsupervised learning, reinforcement learning problems do not have explicit "correct" action labels. The algorithm needs to interact with the environment to obtain reward signals from the environment, and then adjust its decision actions to obtain a larger reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmission power of each user based on the total system throughput feedback from the wireless network, aiming to achieve a higher system throughput. The goal of reinforcement learning is also to learn the mapping relationship between the environment state and a better (e.g., optimal) decision action. However, because the label of the "correct action" cannot be obtained in advance, the network cannot be optimized by calculating the error between the action and the "correct action." Reinforcement learning training is achieved through iterative interaction with the environment.

[0131] Neural networks (NNs) are a specific model in machine learning techniques. According to the general approximation theorem, neural networks can theoretically approximate any continuous function, thus enabling them to learn arbitrary mappings. Traditional communication systems rely on extensive expert knowledge to design communication modules, while deep learning communication systems based on neural networks can automatically discover hidden pattern structures from large datasets, establish mapping relationships between data, and achieve performance superior to traditional modeling methods.

[0132] The idea behind neural networks comes from the neuronal structure of the brain. For example, each neuron performs a weighted summation of its input values ​​and outputs the result through an activation function.

[0133] like Figure 2a The diagram shown is a schematic representation of a neuron structure. Assume the neuron's input is x = [x0, x1, ..., x...]. n The weights corresponding to each input are w = [w0, w1, ..., w] n ], where n is a positive integer, w i and x i It can be any possible type, such as a decimal, an integer (e.g., 0, a positive integer, or a negative integer), or a complex number. i As x i The weights are used to assign weights to x. i Weighting is applied. The bias for the weighted sum of the input values ​​is, for example, b. Activation functions can take many forms. Suppose the activation function of a neuron is: y = f(z) = max(0, z), then the output of that neuron is: For example, if the activation function of a neuron is y = f(z) = z, then the output of that neuron is: Here, b can be any possible type, such as a decimal, an integer (e.g., 0, a positive integer, or a negative integer), or a complex number. The activation functions of different neurons in a neural network can be the same or different.

[0134] Furthermore, neural networks generally consist of multiple layers, each of which may include one or more neurons. Increasing the depth and / or width of a neural network can improve its expressive power, providing more powerful information extraction and abstract modeling capabilities for complex systems. The depth of a neural network can refer to the number of layers it includes, and the number of neurons in each layer can be called the width of that layer. In one implementation, a neural network includes an input layer and an output layer. The input layer processes the received input information through neurons and passes the processing result to the output layer, which then obtains the output of the neural network. In another implementation, a neural network includes an input layer, hidden layers, and an output layer. The input layer processes the received input information through neurons and passes the processing result to the hidden layer. The hidden layer calculates the received processing result and passes the calculation result to the output layer or the next adjacent hidden layer, ultimately obtaining the output of the neural network. A neural network may include one hidden layer or multiple sequentially connected hidden layers, without limitation.

[0135] Neural networks, for example, are deep neural networks (DNNs). Depending on how the network is constructed, DNNs can include feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

[0136] Figure 2b This is a schematic diagram of a Free-Nearest Neural Network (FNN). A key characteristic of FNNs is that neurons in adjacent layers are completely connected pairwise. This characteristic makes FNNs typically require a large amount of storage space, leading to high computational complexity.

[0137] CNNs are neural networks specifically designed to process data with a grid-like structure. For example, time-series data (e.g., discrete sampling along a time axis) and image data (e.g., two-dimensional discrete sampling) can both be considered grid-like data. CNNs do not use all the input information at once for computation; instead, they use a fixed-size window to extract a portion of the information for convolution operations, which significantly reduces the computational cost of model parameters. Furthermore, depending on the type of information extracted by the window (e.g., people and objects in an image represent different types of information), each window can use different convolution kernels, allowing CNNs to better extract features from the input data.

[0138] Recurrent Neural Networks (RNNs) are a type of neural network that utilizes feedback time-series information. The input to an RNN includes the current input value and its own output value from the previous time step. RNNs are suitable for acquiring temporally correlated sequence features, and are applicable to applications such as speech recognition and channel coding / decoding.

[0139] In the model training process described above, a loss function can be defined. The loss function describes the difference between the model's output value and the ideal target value. The loss function can be expressed in various forms, and there are no restrictions on its specific form. The model training process can be viewed as follows: by adjusting some or all of the model's parameters, the value of the loss function is made to be less than a threshold or to meet the target requirement.

[0140] A model can also be called an AI model, a rule, or other names. An AI model can be considered a specific method for implementing AI functions. An AI model represents the mapping relationship or function between the model's input and output. AI functions can include one or more of the following: data collection, model training (or model learning), model information dissemination, model inference (or model reasoning, inference, or prediction, etc.), model monitoring or model validation, or inference result publication, etc. AI functions can also be called AI (related) operations or AI-related functions.

[0141] The implementation process of the neural network will be described below with reference to the accompanying drawings.

[0142] 1. Fully connected neural networks, also known as multilayer perceptrons (MLP).

[0143] like Figure 2c As shown, an MLP consists of an input layer (left side), an output layer (right side), and multiple hidden layers (middle). Each layer of an MLP contains several nodes, called neurons. Neurons in adjacent layers are connected pairwise.

[0144] Optionally, considering neurons in two adjacent layers, the output h of a neuron in the next layer is the weighted sum of all neurons x in the previous layer connected to it, processed by an activation function, and can be expressed as:

[0145] h = f(wx + b).

[0146] Where w is the weight matrix, b is the bias vector, and f is the activation function.

