Performance monitoring method and apparatus

Abstract

Embodiments of the present application provide a performance monitoring method and apparatus. The method comprises: a terminal device receives from a network device configuration information for beam management; the terminal device performs temporal beam prediction and / or spatial beam prediction on the basis of an AI / ML model / function and the configuration information; and the terminal device sends to the network device performance metrics related to performance monitoring.

Description

Performance monitoring methods and devices Technical Field

[0001] The embodiments of this application relate to the field of communication technology. Background Technology

[0002] In NR Rel-18, artificial intelligence / machine learning (AI / ML) for the air interface was studied. 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 (BM case-1) and temporal beam prediction (BM case-2); positioning enhancement can include direct positioning and AI / ML-assisted positioning.

[0003] In some sub-use cases, a two-sided model can be used, meaning the AI / ML model is on both the terminal device side and the network device side. In other sub-use cases, a one-sided model can be used, meaning the AI / ML model is on either the terminal device side or the network device side. For beam management, the AI / ML model can be on both the terminal device side and / or the network device side.

[0004] It should be noted that the above introduction to the technical background is only for the purpose of providing a clear and complete explanation of the technical solutions of this application and facilitating understanding by those skilled in the art. It should not be assumed that these technical solutions are known to those skilled in the art simply because they have been described in the background section of this application.

[0005] Summary of the Invention

[0006] The inventors discovered that terminal devices and / or network devices can utilize AI / ML functions / models for beam management and performance monitoring, such as UE-assisted monitoring. However, further research is needed to determine the specific methods for performance monitoring.

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

[0008] According to one aspect of the embodiments of this application, a performance monitoring method is provided, comprising:

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

[0010] The terminal device, based on an AI / ML model / function, performs temporal beam prediction and / or spatial beam prediction according to the configuration information; and

[0011] The terminal device sends performance metrics related to performance monitoring to the network device.

[0012] According to another aspect of the embodiments of this application, a performance monitoring device is provided, comprising:

[0013] The receiver receives configuration information for beam management from the network device;

[0014] A processor, based on an AI / ML model / function, performs temporal beam prediction and / or spatial beam prediction according to the configuration information; and

[0015] A transmitter that sends performance metrics related to performance monitoring to the network device.

[0016] According to another aspect of the embodiments of this application, a performance monitoring method is provided, comprising:

[0017] The network device sends configuration information for beam management to the terminal device; wherein, the configuration information is used by the terminal device to perform temporal beam prediction and / or spatial beam prediction based on AI / ML models / functions; and

[0018] The network device receives performance monitoring related performance metrics.

[0019] According to another aspect of the embodiments of this application, a performance monitoring device is provided, comprising:

[0020] A transmitter that sends configuration information for beam management to a terminal device; wherein the configuration information is used by the terminal device to perform temporal beam prediction and / or spatial beam prediction based on an AI / ML model / function; and

[0021] Receiver, and related performance metrics for monitoring its receiving performance.

[0022] According to another aspect of the embodiments of this application, a communication system is provided, comprising:

[0023] Network devices that send configuration information for beam management;

[0024] The terminal device receives the configuration information; performs temporal beam prediction and / or spatial beam prediction based on the configuration information according to the AI / ML model / function; and sends performance metrics related to performance monitoring to the network device.

[0025] The beneficial effects of this application's embodiments include: the terminal device receiving configuration information for beam management from the network device; performing temporal beam prediction and / or spatial beam prediction based on the configuration information using an AI / ML model / function; and sending performance metrics related to performance monitoring to the network device. This improves the accuracy of performance monitoring and enhances the performance and efficiency of AI / ML.

[0026] Specific embodiments of this application are disclosed in detail with reference to the following description and accompanying drawings, indicating how the principles of this application can be adopted. It should be understood that the embodiments of this application are not limited in scope. Within the spirit and scope of the appended claims, embodiments of this application include many changes, modifications, and equivalents.

[0027] Features described and / or illustrated for 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 "including / comprises" as used herein refers to the presence of a feature, whole, step, or component, but does not exclude the presence or addition of one or more other features, wholes, steps, or components. Attached Figure Description

[0029] The elements and features described in one drawing or embodiment of this application may be combined with elements and features shown in one or more other drawings or embodiments. Furthermore, in the drawings, similar reference numerals denote corresponding parts in several drawings and can be used to indicate corresponding parts used in more than one embodiment.

[0030] Figure 1 is a schematic diagram of a communication system according to an embodiment of this application;

[0031] Figure 2 is a schematic diagram of AI / ML used for beam management;

[0032] Figure 3 is a schematic diagram of a performance monitoring method according to an embodiment of this application;

[0033] Figure 4 is another schematic diagram of the beam management method according to an embodiment of this application;

[0034] Figure 5 is a schematic diagram of the measurement window and prediction window according to an embodiment of this application;

[0035] Figure 6 is a schematic diagram of a terminal initiating a performance measurement report according to an embodiment of this application;

[0036] Figure 7 is another schematic diagram of a terminal initiating a performance measurement report according to an embodiment of this application;

[0037] Figure 8 is a schematic diagram of a performance monitoring method according to an embodiment of this application;

[0038] Figure 9 is a schematic diagram of a performance monitoring device according to an embodiment of this application;

[0039] Figure 10 is a schematic diagram of a performance monitoring device according to an embodiment of this application;

[0040] Figure 11 is a schematic diagram of a terminal device according to an embodiment of this application;

[0041] Figure 12 is a schematic diagram of a network device according to an embodiment of this application. Detailed Implementation

[0042] Referring to the accompanying drawings, the foregoing and other features of this application will become apparent from the following description. Specific embodiments of this application are specifically disclosed in the description and drawings, illustrating partial implementations in which the principles of this application may be employed. It should be understood that this application is not limited to the described embodiments; rather, it includes all modifications, variations, and equivalents falling within the scope of the appended claims.

