Beam management method and apparatus

Abstract

Embodiments of the present application provide a beam management method and apparatus. The method comprises: a network device receives an uplink signal / channel from a terminal device; and on the basis of an AI / ML model / functionality, the network device performs beam tracking or beam steering on the basis of the uplink signal / channel to determine a beam directed towards the terminal device.

Description

Beam management method and apparatus TECHNICAL FIELD

[0001] Embodiments of the present application relate to the field of communication technology. BACKGROUND

[0002] In NR Rel-18, artificial intelligence / machine learning (AI / ML) for air interface is studied. AI / ML can be used for the following use cases: Channel State Information (CSI) feedback enhancement, beam management, positioning enhancement. CSI feedback enhancement can include CSI prediction, CSI compression; beam management can include spatial beam prediction (BM case-1), temporal beam prediction (BM case-2); positioning enhancement can include direct positioning, AI / ML assisted positioning.

[0003] In some sub-use cases, a bilateral model can be used, i.e., the AI / ML model is at the terminal device side and at the network device side. In other sub-use cases, a unilateral model can be used, i.e., the AI / ML model is at the terminal device side or at the network device side. For beam management, the AI / ML model can be at the terminal device side and / or at the network device side.

[0004] It should be noted that the above introduction to the technical background is only for the convenience of clearly and completely describing the technical solutions of the present application, and for the convenience of understanding by those skilled in the art. The above technical solutions cannot be considered as known to those skilled in the art merely because they are described in the background section of the present application.

[0005] SUMMARY

[0006] The inventors have found that the terminal device and / or the network device can utilize AI / ML functionality / model for beam management, but the current beam management is still based on beam sweeping, which requires sequentially transmitting multiple beams and selecting the best one or more beams, which can result in large signaling overhead and certain degree of latency.

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

[0008] According to an aspect of embodiments of the present application, a beam management method is provided, comprising:

[0009] The network device receives an uplink signal / channel from the terminal device;

[0010] The network device performs beam tracking or beam steering based on the AI / ML model / function according to the uplink signal / channel to determine a beam towards the terminal device

[0011] According to another aspect of embodiments of the present application, a beam management apparatus is provided, comprising:

[0012] a receiving unit configured to receive an uplink signal / channel from a terminal device;

[0013] a processing unit configured to perform beam tracking or beam steering based on an AI / ML model / function according to the uplink signal / channel to determine a beam towards the terminal device.

[0014] According to another aspect of embodiments of the present application, a configuration method of beam management is provided, comprising:

[0015] a terminal device configured to send an uplink signal / channel to a network device;

[0016] wherein the uplink signal / channel is used by the network device to perform beam tracking or beam steering based on an AI / ML model / function to determine a beam towards the terminal device.

[0017] According to another aspect of embodiments of the present application, a configuration apparatus of beam management is provided, comprising:

[0018] a sending unit configured to send an uplink signal / channel to a network device;

[0019] wherein the uplink signal / channel is used by the network device to perform beam tracking or beam steering based on an AI / ML model / function to determine a beam towards the terminal device.

[0020] According to another aspect of embodiments of the present application, a communication system is provided, comprising:

[0021] a terminal device configured to send an uplink signal / channel to a network device;

[0022] a network device configured to perform beam tracking or beam steering based on an AI / ML model / function according to the uplink signal / channel to determine a beam towards the terminal device.

[0023] One of the beneficial effects of the embodiments of the present application is that the network device determines the beam towards the terminal device based on the AI / ML model / function, according to the uplink signal / channel for beam tracking or beam steering. Thus, the signaling overhead can be reduced and the latency can be decreased.

[0024] Specific embodiments of the application are disclosed herein, and summarized above, in order to provide a thorough understanding of the application. It should be understood that the application is not limited to the embodiments described and / or illustrated herein. Rather, the application includes all alternatives, modifications and equivalents falling within the spirit and scope of the appended claims.

[0025] Features described and / or illustrated with respect to one implementation can be used in one or more other implementations in the same or similar manner, in combination with or in place of features in other implementations, or in combination with or in place of one or more features described and / or illustrated with respect to another implementation.

[0026] It should be emphasized that the term "comprises / comprising" when used in this specification is taken to mean the presence of stated features, integers, steps or components but does not preclude the presence or addition of one or more other features, integers, steps, components or groups thereof. BRIEF DESCRIPTION OF DRAWINGS

[0027] Elements and features depicted with respect to one drawing or implementation of the application can be combined with elements and features depicted with respect to one or more other drawings or implementations of the application. Also, in the drawings, like reference numerals designate corresponding parts throughout the several views, and can be used to designate like components in more than one implementation.

[0028] FIG. 1 is a schematic diagram of a communication system according to an embodiment of the present application;

[0029] FIG. 2 is a schematic diagram of AI / ML for beam management;

[0030] FIG. 3 is a schematic diagram of an AI / ML based beam management method;

[0031] FIG. 4 is an example diagram of beam management based on beam sweeping;

[0032] FIG. 5 is a schematic diagram of a beam management method according to an embodiment of the present application;

[0033] FIG. 6 is another schematic diagram of a beam management method according to an embodiment of the present application;

[0034] FIG. 7 is an example diagram of beam management based on beam steering or beam tracking according to an embodiment of the present application;

[0035] FIG. 8 is an example diagram of beam management according to an embodiment of the present application;

[0036] FIG. 9 is a schematic diagram of a beam management method according to an embodiment of the present application;

[0037] FIG. 10 is a schematic diagram of a beam management apparatus according to an embodiment of the present application;

[0038] FIG. 11 is a schematic diagram of a beam management apparatus according to an embodiment of the present application;

[0039] FIG. 12 is a schematic diagram of a network device according to an embodiment of the present application;

[0040] FIG. 13 is a schematic diagram of a terminal device according to an embodiment of the present application. DETAILED DESCRIPTION

[0041] The foregoing and other features of the present application will become more apparent from the following description and accompanying drawings. In the description and drawings, particular embodiments of the present application are disclosed in detail. It should be understood that the present application is not limited to the embodiments described but includes all modifications, variations, and equivalents that fall within the scope of the appended claims.

[0042] In the embodiments of the present application, the terms "first", "second", and the like are used to distinguish different elements from each other, but do not indicate spatial arrangement or time sequence, and the elements should not be limited by these terms. The term "and / or" includes any one and all combinations of the associated listed terms. The terms "comprise", "include", "have", and the like mean the presence of the stated features, elements, components, or assemblies, but do not exclude the presence or addition of one or more other features, elements, components, or assemblies.

[0043] In the embodiments of the present application, the singular forms "a", "an", and "the" include the plural forms, should be broadly understood as "one" or "one kind" rather than the meaning of "one", and in addition, the term "said" should be understood as including both singular and plural forms, unless the context clearly indicates otherwise. In addition, the term "according to" should be understood as "at least partially according to", and the term "based on" should be understood as "at least partially based on", unless the context clearly indicates otherwise.

