Beam management configuration method and apparatus

Abstract

Provided in the embodiments of the present application are a beam management configuration method and apparatus. The beam management configuration method comprises: a terminal device receiving from a network device configuration information of AI / ML functionality / model-based beam management; and the terminal device configuring a carrier aggregation operation and / or a multi-TRP operation.

Description

Configuration method and device for beam management Technical Field

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

[0002] NR Release 18 investigates artificial intelligence / machine learning (AI / ML) over the air interface. AI / ML can be used for the following use cases: channel state information (CSI) feedback enhancement, beam management, and positioning enhancement. CSI feedback enhancement can include CSI prediction and CSI compression; beam management can include spatial beam prediction and temporal beam prediction; and positioning enhancement can include direct positioning and AI / ML-assisted positioning.

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

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

[0005] Summary of the Invention

[0006] The inventors found that terminal devices and / or network devices can use AI / ML functionality / models to predict beams based on beam measurement results, but there is currently no clear solution for how to consider carrier aggregation operations and / or multi-TRP operations.

[0007] To address at least one of the above problems, an embodiment of the present application provides a method and apparatus for configuring beam management.

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

[0009] The terminal device receives configuration information for beam management based on AI / ML functionality / model from the network device; and

[0010] The terminal device is configured to perform carrier aggregation operations and / or multi-TRP operations.

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

[0012] a receiving unit that receives configuration information for beam management based on AI / ML functionality / model from a network device; and

[0013] A processing unit that performs configuration of carrier aggregation operations and / or multi-TRP operations.

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

[0015] The network device sends configuration information for beam management based on AI / ML functionality / model to the terminal device; wherein the terminal device performs carrier aggregation operation and / or multi-TRP operation configuration.

[0016] According to another aspect of an embodiment of the present application, a configuration device for beam management is provided, including:

[0017] A sending unit that sends configuration information for beam management based on AI / ML functionality / model to a terminal device; wherein the terminal device is configured to perform carrier aggregation operation and / or multi-TRP operation.

[0018] According to another aspect of an embodiment of the present application, a communication system is provided, including:

[0019] Network devices that send configuration information for beam management based on AI / ML functionality / models to end devices;

[0020] A terminal device that receives configuration information for beam management based on AI / ML functionality / model, and configures carrier aggregation operations and / or multi-TRP operations.

[0021] One of the beneficial effects of the embodiments of the present application is that the terminal device receives configuration information for beam management based on AI / ML functionality / model from the network device, as well as configuration for carrier aggregation operations and / or multi-TRP operations; thereby improving the efficiency of beam management and improving the accuracy and reliability of beam management.

[0022] 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.

[0023] 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.

[0024] 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

[0025] 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.

[0026] FIG1 is a schematic diagram of a communication system according to an embodiment of the present application;

[0027] FIG2 is a schematic diagram of a configuration method for beam management according to an embodiment of the present application;

[0028] FIG3 is a schematic diagram of a beam management method according to an embodiment of the present application;

[0029] FIG4 is an example diagram of performance monitoring according to CC configuration according to an embodiment of the present application;

[0030] FIG5 is an example diagram of performance monitoring of a specific CC configuration according to an embodiment of the present application;

[0031] FIG6 is an example diagram of CC configuration performance monitoring according to an embodiment of the present application;

[0032] FIG7 is an example diagram of CC configuration performance monitoring based on grouping according to an embodiment of the present application;

[0033] FIG8 is an example diagram of CC configuration performance monitoring based on grouping according to an embodiment of the present application;

[0034] FIG9 is an example diagram of performance monitoring configured according to TRP according to an embodiment of the present application;

[0035] FIG10 is an example diagram of performance monitoring of a specific TRP configuration according to an embodiment of the present application;

[0036] FIG11 is an example diagram of performance monitoring of a specific TRP configuration according to an embodiment of the present application;

[0037] FIG12 is another schematic diagram of a configuration method for beam management according to an embodiment of the present application;

[0038] FIG13 is a schematic diagram of a configuration device for beam management according to an embodiment of the present application;

[0039] FIG14 is a schematic diagram of a configuration device for beam management according to an embodiment of the present application;

[0040] FIG15 is a schematic diagram of a terminal device according to an embodiment of the present application;

[0041] FIG16 is a schematic diagram of a network device according to an embodiment of the present application. DETAILED DESCRIPTION

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] The following describes the scenarios of the embodiments of the present application through examples, but the present application is not limited thereto.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] In NR, carrier aggregation (CC) operation can be configured, that is, multiple component carriers (CC) can be configured. In addition, since Rel-16, multiple transmission reception point (TRP) operation has been introduced, including single downlink control information (single-DCI) and multiple DCI (multi-DCI) operation. In the scenarios of carrier aggregation and multiple TRP operation, further research is needed on how to configure AI / ML functions / models for beam management and performance monitoring.

[0059] In embodiments of the present application, one or more AI / ML models may be configured and run in a network device and / or terminal device. The AI / ML models may be used for various signal processing functions in wireless communications, such as CSI prediction, CSI compression, beamforming, positioning management, and the like; however, the present application is not limited thereto.

[0060] Embodiments of the first aspect

[0061] An embodiment of the present application provides a method for configuring beam management, which is described from the terminal device side.

