Performance monitoring configuration method and apparatus

Abstract

Provided in the embodiments of the present application are a performance monitoring configuration method and apparatus. The performance monitoring configuration method comprises: a terminal device receiving configuration information, which is from a network device, wherein the configuration information is used for beam management based on an AI / ML functionality / model, and performance monitoring of the AI / ML functionality / model.

Description

Performance monitoring configuration 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 investigates artificial intelligence / machine learning (AI / ML) over the air interface. AI / ML can be used for the following use cases: channel state information (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 two-sided model can be used, with the AI / ML model located on both the end device and the network equipment. In other sub-use cases, a one-sided model can be used, with the AI / ML model located on either the end device or the network equipment. For beam management, the AI / ML model can be located on the end device and / or the network equipment.

[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 terminal devices and / or network devices can utilize AI / ML functionality / models to predict beams based on beam measurement results. However, there is currently no clear solution for performance monitoring of the AI / ML functionality / models.

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

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

[0009] The terminal device receives configuration information from the network device;

[0010] The configuration information is used for beam management based on AI / ML functionality / model and performance monitoring of the AI / ML functionality / model.

[0011] According to another aspect of an embodiment of the present application, a performance monitoring configuration device is provided, including:

[0012] a receiving unit, configured to receive configuration information from a network device;

[0013] The configuration information is used for beam management based on AI / ML functionality / model and performance monitoring of the AI / ML functionality / model.

[0014] According to another aspect of an embodiment of the present application, a performance monitoring configuration method is provided, including:

[0015] The network device sends configuration information to the terminal device;

[0016] The configuration information is used for beam management based on AI / ML functionality / model and performance monitoring of the AI / ML functionality / model.

[0017] According to another aspect of an embodiment of the present application, a performance monitoring configuration device is provided, including:

[0018] a sending unit, configured to send configuration information to a terminal device;

[0019] The configuration information is used for beam management based on AI / ML functionality / model and performance monitoring of the AI / ML functionality / model.

[0020] According to another aspect of an embodiment of the present application, a communication system is provided, including:

[0021] A network device that sends configuration information to a terminal device;

[0022] A terminal device receives the configuration information, where the configuration information is used for beam management based on AI / ML functionality / model and performance monitoring of the AI / ML functionality / model.

[0023] One of the beneficial effects of the embodiments of the present application is that: the terminal device receives configuration information from the network device; the configuration information is used for beam management based on AI / ML functionality / model and performance monitoring of the AI / ML functionality / model; thereby, the performance and efficiency of beam management can be improved, and the accuracy and reliability of beam management can be improved.

[0024] 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.

[0025] 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.

[0026] 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

[0027] 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.

[0028] FIG1 is a schematic diagram of a communication system according to an embodiment of the present application;

[0029] FIG2 is a schematic diagram of a performance monitoring configuration method according to an embodiment of the present application;

[0030] FIG3 is a schematic diagram of a beam management method according to an embodiment of the present application;

[0031] FIG4 is another schematic diagram of the performance monitoring configuration method according to an embodiment of the present application;

[0032] FIG5 is a schematic diagram of a performance monitoring configuration device according to an embodiment of the present application;

[0033] FIG6 is another schematic diagram of a performance monitoring configuration device according to an embodiment of the present application;

[0034] FIG7 is a schematic diagram of a terminal device according to an embodiment of the present application;

[0035] FIG8 is a schematic diagram of a network device according to an embodiment of the present application. DETAILED DESCRIPTION

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] The following describes the scenarios of the embodiments of the present application through examples, but the present application is not limited thereto.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] The performance of the AI / ML functions / models used for beam management needs to be monitored so that corresponding control of the AI / ML functions / models, such as activation / deactivation / selection / switching / fallback, can be performed.

[0053] For example, network-side monitoring can be performed, with the network side monitoring performance metrics and making activation / deactivation / selection / switching / fallback decisions.

[0054] For another example, UE-side monitoring may be performed, where the terminal side monitors performance metrics and makes activation / deactivation / selection / switching / fallback decisions.

[0055] For another example, hybrid monitoring can be performed, where the terminal side monitors performance metrics, and the network side makes activation / deactivation / selection / switching / fallback decisions.

[0056] 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; the present application is not limited thereto.

[0057] Embodiments of the first aspect

[0058] An embodiment of the present application provides a performance monitoring configuration method, which is described from the perspective of a terminal device.

[0059] FIG2 is a schematic diagram of a performance monitoring configuration method according to an embodiment of the present application. As shown in FIG2 , the method includes:

[0060] 201, the terminal device receives configuration information from the network device;

[0061] The configuration information is used for beam management based on AI / ML functionality / model and performance monitoring of the AI / ML functionality / model.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] In some embodiments, the configuration information for beam management may include configuration information of one or more reference signals used for beam management or beam measurement, such as CSI-RS configuration information, etc. The present application is not limited thereto, and reference may be made to related technologies for specific configuration information.

[0067] In some embodiments, one or more reference signals are used for measurement and the measurement results are input into 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.