[0044] In the embodiments of the present application, the term "communication network" or "wireless communication network" can refer to a network conforming to any communication standard, such as Long Term Evolution (LTE), LTE-Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), High-Speed Packet Access (HSPA), and the like.

[0045] In addition, the communication between devices in the communication system can be performed according to any phase communication protocol, which can include but is 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, and the like, and / or other currently known or to be developed communication protocols.

[0046] In the embodiments of the present application, the term "network device" refers to a device that accesses a terminal device to a communication network and provides services for the terminal device in the communication system. The network device can include but is 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), and the like.

[0047] Among them, the base station can include but is not limited to: Node B (NodeB or NB), evolved Node B (eNodeB or eNB), and 5G base station (gNB), IAB donor, etc., and can also include remote radio head (RRH), remote radio unit (RRU), relay or low-power node (such as femto, pico, etc.). In addition, the term "base station" can include some or all functions thereof, and each base station can provide communication coverage for a specific geographic area. The term "cell" can refer to a base station and / or its coverage area, depending on the context in which the term is used.

[0048] 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. The terminal equipment can be fixed or mobile, and can also be referred to as a mobile station (MS), a terminal, a subscriber station (SS), an access terminal (AT), a station, and the like.

[0049] The terminal equipment can include, but is not limited to, the following devices: a cellular phone, a personal digital assistant (PDA), a wireless modem, a wireless communication device, a handheld device, a machine type communication device, a laptop computer, a cordless phone, a smart phone, a smart watch, a digital camera, and the like.

[0050] For another example, in an Internet of Things (IoT) scenario or the like, the terminal equipment can also be a machine or device that performs monitoring or measurement, and can include, but is not limited to, the following devices: a machine type communication (MTC) terminal, a vehicle-mounted communication terminal, a device-to-device (D2D) terminal, a machine-to-machine (M2M) terminal, and the like.

[0051] In addition, the term "network side" or "network device side" refers to the side of the network, which can be a certain base station, or can include one or more network devices as described above. The term "user side" or "terminal side" or "terminal equipment side" refers to the side of the user or terminal, which can be a certain UE, or can include one or more terminal devices as described above. In this document, "device" can refer to a network device or a terminal device unless otherwise specified.

[0052] The following describes the scenarios of the embodiments of the present application by way of examples, but the present application is not limited thereto.

[0053] FIG. 1 is a schematic diagram of a communication system according to an embodiment of the present application, which schematically illustrates a case taking a terminal equipment and a network device as an example. As shown in FIG. 1, the communication system 100 can include a network device 101 and terminal equipments 102 and 103. For simplicity, FIG. 1 only takes two terminal equipments and one network device as an example for illustration, but the embodiments of the present application are not limited thereto.

[0054] In the embodiments of the present application, the network device 101 and the terminal devices 102, 103 can perform existing services or future implementable service transmission. For example, the services can include, but are not limited to, enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra-reliable and low-latency communication (URLLC), etc.

[0055] It is worth noting that FIG. 1 shows that both terminal devices 102, 103 are within the coverage of the network device 101, but the present application is not limited thereto. Both terminal devices 102, 103 can not be within the coverage of the network device 101, or one terminal device 102 is within the coverage of the network device 101 while the other terminal device 103 is outside the coverage of the network device 101.

[0056] In the embodiments of the present application, the higher layer signaling can be, for example, radio resource control (RRC) signaling; for example, referred to as an RRC message, for example, including MIB, system information, dedicated RRC message; or referred to as an RRC IE. The higher layer signaling can also be, for example, MAC (Medium Access Control) signaling; or referred to as a MAC CE. However, the present application is not limited thereto.

[0057] Since Rel-15, NR 5G has introduced beam management. The beam management procedure is based on beam sweeping. For example, the gNB needs to send beams to the UE in sequence, and the UE can select the best one or multiple beams and feed back.

[0058] A gNB can configure a reference signal, e.g., channel state information reference signal (CSI-RS) / synchronization signal block (SSB) for beam sweeping and beam measurement, for a UE. To indicate which beam to select for communication, a gNB configures transmission configuration indication (TCI) states and indicates which TCI state is used. For example, a gNB can configure a list of TCI states by RRC, then can select a subset of the configured TCI states (e.g., 8 TCI states) for activation by MAC CE, and can indicate the activated TCI state to a UE by DCI (e.g., via a TCI indication field in the DCI).

[0059] In Rel-19, AI / ML based beam management is introduced. AI / ML based beam management (BM Case-1 and BM Case-2, with NW side model and / or UE side model) in Rel-19 can reduce overhead.

[0060] FIG. 2 is a schematic diagram of AI / ML for beam management. As shown in FIG. 2, one or more reference signals in a second reference signal resource set (set B), which can be referred to as RS for measurement or RS for inference, can be received and measured by a terminal device, and the measurement results can be used as input of AI / ML. One or more reference signals in a first reference signal resource set (set A), which can be referred to as RS for prediction, can be used by the terminal device for output of AI / ML, e.g., the measurement results as label data or ground truth data of AI / ML. Details about AI / ML and set A and set B can refer to related technologies, which will not be described here.

[0061] FIG. 3 is a schematic diagram of an AI / ML based beam management method, taking the case of a network device configured with AI / ML as an example. FIG. 3 can be applicable to AI / ML based beam sweeping of embodiments of the present application. As shown in FIG. 3, the method includes:

[0062] 301, a terminal device receives configuration information from a network device; for example, the configuration information includes a second reference signal resource set (set B) for measurement and a first reference signal resource set (set A) for prediction;

[0063] 302, the terminal device receives a reference signal and performs reference signal measurement;

[0064] 303, the terminal device feeds back the measurement results to the network device;

[0065] 304, the network device makes a prediction based on AI / ML; that is, the network device can input the measurement results into an AI / ML functionality / model; for example, the measurement results of the reference signals in set B are input as the input of AI / ML, and the reference signals in set A are used for prediction (or inference).

[0066] As shown in FIG. 2 and FIG. 3, for example, the AI / ML-based beam management can predict the information corresponding to set A (a larger number of reference signals) according to the measurement results of set B (a smaller number of reference signals), and can perform beam prediction in the spatial domain / time domain.

[0067] However, the inventors find that the conventional beam management and the AI / ML-based beam management are still based on beam sweeping, and need to send beams in sequence to select the best one or more beams, resulting in a large amount of overhead. The TCI state configuration and update also introduce a large amount of signaling overhead and delay.

[0068] FIG. 4 is an example diagram of beam management based on beam sweeping. As shown in FIG. 4, whether it is conventional beam management or AI / ML-based beam management, it is based on beam sweeping, and needs to send beams in sequence to select the best one or more beams, so there is still a large signaling overhead and a certain degree of delay.

[0069] In addition, in the AI / ML-based beam management of Rel-19, the TCI configuration and indication are based on the conventional framework, so there is still a large signaling overhead and a certain degree of delay. Considering that the number of beams may be larger and the width of the beam may be narrower in future communications (such as 6G), the beam management scheme based on beam sweeping needs to be improved.