[0062] FIG2 is a schematic diagram of a beam management configuration method according to an embodiment of the present application. As shown in FIG2 , the method includes:

[0063] 201, the terminal device receives configuration information for beam management based on AI / ML functionality / model from the network device; and

[0064] 202. The terminal device performs configuration of carrier aggregation operation and / or multi-TRP operation.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] For another example, the function can be to use AI / ML for spatial beam prediction, or to use AI / ML for time beam prediction, or to use AI / ML for CSI prediction, or to use AI / ML for direct positioning, or to use AI / ML for assisted positioning, and so on.

[0069] In some embodiments, the configuration information for beam management may include configuration information of one or more reference signals used for beam management or beam measurement, such as CSI-RS configuration information, etc. The present application is not limited thereto, and reference may be made to related technologies for specific configuration information.

[0070] In some embodiments, one or more reference signals are used for measurement and the measurement results are input into the AI / ML functionality / model, and another one or more reference signals are used for the output of the AI / ML functionality / model for inference.

[0071] FIG3 is another schematic diagram of the beam management method according to an embodiment of the present application, which is illustrated by taking a terminal device configured with AIML as an example. As shown in FIG3 , the method includes:

[0072] 301. A terminal device receives configuration information from a network device; for example, the configuration information includes a second reference signal resource set (set B) for beam measurement and a first reference signal resource set (set A) for beam prediction.

[0073] 302. The terminal device performs beam measurement and inputs the beam measurement results into the AI / ML functionality / model. For example, the measurement results of the reference signals in set B are used as input to the AI / ML, and the reference signals in set A are used for prediction (or inference).

[0074] 303. The terminal device sends the beam prediction result to the network device.

[0075] 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.

[0076] It is worth noting that FIG3 above is merely a schematic illustration of an embodiment of the present application, and the present application is not limited thereto. For example, the execution order of the various operations may be appropriately adjusted, and other operations may be added or some operations may be reduced. Those skilled in the art may make appropriate modifications based on the above description, and are not limited to the description of FIG3 above.

[0077] The above schematically illustrates AI / ML-based beam management. The following describes carrier aggregation and multiple TRPs.

[0078] In some embodiments, the AI / ML functionality / model for beam management is configured per CC or per BWP.

[0079] For example, in a multi-carrier (carrier aggregation) operation scenario, the AI / ML function / model for beam management can be configured or enabled by CC (component carrier) or by bandwidth part (BWP).

[0080] In some embodiments, the second reference signal set (set B) for measurement and / or the first reference signal set (set A) for prediction and / or the reporting configuration are configured per CC (per CC) or per BWP (per BWP).

[0081] For example, in a multi-carrier (carrier aggregation) operation scenario, the second reference signal set (set B) for measurement can be configured according to CC or according to BWP, and / or the first reference signal set (set A) for prediction can be configured according to CC or according to BWP, and / or the reporting configuration can be configured according to CC or according to BWP.

[0082] In some embodiments, AI / ML functionality / model is enabled for all CCs or BWPs.

[0083] Alternatively, a portion of CCs or BWPs are configured or enabled with AI / ML functionality / model, while another portion of CCs or BWPs are not configured or enabled with AI / ML functionality / model.

[0084] For example, in a multi-carrier (carrier aggregation) operation scenario, AI / ML operation can be enabled for all CCs. For another example, in a multi-carrier (carrier aggregation) operation scenario, some CCs are configured (enabled) for AI / ML operation, while some CCs are not configured (disabled) for AI / ML operation.

[0085] In some embodiments, the second reference signal set (set B) for measurement and / or the first reference signal set (set A) for prediction and / or the reporting configuration are shared among all or a portion of the CCs or BWPs configured / enabled with the AI / ML functionality / model for beam management.

[0086] For example, reference signals (set A and / or set B) and / or reporting configurations for measurement and / or prediction may be shared among all CCs (or a subset thereof) configured (enabled) with AI / ML operations for beam management. For example, AI / ML-based cross-carrier / inter-carrier beam prediction may be applied, and / or cross-carrier reporting may be applied.

[0087] For another example, the AI / ML function / model for beam management is configured (enabled) on one CC, and the reference signals (set A and / or set B) and / or reporting configuration for measurement and / or prediction are configured on the same CC. The predicted beam can be applied to all CCs (or multiple CCs).

[0088] In some embodiments, a generalized AI / ML functionality / model is configured or enabled, and the generalized AI / ML functionality / model is applied to multiple CCs or BWPs.

[0089] For example, a generalized AI / ML function / model may be configured / enabled, which may be applied to multiple CCs, i.e., one AI / ML function / model is applied and multiple CCs are enabled for AI / ML operations for beam management.

[0090] For another example, whether general AI / ML functions / models are supported may depend on the capabilities of the UE.

[0091] In some embodiments, the second reference signal set (set B) for measurement and / or the first reference signal set (set A) for prediction and / or the reporting configuration are configured in each CC or BWP where AI / ML is enabled.

[0092] In some embodiments, the second reference signal set (set B) for measurement and / or the first reference signal set (set A) for prediction and / or the reporting configuration are shared among all CCs or BWPs (or one or more subsets) enabled with AI / ML.

[0093] In some embodiments, AI / ML functionality / model for beam management is configured per TRP.

[0094] For example, in scenarios of multi-TRP operation (including single DCI and multi-DCI operation), the AI / ML function / model for beam management can be configured (enabled) based on TRP (per TRP).

