Performance monitoring method and device

Abstract

The embodiment of the invention provides a performance monitoring method and device. The method comprises: a terminal device receives a first configuration sent by a network device; the terminal equipment monitors the performance of an AI / ML model of which the function is enabled or activated according to the first configuration, wherein the function comprises a feature or a feature group enabled by the configuration indicated by the capability of the terminal equipment; and the terminal equipment sends performance information corresponding to the function to the network equipment.

Description

Performance monitoring method and device Technical Field

[0001] The embodiments of the present application relate to the field of communication technologies. Background Art

[0002] NR Release 18 studies AI / ML over the air interface. AI / ML can be used for the following use cases: CSI feedback enhancement, beam management, and positioning enhancement. CSI feedback enhancement can include CSI prediction and CSI compression; beam management can include spatial beam prediction and temporal beam prediction; and positioning enhancement can include direct positioning and AI / ML-assisted positioning.

[0003] In some sub-use cases, a bilateral model can be used, with the AI / ML model located on both the terminal device side and the network device side. For example, CSI compression can be a representative use case for a bilateral model. In other sub-use cases, a unilateral model can be used, with the AI / ML model located either on the terminal device side or the network device side.

[0004] It should be noted that the above introduction to the technical background is merely intended to provide a clear and complete description of the technical solutions of this application and facilitate understanding by those skilled in the art. Simply because these solutions are described in the background technology section of this application, it should not be assumed that the above technical solutions are well known to those skilled in the art.

[0005] Summary of the Invention

[0006] The inventors discovered that the performance of AI / ML models located on the terminal device side should be monitored by the terminal device. However, there is currently no clear and accurate solution for how to monitor this.

[0007] To address at least one of the above problems, embodiments of the present application provide a performance monitoring method and apparatus.

[0008] According to one aspect of an embodiment of the present application, a performance monitoring method is provided, including:

[0009] The terminal device receives the first configuration sent by the network device;

[0010] monitoring the performance of an AI / ML model of an enabled or activated function according to the first configuration, the function comprising a feature or feature group enabled by the configuration indicated by the terminal device capability; and

[0011] Sending performance information corresponding to the function to the network device.

[0012] According to another aspect of an embodiment of the present application, a performance monitoring device is provided, including:

[0013] a receiving unit configured to receive a first configuration sent by a network device;

[0014] a monitoring unit configured to monitor, according to the first configuration, a performance of an AI / ML model of an enabled or activated function, the function comprising a feature or feature group enabled by the configuration indicated by the terminal device capability; and

[0015] A sending unit is configured to send performance information corresponding to the function to the network device.

[0016] According to another aspect of an embodiment of the present application, a performance monitoring method is provided, including:

[0017] The network device sends a first configuration to the terminal device; wherein the terminal device monitors the performance of the AI / ML model of an enabled or activated function according to the first configuration, the function including a feature or feature group enabled by the configuration indicated by the terminal device capability; and

[0018] The network device receives the performance information corresponding to the function sent by the terminal device.

[0019] According to another aspect of an embodiment of the present application, a performance monitoring device is provided, including:

[0020] a sending unit configured to send a first configuration to a terminal device; wherein the terminal device monitors the performance of an AI / ML model of an enabled or activated function according to the first configuration, the function including a feature or feature group enabled by the configuration indicated by the terminal device capability; and

[0021] A receiving unit receives performance information corresponding to the function sent by the terminal device.

[0022] According to another aspect of an embodiment of the present application, a communication system is provided, including:

[0023] A network device that sends a first configuration to a terminal device;

[0024] A terminal device that monitors the performance of an AI / ML model of an enabled or activated function according to the first configuration, where the function includes a feature or feature group enabled by the configuration indicated by the terminal device capability; and sends performance information corresponding to the function to the network device.

[0025] One of the beneficial effects of the embodiments of the present application is that it can accurately monitor the AI / ML model located on the terminal device side, thereby improving the accuracy and reliability of AI / ML.

[0026] With reference to the following description and accompanying drawings, specific embodiments of the present application are disclosed in detail, indicating the manner in which the principles of the present application can be employed. It should be understood that the embodiments of the present application are not limited in scope. Within the spirit and scope of the appended claims, the embodiments of the present application include many variations, modifications and equivalents.

[0027] Features described and / or illustrated with respect to one embodiment may be used in the same or similar manner in one or more other embodiments, combined with features in other embodiments, or substituted for features in other embodiments.

[0028] It should be emphasized that the term "include / comprising" when used herein refers to the presence of features, integers, steps or components, but does not exclude the presence or addition of one or more other features, integers, steps or components. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The elements and features described in one figure or one embodiment of the present application can be combined with the elements and features shown in one or more other figures or embodiments. In addition, in the accompanying drawings, similar reference numerals represent corresponding parts in several figures and can be used to indicate corresponding parts used in more than one embodiment.

[0030] FIG1 is a schematic diagram of a communication system according to an embodiment of the present application;

[0031] FIG2 is a schematic diagram of a performance monitoring method according to an embodiment of the present application;

[0032] FIG3 is a schematic diagram of performance monitoring of a function according to an embodiment of the present application;

[0033] FIG4 is an example diagram of performance information reporting according to an embodiment of the present application;

[0034] FIG5 is another example diagram of performance information reporting according to an embodiment of the present application;

[0035] FIG6 is another example diagram of performance information reporting according to an embodiment of the present application;

[0036] FIG7 is a schematic diagram of a performance monitoring method according to an embodiment of the present application;

[0037] FIG8 is a schematic diagram of a performance monitoring device according to an embodiment of the present application;

[0038] FIG9 is a schematic diagram of a performance monitoring device according to an embodiment of the present application;

[0039] FIG10 is a schematic diagram of a network device according to an embodiment of the present application;

[0040] FIG11 is a schematic diagram of a terminal device according to an embodiment of the present application. DETAILED DESCRIPTION

[0041] The above and other features of the present application will become apparent through the following description with reference to the accompanying drawings. In the description and the accompanying drawings, specific embodiments of the present application are disclosed in detail, which illustrate some embodiments in which the principles of the present application can be adopted. It should be understood that the present application is not limited to the described embodiments. On the contrary, the present application includes all modifications, variations and equivalents that fall within the scope of the appended claims.

