Method and apparatus for transmitting information

Abstract

Embodiments of the present application provide a method and apparatus for transmitting information. The method comprises: a terminal device sending to a network device request information related to AI / ML; receiving monitoring configuration information and / or reporting configuration information sent by the network device; reporting AI / ML monitoring information to the network device; and receiving first identifier information related to the AI / ML of the terminal device.

Description

Information transmission method and apparatus TECHNICAL FIELD

[0001] Embodiments of the present application relate to the field of communication technology. BACKGROUND

[0002] With low-frequency spectrum resources becoming scarce, millimeter wave bands can provide larger bandwidth and become an important frequency band for 5G New Radio (NR) systems. Millimeter waves have different propagation characteristics from traditional low-frequency bands due to their shorter wavelengths, such as higher propagation loss, poor reflection and diffraction performance, etc. Therefore, a larger antenna array is usually used to form a larger gain beam to overcome the propagation loss and ensure system coverage.

[0003] With the development of artificial intelligence (AI) and machine learning (ML) technologies, applying AI / ML technologies to wireless communication to solve the difficulties of traditional methods has become a current technical direction. AI / ML models are applied to wireless communication systems, especially to air interface transmission, which is a new technology in the 5G-Advanced and 6G stages.

[0004] For example, for channel state information (CSI) reporting, an AI encoder (AI encoder) in an Autoencoder network in deep learning is used to encode / compress CSI at the terminal device side, and an AI decoder (AI decoder) is used to decode / decompress CSI at the network device side, which can reduce feedback overhead. For another example, for beam management, an AI / ML functionality / model is used to predict the optimal beam pair in space according to the results of a small amount of beam measurement, which can reduce the load and delay of the system. Other use cases include applications in timing and mobility.

[0005] It should be noted that the above introduction to the technical background is only to facilitate a clear and complete description of the technical solutions of the present application, and to facilitate the understanding of those skilled in the art. The above technical solutions cannot be considered as known to those skilled in the art merely because they are described in the background section of the present application.

[0006] SUMMARY

[0007] The inventors find that due to the complexity of transmitting radio frequency signals and the protection requirements of device information and intellectual property information of network equipment manufacturers, it is difficult to provide all the auxiliary information to the terminal device. Thus, in the training and use of the model, it is difficult to achieve consistency between the training data (signals) of the model and the data (signals) encountered in the use of the model through the interaction of the auxiliary information. The ambiguity or uncertainty of such consistency has a great impact on the performance of AI / ML.

[0008] To solve at least one of the above problems, embodiments of the present application provide an information transmission method and device.

[0009] According to an aspect of embodiments of the present application, an information transmission method is provided, comprising:

[0010] The terminal device sends request information related to AI / ML to the network device;

[0011] The terminal device receives monitoring configuration information and / or reporting configuration information sent by the network device;

[0012] The terminal device reports AI / ML monitoring information to the network device; and

[0013] The terminal device receives first identification information related to AI / ML of the terminal device.

[0014] According to another aspect of embodiments of the present application, an information transmission device configured in a terminal device is provided, comprising:

[0015] A sending unit that sends request information related to AI / ML to the network device; and

[0016] A receiving unit that receives monitoring configuration information and / or reporting configuration information sent by the network device;

[0017] The sending unit also reports AI / ML monitoring information to the network device;

[0018] The receiving unit also receives first identification information related to AI / ML of the terminal device.

[0019] According to another aspect of embodiments of the present application, an information transmission method is provided, comprising:

[0020] The network device receives request information related to AI / ML from the terminal device; and

[0021] The network device sends monitoring configuration information and / or reporting configuration information to the terminal device;

[0022] The network device receives AI / ML monitoring information reported by the terminal device;

[0023] The network device sends first identification information related to AI / ML of the terminal device to the terminal device.

[0024] According to another aspect of the embodiments of the present application, an information transmission apparatus is provided, configured in a network device, the information transmission apparatus comprising:

[0025] a receiving unit configured to receive request information related to AI / ML from a terminal device; and

[0026] a sending unit configured to send monitoring configuration information and / or reporting configuration information to the terminal device;

[0027] The receiving unit is further configured to receive AI / ML monitoring information reported by the terminal device.

[0028] The sending unit is further configured to send first identification information related to AI / ML of the terminal device to the terminal device.

[0029] According to another aspect of the embodiments of the present application, a communication system is provided, comprising:

[0030] a network device configured to receive request information related to AI / ML from a terminal device, send monitoring configuration information and / or reporting configuration information to the terminal device, receive AI / ML monitoring information reported by the terminal device, and send first identification information related to AI / ML of the terminal device to the terminal device;

[0031] a terminal device configured to send request information related to AI / ML to a network device, receive monitoring configuration information and / or reporting configuration information sent by the network device, report AI / ML monitoring information to the network device, and receive first identification information related to AI / ML of the terminal device.

[0032] One of the beneficial effects of the embodiments of the present application is that the terminal device receives first identification information related to AI / ML from the network device. Thus, the consistency of data (signals) during AI / ML training and data (signals) encountered during use can be improved, and the performance and efficiency of AI / ML can be improved.

[0033] Specific embodiments of the application are now described with reference to the following description and drawings. The following description and drawings are included to provide a thorough understanding of the application. The application may, however, be practiced without the specific details. The following description and drawings are included to provide a thorough understanding of the application. The application may, however, be practiced without the specific details. In some instances, well-known methods, structures and techniques have not been described in detail in order to avoid obscuring the application. The application is capable of other embodiments or of being practiced with other components or using other structures and techniques. Accordingly, the application is not intended to be limited by the recited embodiment, but is intended to be defined by the appended claims and equivalents thereof.

[0034] Features described and / or illustrated with respect to one implementation can be used in one or more other implementations in the same or similar manner, in combination with or in place of one or more features of the other implementations.

[0035] It should be emphasized that the term "comprises / comprising" when used in this specification is taken to mean the presence of stated features, integers, steps or components but does not preclude the presence or addition of one or more other features, integers, steps, components or groups thereof. BRIEF DESCRIPTION OF DRAWINGS

[0036] Elements and features described with respect to one drawing or implementation of an embodiment of the application can be combined with elements and features illustrated with respect to one or more other drawings or implementations of the application. Also, in the drawings, like reference numerals designate corresponding parts throughout the several views, and can be used to designate corresponding parts in more than one implementation.

[0037] Figure 1 is a schematic illustration of a communication system according to an embodiment of the application;

[0038] Figure 2 is a schematic illustration of a method of information transmission according to an embodiment of the application;

[0039] Figure 3 is a schematic illustration of AI / ML according to an embodiment of the application;

[0040] Figure 4 is a schematic illustration of a method of information transmission according to another embodiment of the application;

[0041] Figure 5 is a schematic illustration of a method of information transmission according to another embodiment of the application;

[0042] Figure 6 is a schematic illustration of a method of information transmission according to another embodiment of the application;

[0043] Figure 7 is a schematic illustration of a method of information transmission according to another embodiment of the application;

[0044] Figure 8 is a schematic illustration of a method of information transmission according to another embodiment of the application;

[0045] Figure 9 is a schematic illustration of a method of information transmission according to another embodiment of the application;

[0046] Figure 10 is a schematic illustration of a method of information transmission according to another embodiment of the application;

[0047] Figure 11 is a schematic illustration of a method of information transmission according to another embodiment of the application;

[0048] Figure 12 is a schematic illustration of an apparatus for information transmission according to an embodiment of the application;

[0049] Figure 13 is a schematic illustration of an apparatus for information transmission according to another embodiment of the application;

[0050] Figure 14 is a schematic illustration of a terminal device according to an embodiment of the application;

[0051] FIG. 15 is a schematic diagram of a network device according to an embodiment of the present application. DETAILED DESCRIPTION

[0052] The foregoing and other features of the present application will become apparent to those skilled in the art upon consideration of the following description of specific embodiments of the application, taken in conjunction with the accompanying drawings. In the description of embodiments of the application, specific terminology is employed for the sake of clarity. However, the application is not intended to be limited to the specific embodiments described, but rather, is intended to include all modifications, equivalents, and alternatives that fall within the scope of the appended claims.