[0147] Alternatively, the output of the neural network can be recursively expressed as:

[0148] y = f z (w z f z-1 (…)+b z ).

[0149] Where z is the index of the neural network layer, z is greater than or equal to 1 and z is less than or equal to Z, where Z is the total number of layers in the neural network.

[0150] In other words, a neural network can be understood as a mapping from an input data set to an output data set. Neural networks are typically initialized randomly; the process of obtaining this mapping from random values ​​w and b using existing data is called training the neural network.

[0151] Optionally, the training process may involve evaluating the output of the neural network using a loss function.

[0152] like Figure 2d As shown, the error can be backpropagated, and the neural network parameters (including w and b) can be iteratively optimized using gradient descent until the output of the loss function reaches its minimum value. Figure 2d The term "relative advantage (e.g., optimal advantage)" is used. This is understandable. Figure 2d The neural network parameters corresponding to the "better points (e.g., the best points)" in the data can be used as neural network parameters in the trained AI model information.

[0153] Alternatively, the gradient descent process can be represented as:

[0154]

[0155] Where θ represents the parameters to be optimized (including w and b), L is the loss function, and η is the learning rate, controlling the step size of gradient descent. This represents the differentiation operation. This indicates taking the derivative of θ with respect to L.

[0156] Alternatively, the backpropagation process may utilize the chain rule for partial derivatives.

[0157] like Figure 2e As shown, the gradient of the parameters in the previous layer can be recursively calculated from the gradient of the parameters in the next layer, and can be expressed as:

[0158]

[0159] Among them, w ij Let s be the weight of the connection between node j and node i. i The weighted sum of the inputs at node i.

[0160] 2. Federated Learning (FL).

[0161] The concept of federated learning effectively addresses the current challenges in the development of artificial intelligence. While fully protecting user data privacy and security, it enables various edge devices and central servers to collaborate efficiently to complete the model's learning task.

[0162] like Figure 2f As shown, the FL architecture is currently the most widely used training architecture in the FL field, and the FedAvg algorithm is the foundational algorithm of FL. The FedAvg algorithm flow is roughly as follows:

[0163] (1) Initialize the model to be trained at the center end. And broadcast it to all clients.

[0164] (2) In the t∈[1,T] round, the client k∈[1,K] is based on the local dataset. For the received global model Perform E epochs of training to obtain the local training results. Report it to the central node. Figure 2f In the example shown, the local training results sent by distributed nodes n, k, and m are denoted as G, respectively. n G k G m .

[0165] (3) The central node collects local training results from all (or some) clients. Assume the set of clients uploading local models in round t is... The central server will use the number of samples from the corresponding client as weights to calculate the new global model. The specific update rule is as follows: Then the central end will send the latest version of the global model. The broadcast is sent to all clients for a new round of training.

[0166] (4) Repeat steps (2) and (3) until the model finally converges or the number of training rounds reaches the upper limit.

[0167] Optionally, in addition to reporting the local model, the client can also... It can also train local gradients The central node averages the local gradients reported by all clients and updates the global model based on this average gradient.

[0168] As can be seen in the FL framework, the dataset resides on distributed nodes (such as clients). These distributed nodes collect their local datasets, perform local training, and report the local results (model or gradients) to the central node. The central node itself may not have a dataset; it can be responsible for fusing the training results from the distributed nodes to obtain a global model, which is then distributed back to the distributed nodes.

[0169] 3. Decentralized learning.

[0170] like Figure 2g The diagram shows a fully distributed system without a central node. The design goal of decentralized learning systems is generally that each node's objective f... i The mean of (x), i.e. Where n is the number of distributed nodes, and x is the parameter to be optimized; in machine learning, x is the parameter of the machine learning model (such as a neural network). Each node utilizes local data and its local target f. i (x) Calculate the local gradient Then it is sent to its communicatively reachable neighboring nodes. Upon receiving the gradient information from its neighbor, any node can update the parameters x of its local model according to the following formula:

[0171]

[0172] in, This represents the parameters of the local model after the (k+1)th update (k is a natural number) in the i-th node. This represents the parameters of the local model for the i-th node after the k-th update (if k is 0, then it represents...). (where α is the parameter of the local model of the i-th node that is not involved in the update) k N represents the tuning coefficient. i It is the set of neighboring nodes of node i, |N i | represents the number of elements in the set of neighboring nodes of node i, that is, the number of neighboring nodes of node i. Through information interaction between nodes, the decentralized learning system will eventually learn a unified model.

[0173] The technical solution provided in this application can be applied to communication systems (e.g.) Figure 1a or Figure 1b or Figure 1c In a communication system (as shown in the diagram), communication nodes typically possess both signal transmission and reception capabilities and computational capabilities. Taking a network device with computational capabilities as an example, the network device's computational capabilities primarily provide computing power support for signal transmission and reception capabilities (e.g., processing signals for transmission and reception) to enable the network device to perform communication tasks with other communication nodes.

[0174] With the development of communication technology, communication equipment in communication systems can now perform not only traditional communication services but also other new types of services, such as AI services. Generally speaking, a system capable of handling AI services, such as a communication system, can also be called an AI system.

[0175] In AI systems, different nodes can transmit data such as model parameters via wireless networks. Figure 3This paper illustrates an application scenario applicable to embodiments of this application, where a transmitting device sends model parameters to a receiving device. Exemplarily, the transmitting device can be a central node in distributed learning, and the receiving device can be a distributed node in distributed learning. The transmitting device can be a network device, a component or device applied to a network device (e.g., a processor, circuit, chip, or chip system), or a logic module or software capable of implementing all or part of the network device's functions (e.g., a central unit (CU), a distributed unit (DU), or a radio unit (RU)). The transmitting device can also be a terminal device, a component or device applied to a terminal device (e.g., a processor, circuit, chip, or chip system), or a circuit or chip in the terminal device responsible for communication functions (e.g., a modem chip, also known as a baseband chip, or a system-on-a-chip (SoC) chip containing a modem core, or a system-in-package (SIP) chip).