[0043] In the embodiments of this application, the terms "first," "second," etc., are used to distinguish different elements by name, but do not indicate the spatial arrangement or chronological order of these elements, and these elements should not be limited by these terms. The term "and / or" includes any one or more of the terms listed in association and all combinations thereof. The terms "comprising," "including," "having," etc., refer to the presence of the stated features, elements, components, or assemblies, but do not exclude the presence or addition of one or more other features, elements, components, or assemblies.

[0044] In the embodiments of this application, the singular forms "a," "the," etc., including the plural forms, should be broadly interpreted as "a kind" or "a class" rather than limited to the meaning of "an." Furthermore, the term "the" should be understood to include both the singular and plural forms, unless the context explicitly indicates otherwise. Additionally, the term "according to" should be understood as "at least partially based on…," and the term "based on" should be understood as "at least partially based on…," unless the context explicitly indicates otherwise.

[0045] In the embodiments of this application, the term "communication network" or "wireless communication network" may refer to a network that conforms to any of the following communication standards, such as Long Term Evolution (LTE), LTE-Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), High-Speed ​​Packet Access (HSPA), etc.

[0046] Furthermore, communication between devices in a communication system can be carried out according to communication protocols at any stage, 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 currently known or future communication protocols.

[0047] In the embodiments of this application, the term "network device" refers, for example, to a device in a communication system that connects a terminal device to a communication network and provides services to that 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.

[0048] Base stations can include, but are not limited to: NodeBs (or NBs), evolved NodeBs (eNodeBs or eNBs), and 5G base stations (gNBs), IAB hosts, etc. They can also include Remote Radio Heads (RRHs), Remote Radio Units (RRUs), relays, or low-power nodes (e.g., femeto, pico, etc.). The term "base station" can encompass some or all of their functions, and each base station can provide communication coverage to a specific geographic 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.

[0049] In the embodiments of this application, the terms "User Equipment" (UE) or "Terminal Equipment" (TE) refer, for example, to a device that accesses a communication network and receives network services through a network device. A terminal device can be fixed or mobile, and may also be referred to as a mobile station (MS), terminal, subscriber station (SS), access terminal (AT), station, etc.

[0050] The terminal device may include, but is not limited to, the following devices: cellular phone, personal digital assistant (PDA), wireless modem, wireless communication device, handheld device, machine-type communication device, laptop computer, cordless phone, smartphone, smartwatch, digital camera, etc.

[0051] For example, in scenarios such as the Internet of Things (IoT), terminal devices can also be machines or devices for monitoring or measurement, such as including but not limited to: machine-type communication (MTC) terminals, vehicle communication terminals, device-to-device (D2D) terminals, machine-to-machine (M2M) terminals, and so on.

[0052] Furthermore, the terms "network side" or "network equipment side" refer to one side of the network, which can be a base station or include one or more network devices as described above. The terms "user side," "terminal side," or "terminal equipment side" refer to the side of the user or terminal, which can be a UE or include one or more terminal devices as described above. Unless otherwise specified, "equipment" can refer to either network equipment or terminal equipment.

[0053] The following examples illustrate the scenarios of embodiments of this application, but this application is not limited thereto.

[0054] Figure 1 is a schematic diagram of a communication system according to an embodiment of this application, illustrating the case of a terminal device and a network device as examples. As shown in Figure 1, the communication system 100 may include a network device 101 and terminal devices 102 and 103. For simplicity, Figure 1 only illustrates the case of two terminal devices and one network device, but the embodiments of this application are not limited to this.

[0055] In this embodiment of the application, network device 101 and terminal devices 102 and 103 can transmit existing services or services that can be implemented in the future. 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.

[0056] It is worth noting that Figure 1 shows that both terminal devices 102 and 103 are within the coverage area of ​​network device 101, but this application is not limited to this. Both terminal devices 102 and 103 may be outside the coverage area of ​​network device 101, or one terminal device 102 may be within the coverage area of ​​network device 101 while the other terminal device 103 may be outside the coverage area of ​​network device 101.

[0057] In the embodiments of this application, higher-layer signaling may be, for example, Radio Resource Control (RRC) signaling; for example, referred to as an RRC message, including MIB, system information, dedicated RRC messages; or referred to as an RRC information element. Higher-layer signaling may also be, for example, Medium Access Control (MAC) signaling; or referred to as a MAC control element. However, this application is not limited to these.

[0058] Since Rel-15, NR 5G has introduced beam management. The beam management process is based on beam sweeping. For example, the gNB needs to send beams to the UE in sequence, and the UE can select the best one or more beams and provide feedback.

[0059] The gNB can configure reference signals for the UE, such as Channel State Information Reference Signal (CSI-RS) / Synchronization Signal Block (SSB) for beam scanning and beam measurement. To indicate which beam to select for communication, the gNB configures Transmission Configuration Indication (TCI) states and indicates which TCI states are used. For example, the gNB can configure a list of TCI states via RRC, and then select a subset of the configured TCI states (e.g., 8 TCI states) to be active via MAC CE, and indicate the active TCI state to the UE via DCI (e.g., via the TCI indication field in the DCI).

[0060] In Rel-19, AI / ML-based beam management was introduced. AI / ML-based beam management in Rel-19 (BM Case-1 and BM Case-2, with NW-side and / or UE-side models) can reduce overhead.