[0095] In some embodiments, the second reference signal set (set B) used for measurement and / or the first reference signal set (set A) used for prediction and / or the reporting configuration are configured per TRP (per TRP).

[0096] For example, in a multi-TRP operation scenario, the second reference signal set (set B) for measurement can be configured according to TRP, and / or, the first reference signal set (set A) for prediction can be configured according to TRP, and / or, the reporting configuration can be configured according to TRP.

[0097] In some embodiments, AI / ML functionality / model is enabled for all TRPs.

[0098] Alternatively, a portion of the TRPs are configured or enabled with AI / ML functionality / model, while another portion of the TRPs are not configured or enabled with AI / ML functionality / model.

[0099] For example, in a scenario where multiple TRPs operate, AI / ML operations can be enabled for all TRPs. For another example, in a scenario where multiple TRPs operate, some TRPs are configured (enabled) for AI / ML operations, while some TRPs are not configured (disabled) for AI / ML operations.

[0100] In some embodiments, the second reference signal set (set B) for measurement and / or the first reference signal set (set A) for prediction and / or the reporting configuration are shared among all or a portion of the TRPs configured / enabled with the AI / ML functionality / model for beam management.

[0101] For example, reference signals (set A and / or set B) and / or reporting configurations for measurement and / or prediction can be shared among all TRPs (or subsets of TRPs) that have AI / ML operations configured (enabled) for beam management. For example, one set A and one set B (or one set A and two set Bs) are configured for multiple TRPs, thereby enabling prediction of beams from multiple TRPs.

[0102] As another example, set A contains RS / beams to be predicted from multiple TRPs.

[0103] In some embodiments, a generalized AI / ML functionality / model is configured or enabled, and the generalized AI / ML functionality / model is applied to multiple TRPs.

[0104] For example, a generalized AI / ML function / model may be configured / enabled, which may be applied to multiple TRPs, i.e., one AI / ML function / model is applied and multiple TRPs are enabled for AI / ML operations for beam management.

[0105] For another example, whether general AI / ML functions / models are supported may depend on the capabilities of the UE.

[0106] In some embodiments, the second reference signal set (set B) for measurement and / or the first reference signal set (set A) for prediction and / or the reporting configuration are configured in each TRP where AI / ML is enabled.

[0107] In some embodiments, the second reference signal set (set B) for measurement and / or the first reference signal set (set A) for prediction and / or the reporting configuration are shared among all TRPs (or one or more subsets) enabled with AI / ML.

[0108] The above schematically illustrates the configuration of carrier aggregation and / or multiple TRPs related to AI / ML. The following schematically illustrates the performance monitoring related to AI / ML.

[0109] In some embodiments, for multiple carriers or carrier aggregation, the number of performance monitoring processes is the same as the number of AI / ML functionalities / models configured or enabled on all carriers.

[0110] For example, in a multi-carrier (carrier aggregation) operation scenario, the total number of performance monitoring processes is the same as the total number of AI / ML functions / models configured (enabled) on all CCs.

[0111] In some embodiments, for multiple carriers or carrier aggregation, the number of performance monitoring processes is different from the number of AI / ML functionalities / models configured or enabled on all carriers.

[0112] For example, in a multi-carrier (carrier aggregation) operation scenario, the total number of performance monitoring processes is different from the total number of AI / ML functions / models configured (enabled) on all CCs. For example, the total number of performance monitoring processes is less than the total number of AI / ML functions / models configured (enabled).

[0113] In some embodiments, for multi-carrier or carrier aggregation, the AI / ML functionality / model for beam management is configured or enabled per CC (per CC) or per BWP (per BWP), and the performance monitoring of the AI / ML functionality / model is configured or enabled per CC (per CC) or per BWP (per BWP).

[0114] Figure 4 illustrates an example of CC-based performance monitoring configuration in accordance with an embodiment of the present application. For example, if CC#1 has AI / ML function / model #1 enabled, then performance monitoring #1 is also configured or enabled; if CC#2 has AI / ML function / model #2 enabled, then performance monitoring #2 is also configured or enabled; if CC#3 has AI / ML function / model #3 enabled, then performance monitoring #3 is also configured or enabled; and if CC#4 has AI / ML function / model #4 enabled, then performance monitoring #4 is also configured or enabled.

[0115] For another example, if one or more CCs (or BWPs) are configured (enabled) with AI / ML functions / models for beam management operations, performance monitoring of the AI / ML functions / models is configured (enabled) on the one or more CCs (or BWPs).

[0116] In some embodiments, for multi-carrier or carrier aggregation, performance monitoring of the AI / ML functionality / model is configured or enabled on one or more CCs or BWPs; wherein the CCs or BWPs configured or enabled for performance monitoring are predefined or configured.

[0117] For example, in a multi-carrier (carrier aggregation) operation scenario, performance monitoring of an AI / ML function / model is configured (enabled) on one or more specific CCs. The CCs configured (enabled) for performance monitoring may be predefined or configurable. For example, the performance of an AI / ML function / model on one CC may be indicative of the performance of an AI / ML function / model on one or more other CCs.

[0118] For example, the CC with the smallest ID will have performance monitoring configured (enabled). Alternatively, among CCs configured (enabled) with AI / ML functions / models for beam management operations, the CC with the smallest ID will have performance monitoring configured (enabled).

