Beam management method and apparatus

Abstract

Provided in the embodiments of the present application are a beam management method and apparatus. The method comprises: a terminal device receiving configuration information, which is from a network device and comprises at least beam pattern information; and the terminal device measuring a reference signal on the basis of the configuration information, so as to perform training data collection and / or model inference and / or performance monitoring for an AI / ML functionality / model.

Description

Beam management 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 (BM case-1) and temporal beam prediction (BM case-2); 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 leverage AI / ML functionality / models to predict beams based on beam measurement results, but there is currently no clear solution for how to define and configure beam patterns.

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

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

[0009] The terminal device receives configuration information including at least beam pattern information from the network device;

[0010] The terminal device measures the reference signal according to the configuration information to collect training data and / or model inference and / or performance monitoring for AI / ML functionality / model.

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

[0012] a receiving unit configured to receive configuration information including at least beam pattern information from a network device;

[0013] A processing unit measures a reference signal according to the configuration information to collect training data for AI / ML functionality / model and / or perform model inference and / or performance monitoring.

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

[0015] The network device sends configuration information including at least beam pattern information to the terminal device;

[0016] The configuration information is used by the terminal device to measure a reference signal for training data collection and / or model inference and / or performance monitoring for AI / ML functionality / model.

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

[0018] a sending unit configured to send configuration information including at least beam pattern information to a terminal device;

[0019] The configuration information is used by the terminal device to measure a reference signal for training data collection and / or model inference and / or performance monitoring for 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 including at least beam pattern information to a terminal device;

[0022] A terminal device measures a reference signal according to the configuration information to collect training data and / or perform model inference and / or performance monitoring for AI / ML functionality / model.

[0023] One of the beneficial effects of the embodiments of the present application is that a terminal device measures a reference signal based on configuration information including at least beam pattern information to collect training data for AI / ML functionality / models and / or perform model inference and / or performance monitoring. This ensures consistency between training and inference, improving the performance and efficiency of AI / ML.

[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 beam management method according to an embodiment of the present application;

[0030] FIG3 is a schematic diagram of AI / ML for beam management according to an embodiment of the present application;

[0031] FIG4 is a schematic diagram of a beam management method according to an embodiment of the present application;

[0032] FIG5 is an example diagram of beam patterns of set A and set B according to an embodiment of the present application;

[0033] FIG6 is another example diagram of the beam patterns of set A and set B according to an embodiment of the present application;

[0034] FIG7 is another example diagram of the beam patterns of set A and set B according to an embodiment of the present application;

[0035] FIG8 is a schematic diagram of a beam management method according to an embodiment of the present application;

[0036] FIG9 is a schematic diagram of a beam management device according to an embodiment of the present application;

[0037] FIG10 is a schematic diagram of a beam management device according to an embodiment of the present application;

[0038] FIG11 is a schematic diagram of a terminal device according to an embodiment of the present application;

[0039] FIG12 is a schematic diagram of a network device according to an embodiment of the present application. DETAILED DESCRIPTION

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] The following describes the scenarios of the embodiments of the present application through examples, but the present application is not limited thereto.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] For example, network-side monitoring can be performed, where the network monitors performance metrics and makes activation / deactivation / selection / switching / fallback decisions.

[0058] For another example, UE-side monitoring may be performed, where the UE monitors performance metrics and makes activation / deactivation / selection / switching / fallback decisions.

[0059] 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.

[0060] In embodiments of the present application, one or more AI / ML models may be configured and run in a network device and / or a terminal device. The AI / ML models may be used for various signal processing functions in wireless communications, such as CSI prediction, CSI compression, beamforming, positioning management, and the like; however, the present application is not limited thereto.

[0061] Embodiments of the first aspect

[0062] An embodiment of the present application provides a beam management method, which is described from the perspective of a terminal device.

[0063] FIG2 is a schematic diagram of a beam management method according to an embodiment of the present application. As shown in FIG2 , the method includes:

[0064] 201. A terminal device receives configuration information including at least beam pattern information from a network device.

[0065] 202. The terminal device measures a reference signal according to the configuration information to collect training data and / or perform model inference and / or performance monitoring for AI / ML functionality / model.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] For another example, the function may 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.

[0070] In some embodiments, an AI / ML functionality / model can be used for beam management. One or more reference signals are used for measurement, and the measurement results are input into the AI / ML functionality / model. Another one or more reference signals are used for inference at the output of the AI / ML functionality / model.

[0071] For ease of description, beam management based on AI / ML functionality / models is referred to as model inference, training data collection based on AI / ML functionality / models is referred to as training data collection (non-AI / ML methods can also be used for training data collection), and performance monitoring based on AI / ML functionality / models is referred to as performance monitoring.

[0072] In some embodiments, the configuration information may include configuration information of one or more reference signals, such as CSI-RS configuration information, etc. The present application is not limited thereto, and for specific configuration information, reference may be made to 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.

