Beam failure recovery method and apparatus

Abstract

Provided in the embodiments of the present application are a beam failure recovery (BFR) method and apparatus. The method comprises: a terminal device receiving BFR configuration information from a network device; and the terminal device performing BFR of a secondary cell (SCell) on the basis of the BFR configuration information, wherein a candidate beam for BFR is determined on the basis of an AI / ML functionality / model.

Description

Beam failure recovery 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 leverage AI / ML functionality / models to predict future beams based on beam measurement results. However, there is currently no clear solution for configuring and instructing beam failure recovery.

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

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

[0009] The terminal device receives beam failure recovery (BFR) configuration information from the network device;

[0010] The terminal device performs beam failure recovery of the secondary cell (SCell) according to the beam failure recovery (BFR) configuration information; wherein the candidate beams for the beam failure recovery are determined based on AI / ML functionality / model.

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

[0012] a receiving unit configured to receive beam failure recovery (BFR) configuration information from a network device;

[0013] A processing unit that performs beam failure recovery of a secondary cell (SCell) according to the beam failure recovery (BFR) configuration information; wherein candidate beams for the beam failure recovery are determined based on AI / ML functionality / model.

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

[0015] The network device sends beam failure recovery (BFR) configuration information to the terminal device;

[0016] The terminal device performs beam failure recovery of the secondary cell (SCell) according to the beam failure recovery (BFR) configuration information; wherein the candidate beam for beam failure recovery is determined based on AI / ML functionality / model.

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

[0018] a sending unit, configured to send beam failure recovery (BFR) configuration information to a terminal device;

[0019] The terminal device performs beam failure recovery of the secondary cell (SCell) according to the beam failure recovery (BFR) configuration information; wherein the candidate beam for beam failure recovery is determined based on AI / ML functionality / model.

[0020] One of the beneficial effects of the embodiments of the present application is that: based on the AI / ML functionality / model, the candidate beams for beam failure recovery are determined, and the terminal device can perform beam failure recovery of the secondary cell (SCell), thereby improving the accuracy and reliability of beam failure recovery.

[0021] 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.

[0022] 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.

[0023] 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

[0024] 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.

[0025] FIG1 is a schematic diagram of a communication system according to an embodiment of the present application;

[0026] FIG2 is a schematic diagram of a beam failure recovery method according to an embodiment of the present application;

[0027] FIG3 is a schematic diagram of AI / ML according to an embodiment of the present application;

[0028] FIG4 is another schematic diagram of a beam failure recovery method according to an embodiment of the present application;

[0029] FIG5 is an exemplary diagram of sending a reference signal through a primary cell according to an embodiment of the present application;

[0030] FIG6 is an exemplary diagram of sending a reference signal through a secondary cell according to an embodiment of the present application;

[0031] FIG7 is another schematic diagram of a beam failure recovery method according to an embodiment of the present application;

[0032] FIG8 is an example diagram of not sending a reference signal according to an embodiment of the present application;

[0033] FIG9 is an example diagram of candidate beam indication according to an embodiment of the present application;

[0034] FIG10 is another schematic diagram of a beam failure recovery method according to an embodiment of the present application;

[0035] FIG11 is another schematic diagram of a beam failure recovery method according to an embodiment of the present application;

[0036] FIG12 is another schematic diagram of a beam failure recovery method according to an embodiment of the present application;

[0037] FIG13 is a schematic diagram of a beam failure recovery device according to an embodiment of the present application;

[0038] FIG14 is another schematic diagram of a beam failure recovery device according to an embodiment of the present application;

[0039] FIG15 is a schematic diagram of a terminal device according to an embodiment of the present application;

[0040] FIG16 is a schematic diagram of a network device according to an embodiment of the present application. DETAILED DESCRIPTION

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] Furthermore, communication between devices in the communication system may be carried out according to communication protocols of any stage, for example, including but not limited to the following communication protocols: 1G (generation), 2G, 2.5G, 2.75G, 3G, 4G, 4.5G and 5G, New Radio (NR), future 6G, etc., and / or other communication protocols currently known or to be developed in the future.

[0046] 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.

[0047] 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.

[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. 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.

[0049] 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.

[0050] 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.

[0051] 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.

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

[0053] 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.

[0054] 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.

[0055] 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.

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

[0057] 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).

[0058] 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.

[0059] In an embodiment 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 model may be used for various signal processing functions of wireless communications, such as CSI prediction, CSI compression, beam prediction, positioning management, and the like; the present application is not limited thereto. Hereinafter, the beam predicted (inferred) or selected by the AI / ML model is referred to as a candidate beam or a new beam; the present application is not limited thereto, and the terms "inference" and "prediction" are interchangeable, and the terms "candidate beam" and "new beam" are interchangeable.

[0060] Embodiments of the first aspect

[0061] An embodiment of the present application provides a beam failure recovery method, which is described from the perspective of a network device.

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

[0063] 201, the terminal device receives beam failure recovery (BFR) configuration information from the network device; and

[0064] 202. The terminal device performs beam failure recovery of the secondary cell (SCell) according to beam failure recovery (BFR) configuration information; wherein candidate beams for beam failure recovery are determined based on AI / ML functionality / model.

