Method and apparatus for beam management

Abstract

Embodiments of the present application provide a method and apparatus for beam management. The method for beam management comprises: a terminal device receives beam management configuration information and / or beam failure recovery configuration information from a network device; on the basis of an AI / ML functionality / model, the terminal device determines a candidate beam for beam failure recovery.

Description

Beam management method and device Technical Field

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

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

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

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

[0005] Summary of the Invention

[0006] The inventors discovered that terminal devices and / or network devices can use AI / ML functionality / models to predict future beams based on beam measurement results, but there is currently no clear solution for considering beam failure recovery.

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

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

[0009] The terminal device receives beam management configuration information and / or beam failure recovery configuration information from the network device;

[0010] The terminal device determines candidate beams for beam failure recovery based on AI / ML functionality / model.

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

[0012] a receiving unit configured to receive beam management configuration information and / or beam failure recovery configuration information from a network device;

[0013] A processing unit determines candidate beams for beam failure recovery based on AI / ML functionality / model.

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

[0015] The network device sends beam management configuration information and / or beam failure recovery configuration information to the terminal device;

[0016] The terminal device determines a candidate beam for beam failure recovery based on AI / ML functionality / model.

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

[0018] a sending unit, configured to send beam management configuration information and / or beam failure recovery configuration information to a terminal device;

[0019] The terminal device determines a candidate beam for beam failure recovery based on AI / ML functionality / model.

[0020] According to another aspect of an embodiment of the present application, a communication system is provided, including:

[0021] A network device that sends beam management configuration information and / or beam failure recovery configuration information to a terminal device;

[0022] A terminal device that determines candidate beams for beam failure recovery based on AI / ML functionality / model.

[0023] One of the beneficial effects of the embodiments of the present application is that the terminal device determines the candidate beam for beam failure recovery based on AI / ML functionality / model, and AI / ML can be used for new candidate beam identification for beam failure recovery, thereby improving the accuracy and reliability of beam failure recovery.

[0024] With reference to the following description and accompanying drawings, specific embodiments of the present application are disclosed in detail, indicating the manner in which the principles of the present application can be employed. It should be understood that the embodiments of the present application are not limited in scope. Within the spirit and scope of the appended claims, the embodiments of the present application include many variations, modifications and equivalents.

[0025] Features described and / or illustrated with respect to one embodiment may be used in the same or similar manner in one or more other embodiments, combined with features in other embodiments, or substituted for features in other embodiments.

[0026] It should be emphasized that the term "include / comprising" when used herein refers to the presence of features, integers, steps or components, but does not exclude the presence or addition of one or more other features, integers, steps or components. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The elements and features described in one figure or one embodiment of the present application can be combined with the elements and features shown in one or more other figures or embodiments. In addition, in the accompanying drawings, similar reference numerals represent corresponding parts in several figures and can be used to indicate corresponding parts used in more than one embodiment.

[0028] FIG1 is a schematic diagram of a communication system according to an embodiment of the present application;

[0029] FIG2 is a schematic diagram of a beam management method according to an embodiment of the present application;

[0030] FIG3 is another schematic diagram of the beam management method according to an embodiment of the present application;

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

[0032] FIG5 is another schematic diagram of the beam management method according to an embodiment of the present application;

[0033] FIG6 is another schematic diagram of the beam management method according to an embodiment of the present application;

[0034] FIG7 is a schematic diagram of a beam management device according to an embodiment of the present application;

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

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

[0037] FIG10 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 currently known or future developed communication protocols.

[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] Among them, terminal devices may include but are not limited to the following devices: cellular phones, personal digital assistants (PDAs), wireless modems, wireless communication devices, handheld devices, machine-type communication devices, laptop computers, cordless phones, smart phones, smart watches, digital cameras, etc.

[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, an MIB, system information, or a dedicated RRC message; or an RRC information element (RRC IE). The high-layer signaling may also be, for example, MAC (Medium Access Control) signaling; or a MAC control element (MAC CE). However, the present application is not limited thereto.

[0054] In NR Rel-15, beam failure recovery operations were introduced. Beam failure recovery operations include beam failure detection, new candidate beam identification, and a beam failure recovery request. For new candidate beam identification, the gNB can configure a candidate beam list for the UE. When a beam failure occurs, the UE selects a candidate beam for communication, and the new beam information is sent to the gNB via a dedicated physical random access channel (PRACH).

[0055] In NR Rel-16, beam failure recovery is extended to secondary cells (SCells). That is, if a beam failure occurs on an SCell, communication through the primary cell (PCell) can be maintained. In this case, new beam information can be sent via MAC-CE on the PCell.

[0056] 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.

[0057] Embodiments of the first aspect

[0058] An embodiment of the present application provides a beam management method, which is described from the perspective of a terminal device.

[0059] FIG2 is a schematic diagram of a beam management method according to an embodiment of the present application. As shown in FIG2 , the method includes:

[0060] 201, the terminal device receives beam management configuration information and / or beam failure recovery configuration information from the network device; and

[0061] 202. The terminal device determines a candidate beam for beam failure recovery based on AI / ML functionality / model.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] In some embodiments, the beam management configuration information may include configuration information of one or more reference signals used for beam management or beam measurement, such as CSI-RS configuration information. The beam failure recovery configuration information may include configuration information of one or more reference signals used for beam failure recovery, such as a CSI-RS list for BFR. The present application is not limited thereto, and for specific configuration information, reference may be made to related technologies.

[0067] In some embodiments, one or more reference signals are used for measurement and the measurement results are input into the AI / ML functionality / model, and another one or more reference signals are used for the output of the AI / ML functionality / model for inference.

