A communication method and a communication device

Abstract

The application provides a communication method and a communication device. The method comprises: a first device obtaining first data; each of N first identifiers indicating a state of a second device when the first device obtains the first data, each of the N first identifiers belonging to one of M groups of first identifiers, any two of the N first identifiers belonging to different groups, M being an integer greater than or equal to 0, and N being an integer greater than or equal to 0 and less than or equal to M. The method provided by the application can reduce communication overhead.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and more specifically, to a communication method and a communication device. Background Technology

[0002] Currently, artificial intelligence (AI) has been introduced into wireless communication networks and can be applied to many air interface-related application scenarios, such as AI-based channel state information (CSI) prediction, AI-based beam management, AI-based CSI feedback, and AI-based positioning.

[0003] In some application scenarios related to air interface, the data used for AI models may correspond to different devices and device states. In order to distinguish different data, a large number of data identifiers need to be stored, which leads to high storage overhead. Summary of the Invention

[0004] This application provides a communication method and apparatus to reduce storage overhead.

[0005] Firstly, a method is provided that can be performed by an apparatus (e.g., a communication apparatus). The apparatus can be a device (such as a terminal device or a network device), or it can be a component of a device (e.g., a chip (such as a modem chip, also known as a baseband chip, or a system-on-chip (SoC) chip containing a modem core, or a system-in-package (SIP) chip), a chip system, or a circuit), which is not limited in this application. The following description primarily uses a first apparatus as an example.

[0006] The method includes: sending or receiving N first identifiers, each of the N first identifiers indicating a state of the second device when acquiring first data, each of the N first identifiers belonging to one of M groups of first identifiers, any two of the N first identifiers belonging to different groups, M being an integer greater than or equal to 0, and N being an integer greater than or equal to 0 and less than or equal to M; acquiring first data, the first data being used to train an AI model.

[0007] Based on the above scheme, M groups of first identifiers indicate the M states of the second device. N first identifiers are combined to indicate the state of the second device when the first device acquires the first data. In other words, the first device can determine the state of the second device when it acquires the first data by storing different combinations of N first identifiers, thereby reducing storage overhead.

[0008] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: obtaining a second identifier, the second identifier indicating information of the second device.

[0009] Based on the above scheme, the second identifier can identify the information of the second device. Specifically, after the first device determines the information of the second device based on the second identifier, the second identifier will not be updated if the second device is not updated (or changed). In other words, the first device does not need to repeatedly store the information of the second device, thereby reducing storage overhead.

[0010] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: sending N first identifiers to the second device, the N first identifiers indicating the state of the second device when receiving the second data; sending the second data to the second device; and receiving the first data, the first data being obtained by the second device from measuring the second data.

[0011] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: sending N first identifiers to the second device, the N first identifiers indicating the state of the second device when sending the second data; receiving the second data; and determining the first data based on the second data.

[0012] Based on the above scheme, the first device can actively request to obtain the first data, and the second device can adjust to the corresponding state based on N first identifiers, thereby making the communication process more flexible and efficient.

[0013] In conjunction with the first aspect, in some implementations of the first aspect, the number N of the first identifiers is related to one or more of the following: first data, the state of the second device.

[0014] In conjunction with the first aspect, some implementations of the first aspect include: the higher the correlation between the first data and the state of the second device, the larger the value of N; or, the lower the correlation between the first data and the state of the second device, the smaller the value of N.

[0015] Based on the above scheme, communication overhead is further reduced by classifying the first data. The larger the N value, the shorter the update cycle of the identifier of the first data; the lower the N value, the longer the update cycle of the identifier of the first data.

[0016] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: obtaining N2 first identifiers, the N2 first identifiers indicating the state of the second device when the third data is obtained, each of the N2 first identifiers belonging to one of the M groups of first identifiers, any two of the N2 first identifiers belonging to different groups, and N2 being an integer greater than or equal to 0 and less than or equal to M; determining a first model based on the N2 first identifiers, the first model being trained using the first data.

[0017] The third set of data is used in the model inference process.

[0018] Based on the above scheme, the first device determines the first model based on the N2 first identifiers corresponding to the third data. As a possible implementation, the N2 first identifiers are the same as the N first identifiers of the first data. The first model determined in this way is more compatible with the third data.

[0019] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: obtaining N3 first identifiers, wherein the N3 first identifiers are the updated identifiers among the N2 first identifiers, and N3 is an integer greater than or equal to 0 and less than or equal to N2; determining a second model based on the N3 first identifiers, wherein the second model is obtained by training with the first data.

[0020] Based on the above scheme, when the second device is updated, the first device can determine the second model based on the N3 first identifiers and the first identifiers among the N2 first identifiers that have not been updated, thereby reducing communication overhead.

[0021] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: receiving a first model from a first node, the first model being trained via first data; the first model being determined based on N2 first identifiers, the N2 first identifiers indicating the state of the second device when acquiring the third data, each of the N2 first identifiers belonging to one of M groups of first identifiers, any two of the N2 first identifiers belonging to different groups, and N2 being an integer greater than or equal to 0 and less than or equal to M.

[0022] Based on the above scheme, the first node determines the first model based on N2 first identifiers and sends it to the first device. This method makes the communication process more flexible and efficient, while also improving the security of the communication process.

[0023] In conjunction with the first aspect, some implementations of the first aspect include: the first identifier indicating any of the following: port mapping status, transmit power, beam shape.

[0024] In conjunction with the first aspect, some implementations of the first aspect include: the second identifier indicating one or more of the following: the manufacturer identifier of the second device, the device type of the second device, and the unique identifier of the second device.

[0025] In conjunction with the first aspect, some implementations of the first aspect include: receiving M sets of first identifiers from the first node, wherein the M sets of first identifiers are determined by the first node based on a second identifier.

[0026] Based on the above scheme, the security of the communication process is improved by avoiding the first device directly obtaining the second identifier. In one possible scenario, the second identifier is sensitive information.

[0027] Secondly, a method is provided that can be performed by an apparatus (e.g., a communication apparatus). This apparatus can be a device (such as a terminal device or a network device), or it can be a component of a device (e.g., a chip (such as a modem chip, also known as a baseband chip, or a SoC chip or SIP chip containing a modem core) or a chip system or circuit), and this application does not limit this. The following description primarily uses the second apparatus as an example.

[0028] The method includes: receiving or sending N first identifiers, each of the N first identifiers indicating a state of the second device when the first device acquires the first data, each of the N first identifiers belonging to one of M groups of first identifiers, any two of the N first identifiers belonging to different groups, M being an integer greater than or equal to 0, and N being an integer greater than or equal to 0 and less than or equal to M; and sending the first data, which is used to train an AI model.

[0029] In conjunction with the second aspect, in some implementations of the second aspect, the method further includes: sending a second identifier that indicates information of the second device.

[0030] In conjunction with the second aspect, in some implementations of the second aspect, the method further includes: receiving N first identifiers from the first device, the N first identifiers indicating the state of the second device when receiving the second data; receiving the second data from the first device; and measuring the second data to determine the first data.

[0031] In conjunction with the second aspect, in some implementations of the second aspect, the method further includes: receiving N first identifiers from the first device, the N first identifiers indicating the state of the second device when transmitting the second data; and transmitting the second data, which is used to determine the first data.

[0032] In conjunction with the second aspect, in some implementations of the second aspect, the number N of the first identifiers is related to one or more of the following: first data, the state of the second device.

[0033] In conjunction with the second aspect, some implementations of the second aspect include: the higher the correlation between the first data and the state of the second device, the larger the value of N.

[0034] In conjunction with the second aspect, in some implementations of the second aspect, the method further includes: sending N2 first identifiers, the N2 first identifiers indicating the state of the second device when the first device acquires the third data, each of the N2 first identifiers belonging to one of the M groups of first identifiers, any two of the N2 first identifiers belonging to different groups, and N2 being an integer greater than or equal to 0 and less than or equal to M; the N2 first identifiers are used to determine the first model, the first model being obtained by training with the first data.

[0035] In conjunction with the second aspect, in some implementations of the second aspect, the method further includes: sending N3 first identifiers, wherein the N3 first identifiers are the updated identifiers among the N2 first identifiers, and N3 is an integer greater than or equal to 0 and less than or equal to N2; the N3 first identifiers are used to determine the second model, which is obtained by training the first data.

[0036] In conjunction with the second aspect, some implementations of the second aspect include: the first identifier indicating any of the following: port mapping status, transmit power, beam shape.

[0037] In conjunction with the second aspect, some implementations of the second aspect include: the second identifier indicating one or more of the following: the manufacturer's identifier of the second device, the device type of the second device, and the unique identifier of the second device.

[0038] The beneficial effects of the second aspect and its possible implementation methods can be found in the description of the first aspect, and will not be repeated here.

[0039] Thirdly, a method is provided that can be performed by an apparatus (e.g., a communication device). This apparatus can be a device (such as a core network device), or it can be a component of a device (e.g., a chip (such as a modem chip, also known as a baseband chip, or a SoC chip or SIP chip containing a modem core) or a chip system or circuit), which is not limited in this application. The following description primarily uses the first node as an example.

[0040] Optionally, the first node is a core network device.

[0041] The method includes: receiving a second identifier from a second device, the second identifier indicating information of the second device; obtaining M groups of first identifiers and / or the correspondence between the model and the identifiers of the first data based on the second identifier; wherein, the M groups of first identifiers indicate all states of the second device, the first data is used to train an AI model, the identifiers of the first data include N first identifiers, each of the N first identifiers belongs to one of the M groups of first identifiers, any two of the N first identifiers belong to different groups, M is an integer greater than or equal to 0, and N is an integer greater than or equal to 0 and less than or equal to M.