[0042] In the embodiments of the present application, the terms "first", "second", etc. are used to distinguish different elements from the name, but do not indicate the spatial arrangement or temporal order of these elements, and these elements should not be limited by these terms. The term "and / or" includes any one and all combinations of one or more of the associated listed terms. The terms "comprising", "including", "having", etc. refer to the presence of the stated features, elements, components or components, but do not exclude the presence or addition of one or more other features, elements, components or components.

[0043] In the embodiments of this application, the singular forms "a," "the," etc. include plural forms and should be broadly understood to mean "a" or "a type" rather than being limited to "one." Furthermore, the term "said" should be understood to include both singular and plural forms, unless the context clearly indicates otherwise. Furthermore, the term "according to" should be understood to mean "at least in part based on...", and the term "based on" should be understood to mean "at least in part based on...", unless the context clearly indicates otherwise.

[0044] In the embodiments of the present application, the term "communication network" or "wireless communication network" may refer to a network that complies with any of the following communication standards, such as Long Term Evolution (LTE), enhanced Long Term Evolution (LTE-A, LTE-Advanced), Wideband Code Division Multiple Access (WCDMA), High-Speed ​​Packet Access (HSPA), etc.

[0045] Furthermore, communication between devices in the communication system may be carried out according to communication protocols of any stage, for example, including but 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, etc., and / or other communication protocols currently known or to be developed in the future.

[0046] In the embodiments of the present application, the term "network device" refers to, for example, a device in a communication system that connects a terminal device to the communication network and provides services to the terminal device. Network devices may include, but are 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), etc.

[0047] Among them, base stations may include but are not limited to: NodeB (NodeB or NB), evolved NodeB (eNodeB or eNB) and 5G base station (gNB), IAB host, etc., and may also include remote radio head (RRH, Remote Radio Head), remote radio unit (RRU, Remote Radio Unit), relay (relay) or low-power node (such as femeto, pico, etc.). The term "base station" can include some or all of their functions. Each base station can provide communication coverage for a specific geographical area. The term "cell" can refer to a base station and / or its coverage area, depending on the context in which the term is used.

[0048] In the 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. A terminal device can be fixed or mobile and may also be referred to as a mobile station (MS), a terminal, a subscriber station (SS), an access terminal (AT), a station, and so on.

[0049] Among them, terminal devices may include but are not limited to the following devices: cellular phones, personal digital assistants (PDAs), wireless modems, wireless communication devices, handheld devices, machine-type communication devices, laptop computers, cordless phones, smart phones, smart watches, digital cameras, etc.

[0050] For another example, in scenarios such as the Internet of Things (IoT), the terminal device can also be a machine or device for monitoring or measurement, including but not limited to: machine type communication (MTC) terminal, vehicle-mounted communication terminal, device-to-device (D2D) terminal, machine-to-machine (M2M) terminal, and so on.

[0051] In addition, the term "network side" or "network device side" refers to one side of the network, which can be a base station or one or more network devices as described above. The term "user side" or "terminal side" or "terminal device side" refers to the user or terminal side, which can be a UE or one or more terminal devices as described above. Unless otherwise specified herein, "device" can refer to either network equipment or terminal equipment.

[0052] The following describes the scenarios of the embodiments of the present application through examples, but the present application is not limited thereto.

[0053] FIG1 is a schematic diagram of a communication system according to an embodiment of the present application, schematically illustrating a situation using a terminal device and a network device as an example. As shown in FIG1 , a communication system 100 may include a network device 101 and terminal devices 102 and 103. For simplicity, FIG1 illustrates only two terminal devices and one network device as an example, but the embodiments of the present application are not limited thereto.

[0054] In the embodiment of the present application, existing services or future services can be transmitted between the network device 101 and the terminal devices 102 and 103. For example, these services may include but are not limited to: enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra-reliable and low-latency communication (URLLC), etc.

[0055] It is worth noting that FIG1 shows that both terminal devices 102 and 103 are within the coverage range of network device 101, but the present application is not limited thereto. Both terminal devices 102 and 103 may not be within the coverage range of network device 101, or one terminal device 102 may be within the coverage range of network device 101 while the other terminal device 103 is outside the coverage range of network device 101.

[0056] In the embodiments of the present application, the high-layer signaling may be, for example, radio resource control (RRC) signaling; for example, an RRC message, including, for example, an MIB, system information, or a dedicated RRC message; or an RRC information element (RRC IE). The high-layer signaling may also be, for example, MAC (Medium Access Control) signaling; or a MAC control element (MAC CE). However, the present application is not limited thereto.

[0057] In embodiments of the present application, one or more AI / ML models may be configured and run in a network device and / or terminal device. The AI / ML models may be used for various signal processing functions in wireless communications, such as CSI prediction, CSI compression, beamforming, positioning management, and the like; however, the present application is not limited thereto.

[0058] Embodiments of the first aspect

[0059] The embodiment of the present application provides a performance monitoring method. FIG2 is a schematic diagram of the performance monitoring method of the embodiment of the present application. As shown in FIG2 , the method includes:

[0060] 201. A terminal device receives a first configuration sent by a network device.

[0061] 202, the terminal device monitors the performance of an AI / ML model of an enabled or activated function according to a first configuration, wherein the function includes a feature or feature group enabled by the configuration indicated by the terminal device capability; and

[0062] 203. The terminal device sends performance information corresponding to the function to the network device.

[0063] It is worth noting that FIG2 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 FIG2 above.

[0064] In some embodiments, functionality refers to an AI / ML feature / feature group enabled by a configuration, where the configuration is supported based on conditions indicated by UE capabilities.