[0068] FIG3 is another schematic diagram of the beam management method according to an embodiment of the present application, which is illustrated by taking a terminal device configured with AIML as an example. As shown in FIG3 , the method includes:

[0069] 301. A terminal device receives configuration information from a network device; for example, the configuration information includes a second reference signal resource set (set B) for beam measurement and a first reference signal resource set (set A) for beam prediction.

[0070] 302. The terminal device performs beam measurement and inputs the beam measurement results into the AI / ML functionality / model. For example, the measurement results of the reference signals in set B are used as input to the AI / ML, and the reference signals in set A are used for prediction (or inference).

[0071] 303. The terminal device sends the beam prediction result to the network device.

[0072] For example, the AI / ML function is located on the terminal device side. After the AI / ML function is enabled or activated, the terminal device performs beam measurement based on the reference signal from the network side, uses AI / ML to perform beam prediction based on the beam measurement results, and sends the prediction results to the network device.

[0073] 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.

[0074] The above schematically illustrates AI / ML-based beam management. The following describes performance monitoring.

[0075] In some embodiments, for network-side performance monitoring, a second reference signal set (set B) for measurement is configured, and one or more reference signals for performance monitoring are configured;

[0076] The reference signal for performance monitoring is identical or at least partially identical to the reference signal in the first reference signal set (set A) for reasoning, or the reference signal for performance monitoring is a subset of the first reference signal set (set A) for reasoning.

[0077] For example, for network-side (gNB-side) performance monitoring (i.e., the AI / ML functions / models for beam management are on the network / gNB side), separate reference signals can be configured for performance monitoring in addition to the reference signals used for measurement (set B).

[0078] For example, the reference signal used for performance monitoring can be entirely the same as the reference signal used for reasoning (i.e., the RS in set A), or the reference signal used for performance monitoring can be partially the same as the reference signal used for reasoning (i.e., the RS in set A), or the reference signal used for performance monitoring can be a subset of set A.

[0079] In some embodiments, for network-side performance monitoring, reporting for measurements is configured, and reporting for performance monitoring is configured.

[0080] For example, for network-side (gNB-side) performance monitoring, in addition to measurement reports (for Set B), a separate report can be configured for the RS for performance monitoring. The report quantity can reuse existing reporting quantities or be a newly introduced reporting quantity.

[0081] In some embodiments, for network-side performance monitoring, a reference signal for measurement and a reference signal for performance monitoring are configured together, and measurement results for the reference signal for measurement and the reference signal for performance monitoring are reported together.

[0082] For example, for network-side (gNB-side) performance monitoring, the RS used for performance monitoring and the RS used for measurement can be configured together, i.e., through the same reporting configuration. The measurement results of set B and the measurement results of the performance monitoring RS can be provided to the network device in the same report.

[0083] In some embodiments, for network-side performance monitoring, the number of reference signals in the second reference signal set (set B) used for measurement is M, and the measurement results for M reference signals are reported, or the measurement results for N reference signals are reported, where N is less than or equal to M.

[0084] For example, for network-side (gNB-side) performance monitoring (i.e., the AI / ML function / model for beam management is on the network / gNB side), if the number of reference signals used for measurement (set B) is M, the measurement results of all M reference signals in set B are reported.

[0085] For another example, for network-side (gNB-side) performance monitoring (i.e., the AI / ML function / model for beam management is on the network / gNB side), if the number of reference signals used for measurement (set B) is M, then a subset of the reference signals in set B is reported, for example, the measurement results of N reference signals are reported, where N <= M.

[0086] In some embodiments, the minimum number of reference signals for which measurement results are reported in the second reference signal set (set B) is predefined or configured, and / or the maximum number of reference signals for which measurement results are reported in the second reference signal set (set B) is predefined or configured.

[0087] For example, the minimum number of RSs for reporting measurement results in set B, such as X1, may be predefined or configured (in one example, X1 may be greater than 4). Alternatively, the maximum number of RSs for reporting measurement results in set B, such as X2, may be predefined or configured (in one example, X2 may be greater than 4).

[0088] In some embodiments, for network-side performance monitoring, the number of reference signals used for performance monitoring is K, and measurement results for K reference signals are reported, or measurement results for L reference signals are reported, where L is less than or equal to K.

[0089] For example, for network-side (gNB-side) performance monitoring (i.e., the AI / ML function / model for beam management is on the network / gNB side), if the number of reference signals used for performance monitoring is K, the measurement results of all K reference signals used for performance monitoring are reported.

[0090] For another example, for network-side (gNB-side) performance monitoring (i.e., the AI / ML function / model for beam management is on the network / gNB side), if the number of reference signals used for performance monitoring is K, a subset of the reference signals used for performance monitoring is reported, for example, the measurement results of L reference signals are reported, where L <= K.

[0091] In some embodiments, a minimum number of reference signals for which measurement results for performance monitoring are reported is predefined or configured, and / or a maximum number of reference signals for which measurement results for performance monitoring are reported is predefined or configured.