[0065] It is worth noting that FIG2 above is merely a schematic illustration of an embodiment of the present application, and the present application is not limited thereto. For example, the execution order of the various operations may be appropriately adjusted, and other operations may be added or some operations may be reduced. Those skilled in the art may make appropriate modifications based on the above description, and are not limited to the description of FIG2 above.

[0066] In some embodiments, functionality refers to an AI / ML feature / feature group enabled by a configuration, where the configuration is supported based on conditions indicated by UE capabilities.

[0067] For example, the AL / ML function may be one or more functions, or one or more logical models, or one or more sub-functions, or one or more features, or one or more feature groups.

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

[0069] In some embodiments, beam failure recovery (BFR) 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. In addition, the terminal device may also be configured with information for beam management, which may include information of one or more reference signals used for beam management or beam measurement, such as CSI-RS configuration information. The present application is not limited thereto, and reference may be made to related technologies for specific configuration information.

[0070] In some embodiments, the AI / ML functionality / model used to determine candidate beams can be 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 determining (identifying) candidate beams for beam failure recovery.

[0071] In other embodiments, the AI / ML functionality / model used to determine candidate beams may be 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 determining (identifying) candidate beams for beam failure recovery.

[0072] Figure 3 is a schematic diagram of AI / ML in an embodiment of the present application, which can be used for both beam management and BFR, for example. As shown in Figure 3, one or more reference signals in the second reference signal resource set (set B) can be received and measured by a terminal device, and the measurement results can be used as input for AI / ML. One or more reference signals in the first reference signal resource set (set A) can be used by the terminal device for output of AI / ML, for example, the measurement results can be used as label data or ground truth data for AI / ML. For the specific content of AI / ML and set A and set B, please refer to the relevant technology and will not be repeated here.

[0073] In some embodiments, the AI / ML functionality / model is located on the network device side, which may be referred to as a network-side model (NW-side model or gNB-side model).

[0074] FIG4 is another schematic diagram of a beam failure recovery method according to an embodiment of the present application. As shown in FIG4 , the method includes:

[0075] 401, the terminal device detects a beam failure on the secondary cell (SCell);

[0076] 402. The terminal device sends a beam failure recovery request and / or a reference signal transmission request to the network device on the primary cell (PCell);

[0077] 403. The terminal device receives a reference signal from the network device and performs measurement.

[0078] 404, the terminal device reports the measurement result to the network device;

[0079] 405. The network device performs inference based on the measurement results and AI / ML functionality / model to select the candidate beam. For example, the measurement results of the received reference signal are used as input to the AI / ML for beam inference to obtain candidate beam information.

[0080] 406. The terminal device receives candidate beam information from the network device.

[0081] 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.

[0082] For example, the AI / ML function / model is located on the network device side. After the terminal device detects a beam failure on the secondary cell, it sends a beam failure recovery request and / or a reference signal transmission request to the network device. The network device can send a reference signal, and the terminal device measures the received reference signal and reports the measurement results. The network device uses AI / ML to perform beam inference based on the measurement results and sends the inferred candidate beam information to the terminal device.

[0083] In some embodiments, the beam failure recovery request and / or the reference signal transmission request are used by the terminal device to trigger the network device to send reference signals in the second reference signal set (set B) for beam management measurements.

[0084] For example, the UE can request / trigger / initiate the transmission of reference signals in Set B. Upon receiving a beam failure recovery request, the gNB can transmit reference signals in Set B for beam measurement. In other words, the transmission (or inferred operation) of Set B can be requested / triggered / initiated by the UE. UE-requested / triggered / initiated Set B transmissions may also be applicable to non-BFR operations, such as UE-initiated beam management.

[0085] For another example, in a MAC CE carrying a beam failure recovery request, the MAC CE does not contain any new candidate beam information. When the gNB receives such a MAC CE, the gNB can send the reference signal of set B to the UE and perform beam inference based on the measurement results reported by the UE.

[0086] Figure 5 illustrates an example of transmitting a reference signal via a primary cell according to an embodiment of the present application. For example, as shown in Figure 5 , after a UE detects a beam failure in a secondary cell (SCell), it can send a beam failure recovery request to the gNB via the primary cell (PCell). This beam failure recovery request can be carried in a dedicated scheduling request (SR) and / or MAC-CE, and can indicate the secondary cell (SCell) in which the beam failure occurred.

[0087] As shown in Figure 5, the reference signals for set B can be transmitted on the PCell. The UE can measure the reference signals for set B transmitted on the PCell and send the measurement results back to the gNB via the PCell. The gNB can perform inference across different component carriers (CCs). That is, based on the measurement results of the reference signals for set B on the PCell, the AI / ML function / model on the gNB can predict good beams on different CCs (or the same CC). The inference operation can be performed by the AI / ML function / model configured on the PCell or the AI / ML function / model configured on the SCell.