[0061] Figure 2 is a schematic diagram of AI / ML used for beam management. As shown in Figure 2, one or more reference signals (which may be referred to as RS for measurement or RS for inference) in the second reference signal resource set (set B) can be received and measured by the terminal device. The measurement results can be used as input to AI / ML. One or more reference signals (which may be referred to as RS for prediction) in the first reference signal resource set (set A) can be used by the terminal device as output to AI / ML, for example, the measurement results can be used as labeled data or ground truth data for AI / ML. For details regarding AI / ML and sets A and B, please refer to relevant technologies, which will not be elaborated here.

[0062] As shown in Figure 2, for example, AI / ML-based beam management can predict the information corresponding to set A (a larger number of reference signals) based on the measurement results of set B (a smaller number of reference signals), and can perform beam prediction in the spatial / temporal domains.

[0063] The above provides an illustrative description of beam management, but this application is not limited thereto. Furthermore, the above embodiments can be considered as part of the embodiments of this application, applicable to this application, and can also be implemented in combination with one or more of the following embodiments.

[0064] In the embodiments of this application, one or more AI / ML models can be configured and run in network devices and / or terminal devices. AI / ML models can be used for various signal processing functions in wireless communication, such as CSI prediction, CSI compression, beam prediction, positioning management, etc.; this application is not limited thereto.

[0065] First aspect of the embodiments

[0066] This application provides a performance monitoring method, which is described from the perspective of the terminal device.

[0067] Figure 3 is a schematic diagram of a performance monitoring method according to an embodiment of this application. As shown in Figure 3, the method includes:

[0068] 301. The terminal device receives configuration information for beam management from the network device;

[0069] 302, the terminal device, based on an AI / ML model / function, performs temporal beam prediction and / or spatial beam prediction according to the configuration information; and

[0070] 303, the terminal device sends the performance metrics related to performance monitoring to the network device.

[0071] It is worth noting that Figure 3 above is only an illustrative description of the embodiments of this application, but this application is not limited thereto. For example, the execution order between various operations can be appropriately adjusted, and other operations can be added or some operations can be removed. Those skilled in the art can make appropriate modifications based on the above content, and are not limited to the description in Figure 3 above.

[0072] In some embodiments, a functionality refers to an AI / ML feature / feature group enabled by a configuration, wherein the configuration is supported based on conditions indicated by UE capabilities.

[0073] For example, an AL / ML function can 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.

[0074] For example, the function could be to use AI / ML for spatial beam prediction, or to use AI / ML for temporal 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.

[0075] In some embodiments, the AI / ML function / model can be used for beam management. One or more reference signals are used for measurement, and the measurement results are input to the AI / ML function / model. Another one or more reference signals are used to input the output of the AI / ML function / model for inference.

[0076] For ease of description, beam management based on AI / ML functionality / model will be referred to as model inference or inference operation, training data collection based on AI / ML functionality / model will be referred to as training data collection (training data collection can also use non-AI / ML methods), and performance monitoring based on AI / ML functionality / model will be referred to as performance monitoring.

[0077] In some embodiments, the configuration information may include configuration information for one or more reference signals, such as CSI-RS configuration information, etc. This application is not limited thereto; further details regarding specific configuration information can be found in related technologies. The configuration information may include configuration information for training data collection, and / or configuration information for model inference, and / or configuration information for performance monitoring.

[0078] Figure 4 is another schematic diagram of the beam management method according to an embodiment of this application, illustrated using a terminal device configured with AI / ML as an example. As shown in Figure 4, the method includes:

[0079] 401. The terminal device receives configuration information from the network device; for example, the configuration information includes a second set of reference signal resources (set B) for measurement and a first set of reference signal resources (set A) for prediction.

[0080] 402, The terminal device receives the reference signal;

[0081] 403. The terminal device performs reference signal measurements and inputs the measurement results into the AI / ML function / model; for example, the measurement results of the reference signal in set B are used as input to AI / ML, and the reference signal in set A is used for prediction (or inference); and

[0082] 404, the terminal device sends the prediction result to the network device.

[0083] For example, the AI / ML function resides on the terminal device side. After enabling or activating the AI / ML function, the terminal device performs measurements based on reference signals from the network side, uses AI / ML to perform beam prediction based on the measurement results, and sends the prediction results to the network device.

[0084] It is worth noting that Figure 4 above is only an illustrative description of the embodiments of this application, but this application is not limited thereto. For example, the execution order between various operations can be appropriately adjusted, and other operations can be added or some operations can be removed. Those skilled in the art can make appropriate modifications based on the above content, and are not limited to the description in Figure 4 above.

[0085] In some embodiments, for BM case-2 with a terminal-side model, the terminal device can measure reference signals transmitted over one or more time instances within a measurement / observation window. The terminal-side AI / ML model / function can predict the beam quality of one or more future time instances (i.e., the prediction window).

[0086] Figure 5 is a schematic diagram of the measurement window and prediction window according to an embodiment of this application. As shown in Figure 5, the measurement window may include multiple time instances (T1 to T4), on which CSI-RS can be sent. The terminal device can measure these CSI-RS and input the measurement results into an AI / ML model / function for inference, thereby predicting the beam quality of multiple time instances (T5 to T8) in the prediction window. As shown in Figure 5, the terminal device can report the inference results to the network device.

[0087] Figure 5 illustrates the BM case-2 scenario, but this application is not limited to this. For example, BM case-1 involves one or more time instances, or other scenarios involving time instances can also refer to Figure 5.

[0088] The above illustrations demonstrate AI / ML-based beam management, but this application is not limited thereto.

[0089] In some embodiments, the reference signal used for the performance monitoring and / or the performance metric report related to the performance monitoring are configured via a Channel State Information (CSI) report.