[0092] For example, the minimum number of RSs for reporting measurement results in a reference signal for performance monitoring, such as Y1, may be predefined or configured (in one example, Y1 may be greater than 4). Alternatively, the maximum number of RSs for reporting measurement results in a reference signal for performance monitoring, such as Y2, may be predefined or configured (in one example, Y2 may be greater than 4).

[0093] The above examples illustrate some situations of network-side monitoring. The following will further explain performance monitoring metrics.

[0094] In some embodiments, for network-side performance monitoring, the strongest one or more beams are reported by the terminal device; the network device calculates a performance metric, i.e., beam prediction accuracy, based on the strongest one or more beams; wherein a first report quantity is introduced, or the report quantity of L1-RSRP / L1-SINR is reused.

[0095] For example, for NW-side monitoring, the UE can report the Top K / Top-1 beam, i.e., the CSI-RS Resource Indicator (CRI). A new reporting quantity, CRI-only reporting (e.g., the RRC parameter reportQuantity is set to "criOnly"), can be introduced. The UCI format is also enhanced to report only CRI. In the report, only CRI is reported (K CRIs or 1 CRI). The network side, i.e., the gNB side, will calculate the performance metric (beam prediction accuracy). In another example, the existing reporting quantity of L1-RSRP / L1-SINR can be reused.

[0096] In some embodiments, for network-side performance monitoring, a hypothetical BLER is used as a performance metric, and a second report quantity is introduced; the hypothetical BLER is included in the report for performance monitoring.

[0097] For example, for NW-side monitoring, if hypothetical BLER is used as a performance metric, a new reporting quantity can be introduced. The hypothetical BLER can be reported in the monitoring report. For example, the RRC parameter reportQuantity can be set to "BLER." The UCI format can also be enhanced to report the hypothetical BLER.

[0098] In some embodiments, for network-side performance monitoring, the terminal device reports the strongest L1-RSRP / L1-SINR of one or more or all beams used for performance monitoring, wherein the reported amount of L1-RSRP / L1-SINR is reused.

[0099] For example, for NW-side monitoring, the UE may report the L1-RSRP / L1-SINR of the top K beams / top-1 beam / all beams of the configured reference signals for monitoring. Existing reported L1-RSRP / L1-SINR values ​​may be reused.

[0100] In some embodiments, the network device calculates a performance metric based on the L1-RSRP / L1-SINR of the strongest beam(s) or all beams, ie, the difference between the monitored L1-RSRP / L1-SINR and the predicted L1-RSRP / L1-SINR.

[0101] For example, the network side may calculate a performance metric, namely, the difference between the measured L1-RSRP / L1-SINR for performance monitoring and the measured L1-RSRP / L1-SIN for beam prediction.

[0102] In some embodiments, multiple reports are configured, one of which reports measurement results for a reference signal used for measurement, and another reports measurement results for a reference signal used for performance monitoring.

[0103] In some embodiments, one report is configured, the report including measurement results for a reference signal used for measurement and measurement results for a reference signal used for performance monitoring.

[0104] For example, multiple reports can be configured, such as two reports of L1-RSRP / L1-SINR. One report is used for the measurement results of set B, and the other report is used for the measurement results of performance monitoring. Alternatively, the same report can be used to carry L1-RSRP / L1-SINR for both the measurement results of set B and the measurement results of performance monitoring.

[0105] In some embodiments, for hybrid performance monitoring, one or more reference signals for performance monitoring are configured; wherein the reference signals for performance monitoring are the same as or at least partially the same as the reference signals in a first reference signal set (set A) for reasoning, or the reference signals for performance monitoring are a subset of the first reference signal set (set A) for reasoning.

[0106] For example, for hybrid monitoring (i.e., the AI / ML function / model is on the UE side, the UE reports measurements / performance metrics to the gNB side, and the gNB side makes the decision), the reference signals used for performance monitoring can be all the same as the reference signals of set A (reference signals used for inference), or the reference signals used for performance monitoring can be partially the same as the reference signals of set A (reference signals used for inference), or the reference signals used for performance monitoring can be a subset of set A.

[0107] In some embodiments, the terminal device calculates a performance metric (i.e., beam prediction accuracy) based on the strongest one or more beams and includes it in a report for performance monitoring; wherein a first report quantity is introduced, or the report quantity of L1-RSRP / L1-SINR is reused.

[0108] For example, the UE can calculate the prediction accuracy of the Top-K / Top-1 beam and report the prediction accuracy. In the reporting for performance monitoring, the UE can report the prediction accuracy. For example, a new reporting quantity can be introduced (for example, the RRC parameter reportQuantity is set to 'criOnly'), and the UCI format is also enhanced to report only CRI.

[0109] In some embodiments, the strongest beam or beams are included by the terminal device in a report for performance monitoring; the network device calculates a performance metric (i.e., beam prediction accuracy) based on the strongest beam or beams; wherein a first report quantity is introduced, or the report quantity of L1-RSRP / L1-SINR is reused.

[0110] In some embodiments, the strongest one or more beams based on the inference are included by the terminal device in the report for the inference; and the network device calculates the beam prediction accuracy based on the strongest one or more beams.