[0065] For example, the AL / ML function may be one or more functions, or one or more logical models, or one or more sub-functions, or one or more features, or one or more feature groups.

[0066] For another example, the function can be to use AI / ML for spatial beam prediction, or to use AI / ML for time beam prediction, or to use AI / ML for CSI prediction, or to use AI / ML for direct positioning, or to use AI / ML for assisted positioning, and so on.

[0067] In some embodiments, the AI / ML model resides on a terminal device. The terminal device monitors the performance of the AI / ML model and reports the performance information to a network device, which then determines (determines, detects) whether the AI / ML performance corresponding to one or more functions of the AI / ML model is functioning properly.

[0068] In some embodiments, the first configuration includes at least one of the following: a performance metric, a parameter for controlling the monitoring, and a reference signal configuration. This application is not limited thereto, and the first configuration may include other information / parameters / conditions / resource configurations used for performance monitoring. Furthermore, the first configuration may include any one of the aforementioned information, or any combination of two or more.

[0069] For example, the AI / ML function is located on the UE side. After the AI / ML function is enabled or activated, the UE monitors the AI / ML operation according to a first configuration from the network side. The first configuration may include performance metrics, parameters for controlling monitoring (e.g., parameters for event triggering, parameters for activation / deactivation triggering, such as counters, timers, thresholds, conditions), CSI-RS resource configuration, etc. The performance metrics may include AI / ML output performance, data input / output distribution, measurement statistics compared with input statistics, etc.

[0070] In some embodiments, the terminal device receives a second configuration sent by the network device; and the terminal device sends performance information corresponding to the function to the network device according to the second configuration.

[0071] In some embodiments, the second configuration includes at least one of the following: a reporting configuration, uplink resources for transmitting the performance information, and a method for reporting the performance information; the method includes periodic reporting, semi-continuous reporting, or aperiodic reporting; the performance information includes at least one of the following: a performance metric, input data drift, and output data drift; the present application is not limited thereto. Furthermore, the second configuration or performance information may include any one of the aforementioned information, or any combination of two or more.

[0072] In some embodiments, the function is one or more, and the terminal device performs AI / ML performance monitoring for each function separately, and the network device determines whether the function fails or whether the function is deactivated based on the performance information.

[0073] FIG3 is a schematic diagram of a function performance monitoring process according to an embodiment of the present application. As shown in FIG3 , the process includes:

[0074] 301. The terminal device receives a reference signal for AI / ML performance monitoring sent by a network device.

[0075] 302. The terminal device monitors the AI / ML performance corresponding to the function according to the reference signal and calculates the performance information.

[0076] For example, the UE monitors the AI / ML performance of a certain function and detects whether the performance has degraded, and the UE can calculate the performance information.

[0077] 303. The terminal device sends performance information corresponding to one or more functions to the network device;

[0078] For example, the performance information sent from the UE to the base station may be used for one function, or may also be used for multiple functions (for example, may be for N functions, where the value of N may be configured or predefined).

[0079] 304, the network device determines whether the corresponding function fails or whether the function is deactivated according to the performance information; and

[0080] 305. The terminal device receives response information fed back by the network device. The response information includes at least one of the following: functionality activation information, functionality deactivation information, functionality fallback information, functionality switching information, or reconfiguration information. The present application is not limited thereto. Furthermore, the response information may include any one of the above information, or any combination of two or more.

[0081] It is worth noting that FIG3 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 FIG3 above.

[0082] In some embodiments, the reference signal used for AI / ML performance monitoring is different from the reference signal used for measurement or inference; or, the reference signal used for AI / ML performance monitoring is the same as the reference signal used for measurement or inference; or, the reference signal used for AI / ML performance monitoring is at least a portion of the reference signal used for measurement or inference.

[0083] In some embodiments, the reference signal is a periodic signal for AI / ML performance monitoring of a function, for example, a periodic CSI-RS.

[0084] In some embodiments, the reference signal for AI / ML performance monitoring of a function is configured by radio resource control (RRC), or the reference signal for AI / ML performance monitoring of a function is determined by the terminal device.

[0085] For example, a base station may configure a periodic reference signal for performance monitoring of a function. For example, whether a reference signal is used for monitoring, measurement, or prediction may be explicitly identified through RRC configuration. For another example, it is also possible not to explicitly configure whether a reference signal is used for monitoring, measurement, or prediction, and the UE may independently determine whether a reference signal is used for monitoring, measurement, or prediction.

[0086] For another example, if no reference signal for performance monitoring is configured, the reference signal used by the UE for measurement and / or inference may be used for performance monitoring of one or more functions.

[0087] For another example, the reference signal may be semi-persistent or aperiodic.

[0088] In some embodiments, the performance information sent by the terminal device to the network device is for one or more functions.

[0089] In some embodiments, the performance information is reported periodically; a periodic reference signal is configured for the monitoring and uplink resources are configured for reporting the performance information; or, the performance information is reported via two-step random access.

[0090] For example, the performance information may be physical layer (layer 1) information. The performance information may be sent via the PUCCH or PUSCH. Alternatively, the performance information may be sent via a two-step RACH; for example, the UE may send a preamble and a PUSCH to the base station, with the performance information included in the PUSCH, and the UE may receive an RA response from the base station.

[0091] For another example, the terminal device may periodically report performance information. The base station may configure a periodic reference signal for performance monitoring and corresponding uplink resources (PUCCH or PUSCH) for the terminal device, so that the terminal device periodically sends the performance information.

[0092] In some embodiments, the performance information is reported semi-persistently; a periodic or semi-persistent reference signal is configured for the monitoring and uplink resources are configured for reporting the performance information; and the semi-persistent reporting is activated / deactivated via MAC CE or DCI.

[0093] For example, the terminal device may semi-persistently report the performance information. The base station may configure a periodic / semi-persistent reference signal and corresponding uplink resources (PUCCH or PUSCH) for performance monitoring for the terminal device, so that the terminal device sends the performance information.

