Beam management method and apparatus, and communication system

Abstract

Provided in the embodiments of the present application are a beam management method and apparatus, and a communication system. The beam management apparatus is applied to a terminal device. The apparatus comprises: a first receiving part, which receives a beam failure recovery configuration from a network device; and a first processing part, which uses an artificial intelligence model or function to predict a beam failure.

Description

Beam management methods and devices, communication systems Technical Field

[0001] The embodiments of this application relate to the field of communication technology. Background Technology

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

[0003] Beam management includes: spatial domain beam prediction and temporal beam prediction. The network device can configure a set of beams (i.e., reference signals) for measurement (e.g., set B) for the terminal device, and measurements for set B are used as input to AI / ML functions / models. The network device can also configure a set of beams (i.e., reference signals) for prediction (e.g., set A), for example, set A can be used for inference.

[0004] In some sub-use cases, a two-sided model can be used, where the AI / ML function / model resides on both the terminal device side and the network device side. In other sub-use cases, a one-sided model can be used, where the AI / ML function / model resides either on the terminal device side or on the network device side. For beam management, the AI / ML model can reside on both the terminal device side and / or on the network device side.

[0005] It should be noted that the above introduction to the technical background is only for the purpose of providing a clear and complete explanation of the technical solutions of this application and facilitating understanding by those skilled in the art. It should not be assumed that these technical solutions are known to those skilled in the art simply because they have been described in the background section of this application.

[0006] Summary of the Invention

[0007] The inventors discovered that although terminal devices can recover from blocking through beam failure recovery, the beam failure recovery operation still takes time and causes communication interruption.

[0008] To address at least one of the aforementioned problems, embodiments of this application provide a beam management method, apparatus, and communication system, wherein a terminal device can use an artificial intelligence model or function to predict beam failure, thereby facilitating network devices to switch to another beam to avoid communication interruption and increasing the robustness of the communication link.

[0009] According to one aspect of the embodiments of this application, a beam management device is provided, applied to a terminal device, the device comprising:

[0010] The first receiving unit receives the beam failure recovery configuration from the network device; and

[0011] The first processing unit uses artificial intelligence models or functional predictions to identify beam failures.

[0012] According to another aspect of the embodiments of this application, a beam management device is provided, applied to a network device, the device comprising:

[0013] The second transmitting unit sends the beam failure recovery configuration to the terminal device; and

[0014] The second receiving unit receives a report from the terminal device, the report being used to notify the terminal device of potential beam failure events predicted using an artificial intelligence model or function.

[0015] According to another aspect of the embodiments of this application, a beam management method is provided, applied to a terminal device, the method comprising:

[0016] Configuration for recovering from beam reception failure from network devices; and

[0017] The terminal device uses an artificial intelligence model or function to predict beam failure.

[0018] According to another aspect of the embodiments of this application, a beam management method is provided, applied to a network device, the method comprising:

[0019] Send beam failure recovery configuration to the terminal device; and

[0020] The terminal device receives a report that notifies it of potential beam failure events predicted using an artificial intelligence model or function.

[0021] One of the beneficial effects of the embodiments of this application is that: using artificial intelligence models or functions to predict beam failure, thereby facilitating network devices to switch to another beam to avoid communication interruption, thereby increasing the robustness of the communication link.

[0022] Specific embodiments of this application are disclosed in detail with reference to the following description and accompanying drawings, indicating how the principles of this application can be adopted. It should be understood that the embodiments of this application are not limited in scope. Within the spirit and scope of the appended claims, embodiments of this application include many changes, modifications, and equivalents.

[0023] Features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, combined with features in other embodiments, or substituted for features in other embodiments.

[0024] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, whole, step, or component, but does not exclude the presence or addition of one or more other features, wholes, steps, or components. Attached Figure Description

[0025] The elements and features described in one drawing or embodiment of this application may be combined with elements and features shown in one or more other drawings or embodiments. Furthermore, in the drawings, similar reference numerals denote corresponding parts in several drawings and can be used to indicate corresponding parts used in more than one embodiment.

[0026] Figure 1 is a schematic diagram of a communication system according to an embodiment of this application;

[0027] Figure 2 is a schematic diagram of traditional beam failure detection;

[0028] Figure 3 is a schematic diagram of a beam management method according to an embodiment of this application;

[0029] Figure 4 is a schematic diagram of an artificial intelligence model or function according to an embodiment of this application;

[0030] Figure 5 is a schematic diagram of beam failure prediction using an artificial intelligence model or function according to an embodiment of this application;

[0031] Figure 6 is a schematic diagram for monitoring the performance of an artificial intelligence model or function;

[0032] Figure 7 is a schematic diagram of a beam management method according to an embodiment of this application;

[0033] Figure 8 is a schematic diagram of a beam management device according to an embodiment of this application;

[0034] Figure 9 is a schematic diagram of a beam management device according to an embodiment of this application;

[0035] Figure 10 is a schematic diagram of a terminal device according to an embodiment of this application;

[0036] Figure 11 is a schematic diagram of the network device configuration according to an embodiment of this application. Detailed Implementation

[0037] Referring to the accompanying drawings, the foregoing and other features of this application will become apparent from the following description. Specific embodiments of this application are specifically disclosed in the description and drawings, illustrating partial implementations in which the principles of this application may be employed. It should be understood that this application is not limited to the described embodiments; rather, it includes all modifications, variations, and equivalents falling within the scope of the appended claims.

[0038] In the embodiments of this application, the terms "first," "second," etc., are used to distinguish different elements by name, but do not indicate the spatial arrangement or chronological order of these elements, and these elements should not be limited by these terms. The term "and / or" includes any one or more of the terms listed in association and all combinations thereof. The terms "comprising," "including," "having," etc., refer to the presence of the stated features, elements, components, or assemblies, but do not exclude the presence or addition of one or more other features, elements, components, or assemblies.

[0039] In the embodiments of this application, the singular forms "a," "the," etc., including the plural forms, should be broadly understood as "a kind" or "a class" rather than limited to the meaning of "an." Furthermore, the term "the" should be understood to include both the singular and plural forms, unless the context explicitly indicates otherwise. Additionally, the term "according to" should be understood as "at least partially based on…," and the term "based on" should be understood as "at least partially based on…," unless the context explicitly indicates otherwise.