[0176] The receiving end device can be a network device, a component or device applied to a network device (such as a processor, circuit, chip, or chip system), or a logic module or software (such as a CU, DU, or RU) that can implement all or part of the functions of the network device. The receiving end device can also be a terminal device, a component or device applied to a terminal device (such as a processor, circuit, chip, or chip system), or a logic module or software that can implement all or part of the functions of the terminal device.

[0177] It should be noted that the sending and receiving devices can be of the same type, such as both being network devices, or both being terminal devices. Alternatively, the sending and receiving devices can be of different types, such as the sending device being a network device and the receiving device being a terminal device; or, for example, the sending device being a terminal device and the receiving device being a network device. No specific limitation is made here.

[0178] Since the model parameters sent by the sending device to the receiving device should meet the requirement of sending all available parameters, and air interface resources are limited, the sending device may not be able to send all the model parameters to the receiving device at once, which may lead to service latency or service interruption.

[0179] Based on this, an embodiment of this application provides a method. Please refer to... Figure 4 One method in this application embodiment includes:

[0180] 401. The sending device determines the first information.

[0181] The transmitting device uses data units to carry AI data and uses first information to indicate the AI ​​data carried in the data unit. Specifically, the first information indicates X AI data points, where X is a positive integer.

[0182] In this embodiment of the application, the data unit is a transmission resource used to transmit AI data. The data unit can be a transport block (TB) or a code block (CB), or a service data unit (SDU), or a frame or subframe of the physical layer, or a protocol data unit (PDU) of the MAC layer. The specifics are not limited here.

[0183] In this embodiment of the application, AI data refers to data used to provide AI services. AI data can be AI models, AI model parameters, AI model input data, or AI model output results. This data can be structured, such as tabular data in a database, or unstructured, such as text, images, and videos. No specific limitation is made here.

[0184] The transmitting device transmits X AI data points using Y data units, where Y data units are used to transmit M AI data points, Y is a positive integer, and M is an integer greater than or equal to X.

[0185] In this embodiment, since the transmitting device transmits X AI data points through Y data units, and M is greater than or equal to X, the receiving device can release idle resources in the data units for use in other service transmissions. Furthermore, indicating at the data unit level reduces transmission latency.

[0186] Taking AI model parameters as an example, in the process of neural network training, in order to reduce training complexity, the importance of model parameters can be evaluated, and parameters with less impact on performance can be pruned to obtain a lightweight model. Even after lightweighting, the model parameters still have a large data volume, requiring multiple transmission opportunities to complete the full transmission. The sending device selects the model parameters to be uploaded from its local model parameters. The selection method can be based on importance principles or predefined principles; the specific method is not limited here.

[0187] It should be noted that the number of data units Y is determined based on the amount of AI data the sending device needs to transmit and the capacity of each data unit. Specifically, if the sending device needs to transmit a total of M AI data points, and each data unit can transmit N AI data points, then... This indicates that the sending device needs at least Y data units to send M AI data.

[0188] In one possible implementation, X AI data points and the first information are both carried within the first data unit of Y data units. For example... Figure 5 As shown, the first data unit carries indication information. The first data unit is TB1, and the indication information is the first information. In addition to the indication information, the part of TB1 carries X AI data.

[0189] Optionally, the first data unit is the first data unit among Y data units.

[0190] Since Y data units are used to transmit M AI data, each data unit is used to transmit... AI data, among which This indicates rounding up, where X is less than or equal to the nearest integer. This method is also known as bitmap.

[0191] Optional, the first information is The information is indicated by a single bit. X bits out of 1 are 1, or, the In this context, X bits are 0. That is, the transmitting device can use X 1s to represent X AI data, or it can use X 0s to represent X AI data; the specific choice is not limited here.

[0192] For example, such as Figure 6 As shown, the first information is 011001. This first information can be used to indicate that the first data unit carries parameters 2, 3, and 6; that is, a bit of 1 in the first information indicates that the parameter is carried in the first data unit. The first information can also be used to indicate that the first data unit carries parameters 1, 4, and 5; that is, a bit of 0 in the first information indicates that the parameter is carried in the first data unit.

[0193] It should be noted that the first information can be used to indicate the quantity X of AI data, or it can be used to indicate the specific X AI data points. For example, if the first information is 101, it means that the sending device sent 2 model parameters. Or, if the first information is 101, it means the first and third model parameters out of the first to third model parameters sent by the sending device.

[0194] In another possible implementation, the first piece of information is carried in the first data unit, and X pieces of AI data are carried in the second data unit. For example... Figure 7As shown, the first data unit is TB1, which carries indication information. X AI data are carried in TB2, where the first information in TB1 is used to indicate the AI ​​data in TB2.

[0195] The first information could be The indication information is provided by each bit. The specific indication method can be referred to the above description, and is not limited here.

[0196] For example, if the first information in TB1 is 001, it means that the X AI data in TB2 are the 6th model parameter among the 4th to 6th model parameters.

[0197] It should be noted that the AI ​​data in TB1 can be indicated by additional indication information or by the first information. For example, if the first information is 001101, it means that TB1 carries the third model parameter out of the first to third model parameters, and TB2 carries the fourth and sixth model parameters out of the fourth to sixth model parameters. Specific details are not limited here.