[0068] FIG3 is another schematic diagram of the beam management method according to an embodiment of the present application, which is illustrated by taking a terminal device configured with AIML as an example. As shown in FIG3 , the method includes:

[0069] 301. A terminal device receives configuration information from a network device; for example, the configuration information includes a second reference signal resource set (set B) for beam measurement and a first reference signal resource set (set A) for beam prediction.

[0070] 302. The terminal device performs beam measurement and inputs the beam measurement results into the AI / ML functionality / model. For example, the measurement results of the reference signals in set B are used as input to the AI / ML, and the reference signals in set A are used for prediction (or inference).

[0071] 303. The terminal device sends the beam prediction result to the network device.

[0072] 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.

[0073] It is worth noting that FIG3 above is merely a schematic illustration of an embodiment of the present application, and the present application is not limited thereto. For example, the execution order of the various operations may be appropriately adjusted, and other operations may be added or some operations may be reduced. Those skilled in the art may make appropriate modifications based on the above description, and are not limited to the description of FIG3 above.

[0074] In some embodiments, candidate beams for beam failure recovery are selected from prediction results output by the AI / ML functionality / model.

[0075] For example, AI / ML functions / models for beam prediction (including spatial beam prediction and / or temporal beam prediction) can be used to identify new candidate beams in beam failure recovery operations. One or more reference signals (or sets / lists of reference signals) can be used for measurement, and the measurement results are used as input to the AI / ML for beam prediction. Another one or more reference signals (or sets / lists of reference signals) can be used for inference, for example, as output from the AI / ML for beam prediction.

[0076] For example, new candidate beams can be selected from the output of AI / ML for beam prediction. Therefore, compared with the method of selecting from a configured candidate beam reference signal list, the embodiments of the present application can improve the accuracy and reliability of beam failure recovery.

[0077] In some embodiments, the AI / ML functionality / model used to determine the candidate beams is the same as the AI / ML functionality / model used for beam management. For example, a single AI / ML can be used for both beam management and for identifying new candidate beams for beam failure recovery.

[0078] In other embodiments, the AI / ML functionality / model used to determine the candidate beams is different from the AI / ML functionality / model used for beam management. For example, two AI / MLs may be used: one for beam management and another for identifying new candidate beams for beam failure recovery.

[0079] In some embodiments, the one or more reference signals for measurement and the one or more reference signals for inference are configured for the terminal device.

[0080] For example, using AI / ML for identifying new candidate beams, the UE may be configured with a reference signal (or a set / list of reference signals) for measurement as input to the AI / ML and / or a reference signal (or a set / list of reference signals) for AI / ML reasoning.

[0081] In some embodiments, the one or more reference signals used for measurement are identical to reference signals in a second reference signal set (set B) used for beam management. In some embodiments, the one or more reference signals used for measurement are different from reference signals in a second reference signal set (set B) used for beam management. In some embodiments, the one or more reference signals used for measurement are partially identical to reference signals in a second reference signal set (set B) used for beam management. In some embodiments, the one or more reference signals used for measurement are a subset of the second reference signal set (set B) used for beam management.

[0082] For example, the reference signal (or set / list of reference signals) used for measurement as AI / ML input can be the same as / different from / partially overlap with the reference signals used for beam management in set B, or can be a subset of the reference signals used for beam management in set B.

[0083] In some embodiments, the one or more reference signals used for inference are identical to reference signals in a first reference signal set (set A) used for beam management. In some embodiments, the one or more reference signals used for inference are different from reference signals in the first reference signal set (set A) used for beam management. In some embodiments, the one or more reference signals used for inference are partially identical to reference signals in the first reference signal set (set A) used for beam management. In some embodiments, the one or more reference signals used for inference are a subset of the first reference signal set (set A) used for beam management.

[0084] For example, the reference signal (or set / list of reference signals) used for inference can be the same as / different from / partially overlap with the reference signal used for beam management in set A, or can be a subset of the reference signal used for beam management in set A.

[0085] In some embodiments, the terminal device is configured with a candidate beam reference signal list for beam failure recovery. In another embodiment, the terminal device is not configured with a candidate beam reference signal list for beam failure recovery.

[0086] For example, the new candidate beam RS list is still configured for the UE (for example, if AI / ML for identifying new candidate beams is enabled, the new candidate beam RS list is not used by the UE). For another example, the new candidate beam RS list is not configured for the UE.

[0087] In some embodiments, the terminal device is configured with a candidate beam reference signal list for beam failure recovery, the measurement results of the candidate beam reference signal list are input into the AI / ML functionality / model, and one or more beams from the prediction results output by the AI / ML functionality / model are selected as the candidate beams for beam failure recovery.

[0088] FIG4 is another schematic diagram of a beam management method according to an embodiment of the present application. As shown in FIG4 , the method includes:

[0089] 401. The gNB configures a candidate beam RS list. The candidate beam RS list is used as an AI / ML input for identifying new candidate beams. The gNB also configures a reference signal for the AI / ML output.

[0090] 402. The gNB sends a reference signal for beam failure recovery.

[0091] 403: UE fails to detect beam;

[0092] 404. If beam failure occurs, the UE selects a new candidate beam based on AI / ML prediction;

[0093] 405. The UE sends a beam failure recovery request and new beam information.

[0094] 406. The gNB sends a response message.

[0095] 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.

[0096] In some embodiments, the reference signals in the candidate beam reference signal list are identical to the reference signals in the second reference signal set (set B) used for beam management. In some embodiments, the reference signals in the candidate beam reference signal list are different from the reference signals in the second reference signal set (set B) used for beam management. In some embodiments, the reference signals in the candidate beam reference signal list are partially identical to the reference signals in the second reference signal set (set B) used for beam management. In some embodiments, the reference signals in the candidate beam reference signal list are a subset of the second reference signal set (set B) used for beam management.