[0088] Figure 6 illustrates an example of transmitting a reference signal via a secondary cell according to an embodiment of the present application. For example, as shown in Figure 6, after a UE detects a beam failure in a secondary cell (SCell), it can send a beam failure recovery request to the gNB via the primary cell (PCell). This beam failure recovery request can be carried in a dedicated scheduling request (SR) and / or MAC-CE, and can indicate the secondary cell (SCell) in which the beam failure occurred.

[0089] As shown in Figure 6, reference signals for set B can be transmitted on the SCell. The UE can measure the reference signals for set B transmitted on the SCell and send the measurement results back to the gNB via the PCell. The gNB can perform inference across different CCs. Specifically, based on the measurement results of the reference signals for set B on the SCell, the gNB's AI / ML function / model can predict good beams on different CCs (or the same CC). The inference operation can be performed by the AI / ML function / model configured on the PCell or the AI / ML function / model configured on the SCell.

[0090] FIG7 is another schematic diagram of a beam failure recovery method according to an embodiment of the present application. As shown in FIG7 , the method includes:

[0091] 701. The terminal device detects that a beam failure occurs on the secondary cell (SCell);

[0092] 702. The terminal device sends a beam failure recovery request and / or a measurement result for model input to the network device on the primary cell (PCell).

[0093] 703. The network device performs inference based on the beam failure recovery request and / or the measurement result and the AI / ML functionality / model to select a candidate beam.

[0094] 704. The terminal device receives candidate beam information from the network device.

[0095] It is worth noting that FIG7 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 FIG7 above.

[0096] For example, the AI / ML function / model is located on the network device side. After the terminal device detects a beam failure on the secondary cell, it sends a beam failure recovery request and / or measurement results to the network device. The network device can use AI / ML to perform beam inference and send the inferred candidate beam information to the terminal device. As a result, the network device does not need to send a reference signal to the terminal device for measurement. Instead, the terminal device reports the measurement results at the same time as sending the BFR request.

[0097] In some embodiments, the beam failure recovery request and / or the measurement result include RSRP / L1-RSRP of beams in a candidate reference signal list (candidate RS list) of one or more secondary cells.

[0098] For example, in a beam failure recovery request, beams in the candidate RS list of the secondary cell are used regardless of whether the RSRP / L1-RSRP quality is good (e.g., whether the RSRP / L1-RSRP is greater than a preset threshold). For example, the beams in the candidate RS list and their corresponding RSRP / L1-RSRP are used as input to the AI / ML function / model. The gNB performs inference based on this input to select candidate beams for the secondary cell (SCell) experiencing beam failure.

[0099] For another example, the RSRP / L1-RSRP of the beams in the candidate reference signal list is included in the MAC CE. The existing MAC CE for BFR can be enhanced, or a new MAC CE can be used. The MAC CE is sent to the gNB via the PCell. Alternatively, before a beam failure occurs (before a beam failure recovery request is received), the gNB performs inference based on the latest measurement results of set B to predict / select candidate beams. Alternatively, before a beam failure occurs (before a beam failure recovery request is received), the gNB reuses the latest inference results to select candidate beams.

[0100] For example, in a MAC CE carrying a beam failure recovery request, if the RSRP / L1-RSRP of all beams in the candidate RS list of a certain SCell is lower than a certain threshold, it means that the UE has not identified the candidate beam. When the gNB receives such a MAC CE, it performs inference operations to predict / select candidate beams for the failed SCell.

[0101] In some examples, an explicit indication may be included in the MAC CE carrying the beam failure recovery request to indicate that the UE has not identified a candidate beam and the gNB needs to perform inference operations to determine the candidate beam.

[0102] Figure 8 is an example diagram of not sending reference signals in an embodiment of the present application. For example, as shown in Figure 8, after the UE detects a beam failure in a secondary cell (SCell), it can send a beam failure recovery request to the gNB through the primary cell (PCell). This beam failure recovery request can be carried in a dedicated scheduling request (SR) and / or MAC-CE, and can indicate the secondary cell (SCell) in which the beam failure occurred.

[0103] As shown in Figure 8, the measurement results may include the RSRP / L1-RSRP of all beams in the candidate reference signal list (candidate RS list). Based on the measurement results (e.g., below a threshold), the AI / ML function / model on the gNB can predict beams with good performance. The inference operation can be performed by the AI / ML function / model configured on the PCell or the AI / ML function / model configured on the SCell.

[0104] The above schematically illustrates the BFR-related processes. The following describes the signals of the network-side model.

[0105] In some embodiments, the beams in the candidate reference signal list may be configured to be different from set B. For example, the beams in the candidate reference signal list may be configured to be a subset of set B.

[0106] In some embodiments, the beams in the candidate reference signal list can be configured to be the same as those in set B. Alternatively, the reference signals in set B can be configured to be periodic. Alternatively, if the reference signals in set B are configured to be periodic, the periodic reference signals in set B can be configured / processed as candidate reference signals. Alternatively, the periodic reference signals in set B can be reused for beam management.

[0107] In some cases, the UE reports the measurement results for Set B after each Set B transmission or after each candidate reference signal transmission. Therefore, after a beam failure occurs, the gNB can use the most recent measurement results for Set B before the beam failure (before receiving the beam failure recovery request) to perform inference operations to predict / select candidate beams. Alternatively, the gNB can reuse the most recent inference results before the beam failure (before receiving the beam failure recovery request) to select candidate beams.