[0040] In the embodiments of this application, the term "communication network" or "wireless communication network" may refer to a network that conforms to any of the following communication standards, such as Long Term Evolution (LTE), Enhanced Long Term Evolution (LTE-A), Wideband Code Division Multiple Access (WCDMA), High-Speed ​​Packet Access (HSPA), etc.

[0041] Furthermore, communication between devices in a communication system can be carried out according to communication protocols at any stage, 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 communication protocols.

[0042] In the embodiments of this application, the term "network device" refers, for example, to a device in a communication system that connects a terminal device to a communication network and provides services to that 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.

[0043] Base stations can include, but are not limited to: NodeBs (or NBs), evolved NodeBs (eNodeBs or eNBs), and 5G base stations (gNBs), IAB hosts, etc. They can also include Remote Radio Heads (RRHs), Remote Radio Units (RRUs), relays, or low-power nodes (e.g., femeto, pico, etc.). The term "base station" can encompass some or all of their functions, and each base station can provide communication coverage to a specific geographic area. The term "cell" can refer to a base station and / or its coverage area, depending on the context in which the term is used.

[0044] In the embodiments of this application, the terms "User Equipment" (UE) or "Terminal Equipment" (TE) refer, for example, to a device that accesses a communication network and receives network services through a network device. Terminal equipment can be fixed or mobile, and may also be referred to as a mobile station (MS), terminal, subscriber station (SS), access terminal (AT), station, mobile terminal (MT), etc.

[0045] The terminal device may include, but is not limited to, the following devices: cellular phone, personal digital assistant (PDA), wireless modem, wireless communication device, handheld device, machine-type communication device, laptop computer, cordless phone, smartphone, smartwatch, digital camera, etc.

[0046] For example, in scenarios such as the Internet of Things (IoT), terminal devices can also be machines or devices for monitoring or measurement, such as including but not limited to: machine-type communication (MTC) terminals, vehicle communication terminals, device-to-device (D2D) terminals, machine-to-machine (M2M) terminals, and so on.

[0047] Furthermore, the terms "network side" or "network equipment side" refer to one side of the network, which can be a base station or include one or more network devices as described above. The terms "user side," "terminal side," or "terminal equipment side" refer to the side of the user or terminal, which can be a UE or include one or more terminal devices as described above. Unless otherwise specified, "equipment" can refer to either network equipment or terminal equipment.

[0048] In the following description, without causing confusion, the terms “uplink control signal” and “uplink control information (UCI)” or “physical uplink control channel (PUCCH)” are used interchangeably, as are the terms “uplink data signal” and “uplink data information” or “physical uplink shared channel (PUSCH)”.

[0049] The terms “downlink control signal” and “downlink control information (DCI)” or “physical downlink control channel (PDCCH)” are interchangeable, as are the terms “downlink data signal” and “downlink data information (PDSCH)” or “physical downlink shared channel (PDSCH)”.

[0050] Additionally, uplink signals can include uplink data signals and / or uplink control signals and / or PRACH and / or SRS, etc., and can also be referred to as uplink transmission (UL transmission), uplink information, or uplink channel. Sending / receiving uplink transmission on uplink resources can be understood as using that uplink resource to send / receive the uplink transmission. Downlink signals can include downlink data signals and / or downlink control signals and / or synchronization signals (SS, such as PSS / SSS) and / or broadcast channel (PBCH) and / or SSB (SS / PBCH block, including PSS, SSS, and PBCH and their DMRS) and / or CSI-RS, etc., and can also be referred to as downlink transmission (DL transmission), downlink information, or downlink channel. Sending / receiving downlink transmission on downlink resources can be understood as using that downlink resource to send / receive the downlink transmission.

[0051] In the embodiments of this application, higher-layer signaling may be, for example, Radio Resource Control (RRC) signaling; RRC signaling may include, for example, RRC messages, such as broadcast / public RRC messages / signaling (e.g., Master Information Block (MIB), system information), dedicated RRC messages / signaling; or RRC information elements (RRC IE); or information fields (or information fields included in information fields) included in RRC messages or RRC information elements. Higher-layer signaling may also be, for example, Medium Access Control (MAC) signaling; or referred to as MAC control elements (MAC CE). However, this application is not limited to these.

[0052] In the embodiments of this application, "multiple" refers to at least two, or two or more.

[0053] In this application embodiment, "predefined" refers to what is specified by the protocol or determined according to the rules specified by the protocol, and does not require additional configuration. "Configuration / instruction" refers to what the network device directly or indirectly configures / instructs through higher-layer signaling and / or physical layer signaling. Configuration / instruction can be achieved by introducing higher-layer parameters into the higher-layer signaling. Higher-layer parameters refer to information fields and / or information elements / information units / information cells (IEs) in the higher-layer signaling. Physical layer signaling refers to, for example, control information (DCI) carried by the physical downlink control channel or control information carried by the sequence, but is not limited to these.

[0054] For ease of description, the following description uses a base station as an example of an access network device. In the following description, without causing confusion, "if...", "in the case of...", and "when..." can be used interchangeably.

[0055] The following examples illustrate the scenarios of embodiments of this application, but this application is not limited thereto.

[0056] Figure 1 is a schematic diagram of a communication system according to an embodiment of this application, illustrating the case of a terminal device and a network device as examples. As shown in Figure 1, the communication system 100 may include a network device 101 and terminal devices 102 and 103. For simplicity, Figure 1 only illustrates the case of two terminal devices and one network device, but the embodiments of this application are not limited to this.

[0057] In this embodiment of the application, network device 101 and terminal devices 102 and 103 can transmit existing services or services that can be implemented in the future. 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.

[0058] It is worth noting that Figure 1 shows that both terminal devices 102 and 103 are within the coverage area of ​​network device 101, but this application is not limited to this. Both terminal devices 102 and 103 may be outside the coverage area of ​​network device 101, or one terminal device 102 may be within the coverage area of ​​network device 101 while the other terminal device 103 may be outside the coverage area of ​​network device 101.