[0111] For example, in reports for performance monitoring, the UE can report the Top-K / Top-1 CRI, that is, only report the CRI. In reports for reasoning, the UE reports the Top-K / Top-1 CRI based on reasoning. Alternatively, the Top-K / Top-1 CRI for performance monitoring and the Top-K-Top-1 CRIs for reasoning can be sent in the same report. The network side calculates the beam prediction accuracy. For example, a new reporting quantity of CRI only can be introduced, or the existing reporting quantity of L1-RSRP / L1-SINR can be reused.

[0112] In some embodiments, L1-RSRP / L1-SINR is used for performance measurement, the terminal device reports L1-RSRP / L1-SINR for performance monitoring and L1-RSRP / L1-SINR for reasoning, and the network device calculates the difference between the L1-RSRP / L1-SINR for performance monitoring and the L1-RSRP / L1-SINR for reasoning.

[0113] For example, if L1-RSRP / L1-SINR is used as a performance metric, the UE reports the L1-RSRP / L1-SINR used for monitoring measurement and may also report the L1-RSRP / L1-SINR used for inference. The network side may calculate the difference between the L1-RSRP / L1-SINR used for monitoring measurement and the L1-RSRP / L1-SINR used for prediction.

[0114] In some embodiments, the L1-RSRP / L1-SINR difference is used for performance measurement, and the terminal device calculates the difference between the L1-RSRP / L1-SINR for performance monitoring and the L1-RSRP / L1-SINR for inference, and reports the difference.

[0115] For example, the UE calculates the difference between the L1-RSRP / L1-SINR used for monitoring and measurement and the L1-RSRP / L1-SIN used for prediction. The UE reports the L1-RSRP / L1-SINR difference. For example, a new reporting quantity can be introduced. For example, the RRC parameter reportQuantity can be set to "RSRPDifference". The UCI format can also be enhanced to report the L1-RSRP / L1-SINR difference.

[0116] In some embodiments, a hypothetical BLER is used for performance metrics, and the terminal device reports the hypothetical BLER for performance monitoring and / or the hypothetical BLER for inference.

[0117] For example, if hypothetical BLER is used as a performance metric, the UE reports the hypothetical BLER used for monitoring measurements and / or the hypothetical BLER used for beam prediction. New reporting quantities can be introduced, for example, by setting the RRC parameter reportQuantity to "BLER." The UCI format can also be enhanced to report the hypothetical BLER.

[0118] The following further describes the performance monitoring with discontinuous reception (DRX) / discontinuous transmission (DTX).

[0119] In some embodiments, DRX / DTX is enabled and a timer or counter is enlarged or scaled for performance monitoring of AI / ML functionality / model.

[0120] For example, when DRX / DTX is enabled, e.g., DRX is enabled at the UE and / or DTX is enabled at the gNB, then for performance monitoring of AI / ML functions / models, the timer / counter is amplified / scaled. For example, for non-DRX case, the evaluation window length is T nonDRX , then when DRX is enabled, the evaluation window length can be s×T nonDRX , where s is the scaling factor, which can be predefined or configured.

[0121] In some embodiments, DRX / DTX is enabled and performance monitoring of AI / ML functionality / model is suspended or stopped during DRX / DTX.

[0122] In some embodiments, DRX / DTX is enabled and performance monitoring of AI / ML functionality / models remains running during DRX / DTX.

[0123] For example, when DRX / DTX is enabled, the performance monitoring of the AI / ML function / model can be paused / stopped during DRX / DTX. For another example, when DRX / DTX is enabled, the performance monitoring of the AI / ML function / model can remain running during DRX / DTX.

[0124] In some embodiments, DRX / DTX is enabled, a time window for time beam prediction falls within or overlaps with a DRX / DTX period, and prediction results for some or all time instances are skipped.

[0125] In some embodiments, DRX / DTX is enabled, the time window of time beam prediction falls within or overlaps with the DRX / DTX period, and prediction results for some or all time instances are reported.

[0126] For example, for BM case-1 (time beam prediction), if the prediction window falls within a DRX / DTX period, or the prediction window overlaps with the DRX / DTX period, the prediction results for some (or all) time instances can be skipped. For another example, for BM case-1 (time beam prediction), if the prediction window falls within a DRX / DTX period, or the prediction window overlaps with the DRX / DTX period, the prediction results for all time instances are still reported.

[0127] In some embodiments, if the AI / ML-related configuration is changed, updated, or switched, the performance monitoring of the AI / ML functionality / model is reset or restarted; wherein the AI / ML-related configuration change, update, or switch includes at least one of the following: AI / ML functionality / model change or update, AI / ML functionality / model switching, reference signal configuration change for performance monitoring, cell configuration change, scenario configuration change, change in the number of prediction time instances, and BWP switching.

[0128] For example, resetting / restarting performance monitoring (e.g., resetting or restarting timers / counters) of AI / ML for beam management when:

[0129] -Configuration changes related to AI / ML, such as

[0130] -- AI / ML function / model changes / updates or AI / ML function / model switching occurs;

[0131] --Changes in reference signal configuration for performance monitoring;

[0132] --Cell / scenario-specific configuration / additional condition changes; for example, the UE moves to a cell with a different configuration, the number of prediction time instances changes due to UE speed changes, etc.