[0097] In some embodiments, for example, the RS set for AI / ML reasoning (output output) for new beam identification can be configured separately, for example, the RS set is independent of set A for beam management. The reference signals in the RS set for AI / ML reasoning for new beam identification can be the same / different / partially overlapping with the reference signals in set A for beam management, or can be a subset of the reference signals in set A for beam management. For another example, the RS set for beam management reasoning (e.g., set A for beam management) can be used as the RS set for AI / ML reasoning for new beam identification. For another example, the RS set used to reason about new beam identification can be predefined / implicitly configured, for example, it can be all SSB indices.

[0098] In some embodiments, the terminal device is configured with a candidate beam reference signal list for beam failure recovery, the measurement results of the candidate beam reference signal list are used for reasoning of the AI / ML functionality / model, and one or more beams from the prediction results output by the AI / ML functionality / model are selected as the candidate beams for beam failure recovery.

[0099] FIG5 is another schematic diagram of a beam management method according to an embodiment of the present application. As shown in FIG5 , the method includes:

[0100] 501. The gNB configures a candidate beam RS list. The candidate beam RS list is used as the AI / ML output for identifying new candidate beams. The gNB also configures reference signals for AI / ML input.

[0101] 502. The gNB sends a reference signal for beam failure recovery.

[0102] 503, UE fails to detect beam;

[0103] 504, if beam failure occurs, the UE selects a new candidate beam based on AI / ML prediction;

[0104] 505, UE sends a beam failure recovery request and new beam information;

[0105] 506. The gNB sends a response message.

[0106] 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.

[0107] In some embodiments, the reference signals in the candidate beam reference signal list are the same as the reference signals in the first reference signal set (set A) used for beam management, or the reference signals in the candidate beam reference signal list are different from the reference signals in the first reference signal set (set A) used for beam management, or the reference signals in the candidate beam reference signal list are partially the same as the reference signals in the first reference signal set (set A) used for beam management, or the reference signals in the candidate beam reference signal list are a subset of the first reference signal set (set A) used for beam management.

[0108] In some embodiments, for example, the RS set used for AI / ML input for new beam identification can be configured separately, for example, the RS set is independent of set B used for beam management. The reference signals in the RS set used for AI / ML input for new beam identification can be the same as / different from / partially overlap with the reference signals in set B used for beam management, or can be a subset of the reference signals in set B used for beam management. For another example, the RS set used for beam management input (e.g., set B used for beam management) can be used as the RS set used for AI / ML input for new beam identification.

[0109] In some embodiments, all / a portion / a subset of reference signals in set A used for beam management are used as new candidate beams. A new candidate beam RS list may still be configured for the UE, or the new candidate beam RS list may not be configured for the UE. In one example, if AI / ML for identifying new candidate beams is enabled, the UE does not use the configured new candidate beam RS list.

[0110] In some embodiments, all / a portion / a subset of reference signals in set B used for beam management are used as new candidate beams. A new candidate beam RS list may still be configured for the UE, or the new candidate beam RS list may not be configured for the UE. In one example, if AI / ML for identifying new candidate beams is enabled, the UE does not use the configured new candidate beam RS list.

[0111] In some embodiments, a new candidate beam RS list is configured to the UE. When the UE sends new beam information to the gNB, the UE can further indicate whether the new beam is selected from the configured new candidate beam RS list or from the RS set used for AI / ML inference for new beam identification.

[0112] In some embodiments, the terminal device may send beam failure recovery information to the network device via at least one of the following: MAC-CE, dedicated PRACH, contention-based PRACH. The present application is not limited thereto, and for example, the above methods may be combined or other methods may be used.

[0113] For example, the new beam information may be sent via a MAC-CE. For example, an existing MAC-CE (e.g., a BFR MAC-CE) may be reused, or a new MAC-CE may be introduced. For another example, the new beam information may be sent via a dedicated PRACH or a contention-based PRACH.

[0114] In some embodiments, spatial beam prediction and / or temporal beam prediction is applied to determine candidate beams for beam failure recovery.

[0115] For example, BM case 1 (spatial beam prediction) and / or BM case 2 (temporal beam prediction) can be applied to new beam identification during beam failure recovery. For another example, for temporal beam prediction, if the predicted time instance falls during a beam failure (the interval from beam failure occurrence to beam failure recovery), the predicted beam can be used for new candidate beam identification.

[0116] In some embodiments, the AI / ML functionality / model is further configured to predict a beam failure event; wherein L1-RSRP / L1-SINR or hypothetical BLER of one or more reference signals is input into the AI / ML functionality / model.

[0117] For example, AI / ML can be used to predict beam failure events, thereby reducing the workload of beam failure detection and accelerating or avoiding beam failure recovery operations. For example, if a beam failure event is predicted, the UE can report another candidate beam for communication, thereby reducing beam failure. The input to the AI / ML for beam failure event prediction can be the L1-RSRP / L1-SINR / hypothesized BLER of one or more reference signals.

[0118] In some embodiments, temporal prediction can be applied. For example, based on historical measurements, the UE can predict future beam quality. In other embodiments, spatial prediction can also be applied. For example, based on measurements of one or more core set beams, the UE can predict the quality of other core set beams.

[0119] The above schematically illustrates the identification of new candidate beams in beam failure recovery in an embodiment of the present application. The following further illustrates the relationship or interaction between AI / ML-based beam management and beam failure recovery. For example, if all (or some) failed beams are the same as the input / output of the AI / ML for beam management, or if all (or some) beams of the AI / ML input / output for beam management fail, then there is some interaction between the beam failure recovery operation and the AI / ML-based beam management operation.

[0120] In some embodiments, if all input beams to an AI / ML functionality / model for beam management fail, the AI / ML functionality / model for beam management is stopped.

