Reference signal configuration method and apparatus for beam management

Abstract

Embodiments of the present application provide a reference signal configuration method and apparatus for beam management. The method comprises: a terminal device receives reference signal configuration information from a network device, receives and measures a reference signal on the basis of the reference signal configuration information, and performs training data collection and / or model inference and / or performance monitoring for an AI / ML functionality / model according to a measurement result.

Description

Reference signal 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 (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 found that terminal devices and / or network devices can use AI / ML functionality / models to predict beams based on beam measurement results, but the measurement results may not be accurate enough and the reference signal configuration needs to be enhanced.

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

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

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

[0010] The terminal device receives and measures a reference signal according to the reference signal configuration information; and

[0011] The terminal device collects training data and / or performs model reasoning and / or performance monitoring for AI / ML functions / models based on the measurement results.

[0012] According to another aspect of an embodiment of the present application, a reference signal configuration apparatus for beam management is provided, including:

[0013] a receiving unit, configured to receive reference signal configuration information from a network device, and receive and measure a reference signal according to the reference signal configuration information;

[0014] A processing unit that performs training data collection and / or model inference and / or performance monitoring for AI / ML functions / models based on the measurement results.

[0015] According to another aspect of an embodiment of the present application, a reference signal configuration method for beam management is provided, including:

[0016] The network device sends reference signal configuration information to the terminal device;

[0017] The network device sends a reference signal to the terminal device according to the reference signal configuration information; wherein, the reference signal is measured by the terminal device and training data collection and / or model inference and / or performance monitoring for AI / ML functions / models are performed based on the measurement results.

[0018] According to another aspect of an embodiment of the present application, a reference signal configuration apparatus for beam management is provided, including:

[0019] a sending unit, configured to send reference signal configuration information to a terminal device, and send a reference signal to the terminal device according to the reference signal configuration information;

[0020] The reference signal is measured by the terminal device and training data collection and / or model reasoning and / or performance monitoring for AI / ML functions / models are performed based on the measurement results.

[0021] According to another aspect of an embodiment of the present application, a communication system is provided, including:

[0022] a network device, configured to send reference signal configuration information to a terminal device, and to send a reference signal to the terminal device according to the reference signal configuration information;

[0023] A terminal device receives and measures the reference signal according to the reference signal configuration information; and performs training data collection and / or model inference and / or performance monitoring for AI / ML functions / models based on the measurement results.

[0024] One of the beneficial effects of the embodiments of the present application is that: a terminal device receives reference signal configuration information from a network device, receives and measures a reference signal based on the reference signal configuration information, and performs training data collection and / or model inference and / or performance monitoring for AI / ML functionality / model based on the measurement results. This enhances the reference signal configuration, improves the accuracy of the measurement results, and improves the performance and efficiency of AI / ML.

[0025] 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.

[0026] 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.

[0027] 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

[0028] 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.

[0029] FIG1 is a schematic diagram of a communication system according to an embodiment of the present application;

[0030] FIG2 is a schematic diagram of a reference signal configuration method for beam management according to an embodiment of the present application;

[0031] FIG3 is a schematic diagram of AI / ML for beam management according to an embodiment of the present application;

[0032] FIG4 is a schematic diagram of a beam management method according to an embodiment of the present application;

[0033] FIG5 is a schematic diagram of a reference signal configuration method for beam management according to an embodiment of the present application;

[0034] FIG6 is a schematic diagram of a reference signal configuration device for beam management according to an embodiment of the present application;

[0035] FIG7 is a schematic diagram of a reference signal configuration device for beam management according to an embodiment of the present application;

[0036] FIG8 is a schematic diagram of a terminal device according to an embodiment of the present application;

[0037] FIG9 is a schematic diagram of a network device according to an embodiment of the present application. DETAILED DESCRIPTION

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] The terminal device may include but is not limited to the following devices: cellular phone, personal digital assistant (PDA), wireless modem, wireless communication device, handheld device, machine type communication device, laptop computer, cordless phone, smart phone, smart watch, digital camera, etc.

[0047] 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.

[0048] 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.

[0049] The following describes the scenarios of the embodiments of the present application through examples, but the present application is not limited thereto.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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, 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.

[0054] 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.

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

[0056] For another example, UE-side monitoring may be performed, where the UE monitors performance metrics and makes activation / deactivation / selection / switching / fallback decisions.

[0057] 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.

[0058] 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.

[0059] Embodiments of the first aspect

[0060] An embodiment of the present application provides a reference signal configuration method for beam management, which is described from the perspective of a terminal device.

[0061] FIG2 is a schematic diagram of a reference signal configuration method for beam management according to an embodiment of the present application. As shown in FIG2 , the method includes:

[0062] 201, a terminal device receives reference signal configuration information from a network device;

[0063] 202. The terminal device receives and measures a reference signal according to the reference signal configuration information;

[0064] 203. The terminal device collects training data and / or performs model inference and / or performance monitoring for AI / ML functionality / model based on the measurement results.

[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, 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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:

[0074] 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.

[0075] 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).

[0076] 403. The terminal device sends the beam prediction result to the network device.

[0077] 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.

[0078] 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.

[0079] The above schematically illustrates AI / ML-based beam management, and the present application is not limited thereto. For ease of description, the reference signal whose measurement results are used as input to the AI / ML model is referred to as the second reference signal, and the reference signal whose measurement results are used as output from the AI / ML model is referred to as the first reference signal. The reference signal can be used for the training data collection and / or the model inference and / or the performance monitoring.

[0080] The following first schematically illustrates the training data collection.

[0081] In some embodiments, the AI / ML function / model for beam management is located on the terminal device side, and the training data collection is performed through non-layer-1 signaling.

[0082] For example, for beam management with AI / ML functionality / models on the UE side, training data collection is reported to the gNB via non-Layer 1 signaling, such as Layer 3 signaling (RRC). Reference signals for model input data collection (e.g., which may be the same as set B) and reference signals for baseline real data collection (e.g., which may be the same as set A) may be configured and / or associated, including BM case 1 and / or BM case 2. For example, new parameters (e.g., RRC parameters) may be added to the reference signal configuration to instruct the UE to perform data collection operations.

[0083] In some embodiments, the same receive beam is used to receive a second reference signal for model input data collection and a corresponding or associated first reference signal for reference real data collection.

