Information acquisition method and apparatus, information transmission method and apparatus, and communication device

Abstract

The present application relates to the technical field of communications, and discloses an information acquisition method and apparatus, an information transmission method and apparatus, and a communication device. The information acquisition method in embodiments of the present application comprises: a terminal acquires target information, the target information being used for indicating at least one of the following: data characteristic related information of N network devices and prediction related information, and N being an integer greater than 1; and the terminal executes a first operation on the basis of the target information, the first operation being an artificial intelligence (AI) related operation.

Description

Information acquisition methods, information transmission methods, devices and communication equipment

[0001] Cross-reference to related applications

[0002] This application claims priority to Chinese Patent Application No. 202410923808.0, filed on July 10, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application belongs to the field of communication technology, specifically relating to an information acquisition method, an information transmission method, an apparatus, and a communication device. Background Technology

[0004] In some communication scenarios (such as cell handover or cell reselection), communication equipment can use artificial intelligence (AI) models (or AI units or AI functions) to make relevant predictions. However, in practical applications, the performance of AI models is unstable, which affects communication performance. Summary of the Invention

[0005] This application provides an information acquisition method, an information transmission method, an apparatus, and a communication device that can solve the problem of unstable performance of AI models.

[0006] Firstly, an information acquisition method is provided, executed by a terminal, the method comprising:

[0007] The terminal acquires target information, which is used to indicate at least one of the following: data characteristic related information of N network devices; prediction related information; N is an integer greater than 1;

[0008] Based on the target information, the terminal performs a first operation, which is an artificial intelligence (AI) related operation.

[0009] Secondly, an information transmission method is provided, executed by a first network device, the method comprising:

[0010] The first network device sends target information or second information to the terminal;

[0011] The target information is used to indicate at least one of the following: data characteristic-related information of N network devices; prediction-related information; where N is an integer greater than 1;

[0012] The second information is used to indicate at least one of the following: data characteristic-related information of the first network device; the prediction-related information.

[0013] Thirdly, a method for transmitting information is provided, executed by a second network device, the method comprising:

[0014] The second network device receives fourth information from the first network device, the fourth information being used to request at least one of the following:

[0015] Information related to the data characteristics of the second network device;

[0016] The data from the second network device.

[0017] Fourthly, an information acquisition device is provided, the device comprising:

[0018] The first processing module is used to acquire target information, which indicates at least one of the following: data characteristic related information of N network devices; prediction related information; N is an integer greater than 1;

[0019] The second processing module is used to perform a first operation based on the target information, wherein the first operation is an artificial intelligence (AI) related operation.

[0020] Fifthly, an information transmission device is provided, the device comprising:

[0021] The first sending module is used to send target information or second information to the terminal;

[0022] The target information is used to indicate at least one of the following: data characteristic-related information of N network devices; prediction-related information; where N is an integer greater than 1;

[0023] The second information is used to indicate at least one of the following: data characteristic-related information of the first network device; the prediction-related information.

[0024] Sixthly, an information transmission device is provided, the device comprising:

[0025] The receiving module is configured to receive fourth information from the first network device, the fourth information being used to request at least one of the following:

[0026] Information related to the data characteristics of the second network device;

[0027] The data from the second network device.

[0028] In a seventh aspect, an information acquisition apparatus is provided, the apparatus being configured to perform the steps of the method described in the first aspect.

[0029] Eighthly, an information transmission apparatus is provided, the apparatus being configured to perform the steps of the method described in the second aspect, or the steps of the method described in the third aspect.

[0030] In a ninth aspect, a terminal is provided, the terminal including a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the method as described in the first aspect.

[0031] In a tenth aspect, a terminal is provided, including a processor and a communication interface, wherein the processor is configured to: acquire target information, the target information being configured to indicate at least one of the following: data characteristic related information of N network devices; prediction related information; N being an integer greater than 1; and, based on the target information, perform a first operation, the first operation being an artificial intelligence (AI) related operation.

[0032] Eleventhly, a network-side device is provided, the network-side device including a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the method as described in the second or third aspect.

[0033] In a twelfth aspect, a network-side device is provided, including a processor and a communication interface, wherein the communication interface is used to: send target information or second information to a terminal; wherein the target information is used to indicate at least one of the following: data characteristic related information of N network devices; prediction related information; N is an integer greater than 1; the second information is used to indicate at least one of the following: data characteristic related information of the first network device; the prediction related information.

[0034] In a thirteenth aspect, a network-side device is provided, including a processor and a communication interface, wherein the communication interface is configured to: receive fourth information from a first network device, the fourth information being configured to request at least one of the following: information related to the data characteristics of the second network device; and data of the second network device.

[0035] In a fourteenth aspect, a readable storage medium is provided, on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect, or the steps of the method described in the second aspect, or the steps of the method described in the third aspect.

[0036] In a fifteenth aspect, a wireless communication system is provided, comprising: a terminal and a network-side device, wherein the terminal is configured to perform the steps of the method described in the first aspect, and the network-side device is configured to perform the steps of the method described in the second or third aspect.

[0037] In a sixteenth aspect, a chip is provided, the chip including a processor and a communication interface coupled to the processor, the processor being configured to run a program or instructions to implement the method as described in the first aspect, or the method as described in the second aspect, or the method as described in the third aspect.

[0038] In a seventeenth aspect, a computer program / program product is provided, the computer program / program product being stored in a storage medium, the computer program / program product being executed by at least one processor to implement the steps of the information acquisition method as described in the first aspect, or the steps of the information transmission method as described in the second aspect, or the steps of the information transmission method as described in the third aspect.

[0039] In this embodiment, the terminal acquires target information, which indicates at least one of the following: data characteristic-related information of N network devices; prediction-related information; where N is an integer greater than 1. Based on the target information, the terminal performs a first operation, which is an artificial intelligence (AI) related operation. In this way, by acquiring the target information, the terminal can obtain the data characteristics of multiple network devices, and thus, based on the target information, perform AI-related operations. This ensures that the AI-related operations match the data characteristics of the multiple network devices, or that the AI-related operations conform to the indicated prediction-related information, which helps improve the performance stability of the AI ​​unit or AI function. Attached Figure Description

[0040] Figure 1 is a schematic diagram of a network structure applicable to the embodiments of this application;

[0041] Figure 2 is a flowchart of an information acquisition method provided in an embodiment of this application;

[0042] Figure 3 is a flowchart of an information transmission method provided in an embodiment of this application;

[0043] Figure 4 is a flowchart of another information transmission method provided in an embodiment of this application;

[0044] Figures 5 and 6 are flowcharts of Example 1;

[0045] Figure 7 is a flowchart of Example 2;

[0046] Figures 8 and 9 are flowcharts of Example 3;

[0047] Figure 10 is a flowchart of Example 4;

[0048] Figure 11 is a flowchart of Example 5;

[0049] Figure 11a is a flowchart of Example 6;

[0050] Figures 11b to 11c are flowcharts of Example 7;

[0051] Figure 12 is a structural diagram of an information acquisition device provided in an embodiment of this application;

[0052] Figure 13 is a structural diagram of an information transmission device provided in an embodiment of this application;

[0053] Figure 14 is a structural diagram of an information transmission device provided in an embodiment of this application;

[0054] Figure 15 is a structural diagram of a communication device provided in an embodiment of this application;

[0055] Figure 16 is a structural diagram of a terminal provided in an embodiment of this application;

[0056] Figure 17 is a structural diagram of a network-side device provided in an embodiment of this application;

[0057] Figure 18 is a structural diagram of another network-side device provided in an embodiment of this application. Detailed Implementation

[0058] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0059] The terms "first," "second," etc., used in this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same class, not limited in number; for example, the first object can be one or more. Furthermore, "or" in this application indicates at least one of the connected objects. For example, the scope of protection for "A or B" covers at least three scenarios: Scenario 1: including A but not B; Scenario 2: including B but not A; Scenario 3: including both A and B. In addition, the terms "A and / or B," "at least one of A and B," and "at least one of A or B" also cover at least the above three scenarios. The character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0060] The term "instruction" in this application can be either a direct instruction (or explicit instruction) or an indirect instruction (or implicit instruction). A direct instruction can be understood as one in which the sender explicitly informs the receiver of specific information, the operation to be performed, or the requested result, etc., in the instruction sent. An indirect instruction can be understood as one in which the receiver determines the corresponding information based on the instruction sent by the sender, or makes a judgment and determines the operation to be performed or the requested result, etc., based on the judgment result.

[0061] It is worth noting that the technologies described in this application are not limited to Long Term Evolution (LTE) / LTE-Advanced (LTE-A) systems, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency-Division Multiple Access (SC-FDMA), or other systems. The terms "system" and "network" in this application are often used interchangeably, and the described technologies can be used with the systems and radio technologies mentioned above, as well as with other systems and radio technologies. The following description describes New Radio (NR) systems for illustrative purposes, and the term NR is used in most of the following description; however, these technologies can also be applied to systems other than NR systems, such as 6th generation (6G) radio systems. th Generation 6G communication system.

[0062] Figure 1 shows a block diagram of a wireless communication system applicable to an embodiment of this application. The wireless communication system includes a terminal 11 and a network-side device 12. The terminal 11 can be a mobile phone, tablet computer, laptop computer, notebook computer, personal digital assistant (PDA), handheld computer, netbook, ultra-mobile personal computer (UMPC), mobile internet device (MID), augmented reality (AR), virtual reality (VR) device, robot, wearable device, flight vehicle, vehicle user equipment (VUE), shipboard equipment, pedestrian user equipment (PUE), smart home (home devices with wireless communication capabilities, such as refrigerators, televisions, washing machines, or furniture), game console, personal computer (PC), ATM, or self-service machine, etc. Wearable devices include: smartwatches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart chains, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. Among these, in-vehicle devices can also be referred to as in-vehicle terminals, in-vehicle controllers, in-vehicle modules, in-vehicle components, in-vehicle chips, or in-vehicle units, etc. It should be noted that the specific type of terminal 11 is not limited in this application embodiment. Network-side equipment 12 may include access network equipment or core network equipment, wherein access network equipment may also be referred to as Radio Access Network (RAN) equipment, radio access network function, or radio access network unit. Access network equipment may include base stations, Wireless Local Area Network (WLAN) access points (APs), or Wireless Fidelity (WiFi) nodes, etc.The term "base station" can be referred to as Node B (NB), Evolved Node B (eNB), Next Generation Node B (gNB), New Radio Node B (NR Node B), Access Point, Relay Base Station (RBS), Serving Base Station (SBS), Base Transceiver Station (BTS), Radio Base Station, Radio Transceiver, Basic Service Set (BSS), Extended Service Set (ESS), Home Node B (HNB), Home Evolved Node B, Transmit / Receive Point (TRP), or any other suitable term in the relevant field, as long as the same technical effect is achieved. The term "base station" is not limited to any specific technical terminology. It should be noted that this application embodiment only uses a base station in an NR system as an example for description and does not limit the specific type of base station.

[0063] Core network equipment, also known as core network nodes, core network functions, or core network elements, includes, but is not limited to, at least one of the following: Mobility Management Entity (MME), Access and Mobility Management Function (AMF), Session Management Function (SMF), User Plane Function (UPF), Policy Control Function (PCF), Policy and Charging Rules Function (PCRF), Edge Application Server Discovery Function (EASDF), Unified Data Management (UDM), Unified Data Repository (UDR), Home Subscriber Server (HSS), Centralized network configuration (CNC), Network Repository Function (NRF), Network Exposure Function (NEF), Local NEF (or L-NEF), and Binding Support. The core network functions include: BSF (Block Network Function), Application Function (AF), Location Management Function (LMF), Gateway Mobile Location Centre (GMLC), and Network Data Analytics Function (NWDAF). It should be noted that this application embodiment only uses core network equipment in the NR system as an example and does not limit the specific type of core network equipment. If the name of the core network equipment mentioned in this application embodiment changes in subsequent protocol versions (e.g., 6G), it will still be within the scope of protection of this application.

[0064] Optionally, the core network equipment can be implemented by one or more functional modules in a single device, or by multiple devices working together; this application does not specifically limit this. It is understood that the aforementioned functional modules can be network elements in hardware devices, software functional modules running on dedicated hardware, or virtualized functional modules instantiated on a platform (e.g., a cloud platform).

[0065] Before describing the embodiments of this application, the relevant technologies are briefly introduced below:

[0066] I. AI-based Cell Reselection

[0067] In related technologies, AI-based cell reselection involves inputting terminal-side information, information about the currently active cell, and information about neighboring cells into the AI ​​unit. The output of the AI ​​unit is information related to the reselected cell. The network-side information includes cell load information and antenna configuration information.

[0068] II. AI-based Measurement of Relaxation

[0069] Among related technologies, AI-based relaxation measurement solutions fall into the following two categories:

[0070] Category 1: Using two AI units, such as: first AI unit and second AI unit.

[0071] First AI Unit: The input of the AI ​​unit includes the channel quality of the target cell (the stationed cell and neighboring cells) measured by the terminal over multiple historical time units, and may also include information about the stationed cell or neighboring cells. The output of the AI ​​unit includes the predicted channel quality of the target cell.

[0072] The second AI unit: The input to the AI ​​unit includes the predicted target cell channel quality, and the output of the AI ​​unit includes the measurement relaxation period multiple.

[0073] The second type: using an AI unit, such as the third AI unit.

[0074] The third AI unit: The input of the AI ​​unit includes the channel quality of the target cell in multiple historical time units measured by the terminal, and may also include information of the camped cell or neighboring cells. The output of the AI ​​unit includes the predicted channel quality of the target cell, and may also include the period multiple of the measurement relaxation.

[0075] The AI ​​units described above all take as input channel quality data of the target cell obtained from multiple historical time units measured by the terminal. The main function of these AI units is to predict future wireless channel quality by extracting changes in historical wireless channel measurement results. However, these changes are often strongly correlated with the cell's wireless environment. Therefore, these AI units are not applicable to multiple scenarios and are often only suitable for specific ones. This raises the issue of matching the characteristics of the data collected during inference with those collected during training.

[0076] III. AI-based Mobility Management

[0077] Mobility management can assist the network in achieving load balancing and provide a better user experience. Connected-mode mobility management is mainly achieved through network-controlled handover and redirection processes; disconnected-mode mobility management is mainly achieved through terminal-controlled cell selection and cell reselection processes. Since the prediction accuracy of existing non-AI methods (such as filtering methods) is far lower than that of AI-based predictions, AI-based mobility management is now being discussed. When the terminal is in a disconnected state, AI-based cell reselection can be used.

[0078] In AI-based mobility management, taking inference at the terminal as an example, there are two scenarios.

[0079] Scenario 1: The input data for the AI ​​unit comes only from the UE's data.

[0080] Scenario 2: The input data of the AI ​​unit comes from both the UE's data and the network's data.

[0081] In scenario 1, the input data for the AI ​​unit includes the terminal's wireless measurement results. The AI ​​unit's main function is to predict future wireless channel quality by extracting changes in historical wireless channel measurement results. However, these changes are often strongly correlated with the cell's wireless environment. Therefore, this type of AI unit cannot be applied to multiple scenarios and is often only suitable for specific scenarios. There is no corresponding technical solution for how the terminal can determine whether the current AI unit matches the current cell's wireless characteristics, which leads to unstable inference performance of the AI ​​unit. In cell handover or reselection scenarios, if the terminal uses a model with mismatched wireless characteristics, it may cause handover failure or reselection to an unsuitable cell.

[0082] In view of this, embodiments of this application provide an information acquisition method, an information transmission method, an apparatus, and a communication device to solve the problem of unstable inference performance of AI units in related technologies.

[0083] The information acquisition method provided in this application will be described in detail below with reference to the accompanying drawings and through some embodiments and application scenarios.

[0084] Figure 2 shows a flowchart of an information acquisition method provided in an embodiment of this application. As shown in Figure 2, the information acquisition method includes the following steps:

[0085] Step 201: The terminal obtains target information, which is used to indicate at least one of the following: data characteristic related information of N network devices; prediction related information; N is an integer greater than 1;

[0086] Step 202: The terminal performs a first operation based on the target information, the first operation being an artificial intelligence (AI) related operation.

[0087] In this application embodiment, AI-related operations may include, for example, data characteristic consistency judgment related to AI unit or AI function, model activation of AI unit, model deactivation of AI unit, model inference of AI unit, model training of AI unit, collection of model training dataset of AI unit, module monitoring of AI unit, and model fine-tuning of AI unit, etc. This application embodiment does not limit these operations.

[0088] In this embodiment, the terminal can be understood as a device performing AI model inference. The N network devices may include, for example, a first network device and a second network device. The first network device can be understood as the base station of the cell where the terminal is camped in a non-connected state, or the serving base station in a connected state, or a core network function (e.g., LMF or other network elements). The first network device can perform data characteristic consistency judgment related to AI model inference data collection. The second network device may be, for example, a base station of a neighboring cell or a core network function (e.g., LMF or other network elements). The first network device can be understood as a mapping device between data characteristics and associated information. Additionally, the first network device may also be a device performing data characteristic consistency judgment related to AI unit model inference data collection.

[0089] The explanation of AI units / AI models is as follows:

[0090] In this application embodiment, the AI ​​unit / AI model may also be referred to as an AI unit, AI model, machine learning (ML) model, ML unit, AI structure, AI function, AI characteristic, machine learning model, neural network, neural network function, neural network functionality, etc. Alternatively, the AI ​​unit / AI model may refer to a processing unit capable of implementing specific algorithms, formulas, processing flows, capabilities, etc. related to AI. Alternatively, the AI ​​unit / AI model may be a processing method, algorithm, function, module, or unit for a specific dataset. Alternatively, the AI ​​unit / AI model may be a processing method, algorithm, function, module, or unit running on AI / ML related hardware such as GPU, NPU, TPU, and ASIC. This application embodiment does not specifically limit these aspects. Optionally, the specific dataset includes the input and / or output of the AI ​​unit / AI model.

[0091] Optionally, AI units / AI models can be identified by AI unit / AI model identifiers. The AI ​​unit / AI model identifier can be an AI model identifier, an AI structure identifier, an AI algorithm identifier, or an identifier of a specific dataset associated with the AI ​​unit / AI model, or an identifier of a specific scenario, environment, channel characteristics, or device related to the AI / ML, or an identifier of a function, characteristic, capability, or module related to the AI / ML.

[0092] In this embodiment, the data characteristic information related to the device can be understood as information related to the data characteristics of the device. The data characteristics of the device can be understood as the device's wireless characteristics (that is, there is a correspondence between the device's data characteristics and its wireless characteristics), or as the device's related configuration (that is, there is a correspondence between the device's data characteristics and its related configuration), or as the characteristics of the wireless environment in which the device exists (that is, there is a correspondence between the device's data characteristics and its wireless environment), or as the mapping relationship between the device's related configuration and the mapped data. The device's wireless characteristics can be divided into two categories: first wireless characteristics and second wireless characteristics. The first wireless characteristics can be understood as fixed wireless characteristics, or hardware-related wireless characteristics, or station-type-related wireless characteristics. The second wireless characteristics can be understood as adjustable wireless characteristics, or software-adjustable wireless characteristics.

[0093] The embodiments of this application are applicable to scenarios involving multi-cell prediction, such as cell handover, cell reselection, and location.

[0094] In step 201, the terminal obtains the target information, which can be done by obtaining information from each of the N network devices, or by obtaining information from the first network device. This application embodiment does not limit this to either method.