[0121] In some embodiments, if all input beams to an AI / ML functionality / model for beam management fail, the AI / ML functionality / model for beam management continues to run.

[0122] In some embodiments, if some input beams to an AI / ML functionality / model for beam management fail, the failed beams are not input to the AI / ML functionality / model for beam management.

[0123] For example, if a subset of the input beams to an AI / ML for beam management fails, then the subset (ie, the failed beams) should be filtered out from the input to the AI / ML.

[0124] In some embodiments, if some input beams to an AI / ML functionality / model for beam management fail, the failed beams are still input to the AI / ML functionality / model for beam management.

[0125] For example, if a subset of the input beams of an AI / ML for beam management fails, the subset (i.e., the failed beams) still serves as the input of the AI / ML.

[0126] In some embodiments, the AI / ML functionality / model for beam management is located on the network device side; if at least part of the input beams of the AI / ML functionality / model for beam management fail, the terminal device does not report the failed beams to the network device.

[0127] For example, if the AI / ML for beam management is on the network side (e.g., gNB side), when all (or one or more subsets) of the input beams of the AI / ML for beam management fail, the UE does not report the failed beams to the gNB.

[0128] In some embodiments, the AI / ML functionality / model for beam management is located on the network device side; if at least part of the input beams of the AI / ML functionality / model for beam management fail, the terminal device reports the failed beams to the network device.

[0129] For example, if the AI / ML for beam management is on the network side (e.g., gNB side), when all (or one or more subsets) of the input beams of the AI / ML for beam management fail, the UE still reports the failed beams to the gNB.

[0130] In some embodiments, if all output beams of an AI / ML functionality / model for beam management fail, the AI / ML functionality / model for beam management is stopped.

[0131] In some embodiments, if all output beams of an AI / ML functionality / model for beam management fail, the AI / ML functionality / model for beam management continues to operate.

[0132] In some embodiments, if at least some of the output beams (all output beams, or one or more subsets) of the AI / ML functionality / model for beam management fails, the terminal device does not report the failed beams to the network device.

[0133] In some embodiments, if at least some of the output beams (all output beams, or one or more subsets) of the AI / ML functionality / model for beam management fail, the terminal device reports the failed beams to the network device.

[0134] In some embodiments, if a beam failure occurs but the quality of the failed beam predicted based on AI / ML functionality / model is still above a threshold, the beam failure is not declared (or is not determined).

[0135] For example, if a beam failure occurs, but the quality of the failed beam based on AI / ML prediction is still good, the beam failure is not declared.

[0136] In some embodiments, beam failure is declared (or determined) when a beam failure occurs but the quality of the failed beam predicted based on AI / ML functionality / model is still above a threshold.

[0137] For example, if a beam failure occurs, but the quality of the failed beam based on AI / ML prediction is still good, the beam failure is still declared.

[0138] In some embodiments, whether beam failure is declared (or determined) is determined based on recent events.

[0139] For example, whether a beam failure is declared depends on the most recent event. For example, if the most recent event is a beam failure, then the beam failure is declared.

[0140] In some embodiments, if a candidate beam is determined based on AI / ML functionality / model for beam management but the candidate beam fails, the terminal device falls back to not using AI / ML to determine the candidate beam for beam failure recovery.

[0141] For example, if the new beam identification is based on AI / ML for beam management, when the predicted beam also fails, the UE needs to fall back to the legacy scheme for new candidate beam identification, such as using a new candidate beam identification scheme that does not use AI / ML.

[0142] The following further describes the control of AI / ML functionality / models that take beam failure into account. For example, when controlling (e.g., activating / deactivating) the operation of AI / ML functionality / models for beam management, beam failure recovery operations are considered.

[0143] In some embodiments, when a beam failure occurs, the AI / ML functionality / model for beam management is deactivated, and when the beam failure is recovered, the AI / ML functionality / model for beam management is activated or reactivated.

[0144] For example, after a beam failure occurs, the AI / ML function / model for beam management is deactivated. After the beam failure is recovered, the AI / ML function / model for beam management can be reactivated through explicit signaling and / or implicit signaling.

[0145] In some embodiments, when beam failure occurs, a first (generalized or default) AI / ML functionality / model for beam management is used, and when beam failure recovery occurs, a second (local) AI / ML functionality / model for beam management is used.

[0146] For example, after a beam failure occurs, the AI / ML function / model for beam management can use a generalized model or a default model. After the beam failure is recovered, the dedicated model (local model) can be enabled through explicit signaling and / or implicit signaling.

[0147] In some embodiments, the control of the AI / ML functionality / model for beam management is independent of beam failure recovery.

[0148] The following further describes the performance monitoring of AI / ML functionality / models that take beam failure into account. For example, when monitoring the performance of AI / ML functionality / models used for beam management, beam failure recovery operations are considered.

[0149] In some embodiments, when a beam failure occurs, performance monitoring of the AI / ML functionality / model for beam management is restarted.

[0150] For example, after a beam failure occurs, performance monitoring of the AI / ML function / model used for beam management is restarted, such as resetting and restarting a timer / counter.

[0151] In some embodiments, upon beam failure recovery, performance monitoring of the AI / ML functionality / model for beam management is restarted.

[0152] For example, after beam failure recovery, performance monitoring of the AI / ML function / model used for beam management is restarted, such as resetting and restarting a timer / counter.

[0153] In some embodiments, when a beam failure occurs, performance monitoring of the AI / ML functionality / model for beam management is paused, and when the beam failure is recovered, performance monitoring of the AI / ML functionality / model for beam management is resumed.

[0154] For example, after a beam failure occurs, performance monitoring of the AI / ML function / model for beam management is paused, and after the beam failure is recovered, performance monitoring of the AI / ML function / model for beam management is resumed. For example, a timer / counter is paused after a beam failure occurs, and resumed after the beam failure is recovered.