[0073] Figure 3 is a schematic diagram of AI / ML for beam management in an embodiment of the present application. As shown in Figure 3, one or more reference signals in the second reference signal resource set (set B) can be received and measured by the terminal device, and the measurement results can be used as input to the AI / ML. One or more reference signals in the first reference signal resource set (set A) can be used by the terminal device for the output of the AI / ML, for example, the measurement results can be used as label data or ground truth data for the AI / ML. For the specific content of AI / ML and set A and set B, please refer to the relevant technology and will not be repeated here.

[0074] FIG4 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 AI / ML as an example. As shown in FIG4 , the method includes:

[0075] 401. 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.

[0076] At 402 , the terminal device performs beam measurement and inputs the beam measurement results into an 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).

[0077] 403. The terminal device sends the beam prediction result to the network device.

[0078] 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.

[0079] It is worth noting that FIG4 above is merely a schematic illustration of an embodiment of the present application, and the present application is not limited thereto. For example, the execution order of the various operations may be appropriately adjusted, and other operations may be added or some operations may be reduced. Those skilled in the art may make appropriate modifications based on the above description, and are not limited to the description of FIG4 above.

[0080] The above schematically illustrates AI / ML-based beam management. The following describes the beam pattern.

[0081] In some embodiments, a downlink transmit beam pattern is defined for a reference signal used for the training data collection and / or the model inference and / or the performance monitoring.

[0082] For example, for beam management with AI / ML operations, a downlink transmit beam pattern (gNB DL Tx beam pattern) may be defined for reference signals used for training data collection and / or model inference and / or performance monitoring. For example, the beam pattern may represent the position distribution (e.g., a two-dimensional distribution) of downlink beams projected onto the same plane (e.g., the ground plane), but the present application is not limited thereto.

[0083] For beam management with AI / ML operations, set B can be a subset of set A (set A and set B can have the same beam shape). Alternatively, set B can be different from set A (set A and set B can have different beam shapes, for example, set B is a wide beam and set A is a narrow beam).

[0084] Figure 5 shows an example of the beam patterns of Set A and Set B according to an embodiment of the present application, and Figure 6 shows another example of the beam patterns of Set A and Set B according to an embodiment of the present application. As shown in Figures 5 and 6, Set B can be a subset of Set A, and the beams of Set A and Set B are evenly distributed. In Figure 5, the beams of Set A are part of all beams of the gNB, that is, there are beams other than the beams of Set A. In Figure 6, the beams of Set A are composed of all beams of the gNB.

[0085] Figure 7 is another example diagram of the beam patterns of Set A and Set B in an embodiment of the present application. In Figure 7, the beams of Set A are composed of all beams of the gNB. In addition, the beams of Set B are distributed in an interlaced manner. Figures 5 to 7 illustrate the beam patterns for illustrative purposes only and the present application is not limited thereto.

[0086] In some embodiments, the terminal device is configured with an N×M beam matrix, where N is the number of beams in the horizontal direction and M is the number of beams in the vertical direction;

[0087] N and / or M are configured to the terminal device; the beam pattern of the second reference signal set (set B) for measurement and / or the beam pattern of the first reference signal set (set A) for prediction corresponds to one or more elements in the beam matrix.

[0088] For example, the values ​​of N and M may be included in the configuration information as beam pattern information.

[0089] For another example, the beam matrix may include all beams of the gNB, with each element corresponding to a beam.

[0090] In some embodiments, the terminal device is configured with an N×M beam codebook matrix, where N is the number of beams in the horizontal direction and M is the number of beams in the vertical direction;

[0091] N and / or M are configured to the terminal device; the beam pattern of the second reference signal set (set B) for measurement and / or the beam pattern of the first reference signal set (set A) for prediction corresponds to one or more elements in the beam codebook matrix.

[0092] For example, instead of directly configuring the beam matrix, a beam codebook matrix can be configured for the terminal device through DFT vectors, etc. Each element in the beam codebook matrix can correspond to a beam. For more information about codebooks and DFT vectors, please refer to related technologies.

[0093] In some embodiments, bitmap information corresponding to all beams of the network device is configured for the terminal device.

[0094] For example, each bit in the bitmap corresponds to a beam.

[0095] In some embodiments, for the second reference signal set (set B) for measurement and / or the first reference signal set (set A) for prediction, the coordinates or index of the first beam in the beam matrix or beam codebook matrix, and the step size of the beam in the horizontal direction and / or vertical direction of the beam matrix or beam codebook matrix are configured.

[0096] For example, the coordinates of the first beam, the step size in the horizontal direction (e.g., the first interval between two adjacent beams in the horizontal direction), and the step size in the vertical direction (e.g., the second interval between two adjacent beams in the vertical direction) can be included in the configuration information as beam pattern information.

[0097] In some embodiments, the number of beams of the second reference signal set (set B) and / or the first reference signal set (set A) in the horizontal direction and / or vertical direction of the beam matrix or the beam code book matrix is ​​configured, or the number of all beams in the horizontal direction and / or vertical direction of the beam matrix or the beam code book matrix is ​​configured.

[0098] For example, the number of beams of set B in the horizontal direction, the number of beams of set A in the horizontal direction, the number of beams of set B in the vertical direction, and the number of beams of set A in the vertical direction can be included in the configuration information as beam pattern information.