[0084] In some embodiments, a repetition parameter is configured for a second reference signal used for model input data collection and a corresponding or associated first reference signal used for baseline real data collection.

[0085] For example, the same UE Rx beam can be used to measure reference signals used for model input data collection and corresponding / associated reference signals used for baseline real data collection, including BM case 1 and / or BM case 2. Furthermore, to improve measurement accuracy, "repetition" can be configured for the reference signals used for model input data collection and / or the corresponding / associated reference signals used for baseline real data collection, or the reference signals in set B / set A can be sent multiple times. For example, if the reference signals are sent multiple times, the UE can average / filter the measurement results to improve measurement accuracy.

[0086] For another example, even if repetition is configured (e.g., the "repetition" factor in the RRC parameters is set to "on"), the UE maintains the same Rx beam for reference signal measurement without changing the UE's Rx beam. For example, when the "repetition" factor in the reference signal configuration is set to "on," the UE further checks whether the reference signal is used for data collection; if so, the UE maintains the same Rx beam.

[0087] In some examples, since training data collection is performed, for example, via layer 3 signaling, a report quantity of "none" may be applied. Alternatively, a new report quantity may be introduced to indicate that no layer 1 reporting is used for data collection.

[0088] In some examples, the existing report quantity may be applied to the reference signals used for model input data collection (e.g., may be the same as set B) and / or the reference signals used for baseline real data collection (e.g., may be the same as set A). In addition to data collection, the UE may also perform beam measurement operations configured by the reference signal configuration and report the measurement results via Layer 1 signaling (UCI) indicated by the report quantity.

[0089] For example, if one CSI-RS resource set is configured for input data collection and another CSI / RS resource set is configured for baseline real data collection, and both resource sets are configured with the reporting quantity "cri-RSRP", the UE performs data collection operations and also reports the best several beams and corresponding RSRPs after measurement.

[0090] In some examples, the UE Rx beam can be refined before measuring the reference signal for data collection. For example, the gNB can trigger the UE Rx beam refinement process before sending the RS for data collection.

[0091] In some embodiments, the AI / ML function / model for beam management is located on the terminal device side, and the training data collection is performed through layer 1 signaling.

[0092] For example, for beam management with AI / ML functionality / models on the UE side, training data collection is reported to the gNB via Layer 1 signaling, such as uplink control information (UCI). Reference signals for model input data collection (e.g., which may be the same as set B) and reference signals for baseline real data collection (e.g., which may be the same as set A) may be configured and / or associated, including BM case 1 and / or BM case 2. Existing or new report quantities may be used. For example, new parameters (e.g., RRC parameters) may be added to the reference signal configuration to instruct the UE to perform data collection operations.

[0093] In some embodiments, the same receive beam is used to receive a second reference signal for model input data collection and a corresponding or associated first reference signal for reference real data collection.

[0094] In some embodiments, a repetition parameter is configured for a second reference signal used for model input data collection and a corresponding or associated first reference signal used for baseline real data collection.

[0095] For example, the same UE Rx beam can be used to measure reference signals used for model input data collection and corresponding / associated reference signals used for baseline real data collection, including BM case 1 and / or BM case 2. Furthermore, to improve measurement accuracy, "repetition" can be configured for the reference signals used for model input data collection and / or the corresponding / associated reference signals used for baseline real data collection, or the reference signals in set B / set A can be sent multiple times. For example, if the reference signals are sent multiple times, the UE can average / filter the measurement results to improve measurement accuracy.

[0096] For another example, even if repetition is configured (e.g., the "repetition" factor in the RRC parameters is set to "on"), the UE maintains the same Rx beam for reference signal measurement without changing the UE's Rx beam. For example, when the "repetition" factor in the reference signal configuration is set to "on," the UE further checks whether the reference signal is used for data collection; if so, the UE maintains the same Rx beam.

[0097] In some examples, existing report quantities can be applied to reference signals used for model input data collection (e.g., which may be the same as set B) and / or reference signals used for baseline real-world data collection (e.g., which may be the same as set A). Newly defined report quantities can also be used. The measurement results of the model input data and baseline real-world data can be delivered to the network device via separate reports or via the same report.

[0098] In some examples, the UE Rx beam can be refined before measuring the reference signal for data collection. For example, the gNB can trigger the UE Rx beam refinement process before sending the RS for data collection.

[0099] In some embodiments, the AI / ML function / model for beam management is located on the terminal device side, and the training data collection is reported to the network device through non-layer-1 signaling. For example, the non-layer-1 signaling is layer 3 signaling (RRC). The reference signal for model input data collection (for example, which may be the same as set B) and the reference signal for benchmark real data collection (for example, which may be the same as set A) can be configured and / or associated through a dedicated RRC configuration, which may include BM case 1 and / or BM case 2. For example, configuration is not performed through the CSI reporting framework.

[0100] In some examples, L1-RSRP is collected for model input data and / or baseline real data.

[0101] In some examples, the same UE Rx beam can be used to measure reference signals used for model input data collection and corresponding / associated reference signals used for baseline real data collection, including BM case 1 and / or BM case 2. Furthermore, to improve measurement accuracy, "repetition" can be configured for the reference signals used for model input data collection and / or the corresponding / associated reference signals used for baseline real data collection, or the reference signals in set B / set A can be transmitted multiple times. For example, if the reference signals are transmitted multiple times, the UE can average / filter the measurement results to improve measurement accuracy.

[0102] For another example, even if repetition is configured (e.g., the "repetition" factor in the RRC parameters is set to "on"), the UE maintains the same Rx beam for reference signal measurement without changing the UE's Rx beam. For example, when the "repetition" factor in the reference signal configuration is set to "on," the UE further checks whether the reference signal is used for data collection; if so, the UE maintains the same Rx beam.

[0103] In some examples, the UE Rx beam can be refined before measuring the reference signal for data collection. For example, the gNB can trigger the UE Rx beam refinement process before sending the RS for data collection.

[0104] In some embodiments, the AI / ML function / model for beam management is located on the network device side, and the training data collection is performed through layer 1 signaling.

[0105] For example, for beam management with AI / ML functionality / models on the gNB side, training data collection is reported to the gNB via Layer 1 signaling, e.g., Uplink Control Information (UCI). Reference signals for model input data collection (e.g., which may be the same as set B) and reference signals for baseline real data collection (e.g., which may be the same as set A) may be configured and / or associated, including BM case 1 and / or BM case 2. Existing or new report quantities may be used. For example, new parameters (e.g., RRC parameters) may be added to the reference signal configuration to instruct the UE to perform data collection operations. Alternatively, there may be no explicit RRC parameters to indicate the reference signals used for data collection operations.