[0053] In the embodiments of the present application, the terms "first", "second", and the like are used to distinguish different elements from one another, but do not indicate spatial arrangement or temporal order, and the elements should not be limited by these terms. The term "and / or" includes any one and all combinations of the associated listed terms. The terms "comprise", "include", "have", and the like, mean the presence of the stated feature, element, component, or assembly, but do not exclude the presence or addition of one or more other features, elements, components, or assemblies.

[0054] In the embodiments of the present application, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. The term "said" should be interpreted to mean "one or more" unless the context clearly dictates otherwise. In addition, the term "according to" should be interpreted as "based, at least in part, on", and the term "based on" should be interpreted as "based, at least in part, on" unless the context clearly dictates otherwise.

[0055] In the embodiments of the present application, the term "communication network" or "wireless communication network" can refer to a network that complies with any communication standard, such as Long Term Evolution (LTE), LTE-Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), High-Speed Packet Access (HSPA), and the like.

[0056] In addition, communication between devices in a communication system can be conducted according to any phase of a communication protocol, such as can include, but is not limited to, the following communication protocols: 1G (generation), 2G, 2.5G, 2.75G, 3G, 4G, 4.5G, and 5G, New Radio (NR), future 6G, and the like, and / or other communication protocols that are currently known or will be developed in the future.

[0057] In embodiments of the present application, the term "network device" refers to, for example, a device that accesses a terminal device to a communication network and provides services for the terminal device in a communication system. The network device can include, but is not limited to, the following devices: base station (BS), access point (AP), transmission reception point (TRP), broadcast transmitter, mobile management entity (MME), gateway, server, radio network controller (RNC), base station controller (BSC), and the like.

[0058] In embodiments of the present application, the term "network device" refers to, for example, a device that accesses a terminal device to a communication network and provides services for the terminal device in a communication system. The network device can include, but is not limited to, the following devices: base station (BS), access point (AP), transmission reception point (TRP), broadcast transmitter, mobile management entity (MME), gateway, server, radio network controller (RNC), base station controller (BSC), and the like.

[0059] In embodiments of the present application, the term "user equipment" (UE) or "terminal equipment" (TE) refers to, for example, a device that accesses a communication network through a network device and receives network services. The terminal device can be fixed or mobile, and can also be referred to as a mobile station (MS), a terminal, a subscriber station (SS), an access terminal (AT), a station, and the like.

[0060] In embodiments of the present application, the term "user equipment" (UE) or "terminal equipment" (TE) refers to, for example, a device that accesses a communication network through a network device and receives network services. The terminal device can be fixed or mobile, and can also be referred to as a mobile station (MS), a terminal, a subscriber station (SS), an access terminal (AT), a station, and the like.

[0061] For another example, in scenarios such as Internet of Things (IoT), a terminal device can also be a machine or device that performs monitoring or measurement, which can include but is not limited to: a machine type communication (MTC) terminal, a vehicle-mounted communication terminal, a device-to-device (D2D) terminal, a machine-to-machine (M2M) terminal, and the like.

[0062] In addition, the term "network side" or "network device side" refers to the side of the network, which can be a certain base station or can include one or more network devices as described above. The term "user side" or "terminal side" or "terminal device side" refers to the side of the user or terminal, which can be a certain UE or can include one or more terminal devices as described above. In this article, "device" can refer to a network device or a terminal device unless otherwise specified.

[0063] The following describes the scenarios of the embodiments of the present application by way of examples, but the present application is not limited thereto.

[0064] FIG. 1 is a schematic diagram of a communication system according to an embodiment of the present application, which schematically illustrates a case taking a terminal device and a network device as an example. As shown in FIG. 1, the communication system 100 can include a network device 101 and terminal devices 102 and 103. For simplicity, FIG. 1 illustrates only two terminal devices and one network device as an example, but the embodiments of the present application are not limited thereto.

[0065] In the embodiments of the present application, the network device 101 and the terminal devices 102 and 103 can perform existing services or future implementable services transmission. For example, these services can include but are not limited to: enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra-reliable and low-latency communication (URLLC), and the like.

[0066] It is worth noting that FIG. 1 shows that both terminal devices 102 and 103 are within the coverage of the network device 101, but the present application is not limited thereto. Both terminal devices 102 and 103 can not be within the coverage of the network device 101, or one terminal device 102 is within the coverage of the network device 101 while the other terminal device 103 is outside the coverage of the network device 101.

[0067] In the embodiments of the present application, the high-layer signaling may be, for example, radio resource control (RRC) signaling; for example, referred to as an RRC message (RRC message), for example, including an MIB, system information, a dedicated RRC message; or referred to as an RRC IE (RRC information element). The high-layer signaling may also be, for example, MAC (Medium Access Control) signaling; or referred to as a MAC CE (MAC control element). However, the present application is not limited thereto.

[0068] For various use cases in which artificial intelligence (AI) and machine learning (ML) technologies are applied to air interface wireless transmission, some AI / ML is deployed in terminal devices, and some AI / ML is deployed in network devices. For AI / ML of terminal devices, relevant condition information of network devices needs to be known in the model training phase thereof.

[0069] For example, for the use case in which AI / ML is applied to beam management, beam shape information, angle information, and pattern information of a beam of a network device, as well as other information related to radio frequency of the network device, will affect the radio wave characteristics of the reference signal transmitted thereby. In the positioning use case, in addition to the above-mentioned beam information, network device timing error information may also be included. Other parameters of a radio frequency unit transmitted by a related network device, such as beam horizontal and vertical angles, phase rotation, antenna configuration, polarization direction, and the like, will all affect the characteristics of the wireless signal related to the reference signal received by the terminal device. Such parameter information may be collectively referred to as auxiliary condition information of a network device (or network auxiliary condition information) herein, and the present application is not limited thereto.

[0070] For terminal devices, in order to perform model training, relevant reference signals transmitted by network devices need to be received and measured. If the auxiliary condition information of the network device can be labeled and classified for these signals (or training data), it will be helpful to train a model that meets specific auxiliary condition information or multiple auxiliary condition information. In the use phase of the model, the terminal device can report the auxiliary condition information used for the training of the model to the network device, so as to facilitate the network side to configure the corresponding transmission conditions. Alternatively, the network side transmits auxiliary condition information to the terminal device, and the terminal device selects the corresponding model accordingly. It should be noted that for different use cases, the types of auxiliary condition information that are sensitive to the performance of the corresponding model may be different.

[0071] However, the inventors found that it is difficult to provide all the assistance information to the terminal device due to the complexity of transmitting radio frequency signals, and the protection requirements of device information and intellectual property information of network equipment manufacturers. Thus, when training and using the model, it is difficult to achieve consistency between the data (signals) encountered during model training and model use through the interaction of the assistance information. This ambiguity or uncertainty has a great impact on the performance of AI / ML.

[0072] In embodiments of the present application, the performance of the AI / ML function / model can be monitored so that the control of the corresponding AI / ML function / model can be performed, such as activation / deactivation / selection / switching / fallback.

[0073] For example, network-side monitoring can be performed, and the performance metric is monitored by the network side, and the decision of activation / deactivation / selection / switching / fallback is made by the network side.

[0074] For another example, UE-side monitoring can be performed, and the performance metric is monitored by the terminal side, and the decision of activation / deactivation / selection / switching / fallback is made by the terminal side.

[0075] For yet another example, hybrid monitoring can be performed, and the performance metric is monitored by the terminal side, and the decision of activation / deactivation / selection / switching / fallback is made by the network side.

[0076] In embodiments of the present application, one or more AI / ML models can be configured and run in the network device and / or the terminal device. The AI / ML model can be used for various signal processing functions of wireless communication, such as CSI prediction, CSI compression, beam prediction, positioning management, etc.; the present application is not limited thereto.