[0099] For another example, the number of all beams in the horizontal direction and the number of all beams in the vertical direction may also be included in the configuration information as beam pattern information.

[0100] In some embodiments, an association / mapping between CSI-RS resources and beams or an association / mapping between CSI-RS resources and beam codebooks is configured for the terminal device.

[0101] In some embodiments, the association / mapping between CSI-RS resources and beams or the association / mapping between CSI-RS resources and beam codebooks is predefined.

[0102] In some embodiments, the beam patterns of at least some of the following reference signals are the same: a second reference signal used for model input data collection in training data collection, a second reference signal set (set B) used for model input in model inference, and a second reference signal used for measurement in performance monitoring.

[0103] For example, the beam pattern of the second reference signal used for model input data collection in training data collection is pattern 1, the beam pattern of the second reference signal set (set B) used for model input in model inference is pattern 2, and the beam pattern of the second reference signal used for measurement in performance monitoring is pattern 3; then patterns 1, 2, and 3 are all the same.

[0104] In some embodiments, the beam patterns of at least some of the following reference signals are different: a second reference signal used for model input data collection in training data collection, a second reference signal set (set B) used for model input in model inference, and a second reference signal used for measurement in performance monitoring.

[0105] For example, the beam pattern of the second reference signal used for model input data collection in training data collection is pattern 1, the beam pattern of the second reference signal set (set B) used for model input in model inference is pattern 2, and the beam pattern of the second reference signal used for measurement in performance monitoring is pattern 3; then at least two patterns among pattern 1, pattern 2 and pattern 3 are different.

[0106] In some embodiments, the beam patterns of at least some of the following reference signals are the same: a first reference signal used for collecting ground truth data in training data collection, a first reference signal set (set A) used for model output in model inference, and a first reference signal used for monitoring in performance monitoring.

[0107] For example, the beam pattern of the first reference signal used for collecting ground truth data in training data collection is pattern 4, the beam pattern of the first reference signal set (set A) used for model output in model inference is pattern 5, and the beam pattern of the first reference signal used for monitoring in performance monitoring is pattern 6; then patterns 4, 5, and 6 are all the same.

[0108] In some embodiments, the beam patterns of at least some of the following reference signals are different: a first reference signal used for collecting ground truth data in training data collection, a first reference signal set (set A) used for model output in model inference, and a first reference signal used for monitoring in performance monitoring.

[0109] For example, the beam pattern of the first reference signal used for collecting ground truth data in training data collection is pattern 4, the beam pattern of the first reference signal set (set A) used for model output in model inference is pattern 5, and the beam pattern of the first reference signal used for monitoring in performance monitoring is pattern 6; then at least two of patterns 4, 5, and 6 are different.

[0110] In some embodiments, a terminal device may be configured with one or all of the following types of beam patterns:

[0111] - A second reference signal used for model input data collection during training data collection;

[0112] -The first reference signal used for collecting ground truth data in training data collection.

[0113] In some embodiments, the beam patterns for one or all of the following reference signal types may not be configured for the terminal device:

[0114] - A second reference signal used for model input data collection during training data collection;

[0115] -The first reference signal used for collecting ground truth data in training data collection.

[0116] In some embodiments, a terminal device may be configured with one or all of the following types of beam patterns:

[0117] -The second reference signal used as input to the AI / ML model during model inference, i.e., set B;

[0118] -The first reference signal used for AI / ML model output / prediction in model inference, i.e. set A.

[0119] In some embodiments, the beam patterns for one or all of the following reference signal types may not be configured for the terminal device:

[0120] -The second reference signal used as input to the AI / ML model during model inference, i.e., set B;

[0121] -The first reference signal used for AI / ML model output / prediction in model inference, i.e. set A.

[0122] In some embodiments, a terminal device may be configured with one or all of the following types of beam patterns:

[0123] - A second reference signal for measurement in performance monitoring;

[0124] - A first reference signal for monitoring in performance monitoring.

[0125] In some embodiments, the beam patterns for one or all of the following reference signal types may not be configured for the terminal device:

[0126] - A second reference signal for measurement in performance monitoring;

[0127] - A first reference signal for monitoring in performance monitoring.

[0128] In some embodiments, the beam pattern may be configured together with the reference signal configuration. In other embodiments, the beam pattern may be configured separately from the reference signal configuration.

[0129] In some embodiments, for the terminal side model, the terminal device supports multiple beam patterns, the terminal device reports the supported beam patterns to the network device, and the network device configures the corresponding beam pattern for the reference signal.

[0130] In some embodiments, the configured beam pattern is indicated to the terminal device. In other embodiments, the configured beam pattern is not indicated to the terminal device. In some embodiments, the configured beam pattern is the same in different cells. In other embodiments, the configured beam pattern is different in different cells.

[0131] For example, for the UE-side model, the UE may support multiple beam patterns. The UE may report the supported beam patterns to the gNB. The gNB may configure the corresponding beam pattern for the reference signal. Furthermore, the gNB may or may not indicate the configured beam pattern to the UE. The configured beam pattern may be the same across different cells, or may differ across different cells.