[0059] In the embodiments of this application, one or more AI / ML functions or models may be configured and run in the network device and / or terminal device. The AI / ML functions or models can be used for various signal processing functions of wireless communication, such as channel state information (CSI) prediction, CSI compression, beam prediction, positioning management, etc.; this application is not limited thereto.

[0060] In NR Rel-15, beam failure recovery operations were introduced. Beam failure recovery operations include beam failure detection, identification of new candidate beams, and beam failure recovery request.

[0061] Network devices can configure reference signals (i.e., beams) for beam failure detection for terminal devices, and terminal devices can evaluate the quality of the beam, for example, the hypothetical block error rate (BLER).

[0062] Figure 2 is a schematic diagram of traditional beam failure detection. As shown in Figure 2, if the beam measurement result is below a certain threshold, a beam failure instance is detected; if multiple (e.g., N in Figure 2) consecutive beam failure instances are detected, a beam failure event is declared.

[0063] In this application, a time instance is, for example, a moment or a moment represented by other time units. These other time units include, for example, a slot, a frame, or a sub-frame.

[0064] First aspect of the embodiments

[0065] This application provides a beam management method, which is described from the perspective of the terminal device.

[0066] Figure 3 is a schematic diagram of a beam management method according to an embodiment of this application. As shown in Figure 3, the beam management method includes:

[0067] 301. Configuration for recovering from beam reception failure from network devices; and

[0068] 302. The terminal device failed to predict the beam using an artificial intelligence model or function.

[0069] In the embodiments of this application, the following terms have the same meaning and can be used interchangeably: AI / ML, Artificial Intelligence, or Machine Learning. Therefore, the artificial intelligence model or function in operation 302 can also be written as AI / ML model / function.

[0070] In some embodiments, a functionality refers to an AI / ML feature / feature group enabled by a configuration, wherein the configuration is supported based on conditions indicated by the capabilities of the end device.

[0071] For example, an AL / ML function can 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.

[0072] Figure 4 is a schematic diagram of an artificial intelligence model or function according to an embodiment of this application.

[0073] As shown in Figure 4, the artificial intelligence model or function used in operation 302 is set on the side of the terminal device.

[0074] The input to this AI model or function includes: measurement results of multiple first time instances for one or more reference signals. These one or more reference signals are configured for beam failure detection; the measurement results of the first time instances are, for example, a hypothetical BLER or Layer 1 Reference Signal Received Power (L1-RSRP). The number of these multiple first time instances can be predefined, configured, or determined by the capabilities of the terminal device.

[0075] In some examples, the input to this AI model or function can be based on a sliding window. For instance, multiple first-time instance measurement results are M1, M2, M3, ..., Mn (where i and n are natural numbers). The first input is M1, ..., Mi (where 1 < i ≤ n), the second input is M1+j, ..., Mi+j (where j is a natural number, 2 ≤ i + j ≤ n), and so on. This allows for the full utilization of measurement results from multiple first-time instances to predict beam failure.

[0076] As shown in Figure 4, the output of the artificial intelligence model or function used in operation 302 includes: prediction results for multiple second time instances of one or more reference signals. For example, the BLER or L1-RSRP of the predictions for one or more reference signals in multiple second time instances. The second time instance can be later than the first time instance; that is, the second time instance is a future time instance of the first time instance. The terminal device can compare the prediction results for multiple second time instances of one or more reference signals with a threshold, and predict whether a potential beam failure event exists based on the comparison results.

[0077] As shown in Figure 4, the output of the artificial intelligence model or function used in operation 302 may also include: potential beam failure events.

[0078] In some cases, the prediction result is BLER. If the predicted BLER is greater than a threshold, it is determined that a potential beam failure instance has been detected. If a potential beam failure instance is detected N times consecutively, it is determined that a potential beam failure event has been detected. The artificial intelligence model or function can output the potential beam failure event.

[0079] In some cases, the prediction result is L1-RSRP. If the predicted L1-RSRP is less than a threshold, it is determined that a potential beam failure instance has been detected. If a potential beam failure instance is detected N times consecutively, it is determined that a potential beam failure event has been detected. The artificial intelligence model or function can output this potential beam failure event.

[0080] Terminal devices can use artificial intelligence models or functional predictions of beam failures for primary cell (PCell) beam failure recovery operations and / or secondary cell (SCell) beam failure recovery operations.

[0081] Figure 5 is a schematic diagram of using an artificial intelligence model or function to predict beam failure according to an embodiment of this application. As shown in Figure 5, the measurement results of N1 first time instances are input into the artificial intelligence model or function; the artificial intelligence model or function detects that there is a potential beam failure instance in a future second time instance, for example, if N consecutive future second time instances are detected to have potential beam failure instances, it is determined that there is a potential beam failure event.

[0082] As shown in Figure 3, the beam management method also includes:

[0083] 303. In the event of a predicted potential beam failure event, the terminal device sends a report to the network device to notify of the potential beam failure event.

[0084] For example, as shown in Figure 5, if the terminal device detects that there are potential beam failure instances in multiple second time instances and thus determines that there is a potential beam failure event, it can send a report to the network device to notify the potential beam failure event at a time instance (e.g., time T1 in Figure 5) before the second time instance (e.g., the earliest second time instance in which the potential beam failure instance exists).

[0085] By using Operation 303, the terminal device can initiate a report to notify of potential beam failure events.

[0086] In the first embodiment of operation 303, the report may be based on Layer-1 signaling, such as uplink control information (UCI). The report may be transmitted via the Physical Uplink Control Channel (PUCCH) and / or the Physical Uplink Shared Channel (PUSCH).

[0087] In the first example of this first embodiment, the report includes an event to indicate the potential beam failure event. For example, a new time can be defined or introduced in the report to indicate the potential beam failure event.

[0088] In the second example of this first embodiment, the report may be based on the Physical Random Access Channel (PRACH). The PRACH may be based on dedicated PRACH resources or non-dedicated PRACH resources.