[0094] For semi-persistent performance information reporting, the base station can send a command to activate / deactivate semi-persistent reporting. This command can be a MAC CE or DCI. For example, this MAC CE or DCI can be newly defined; for another example, an existing MAC CE or DCI can be reused; for another example, if the DCI is used to activate / deactivate semi-persistent performance information reporting, a new RNTI can be introduced.

[0095] In some embodiments, the performance information is reported aperiodically; and the aperiodic reporting is triggered by DCI.

[0096] For example, the terminal device can report performance information non-periodically. The base station can configure periodic / semi-persistent / aperiodic reference signals for the terminal device for performance monitoring. Performance information can be transmitted via PUSCH or PUCCH. The base station can trigger non-periodic performance information reporting via DCI. For example, a new DCI domain (field) can be introduced, or an existing DCI domain can be reused.

[0097] In some embodiments, the first configuration for performance monitoring and / or the second configuration for performance information reporting is different from the configuration for AI / ML reporting, or the first configuration for performance monitoring and / or the second configuration for performance information reporting is the same as the configuration for AI / ML reporting; or the first configuration for performance monitoring and / or the second configuration for performance information reporting is at least a portion of the configuration for AI / ML reporting;

[0098] The AI / ML reporting includes at least one of the following: CSI reporting, beam reporting, and positioning information reporting.

[0099] For example, the configuration for performance monitoring and performance information reporting can be separated from the configuration for AI / ML reporting, where AI / ML reporting can include CSI reporting, beam reporting, and positioning reporting. For example, a new CSI-ReportConfig and / or CSI-ResourceConfig and / or CSI-RS Resource set can be introduced for performance monitoring and / or performance information reporting of one or more functions. For another example, a positioning reference signal (PRS) or a PRS resource set can be configured in the CSI-ResourceConfig.

[0100] For another example, the configuration of performance monitoring and performance information reporting can use the configuration of AI / ML reporting, or use part of the configuration of AI / ML reporting, where AI / ML reporting can be CSI reporting, beam reporting, or positioning reporting. For example, the existing CSI-ReportConfig and / or CSI-ResourceConfig and / or CSI-RS Resource set can be used for performance monitoring and / or performance information reporting of one or more functions.

[0101] For another example, for different functions, the first configuration for performance monitoring and / or the second configuration for performance information reporting are different; or, for different functions, the first configuration for performance monitoring and / or the second configuration for performance information reporting are the same.

[0102] For another example, a first configuration for performance monitoring and / or a second configuration for performance information reporting is applied to one function, or a first configuration for performance monitoring and / or a second configuration for performance information reporting is applied to multiple functions.

[0103] In some embodiments, the method for reporting performance information is different from the method for reporting AI / ML, or the method for reporting performance information is the same as the method for reporting AI / ML; the method includes periodic reporting, non-periodic reporting or semi-continuous reporting; the AI / ML reporting includes at least one of the following: CSI reporting, beam reporting, positioning information reporting.

[0104] For example, the time domain behavior (periodic / semi-persistent / aperiodic) of performance information reporting can be the same as the time domain behavior of AI / ML reporting. Alternatively, the time domain behavior (periodic / semi-persistent / aperiodic) of performance information reporting can be different from the time domain behavior of AI / ML reporting.

[0105] In some embodiments, for different functions, the method used for performance information reporting is different from the method used for AI / ML reporting; or, for different functions, the method used for performance information reporting is the same as the method used for AI / ML reporting; the method includes periodic reporting, non-periodic reporting or semi-continuous reporting; the AI / ML reporting includes at least one of the following: CSI reporting, beam reporting, positioning information reporting.

[0106] For example, for different functions, the time domain behavior (periodic / semi-persistent / aperiodic) of performance information reporting can be the same. Alternatively, for different functions, the time domain behavior (periodic / semi-persistent / aperiodic) of performance information reporting can be different.

[0107] In some embodiments, performance information is reported through MAC CE; the MAC CE includes performance information for one function or multiple functions; or, performance information is reported through PUCCH and MAC CE; the MAC CE includes performance information for one function or multiple functions; or, performance information is reported through RRC message; the RRC message includes performance information for one function or multiple functions.

[0108] For example, the UE may initiate performance information reporting of one or more functions to the base station to help the base station make a decision on whether to activate / deactivate one or more functions. For example, the performance information may be included in a new MAC CE, and the MAC CE may include performance information for one function or for multiple functions.

[0109] In some embodiments, the performance information is sent periodically via a timer and is sent non-periodically based on a condition or event; or, the performance information is sent periodically via a timer; or, the performance information is sent non-periodically based on a condition or event.

[0110] For example, performance information can be sent to the network periodically and based on some predefined conditions / events. For example, the UE periodically monitors the AI / ML performance of one or more functions. If the performance metric of at least one function falls below a certain threshold, performance information reporting should be triggered, such as sending a MAC CE.

[0111] FIG4 is an example diagram of performance information reporting according to an embodiment of the present application. As shown in FIG4 , the UE may maintain a timer. After a function is enabled / activated (or after performance information is reported), the UE starts the timer (see 401 in FIG4 ). The UE periodically performs performance monitoring (see 402 in FIG4 ) and may determine whether a condition / event for performance information reporting has been triggered (see 403 in FIG4 ).

[0112] If performance information reporting is not triggered, a timer counts down (see 404 in Figure 4 ). If performance information reporting is triggered, the UE reports the performance information via a MAC CE (see 405 in Figure 4 ). The MAC CE includes performance information corresponding to one or more functions. The UE also determines whether the timer has expired (see 406 in Figure 4 ). If the timer has expired, the UE sends the performance information. After sending the performance information, the UE restarts the timer.

[0113] Figure 5 is another example diagram of performance information reporting in an embodiment of the present application. As shown in Figure 5, the UE can maintain a timer. After the function is enabled / activated (or after the performance information is reported), the UE starts the timer (see 501 in Figure 5). The UE periodically performs performance monitoring (see 502 in Figure 5) and the timer counts down (see 503 in Figure 5). The UE also determines whether the timer has timed out (see 504 in Figure 5). If the timer has timed out, the UE reports the performance information via MAC CE (see 505 in Figure 5), which includes performance information corresponding to one or more functions. After sending the performance information, the UE restarts the timer.