[0108] In other examples, after each Set B transmission or Candidate Reference Signal transmission, if no beam failure occurs, the UE does not report measurement results. After a beam failure occurs, the UE reports the Set B or Candidate Reference Signal measurement results to the gNB in ​​a MAC CE carrying a Beam Failure Recovery Request so that the gNB can perform inference operations.

[0109] In some embodiments, the candidate beams are selected from a first reference signal set (set A) for beam management prediction, and / or the candidate beam information is sent via RRC and / or MAC CE and / or DCI.

[0110] In some embodiments, candidate beam information is sent through the primary cell (PCell).

[0111] In some embodiments, the terminal device sends a confirmation message to the network device on the primary cell (PCell); wherein, the candidate beam is used on the secondary cell (SCell) after a first time of sending the confirmation message, and the first time is predefined or configured.

[0112] Figure 9 is an example diagram of a candidate beam indication according to an embodiment of the present application. Figure 9 omits the process of sending a beam failure recovery request and set B. For details, please refer to the previous description. As shown in Figure 9, the terminal device detects a beam failure on the secondary cell (SCell); the network device uses AI / ML functions / models to perform beam inference operations and predict candidate beams for the secondary cell; the network device indicates the candidate beam information to the terminal device via MAC CE or DCI; and the terminal device sends an acknowledgment (ACK) to the network device on the primary cell (PCell).

[0113] As shown in Figure 9, for secondary cell (SCell) beam failure recovery using network-side AI / ML capabilities / models, the gNB performs inference and selects candidate beams from set A. The candidate beam information is sent via the primary cell (PCell). Upon receiving the candidate beam information, the UE sends an acknowledgment (ACK) via the PCell. After a first time T, starting from the UE's feedback of the ACK, both the gNB and the UE begin communicating on the SCell using the candidate beam.

[0114] For example, the first time T may also be referred to as a time window, which may be predefined or configured.

[0115] In some embodiments, a MAC CE is used to indicate candidate beams. This can be a newly defined MAC CE or an enhancement to an existing MAC CE. For example, the MAC CE can include one or any combination of the following information: candidate beam information, SSB index or CSI-RS resource ID, SCell ID, and CC ID. After receiving the MAC CE, the UE provides confirmation of the MAC CE.

[0116] In some embodiments, DCI can be used to indicate candidate beams. For example, a new DCI field can be introduced, an existing DCI field can be used, or some unused bits in the non-scheduling DCI can be reused to indicate candidate beams. The DCI can indicate one or any combination of the following information: candidate beam information, SSB index or CSI-RS resource ID, SCell ID, CC ID. After receiving the DCI, the UE feeds back confirmation information for the DCI.

[0117] In some embodiments, candidate beam information is sent via a secondary cell (SCell).

[0118] In some embodiments, the terminal device measures the candidate beam; and uses the candidate beam on the secondary cell (SCell) if the measured beam quality is greater than a threshold.

[0119] For example, for SCell beam failure recovery using network-side AI / ML functions / models, after the gNB indicates a candidate beam via MAC CE or DCI, the UE can further check the quality of the candidate beam. Specifically, the candidate beam can be sent via the SCell, and the UE can measure the quality of the candidate beam. If the candidate beam quality is good, the gNB and UE can use it in the next communication. If the candidate beam quality is poor, the gNB selects another candidate beam.

[0120] For another example, the UE may send feedback information to indicate whether the quality of the candidate beam is good.

[0121] For another example, when the gNB indicates candidate beams through MAC CE, the MAC CE may indicate multiple beams on the SCell. The UE may measure the indicated multiple candidate beams and select one or multiple beams (e.g., the first few beams) for further communication between the UE and the gNB.

[0122] The above schematically illustrates the NW side model. The following describes the UE side model.

[0123] In some embodiments, the AI / ML functionality / model is located on the terminal device side, which is referred to as a UE-side model.

[0124] FIG10 is another schematic diagram of a beam failure recovery method according to an embodiment of the present application. As shown in FIG10 , the method includes:

[0125] 1001: The terminal device detects a beam failure on the secondary cell (SCell);

[0126] 1002. The terminal device performs inference based on AI / ML functionality / model to select a candidate beam.

[0127] 1003. The terminal device sends a beam failure recovery request and / or candidate beam information to the network device on the primary cell (PCell);

[0128] 1004. The terminal device receives confirmation information from the network device.

[0129] It is worth noting that FIG10 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 FIG10 above.

[0130] In some embodiments, the beam failure recovery request and / or candidate beam information is carried by a MAC CE, and / or the candidate beam is selected from a first reference signal set (set A) used for beam management prediction.

[0131] FIG11 is another schematic diagram of a beam failure recovery method according to an embodiment of the present application. As shown in FIG11 , the method includes:

[0132] 1101, the terminal device detects a beam failure on the secondary cell (SCell);

[0133] 1102, the terminal device receives a reference signal from the network device and performs measurement;

[0134] 1103. The terminal device performs inference based on the measurement results and AI / ML functionality / model to select a candidate beam.