[0089] In the third example of this first embodiment, the report can be based on conventional beam reporting, for example, a special value of L1-RSRP can be predefined to indicate potential beam failure events.

[0090] In the second embodiment of operation 303, the report may be based on higher layer signaling. This higher layer signaling may be, for example, Media Access Control Element (MAC-CE) or Radio Resource Control (RRC) signaling.

[0091] In the third embodiment of operation 303, the report may be based on a combination of Layer-1 signaling and higher-layer signaling. For example, the report may be a scheduling request (SR) or similar SR signaling plus MAC-CE.

[0092] In some embodiments of this application, the report of operation 303, in addition to notifying the potential beam failure event, may also include information on more than one candidate beam. The candidate beam may be based on a configured reference signal, which may be periodic or semi-persistent. The candidate beam is used for primary cell (PCell) beam failure recovery operations and / or secondary cell (SCell) beam failure recovery operations. Therefore, the report of operation 303 can be used for beam failure recovery operations.

[0093] In some examples, the terminal device may send candidate beam information along with information about the potential beam failure event in the report. In other examples, the terminal device may request the network device to send a set of reference signals for prediction (e.g., set B).

[0094] In some other embodiments of this application, candidate beam information may not be included in the report transmitted in operation 303. This provides greater flexibility in transmitting candidate beam information.

[0095] As shown in Figure 3, the beam management method may also include:

[0096] 304. The terminal device receives a response from the network device to confirm the report.

[0097] For example, after the terminal device sends the report to the network device via Operation 303, the network device sends a response to the terminal device to acknowledge it.

[0098] In this application, after the terminal device sends a report through operation 303, the terminal device monitors either a dedicated control resource set (CORESET) (i.e., the response to operation 304 is transmitted through a dedicated CORESET) or a non-dedicated control resource set (CORESET) (i.e., the response to operation 304 is transmitted through a non-dedicated CORESET) to receive the response to operation 304.

[0099] In some embodiments of this application, if the report sent by operation 303 includes information on more than one candidate beam, after receiving the response via operation 304 for a first time period, the terminal device switches to using one of the candidate beams to communicate with the network device; correspondingly, after sending the response for the first time period, the network device switches to using one of the candidate beams to communicate with the terminal device. The first time period is predefined, configured, or determined by the capabilities of the terminal device.

[0100] The candidate beam used after the terminal device and / or network device switchover includes: a predetermined candidate beam among the more than one candidate beams, such as the first reported candidate beam; or a candidate beam specified by signaling among the more than one candidate beams, such as a candidate beam specified by signaling sent by the network device to the terminal device, or a candidate beam specified by signaling sent by the terminal device to the network device.

[0101] In some examples, if the report for Operation 303 is sent via Layer 1 (L1) signaling, the response for Operation 304 can be received via Downlink Control Information (DCI), and the DCI can be either scheduled DCI or non-scheduling DCI. Specific values ​​or codepoint(s) for one or more DCI fields can be predefined for DCI validation.

[0102] In other examples, if the report for Operation 303 is sent via MAC-CE signaling, then the response for Operation 304 can be received via MAC-CE.

[0103] In some other embodiments of this application, if the report sent in operation 303 does not include candidate beam information, then in operation 304, the terminal device receives a first command sent by the network device as a response. This first command instructs the terminal device to switch to another Transmission Configuration Indication (TCI) state or activate another TCI state. This first command can be sent via Downlink Control Information (DCI), Media Access Control Element (MAC-CE) signaling, or Radio Resource Control (RRC) signaling.

[0104] As shown in Figure 3, the beam management method may also include:

[0105] 305. Terminal equipment monitors the performance of artificial intelligence models or functions used to predict beam failure.

[0106] In operation 305, the artificial intelligence model or function detected by the terminal device is the same artificial intelligence model or function used in operation 302 to predict beam failure.

[0107] In some embodiments of operation 305, the terminal device may receive a first reference signal corresponding to the original beam (i.e., the beam used by the terminal device and the network device for communication before switching to the candidate beam) during a second time period, and compare the measurement results obtained based on the first reference signal with the prediction results output by the artificial intelligence model or function to monitor the performance of the artificial intelligence model or function. The length of the second time period may be predefined or configurable.

[0108] Figure 6 is a schematic diagram of monitoring the performance of an artificial intelligence model or function. As shown in Figure 6, even if the terminal device sends a report to the network device at time T1 to notify of a potential beam failure event in the original beam, the network device still sends a first reference signal corresponding to the original beam in a second time period after time T1. The terminal device can compare the prediction result for this second time period in operation 302 (e.g., the prediction result for the second time instance) with the measurement result obtained based on the first reference signal (i.e., the actual measurement result) to determine whether the artificial intelligence model or function used in operation 302 is working well. For example, if the difference between the prediction result and the measurement result is less than a threshold, it is judged that the performance of the artificial intelligence model or function used in operation 302 is good; if the difference between the prediction result and the measurement result is greater than a threshold, it is judged that the performance of the artificial intelligence model or function used in operation 302 is poor.

[0109] In some other embodiments of operation 305, the terminal device can monitor the performance of the artificial intelligence model or function based on the distribution of its input data. For example, the artificial intelligence model or function is trained on data, and the training data statistically conforms to a certain distribution. Therefore, the distribution of the input data of the artificial intelligence model or function can be used as a metric for monitoring its performance. That is, if the distribution of the input data of the artificial intelligence model or function deviates significantly from the predetermined distribution, it indicates that the performance of the artificial intelligence model or function has deteriorated.

[0110] It is worth noting that Figure 3 above is only an illustrative description of the embodiments of this application, but this application is not limited thereto. For example, the execution order between various operations can be appropriately adjusted, and other operations can be added or some operations can be removed. Those skilled in the art can make appropriate modifications based on the above content, and are not limited to the description in Figure 3 above.

[0111] The embodiments of this application can be applied to the UE-side model or the gNB-side model, but this application is not limited thereto.

[0112] The above embodiments are merely illustrative examples of embodiments of this application, but this application is not limited thereto, and appropriate modifications can be made based on the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0113] As can be seen from the above embodiments, the terminal device uses artificial intelligence models or functions to predict beam failure, thereby facilitating the network device to switch to another beam to avoid communication interruption, thus increasing the robustness of the communication link.