[0106] In some embodiments, the same receive beam is used to receive a second reference signal for model input data collection and a corresponding or associated first reference signal for reference real data collection.

[0107] In some embodiments, a repetition parameter is configured for a second reference signal used for model input data collection and a corresponding or associated first reference signal used for baseline real data collection.

[0108] For example, the same UE Rx beam can be used to measure reference signals used for model input data collection and corresponding / associated reference signals used for baseline real data collection, including BM case 1 and / or BM case 2. Furthermore, to improve measurement accuracy, "repetition" can be configured for the reference signals used for model input data collection and / or the corresponding / associated reference signals used for baseline real data collection, or the reference signals in set B / set A can be sent multiple times. For example, if the reference signals are sent multiple times, the UE can average / filter the measurement results to improve measurement accuracy.

[0109] For another example, even if repetition is configured (e.g., the "repetition" factor in the RRC parameters is set to "on"), the UE maintains the same Rx beam for reference signal measurement without changing the UE's Rx beam. For example, when the "repetition" factor in the reference signal configuration is set to "on," the UE further checks whether the reference signal is used for data collection; if so, the UE maintains the same Rx beam.

[0110] In some examples, for the NW-side model, the reported amount and / or L1-RSRP quantization used for training data collection can be the same as the reported amount and / or L1-RSRP quantization used for model inference, or the reported amount and / or L1-RSRP quantization used for training data collection can be different from the reported amount and / or L1-RSRP quantization used for model inference.

[0111] In some examples, existing report quantities can be applied to reference signals used for model input data collection (e.g., which may be the same as set B) and / or reference signals used for baseline real-world data collection (e.g., which may be the same as set A). Newly defined report quantities can also be used. The measurement results of the model input data and baseline real-world data can be delivered to the network device via separate reports or via the same report.

[0112] In some embodiments, the second reference signal used for model input data collection and the first reference signal used for baseline real data collection are correlated, or the second reference signal used for model input data collection and the first reference signal used for baseline real data collection are uncorrelated.

[0113] For example, the reference signal used for collecting model input data (e.g., the same as set B) and the reference signal used for collecting baseline real data (e.g., the same as set A) may be associated, including BM sase 1 and / or BM case 2. For another example, the reference signal used for collecting baseline real data (e.g., the same as set A) may not be associated with the reference signal used for collecting model input data (e.g., the same as set B), that is, this association may be transparent to the UE.

[0114] In some examples, for BM case 2, before triggering training data collection, the gNB may first query the UE's conditions, such as the UE's speed and / or preferred number of time instances for model input data and / or ground truth data.

[0115] In some examples, the UE Rx beam can be refined before measuring the reference signal for data collection. For example, the gNB can trigger the UE Rx beam refinement process before sending the RS for data collection.

[0116] In some embodiments, the AI / ML functionality / model for beam management resides on the network device side, and the training data is reported to the network device via non-layer-1 signaling, such as layer-3 signaling (RRC).

[0117] For example, for beam management with AI / ML functionality / models on the gNB side, training data collection is reported to the gNB via non-Layer 1 signaling, such as Layer 3 signaling (RRC). Reference signals for model input data collection (e.g., which may be the same as set B) and reference signals for baseline real data collection (e.g., which may be the same as set A) can be configured and / or associated. For example, new parameters (e.g., RRC parameters) can be added to the reference signal configuration to instruct the UE to perform data collection operations. Alternatively, there may be no explicit RRC parameters indicating the reference signals used for data collection operations.

[0118] In some embodiments, the same receive beam is used to receive a second reference signal for model input data collection and a corresponding or associated first reference signal for reference real data collection.

[0119] In some embodiments, a repetition parameter is configured for a second reference signal used for model input data collection and a corresponding or associated first reference signal used for baseline real data collection.

[0120] For example, the same UE Rx beam can be used to measure reference signals used for model input data collection and corresponding / associated reference signals used for baseline real data collection, including BM case 1 and / or BM case 2. Furthermore, to improve measurement accuracy, "repetition" can be configured for the reference signals used for model input data collection and / or the corresponding / associated reference signals used for baseline real data collection, or the reference signals in set B / set A can be sent multiple times. For example, if the reference signals are sent multiple times, the UE can average / filter the measurement results to improve measurement accuracy.

[0121] For another example, even if repetition is configured (e.g., the "repetition" factor in the RRC parameters is set to "on"), the UE maintains the same Rx beam for reference signal measurement without changing the UE's Rx beam. For example, when the "repetition" factor in the reference signal configuration is set to "on," the UE further checks whether the reference signal is used for data collection; if so, the UE maintains the same Rx beam.

[0122] In some examples, for the NW-side model, since training data collection is performed, for example, via layer 3 signaling, a report quantity of "none" may be applied. Alternatively, a new report quantity may be introduced to indicate that there is no layer 1 reporting for data collection.

[0123] In some examples, the existing report quantity may be applied to the reference signals used for model input data collection (e.g., may be the same as set B) and / or the reference signals used for baseline real data collection (e.g., may be the same as set A). In addition to data collection, the UE may also perform beam measurement operations configured by the reference signal configuration and report the measurement results via Layer 1 signaling (UCI) indicated by the report quantity.

[0124] For example, if one CSI-RS resource set is configured for input data collection and another CSI / RS resource set is configured for baseline real data collection, and both resource sets are configured with the reporting quantity "cri-RSRP", the UE performs data collection operations and also reports the best several beams and corresponding RSRPs after measurement.

[0125] In some embodiments, the second reference signal used for model input data collection and the first reference signal used for baseline real data collection are correlated, or the second reference signal used for model input data collection and the first reference signal used for baseline real data collection are uncorrelated.

[0126] For example, the reference signal used for collecting model input data (e.g., the same as set B) and the reference signal used for collecting baseline real data (e.g., the same as set A) may be associated, including BM sase 1 and / or BM case 2. For another example, the reference signal used for collecting baseline real data (e.g., the same as set A) may not be associated with the reference signal used for collecting model input data (e.g., the same as set B), that is, this association may be transparent to the UE.