[0114] Figure 6 is another example diagram of performance information reporting according to an embodiment of the present application. As shown in Figure 6, after the function is enabled / activated, the UE periodically performs performance monitoring (see 601 of Figure 6) and can determine whether a condition / event for performance information reporting is triggered (see 602 of Figure 6).

[0115] If performance information reporting is not triggered, the UE continues to perform performance monitoring. If performance information reporting is triggered, the UE reports performance information via MAC CE (see 603 in FIG6 ), and the MAC CE includes performance information corresponding to one or more functions.

[0116] The above examples illustrate performance information reporting, but the present application is not limited thereto.

[0117] In some embodiments, the performance information of one or more functions may be sent via at least one of the following: uplink control channel resources, MAC CE, two-step random access, and radio resource control (RRC) messages.

[0118] For example, the UE may be configured with PUCCH SR-like resources for performance information reporting. The performance information may be included in a new MAC-CE, which may contain performance information for one function or for multiple functions. For example, if the performance metric of at least one function falls below a certain threshold, the UE may send a PUCCH SR-like resource to the base station. After receiving the PUCCH SR-like resource, the base station sends a DCI containing an uplink grant for PUSCH transmission, and the UE sends a MAC CE to the base station based on the uplink grant.

[0119] For another example, performance information can be included in an RRC message. An RRC message can include performance information for a single function or for multiple functions. Performance information reporting can be triggered based on some predefined conditions / events. For example, if the performance metric of at least one function falls below a certain threshold, performance information reporting is triggered, and the UE sends an RRC message. Alternatively, the UE can periodically send RRC messages, for example, by maintaining a timer, and after the timer expires, the UE sends the RRC message.

[0120] For another example, RRC messages can be sent periodically or based on some predefined conditions / events. For example, if the performance metric of at least one function falls below a certain threshold, the UE sends an RRC message. Furthermore, the UE can maintain a timer. After the function is enabled / activated, the UE starts the timer. If the timer expires, the UE sends the RRC message. After sending performance information (triggered by an event or timer expiration), the UE restarts the timer.

[0121] The above embodiments are merely exemplary of the present invention, but the present invention is not limited thereto. Appropriate modifications may be made based on the above embodiments. For example, the above embodiments may be used alone, or one or more of the above embodiments may be combined.

[0122] It can be seen from the above embodiments that the embodiments of the present application can accurately monitor the AI / ML model located on the terminal device side, thereby improving the accuracy and reliability of AI / ML.

[0123] Embodiments of the second aspect

[0124] The embodiment of the present application provides a performance monitoring method, which is described from the perspective of a network device. The embodiment of the second aspect can be combined with the embodiment of the first aspect, or implemented separately, and the same contents as the embodiment of the first aspect will not be repeated.

[0125] FIG7 is a schematic diagram of a performance monitoring method according to an embodiment of the present application. As shown in FIG7 , the method includes:

[0126] 701. A network device sends a first configuration to a terminal device; wherein the terminal device monitors the performance of an AI / ML model of an enabled or activated function according to the first configuration, where the function includes a feature or feature group enabled by the configuration indicated by the terminal device capability.

[0127] 702. The network device receives the performance information corresponding to the function sent by the terminal device.

[0128] It is worth noting that FIG7 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 FIG7 above.

[0129] The above embodiments are merely exemplary of the present invention, but the present invention is not limited thereto. Appropriate modifications may be made based on the above embodiments. For example, the above embodiments may be used alone, or one or more of the above embodiments may be combined.

[0130] It can be seen from the above embodiments that the embodiments of the present application can accurately monitor the AI / ML model located on the terminal device side, thereby improving the accuracy and reliability of AI / ML.

[0131] Embodiments of the third aspect

[0132] The embodiment of the present application provides a performance monitoring device, which may be, for example, a terminal device, or one or more components or assemblies configured in the terminal device, and the same contents as those in the first to third aspects of the embodiment will not be repeated.

[0133] FIG8 is a schematic diagram of a performance monitoring device according to an embodiment of the present application. As shown in FIG8 , the performance monitoring device 800 includes:

[0134] A receiving unit 801 receives a first configuration sent by a network device;

[0135] a monitoring unit 802 configured to monitor, according to the first configuration, the performance of an AI / ML model of an enabled or activated function, the function comprising a feature or feature group enabled by the configuration indicated by the terminal device capability; and

[0136] The sending unit 803 sends the performance information corresponding to the function to the network device.

[0137] In some embodiments, the AI / ML model is located on the terminal device side.

[0138] In some embodiments, the first configuration includes at least one of the following: a performance metric, a parameter for controlling the monitoring, and a reference signal configuration.

[0139] In some embodiments, the receiving unit 801 further receives a second configuration sent by the network device; and the sending unit 803 sends the performance information corresponding to the function to the network device according to the second configuration.

[0140] In some embodiments, the second configuration includes at least one of the following: reporting configuration, uplink resources for sending the performance information, and a method for reporting the performance information; the method includes periodic reporting, semi-continuous reporting, or non-periodic reporting; the performance information includes at least one of the following: performance metric, input data drift, and output data drift.

[0141] In some embodiments, the function is one or more, the terminal device performs AI / ML performance monitoring for each function separately, and the network device determines whether the function fails or whether the function is deactivated based on the performance information.

[0142] In some embodiments, the receiving unit 801 receives a reference signal for AI / ML performance monitoring sent by the network device; the monitoring unit 802 monitors the AI / ML performance corresponding to the function according to the reference signal and calculates the performance information.

[0143] In some embodiments, the receiving unit 801 also receives response information fed back by the network device based on the indication information, and the response information includes at least one of the following: function activation information, function deactivation information, function fallback information, function switching information or reconfiguration information.