[0114] Second aspect of the embodiments

[0115] This application provides a beam management configuration method, described from the perspective of a network device. The embodiments of the second aspect can be combined with the embodiments of the first aspect.

[0116] Figure 7 is a schematic diagram of a beam management method according to an embodiment of this application. As shown in Figure 7, the method includes:

[0117] 701. Send beam failure recovery configuration to the terminal device; and

[0118] 702. Receive a report from the terminal device that notifies the terminal device of potential beam failure events predicted using an artificial intelligence model or function.

[0119] In some embodiments, the input to the artificial intelligence model or function includes measurement results for multiple first time instances of one or more reference signals. The one or more reference signals are configured for beam failure detection; the number of the multiple first time instances is predefined, configured, or determined by the capabilities of the terminal device.

[0120] In some embodiments, the output of the artificial intelligence model or function includes: prediction results for multiple second time instances of one or more reference signals, and / or, potential beam failure events.

[0121] In some embodiments, the terminal device further compares the prediction result with a threshold, and predicts whether a potential beam failure event exists based on the comparison result. This enables the potential beam failure event to be sent to the network device.

[0122] In some examples, the report is based on Layer-1 signaling, where it is transmitted via the Physical Uplink Control Channel (PUCCH) and / or the Physical Uplink Shared Channel (PUSCH); or, the report is based on the Physical Random Access Channel (PRACH).

[0123] For example, the report may include an event used to indicate the potential beam failure event; or the Physical Random Access Channel (PRACH) may be based on dedicated or non-dedicated Physical Random Access Channel (PRACH) resources.

[0124] In other embodiments, the report is based on higher layer signaling.

[0125] In some other embodiments, the report is based on a combination of Layer-1 signaling and higher layer signaling.

[0126] In some embodiments, the report may also include candidate beam information.

[0127] In some embodiments, the beam management method further includes:

[0128] 703. The network device sends a response to the terminal device to confirm the report.

[0129] In some instances, where the report includes information on more than one candidate beam, the terminal device, after a first time period following receiving the response, switches to using one of the candidate beams to communicate with the network device. This first time period is predefined, configured, or determined by the terminal device's capabilities.

[0130] One candidate beam includes:

[0131] The predetermined candidate beam among one or more candidate beams; or

[0132] The candidate beam is selected from one or more candidate beams by signaling.

[0133] In other examples, where candidate beam information is not included in the report, the network device sends a first command to the terminal device as a response. This first command instructs the terminal device to switch to or activate another Transmission Configuration Indication (TCI) state. This first command is sent via Downlink Control Information (DCI), Media Access Control Element (MAC-CE) signaling, or Radio Resource Control (RRC) signaling.

[0134] In some embodiments of this application, the network device sends the response via a dedicated control resource set (CORESET) or a non-dedicated control resource set (CORESET).

[0135] It is worth noting that Figure 7 above is only an illustrative description of the embodiments of this application, but this application is not limited thereto. For example, the execution order between various operations can be appropriately adjusted, and other operations can be added or some operations can be removed. Those skilled in the art can make appropriate modifications based on the above content, and are not limited to the description in Figure 7 above.

[0136] The above embodiments are merely illustrative examples of embodiments of this application, but this application is not limited thereto, and appropriate modifications can be made based on the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0137] As can be seen from the above embodiments, the terminal device uses an artificial intelligence model or function to predict beam failure, thereby enabling the network device to switch to another beam to avoid communication interruption, thus increasing the robustness of the communication link.

[0138] Third aspect of the embodiments

[0139] This application provides a beam management device. This device may be, for example, a terminal device, or one or more components or parts configured within a terminal device; details identical to those in the first and second aspects will not be repeated.

[0140] Figure 8 is a schematic diagram of a beam management device according to an embodiment of this application. As shown in Figure 8, the beam management device 800 according to an embodiment of this application includes:

[0141] The first receiving unit 801 receives the beam failure recovery configuration from the network device; and

[0142] The first processing unit 802 uses artificial intelligence models or functions to predict beam failure.

[0143] The input to the artificial intelligence model or function includes measurement results for multiple first-time instances of one or more reference signals, wherein the one or more reference signals are configured for beam failure detection, and the number of the multiple first-time instances is predefined, configured, or determined by the capabilities of the terminal device.

[0144] In some embodiments, the output of the artificial intelligence model or function includes:

[0145] Prediction results for multiple second time instances of one or more reference signals, and / or, potential beam failure events.

[0146] In some embodiments, the first processing unit further compares the prediction result with a threshold and predicts whether there is a potential beam failure event based on the comparison result.

[0147] In some embodiments, the apparatus 800 further includes a first transmitting unit 803. Upon predicting a potential beam failure event, the first transmitting unit 803 sends a report to the network device to notify of the potential beam failure event.

[0148] In some embodiments, the report is based on Layer-1 signaling, wherein the report is transmitted via the Physical Uplink Control Channel (PUCCH) and / or the Physical Uplink Shared Channel (PUSCH); or, the report is based on the Physical Random Access Channel (PRACH).

[0149] In some embodiments, the report includes an event indicating the potential beam failure event; or, the Physical Random Access Channel (PRACH) is based on dedicated Physical Random Access Channel (PRACH) resources or non-dedicated Physical Random Access Channel (PRACH) resources.

[0150] In some embodiments, the report is based on higher layer signaling.

[0151] In some embodiments, the report is based on a combination of Layer-1 signaling and higher layer signaling.

[0152] In some embodiments, the report may also include candidate beam information.

[0153] In some embodiments, the first receiving unit 801 receives a response from the network device to confirm the report.

[0154] In some embodiments, where the report also includes information on more than one candidate beam, after a first time period following the receipt of the response by the first receiving unit, the first processing unit switches to using one of the candidate beams to communicate with the network device. The first time period is predefined, configured, or determined by the capabilities of the terminal device.

[0155] One candidate beam includes:

[0156] The predetermined candidate beam among one or more candidate beams; or

[0157] The candidate beam is selected from one or more candidate beams by signaling.