[0127] In some examples, for BM case 2, before triggering training data collection, the gNB may first query the UE's conditions, such as the UE's speed and / or preferred number of time instances for model input data and / or ground truth data.

[0128] In some examples, the UE Rx beam can be refined before measuring the reference signal for data collection. For example, the gNB can trigger the UE Rx beam refinement process before sending the RS for data collection.

[0129] In some embodiments, the AI / ML function / model for beam management is located on the network device side, and the training data collection is reported to the network device via non-layer-1 signaling. For example, the non-layer-1 signaling is layer-3 signaling (RRC). The reference signal for model input data collection (for example, which may be the same as set B) and the reference signal for baseline real data collection (for example, which may be the same as set A) can be configured and / or associated through a dedicated RRC configuration, which may include BM case 1 and / or BM case 2. For example, configuration is not performed through the CSI reporting framework.

[0130] In some embodiments, the same receive beam is used to receive a second reference signal for model input data collection and a corresponding or associated first reference signal for reference real data collection.

[0131] In some embodiments, a repetition parameter is configured for a second reference signal used for model input data collection and a corresponding or associated first reference signal used for baseline real data collection.

[0132] For example, the same UE Rx beam can be used to measure reference signals used for model input data collection and corresponding / associated reference signals used for baseline real data collection, including BM case 1 and / or BM case 2. Furthermore, to improve measurement accuracy, "repetition" can be configured for the reference signals used for model input data collection and / or the corresponding / associated reference signals used for baseline real data collection, or the reference signals in set B / set A can be sent multiple times. For example, if the reference signals are sent multiple times, the UE can average / filter the measurement results to improve measurement accuracy.

[0133] For another example, even if repetition is configured (e.g., the "repetition" factor in the RRC parameters is set to "on"), the UE maintains the same Rx beam for reference signal measurement without changing the UE's Rx beam. For example, when the "repetition" factor in the reference signal configuration is set to "on," the UE further checks whether the reference signal is used for data collection; if so, the UE maintains the same Rx beam.

[0134] In some examples, for the NW-side model, L1-RSRP is collected for model input data and / or ground truth data.

[0135] In some embodiments, the second reference signal used for model input data collection and the first reference signal used for baseline real data collection are correlated, or the second reference signal used for model input data collection and the first reference signal used for baseline real data collection are uncorrelated.

[0136] For example, the reference signal used for model input data collection (e.g., may be the same as set B) and the reference signal used for baseline real data collection (e.g., may be the same as set A) may be associated, including BM sase 1 and / or BM case 2. For another example, the reference signal used for baseline real data collection (e.g., may be the same as set A) may not be associated with the reference signal used for model input data collection (e.g., may be the same as set B), that is, this association may be transparent to the UE.

[0137] In some examples, for BM case 2, before triggering training data collection, the gNB may first query the UE's conditions, such as the UE's speed and / or preferred number of time instances for model input data and / or ground truth data.

[0138] In some examples, the UE Rx beam can be refined before measuring the reference signal for data collection. For example, the gNB can trigger the UE Rx beam refinement process before sending the RS for data collection.

[0139] The above describes the training data collection. The following is a schematic description of model inference.

[0140] In some embodiments, the AI / ML function / model for beam management is located on the terminal device side, and the second reference signal set for measurement and the first reference signal set for prediction are configured and / or associated.

[0141] In some embodiments, the same receive beam is used to receive a second reference signal for model input.

[0142] In some embodiments, a repetition parameter is configured for a second reference signal used as an input to the model.

[0143] For example, the same UE Rx beam can be used to measure the reference signals used for model input (e.g., set B). Furthermore, to improve measurement accuracy, "repetition" can be configured for the reference signals used for model input (or set B), or the reference signals in set B can be sent multiple times. For example, if the reference signals are sent multiple times, the UE can average / filter the measurement results, thereby improving measurement accuracy.

[0144] For another example, even if repetition is configured (e.g., the "repetition" factor in the RRC parameters is set to "on"), the UE maintains the same Rx beam for reference signal measurement without changing the UE's Rx beam. For example, when the "repetition" factor in the reference signal configuration is set to "on," the UE further checks whether the reference signal is used for model input; if so, the UE maintains the same Rx beam.

[0145] In some embodiments, the terminal device reports an inference or prediction result; wherein the report includes a component carrier (CC) identifier and / or a TRP identifier.

[0146] For example, when a UE reports an inference result (e.g., a predicted beam from set A), the component carrier (CC) ID may be included in the report. For example, the CC ID may be added to the beam information of the inference result. For another example, for multi-TRP operation, the TRP ID (e.g., CORESETPoolIndex) may be included in the report of the inference result.

[0147] For another example, the reference signal configuration of set B may be associated with set A from different CCs. Alternatively, the reference signal configuration of set B may be associated with set A from multiple CCs.

[0148] In some embodiments, the second reference signal set is a subset of the first reference signal set; when determining the multiple beams with the strongest signals, the predicted RSRP is used for the beams in the second reference signal set, or the measured RSRP is used for the beams in the second reference signal set. Alternatively, whether to use the predicted RSRP or the measured RSRP depends on the performance of the AI / ML function / model. For example, when the performance of the AI / ML function / model is better than a certain threshold, the predicted RSRP is used; otherwise, the measured RSRP is used.

[0149] For example, if set B is a subset of set A, then when determining the top K (i.e., the strongest K) beams, the predicted RSRP can be applied to the beams in set B. Alternatively, the measured RSRP can be applied to the beams in set B. In the reporting of the inference results, if the beams in set B are included, the predicted RSRP or measured RSRP of the beams in set B is reported accordingly.

[0150] In some examples, the UE Rx beam can be refined before measuring the reference signal for data collection. For example, the gNB can trigger the UE Rx beam refinement process before sending the RS for data collection.

[0151] In some embodiments, the AI / ML function / model for beam management is located on the network device side, and the second reference signal set (set B) for measurement and the first reference signal set (set A) for prediction are configured and / or associated, or the first reference signal set (set A) for prediction is not configured to the terminal device.

[0152] In some embodiments, the same receive beam is used to receive a second reference signal for model input.

[0153] In some embodiments, a repetition parameter is configured for a second reference signal used as an input to the model.