[0095] The target information can be carried in at least one of the following messages:

[0096] Broadcast messages, such as System Information Block (SIB) messages;

[0097] Specialized signaling, such as Radio Resource Control (RRC) signaling, can be sent in the release message for cell reselection (specifically, RRC Release with Suspend config).

[0098] By acquiring target information, where the target information is used to indicate data characteristic information of N network devices, the terminal can know whether the AI ​​unit or AI function that its device can acquire matches the data characteristics of the N network devices. The terminal can determine the matching AI unit or AI function based on the target information. This not only avoids the mismatch between the AI ​​unit or AI function enabled by the terminal and the corresponding data characteristics of the network devices, but also reduces the computing power and time overhead of the terminal in blindly searching to determine whether there is a matching AI unit or AI function.

[0099] When the target information is used to indicate prediction-related information, it can be understood that the AI ​​units or AI functions available to the terminal match the data characteristics corresponding to the first network device. In other words, the first network device has made its own judgment. By judging, the first network device determines that the AI ​​units or AI functions available to the terminal match the data characteristics corresponding to the first network device, and thus the first network device directly sends the second indication information to request the terminal to make a prediction. It should be noted that "prediction" in the embodiments of this application can be replaced with "inference".

[0100] In this embodiment, the terminal can acquire target information to obtain the data characteristics of multiple network devices. Based on this target information, it can perform AI-related operations, ensuring that the AI-related operations match the data characteristics of the multiple network devices, or that the AI-related operations conform to the indicated prediction-related information. This helps improve the performance stability of the AI ​​unit or AI function. For cell handover and cell reselection scenarios, it can prevent handover failures or reselection to an unsuitable cell due to the terminal using AI units or AI functions with mismatched data characteristics.

[0101] In some embodiments, the N network devices include a first network device and a second network device;

[0102] The target information includes at least one of the following:

[0103] The first data characteristic corresponding to the first network device;

[0104] AI information related to the first data characteristic;

[0105] Cell information related to the first data characteristic;

[0106] The second data characteristic corresponding to the second network device;

[0107] AI information related to the second data characteristic;

[0108] The cell information related to the second data characteristic.

[0109] In this application embodiment, AI information may include, for example, AI features or AI functions. AI features and AI functions are explained as follows: AI feature + specific configuration, i.e., AI function. For example, the AI ​​feature is beam management, and the AI ​​function is time-domain beam prediction configured for a base station with 32 transmit beams.

[0110] For target information including the aforementioned AI information, the first network device directly helps the terminal determine the AI ​​features or functions that can be activated, which can save the terminal's computing power and computational complexity.

[0111] In this embodiment of the application, the cell information may include at least one of the following:

[0112] Cell ID;

[0113] Cell group ID;

[0114] List of residential communities;

[0115] Public Land Mobile Network (PLMN) ID;

[0116] PLMN group ID;

[0117] NR Cell Global Identifier (NCGI);

[0118] Tracking Area Identity (TAI);

[0119] Track the region group ID;

[0120] RAN-based Notification Area (RNA);

[0121] RAN group ID;

[0122] Cell Global Identity (CGI) is a unified identifier for the community.

[0123] Cell group global identifier (CGGI);

[0124] Carrier frequency information;

[0125] Physical Cell Identifier (PCI);

[0126] Physical cell group identifier;

[0127] Geographical location area.

[0128] For target information including the aforementioned cell information, the target information indicates cells whose data characteristics are consistent with the first data characteristic (and / or the second data characteristic). Cell information can be understood or replaced as a list of consistent cells (the same understanding applies to subsequent descriptions, and will not be repeated here). In this way, the terminal can determine whether to switch to a neighboring cell based on the aforementioned cell information, which ensures that the terminal switches to a suitable cell.

[0129] In this implementation, the target information includes the data characteristics of multiple network devices (i.e., multiple cells). In this way, the terminal can determine whether the data characteristics of multiple cells match based on the data characteristics of multiple cells. The terminal can then quickly determine the matching AI function for multi-cell prediction without having to blindly search to determine whether a matching AI function exists. This can reduce the terminal's computing power and time costs.

[0130] In some embodiments, the N network devices include a first network device and a second network device;

[0131] The target information includes at least one of the following:

[0132] At least one first association information, wherein the at least one first association information is associated with the first data characteristic;

[0133] AI information related to at least one first associated information;

[0134] Cell information related to at least one first associated information;

[0135] At least one second association information, wherein the at least one second association information is associated with the second data characteristic;

[0136] AI information related to at least one second associated information;

[0137] The cell information related to at least one second associated information.

[0138] In this embodiment, by using the first association information to indicate the first data characteristics, the physical characteristics (or wireless characteristics) of the first network device can be avoided, thereby protecting the data privacy of the first network device.

[0139] Accordingly, by using the second association information to indicate the second data characteristics, the physical characteristics (or wireless characteristics) of the second network device can be avoided, thereby protecting the data privacy of the second network device.

[0140] The terminal can see any associated information, such as the first associated information and the second associated information, but it cannot parse the corresponding configuration information and therefore cannot know the physical characteristics of the network-side devices such as the first network device and the second network device.

[0141] Any association information, such as the first association information and the second association information, may include at least one of the association ID and association mapping data. The association mapping data may include, for example, a vector, a two-dimensional matrix, or a multi-dimensional matrix, or other forms of data; however, this application embodiment will not provide specific examples of these.

[0142] In some embodiments, the first association information includes a first association identifier (ID), or the first association information includes at least one of a first association ID, a first cell ID, and a first timestamp, wherein the first cell ID is the cell ID of the first network device.

[0143] In some embodiments, the second association information includes a second association ID, or the second association information includes at least one of a second association ID, a second cell ID, and a second timestamp, wherein the second cell ID is the cell ID of the second network device.

[0144] In this embodiment of the application, the first association ID, the second association ID, and other association IDs can all be understood as global association information. The same association ID corresponds to the same physical meaning in any cell.

[0145] Alternatively, in this embodiment, the first association ID, second association ID, and other association IDs can all be understood as local association information. For example, the first association ID is only valid within the scope associated with the first cell ID, and the second association ID is only valid within the scope associated with the second cell ID. The association ID corresponding to cell 1 is 1, and the association ID corresponding to cell 2 is also 1, which can represent different network physical implementations. In this case, the association ID needs to be combined with the cell ID, or both the cell ID and the timestamp, to represent complete association information, so that the network device can determine the corresponding data characteristics or network configuration based on the association information. Whether the data characteristics or network configuration represented by the association ID of cell 1 and the association ID of cell 2 match can be determined by cell 1 or cell 2. By introducing cell-local association IDs, the problem of excessively high maintenance costs for global association IDs can be solved.

[0146] First timestamp, second timestamp, and other timestamps can be understood as timestamps associated with related information. By introducing timestamps, associated IDs within a cell can be recycled and redistributed at different times, avoiding the problem of an excessive number of associated IDs within a cell and reducing the maintenance cost of associated IDs within a cell.

[0147] For the second cell ID, the first network device indicates the identifier of the corresponding second network device. This allows the terminal to be shown the association between the second association information and the second cell ID, or the matching relationship between the third association information and the second cell ID. This enables the terminal to determine whether multi-cell prediction can include predictions targeting the second network device. Furthermore, it avoids the situation where the terminal, after switching to the cell where the second network device is located, still needs to interact with the second network device to determine whether to initiate prediction; alternatively, the terminal can determine whether to switch to the cell where the second network device is located based on whether the data characteristics match, ensuring that the terminal switches to the appropriate cell.

[0148] In addition, the first association information, the second association information, and other association information may also include association mapping data. The association mapping data may include vectors, two-dimensional matrices, or multi-dimensional matrices, or other forms of data. This application embodiment will not provide examples of each of these.

[0149] In some embodiments, the first operation includes at least one of the following:

[0150] Identify AI units or AI functions that match the data characteristics of the N network devices;

[0151] Determine a first dataset that matches the data characteristics of all N network devices;

[0152] Determine the AI ​​unit or AI function corresponding to the first dataset;

[0153] Initiate prediction for the cells where the N network devices are located;

[0154] Activate the second AI unit or the second AI function, wherein the second AI unit or the second AI function is an AI unit or AI function whose data characteristics match the data characteristics corresponding to the N network devices;

[0155] Deactivate the third AI unit or the third AI function, wherein the third AI unit or the third AI function is an AI unit or AI function whose data characteristics do not match the data characteristics corresponding to at least one of the N network devices;

[0156] A non-AI algorithm is used to determine whether to initiate the prediction of the cell where the target network device is located. The target network device is the network device whose data characteristics cannot be matched among the N network devices.

[0157] Prediction of the cell where the target network device is located is initiated using a non-AI algorithm;

[0158] Collect the first training data and associate the first training data with the data characteristics corresponding to the N network devices;

[0159] Based on the first training data, a fourth AI unit or a fourth AI function is generated, and the fourth AI unit or the fourth AI function is associated with the data characteristics corresponding to the N network devices.

[0160] Taking the first operation as an example, which includes activating the second AI unit or the second AI function, the terminal can use the activated AI unit or AI function to make predictions through the first operation, which can ensure the prediction effect and thus improve the communication performance of the terminal.

[0161] Taking the first operation, which includes deactivating the third AI unit or the third AI function, as an example, it can be understood as turning off prediction. This can avoid the problem that the predicted value does not meet expectations due to the use of an AI unit or AI function whose data characteristics do not match the data characteristics of at least one of the N network devices, thereby avoiding the waste of the terminal's computing power.

[0162] Deactivate the third AI unit or third AI function, including the following two situations:

[0163] If the data characteristics of one of the N network devices do not match the data characteristics of the third AI unit or the third AI function, then activate it.

[0164] If the data characteristics of some of the N network devices do not match the data characteristics of the third AI unit or the third AI function, the third AI unit or the third AI function will remain active, and non-AI algorithms will be used for the mismatched ones.

[0165] Taking the first operation as including using a non-AI algorithm to determine whether to start the prediction of the cell where the target network device is located, and using a non-AI algorithm to start the prediction of the cell where the target network device is located, if the data characteristics of the terminal's AI unit or AI function do not match the data characteristics corresponding to at least one of the N network devices, the terminal can make a prediction start judgment or start prediction through a non-AI algorithm.

[0166] If the first operation includes determining a first dataset that matches the data characteristics corresponding to the first network device, and determining the AI ​​unit or AI function corresponding to the first dataset, the terminal can ensure that the dataset used matches the data characteristics corresponding to the first network device through the first operation, thereby ensuring that the inference performance of the AI ​​unit or AI function used by the terminal is stable (the AI ​​unit or AI function is associated with the dataset).

[0167] Taking the first operation as an example, which includes collecting first training data and associating the first training data with the data characteristics corresponding to the first network device, the terminal can add data characteristic attributes to the collected training data through this first operation, thereby facilitating subsequent use or serving as a basis for judgment.

[0168] Taking the first operation as an example, which includes generating a fourth AI unit or a fourth AI function based on the first training data, and associating the fourth AI unit or the fourth AI function with the data characteristics corresponding to the first network device, the terminal can add data characteristic attributes to the generated AI unit or AI function through this first operation, thereby facilitating subsequent use or serving as a basis for judgment.

[0169] Taking the first operation as an example, which includes collecting first training data and associating the first training data with the association information corresponding to the first network device, the terminal can add the association information of the network device to the collected training data through this first operation, which is beneficial for subsequent use or as a basis for judgment.

[0170] Taking the first operation as an example, which includes generating a fourth AI unit or a fourth AI function based on the first training data, and associating the fourth AI unit or the fourth AI function with the association information corresponding to the first network device, the terminal can add the association information of the network device to the generated AI unit or AI function through the first operation, which is beneficial for subsequent use or as a basis for judgment.

[0171] Regardless of which of the above operations are included in the first operation, it ensures that the training or inference of the AI ​​unit or AI function matches the data characteristics, which helps improve the inference stability of the AI ​​unit or AI function. In scenarios such as cell handover and cell reselection, it can avoid situations where the terminal uses an AI unit or AI function with mismatched data characteristics, resulting in low prediction accuracy, handover failure, or reselection to an unsuitable cell.

[0172] In some embodiments, the method further includes:

[0173] The terminal sends first information to the first network device. The first information includes first indication information, which is used to indicate data characteristic related information corresponding to the first AI unit or the first AI function. The first AI unit or the first AI function is an AI unit or AI function that the terminal can obtain.

[0174] In this embodiment, the terminal actively sends first information to the first network device (i.e., the serving cell in the connected state) to request the data characteristic matching status corresponding to one or more AI units or AI functions. In this way, the terminal does not need to continuously receive first information from the first network device, which can further reduce the signaling overhead of the terminal.

[0175] In some embodiments, the terminal sends first information to the first network device, including:

[0176] When the first condition is met, the terminal sends the first information to the first network device;

[0177] The first condition includes at least one of the following:

[0178] Prepare to activate or start the first AI unit or the first AI function;

[0179] The communication index of the cell where the first network device or the second network device is located is detected to be lower than a preset threshold;

[0180] The performance degradation of the prediction of at least one cell by the first AI unit or the first AI function was detected.

[0181] The terminal is undergoing cell handover or cell reselection.

[0182] Preparing to activate or start the first AI unit or the first AI function can be understood as preparing to activate or start a certain AI feature / AI use case.

[0183] In this embodiment, the terminal sends the first information to the first network device only when the first condition is met, which can avoid the frequent transmission of the first information and thus save signaling interaction between the terminal and the first network device.

[0184] In some embodiments, the first indication information includes at least one of the following:

[0185] The third data characteristic corresponding to the first AI unit or the first AI function;

[0186] The AI ​​information related to the third data characteristic.

[0187] Here, the first information can be understood as the matching status between the network data characteristics and the terminal AI function data characteristics. The first information can be carried in at least one of the following: a scheduling request (SR) / buffer status report (BSR) (i.e., the mechanism for reusing uplink scheduling requests) and a CSI report.

[0188] In some embodiments, the first indication information includes at least one of the following:

[0189] At least one third association information, wherein the at least one third association information is associated with the third data characteristic;

[0190] The first set includes multiple sets of the third associated information;

[0191] AI information related to the third associated information;

[0192] AI information related to the first set.

[0193] In this embodiment, the third data characteristics can be indicated by the third association information, which can avoid exposing the physical characteristics (or wireless characteristics) of the first AI unit or the first AI function, thereby protecting the data privacy of the corresponding device (such as the network-side device that provides training data for training the first unit or the first AI function).

[0194] In addition, the first instruction information may also include information such as the number of third related information and the number of first sets.

[0195] When the first instruction information includes a first set, the terminal can collect data from multiple related information together for training during training data collection.

[0196] In some embodiments, the third association information includes a third association ID, or the third association information includes at least one of a third association ID, a third cell ID, and a third timestamp, wherein the third cell ID is the cell ID associated with the first AI unit or the first AI function.

[0197] In some embodiments, the N network devices include a first network device and a second network device, and when the target information is used to indicate data characteristic-related information of the N network devices, the target information includes at least one of the following:

[0198] The second indication information is used to indicate whether the first data characteristic corresponding to the first network device matches the third data characteristic corresponding to the first AI unit or the first AI function.

[0199] The third indication information is used to indicate whether the second data characteristic corresponding to the second network device matches the third data characteristic;

[0200] The third data characteristic;

[0201] AI information related to the third data characteristic;

[0202] The cell information related to the third data characteristic;

[0203] The first data characteristic;

[0204] AI information related to the first data characteristic;

[0205] Cell information related to the first data characteristic;

[0206] The second data characteristic;

[0207] AI information related to the second data characteristic;

[0208] The cell information related to the second data characteristic.

[0209] In this embodiment, for target information including second indication information, the terminal can know whether the data characteristics of the AI ​​functions or AI units that the device can obtain match the data characteristics of the first network device.

[0210] Regarding the target information, including the third instruction information, the terminal can determine whether the AI ​​functions or data characteristics of the AI ​​unit that this device can acquire match the data characteristics of the second network device.

[0211] For AI information whose target information includes data characteristics that are consistent with the third data characteristic, the first network device directly indicates the AI ​​function or AI characteristic that matches the data characteristics of the AI ​​function or AI unit that the terminal can obtain based on the third data characteristic. In this way, the terminal can save computing power for decision-making and reduce computational complexity.

[0212] For target information including cell information whose data characteristics are consistent with the third data characteristic, the first network device directly indicates the cell whose data characteristics match the AI ​​function or AI unit that the terminal can obtain, based on the third data characteristic. In this way, the terminal can save signaling interaction with other cells.

[0213] For the rest, please refer to the relevant explanations mentioned above, which will not be repeated here.

[0214] In some embodiments, the target information includes at least one of the following:

[0215] At least one first association information, wherein the at least one first association information is associated with the first data characteristic;

[0216] AI information related to at least one first associated information;

[0217] Cell information related to at least one first associated information;

[0218] At least one second association information, wherein the at least one second association information is associated with the second data characteristic;

[0219] AI information related to at least one second associated information;

[0220] The cell information related to at least one second associated information;

[0221] At least one third association information, wherein the at least one third association information is associated with the third data characteristic;

[0222] AI information related to at least one third-party associated information;

[0223] The cell information related to at least one third-party associated information;

[0224] The fourth indication information is used to indicate whether the third data characteristic associated with the at least one third association information matches the first data characteristic;

[0225] The fifth indication information is used to indicate whether the third data characteristic associated with the at least one third association information matches the second data characteristic.

[0226] In this embodiment, compared with the target information in the previous embodiment, the last five items are added. That is, if the terminal sends the third association information to the first network device in the first information, the target information may also include the last five items.

[0227] Regarding third-party association information, for example, if the first information includes 10 third-party association information items, the target information can include one or more of the third-party association information items that match the data characteristics corresponding to the first network device. For another example, if the first network device receives a third-party association information item from a terminal at 8:00 AM, performs a model download update at 9:00 AM, and then sends another third-party association information item to the first network device, the first network device can indicate in the target information which third-party association information's data characteristics match the first network device's data characteristics.

[0228] For AI information related to the third associated information, the first network device directly indicates the AI ​​information (e.g., AI functions) associated with the third associated information, which can also reduce the computing power required for terminal decision-making. In one possible implementation, if the first information includes 10 AI functions, the target information may include one or more AI functions that match the data characteristics corresponding to the first network device.

[0229] For cell information related to the third association information, the first network device directly indicates the cell associated with the third association information, which can save the terminal from signaling interaction with other cells.

[0230] In some embodiments, where the target information is used to indicate the prediction-related information, the target information includes at least one of the following:

[0231] The terminal is requested to execute the prediction amount corresponding to the multi-cell prediction.

[0232] The terminal is requested to perform multi-cell prediction of the corresponding cell information;

[0233] The third association information is associated with the third data characteristic corresponding to the first AI unit or the first AI function;

[0234] The sixth indication information is used to indicate whether the matched third data feature can be used for training data collection, model fine-tuning, or model training of the AI ​​unit.

[0235] In this implementation, the terminal can perform multi-cell prediction and reporting based on target information.

[0236] Optionally, the predicted quantity includes at least one of the following:

[0237] Beam quality, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Received Signal Strength Indication (RSSI), and Signal-to-noise and interference ratio (SINR);

[0238] Cellular quality, such as RSRP, RSRQ, RSSI, SINR, etc.;

[0239] Should we initiate same-frequency measurement or different-frequency measurement?

[0240] Threshold value for initiating same-frequency or different-frequency measurements;

[0241] Measure the relaxation cycle multiples;

[0242] List of cells to be measured within the target frequency range;

[0243] Frequency priority;

[0244] The observation time for heterogeneous frequency measurements;

[0245] Threshold value for heterogeneous frequency measurement.