[0077] Embodiments of the first aspect

[0078] Embodiments of the present application provide an information transmission method.

[0079] FIG. 2 is a schematic diagram of an information transmission method according to an embodiment of the present application. As shown in FIG. 2, the method comprises the following steps.

[0080] 201. The terminal device sends AI / ML related request information to the network device.

[0081] 202. The terminal device receives monitoring configuration information and / or reporting configuration information sent by the network device.

[0082] 203. The terminal device reports AI / ML monitoring information to the network device.

[0083] 204. The terminal device receives first identification information related to AI / ML of the terminal device.

[0084] It is worth noting that the above FIG. 2 only schematically illustrates the embodiments of the present application, but the present application is not limited thereto. For example, the execution order between the operations can be appropriately adjusted, and in addition, some operations can be added or some operations can be reduced. Those skilled in the art can make appropriate modifications based on the above description, and the present application is not limited to the above FIG. 2.

[0085] In some embodiments, AI / ML can be a short form of AI / ML function / model, and the present application is not limited thereto, but can also be other terms. Functionality refers to AI / ML features / feature groups enabled by configuration, wherein the configuration is supported based on conditions indicated by UE capability.

[0086] For example, the AL / ML function can be one or more functions, or can be one or more logical models, or can be one or more sub-functions, or can be one or more features, or can be one or more feature groups.

[0087] For another example, the function can be spatial beam prediction using AI / ML, or can be time beam prediction using AI / ML, or can be CSI prediction using AI / ML, or can be direct positioning using AI / ML, or can be AI / ML assisted positioning, etc.

[0088] In some embodiments, AI / ML functionality / model can be used for beam management, etc. One or more reference signals are used for measurement and the measurement results are input to the AI / ML functionality / model, and another one or more reference signals are used for the output of the AI / ML functionality / model for inference.

[0089] In some embodiments, the monitoring configuration information can include configuration information of one or more reference signals, such as CSI-RS configuration information, etc. The present application is not limited thereto, and specific configuration information can also be referred to related technologies. Based on the monitoring configuration information, the network device can send reference signals for performance monitoring to the terminal device, and the terminal device can input the measurement results of these reference signals to AI / ML, thereby generating AI / ML monitoring information. In addition, the reporting configuration information can be used for the terminal device to report; the terminal device can report the AI / ML monitoring information to the network device according to the reporting configuration information.

[0090] FIG. 3 is a schematic diagram of AI / ML of an embodiment of the present application. As shown in FIG. 3, one or more reference signals in the second reference signal resource set (set B) can be received and measured by the terminal device, and the measurement results can be input to AI / ML, and one or more reference signals in the first reference signal resource set (set A) can be used by the terminal device for the output of AI / ML, for example, the measurement results as the label data or ground truth data of AI / ML. The specific content of AI / ML and set A and set B can be referred to related technologies, which will not be repeated here.

[0091] The above illustrates AI / ML in a schematic manner, and the present application is not limited thereto. The present application is further described below.

[0092] In some embodiments, the first identification information is related to the performance of AI / ML, and the first identification information indicates the availability of the AI / ML in the cell corresponding to the network device.

[0093] In some embodiments, the first identification information is related to the performance of AI / ML, and the first identification information indicates the availability of the AI / ML in the cell corresponding to the network device under a specific network assistance condition.

[0094] Regarding the first identification information in the embodiments of the present application, the identification information may be an identifier of the performance of the corresponding AI / ML model. For example, the identification information is determined by monitoring and / or evaluating the AI / ML model. Once the performance meets the requirements, the network device provides the terminal device with the identification information of the AI / ML model.

[0095] In some embodiments, the first identification information is associated with at least one of the following associated information: a global cell identifier (GCI), auxiliary condition information identification information of a network device, training data or data set identification information of the AI / ML, and model information of the AI / ML; the present application is not limited to this, and for example, other information may also be associated.

[0096] For example, a cell to which a network device belongs has a corresponding global cell identifier (Global Cell ID). This identifier is unique and can be obtained by a terminal device when connecting to the network device. The first identification information received by the terminal device from the network device can be associated with the global cell identifier (GCI). Table 1 shows an example of the association between the global cell identifier and the first identification information.

[0097] Table 1

[0098] For example, the association between the first identification information and the global cell identification indicates the availability of a certain model of the terminal device in this cell. For example, the first identification information is 1 bit, 0 indicates that the model is not available in the cell, and 1 indicates that the model is available in the cell.

[0099] For another example, the network device may send a reference signal for the model based on auxiliary conditions jointly established by the network and the terminal. Thus, for example, in addition to being associated with the GCI, the first identification information may also be associated with an identifier of the auxiliary condition of the network device. Table 2 shows an example of associating the first identification information with the global cell identifier and auxiliary condition information (e.g., auxiliary condition ID).

[0100] Table 2

[0101] For example, by associating the first identification information with the global cell identification and the auxiliary condition information, it is indicated that a certain model possessed by the terminal device is available under the auxiliary condition in the cell. For example, the first identification information is 1 bit, 0 indicates that the model is not available under the auxiliary condition in the cell, and 1 indicates that the model is available under the auxiliary condition in the cell.

[0102] For another example, the network device may send a reference signal for the model based on certain auxiliary conditions jointly established by the network device and the terminal device. Thus, for example, the first identification information may be associated with the identification of the auxiliary condition of the network device. Table 3 shows an example of the association between the first identification information and the auxiliary condition information.

[0103] Table 3

[0104] For example, the association between the first identification information and the auxiliary condition information indicates the availability of a certain model of the terminal device under the auxiliary condition. For example, the first identification information is 1 bit, 0 indicates that the model is not available under the auxiliary condition, and 1 indicates that the model is available under the auxiliary condition.

[0105] For another example, if the model identifier is reported when the model evaluation is reported, the first identifier information may also be associated with the model identifier. Table 4 shows an example of the association between the first identifier information and the model identifier.

[0106] Table 4

[0107] For example, the association between the first identification information and the model identification indicates the availability of a certain model of the terminal device. For example, the first identification information is 1 bit, 0 indicates that the model is unavailable, and 1 indicates that the model is available.

[0108] In some examples, the first identification information may also be associated with the identification information of the data or data set used for AI / ML training of the terminal device. In other examples, the first identification information may also be independent model identification information. Different models have different identifications. The specific naming method is determined by the network side, and the number of bits may be predefined. The identification information may be globally unique, or unique within an operator, or unique within a region, or unique within a cell. Table 5 shows an example of the first identification information.

[0109] Table 5

[0110] For example, the first identification information may be single-bit information corresponding to the model performance, such as 1 indicating that the model performance is available and 0 indicating that the model performance is unavailable. Alternatively, the first identification information may be multi-bit information corresponding to the performance, such as using multi-bit information to indicate the model performance.

[0111] In some embodiments, the association between the first identification information and the association information is stored on the network device side and / or the terminal device side; wherein, the first identification information is attached before or after the association information, or, the first identification information and the association information are coupled and marked, or, the first identification information is bound or mapped to the association information or other identification information.

[0112] For example, as shown in Tables 1 to 4, the first identification information can be attached to the information. For another example, the first identification information and the associated information can be coupled, for example, by performing a logical operation (XOR, XOR, etc.) and then marking them. For another example, the first identification information can be mapped to a certain information. This application is not limited to this.

[0113] In some embodiments, the first identification information is identification information for distinguishing different models configured based on the AI / ML monitoring information reported by the terminal device, including availability information of the AI / ML.

[0114] For example, the first identification information may also correspond to a network device auxiliary condition identifier, which may be partially derived from reports from the terminal device or may not rely on reports from the terminal device. The naming method may be determined by the network device or predefined, and the number of bits is determined by the predefined method. For example, it can be multi-bit information, indicating the network device auxiliary condition corresponding to good model monitoring and evaluation performance.