[0154] For example, the same UE Rx beam can be used to measure the reference signals used for model input (e.g., set B). Furthermore, to improve measurement accuracy, "repetition" can be configured for the reference signals used for model input (or set B), or the reference signals in set B can be sent multiple times. For example, if the reference signals are sent multiple times, the UE can average / filter the measurement results, thereby improving measurement accuracy.

[0155] For another example, even if repetition is configured (e.g., the "repetition" factor in the RRC parameters is set to "on"), the UE maintains the same Rx beam for reference signal measurement without changing the UE's Rx beam. For example, when the "repetition" factor in the reference signal configuration is set to "on," the UE further checks whether the reference signal is used for model input; if so, the UE maintains the same Rx beam.

[0156] In some examples, the reference signal configuration of set B may be associated with set A from a different CC. Alternatively, the reference signal configuration of set B may be associated with set A from multiple CCs.

[0157] In some examples, the reference signal configuration of set B and / or set A in the NW-side model can be the same as that in the UE-side model, or the reference signal configuration of set B and / or set A in the NW-side model can be different from that in the UE-side model. The association between set B and set A in the NW-side model can be the same as that in the UE-side model, or the association between set B and set A in the NW-side model can be different from that in the UE-side model. Alternatively, set A may not be configured for the UE because model inference is performed on the NW side.

[0158] In some examples, the UE Rx beam can be refined before measuring the reference signal for data collection. For example, the gNB can trigger the UE Rx beam refinement process before sending the RS for data collection.

[0159] The above describes model reasoning. The following describes performance monitoring.

[0160] In some embodiments, the AI / ML function / model for beam management is located on the terminal device side, and the second reference signal for measurement and the first reference signal for performance monitoring are configured and / or associated.

[0161] In some embodiments, the same receive beam is used to receive the second reference signal for measurement and the first reference signal for performance monitoring.

[0162] In some embodiments, a repetition parameter is configured for the second reference signal used for measurement and the first reference signal used for performance monitoring.

[0163] For example, the same UE Rx beam can be used to measure both the reference signal used for measurement and the reference signal used for performance monitoring. Furthermore, to improve measurement accuracy, repetition can be configured for the reference signal used for measurement and / or the reference signal used for performance monitoring, or the reference signal can be sent multiple times. For example, if the reference signal is sent multiple times, the UE can average / filter the measurement results to improve measurement accuracy.

[0164] For another example, even if repetition is configured (e.g., the "repetition" factor in the RRC parameters is set to "on"), the UE maintains the same Rx beam for reference signal measurement without changing the UE's Rx beam. For example, when the "repetition" factor in the reference signal configuration is set to "on", the UE further checks whether the reference signal is used for performance monitoring; if so, the UE maintains the same Rx beam.

[0165] In some examples, the UE Rx beam can be refined before measuring the reference signals for measurement and the reference signals for performance monitoring. For example, the gNB can trigger the UE Rx beam refinement process before sending the RS for measurement and the RS for performance monitoring.

[0166] In some examples, the reference signals used for measurement may be set B (or a subset of set B), and the reference signals used for performance monitoring may also be set B (or a subset of set B). Alternatively, the reference signals used for measurement may be set B (or a subset of set B), and the reference signals used for performance monitoring may be set A (or a subset of set A). Alternatively, set B is used for model input, and the reference signals used for measurement may be set A (or a subset of set A), and the reference signals used for performance monitoring may also be set A (or a subset of set A).

[0167] In some examples, if the performance of the UE-side model is monitored by the NW side and the performance metric is beam prediction accuracy, set B is configured to the UE for measurement and used as input to the UE-side AI / ML model. Set A is also configured to the UE and associated with set B. Set A is used for both inference and performance monitoring. The reference signals in set A (or a subset of set A used for performance monitoring) are sent to the UE. In the report, the UE reports the top K beams based on the beam prediction of set A, and also reports the top K beams based on the measurement of the reference signals of set A. A new report quantity can be introduced.

[0168] In some examples, if the performance of the UE-side model is monitored by the NW side and the performance metric is the RSRP difference between the measurement and prediction, set B is configured to the UE for measurement and used as input to the UE-side AI / ML model. Set A is also configured to the UE and associated with set B. Set A is used for both inference and performance monitoring. The reference signals in set A (or the subset of set A used for performance monitoring) are sent to the UE. In the report, the UE reports the predicted RSRP and measured RSRP of the reference signals in set A (or the subset of reference signals in set A). The predicted RSRP and measured RSRP can be delivered through the same report, or they can be delivered through separate reports. New reporting quantities / new RRC parameters can be introduced to indicate whether the predicted RSRP and / or measured RSRP should be reported.

[0169] In some examples, if the performance of the UE-side model is monitored by the NW side and the performance metric is the RSRP difference between the measurement and prediction, set B is configured to the UE for measurement and used as input to the UE-side AI / ML model. Set B is also used for performance monitoring. In the report, the UE reports the predicted RSRP and measured RSRP of the reference signals in set B (or a subset of the reference signals in set B). The predicted RSRP and measured RSRP can be delivered through the same report, or through separate reports. New reporting quantities / new RRC parameters can be introduced to indicate whether the predicted RSRP and / or measured RSRP should be reported. This situation can also be applied to hybrid monitoring and UE-side monitoring.

[0170] In some examples, for beam management with AI / ML functions / models on the UE side, reference signals for training data collection (including reference signals for model input data and / or reference signals for benchmark real data) and / or reference signals for inference (including set B and / or set A) and / or reference signals for performance monitoring can be configured or associated together. Reports for data collection, inference results, and performance monitoring can be reported to the network device together through the same report, or can be reported to the network device separately through separate reports.

[0171] In some embodiments, the AI / ML function / model for beam management is located on the network device side, and the second reference signal for measurement and the first reference signal for performance monitoring are configured and / or associated.

[0172] For example, set B can be configured for measurement, and the measurement results can be used as model input. If the performance metric is the RSRP difference between the measurement and prediction, the measurements of set B can also be used for performance monitoring. For example, the gNB can also check the predicted RSRP of the beams in set B based on inference. In this case, set A can not be configured to the UE for performance monitoring.

[0173] For another example, set B can be configured for measurement, and the measurement results can be used as model input. Set A (or a subset of set A) can also be configured and sent to the UE. The measurements of set A (or a subset of set A) can also be used for performance monitoring. Set A can be associated with set B or not, that is, the configuration of set A can be separated from the configuration of set A, and the association between set B and set A can be transparent to the UE. For another example, only set A (or a subset of set A) can be configured and sent to the UE for performance monitoring.