[0144] In some embodiments, the reference signal used for AI / ML performance monitoring is different from the reference signal used for measurement or inference.

[0145] In some embodiments, the reference signal used for AI / ML performance monitoring is the same as the reference signal used for measurement or inference.

[0146] In some embodiments, the reference signal for AI / ML performance monitoring is at least a portion of a reference signal used for measurement or inference.

[0147] In some embodiments, the performance information sent by the terminal device to the network device is for one or more functions.

[0148] In some embodiments, the performance information is reported periodically; a periodic reference signal is configured for the monitoring and uplink resources are configured for reporting the performance information; or, the performance information is reported via two-step random access.

[0149] In some embodiments, the performance information is reported semi-persistently; a periodic or semi-persistent reference signal is configured for the monitoring and uplink resources are configured for reporting the performance information; and the semi-persistent reporting is activated / deactivated via MAC CE or DCI.

[0150] In some embodiments, the performance information is reported aperiodically; and the aperiodic reporting is triggered by DCI.

[0151] In some embodiments, the first configuration for performance monitoring and / or the second configuration for performance information reporting is different from the configuration for AI / ML reporting.

[0152] In some embodiments, the first configuration for performance monitoring and / or the second configuration for performance information reporting is the same as the configuration for AI / ML reporting.

[0153] In some embodiments, the first configuration for performance monitoring and / or the second configuration for performance information reporting is at least a portion of the configuration for AI / ML reporting;

[0154] The AI / ML reporting includes at least one of the following: CSI reporting, beam reporting, and positioning information reporting.

[0155] In some embodiments, for different functions, the first configuration for performance monitoring and / or the second configuration for performance information reporting are different; or, for different functions, the first configuration for performance monitoring and / or the second configuration for performance information reporting are the same.

[0156] In some embodiments, a first configuration for performance monitoring and / or a second configuration for performance information reporting is applied to one function, or a first configuration for performance monitoring and / or a second configuration for performance information reporting is applied to multiple functions.

[0157] In some embodiments, the method for reporting performance information is different from the method for reporting AI / ML, or the method for reporting performance information is the same as the method for reporting AI / ML; the method includes periodic reporting, non-periodic reporting or semi-continuous reporting; the AI / ML reporting includes at least one of the following: CSI reporting, beam reporting, positioning information reporting.

[0158] In some embodiments, for different functions, the method used for performance information reporting is different from the method used for AI / ML reporting; or, for different functions, the method used for performance information reporting is the same as the method used for AI / ML reporting; the method includes periodic reporting, non-periodic reporting or semi-continuous reporting; the AI / ML reporting includes at least one of the following: CSI reporting, beam reporting, positioning information reporting.

[0159] In some embodiments, performance information is reported via MAC CE; the MAC CE includes performance information for one function or multiple functions.

[0160] In some embodiments, performance information is reported via PUCCH and MAC CE; the MAC CE includes performance information for one function or multiple functions.

[0161] In some embodiments, performance information is reported via an RRC message; the RRC message includes performance information for one function or multiple functions.

[0162] In some embodiments, the performance information is sent periodically via a timer and is sent aperiodically based on a condition or an event.

[0163] In some embodiments, the performance information is sent periodically via a timer.

[0164] In some embodiments, the performance information is sent aperiodically based on a condition or event.

[0165] The above embodiments are merely exemplary of the present invention, but the present invention is not limited thereto. Appropriate modifications may be made based on the above embodiments. For example, the above embodiments may be used alone, or one or more of the above embodiments may be combined.

[0166] It is worth noting that the above description only describes the components or modules related to the present application, but the present application is not limited thereto. The performance monitoring device 800 may also include other components or modules. For details of these components or modules, reference may be made to related technologies.

[0167] In addition, for the sake of simplicity, FIG8 only illustrates the connection relationship or signal direction between various components or modules. However, it should be clear to those skilled in the art that various related technologies such as bus connection can be used. The above-mentioned components or modules can be implemented by hardware facilities such as processors, memories, transmitters, and receivers; the implementation of this application is not limited to this.

[0168] It can be seen from the above embodiments that the embodiments of the present application can accurately monitor the AI / ML model located on the terminal device side, thereby improving the accuracy and reliability of AI / ML.

[0169] Embodiments of the fourth aspect

[0170] The embodiment of the present application provides a performance monitoring device, which may be, for example, a network device, or one or more components or assemblies configured on the network device, and the same contents as those in the first to third aspects of the embodiment will not be repeated.

[0171] FIG9 is a schematic diagram of a performance monitoring device according to an embodiment of the present application. As shown in FIG9 , the performance monitoring device 900 includes:

[0172] a sending unit 901, which sends a first configuration to a terminal device; wherein the terminal device monitors the performance of an AI / ML model of an enabled or activated function according to the first configuration, wherein the function includes a feature or feature group enabled by the configuration indicated by the terminal device capability; and

[0173] The receiving unit 902 receives the performance information corresponding to the function sent by the terminal device.

[0174] The above embodiments are merely exemplary of the present invention, but the present invention is not limited thereto. Appropriate modifications may be made based on the above embodiments. For example, the above embodiments may be used individually, or one or more of the above embodiments may be combined.

[0175] It is worth noting that the above description only describes the components or modules related to the present application, but the present application is not limited thereto. The performance monitoring device 900 may also include other components or modules. For details of these components or modules, reference may be made to related technologies.

[0176] In addition, for the sake of simplicity, FIG9 only illustrates the connection relationship or signal direction between various components or modules. However, it should be clear to those skilled in the art that various related technologies such as bus connection can be used. The above-mentioned components or modules can be implemented by hardware facilities such as processors, memories, transmitters, and receivers; the implementation of this application is not limited to this.

[0177] It can be seen from the above embodiments that the embodiments of the present application can accurately monitor the AI / ML model located on the terminal device side, thereby improving the accuracy and reliability of AI / ML.