[0042] In conjunction with the third aspect, in some implementations of the third aspect, the method further includes: sending M sets of first identifiers and / or the correspondence between the model and the identifiers of the first data to the first device, wherein the correspondence between the model and the identifiers of the first data is used to determine the first model.

[0043] In conjunction with the third aspect, in some implementations of the third aspect, the method further includes: acquiring third data; determining a first model based on the correspondence between the identifier and model of the third data and the identifier of the first data; and sending the first model to the first device.

[0044] The third set of data is used in the model inference process.

[0045] The beneficial effects of the third aspect and its possible implementation methods can be found in the description of the first aspect, and will not be elaborated here.

[0046] Fourthly, a communication apparatus is provided for performing the method provided in any one of the first to third aspects. Specifically, the apparatus may include units and / or modules for performing the method provided in any of the above-described implementations of the first to third aspects, such as processing units and / or communication units.

[0047] In one implementation, the device is a communication device (such as a terminal device or a network device). When the device is a communication device, the communication unit can be a transceiver or an input / output interface; the processing unit can be at least one processor. Optionally, the transceiver can be a transceiver circuit. Optionally, the input / output interface can be an input / output circuit.

[0048] In another implementation, the device is a chip, chip system, or circuit used in a communication device. When the device is a chip, chip system, or circuit used in a communication device, the communication unit can be an input / output interface, interface circuit, output circuit, input circuit, pin, or related circuit on the chip, chip system, or circuit; the processing unit can be at least one processor, processing circuit, or logic circuit.

[0049] Fifthly, a communication apparatus is provided, the apparatus comprising: a memory for storing a program; and at least one processor for executing the computer program or instructions stored in the memory to perform the method provided by any of the above-described implementations of any of the first to third aspects.

[0050] In one implementation, the device is a communication device (such as a terminal device or a network device).

[0051] In another implementation, the device is a chip, chip system, or circuit used in a communication device.

[0052] Sixthly, this application provides a processor for performing the methods provided in the above aspects.

[0053] Unless otherwise specified, or if it does not contradict its actual function or internal logic in the relevant description, the transmission and acquisition / reception operations involved in the processor can be understood as processor output and input operations, or as transmission and reception operations performed by radio frequency circuits and antennas. This application does not limit them in this regard.

[0054] In a seventh aspect, a computer-readable storage medium is provided for program code executed by a device, the program code including a method for performing any of the above-described implementations of any of the first to third aspects.

[0055] Eighthly, a computer program product comprising instructions is provided, which, when executed by a processor on a computer, causes the computer to perform the method provided by any of the above-described implementations of any of the first to third aspects.

[0056] Ninth aspect, a chip is provided, the chip including a processor and a communication interface, the processor reading instructions stored in a memory through the communication interface and executing the method provided by any of the above implementations of any of the first to third aspects.

[0057] Optionally, as one implementation, the chip further includes a memory storing computer programs or instructions, and a processor for executing the computer programs or instructions stored in the memory. When the computer programs or instructions are executed, the processor is used to execute the method provided by any of the above implementations of any of the first to third aspects.

[0058] A tenth aspect provides a communication system, including a first communication device, a second communication device, and a third communication device. The first communication device is used to execute the method provided in any implementation of the first aspect, the second communication device is used to execute the method provided in any implementation of the second aspect, and the third communication device is used to execute the method provided in any implementation of the third aspect.

[0059] The beneficial effects of aspects four through ten and possible implementation methods can be found in the relevant description of aspect one, and will not be repeated here. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of a possible application framework in a communication system.

[0061] Figure 2 This is a schematic diagram of another possible application framework in a communication system.

[0062] Figure 3 This is a schematic diagram of a communication system applicable to the communication method in the embodiments of this application.

[0063] Figure 4 This is a schematic diagram of another communication system applicable to the communication method in the embodiments of this application.

[0064] Figure 5 A schematic diagram of the neuron structure is shown.

[0065] Figure 6 A schematic diagram of the AI ​​model design is shown.

[0066] Figure 7 This is a schematic diagram of method 700 provided in an embodiment of this application.

[0067] Figure 8 This is a schematic diagram of method 800 proposed in an embodiment of this application.

[0068] Figure 9 This is a schematic diagram of method 900 provided in an embodiment of this application.

[0069] Figure 10 This is a schematic block diagram of a communication device 1000 provided in an embodiment of this application.

[0070] Figure 11 This is a schematic diagram of another communication device 1100 provided in an embodiment of this application.

[0071] Figure 12 This is a schematic block diagram of the chip system 1200 provided in the embodiments of this application. Detailed Implementation

[0072] Before introducing the scheme of this application, the following points should be noted.

[0073] (1) In this application, “instruction” may include direct instruction, indirect instruction, explicit instruction, and implicit instruction. When describing a certain instruction information for the purpose of instructing A, it can be understood that the instruction information carries A, directly instructs A, or indirectly instructs A.

[0074] In this application, the information indicated by the instruction information is called the information to be instructed. In specific implementations, there are many ways to indicate the information to be instructed, such as, but not limited to, directly indicating the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly indicate the information to be instructed by indicating other information, where there is a relationship between the other information and the information to be instructed. It can also indicate only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. Furthermore, the information to be instructed can be sent as a whole or divided into multiple sub-information pieces, and the sending period and / or timing of these sub-information pieces can be the same or different.

[0075] (2) In this application, "send" and "receive" indicate the direction of signal transmission. For example, "send information to XX" can be understood as the destination of the information being XX, which may include direct transmission via the air interface or indirect transmission via the air interface by other units or modules. "Receive information from YY" can be understood as the source of the information being YY, which may include direct reception from YY via the air interface or indirect reception from YY via the air interface by other units or modules. "Send" can also be understood as the "output" of the chip interface, and "receive" can also be understood as the "input" of the chip interface. In other words, sending and receiving can occur between devices, such as between network devices and terminal devices, or within a device, such as between components, modules, chips, software modules, or hardware modules within the device via a bus, wiring, or interface.

[0076] (3) In the various embodiments of this application, unless otherwise specified or logically conflicting, the terms and / or descriptions of different embodiments are consistent and can be referenced by each other. The technical features of different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0077] (4) In this application, "first," "second," and "#1," "#2," etc., are merely for descriptive convenience and are used to distinguish objects, and are not intended to limit the scope of the embodiments of this application. They are not used to describe the order or sequence of features. It should be understood that such described objects can be interchanged where appropriate so as to describe solutions other than those in the embodiments of this application.

[0078] (5) In this application, “predefined” may mean a standard protocol predefined, or it may mean that the devices have agreed or negotiated in advance.

[0079] (6) In this application, the words “exemplary,” “for example,” etc., are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as an “example” in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word “example” is intended to present the concept in a concrete manner. In the embodiments of this application, “of,” “corresponding, relevant,” and “corresponding” may sometimes be used interchangeably, and it should be noted that their intended meanings are consistent unless their distinction is emphasized.

[0080] (7) In this document, "at least one" means one or more. "More than one" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, or B exists alone, where A and B can be singular or plural. In the textual description of this application, the character " / " generally indicates that the related objects before and after are in an "or" relationship; in the formula of this application, the character " / " indicates that the related objects before and after are in a "division" relationship. "Including at least one of A, B and C" can mean: including A; including B; including C; including A and B; including A and C; including B and C; including A, B and C.

[0081] (8) The arrows or boxes indicated by dashed lines in the schematic diagrams in the accompanying drawings of this application indicate optional steps or optional modules.

[0082] (9) In this application, the data identifier refers to an identifier (or ID) used to distinguish data. Its name does not limit the embodiments of this application. For example, "data identifier" can also be called "identifier" or "ID". For ease of description, this application uses "identifier" as an example, but it should be understood that this application is not limited thereto.

[0083] The technical solutions in this application will now be described with reference to the accompanying drawings.

[0084] The technical solutions provided in this application can be applied to various communication systems, such as: future mobile communication systems, 5th generation (5G) or new radio (NR) systems, long term evolution (LTE) systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD) systems, wireless local area network (WLAN) systems, satellite communication systems, future communication systems, or integrated systems of multiple systems. The technical solutions provided in this application can also be applied to device-to-device (D2D) communication, vehicle-to-everything (V2X) communication, machine-to-machine (M2M) communication, machine-type communication (MTC), and Internet of Things (IoT) communication systems or other communication systems.

[0085] In a communication system, a device can send signals to or receive signals from another device. These signals can include information, signaling, or data. The term "device" can also be replaced by an entity, network entity, communication device, mobile device, network element, communication module, node, communication node, communication apparatus, etc. This disclosure uses "device" as an example. For instance, a communication system can include at least one terminal device and at least one network device. The network device can send downlink signals to the terminal device, and / or the terminal device can send uplink signals to the network device.

[0086] In the embodiments of this application, the terminal device may also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent, or user apparatus.

[0087] Terminal devices can be devices that provide voice / data, such as handheld devices with wireless connectivity, in-vehicle devices, etc. Currently, examples of terminals include: mobile phones, tablets, laptops, PDAs, mobile internet devices (MIDs), wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, cellular phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), handheld devices with wireless communication capabilities, computing devices or other processing devices connected to a wireless modem, wearable devices, terminal devices in 5G networks, or future public land mobile communication networks. Terminal devices in a network (PLMN), etc., are not limited to this in the embodiments of this application.