[0174] In some embodiments, the same receive beam is used to receive the second reference signal for measurement and the first reference signal for performance monitoring.

[0175] In some embodiments, a repetition parameter is configured for the second reference signal used for measurement and the first reference signal used for performance monitoring.

[0176] For example, the same UE Rx beam can be used to measure both the reference signal used for measurement and the reference signal used for performance monitoring. Furthermore, to improve measurement accuracy, repetition can be configured for the reference signal used for measurement and / or the reference signal used for performance monitoring, or the reference signal can be sent multiple times. For example, if the reference signal is sent multiple times, the UE can average / filter the measurement results to improve measurement accuracy.

[0177] For another example, even if repetition is configured (e.g., the "repetition" factor in the RRC parameters is set to "on"), the UE maintains the same Rx beam for reference signal measurement without changing the UE's Rx beam. For example, when the "repetition" factor in the reference signal configuration is set to "on", the UE further checks whether the reference signal is used for performance monitoring; if so, the UE maintains the same Rx beam.

[0178] In some examples, the UE Rx beam can be refined before measuring the reference signals for measurement and the reference signals for performance monitoring. For example, the gNB can trigger the UE Rx beam refinement process before sending the RS for measurement and the RS for performance monitoring.

[0179] In some examples, for beam management with AI / ML functions / models on the NW side, reference signals for training data collection (including reference signals for model input data and / or reference signals for benchmark real data) and / or reference signals for inference (including set B and / or set A) and / or reference signals for performance monitoring can be configured or associated together. Reports for data collection, inference results, and performance monitoring can be reported to the network device together through the same report, or can be reported to the network device separately through separate reports.

[0180] 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.

[0181] The above embodiments are merely exemplary of the present invention, but the present invention is not limited thereto. Appropriate modifications may be made based on the above embodiments. For example, the above embodiments may be used alone, or one or more of the above embodiments may be combined.

[0182] As can be seen from the above embodiments, a terminal device receives reference signal configuration information from a network device, receives and measures reference signals based on the reference signal configuration information, and performs training data collection and / or model inference and / or performance monitoring for AI / ML functionality / models based on the measurement results. This enhances the reference signal configuration, improves the accuracy of measurement results, and improves the performance and efficiency of AI / ML.

[0183] Embodiments of the second aspect

[0184] The embodiment of the present application provides a reference signal 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.

[0185] FIG5 is a schematic diagram of a reference signal configuration method for beam management according to an embodiment of the present application. As shown in FIG5 , the method includes:

[0186] 501, the network device sends reference signal configuration information to the terminal device;

[0187] 502. The network device sends a reference signal to the terminal device according to the reference signal configuration information;

[0188] As shown in FIG5 , the method may further include:

[0189] 503. The terminal device collects training data and / or performs model inference and / or performance monitoring for AI / ML functionality / model based on the measurement results.

[0190] It is worth noting that FIG5 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 FIG5 above.

[0191] The above embodiments are merely exemplary of the present invention, but the present invention is not limited thereto. Appropriate modifications may be made based on the above embodiments. For example, the above embodiments may be used alone, or one or more of the above embodiments may be combined.

[0192] As can be seen from the above embodiments, a terminal device receives reference signal configuration information from a network device, receives and measures reference signals based on the reference signal configuration information, and performs training data collection and / or model inference and / or performance monitoring for AI / ML functionality / models based on the measurement results. This enhances the reference signal configuration, improves the accuracy of measurement results, and improves the performance and efficiency of AI / ML.

[0193] Embodiments of the third aspect

[0194] The present application provides a reference signal 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.

[0195] FIG6 is a schematic diagram of a reference signal configuration apparatus for beam management according to an embodiment of the present application. As shown in FIG6 , a reference signal configuration 600 for beam management according to an embodiment of the present application includes:

[0196] a receiving unit 601, which receives reference signal configuration information from a network device, and receives and measures a reference signal according to the reference signal configuration information;

[0197] The processing unit 602 performs training data collection and / or model inference and / or performance monitoring for AI / ML functions / models based on the measurement results.

[0198] In some embodiments, the AI / ML function / model for beam management is located on the terminal device side and / or the network device side, and the training data collection is performed through non-layer 1 signaling.

[0199] In some embodiments, the same receive beam is used to receive a second reference signal for model input data collection and a corresponding or associated first reference signal for reference real data collection.

[0200] In some embodiments, a repetition parameter is configured for a second reference signal used for model input data collection and a corresponding or associated first reference signal used for baseline real data collection.

[0201] In some embodiments, the AI / ML function / model for beam management is located on the terminal device side, and the training data collection is performed through layer 1 signaling.

[0202] In some embodiments, the same receive beam is used to receive a second reference signal for model input data collection and a corresponding or associated first reference signal for reference real data collection.

[0203] In some embodiments, a repetition parameter is configured for a second reference signal used for model input data collection and a corresponding or associated first reference signal used for baseline real data collection.

[0204] In some embodiments, the AI / ML function / model for beam management is located on the network device side, and the training data collection is performed through layer 1 signaling.

[0205] In some embodiments, the same receive beam is used to receive a second reference signal for model input data collection and a corresponding or associated first reference signal for reference real data collection.

[0206] In some embodiments, a repetition parameter is configured for a second reference signal used for model input data collection and a corresponding or associated first reference signal used for baseline real data collection.

[0207] In some embodiments, the second reference signal used for model input data collection and the first reference signal used for baseline real data collection are correlated, or the second reference signal used for model input data collection and the first reference signal used for baseline real data collection are uncorrelated.

[0208] In some embodiments, the AI / ML function / model for beam management is located on the terminal device side, and the second reference signal set for measurement and the first reference signal set for prediction are configured and / or associated.

[0209] In some embodiments, the same receive beam is used to receive a second reference signal for model input.

[0210] In some embodiments, a repetition parameter is configured for a second reference signal used as an input to the model.

[0211] In some embodiments, the terminal device reports an inference or prediction result; wherein the report includes a component carrier (CC) identifier and / or a TRP identifier.