[0132] For another example, for the NW-side model, the gNB can support multiple beam patterns. The gNB can configure the corresponding beam pattern for the reference signal. In addition, the gNB may or may not indicate the configured beam pattern to the UE. The configured beam pattern can be the same across different cells, or the configured beam pattern can be different across different cells.

[0133] In some embodiments, performance monitoring is restarted if the configured beam pattern changes.

[0134] The beam pattern is schematically described above, and the related data processing is described below.

[0135] In some embodiments, for training data collection, if the quality of one or more optimal beams in the measurement results of the first reference signal used for collecting ground truth data and / or the second reference signal used for collecting model input data is lower than a threshold value, the measurement results are discarded and / or not reported.

[0136] For example, for training data collection, when the UE performs measurements on one or more reference signals used for collecting ground truth data and one or more reference signals used for collecting model input data (for example, if the reference signal for the model input data is configured), if the measurement results of the best beam or the best several (for example, K) beams (i.e., the beam quality of the Top-1 beam or Top-K beams, such as L1-RSRP / L1-SINR / SNR) are lower than a threshold, the measurement results of the ground truth data and the corresponding results of the model input data (if the reference signal for the model input data is configured) can be considered to be of low quality and can be discarded, where the threshold value and / or K value can be predefined or configurable.

[0137] For another example, if a certain percentage (M%) of the measurement results of the ground truth data and the corresponding results of the model input data (if a reference signal for the model input data is configured) is lower than a threshold value, the measurement result is invalid and can be discarded, where the threshold value and / or M value can be predefined or configurable.

[0138] In some embodiments, when determining the quality of training data, the measurement results of the reference real data (ground truth data) and the corresponding results of the model input data (if a reference signal for the model input data is configured) can be considered together, or the measurement results of the reference real data (ground truth data) and the corresponding results of the model input data (if a reference signal for the model input data is configured) can be considered separately.

[0139] For example, if the measurement results of the best beam or the best several (e.g., K) beams within the measurement results of the reference real data (ground truth data) (i.e., the beam quality of the Top-1 beam or Top-K beams, e.g., L1-RSRP / L1-SINR / SNR) are lower than the threshold value, the measurement results of the reference real data (ground truth data) and the corresponding results of the model input data (if a reference signal for the model input data is configured) can be considered as low quality and can be discarded. Alternatively, only the measurement results of the reference real data (ground truth data) can be considered as low quality and can be discarded.

[0140] For another example, if the measurement results of the reference true data (ground truth data) and the measurement results of the best beam or the best several (for example, K) beams within the corresponding results of the model input data (i.e., the beam quality of the Top-1 beam or Top-K beams, for example, L1-RSRP / L1-SINR / SNR) are lower than the threshold value, the measurement results of the reference true data (ground truth data) and the corresponding results of the model input data (if a reference signal for the model input data is configured) can be regarded as low quality and can be discarded.

[0141] For another example, if the measurement results of the best beam or the best several (e.g., K) beams within the measurement results of the model input data (i.e., the beam quality of the Top-1 beam or Top-K beams, e.g., L1-RSRP / L1-SINR / SNR) are lower than a threshold value, the measurement results of the ground truth data and the corresponding results of the model input data (if a reference signal for the model input data is configured) can be considered to be of low quality and can be discarded. Alternatively, only the measurement results of the model input data can be considered to be of low quality and can be discarded.

[0142] In some embodiments, for training data collection, if one or more reference signals cannot be detected or the quality of the measurement result is lower than a threshold, the one or more reference signals are marked and corresponding data is reported.

[0143] For example, for training data collection, if some reference signals are undetectable, for example, the measured L1-RSRP / L1-SINR / SNR is too low to be detected (or the measurement result is lower than some predefined / configurable threshold), some special tags / indications, such as "nondetectable", can be introduced to indicate that the UE cannot detect these beams (or the signal quality is too low). In the collected data, these reference signals are marked as "undetectable" and the corresponding data is collected and reported (i.e., it should not be discarded).

[0144] For another example, if the measurement results of the best beam or the best several (e.g., K) beams within the measurement results of the reference real data (ground truth data) (i.e., the beam quality of the top-1 beam or the top-K beams, such as L1-RSRP / L1-SINR / SNR) are higher than a certain threshold value, then the measurement results of the reference real data (ground truth data) and the corresponding results of the model input data (if the reference signal for the model input data is configured) can be considered valid and collected by the UE. In the measurement results of the reference real data (ground truth data) and the corresponding results of the model input data, if some reference signals are undetectable, for example, the measured L1-RSRP / L1-SINR / SNR is too low to be detected (or the measured results are lower than a certain predefined / configurable threshold value), these reference signals are marked as "undetectable" and the corresponding data is collected.

[0145] The above describes the data collection process. The following describes the performance monitoring process.

[0146] In some embodiments, for performance monitoring, if one or more reference signals cannot be detected or the quality of the measurement result is lower than a threshold, the data corresponding to the one or more reference signals are discarded or not used for performance monitoring.

[0147] For example, for performance monitoring, among the reference signals used for monitoring, if some reference signals are undetectable, for example, the measured L1-RSRP / L1-SINR / SNR is too low to be detected (or the measurement result is lower than a predefined / configurable threshold), the measurement result is discarded and not used for performance monitoring.