[0135] 1104. The terminal device sends candidate beam information to the network device on the primary cell (PCell).

[0136] 1105. The terminal device receives confirmation information from the network device.

[0137] It is worth noting that FIG11 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 FIG11 above.

[0138] In some embodiments, the terminal device triggers the network device to send reference signals in the second reference signal set (set B) for beam management measurement by sending a beam failure recovery request to the network device.

[0139] In some embodiments, a second set of reference signals (set B) for beam management measurements is used as input to an AI / ML functionality / model to select the candidate beams.

[0140] For example, upon receiving a beam failure recovery request, the gNB can transmit a reference signal for set B for beam measurement. In other words, the transmission (or inferred operation) of set B can be requested / triggered / initiated by the UE. UE-requested / triggered / initiated set B transmissions may also be applicable to non-beam failure recovery operations, such as UE-initiated beam management.

[0141] For another example, the beams in the candidate RS list are used as the output of the AI / ML function / model. For another example, the beams in the first reference signal set (set A) are used as the output of the AI / ML function / model, for example, the candidate beams are selected from set A, and the identities of the candidate beams are included in the MAC CE.

[0142] For another example, the beams in the candidate reference signal list can be configured to be the same as those in set B. Alternatively, the reference signals in set B can be configured to be periodic. Alternatively, if the reference signals in set B are configured to be periodic, the periodic reference signals in set B are configured / processed as candidate reference signals. Alternatively, the periodic reference signals in set B are reused for beam management.

[0143] In some examples, after each transmission of set B or candidate reference signals, the UE performs measurements and inference on set B. After a beam failure occurs, the UE can use the latest measurement and inference results of set B before the beam failure to predict / select candidate beams.

[0144] In other examples, after each set B transmission or candidate reference signal transmission, if no beam failure occurs, the UE measures the reference signals of set B but does not perform inference. After a beam failure occurs, the UE can perform inference using the latest set B measurement results before the beam failure to predict / select a candidate beam.

[0145] In some embodiments, the reference signals in the candidate RS list are used as input to the AI / ML functionality / model to select the candidate beams. For example, the candidate beams may be selected from set A.

[0146] For example, the beams in the candidate reference signal list may be configured to be different from set B. For example, the beams in the candidate reference signal list may be configured to be a subset of set B.

[0147] For another example, the beams in the candidate reference signal list may be configured to be the same as those in set B. Alternatively, if the reference signals in set B are configured to be periodic, the periodic reference signals in set B are configured / processed as candidate reference signals.

[0148] In some examples, after candidate reference signals are transmitted, the UE performs measurements and inference on the candidate reference signal list. After a beam failure occurs, the UE can use the most recent inference results before the beam failure to predict / select a candidate beam.

[0149] In other examples, after candidate reference signal transmission, if beam failure does not occur, the UE measures reference signals in the candidate reference signal list but does not perform inference. After beam failure occurs, the UE can perform inference using the most recent measurement results before the beam failure to predict / select a candidate beam.

[0150] The embodiments of the present application can be applied to the terminal device side model (UE-side model) or the network device side model (gNB-side model), but the present application is not limited thereto. In addition, the AI / ML of the embodiments of the present application can be used for beam management, such as time beam prediction and / or spatial beam prediction. In addition, the embodiments of the present application can also be applied to SR-based beam failure recovery operations in multi-TRP scenarios (including single-DCI multi-TRP and multi-DCI multi-TRP), but the present application is not limited thereto.

[0151] 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.

[0152] It can be seen from the above embodiments that by determining the candidate beams for beam failure recovery based on AI / ML functionality / model, the terminal device can perform beam failure recovery of the secondary cell (SCell), thereby improving the accuracy and reliability of beam failure recovery.

[0153] Embodiments of the second aspect

[0154] The embodiment of the present application provides a beam failure recovery 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.

[0155] FIG12 is a schematic diagram of a beam failure recovery method according to an embodiment of the present application. As shown in FIG12 , the method includes:

[0156] 1201. The network device sends beam failure recovery (BFR) configuration information to the terminal device.

[0157] As shown in FIG12 , the method may further include:

[0158] 1202. The terminal device performs beam failure recovery of the secondary cell (SCell) according to the beam failure recovery (BFR) configuration information; wherein the candidate beam for the beam failure recovery is determined based on the AI / ML functionality / model.

[0159] It is worth noting that FIG12 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 FIG12 above.

[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 by determining the candidate beams for beam failure recovery based on AI / ML functionality / model, the terminal device can perform beam failure recovery of the secondary cell (SCell), thereby improving the accuracy and reliability of beam failure recovery.

[0162] Embodiments of the third aspect

[0163] The embodiment of the present application provides a beam failure recovery device, which can be, for example, a terminal device, or one or more components or assemblies configured in the terminal device, and the same contents as those in the first and second aspects of the embodiment are not repeated here.