[0155] In some embodiments, when a beam failure occurs, performance monitoring of the AI / ML functionality / model for beam management is stopped, and when the beam failure is recovered, performance monitoring of the AI / ML functionality / model for beam management is restarted.

[0156] For example, after a beam failure occurs, performance monitoring of the AI / ML function / model for beam management is stopped, and after the beam failure is recovered, performance monitoring of the AI / ML function / model for beam management is restarted. For example, a timer / counter is stopped after a beam failure occurs, and is reset or restarted after the beam failure is recovered.

[0157] In some embodiments, performance monitoring of the AI / ML functionality / model for beam management is independent of beam failure recovery.

[0158] In embodiments of the present application, AI / ML operations can be used to identify new candidate beams during beam failure recovery operations. Embodiments of the present application define the interaction between AI / ML for beam management and beam failure recovery. Furthermore, control and performance monitoring of AI / ML functions / models that account for beam failures are defined.

[0159] The embodiments of the present application can be applied to the UE-side model and / or the gNB-side model. Furthermore, the embodiments of the present application can be applied to BM case 1 (spatial beam prediction) and / or BM case 2 (temporal beam prediction).

[0160] 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.

[0161] It can be seen from the above embodiments that the terminal device determines the candidate beam for beam failure recovery based on AI / ML functionality / model, and AI / ML can be used for new candidate beam identification for beam failure recovery, thereby improving the accuracy and reliability of beam failure recovery.

[0162] Embodiments of the second aspect

[0163] The embodiment of the present application provides a beam management method, which is described from the perspective of a network device. The embodiment of the second aspect can be combined with the embodiment of the first aspect, and the same contents as the embodiment of the first aspect will not be repeated.

[0164] FIG6 is another schematic diagram of a beam management method according to an embodiment of the present application. As shown in FIG6 , the method includes:

[0165] 601. The network device sends beam management configuration information and / or beam failure recovery configuration information to the terminal device; wherein the terminal device determines a candidate beam for beam failure recovery based on AI / ML functionality / model.

[0166] 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.

[0167] It can be seen from the above embodiments that the terminal device determines the candidate beam for beam failure recovery based on AI / ML functionality / model, and AI / ML can be used for new candidate beam identification for beam failure recovery, thereby improving the accuracy and reliability of beam failure recovery.

[0168] Embodiments of the third aspect

[0169] The embodiment of the present application provides a beam management device. The device may be, for example, a terminal device, or one or more components or assemblies configured in the terminal device. The contents that are the same as those in the first and second aspects of the embodiment are not repeated here.

[0170] FIG7 is another schematic diagram of a beam management device according to an embodiment of the present application. As shown in FIG7 , a beam management device 700 according to an embodiment of the present application includes:

[0171] a receiving unit 701, which receives beam management configuration information and / or beam failure recovery configuration information from a network device;

[0172] The processing unit 702 determines candidate beams for beam failure recovery based on AI / ML functionality / model.

[0173] In some embodiments, one or more reference signals are used for measurement and the measurement results are input into the AI / ML functionality / model, and another one or more reference signals are used for the output of the AI / ML functionality / model for inference.

[0174] In some embodiments, the candidate beams for beam failure recovery are selected from prediction results output by the AI / ML functionality / model.

[0175] In some embodiments, the AI / ML functionality / model used to determine the candidate beams is the same as the AI / ML functionality / model used for beam management.

[0176] In some embodiments, the AI / ML functionality / model used to determine the candidate beams is different from the AI / ML functionality / model used for beam management.

[0177] In some embodiments, the one or more reference signals for measurement and the one or more reference signals for inference are configured for a terminal device.

[0178] In some embodiments, the one or more reference signals used for measurement are the same as reference signals in a second reference signal set (set B) used for beam management.

[0179] In some embodiments, the one or more reference signals used for measurement are different from reference signals in a second reference signal set (set B) used for beam management.

[0180] In some embodiments, the one or more reference signals used for measurement are partially identical to reference signals in a second reference signal set (set B) used for beam management.

[0181] In some embodiments, the one or more reference signals used for measurement are a subset of a second set of reference signals (set B) used for beam management.

[0182] In some embodiments, the one or more reference signals used for inference are the same as reference signals in a first reference signal set (set A) used for beam management.

[0183] In some embodiments, the one or more reference signals used for inference are different from reference signals in a first reference signal set (set A) used for beam management.

[0184] In some embodiments, the one or more reference signals used for inference are partially identical to reference signals in a first reference signal set (set A) used for beam management.

[0185] In some embodiments, the one or more reference signals used for inference are a subset of a first set of reference signals (set A) used for beam management.

[0186] In some embodiments, the terminal device is configured with a candidate beam reference signal list for beam failure recovery.

[0187] In some embodiments, the terminal device is not configured with a candidate beam reference signal list for beam failure recovery.

[0188] In some embodiments, the terminal device is configured with a candidate beam reference signal list for beam failure recovery, the measurement results of the candidate beam reference signal list are input into the AI / ML functionality / model, and one or more beams from the prediction results output by the AI / ML functionality / model are selected as the candidate beams for beam failure recovery.

[0189] In some embodiments, the reference signals in the candidate beam reference signal list are the same as the reference signals in a second reference signal set (set B) used for beam management.

[0190] In some embodiments, the reference signals in the candidate beam reference signal list are different from the reference signals in the second reference signal set (set B) used for beam management.

[0191] In some embodiments, the reference signals in the candidate beam reference signal list are partially identical to reference signals in a second reference signal set (set B) used for beam management.

[0192] In some embodiments, the reference signals in the candidate beam reference signal list are a subset of a second reference signal set (set B) used for beam management.