[0246] In some embodiments, the first information is also used to request network-side data.

[0247] In multi-cell prediction scenarios, the model input of the AI ​​unit can include only terminal-side data, or it can include both terminal-side and network-side data. When the AI ​​unit's model input includes network-side data, the terminal can request this data from the network-side device. This improves the inference accuracy of the AI ​​unit, thereby enhancing its prediction performance.

[0248] In some embodiments, the first information may further include the network device identifier of the target area. The target area may include, for example, a target cell, a target tracking area (TA), etc.

[0249] In some embodiments, the target information further includes at least one of the following:

[0250] The first data is data whose characteristics match the third data characteristics corresponding to the first AI unit or the first AI function. The first data is data from the first network device, or the first data is data obtained after performing a first processing on the data from the first network device. The data processing method of the first processing is determined based on the first data characteristics.

[0251] The second data is data whose characteristics match the third data characteristics corresponding to the first AI unit or the first AI function. The second data is the third data, or the second data is data obtained after performing a second processing on the third data. The data processing method of the second processing is determined based on the first data characteristics.

[0252] The third data is either data from the second network device or data obtained by performing a third processing on the data from the second network device. The data processing method of the third processing is determined based on the second data characteristics corresponding to the second network device.

[0253] The first, second, and third processing described above can be understood as privacy processing. In this way, after the above privacy processing, the terminal cannot interpret sensitive network-side information such as cell load and antenna configuration, thereby avoiding the exposure of network-side privacy.

[0254] In this embodiment, the data processing method for privacy processing is determined based on the data characteristics of the network device itself. In this way, the data characteristics of the data obtained after privacy processing can be consistent with the data characteristics of the terminal AI unit, thereby improving the stability of the AI ​​unit's inference performance.

[0255] The data from the first network device mentioned above can be understood as the raw data of the first network device; correspondingly, the data from the second network device mentioned above can be understood as the raw data of the second network device.

[0256] In some embodiments, the N network devices include a first network device and a second network device;

[0257] The terminal acquires target information, including:

[0258] The terminal obtains the target information from the first network device; or...

[0259] The terminal obtains second information from the first network device, and the terminal obtains third information from the second network device, wherein the target information includes the second information and the third information;

[0260] Wherein, the second information is used to indicate at least one of the following: data characteristic-related information of the first network device; the prediction-related information;

[0261] The third information is used to indicate data characteristic-related information of the second network device.

[0262] In some embodiments, the data characteristics are used to indicate at least one of the first configuration information, the second configuration information, and the mapping characteristics;

[0263] The first configuration information includes at least one of the following:

[0264] Number of antenna elements;

[0265] The number of antenna elements in the horizontal dimension;

[0266] The number of antenna elements in the vertical dimension;

[0267] Antenna spacing;

[0268] Antenna spacing in the horizontal dimension;

[0269] Antenna spacing in the vertical dimension;

[0270] Mechanical tilt angle;

[0271] Antenna height;

[0272] The orientation of the antenna panel;

[0273] The second configuration information includes at least one of the following:

[0274] Number of beams;

[0275] Number of beams in the horizontal dimension;

[0276] Number of beams in the vertical dimension;

[0277] Beam pointing;

[0278] Half-power beamwidth, also known as 3dB beamwidth;

[0279] Beamforming codebook;

[0280] Beamforming codebook in the horizontal dimension;

[0281] Beamforming codebook in the vertical dimension;

[0282] Electron downtilt angle;

[0283] The mapping property includes at least one of the following:

[0284] The mapping relationship between the first configuration information and the associated information;

[0285] The mapping relationship between the second configuration information and the associated information;

[0286] Mapping relationship between physical beam and reference signal RS identifier;

[0287] Mapping relationship between physical beams and beam identifiers.

[0288] Here, the wireless characteristics corresponding to the first configuration information can be understood as the first wireless characteristics, and the wireless characteristics corresponding to the second configuration information can be understood as the second wireless characteristics.

[0289] The above are implementation examples of the method on the terminal side. The following describes implementation examples of the method on the first network device side.

[0290] Figure 3 shows a flowchart of an information transmission method provided in an embodiment of this application. As shown in Figure 3, the information transmission method includes the following steps:

[0291] Step 301: The first network device sends target information or second information to the terminal;

[0292] The target information is used to indicate at least one of the following: data characteristic-related information of N network devices; prediction-related information; where N is an integer greater than 1;

[0293] The second information is used to indicate at least one of the following: data characteristic-related information of the first network device; the prediction-related information.

[0294] In this embodiment, the first network device sends target information or second information to the terminal, enabling the terminal to obtain data characteristics or prediction value indications corresponding to at least one device. The terminal can then collect training data based on the data characteristics (or associated information) of at least one device and associate the dataset with the data characteristics (or associated information) of at least one device. Alternatively, it can generate AI units based on the training dataset and associate the AI ​​units or AI functions with the data characteristics (or associated information) of at least one device. This allows the terminal to determine whether the associated data characteristics (or associated information) match the data characteristics (or associated information) of the cell containing the network device to be predicted before the AI ​​unit or AI function is activated. This helps improve the inference stability of the AI ​​unit or AI function. Alternatively, the first network device can directly configure prediction value indications for prediction by determining whether the associated data characteristics (or associated information) of the AI ​​unit or AI function to be activated match the data characteristics (or associated information) of the cell containing the network device to be predicted. This saves the signaling interaction required for the terminal to initiate a corresponding prediction request to the first network device based on the matched AI unit or AI function.

[0295] In some embodiments, the N network devices include a first network device and a second network device;

[0296] The target information includes at least one of the following:

[0297] The first data characteristic corresponding to the first network device;

[0298] AI information related to the first data characteristic;

[0299] Cell information related to the first data characteristic;

[0300] The second data characteristic corresponding to the second network device;

[0301] AI information related to the second data characteristic;

[0302] Cell information related to the second data characteristic;

[0303] or,

[0304] The target information includes at least one of the following:

[0305] At least one first association information, wherein the at least one first association information is associated with the first data characteristic;

[0306] AI information related to at least one first associated information;

[0307] Cell information related to at least one first associated information;

[0308] At least one second association information, wherein the at least one second association information is associated with the second data characteristic;

[0309] AI information related to at least one second associated information;

[0310] The cell information related to at least one second associated information.

[0311] In some embodiments, the first association information includes a first association identifier ID, or the first association information includes at least one of a first association ID, a first cell ID, and a first timestamp, wherein the first cell ID is the cell ID of the first network device;

[0312] or,

[0313] The second association information includes a second association ID, or the second association information includes at least one of a second association ID, a second cell ID, and a second timestamp, wherein the second cell ID is the cell ID of the second network device.

[0314] In some embodiments, the first network device sends target information or second information to the terminal, including:

[0315] When the second condition is met, the first network device sends target information or second information to the terminal;

[0316] The second condition includes at least one of the following:

[0317] The data characteristics of the first network device have changed;

[0318] The data characteristics of network devices within the target area change, and the first network device is located within the target area;

[0319] The first network device is ready to activate the AI ​​unit or AI function of the terminal.

[0320] In this embodiment, the first network device sends target information or second information to the terminal only when the second condition is met, which can avoid the frequent sending of target information or second information, thereby saving signaling interaction between the first network device and the terminal.

[0321] In some embodiments, the method further includes:

[0322] The first network device receives first information from the terminal. The first information includes first indication information, which is used to indicate data characteristic related information corresponding to the first AI unit or the first AI function. The first AI unit or the first AI function is an AI unit or AI function that can be obtained by the terminal.

[0323] In some embodiments, the first indication information includes at least one of the following:

[0324] The third data characteristic corresponding to the first AI unit or the first AI function;

[0325] AI information related to the third data characteristic;

[0326] or,

[0327] The first indication information includes at least one of the following:

[0328] At least one third association information, wherein the at least one third association information is associated with the third data characteristic;

[0329] The first set includes multiple sets of the third associated information;

[0330] AI information related to the third associated information;

[0331] AI information related to the first set.

[0332] In some embodiments, the third association information includes a third association ID, or the third association information includes at least one of a third association ID, a third cell ID, and a third timestamp, wherein the third cell ID is the cell ID associated with the first AI unit or the first AI function.

[0333] In some embodiments, the N network devices include a first network device and a second network device, and when the target information is used to indicate data characteristic-related information of the N network devices, the target information includes at least one of the following:

[0334] The second indication information is used to indicate whether the first data characteristic corresponding to the first network device matches the third data characteristic corresponding to the first AI unit or the first AI function.

[0335] The third indication information is used to indicate whether the second data characteristic corresponding to the second network device matches the third data characteristic;

[0336] The third data characteristic;

[0337] AI information related to the third data characteristic;

[0338] The cell information related to the third data characteristic;

[0339] The first data characteristic;

[0340] AI information related to the first data characteristic;

[0341] Cell information related to the first data characteristic;

[0342] The second data characteristic;

[0343] AI information related to the second data characteristic;

[0344] Cell information related to the second data characteristic;

[0345] or,

[0346] The target information includes at least one of the following:

[0347] At least one first association information, wherein the at least one first association information is associated with the first data characteristic;

[0348] AI information related to at least one first associated information;

[0349] Cell information related to at least one first associated information;

[0350] At least one second association information, wherein the at least one second association information is associated with the second data characteristic;

[0351] AI information related to at least one second associated information;

[0352] The cell information related to at least one second associated information;

[0353] At least one third association information, wherein the at least one third association information is associated with the third data characteristic;

[0354] AI information related to at least one third-party associated information;

[0355] The cell information related to at least one third-party associated information;

[0356] The fourth indication information is used to indicate whether the third data characteristic associated with the at least one third association information matches the first data characteristic;

[0357] The fifth indication information is used to indicate whether the third data characteristic associated with the at least one third association information matches the second data characteristic.

[0358] In some embodiments, when the target information or the second information is used to indicate the prediction-related information, the target information or the second information includes at least one of the following:

[0359] The terminal is requested to execute the prediction amount corresponding to the multi-cell prediction.

[0360] The terminal is requested to perform multi-cell prediction of the corresponding cell information;

[0361] The third association information is associated with the third data characteristic corresponding to the first AI unit or the first AI function;

[0362] The sixth indication information is used to indicate whether the matched third data feature can be used for training data collection, model fine-tuning, or model training of the AI ​​unit.

[0363] In some embodiments, the predicted quantity includes at least one of the following:

[0364] Beam quality;

[0365] Community quality;

[0366] Should we initiate same-frequency measurement or different-frequency measurement?

[0367] Threshold value for initiating same-frequency or different-frequency measurements;

[0368] Measure the relaxation cycle multiples;

[0369] List of cells to be measured within the target frequency range;

[0370] Frequency priority;

[0371] The observation time for heterogeneous frequency measurements;

[0372] Threshold value for heterogeneous frequency measurement.

[0373] In some embodiments, the first information is also used to request network-side data.

[0374] In some embodiments, the target information further includes at least one of the following:

[0375] First data, the data characteristics of the first data match the third data characteristics, the first data is the data of the first network device, or, the first data is the data obtained after performing a first processing on the data of the first network device, the data processing method of the first processing is determined based on the first data characteristics;

[0376] The second data is the third data, and its characteristics match those of the third data. Alternatively, the second data is the data obtained after performing a second processing on the third data, and the data processing method of the second processing is determined based on the first data characteristics.

[0377] In some embodiments, the method further includes:

[0378] The first network device sends a fourth message to the second network device, the fourth message being used to request at least one of the following:

[0379] The second network device has data characteristic information.

[0380] The data from the second network device.

[0381] In this embodiment, by sending the fourth information to the second network device, the data of the second network device can be obtained, thereby enabling the terminal to obtain more model input data, which is beneficial to improving the model inference performance.

[0382] In some embodiments, the fourth information includes at least one of the following:

[0383] A target set, which is determined based on a first set provided by the terminal;

[0384] Target association information, which is determined based on third association information provided by the terminal or first association information corresponding to the first network device;

[0385] AI information related to the target association information;

[0386] The target-related information includes cell information;

[0387] The seventh instruction information is used to instruct the second network device to provide data that matches the target association information;

[0388] The eighth instruction information is used to indicate a request for the second network device to provide cell information whose data characteristics are consistent with the second data characteristics of the second network device;

[0389] The ninth instruction information is used to instruct the second network device to provide AI information related to the second data characteristic;

[0390] The tenth instruction information is used to instruct the second network device to confirm whether the second data characteristic matches the data characteristic corresponding to the target association information;

[0391] The eleventh instruction information is used to indicate a request for the second network device to provide cell information whose data characteristics match those of the target associated information;

[0392] The twelfth instruction information is used to instruct the second network device to provide AI information that matches the data characteristics corresponding to the target associated information.

[0393] In this implementation, the number of target-related information can be one or more.

[0394] In some embodiments, when the first network device receives the third data characteristic provided by the terminal, the target association information is determined based on the third data characteristic provided by the terminal;

[0395] If the first network device does not receive the third data characteristic provided by the terminal, the target association information is determined based on the first data characteristic corresponding to the first network device.

[0396] Optionally, the target association information includes the target association ID, or the target association information includes at least one of the target association ID, the target cell ID, and the target timestamp.

[0397] For example, if the first network device receives a third cell ID provided by the terminal, the target cell ID is determined based on the third cell ID provided by the terminal; if the first network device does not receive a third cell ID provided by the terminal, the target cell ID is determined based on the first cell ID corresponding to the first network device.

[0398] or,

[0399] If the first network device receives a third timestamp provided by the terminal, the target timestamp is determined based on the third timestamp provided by the terminal; if the first network device does not receive a third timestamp provided by the terminal, the target timestamp is determined based on a first timestamp provided by the first network device.

[0400] or,

[0401] If the first network device receives a third associated ID provided by the terminal, the target associated ID is determined based on the third associated ID provided by the terminal; if the first network device does not receive a third associated ID provided by the terminal, the target associated ID is determined based on the first associated ID corresponding to the first network device.

[0402] In some embodiments, the method further includes:

[0403] The first network device receives the fifth information from the second network device;

[0404] The fifth piece of information includes at least one of the following:

[0405] The thirteenth indication information is used to indicate whether the second data characteristic matches the data characteristic associated with the target association information;

[0406] The second data characteristic;

[0407] AI information related to the second data characteristic;

[0408] Cell information related to the second data characteristic;

[0409] Third data whose data characteristics are consistent with the second data characteristic;

[0410] or,

[0411] The fifth piece of information includes at least one of the following:

[0412] The thirteenth indication information is used to indicate whether the second data characteristic matches the data characteristic associated with the target association information;

[0413] The second association information is associated with the second data characteristic;

[0414] The second related information is related to the cell information;

[0415] AI information related to the second association information;

[0416] AI information whose data characteristics are consistent with the data characteristics associated with the target information;

[0417] Cell information whose data characteristics are consistent with the data characteristics associated with the target association information;

[0418] Third data whose data characteristics match the target association information;

[0419] The third data is either the data of the second network device or the data obtained after performing a third processing on the data of the second network device, wherein the data processing method of the third processing is determined based on the characteristics of the second data.

[0420] It should be noted that the second network device can also proactively send information from the fifth message, excluding the third data, to the first network device. In other words, even if the first network device does not send the fourth message to the second network device, the second network device can still send information from the fifth message, excluding the third data, to the first network device.

[0421] For related descriptions of the embodiments of this application, please refer to the related descriptions of the method embodiments in Figure 2, which can achieve the same technical effects. To avoid repetition, they will not be described again.

[0422] The above are method embodiments on the first network device side. The following describes method embodiments on the second network device side.

[0423] Figure 4 shows a flowchart of an information transmission method provided in an embodiment of this application. As shown in Figure 4, the information transmission method includes the following steps:

[0424] Step 401: The second network device receives fourth information from the first network device, the fourth information being used to request at least one of the following:

[0425] Information related to the data characteristics of the second network device;

[0426] The data from the second network device.

[0427] In some embodiments, the fourth information includes at least one of the following:

[0428] A target set, which is determined based on a first set provided by the terminal;

[0429] Target association information, which is determined based on third association information provided by the terminal or first association information corresponding to the first network device;

[0430] AI information related to the target association information;

[0431] The target-related information includes cell information;

[0432] The seventh instruction information is used to instruct the second network device to provide data that matches the target association information;

[0433] The eighth instruction information is used to indicate a request for the second network device to provide cell information whose data characteristics are consistent with the second data characteristics of the second network device;

[0434] The ninth instruction information is used to instruct the second network device to provide AI information related to the second data characteristic;

[0435] The tenth instruction information is used to instruct the second network device to confirm whether the second data characteristic matches the data characteristic corresponding to the target association information;

[0436] The eleventh instruction information is used to indicate a request for the second network device to provide cell information whose data characteristics match those of the target associated information;

[0437] The twelfth instruction information is used to instruct the second network device to provide AI information that matches the data characteristics corresponding to the target associated information.

[0438] In some embodiments, the method further includes:

[0439] The second network device sends the fifth message to the first network device;

[0440] The fifth piece of information includes at least one of the following:

[0441] The thirteenth indication information is used to indicate whether the second data characteristic matches the data characteristic associated with the target association information;

[0442] The second data characteristic;

[0443] AI information related to the second data characteristic;

[0444] Cell information related to the second data characteristic;

[0445] Third data whose data characteristics are consistent with the second data characteristic;

[0446] or,

[0447] The fifth piece of information includes at least one of the following:

[0448] The thirteenth indication information is used to indicate whether the second data characteristic matches the data characteristic associated with the target association information;

[0449] The second association information is associated with the second data characteristic;

[0450] The second related information is related to the cell information;

[0451] AI information related to the second association information;

[0452] AI information whose data characteristics are consistent with the data characteristics associated with the target information;

[0453] Cell information whose data characteristics are consistent with the data characteristics associated with the target association information;

[0454] Third data whose data characteristics match the target association information;

[0455] The third data is either the data of the second network device or the data obtained after performing a third processing on the data of the second network device, wherein the data processing method of the third processing is determined based on the characteristics of the second data.

[0456] For related descriptions of the embodiments of this application, please refer to the descriptions of the method embodiments in Figures 2 and 3, which can achieve the same technical effects. To avoid repetition, they will not be described again.

[0457] The following provides several specific embodiments to illustrate the interaction process between the terminal, the first network device, and the second network device. In the following embodiments, the first network device is a serving base station (i.e., serving cell) in a connected state or a camped cell in a disconnected state, the second network device is a neighboring base station (i.e., neighboring cell), and the associated information is the association ID.

[0458] Example 1: Multiple network devices actively instruct

[0459] The main idea of ​​this embodiment is that the input to the UE inference and AI unit only includes data from the UE side, and the network actively indicates the associated ID. This solves the problem of low multi-cell prediction accuracy caused by the UE using a mismatched model, which leads to handover failure or reselection to an unsuitable cell. In addition, it can also solve the problems of power consumption, computing power, and time costs caused by monitoring and finding a matching AI unit through an inactive model. It can save UE computing power and power consumption and can quickly find a matching AI function.

[0460] As shown in Figure 5, the steps include:

[0461] Step 1: The terminal obtains target information, which can indicate the data characteristics of multiple cells. As an example, the target information includes the association IDs of multiple cells.

[0462] In this way, the terminal can determine whether there is an AI function that matches the associated IDs of multiple cells based on the target information.