[0148] In some embodiments, for performance monitoring of the network side model, if one or more reference signals cannot be detected or the quality of the measurement results is lower than a threshold value, the data corresponding to the one or more reference signals is discarded or not reported, or the one or more reference signals are marked and the corresponding data is reported.

[0149] For example, for performance monitoring on the NW side, if some reference signals are undetectable, for example, the measured L1-RSRP / L1-SINR / SNR is too low to be detected (or the measurement result is lower than a predefined / configurable threshold), the measurement results of these reference signals are not reported.

[0150] For another example, for performance monitoring on the NW side, if some reference signals are undetectable, for example, the measured L1-RSRP / L1-SINR / SNR is too low to be detected (or the measurement result is lower than a predefined / configurable threshold), then these reference signals can be marked as "undetectable" and the corresponding data can be reported.

[0151] In some embodiments, one of the following options may be used to determine whether the measurement result is applied for performance monitoring:

[0152] -If the measurement results of the best beam or the best several (e.g., K) beams within the measurement results of the reference signal (or set A) used for monitoring (i.e., the beam quality of the Top-1 beam or Top-K beams, e.g., L1-RSRP / L1-SINR / SNR) are higher than a specific threshold value, the measurement result can be considered valid and used for performance monitoring; otherwise, the entire measurement result will not be used for performance monitoring.

[0153] -If the measurement results of the best beam or the best several (e.g., K) beams within the measurement results of the reference signal (or set B) used for measurement (i.e., the beam quality of the Top-1 beam or Top-K beams, e.g., L1-RSRP / L1-SINR / SNR) are higher than a certain threshold value, the measurement result can be considered valid and used for performance monitoring; otherwise, the entire measurement result will not be used for performance monitoring.

[0154] If the measurement result is valid for performance monitoring, whether the measurement result of each reference signal is invalid for performance monitoring can be further determined by determining whether the measurement result of the reference signal is lower than a certain threshold.

[0155] In some embodiments, for performance monitoring, reference signals of one or more optimal beams in the measurement results and / or prediction results are configured as reference signals for monitoring, or one or more beams near the current beam are configured as reference signals for monitoring.

[0156] For example, for performance monitoring, the reference signal of the best beam or the best several (e.g., K) beams (i.e., Top-1 / Top-K predicted beams) can be configured as the reference signal for performance monitoring. Alternatively, one or more beams surrounding the current beam can be configured as the reference signal for performance monitoring.

[0157] The embodiments of the present application can be applied to both the UE-side model and the gNB-side model, but the present application is not limited thereto. Furthermore, the AI / ML of the embodiments of the present application can be used for beam management, such as temporal beam prediction and / or spatial beam prediction, but the present application is not limited thereto. For example, non-AI / ML methods can also be used for data collection.

[0158] 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.

[0159] As can be seen from the above embodiments, the terminal device measures the reference signal based on configuration information including at least beam pattern information to collect training data for AI / ML functionality / models and / or perform model inference and / or performance monitoring. This ensures consistency in training and inference, improving the performance and efficiency of AI / ML.

[0160] Embodiments of the second aspect

[0161] The embodiment of the present application provides a beam management 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.

[0162] FIG8 is a schematic diagram of a beam management method according to an embodiment of the present application. As shown in FIG8 , the method includes:

[0163] 801. The network device sends configuration information including at least beam pattern information to the terminal device.

[0164] As shown in FIG8 , the method may further include:

[0165] 802. The terminal device measures a reference signal according to the configuration information to collect training data and / or perform model inference and / or performance monitoring for AI / ML functionality / model.

[0166] It is worth noting that FIG8 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 FIG8 above.

[0167] 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.

[0168] As can be seen from the above embodiments, the terminal device measures the reference signal based on configuration information including at least beam pattern information to collect training data for AI / ML functionality / models and / or perform model inference and / or performance monitoring. This ensures consistency in training and inference, improving the performance and efficiency of AI / ML.

[0169] Embodiments of the third aspect

[0170] The embodiment of the present application provides a beam management device. The device may be, for example, a terminal device, or one or more components or assemblies configured in the terminal device. The contents that are the same as those in the first and second aspects of the embodiment are not repeated here.

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

[0172] a receiving unit 901 configured to receive configuration information including at least beam pattern information from a network device;

[0173] The processing unit 902 measures the reference signal according to the configuration information to collect training data for AI / ML functionality / model and / or perform model inference and / or performance monitoring.

[0174] In some embodiments, a downlink transmit beam pattern is defined for a reference signal used for the training data collection and / or the model inference and / or the performance monitoring.

[0175] In some embodiments, the terminal device is configured with an N×M beam matrix, where N is the number of beams in the horizontal direction and M is the number of beams in the vertical direction;

[0176] N and / or M are configured to the terminal device; the beam pattern of the second reference signal set (set B) for measurement and / or the beam pattern of the first reference signal set (set A) for prediction corresponds to one or more elements in the beam matrix.