[0115] For another example, the first identification information may be information sent to the terminal device based on the results of a model-based online evaluation on the network side. The naming of the identification information may refer to the GCI, network auxiliary conditions, and the like. It may be generated by predefined rules through identification coupling based on the GCI, network auxiliary condition identifier, and the area ID of the network device. The network device and the terminal device have a common understanding of this coupling processing method.

[0116] It should be noted that the identifiers for network device auxiliary conditions in the above implementation examples can also be referred to as data identifiers or dataset identifiers. Alternatively, different air interface use cases may have specific names. For example, when collecting training data, auxiliary information sent by the network device to the terminal device, in addition to the data content, is typically used to label the data. Table 6 shows an example of the association between data and network device auxiliary conditions.

[0117] Table 6

[0118] In an embodiment of the present application, the first identification information may be a global identification, or an identification associated with a certain global identification, or an identification of a certain cell, or a local identification within a certain area; the present application is not limited thereto.

[0119] In some embodiments, the AI / ML monitoring information includes at least one of the following:

[0120] Identification information related to the AI / ML of the terminal device;

[0121] Auxiliary condition information of network devices related to the AI / ML of the terminal device;

[0122] Model serial number and / or quantity information related to the AI / ML of the terminal device;

[0123] Model performance information related to the AI / ML of the terminal device.

[0124] The above schematically illustrates various information of the embodiments of the present application, and the interaction is further explained below.

[0125] In some embodiments, the network device sends an AI / ML capability query to the terminal device; wherein the AI / ML-related request information is an AI / ML capability report fed back by the terminal device to the network device.

[0126] FIG4 is another schematic diagram of the information transmission method according to an embodiment of the present application. As shown in FIG4 , the method includes:

[0127] 401: The network device sends an AI / ML capability query to the terminal device;

[0128] 402. The terminal device feeds back an AI / ML capability report to the network device.

[0129] 403, the terminal device receives the monitoring configuration information and / or reporting configuration information sent by the network device;

[0130] For example, the network device may send a reference signal to the terminal device according to the monitoring configuration information, and the terminal device may receive the reference signal according to the monitoring configuration information. The present application is not limited thereto, for example, sending the reference signal is optional.

[0131] 404, the terminal device performs AI / ML performance monitoring;

[0132] For example, the terminal device can measure the reference signal and monitor the AI / ML performance based on the measurement results. For example, it can also select one or more AI / ML models with good performance and generate AI / ML monitoring information.

[0133] 405. The terminal device reports AI / ML monitoring information to the network device.

[0134] For example, the terminal device can report AI / ML monitoring information based on the reporting configuration information.

[0135] 406. The terminal device receives first identification information related to the AI / ML of the terminal device.

[0136] It is worth noting that FIG4 above is merely a schematic illustration of an embodiment of the present application, and the present application is not limited thereto. For example, the execution order of the various operations may be appropriately adjusted, and other operations may be added or some operations may be reduced. Those skilled in the art may make appropriate modifications based on the above description, and are not limited to the description of FIG4 above.

[0137] In some cases, the network device queries the terminal device for the AI / ML capabilities of the terminal. This capability query may be based on a specific function, such as beam management, positioning, CSI prediction, CSI compression, mobility prediction, etc. The terminal device reports its AI / ML capabilities.

[0138] To ensure AI / ML performance, network devices may send configuration information and performance requirements related to AI / ML performance evaluation and model selection to terminal devices. The terminal devices monitor the models and evaluate their performance, reporting the monitoring results (AI / ML monitoring information) to the network devices.

[0139] In some examples, the result information may be one or more of the following: identification information of the selected model, auxiliary condition information of the network device related to the selected model, and the model's sequence number, quantity, and performance information. Based on the received result information, the network device sends the first identification information of the AI / ML to the terminal device.

[0140] In some examples, the first identification information indicates that the performance of the selected AI / ML model has been tested and meets the requirements of the terminal device. For example, in addition to generally identifying the model, the identification information may also indicate that the performance of the selected model has been tested online on the network device and meets the performance requirements of the device.

[0141] In some embodiments, the AI / ML-related request information is an evaluation request sent by the terminal device to the network device; and the monitoring configuration information also includes evaluation configuration information.

[0142] FIG5 is another schematic diagram of the information transmission method according to an embodiment of the present application. As shown in FIG5 , the method includes:

[0143] 501, the terminal device sends an AI / ML monitoring and evaluation request to the network device;

[0144] 502, the terminal device receives monitoring configuration information and / or reporting configuration information sent by the network device;

[0145] For example, the network device may send a reference signal to the terminal device according to the monitoring configuration information (evaluation configuration information), and the terminal device may receive the reference signal according to the monitoring configuration information. The present application is not limited thereto, for example, sending the reference signal is optional.

[0146] 503, the terminal device performs AI / ML performance monitoring;

[0147] For example, the terminal device can measure the reference signal and monitor / evaluate the AI / ML performance based on the measurement results. For example, it can also select one or more AI / ML models with good performance and generate AI / ML monitoring information.

[0148] 504, the terminal device reports AI / ML monitoring information to the network device;

[0149] For example, the terminal device can report AI / ML monitoring information based on the reporting configuration information.

[0150] 505. The terminal device receives first identification information related to the AI / ML of the terminal device.

[0151] It is worth noting that FIG5 above is merely a schematic illustration of an embodiment of the present application, and the present application is not limited thereto. For example, the execution order of the various operations may be appropriately adjusted, and other operations may be added or some operations may be reduced. Those skilled in the art may make appropriate modifications based on the above description, and are not limited to the description of FIG5 above.

[0152] In some examples, the terminal device includes AI / ML functionality / models and sends a request to a network device for model performance monitoring or evaluation. Upon receiving the request, the network device configures reference signal resources and sends the configuration information and / or reporting information to the terminal device.

[0153] For example, the above information may also include performance indicator information related to performance monitoring or evaluation. The resource configuration information may also include monitoring or evaluation time information, reference signal period information, etc. The configuration information can be formulated based on the request information and the information obtained from the AI / ML capability report of the terminal device. The terminal device measures the reference signal based on the configuration information and starts the terminal-side AI / ML for performance monitoring or evaluation.

[0154] The terminal device selects one or more models that meet the performance requirements based on performance indicators configured by the network device, performance indicators developed by the terminal device, or predefined performance indicators. The terminal device reports AI / ML information related to the monitoring or evaluation results to the network device. Based on this information, the network device sends first identification information to the terminal device.

[0155] In some examples, the first identification information indicates that the performance of the selected AI / ML model has been tested and meets the performance requirements of the evaluation. For example, in addition to generally identifying the model, the identification information may also indicate that the performance of the selected model has been tested online on the network device and meets the performance requirements of the device.

[0156] FIG6 is another schematic diagram of the information transmission method according to an embodiment of the present application. As shown in FIG6 , the method includes:

[0157] 601, the terminal device sends an AI / ML monitoring and evaluation request to the network device;

[0158] 602. The terminal device receives monitoring configuration information and / or reporting configuration information sent by the network device;

[0159] 603. The terminal device receives a reference signal sent by the network device;

[0160] For example, the network device may send a reference signal to the terminal device according to the monitoring configuration information (evaluation configuration information), and the terminal device may receive the reference signal according to the monitoring configuration information.

[0161] 604, the terminal device performs AI / ML performance monitoring;

[0162] For example, the terminal device can measure the reference signal and monitor / evaluate the AI / ML performance based on the measurement results. For example, it can also select one or more AI / ML models with good performance and generate AI / ML monitoring information.

[0163] 605, the terminal device reports AI / ML monitoring information to the network device;

[0164] For example, the terminal device can report the AI / ML monitoring information according to the reporting configuration information.

[0165] 606, the terminal device receives first identification information related to AI / ML of the terminal device.