[0164] FIG13 is a schematic diagram of a beam failure recovery device according to an embodiment of the present application. As shown in FIG13 , the beam failure recovery device 1300 according to an embodiment of the present application includes:

[0165] a receiving unit 1301, which receives beam failure recovery (BFR) configuration information from a network device;

[0166] The processing unit 1302 performs beam failure recovery of the secondary cell (SCell) according to the beam failure recovery (BFR) configuration information; wherein the candidate beams for the beam failure recovery are determined based on AI / ML functionality / model.

[0167] In some embodiments, the AI / ML functionality / model is located on the network device side.

[0168] In some embodiments, the processing unit 1302 detects that a beam failure occurs on the secondary cell; and the apparatus further includes:

[0169] a sending unit 1303, configured to send a beam failure recovery request and / or a reference signal transmission request to the network device on a primary cell (PCell);

[0170] The receiving unit 1301 also receives a reference signal from the network device and performs measurement;

[0171] The sending unit 1303 further reports the measurement result to the network device; wherein the network device performs inference based on the measurement result and the AI / ML functionality / model to select the candidate beam; and

[0172] The receiving unit 1301 also receives candidate beam information indicated by the network device.

[0173] In some embodiments, the beam failure recovery request and / or reference signal transmission request is used by the terminal device to trigger the network device to send a reference signal in a second reference signal set (set B) for beam management measurement.

[0174] In some embodiments, the processing unit 1302 detects that there is a beam failure on the secondary cell;

[0175] The sending unit 1303 sends a beam failure recovery request and / or a measurement result for model input to the network device on the primary cell (PCell); wherein the network device performs inference based on the beam failure recovery request and / or the measurement result and the AI / ML functionality / model to select the candidate beam;

[0176] The receiving unit 1301 also receives candidate beam information indicated by the network device.

[0177] In some embodiments, the beam failure recovery request and / or the measurement result include RSRP / L1-RSRP of beams in a candidate reference signal list (candidate RS list) of one or more secondary cells.

[0178] In some embodiments, the candidate beams are selected from a first reference signal set (set A) for beam management prediction, and / or the candidate beam information is sent via RRC and / or MAC CE and / or DCI.

[0179] In some embodiments, candidate beam information is sent through the primary cell (PCell).

[0180] In some embodiments, the sending unit 1303 sends confirmation information to the network device on the primary cell (PCell); wherein, the candidate beam is used on the secondary cell (SCell) after a first time of sending the confirmation information, and the first time is predefined or configured.

[0181] In some embodiments, candidate beam information is sent via a secondary cell (SCell).

[0182] In some embodiments, the processing unit 1302 is further configured to: measure the candidate beam; and use the candidate beam on the secondary cell (SCell) when the measured beam quality is greater than a threshold.

[0183] In some embodiments, the AI / ML functionality / model is located on the terminal device side.

[0184] In some embodiments, the processing unit 1302 is further configured to: detect a beam failure on the secondary cell, and perform inference based on the AI / ML functionality / model to select the candidate beam;

[0185] The sending unit 1303 sends a beam failure recovery request and / or candidate beam information to the network device on the primary cell (PCell);

[0186] The receiving unit 1301 also receives confirmation information from the network device.

[0187] In some embodiments, the beam failure recovery request and / or candidate beam information is carried by a MAC CE, and / or the candidate beam is selected from a first reference signal set (set A) used for beam management prediction.

[0188] In some embodiments, the processing unit 1302 detects that there is a beam failure on the secondary cell;

[0189] The receiving unit 1301 also receives a reference signal from the network device and performs measurement;

[0190] The processing unit 1302 performs inference based on the measurement results and the AI / ML functionality / model to select the candidate beam;

[0191] The sending unit 1303 sends the candidate beam information to the network device on the primary cell (PCell); and

[0192] The receiving unit 1301 also receives confirmation information from the network device.

[0193] In some embodiments, the sending unit 1303 further sends a reference signal transmission request to the network device to trigger the network device to send a reference signal in a second reference signal set (set B) for beam management measurement.

[0194] In some embodiments, a second set of reference signals (set B) for beam management measurements is used as input to the AI / ML functionality / model to select the candidate beams.

[0195] In some embodiments, reference signals in a candidate RS list are used as input to the AI / ML functionality / model to select the candidate beams.

[0196] 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.

[0197] 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 failure recovery device 1300 may also include other components or modules. For the specific contents of these components or modules, reference may be made to the relevant art.

[0198] In addition, for the sake of simplicity, FIG13 only illustrates the connection relationship or signal direction between various components or modules. However, it should be clear to those skilled in the art that various related technologies such as bus connection can be used. The above-mentioned components or modules can be implemented by hardware facilities such as processors, memories, transmitters, and receivers; the implementation of this application is not limited to this.

[0199] It can be seen from the above embodiments that by determining the candidate beams for beam failure recovery based on AI / ML functionality / model, the terminal device can perform beam failure recovery of the secondary cell (SCell), thereby improving the accuracy and reliability of beam failure recovery.

[0200] Embodiments of the fourth aspect

[0201] The embodiment of the present application provides a beam failure recovery device. The device may be, for example, a network device, or one or more components or assemblies configured in the network device. The contents that are the same as those in the first to third aspects of the embodiment are not repeated here.