[0177] In some embodiments, the terminal device is configured with an N×M beam codebook matrix, where N is the number of beams in the horizontal direction and M is the number of beams in the vertical direction;

[0178] N and / or M are configured to the terminal device; the beam pattern of the second reference signal set (set B) for measurement and / or the beam pattern of the first reference signal set (set A) for prediction corresponds to one or more elements in the beam codebook matrix.

[0179] In some embodiments, bitmap information corresponding to all beams of the network device is configured for the terminal device.

[0180] In some embodiments, for the second reference signal set (set B) for measurement and / or the first reference signal set (set A) for prediction, the coordinates or index of the first beam in the beam matrix or beam codebook matrix, and the step size of the beam in the horizontal direction and / or vertical direction of the beam matrix or beam codebook matrix are configured.

[0181] In some embodiments, the number of beams of the second reference signal set (set B) and / or the first reference signal set (set A) in the horizontal direction and / or vertical direction of the beam matrix or the beam code book matrix is ​​configured, or the number of all beams in the horizontal direction and / or vertical direction of the beam matrix or the beam code book matrix is ​​configured.

[0182] In some embodiments, an association / mapping between CSI-RS resources and beams or an association / mapping between CSI-RS resources and beam codebooks is configured for the terminal device.

[0183] In some embodiments, the association / mapping between CSI-RS resources and beams or the association / mapping between CSI-RS resources and beam codebooks is predefined.

[0184] In some embodiments, the beam patterns of at least some of the following reference signals are the same: a second reference signal used for model input data collection in training data collection, a second reference signal set (set B) used for model input in model inference, and a second reference signal used for measurement in performance monitoring.

[0185] In some embodiments, the beam patterns of at least some of the following reference signals are different: a second reference signal used for model input data collection in training data collection, a second reference signal set (set B) used for model input in model inference, and a second reference signal used for measurement in performance monitoring.

[0186] In some embodiments, the beam patterns of at least some of the following reference signals are the same: a first reference signal used for collecting ground truth data in training data collection, a first reference signal set (set A) used for model output in model inference, and a first reference signal used for monitoring in performance monitoring.

[0187] In some embodiments, the beam patterns of at least some of the following reference signals are different: a first reference signal used for collecting ground truth data in training data collection, a first reference signal set (set A) used for model output in model inference, and a first reference signal used for monitoring in performance monitoring.

[0188] In some embodiments, for the terminal side model, the terminal device supports multiple beam patterns, the terminal device reports the supported beam patterns to the network device, and the network device configures the corresponding beam pattern for the reference signal.

[0189] In some embodiments, the configured beam pattern is indicated to the terminal device.

[0190] In some embodiments, the configured beam pattern is not indicated to the terminal device.

[0191] In some embodiments, the configured beam pattern is the same in different cells.

[0192] In some embodiments, the configured beam patterns are different in different cells.

[0193] In some embodiments, for training data collection, if the quality of one or more optimal beams in the measurement results of the first reference signal used for collecting ground truth data and / or the second reference signal used for collecting model input data is lower than a threshold value, the measurement results are discarded and / or not reported.

[0194] In some embodiments, for training data collection, if one or more reference signals cannot be detected or the quality of the measurement result is lower than a threshold, the one or more reference signals are marked and corresponding data is reported.

[0195] In some embodiments, for performance monitoring, if one or more reference signals cannot be detected or the quality of the measurement result is lower than a threshold, the data corresponding to the one or more reference signals are discarded or not used for performance monitoring.

[0196] In some embodiments, for performance monitoring of the network side model, if one or more reference signals cannot be detected or the quality of the measurement results is lower than a threshold value, the data corresponding to the one or more reference signals is discarded or not reported, or the one or more reference signals are marked and the corresponding data is reported.

[0197] In some embodiments, for performance monitoring, reference signals of one or more optimal beams in the measurement results and / or prediction results are configured as reference signals for monitoring, or one or more beams near the current beam are configured as reference signals for monitoring.

[0198] In some embodiments, as shown in FIG9 , the apparatus may further include a sending unit 903 that sends feedback information / data to the network device.

[0199] 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.

[0200] 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 beam management device 900 may also include other components or modules. For the specific contents of these components or modules, reference may be made to the relevant art.

[0201] In addition, for the sake of simplicity, FIG9 only illustrates the connection relationship or signal direction between various components or modules. However, it should be clear to those skilled in the art that various related technologies such as bus connection can be used. The above-mentioned components or modules can be implemented by hardware facilities such as processors, memories, transmitters, and receivers; the implementation of this application is not limited to this.

[0202] As can be seen from the above embodiments, the terminal device measures the reference signal based on configuration information including at least beam pattern information to collect training data for AI / ML functionality / models and / or perform model inference and / or performance monitoring. This ensures consistency in training and inference, improving the performance and efficiency of AI / ML.

[0203] Embodiments of the fourth aspect

[0204] The embodiment of the present application provides a beam management device. The device may be, for example, a network device, or one or more components or assemblies configured in the network device. The contents that are the same as those in the first to third aspects of the embodiment are not repeated here.