[0133] -BWP switches to target BWP with different AI / ML configuration.

[0134] The embodiments of the present application can be applied to the UE-side model and / or the gNB-side model. Furthermore, the embodiments of the present application can be applied to BM case 1 (spatial beam prediction) and / or BM case 2 (temporal beam prediction).

[0135] 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.

[0136] It can be seen from the above embodiments that the terminal device receives configuration information from the network device; the configuration information is used for beam management based on AI / ML functionality / model and performance monitoring of the AI / ML functionality / model; thereby, the performance and efficiency of beam management can be improved, and the accuracy and reliability of beam management can be improved.

[0137] Embodiments of the second aspect

[0138] The embodiment of the present application provides a performance monitoring configuration 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, and the same contents as the embodiment of the first aspect will not be repeated.

[0139] FIG4 is another schematic diagram of a performance monitoring configuration method according to an embodiment of the present application. As shown in FIG4 , the method includes:

[0140] 401, the network device sends configuration information to the terminal device;

[0141] The configuration information is used for beam management based on AI / ML functionality / model and performance monitoring of the AI / ML functionality / model.

[0142] 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.

[0143] It can be seen from the above embodiments that the terminal device receives configuration information from the network device; the configuration information is used for beam management based on AI / ML functionality / model and performance monitoring of the AI / ML functionality / model; thereby, the performance and efficiency of beam management can be improved, and the accuracy and reliability of beam management can be improved.

[0144] Embodiments of the third aspect

[0145] The embodiment of the present application provides a performance monitoring configuration device, which can 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 and second aspects of the embodiment will not be repeated.

[0146] FIG5 is a schematic diagram of a performance monitoring configuration device according to an embodiment of the present application. As shown in FIG5 , a performance monitoring configuration device 500 according to an embodiment of the present application includes:

[0147] A receiving unit 501 receives configuration information from a network device; the configuration information is used for beam management based on AI / ML functionality / model and performance monitoring of the AI / ML functionality / model.

[0148] In some embodiments, for network-side performance monitoring, a second reference signal set (set B) for measurement is configured, and one or more reference signals for performance monitoring are configured.

[0149] In some embodiments, the reference signals used for performance monitoring are identical or at least partially identical to reference signals in the first reference signal set (set A) used for inference.

[0150] In some embodiments, the reference signals used for performance monitoring are a subset of a first set of reference signals used for inference (set A).

[0151] In some embodiments, for network-side performance monitoring, reporting for measurements is configured, and reporting for performance monitoring is configured.

[0152] In some embodiments, for network-side performance monitoring, a reference signal for measurement and a reference signal for performance monitoring are configured together, and measurement results for the reference signal for measurement and the reference signal for performance monitoring are reported together.

[0153] In some embodiments, for network-side performance monitoring, the number of reference signals in the second reference signal set (set B) used for measurement is M, and measurement results for M reference signals are reported. In some embodiments, measurement results for N reference signals are reported, where N is less than or equal to M.

[0154] In some embodiments, the minimum number of reference signals for which measurement results are reported in the second reference signal set (set B) is predefined or configured, and / or the maximum number of reference signals for which measurement results are reported in the second reference signal set (set B) is predefined or configured.

[0155] In some embodiments, for network-side performance monitoring, the number of reference signals used for performance monitoring is K, and measurement results for K reference signals are reported. In some embodiments, measurement results for L reference signals are reported, where L is less than or equal to K.

[0156] In some embodiments, a minimum number of reference signals for which measurement results for performance monitoring are reported is predefined or configured, and / or a maximum number of reference signals for which measurement results for performance monitoring are reported is predefined or configured.

[0157] In some embodiments, for network-side performance monitoring, the strongest one or more beams are reported by the terminal device; the network device calculates a performance metric, i.e., beam prediction accuracy, based on the strongest one or more beams.

[0158] In some embodiments, a first report quantity is introduced, or the report quantity of L1-RSRP / L1-SINR is reused.

[0159] In some embodiments, for network-side performance monitoring, a hypothetical BLER is used as a performance metric, and a second report quantity is introduced; the hypothetical BLER is included in the report for performance monitoring.

[0160] In some embodiments, for network-side performance monitoring, the terminal device reports the strongest L1-RSRP / L1-SINR of one or more or all beams used for performance monitoring, wherein the reported amount of L1-RSRP / L1-SINR is reused.

[0161] In some embodiments, the network device calculates a performance metric based on the L1-RSRP / L1-SINR of the strongest one or more or all beams, i.e., the difference between the monitored L1-RSRP / L1-SINR and the predicted L1-RSRP / L1-SINR.

[0162] In some embodiments, multiple reports are configured, one of which reports measurement results for a reference signal used for measurement, and another reports measurement results for a reference signal used for performance monitoring.

[0163] In some embodiments, one report is configured, the report including measurement results for a reference signal used for measurement and measurement results for a reference signal used for performance monitoring.