[0088] By way of example and not limitation, in this embodiment, the terminal device can also be a wearable device. Wearable devices, also known as wearable smart devices, are a general term for devices that utilize wearable technology to intelligently design and develop everyday wearables, such as glasses, gloves, watches, clothing, and shoes. Wearable devices are portable devices that are worn directly on the body or integrated into the user's clothing or accessories. Wearable devices are not merely hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are feature-rich, large in size, and can achieve complete or partial functions without relying on a smartphone, such as smartwatches or smart glasses, as well as those that focus on a specific type of application function and require the use of other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0089] In this embodiment, the device for implementing the functions of the terminal device can be the terminal device itself, or it can be any device capable of supporting the terminal device in implementing those functions, such as a chip system. This device can be installed in or used in conjunction with the terminal device. In this embodiment, the chip system can consist of chips or include chips and other discrete components. This embodiment only uses the terminal device as an example to illustrate the device for implementing the functions of the terminal device, and does not constitute a limitation on the solutions of this embodiment.

[0090] The network device in this application embodiment may include a device for communicating with a terminal device. For example, the network device may include an access network device or a wireless access network device, such as a base station (BS). The wireless access network device in this application embodiment may refer to a radio access network (RAN) node (or device) that connects the terminal device to the wireless network. A base station can broadly encompass, or be replaced by, various names including: NodeB, evolved NodeB (eNB), next-generation NodeB (gNB), relay station, access point, transmitting and receiving point (TRP), transmitting point (TP), master station, auxiliary station, motor slide retainer (MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), radio unit (RU), positioning node, etc. A base station can be a macro base station, micro base station, relay node, donor node, or similar entities, or combinations thereof. A base station can also refer to a communication module, modem, or chip installed within the aforementioned equipment or apparatus. A base station can also be a mobile switching center, a device that performs base station functions in D2D, V2X, and M2M communications, or a device that performs base station functions in future communication systems. A base station can support networks using the same or different access technologies. Optionally, a RAN node can also be a server, wearable device, vehicle, or in-vehicle equipment. For example, the access network equipment in vehicle-to-everything (V2X) technology can be a roadside unit (RSU). The embodiments of this application do not limit the specific technologies or equipment forms used in the network equipment.

[0091] Base stations can be fixed or mobile. For example, a helicopter or drone can be configured to act as a mobile base station, and one or more cells can move depending on the location of the mobile base station. In other examples, a helicopter or drone can be configured as a device to communicate with another base station.

[0092] In some deployments, the network devices mentioned in the embodiments of this application may be devices including CU, DU, or CU and DU, or devices with control plane CU nodes (central unit-control plane (CU-CP)) and user plane CU nodes (central unit-user plane (CU-UP)) and DU nodes. For example, the network devices may include gNB-CU-CP, gNB-CU-UP, and gNB-DU.

[0093] In some deployments, multiple RAN nodes collaborate to assist terminals in achieving wireless access, with different RAN nodes each implementing some of the base station's functions. For example, RAN nodes can be CUs, DUs, CU-CPs, CU-UPs, or RUs. CUs and DUs can be configured separately or included in the same network element, such as a BBU. RUs can be included in radio frequency equipment or radio frequency units, such as RRUs, AAUs, or RRHs.

[0094] RAN nodes can support one or more types of fronthaul interfaces, each corresponding to a DU and RU with different functions. If the fronthaul interface between the DU and RU is a common public radio interface (CPRI), the DU is configured to implement one or more baseband functions, and the RU is configured to implement one or more radio frequency functions. If the fronthaul interface between the DU and RU is another type of interface, relative to CPRI, some downlink and / or uplink baseband functions, such as, for downlink, precoding, digital beamforming (BF), or one or more of inverse fast Fourier transform (IFFT) / cyclic prefix addition (CP), are moved from the DU to the RU; and for uplink, digital beamforming (BF), or one or more of fast Fourier transform (FFT) / cyclic prefix removal (CP), are moved from the DU to the RU. In one possible implementation, the interface can be an enhanced common public radio interface (eCPRI). Under the eCPRI architecture, the segmentation between DU and RU differs, corresponding to different categories (Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, F.

[0095] Taking eCPRI Cat A as an example, for downlink transmission, layer mapping is used as the dividing line. DU is configured to implement one or more functions preceding layer mapping (i.e., coding, rate matching, scrambling, modulation, and layer mapping), while other functions following layer mapping (e.g., RE mapping, digital beamforming (BF), or one or more inverse fast Fourier transform (IFFT) / cyclic prefix (CP) addition) are moved to RU. For uplink transmission, deRE mapping is used as the dividing line. DU is configured to implement one or more functions preceding deRE mapping (i.e., decoding, rate matching de-matching, descrambling, demodulation, inverse discrete Fourier transform (IDFT), channel equalization, and deRE mapping), while other functions following deRE mapping (e.g., digital BF or fast Fourier transform (FFT) / CP removal) are moved to RU. It is understandable that the functional descriptions of the DU and RU corresponding to various types of eCPRI can be found in the eCPRI protocol, and will not be elaborated here.

[0096] In one possible design, the processing unit in the BBU used to implement baseband functions is called the baseband high (BBH) unit, and the processing unit in the RRU / AAU / RRH used to implement baseband functions is called the baseband low (BBL) unit.

[0097] 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 open RAN (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. 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 modules and hardware modules.

[0098] In this embodiment, the apparatus for implementing the functions of a network device can be a network device itself; it can also be an apparatus capable of supporting the network device in implementing those functions, such as a chip system, hardware circuit, software module, or a hardware circuit plus a software module. This apparatus can be installed in the network device or used in conjunction with the network device. In this embodiment, the example of a network device being used to implement the functions of a network device is provided only and does not constitute a limitation on the solutions described in this embodiment.

[0099] Network devices and / or terminal devices can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; and they can also be deployed in the air on airplanes, balloons, and satellites. This application does not limit the scenario in which the network devices and terminal devices are located. Furthermore, terminal devices and network devices can be hardware devices, or software functions running on dedicated hardware or general-purpose hardware, such as virtualization functions instantiated on a platform (e.g., a cloud platform), or entities that include dedicated or general-purpose hardware devices and software functions. This application does not limit the specific form of the terminal devices and network devices.

[0100] In wireless communication networks, such as mobile communication networks, the services supported by the networks are becoming increasingly diverse, leading to increasingly diverse requirements. For example, networks need to support ultra-high speeds, ultra-low latency, and / or massive connectivity. This characteristic makes network planning, network configuration, and / or resource scheduling increasingly complex. Furthermore, as network functions become more powerful, such as supporting higher spectrum levels, supporting higher-order multiple-input multiple-output (MIMO) technologies, supporting beamforming, and / or supporting beam management, network energy efficiency has become a hot research topic. These new requirements, new scenarios, and new characteristics bring unprecedented challenges to network planning, operation, and efficient operation. To meet these challenges, artificial intelligence technology can be introduced into wireless communication networks to achieve network intelligence.

[0101] To support artificial intelligence (AI) technology in wireless networks, AI nodes may also be introduced into the network.

[0102] Optionally, the AI ​​node can be deployed in one or more of the following locations within the communication system: access network equipment, terminal equipment, or core network equipment, etc. Alternatively, the AI ​​node can be deployed independently, for example, in a location other than any of the aforementioned devices, such as in the host or cloud server of an over-the-top (OTT) system. The AI ​​node can communicate with other devices in the communication system, which can be, for example, one or more of the following: wireless access network equipment, terminal equipment, or core network elements, etc.

[0103] It is understood that this application does not limit the number of AI nodes. For example, when there are multiple AI nodes, these nodes can be divided based on function, such as different AI nodes being responsible for different functions.

[0104] It can also be understood that AI nodes can be independent devices, or they can be integrated into the same device to achieve different functions. Alternatively, they can be network elements in hardware devices, software functions running on dedicated hardware, or virtualization functions instantiated on a platform (e.g., a cloud platform). This application does not limit the specific form of the aforementioned AI nodes.

[0105] AI nodes can be AI network elements or AI modules.

[0106] Figure 1 This is a schematic diagram of a possible application framework in a communication system. For example... Figure 1 As shown, network elements in a communication system are connected via interfaces (e.g., next-generation (NG) interfaces, Xn interfaces) or air interfaces. These network element nodes, such as core network equipment, access network nodes or equipment (RAN nodes or equipment), terminals, or one or more devices in operation administration and maintenance (OAM), are equipped with one or more AI modules (for clarity, ...). Figure 1 (Only one is shown in the image). The access network node can be a single RAN node or can include multiple RAN nodes, such as CU and DU. The CU and / or DU can also be configured with one or more AI modules. Optionally, the CU can also be split into CU-CP and CU-UP. One or more AI models are configured in CU-CP and / or CU-UP.

[0107] The AI ​​module is used to implement corresponding AI functions. AI modules deployed in different network elements can be the same or different. Depending on the parameter configuration, the AI ​​module can implement different functions. The AI ​​module model can be configured based on one or more of the following parameters: structural parameters (e.g., at least one of the following: number of neural network layers, neural network width, inter-layer connections, neuron weights, neuron activation function, or bias in the activation function), input parameters (e.g., type and / or dimension of input parameters), or output parameters (e.g., type and / or dimension of output parameters). The bias in the activation function can also be referred to as the neural network bias.

[0108] An AI module can have one or more models. A model can infer an output, which includes one or more parameters. The learning, training, or inference processes of different models can be deployed on different nodes or devices, or they can be deployed on the same node or device.

[0109] Figure 2 This is a schematic diagram illustrating another possible application framework in a communication system. For example... Figure 2 As shown, the communication system includes a RAN intelligent controller (RIC). For example, the RIC can be... Figure 1 The AI ​​module shown is used to implement AI-related functions. The RIC includes near-real-time RIC (near-RTRIC) and non-real-time RIC (non-RTRIC). Non-real-time RIC primarily processes non-real-time information, such as data that is not sensitive to latency, with latency in the order of seconds. Real-time RIC primarily processes near-real-time information, such as data that is relatively sensitive to latency, with latency in the order of tens of milliseconds.