[0166] It is worth noting that the above Figure 6 only schematically illustrates the embodiments of the present application, but the present application is not limited thereto. For example, the execution order between the operations can be appropriately adjusted, and in addition, some operations can be added or some operations can be reduced. Those skilled in the art can make appropriate modifications according to the above description, and the above Figure 6 is not limited thereto.

[0167] In some embodiments, the network device sends a reference signal to the terminal device according to auxiliary condition information. In some embodiments, the first identification information includes or indicates the auxiliary condition information.

[0168] For example, the network device sends a reference signal based on a specific implementation corresponding to the network device auxiliary condition, such as the pattern of the beam, the down tilt angle of the transmission, the width of the beam, and the configuration of the radio frequency and antenna device. These network device radio frequency and antenna configuration parameters for model evaluation can be considered stable during evaluation. The network device itself can record the network device auxiliary condition used.

[0169] When the evaluation is completed, the network device receives the AI / ML information reported by the terminal device, and the network device sends the first identification information to the terminal device. At this time, the first identification information can also include the network device auxiliary condition information. The mapping of this auxiliary condition information to the first identification information is determined by the network device, and the corresponding bit number can be predefined.

[0170] In some embodiments, the network device explicitly or implicitly indicates the network device auxiliary condition information to the terminal device during the AI / ML data collection phase; and the AI / ML related request information is the network device auxiliary condition information corresponding to the AI / ML training.

[0171] Figure 7 is another schematic diagram of an information transmission method according to an embodiment of the present application. As shown in Figure 7, the method includes:

[0172] 701, the network device explicitly or implicitly indicates the network device auxiliary condition information to the terminal device during the AI / ML data collection phase;

[0173] 702, the network device receives the network device auxiliary condition information during the AI / ML running phase;

[0174] For example, the network device assistance condition information is a network device assistance condition ID (e.g., ID-1).

[0175] 703. The terminal device receives the monitoring configuration information and / or the reporting configuration information sent by the network device.

[0176] For example, the network device can send a reference signal to the terminal device according to the monitoring configuration information (evaluation configuration information), and the terminal device can receive the reference signal according to the monitoring configuration information. The present application is not limited thereto, for example, the sending of the reference signal is optional.

[0177] 704. The terminal device performs AI / ML performance monitoring.

[0178] For example, the terminal device can measure the reference signal, perform AI / ML performance monitoring / evaluation according to the measurement result, for example, can also select one or more AI / ML models with good performance, and generate AI / ML monitoring information.

[0179] 705. The terminal device reports the AI / ML monitoring information to the network device.

[0180] For example, the terminal device can report the AI / ML monitoring information according to the reporting configuration information.

[0181] 706. The terminal device receives first identification information related to AI / ML of the terminal device.

[0182] It is worth noting that the above Figure 7 only schematically illustrates the embodiments of the present application, but the present application is not limited thereto. For example, the execution order between the operations can be appropriately adjusted, and in addition, some operations can be added or some operations can be reduced. Those skilled in the art can make appropriate modifications based on the above content, and the present application is not limited to the above Figure 7.

[0183] In some examples, in the data collection phase related to AI / ML training, the network device provides network device assistance condition identification for sending training signals, such as identification corresponding to beam pattern, beam codebook identification, etc. The network device assistance condition identification can be one or more.

[0184] In the running phase of the AI / ML model of the terminal device, in order to maintain the data consistency between the training and running phases, the terminal device reports the known network device auxiliary condition identifier used for training, which can be one or more. In addition, it can also be reported together with the identifier of the model. After receiving the information, the network device configures the configuration information related to model evaluation or monitoring accordingly. Moreover, the network device sends the evaluation reference signal after adjusting according to the received auxiliary condition information. The terminal device evaluates or monitors the performance of the model according to the reference signal, and reports the evaluation result. The network device then sends the first identifier information to the terminal device.

[0185] In some examples, the first identifier information is performance confirmation information of the AI / ML running in the auxiliary condition of the network device, and / or the performance confirmation information of the AI / ML of the terminal device in the model evaluation process.

[0186] In some embodiments, the network device performs an LCM process based on the first identifier information.

[0187] FIG. 8 is another schematic diagram of an information transmission method according to an embodiment of the present application. As shown in FIG. 8, the method comprises:

[0188] 801, the network device and the terminal device perform AI / ML monitoring and evaluation. For details, please refer to the foregoing embodiments.

[0189] 802, the terminal device receives first identifier information related to the AI / ML of the terminal device.

[0190] 803, the network device and the terminal device perform an LCM process based on the first identifier information.

[0191] It is worth noting that the above FIG. 8 only schematically illustrates the embodiments of the present application, but the present application is not limited thereto. For example, the execution order between the operations can be appropriately adjusted, and in addition, some operations can be added or some operations can be reduced. Those skilled in the art can make appropriate modifications based on the above content, and the present application is not limited to the above FIG. 8.

[0192] In some examples, the network device and the terminal device perform online performance monitoring and evaluation of the AI / ML of the terminal device. Based on the evaluation result, after the network device sends the first identifier information to the terminal device, the terminal device and the network device start an LCM process based on the first identifier information. For example, activation, deactivation, switching, selection, etc. of the AI / ML model.

[0193] In some examples, the terminal device performs AI / ML operations, including one or any combination of the following:

[0194] activation, deactivation, fallback of AI / ML;

[0195] activation, deactivation of a certain model within AI / ML;

[0196] selection, switching of a model within AI / ML.

[0197] The above illustrates the case of AI / ML monitoring and sending the first identification information, but the present application is not limited thereto.

[0198] In some embodiments, the first identification information is sent to the terminal device based on model evaluation and / or model monitoring after AI / ML is activated.

[0199] FIG. 9 is another schematic diagram of an information transmission method according to an embodiment of the present application. As shown in FIG. 9, the method comprises:

[0200] 901, the network device and the terminal device activate AI / ML;

[0201] 902, the network device and the terminal device perform AI / ML monitoring evaluation; for details, refer to the foregoing embodiments.

[0202] 903, the terminal device receives first identification information related to AI / ML of the terminal device.

[0203] It is worth noting that the above FIG. 9 only illustrates the embodiments of the present application schematically, but the present application is not limited thereto. For example, the execution order between the operations can be adjusted appropriately, and in addition, some operations can be added or some operations can be reduced. Those skilled in the art can make appropriate modifications based on the above content, and the present application is not limited to the above FIG. 9. For example, FIG. 9 can be independent of FIG. 4 to FIG. 8 in the foregoing embodiments.

[0204] In some examples, model monitoring and evaluation can refer to model evaluation and model selection before AI / ML function or model activation, or can refer to model monitoring after AI / ML function or model activation. In FIG. 9, model selection is not required. The network device can determine whether the model performance meets the requirements based on the results of model monitoring after the AI / ML function or model runs for a period of time, and accordingly send the first identification information to the terminal device.

[0205] In some embodiments, the network device sends a query related to the first identification information to the terminal device; receives a report related to the first identification information from the terminal device, and activates / deactivates AI / ML according to the report.

[0206] FIG. 10 is another schematic diagram of the information transmission method of the embodiments of the present application, as shown in FIG. 10, the method comprises:

[0207] 1001, the network device sends a query of the related information of the first identification information to the terminal device;

[0208] 1002, the terminal device sends the related information of the first identification information to the network device.

[0209] 1003, the network device and the terminal device activate the AI / ML.

[0210] It is worth noting that the above FIG. 10 only schematically illustrates the embodiments of the present application, but the present application is not limited thereto. For example, the execution order between the operations can be appropriately adjusted, and in addition, some operations can be added or some operations can be reduced. Those skilled in the art can make appropriate modifications based on the above content, and the present application is not limited to the above FIG. 10. For example, FIG. 10 can be independent of FIGS. 4 to 9 in the foregoing embodiments.

[0211] In some examples, when the terminal device with the first identification information connects with the cell associated with the first identification information again, the AI / ML with the first identification information can be activated / deactivated, so that the required model evaluation and selection process can be reduced or avoided.