[0212] In some embodiments, the second reference signal set is a subset of the first reference signal set; when determining the multiple beams with the strongest signals, the predicted RSRP is used for the beams in the second reference signal set, or the measured RSRP is used for the beams in the second reference signal set. Alternatively, whether to use the predicted RSRP or the measured RSRP depends on the performance of the AI / ML function / model. For example, when the performance of the AI / ML function / model is better than a certain threshold, the predicted RSRP is used; otherwise, the measured RSRP is used.

[0213] In some embodiments, the AI / ML function / model for beam management is located on the network device side, and the second reference signal set for measurement and the first reference signal set for prediction are configured and / or associated, or the first reference signal set for prediction is not configured to the terminal device.

[0214] In some embodiments, the same receive beam is used to receive a second reference signal for model input.

[0215] In some embodiments, a repetition parameter is configured for a second reference signal used as an input to the model.

[0216] In some embodiments, the AI / ML function / model for beam management is located on the terminal device side, and the second reference signal for measurement and the first reference signal for performance monitoring are configured and / or associated.

[0217] In some embodiments, the same receive beam is used to receive the second reference signal for measurement and the first reference signal for performance monitoring.

[0218] In some embodiments, a repetition parameter is configured for the second reference signal used for measurement and the first reference signal used for performance monitoring.

[0219] In some embodiments, the AI / ML function / model for beam management is located on the network device side, and the second reference signal for measurement and the first reference signal for performance monitoring are configured and / or associated.

[0220] In some embodiments, the same receive beam is used to receive the second reference signal for measurement and the first reference signal for performance monitoring.

[0221] In some embodiments, a repetition parameter is configured for the second reference signal used for measurement and the first reference signal used for performance monitoring.

[0222] In some embodiments, as shown in FIG6 , the apparatus may further include a sending unit 603 that sends feedback information / data to the network device.

[0223] 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.

[0224] 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 reference signal configuration device 600 for beam management may also include other components or modules. For the specific contents of these components or modules, please refer to the relevant art.

[0225] In addition, for the sake of simplicity, FIG6 only illustrates the connection relationship or signal direction between various components or modules. However, it should be clear to those skilled in the art that various related technologies such as bus connection can be used. The above-mentioned components or modules can be implemented by hardware facilities such as processors, memories, transmitters, and receivers; the implementation of this application is not limited to this.

[0226] As can be seen from the above embodiments, a terminal device receives reference signal configuration information from a network device, receives and measures reference signals based on the reference signal configuration information, and performs training data collection and / or model inference and / or performance monitoring for AI / ML functionality / models based on the measurement results. This enhances the reference signal configuration, improves the accuracy of measurement results, and improves the performance and efficiency of AI / ML.

[0227] Embodiments of the fourth aspect

[0228] The present embodiment provides a reference signal 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.

[0229] FIG7 is another schematic diagram of a reference signal configuration apparatus for beam management according to an embodiment of the present application. As shown in FIG7 , the reference signal configuration apparatus 700 for beam management includes:

[0230] A transmitting unit 701 transmits reference signal configuration information to a terminal device and transmits a reference signal to the terminal device based on the reference signal configuration information; wherein the reference signal is measured by the terminal device and, based on the measurement results, training data collection and / or model inference and / or performance monitoring for AI / ML functions / models are performed.

[0231] In some embodiments, as shown in FIG7 , the reference signal configuration apparatus 700 for beam management may further include:

[0232] The receiving unit 702 receives data / information fed back by the terminal device.

[0233] 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.

[0234] 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 reference signal configuration device 700 for beam management may also include other components or modules. For the specific contents of these components or modules, please refer to the relevant art.

[0235] In addition, for the sake of simplicity, FIG7 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.

[0236] As can be seen from the above embodiments, a terminal device receives reference signal configuration information from a network device, receives and measures reference signals based on the reference signal configuration information, and performs training data collection and / or model inference and / or performance monitoring for AI / ML functionality / models based on the measurement results. This enhances the reference signal configuration, improves the accuracy of measurement results, and improves the performance and efficiency of AI / ML.

[0237] Embodiments of the fifth aspect

[0238] 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.

[0239] In some embodiments, the communication system 100 may include at least:

[0240] a network device, configured to send reference signal configuration information to a terminal device, and to send a reference signal to the terminal device according to the reference signal configuration information;

[0241] A terminal device receives and measures the reference signal according to the reference signal configuration information; and performs training data collection and / or model inference and / or performance monitoring for AI / ML functions / models based on the measurement results.

[0242] 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.

[0243] Figure 8 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in Figure 8 , terminal device 800 may include a processor 810 and a memory 820. Memory 820 stores data and programs and is coupled to processor 810. 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.

[0244] For example, the processor 810 may be configured to execute a program to implement the reference signal configuration method for beam management as described in the embodiment of the first aspect. For example, the processor 810 may be configured to perform the following control: receiving reference signal configuration information from a network device, receiving and measuring a reference signal according to the reference signal configuration information; and performing training data collection and / or model inference and / or performance monitoring for AI / ML functions / models based on the measurement results.

[0245] As shown in Figure 8 , the terminal device 800 may further include: a communication module 830, an input unit 840, a display 850, and a power supply 860. 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 800 does not necessarily include all of the components shown in Figure 8 , and these components are not essential. Furthermore, the terminal device 800 may also include components not shown in Figure 8 , for which reference may be made to the prior art.

[0246] 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.

[0247] Figure 9 is a schematic diagram illustrating the structure of a network device according to an embodiment of the present application. As shown in Figure 9 , network device 900 may include a processor 910 (e.g., a central processing unit (CPU)) and a memory 920 ; the memory 920 is coupled to the processor 910 . The memory 920 may store various data and may also store an information processing program 930 , which is executed under the control of the processor 910 .

[0248] For example, the processor 910 may be configured to execute a program to implement the reference signal configuration method for beam management as described in the embodiment of the second aspect. For example, the processor 910 may be configured to perform the following control: sending reference signal configuration information to a terminal device, and sending a reference signal to the terminal device based on the reference signal configuration information; wherein the reference signal is measured by the terminal device and training data collection and / or model inference and / or performance monitoring for AI / ML functions / models are performed based on the measurement results.

[0249] In addition, as shown in Figure 9, network device 900 may further include: a transceiver 940 and an antenna 950; wherein, the functions of these components are similar to those in the prior art and are not further described here. It is worth noting that network device 900 does not necessarily include all the components shown in Figure 9; in addition, network device 900 may also include components not shown in Figure 9, and reference may be made to the prior art for details.