[0070] The above is a schematic description of beam management and beam sweeping, and the present application is not limited thereto. In addition, the above-mentioned embodiments can be part of the embodiments of the present application, can be applicable to the present application, and can also be combined with one or more of the following embodiments.

[0071] In the embodiments of the present application, one or more AI / ML models can be configured and run in the network device and / or the terminal device. The AI / ML model can be used for various signal processing functions of wireless communication, such as CSI prediction, CSI compression, beam prediction, positioning management, etc.; the present application is not limited thereto.

[0072] Embodiments of the first aspect

[0073] The embodiments of the present application provide a beam management method, which is described from the network device side.

[0074] FIG. 5 is a schematic diagram of a beam management method according to an embodiment of the present application. As shown in FIG. 5, the method comprises the following steps.

[0075] 501. The network device receives an uplink signal / channel from a terminal device.

[0076] 502. The network device performs beam tracking or beam steering based on an AI / ML model / functionality to determine a beam towards the terminal device according to the uplink signal / channel.

[0077] It is worth noting that the above FIG. 5 only schematically illustrates the embodiments of the present application, but the present application is not limited thereto. For example, the execution order between the operations can be appropriately adjusted, and in addition, some operations can be added or some operations can be reduced. Those skilled in the art can appropriately modify the above description, and the present application is not limited to the above FIG. 5.

[0078] In some embodiments, the functionality refers to an AI / ML feature / feature group enabled by a configuration, wherein the configuration is supported based on a condition indicated by a UE capability.

[0079] For example, the AL / ML functionality can be one or more functions, or can be one or more logical models, or can be one or more sub-functions, or can be one or more features, or can be one or more feature groups.

[0080] For another example, the functionality can be spatial beam prediction using AI / ML, or can be time beam prediction using AI / ML, or can be CSI prediction using AI / ML, or can be direct positioning using AI / ML, or can be positioning assisted using AI / ML, etc.

[0081] In some embodiments, the 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 to the AI / ML functionality / model, and another one or more reference signals are used for the output of the AI / ML functionality / model for inference.

[0082] For convenience of description, the beam management based on AI / ML functionality / model is referred to as model inference or inference operation, the training data collection based on AI / ML functionality / model is referred to as training data collection (the training data collection can also use a non-AI / ML manner), and the performance monitoring based on AI / ML functionality / model is referred to as performance monitoring.

[0083] In some embodiments, the configuration information can include configuration information of one or more reference signals, such as CSI-RS configuration information, and the like. The present application is not limited thereto, and specific configuration information can also be referred to related technologies. The configuration information can include configuration information for training data collection, and / or configuration information for model inference, and / or configuration information for performance monitoring.

[0084] FIG. 6 is another schematic diagram of a beam management method according to an embodiment of the present application, which is illustrated by taking an example of a network device configured with AI / ML. FIG. 6 can be applied to AI / ML-based beam steering or beam tracking according to an embodiment of the present application. As shown in FIG. 6, the method includes:

[0085] 601, the terminal device receives configuration information from the network device; for example, configuration information for the terminal device to send an uplink signal / channel (such as SRS configuration, PRACH configuration, and the like); this step is optional, for example.

[0086] 602, the terminal device sends an uplink signal / channel to the network device; for example, the uplink signal / channel can be SRS, PRACH, and the like, and the present application is not limited thereto.

[0087] 603, the network device performs prediction based on AI / ML; that is, the network device receives the uplink signal / channel and performs measurement, and inputs the measurement result into the AI / ML functionality / model; for example, the measurement result of the SRS is used as the input of the AI / ML for prediction (or inference).

[0088] As shown in FIG. 6, the method can further include:

[0089] 604, the network device sends the predicted beam indication to the terminal device for subsequent transmission.

[0090] It is noticeable that the above Figure 6 only schematically illustrates the embodiments of the present application, but the present application is not limited thereto. For example, the execution order between the respective operations can be appropriately adjusted, and furthermore, some operations can be added or some operations can be reduced. Those skilled in the art can appropriately modify the above based on the above, and the above is not limited to the above Figure 6.

[0091] Figure 7 is an example diagram of beam management based on beam steering or beam tracking according to an embodiment of the present application. As shown in Figure 7, in the AI / ML based beam management according to an embodiment of the present application, the usable beam can be predicted according to the uplink signal / channel; as the UE moves, the beam direction can change accordingly, which can be referred to as beam steering or beam tracking. As shown in Figure 7, the embodiments of the present application do not need to be based on beam sweeping, and do not need to send beams in sequence.

[0092] Thus, the network device determines the beam toward the terminal device based on the AI / ML model / function, and performs beam tracking or beam steering according to the uplink signal / channel. Thus, the usable beam can be predicted without sending multiple beams in sequence, which can reduce signaling overhead and reduce latency.

[0093] The above schematically illustrates the AI / ML based beam management, and the present application is not limited thereto. For convenience of description, the reference signal whose measurement result is used as the input of the AI / ML model is referred to as the second reference signal, and the reference signal whose measurement result is used as the output of the AI / ML model is referred to as the first reference signal. The reference signal can be used for training data collection and / or model inference and / or performance monitoring.

[0094] In some embodiments, the AI / ML model / function is located at the network device side; the measurement result of the uplink signal / channel is input into the AI / ML model / function, and the AI / ML model / function outputs the beam direction, or outputs information capable of indicating the direction or position of the terminal device.

[0095] For example, for beam management, the AI / ML can perform beam tracking or beam steering for the UE, i.e., as shown in Figure 7, the beam direction can follow the UE movement and automatically target the UE. The AI / ML model / function can be at the NW side, i.e., the gNB side.

[0096] For example, the input of the AI / ML model / function for beam tracking or beam steering can be the channel measurement results of the uplink reference signal and / or uplink channel sent by the UE. For example, the input of the AI / ML model / function can be based on the measurement of SRS and / or PRACH.

[0097] In some embodiments, the AI / ML model / function outputs one or more beam directions, one of which is used by the network device for downlink transmission to the terminal device.

[0098] For example, the output of the AI / ML model / function for beam tracking or beam steering can be one beam direction (or multiple beam directions) for the UE. The gNB can apply the one beam direction determined by the AI / ML model / function (or one selected from multiple beam directions) to the downlink transmission to the UE.

[0099] In some embodiments, the AI / ML model / function outputs one or more parameters, which are used by the network device to determine one or more beam directions, and one of which is used by the network device for downlink transmission to the terminal device.

[0100] For example, the output of the AI / ML model / function for beam tracking or beam steering can be some intermediate parameters, such as the UE position, and then the gNB can further determine one beam direction (or multiple beam directions) for the UE. The gNB can apply the one beam direction determined (or one selected from multiple beam directions) to the downlink transmission to the UE.

[0101] In some embodiments, the network device configures one or more reference signals and / or one or more TCI states according to the output of the AI / ML model / function.