[0212] For example, the network device sends a query of the first identification information to the terminal device, and if the terminal device can report the corresponding first identification information, the network device can directly activate the AI / ML model or function. In this way, the AI / ML can be quickly activated with performance guarantee.

[0213] The embodiments of the present application can be applied to the terminal device side model (UE-side model) and can be applied to the network device side model (gNB-side model), and the present application is not limited thereto. In addition, the AI / ML of the embodiments of the present application can be used for beam management, such as time beam prediction and / or spatial beam prediction, and the present application is not limited thereto, for example, non-AI / ML can also be used for data collection.

[0214] The above various embodiments only exemplarily illustrate the embodiments of the present application, but the present application is not limited thereto, and appropriate modifications can be made on the basis of the above various embodiments. For example, the above various embodiments can be used independently, or one or more of the above various embodiments can be combined.

[0215] From the above embodiments, the terminal device receives the first identification information related to AI / ML from the network device. In this way, the consistency of data (signals) during AI / ML training and data (signals) encountered during use can be improved, and the performance and efficiency of AI / ML can be improved.

[0216] Embodiments of the second aspect

[0217] Embodiments of the present application provide an information transmission method, which is described from the network device side. Embodiments of the second aspect can be combined with embodiments of the first aspect, and the same content as that of the first aspect will not be described again.

[0218] FIG. 11 is a schematic diagram of an information transmission method according to an embodiment of the present application. As shown in FIG. 11, the method comprises:

[0219] 1101, the network device receives request information related to AI / ML from the terminal device;

[0220] 1102, the network device sends monitoring configuration information and / or reporting configuration information to the terminal device;

[0221] 1103, the network device receives AI / ML monitoring information reported by the terminal device;

[0222] 1104, the network device sends first identification information related to AI / ML of the terminal device to the terminal device.

[0223] It is worth noting that the above FIG. 11 only schematically illustrates the embodiments of the present application, but the present application is not limited thereto. For example, the execution order between the operations can be appropriately adjusted, and in addition, some operations can be added or some operations can be reduced. Those skilled in the art can make appropriate modifications based on the above content, and the present application is not limited to the above FIG. 11.

[0224] In some embodiments, the first identification information is related to the performance of AI / ML, and the first identification information indicates the availability of the AI / ML in the cell corresponding to the network device.

[0225] In some embodiments, the first identification information is related to the performance of AI / ML, and the first identification information indicates the availability of the AI / ML in the cell corresponding to the network device under a specific network assistance condition.

[0226] In some embodiments, the AI / ML monitoring information comprises at least one of:

[0227] Identification information related to AI / ML of the terminal device;

[0228] auxiliary condition information of the network device related to AI / ML of the terminal device;

[0229] model serial number and / or quantity information related to AI / ML of the terminal device;

[0230] model performance information related to AI / ML of the terminal device.

[0231] In some embodiments, the network device sends an AI / ML capability query to the terminal device.

[0232] The AI / ML related request information is AI / ML capability report fed back by the terminal device to the network device.

[0233] In some embodiments, the first identification information indicates that the performance of the selected AI / ML has been tested and meets the requirements of the terminal device.

[0234] In some embodiments, the AI / ML related request information is an evaluation request sent by the terminal device to the network device.

[0235] The monitoring configuration information further includes evaluation configuration information.

[0236] In some embodiments, the first identification information indicates that the performance of the selected AI / ML has been tested and meets the evaluation performance requirements.

[0237] In some embodiments, the network device sends a reference signal to the terminal device according to the auxiliary condition information.

[0238] In some embodiments, the first identification information includes or indicates the auxiliary condition information.

[0239] In some embodiments, the network device explicitly or implicitly indicates the auxiliary condition information of the network device to the terminal device in the AI / ML data collection phase.

[0240] The AI / ML related request information is the auxiliary condition information of the network device corresponding to the AI / ML training.

[0241] In some embodiments, the first identification information is performance confirmation information of the AI / ML running under the auxiliary conditions of the network device, and / or performance confirmation information of the AI / ML in the model evaluation process reported by the terminal device.

[0242] In some embodiments, the network device sends the first identification information to the terminal device based on model evaluation and / or model monitoring.

[0243] And / or, the network device performs an LCM procedure based on the first identification information.

[0244] In some embodiments, the network device further sends a query related to the first identification information to the terminal device; receives a report related to the first identification information of the terminal device, and activates / deactivates the AI / ML according to the report.

[0245] In some embodiments, the first identification information is associated with the following at least one of the associated information:

[0246] Global Cell Identity (GCI),

[0247] auxiliary condition information identification information of the network device,

[0248] training data or data set identification information of the AI / ML,

[0249] model information of the AI / ML.

[0250] In some embodiments, the association between the first identification information and the associated information is stored at the network device side and / or the terminal device side.

[0251] Wherein, the first identification information is attached before or after the associated information, or the first identification information is coupled with the associated information, or the first identification information is bound or mapped with the associated information or other identification information.

[0252] In some embodiments, the first identification information is the identification information of different models configured according to the AI / ML monitoring information reported by the terminal device, which includes the availability information of the AI / ML.

[0253] The above various embodiments are only exemplarily described, but the present application is not limited thereto, and appropriate modifications can be made on the basis of the above various embodiments. For example, each of the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0254] As can be seen from the above embodiments, the terminal device receives the first identification information related to the AI / ML from the network device. Therefore, the consistency of the data (signals) during AI / ML training and the data (signals) encountered during use can be improved, and the performance and efficiency of the AI / ML can be improved.

[0255] Embodiments of the third aspect

[0256] The embodiment of the present application provides an information transmission device. The device can be a terminal device, or can be one or more components or assemblies arranged in the terminal device. The same content as the embodiment of the first and second aspects will not be described again.

[0257] FIG. 12 is a schematic diagram of the information transmission device according to the embodiment of the present application. As shown in FIG. 12, the information transmission device 1200 according to the embodiment of the present application includes a sending unit 1201 and a receiving unit 1202.

[0258] The sending unit 1201 sends AI / ML related request information to a network device; and

[0259] The receiving unit 1202 receives monitoring configuration information and / or reporting configuration information sent by the network device.

[0260] The sending unit 1201 further reports AI / ML monitoring information to the network device.

[0261] The receiving unit 1202 further receives first identification information related to AI / ML of the terminal device.

[0262] In some embodiments, as shown in FIG. 12, the device can further include:

[0263] A processing unit 1203 generates the AI / ML monitoring information according to a reference signal and based on AI / ML.

[0264] The above various embodiments are only exemplary descriptions of the embodiments of the present application, but the present application is not limited thereto, and can be appropriately modified on the basis of the above various embodiments. For example, the above various embodiments can be used alone, or one or more of the above various embodiments can be combined.

[0265] It should be noted that the above only describes the components or modules related to the present application, but the present application is not limited thereto. The information transmission device 1200 can further include other components or modules, and the specific content of these components or modules can be referred to the related art.

[0266] In addition, for the sake of simplicity, only the connection relationship or signal path between the components or modules is exemplarily shown in FIG. 12, but it should be clear to those skilled in the art that various related technologies such as bus connection can be used. The above various components or modules can be realized by hardware facilities such as processors, memories, transmitters, receivers, etc.; the present application is not limited thereto.

[0267] From the above embodiments, the terminal device receives the first identification information related to AI / ML from the network device. In this way, the consistency of data (signals) during AI / ML training and data (signals) encountered during use can be improved, and the performance and efficiency of AI / ML can be improved.

[0268] Embodiments of the fourth aspect

[0269] Embodiments of the present application provide an information transmission device. The device may, for example, be a network device, or one or more components or components configured in the network device. The same content as the embodiments of the first to third aspects will not be repeated.

[0270] FIG. 13 is another schematic diagram of an information transmission device according to an embodiment of the present application. As shown in FIG. 13, the information transmission device 1300 includes a receiving unit 1301 and a sending unit 1302.