[0164] In some embodiments, for hybrid performance monitoring, one or more reference signals for performance monitoring are configured.

[0165] In some embodiments, the reference signals used for performance monitoring are identical or at least partially identical to reference signals in the first reference signal set (set A) used for inference.

[0166] In some embodiments, the reference signals used for performance monitoring are a subset of a first set of reference signals used for inference (set A).

[0167] In some embodiments, the terminal device calculates a performance metric (i.e., beam prediction accuracy) based on the strongest one or more beams and includes it in a report for performance monitoring; wherein a first report quantity is introduced, or the report quantity of L1-RSRP / L1-SINR is reused.

[0168] In some embodiments, the strongest beam or beams are included by the terminal device in a report for performance monitoring; the network device calculates a performance metric (i.e., beam prediction accuracy) based on the strongest beam or beams; wherein a first report quantity is introduced, or the report quantity of L1-RSRP / L1-SINR is reused.

[0169] In some embodiments, one or more previous beams based on inference are included by the terminal device in a report for inference; and the network device calculates beam prediction accuracy based on the one or more previous beams.

[0170] In some embodiments, L1-RSRP / L1-SINR is used for performance measurement, the terminal device reports L1-RSRP / L1-SINR for performance monitoring and L1-RSRP / L1-SINR for reasoning, and the network device calculates the difference between the L1-RSRP / L1-SINR for performance monitoring and the L1-RSRP / L1-SINR for reasoning.

[0171] In some embodiments, the L1-RSRP / L1-SINR difference is used for performance measurement, and the terminal device calculates the difference between the L1-RSRP / L1-SINR for performance monitoring and the L1-RSRP / L1-SINR for inference, and reports the difference.

[0172] In some embodiments, a hypothetical BLER is used for performance metrics, and the terminal device reports the hypothetical BLER for performance monitoring and / or the hypothetical BLER for inference.

[0173] In some embodiments, DRX / DTX is enabled and a timer or counter is incremented or amplified for performance monitoring of AI / ML functionality / model.

[0174] In some embodiments, DRX / DTX is enabled and performance monitoring of the AI / ML functionality / model is suspended or stopped during DRX / DTX. In some embodiments, performance monitoring of the AI / ML functionality / model remains running during DRX / DTX.

[0175] In some embodiments, when DRX / DTX is enabled and the time window for time beam prediction falls within or overlaps with a DRX / DTX period, prediction results for some or all time instances are skipped. In some embodiments, prediction results for some or all time instances are reported.

[0176] In some embodiments, when the AI / ML related configuration is changed, updated, or switched, the performance monitoring of the AI / ML functionality / model is reset or restarted.

[0177] In some embodiments, the AI / ML-related configuration change, update, or switch includes at least one of the following: AI / ML functionality / model change or update, AI / ML functionality / model switch, reference signal configuration change for performance monitoring, cell configuration change, scenario configuration change, change in the number of prediction time instances, and BWP switch.

[0178] In some embodiments, as shown in FIG5 , the performance monitoring configuration apparatus 500 may further include:

[0179] a processing unit 502 that performs beam measurement and / or beam prediction; and

[0180] The sending unit 503 sends beam measurement information and / or beam prediction information to the network device.

[0181] 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.

[0182] It is worth noting that the above only describes the components or modules related to the present application, but the present application is not limited thereto. The performance monitoring configuration device 500 may also include other components or modules. For details of these components or modules, reference may be made to related technologies.

[0183] In addition, for simplicity, FIG5 only illustrates the connection relationship or signal path between various components or modules. However, those skilled in the art should be aware 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; this application is not limited to this.

[0184] It can be seen from the above embodiments that the terminal device receives configuration information from the network device; the configuration information is used for beam management based on AI / ML functionality / model and performance monitoring of the AI / ML functionality / model; thereby, the performance and efficiency of beam management can be improved, and the accuracy and reliability of beam management can be improved.

[0185] Embodiments of the fourth aspect

[0186] The embodiment of the present application provides a performance monitoring configuration device, which can be, for example, a network device, or one or more components or assemblies configured on the network device, and the same contents as the first to third embodiments are not repeated here.

[0187] FIG6 is another schematic diagram of a performance monitoring configuration device according to an embodiment of the present application. As shown in FIG6 , the performance monitoring configuration device 600 includes:

[0188] A sending unit 601 sends configuration information to a terminal device; wherein the configuration information is used for beam management based on AI / ML functionality / model and performance monitoring of the AI / ML functionality / model.

[0189] In some embodiments, as shown in FIG6 , the performance monitoring configuration apparatus 600 may further include:

[0190] The receiving unit 602 receives the beam measurement information and / or beam prediction information sent by the terminal device.

[0191] 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.

[0192] 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 configuration device 600 may also include other components or modules. For details of these components or modules, reference may be made to related technologies.

[0193] In addition, for the sake of simplicity, FIG6 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.

[0194] It can be seen from the above embodiments that the terminal device receives configuration information from the network device; the configuration information is used for beam management based on AI / ML functionality / model and performance monitoring of the AI / ML functionality / model; thereby, the performance and efficiency of beam management can be improved, and the accuracy and reliability of beam management can be improved.