[0202] FIG14 is another schematic diagram of a beam failure recovery device according to an embodiment of the present application. As shown in FIG14 , the beam failure recovery device 1400 includes:

[0203] a transmitting unit 1401, which transmits beam failure recovery (BFR) configuration information to a terminal device;

[0204] The terminal device performs beam failure recovery of the secondary cell (SCell) according to the beam failure recovery (BFR) configuration information; wherein the candidate beam for beam failure recovery is determined based on AI / ML functionality / model.

[0205] In some embodiments, as shown in FIG14 , the beam failure recovery apparatus 1400 may further include:

[0206] The receiving unit 1402 receives data / information fed back by the terminal device.

[0207] 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.

[0208] 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 failure recovery device 1400 may also include other components or modules. For the specific contents of these components or modules, reference may be made to the relevant art.

[0209] In addition, for the sake of simplicity, FIG14 only illustrates the connection relationship or signal direction between various components or modules. However, it should be clear to those skilled in the art that various related technologies such as bus connection can be used. The above-mentioned components or modules can be implemented by hardware facilities such as processors, memories, transmitters, and receivers; the implementation of this application is not limited to this.

[0210] It can be seen from the above embodiments that by determining the candidate beams for beam failure recovery based on AI / ML functionality / model, the terminal device can perform beam failure recovery of the secondary cell (SCell), thereby improving the accuracy and reliability of beam failure recovery.

[0211] Embodiments of the fifth aspect

[0212] 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.

[0213] In some embodiments, the communication system 100 may include at least:

[0214] a network device that sends beam failure recovery (BFR) configuration information to a terminal device;

[0215] A terminal device performs beam failure recovery of a secondary cell (SCell) according to the beam failure recovery (BFR) configuration information; wherein candidate beams for the beam failure recovery are determined based on AI / ML functionality / model.

[0216] 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.

[0217] Figure 15 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in Figure 15 , terminal device 1500 may include a processor 1510 and a memory 1520. Memory 1520 stores data and programs and is coupled to processor 1510. It should be noted that this diagram is exemplary; other types of structures may be used to supplement or replace this structure to implement telecommunication or other functions.

[0218] For example, the processor 1510 may be configured to execute a program to implement the beam failure recovery method as described in the embodiment of the first aspect. For example, the processor 1510 may be configured to perform the following control: receiving beam failure recovery (BFR) configuration information from a network device; performing beam failure recovery of a secondary cell (SCell) according to the beam failure recovery (BFR) configuration information; wherein candidate beams for beam failure recovery are determined based on AI / ML functionality / model.

[0219] As shown in Figure 15 , the terminal device 1500 may further include: a communication module 1530, an input unit 1540, a display 1550, and a power supply 1560. The functions of these components are similar to those in the prior art and are not described in detail here. It is worth noting that the terminal device 1500 does not necessarily include all of the components shown in Figure 15 , and these components are not essential. Furthermore, the terminal device 1500 may also include components not shown in Figure 15 , for which reference may be made to the prior art.

[0220] 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.

[0221] Figure 16 is a schematic diagram illustrating the structure of a network device according to an embodiment of the present application. As shown in Figure 16 , network device 1600 may include a processor 1610 (e.g., a central processing unit (CPU)) and a memory 1620; memory 1620 is coupled to processor 1610. Memory 1620 may store various data and may also store an information processing program 1630, which is executed under the control of processor 1610.

[0222] For example, the processor 1610 may be configured to execute a program to implement the beam failure recovery method as described in the embodiment of the second aspect. For example, the processor 1610 may be configured to perform the following control: sending beam failure recovery (BFR) configuration information to a terminal device; wherein the terminal device performs beam failure recovery of a secondary cell (SCell) according to the beam failure recovery (BFR) configuration information; wherein the candidate beam for beam failure recovery is determined based on AI / ML functionality / model.

[0223] In addition, as shown in FIG16 , network device 1600 may further include: a transceiver 1640 and an antenna 1650, etc.; wherein, the functions of the above components are similar to those in the prior art and are not described in detail here. It is worth noting that network device 1600 does not necessarily include all the components shown in FIG16 ; in addition, network device 1600 may also include components not shown in FIG16 , and reference may be made to the prior art for details.

[0224] An embodiment of the present application also provides a computer program, wherein when the program is executed in a terminal device, the program causes the terminal device to execute the beam failure recovery method described in the embodiment of the first aspect.

[0225] 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 failure recovery method described in the embodiment of the first aspect.

[0226] An embodiment of the present application also provides a computer program, wherein when the program is executed in a network device, the program causes the network device to execute the beam failure recovery method described in the embodiment of the second aspect.

[0227] 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 failure recovery method described in the embodiment of the second aspect.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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.

[0233] Regarding the implementation methods including the above embodiments, the following additional notes are also disclosed:

[0234] 1. A beam failure recovery method, comprising:

[0235] The terminal device receives beam failure recovery (BFR) configuration information from the network device;

[0236] The terminal device performs beam failure recovery of the secondary cell (SCell) according to the beam failure recovery (BFR) configuration information; wherein the candidate beams for the beam failure recovery are determined based on AI / ML functionality / model.