[0158] In some embodiments, where candidate beam information is not included in the report, the first receiving unit receives a first command sent by the network device as a response, the first command being used to instruct the terminal device to switch to another Transmission Configuration Indication (TCI) state or activate another Transmission Configuration Indication (TCI) state.

[0159] The first command is sent via Downlink Control Information (DCI), Media Access Control Element (MAC-CE) signaling, or Radio Resource Control (RRC) signaling.

[0160] After sending the report, the first receiving unit monitors either the dedicated control resource set (CORESET) or the non-dedicated control resource set (CORESET) to receive the response.

[0161] The first receiving unit 801 receives a first reference signal corresponding to the original beam during the second time period, and the first processing unit compares the measurement result obtained based on the first reference signal with the prediction result output by the artificial intelligence model or function to monitor the performance of the artificial intelligence model or function; or, the first processing unit 802 monitors the performance of the artificial intelligence model or function based on the distribution of the input.

[0162] The above embodiments are merely illustrative examples of embodiments of this application, but this application is not limited thereto, and appropriate modifications can be made based on the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0163] It is worth noting that the above description only covers the components or modules relevant to this application, but this application is not limited thereto. The beam management device 800 may also include other components or modules, and for details regarding these components or modules, please refer to relevant technologies.

[0164] Furthermore, for simplicity, Figure 8 only illustrates the connection relationships or signal flow between the various components or modules, but those skilled in the art should understand that various related technologies such as bus connections can be used. The aforementioned components or modules can be implemented using hardware facilities such as processors, memory, transmitters, and receivers; this application does not limit this implementation.

[0165] As can be seen from the above embodiments, the beam management device of the terminal device uses artificial intelligence models or functions to predict beam failure, thereby facilitating the network device to switch to another beam to avoid communication interruption, thereby increasing the robustness of the communication link.

[0166] Fourth aspect of the embodiment

[0167] This application provides a beam management device. This device may be, for example, a network device, or one or more components or parts configured within a network device; details identical to those in the embodiments of the first to third aspects will not be repeated.

[0168] Figure 9 is a schematic diagram of a beam management device according to an embodiment of this application. As shown in Figure 9, the beam management device 900 includes:

[0169] The second transmitting unit 901 transmits the beam failure recovery configuration to the terminal device; and

[0170] The second receiving unit 902 receives a report from the terminal device, which is used to notify the terminal device of potential beam failure events predicted by artificial intelligence models or functions.

[0171] The input to the artificial intelligence model or function includes measurement results of multiple first-time instances for one or more reference signals. These one or more reference signals are configured for beam failure detection, and the number of these multiple first-time instances is predefined, configured, or determined by the capabilities of the terminal device.

[0172] In some embodiments, the output of the artificial intelligence model or function includes: predictions for multiple second time instances of one or more reference signals, and / or potential beam failure events.

[0173] In some embodiments, the terminal device also compares the prediction result with a threshold and predicts whether there is a potential beam failure event based on the comparison result.

[0174] In some embodiments, the report is based on Layer-1 signaling, wherein the report is transmitted via the Physical Uplink Control Channel (PUCCH) and / or the Physical Uplink Shared Channel (PUSCH); or, the report is based on the Physical Random Access Channel (PRACH).

[0175] In some embodiments, the report includes an event indicating the potential beam failure event; or, the Physical Random Access Channel (PRACH) is based on dedicated Physical Random Access Channel (PRACH) resources or non-dedicated Physical Random Access Channel (PRACH) resources.

[0176] In some embodiments, the report is based on higher layer signaling.

[0177] In some embodiments, the report is based on a combination of Layer-1 signaling and higher layer signaling.

[0178] In some embodiments, the report may also include candidate beam information.

[0179] In some embodiments, the second sending unit 901 also sends a response to the terminal device to confirm the report.

[0180] If the report also includes information on more than one candidate beam, the terminal device, after a first time period following receiving the response, switches to using one of the candidate beams to communicate with the network device, wherein the first time period is predefined, configured, or determined by the capabilities of the terminal device.

[0181] One candidate beam includes:

[0182] The predetermined candidate beam among one or more candidate beams; or

[0183] The candidate beam is selected from one or more candidate beams by signaling.

[0184] In some embodiments, where candidate beam information is not included in the report, the second transmitting unit sends a first command to the terminal device as a response. This first command instructs the terminal device to switch to or activate another Transmission Configuration Indication (TCI) state. The first command is transmitted via Downlink Control Information (DCI), Media Access Control Element (MAC-CE) signaling, or Radio Resource Control (RRC) signaling.

[0185] In some embodiments, the second sending unit 901 sends the response via a dedicated control resource set (CORESET) or a non-dedicated control resource set (CORESET).

[0186] The above embodiments are merely illustrative examples of embodiments of this application, but this application is not limited thereto, and appropriate modifications can be made based on the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0187] It is worth noting that the above description only covers the components or modules relevant to this application, but this application is not limited thereto. The beam management configuration device 900 may also include other components or modules, and for details regarding these components or modules, please refer to related technologies.

[0188] Furthermore, for simplicity, Figure 9 only illustrates the connection relationships or signal flow between the various components or modules, but those skilled in the art should understand that various related technologies such as bus connections can be used. The aforementioned components or modules can be implemented using hardware facilities such as processors, memory, transmitters, and receivers; this application does not limit this implementation.

[0189] As can be seen from the above embodiments, the beam management device of the terminal device uses artificial intelligence models or functions to predict beam failure, thereby enabling the network device to switch to another beam to avoid communication interruption, thus increasing the robustness of the communication link.

[0190] Fifth aspect of the embodiment

[0191] This application also provides a communication system, which can be referred to FIG1. ​​The contents that are the same as those in the embodiments of the first to fourth aspects will not be repeated.

[0192] In some embodiments, the communication system 100 may include at least:

[0193] A network device that sends beam failure recovery configuration to a terminal device and receives a report from the terminal device informing it of potential beam failure events predicted using an artificial intelligence model or function; and

[0194] The terminal device receives beam failure recovery configuration from the network device and uses artificial intelligence models or functions to predict beam failure.