[0193] In some embodiments, the terminal device is configured with a candidate beam reference signal list for beam failure recovery, the measurement results of the candidate beam reference signal list are used for reasoning of the AI / ML functionality / model, and one or more beams from the prediction results output by the AI / ML functionality / model are selected as the candidate beams for beam failure recovery.

[0194] In some embodiments, the reference signals in the candidate beam reference signal list are the same as the reference signals in the first reference signal set (set A) used for beam management.

[0195] In some embodiments, the reference signals in the candidate beam reference signal list are different from the reference signals in the first reference signal set (set A) used for beam management.

[0196] In some embodiments, the reference signals in the candidate beam reference signal list are partially identical to reference signals in a first reference signal set (set A) used for beam management.

[0197] In some embodiments, the reference signals in the candidate beam reference signal list are a subset of a first reference signal set (set A) used for beam management.

[0198] In some embodiments, as shown in FIG7 , the apparatus further comprises:

[0199] The sending unit 703 sends beam failure recovery information to the network device through at least one of the following: MAC-CE, dedicated PRACH, and contention-based PRACH.

[0200] In some embodiments, spatial beam prediction and / or temporal beam prediction is applied to determine candidate beams for beam failure recovery.

[0201] In some embodiments, the AI / ML functionality / model is further configured to predict a beam failure event; wherein L1-RSRP / L1-SINR or hypothetical BLER of one or more reference signals is input into the AI / ML functionality / model.

[0202] In some embodiments, if all input beams to an AI / ML functionality / model for beam management fail, the AI / ML functionality / model for beam management is stopped.

[0203] In some embodiments, if all input beams to an AI / ML functionality / model for beam management fail, the AI / ML functionality / model for beam management continues to run.

[0204] In some embodiments, if some of the input beams to the AI / ML functionality / model for beam management fail, the failed beams are not input to the AI / ML functionality / model for beam management.

[0205] In some embodiments, if some input beams to an AI / ML functionality / model for beam management fail, the failed beams are still input to the AI / ML functionality / model for beam management.

[0206] In some embodiments, the AI / ML functionality / model for beam management is located on the network device side;

[0207] If at least part of the input beam of the AI / ML functionality / model for beam management fails, the terminal device does not report the failed beam to the network device, or if at least part of the input beam of the AI / ML functionality / model for beam management fails, the terminal device reports the failed beam to the network device.

[0208] In some embodiments, if all output beams of an AI / ML functionality / model for beam management fail, the AI / ML functionality / model for beam management is stopped.

[0209] In some embodiments, if all output beams of an AI / ML functionality / model for beam management fail, the AI / ML functionality / model for beam management continues to operate.

[0210] In some embodiments, if at least part of the output beam of the AI / ML functionality / model for beam management fails, the terminal device does not report the failed beam to the network device.

[0211] In some embodiments, if at least part of the output beam of the AI / ML functionality / model for beam management fails, the terminal device reports the failed beam to the network device.

[0212] In some embodiments, if a beam failure occurs but the quality of the failed beam predicted based on AI / ML functionality / model is still above a threshold, the beam failure is not declared (or is not determined).

[0213] In some embodiments, beam failure is declared (or determined) when a beam failure occurs but the quality of the failed beam predicted based on AI / ML functionality / model is still above a threshold.

[0214] In some embodiments, whether beam failure is declared (or determined) is determined based on recent events.

[0215] In some embodiments, if a candidate beam is determined based on AI / ML functionality / model for beam management but the candidate beam fails, the terminal device falls back to not using AI / ML to determine the candidate beam for beam failure recovery.

[0216] In some embodiments, when a beam failure occurs, the AI / ML functionality / model for beam management is deactivated, and when the beam failure is recovered, the AI / ML functionality / model for beam management is activated or reactivated.

[0217] In some embodiments, when beam failure occurs, a first (generalized or default) AI / ML functionality / model for beam management is used, and when beam failure recovery occurs, a second (local) AI / ML functionality / model for beam management is used.

[0218] In some embodiments, the control of the AI / ML functionality / model for beam management is independent of beam failure recovery.

[0219] In some embodiments, when a beam failure occurs, performance monitoring of the AI / ML functionality / model for beam management is restarted.

[0220] In some embodiments, upon beam failure recovery, performance monitoring of the AI / ML functionality / model for beam management is restarted.

[0221] In some embodiments, when a beam failure occurs, performance monitoring of the AI / ML functionality / model for beam management is paused, and when the beam failure is recovered, performance monitoring of the AI / ML functionality / model for beam management is resumed.

[0222] In some embodiments, when a beam failure occurs, performance monitoring of the AI / ML functionality / model for beam management is stopped, and when the beam failure is recovered, performance monitoring of the AI / ML functionality / model for beam management is restarted.

[0223] In some embodiments, performance monitoring of the AI / ML functionality / model for beam management is independent of beam failure recovery.

[0224] 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.

[0225] It is worth noting that the above only describes the components or modules related to the present application, but the present application is not limited thereto. The beam management device 700 may also include other components or modules. For the specific contents of these components or modules, reference may be made to the relevant art.

[0226] 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.

[0227] It can be seen from the above embodiments that the terminal device determines the candidate beam for beam failure recovery based on AI / ML functionality / model, and AI / ML can be used for new candidate beam identification for beam failure recovery, thereby improving the accuracy and reliability of beam failure recovery.

[0228] Embodiments of the fourth aspect

[0229] The embodiment of the present application provides a beam management configuration. The device may be, for example, a network device, or one or more components or assemblies configured in the network device, and the contents that are the same as those in the first to third aspects of the embodiment are not repeated here.

[0230] FIG8 is another schematic diagram of a beam management device according to an embodiment of the present application. As shown in FIG8 , the beam management device 800 includes:

[0231] A sending unit 801 sends beam management configuration information and / or beam failure recovery configuration information to a terminal device; wherein the terminal device determines a candidate beam for beam failure recovery based on AI / ML functionality / model.