[0090] For example, the UE side has AI / ML functions / models for UE-assisted performance monitoring regarding BM Case-1 or BM Case-2, and performance monitoring is configured through CSI reports.

[0091] In some embodiments, the performance metric is calculated based on a time instance of a transmitted reference signal used for the performance monitoring, and the performance metric is reported after a reference signal measurement is performed at each time instance used for the performance monitoring.

[0092] For example, performance metrics can be calculated on a time instance of a transport CSI-RS configured for performance monitoring. Performance metrics are reported after each time instance's CSI-RS measurement.

[0093] For example, taking BM case-2 in Figure 5 as an example, for the prediction window used for performance monitoring, a performance metric can be calculated for time instance T5 of CSI-RS transmission and reported after the CSI-RS measurement for time instance T5; a performance metric can be calculated for time instance T6 of CSI-RS transmission and reported after the CSI-RS measurement for time instance T6; a performance metric can be calculated for time instance T7 of CSI-RS transmission and reported after the CSI-RS measurement for time instance T7; a performance metric can be calculated for time instance T8 of CSI-RS transmission and reported after the CSI-RS measurement for time instance T8. This application is not limited to this; for example, it is also applicable to scenarios involving one or more time instances, such as BM case-1, or other scenarios involving time instances.

[0094] In some embodiments, the performance metric is calculated based on a time window comprising multiple time instances of a transmitted reference signal used for performance monitoring, and the performance metric is reported after reference signal measurements are taken on all time instances within the time window used for performance monitoring.

[0095] For example, performance metrics are calculated within a configurable time window that includes multiple time instances of a transmission reference signal configured for performance monitoring, and the performance metrics are reported after measurement of all time instances within that time window.

[0096] In some embodiments, a performance metric is calculated based on reference signal measurements (averaging and / or weighting, etc.) from multiple time instances, and said performance metric is reported.

[0097] For example, performance metrics can be calculated based on measurements from multiple time instances, such as averaging measurements from multiple time instances and reporting a single performance metric.

[0098] For example, taking BM case-2 in Figure 5 as an example, for the prediction window used for performance monitoring, CSI-RS measurements can be performed on time instance T5, time instance T6, time instance T7, and time instance T8 of the CSI-RS transmission. The measurements from these four time instances are then averaged to calculate a performance metric, which is reported after the CSI-RS measurement of time instance T8. This application is not limited to this; it is also applicable to scenarios involving one or more time instances, such as BM case-1, or other scenarios involving time instances.

[0099] In some embodiments, multiple performance metrics are calculated separately based on reference signal measurements of the multiple time instances, and the multiple performance metrics are reported together.

[0100] For example, calculate performance metrics for each time instance and transmit multiple performance metric results through a single report.

[0101] For example, taking BM case-2 in Figure 5 as an example, for the prediction window used for performance monitoring, performance metrics can be calculated for time instance T5 of CSI-RS transmission, performance metrics can be calculated for time instance T6 of CSI-RS transmission, performance metrics can be calculated for time instance T7 of CSI-RS transmission, and performance metrics can be calculated for time instance T8 of CSI-RS transmission; then, these four performance metrics are reported together after the CSI-RS measurement at time instance T8. This application is not limited to this; for example, it is also applicable to scenarios involving one or more time instances, such as BM case-1, or other scenarios involving time instances.

[0102] In some embodiments, the reporting of the performance metric is configured by the network device; the performance metric is sent via at least one of the following: Layer 1 signaling, MAC CE, or RRC.

[0103] For example, the UE side has AI / ML functions / models for UE-assisted performance monitoring regarding BM Case-1 or BM Case-2, and performance monitoring is configured via CSI reports. Performance measurement reports are configured by the network side, i.e., the gNB, meaning they are controlled by the gNB. For example, performance measurements are sent via Layer 1 signaling. As another example, performance measurements are sent via MAC CE and / or RRC.

[0104] In some embodiments, the reporting of the performance metrics is triggered by the terminal device via an event.

[0105] For example, the UE has AI / ML functions / models, and UE-assisted performance monitoring for BM Case-1 or BM Case-2 is configured via CSI reports. Performance metric reports can be triggered by certain events, meaning the performance metric report is initiated by the UE. For example, if the performance metric falls below a certain threshold, the UE can trigger a performance metric report.

[0106] In some embodiments, the performance metric is sent via Layer 1 signaling.

[0107] In some embodiments, performance metric reports are based on dynamic scheduling. For example, a terminal device sends uplink control information (PUCCH) to request resources; the terminal device detects downlink control information (DCI) to indicate uplink resources; and the terminal device sends the performance metric to the network device based on the PUCCH / PUSCH resources indicated by the downlink control information (DCI).

[0108] Figure 6 is a schematic diagram of a terminal initiating a performance measurement report according to an embodiment of this application. As shown in Figure 6, when a performance measurement report is triggered (as shown in 601), the UE can first request resources via PUCCH to carry the performance measurement report (as shown in 602). The resources for sending this request can be PUCCH or PUSCH (including dynamically scheduled PUSCH and / or configuration-authorized PUSCH). The request on PUCCH can be an SR or a similar SR message or a new UCI type.

[0109] After sending the request, the UE can detect the DCI (as shown in 603), which indicates the resource to be used to carry the performance metric report. Upon receiving the DCI, the UE transmits the performance metric on the indicated resource on the PUCCH or PUSCH (as shown in 604).

[0110] In some embodiments, performance metric reports are based on pre-configured resources. For example, the terminal device sends uplink control information / PUCCH to indicate performance metric transmission; and the terminal device sends the performance metric to the network device according to pre-configured PUCCH / PUSCH resources.