[0198] exist Figure 7 In the illustrated embodiment, the first data unit is the first data unit among Y data units. In practical applications, the first data unit can be the i-th data unit among Y data units, and the second data unit can be the (i+1)-th data unit among Y data units, where i is less than or equal to Y-1.

[0199] Optionally, the indication information carried by the i-th data unit can also be used to indicate the transmission attributes of the AI ​​data in the (i+1)-th data unit, such as the quantization method and repetition count of the AI ​​data. If all AI data is divided into several sets, the indication information can also indicate the set number. Specific details are not limited here.

[0200] It should be noted that the number Y of data units is related to the amount of AI data the sending device needs to send and the amount of AI data each data unit can transmit. For example, if the sending device needs to send a total of N AI data points, and each data unit can send M data points...

[0201] In another possible implementation, the first information is carried in the first data unit, and X AI data points are carried in one or more data units among Y data units, where the Y data units are data units of the same period. For example... Figure 8 As shown, the transmitting device determines the transmission period. One transmission period includes Y data units, and X data units can be carried by one or more of the Y data units.

[0202] It should be noted that a transmission cycle can be one or more transmission time intervals (TTI), slots, mini-slots, frames, half-frames, or orthogonal frequency division multiplexing (OFDM) symbols, without being limited here.

[0203] For example, the first data unit is the first data unit among Y data units, the first information indicates which AI data is carried by the Y data units, and the first information can also be used to indicate which data units among the Y data units can be released.

[0204] The first information is an indication information consisting of M bits, where X bits are 1, or M bits are 0.

[0205] For example, if a transmission cycle consists of 6 data units, and each data unit can transmit one model parameter, and the 6 data units carry the first 3 model parameters from the 1st to the 6th model parameters (i.e., the 1st to 3rd model parameters), then the first information is 111000. Since each data unit can transmit one model parameter, the first information can also indicate that the 1st to 3rd data units carry model parameters, while the 4th to 6th data units do not.

[0206] For example, if a transmission cycle has four data units, and each data unit can transmit two model parameters, and the four data units carry the third to seventh model parameters out of the first to eighth model parameters, then the first information is 0011111110. Since each data unit can transmit two model parameters, the first information can also indicate that the second to fourth data units out of the four data units carry model parameters, where the fourth data unit carries one model parameter, namely the seventh model parameter, and the first data unit does not carry any model parameters.

[0207] In another possible implementation, the first information is carried in signaling, and X AI data points are carried in one or more data units out of Y data units. For example... Figure 9 As shown, the signaling can be a MAC control element (CE), and the data unit can be an SDU.

[0208] Specifically, the transmitting end transmits AI data based on MAC subPDU. MAC PDU consists of a MAC header, MAC SDU, MAC CE, and padding. The subheader in the MAC header corresponds to either the MAC PDU, CE, or padding. The SDU that constitutes the subPDU carries the AI ​​data, and the subheader uses different parameter sets from the Logical Channel Identification (LCID) model.

[0209] Optionally, different LCIDs correspond to different priorities.

[0210] Optionally, the sending device can directly indicate the size of the next transmission resource in the MAC CE.

[0211] It should be understood that the order or index of AI data can be agreed upon by the sending and receiving devices, or predefined by the protocol, or indicated by the network device (e.g., when the sending device is a terminal device), and no specific limitation is made here.

[0212] It should be noted that in the above embodiments, the first information can also be used to indicate one or more sets of AI data. For example, nine model parameters are divided into three parameter groups: parameters 1 to 3 form parameter group 1, parameters 4 to 6 form parameter group 2, and parameters 7 to 9 form parameter group 3. The first information is a 3-bit indication, with each bit indicating a parameter group containing multiple model parameters. Assuming the first information is 101, it indicates that Y data units carry parameters 1 and parameter group 3; the specific meaning is not limited here.

[0213] Optionally, multiple model parameters in a parameter group can be multiple model parameters with consecutive parameter numbers or parameter indices.

[0214] Optionally, for neural network models, if the neural network predefines several simplified structures, the first information can also be used to carry indexes of these simplified structures to indicate which structure corresponds to the AI ​​data carried in the data unit.

[0215] Optionally, X AI data points may have the same priority, and the first information can also be used to indicate the priority of the X AI data points. Specifically, the AI ​​data is divided into several priorities. For example, when the AI ​​data is model parameters, the division can be based on the model structure, the characteristics of the model parameters (such as the magnitude of the parameter values), or other principles used in existing technologies to evaluate the importance of parameters. The specifics are not limited here.

[0216] Optionally, the priority division can be static, meaning the index of the AI ​​data contained in different priority sets remains unchanged; or it can be dynamic, meaning the index of the AI ​​data contained in different priority sets changes with the application scenario.

[0217] 402. The transmitting device sends the first information and Y data units to the receiving device, and correspondingly, the receiving device receives the first information and Y data units from the transmitting device.

[0218] In one possible implementation, the first information is carried within Y data units, as detailed in the above embodiments, and will not be repeated here. In this case, the first information and the Y data units can be sent via the same message.

[0219] In another possible implementation, the first information is sent via signaling (e.g., MAC CE), and the first information and the Y data units can be sent via different messages.

[0220] Optionally, for AI data of different priorities, the sending device can send it to the receiving device through other data units (e.g., Z data units, which are different from Y data units).