[0102] In some embodiments, the AI / ML model / function outputs multiple beam directions or the network device determines multiple beam directions according to the parameters output by the AI / ML model / function.

[0103] The number of reference signals is the same as the number of TCI states, and / or the number of reference signals or TCI states is the same as the number of beam directions output by the AI / ML model / function, and / or the number of reference signals or TCI states is the same as the number of beam directions determined according to the output of the AI / ML model / function.

[0104] For example, if the output of the AI / ML model / function is multiple beam directions (or multiple beam directions can be determined based on intermediate parameters), the gNB can configure multiple reference signals (or a list of reference signals), e.g., CSI-RS and / or SSB, and correspondingly configure multiple TCI states (or a list of TCI states).

[0105] For another example, the number of TCI states can be the same as the number of reference signals, and there can be one-to-one mapping between TCI states and reference signals. The number of reference signals / TCI states can be the same as the number of beam directions output by the AI / ML or determined according to the AI / ML output. There can be one-to-one mapping between TCI states / reference signals and beam directions.

[0106] In some embodiments, the network device maps the multiple beam directions output by the AI / ML model / function or determined according to the AI / ML model / function output to one-to-one mapping to reference signals and / or TCI states.

[0107] In some embodiments, the network device sends indication information to the terminal device for indicating the beam direction.

[0108] For example, after making a prediction (or inference), the gNB can map the beam direction to the reference signal and the TCI state. It can further indicate to the UE which beam direction is used for transmission, e.g., through the beam indication (TCI indication) of the DCI. In this way, compared with the traditional operation, there is no need for TCI state reconfiguration through RRC and / or no need for TCI state activation through MAC-CE, so that the signaling overhead can be further reduced.

[0109] In some embodiments, the AI / ML model / function outputs one beam direction or the network device determines one beam direction according to the parameters output by the AI / ML model / function.

[0110] In some embodiments, the network device configures one reference signal and one TCI state; the network device maps the one beam direction output by the AI / ML model / function or determined according to the AI / ML model / function output to the reference signal and the TCI state.

[0111] For example, if the output of the AI / ML model / function is only one beam direction (or only one beam direction is determined based on the intermediate parameters), the gNB can configure one reference signal, e.g., CSI-RS or SSB, and accordingly one TCI state. After the prediction (or inference), the gNB maps the beam direction to the reference signal and the TCI state. The TCI state is used for transmission, i.e., no beam indication (TCI indication) is needed.

[0112] In some embodiments, the TCI state is not configured; the network device uses the one beam direction output by the AI / ML model / function or determined based on the AI / ML model / function output for downlink transmission to the terminal device.

[0113] For example, if the output of the AI / ML model / function is only one beam direction (or only one beam direction is determined based on the intermediate parameters), the TCI state can not be configured. The beam direction output by the AI / ML can be directly used for transmission. In this case, the beam management is TCI-less operation.

[0114] The above is a schematic description of beam management, and the following describes training data collection.

[0115] In some embodiments, the network device performs training data collection for the AI / ML model / function.

[0116] In some examples, the training data collection is performed at the network device side, and beam sweeping is used to obtain ground truth data; wherein a reference signal is configured to the terminal device and the terminal device reports measurement results after measuring the reference signal. The reference signal is, for example, CSI-RS and / or SSB, and the present application is not limited thereto.

[0117] For example, in order to obtain ground truth data, traditional beam sweeping can be applied (for example, reference can be made to FIG. 4). One or more groups of reference signals (e.g., CSI-RS and / or SSB) can be configured to the UE for beam sweeping. The UE can measure the reference signals and report the measurement results to the gNB. The reported data can include the best beam (Top-1 beam) or the best several beams (Top-K beams).

[0118] In some examples, the uplink signal / channel used for training data collection is measured by the network device, and the measurement results are used as input data for the AI / ML model / function.

[0119] For example, the input data of the AI / ML model / function can be based on the measurement results of the gNB on the uplink reference signals and / or uplink channels (e.g., SRS and / or PRACH).

[0120] In some examples, the measurement results of the input data for the AI / ML model / function are associated with the ground truth data, and the reference signals for obtaining the ground truth data are associated with the uplink signals / channels for the training data collection.

[0121] For example, the input data and the ground truth data are associated. The reference signals (CSI-RS / SSB) of the ground truth data and the reference signals / channels (SRS / PRACH) of the input data are associated. For example, the CSI-RS (or SSB) and the SRS (or PRACH) are associated.

[0122] The above is illustratively described for the training data collection, and the performance monitoring is further described below.

[0123] In some embodiments, the network device performs the performance monitoring for the AI / ML model / function.

[0124] In some examples, the performance monitoring is performed at the network device side, and the beam sweeping is used to obtain the ground truth data; wherein the reference signals are configured to the terminal device and the terminal device reports the measurement results after measuring the reference signals. The reference signals can be CSI-RS and / or SSB, and the present application is not limited thereto.

[0125] For example, in order to obtain the ground truth data, the traditional beam sweeping can be applied (for example, refer to FIG. 4). One or more groups of reference signals (e.g., CSI-RS and / or SSB) can be configured to the UE for beam sweeping. The UE can measure the reference signals and report the measurement results to the gNB. The top beam (Top-1 beam) or the top several beams (Top-K beams) can be included in the reported data.

[0126] In some examples, the uplink signals / channels for the performance monitoring are measured by the network device, and the measurement results are used as the input data of the AI / ML model / function.

[0127] For example, the input data of the AI / ML model / function can be based on the measurement results of the gNB on the uplink reference signals and / or uplink channels (e.g., SRS and / or PRACH).

[0128] In some examples, the measurements of the input data for the AI / ML model / function are associated with the ground truth data, the reference signals for obtaining the ground truth data are associated with the uplink signals / channels for performance monitoring.

[0129] For example, the input data and the ground truth data are associated. The reference signals (CSI-RS / SSB) of the ground truth data and the reference signals / channels (SRS / PRACH) of the input data are associated. For example, the CSI-RS (or SSB) and the SRS (or PRACH) are associated.

[0130] In some embodiments, the network device transmits first and second beams with different widths.

[0131] For example, the AI / ML based beam steering / beam tracking is applied for beam management, hierarchical beam management can be applied, for example, the gNB can transmit wide beams and narrow beams. In one example, the wide beams can be mapped to SSBs, and the narrow beams can be mapped to CSI-RSs, but not limited thereto.

[0132] In some examples, the AI / ML based beam tracking or beam steering is applied to the first beam, and the AI / ML based beam tracking or beam steering is applied to the second beam. For example, the AI / ML based beam steering / beam tracking is applied to the wide beam, and the AI / ML based beam steering / beam tracking is also applied to the narrow beam.

[0133] In some examples, the AI / ML based beam tracking or beam steering is applied to the first beam, and the legacy beam sweeping is applied to the second beam. For example, the AI / ML based beam tracking or beam steering is applied to the wide beam, and the legacy beam sweeping (can refer to FIG. 4) is applied to the narrow beam.