[0119] In some embodiments, the AI / ML functionality / model for beam management is configured or enabled in multiple CCs or BWPs, and the performance monitoring of the AI / ML functionality / model is configured or enabled in a certain CC or BWP.

[0120] Figure 5 is an example diagram of performance monitoring for a specific CC configuration according to an embodiment of the present application. For example, CC#1 has AI / ML function / model #1 enabled, CC#2 has AI / ML function / model #2 enabled, CC#3 has AI / ML function / model #3 enabled, and CC#4 has AI / ML function / model #4 enabled.

[0121] As shown in Figure 5, AI / ML functions / models for beam management can be configured (enabled) on multiple CCs. Performance monitoring can also be configured on a specific CC, for example, the CC with the smallest ID (CC#1) configures or enables performance monitoring #1.

[0122] In some embodiments, a generalized AI / ML functionality / model is configured or enabled, and the generalized AI / ML functionality / model is applied to multiple CCs or BWPs, and performance monitoring of the AI / ML functionality / model is configured or enabled in one or more CCs or BWPs.

[0123] For example, an AI / ML function / model for beam management is used in multiple CCs, e.g., trained with data from all CCs. In this case, the AI / ML for beam management can be enabled on multiple CCs, and performance monitoring can be configured or enabled on a specific CC, e.g., the CC with the smallest ID.

[0124] For another example, whether general AI / ML functions / models are supported may depend on UE capabilities.

[0125] Figure 6 is an example diagram of CC configuration performance monitoring according to an embodiment of the present application. As shown in Figure 6, CC#1, CC#2, CC#3, and CC#4 have common AI / ML functions / models enabled, and performance monitoring #1 is configured or enabled on the CC with the smallest ID (CC#1).

[0126] In another example, performance monitoring can be configured for each CC (or one or more subsets), and if performance on one CC (or all CCs) fails, then the performance of the AI / ML function / model also fails.

[0127] In some embodiments, for multiple carriers or carrier aggregation, the AI / ML functionality / model for beam management is divided into multiple groups, each group is configured or enabled in one or more CCs or BWPs, and the performance monitoring of the AI / ML functionality / model of each group is configured or enabled in one or more CCs or BWPs; wherein the CCs or BWPs configured or enabled for performance monitoring are predefined or configured.

[0128] For example, AI / ML functions / models can be divided into multiple groups, e.g., one group corresponds to an AI / ML function / model on one or more CCs. Within each group, performance monitoring of the AI / ML function / model is configured (enabled) by one or more specific CCs. Which CCs are configured (enabled) for performance monitoring can be predefined or configurable. For example, the CC with the smallest ID in the group will have performance monitoring configured (enabled).

[0129] Figure 7 is an example diagram of group-based CC configuration performance monitoring according to an embodiment of the present application. As shown in Figure 7 , for example, CC#1 and CC#2 form Group #1, and CC#3 and CC#4 form Group #2. CC#1 has AI / ML function / model #1 enabled, CC#2 has AI / ML function / model #2 enabled, CC#3 has AI / ML function / model #3 enabled, and CC#4 has AI / ML function / model #4 enabled. As shown in Figure 7 , in Group #1, performance monitoring #1 is configured or enabled on the CC with the smallest ID (CC#1). In Group #2, performance monitoring #2 is configured or enabled on the CC with the smallest ID (CC#3).

[0130] For another example, AI / ML functions / models can be divided into multiple groups, e.g., one group corresponds to a common AI / ML function / model for one or more CCs. Within each group, performance monitoring of the AI / ML function / model is configured (enabled) by one or more specific CCs. Which CCs are configured (enabled) for performance monitoring can be predefined or configurable. For example, the CC with the smallest ID in the group will have performance monitoring configured (enabled).

[0131] Figure 8 illustrates an example of group-based CC configuration performance monitoring according to an embodiment of the present application. As shown in Figure 8 , CC#1 and CC#2 form Group #1, with Common AI / ML Function / Model #1 enabled. CC#3 and CC#4 form Group #2, with Common AI / ML Function / Model #2 enabled. As shown in Figure 8 , within Group #1, Performance Monitoring #1 is configured or enabled on the CC with the smallest ID (CC#1). Within Group #2, Performance Monitoring #2 is configured or enabled on the CC with the smallest ID (CC#3).

[0132] In some embodiments, for multiple TRPs, the number of performance monitoring processes is the same as the number of AI / ML functionalities / models configured or enabled on all TRPs.

[0133] For example, in a scenario of multi-TRP operation (including single DCI and / or multi-DCI operation), the total number of performance monitoring processes is the same as the total number of AI / ML functions / models configured (enabled) on all TRPs.

[0134] In some embodiments, for multiple TRPs, the number of performance monitoring processes is different from the number of AI / ML functionalities / models configured or enabled on all TRPs.

[0135] For example, in a scenario of multi-TRP operation (including single DCI and / or multi-DCI operation), the total number of performance monitoring processes is different from the total number of AI / ML functions / models configured (enabled) on all TRPs. For example, the total number of performance monitoring processes can be less than the total number of configured (enabled) AI / ML functions / models.

[0136] In some embodiments, for multiple TRPs, the AI / ML functionality / model for beam management is configured or enabled based on TRP (per TRP), and the performance monitoring of the AI / ML functionality / model is configured or enabled according to TRP (per TRP).