[0195] Embodiments of the fifth aspect

[0196] 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.

[0197] In some embodiments, the communication system 100 may include at least:

[0198] A network device that sends configuration information to a terminal device;

[0199] A terminal device receives the configuration information, where the configuration information is used for beam management based on AI / ML functionality / model and performance monitoring of the AI / ML functionality / model.

[0200] 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.

[0201] Figure 7 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in Figure 7 , terminal device 700 may include a processor 710 and a memory 720. Memory 720 stores data and programs and is coupled to processor 710. 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.

[0202] For example, the processor 710 may be configured to execute a program to implement the performance monitoring configuration method as described in the embodiment of the first aspect. For example, the processor 710 may be configured to perform the following control: receiving configuration information from a network device; wherein the configuration information is used for beam management based on AI / ML functionality / model and performance monitoring of the AI / ML functionality / model.

[0203] As shown in Figure 7 , the terminal device 700 may further include: a communication module 730, an input unit 740, a display 750, and a power supply 760. 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 700 does not necessarily include all of the components shown in Figure 7 , and these components are not essential. Furthermore, the terminal device 700 may also include components not shown in Figure 7 , for which reference may be made to the prior art.

[0204] 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.

[0205] Figure 8 is a schematic diagram illustrating the structure of a network device according to an embodiment of the present application. As shown in Figure 8 , network device 800 may include a processor 810 (e.g., a central processing unit (CPU)) and a memory 820 ; the memory 820 is coupled to the processor 810 . The memory 820 may store various data and may also store an information processing program 830 , which is executed under the control of the processor 810 .

[0206] For example, the processor 810 may be configured to execute a program to implement the performance monitoring configuration method as described in the embodiment of the second aspect. For example, the processor 810 may be configured to perform the following control: sending configuration information to a terminal device; wherein the configuration information is used for beam management based on AI / ML functionality / model and performance monitoring of the AI / ML functionality / model.

[0207] In addition, as shown in FIG8 , network device 800 may further include: a transceiver 840 and an antenna 850, etc.; 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 network device 800 does not necessarily include all the components shown in FIG8 ; in addition, network device 800 may also include components not shown in FIG8 , and reference may be made to the prior art for details.

[0208] An embodiment of the present application also provides a computer program, wherein when the program is executed in a terminal device, the program enables the terminal device to execute the performance monitoring configuration method described in the embodiment of the first aspect.

[0209] 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 configuration method described in the embodiment of the first aspect.

[0210] An embodiment of the present application also provides a computer program, wherein when the program is executed in a network device, the program enables the network device to execute the performance monitoring configuration method described in the embodiment of the second aspect.

[0211] 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 configuration method described in the embodiment of the second aspect.

[0212] 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.

[0213] The methods / apparatus described in conjunction with the embodiments of the present application may be directly embodied as hardware, software modules 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 one or more combinations of functional block diagrams may correspond to either software modules of a computer program flow or hardware modules. These software modules may correspond to the steps shown in the figures. These hardware modules may be implemented by solidifying these software modules, for example, using a field programmable gate array (FPGA).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] Regarding the implementation methods including the above embodiments, the following additional notes are also disclosed:

[0218] 1. A performance monitoring configuration method, comprising:

[0219] The terminal device receives configuration information from the network device;

[0220] The configuration information is used for beam management based on AI / ML functionality / model and performance monitoring of the AI / ML functionality / model.

[0221] 2. A performance monitoring configuration method, comprising:

[0222] The network device sends configuration information to the terminal device;

[0223] The configuration information is used for beam management based on AI / ML functionality / model and performance monitoring of the AI / ML functionality / model.

[0224] 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 configuration method as described in Note 1.

[0225] 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 configuration method as described in Note 2.

Claims

1. A performance monitoring configuration device, comprising: A receiving unit that receives configuration information from a network device; Wherein, the configuration information is used for beam management based on AI / ML functions / models and performance monitoring of the AI / ML functions / models.

2. The device according to claim 1, wherein, For network-side performance monitoring, a second set of reference signals for measurement is configured, and one or more reference signals for performance monitoring are configured; Wherein, the reference signals for performance monitoring are the same as or at least partially the same as the reference signals in the first set of reference signals for inference, or, the reference signals for performance monitoring are a subset of the first set of reference signals for inference.

3. The apparatus according to claim 1, wherein For network-side performance monitoring, reporting for measurement is configured, and reporting for performance monitoring is configured.

4. The device according to claim 1, wherein, For network-side performance monitoring, the reference signals for measurement and the reference signals for performance monitoring are configured together, and the measurement results for the reference signals for measurement and the measurement results for the reference signals for performance monitoring are reported together.

5. The apparatus according to claim 1, wherein, For network-side performance monitoring, the number of reference signals in the second set of reference signals for measurement is M, the measurement results for the M reference signals are reported, or the measurement results for N reference signals are reported, where N is less than or equal to M; Wherein, the minimum number of reference signals in the second set of reference signals for which the measurement results are reported is predefined or configured, and / or, the maximum number of reference signals in the second set of reference signals for which the measurement results are reported is predefined or configured.