[0195] In this application embodiment, a scenario including network devices and / or terminal devices is taken as an example.

[0196] In the above scenario, network devices may include at least one of core network devices, third-party application devices, operation administration and maintenance (OAM) devices, and access network devices.

[0197] Core network equipment refers to equipment in the core network (CN) that provides service support to terminal equipment. As examples, core network equipment can be at least one of the following: Mobility and Management Entity (MME), Access and Mobility Management Function (AMF) entity, Session Management Function (SMF) entity, User Plane Function (UPF) entity, Location Management Function (LMF) entity, etc., and not all will be listed here. The AMF entity is responsible for terminal access management and mobility management; the SMF entity is responsible for session management, such as user session establishment; the UPF entity can be a user plane function entity, mainly responsible for connecting to external networks; and the LMF entity manages the overall coordination and scheduling of resources required for the location of terminal equipment registered with or accessing the core network equipment. It should be noted that in the embodiments of this application, an entity can also be called a network element or functional entity; for example, an AMF entity can also be called an AMF network element or an AMF functional entity, etc.

[0198] Third-party application devices can be OTT services (over the top server) or other third-party devices.

[0199] OAM (Operation, Administration, Maintenance) is a network device that performs network management tasks such as operation, administration, and maintenance according to the actual needs of the operator's network operation.

[0200] Access network equipment is an access device that allows terminal devices to wirelessly access a communication system. Access network equipment can be a base station (BS), a node, an evolved NodeB (eNodeB), a transmission reception point (TRP), a base station in a 5G mobile communication system (gNB), a base station in a 6G mobile communication system, a base station in a future mobile communication system, or an access node in a WiFi system, etc. Access network equipment can also be a module or unit that performs some of the functions of a base station. For example, it can be at least one of the following modules or units: a central unit (CU), a distributed unit (DU), a CU control plane (CU-CP), a CU user plane (CU-CP), an integrated access backhaul (IAB), or other modules or units. This application does not limit the specific technology and / or specific equipment form used in the access network equipment. Access network equipment can be deployed on land, including indoors / outdoors, and can be handheld or vehicle-mounted; it can also be deployed on water, on airplanes, balloons, or satellites; access network equipment can be deployed in fixed locations or on mobile carriers, and this application embodiment does not limit this.

[0201] In the above scenarios, the terminal device can be a device with wireless transceiver capabilities, capable of sending signals to and / or receiving signals from the access network device. The terminal device can also be called a terminal, mobile station, mobile terminal, etc. It can be a mobile phone, tablet, or other device with wireless intelligent transceiver capabilities. Terminal devices can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IoT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, or various smart scenarios.

[0202] In the above scenarios, communication between access network devices and terminal devices, and between terminal devices, can be conducted using licensed spectrum, unlicensed spectrum, or both simultaneously. This application does not limit the spectrum resources used for wireless communication.

[0203] This application also provides a terminal device, but the application is not limited thereto and may also include other devices.

[0204] Figure 10 is a schematic diagram of a terminal device according to an embodiment of this application. As shown in Figure 10, the terminal device 1000 may include a processor 1010 and a memory 1020; the memory 1020 stores data and programs and is coupled to the processor 1010. It is worth noting that this figure is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunications functions or other functions.

[0205] For example, processor 1010 may be configured to execute a program to implement the beam management method as described in the embodiments of the first aspect. For example, processor 1010 may be configured to perform the following controls: receive beam failure recovery configuration from a network device; and predict beam failure using an artificial intelligence model or function.

[0206] As shown in Figure 10, the terminal device 1000 may further include: a communication module 1030, an input unit 1040, a display 1050, and a power supply 1060. The functions of these components are similar to those in the prior art and will not be described in detail here. It is worth noting that the terminal device 1000 does not necessarily include all the components shown in Figure 10; these components are not essential. Furthermore, the terminal device 1000 may also include components not shown in Figure 10, which can be referred to in the prior art.

[0207] This application also provides a network device, such as a base station, but this application is not limited to this and may also include other network devices.

[0208] Figure 11 is a schematic diagram of the network device according to an embodiment of this application. As shown in Figure 11, the network device 1100 may include: a processor 1110 (e.g., a central processing unit CPU) and a memory 1120; the memory 1120 is coupled to the processor 1110. The memory 1120 can store various data; in addition, it also stores an information processing program 1130, and executes the program 1130 under the control of the processor 1110.

[0209] For example, processor 1110 may be configured to execute a program to implement the beam management configuration method as described in the embodiments of the second aspect. For example, processor 1110 may be configured to perform the following controls: sending beam failure recovery configuration to a terminal device; and receiving a report from the terminal device, the report being used to notify the terminal device of potential beam failure events predicted using an artificial intelligence model or function.

[0210] In addition, as shown in Figure 11, network device 1100 may also include: transceiver 1140 and antenna 1150, etc.; the functions of the above components are similar to those in the prior art, and will not be described in detail here. It is worth noting that network device 1100 does not necessarily have to include all the components shown in Figure 11; in addition, network device 1100 may also include components not shown in Figure 11, which can be referred to in the prior art.

[0211] This application also provides a computer program, wherein when the program is executed in a terminal device, the program causes the terminal device to perform the beam management method described in the first aspect of the embodiment.

[0212] This application also provides a storage medium storing a computer program, wherein the computer program causes a terminal device to execute the beam management method described in the first aspect of the embodiment.

[0213] This application also provides a computer program, wherein when the program is executed in a network device, the program causes the network device to perform the beam management method described in the second aspect of the embodiment.

[0214] This application also provides a storage medium storing a computer program, wherein the computer program causes a network device to perform the beam management method described in the second aspect of the embodiment.

[0215] The apparatus and methods described above in this application can be implemented in hardware or in combination with software. This application relates to a computer-readable program that, when executed by a logic component, enables the logic component to implement the apparatus or components described above, or to implement the various methods or steps described above. This application also relates to storage media for storing the above programs, such as hard disks, magnetic disks, optical disks, DVDs, flash memory, etc.