[0178] Embodiments of the fifth aspect

[0179] An embodiment of the present application also provides a communication system, and reference may be made to FIG1 . The contents that are the same as those in the first to fourth aspects of the embodiments will not be repeated.

[0180] In some embodiments, the communication system 100 may include at least:

[0181] A network device that sends a first configuration to a terminal device;

[0182] A terminal device that monitors the performance of an AI / ML model of an enabled or activated function according to the first configuration, where the function includes a feature or feature group enabled by the configuration indicated by the terminal device capability; and sends performance information corresponding to the function to the network device.

[0183] An embodiment of the present application further provides a network device, which may be, for example, a base station, but the present application is not limited thereto and may also be other network devices.

[0184] Figure 10 is a schematic diagram illustrating the structure of a network device according to an embodiment of the present application. As shown in Figure 10 , network device 1000 may include a processor 1010 (e.g., a central processing unit (CPU)) and a memory 1020; memory 1020 is coupled to processor 1010. Memory 1020 may store various data and may also store an information processing program 1030, which is executed under the control of processor 1010.

[0185] For example, the processor 1010 may be configured to execute a program to implement the performance monitoring method as described in the embodiment of the second aspect. For example, the processor 1010 may be configured to perform the following control: sending a first configuration to a terminal device; wherein the terminal device monitors the performance of an AI / ML model of an enabled or activated function according to the first configuration, where the function includes a feature or feature group enabled by the configuration indicated by the terminal device capability; and receiving performance information corresponding to the function sent by the terminal device.

[0186] In addition, as shown in FIG10 , the network device 1000 may further include: a transceiver 1040 and an antenna 1050; wherein, the functions of the above components are similar to those in the prior art and are not described in detail here. It is worth noting that the network device 1000 does not necessarily include all the components shown in FIG10 ; in addition, the network device 1000 may also include components not shown in FIG10 , and reference may be made to the prior art for details.

[0187] The embodiment of the present application also provides a terminal device, but the present application is not limited thereto and may also be other devices.

[0188] Figure 11 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in Figure 11 , terminal device 1100 may include a processor 1110 and a memory 1120. Memory 1120 stores data and programs and is coupled to processor 1110. It should be noted that this diagram is exemplary; other types of structures may be used to supplement or replace this structure to implement telecommunication or other functions.

[0189] For example, the processor 1110 may be configured to execute a program to implement the performance monitoring method as described in the embodiment of the first aspect. For example, the processor 1110 may be configured to perform the following control: receiving a first configuration sent by a network device; monitoring the performance of an AI / ML model of an enabled or activated function according to the first configuration, where the function includes a feature or feature group enabled by the configuration indicated by the terminal device capability; and sending performance information corresponding to the function to the network device.

[0190] As shown in Figure 11 , the terminal device 1100 may further include: a communication module 1130, an input unit 1140, a display 1150, and a power supply 1160. The functions of these components are similar to those in the prior art and are not described in detail here. It is worth noting that the terminal device 1100 does not necessarily include all of the components shown in Figure 11 , and these components are not essential. Furthermore, the terminal device 1100 may also include components not shown in Figure 11 , for which reference may be made to the prior art.

[0191] An embodiment of the present application also provides a computer program, wherein when the program is executed in a terminal device, the program causes the terminal device to execute the performance monitoring method described in the embodiment of the first aspect.

[0192] An embodiment of the present application also provides a storage medium storing a computer program, wherein the computer program enables a terminal device to execute the performance monitoring method described in the embodiment of the first aspect.

[0193] An embodiment of the present application also provides a computer program, wherein when the program is executed in a network device, the program causes the network device to execute the performance monitoring method described in the embodiment of the second aspect.

[0194] An embodiment of the present application also provides a storage medium storing a computer program, wherein the computer program enables a network device to execute the performance monitoring method described in the embodiment of the second aspect.

[0195] The above devices and methods of the present application can be implemented by hardware or by a combination of hardware and software. The present application relates to such a computer-readable program that, when executed by a logic component, enables the logic component to implement the devices or 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.

[0196] The method / device described in conjunction 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 figure and / or one or more combinations of functional block diagrams can correspond to various software modules of the computer program flow or to various hardware modules. These software modules can respectively correspond to the various steps shown in the figure. These hardware modules can be implemented by solidifying these software modules, for example, using a field programmable gate array (FPGA).

[0197] The software module may be located in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. A storage medium may be coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium; or the storage medium may be an integral part of the processor. The processor and the storage medium may be located in an ASIC. The software module may be stored in the memory of the mobile terminal or in a memory card that can be inserted into the mobile terminal. For example, if the device (such as a mobile terminal) uses a large-capacity MEGA-SIM card or a large-capacity flash memory device, the software module may be stored in the MEGA-SIM card or the large-capacity flash memory device.

[0198] One or more of the functional blocks and / or one or more combinations of functional blocks described in the accompanying drawings may 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, a discrete gate or transistor logic device, a discrete hardware component, or any appropriate combination thereof for performing the functions described in this application. One or more of the functional blocks and / or one or more combinations of functional blocks described in the accompanying drawings may 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 communication with a DSP, or any other such configuration.

[0199] The present application has been described above in conjunction with specific embodiments. However, those skilled in the art should understand that these descriptions are merely illustrative and are not intended to limit the scope of protection of the present application. Those skilled in the art may make various modifications and variations to the present application based on the spirit and principles of the present application, and such modifications and variations are also within the scope of the present application.

[0200] Regarding the implementation methods including the above embodiments, the following additional notes are also disclosed:

[0201] 1. A performance monitoring method, comprising:

[0202] The terminal device receives the first configuration sent by the network device;

[0203] monitoring the performance of an AI / ML model of an enabled or activated function according to the first configuration, the function comprising a feature or feature group enabled by the configuration indicated by the terminal device capability; and

[0204] Sending performance information corresponding to the function to the network device.