[0134] In some examples, the AI / ML based beam tracking or beam steering is applied to the first beam, and the AI / ML based beam sweeping is applied to the second beam. For example, the AI / ML based beam tracking or beam steering is applied to the wide beam, and the AI / ML based beam sweeping (can refer to FIG. 2 to 4) is applied to the narrow beam.

[0135] In some examples, the AI / ML model / function based beam tracking or beam steering is applied to the second beam, and the non-AI / ML model / function based beam sweeping is applied to the first beam. For example, the AI / ML model / function based beam tracking or beam steering is applied to the narrow beam, and the legacy beam sweeping (see FIG. 4) is applied to the wide beam.

[0136] In some examples, the AI / ML model / function based beam tracking or beam steering is applied to the second beam, and the AI / ML model / function based beam sweeping is applied to the first beam. For example, the AI / ML model / function based beam tracking or beam steering is applied to the narrow beam, and the AI / ML based beam sweeping (see FIGS. 2-4) is applied to the wide beam.

[0137] FIG. 8 is an example diagram of beam management of an embodiment of the present application, showing an example of hierarchical beam management. As shown in FIG. 8, beam steering / beam tracking can be used for wide beams, and beam sweeping can be used for narrow beams (the beam sweeping can be based on non-AI / ML or can also be based on AI / ML).

[0138] As shown in FIG. 8, the gNB can configure multiple narrow beams within the coverage of a wide beam. As the UE moves, AI / ML based beam steering / beam tracking can be applied to the wide beam so that the direction of the wide beam can be directed to the UE; the narrow beams can be beam swept, and the best one or more narrow beams can be selected. FIG. 8 is only illustrative of the present application, and the present application is not limited thereto.

[0139] In the embodiments of the present application, the uplink signal / channel can be periodic, aperiodic, or semi-persistent, and the present application is not limited thereto. The embodiments of the present application can be applied to TDD or FDD, and can also be applied to half duplex and / or full duplex; in addition, the present application can be applied to 6G, 5G advanced, or 5G, and the present application is not limited thereto.

[0140] The above various embodiments are only illustrative of the embodiments of the present application, but the present application is not limited thereto, and appropriate modifications can be made on the basis of the above various embodiments. For example, the above various embodiments can be used alone, or one or more of the above various embodiments can be combined.

[0141] From the above embodiments, the network device determines the beam towards the terminal device based on the AI / ML model / function and according to the uplink signal / channel for beam tracking or beam steering. In this way, the signaling overhead can be reduced and the latency can be reduced.

[0142] Embodiments of the second aspect

[0143] Embodiments of the present application provide a beam management method, which is described from the terminal device side. Embodiments of the second aspect can be combined with embodiments of the first aspect, and the same content as that of the first aspect will not be described again.

[0144] FIG. 9 is a schematic diagram of a beam management method according to an embodiment of the present application. As shown in FIG. 9, the method comprises:

[0145] 901, the terminal device sends an uplink signal / channel to the network device.

[0146] As shown in FIG. 9, the method can further comprise:

[0147] 902, the uplink signal / channel is used by the network device to determine the beam towards the terminal device based on the AI / ML model / function for beam tracking or beam steering.

[0148] It is worth noting that the above FIG. 9 only schematically illustrates the embodiments of the present application, but the present application is not limited thereto. For example, the execution order between the operations can be appropriately adjusted, and in addition, some operations can be added or some operations can be reduced. Those skilled in the art can make appropriate modifications based on the above description, and the present application is not limited to the above FIG. 9.

[0149] In some embodiments, the AI / ML model / function is located at the network device side; the measurement result of the uplink signal / channel is input into the AI / ML model / function, and the AI / ML model / function outputs the beam direction or outputs the information capable of indicating the direction or position of the terminal device.

[0150] In some embodiments, the AI / ML model / function outputs one or more beam directions, and one of the one or more beam directions is used by the network device for downlink transmission to the terminal device.

[0151] In some embodiments, the AI / ML model / function outputs one or more parameters, which are used by the network device to determine one or more beam directions, and one of the one or more beam directions is used by the network device for downlink transmission to the terminal device.

[0152] In some embodiments, the network device further configures one or more reference signals and / or one or more TCI states according to the output of the AI / ML model / function.

[0153] In some embodiments, the AI / ML model / function outputs multiple beam directions or the network device determines multiple beam directions according to the parameters output by the AI / ML model / function.

[0154] In some embodiments, the number of the reference signals is the same as the number of the TCI states, and / or the number of the reference signals or TCI states is the same as the number of the beam directions output by the AI / ML model / function, and / or the number of the reference signals or TCI states is the same as the number of the beam directions determined according to the output of the AI / ML model / function.

[0155] In some embodiments, the network device maps the multiple beam directions output by the AI / ML model / function or determined according to the output of the AI / ML model / function to one-to-one mapping to reference signals and / or TCI states; and sends indication information indicating the beam directions to the terminal device.

[0156] In some embodiments, the AI / ML model / function outputs one beam direction or the network device determines one beam direction according to the parameters output by the AI / ML model / function.

[0157] In some embodiments, the network device configures one reference signal and one TCI state; and maps the one beam direction output by the AI / ML model / function or determined according to the output of the AI / ML model / function to the reference signal and the TCI state.

[0158] In some embodiments, the TCI state is not configured; the network device uses the one beam direction output by the AI / ML model / function or determined according to the output of the AI / ML model / function for downlink transmission to the terminal device.

[0159] In some embodiments, the network device collects training data for the AI / ML model / function;

[0160] The training data collection is conducted at the network device side, and beam sweeping is used to obtain ground truth data; wherein reference signals are configured to the terminal device and the terminal device reports measurement results after measuring the reference signals.

[0161] In some embodiments, the reference signals are CSI-RS and / or SSB.

[0162] In some embodiments, the uplink signals / channels used for training data collection are measured by the network device, and the measurement results are used as input data for the AI / ML model / function.

[0163] In some embodiments, the measurement results of the input data for the AI / ML model / function are associated with the ground truth data, and the reference signals used to obtain the ground truth data are associated with the uplink signals / channels used for training data collection.

[0164] In some embodiments, the network device performs performance monitoring for the AI / ML model / function;

[0165] The performance monitoring is conducted at the network device side, and beam sweeping is used to obtain ground truth data; wherein reference signals are configured to the terminal device and the terminal device reports measurement results after measuring the reference signals.

[0166] In some embodiments, the reference signals are CSI-RS and / or SSB.

[0167] In some embodiments, the uplink signals / channels used for performance monitoring are measured by the network device, and the measurement results are used as input data for the AI / ML model / function.

[0168] In some embodiments, the measurement results of the input data for the AI / ML model / function are associated with the ground truth data, and the reference signals used to obtain the ground truth data are associated with the uplink signals / channels used for performance monitoring.

[0169] In some embodiments, the network device can send first and second beams with different widths.