[0271] The receiving unit 1301 receives request information related to AI / ML from the terminal device; and

[0272] The sending unit 1302 sends monitoring configuration information and / or reporting configuration information to the terminal device;

[0273] The receiving unit 1301 also receives AI / ML monitoring information reported by the terminal device;

[0274] The sending unit 1302 also sends first identification information related to AI / ML of the terminal device to the terminal device.

[0275] In some embodiments, the first identification information is related to the performance of AI / ML, and the first identification information indicates the availability of the AI / ML in the cell corresponding to the network device.

[0276] In some embodiments, the first identification information is related to the performance of AI / ML, and the first identification information indicates the availability of the AI / ML in the cell corresponding to the network device under a specific network assistance condition.

[0277] In some embodiments, the AI / ML monitoring information includes at least one of the following:

[0278] Identification information related to AI / ML of the terminal device;

[0279] Assistance condition information of the network device related to AI / ML of the terminal device;

[0280] Model serial number and / or quantity information related to AI / ML of the terminal device;

[0281] Model performance information related to AI / ML of the terminal device.

[0282] In some embodiments, the sending unit 1302 further sends AI / ML capability query to the terminal device.

[0283] The AI / ML related request information is AI / ML capability report fed back by the terminal device to the network device.

[0284] In some embodiments, the first identification information indicates that the performance of the selected AI / ML has been tested and meets the needs of the terminal device.

[0285] In some embodiments, the AI / ML related request information is an evaluation request sent by the terminal device to the network device.

[0286] The monitoring configuration information further includes evaluation configuration information.

[0287] In some embodiments, the first identification information indicates that the performance of the selected AI / ML has been tested and meets the evaluation performance needs.

[0288] In some embodiments, the sending unit 1302 further sends reference signal to the terminal device according to auxiliary condition information.

[0289] In some embodiments, the first identification information includes or indicates the auxiliary condition information.

[0290] In some embodiments, the sending unit 1302 explicitly or implicitly indicates the auxiliary condition information of the network device to the terminal device in the AI / ML data collection stage.

[0291] The AI / ML related request information is the auxiliary condition information of the network device corresponding to the AI / ML training.

[0292] In some embodiments, the first identification information is performance confirmation information of the AI / ML running in the auxiliary condition of the network device, and / or performance confirmation information of the AI / ML in the model evaluation process reported by the terminal device.

[0293] In some embodiments, the sending unit 1302 sends the first identification information to the terminal device based on model evaluation and / or model monitoring.

[0294] And / or, the network device performs LCM process based on the first identification information.

[0295] In some embodiments, the sending unit 1302 further sends a query related to the first identification information to the terminal device.

[0296] The receiving unit 1301 further receives a report related to the first identification information of the terminal device, and activates / deactivates the AI / ML according to the report.

[0297] In some embodiments, the first identification information is associated with the association information of at least one of the following:

[0298] a global cell identity (GCI),

[0299] auxiliary condition information identification information of the network device,

[0300] training data or data set identification information of the AI / ML,

[0301] model information of the AI / ML.

[0302] In some embodiments, the association between the first identification information and the association information is stored at the network device side and / or the terminal device side.

[0303] The first identification information is attached before or after the association information, or the first identification information is coupled with the association information, or the first identification information is bound or mapped with the association information or other identification information.

[0304] In some embodiments, the first identification information is identification information of different models configured according to the AI / ML monitoring information reported by the terminal device, which includes availability information of the AI / ML.

[0305] The above various embodiments are only exemplarily described, but the present application is not limited thereto, and appropriate variations can be made on the basis of the above various embodiments. For example, each of the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0306] It is worth noting that the above only describes each component or module related to the present application, but the present application is not limited thereto. The information transmission device 1300 can further include other components or modules, and the specific content of these components or modules can be referred to the related technology.

[0307] In addition, for the sake of simplicity, only the connection relationship or signal direction between each component or module is exemplarily shown in FIG. 13, but it should be clear to those skilled in the art that various related technologies such as bus connection can be adopted. Each component or module described above can be implemented by hardware facilities such as processors, memories, transmitters, receivers, etc.; the implementation of the present application is not limited thereto.

[0308] As can be seen from the above embodiments, the terminal device receives the first identification information related to AI / ML from the network device. In this way, the consistency of data (signals) during AI / ML training and data (signals) encountered during use can be improved, and the performance and efficiency of AI / ML can be improved.

[0309] Embodiments of the fifth aspect

[0310] The embodiments of the present application also provide a communication system, which can be referred to FIG. 1, and the same content as the embodiments of the first to fourth aspects will not be described herein.

[0311] In some embodiments, the communication system 100 can at least include:

[0312] The network device receives the request information related to AI / ML from the terminal device, sends the monitoring configuration information and / or the reporting configuration information to the terminal device, receives the AI / ML monitoring information reported by the terminal device, and sends the first identification information related to the AI / ML of the terminal device to the terminal device.

[0313] The terminal device sends the request information related to AI / ML to the network device, receives the monitoring configuration information and / or the reporting configuration information sent by the network device, reports the AI / ML monitoring information to the network device, and receives the first identification information related to the AI / ML of the terminal device.

[0314] The embodiments of the present application also provide a terminal device, but the present application is not limited thereto, and other devices can also be used.

[0315] FIG. 14 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in FIG. 14, the terminal device 1400 can include a processor 1410 and a memory 1420; the memory 820 stores data and programs and is coupled to the processor 1410. It is worth noting that this figure is exemplary; other types of structures can also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0316] For example, the processor 1410 can be configured to execute a program to implement the information transmission method as described in the embodiments of the first aspect. For example, the processor 1410 can be configured to perform the following control: sending, to a network device, request information related to AI / ML, receiving monitoring configuration information and / or reporting configuration information sent by the network device, reporting AI / ML monitoring information to the network device, and receiving first identification information related to AI / ML of the terminal device.

[0317] As shown in FIG. 14, the terminal device 1400 can further include a communication module 1430, an input unit 1440, a display 1450, and a power supply 1460. The functions of the above components are similar to those of the prior art, and will not be described here. It should be noted that the terminal device 1400 does not necessarily include all the components shown in FIG. 14, and the above components are not essential; in addition, the terminal device 1400 can also include components not shown in FIG. 14, which can be referred to the prior art.

[0318] The embodiments of the present application also provide a network device, which can be a base station for example, but the present application is not limited thereto, and can also be other network devices.

[0319] FIG. 15 is a schematic diagram of the network device according to the embodiments of the present application. As shown in FIG. 15, the network device 1500 can include a processor 1510 (such as a central processing unit CPU) and a memory 1520; the memory 1520 is coupled to the processor 1510. The memory 1520 can store various data; in addition, it also stores a program 1530 for information processing, and executes the program 1530 under the control of the processor 1510.

[0320] For example, the processor 1510 can be configured to execute a program to implement the information transmission method as described in the embodiments of the second aspect. For example, the processor 1510 can be configured to perform the following control: receiving request information related to AI / ML from a terminal device, sending monitoring configuration information and / or reporting configuration information to the terminal device, receiving AI / ML monitoring information reported by the terminal device, and sending first identification information related to AI / ML of the terminal device to the terminal device.

[0321] In addition, as shown in FIG. 15, the network device 1500 can further include a transceiver 1540, an antenna 1550, and the like; the functions of the above components are similar to those of the prior art, and will not be described here. It should be noted that the network device 1500 does not necessarily include all the components shown in FIG. 15; in addition, the network device 1500 can also include components not shown in FIG. 15, which can be referred to the prior art.

[0322] The embodiment of the present application further provides a computer program, wherein when the program is executed in a terminal device, the program enables the terminal device to perform the information transmission method in the embodiment of the first aspect.

[0323] The embodiment of the present application further provides a storage medium storing a computer program, wherein the computer program enables a terminal device to perform the information transmission method in the embodiment of the first aspect.