[0137] For example, in a scenario of multi-TRP operation (including single DCI and / or multi-DCI operation), if the AI / ML function / model of beam management is configured (enabled) according to TRP (per TRP), the performance monitoring of the AI / ML function / model is configured (enabled) according to TRP (per TRP).

[0138] For another example, if a TRP is configured (enabled) with an AI / ML function / model for beam management operations, performance monitoring of the AI / ML function / model is configured (enabled) on that TRP. Alternatively, for a UE-side model, performance monitoring is configured (enabled) for the AI / ML function / model associated with each TRP.

[0139] Figure 9 is an example diagram of configuring performance monitoring based on TRPs in an embodiment of the present application. As shown in Figure 9 , for example, a UE configures or enables AI / ML function / model #1 in association with TRP #1 and AI / ML function / model #2 in association with TRP #2. Accordingly, performance monitoring #1 is enabled for AI / ML function / model #1, and performance monitoring #2 is enabled for AI / ML function / model #2.

[0140] In some embodiments, for multiple TRPs, performance monitoring of AI / ML functionality / models is configured or enabled in one or more TRPs; wherein the TRPs configured or enabled for performance monitoring are predefined or configured.

[0141] For example, in a scenario of multi-TRP operation (including single DCI and / or multi-DCI operation), performance monitoring of AI / ML functions / models is configured (enabled) on one or more specific TRPs. Which TRPs are configured (enabled) for performance monitoring can be predefined or configurable.

[0142] For example, the performance of an AI / ML function / model on one TRP may represent the performance of the AI / ML function / model on another TRP. For example, the first TRP (or the first of multiple TRPs with the AI / ML function / model configured / enabled for beam management operations) is configured (enabled) for performance monitoring. Alternatively, for the UE-side model, the AI / ML function / model associated with the first TRP will be configured (enabled) for performance monitoring.

[0143] In some embodiments, for the network side model, the AI / ML functionality / model for beam management is configured or enabled in multiple TRPs, and the performance monitoring of the AI / ML functionality / model is configured or enabled in a certain TRP.

[0144] Figure 10 is an example diagram of configuring performance monitoring for a specific TRP according to an embodiment of the present application. As shown in Figure 10 , for example, for a network-side model, the AI / ML function / model for beam management can be configured (enabled) on multiple TRPs, for example, TRP#1 enables AI / ML function / model #1, and TRP#2 enables AI / ML function / model #2. As shown in Figure 10 , performance monitoring is configured on a specific TRP, for example, TRP#1 configures performance monitoring #1.

[0145] In some embodiments, for the terminal-side model, a generalized AI / ML functionality / model is configured or enabled, and the generalized AI / ML functionality / model is applied to multiple TRPs, and performance monitoring of the AI / ML functionality / model is configured or enabled for a particular TRP.

[0146] Figure 11 is an example diagram of performance monitoring configured for a specific TRP in an embodiment of the present application. As shown in Figure 11, for example, for a UE-side model, the AI / ML function / model for beam management is commonly used for multiple TRPs, and the common AI / ML function / model is trained with data from all TRPs. In this case, the AI / ML for beam management operations can be used for communication with multiple TRPs. In one example, whether the common AI / ML function / model is supported may depend on the capabilities of the UE. As shown in Figure 11, performance monitoring can be performed for the AI / ML operation of one TRP, for example, configuring or enabling performance monitoring #1 for TRP#1.

[0147] The embodiments of the present application can be applied to the UE-side model and / or the gNB-side model. Furthermore, the embodiments of the present application can be applied to BM case 1 (spatial beam prediction) and / or BM case 2 (temporal beam prediction).

[0148] 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.

[0149] It can be seen from the above embodiments that the terminal device receives configuration information for beam management based on AI / ML functionality / model from the network device, as well as configuration for carrier aggregation operations and / or multi-TRP operations; thereby improving the efficiency of beam management and improving the accuracy and reliability of beam management.

[0150] Embodiments of the second aspect

[0151] The embodiment of the present application provides a configuration method for beam management, 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.

[0152] FIG12 is another schematic diagram of a beam management configuration method according to an embodiment of the present application. As shown in FIG12 , the method includes:

[0153] 1201. The network device sends configuration information for beam management based on AI / ML functionality / model to the terminal device; wherein the terminal device is configured to perform carrier aggregation operation and / or multi-TRP operation.

[0154] 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.

[0155] It can be seen from the above embodiments that the terminal device receives configuration information for beam management based on AI / ML functionality / model from the network device, as well as configuration for carrier aggregation operations and / or multi-TRP operations; thereby improving the efficiency of beam management and improving the accuracy and reliability of beam management.

[0156] Embodiments of the third aspect

[0157] The embodiment of the present application provides a configuration device for beam management. 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.

[0158] FIG13 is another schematic diagram of a beam management configuration apparatus according to an embodiment of the present application. As shown in FIG13 , the beam management configuration apparatus 1300 according to an embodiment of the present application includes:

[0159] A receiving unit 1301 receives configuration information for beam management based on AI / ML functionality / model from a network device; and

[0160] Processing unit 1302, which performs configuration of carrier aggregation operation and / or multi-TRP operation.

[0161] In some embodiments, the AI / ML functionality / model for beam management is configured per CC or per BWP.

[0162] In some embodiments, the second reference signal set (set B) for measurement and / or the first reference signal set (set A) for prediction and / or the reporting configuration are configured per CC (per CC) or per BWP (per BWP).