[0170] In some embodiments, AI / ML model / function-based beam tracking or beam steering is applied to the first beam, and AI / ML model / function-based beam tracking or beam steering is applied to the second beam.

[0171] In some embodiments, AI / ML model / function based beam tracking or beam steering is applied to the first beam, and non-AI / ML model / function based beam sweeping is applied to the second beam.

[0172] In some embodiments, AI / ML model / function based beam tracking or beam steering is applied to the first beam, and AI / ML model / function based beam sweeping is applied to the second beam.

[0173] In some embodiments, AI / ML model / function based beam tracking or beam steering is applied to the second beam, and non-AI / ML model / function based beam sweeping is applied to the first beam.

[0174] In some embodiments, AI / ML model / function based beam tracking or beam steering is applied to the second beam, and AI / ML model / function based beam sweeping is applied to the first beam.

[0175] In some embodiments, the network device can send configuration information, etc. to the terminal device. The network device can receive feedback information and / or report information sent by the terminal device. For example, the terminal device can report inference results and / or performance monitoring results and / or training data collection results to the network device, and the present application is not limited thereto.

[0176] The above various embodiments only exemplarily illustrate the embodiments of the present application, but the present application is not limited thereto, and can be appropriately modified on the basis of the above various embodiments. For example, the above various embodiments can be used alone, or one or more of the above various embodiments can be combined.

[0177] As can be seen from the above embodiments, the network device determines the beam toward the terminal device based on the AI / ML model / function and performs beam tracking or beam steering according to the uplink signal / channel. In this way, the signaling overhead can be reduced and the latency can be reduced.

[0178] Embodiments of the third aspect

[0179] The embodiments of the present application provide a beam management apparatus. The apparatus may, for example, be a network device, or one or more components or components configured in the network device, and the same content as the embodiments of the first and second aspects will not be described herein.

[0180] FIG. 10 is a schematic diagram of a beam management apparatus according to an embodiment of the present application. As shown in FIG. 10, the beam management apparatus 1000 according to an embodiment of the present application includes:

[0181] a receiving unit 1001 configured to receive an uplink signal / channel from a terminal device;

[0182] a processing unit 1002 configured to perform beam tracking or beam steering based on an AI / ML model / function according to the uplink signal / channel to determine a beam direction towards the terminal device.

[0183] In some embodiments, as shown in FIG. 10, the beam management apparatus 1000 can further include a sending unit 1003 configured to send configuration information / indication information to the terminal device, and the present application is not limited thereto.

[0184] In some embodiments, the AI / ML model / function is located at a network device side; a measurement result of the uplink signal / channel is input into the AI / ML model / function, and the AI / ML model / function outputs a beam direction or outputs information capable of indicating a direction or a position of the terminal device.

[0185] In some embodiments, the AI / ML model / function outputs one or more beam directions, and one of the one or more beam directions is used by the network device for downlink transmission to the terminal device.

[0186] In some embodiments, the AI / ML model / function outputs one or more parameters, the one or more parameters are used by the network device to determine one or more beam directions, and one of the one or more beam directions is used by the network device for downlink transmission to the terminal device.

[0187] In some embodiments, the processing unit 1002 is further configured to configure one or more reference signals and / or one or more TCI states according to an output of the AI / ML model / function.

[0188] In some embodiments, the AI / ML model / function outputs a plurality of beam directions or the network device determines a plurality of beam directions according to parameters output by the AI / ML model / function.

[0189] In some embodiments, the number of the reference signals is the same as the number of the TCI states, and / or the number of the reference signals or the TCI states is the same as the number of the beam directions output by the AI / ML model / function, and / or the number of the reference signals or the TCI states is the same as the number of the beam directions determined according to the output of the AI / ML model / function.

[0190] In some embodiments, the processing unit 1002 maps the multiple beam directions output by the AI / ML model / function or determined according to the AI / ML model / function output to one-to-one mapping to reference signals and / or TCI states; and sends indication information indicating the beam directions to the terminal device.

[0191] In some embodiments, the AI / ML model / function outputs one beam direction or the network device determines one beam direction according to parameters output by the AI / ML model / function.

[0192] In some embodiments, the processing unit 1002 configures one reference signal and one TCI state; and maps the one beam direction output by the AI / ML model / function or determined according to the AI / ML model / function output to the reference signal and the TCI state.

[0193] In some embodiments, the TCI state is not configured; the processing unit 1002 uses the one beam direction output by the AI / ML model / function or determined according to the AI / ML model / function output for downlink transmission to the terminal device.

[0194] In some embodiments, the processing unit 1002 performs training data collection for the AI / ML model / function;

[0195] The training data collection is performed at the network device side, and beam sweeping is used to obtain ground truth data; wherein a reference signal is configured to the terminal device and the terminal device reports measurement results after measuring the reference signal.

[0196] In some embodiments, the reference signal is CSI-RS and / or SSB.

[0197] In some embodiments, the uplink signal / channel used for training data collection is measured by the network device, and the measurement results are used as input data of the AI / ML model / function.

[0198] In some embodiments, the measurement results of the input data of the AI / ML model / function are associated with the ground truth data, and the reference signal used to obtain the ground truth data is associated with the uplink signal / channel used for training data collection.

[0199] In some embodiments, the processing unit 1002 performs performance monitoring for the AI / ML model / function;

[0200] The performance monitoring is conducted at the network device side, and beam sweeping is used to obtain ground truth data; wherein a reference signal is configured to the terminal device and the terminal device reports measurement results after measuring the reference signal.

[0201] In some embodiments, the reference signal is CSI-RS and / or SSB.

[0202] In some embodiments, the uplink signal / channel for performance monitoring is measured by the network device, and the measurement results are used as input data for the AI / ML model / function.

[0203] In some embodiments, the measurement results of the input data for the AI / ML model / function are associated with the ground truth data, and the reference signal used to obtain the ground truth data is associated with the uplink signal / channel for performance monitoring.

[0204] In some embodiments, the transmission unit 1003 transmits first and second beams with different widths.

[0205] In some embodiments, AI / ML model / function-based beam tracking or beam steering is applied to the first beam, and AI / ML model / function-based beam tracking or beam steering is applied to the second beam.

[0206] In some embodiments, AI / ML model / function-based beam tracking or beam steering is applied to the first beam, and non-AI / ML model / function-based beam sweeping is applied to the second beam.

[0207] In some embodiments, AI / ML model / function-based beam tracking or beam steering is applied to the first beam, and AI / ML model / function-based beam sweeping is applied to the second beam.

[0208] In some embodiments, AI / ML model / function-based beam tracking or beam steering is applied to the second beam, and non-AI / ML model / function-based beam sweeping is applied to the first beam.

[0209] In some embodiments, AI / ML model / function-based beam tracking or beam steering is applied to the second beam, and AI / ML model / function-based beam sweeping is applied to the first beam.