[0110] The near real-time RIC is used for model training and inference. For example, it is used to train an AI model and then use that AI model for inference. The near real-time RIC can obtain network-side and / or terminal-side information from RAN nodes (e.g., CU, CU-CP, CU-UP, DU, and / or RU) and / or terminals. This information can be used as training data or inference data. Optionally, the near real-time RIC can deliver the inference results to the RAN nodes and / or terminals. Optionally, inference results can be exchanged between CU and DU, and / or between DU and RU. For example, the near real-time RIC delivers the inference results to the DU, and the DU then sends the inference results to the RU.

[0111] The non-real-time RIC is also used for model training and inference. For example, it can be used to train an AI model and then use that model for inference. The non-real-time RIC can obtain network-side and / or terminal-side information from RAN nodes (e.g., CU, CU-CP, CU-UP, DU, and / or RU) and / or terminals. This information can be used as training data or inference data, and the inference results can be delivered to RAN nodes and / or terminals. Optionally, inference results can be exchanged between CU and DU, and / or between DU and RU. For example, the non-real-time RIC delivers the inference results to the DU, and the DU then sends the inference results to the RU.

[0112] The near real-time RIC and non-real-time RIC can also be set up as separate network elements. Optionally, the near real-time RIC and non-real-time RIC can also be part of other devices. For example, the near real-time RIC can be set in the RAN node (e.g., in CU, DU), while the non-real-time RIC can be set in the OAM, cloud server, core network device, or other network device.

[0113] Figure 3 This is a schematic diagram of a communication system applicable to the communication method in the embodiments of this application. For example... Figure 3 As shown, the communication system 100 may include at least one network device, such as Figure 3 The network device 110 shown; the communication system 100 may also include at least one terminal device, such as Figure 3 The terminal devices 120 and 130 are shown. Network device 110 can communicate with the terminal devices (such as terminal devices 120 and 130) via a wireless link. Communication devices in this communication system, for example, network device 110 and terminal device 120, can communicate via multi-antenna technology.

[0114] Figure 4 This is a schematic diagram of another communication system applicable to the communication method in the embodiments of this application. Compared to Figure 3 Regarding the communication system 100 shown, Figure 4 The communication system 200 shown also includes an AI network element 140. The AI ​​network element 140 is used to perform AI-related operations, such as building training datasets or training AI models.

[0115] In one possible implementation, network device 110 can send data related to the training of the AI ​​model to AI network element 140, which then constructs a training dataset and trains the AI ​​model. For example, the data related to the training of the AI ​​model may include data reported by the terminal device. AI network element 140 can send the results of operations related to the AI ​​model to network device 110, which then forwards them to the terminal device. For example, the results of operations related to the AI ​​model may include at least one of the following: a trained AI model, model evaluation results, or test results. Exemplarily, a portion of the trained AI model may be deployed on network device 110, and another portion on the terminal device. Alternatively, the trained AI model may be deployed on network device 110. Or, the trained AI model may be deployed on the terminal device.

[0116] It should be understood that Figure 4 This explanation only uses the direct connection between AI network element 140 and network device 110 as an example. In other scenarios, AI network element 140 can also be connected to a terminal device. Alternatively, AI network element 140 can be connected to both network device 110 and a terminal device simultaneously. Alternatively, AI network element 140 can also be connected to network device 110 through a third-party network element. This application embodiment does not limit the connection relationship between AI network element and other network elements.

[0117] The AI ​​Network Element 140 can also be configured as a module in network devices and / or terminal devices, for example, configured in Figure 3 In the network device 110 or terminal device 120 shown.

[0118] It should be noted that, Figure 3 and Figure 4 This is a simplified illustration for ease of understanding only. For example, the communication system may also include other devices, such as wireless relay devices and / or wireless backhaul devices. Figure 3 and Figure 4 The figures are not shown. In practical applications, this communication system may include multiple network devices or multiple terminal devices. This application does not limit the number of network devices and terminal devices included in the communication system.

[0119] To facilitate understanding of the solutions in the embodiments of this application, the terms that may be involved in the embodiments of this application are explained below.

[0120] (1) Artificial Intelligence: This refers to enabling machines to learn, accumulate experience, and solve problems that humans can solve through experience, such as natural language understanding, image recognition, and chess. Artificial intelligence can be understood as the intelligence exhibited by machines created by humans. Generally, artificial intelligence refers to the technology of presenting human intelligence through computer programs. The goals of artificial intelligence include understanding intelligence by constructing computer programs that demonstrate symbolic reasoning or reasoning.

[0121] (2) Machine learning (ML): This is a method of implementing artificial intelligence. Machine learning is a method that endows machines with the ability to perform functions that cannot be accomplished through direct programming. In practical terms, machine learning is a method that uses data to train a model and then uses the model to make predictions. There are many methods of machine learning, such as neural networks (NN), decision trees, and support vector machines. Machine learning theory mainly involves designing and analyzing algorithms that enable computers to learn automatically. Machine learning algorithms are a class of algorithms that automatically analyze data to obtain patterns and use these patterns to predict unknown data.

[0122] (3) Neural Networks: Neural networks are a specific manifestation of machine learning methods. A neural network is a mathematical model that mimics the behavioral characteristics of animal neural networks to process information. The following section combines... Figure 5 A brief introduction to neural networks.

[0123] Figure 5 A schematic diagram of the neuron structure is shown, such as Figure 5 As shown, a neural network can be composed of three types of computational layers: input layer, hidden layer, and output layer. Each layer has one or more logical decision units, called neurons. Common neural network structures include feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), all of which are based on neurons. Each neuron performs a weighted summation operation on its input values ​​and outputs the result through a nonlinear function. The weights of the neuron's weighted summation operation and the nonlinear function are called the parameters of the neural network. The connections between neurons in the neural network are called the structure of the neural network, and the parameters of all neurons constitute the parameters of the neural network.

[0124] (4) Deep neural network: A neural network with multiple hidden layers.

[0125] (5) Deep learning: Machine learning using deep neural networks.

[0126] (6) AI Model: An AI model is an algorithm or computer program that can implement AI functions. An AI model represents the mapping relationship between the model's input and output; in other words, an AI model is a function model that maps an input of a certain dimension to an output of a certain dimension. The parameters of the function model can be obtained through machine learning training. For example, f(x) = ax 2 +b is a quadratic function model, which can be viewed as an AI model. a and b are the parameters of this AI model, and a and b can be obtained through machine learning training. For example, the AI ​​model mentioned in the following embodiments of this application is not limited to neural networks, linear regression models, decision tree models, support vector machines (SVM), Bayesian networks, Q-learning models, or other machine learning (ML) models.

[0127] The implementation of an AI model can be a hardware circuit, software, or a combination of both; there are no restrictions. Non-restrictive examples of software include: program code, program, subroutine, instruction, instruction set, code, code segment, software module, application program, or software application, etc.

[0128] (7) AI model design:

[0129] The design of an AI model mainly includes the data collection phase (e.g., collecting training data and / or inference data), model training phase, model management phase, model storage phase, and model inference phase. It can also further include the application of inference results.

[0130] Figure 6 An AI application framework is illustrated. For example... Figure 6As shown, in the data collection phase, the data source provides both training and inference data. In the model training phase, the AI ​​model is obtained by analyzing or training the training data provided by the data source. The AI ​​model represents the mapping relationship between the model's input and output. Learning the AI ​​model through model training nodes is equivalent to learning the mapping relationship between the model's input and output using the training data. In the model inference phase, the AI ​​model trained in the model training phase is used to perform inference based on the inference data provided by the data source, resulting in an inference output. This phase can also be understood as: inputting inference data into the AI ​​model, obtaining the output through the AI ​​model, which is the inference result. This inference result can indicate the configuration parameters used (executed) by the execution object, and / or the operations performed by the execution object. In the inference result application phase, the inference result is published. For example, the inference result can be uniformly planned by the actor entity, which can send the inference result to one or more execution objects (e.g., network devices or terminal devices) for execution. For example, the execution entity can also provide feedback on the model's performance to the data source, facilitating subsequent model updates and training. In the model management phase, the model's performance is monitored based on the monitoring data provided by the data source, and actions such as model activation, deactivation, switching, selection, or rollback are performed according to the model's performance.

[0131] It is understood that communication systems may include network elements with artificial intelligence (AI) capabilities. The AI ​​model design-related steps described above can be performed by one or more network elements with AI capabilities. In one possible design, AI functions (such as AI modules or AI entities) can be configured within existing network elements in the communication system to implement AI-related operations, such as AI model training and / or inference. For example, this existing network element could be a network device or a terminal device. Alternatively, in another possible design, an independent network element can be introduced into the communication system to perform AI-related operations, such as training an AI model. This independent network element can be called an AI network element or an AI node, etc., and this application embodiment does not limit the use of this name. Exemplarily, the AI ​​network element can be directly connected to network devices in the communication system, or it can be indirectly connected to network devices through a third-party network element. The third-party network element can be a core network element such as an authentication management function (AMF) network element, a user plane function (UPF) network element, an operation administration and maintenance (OAM) network element, a cloud server, or other network elements, without limitation. For example, this independent network element can be deployed on one or more of the following: the network device side, the terminal device side, or the core network side. Optionally, it can be deployed on a cloud server. For example, as shown... Figure 4 The communication system shown incorporates AI network element 140.