[0221] For example, X AI data points have the highest priority (first priority), and K AI data points have the highest priority (second priority). The first priority is higher than the second priority, unlike the K AI data points. If X AI data points are carried on Y data units and K AI data points are carried on Z data units, the sending device can first send Y data units. After sending the Y data units, the sending device then sends the Z data units; the specific details are not limited here.

[0222] Optionally, the transmission configurations for AI data with different priorities can differ. For example, high-priority AI data supports hybrid automatic repeat-request (HARQ), supports higher repetition counts, and supports lower-order modulation and coding schemes. Correspondingly, the size of the number of data units Y is equal to the maximum transmission resource requirement corresponding to the transmission configuration, which is not specified here.

[0223] Taking Y data units as the same cycle as an example, the transmitting device can periodically send AI data. The transmission interval for AI data with different priorities is different. For example, if X AI data units have the highest priority, they require 6 transmission cycles to complete transmission; if K AI data units have the highest priority, they require 3 transmission cycles to complete transmission. Since the first priority is higher than the second priority, the 6 transmission cycles used to transmit X AI data units can be 6 consecutive cycles, meaning the transmitting device transmits a portion of the X AI data units in each cycle. For K AI data units, the transmitting device transmits a portion of the K AI data units every 2 cycles; the specific interval is not limited here.

[0224] 403. The receiving device obtains X AI data points based on the first information.

[0225] After receiving the first information and Y data units, the receiving device obtains X AI data carried by the Y data units based on the first information. If the receiving device is a network device, it can also release idle resources in the Y data units for use in other service transmissions.

[0226] In this embodiment of the application, the receiving device can determine which AI data are carried by the Y data units based on the first information, thereby releasing the idle resources in the Y data units.

[0227] Optional, Figure 4 The illustrated embodiment also includes step 400. Step 400 may be performed before step 401.

[0228] 400. The sending device obtains the second information.

[0229] The second information is used to indicate the index of X data items, the priority of X data items, and one or more items from the set corresponding to X data items. The sending device determines the first information based on the second information.

[0230] In one possible implementation, the sending device is a network device. The sending device can determine the second information through a predefined protocol or through a method agreed upon in advance with the receiving device. The specific method is not limited here.

[0231] In another possible implementation, the sending device is a terminal device, in which case the sending device can receive second information from the network device.

[0232] Optionally, if the receiving device is a network device, the sending device may receive second information from the receiving device; the specifics are not limited here.

[0233] It should be noted that if the sending device is a terminal device, the sending device will also receive a message indicating the Y data units, which means that the Y data units are transmission resources allocated to the sending device by the network side.

[0234] The communication method in the embodiments of this application has been described above. The communication device in the embodiments of this application is described below. Please refer to [link / reference]. Figure 10 The communication device 1000 can be used to perform Figure 4 The process executed by the sending device in the illustrated embodiment can be specifically described in the relevant descriptions of the foregoing method embodiments. The communication device 1000 can be a network device, or a component or device applied to a network device (e.g., a processor, circuit, chip, or chip system), or a logic module or software capable of implementing all or part of the functions of a network device. The communication device can also be a terminal device, or a component or device applied to a terminal device (e.g., a processor, circuit, chip, or chip system), or a logic module or software capable of implementing all or part of the functions of a terminal device.

[0235] The communication device 1000 includes an interface module 1001 and a processing module 1002.

[0236] The processing module 1002 is used for data processing. The interface module 1001 can implement corresponding communication functions. The interface module 1001 can also be called a communication interface or a communication module.

[0237] Optionally, the communication device 1000 may further include a storage module, which can be used to store program code, program instructions and / or data. The processing module 1002 can read the instructions and / or data in the storage module so that the communication device 1000 can implement the aforementioned method embodiments.

[0238] The communication device 1000 can be used to perform the actions performed by the transmitting device in the above method embodiments. For example, it can be the transmitting device itself, a communication module within the transmitting device, or a circuit or chip within the transmitting device responsible for communication functions. The communication device 1000 can be the transmitting device or a component configurable on the transmitting device. The processing module 1002 is used to perform processing-related operations on the transmitting device side in the above method embodiments. The interface module 1001 is used to perform reception-related operations on the transmitting device side in the above method embodiments.

[0239] Optionally, the interface module 1001 may include a sending module and a receiving module. The sending module is used to perform the sending operation in the above method embodiments. The receiving module is used to perform the receiving operation in the above method embodiments.

[0240] It should be noted that the communication device 1000 may include a transmitting module but not a receiving module. Alternatively, the communication device 1000 may include a receiving module but not a transmitting module. Specifically, it depends on whether the above-described scheme performed by the communication device 1000 includes both transmitting and receiving actions. For example, the communication device 1000 is used to perform the above-described... Figure 4 The actions performed by the transmitting device in the illustrated embodiment. For details, please refer to the above. Figure 4 The relevant descriptions in the illustrated embodiments will not be elaborated here.

[0241] For example, the communication device 1000 is used to execute the following scheme:

[0242] Processing module 1002 is used to determine first information, which indicates X AI data, where X is a positive integer;

[0243] Interface module 1001 is used to send the first information and Y data units. The Y data units are used to transmit M AI data. The Y data units carry X AI data, where Y is a positive integer and M is an integer greater than or equal to X.

[0244] In one possible implementation, the data unit is a transport block or a service data unit. N is used to indicate that each of the Y data units is used to transmit N AI data units.

[0245] In another possible implementation, the Y data units include a first data unit, X AI data and first information carried in the first data unit, the first data unit being the first data unit among the Y data units, and X being less than or equal to N.