[0324] The embodiment of the present application further provides a computer program, wherein when the program is executed in a network device, the program enables the network device to perform the information transmission method in the embodiment of the second aspect.

[0325] The embodiment of the present application further provides a storage medium storing a computer program, wherein the computer program enables a network device to perform the information transmission method in the embodiment of the second aspect.

[0326] The apparatus and method described above can be implemented by hardware, or by hardware in combination with software. The present application relates to a computer readable program, which, when executed by a logic component, enables the logic component to implement the apparatus or constituent components described above, or enables the logic component to implement the various methods or steps described above. The present application also relates to a storage medium for storing the above program, such as a hard disk, a magnetic disk, an optical disk, a DVD, a flash memory, etc.

[0327] The method / apparatus described in combination with the embodiments of the present application can be directly embodied as hardware, a software module executed by a processor, or a combination of the two. For example, one or more of the functional block diagrams shown in the figures and / or a combination of one or more of the functional block diagrams can correspond to each software module of a computer program flow, or can correspond to each hardware module. The software modules can respectively correspond to each step shown in the figures. These hardware modules can be implemented by, for example, fixing the software modules with a field programmable gate array (FPGA).

[0328] The software modules can reside in RAM, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. The storage medium can be coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. The software modules can be stored in a memory of the mobile terminal, or in a memory card that can be inserted into the mobile terminal. For example, if the device (e.g., mobile terminal) is a MEGA-SIM card or a large capacity flash memory device, the software modules can be stored in the MEGA-SIM card or the large capacity flash memory device.

[0329] One or more of the functional blocks described in the accompanying drawings and / or one or more combinations of the functional blocks can be implemented as a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any appropriate combination thereof, for performing the functions described in this application. One or more of the functional blocks described in the accompanying drawings and / or one or more combinations of the functional blocks can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0330] The application has been described above with the attachment to the specific embodiments, but it should be clear to those skilled in the art that these descriptions are exemplary and are not a limitation on the scope of protection of the application. Those skilled in the art can make various modifications and changes to the application according to the spirit and principles of the application, and these modifications and changes are also within the scope of the application.

[0331] With regard to the embodiments including the above embodiments, the following notes are also disclosed:

[0332] 1. An information transmission method, comprising:

[0333] A terminal device sends AI / ML related request information to a network device;

[0334] The terminal device receives monitoring configuration information and / or reporting configuration information sent by the network device;

[0335] The terminal device reports AI / ML monitoring information to the network device; and

[0336] The terminal device receives first identification information related to the AI / ML of the terminal device.

[0337] 2. An information transmission method, comprising:

[0338] receiving, by a network device, AI / ML related request information from a terminal device; and

[0339] sending, by the network device, monitoring configuration information and / or reporting configuration information to the terminal device;

[0340] receiving, by the network device, AI / ML monitoring information reported by the terminal device;

[0341] sending, by the network device, first identification information related to AI / ML of the terminal device to the terminal device.

[0342] 3. A terminal device comprising a memory and a processor, the memory storing a computer program, and the processor being configured to execute the computer program to implement the information transmission method of the appended note 1.

[0343] 4. A network device comprising a memory and a processor, the memory storing a computer program, and the processor being configured to execute the computer program to implement the information transmission method of the appended note 2.

[0344] 5. A computer program product comprising at least a computer program, the computer program being executed by a processor to cause a terminal device to execute the information transmission method of the appended note 1.

[0345] 6. A computer program product comprising at least a computer program, the computer program being executed by a processor to cause a network device to execute the information transmission method of the appended note 2.

Claims

1. An information transmission apparatus configured in a network device, comprising: a receiving unit configured to receive AI / ML related request information from a terminal device; and a sending unit configured to send monitoring configuration information and / or reporting configuration information to the terminal device; the receiving unit is further configured to receive AI / ML monitoring information reported by the terminal device; the sending unit is further configured to send first identification information related to AI / ML of the terminal device to the terminal device.

2. The apparatus of claim 1, wherein, The first identification information is related to the performance of AI / ML, and the first identification information indicates the availability of the AI / ML in a cell corresponding to the network device.

3. The apparatus of claim 1, wherein, The first identification information is related to the performance of AI / ML, and the first identification information indicates the availability of the AI / ML in a cell corresponding to the network device under specific network assistance conditions.

4. The apparatus of claim 1, wherein, The AI / ML monitoring information includes at least one of: identification information related to AI / ML of the terminal device; assistance condition information of the network device related to AI / ML of the terminal device; model serial number and / or quantity information related to AI / ML of the terminal device; model performance information related to AI / ML of the terminal device. 5.The apparatus of claim 1, wherein the sending unit is further configured to send AI / ML capability query to the terminal device; wherein the AI / ML related request information is AI / ML capability reporting feedback from the terminal device to the network device.

6. The apparatus of claim 5, wherein, The first identification information indicates that the performance of the selected AI / ML has been tested and meets the needs of the terminal device. 7.The apparatus of claim 1, wherein the AI / ML related request information is an evaluation request sent by the terminal device to the network device; the monitoring configuration information further includes evaluation configuration information.

8. The apparatus of claim 7, wherein, The first identification information indicates that the performance of the selected AI / ML has been tested and meets the evaluation performance requirements. 9.The apparatus of claim 7, wherein the sending unit is further configured to send reference signals to the terminal device according to assistance condition information.

10. The apparatus of claim 9, wherein, The first identification information includes or indicates the assistance condition information. 11.The apparatus of claim 1, wherein the sending unit is configured to explicitly or implicitly indicate assistance condition information of the network device to the terminal device in an AI / ML data collection phase; the AI / ML related request information is assistance condition information of the network device corresponding to the AI / ML training.

12. The apparatus of claim 11, wherein, The first identification information is performance confirmation information of the AI / ML running under the assistance conditions of the network device, and / or performance confirmation information of the AI / ML reported by the terminal device in the model evaluation process. 13.The apparatus of claim 1, wherein the sending unit is configured to send the first identification information to the terminal device based on model evaluation and / or model monitoring; and / or the network device performs LCM process based on the first identification information. 14.The apparatus of claim 1, wherein The sending unit also sends a query related to the first identification information to the terminal device; The receiving unit also receives a report related to the first identification information of the terminal device, and activates / deactivates the AI / ML according to the report.

15. The apparatus of claim 1, wherein, The first identification information is associated with the association information of at least one of the following: Global cell identifier, Auxiliary condition information identifier of network device, Training data or data set identifier of the AI / ML, Model information of the AI / ML.

16. The apparatus of claim 15, wherein, The association between the first identification information and the association information is stored on the network device side and / or the terminal device side; Wherein, the first identification information is attached before or after the association information, or the first identification information is coupled with the association information, or the first identification information is bound or mapped with the association information or other identification information.

17. The apparatus of claim 15, wherein, The first identification information is the identification information of different models configured according to the AI / ML monitoring information reported by the terminal device, which includes the availability information of the AI / ML.

18. An information transmission device configured in a terminal device, the information transmission device comprising: a sending unit configured to send request information related to AI / ML to a network device; and a receiving unit configured to receive monitoring configuration information and / or reporting configuration information sent by the network device; The sending unit also reports AI / ML monitoring information to the network device; The receiving unit also receives first identification information related to the AI / ML of the terminal device.

19. The apparatus of claim 18, wherein, The receiving unit also receives a reference signal from the network device; the device further comprises: a processing unit configured to generate the AI / ML monitoring information based on the reference signal and AI / ML.

20. A communication system comprising: a network device configured to receive request information related to AI / ML from a terminal device, send monitoring configuration information and / or reporting configuration information to the terminal device, receive AI / ML monitoring information reported by the terminal device, and send first identification information related to the AI / ML of the terminal device to the terminal device; a terminal device configured to send request information related to AI / ML to a network device, receive monitoring configuration information and / or reporting configuration information sent by the network device, report AI / ML monitoring information to the network device, and receive first identification information related to the AI / ML of the terminal device.

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