[0163] In some embodiments, AI / ML functionality / model is enabled for all CCs or BWPs.

[0164] In some embodiments, a portion of CCs or BWPs are configured or enabled with AI / ML functionality / model, while another portion of CCs or BWPs are not configured or enabled with AI / ML functionality / model.

[0165] In some embodiments, the second reference signal set (set B) for measurement and / or the first reference signal set (set A) for prediction and / or the reporting configuration are shared among all or a portion of the CCs or BWPs configured / enabled with the AI / ML functionality / model for beam management.

[0166] In some embodiments, a generalized AI / ML functionality / model is configured or enabled, and the generalized AI / ML functionality / model is applied to multiple CCs or BWPs.

[0167] In some embodiments, the second reference signal set (set B) for measurement and / or the first reference signal set (set A) for prediction and / or the reporting configuration are configured in each CC or BWP where AI / ML is enabled.

[0168] In some embodiments, the second reference signal set (set B) for measurement and / or the first reference signal set (set A) for prediction and / or the reporting configuration are shared among all CCs or BWPs enabled with AI / ML.

[0169] In some embodiments, AI / ML functionality / model for beam management is configured per TRP.

[0170] In some embodiments, the second reference signal set (set B) used for measurement and / or the first reference signal set (set A) used for prediction and / or the reporting configuration are configured per TRP (per TRP).

[0171] In some embodiments, AI / ML functionality / model is enabled for all TRPs.

[0172] In some embodiments, a portion of the TRPs are configured or enabled with AI / ML functionality / model, while another portion of the TRPs are not configured or enabled with AI / ML functionality / model.

[0173] In some embodiments, the second reference signal set (set B) for measurement and / or the first reference signal set (set A) for prediction and / or the reporting configuration are shared among all or a portion of the TRPs configured / enabled with the AI / ML functionality / model for beam management.

[0174] In some embodiments, a generalized AI / ML functionality / model is configured or enabled, and the generalized AI / ML functionality / model is applied to multiple TRPs.

[0175] In some embodiments, the second reference signal set (set B) for measurement and / or the first reference signal set (set A) for prediction and / or the reporting configuration are configured in each TRP where AI / ML is enabled.

[0176] In some embodiments, the second reference signal set (set B) for measurement and / or the first reference signal set (set A) for prediction and / or the reporting configuration are shared among all TRPs enabled with AI / ML.

[0177] In some embodiments, for multiple carriers or carrier aggregation, the number of performance monitoring processes is the same as the number of AI / ML functionalities / models configured or enabled on all carriers.

[0178] In some embodiments, the number of performance monitoring processes is different from the number of AI / ML functionalities / models configured or enabled across all carriers.

[0179] In some embodiments, for multi-carrier or carrier aggregation, the AI / ML functionality / model for beam management is configured or enabled per CC (per CC) or per BWP (per BWP), and the performance monitoring of the AI / ML functionality / model is configured or enabled per CC (per CC) or per BWP (per BWP).

[0180] In some embodiments, for multi-carrier or carrier aggregation, performance monitoring of AI / ML functionality / model is configured or enabled on one or more CCs or BWPs; wherein the CCs or BWPs configured or enabled for performance monitoring are predefined or configured.

[0181] In some embodiments, the AI / ML functionality / model for beam management is configured or enabled in multiple CCs or BWPs, and the performance monitoring of the AI / ML functionality / model is configured or enabled in a certain CC or BWP.

[0182] In some embodiments, a generalized AI / ML functionality / model is configured or enabled, and the generalized AI / ML functionality / model is applied to multiple CCs or BWPs, and performance monitoring of the AI / ML functionality / model is configured or enabled in a particular CC or BWP.

[0183] In some embodiments, for multiple carriers or carrier aggregation, the AI / ML functionality / model for beam management is divided into multiple groups, each group is configured or enabled in one or more CCs or BWPs, and the performance monitoring of the AI / ML functionality / model of each group is configured or enabled in one or more CCs or BWPs; wherein the CCs or BWPs configured or enabled for performance monitoring are predefined or configured.

[0184] In some embodiments, for multiple TRPs, the number of performance monitoring processes is the same as the number of AI / ML functionalities / models configured or enabled on all TRPs.

[0185] In some embodiments, the number of performance monitoring processes is different from the number of AI / ML functionalities / models configured or enabled on all TRPs.

[0186] In some embodiments, for multiple TRPs, the AI / ML functionality / model for beam management is configured or enabled based on TRP (per TRP), and the performance monitoring of the AI / ML functionality / model is configured or enabled according to TRP (per TRP).

[0187] In some embodiments, for multiple TRPs, performance monitoring of AI / ML functionality / models is configured or enabled in one or more TRPs; wherein the TRPs configured or enabled for performance monitoring are predefined or configured.

[0188] In some embodiments, for the network side model, the AI / ML functionality / model for beam management is configured or enabled in multiple TRPs, and the performance monitoring of the AI / ML functionality / model is configured or enabled in a certain TRP.

[0189] In some embodiments, for the terminal-side model, a generalized AI / ML functionality / model is configured or enabled, and the generalized AI / ML functionality / model is applied to multiple TRPs, and performance monitoring of the AI / ML functionality / model is configured or enabled for a particular TRP.

[0190] In some embodiments, as shown in FIG13 , the beam management configuration apparatus 1300 may further include:

[0191] The sending unit 1303 sends beam measurement information and / or beam prediction information to the network device.