[0463] In this embodiment, the terminal obtains target information, which indicates the data characteristics of multiple cells. If the data characteristics of multiple cells match, the terminal can quickly determine the matching AI function for multi-cell prediction, thereby reducing the computing power and time overhead of the UE in blindly searching to determine whether a matching AI function exists.

[0464] Optionally, the terminal obtains second information and third information from a first network device and a second network device within the target area, respectively. The second information and third information include the association ID of the cell that sent the respective information, such as the first association ID of the first network device and the second association ID of the second network device.

[0465] The terminal obtains the association ID from the first network device and the second network device respectively, thus obtaining the data characteristics of the first network device and the second network device.

[0466] Optionally, the target information includes at least one of the following:

[0467] AI use cases or AI features related to the first associated ID;

[0468] A list of cells that maintain consistent wireless characteristics with the first associated ID;

[0469] Data characteristic indications corresponding to the first associated ID;

[0470] AI use cases, or AI features, related to the second associated ID;

[0471] A list of cells that maintain consistent wireless characteristics with the second associated ID;

[0472] The data characteristic indication corresponding to the second associated ID.

[0473] Optionally, the model input for the AI ​​function includes the terminal's wireless measurement results.

[0474] Optionally, the data characteristics include at least one of the following:

[0475] The first wireless characteristic is the fixed wireless characteristic related to the antenna, or the wireless characteristic directly related to the hardware, or the wireless characteristic related to the station type.

[0476] The second wireless characteristic is an antenna-dependent adjustable wireless characteristic.

[0477] The third wireless characteristic is the wireless characteristic related to the cell environment, including large-scale fading characteristics, scattering characteristics, and reflection characteristics.

[0478] Optionally, when the second condition is met, the network device (including the first network device and the second network device) sends target information, second information, or third information to the terminal, wherein the second condition includes at least one of the following:

[0479] The wireless characteristics of network devices (within the target area) change;

[0480] The network device accurately activates the AI ​​unit on the terminal side.

[0481] The first information can be carried in the following two ways:

[0482] Broadcasts, such as SIB messages, carry value tags to indicate whether the wireless characteristics of network devices have changed. The value tag is a constant. The terminal compares the currently stored value tag with the value tag in the received broadcast message. If they are the same, it means there is no change; otherwise, it means there is a change and the corresponding SIBx needs to be read.

[0483] Specialized signaling, such as RRC signaling (for reselection, it can be sent in the release message, specifically RRC Release with Suspend config).

[0484] Step 2: The terminal performs a first operation based on the association IDs of each network device within the target area, the first operation including at least one of the following:

[0485] Collect training data and associate it with the first association ID and the second association ID;

[0486] Training data is collected to generate AI units, which are then associated with the first and second association IDs.

[0487] Identify AI functions / datasets / AI units that match the first and second association IDs;

[0488] Determine if there is an AI function / dataset / AI unit that matches the first association ID and the second association ID.

[0489] If an AI function / dataset / AI unit exists that matches the first association ID and the second association ID, the terminal also performs the following first operation:

[0490] Activate the first AI unit, which is an AI unit that matches the wireless data characteristics of the first associated ID;

[0491] The first AI unit is monitored in an inactive state (i.e., the prediction results are not used at first).

[0492] If the AI ​​units available to the terminal cannot all match the wireless characteristics of the target area network devices, the terminal will also perform the following first operation:

[0493] For network devices in the target area that cannot match wireless characteristics, fall back to non-AI algorithms to obtain prediction results, such as filtering methods;

[0494] For network devices in the target area that cannot match the wireless characteristics, the algorithm reverts to a non-AI algorithm to determine the start of same-frequency / different-frequency measurement.

[0495] Deactivate the second AI unit, which is currently running and whose wireless data characteristics do not match the first associated ID of multiple network devices in the target area;

[0496] The second AI unit is monitored in an inactive state (i.e., the prediction results are not used at first);

[0497] The results obtained from the AI ​​unit and the results obtained from the filtering method are processed using different weights.

[0498] Figure 6 shows a specific flowchart of step 2.

[0499] Example 2: First network device actively instructs

[0500] In Example 1, each network device includes its own data characteristic indication in the second or third information. The main idea of ​​this example is that the first network device (i.e., the stationary cell or serving cell) sends the data characteristic indication to the terminal instead of the second network device, thereby solving the problem that the terminal needs to continuously receive messages from the second network device and further reducing the signaling overhead of the terminal.

[0501] As shown in Figure 7, the steps include:

[0502] Step 0a (optional): Before step 0b, the first network device sends fourth information to the second network device, the fourth information being used to request data characteristics from the second network device. The fourth information includes at least one of the following:

[0503] Target associated ID (can be one or more);

[0504] AI functions corresponding to the target association ID;

[0505] The cell identifier corresponding to the target associated ID;

[0506] The number of target associated IDs;

[0507] The first instruction is used to instruct a second network device to provide its associated ID;

[0508] The second instruction is used to instruct the second network device to provide a list of cells whose associated IDs are consistent.

[0509] Optionally, the fourth information includes the target association ID, or the first indication, or the first and second indications.

[0510] Optionally, the first network device determines the target association ID based on the association ID of the first network device (i.e., the first association ID).

[0511] Step 0b: Before step 1, the first network device obtains the fifth information from the second network device.

[0512] If the second network device does not receive the target association ID from the first network device, the fifth information includes at least one of the following:

[0513] At least one second associated ID of the second network device;

[0514] A list of cells that maintains the same second association ID as the second network device;

[0515] The identifier of the cell where the second network device is located.

[0516] If the second network device receives the target association ID from the first network device, the fifth information includes at least one of the following:

[0517] An indication of whether the data characteristics of the ID associated with the target match;

[0518] AI functionality that matches data characteristics associated with the target ID;

[0519] A list of cells whose data characteristics match the target's associated ID.

[0520] Optionally, the second network device performs a third operation, which includes at least one of the following:

[0521] Based on the fifth piece of information, determine whether the data characteristics of the second network device match the data characteristics of the target associated ID;

[0522] Identify a list of cells whose ID data characteristics are consistent with the target;

[0523] Identify AI features that match the characteristics of the target associated ID data.

[0524] Step 1: The terminal obtains target information from the first network device (serving cell or camped cell). The target information includes data characteristic information of the first network device and data characteristic information of the second network device (the number of second network devices can be one or more).

[0525] Optionally, the data characteristic information of the first network device includes the first associated ID of the first network device (which may be one or more), and may also include at least one of the following:

[0526] The identifier of the cell for the first network device;

[0527] AI use cases, or AI features, related to the first associated ID of the first network device;

[0528] A list of cells whose wireless characteristics are consistent with the first association ID of the first network device;

[0529] Data characteristic indication corresponding to the first associated ID of the first network device;

[0530] The data characteristic information of the second network device includes the second association ID of the second network device, and may also include at least one of the following:

[0531] The identifier of the cell for the second network device;

[0532] AI use cases, or AI features, related to the second associated ID of the second network device;

[0533] A list of cells whose wireless characteristics are consistent with the second association ID of the second network device;

[0534] Data characteristic indication corresponding to the second associated ID of the second network device.

[0535] After obtaining the target information, the terminal can determine whether there is a matching AI unit, and thus decide whether to start multi-cell prediction.

[0536] Optionally, the data characteristic information of the first network device includes the first association ID of the first network device and the identifier of the cell of the first network device; the data characteristic information of the second network device includes the second association ID of the second network device and the identifier of the cell of the second network device.

[0537] Optionally, the data characteristic information of the first network device includes the first association ID of the first network device, the identifier of the cell of the first network device, and a list of cells whose data characteristics are consistent with the first association ID of the first network device; the data characteristic information of the second network device includes the first association ID of the second network device and the identifier of the cell of the second network device, and a list of cells whose data characteristics are consistent with the second association ID of the second network device.

[0538] Step 2: Same as Example 1.

[0539] Example 3: UE actively requests matching information from the serving cell

[0540] Examples 1 and 2 involve the network actively instructing network devices on data characteristics. The main idea of ​​this example is that the terminal actively requests the data characteristic matching of the third associated ID from the first network device (the serving cell in the connected state), thereby solving the problem that the terminal needs to continuously receive messages from the first network device and further reducing the signaling overhead of the terminal.

[0541] As shown in Figure 8, the steps include:

[0542] Step 1 (optional): Before Step 2, the terminal determines whether a first condition is met. If the first condition is met, the terminal sends first information to the first network device. The first condition includes at least one of the following:

[0543] Prepare to activate or start the fourth AI unit or the fourth AI function;

[0544] The communication index of the cell where the first network device or the second network device is located is detected to be lower than a preset threshold;

[0545] The fourth AI unit or the fourth AI function is detected to have caused performance degradation in the prediction of at least one cell.

[0546] The terminal is undergoing cell handover or cell reselection.

[0547] The terminal only sends the first information when the first condition is met, thus avoiding the frequent transmission of the first information and saving signaling interaction.

[0548] Step 2: The terminal sends first information to the first network device. The first information is used to request a match between network data characteristics and the terminal's AI function data characteristics. The first information includes at least one of the following:

[0549] The third association ID (a certain AI unit is only associated with one association ID);

[0550] The first set (the set of third association IDs, where a certain AI unit is associated with multiple third association IDs);

[0551] AI functions associated with the third-party ID or the first set;

[0552] The third associated ID or the characteristic indication of the first set association;

[0553] The number of third-party associated IDs;

[0554] The number of elements in the first set;

[0555] Network device identifiers for the target area.

[0556] The first information may be contained in at least one of the following:

[0557] SR / BSR (Mechanism for reusing uplink scheduling requests);

[0558] CSI report.

[0559] Step 3a (optional): Before step 3b, the first network device sends fourth information to the second network device, the fourth information being used to request data characteristics from the second network device. The fourth information includes at least one of the following:

[0560] Target associated ID (can be one or more);

[0561] AI functions corresponding to the target association ID;

[0562] The number of target associated IDs;

[0563] The first instruction is used to instruct a second network device to provide its second associated ID;

[0564] The second instruction is used to instruct the second network device to provide a list of cells whose second associated ID is consistent with its own.

[0565] Optionally, the fourth information includes the target association ID, or the first indication, or the first and second indications.

[0566] Optionally, the first network device determines the target association ID based on the third association ID or the first set.

[0567] Step 3b: The first network device obtains the fifth information from the second network device.

[0568] If the second network device does not receive the target association ID from the first network device, the fifth information includes at least one of the following:

[0569] At least one second associated ID of the second network device;

[0570] A cell ID or cell list that is consistent with the second associated ID of the second network device;

[0571] The identifier of the cell where the second network device is located.

[0572] If the second network device receives the target association ID from the first network device, the fifth information includes at least one of the following:

[0573] Does the data characteristic match the target ID?

[0574] AI capabilities for matching data characteristics associated with the target ID

[0575] A list of cells whose data characteristics match the target's associated ID.

[0576] Optionally, the second network device performs a third operation, which includes at least one of the following:

[0577] Based on the fifth piece of information, determine whether the data characteristics of the second network device match the data characteristics of the target associated ID;

[0578] Identify cell IDs or cell lists that maintain consistent ID data characteristics with the target;

[0579] Identify AI features that match the characteristics of the target associated ID data.

[0580] Step 4: The first network device performs a second operation, which includes at least one of the following:

[0581] Based on the first information, determine whether the data characteristics of the first network device match the data characteristics of the third associated ID;

[0582] Based on the fifth piece of information, determine whether the data characteristics of the second network device match the data characteristics of the third associated ID;

[0583] Based on the first and fifth information, determine the cell ID or cell list that is consistent with the characteristics of the third associated ID data;

[0584] Based on the first and fifth pieces of information, determine the AI ​​function that matches the characteristics of the third associated ID data;

[0585] Based on the first information, determine whether the data characteristics of the first network device match the data characteristics of the first set;

[0586] Based on the fifth piece of information, determine whether the data characteristics of the second network device match the data characteristics of the first set;

[0587] Based on the first and fifth information, determine the cell ID or cell list that is consistent with the characteristics of the first set of data;

[0588] Based on the first and fifth pieces of information, determine the AI ​​function that matches the characteristics of the first set of data.

[0589] Step 5: The first network device sends target information to the terminal, the target information including at least one of the following:

[0590] The third indication is used to indicate a cell ID or cell list that is consistent with the data characteristics of the third associated ID (the indication may or may not be based on the target area in the first information);

[0591] The fourth indicator is used to indicate AI functions that match the characteristics of the third associated ID data;

[0592] The fifth indication is used to indicate a cell ID or cell list that is consistent with the data characteristics of the first set (the indication may or may not be based on the target area in the first information);

[0593] The sixth instruction is used to indicate AI functions that match the data characteristics of the first set.

[0594] Step 6: Based on the target information, the terminal performs a first operation, which includes at least one of the following:

[0595] Determine whether the data characteristics of the cell where the first network device is located match the third associated ID;

[0596] Determine whether the data characteristics of the cell where the second network device is located match the third associated ID;

[0597] Determine whether the data characteristics of the cell where the first network device is located match the first set;

[0598] Determine whether the data characteristics of the cell where the second network device is located match the first set;

[0599] Initiate multi-cell prediction.

[0600] Figure 9 shows a specific flowchart of step 6.

[0601] Example 4: The UE actively requests matching information from the serving cell, and the network directly requests the prediction result.

[0602] Example 3 involves the terminal actively requesting the data characteristic matching status of the third associated ID from the first network device (the serving cell in the connected state). The main idea of ​​this example is that the network directly requests the corresponding predicted value from the terminal based on the first information and the data characteristic matching information obtained through interaction between network devices. This solves the problem of high terminal computing power consumption, power consumption, and signaling overhead caused by the terminal needing to judge the matching of AI functions.

[0603] As shown in Figure 10, the steps include the following:

[0604] Step 1: Same as Example 3;

[0605] Step 2: Same as Example 3;

[0606] Step 3: Same as Example 3;

[0607] Step 4: The first network device performs a second operation, which includes at least one of the following:

[0608] Based on the first and fifth pieces of information, determine the predicted quantity (or predicted value) to be requested from the terminal;

[0609] Based on the first and fifth pieces of information, a list of multiple cells to be requested from the terminal to perform prediction is determined.

[0610] For the second operation that determines the predicted quantity requested from the terminal based on the first information and the fifth information, since the first information reflects the data characteristics of the terminal AI unit, the predicted quantity requested by the second operation can match the data characteristics of the terminal AI unit, which can improve the accuracy of the prediction.

[0611] For the second operation of requesting the terminal to perform prediction of the multi-cell list based on the first and fifth information, the first network device directly determines which network devices' data characteristics match the third associated ID or the first set, which can save the terminal's judgment overhead and save the terminal's computing power and power consumption.

[0612] Step 5: The first network device sends target information to the terminal based on the first information, wherein the first information includes at least one of the following:

[0613] The predicted value requested from the terminal;

[0614] Request the list of multiple cells from the terminal to perform the prediction;

[0615] Third associated ID.

[0616] Step 6: The terminal acquires target information and performs multi-cell prediction and reporting based on the target information.

[0617] Example 5: The model input for multi-cell prediction also includes information from the network device side.

[0618] Examples 1 to 4 describe how, when a UE performs multi-cell prediction, the model input of the AI ​​unit only includes data from the UE side. The main idea of ​​this example is that the model input of the AI ​​unit also includes information from the network device side. However, this information has undergone special processing, such as privacy protection, so the UE cannot interpret sensitive network-side information such as cell load and antenna configuration, thus avoiding the exposure of network device privacy. Because the model input of the AI ​​unit also includes information from the network device side, inference accuracy can also be improved.

[0619] As shown in Figure 11, the steps include the following:

[0620] Step 1: Same as Example 3 / Example 4

[0621] Step 2: The terminal sends first information to the first network device. The first information is used to request a match between network data characteristics and the terminal's AI function data characteristics, as well as data from the network device side. The first information includes at least one of the following:

[0622] Third association ID (a certain AI unit is only associated with one third association ID);

[0623] The first set (the set of third association IDs, where a certain AI unit is associated with multiple third association IDs);

[0624] AI functions associated with the third-party ID or the first set;

[0625] The third associated ID or the characteristic indication of the first set association;

[0626] The number of third-party associated IDs;

[0627] The number of elements in the first set;

[0628] Network device identifiers for the target area.

[0629] Step 3a: The first network device sends fourth information to the second network device. This fourth information requests data characteristics and corresponding network-side data from the second network device. The fourth information includes at least one of the following:

[0630] Target associated ID (can be one or more);

[0631] AI functions corresponding to the target association ID;

[0632] The number of target associated IDs.

[0633] Optionally, the first network device determines the target association ID based on the third association ID or the first set.

[0634] Step 3b: The first network device obtains fifth information from the second network device, the fifth information including at least one of the following:

[0635] Does the data characteristic match the target ID?

[0636] AI functionality that matches data characteristics associated with the target ID;

[0637] Data from the second network device side that matches the target's associated ID.

[0638] Optionally, the second network device performs a third operation, which includes at least one of the following:

[0639] Based on the third information, determine whether the data characteristics of the second network device match the data characteristics of the target associated ID;

[0640] Identify AI functions that match the characteristics of the target-related ID data;

[0641] The data and data processing method provided by the second network device are determined based on the target association ID.

[0642] Step 4: The first network device performs a second operation, which includes at least one of the following:

[0643] Based on the first and fifth pieces of information, determine the predicted quantity to be requested from the terminal;

[0644] Based on the first and fifth pieces of information, determine the list of multiple cells to request the terminal to perform prediction.

[0645] Based on the third associated ID or the first set, determine the data and data processing method provided by the first network device;

[0646] Based on the fifth piece of information, obtain the data (unprocessed or processed) of the second network device associated with the third association ID or the first set.

[0647] For the second operation that determines the data and data processing method provided by the first network device based on the third association ID or the first set, the first network device can know what data to provide to the UE and how to perform preprocessing based on the third association ID to hide the original sensitive information, and match it with the model input of the AI ​​unit corresponding to the third association ID, so as to protect network-side privacy and ensure the inference performance of the AI ​​unit.

[0648] For the second operation of obtaining data of the second network device associated with the third association ID or the first set based on the fifth information, it is possible to expand the model input of the AI ​​unit corresponding to the third association ID, thereby improving the inference performance of the AI ​​unit.

[0649] Step 5: The first network device sends target information to the terminal, the target information including at least one of the following:

[0650] The predicted value requested from the terminal;

[0651] Request the list of multiple cells from the terminal to perform the prediction;

[0652] Third-party associated ID;

[0653] Data from the first network device side that matches the third associated ID or the first set;

[0654] Data from the second network device side that matches the third associated ID or the first set.

[0655] Step 6: The terminal acquires target information and performs multi-cell prediction and reporting based on the target information.

[0656] Example 6: Multiple network devices actively instruct each other (applicable to model training; associated IDs only apply to local IDs within the cell).

[0657] The main assumption of Example 1 is the AI ​​unit's model inference, model monitoring, or training data collection process when the associated ID sent to the UE between different cells is a global ID. The main idea of ​​this example is to assume that the associated ID is a cell-local ID (i.e., cell ID), thereby avoiding the maintenance cost of a global associated ID.

[0658] The flowchart of Embodiment 1 (Figure 5) includes the following steps:

[0659] Step 1: The terminal obtains target information, which may indicate the association information of multiple cells. The association information includes at least one of the following: association ID, cell ID, and timestamp.

[0660] In this way, the terminal can determine which cells to collect data from based on the target information, and associate the collected data with the corresponding cell's information.