[0246] In another possible implementation, Y is a positive integer greater than 2, Y data units include a first data unit and a second data unit, the first information is carried in the first data unit, X AI data are carried in the second data unit, the first data unit is the i-th data unit among the Y data units, the second data unit is the (i+1)-th data unit among the Y data units, i is a positive integer less than Y-1, and X is less than or equal to N.

[0247] In another possible implementation, the first information is N bits of bit information, where the N bits correspond to N AI data, and X bits of the N bits are 1, or X bits of the N bits are 0.

[0248] In another possible implementation, the Y data units include a first data unit, the first information is carried in the first data unit, the Y data units are data units of the same transmission cycle, and X AI data are carried in one or more of the Y data units.

[0249] In another possible implementation, the first information is M bits of bit information, the M bits correspond to M AI data, and X bits of the M bits are 1, or X bits of the M bits are 0.

[0250] In another possible implementation, the first information is carried in the signaling, and X AI data are carried in one or more data units among Y data units.

[0251] In another possible implementation, the characteristic is that X AI data points have the same priority, and the first information is also used to indicate the priority of the X AI data points.

[0252] In another possible implementation, the interface module 1001 is also used to receive second information, which indicates the index of X data, the priority of X data, and one or more items in the set corresponding to X data, and the second information is used to determine the first information.

[0253] It should be understood that the specific procedures for each module to perform the above-mentioned corresponding processes have been described in detail in the above method embodiments, and will not be repeated here for the sake of brevity.

[0254] Optionally, when the communication device 1000 is a terminal device or a communication module within a terminal device, the processing module 1002 in the above embodiments can be implemented by at least one processor or processor-related circuitry. Specifically, the processor may include a modem chip, or a system-on-chip (SoC) chip containing a modem core, or a system-in-package (SIP) chip. The interface module 1001 can be implemented by a transceiver or transceiver-related circuitry. The interface module 1001 may also be referred to as a communication module or communication interface. The storage module can be implemented by at least one memory.

[0255] Optionally, when the communication device 1000 is a circuit or chip in a terminal device responsible for communication functions, such as a modem chip or a SoC chip or SIP chip containing a modem core, the function of the processing module 1002 can be implemented by a circuit system in the aforementioned chip that includes one or more processors or processing cores. The function of the interface module 1001 can be implemented by the interface circuit or data transceiver circuit on the aforementioned chip.

[0256] The following is another structural schematic diagram of the communication device according to an embodiment of this application. Please refer to... Figure 11 Communication devices can be used to perform Figure 4 The process executed by the receiving device in the illustrated embodiment can be found in the relevant descriptions in the foregoing method embodiments.

[0257] The communication device 1100 includes an interface module 1101. Optionally, a processing module 1102.

[0258] The processing module 1102 is used for data processing. The interface module 1101 can implement corresponding communication functions. The interface module 1101 can also be called a communication interface or a communication module.

[0259] Optionally, the communication device 1100 may further include a storage module, which can be used to store program code, program instructions and / or data. The processing module 1102 can read the instructions and / or data in the storage module so that the communication device 1100 can implement the aforementioned method embodiments.

[0260] The communication device 1100 can be used to perform the actions performed by the receiving device in the above method embodiments. For example, it can be the receiving device itself, a communication module within the receiving device, or a circuit or chip within the receiving device responsible for communication functions. The communication device 1100 can be the receiving device or a component configurable on the receiving device. The processing module 1102 is used to perform processing-related operations on the receiving device side in the above method embodiments. The interface module 1101 is used to perform reception-related operations on the receiving device side in the above method embodiments.

[0261] Optionally, interface module 1101 may include a sending module and a receiving module. The sending module is used to perform the sending operation in the above method embodiments. The receiving module is used to perform the receiving operation in the above method embodiments.

[0262] It should be noted that the communication device 1100 may include a transmitting module but not a receiving module. Alternatively, the communication device 1100 may include a receiving module but not a transmitting module. Specifically, it depends on whether the above-described scheme performed by the communication device 1100 includes both transmitting and receiving actions. For example, the communication device 1100 is used to perform the above-described... Figure 4 The actions performed by the receiving device in the illustrated embodiment are shown above. For details, please refer to the above. Figure 4 The relevant descriptions in the illustrated embodiments will not be elaborated here.

[0263] For example, the communication device 1100 is used to execute the following scheme:

[0264] Interface module 1101 is used to receive first information and Y data units. The Y data units are used to transmit M AI data. Each Y data unit carries X AI data, where X and Y are positive integers and M is an integer greater than or equal to X.

[0265] The processing module 1102 is used to obtain X AI data from Y data units based on the first information.

[0266] In one possible implementation, the data unit is a transport block or a service data unit. N is used to indicate that each of the Y data units is used to transmit N AI data units.

[0267] In another possible implementation, the Y data units include a first data unit, X AI data and first information carried in the first data unit, the first data unit being the first data unit among the Y data units, and X being less than or equal to N.

[0268] In another possible implementation, Y is a positive integer greater than 2, Y data units include a first data unit and a second data unit, the first information is carried in the first data unit, X AI data are carried in the second data unit, the first data unit is the i-th data unit among the Y data units, the second data unit is the (i+1)-th data unit among the Y data units, i is a positive integer less than Y-1, and X is less than or equal to N.

[0269] In another possible implementation, the first information is N bits of bit information, where the N bits correspond to N AI data, and X bits of the N bits are 1, or X bits of the N bits are 0.

[0270] In another possible implementation, the Y data units are data units of the same transmission cycle, and the X AI data are carried in one or more data units within the Y data units.