[0205] 2. A performance monitoring method, comprising:

[0206] The network device sends a first configuration to the terminal device; wherein the terminal device monitors the performance of the AI / ML model of an enabled or activated function according to the first configuration, the function including a feature or feature group enabled by the configuration indicated by the terminal device capability; and

[0207] Receive performance information corresponding to the function sent by the terminal device.

[0208] 3. A terminal device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the performance monitoring method as described in Note 1.

[0209] 4. A network device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the performance monitoring method as described in Note 2.

Claims

1. A performance monitoring device, comprising: A receiving unit, configured to receive a first configuration sent by a network device; a monitoring unit, which monitors the performance of the AI / ML model of an enabled or activated function according to the first configuration, wherein the function includes a feature or a feature group enabled by the configuration indicated by the terminal device capability; as well as A sending unit, which sends performance information corresponding to the function to the network device.

2. The device according to claim 1, wherein: The AI / ML model is located on the terminal device side, and the first configuration includes at least one of the following: performance metrics, parameters for controlling the monitoring, and reference signal configuration.

3. The device according to claim 1, wherein: The receiving unit also receives a second configuration sent by the network device; and the sending unit sends performance information corresponding to the function to the network device according to the second configuration.

4. The device according to claim 3, wherein: The second configuration includes at least one of the following: reporting configuration, uplink resources for sending the performance information, and a method for reporting the performance information; the method includes periodic reporting, semi-continuous reporting or non-periodic reporting; the performance information includes at least one of the following: performance measurement, input data drift, and output data drift.

5. The device according to claim 1, wherein: The function is one or more, the terminal device performs AI / ML performance monitoring on each function respectively, and the network device determines whether the function fails or is deactivated based on the performance information.

6. The device according to claim 1, wherein: The receiving unit receives a reference signal for AI / ML performance monitoring sent by the network device; The monitoring unit monitors the AI / ML performance corresponding to the function according to the reference signal and calculates the performance information.

7. The device according to claim 6, wherein: The receiving unit also receives response information fed back by the network device according to the indication information, and the response information includes at least one of the following: function activation information, function deactivation information, function rollback information, function switching information or reconfiguration information.

8. The device according to claim 6, wherein: The reference signal used for AI / ML performance monitoring is different from the reference signal used for measurement or inference; Alternatively, the reference signal for AI / ML performance monitoring is the same as the reference signal used for measurement or inference; Alternatively, the reference signal for AI / ML performance monitoring is a reference signal for measurement or inference. Less part.

9. The device according to claim 6, wherein: The performance information sent by the terminal device to the network device is for one or more functions.

10. The device according to claim 6, wherein: The performance information is periodically reported; a periodic reference signal is configured for the monitoring and an uplink resource is configured for reporting the performance information; Alternatively, the performance information is reported via two-step random access.

11. The device according to claim 6, wherein: The performance information is reported semi-persistently; a periodic or semi-persistent reference signal is configured for the monitoring and an uplink resource is configured for reporting the performance information; And the semi-persistent reporting is activated / deactivated through MAC CE or DCI.

12. The device according to claim 6, wherein: The performance information is reported aperiodically; and the aperiodic reporting is triggered by DCI.

13. The device according to claim 1, wherein: The first configuration for performance monitoring and / or the second configuration for performance information reporting is different from the configuration for AI / ML reporting; Alternatively, the first configuration for performance monitoring and / or the second configuration for performance information reporting is the same as the configuration for AI / ML reporting; Alternatively, the first configuration for performance monitoring and / or the second configuration for performance information reporting is at least a part of the configuration for AI / ML reporting; The AI / ML reporting includes at least one of the following: CSI reporting, beam reporting, and positioning information reporting.

14. The device according to claim 1, wherein: For different functions, the first configuration for performance monitoring and / or the second configuration for performance information reporting are different; or, for different functions, the first configuration for performance monitoring and / or the second configuration for performance information reporting are the same; A first configuration for performance monitoring and / or a second configuration for performance information reporting is applied to one function, or a first configuration for performance monitoring and / or a second configuration for performance information reporting is applied to multiple functions.

15. The device according to claim 1, wherein: The method used for performance information reporting is different from the method used for AI / ML reporting, or the method used for performance information reporting is the same as the method used for AI / ML reporting; The method includes periodic reporting, non-periodic reporting or semi-continuous reporting; the AI / ML reporting includes at least one of the following: CSI reporting, beam reporting, and positioning information reporting.

16. The device according to claim 1, wherein For different functions, the method used for reporting performance information is different from the method used for AI / ML reporting; or, for different functions, the method used for reporting performance information is the same as the method used for AI / ML reporting; The method includes periodic reporting, non-periodic reporting or semi-continuous reporting; the AI / ML reporting includes at least one of the following: CSI reporting, beam reporting, and positioning information reporting.

17. The device according to claim 1, wherein: Reporting performance information through MAC CE; the MAC CE includes performance information for one function or multiple functions; Alternatively, the performance information is reported through PUCCH and MAC CE; the MAC CE includes performance information for one function or multiple functions; Alternatively, the performance information is reported via an RRC message; the RRC message includes performance information for one function or multiple functions.

18. The device according to claim 17, wherein: The performance information is sent periodically via a timer and is sent aperiodically based on a condition or an event; Alternatively, the performance information is sent periodically via a timer; Alternatively, the performance information is sent non-periodically based on a condition or event.

19. A performance monitoring device, comprising: a sending unit, which sends a first configuration to a terminal device; wherein the terminal device monitors the performance of an AI / ML model of an enabled or activated function according to the first configuration, wherein the function includes a feature or feature group enabled by the configuration indicated by the terminal device capability; and A receiving unit receives performance information corresponding to the function sent by the terminal device.

20. A communication system comprising: A network device, which sends a first configuration to a terminal device; A terminal device that monitors the performance of an AI / ML model of an enabled or activated function according to the first configuration, wherein the function includes a feature or a feature group enabled by the configuration indicated by the terminal device capability; and sends performance information corresponding to the function to the network device.

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