[0661] Optionally, the terminal obtains second information and third information from a first network device and a second network device within the target area, respectively. The second and third information include association information of the cells that sent their respective information, such as the first association information of the first network device and the second association information of the second network device.

[0662] The terminal obtains association information from the first network device and the second network device respectively, that is, it obtains the implicit data characteristics of the first network device and the second network device respectively.

[0663] Optionally, the target information includes at least one of the following:

[0664] AI use cases or AI features related to the primary associated information;

[0665] A list of cells whose wireless characteristics are consistent with the first associated information;

[0666] Data characteristic indications corresponding to the first associated information;

[0667] AI use cases or AI features related to the second piece of information;

[0668] A list of cells whose wireless characteristics are consistent with the second associated information;

[0669] The second related information corresponds to the data characteristic indication.

[0670] Optionally, the model input for the AI ​​function includes the terminal's wireless measurement results.

[0671] Optionally, the data characteristics include at least one of the following:

[0672] The first wireless characteristic is the fixed wireless characteristic related to the antenna, or the wireless characteristic directly related to the hardware, or the wireless characteristic related to the station type.

[0673] The second wireless characteristic is an antenna-dependent adjustable wireless characteristic.

[0674] The third wireless characteristic is the wireless characteristic related to the cell environment, including large-scale fading characteristics, scattering characteristics, and reflection characteristics.

[0675] Optionally, when the second condition is met, the network device (including the first network device and the second network device) sends target information, second information, or third information to the terminal, wherein the second condition includes at least one of the following:

[0676] The wireless characteristics of network devices (within the target area) change;

[0677] Network devices accurately activate AI functions or AI units on the terminal side.

[0678] The first information can be carried in the following two ways:

[0679] Broadcasts, such as SIB messages, carry value tags to indicate whether the wireless characteristics of network devices have changed. The value tag is a constant. The terminal compares the currently stored value tag with the value tag in the received broadcast message. If they are the same, it means there is no change; otherwise, it means there is a change and the corresponding SIBx needs to be read.

[0680] Specialized signaling, such as RRC signaling (for reselection, it can be sent in the release message, specifically RRC Release with Suspend config).

[0681] Step 2: As shown in Figure 11a, the terminal performs a first operation based on the association information of each network device within the target area. The first operation includes at least one of the following:

[0682] Training data is collected and correlated with the first and second association information;

[0683] Training data is collected to generate AI units, which are then associated with the first and second association information.

[0684] Optionally, the first association information includes at least one of the following:

[0685] First associated ID;

[0686] The first timestamp is either the start and / or end timestamp of data collection, or the timestamp of receiving the first information.

[0687] The first cell identifier is the cell identifier of the first network device.

[0688] Optionally, the second associated information includes at least one of the following:

[0689] Second associated ID;

[0690] The second timestamp is either the start and / or end timestamp of data collection, or the timestamp of receiving the first information;

[0691] The second cell identifier is the cell identifier of the second network device.

[0692] Example 7: First network device actively instructs (assuming the associated ID is a local ID, training data collection)

[0693] Example 2 describes the model inference, model monitoring, or training data collection process for an AI unit assuming that the association ID sent to the UE between different cells is a global ID. The main idea of ​​this example is to assume that the association ID is a cell-local ID, thereby avoiding the maintenance cost of a global association ID.

[0694] In detail, the following schemes are included:

[0695] Option 1: The target information includes the cell ID of the first network device, the first associated ID, and a list of cells that maintain consistency.

[0696] Option 1a: The second network device maps the local first association ID of the first network device to the actual physical data characteristics, and then determines whether it matches the target data characteristics. This result is then fed back to the first network device.

[0697] Option 1b: The first network device receives the local second association ID of the second network device, maps it to the actual physical data characteristics, and then determines whether it matches the first data characteristics represented by the local first association ID of the first network device.

[0698] Option 2: The target information includes (Cell ID = Cell ID of the first network device, Association ID = First Association ID) and (Cell ID = Cell ID of the second network device, Association ID = Second Association ID). In this case, the Second Association ID is the local association ID of the cell of the second network device. Even if the Second Association ID is equal to the First Association ID, it does not indicate that the second data characteristic is the same as the first data characteristic.

[0699] The following describes this embodiment with reference to scheme 1a:

[0700] The flowchart of Embodiment 2 (Figure 7) includes the following steps:

[0701] Step 0a: The first network device sends fourth information to the second network device, the fourth information being used to request data characteristics from the second network device. The fourth information includes target association information, and may also include an indication for requesting consistency information with the target data characteristics (the second network device maps the local association ID of the first network device to the actual physical configuration, and then determines consistency).

[0702] Optionally, the target association information includes at least one of the following:

[0703] Target cell ID (in this embodiment, the cell ID of the first network device);

[0704] Target association ID (can be one or more, without limitation; in this embodiment, it is the local first association ID of the first network device);

[0705] Target timestamp (used by the second network device to determine the characteristics of the target data, as explained in Table 1);

[0706] AI functions corresponding to target association information;

[0707] The cell identifier corresponding to the target association information;

[0708] The number of target-related information items;

[0709] The target association information includes the target cell ID and the target association ID, or it may include the target cell ID, the target association ID, and the target timestamp.

[0710] Optionally, the fourth information includes at least one of the following:

[0711] The tenth indication information is used to request whether the data characteristics corresponding to the second association information are consistent with the data characteristics of the target association information. The target association information includes the target cell ID and the target association ID, or includes the target cell ID, the target association ID, and the target timestamp.

[0712] The eleventh instruction message is used to instruct the second network device to provide a list of cells that are consistent with the target-related information.

[0713] Optionally, the first network device determines the target association information based on the association information of the first network device;

[0714] Step 0b: The first network device obtains fifth information from the second network device, the fifth information including at least one of the following:

[0715] An indication of whether the data characteristics of the second related information are consistent with the data characteristics of the target related information;

[0716] Target cell ID;

[0717] Target associated ID;

[0718] Target timestamp;

[0719] The cell ID of the second network device;

[0720] A list of cells whose data characteristics are consistent with those associated with the target;

[0721] AI functionality that matches information associated with a target.

[0722] Optionally, the second network device performs a third operation, which includes at least one of the following:

[0723] Determine the characteristics of the target data based on the fourth information;

[0724] Based on the fourth information, determine whether the data characteristics corresponding to the second associated information match the target data characteristics;

[0725] Identify a list of cells whose data characteristics are consistent with those associated with the target;

[0726] AI functions that identify information associated with the target;

[0727] The target association information includes at least one of the following: target cell ID, target association ID, and target timestamp.

[0728] For example, the second network device may have obtained mapping table 1-1 (which contains the cell, target ID, and timestamp information of the first network device, as well as the corresponding actual physical configuration) from OAM or the first network device.

[0729] Mapping Table 1-1

[0730] The target timestamp is a value in mapping table 1-1, so the actual physical configuration can be obtained by looking up the table.

[0731] Mapping Table 1-2

[0732] The target timestamp can also be a specific value, but it can be determined through mapping table 1-2 that it falls within a certain time range in the table. Therefore, the actual physical configuration can also be obtained based on mapping table 1-2.

[0733] At this point, the target timestamp can be a timestamp specified by the first network device.

[0734] Step 1: The terminal obtains target information from the first network device (serving cell or camped cell). The target information includes the first association information of the first network device and the second association information of the second network device (the number of second network devices can be one or more, without limitation).

[0735] Optionally, the association information of the first network device includes the first association ID of the first network device (which may be one or more without limitation) and the identifier of the cell of the first network device, and may also include at least one of the following:

[0736] A list of cells whose data characteristics are consistent with the first associated information;

[0737] AI use cases, or AI features, related to the first associated information of the first network device;

[0738] The data characteristic indication corresponding to the association information of the first network device.

[0739] In this way, after obtaining the target information, the terminal can initiate the data collection process and determine which cells' data to collect for training data collection based on its own capabilities. If its capabilities are strong, it can collect cell data with different data characteristics for training data collection; if its capabilities are weak, it can only collect cell data with the same data characteristics for training data collection.

[0740] Step 2: As shown in Figure 11b, the first operation includes:

[0741] Collect training data and associate it with the first association information;

[0742] Training data is collected to generate AI units, which are then associated with the first set of related information.

[0743] The first association information in step 2 includes the cell ID, first association ID, and first timestamp of the first network device, or it includes the cell ID, first association ID, first timestamp, and a list of cells corresponding to data collection of the first network device.

[0744] The first timestamp includes at least one of the following:

[0745] The start time of data collection;

[0746] The deadline for data collection;

[0747] The time of receiving target information.

[0748] The following describes this embodiment with reference to scheme 1b:

[0749] Includes the following steps:

[0750] Step 0a: The first network device sends fourth information to the second network device, the fourth information being used to request data characteristics from the second network device. The fourth information includes indication information for requesting association information from the second network device.

[0751] Optionally, the fourth information may further include an eighth indication information for requesting a list of cells consistent with the second data characteristics.

[0752] Step 0b: Before step 1, the first network device obtains fifth information from the second network device, the fifth information including at least one of the following:

[0753] The cell ID of the second network device;

[0754] Second associated ID (local ID of the second network device);

[0755] The second timestamp enables the first network device to determine the second data characteristic (see Table 2 for details);

[0756] Cell indications that maintain consistent data characteristics with the second associated information;

[0757] AI functionality that matches the second related information.

[0758] Optionally, the second network device performs a third operation, which includes at least one of the following:

[0759] Determine a list of cells whose data characteristics are consistent with the second associated information;

[0760] Identify AI functions that match the second set of related information.

[0761] Optionally, the first network device instructs a second operation, the second operation including at least one of the following:

[0762] The second data characteristic is determined based on the fifth piece of information;

[0763] Determine whether the second data characteristic is consistent with the data characteristic corresponding to the first associated information;

[0764] Determine a list of cells whose data characteristics are consistent with the first associated information;

[0765] The second association information includes at least one of the cell ID of the second network device, the second association ID, and the second timestamp.

[0766] For example, the first network device may have previously obtained this mapping table 2 from the OAM or the second network device (which contains the cell, second association ID, and timestamp information of the second network device, as well as the corresponding actual physical configuration).

[0767] Mapping Table 2

[0768] Step 1: The terminal obtains target information from the first network device (serving cell or camped cell). The target information includes first association information and second association information (the number of second network devices can be one or more, without limitation).

[0769] Optionally, the first association information includes a first association ID (which may be one or more without limitation) and the identifier of the cell of the first network device, and may also include at least one of the following:

[0770] AI use cases or AI features that match the first associated information;

[0771] A list of cells whose data characteristics are consistent with the wireless characteristics of the first associated information;

[0772] Data characteristic indications corresponding to the first associated information;

[0773] Step 2: The first operation includes:

[0774] Determine whether data collection is based on the same data characteristics;

[0775] Training data is collected and correlated with the characteristics of the first associated information data;

[0776] Training data is collected to generate AI units, which are then associated with the first set of related information.

[0777] The first association information in step 2 includes the cell ID, first association ID, and first timestamp of the first network device, or it includes the cell ID, first association ID, first timestamp, and a list of cells corresponding to data collection of the first network device.

[0778] The first timestamp includes at least one of the following:

[0779] The start time of data collection;

[0780] The deadline for data collection;

[0781] The time of receiving target information.

[0782] The following describes this embodiment with reference to Scheme 2:

[0783] The flowchart of Embodiment 2 (Figure 7) includes the following steps:

[0784] Step 0a: Same as scheme 1b.

[0785] Step 0b: The first network device obtains fifth information from the second network device, the fifth information including at least one of the following:

[0786] The cell ID of the second network device;

[0787] Second associated ID (local ID of the second network device);

[0788] The second timestamp allows other network devices to determine the second data characteristics based on it (see Table 2 for details);

[0789] Cell indications that are consistent with the data characteristics of the second associated information;

[0790] AI functionality that matches the second related information.

[0791] Optionally, the second network device performs a third operation, which includes at least one of the following:

[0792] Determine a list of cells whose data characteristics are consistent with the second associated information;

[0793] Identify AI functions that match the second set of related information.

[0794] Step 1: The terminal obtains target information from the first network device (serving cell or camped cell). The target information includes first association information and second association information (the number of second network devices can be one or more, without limitation).

[0795] Optionally, the first association information includes the first association ID of the first network device (which may be one or more without limitation) and the identifier of the cell of the first network device, and may also include at least one of the following:

[0796] AI use cases or AI features related to the ID associated with the first associated information;

[0797] A list of cells whose data characteristics are consistent with the wireless characteristics of the first associated information (neither the first network device nor the second network device has undergone additional mapping and conversion processing, so this is not included);

[0798] Data characteristic indications corresponding to the first associated information;

[0799] The second association information includes the second association ID of the second network device, and may also include at least one of the following:

[0800] AI use cases or AI features related to the second piece of information;

[0801] A list of cells whose data characteristics are consistent with those of the second associated information, maintaining wireless characteristics.

[0802] The second related information corresponds to the data characteristic indication.

[0803] Step 2: As shown in Figure 11c, the first operation includes:

[0804] Determine whether data collection is based on the same data characteristics;

[0805] Collect training data and associate it with the first association information;

[0806] Training data is collected to generate AI units, which are then associated with the first set of related information.

[0807] Training data is collected and correlated with the first and second association information;

[0808] Training data is collected to generate AI units, which are then associated with related information and second related information.

[0809] The first association information in step 2 includes the cell ID, first association ID, and first timestamp of the first network device, or it includes the cell ID, first association ID, first timestamp, and a list of cells corresponding to data collection of the first network device.

[0810] The second association information in step 2 includes the cell ID of the second network device, the second association ID, and the second timestamp, or it includes the cell ID of the second network device, the second association ID, the second timestamp, and the cell list corresponding to the data collection.

[0811] The first timestamp includes at least one of the following:

[0812] The start time of data collection;

[0813] The deadline for data collection;

[0814] The time of receiving target information.

[0815] Example 8: UE actively requests, network directly configures predicted values ​​(applicable to model inference or model supervision; associated ID only applies to local IDs within the cell).

[0816] The main assumption of Example 4 is the AI ​​unit's model inference, model monitoring, or training data collection process when the associated ID sent to the UE between different cells is a global ID. The main idea of ​​this example is to assume that the associated ID is a cell-local ID, thereby avoiding the maintenance cost of a global associated ID.

[0817] Refer to the flowchart of Embodiment 4 (Figure 10), which includes the following steps:

[0818] Step 1: Same as Example 4.

[0819] Step 2: The terminal sends first information to the network. This first information is used to request a match between network data characteristics and the terminal's AI function data characteristics. The first information includes either third association information or a first set. The third association information includes at least one of the following:

[0820] The third associated ID is a local information;

[0821] Third cell ID (the cell ID in the associated information during training data collection);

[0822] The third timestamp (the timestamp when training data was collected, such as the start and / or end timestamp of data collection).

[0823] The first set is a set of third-related information.

[0824] Optionally, the target information may further include at least one of the following:

[0825] The third set of related information or the AI ​​functions associated with the first set;

[0826] The number of third-party related information;

[0827] The number of elements in the first set;

[0828] Network device identifiers for the target area.

[0829] Scheme 1 for steps 3 to 4: The second network device determines whether the data characteristics are consistent with the target data. The second network device performs mapping and judgment of the actual physical characteristics (similar to Scheme 1a, the difference is that the target cell ID, target association ID, and target timestamp are determined differently).

[0830] Step 3a (mandatory, as this step is mandatory because it is determined by the second network device): The first network device sends fourth information to the second network device. The fourth information includes at least one target association information or at least one target set, and may also include an indication for requesting consistency information of the data characteristics of the target association information (the second network device maps the local association ID of the first network device to the actual physical configuration, and then determines the consistency).

[0831] A target set is a collection of multiple target-related information. Each piece of related information or target set is associated with a dataset or an AI unit. At least one target-related information is associated with at least one dataset or multiple AI units, while a target set is associated with a dataset or an AI unit.

[0832] Optionally, the target association information includes at least one of the following:

[0833] Target cell ID (in this embodiment, it is the third cell ID in the first information);

[0834] Target association ID (can be one or more, without limitation; in this embodiment, it is the third association ID in the first information);

[0835] Target timestamp (used by the second network device to determine the characteristics of the target data; in this embodiment, it is the third timestamp in the first information);

[0836] Target association information

[0837] AI functions corresponding to target association information;

[0838] The number of related information items for the target.

[0839] The target association information includes at least one of the following: target cell ID, target association ID, and target timestamp.

[0840] Optionally, the fourth information further includes at least one of the following:

[0841] The tenth indication information is used to request whether the data characteristics of the second association information are consistent with those of the target association information. The target association information includes at least one of the following: target cell ID, target association ID, and target timestamp.

[0842] The eleventh instruction information is used to instruct the second network device to provide a list of cells whose data characteristics are consistent with those of the target-related information;

[0843] The twelfth instruction is used to request the AI ​​function of the second network device to match the target association information.

[0844] Optionally, the first network device determines the target association information based on the third association information.

[0845] Step 3b: The first network device obtains fifth information from the second network device, the fifth information including at least one of the following:

[0846] The cell ID of the second network device;

[0847] The second association information indicates whether it matches the data characteristics of the target set (for example, if there are 3 target sets, each target set indicates whether it matches the second data characteristic, or only the matching target sets are indicated. If a target set contains 2 target data characteristics, then the second data characteristic belongs to one of them, and it can be considered a match. The specific matching judgment can belong to the implementation algorithm of the network device).

[0848] The target set that matches the second related information;

[0849] An indication of whether the second related information matches the target related information (for example, if there are 3 target related information, this indication indicates whether each target related information matches the second related information, or only the set of matching targets is indicated).

[0850] Indication of the number of target association information matching the second association information;

[0851] A list of cells whose data characteristics are consistent with those of the target-related information (e.g., there are 3 target-related information, each of which has a list of cells that are consistent with the related information, or only one or some of the target data characteristics indicate a list of cells whose data characteristics are consistent).

[0852] AI functions that match the data characteristics of the target-related information (e.g., there are 3 target-related information, each of which has an AI function that matches the related information, or only one or some target data characteristics indicate the matching AI function).

[0853] A list of cells that maintain consistent data characteristics with the target set (e.g., there are 3 target sets, each target set has a list of cells that maintain consistent data characteristics, or only one or some target sets indicate a list of cells that maintain consistent data characteristics).

[0854] AI features that match the data characteristics of the target set (e.g., there are 3 target sets, each with one AI feature that matches the data characteristics, or only one or some target sets indicate the AI ​​feature that matches the data characteristics).

[0855] The target association information includes at least one of the following: target cell ID, target association ID, and target timestamp.

[0856] Optionally, the second network device performs a third operation, which includes at least one of the following:

[0857] At least one target data characteristic is determined based on the fourth information;

[0858] Determine the data characteristics of at least one target set based on the fourth information;

[0859] Based on the fourth information, determine whether the second data characteristic matches at least one target number characteristic;

[0860] Based on the fourth information, determine whether the second data characteristic matches the data characteristic of at least one target set;

[0861] Identify a list of cells whose data characteristics are consistent with those of the target-related information;

[0862] Determine a list of cells whose data characteristics are consistent with the target set;

[0863] Identify AI functions that match the characteristics of the target data;

[0864] Identify AI functions that match the data characteristics of the target set.

[0865] Among them, the target data characteristics are determined based on the target cell ID, the target association ID, and the target timestamp.