[0111] Figure 7 is another schematic diagram illustrating the terminal initiating a performance measurement report according to an embodiment of this application. As shown in Figure 6, when a performance measurement report is triggered (as shown in 701), the UE can first send a notification message through a first channel (e.g., PUCCH) to notify the gNB that it will transmit the performance measurement report through a second channel (as shown in 702). The notification on the first channel (e.g., PUCCH) can be an SR or a similar SR message or a new UCI type. Then, the UE transmits the performance measurement report on the second channel (as shown in 703); the second channel can be PUCCH or PUSCH (e.g., a configured authorized PUSCH, including type 1CG-PUSCH and / or type 2CG-PUSCH).

[0112] In some embodiments, the performance metric is sent via MAC CE.

[0113] For example, performance metric reports can be generated via MAC CE. When a performance metric report is triggered, the UE can send a MAC CE to transmit the performance metrics.

[0114] In some embodiments, the terminal device sends a scheduling request (SR) to request uplink resources; and the terminal device sends the performance metric to the network device via the MAC CE through the uplink resources allocated by the network device to the MAC CE.

[0115] For example, if there are no uplink resources available for transmitting a MAC CE, the UE can first send an SR to request MAC CE resources, and the network device can allocate uplink resources for the MAC CE. When a performance metric report is triggered, the UE can transmit performance metrics through this MAC CE.

[0116] In some embodiments, the performance metric is sent via RRC signaling.

[0117] In some embodiments, a subset of the first set of reference signal resources (set A) used for prediction is configured for the performance monitoring.

[0118] For example, if the UE has AI / ML functions / models, for UE-assisted performance monitoring of BM Case-1 or BM Case-2, a subset of set A can be configured to the UE for performance monitoring to obtain baseline real data (ground truth).

[0119] In some embodiments, the performance metric is beam prediction accuracy, and the beam prediction accuracy is determined based on the subset.

[0120] For example, a performance metric could be beam prediction accuracy. Beam prediction accuracy can be determined based on a subset of set A. For example, beam prediction accuracy is based on the predicted Top-1 or Top-K beams in a subset of set A, and the measured Top-1 or Top-K beams in a subset of set A, where Top-1 represents the best beam or Top-K represents the best few (K) beams.

[0121] The embodiments of this application can be applied to both the UE-side model and the gNB-side model, but this application is not limited thereto. Furthermore, the AI / ML in the embodiments of this application can be used for beam management, such as temporal beam prediction and / or spatial beam prediction, but this application is not limited thereto; for example, non-AI / ML methods can also be used for data collection.

[0122] The above embodiments are merely illustrative examples of embodiments of this application, but this application is not limited thereto, and appropriate modifications can be made based on the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0123] As can be seen from the above embodiments, the terminal device receives configuration information for beam management from the network device; performs temporal beam prediction and / or spatial beam prediction based on the configuration information according to the AI / ML model / function; and sends performance metrics related to performance monitoring to the network device. This improves the accuracy of performance monitoring and enhances the performance and efficiency of AI / ML.

[0124] Second aspect of the embodiments

[0125] This application provides a performance monitoring method, described from the perspective of a network device. The embodiments of the second aspect can be combined with the embodiments of the first aspect, and the content identical to that of the embodiments of the first aspect will not be repeated.

[0126] Figure 8 is a schematic diagram of a performance monitoring method according to an embodiment of this application. As shown in Figure 8, the method includes:

[0127] 801, the network sends configuration information for beam management to a terminal device; wherein the terminal device performs temporal beam prediction and / or spatial beam prediction based on the configuration information, using an AI / ML model / function; and

[0128] 802, the network device receives performance metrics related to performance monitoring sent by the terminal device.

[0129] It is worth noting that Figure 8 above is only an illustrative description of the embodiments of this application, but this application is not limited thereto. For example, the execution order between various operations can be appropriately adjusted, and other operations can be added or some operations can be removed. Those skilled in the art can make appropriate modifications based on the above content, and are not limited to the description in Figure 8 above.

[0130] In some embodiments, the reference signal used for the performance monitoring and / or the performance metric report related to the performance monitoring are configured via a Channel State Information (CSI) report.

[0131] In some embodiments, the performance metric is calculated based on a time instance of a transmitted reference signal used for the performance monitoring, and the performance metric is reported after a reference signal measurement is performed at each time instance used for the performance monitoring.

[0132] In some embodiments, the performance metric is calculated based on a time window comprising multiple time instances of the transmitted reference signal used for performance monitoring, and the performance metric is reported after the reference signal is measured on all time instances within the time window used for performance monitoring.

[0133] In some embodiments, a performance metric is calculated based on reference signal measurements (average and / or weighted, etc.) of the plurality of time instances, and the performance metric is reported.

[0134] In some embodiments, multiple performance metrics are calculated separately based on reference signal measurements of the multiple time instances, and the multiple performance metrics are reported together.

[0135] In some embodiments, the reporting of the performance metrics is configured by the network device;

[0136] The performance metric is sent via at least one of the following: Layer 1 signaling, MAC CE, or RRC.

[0137] In some embodiments, the reporting of the performance metrics is triggered by the terminal device via an event.

[0138] In some embodiments, the performance metric is sent via Layer 1 signaling.

[0139] In some embodiments, the network device receives uplink control information (PUCCH) for requesting resources; sends downlink control information (DCI) for indicating uplink resources; wherein the terminal device sends the performance metric to the network device according to the PUCCH / PUSCH resources indicated by the downlink control information (DCI).

[0140] In some embodiments, the network device receives uplink control information / PUCCH for instructing the transmission of performance metrics; wherein the terminal device sends the performance metrics to the network device according to pre-configured PUCCH / PUSCH resources.