[0192] 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.

[0193] 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 configuration device 1300 may also include other components or modules. For the specific contents of these components or modules, reference may be made to the relevant art.

[0194] In addition, for the sake of simplicity, FIG13 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.

[0195] It can be seen from the above embodiments that the terminal device receives configuration information for beam management based on AI / ML functionality / model from the network device, as well as configuration for carrier aggregation operations and / or multi-TRP operations; thereby improving the efficiency of beam management and improving the accuracy and reliability of beam management.

[0196] Embodiments of the fourth aspect

[0197] The embodiment of the present application provides a configuration device for beam management. The device may be, for example, a network device, or one or more components or assemblies configured on the network device. The contents that are the same as those in the first to third aspects of the embodiment are not repeated here.

[0198] FIG14 is another schematic diagram of a beam management configuration apparatus according to an embodiment of the present application. As shown in FIG14 , the beam management configuration apparatus 1400 includes:

[0199] A sending unit 1401 sends configuration information for beam management based on AI / ML functionality / model to a terminal device; wherein the terminal device is configured to perform carrier aggregation operation and / or multi-TRP operation.

[0200] In some embodiments, as shown in FIG14 , the beam management configuration apparatus 1400 may further include:

[0201] The receiving unit 1402 receives the beam measurement information and / or beam prediction information sent by the terminal device.

[0202] 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.

[0203] 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 configuration device 1400 may also include other components or modules. For the specific contents of these components or modules, reference may be made to the relevant art.

[0204] In addition, for the sake of simplicity, FIG14 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.

[0205] It can be seen from the above embodiments that the terminal device receives configuration information for beam management based on AI / ML functionality / model from the network device, as well as configuration for carrier aggregation operations and / or multi-TRP operations; thereby improving the efficiency of beam management and improving the accuracy and reliability of beam management.

[0206] Embodiments of the fifth aspect

[0207] 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.

[0208] In some embodiments, the communication system 100 may include at least:

[0209] Network devices that send configuration information for beam management based on AI / ML functionality / models to end devices;

[0210] A terminal device that receives configuration information for beam management based on AI / ML functionality / model, and configures carrier aggregation operations and / or multi-TRP operations.

[0211] 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.

[0212] Figure 15 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in Figure 15 , terminal device 1500 may include a processor 1510 and a memory 1520. Memory 1520 stores data and programs and is coupled to processor 1510. 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.

[0213] For example, the processor 1510 may be configured to execute a program to implement the configuration method for beam management as described in the embodiment of the first aspect. For example, the processor 1510 may be configured to perform the following control: receive configuration information for beam management based on AI / ML functionality / model, and perform configuration of carrier aggregation operation and / or multi-TRP operation.

[0214] As shown in Figure 15 , the terminal device 1500 may further include: a communication module 1530, an input unit 1540, a display 1550, and a power supply 1560. 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 1500 does not necessarily include all of the components shown in Figure 15 , and these components are not essential. Furthermore, the terminal device 1500 may also include components not shown in Figure 15 , for which reference may be made to the prior art.

[0215] 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.

[0216] Figure 16 is a schematic diagram illustrating the structure of a network device according to an embodiment of the present application. As shown in Figure 16 , network device 1600 may include a processor 1610 (e.g., a central processing unit (CPU)) and a memory 1620; memory 1620 is coupled to processor 1610. Memory 1620 may store various data and may also store an information processing program 1630, which is executed under the control of processor 1610.

[0217] For example, the processor 1610 may be configured to execute a program to implement the beam management configuration method as described in the embodiment of the second aspect. For example, the processor 1610 may be configured to perform the following control: sending configuration information for beam management based on AI / ML functionality / model to a terminal device; wherein the terminal device performs carrier aggregation operation and / or multi-TRP operation configuration.

[0218] In addition, as shown in FIG16 , network device 1600 may further include: a transceiver 1640 and an antenna 1650, 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 1600 does not necessarily include all the components shown in FIG16 ; in addition, network device 1600 may also include components not shown in FIG16 , and reference may be made to the prior art for details.

[0219] An embodiment of the present application also provides a computer program, wherein when the program is executed in a terminal device, the program enables the terminal device to execute the beam management configuration method described in the embodiment of the first aspect.

[0220] 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 configuration method described in the embodiment of the first aspect.

[0221] An embodiment of the present application also provides a computer program, wherein when the program is executed in a network device, the program enables the network device to execute the beam management configuration method described in the embodiment of the second aspect.

[0222] 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 configuration method described in the embodiment of the second aspect.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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.

[0228] Regarding the implementation methods including the above embodiments, the following additional notes are also disclosed:

[0229] 1. A method for configuring beam management, comprising:

[0230] The terminal device receives configuration information for beam management based on AI / ML functionality / model from the network device; and

[0231] The terminal device is configured to perform carrier aggregation operations and / or multi-TRP operations.

[0232] 2. A beam management configuration method, comprising:

[0233] The network device sends configuration information for beam management based on AI / ML functionality / model to the terminal device; wherein the terminal device performs carrier aggregation operation and / or multi-TRP operation configuration.

[0234] 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 configuration method as described in Note 1.

[0235] 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 configuration method as described in Note 2.

Claims

1. A beam management configuration device, comprising: a receiving unit that receives configuration information for beam management based on an AI / ML function / model from a network device; and a processing unit that configures carrier aggregation operations and / or multi-TRP operations.