[0132] The training processes of different models can be deployed on different devices or nodes, or on the same device or node. Similarly, the inference processes of different models can be deployed on different devices or nodes, or on the same device or node. Taking a terminal device completing the model training phase as an example, after training its corresponding encoder and decoder, the terminal device sends the decoder's model parameters to the network device. Similarly, taking a network device completing the model training phase as an example, after training its corresponding encoder and decoder, the network device can send the encoder's model parameters to the terminal device and the decoder's model parameters to the network device. Then, the model inference phase corresponding to the encoder is performed on the terminal device, and the model inference phase corresponding to the decoder is performed on the network device.

[0133] The model parameters can include one or more of the following: model structure parameters (e.g., number of layers, and / or weights), model input parameters (e.g., input dimension, number of input ports), or model output parameters (e.g., output dimension, number of output ports). The input dimension refers to the size of an input data set; for example, if the input data is a sequence, the corresponding input dimension indicates the length of the sequence. The number of input ports refers to the quantity of input data. Similarly, the output dimension refers to the size of an output data set; for example, if the output data is a sequence, the corresponding output dimension indicates the length of the sequence. The number of output ports refers to the quantity of output data.

[0134] As mentioned above, communication devices need to use different types of data to perform operations related to AI models (such as training AI models, performing inference tasks based on AI models, and strengthening AI models). For example, model training requires a large amount of diverse data, model inference requires real-time data, and model management (i.e., model monitoring) requires a large amount of near real-time data. However, since data depends on the data source (i.e., the second device, such as a terminal device or a network device) or the state of the data source, in other words, different second devices or second devices in different states provide different data. In order to distinguish different data or the corresponding second device or the state of the second device when acquiring data, it is necessary to save the data identifier corresponding to the data. Based on this, it may lead to high storage overhead.

[0135] In view of this, this application provides a communication method that enables a data identifier to identify information of a second device and / or the state of the second device when data is acquired, thereby reducing communication overhead.

[0136] The communication method provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings. The embodiments provided in this application can be applied to the above-described embodiments. Figure 3 or Figure 4 The communication system shown is not limited.

[0137] In this embodiment of the application, the first device obtains first data from the second device, wherein identifier #1 (i.e., the second identifier) ​​indicates the information of the second device, and identifier #2 (i.e., the first identifier) ​​indicates the state of the second device when the first device obtains the first data.

[0138] The first set of data is used to train the AI ​​model.

[0139] The following sections will introduce identifier #1 and identifier #2 respectively.

[0140] (1) Identifier #1 indicates information about the second device.

[0141] The information of the second device may include one or more of the following: the manufacturer's identifier of the second device, the device type of the second device, and the unique identifier assigned to the second device by the network.

[0142] 1) Manufacturer identification of the second device: used to distinguish devices from different manufacturers.

[0143] 2) Device type of the second device: used to distinguish the characteristics or configuration of the device. For example, the characteristics or configuration of the second device can be determined based on the device type, or the category of the characteristics or configuration of the second device can be determined.

[0144] 3) A unique identifier assigned by the network to the second device: used to distinguish different devices or different types of devices in the network.

[0145] (2) A identifier #2 indicates the state of the second device when the first device acquires the first data.

[0146] The status of the second device may include one or more of the following: port mapping status, beam shape, and transmit power.

[0147] 1) Port mapping status of the second device: Different port mapping statuses affect the correlation of data.

[0148] 2) Beam shape used by the second device: When transmitting data, the direction and shape of the electromagnetic waves radiated by the antenna affect the distribution of data.

[0149] 3) Transmission power of the second device: Different transmission powers affect the response of the signal when it passes through the device.

[0150] The following explains the status of the second device indicated by the identifier #2.

[0151] In this embodiment of the application, it is assumed that M group identifiers #2 indicate all possible states of the second device, or in other words, M group identifiers #2 characterize the M states of the second device. For example, M=2, and the two states of the second device include port mapping state and beamform. Then, one group of identifiers #2 in M ​​group identifiers #2 (for example, denoted as group A) characterizes the port mapping state of the second device, and another group of identifiers #2 in M ​​group identifiers #2 (for example, denoted as group B) characterizes the beamform adopted by the second device.

[0152] Specifically, each group of identifiers #2 in the M group of identifiers #2 includes one or more identifiers #2. In other words, an identifier #2 belongs to one group in the M group of identifiers #2.

[0153] The following is an example illustration.

[0154] Example 1: Group A represents the port mapping status of the second device when the first device obtains the first data. Group A includes 3 identifiers #2. Assuming that ID-x is used to represent the 3 identifiers #2 included in Group A, for example, ID-1 represents port mapping status #1, ID-2 represents port mapping status #2, and ID-3 represents port mapping status #3.

[0155] Example 2: Group B represents the beam shape used by the second device when the first device acquires the first data. Group B includes 3 identifiers #2. Assuming ID-y is used to represent the 3 identifiers #2 included in Group B, for example, ID-A represents beam shape #1, ID-B represents beam shape #2, and ID-C represents beam shape #3.

[0156] The following describes a scheme proposed in this application for determining the level of the first data based on the correlation between the first data and the state of the second device.

[0157] Specifically, when the correlation between the first data and the state of the second device is low, the update of the state of the second device has a smaller impact on the first data; in other words, the update cycle of the identifier of the first data is longer. Alternatively, when the correlation between the first data and the state of the second device is high, the update of the state of the second device has a larger impact on the first data; in other words, the update cycle of the identifier of the first data is shorter.

[0158] Taking CSI data as the first data as an example, the classification scheme of the first data is explained in conjunction with Table 1. ID-x belongs to one of the M group identifiers #2 (for example, denoted as group A), x = 1, 2, 3, 4..., ID-x represents the port mapping status of the second device when the first device acquires the first data, ID-y belongs to one of the M group identifiers #2 (for example, denoted as group B), y = A, B, C, D, ..., ID-y represents the beam shape adopted by the second device when the first device acquires the first data.

[0159] The first data includes, for example, multipath information (e.g., multipath component (MPC) or power delay profile (PDP)), channel information (e.g., time-frequency domain channel information or precoding matrix index (PMI)), and beam domain channel information (e.g., beam received power, channel quality, etc.).

[0160] Table 1

[0161]

[0162] As shown in Table 1, if the multipath information is not related to the status information of the second device, then the identifier of the multipath information does not include identifier #2; if the status information of the second device related to the channel information includes the port mapping status, then the identifier of the channel information includes one identifier #2, for example, denoted as ID-x. In other words, the identifier of the channel information includes one identifier #2 from group A; if the status information of the second device related to the channel information in the beam domain includes the port mapping status and the beam shape adopted by the second device, then the identifier of the channel information in the beam domain includes two identifiers #2, for example, denoted as [ID-x]+[ID-y]. In other words, the identifier of the channel information in the beam domain includes one identifier #2 from group A and one identifier #2 from group B.

[0163] It should be understood that the specific forms of the identifiers of the first data listed in Table 1 are only illustrative examples and do not limit the embodiments of this application. For example, the identifier corresponding to the channel information of the beam domain can also be recorded as [ID-x]&[ID-y]. The parameters characterizing the channel information related to the beam domain include the port mapping status of the second device and the beam shape adopted by the second device.

[0164] As shown in Table 1, the number of identifiers #2 corresponding to the identifier of the first data can characterize the degree of correlation between the first data and the state of the second device. In other words, the number of identifiers #2 corresponding to the identifier of the first data is related to one or more of the following: the first data and the state of the second device.

[0165] The first data is classified based on the number of identifiers #2 corresponding to the identifiers of the first data.

[0166] As shown in Table 1, if the identifier corresponding to multipath information does not include identifier #2, then the multipath information belongs to the first level; if the identifier corresponding to channel information includes one identifier #2, then the channel information belongs to the second level; if the identifier corresponding to channel information in the beam domain includes two identifiers #2, then the channel information in the beam domain belongs to the third level.

[0167] Specifically, the level of the first data can characterize the degree of correlation between the first data and the state of the second device. As shown in Table 1, the degree of correlation between the first data and the state of the second device can be characterized from low to high as: first-level first data, second-level first data, and third-level data.

[0168] This application provides a method for a first device to acquire first data, which is described below in conjunction with... Figure 7 Describe it.

[0169] Figure 7 This is a schematic diagram of method 700 provided in an embodiment of this application, wherein the first device is a terminal device and the second device is a network device; or, the first device is a network device and the second device is a terminal device, which is not limited.

[0170] S701, the second device sends identifier #1 and M group identifier #2, and correspondingly, the first device receives identifier #1 and M group identifier #2, where M is an integer greater than or equal to 0.

[0171] In this context, identifier #1 indicates information about the second device.

[0172] Among them, M group identifier #2 indicates all possible states of the second device, each group identifier #2 in M ​​group identifier #2 includes at least one identifier #2, and each group identifier #2 in M ​​group identifier #2 indicates one state of the second device.

[0173] The embodiments of this application do not limit the transmission method of identifier #1 and M group identifier #2. For example, identifier #1 and M group identifier #2 are transmitted in the same information; or, identifier #1 and M group identifier #2 are transmitted in different information.

[0174] This application embodiment does not limit the transmission method of identifier #1 and M group identifier #2. For example, identifier #1 is transmitted in information such as system information block (SIB), UE capability information, or RRC connection message (RRCSetup).

[0175] In one possible implementation, S701 can be replaced by: the second device sending identifier #1 and M sets of identifiers #2 to the first node, and the first node sending M sets of identifiers #2 to the first device based on identifier #1. This method avoids the first device directly obtaining identifier #1, thereby improving the security of the communication process.

[0176] The embodiments of this application do not limit the first node. As an example, the first node is a core network device.

[0177] The following describes the specific implementation method of the first device acquiring the first data.