[0237] 2. A beam failure recovery method, comprising:

[0238] The network device sends beam failure recovery (BFR) configuration information to the terminal device;

[0239] The terminal device performs beam failure recovery of the secondary cell (SCell) according to the beam failure recovery (BFR) configuration information; wherein the candidate beam for beam failure recovery is determined based on AI / ML functionality / model.

[0240] 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 failure recovery method as described in Note 1.

[0241] 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 failure recovery method as described in Note 2.

[0242] 5. A computer program product, comprising at least a computer program, which, when executed by a processor, enables a terminal device to execute the beam failure recovery method as described in Note 1.

[0243] 6. A computer program product, comprising at least a computer program, wherein when the computer program is executed by a processor, the network device executes the beam failure recovery method as described in Note 2.

Claims

1. A beam failure recovery device, comprising: a receiving unit configured to receive beam failure recovery configuration information from a network device; A processing unit that performs beam failure recovery of the secondary cell according to the beam failure recovery configuration information; wherein the candidate beams for beam failure recovery are determined based on an AI / ML function / model.

2. The device according to claim 1, wherein The AI / ML functions / models are located on the network device side.

3. The device according to claim 2, wherein The processing unit detects that a beam failure occurs on the secondary cell; and the apparatus further includes: a sending unit, configured to send a beam failure recovery request and / or a reference signal transmission request to the network device on a primary cell; The receiving unit further receives a reference signal from the network device and performs measurement; The sending unit further reports the measurement result to the network device; wherein the network device performs reasoning based on the measurement result and the AI / ML function / model to select the candidate beam; and The receiving unit further receives candidate beam information indicated by the network device.

4. The device according to claim 3, wherein The beam failure recovery request and / or the reference signal transmission request is used by the terminal device to trigger the network device to send a reference signal in the second reference signal set for beam management measurement.

5. The device according to claim 2, wherein The processing unit detects that a beam failure occurs on the secondary cell; and the apparatus further includes: a sending unit configured to send, on a primary cell, a beam failure recovery request and / or a measurement result for model input to the network device; wherein the network device performs reasoning based on the AI / ML function / model and according to the beam failure recovery request and / or the measurement result to select the candidate beam; The receiving unit further receives candidate beam information indicated by the network device.

6. The device according to claim 5, wherein The beam failure recovery request and / or the measurement result include RSRP / L1-RSRP of beams in the candidate reference signal list of one or more secondary cells.

7. The device according to claim 2, wherein The candidate beam is selected from a first reference signal set used for beam management prediction, and / or candidate beam information is sent through RRC and / or MAC CE and / or DCI.

8. The device according to claim 2, wherein The candidate beam information is sent through the primary cell.

9. The device according to claim 8, wherein The device further comprises: A sending unit, which sends confirmation information to the network device on the primary cell; wherein, the candidate beam is used on the secondary cell after a first time of sending the confirmation information, and the first time is predefined or configured.

10. The device according to claim 2, wherein The candidate beam information is sent through the secondary cell.

11. The device according to claim 10, wherein The processing unit is further configured to: measure the candidate beam; and use the candidate beam on the secondary cell if the measured beam quality is greater than a threshold.

12. The device according to claim 1, wherein The AI / ML function / model is located on the terminal device side.

13. The device according to claim 12, wherein The processing unit is further configured to: detect a beam failure on the secondary cell, and perform reasoning based on the AI / ML function / model to select the candidate beam; and the apparatus further includes: a sending unit, configured to send a beam failure recovery request and / or candidate beam information to the network device on a primary cell; The receiving unit further receives confirmation information from the network device.

14. The device according to claim 13, wherein The beam failure recovery request and / or candidate beam information is carried by a MAC CE, and / or the candidate beam is selected from a first reference signal set used for beam management prediction.

15. The device according to claim 12, wherein The processing unit detects that a beam failure occurs on the secondary cell; The receiving unit further receives a reference signal from the network device and performs measurement; The processing unit performs reasoning based on the AI / ML function / model according to the measurement results to select the candidate beam; The device further comprises: a sending unit, configured to send candidate beam information to the network device on a primary cell; as well as The receiving unit further receives confirmation information from the network device.

16. The device according to claim 15, wherein The sending unit further sends a reference signal transmission request to the network device to trigger the network device to send a reference signal in a second reference signal set for beam management measurement.

17. The device according to claim 12, wherein A second set of reference signals for beam management measurements is used as input to the AI / ML function / model to select the candidate beams.

18. The device according to claim 12, wherein The reference signals in the candidate reference signal list are used as input to the AI / ML function / model to select the candidate beams.

19. A beam failure recovery device, comprising: a sending unit, configured to send beam failure recovery configuration information to a terminal device; The terminal device performs beam failure recovery of the secondary cell according to the beam failure recovery configuration information; wherein the candidate beam for beam failure recovery is determined based on the AI / ML function / model.

20. A communication system comprising: A network device that sends beam failure recovery configuration information to a terminal device; A terminal device performs beam failure recovery of a secondary cell according to the beam failure recovery configuration information; wherein the candidate beams for beam failure recovery are determined based on an AI / ML function / model.

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