2. The device according to claim 1, wherein, The AI / ML function / model for beam management is configured according to a CC or according to a BWP; The second reference signal set for measurement and / or the first reference signal set for prediction and / or the reporting configuration are configured according to a CC or according to a BWP; The AI / ML function / model is enabled for all CCs or BWPs, or, some CCs or BWPs are configured or enabled with the AI / ML function / model, and some other CCs or BWPs are not configured or not enabled with the AI / ML function / model.

3. The device according to claim 1, wherein The second reference signal set for measurement and / or the first reference signal set for prediction and / or the reporting configuration are shared among all or some of the CCs or BWPs that are configured / enabled with the AI / ML function / model for beam management.

4. The device according to claim 1, wherein A general AI / ML function / model is configured or enabled, and the general AI / ML function / model is applied to multiple CCs or BWPs.

5. The apparatus according to claim 4, wherein The second reference signal set for measurement and / or the first reference signal set for prediction and / or the reporting configuration are configured for each CC or BWP enabled with AI / ML, or, the second reference signal set for measurement and / or the first reference signal set for prediction and / or the reporting configuration are shared among all CCs or BWPs enabled with AI / ML.

6. The device according to claim 1, wherein, The AI / ML function / model for beam management is configured according to a TRP; The second reference signal set for measurement and / or the first reference signal set for prediction and / or the reporting configuration are configured according to a TRP; The AI / ML function / model is enabled for all TRPs, or, some TRPs are configured or enabled with the AI / ML function / model, and some other TRPs are not configured or not enabled with the AI / ML function / model.

7. The device according to claim 1, wherein The second reference signal set for measurement and / or the first reference signal set for prediction and / or the reporting configuration are shared among all or some of the TRPs that are configured / enabled with the AI / ML function / model for beam management.

8. The device according to claim 1, wherein A general AI / ML function / model is configured or enabled, and the general AI / ML function / model is applied to multiple TRPs.

9. The device according to claim 8, wherein The second reference signal set for measurement and / or the first reference signal set for prediction and / or the reporting configuration are configured for each TRP enabled with AI / ML, or, the second reference signal set for measurement and / or the first reference signal set for prediction and / or the reporting configuration are shared among all TRPs enabled with AI / ML.

10. The apparatus according to claim 1, wherein, For multi-carrier or carrier aggregation, the number of performance monitoring processes is the same as the number of AI / ML functions / models configured or enabled on all carriers, or, the number of performance monitoring processes is different from the number of AI / ML functions / models configured or enabled on all carriers.

11. The device according to claim 1, wherein, For multi-carrier or carrier aggregation, if the AI / ML function / model for beam management is configured or enabled per CC or per BWP, then the performance monitoring of the AI / ML function / model is configured or enabled per CC or per BWP.

12. The device according to claim 1, wherein, For multi-carrier or carrier aggregation, the performance monitoring of the AI / ML function / model is configured or enabled on one or more CCs or BWPs; wherein the CCs or BWPs for which performance monitoring is configured or enabled are predefined or configured.

13. The device according to claim 12, wherein The AI / ML function / model for beam management is configured or enabled on multiple CCs or BWPs, and the performance monitoring of the AI / ML function / model is configured or enabled on one CC or BWP.

14. The device according to claim 12, wherein, The general AI / ML function / model is configured or enabled and is applied to multiple CCs or BWPs, and the performance monitoring of the AI / ML function / model is configured or enabled on one CC or BWP.

15. The apparatus according to claim 1, wherein For multi-carrier or carrier aggregation, the AI / ML function / model for beam management is divided into multiple groups, each group is configured or enabled on one or more CCs or BWPs, and the performance monitoring of the AI / ML function / model of each group is configured or enabled on one or more CCs or BWPs; wherein the CCs or BWPs for which performance monitoring is configured or enabled are predefined or configured.

16. The device according to claim 1, wherein For multi-TRP, the number of performance monitoring processes is the same as the number of AI / ML functions / models configured or enabled on all TRPs, or the number of performance monitoring processes is different from the number of AI / ML functions / models configured or enabled on all TRPs.

17. The device according to claim 1, wherein, For multi-TRP, if the AI / ML function / model for beam management is configured or enabled per TRP, then the performance monitoring of the AI / ML function / model is configured or enabled per TRP.

18. The device according to claim 1, wherein For multi-TRP, the performance monitoring of the AI / ML function / model is configured or enabled on one or more TRPs; wherein the TRPs for which performance monitoring is configured or enabled are predefined or configured; For the network-side model, the AI / ML function / model for beam management is configured or enabled on multiple TRPs, and the performance monitoring of the AI / ML function / model is configured or enabled on one TRP. For the terminal-side model, the general AI / ML function / model is configured or enabled and is applied to multiple TRPs, and the performance monitoring of the AI / ML function / model is configured or enabled for one TRP.

19. A configuration device for beam management, comprising: A sending unit that sends configuration information for beam management based on the AI / ML function / model to a terminal device; wherein the terminal device is configured for carrier aggregation operation and / or multi-TRP operation.

20. A communication system, comprising: A network device that sends configuration information for beam management based on the AI / ML function / model to a terminal device; A terminal device that receives configuration information for beam management based on AI / ML functions / models and configures carrier aggregation operations and / or multi-TRP operations.

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