[0250] 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 reference signal configuration method for beam management described in the embodiment of the first aspect.

[0251] 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 reference signal configuration method for beam management described in the embodiment of the first aspect.

[0252] 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 reference signal configuration method for beam management described in the embodiment of the second aspect.

[0253] 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 reference signal configuration method for beam management described in the embodiment of the second aspect.

[0254] 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.

[0255] 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).

[0256] 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.

[0257] 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.

[0258] 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.

[0259] Regarding the implementation methods including the above embodiments, the following additional notes are also disclosed:

[0260] 1. A method for configuring a reference signal for beam management, comprising:

[0261] The terminal device receives reference signal configuration information from the network device;

[0262] The terminal device receives and measures the reference signal according to the reference signal configuration information;

[0263] The terminal device collects training data and / or performs model reasoning and / or performance monitoring for AI / ML functions / models based on the measurement results.

[0264] 2. A reference signal configuration method for beam management, comprising:

[0265] The network device sends reference signal configuration information to the terminal device;

[0266] The network device sends a reference signal to the terminal device according to the reference signal configuration information; wherein, the reference signal is measured by the terminal device and training data collection and / or model inference and / or performance monitoring for AI / ML functions / models are performed based on the measurement results.

[0267] 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 reference signal configuration method for beam management as described in Note 1.

[0268] 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 reference signal configuration method for beam management as described in Note 2.

[0269] 5. A computer program product, comprising at least a computer program, which, when executed by a processor, enables a terminal device to execute the reference signal configuration method for beam management as described in Note 1.

[0270] 6. A computer program product, comprising at least a computer program, which, when executed by a processor, enables a network device to execute the reference signal configuration method for beam management as described in Note 2.

Claims

1. A reference signal configuration device for beam management, comprising: a receiving unit, configured to receive reference signal configuration information from a network device, and receive and measure a reference signal according to the reference signal configuration information; A processing unit that performs training data collection and / or model inference and / or performance monitoring for AI / ML functions / models based on the measurement results.

2. The device according to claim 1, wherein The AI / ML functions / models for beam management are located on the terminal device side and / or the network device side, and the training data collection is performed through non-layer 1 signaling.

3. The device according to claim 2, wherein The same receive beam is used to receive a second reference signal for model input data collection and a corresponding or associated first reference signal for reference real data collection; and / or The repetition parameter is configured for the second reference signal used for model input data collection and the corresponding or associated first reference signal used for reference real data collection.

4. The device according to claim 1, wherein The AI / ML functions / models for beam management are located on the terminal device side, and the training data collection is performed through Layer 1 signaling.

5. The device according to claim 4, wherein The same receive beam is used to receive a second reference signal for model input data collection and a corresponding or associated first reference signal for reference real data collection; and / or The repetition parameter is configured for the second reference signal used for model input data collection and the corresponding or associated first reference signal used for reference real data collection.

6. The device according to claim 1, wherein The AI / ML functions / models for beam management are located on the network equipment side, and the training data collection is performed through layer 1 signaling.

7. The device according to claim 6, wherein The same receive beam is used to receive a second reference signal for model input data collection and a corresponding or associated first reference signal for reference real data collection; and / or The repetition parameter is configured for the second reference signal used for model input data collection and the corresponding or associated first reference signal used for reference real data collection.

8. The device according to claim 6, wherein The second reference signal used for model input data collection and the first reference signal used for reference real data collection are correlated, or the second reference signal used for model input data collection and the first reference signal used for reference real data collection are uncorrelated.

9. The device according to claim 1, wherein The AI / ML function / model for beam management is located on the terminal device side, and the second reference signal set for measurement and the first reference signal set for prediction are configured and / or associated.

10. The device according to claim 9, wherein The same receive beam is used to receive a second reference signal for model input; and / or The repetition parameter is configured for the second reference signal used for model input.

11. The device according to claim 9, wherein The terminal device reports the inference or prediction result; The report includes a component carrier identifier and / or a TRP identifier.

12. The device according to claim 9, wherein The second reference signal set is a subset of the first reference signal set; when determining the plurality of beams with the strongest signals, estimating RSRP is used for the beams in the second reference signal set, or measuring RSRP is used for the beams in the second reference signal set; Alternatively, whether to use predicted RSRP or measured RSRP depends on the performance of the AI / ML function / model; wherein, when the performance of the AI / ML function / model is better than a certain threshold, predicted RSRP is used, otherwise measured RSRP is used.

13. The device according to claim 1, wherein The AI / ML function / model for beam management is located on the network device side, and the second reference signal set for measurement and the first reference signal set for prediction are configured and / or associated, or the first reference signal set for prediction is not configured to the terminal device.

14. The device according to claim 13, wherein The same receive beam is used to receive a second reference signal for model input; and / or The repetition parameter is configured for the second reference signal used for model input.

15. The device according to claim 1, wherein The AI / ML function / model for beam management is located on the terminal device side, and the second reference signal for measurement and the first reference signal for performance monitoring are configured and / or associated.

16. The device according to claim 15, wherein The same receive beam is used to receive the second reference signal for measurement and the first reference signal for performance monitoring; and / or The repetition parameter is configured for the second reference signal used for measurement and the first reference signal used for performance monitoring.

17. The device according to claim 1, wherein The AI / ML function / model for beam management is located on the network device side, and the second reference signal for measurement and the first reference signal for performance monitoring are configured and / or associated.

18. The device according to claim 17, wherein The same receive beam is used to receive the second reference signal for measurement and the first reference signal for performance monitoring; and / or The repetition parameter is configured for the second reference signal used for measurement and the first reference signal used for performance monitoring.

19. A reference signal configuration device for beam management, comprising: a sending unit, configured to send reference signal configuration information to a terminal device, and send a reference signal to the terminal device according to the reference signal configuration information; The reference signal is measured by the terminal device and training data collection and / or model reasoning and / or performance monitoring for AI / ML functions / models are performed based on the measurement results.

20. A communication system comprising: a network device, configured to send reference signal configuration information to a terminal device, and to send a reference signal to the terminal device according to the reference signal configuration information; A terminal device receives and measures the reference signal according to the reference signal configuration information; and performs training data collection and / or model inference and / or performance monitoring for AI / ML functions / models based on the measurement results.

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