[0141] In some embodiments, the performance metric is sent via MAC CE.

[0142] In some embodiments, the network device receives a scheduling request (SR) for requesting uplink resources and allocates corresponding uplink resources according to the scheduling request (SR); wherein, the terminal device sends the performance metric to the network device through the MAC CE using the uplink resources allocated by the network device for the MAC CE.

[0143] In some embodiments, the performance metric is sent via RRC signaling.

[0144] In some embodiments, a subset of the first set of reference signal resources (set A) used for prediction is configured for the performance monitoring.

[0145] In some embodiments, the performance metric is beam prediction accuracy, and the beam prediction accuracy is determined based on the subset.

[0146] In some embodiments, the network device may receive feedback information and / or report information sent by the terminal device. For example, the terminal device may report inference results and / or performance monitoring results and / or training data collection results to the network device, but this application is not limited thereto.

[0147] The above embodiments are merely illustrative examples of embodiments of this application, but this application is not limited thereto, and appropriate modifications can be made based on the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0148] As can be seen from the above embodiments, the terminal device receives configuration information for beam management from the network device; performs temporal beam prediction and / or spatial beam prediction based on the configuration information according to the AI / ML model / function; and sends performance metrics related to performance monitoring to the network device. This improves the accuracy of performance monitoring and enhances the performance and efficiency of AI / ML.

[0149] Third aspect of the embodiments

[0150] This application provides a performance monitoring device. This device may be, for example, a terminal device, or one or more components or parts configured within a terminal device; details identical to those in the first and second aspects will not be repeated.

[0151] Figure 9 is a schematic diagram of a performance monitoring device according to an embodiment of this application. As shown in Figure 9, the performance monitoring device 900 according to an embodiment of this application includes:

[0152] Receiver 901 receives configuration information for beam management from network devices;

[0153] Processor 902, based on an AI / ML model / function, performs temporal beam prediction and / or spatial beam prediction according to the configuration information; and

[0154] Transmitter 903 sends performance metrics related to performance monitoring to the network device.

[0155] In some embodiments, the reference signal used for the performance monitoring and / or the performance metric report related to the performance monitoring are configured via a Channel State Information (CSI) report.

[0156] In some embodiments, the performance metric is calculated based on a time instance of a transmitted reference signal used for the performance monitoring, and the performance metric is reported after a reference signal measurement is performed at each time instance used for the performance monitoring.

[0157] In some embodiments, the performance metric is calculated based on a time window comprising multiple time instances of the transmitted reference signal used for performance monitoring, and the performance metric is reported after the reference signal is measured on all time instances within the time window used for performance monitoring.

[0158] In some embodiments, a performance metric is calculated based on reference signal measurements (average and / or weighted, etc.) of the plurality of time instances, and the performance metric is reported.

[0159] In some embodiments, multiple performance metrics are calculated separately based on reference signal measurements of the multiple time instances, and the multiple performance metrics are reported together.

[0160] In some embodiments, the reporting of the performance metrics is configured by the network device;

[0161] The performance metric is sent via at least one of the following: Layer 1 signaling, MAC CE, or RRC.

[0162] In some embodiments, the reporting of the performance metrics is triggered by the terminal device via an event.

[0163] In some embodiments, the performance metric is sent via Layer 1 signaling.

[0164] In some embodiments, transmitter 903 further transmits uplink control information / PUCCH for requesting resources; processor 902 further detects downlink control information (DCI) for indicating uplink resources; and transmitter 903 transmits the performance metric to the network device based on the PUCCH / PUSCH resources indicated by the downlink control information (DCI).

[0165] In some embodiments, transmitter 903 also transmits uplink control information / PUCCH for instructing performance metric transmission; and transmits the performance metric to the network device according to pre-configured PUCCH / PUSCH resources.

[0166] In some embodiments, the performance metric is sent via MAC CE.

[0167] In some embodiments, transmitter 903 also sends a scheduling request (SR) for requesting uplink resources; and sends the performance metric to the network device via the MAC CE using the uplink resources allocated by the network device for the MAC CE.

[0168] In some embodiments, the performance metric is sent via RRC signaling.

[0169] In some embodiments, a subset of the first set of reference signal resources (set A) used for prediction is configured for the performance monitoring.

[0170] In some embodiments, the performance metric is beam prediction accuracy, and the beam prediction accuracy is determined based on the subset.

[0171] The above embodiments are merely illustrative examples of embodiments of this application, but this application is not limited thereto, and appropriate modifications can be made based on the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0172] It is worth noting that the above description only covers the components or modules relevant to this application, but this application is not limited thereto. The performance monitoring device 900 may also include other components or modules, and for details regarding these components or modules, please refer to relevant technologies.

[0173] Furthermore, for simplicity, Figure 9 only illustrates the connection relationships or signal flow between the various components or modules, but those skilled in the art should understand that various related technologies such as bus connections can be used. The aforementioned components or modules can be implemented using hardware facilities such as processors, memory, transmitters, and receivers; this application does not limit this implementation.

[0174] As can be seen from the above embodiments, the terminal device receives configuration information for beam management from the network device; performs temporal beam prediction and / or spatial beam prediction based on the configuration information according to the AI / ML model / function; and sends performance metrics related to performance monitoring to the network device. This improves the accuracy of performance monitoring and enhances the performance and efficiency of AI / ML.

[0175] Fourth aspect of the embodiment

[0176] This application provides a performance monitoring device. This device may be, for example, a network device, or one or more components or parts configured within a network device; details identical to those in the embodiments of the first to third aspects will not be repeated.