[0866] Step 4: The first network device performs a second operation, which includes at least one of the following:

[0867] (Based on target information) Determine at least one data characteristic corresponding to a third related piece of information;

[0868] (Based on target information) Determine at least one data characteristic indicated by a first set;

[0869] (Based on target information) Determine whether the first data characteristic matches at least one third data characteristic;

[0870] (Based on target information) Determine whether the first data characteristic matches at least one data characteristic of the first set;

[0871] (Based on the target information and the fifth information) determine whether the second data characteristic matches the data characteristic indicated by at least one third associated information;

[0872] (Based on the target information and the fifth information) determine whether the second data characteristic matches at least one data characteristic corresponding to the first set;

[0873] Determine the predicted value requested from the terminal;

[0874] Determine the list of multiple cells from which the terminal will request the execution of prediction;

[0875] Determine the matching third characteristic indication to be sent to the terminal;

[0876] Determine the first set of matching indications to be sent to the terminal.

[0877] Scheme 2 for steps 3 to 4: The first network device determines whether the data characteristics of the second network device are consistent with the target data characteristics. The first network device performs the mapping and judgment of the actual physical characteristics, similar to Scheme 1b.

[0878] Step 3a (optional): The first network device sends a fourth message to the second network device, the fourth message being used to request data characteristics from the second network device. The fourth message includes indication information for requesting the data characteristics from the second network device.

[0879] Optionally, the fourth information may further include an eighth indication information for requesting a list of cells consistent with the second data characteristics.

[0880] Step 3b: Step 0b of scheme 1b.

[0881] Optionally, the first network device obtains fifth information from the second network device, the fifth information including at least one of the following:

[0882] The cell ID of the second network device;

[0883] Second associated ID (local ID of the second network device);

[0884] The second timestamp enables the first network device to determine the second data characteristic (see Table 2 for details);

[0885] Cell indications that are consistent with the data characteristics of the second associated information;

[0886] AI functionality that matches the second related information.

[0887] Optionally, the second network device performs a third operation, which includes at least one of the following:

[0888] Determine a list of cells whose data characteristics are consistent with the second associated information;

[0889] Identify AI functions that match the second set of related information.

[0890] Step 4: The first network device instructs a second operation, the second operation including at least one of the following:

[0891] (Based on the first information) determine at least one third data characteristic indicating the corresponding data characteristic;

[0892] (Based on the first information) determine at least one data characteristic indicated by the first set;

[0893] (Based on the first information) Determine whether the first data characteristic matches at least one third data characteristic;

[0894] (Based on the first information) Determine whether the first data characteristic matches the data characteristics of at least one first set;

[0895] (Based on the fifth information) Determine the second data characteristic, which is determined based on the cell ID, second association ID, and second timestamp of the second network device (the difference between Scheme 2 and Scheme 1);

[0896] (Based on the first and fifth information) determine whether the second data characteristic matches the data characteristic corresponding to at least one third data characteristic indication;

[0897] (Based on the first information and the fifth information) determine whether the second data characteristic matches at least one data characteristic corresponding to the first set;

[0898] Determine the predicted value requested from the terminal;

[0899] Determine the list of multiple cells from which the terminal will request the execution of prediction;

[0900] Determine the matching third characteristic indication to be sent to the terminal;

[0901] Determine the first set of matching indications to be sent to the terminal.

[0902] The first four operations of the second step described above can be performed before step 3. For example, the process after step 3 is only performed after it is known that the first data characteristic matches the third data characteristic, or matches the data characteristics of the first set. Otherwise, it is not performed. The other operations of the second step described above are performed after the fifth information is obtained.

[0903] Step 5: The first network device sends target information to the terminal, the target information including at least one of the following:

[0904] Associated / matched third association information (including the matched third association ID, third cell ID, and third timestamp);

[0905] An indication of the first set of associated / matched information (which may be based on first information or on third associated information);

[0906] The predicted value requested from the terminal;

[0907] Request the list of multiple cells from the terminal to perform the prediction;

[0908] The sixth indication information, the associated third data characteristic indication, or the first set of indications used for model inference or model monitoring of the AI ​​unit.

[0909] Step 6: The terminal acquires target information and performs corresponding multi-cell prediction and reporting based on the target information.

[0910] If a sixth instruction message is received, the associated third data characteristic instruction or the associated first set applies to the AI ​​unit's model inference or model monitoring.

[0911] It should be noted that when the associated ID is a local cell ID, and the cell ID of the first or second network device is different from the third cell ID, the third associated ID is not applicable for training data collection within the cell where the first or second network device is located. This is because training data collection requires recording timestamps, the collected cell ID, and the associated ID. In this case, the first or second cell ID is different from the third cell ID, and the third associated ID is only valid within the cell where the third cell ID is located; it is invalid within the cell where the first or second network device is located.

[0912] If the associated ID is a global ID, then the third associated ID can also be used for training data collection.

[0913] The above are the relevant descriptions of Examples 1 to 8.

[0914] Through the solutions of the above embodiments, the network actively indicates the association ID that implicitly represents the data characteristics of the network device, or the UE actively requests the matching of the association ID of the AI ​​unit with the data characteristics of the network device to be inferred. This can avoid the UE blindly trying multiple AI units and wasting time and computing power, thereby reducing the power consumption and computing power of the terminal and shortening the time to start multi-cell prediction.

[0915] In this embodiment of the application, the correspondence (or mapping relationship) between associated information (such as associated ID) and data characteristics (or wireless characteristics) is explained as follows:

[0916] Table 1: Correspondence between the associated information available to the first network device or the second network device and the first wireless feature

[0917] The mapping relationship between the associated information and the first wireless characteristic is preferably a one-to-one mapping, but in some special cases, a one-to-many mapping relationship is also possible.

[0918] Table 2: Correspondence between the associated information available to the first or second network device and the second wireless characteristics

[0919] The mapping relationship between the associated information and the second wireless characteristic is preferably a one-to-one mapping, but in some special cases, a one-to-many mapping relationship is also possible.

[0920] Table 3: Correspondence between the associated information available to the first or second network device and the first and second wireless characteristics.

[0921] The associated information can be mapped to the first wireless characteristic and the second wireless characteristic. Preferably, it is a one-to-one mapping, but in some special cases, a one-to-many mapping relationship is also possible.

[0922] The information acquisition method provided in this application can be executed by an information acquisition device. This application uses an information acquisition device executing the information acquisition method as an example to illustrate the information acquisition device provided in this application.

[0923] This application provides an information acquisition device. As an example, the information acquisition device may be a communication device or a component within a communication device, such as a chip. The communication device may be a terminal, a network-side device, or a server, etc. Exemplarily, the terminal may include, but is not limited to, the type of terminal 11 listed above, and the network-side device may include, but is not limited to, the type of network-side device 12 listed above. This application does not impose specific limitations.

[0924] The information acquisition device includes a receiving module, a transmitting module, and a processing module. These modules can be implemented in software or hardware. When implemented in hardware, the processing module can be implemented by a processor. For example, the processor can include general-purpose processors, special-purpose processors, such as a Central Processing Unit (CPU), microprocessor, Digital Signal Processor (DSP), Artificial Intelligence (AI) processor, Graphics Processing Unit (GPU), Application Specific Integrated Circuit (ASIC), Network Processor (NP), Field Programmable Gate Array (FPGA), or other programmable logic devices, gate circuits, transistors, discrete hardware components, etc. The receiving and transmitting modules can be implemented by a communication interface, which can include one or more of the following: transceiver, pins, circuits, bus, radio frequency unit, etc.

[0925] Specifically, referring to Figure 12, when the information acquisition device is a terminal or a component within a terminal, the information acquisition device 1200 includes:

[0926] The first processing module 1201 is used to acquire target information, which indicates at least one of the following: data characteristic related information of N network devices; prediction related information; N is an integer greater than 1;

[0927] The second processing module 1202 is used to perform a first operation based on the target information, wherein the first operation is an operation related to artificial intelligence (AI).

[0928] Optionally, the N network devices include a first network device and a second network device;

[0929] The target information includes at least one of the following:

[0930] The first data characteristic corresponding to the first network device;

[0931] AI information related to the first data characteristic;

[0932] Cell information related to the first data characteristic;

[0933] The second data characteristic corresponding to the second network device;

[0934] AI information related to the second data characteristic;

[0935] Cell information related to the second data characteristic;

[0936] or,

[0937] The target information includes at least one of the following:

[0938] At least one first association information, wherein the at least one first association information is associated with the first data characteristic;

[0939] AI information related to at least one first associated information;

[0940] Cell information related to at least one first associated information;

[0941] At least one second association information, wherein the at least one second association information is associated with the second data characteristic;

[0942] AI information related to at least one second associated information;

[0943] The cell information related to at least one second associated information.

[0944] Optionally, the first association information includes a first association identifier ID, or the first association information includes at least one of a first association ID, a first cell ID, and a first timestamp, wherein the first cell ID is the cell ID of the first network device;

[0945] or,

[0946] The second association information includes a second association ID, or the second association information includes at least one of a second association ID, a second cell ID, and a second timestamp, wherein the second cell ID is the cell ID of the second network device.

[0947] Optionally, the device further includes:

[0948] The sending module is used to send first information to the first network device. The first information includes first indication information, which is used to indicate data characteristic related information corresponding to the first AI unit or the first AI function. The first AI unit or the first AI function is an AI unit or AI function that can be obtained by the terminal.

[0949] Optionally, the sending module is specifically used for:

[0950] If the first condition is met, send the first information to the first network device;

[0951] The first condition includes at least one of the following:

[0952] Prepare to activate or start the first AI unit or the first AI function;

[0953] The communication index of the cell where the first network device or the second network device is located is detected to be lower than a preset threshold;

[0954] The performance degradation of the prediction of at least one cell by the first AI unit or the first AI function was detected.

[0955] The terminal is undergoing cell handover or cell reselection.

[0956] Optionally, the first indication information includes at least one of the following:

[0957] The third data characteristic corresponding to the first AI unit or the first AI function;

[0958] AI information related to the third data characteristic;

[0959] or,

[0960] The first indication information includes at least one of the following:

[0961] At least one third association information, wherein the at least one third association information is associated with the third data characteristic;

[0962] The first set includes multiple sets of the third associated information;

[0963] AI information related to the third associated information;

[0964] AI information related to the first set.

[0965] Optionally, the third association information includes a third association ID, or the third association information includes at least one of a third association ID, a third cell ID, and a third timestamp, wherein the third cell ID is the cell ID associated with the first AI unit or the first AI function.

[0966] Optionally, the N network devices include a first network device and a second network device. When the target information is used to indicate data characteristic-related information of the N network devices, the target information includes at least one of the following:

[0967] The second indication information is used to indicate whether the first data characteristic corresponding to the first network device matches the third data characteristic corresponding to the first AI unit or the first AI function.

[0968] The third indication information is used to indicate whether the second data characteristic corresponding to the second network device matches the third data characteristic;

[0969] The third data characteristic;

[0970] AI information related to the third data characteristic;

[0971] The cell information related to the third data characteristic;

[0972] The first data characteristic;

[0973] AI information related to the first data characteristic;

[0974] Cell information related to the first data characteristic;

[0975] The second data characteristic;

[0976] AI information related to the second data characteristic;

[0977] Cell information related to the second data characteristic;

[0978] or,

[0979] The target information includes at least one of the following:

[0980] At least one first association information, wherein the at least one first association information is associated with the first data characteristic;

[0981] AI information related to at least one first associated information;

[0982] Cell information related to at least one first associated information;

[0983] At least one second association information, wherein the at least one second association information is associated with the second data characteristic;

[0984] AI information related to at least one second associated information;

[0985] The cell information related to at least one second associated information;

[0986] At least one third association information, wherein the at least one third association information is associated with the third data characteristic;

[0987] AI information related to at least one third-party associated information;

[0988] The cell information related to at least one third-party associated information;

[0989] The fourth indication information is used to indicate whether the third data characteristic associated with the at least one third association information matches the first data characteristic;

[0990] The fifth indication information is used to indicate whether the third data characteristic associated with the at least one third association information matches the second data characteristic.

[0991] Optionally, when the target information is used to indicate the prediction-related information, the target information includes at least one of the following:

[0992] The terminal is requested to execute the prediction amount corresponding to the multi-cell prediction.

[0993] The terminal is requested to perform multi-cell prediction of the corresponding cell information;

[0994] The third association information is associated with the third data characteristic corresponding to the first AI unit or the first AI function;

[0995] The sixth indication information is used to indicate whether the matched third data feature can be used for training data collection, model fine-tuning, or model training of the AI ​​unit.

[0996] Optionally, the first information may also be used to request network-side data.

[0997] Optionally, the first information may also include the network device identifier of the target area.

[0998] Optionally, the target information further includes at least one of the following:

[0999] The first data is data whose characteristics match the third data characteristics corresponding to the first AI unit or the first AI function. The first data is data from the first network device, or the first data is data obtained after performing a first processing on the data from the first network device. The data processing method of the first processing is determined based on the first data characteristics.

[1000] The second data is data whose characteristics match the third data characteristics corresponding to the first AI unit or the first AI function. The second data is the third data, or the second data is data obtained after performing a second processing on the third data. The data processing method of the second processing is determined based on the first data characteristics.

[1001] The third data is either data from the second network device or data obtained by performing a third processing on the data from the second network device. The data processing method of the third processing is determined based on the second data characteristics corresponding to the second network device.

[1002] Optionally, the N network devices include a first network device and a second network device;

[1003] The terminal acquires target information, including:

[1004] The terminal obtains the target information from the first network device; or...

[1005] The terminal obtains second information from the first network device, and the terminal obtains third information from the second network device, wherein the target information includes the second information and the third information;

[1006] Wherein, the second information is used to indicate at least one of the following: data characteristic-related information of the first network device; the prediction-related information;

[1007] The third information is used to indicate data characteristic-related information of the second network device.

[1008] Optionally, the data characteristics are used to indicate at least one of the first configuration information, the second configuration information, and the mapping characteristics;

[1009] The first configuration information includes at least one of the following:

[1010] Number of antenna elements;

[1011] The number of antenna elements in the horizontal dimension;

[1012] The number of antenna elements in the vertical dimension;

[1013] Antenna spacing;

[1014] Antenna spacing in the horizontal dimension;

[1015] Antenna spacing in the vertical dimension;

[1016] Mechanical tilt angle;

[1017] Antenna height;

[1018] The orientation of the antenna panel;

[1019] The second configuration information includes at least one of the following:

[1020] Number of beams;

[1021] Number of beams in the horizontal dimension;

[1022] Number of beams in the vertical dimension;

[1023] Beam pointing;

[1024] Half-power beamwidth;

[1025] Beamforming codebook;

[1026] Beamforming codebook in the horizontal dimension;

[1027] Beamforming codebook in the vertical dimension;

[1028] Electron undertilt angle;

[1029] The mapping property includes at least one of the following:

[1030] The mapping relationship between the first configuration information and the associated information;

[1031] The mapping relationship between the second configuration information and the associated information;

[1032] Mapping relationship between physical beam and reference signal RS identifier;

[1033] Mapping relationship between physical beams and beam identifiers.

[1034] Optionally, the first operation includes at least one of the following:

[1035] Identify AI units or AI functions that match the data characteristics of the N network devices;

[1036] Determine a first dataset that matches the data characteristics of all N network devices;

[1037] Determine the AI ​​unit or AI function corresponding to the first dataset;

[1038] Initiate prediction for the cells where the N network devices are located;

[1039] Activate the second AI unit or the second AI function, wherein the second AI unit or the second AI function is an AI unit or AI function whose data characteristics match the data characteristics corresponding to the N network devices;

[1040] Deactivate the third AI unit or the third AI function, wherein the third AI unit or the third AI function is an AI unit or AI function whose data characteristics do not match the data characteristics corresponding to at least one of the N network devices;

[1041] A non-AI algorithm is used to determine whether to initiate the prediction of the cell where the target network device is located. The target network device is the network device whose data characteristics cannot be matched among the N network devices.

[1042] Prediction of the cell where the target network device is located is initiated using a non-AI algorithm;

[1043] Collect the first training data and associate the first training data with the data characteristics corresponding to the N network devices;

[1044] Based on the first training data, a fourth AI unit or a fourth AI function is generated, and the fourth AI unit or the fourth AI function is associated with the data characteristics corresponding to the N network devices.

[1045] The information acquisition device provided in this application embodiment can implement the various processes implemented in the method embodiment of FIG2 and achieve the same technical effect. To avoid repetition, it will not be described again here.

[1046] The information transmission method provided in this application can be executed by an information transmission device. This application uses an information transmission device executing the information transmission method as an example to illustrate the information transmission device provided in this application.

[1047] This application provides an information transmission device. As an example, the information transmission device may be a communication device or a component within a communication device, such as a chip. The communication device may be a terminal, a network-side device, or a server, etc. Exemplarily, the terminal may include, but is not limited to, the type of terminal 11 listed above, and the network-side device may include, but is not limited to, the type of network-side device 12 listed above. This application does not impose specific limitations.

[1048] The information transmission device includes a receiving module, a transmitting module, and a processing module. These modules can be implemented in software or hardware. When implemented in hardware, the processing module can be implemented by a processor. For example, the processor can include general-purpose processors, special-purpose processors, such as a Central Processing Unit (CPU), microprocessor, Digital Signal Processor (DSP), Artificial Intelligence (AI) processor, Graphics Processing Unit (GPU), Application Specific Integrated Circuit (ASIC), Network Processor (NP), Field Programmable Gate Array (FPGA), or other programmable logic devices, gate circuits, transistors, discrete hardware components, etc. The receiving and transmitting modules can be implemented by a communication interface, which can include one or more of the following: transceiver, pins, circuits, bus, radio frequency unit, etc.

[1049] Specifically, referring to Figure 13, when the information transmission device is a network-side device or a component within a network-side device, the information transmission device 1300 includes:

[1050] The first sending module 1301 is used to send target information or second information to the terminal;

[1051] The target information is used to indicate at least one of the following: data characteristic-related information of N network devices; prediction-related information; where N is an integer greater than 1;

[1052] The second information is used to indicate at least one of the following: data characteristic-related information of the first network device; the prediction-related information.

[1053] Optionally, the N network devices include a first network device and a second network device;

[1054] The target information includes at least one of the following:

[1055] The first data characteristic corresponding to the first network device;

[1056] AI information related to the first data characteristic;

[1057] Cell information related to the first data characteristic;

[1058] The second data characteristic corresponding to the second network device;

[1059] AI information related to the second data characteristic;

[1060] Cell information related to the second data characteristic;

[1061] or,

[1062] The target information includes at least one of the following:

[1063] At least one first association information, wherein the at least one first association information is associated with the first data characteristic;

[1064] AI information related to at least one first associated information;

[1065] Cell information related to at least one first associated information;

[1066] At least one second association information, wherein the at least one second association information is associated with the second data characteristic;

[1067] AI information related to at least one second associated information;

[1068] The cell information related to at least one second associated information.

[1069] Optionally, the first association information includes a first association identifier ID, or the first association information includes at least one of a first association ID, a first cell ID, and a first timestamp, wherein the first cell ID is the cell ID of the first network device;

[1070] or,

[1071] The second association information includes a second association ID, or the second association information includes at least one of a second association ID, a second cell ID, and a second timestamp, wherein the second cell ID is the cell ID of the second network device.

[1072] Optionally, the first sending module is specifically used for:

[1073] If the second condition is met, target information or second information is sent to the terminal.