[0178] Implementation method one includes S702.

[0179] S702, the second device sends first data and N identifiers #2, and correspondingly, the first device receives the first data and N identifiers #2, where N is an integer greater than or equal to 0 and less than or equal to M.

[0180] Among them, N identifiers #2 are the identifiers corresponding to the first data. Each of the N identifiers #2 belongs to one of the M groups of identifiers #2, and any two identifiers #2 in the N identifiers #2 belong to different groups.

[0181] Specifically, the first device can determine the state of the second device when sending the first data based on N identifiers #2. For example, N=2, and the two identifiers #2 include ID-1 and ID-B, where ID-1 represents port mapping state #1 and ID-B represents beam shape #2. Then, based on ID-1 and ID-B, the first device can determine that the port mapping state of the second device when sending the first data is port mapping state #1, and the beam shape used by the second device when sending the first data is beam shape #2.

[0182] The embodiments of this application do not limit the form of sending N identifiers #2. As an example, N identifiers #2 can be sent as a single identifier, for example, denoted as "[ID-1]+[ID-B]".

[0183] The second implementation method includes S703, S704 and S705.

[0184] S703, the first device sends N identifiers #2, and correspondingly, the second device receives N identifiers #2, where N is an integer greater than or equal to 0 and less than or equal to M.

[0185] Among them, each of the N identifiers #2 belongs to one of the M groups of identifiers #2, and any two identifiers #2 in the N identifiers #2 belong to different groups.

[0186] S704, the first device sends the second data, and correspondingly, the second device receives the second data.

[0187] Specifically, the second device determines its state when receiving the second data based on the N identifiers #2 in S703. Furthermore, the second device receives the second data after adjusting to the corresponding state.

[0188] For example, if N=2, the two identifiers #2 include ID-1 and ID-B, where ID-1 represents port mapping state #1 and ID-B represents beam shape #2. Then, the port mapping state when the second device receives the second data is port mapping state #1, and the beam shape used by the second device when receiving the second data is beam shape #2.

[0189] As an example, the second data is a reference signal used for channel measurements.

[0190] S705, the second device sends the first data, and correspondingly, the first device receives the first data.

[0191] Specifically, the second device determines the first data based on the second data and sends the first data to the first device.

[0192] As an example, if the first data is CSI data, then the first data is obtained by the second device performing channel measurements on the second data.

[0193] The third implementation method includes S703, S706 and S707.

[0194] S706, the second device sends the second data, and correspondingly, the first device receives the second data.

[0195] Specifically, the second device determines the state it is in when sending the second data based on the N identifiers #2 in S703. Furthermore, the second device sends the second data after adjusting to the corresponding state.

[0196] For example, if N=2, the two identifiers #2 include ID-1 and ID-B, where ID-1 represents port mapping state #1 and ID-B represents beam shape #2. Then, the port mapping state when the second device sends the second data is port mapping state #1, and the beam shape used by the second device when sending the second data is beam shape #2.

[0197] As an example, the second data is a reference signal used for channel measurements.

[0198] S707, the first device determines the first data based on the second data.

[0199] As an example, if the first data is CSI data, then the first device performs channel measurement on the second data to obtain the first data.

[0200] The above implementation methods one, two, and three are parallel steps.

[0201] This application provides a method for a first device to select a model, which is described below in conjunction with... Figure 8 Describe it.

[0202] Figure 8 This is a schematic diagram of method 800 proposed in an embodiment of this application, wherein the first device is a terminal device and the second device is a network device; or, the first device is a network device and the second device is a terminal device, which is not limited.

[0203] S801, the second device sends identifier #1 and M group identifier #2, and correspondingly, the first device receives identifier #1 and M group identifier #2, where M is an integer greater than or equal to 0.

[0204] For details regarding identifier #1 and M group identifier #2, please refer to the description in S701, which will not be repeated here.

[0205] In another possible implementation, S801 can be: the second device sends identifier #1 and N1 identifiers #2, and correspondingly, the first device receives identifier #1 and N1 identifiers #2, where N1 is an integer greater than or equal to 0 and less than or equal to M.

[0206] Specifically, the N1 identifiers #2 represent the initial state of the second device; in other words, the N1 identifiers #2 indicate the state of the second device when executing S801. Each of the N1 identifiers #2 belongs to one of the M groups of identifiers #2, and any two identifiers #2 belong to different groups.

[0207] S802, the second device sends the third data and N2 identifiers #2, and correspondingly, the first device receives the third data and N2 identifiers #2, where N2 is an integer greater than or equal to 0 and less than or equal to M.

[0208] The third data is used in the model inference stage. N2 identifiers #2 are the identifiers corresponding to the third data, and the N2 identifiers #2 represent the state of the second device when sending the third data. Each of the N2 identifiers #2 belongs to one of the M groups of identifiers #2, and any two identifiers #2 belong to different groups.

[0209] In another possible implementation, the N2 identifiers #2 are the identifiers among the N1 identifiers that have been updated (or changed), i.e., N2 is an integer greater than or equal to 0 and less than or equal to N1. When N2 is greater than 0, it indicates that the state of the second device when sending the third data is different from the initial state of the second device.

[0210] S803, the first device determines model #1 based on N2 identifiers #2.

[0211] Wherein, model #1 is obtained by training the first data, and the first device determines model #1 based on N2 identifiers #2 and the correspondence between the model and the identifiers of the first data.

[0212] Specifically, the first device determines model #1 based on N2 identifiers #2, wherein the identifier of the first data corresponding to model #1 is the same as the N2 identifiers #2.

[0213] This application does not limit the specific implementation of the correspondence between the identifiers of the model and the first data obtained by the first device. In one possible implementation, the first device determines the correspondence between the identifiers of the model and the first data based on the stored data.

[0214] For example, if N2 = 2, and the two identifiers #2 are ID-1 and ID-B, then the first device determines model #1 based on N2 identifiers. Model #1 is trained using the first data #1, which is identified by ID-1 and ID-B. As an example, ID-1 indicates that the port mapping state of the second device when the first device acquires the first data #1 is port mapping state #1, and ID-B indicates that the beamform used by the second device when the first device acquires the first data #1 is beamform #2.

[0215] If the state of the second device is updated, method 800 also includes S804 and S805.

[0216] S804, the second device sends N3 identifiers #2, and correspondingly, the first device receives N3 identifiers #2, where N3 is an integer greater than or equal to 0 and less than or equal to N2.

[0217] Among them, N3 identifiers #2 represent the identifiers that are updated in the N2 identifiers #2 corresponding to the third data after the state of the second device is updated. Each identifier in N3 identifiers #2 belongs to one group of M groups of identifiers #2, and any two identifiers #2 in N3 identifiers #2 belong to different groups.

[0218] S805, the first device determines model #2 based on N3 identifiers #2.

[0219] Specifically, the first device determines model #2 based on N3 identifiers #2 and N2 identifiers #2 that have not been updated.

[0220] For example, N2 = 2, where the two identifiers #2 are ID-1 and ID-B; N3 = 1, indicating that one of the two identifiers #2 has been updated (e.g., ID-1 is updated to ID-2). The first device then determines model #2 based on ID-2 and ID-B, where model #2 is trained using first data #2, and the identifiers of the first data #2 are ID-2 and ID-B. As an example, ID-2 indicates that the port mapping state of the second device when the first device acquires the first data #2 is port mapping state #2, and ID-B indicates that the beamform used by the second device when the first device acquires the first data #2 is beamform #2.

[0221] In the above, the first device selects a model based on the identifier of the third data. If the state of the second device is updated, the first device can determine the updated model by receiving the updated identifier #2, thereby reducing communication overhead.

[0222] The following example further describes method 800.

[0223] For example, in method 800, the AI ​​model for channel prediction is determined based on third data. Further, the third data may be channel information in the beam domain, and thus the third data is related to the port mapping state and beam shape of the second device. As shown in Table 1, the third data belongs to the third level, and the identifier of the third data is, for example, denoted as [ID-x]+[ID-y], where ID-x represents the port mapping state of the second device when the first device acquires the third data, and ID-y represents the beam shape used by the second device when the first device acquires the third data.

[0224] For example, in S801, the initial state of the second device is, for example, [ID-1] + [ID-A], then N1 = 2. When the second device executes S801, it is in the port mapping state indicated by ID-1 (e.g., port mapping state #1) and the beam shape indicated by ID-A (e.g., beam shape #1). In S802, the second device sends third data and N2 identifiers #2, where N2 identifiers #2 is, for example, [ID-2] + [ID-B], then N2 = 2. When the second device sends the third data, it is in the port mapping state indicated by ID-2 (e.g., port mapping state #2) and the beam shape indicated by ID-B (e.g., beam shape #2). The first device determines model #1 based on N2 identifiers, and the identifier of the first data corresponding to model #1 is [ID-2]+[ID-B]. When the state of the second device is updated, the second device sends N3 identifiers #2, and the N3 identifiers #2 are, for example, [ID-C]. Then N3=1, indicating that the beam shape adopted by the second device is updated from the beam shape #2 indicated by ID-B to the beam shape indicated by ID-C (for example, beam shape #3). The first device determines model #2 based on N3 identifiers and the identifier that has not been updated among the N2 identifiers (i.e., ID-2), and the identifier of the first data corresponding to model #2 is [ID-2]+[ID-C].

[0225] Figure 9 This is a schematic diagram of method 900 provided in an embodiment of this application, wherein the first device is a terminal device and the second device is a network device; or, the first device is a network device and the second device is a terminal device, which is not limited.

[0226] S901, the second device sends identifier #1, and correspondingly, the core network device receives identifier #1.