[0177] Figure 10 is another schematic diagram of a performance monitoring device according to an embodiment of this application. As shown in Figure 10, the performance monitoring device 1000 includes:

[0178] Transmitter 1001 transmits configuration information for beam management; wherein, the configuration information is used by a terminal device to perform temporal beam prediction and / or spatial beam prediction based on an AI / ML model / function; and

[0179] Receiver 1002, its receiving performance monitoring related performance metrics.

[0180] The above embodiments are merely illustrative examples of embodiments of this application, but this application is not limited thereto, and appropriate modifications can be made based on the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0181] It is worth noting that the above description only covers the components or modules relevant to this application, but this application is not limited thereto. The performance monitoring device 1000 may also include other components or modules, and for details regarding these components or modules, please refer to relevant technologies.

[0182] Furthermore, for simplicity, Figure 10 only illustrates the connection relationships or signal flow between the various components or modules, but those skilled in the art should understand that various related technologies such as bus connections can be used. The aforementioned components or modules can be implemented using hardware facilities such as processors, memory, transmitters, and receivers; this application does not limit this implementation.

[0183] As can be seen from the above embodiments, the terminal device receives configuration information for beam management from the network device; performs temporal beam prediction and / or spatial beam prediction based on the configuration information according to the AI / ML model / function; and sends performance metrics related to performance monitoring to the network device. This improves the accuracy of performance monitoring and enhances the performance and efficiency of AI / ML.

[0184] Fifth aspect of the embodiment

[0185] This application also provides a communication system, which can be referred to FIG1. ​​The contents that are the same as those in the embodiments of the first to fourth aspects will not be repeated.

[0186] In some embodiments, the communication system 100 may include at least:

[0187] Network devices that send configuration information for beam management;

[0188] The terminal device receives the configuration information; performs temporal beam prediction and / or spatial beam prediction based on the configuration information according to the AI / ML model / function; and sends performance metrics related to performance monitoring to the network device.

[0189] This application also provides a terminal device, but the application is not limited thereto and may also include other devices.

[0190] Figure 11 is a schematic diagram of a terminal device according to an embodiment of this application. As shown in Figure 11, the terminal device 1100 may include a processor 1110 and a memory 1120; the memory 1120 stores data and programs and is coupled to the processor 1110. It is worth noting that this figure is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunications functions or other functions.

[0191] For example, processor 1110 may be configured to execute a program to implement the performance monitoring method as described in the embodiments of the first aspect. For example, processor 1110 may be configured to perform the following control: receive configuration information for beam management from a network device; perform temporal beam prediction and / or spatial beam prediction based on the configuration information according to an AI / ML model / function; and send performance metrics related to performance monitoring to the network device.

[0192] 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 will not be described again here. It is worth noting that the terminal device 1100 does not necessarily include all the components shown in Figure 11; these components are not essential. Furthermore, the terminal device 1100 may also include components not shown in Figure 11, which can be referred to in the prior art.

[0193] This application also provides a network device, such as a base station, but this application is not limited to this and may also include other network devices.

[0194] Figure 12 is a schematic diagram of the network device according to an embodiment of this application. As shown in Figure 12, the network device 1200 may include: a processor 1210 (e.g., a central processing unit CPU) and a memory 1220; the memory 1220 is coupled to the processor 1210. The memory 1220 can store various data; in addition, it also stores an information processing program 1230, and executes the program 1230 under the control of the processor 1210.

[0195] For example, processor 1210 may be configured to execute a program to implement the performance monitoring method as described in the embodiments of the second aspect. For example, processor 1210 may be configured to perform the following controls: sending configuration information for beam management; the configuration information being used by a terminal device to perform temporal beam prediction and / or spatial beam prediction based on an AI / ML model / function; and receiving performance metrics related to performance monitoring.

[0196] In addition, as shown in Figure 12, network device 1200 may also include a transceiver 1240 and an antenna 1250, etc.; the functions of the above components are similar to those in the prior art, and will not be described in detail here. It is worth noting that network device 1200 does not necessarily include all the components shown in Figure 12; furthermore, network device 1200 may also include components not shown in Figure 12, which can be referred to in the prior art.

[0197] This application also provides a computer program, wherein when the program is executed in a terminal device, the program causes the terminal device to perform the performance monitoring method described in the first aspect of the embodiment.

[0198] This application also provides a storage medium storing a computer program, wherein the computer program causes a terminal device to execute the performance monitoring method described in the first aspect of the embodiment.

[0199] This application also provides a computer program, wherein when the program is executed in a network device, the program causes the network device to perform the performance monitoring method described in the second aspect of the embodiment.

[0200] This application also provides a storage medium storing a computer program, wherein the computer program causes a network device to perform the performance monitoring method described in the second aspect of the embodiment.

[0201] The apparatus and methods described above in this application can be implemented in hardware or in combination with software. This application relates to a computer-readable program that, when executed by a logic component, enables the logic component to implement the apparatus or components described above, or to implement the various methods or steps described above. This application also relates to storage media for storing the above programs, such as hard disks, magnetic disks, optical disks, DVDs, flash memory, etc.

[0202] The methods / apparatus described in conjunction with the embodiments of this application can be directly embodied in hardware, software modules executed by a processor, or a combination of both. For example, one or more and / or combinations of one or more functional block diagrams shown in the figures can correspond to various software modules in a computer program flow, or to various hardware modules. These software modules can correspond to the various steps shown in the figures, respectively. These hardware modules can be implemented, for example, using a field-programmable gate array (FPGA) to embed these software modules.