[1074] The second condition includes at least one of the following:

[1075] The data characteristics of the first network device have changed;

[1076] The data characteristics of network devices within the target area change, and the first network device is located within the target area;

[1077] The first network device is ready to activate the AI ​​unit or AI function of the terminal.

[1078] Optionally, the device further includes:

[1079] The first receiving module is configured to receive first information from the terminal. The first information includes first indication information, which is used to indicate data characteristic related information corresponding to the first AI unit or the first AI function. The first AI unit or the first AI function is an AI unit or AI function that can be obtained by the terminal.

[1080] Optionally, the first indication information includes at least one of the following:

[1081] The third data characteristic corresponding to the first AI unit or the first AI function;

[1082] AI information related to the third data characteristic;

[1083] or,

[1084] The first indication information includes at least one of the following:

[1085] At least one third association information, wherein the at least one third association information is associated with the third data characteristic;

[1086] The first set includes multiple sets of the third associated information;

[1087] AI information related to the third associated information;

[1088] AI information related to the first set.

[1089] Optionally, the third association information includes a third association ID, or the third association information includes at least one of a third association ID, a third cell ID, and a third timestamp, wherein the third cell ID is the cell ID associated with the first AI unit or the first AI function.

[1090] Optionally, the N network devices include a first network device and a second network device. When the target information is used to indicate data characteristic-related information of the N network devices, the target information includes at least one of the following:

[1091] The second indication information is used to indicate whether the first data characteristic corresponding to the first network device matches the third data characteristic corresponding to the first AI unit or the first AI function.

[1092] The third indication information is used to indicate whether the second data characteristic corresponding to the second network device matches the third data characteristic;

[1093] The third data characteristic;

[1094] AI information related to the third data characteristic;

[1095] The cell information related to the third data characteristic;

[1096] The first data characteristic;

[1097] AI information related to the first data characteristic;

[1098] Cell information related to the first data characteristic;

[1099] The second data characteristic;

[1100] AI information related to the second data characteristic;

[1101] Cell information related to the second data characteristic;

[1102] or,

[1103] The target information includes at least one of the following:

[1104] At least one first association information, wherein the at least one first association information is associated with the first data characteristic;

[1105] AI information related to at least one first associated information;

[1106] Cell information related to at least one first associated information;

[1107] At least one second association information, wherein the at least one second association information is associated with the second data characteristic;

[1108] AI information related to at least one second associated information;

[1109] The cell information related to at least one second associated information;

[1110] At least one third association information, wherein the at least one third association information is associated with the third data characteristic;

[1111] AI information related to at least one third-party associated information;

[1112] The cell information related to at least one third-party associated information;

[1113] The fourth indication information is used to indicate whether the third data characteristic associated with the at least one third association information matches the first data characteristic;

[1114] The fifth indication information is used to indicate whether the third data characteristic associated with the at least one third association information matches the second data characteristic.

[1115] Optionally, when the target information or the second information is used to indicate the prediction-related information, the target information or the second information includes at least one of the following:

[1116] The terminal is requested to execute the prediction amount corresponding to the multi-cell prediction.

[1117] The terminal is requested to perform multi-cell prediction of the corresponding cell information;

[1118] The third association information is associated with the third data characteristic corresponding to the first AI unit or the first AI function;

[1119] The sixth indication information is used to indicate whether the matched third data feature can be used for training data collection, model fine-tuning, or model training of the AI ​​unit.

[1120] Optionally, the predicted quantity includes at least one of the following:

[1121] Beam quality;

[1122] Community quality;

[1123] Should we initiate same-frequency measurement or different-frequency measurement?

[1124] Threshold value for initiating same-frequency or different-frequency measurements;

[1125] Measure the relaxation cycle multiples;

[1126] List of cells to be measured within the target frequency range;

[1127] Frequency priority;

[1128] The observation time for heterogeneous frequency measurements;

[1129] Threshold value for heterogeneous frequency measurement.

[1130] Optionally, the first information may also be used to request network-side data.

[1131] Optionally, the target information further includes at least one of the following:

[1132] First data, the data characteristics of the first data match the third data characteristics, the first data is the data of the first network device, or, the first data is the data obtained after performing a first processing on the data of the first network device, the data processing method of the first processing is determined based on the first data characteristics;

[1133] The second data is the third data, and its characteristics match those of the third data. Alternatively, the second data is the data obtained after performing a second processing on the third data, and the data processing method of the second processing is determined based on the first data characteristics.

[1134] Optionally, the device further includes:

[1135] The second sending module is configured to send fourth information to the second network device, the fourth information being used to request at least one of the following:

[1136] The second network device has data characteristic information.

[1137] The data from the second network device.

[1138] Optionally, the fourth information includes at least one of the following:

[1139] A target set, which is determined based on a first set provided by the terminal;

[1140] Target association information, which is determined based on third association information provided by the terminal or first association information corresponding to the first network device;

[1141] AI information related to the target association information;

[1142] The target-related information includes cell information;

[1143] The seventh instruction information is used to instruct the second network device to provide data that matches the target association information;

[1144] The eighth instruction information is used to indicate a request for the second network device to provide cell information whose data characteristics are consistent with the second data characteristics of the second network device;

[1145] The ninth instruction information is used to instruct the second network device to provide AI information related to the second data characteristic;

[1146] The tenth instruction information is used to instruct the second network device to confirm whether the second data characteristic matches the data characteristic corresponding to the target association information;

[1147] The eleventh instruction information is used to instruct the second network device to provide cell information whose data characteristics match those of the target associated information;

[1148] The twelfth instruction information is used to instruct the second network device to provide AI information that matches the data characteristics corresponding to the target associated information.

[1149] Optionally, the device further includes:

[1150] The second receiving module is used to receive fifth information from the second network device;

[1151] The fifth piece of information includes at least one of the following:

[1152] The thirteenth indication information is used to indicate whether the second data characteristic matches the data characteristic associated with the target association information;

[1153] The second data characteristic;

[1154] AI information related to the second data characteristic;

[1155] Cell information related to the second data characteristic;

[1156] Third data whose data characteristics are consistent with the second data characteristic;

[1157] or,

[1158] The fifth piece of information includes at least one of the following:

[1159] The thirteenth indication information is used to indicate whether the second data characteristic matches the data characteristic associated with the target association information;

[1160] The second association information is associated with the second data characteristic;

[1161] The second related information is related to the cell information;

[1162] AI information related to the second association information;

[1163] AI information whose data characteristics are consistent with the data characteristics associated with the target information;

[1164] Cell information whose data characteristics are consistent with the data characteristics associated with the target association information;

[1165] Third data whose data characteristics match the target association information;

[1166] The third data is either the data of the second network device or the data obtained after performing a third processing on the data of the second network device, wherein the data processing method of the third processing is determined based on the characteristics of the second data.

[1167] The information transmission device provided in this application embodiment can implement the various processes implemented in the method embodiment of FIG3 and achieve the same technical effect. To avoid repetition, it will not be described again here.

[1168] Specifically, referring to Figure 14, when the information transmission device is a network-side device or a component within a network-side device, the information transmission device 1400 includes:

[1169] Receiving module 1401 is configured to receive fourth information from the first network device, the fourth information being used to request at least one of the following:

[1170] Information related to the data characteristics of the second network device;

[1171] The data from the second network device.

[1172] Optionally, the fourth information includes at least one of the following:

[1173] A target set, which is determined based on a first set provided by the terminal;

[1174] Target association information, which is determined based on third association information provided by the terminal or first association information corresponding to the first network device;

[1175] AI information related to the target association information;

[1176] The target-related information includes cell information;

[1177] The seventh instruction information is used to instruct the second network device to provide data that matches the target association information;

[1178] The eighth instruction information is used to indicate a request for the second network device to provide cell information whose data characteristics are consistent with the second data characteristics of the second network device;

[1179] The ninth instruction information is used to instruct the second network device to provide AI information related to the second data characteristic;

[1180] The tenth instruction information is used to instruct the second network device to confirm whether the second data characteristic matches the data characteristic corresponding to the target association information;

[1181] The eleventh instruction information is used to instruct the second network device to provide cell information whose data characteristics match those of the target associated information;

[1182] The twelfth instruction information is used to instruct the second network device to provide AI information that matches the data characteristics corresponding to the target associated information.

[1183] Optionally, the device further includes:

[1184] The sending module is used to send the fifth information to the first network device;

[1185] The fifth piece of information includes at least one of the following:

[1186] The thirteenth indication information is used to indicate whether the second data characteristic matches the data characteristic associated with the target association information;

[1187] The second data characteristic;

[1188] AI information related to the second data characteristic;

[1189] Cell information related to the second data characteristic;

[1190] Third data whose data characteristics are consistent with the second data characteristic;

[1191] or,

[1192] The fifth piece of information includes at least one of the following:

[1193] The thirteenth indication information is used to indicate whether the second data characteristic matches the data characteristic associated with the target association information;

[1194] The second association information is associated with the second data characteristic;

[1195] The second related information is related to the cell information;

[1196] AI information related to the second association information;

[1197] AI information whose data characteristics are consistent with the data characteristics associated with the target information;

[1198] Cell information whose data characteristics are consistent with the data characteristics associated with the target association information;

[1199] Third data whose data characteristics match the target association information;

[1200] The third data is either the data of the second network device or the data obtained after performing a third processing on the data of the second network device, wherein the data processing method of the third processing is determined based on the characteristics of the second data.

[1201] The information transmission device provided in this application embodiment can implement the various processes implemented in the method embodiment of FIG4 and achieve the same technical effect. To avoid repetition, it will not be described again here.

[1202] As shown in Figure 15, this application embodiment also provides a communication device 1500, including a processor 1501 and a memory 1502. The memory 1502 stores a program or instructions that can run on the processor 1501. For example, when the communication device 1500 is a terminal, the program or instructions executed by the processor 1501 implement the various steps of the above-described terminal-side method embodiments and achieve the same technical effect. When the communication device 1500 is a network-side device, the program or instructions executed by the processor 1501 implement the various steps of the above-described first network device-side or second network device-side method embodiments and achieve the same technical effect. To avoid repetition, further details are omitted here.

[1203] This application embodiment also provides a terminal, including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the steps in the method embodiment shown in FIG2. This terminal embodiment corresponds to the above-described terminal-side method embodiment, and all implementation processes and methods of the above-described method embodiments can be applied to this terminal embodiment and can achieve the same technical effect. The terminal may be the information acquisition device shown in FIG12. Specifically, FIG16 is a schematic diagram of the hardware structure of a terminal implementing an embodiment of this application.

[1204] The terminal 1600 includes, but is not limited to, at least some of the following components: radio frequency unit 1601, network module 1602, audio output unit 1603, input unit 1604, sensor 1605, display unit 1606, user input unit 1607, interface unit 1608, memory 1609, and processor 1610.

[1205] Those skilled in the art will understand that the terminal 1600 may also include a power supply (such as a battery) for powering various components. The power supply can be logically connected to the processor 1610 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The terminal structure shown in Figure 16 does not constitute a limitation on the terminal. The terminal may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[1206] It should be understood that, in this embodiment, the input unit 1604 may include a graphics processor 16041 and a microphone 16042. The graphics processor 16041 processes image data of still images or videos obtained by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 1606 may include a display panel 16061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 1607 includes at least one of a touch panel 16071 and other input devices 16072. The touch panel 16071 is also called a touch screen. The touch panel 16071 may include a touch detection device and a touch controller. Other input devices 16072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be described in detail here.

[1207] In this embodiment, after receiving downlink data from the network-side device, the radio frequency unit 1601 can transmit it to the processor 1610 for processing; in addition, the radio frequency unit 1601 can send uplink data to the network-side device. Typically, the radio frequency unit 1601 includes, but is not limited to, antennas, amplifiers, transceivers, couplers, low-noise amplifiers, duplexers, etc.

[1208] The memory 1609 can be used to store software programs or instructions, as well as various data. The memory 1609 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 1609 may include volatile memory or non-volatile memory. The non-volatile memory may 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), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 1609 in this embodiment includes, but is not limited to, these and any other suitable types of memory.

[1209] Processor 1610 may include one or more processing units; optionally, processor 1610 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 1610.

[1210] The processor 1610 is used for:

[1211] Obtain target information, which is used to indicate at least one of the following: data characteristic related information of N network devices; prediction related information; N is an integer greater than 1;

[1212] Based on the target information, a first operation is performed, which is an operation related to artificial intelligence (AI).

[1213] In this way, by acquiring target information, the terminal can obtain the data characteristics of multiple network devices, and then perform AI-related operations based on the target information. This ensures that the AI-related operations match the data characteristics of multiple network devices, or that the AI-related operations conform to the indicated prediction-related information, which helps to improve the performance stability of the AI ​​unit or AI function.

[1214] It is understood that the implementation process of each implementation method mentioned in this embodiment can refer to the relevant description of the information acquisition method embodiment and achieve the same or corresponding technical effects. To avoid repetition, it will not be described again here.

[1215] This application also provides a network-side device, including a processor and a communication interface. The communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the steps of the method embodiment shown in FIG3 or FIG4. This network-side device embodiment corresponds to the above-described first network device side or second network device side method embodiment. All implementation processes and methods of the above-described method embodiments can be applied to this network-side device embodiment and can achieve the same technical effect.

[1216] Specifically, this application embodiment also provides a network-side device, which may be the information transmission device shown in FIG13 or FIG14. As shown in FIG17, the network-side device 1700 includes: an antenna 171, a radio frequency device 172, a baseband device 173, a processor 174, and a memory 175. The antenna 171 is connected to the radio frequency device 172. In the uplink direction, the radio frequency device 172 receives information through the antenna 171 and sends the received information to the baseband device 173 for processing. In the downlink direction, the baseband device 173 processes the information to be transmitted and sends it to the radio frequency device 172. The radio frequency device 172 processes the received information and transmits it through the antenna 171.

[1217] The method executed by the network-side device in the above embodiments can be implemented in the baseband device 173, which includes a baseband processor.

[1218] The baseband device 173 may include at least one baseband board, on which multiple chips are disposed, as shown in FIG17. One of the chips is, for example, a baseband processor, which is connected to the memory 175 via a bus interface to call the program in the memory 175 and execute the network device operation shown in the above method embodiment.

[1219] The network-side device may also include a network interface 176, such as a Common Public Radio Interface (CPRI).

[1220] Specifically, the network-side device 1700 in this application embodiment further includes: instructions or programs stored in memory 175 and executable on processor 174. Processor 174 calls the instructions or programs in memory 175 to execute the methods executed by the modules shown in FIG13 or 14 and achieve the same technical effect. To avoid repetition, it will not be described in detail here.

[1221] Specifically, this application also provides a network-side device. As shown in FIG18, the network-side device 1800 includes a processor 1801, a network interface 1802, and a memory 1803. The network-side device may be the information transmission device shown in FIG13 or FIG14. The network interface 1802 is, for example, a common public radio interface (CPRI).

[1222] Specifically, the network-side device 1800 in this application embodiment further includes: instructions or programs stored in memory 1803 and executable on processor 1801. Processor 1801 calls the instructions or programs in memory 1803 to execute the methods executed by the modules shown in FIG13 or 14 and achieve the same technical effect. To avoid repetition, it will not be described in detail here.

[1223] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described information acquisition method embodiments or the various processes of the above-described information transmission method embodiments, and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[1224] The processor mentioned above is the processor in the terminal described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk. In some examples, the readable storage medium may be a non-transient readable storage medium.

[1225] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described information acquisition method embodiment or the various processes of the above-described information transmission method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[1226] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[1227] This application also provides a computer program / program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the above-described information acquisition method embodiment or the various processes of the above-described information transmission method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[1228] This application also provides a communication system, including: a terminal and a network-side device, wherein the terminal can be used to perform the steps of the information acquisition method described above, and the network-side device can be used to perform the steps of the information transmission method described above.

[1229] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[1230] From the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of computer software products plus necessary general-purpose hardware platforms, and of course, they can also be implemented by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and includes several instructions to cause the terminal or network-side device to execute the methods described in the various embodiments of this application.

[1231] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other implementations under the guidance of this application without departing from the spirit and scope of the claims. All of these implementations are within the protection scope of this application.

Claims

1. An information acquisition method, comprising: The terminal acquires target information, which is used to indicate at least one of the following: data characteristic related information of N network devices; Predict relevant information; N is an integer greater than 1; Based on the target information, the terminal performs a first operation, which is an artificial intelligence (AI) related operation.

2. The method according to claim 1, wherein, The N network devices include a first network device and a second network device; The target information includes at least one of the following: The first data characteristic corresponding to the first network device; AI information related to the first data characteristic; Cell information related to the first data characteristic; The second data characteristic corresponding to the second network device; AI information related to the second data characteristic; Cell information related to the second data characteristic; or, The target information includes at least one of the following: At least one first association information, wherein the at least one first association information is associated with the first data characteristic; AI information related to at least one first associated information; Cell information related to at least one first associated information; At least one second association information, wherein the at least one second association information is associated with the second data characteristic; AI information related to at least one second associated information; The cell information related to at least one second associated information.

3. The method according to claim 2, wherein, The first association information includes a first association identifier ID, or the first association information includes at least one of a first association ID, a first cell ID, and a first timestamp, wherein the first cell ID is the cell ID of the first network device; or, The second association information includes a second association ID, or the second association information includes at least one of a second association ID, a second cell ID, and a second timestamp, wherein the second cell ID is the cell ID of the second network device.

4. The method according to any one of claims 1 to 3, wherein, Also includes: The terminal sends first information to the first network device. The first information includes first indication information, which is used to indicate data characteristic related information corresponding to the first AI unit or the first AI function. The first AI unit or the first AI function is an AI unit or AI function that the terminal can obtain.

5. The method according to claim 4, wherein, The terminal sends first information to the first network device, including: When the first condition is met, the terminal sends the first information to the first network device; The first condition includes at least one of the following: Prepare to activate or start the first AI unit or the first AI function; The communication index of the cell where the first network device or the second network device is located is detected to be lower than a preset threshold; The performance degradation of the prediction of at least one cell by the first AI unit or the first AI function was detected. The terminal is undergoing cell handover or cell reselection.

6. The method according to claim 4 or 5, wherein, The first indication information includes at least one of the following: The third data characteristic corresponding to the first AI unit or the first AI function; AI information related to the third data characteristic; or, The first indication information includes at least one of the following: At least one third association information, wherein the at least one third association information is associated with the third data characteristic; The first set includes multiple sets of the third associated information; AI information related to the third associated information; AI information related to the first set.

7. The method according to claim 6, wherein, The third association information includes a third association ID, or the third association information includes at least one of a third association ID, a third cell ID, and a third timestamp, wherein the third cell ID is the cell ID associated with the first AI unit or the first AI function.

8. The method according to any one of claims 4 to 7, wherein, The N network devices include a first network device and a second network device. When the target information is used to indicate data characteristic-related information of the N network devices, the target information includes at least one of the following: The second indication information is used to indicate whether the first data characteristic corresponding to the first network device matches the third data characteristic corresponding to the first AI unit or the first AI function. The third indication information is used to indicate whether the second data characteristic corresponding to the second network device matches the third data characteristic; The third data characteristic; AI information related to the third data characteristic; The cell information related to the third data characteristic; The first data characteristic; AI information related to the first data characteristic; Cell information related to the first data characteristic; The second data characteristic; AI information related to the second data characteristic; Cell information related to the second data characteristic; or, The target information includes at least one of the following: At least one first association information, wherein the at least one first association information is associated with the first data characteristic; AI information related to at least one first associated information; Cell information related to at least one first associated information; At least one second association information, wherein the at least one second association information is associated with the second data characteristic; AI information related to at least one second associated information; The cell information related to at least one second associated information; At least one third association information, wherein the at least one third association information is associated with the third data characteristic; AI information related to at least one third-party associated information; The cell information related to at least one third-party associated information; The fourth indication information is used to indicate whether the third data characteristic associated with the at least one third association information matches the first data characteristic; The fifth indication information is used to indicate whether the third data characteristic associated with the at least one third association information matches the second data characteristic.