[0205] FIG10 is another schematic diagram of a beam management device according to an embodiment of the present application. As shown in FIG10 , the beam management device 1000 includes:

[0206] A sending unit 1001 sends configuration information including at least beam pattern information to a terminal device; wherein the configuration information is used by the terminal device to measure a reference signal for training data collection and / or model inference and / or performance monitoring for AI / ML functionality / model.

[0207] In some embodiments, as shown in FIG10 , the beam management apparatus 1000 may further include:

[0208] The receiving unit 1002 receives data / information fed back by the terminal device.

[0209] 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.

[0210] 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 beam management device 1000 may also include other components or modules. For the specific contents of these components or modules, reference may be made to the relevant art.

[0211] In addition, for the sake of simplicity, FIG10 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.

[0212] As can be seen from the above embodiments, the terminal device measures the reference signal based on configuration information including at least beam pattern information to collect training data for AI / ML functionality / models and / or perform model inference and / or performance monitoring. This ensures consistency in training and inference, improving the performance and efficiency of AI / ML.

[0213] Embodiments of the fifth aspect

[0214] 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.

[0215] In some embodiments, the communication system 100 may include at least:

[0216] A network device that sends configuration information including at least beam pattern information to a terminal device;

[0217] A terminal device measures a reference signal according to the configuration information to collect training data and / or perform model inference and / or performance monitoring for AI / ML functionality / model.

[0218] 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.

[0219] Figure 11 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in Figure 11 , terminal device 1100 may include a processor 1110 and a memory 1120. Memory 1120 stores data and programs and is coupled to processor 1110. It should be noted that this diagram is exemplary; other types of structures may be used to supplement or replace this structure to implement telecommunication or other functions.

[0220] For example, the processor 1110 may be configured to execute a program to implement the beam management method as described in the embodiment of the first aspect. For example, the processor 1110 may be configured to perform the following control: receiving configuration information including at least beam pattern information from a network device; and measuring a reference signal based on the configuration information to collect training data for AI / ML functionality / models and / or perform model inference and / or performance monitoring.

[0221] As shown in Figure 11 , the terminal device 1100 may further include: a communication module 1130, an input unit 1140, a display 1150, and a power supply 1160. The functions of these components are similar to those in the prior art and are not described in detail here. It is worth noting that the terminal device 1100 does not necessarily include all of the components shown in Figure 11 , and these components are not essential. Furthermore, the terminal device 1100 may also include components not shown in Figure 11 , for which reference may be made to the prior art.

[0222] 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.

[0223] Figure 12 is a schematic diagram illustrating the structure of a network device according to an embodiment of the present application. As shown in Figure 12 , 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 may store various data and may also store an information processing program 1230 , which is executed under the control of the processor 1210 .

[0224] For example, the processor 1210 may be configured to execute a program to implement the beam management method as described in the embodiment of the second aspect. For example, the processor 1210 may be configured to perform the following control: sending configuration information including at least beam pattern information to a terminal device; the configuration information is used by the terminal device to measure a reference signal for training data collection and / or model inference and / or performance monitoring for AI / ML functionality / model.

[0225] In addition, as shown in FIG12 , network device 1200 may further include: a transceiver 1240 and an antenna 1250, 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 1200 does not necessarily include all the components shown in FIG12 ; in addition, network device 1200 may also include components not shown in FIG12 , and reference may be made to the prior art for details.

[0226] 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 perform the beam management method described in the embodiment of the first aspect.

[0227] 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 beam management method described in the embodiment of the first aspect.

[0228] 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 perform the beam management method described in the embodiment of the second aspect.

[0229] 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 beam management method described in the embodiment of the second aspect.

[0230] 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.

[0231] The method / device described in conjunction with the embodiments of the present application can be directly embodied as hardware, a software module executed by a processor, or a combination of the two. For example, one or more of the functional block diagrams shown in the figure and / or one or more combinations of functional block diagrams can correspond to various software modules of the computer program flow or to various hardware modules. These software modules can respectively correspond to the various steps shown in the figure. These hardware modules can be implemented by solidifying these software modules, for example, using a field programmable gate array (FPGA).

[0232] 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.

[0233] 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.

[0234] 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.

[0235] Regarding the implementation methods including the above embodiments, the following additional notes are also disclosed:

[0236] 1. A beam management method, comprising:

[0237] The terminal device receives configuration information including at least beam pattern information from the network device;

[0238] The terminal device measures the reference signal according to the configuration information to collect training data and / or model inference and / or performance monitoring for AI / ML functionality / model.

[0239] 2. A beam management method, comprising:

[0240] The network device sends configuration information including at least beam pattern information to the terminal device;

[0241] The configuration information is used by the terminal device to measure a reference signal for training data collection and / or model inference and / or performance monitoring for AI / ML functionality / model.

[0242] 3. A terminal 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 beam management method as described in Note 1.

[0243] 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 beam management method as described in Note 2.

[0244] 5. A computer program product, comprising at least a computer program, wherein when the computer program is executed by a processor, the terminal device executes the beam management method as described in Note 1.

[0245] 6. A computer program product, comprising at least a computer program, wherein when the computer program is executed by a processor, the network device executes the beam management method as described in Note 2.