[0203] The software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. A storage medium can be coupled to the processor, enabling the processor to read information from and write information to the storage medium; or the storage medium can be an integral part of the processor. The processor and storage medium can reside in an ASIC. The software module can be stored in the memory of a 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 high-capacity MEGA-SIM card or a high-capacity flash memory device, the software module can be stored in the MEGA-SIM card or the high-capacity flash memory device.

[0204] One or more and / or one or more combinations of functional blocks described in the accompanying drawings can be implemented as a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, or any suitable combination thereof for performing the functions described herein. One or more and / or one or more combinations of functional blocks described in the accompanying drawings can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in communication with a DSP, or any other such configuration.

[0205] The present application has been described above with reference to specific embodiments. However, those skilled in the art should understand that these descriptions are exemplary and not intended to limit the scope of protection of the present application. Those skilled in the art can make various modifications and variations to the present application based on its spirit and principles, and these modifications and variations are also within the scope of the present application.

[0206] Regarding the implementation methods including the above embodiments, the following notes are also disclosed:

[0207] 1. A performance monitoring method, comprising:

[0208] The terminal device receives configuration information for beam management from the network device;

[0209] The terminal device, based on an AI / ML model / function, performs temporal beam prediction and / or spatial beam prediction according to the configuration information; and

[0210] The terminal device sends performance metrics related to performance monitoring to the network device.

[0211] 2. A performance monitoring method, comprising:

[0212] The network device sends configuration information for beam management to the terminal device; wherein, the configuration information is used by the terminal device to perform temporal beam prediction and / or spatial beam prediction based on AI / ML models / functions; and

[0213] The network device receives performance monitoring related performance metrics.

[0214] 3. A terminal device, comprising a memory and a processor, the memory storing a computer program, the processor being configured to execute the computer program to implement the performance monitoring method as described in Appendix 1.

[0215] 4. A network device comprising a memory and a processor, the memory storing a computer program and the processor being configured to execute the computer program to implement the performance monitoring method as described in Appendix 2.

[0216] 5. A computer program product comprising at least a computer program that, when executed by a processor, causes a terminal device to perform the performance monitoring method as described in Appendix 1.

[0217] 6. A computer program product comprising at least a computer program that, when executed by a processor, causes a network device to perform the performance monitoring method as described in Appendix 2.

Claims

1. A performance monitoring device, comprising: The receiver receives configuration information for beam management from the network device; A processor, based on an AI / ML model / function, performs temporal beam prediction and / or spatial beam prediction according to the configuration information; and A transmitter that sends performance metrics related to performance monitoring to the network device.

2. The apparatus according to claim 1, wherein, The reference signal used for the performance monitoring and / or the performance metric report related to the performance monitoring are configured through the channel state information report.

3. The apparatus according to claim 1, wherein, The performance metric is calculated based on a time instance of the transmitted reference signal used for the performance monitoring, and the performance metric is reported after the reference signal is measured at each time instance used for the performance monitoring.

4. The apparatus according to claim 1, wherein, The performance metric is calculated based on a time window comprising multiple time instances of the transmitted reference signal used for performance monitoring, and the performance metric is reported after the reference signal is measured at all time instances within the time window used for performance monitoring.

5. The apparatus according to claim 4, wherein, A performance metric is calculated based on reference signal measurements from the plurality of time instances, and the performance metric is reported.

6. The apparatus according to claim 4, wherein, Multiple performance metrics are calculated based on reference signal measurements from the multiple time instances, and the multiple performance metrics are reported together.

7. The apparatus according to claim 1, wherein, The reporting of the performance metrics is configured by the network device.

8. The apparatus according to claim 7, wherein, The performance metric is sent via at least one of the following: Layer 1 signaling, MAC CE, or RRC.

9. The apparatus according to claim 1, wherein, The performance metric report is triggered by an event from the terminal device.

10. The apparatus according to claim 9, wherein, The performance metrics are sent via Layer 1 signaling.

11. The apparatus according to claim 10, wherein, The transmitter also sends uplink control information / PUCCH for requesting resources; The processor detects downlink control information used to indicate uplink resources; and The transmitter sends the performance metric to the network device based on the PUCCH / PUSCH resources indicated by the downlink control information.

12. The apparatus according to claim 10, wherein, The transmitter also sends uplink control information / PUCCH for indicating performance metric transmission; and sends the performance metric to the network device according to pre-configured PUCCH / PUSCH resources.

13. The apparatus according to claim 9, wherein, The performance metrics are sent via MAC CE.

14. The apparatus according to claim 13, wherein, The transmitter also sends a scheduling request for uplink resources; and sends the performance metric to the network device via the MAC CE using the uplink resources allocated to the MAC CE by the network device.

15. The apparatus according to claim 9, wherein, The performance metrics are sent via RRC signaling.

16. The apparatus according to claim 1, wherein, A subset of the first set of reference signal resources used for prediction is configured for the performance monitoring.

17. The apparatus according to claim 16, wherein, The performance metric is beam prediction accuracy, and the beam prediction accuracy is determined based on the subset.

18. A performance monitoring device, comprising: A transmitter that sends configuration information for beam management to a terminal device; wherein the configuration information is used by the terminal device to perform temporal beam prediction and / or spatial beam prediction based on an AI / ML model / function; and Receiver, and related performance metrics for monitoring its receiving performance.

19. The apparatus according to claim 18, wherein, The reference signal used for the performance monitoring and / or the performance metric report related to the performance monitoring are configured through the channel state information report.

20. A communication system, comprising: Network devices that send configuration information for beam management; The terminal device receives the configuration information; and performs temporal beam prediction and / or spatial beam prediction based on the configuration information according to the AI / ML model / function. And send performance metrics related to performance monitoring to the network device.

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