9. The method according to any one of claims 4 to 7, wherein, When the target information is used to indicate the prediction-related information, the target information includes at least one of the following: The terminal is requested to execute the prediction amount corresponding to the multi-cell prediction. The terminal is requested to perform multi-cell prediction of the corresponding cell information; The third association information is associated with the third data characteristic corresponding to the first AI unit or the first AI function; The sixth indication information is used to indicate whether the matched third data feature can be used for training data collection, model fine-tuning, or model training of the AI ​​unit.

10. The method according to any one of claims 4 to 7, wherein, The first information is also used to request network-side data.

11. The method according to claim 10, wherein, The first information also includes network device identifiers for the target area.

12. The method according to claim 10 or 11, wherein, The target information also includes at least one of the following: The first data is data whose characteristics match the third data characteristics corresponding to the first AI unit or the first AI function. The first data is data from the first network device, or the first data is data obtained after performing a first processing on the data from the first network device. The data processing method of the first processing is determined based on the first data characteristics. The second data is data whose characteristics match the third data characteristics corresponding to the first AI unit or the first AI function. The second data is the third data, or the second data is data obtained after performing a second processing on the third data. The data processing method of the second processing is determined based on the first data characteristics. The third data is either data from the second network device or data obtained by performing a third processing on the data from the second network device. The data processing method of the third processing is determined based on the second data characteristics corresponding to the second network device.

13. The method according to any one of claims 1 to 12, wherein, The N network devices include a first network device and a second network device; The terminal acquires target information, including: The terminal obtains the target information from the first network device; or... The terminal obtains second information from the first network device, and the terminal obtains third information from the second network device, wherein the target information includes the second information and the third information; Wherein, the second information is used to indicate at least one of the following: data characteristic-related information of the first network device; the prediction-related information; The third information is used to indicate data characteristic-related information of the second network device.

14. The method according to any one of claims 1 to 13, wherein, The data characteristics are used to indicate at least one of the first configuration information, the second configuration information, and the mapping characteristics; The first configuration information includes at least one of the following: Number of antenna elements; The number of antenna elements in the horizontal dimension; The number of antenna elements in the vertical dimension; Antenna spacing; Antenna spacing in the horizontal dimension; Antenna spacing in the vertical dimension; Mechanical tilt angle; Antenna height; The orientation of the antenna panel; The second configuration information includes at least one of the following: Number of beams; Number of beams in the horizontal dimension; Number of beams in the vertical dimension; Beam pointing; Half-power beamwidth; Beamforming codebook; Beamforming codebook in the horizontal dimension; Beamforming codebook in the vertical dimension; Electron undertilt angle; The mapping property includes at least one of the following: The mapping relationship between the first configuration information and the associated information; The mapping relationship between the second configuration information and the associated information; Mapping relationship between physical beam and reference signal RS identifier; Mapping relationship between physical beams and beam identifiers.

15. The method according to any one of claims 1 to 14, wherein, The first operation includes at least one of the following: Identify AI units or AI functions that match the data characteristics of the N network devices; Determine a first dataset that matches the data characteristics of all N network devices; Determine the AI ​​unit or AI function corresponding to the first dataset; Initiate prediction for the cells where the N network devices are located; Activate the second AI unit or the second AI function, wherein the second AI unit or the second AI function is an AI unit or AI function whose data characteristics match the data characteristics corresponding to the N network devices; Deactivate the third AI unit or the third AI function, wherein the third AI unit or the third AI function is an AI unit or AI function whose data characteristics do not match the data characteristics corresponding to at least one of the N network devices; A non-AI algorithm is used to determine whether to initiate the prediction of the cell where the target network device is located. The target network device is the network device whose data characteristics cannot be matched among the N network devices. Prediction of the cell where the target network device is located is initiated using a non-AI algorithm; Collect the first training data and associate the first training data with the data characteristics corresponding to the N network devices; Based on the first training data, a fourth AI unit or a fourth AI function is generated, and the fourth AI unit or the fourth AI function is associated with the data characteristics corresponding to the N network devices; Collect the first training data and associate the first training data with the association information corresponding to the N network devices; Based on the first training data, a fourth AI unit or a fourth AI function is generated, and the fourth AI unit or the fourth AI function is associated with the association information corresponding to the N network devices.

16. An information transmission method, comprising: The first network device sends target information or second information to the terminal; The target information is used to indicate at least one of the following: data characteristic-related information of N network devices; prediction-related information; where N is an integer greater than 1; The second information is used to indicate at least one of the following: data characteristic-related information of the first network device; the prediction-related information.

17. The method according to claim 16, wherein, The N network devices include a first network device and a second network device; The target information includes at least one of the following: The first data characteristic corresponding to the first network device; AI information related to the first data characteristic; Cell information related to the first data characteristic; The second data characteristic corresponding to the second network device; AI information related to the second data characteristic; Cell information related to the second data characteristic; or, The target information includes at least one of the following: At least one first association information, wherein the at least one first association information is associated with the first data characteristic; AI information related to at least one first associated information; Cell information related to at least one first associated information; At least one second association information, wherein the at least one second association information is associated with the second data characteristic; AI information related to at least one second associated information; The cell information related to at least one second associated information.

18. The method according to claim 17, wherein, The first association information includes a first association identifier ID, or the first association information includes at least one of a first association ID, a first cell ID, and a first timestamp, wherein the first cell ID is the cell ID of the first network device; or, The second association information includes a second association ID, or the second association information includes at least one of a second association ID, a second cell ID, and a second timestamp, wherein the second cell ID is the cell ID of the second network device.

19. The method according to any one of claims 16 to 18, wherein, The first network device sends target information or second information to the terminal, including: When the second condition is met, the first network device sends target information or second information to the terminal; The second condition includes at least one of the following: The data characteristics of the first network device have changed; The data characteristics of network devices within the target area change, and the first network device is located within the target area; The first network device is ready to activate the AI ​​unit or AI function of the terminal.

20. The method according to any one of claims 16 to 19, wherein, Also includes: The first network device receives first information from the terminal. The first information includes first indication information, which is used to indicate data characteristic related information corresponding to the first AI unit or the first AI function. The first AI unit or the first AI function is an AI unit or AI function that can be obtained by the terminal.

21. The method according to claim 20, wherein, The first indication information includes at least one of the following: The third data characteristic corresponding to the first AI unit or the first AI function; AI information related to the third data characteristic; or, The first indication information includes at least one of the following: At least one third association information, wherein the at least one third association information is associated with the third data characteristic; The first set includes multiple sets of the third associated information; AI information related to the third associated information; AI information related to the first set.

22. The method according to claim 21, wherein, The third association information includes a third association ID, or the third association information includes at least one of a third association ID, a third cell ID, and a third timestamp, wherein the third cell ID is the cell ID associated with the first AI unit or the first AI function.

23. The method according to any one of claims 20 to 22, wherein, The N network devices include a first network device and a second network device. When the target information is used to indicate data characteristic-related information of the N network devices, the target information includes at least one of the following: The second indication information is used to indicate whether the first data characteristic corresponding to the first network device matches the third data characteristic corresponding to the first AI unit or the first AI function. The third indication information is used to indicate whether the second data characteristic corresponding to the second network device matches the third data characteristic; The third data characteristic; AI information related to the third data characteristic; The cell information related to the third data characteristic; The first data characteristic; AI information related to the first data characteristic; Cell information related to the first data characteristic; The second data characteristic; AI information related to the second data characteristic; Cell information related to the second data characteristic; or, The target information includes at least one of the following: At least one first association information, wherein the at least one first association information is associated with the first data characteristic; AI information related to at least one first associated information; Cell information related to at least one first associated information; At least one second association information, wherein the at least one second association information is associated with the second data characteristic; AI information related to at least one second associated information; The cell information related to at least one second associated information; At least one third association information, wherein the at least one third association information is associated with the third data characteristic; AI information related to at least one third-party associated information; The cell information related to at least one third-party associated information; The fourth indication information is used to indicate whether the third data characteristic associated with the at least one third association information matches the first data characteristic; The fifth indication information is used to indicate whether the third data characteristic associated with the at least one third association information matches the second data characteristic.

24. The method according to any one of claims 20 to 22, wherein, When the target information or the second information is used to indicate the prediction-related information, the target information or the second information includes at least one of the following: The terminal is requested to execute the prediction amount corresponding to the multi-cell prediction. The terminal is requested to perform multi-cell prediction of the corresponding cell information; The third association information is associated with the third data characteristic corresponding to the first AI unit or the first AI function; The sixth indication information is used to indicate whether the matched third data feature can be used for training data collection, model fine-tuning, or model training of the AI ​​unit.

25. The method according to claim 24, wherein, The predicted quantity includes at least one of the following: Beam quality; Community quality; Should we initiate same-frequency measurement or different-frequency measurement? Threshold value for initiating same-frequency or different-frequency measurements; Measure the relaxation cycle multiples; List of cells to be measured within the target frequency range; Frequency priority; The observation time for heterogeneous frequency measurements; Threshold value for heterogeneous frequency measurement.

26. The method according to any one of claims 20 to 25, wherein, The first information is also used to request network-side data.

27. The method according to claim 26, wherein, The target information also includes at least one of the following: The first data is data whose characteristics match the third data characteristics corresponding to the first AI unit or the first AI function. The first data is data from the first network device, or the first data is data obtained after performing a first processing on the data from the first network device. The data processing method of the first processing is determined based on the first data characteristics. The second data is the third data, and its characteristics match those of the third data. Alternatively, the second data is the data obtained after performing a second processing on the third data, and the data processing method of the second processing is determined based on the first data characteristics.

28. The method according to claim 26 or 27, wherein, Also includes: The first network device sends a fourth message to the second network device, the fourth message being used to request at least one of the following: The second network device has data characteristic information. The data from the second network device.

29. The method according to claim 28, wherein, The fourth piece of information includes at least one of the following: A target set, which is determined based on a first set provided by the terminal; Target association information, which is determined based on third association information provided by the terminal or first association information corresponding to the first network device; AI information related to the target association information; The target-related information includes cell information; The seventh instruction information is used to instruct the second network device to provide data that matches the target association information; The eighth instruction information is used to indicate a request for the second network device to provide cell information whose data characteristics are consistent with the second data characteristics of the second network device; The ninth instruction information is used to instruct the second network device to provide AI information related to the second data characteristic; The tenth instruction information is used to instruct the second network device to confirm whether the second data characteristic matches the data characteristic corresponding to the target association information; The eleventh instruction information is used to instruct the second network device to provide cell information whose data characteristics match those of the target associated information; The twelfth instruction information is used to instruct the second network device to provide AI information that matches the data characteristics corresponding to the target associated information.

30. The method according to claim 29, wherein, Also includes: The first network device receives the fifth information from the second network device; The fifth piece of information includes at least one of the following: The thirteenth indication information is used to indicate whether the second data characteristic matches the data characteristic associated with the target association information; The second data characteristic; AI information related to the second data characteristic; Cell information related to the second data characteristic; Third data whose data characteristics are consistent with the second data characteristic; or, The fifth piece of information includes at least one of the following: The thirteenth indication information is used to indicate whether the second data characteristic matches the data characteristic associated with the target association information; The second association information is associated with the second data characteristic; The second related information is related to the cell information; AI information related to the second association information; AI information whose data characteristics are consistent with the data characteristics associated with the target information; Cell information whose data characteristics are consistent with the data characteristics associated with the target association information; Third data whose data characteristics match the target association information; The third data is either the data of the second network device or the data obtained after performing a third processing on the data of the second network device, wherein the data processing method of the third processing is determined based on the characteristics of the second data.

31. An information transmission method, comprising: The second network device receives fourth information from the first network device, the fourth information being used to request at least one of the following: Information related to the data characteristics of the second network device; The data from the second network device.

32. The method according to claim 31, wherein, The fourth piece of information includes at least one of the following: The target set is determined based on a first set provided by the terminal; Target association information, which is determined based on third association information provided by the terminal or first association information corresponding to the first network device; AI information related to the target association information; The target-related information includes cell information; The seventh instruction information is used to instruct the second network device to provide data that matches the target association information; The eighth instruction information is used to indicate a request for the second network device to provide cell information whose data characteristics are consistent with the second data characteristics of the second network device; The ninth instruction information is used to instruct the second network device to provide AI information related to the second data characteristic; The tenth instruction information is used to instruct the second network device to confirm whether the second data characteristic matches the data characteristic corresponding to the target association information; The eleventh instruction information is used to instruct the second network device to provide cell information whose data characteristics match those of the target associated information; The twelfth instruction information is used to instruct the second network device to provide AI information that matches the data characteristics corresponding to the target associated information.

33. The method according to claim 32, wherein, Also includes: The second network device sends the fifth message to the first network device; The fifth piece of information includes at least one of the following: The thirteenth indication information is used to indicate whether the second data characteristic matches the data characteristic associated with the target association information; The second data characteristic; AI information related to the second data characteristic; Cell information related to the second data characteristic; Third data whose data characteristics are consistent with the second data characteristic; or, The fifth piece of information includes at least one of the following: The thirteenth indication information is used to indicate whether the second data characteristic matches the data characteristic associated with the target association information; The second association information is associated with the second data characteristic; The second related information is related to the cell information; AI information related to the second association information; AI information whose data characteristics are consistent with the data characteristics associated with the target information; Cell information whose data characteristics are consistent with the data characteristics associated with the target association information; Third data whose data characteristics match the target association information; The third data is either the data of the second network device or the data obtained after performing a third processing on the data of the second network device, wherein the data processing method of the third processing is determined based on the characteristics of the second data.

34. An information acquisition device, the device comprising: The first processing module is used to acquire target information, the target information being used to indicate at least one of the following: data characteristic related information of N network devices; Predict relevant information; N is an integer greater than 1; The second processing module is used to perform a first operation based on the target information, wherein the first operation is an artificial intelligence (AI) related operation.

35. The apparatus according to claim 34, wherein, Also includes: The sending module is used to send first information to a first network device. The first information includes first indication information, which is used to indicate data characteristic related information corresponding to a first AI unit or a first AI function. The first AI unit or the first AI function is an AI unit or AI function that can be obtained by the terminal.

36. The apparatus according to claim 35, wherein, The first information is also used to request network-side data.

37. The apparatus according to claim 36, wherein, The target information also includes at least one of the following: The first data is data whose characteristics match the third data characteristics corresponding to the first AI unit or the first AI function. The first data is data from the first network device, or the first data is data obtained after performing a first processing on the data from the first network device. The data processing method of the first processing is determined based on the first data characteristics. The second data is data whose characteristics match the third data characteristics corresponding to the first AI unit or the first AI function. The second data is the third data, or the second data is data obtained after performing a second processing on the third data. The data processing method of the second processing is determined based on the first data characteristics. The third data is either data from the second network device or data obtained by performing a third processing on the data from the second network device. The data processing method of the third processing is determined based on the second data characteristics corresponding to the second network device.

38. An information transmission device, the device comprising: The first sending module is used to send target information or second information to the terminal; The target information is used to indicate at least one of the following: data characteristic-related information of N network devices; prediction-related information; where N is an integer greater than 1; The second information is used to indicate at least one of the following: data characteristic-related information of the first network device; the prediction-related information.

39. The apparatus according to claim 38, wherein, Also includes: The first receiving module is configured to receive first information from the terminal. The first information includes first indication information, which is used to indicate data characteristic related information corresponding to the first AI unit or the first AI function. The first AI unit or the first AI function is an AI unit or AI function that can be obtained by the terminal.

40. The apparatus according to claim 39, wherein, The target information also includes at least one of the following: The first data is data whose characteristics match the third data characteristics corresponding to the first AI unit or the first AI function. The first data is data from the first network device, or the first data is data obtained after performing a first processing on the data from the first network device. The data processing method of the first processing is determined based on the first data characteristics. The second data is the third data, and its characteristics match those of the third data. Alternatively, the second data is the data obtained after performing a second processing on the third data, and the data processing method of the second processing is determined based on the first data characteristics.

41. The apparatus according to claim 39 or 40, wherein, Also includes: The second sending module is configured to send fourth information to the second network device, the fourth information being used to request at least one of the following: The second network device has data characteristic information. The data from the second network device.

42. The apparatus according to claim 41, wherein, Also includes: The second receiving module is used to receive fifth information from the second network device; The fifth piece of information includes at least one of the following: The thirteenth instruction information is used to indicate whether the second data characteristic of the second network device matches the data characteristic associated with the target association information; The second data characteristic; AI information related to the second data characteristic; Cell information related to the second data characteristic; Third data whose data characteristics are consistent with the second data characteristic; or, The fifth piece of information includes at least one of the following: The thirteenth indication information is used to indicate whether the second data characteristic matches the data characteristic associated with the target association information; The second association information is associated with the second data characteristic; The second related information is related to the cell information; AI information related to the second association information; AI information whose data characteristics are consistent with the data characteristics associated with the target information; Cell information whose data characteristics are consistent with the data characteristics associated with the target association information; Third data whose data characteristics match the target association information; The third data is either the data of the second network device or the data obtained after performing a third processing on the data of the second network device, wherein the data processing method of the third processing is determined based on the characteristics of the second data.

43. An information transmission device, comprising: The receiving module is configured to receive fourth information from the first network device, the fourth information being used to request at least one of the following: Information related to the data characteristics of the second network device; The data from the second network device.

44. The apparatus according to claim 43, wherein, Also includes: The sending module is used to send the fifth information to the first network device; The fifth piece of information includes at least one of the following: The thirteenth instruction information is used to indicate whether the second data characteristic of the second network device matches the data characteristic associated with the target association information; The second data characteristic; AI information related to the second data characteristic; Cell information related to the second data characteristic; Third data whose data characteristics are consistent with the second data characteristic; or, The fifth piece of information includes at least one of the following: The thirteenth indication information is used to indicate whether the second data characteristic matches the data characteristic associated with the target association information; The second association information is associated with the second data characteristic; The second related information is related to the cell information; AI information related to the second association information; AI information whose data characteristics are consistent with the data characteristics associated with the target information; Cell information whose data characteristics are consistent with the data characteristics associated with the target association information; Third data whose data characteristics match the target association information; The third data is either the data of the second network device or the data obtained after performing a third processing on the data of the second network device, wherein the data processing method of the third processing is determined based on the characteristics of the second data.

45. A communication device comprising a processor and a memory, the memory storing a program or instructions executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the information acquisition method as claimed in any one of claims 1 to 15, or the steps of the information transmission method as claimed in any one of claims 16 to 30, or the steps of the information transmission method as claimed in any one of claims 31 to 33.

46. ​​A readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the information acquisition method as claimed in any one of claims 1 to 15, or the steps of the information transmission method as claimed in any one of claims 16 to 30, or the steps of the information transmission method as claimed in any one of claims 31 to 33.

47. A computer program product comprising computer instructions which, when executed by a processor, implement the steps of the information acquisition method as claimed in any one of claims 1 to 15, or the steps of the information transmission method as claimed in any one of claims 16 to 30, or the steps of the information transmission method as claimed in any one of claims 31 to 33.

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