[0271] In another possible implementation, the first information is M bits of bit information, the M bits correspond to M AI data, and X bits of the M bits are 1, or X bits of the M bits are 0.

[0272] In another possible implementation, the first information is carried in the signaling, and X AI data are carried in one or more data units among Y data units.

[0273] In another possible implementation, X AI data points have the same priority, and the first information is also used to indicate the priority of the X AI data points.

[0274] In another possible implementation, interface module 1101 is also used to send second information, which is used to indicate the index of X data, the priority of X data, and one or more items in the set corresponding to X data, and the second information is used to determine the first information.

[0275] It should be understood that the specific procedures for each module to perform the above-mentioned corresponding processes have been described in detail in the above method embodiments, and will not be repeated here for the sake of brevity.

[0276] Optionally, when the communication device 1100 is a terminal device or a communication module within a terminal device, the processing module 1102 in the above embodiments can be implemented by at least one processor or processor-related circuitry. Specifically, the processor may include a modem chip, or a system-on-chip (SoC) chip containing a modem core, or a system-in-package (SIP) chip. The interface module 1101 can be implemented by a transceiver or transceiver-related circuitry. The interface module 1101 may also be referred to as a communication module or communication interface. The storage module can be implemented by at least one memory.

[0277] Optionally, when the communication device 1100 is a circuit or chip in a terminal device responsible for communication functions, such as a modem chip or a SoC chip or SIP chip containing a modem core, the function of the processing module 1102 can be implemented by a circuit system in the aforementioned chip that includes one or more processors or processing cores. The function of the interface module 1101 can be implemented by the interface circuit or data transceiver circuit on the aforementioned chip.

[0278] The following describes a communication device provided in an embodiment of this application. Please refer to [link / reference]. Figure 12 , Figure 12 This is a schematic diagram of a communication device provided in an embodiment of this application. The communication device can be a transmitting end device or a receiving end device in the above method embodiments, or it can be a chip, chip system, or processor that supports the transmitting end device or receiving end device in implementing the above methods. This communication device can be used to implement the methods described in the above method embodiments, and for details, please refer to the description in the above method embodiments.

[0279] The communication device may include one or more processors 1201, which are connected to a memory 1202, an input / output unit 1203, and a bus 1204. The processor 1201 may be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit (CPU). The baseband processor can be used to process communication protocols and communication data, while the CPU can be used to control the communication device (e.g., base station, baseband chip, terminal, terminal chip, DU or CU, etc.), execute software programs, and process data from the software programs.

[0280] Optionally, the communication device may include one or more memories 1202, which may store instructions that can be executed on the processor 1201, causing the communication device to perform the methods described in the above method embodiments. Optionally, the memories 1202 may also store data. The processor 1201 and the memories 1202 may be configured separately or integrated together.

[0281] Optionally, the communication device may also include a transceiver and an antenna. A transceiver, also called a transceiver unit, transceiver, or transceiver circuit, is used to implement transmission and reception functions. A transceiver may include a receiver and a transmitter; the receiver, also called a receiver circuit, is used to implement the receiving function; the transmitter, also called a transmitter or transmitting circuit, is used to implement the transmitting function.

[0282] In another possible design, the processor 1201 may include a transceiver for implementing receive and transmit functions. For example, the transceiver may be a transceiver circuit, an interface, or an interface circuit. The transceiver circuit, interface, or interface circuit for implementing receive and transmit functions may be separate or integrated. The aforementioned transceiver circuit, interface, or interface circuit may be used for reading and writing code / data, or it may be used for transmitting or relaying signals.

[0283] In another possible design, the processor 1201 may optionally store instructions that, when executed, cause the communication device to perform the methods described in the above method embodiments. The instructions may be stored in the processor 1201; in this case, the processor 1201 may be implemented in hardware.

[0284] In another possible design, the communication device may include circuitry that performs the transmitting or receiving or communication functions of the transmitting or receiving device in the aforementioned method embodiments. The processor and transceiver described in this application can be implemented on integrated circuits (ICs), analog ICs, radio frequency integrated circuits (RFICs), mixed-signal ICs, application-specific integrated circuits (ASICs), printed circuit boards (PCBs), electronic devices, etc. The processor and transceiver can also be manufactured using various IC process technologies, such as complementary metal oxide semiconductors (CMOS), n-type metal-oxide-semiconductor (NMOS), p-type metal oxide semiconductors (PMOS), bipolar junction transistors (BJTs), bipolar CMOS (BiCMOS), silicon germanium (SiGe), gallium arsenide (GaAs), etc.

[0285] The communication device described in the above embodiments can be a transmitting end device or a receiving end device, but the scope of the communication device described in the embodiments of this application is not limited to this, and the structure of the communication device is not limited to this. Figure 12 The communication device can be a standalone device or part of a larger device. For example, the communication device can be:

[0286] (1) Independent integrated circuit IC, or chip, or chip system or subsystem;

[0287] (2) A collection of one or more ICs, optionally including a storage component for storing data and instructions;

[0288] (3) ASIC, such as modem;

[0289] (4) Modules that can be embedded in other devices;

[0290] (5) Receivers, terminals, smart terminals, cellular phones, wireless devices, handheld devices, mobile units, vehicle-mounted devices, network devices, cloud devices, artificial intelligence devices, etc.

[0291] (6) Others, etc.

[0292] For cases where the communication device can be a chip or a chip system, please refer to [link / reference]. Figure 13 The diagram shows the structure of the chip. Figure 13 The chip 1300 shown includes a processor 1301 and an interface 1302. Optionally, it may also include a memory 1303. The number of processors 1301 can be one or more, and the number of interfaces 1302 can be multiple.