[0227] In this context, identifier #1 indicates information about the second device.

[0228] S902, the core network equipment obtains the correspondence between the M group of identifiers #2 and / or the model and the identifiers of the first data based on identifier #1.

[0229] Specifically, the model is trained using the first data. The core network device determines the information of the second device based on identifier #1. Furthermore, the core network device obtains the M sets of identifiers #2 corresponding to the second device and / or the correspondence between the model and the identifiers in the first data based on identifier #1.

[0230] This application embodiment does not limit the specific implementation method of the core network device obtaining the correspondence between the M group identifier #2 and / or model corresponding to the second device and the identifier of the first data. As an example, the core network device obtains the correspondence between the M group identifier #2 and / or model and the identifier of the first data based on the stored data; or, the core network device sends a request information to the second device, requesting to obtain the correspondence between the M group identifier #2 and / or model corresponding to the second device and the identifier of the first data.

[0231] The following describes two implementation methods for determining the first device model #1.

[0232] Implementation method one includes S903 and S904.

[0233] S903, the core network device sends the correspondence between the M group identifier #2 and / or the model and the identifier of the first data, and correspondingly, the first device receives the correspondence between the M group identifier #2 and / or the model and the identifier of the first data.

[0234] S904, the first device determines model #1 based on third data.

[0235] The third data is the reasoning data used in the model reasoning stage.

[0236] Specifically, the first device determines model #1 based on the identifier of the third data and the correspondence between the model and the identifier of the first data, wherein the identifier of the first data corresponding to model #1 is the same as the identifier of the third data.

[0237] Implementation method two includes S905-S906.

[0238] S905, core network equipment determines model #1 based on third-party data.

[0239] The third data refers to inference data used in the model inference process. This application does not limit the specific implementation method of the core network device acquiring the third data; as an example, the first device sends the third data to the core network device.

[0240] Specifically, the core network equipment determines model #1 based on the identifier of the third data and the correspondence between the model and the identifier of the first data, wherein the identifier of the first data corresponding to model #1 is the same as the identifier of the third data.

[0241] S906, the core network equipment sends information about model #1 and / or model #1, and correspondingly, the first equipment receives information about model #1 and / or model #1.

[0242] This application does not limit the specific method by which the core network device indicates model #1 to the first device. As one possible implementation, the information of model #1 may include the identifier of model #1, and the first device determines model #1 based on the identifier of model #1 and the stored data.

[0243] S903-S904 and S905-S906 are parallel steps, and only one of them needs to be executed.

[0244] The above describes how the core network equipment assists in the interactive communication between the first and second devices. This method can improve the security of the communication process. For example, if identifier #1 is sensitive information, the way the core network equipment determines the correspondence between M groups of identifiers #2 and / or the model and the identifier of the first data based on identifier #1 can avoid the security problems caused by the first device directly obtaining identifier #1.

[0245] Figure 10 This is a schematic block diagram of a communication device 1000 provided in an embodiment of this application. The communication device includes a transceiver unit 1010. The transceiver unit 1010 can be used to implement corresponding communication functions. The transceiver unit 1010 can also be referred to as a communication interface or a communication unit. Optionally, the device 1000 further includes a processing unit 1020. The processing unit 1020 can be used to implement processing operations.

[0246] Optionally, the device 1000 may further include a storage unit, which can be used to store instructions and / or data, and the processing unit 1020 can read the instructions and / or data in the storage unit to enable the device to implement the aforementioned method embodiments.

[0247] Optionally, the transceiver unit 1010 includes a sending unit and / or a receiving unit, wherein the sending unit is used to perform the sending operation in the above embodiments, and the receiving unit is used to perform the receiving operation in the above embodiments.

[0248] It should be noted that the communication device 1000 may include a transmitting unit but not a receiving unit; or, the communication device 1000 may include a receiving unit but not a transmitting unit. Specifically, it depends on whether the above-described scheme executed by the communication device 1000 includes both transmitting and receiving actions. For example, the communication device 1000 is used to execute the actions performed by the first device, the second device, or the core network device in the above embodiments. For details, please refer to the relevant descriptions in the above embodiments, which will not be repeated here.

[0249] For example, the communication device 1000 is used to execute the following scheme.

[0250] In one possible design, the device 1000 can be a first device, or a component of the first device (such as a chip, chip system, or circuit). The transceiver unit and processing unit can be used to implement the relevant operations of the first device.

[0251] One possible implementation is that the transceiver unit 1010 is used to send or receive N first identifiers, each of the N first identifiers indicating a state of the second device when acquiring first data, each of the N first identifiers belonging to one of M groups of first identifiers, any two of the N first identifiers belonging to different groups, M being an integer greater than or equal to 0, and N being an integer greater than or equal to 0 and less than or equal to M; the transceiver unit 1010 is also used to acquire first data, which is used to train an AI model.

[0252] Optionally, the transceiver unit 1010 is also used to acquire a second identifier, which indicates information about the second device.

[0253] Optionally, the transceiver unit 1010 is further configured to send N first identifiers to the second device, the N first identifiers indicating the state of the second device when receiving the second data; the transceiver unit 1010 is further configured to send the second data to the second device; the transceiver unit 1010 is further configured to receive first data, the first data being obtained by the second device by measuring the second data.

[0254] Optionally, the transceiver unit 1010 is further configured to send N first identifiers to the second device, the N first identifiers indicating the state of the second device when sending the second data; the transceiver unit 1010 is further configured to receive the second data; and the processing unit 1020 is configured to determine the first data based on the second data.

[0255] Optionally, the number N of the first identifiers is related to one or more of the following: first data, the state of the second device.

[0256] Optionally, the higher the correlation between the first data and the state of the second device, the larger the value of N; or, the lower the correlation between the first data and the state of the second device, the smaller the value of N.

[0257] Optionally, the transceiver unit 1010 is further configured to acquire N2 first identifiers, the N2 first identifiers indicating the state of the second device when acquiring the third data, each of the N2 first identifiers belonging to one of the M groups of first identifiers, any two of the N2 first identifiers belonging to different groups, and N2 being an integer greater than or equal to 0 and less than or equal to M; the processing unit 1020 is further configured to determine a first model based on the N2 first identifiers, the first model being obtained by training with the first data.

[0258] Optionally, the transceiver unit 1010 is further configured to acquire N3 first identifiers, wherein the N3 first identifiers are the updated identifiers among the N2 first identifiers, and N3 is an integer greater than or equal to 0 and less than or equal to N2; the processing unit 1020 is further configured to determine a second model based on the N3 first identifiers, wherein the second model is obtained by training the first data.

[0259] Optionally, the transceiver unit 1010 is further configured to receive a first model from the first node, the first model being obtained by training with first data; the first model is determined based on N2 first identifiers, the N2 first identifiers indicating the state of the second device when acquiring the third data, each of the N2 first identifiers belonging to one of the M groups of first identifiers, any two of the N2 first identifiers belonging to different groups, and N2 being an integer greater than or equal to 0 and less than or equal to M.

[0260] Optionally, the first identifier indicates any of the following: port mapping status, transmit power, or beam shape.

[0261] Optionally, the second identifier indicates one or more of the following: the manufacturer's identifier of the second device, the device type of the second device, and the unique identifier of the second device.

[0262] Optionally, the transceiver unit 1010 is also configured to receive M groups of first identifiers from the first node, wherein the M groups of first identifiers are determined by the first node based on the second identifier.

[0263] In a second possible design, the device 1000 can be a second device, or a component of a second device (such as a chip, chip system, or circuit). The transceiver unit and processing unit can be used to implement the relevant operations of the second device.

[0264] In one possible implementation, the transceiver unit 1010 is used to receive or send N first identifiers, each of the N first identifiers indicating a state of the second device when the first device acquires the first data, each of the N first identifiers belonging to one of M groups of first identifiers, any two of the N first identifiers belonging to different groups, M being an integer greater than or equal to 0, and N being an integer greater than or equal to 0 and less than or equal to M; the transceiver unit 1010 is also used to send the first data, which is used to train the AI ​​model.

[0265] Optionally, the transceiver unit 1010 is also used to transmit a second identifier, which indicates information about the second device.

[0266] Optionally, the transceiver unit 1010 is further configured to receive N first identifiers from the first device, the N first identifiers indicating the state of the second device when receiving the second data; the transceiver unit 1010 is further configured to receive the second data from the first device; the processing unit 1020 is configured to measure the second data to obtain the first data.

[0267] Optionally, the transceiver unit 1010 is further configured to receive N first identifiers from the first device, the N first identifiers indicating the state of the second device when transmitting the second data; the transceiver unit 1010 is further configured to transmit the second data, the second data being used to determine the first data.

[0268] Optionally, the number N of the first identifiers is related to one or more of the following: first data, the state of the second device.

[0269] Optionally, the higher the correlation between the first data and the state of the second device, the larger the value of N.

[0270] Optionally, the transceiver unit 1010 is further configured to send N2 first identifiers, which indicate the state of the second device when the first device acquires the third data. Each of the N2 first identifiers belongs to one of the M groups of first identifiers. Any two of the N2 first identifiers belong to different groups. N2 is an integer greater than or equal to 0 and less than or equal to M. The N2 first identifiers are used to determine the first model, which is obtained by training with the first data.

[0271] Optionally, the transceiver unit 1010 is further configured to send N3 first identifiers, wherein the N3 first identifiers are the updated identifiers among the N2 first identifiers, and N3 is an integer greater than or equal to 0 and less than or equal to N2; the N3 first identifiers are used to determine the second model, which is obtained by training the first data.

[0272] Optionally, the first identifier indicates any of the following: port mapping status, transmit power, or beam shape.