[0210] The above embodiments are only illustrative of the embodiments of the present application, but the present application is not limited thereto, and can be appropriately modified on the basis of the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0211] It is worth noting that the above only illustrates the components or modules related to the present application, but the present application is not limited thereto. The beam management apparatus 1000 can also include other components or modules, and the specific content of these components or modules can be referred to related art.

[0212] In addition, for simplicity, only the connection relationship or signal path between the components or modules is exemplarily shown in FIG. 10, but those skilled in the art should understand that various related technologies such as bus connection can be used. The above components or modules can be implemented by hardware facilities such as processors, memories, transmitters, receivers, etc.; the present application is not limited thereto.

[0213] As can be seen from the above embodiments, the network equipment determines the beam towards the terminal equipment based on the AI / ML model / function and the uplink signal / channel for beam tracking or beam steering. Thus, the signaling overhead and latency can be reduced.

[0214] Embodiments of the fourth aspect

[0215] The embodiments of the present application provide a beam management apparatus. The apparatus can be a terminal device, or one or more components or components configured in the terminal device. The same content as the embodiments of the first to third aspects will not be repeated.

[0216] FIG. 11 is another schematic diagram of the beam management apparatus according to an embodiment of the present application. As shown in FIG. 11, the beam management apparatus 1100 includes:

[0217] The sending unit 1101 sends the uplink signal / channel to the network equipment;

[0218] The uplink signal / channel is used by the network equipment to perform beam tracking or beam steering based on the AI / ML model / function to determine the beam towards the terminal equipment.

[0219] In some embodiments, as shown in FIG. 11, the beam management apparatus 1100 can further include:

[0220] The receiving unit 1102 receives the configuration information / indication information sent by the network equipment, and the present application is not limited thereto.

[0221] The above embodiments are only illustrative of the embodiments of the present application, but the present application is not limited thereto, and can be appropriately modified on the basis of the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0222] It is worth noting that the above only illustrates the components or modules related to the present application, but the present application is not limited thereto. The beam management apparatus 1100 can also include other components or modules, and the specific content of these components or modules can be referred to related technologies.

[0223] In addition, for the sake of simplicity, only the connection relationship or signal path between the components or modules is exemplarily shown in FIG. 11, but it should be clear to those skilled in the art that various related technologies such as bus connection can be used. The above components or modules can be implemented by hardware facilities such as processors, memories, transmitters, receivers, etc.; the present application is not limited thereto.

[0224] As can be seen from the above embodiments, the network device determines the beam towards the terminal device based on the AI / ML model / function and the uplink signal / channel for beam tracking or beam steering. Thus, the signaling overhead can be reduced and the latency can be reduced.

[0225] Embodiments of the fifth aspect

[0226] The embodiments of the present application also provide a communication system, which can be referred to FIG. 1, and the same content as the embodiments of the first to fourth aspects will not be repeated.

[0227] In some embodiments, the communication system 100 can at least include:

[0228] a terminal device, which sends an uplink signal / channel to a network device;

[0229] a network device, which determines a beam towards the terminal device based on an AI / ML model / function and the uplink signal / channel for beam tracking or beam steering.

[0230] The embodiments of the present application also provide a network device, which can be a base station for example, but the present application is not limited thereto, and can also be other network devices.

[0231] Fig. 12 is a schematic diagram of a network device according to an embodiment of the present application. As shown in Fig. 12, the network device 1200 can include a processor 1210 (e.g., a central processing unit, CPU) and a memory 1220 coupled to the processor 1210. The memory 1220 can store various data. In addition, the memory 1220 can store a program 1230 for information processing, and execute the program 1230 under control of the processor 1210.

[0232] For example, the processor 1210 can be configured to execute the program to implement the beam management method according to the embodiments of the first aspect. For example, the processor 1210 can be configured to control receiving an uplink signal / channel from a terminal device, and performing beam tracking or beam steering based on an AI / ML model / function according to the uplink signal / channel to determine a beam towards the terminal device.

[0233] In addition, as shown in Fig. 12, the network device 1200 can further include a transceiver 1240, an antenna 1250, and the like. The functions of the above components are similar to those of the prior art, and will not be described here. It should be noted that the network device 1200 does not necessarily include all the components shown in Fig. 12. In addition, the network device 1200 can include components not shown in Fig. 12, which can be referred to the prior art.

[0234] Embodiments of the present application also provide a terminal device, but the present application is not limited thereto, and can also be other devices.

[0235] Fig. 13 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in Fig. 13, the terminal device 1300 can include a processor 1310 and a memory 1320. The memory 1320 stores data and programs, and is coupled to the processor 1310. It should be noted that the figure is exemplary. Other types of structures can also be used to supplement or replace the structure to implement telecommunication functions or other functions.

[0236] For example, the processor 1310 can be configured to execute the program to implement the beam management method according to the embodiments of the second aspect. For example, the processor 1310 can be configured to control transmitting an uplink signal / channel to a network device, wherein the uplink signal / channel is used by the network device to perform beam tracking or beam steering based on an AI / ML model / function to determine a beam towards the terminal device.

[0237] As shown in FIG. 13, the terminal device 1300 can further include a communication module 1330, an input unit 1340, a display 1350, and a power supply 1360. The functions of the above components are similar to those of the prior art, and will not be described here. It is worth noting that the terminal device 1300 does not necessarily include all the components shown in FIG. 13, and the above components are not essential; in addition, the terminal device 1300 can also include components not shown in FIG. 13, and can refer to the prior art.

[0238] The embodiments of the present application further provide a computer program, which, when executed in a network device, causes the network device to perform the beam management method according to the embodiments of the first aspect.

[0239] The embodiments of the present application further provide a storage medium storing a computer program, which causes a network device to perform the beam management method according to the embodiments of the first aspect.

[0240] The embodiments of the present application further provide a computer program, which, when executed in a terminal device, causes the terminal device to perform the beam management method according to the embodiments of the second aspect.

[0241] The embodiments of the present application further provide a storage medium storing a computer program, which causes a terminal device to perform the beam management method according to the embodiments of the second aspect.

[0242] The apparatuses and methods described above can be implemented by hardware, or by a combination of hardware and software. The present application relates to a computer readable program, which, when executed by a logic component, can cause the logic component to implement the apparatuses or constituent components described above, or 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.

[0243] The methods / apparatuses described in conjunction with the embodiments of the present application can be directly embodied as hardware, software modules executed by a processor, or a combination of the two. For example, one or more of the functional blocks shown in the figures and / or a combination of one or more of the functional blocks can correspond to individual software modules of a computer program flow, or to individual hardware modules. These software modules can correspond to individual steps shown in the figures, respectively. These hardware modules can be implemented by, for example, fixing the software modules with a field programmable gate array (FPGA).

[0244] The software modules can reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, registers, a hard disk, a mobile disk, CD-ROM, or any other form of storage medium known in the art. One storage medium can be coupled to the processor, such that the processor can read information from, and write information to, the storage medium; or the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. The software modules can be stored in a 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 the mobile terminal, uses a MEGA-SIM card or a flash memory device of large capacity, the software modules can be stored in the MEGA-SIM card or the flash memory device of large capacity.