[0232] In some embodiments, as shown in FIG8 , the beam management device 800 may further include:

[0233] The receiving unit 802 receives the beam failure recovery information sent by the terminal device.

[0234] 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.

[0235] It is worth noting that the above only describes the components or modules related to the present application, but the present application is not limited thereto. The beam management device 800 may also include other components or modules. For the specific contents of these components or modules, reference may be made to the relevant art.

[0236] In addition, for the sake of simplicity, FIG8 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.

[0237] It can be seen from the above embodiments that the terminal device determines the candidate beam for beam failure recovery based on AI / ML functionality / model, and AI / ML can be used for new candidate beam identification for beam failure recovery, thereby improving the accuracy and reliability of beam failure recovery.

[0238] Embodiments of the fifth aspect

[0239] 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.

[0240] In some embodiments, the communication system 100 may include at least:

[0241] A network device that sends beam management configuration information and / or beam failure recovery configuration information to a terminal device;

[0242] A terminal device that determines candidate beams for beam failure recovery based on AI / ML functionality / model.

[0243] 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.

[0244] Figure 9 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in Figure 9 , terminal device 900 may include a processor 910 and a memory 920. Memory 920 stores data and programs and is coupled to processor 910. 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.

[0245] For example, the processor 910 may be configured to execute a program to implement the beam management method as described in the embodiment of the first aspect. For example, the processor 910 may be configured to perform the following control: receiving beam management configuration information and / or beam failure recovery configuration information from a network device; and determining candidate beams for beam failure recovery based on AI / ML functionality / model.

[0246] As shown in Figure 9 , the terminal device 900 may further include: a communication module 930, an input unit 940, a display 950, and a power supply 960. 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 900 does not necessarily include all of the components shown in Figure 9 , and these components are not essential. Furthermore, the terminal device 900 may also include components not shown in Figure 9 , for which reference may be made to the prior art.

[0247] 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.

[0248] Figure 10 is a schematic diagram illustrating the structure of a network device according to an embodiment of the present application. As shown in Figure 10 , network device 1000 may include a processor 1010 (e.g., a central processing unit (CPU)) and a memory 1020; memory 1020 is coupled to processor 1010. Memory 1020 may store various data and may also store an information processing program 1030, which is executed under the control of processor 1010.

[0249] For example, the processor 1010 may be configured to execute a program to implement the beam management method as described in the embodiment of the second aspect. For example, the processor 1010 may be configured to perform the following control: sending beam management configuration information and / or beam failure recovery configuration information to a terminal device; wherein the terminal device determines a candidate beam for beam failure recovery based on an AI / ML function / model.

[0250] In addition, as shown in FIG10 , the network device 1000 may further include: a transceiver 1040 and an antenna 1050; wherein, the functions of the above components are similar to those in the prior art and are not described in detail here. It is worth noting that the network device 1000 does not necessarily include all the components shown in FIG10 ; in addition, the network device 1000 may also include components not shown in FIG10 , and reference may be made to the prior art for details.

[0251] An embodiment of the present application also provides a computer program, wherein when the program is executed in a terminal device, the program enables the terminal device to perform the beam management method described in the embodiment of the first aspect.

[0252] An embodiment of the present application also provides a storage medium storing a computer program, wherein the computer program enables a terminal device to execute the beam management method described in the embodiment of the first aspect.

[0253] An embodiment of the present application also provides a computer program, wherein when the program is executed in a network device, the program enables the network device to perform the beam management method described in the embodiment of the second aspect.

[0254] An embodiment of the present application also provides a storage medium storing a computer program, wherein the computer program enables a network device to execute the beam management method described in the embodiment of the second aspect.

[0255] 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.

[0256] 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).

[0257] 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.

[0258] 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.

[0259] 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.

[0260] Regarding the implementation methods including the above embodiments, the following additional notes are also disclosed:

[0261] 1. A beam management method, comprising:

[0262] The terminal device receives beam management configuration information and / or beam failure recovery configuration information from the network device;

[0263] The terminal device determines candidate beams for beam failure recovery based on AI / ML functionality / model.

[0264] 2. A beam management method, comprising:

[0265] The network device sends beam management configuration information and / or beam failure recovery configuration information to the terminal device;

[0266] The terminal device determines a candidate beam for beam failure recovery based on AI / ML functionality / model.

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

Claims

1. A beam management apparatus, comprising: a receiving unit that receives beam management configuration information and / or beam failure recovery configuration information from a network device; a processing unit that determines candidate beams for beam failure recovery based on an AI / ML function / model.

2. The device according to claim 1, wherein One or more reference signals are used for measurement and the measurement results are input into the AI / ML function / model, and another or more reference signals are used for the output of the AI / ML function / model for inference.

3. The device according to claim 2, wherein The candidate beams for beam failure recovery are selected from the prediction results output by the AI / ML function / model; The AI / ML function / model for determining the candidate beams is the same as the AI / ML function / model for beam management, or the AI / ML function / model for determining the candidate beams is different from the AI / ML function / model for beam management.

4. The apparatus according to claim 2, wherein, The one or more reference signals for measurement and the one or more reference signals for inference are configured for a terminal device; The one or more reference signals for measurement are the same as the reference signals in a second reference signal set for beam management, or the one or more reference signals for measurement are different from the reference signals in a second reference signal set for beam management, or the one or more reference signals for measurement are partially the same as the reference signals in a second reference signal set for beam management, or the one or more reference signals for measurement are a subset of a second reference signal set for beam management; The one or more reference signals for inference are the same as the reference signals in a first reference signal set for beam management, or the one or more reference signals for inference are different from the reference signals in a first reference signal set for beam management, or the one or more reference signals for inference are partially the same as the reference signals in a first reference signal set for beam management, or the one or more reference signals for inference are a subset of a first reference signal set for beam management.