6. The device according to claim 1, wherein For network-side performance monitoring, the number of reference signals for performance monitoring is K, the measurement results for the K reference signals are reported, or the measurement results for L reference signals are reported, where L is less than or equal to K; Wherein, the minimum number of reference signals for which the measurement results for performance monitoring are reported is predefined or configured, and / or, the maximum number of reference signals for which the measurement results for performance monitoring are reported is predefined or configured.

7. The apparatus according to claim 1, wherein, For network-side performance monitoring, one or more of the strongest beams are reported by the terminal device; The network device calculates a performance metric, i.e., beam prediction accuracy, based on the one or more of the strongest beams; Wherein, a first reporting quantity is introduced, or, the reporting quantity of L1-RSRP / L1-SINR is reused.

8. The device according to claim 1, wherein For network-side performance monitoring, if BLER is assumed to be used as the performance metric, a second reporting quantity is introduced; the assumed BLER is included in the reporting for performance monitoring.

9. The device according to claim 1, wherein For network-side performance monitoring, the terminal device reports the L1-RSRP / L1-SINR of one or more or all of the strongest beams for performance monitoring, where the reporting quantity of L1-RSRP / L1-SINR is reused; The network device calculates a performance metric based on the L1-RSRP / L1-SINR of one or more or all of the strongest beams, i.e., the difference between the L1-RSRP / L1-SINR for monitoring and the L1-RSRP / L1-SINR for prediction.

10. The device according to claim 9, wherein, Multiple reports are configured, where one report is for the measurement result of the reference signal for measurement, and the other report is for the measurement result of the reference signal for performance monitoring; Or One report is configured, and the report is for the measurement result of the reference signal for measurement and the measurement result of the reference signal for performance monitoring.

11. The device according to claim 1, wherein For hybrid performance monitoring, one or more reference signals for performance monitoring are configured; Wherein, the reference signal for performance monitoring is the same as or at least partially the same as the reference signal in the first set of reference signals for inference, or the reference signal for performance monitoring is a subset of the first set of reference signals for inference.

12. The device according to claim 11, wherein, The terminal device calculates a performance metric based on the strongest one or more beams and includes it in the report for performance monitoring; wherein, a first reporting quantity is introduced, or the reporting quantity of L1-RSRP / L1-SINR is reused; Or, the strongest one or more beams are included by the terminal device in the report for performance monitoring; the network device calculates a performance metric based on the strongest one or more beams; wherein, a first reporting quantity is introduced, or the reporting quantity of L1-RSRP / L1-SINR is reused.

13. The apparatus according to claim 11, wherein The previous one or more beams based on inference are included by the terminal device in the report for inference; the network device calculates the beam prediction accuracy based on the previous one or more beams.

14. The apparatus according to claim 11, wherein, L1-RSRP / L1-SINR is used for the performance metric, the terminal device reports L1-RSRP / L1-SINR for performance monitoring and L1-RSRP / L1-SINR for inference, and the network device calculates the difference between the L1-RSRP / L1-SINR for performance monitoring and the L1-RSRP / L1-SINR for inference.

15. The device according to claim 11, wherein, The L1-RSRP / L1-SINR difference is used for the performance metric, the terminal device calculates the difference between the L1-RSRP / L1-SINR for performance monitoring and the L1-RSRP / L1-SINR for inference, and reports the difference.

16. The apparatus according to claim 11, wherein, Assuming that BLER is used for the performance metric, the terminal device reports the assumed BLER for performance monitoring and / or the assumed BLER for inference.

17. The device according to claim 1, wherein, DRX / DTX is enabled, and for the performance monitoring of the AI / ML function / model, the timer or counter is increased or magnified; And / or DRX / DTX is enabled, and the performance monitoring of the AI / ML function / model is paused or stopped during DRX / DTX, or the performance monitoring of the AI / ML function / model remains running during DRX / DTX; And / or DRX / DTX is enabled, the time window of the time beam prediction falls within or overlaps with the DRX / DTX period, and the prediction results for some or all of the time instances are skipped, or the prediction results for some or all of the time instances are reported.

18. The device according to claim 1, wherein, If the AI / ML-related configuration changes or updates or switches, then the performance monitoring of the AI / ML function / model is reset or restarted; Among them, the AI / ML-related configuration change or update or switch includes at least one of the following: AI / ML function / model change or update, AI / ML function / model switch, reference signal configuration change for performance monitoring, cell configuration change, scenario configuration change, prediction time instance number change, BWP switch.

19. A performance monitoring configuration device, comprising: A sending unit, which sends configuration information to a terminal device; Among them, the configuration information is used for beam management based on the AI / ML function / model and performance monitoring of the AI / ML function / model.

20. A communication system, comprising: A network device, which sends configuration information to a terminal device; A terminal device, which receives the configuration information, and the configuration information is used for beam management based on the AI / ML function / model and performance monitoring of the AI / ML function / model.

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