Claims

1. A beam management device, comprising: a receiving unit configured to receive configuration information including at least beam pattern information from a network device; A processing unit that measures a reference signal based on the configuration information to collect training data and / or perform model inference and / or performance monitoring for AI / ML functions / models.

2. The device according to claim 1, wherein A downlink transmit beam pattern is defined for a reference signal used for the training data collection and / or the model inference and / or the performance monitoring.

3. The device according to claim 1, wherein The terminal device is configured with an N×M beam matrix, where N is the number of beams in the horizontal direction and M is the number of beams in the vertical direction; N and / or M are configured for the terminal device; the beam pattern of the second reference signal set used for measurement and / or the beam pattern of the first reference signal set used for prediction corresponds to one or more elements in the beam matrix.

4. The device according to claim 1, wherein The terminal device is configured with an N×M beam codebook matrix, where N is the number of beams in the horizontal direction and M is the number of beams in the vertical direction; N and / or M are configured for the terminal device; The beam pattern of the second reference signal set used for measurement and / or the beam pattern of the first reference signal set used for prediction corresponds to one or more elements in the beam codebook matrix.

5. The device according to claim 1, wherein Bitmap information corresponding to all beams of the network device is configured to the terminal device.

6. The device according to claim 1, wherein For the second reference signal set for measurement and / or the first reference signal set for prediction, the coordinates or index of the first beam in the beam matrix or the beam codebook matrix, and the step size of the beam in the horizontal direction and / or the vertical direction of the beam matrix or the beam codebook matrix are configured.

7. The device according to claim 1, wherein The number of beams of the second reference signal set for measurement and / or the first reference signal set for prediction in the horizontal direction and / or vertical direction of the beam matrix or the beam code book matrix is configured, or the number of all beams in the horizontal direction and / or vertical direction of the beam matrix or the beam code book matrix is configured.

8. The device according to claim 1, wherein The association / mapping between CSI-RS resources and beams or the association / mapping between CSI-RS resources and beam codebooks is configured to the terminal device.

9. The device according to claim 1, wherein The association / mapping between CSI-RS resources and beams or the association / mapping between CSI-RS resources and beam codebooks is predefined.

10. The device according to claim 1, wherein The beam patterns of at least some of the following reference signals are the same: a second reference signal used for model input data collection in training data collection, a second reference signal set used for model input in model inference, and a second reference signal used for measurement in performance monitoring; Alternatively, the beam patterns of at least some of the following reference signals are different: a second reference signal used for model input data collection in training data collection, a second reference signal set used for model input in model inference, and a second reference signal used for measurement in performance monitoring.

11. The device according to claim 1, wherein The beam patterns of at least some of the following reference signals are the same: a first reference signal used for benchmark real data collection in training data collection, a first reference signal set used for model output in model inference, and a first reference signal used for monitoring in performance monitoring; Alternatively, the beam patterns of at least some of the following reference signals are different: the first reference signal used for benchmark real data collection in training data collection, the first reference signal set used for model output in model inference, and the first reference signal used for monitoring in performance monitoring.

12. The device according to claim 1, wherein For the terminal side model, the terminal device supports multiple beam patterns, the terminal device reports the supported beam patterns to the network device, and the network device configures the corresponding beam pattern for the reference signal.

13. The device according to claim 11, wherein The configured beam pattern is indicated to the terminal device, or the configured beam pattern is not indicated to the terminal device; The configured beam patterns are the same in different cells, or the configured beam patterns are different in different cells.

14. The device according to claim 1, wherein For training data collection, if the quality of one or more optimal beams in the measurement results of the first reference signal used for benchmark real data collection and / or the second reference signal used for model input data collection is lower than a threshold value, the measurement results are discarded and / or not reported.

15. The device according to claim 1, wherein For training data collection, if one or more reference signals cannot be detected or the quality of the measurement result is lower than a threshold, the one or more reference signals are marked and corresponding data is reported.

16. The device according to claim 1, wherein For performance monitoring, if one or more reference signals cannot be detected or the quality of the measurement result is lower than a threshold, the data corresponding to the one or more reference signals are discarded or not used for performance monitoring.

17. The device according to claim 1, wherein For performance monitoring of the network side model, if one or more reference signals cannot be detected or the quality of the measurement results is lower than the threshold value, the data corresponding to the one or more reference signals is discarded or not reported, or the one or more reference signals are marked and the corresponding data is reported.

18. The device according to claim 1, wherein For performance monitoring, the reference signals of one or more best beams in the measurement results and / or prediction results are configured as reference signals for monitoring, or one or more beams near the current beam are configured as reference signals for monitoring.

19. A beam management device, comprising: a sending unit configured to send configuration information including at least beam pattern information to a terminal device; The configuration information is used by the terminal device to measure reference signals for training data collection and / or model reasoning and / or performance monitoring for AI / ML functions / models.

20. A communication system comprising: A network device that sends configuration information including at least beam pattern information to a terminal device; A terminal device measures a reference signal according to the configuration information to collect training data and / or perform model inference and / or performance monitoring for AI / ML functions / models.

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