[0245] One or more of the functional blocks described in the figures and / or one or more combinations of the functional blocks can 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, discrete gate or transistor logic, discrete hardware components, or any appropriate combination thereof, for performing the functions described in this application. One or more of the functional blocks described in the figures and / or one or more combinations of the functional blocks can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0246] The application has been described above with the attachment to the specific embodiments, but it should be clear to those skilled in the art that these descriptions are exemplary and are not a limitation on the scope of protection of the application. Those skilled in the art can make various modifications and changes to the application according to the spirit and principles of the application, and these modifications and changes are also within the scope of the application.

[0247] With regard to the embodiments including the above embodiments, the following notes are also disclosed:

[0248] 1. A beam management method, comprising:

[0249] The network device receives an uplink signal / channel from a terminal device;

[0250] The network device performs beam tracking or beam steering based on an AI / ML model / function to determine a beam towards the terminal device according to the uplink signal / channel.

[0251] 2. A beam management method, comprising:

[0252] The terminal device sends an uplink signal / channel to the network device;

[0253] The uplink signal / channel is used by the network device to perform beam tracking or beam steering based on an AI / ML model / function to determine a beam towards the terminal device.

[0254] 3. A network device comprising a memory and a processor, the memory storing a computer program, the processor configured to execute the computer program to implement the beam management method of clause 1.

[0255] 4. A terminal device comprising a memory and a processor, the memory storing a computer program, the processor configured to execute the computer program to implement the beam management method of clause 2.

[0256] 5. A computer program product comprising at least a computer program, the computer program being executed by a processor to cause a network device to perform the beam management method of clause 1.

[0257] 6. A computer program product comprising at least a computer program, the computer program being executed by a processor to cause a terminal device to perform the beam management method of clause 2.

Claims

1. A beam management apparatus, comprising: a receiving unit configured to receive an uplink signal / channel from a terminal device; a processing unit configured to perform, based on an AI / ML model / function, beam tracking or beam steering according to the uplink signal / channel to determine a beam towards the terminal device.

2. The apparatus of claim 1, wherein, The AI / ML model / function is located at a network device side; a measurement result of the uplink signal / channel is input to the AI / ML model / function, and the AI / ML model / function outputs a beam direction or outputs information capable of indicating a direction or a position of the terminal device.

3. The apparatus of claim 1, wherein, The AI / ML model / function outputs one or more beam directions, one of which is used by the network device for downlink transmission to the terminal device. Alternatively, the AI / ML model / function outputs one or more parameters, which are used by the network device to determine one or more beam directions, and one of which is used by the network device for downlink transmission to the terminal device.

4. The apparatus of claim 1, wherein, The processing unit is further configured to configure one or more reference signals and / or one or more TCI states according to an output of the AI / ML model / function.

5. The apparatus of claim 4, wherein, The AI / ML model / function outputs multiple beam directions or the network device determines multiple beam directions according to the parameters output by the AI / ML model / function.

6. The apparatus of claim 5, wherein, The number of the reference signals is the same as the number of the TCI states, and / or the number of the reference signals or TCI states is the same as the number of the beam directions output by the AI / ML model / function, and / or the number of the reference signals or TCI states is the same as the number of the beam directions determined according to the output of the AI / ML model / function.

7. The apparatus of claim 5, wherein, The processing unit maps the multiple beam directions output by the AI / ML model / function or determined according to the output of the AI / ML model / function to the reference signals and / or the TCI states in a one-to-one manner; and sends indication information indicating the beam directions to the terminal device.

8. The apparatus of claim 4, wherein, The AI / ML model / function outputs one beam direction or the network device determines one beam direction according to the parameters output by the AI / ML model / function.

9. The apparatus of claim 8, wherein, The processing unit configures one reference signal and one TCI state; and maps the one beam direction output by the AI / ML model / function or determined according to the output of the AI / ML model / function to the reference signal and the TCI state.

10. The apparatus of claim 8, wherein, The TCI state is not configured. The processing unit uses the one beam direction output by the AI / ML model / function or determined according to the output of the AI / ML model / function for downlink transmission to the terminal device.

11. The apparatus of claim 1, wherein, The processing unit performs training data collection for the AI / ML model / function. The training data collection is performed at the network device side, and beam sweeping is used to obtain benchmark real data. wherein reference signals are configured to the terminal device and the terminal device reports measurement results after measuring the reference signals; the reference signals are CSI-RS and / or SSB.

12. The apparatus of claim 11, wherein, The uplink signals / channels for training data collection are measured by the network device, and the measurement results are used as input data for the AI / ML model / function.

13. The apparatus of claim 12, wherein, The measurement results of the input data for the AI / ML model / function are associated with the benchmark real data, and the reference signals used to obtain the benchmark real data are associated with the uplink signals / channels for training data collection.

14. The apparatus of claim 1, wherein, The processing unit performs performance monitoring for the AI / ML model / function; The performance monitoring is performed at the network device side, and beam sweeping is used to obtain benchmark real data; wherein reference signals are configured to the terminal device and the terminal device reports measurement results after measuring the reference signals; the reference signals are CSI-RS and / or SSB.

15. The apparatus of claim 14, wherein, The uplink signals / channels for performance monitoring are measured by the network device, and the measurement results are used as input data for the AI / ML model / function.

16. The apparatus of claim 15, wherein, The measurement results of the input data for the AI / ML model / function are associated with the benchmark real data, and the reference signals used to obtain the benchmark real data are associated with the uplink signals / channels for performance monitoring.

17. The apparatus of claim 1, wherein, The apparatus further comprises: a sending unit that sends first and second beams with different widths.

18. The apparatus of claim 17, wherein, Beam tracking or beam steering based on the AI / ML model / function is applied to the first beam, and beam tracking or beam steering based on the AI / ML model / function is applied to the second beam; or, beam tracking or beam steering based on the AI / ML model / function is applied to the first beam, and beam sweeping based on a non-AI / ML model / function is applied to the second beam; or, beam tracking or beam steering based on the AI / ML model / function is applied to the first beam, and beam sweeping based on the AI / ML model / function is applied to the second beam; or, beam tracking or beam steering based on the AI / ML model / function is applied to the second beam, and beam sweeping based on a non-AI / ML model / function is applied to the first beam; or, beam tracking or beam steering based on the AI / ML model / function is applied to the second beam, and beam sweeping based on the AI / ML model / function is applied to the first beam.

19. A beam management apparatus, comprising: a sending unit that sends uplink signals / channels to a network device; wherein the uplink signals / channels are used by the network device to perform beam tracking or beam steering based on an AI / ML model / function to determine a beam towards a terminal device.

20. A communication system, comprising: a terminal device that sends uplink signals / channels to a network device; A network device, based on an AI / ML model / function, performs beam tracking or beam steering to determine a beam towards the terminal device based on the uplink signal / channel.

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