5. The apparatus according to claim 2, wherein The terminal device is configured with a candidate beam reference signal list for beam failure recovery, or the terminal device is not configured with a candidate beam reference signal list for beam failure recovery.

6. The device according to claim 2, wherein The terminal device is configured with a candidate beam reference signal list for beam failure recovery, the measurement results of the candidate beam reference signal list are input into the AI / ML function / model, and one or more beams in the prediction results output by the AI / ML function / model are selected as the candidate beams for beam failure recovery; The reference signals in the candidate beam reference signal list are the same as the reference signals in the second reference signal set for beam management, or the reference signals in the candidate beam reference signal list are different from the reference signals in the second reference signal set for beam management, or the reference signals in the candidate beam reference signal list are partially the same as the reference signals in the second reference signal set for beam management, or the reference signals in the candidate beam reference signal list are a subset of the second reference signal set for beam management.

7. The apparatus according to claim 2, wherein The terminal device is configured with a candidate beam reference signal list for beam failure recovery, and the measurement results of the candidate beam reference signal list are used for the inference of the AI / ML function / model. One or more beams in the prediction results output by the AI / ML function / model are selected as the candidate beams for beam failure recovery; The reference signals in the candidate beam reference signal list are the same as the reference signals in the first reference signal set for beam management, or the reference signals in the candidate beam reference signal list are different from the reference signals in the first reference signal set for beam management, or the reference signals in the candidate beam reference signal list are partially the same as the reference signals in the first reference signal set for beam management, or the reference signals in the candidate beam reference signal list are a subset of the first reference signal set for beam management.

8. The apparatus according to claim 1, wherein The apparatus further includes: A sending unit that sends beam failure recovery information to the network device through at least one of the following: MAC-CE, dedicated PRACH, contention-based PRACH.

9. The device according to claim 1, wherein, Spatial beam prediction and / or temporal beam prediction is applied to determine candidate beams for beam failure recovery.

10. The device according to claim 1, wherein, The AI / ML function / model is further used to predict beam failure events; the L1-RSRP / L1-SINR or assumed BLER of one or more reference signals is input to the AI / ML function / model.

11. The device according to claim 1, wherein, If all the input beam failures of the AI / ML function / model for beam management occur, the AI / ML function / model for beam management is stopped, or if all the input beam failures of the AI / ML function / model for beam management occur, the AI / ML function / model for beam management continues to run; or If some of the input beam failures of the AI / ML function / model for beam management occur, the failed beams are not input to the AI / ML function / model for beam management, or if some of the input beam failures of the AI / ML function / model for beam management occur, the failed beams are still input to the AI / ML function / model for beam management.

12. The apparatus according to claim 1, wherein, The AI / ML function / model for beam management is located on the network device side; If at least some of the input beams of the AI / ML function / model for beam management fail, the terminal device does not report the failed beams to the network device, or if at least some of the input beams of the AI / ML function / model for beam management fail, the terminal device reports the failed beams to the network device.

13. The apparatus according to claim 1, wherein, If all of the output beams of the AI / ML function / model for beam management fail, the AI / ML function / model for beam management is stopped, or if all of the output beams of the AI / ML function / model for beam management fail, the AI / ML function / model for beam management continues to run.

14. The apparatus according to claim 1, wherein, If at least some of the output beams of the AI / ML function / model for beam management fail, the terminal device does not report the failed beams to the network device, or if at least some of the output beams of the AI / ML function / model for beam management fail, the terminal device reports the failed beams to the network device.

15. The apparatus according to claim 1, wherein, If a beam failure occurs but the quality of the failed beam predicted based on the AI / ML function / model is still higher than a threshold, the beam failure is not declared, or if a beam failure occurs but the quality of the failed beam predicted based on the AI / ML function / model is still higher than a threshold, the beam failure is declared; Or, it is determined whether the beam failure is declared according to recent events.

16. The apparatus according to claim 1, wherein If a candidate beam is determined based on the AI / ML function / model for beam management but the candidate beam fails, the terminal device falls back to not using the candidate beam determined by the AI / ML for beam failure recovery.

17. The device according to claim 1, wherein, When a beam failure occurs, the AI / ML function / model for beam management is deactivated, and when beam failure recovery occurs, the AI / ML function / model for beam management is activated or reactivated; or When a beam failure occurs, the first AI / ML function / model for beam management is used, and when beam failure recovery occurs, the second AI / ML function / model for beam management is used; or The control of the AI / ML function / model for beam management is independent of beam failure recovery.

18. The device according to claim 1, wherein When a beam failure occurs, the performance monitoring of the AI / ML function / model for beam management is restarted, or when beam failure recovery occurs, the performance monitoring of the AI / ML function / model for beam management is restarted; Or When a beam failure occurs, the performance monitoring of the AI / ML function / model for beam management is paused, and when beam failure recovery occurs, the performance monitoring of the AI / ML function / model for beam management is continued; Or When a beam failure occurs, the performance monitoring of the AI / ML function / model for beam management is stopped, and when beam failure recovery occurs, the performance monitoring of the AI / ML function / model for beam management is restarted; Or The performance monitoring of the AI / ML function / model for beam management is independent of beam failure recovery.

19. A beam management apparatus, comprising: A sending unit that sends beam management configuration information and / or beam failure recovery configuration information to a terminal device; Among them, the terminal device determines candidate beams for beam failure recovery based on the AI / ML function / model.

20. A communication system, comprising: A network device that sends beam management configuration information and / or beam failure recovery configuration information to a terminal device; A terminal device that determines candidate beams for beam failure recovery based on the AI / ML function / model.

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