[0273] Optionally, the second identifier indicates one or more of the following: the manufacturer's identifier of the second device, the device type of the second device, and the unique identifier of the second device.

[0274] In a third possible design, the device 1000 can be a core network device, or a component of a core network device (such as a chip, chip system, or circuit). The transceiver unit and processing unit can be used to implement the relevant operations of the core network device.

[0275] One possible implementation is as follows: a transceiver unit 1010 is used to receive a second identifier from a second device, the second identifier indicating information of the second device; a processing unit 1020 is used to obtain M groups of first identifiers and / or the correspondence between the model and the identifiers of the first data based on the second identifier; wherein, the M groups of first identifiers indicate all states of the second device, the first data is used to train the AI ​​model, the identifiers of the first data include N first identifiers, each of the N first identifiers belongs to one of the M groups of first identifiers, any two of the N first identifiers belong to different groups, M is an integer greater than or equal to 0, and N is an integer greater than or equal to 0 and less than or equal to M.

[0276] Optionally, the transceiver unit 1010 is further configured to send M sets of first identifiers and / or the correspondence between the model and the identifier of the first data to the first device, wherein the correspondence between the model and the identifier of the first data is used to determine the first model.

[0277] Optionally, the transceiver unit 1010 is further configured to acquire third data; the processing unit 1020 is further configured to determine a first model based on the correspondence between the identifier of the third data and the model and the identifier of the first data; the transceiver unit 1010 is further configured to send the first model to the first device.

[0278] It is understood that the division of units in the above-described device is merely a logical functional division. Each function can correspond to a functional unit, or two or more functions can be integrated into one functional unit. In actual implementation, all or some units can be integrated into a single physical entity, or they can be distributed across different physical entities. Furthermore, the aforementioned functional units can be implemented in hardware, software, or a combination of both. Whether a function is executed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0279] In one example, the functional unit in any of the above devices may be one or more integrated circuits configured to implement the above methods, such as: one or more application-specific integrated circuits (ASICs), or one or more central processing units (CPUs), one or more microcontroller units (MCUs), one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0280] In one example, the storage unit may include random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory and / or registers, etc.

[0281] Figure 11 This is a schematic diagram of another communication device 1100 provided in an embodiment of this application. The device 1100 includes a processor 1110, which is coupled to a memory 1120. The memory 1120 is used to store computer programs or instructions and / or data. The processor 1110 is used to execute the computer programs or instructions stored in the memory 1120, or to read the data stored in the memory 1120, so as to execute the methods in the above method embodiments.

[0282] Optionally, there may be one or more processors 1110.

[0283] Optionally, the memory 1120 may be one or more.

[0284] Optionally, the memory 1120 is integrated with the processor 1110, or the memory 1120 is built into the processor 1110, or the memory 1120 is set separately from the processor 1110.

[0285] Optionally, such as Figure 11 As shown, the device 1100 also includes a transceiver 1130, which is used for receiving and / or transmitting signals. For example, the processor 1110 is used to control the transceiver 1130 to receive and / or transmit signals.

[0286] For example, processor 1110 is used to execute computer programs or instructions stored in memory 1120 to implement the relevant operations of terminal devices or network devices in the various method embodiments described above.

[0287] Optionally, the transceiver 1130 includes a transmitter (or a transmitter module, a transmitting circuit, etc.) and / or a receiver (or a receiver module, a receiving circuit, etc.), wherein the transmitter is used to perform the transmitting operation in the above embodiments, and the receiver is used to perform the receiving operation in the above embodiments.

[0288] It should be noted that the communication device 1100 may include a transmitter but not a receiver; or, the communication device 1100 may include a receiver but not a transmitter. 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 actions performed by the first device, the second device, or the core network device in the above embodiments. For details, please refer to the relevant descriptions in the above embodiments, which will not be repeated here.

[0289] It should be understood that the processor mentioned in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0290] It should also be understood that the memory mentioned in the embodiments of this application can be volatile memory and / or non-volatile memory. 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. Volatile memory can be random access memory (RAM). For example, RAM can be used as an external cache. By way of example and not limitation, RAM includes the following forms: 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).

[0291] It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA, or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, the memory (storage module) can be integrated into the processor.

[0292] It should also be noted that the memory described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0293] Figure 12 This is a schematic block diagram of a chip system 1200 provided in an embodiment of this application. The chip system 1200 (or may also be referred to as a processing system) includes logic circuitry 1210 and an input / output interface 1220.

[0294] The logic circuit 1210 can be a processing circuit in the chip system 1200. The logic circuit 1210 can be coupled to a memory unit, calling instructions from the memory unit, enabling the chip system 1200 to implement the methods and functions of the embodiments of this application. The input / output interface 1220 can be an input / output circuit in the chip system 1200, outputting processed information from the chip system 1200, or inputting data or signaling information to be processed into the chip system 1200 for processing.

[0295] As one approach, the chip system 1200 is used to implement the operations performed by the communication device in the various method embodiments described above.

[0296] For example, logic circuit 1210 is used to implement processing-related operations performed by the communication device in the above method embodiments; input / output interface 1220 is used to implement sending and / or receiving-related operations performed by the communication device in the above method embodiments.

[0297] This application also provides a computer-readable storage medium storing computer instructions for implementing the methods executed by the communication device in the above-described method embodiments.

[0298] For example, when the computer program is executed by the computer, it enables the computer to implement the methods executed by the communication device in the various embodiments of the above methods.

[0299] This application also provides a computer program product comprising instructions which, when executed by a computer, implement the methods performed by the communication device in the above-described method embodiments.

[0300] This application also provides a communication system, which includes the first device and / or the second device and / or the core network device in the above embodiments.

[0301] The explanations and beneficial effects of the relevant contents in any of the devices provided above can be found in the corresponding method embodiments provided above, and will not be repeated here.

[0302] In the several embodiments provided in this application, it should be understood that the disclosed apparatus 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 mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of apparatus or units may be electrical, mechanical, or other forms.

[0303] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program 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. For example, the computer can be a personal computer, a server, or a network device, etc. 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 website, computer, server, or data center 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 that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks, SSDs). For example, the aforementioned available media include, but are not limited to, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, and other media capable of storing program code.

[0304] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A communication method applied to a first device, characterized in that, include: Send or receive N first identifiers, each of the N first identifiers indicating a state of the second device when acquiring the first data, each of the N first identifiers belonging to one of M groups of first identifiers, any two of the N first identifiers belonging to different groups, M being an integer greater than or equal to 0, and N being an integer greater than or equal to 0 and less than or equal to M. Obtain the first data, which is used to train an artificial intelligence (AI) model.

2. The method according to claim 1, characterized in that, The method further includes: Obtain a second identifier, which indicates information about the second device.

3. The method according to claim 1 or 2, characterized in that, The sending or receiving of N first identifiers includes: The N first identifiers are sent to the second device, the N first identifiers indicating the state of the second device when receiving the second data; The method further includes: sending the second data to the second device, wherein the first data is obtained by the second device measuring the second data.

4. The method according to claim 1 or 2, characterized in that, The sending or receiving of N first identifiers includes: The N first identifiers are sent to the second device, the N first identifiers indicating the state of the second device when sending the second data; The method further includes: receiving the second data; The step of obtaining the first data includes: determining the first data based on the second data.

5. The method according to any one of claims 1 to 4, characterized in that, include: The number N of the N first identifiers is related to one or more of the following: the first data, the state of the second device.

6. The method according to claim 5, characterized in that, include: The higher the correlation between the first data and the state of the second device, the larger the value of N; Alternatively, the lower the correlation between the first data and the state of the second device, the smaller the value of N.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: Obtain N2 first identifiers, wherein the N2 first identifiers indicate the state of the second device when the third data is obtained, each of the N2 first identifiers belongs to one of the M groups of first identifiers, and any two of the N2 first identifiers belong to different groups, where N2 is an integer greater than or equal to 0 and less than or equal to M. A first model is determined based on the N2 first identifiers, and the first model is obtained by training with the first data.

8. The method according to claim 7, characterized in that, The method further includes: Obtain N3 first identifiers, wherein the N3 first identifiers indicate the identifiers that have been updated among the N2 first identifiers, and N3 is an integer greater than or equal to 0 and less than or equal to N2; The second model is determined based on the N3 first identifiers, and the second model is obtained by training with the first data.

9. The method according to any one of claims 1 to 6, characterized in that, The method further includes: Receive a first model from the first node, the first model being trained using the first data; The first model is determined based on N2 first identifiers, which indicate the state of the second device when acquiring the third data. Each of the N2 first identifiers belongs to one of the M groups of first identifiers. Any two of the N2 first identifiers belong to different groups. N2 is an integer greater than or equal to 0 and less than or equal to M.

10. The method according to any one of claims 1 to 9, characterized in that, include: The first identifier indicates any of the following: port mapping status, transmit power, beam shape.

11. The method according to any one of claims 2 to 10, characterized in that, include: The second identifier indicates one or more of the following: the manufacturer's identifier of the second device, the device type of the second device, and the unique identifier of the second device.

12. The method according to any one of claims 2 to 11, characterized in that, The method further includes: Receive the M groups of first identifiers from the first node, the M groups of first identifiers being determined by the first node based on the second identifier.

13. A communication device, characterized in that, include: A processor for executing a computer program or instructions stored in a memory to cause the communication device to perform the method as described in any one of claims 1 to 12.

14. A computer program product, characterized in that, The computer program product includes programs or instructions for performing the method as described in any one of claims 1 to 12.

15. A computer-readable storage medium, characterized in that, include: The computer-readable storage medium stores a computer program that, when run on a computer, causes the computer to perform the method as described in any one of claims 1 to 12.

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