[0293] For cases where the chip is used to implement the functions of the transmitting or receiving device in the embodiments of this application:

[0294] The interface 1302 is used to receive or output signals;

[0295] The processor 1301 is used to perform data processing operations of network devices or terminal devices.

[0296] It should be understood that the processor in the embodiments of this application can be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor described above can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0297] It is understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAK are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0298] This application also provides a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the methods described in the foregoing embodiments. The computer-readable storage medium may be a non-volatile storage medium.

[0299] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods described in the foregoing embodiments.

[0300] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0301] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0302] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0303] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0304] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0305] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

Claims

1. A communication method, characterized in that, The method includes: Determine the first information, which is used to indicate X AI data points, where X is a positive integer; Send the first information and Y data units, wherein the Y data units are used to transmit M AI data, and the Y data units carry the X AI data, where Y is a positive integer and M is an integer greater than or equal to X.

2. The method according to claim 1, characterized in that, The data unit is a transport block or a service data unit. N is used to indicate that each of the Y data units is used to transmit N AI data units.

3. The method according to claim 2, characterized in that, The Y data units include a first data unit, in which the X AI data and the first information are carried. The first data unit is the first data unit among the Y data units, and X is less than or equal to N.

4. The method according to claim 2, characterized in that, Y is a positive integer greater than 2. The Y data units include a first data unit and a second data unit. The first information is carried in the first data unit, and the X AI data are carried in the second data unit. The first data unit is the i-th data unit among the Y data units, and the second data unit is the (i+1)-th data unit among the Y data units. i is a positive integer less than Y-1, and X is less than or equal to N.

5. The method according to claim 3 or 4, characterized in that, The first information is N bits of bit information, the N bits correspond to N AI data, and X bits of the N bits are 1, or X bits of the N bits are 0.

6. The method according to claim 1 or 2, characterized in that, The Y data units include a first data unit, the first information is carried in the first data unit, the Y data units are data units of the same transmission period, and the X AI data are carried in one or more of the Y data units.

7. The method according to claim 6, characterized in that, The first information is bit information of M bits, the M bits correspond to M AI data, and X bits of the M bits are 1, or X bits of the M bits are 0.

8. The method according to claim 1 or 2, characterized in that, The first information is carried in the signaling, and the X AI data are carried in one or more of the Y data units.

9. The method according to any one of claims 1 to 8, characterized in that, The X AI data points have the same priority, and the first information is also used to indicate the priority of the X AI data points.

10. The method according to any one of claims 1 to 9, characterized in that, The method further includes: Receive second information, which is used to indicate the index of the X data, the priority of the X data, and one or more items in the set corresponding to the X data, and the second information is used to determine the first information.

11. A communication method, characterized in that, The method includes: Receive first information and Y data units, wherein the Y data units are used to transmit M AI data, and the Y data units carry X AI data, where X and Y are positive integers, and M is an integer greater than or equal to X; Based on the first information, obtain the X AI data from the Y data units.

12. The method according to claim 11, characterized in that, The data unit is a transport block or a service data unit. N is used to indicate that each of the Y data units is used to transmit N AI data units.

13. The method according to claim 12, characterized in that, The Y data units include a first data unit, in which the X AI data and the first information are carried. The first data unit is the first data unit among the Y data units, and X is less than or equal to N.

14. The method according to claim 12, characterized in that, Y is a positive integer greater than 2. The Y data units include a first data unit and a second data unit. The first information is carried in the first data unit, and the X AI data are carried in the second data unit. The first data unit is the i-th data unit among the Y data units, and the second data unit is the (i+1)-th data unit among the Y data units. i is a positive integer less than Y-1, and X is less than or equal to N.

15. The method according to claim 13 or 14, characterized in that, The first information is N bits of bit information, the N bits correspond to N AI data, and X bits of the N bits are 1, or X bits of the N bits are 0.

16. The method according to claim 11 or 12, characterized in that, The Y data units are data units with the same transmission cycle, and the X AI data are carried in one or more data units among the Y data units.

17. The method according to claim 16, characterized in that, The first information is bit information of M bits, the M bits correspond to M AI data, and X bits of the M bits are 1, or X bits of the M bits are 0.

18. The method according to claim 11 or 12, characterized in that, The first information is carried in the signaling, and the X AI data are carried in one or more of the Y data units.

19. The method according to any one of claims 11 to 18, characterized in that, The X AI data points have the same priority, and the first information is also used to indicate the priority of the X AI data points.

20. The method according to any one of claims 11 to 19, characterized in that, The method further includes: Send a second message, which is used to indicate the index of the X data, the priority of the X data, and one or more items in the set corresponding to the X data, and the second message is used to determine the first message.

21. A communication device, characterized in that, Includes modules or units for performing the method as described in any one of claims 1 to 10.

22. A communication device, characterized in that, Includes modules or units for performing the method as described in any one of claims 11 to 20.

23. A communication device, characterized in that, include: A processor for executing a program that causes the communication device to perform the method as described in any one of claims 1 to 10.

24. A communication device, characterized in that, include: A processor for executing a program that causes the communication device to perform the method as described in any one of claims 11 to 20.

25. A computer-readable storage medium comprising instructions that, when executed on a computer, cause the computer to perform the method as claimed in any one of claims 1 to 10, or cause the computer to perform the method as claimed in any one of claims 11 to 20.

26. A computer program product comprising instructions that, when run on a computer, causes the computer to perform the method as claimed in any one of claims 1 to 10, or causes the computer to perform the method as claimed in any one of claims 11 to 20.

Images