[0216] The methods / apparatus described in conjunction with the embodiments of this application can be directly embodied in hardware, software modules executed by a processor, or a combination of both. For example, one or more and / or combinations of one or more functional block diagrams shown in the figures can correspond to various software modules in a computer program flow, or to various hardware modules. These software modules can correspond to the various steps shown in the figures, respectively. These hardware modules can be implemented, for example, using a field-programmable gate array (FPGA) to embed these software modules.

[0217] The software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. A storage medium can be coupled to the processor, enabling the processor to read information from and write information to the storage medium; or the storage medium can be an integral part of the processor. The processor and storage medium can reside in an ASIC. The software module can be stored in the memory of a 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 high-capacity MEGA-SIM card or a high-capacity flash memory device, the software module can be stored in the MEGA-SIM card or the high-capacity flash memory device.

[0218] One or more and / or one or more combinations of functional blocks described in the accompanying drawings can be implemented as a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, or any suitable combination thereof for performing the functions described herein. One or more and / or one or more combinations of functional blocks described in the accompanying drawings can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in communication with a DSP, or any other such configuration.

[0219] The present application has been described above with reference to specific embodiments. However, those skilled in the art should understand that these descriptions are exemplary and not intended to limit the scope of protection of the present application. Those skilled in the art can make various modifications and variations to the present application based on its spirit and principles, and these modifications and variations are also within the scope of the present application.

[0220] Regarding the implementation methods including the above embodiments, the following notes are also disclosed:

[0221] 1. A beam management method applied to a terminal device, the method comprising:

[0222] Configuration for recovering from beam reception failure from network devices; and

[0223] The terminal device uses an artificial intelligence model or function to predict beam failure.

[0224] 2. A beam management method applied to a network device, the method comprising:

[0225] Send beam failure recovery configuration to the terminal device; and

[0226] The terminal device receives a report that notifies it of potential beam failure events predicted using an artificial intelligence model or function.

[0227] 3. A terminal device comprising a memory and a processor, the memory storing a computer program and the processor being configured to execute the computer program to implement the beam management method as described in Appendix 1.

[0228] 4. A network device comprising a memory and a processor, the memory storing a computer program and the processor being configured to execute the computer program to implement the beam management method as described in Appendix 2.

[0229] 5. A computer program product comprising at least a computer program that, when executed by a processor, causes a terminal device to perform the beam management method as described in Appendix 1.

[0230] 6. A computer program product comprising at least a computer program that, when executed by a processor, causes a network device to perform the beam management method as described in Appendix 2.

Claims

1. A beam management device applied to a terminal device, the device comprising: The first receiving unit receives the beam failure recovery configuration from the network device; as well as The first processing unit uses artificial intelligence models or functional predictions to identify beam failures.

2. The apparatus of claim 1, wherein, The input to the artificial intelligence model or function includes measurement results from multiple first-time instances of one or more reference signals. The one or more reference signals are configured for beam failure detection. The number of the plurality of first-time instances is predefined, configured, or determined by the capabilities of the terminal device.

3. The apparatus of claim 1, wherein, The output of the artificial intelligence model or function includes: Prediction results for multiple second time instances of one or more reference signals, and / or, potential beam failure events.

4. The apparatus of claim 3, wherein, The first processing unit also compares the prediction result with a threshold, and predicts whether there is a potential beam failure event based on the comparison result.

5. The apparatus of claim 1, wherein, The device further includes: In the event that a potential beam failure event is predicted, the first transmitting unit sends a report to the network device to notify of the potential beam failure event.

6. The apparatus of claim 5, wherein, The report is based on Layer-1 signaling, wherein the report is transmitted via the Physical Uplink Control Channel (PUCCH) and / or the Physical Uplink Shared Channel (PUSCH); or The report is based on the Physical Random Access Channel (PRACH).

7. The apparatus of claim 6, wherein, The report includes events used to indicate the potential beam failure event; or The Physical Random Access Channel (PRACH) is based on either dedicated PRACH resources or non-dedicated PRACH resources.

8. The apparatus of claim 5, wherein, The report is based on higher layer signaling.

9. The apparatus of claim 5, wherein, The report is based on a combination of Layer-1 signaling and higher layer signaling.

10. The apparatus of claim 5, wherein, The report also includes candidate beam information.

11. The apparatus of claim 5, wherein, The first receiving unit receives a response from the network device to confirm the report.

12. The apparatus of claim 11, wherein, If the report also includes information on more than one candidate beam, After a first time period following the receipt of the response by the first receiving unit, the first processing unit switches to communicating with the network device using one of the more than one candidate beams. The first time period is predefined, configured, or determined by the capabilities of the terminal device.

13. The apparatus of claim 12, wherein, The candidate beam includes: The predetermined candidate beam among the one or more candidate beams; or The candidate beam specified by signaling among the one or more candidate beams.

14. The apparatus of claim 11, wherein, In the absence of candidate beam information in the report, The first receiving unit receives a first command sent by the network device as a response to receive the first command, which is used to instruct the terminal device to switch to another Transmission Configuration Indication (TCI) state or activate another Transmission Configuration Indication (TCI) state.

15. The apparatus of claim 14, wherein, The first command is sent via Downlink Control Information (DCI), Media Access Control Element (MAC-CE) signaling, or Radio Resource Control (RRC) signaling.

16. The apparatus of claim 11, wherein, After sending the report The first receiving unit monitors a dedicated control resource set (CORESET) or a non-dedicated control resource set (CORESET) to receive the response.

17. The apparatus of claim 1, wherein, The first receiving unit receives a first reference signal corresponding to the original beam during a second time period, and the first processing unit compares the measurement result obtained based on the first reference signal with the prediction result output by the artificial intelligence model or function to monitor the performance of the artificial intelligence model or function; or The first processing unit monitors the performance of the artificial intelligence model or function based on the distribution of the input.

18. A beam management device applied to a network device, the device comprising: The second transmitting unit sends the beam failure recovery configuration to the terminal device; as well as The second receiving unit receives a report from the terminal device, the report being used to notify the terminal device of potential beam failure events predicted using an artificial intelligence model or function.

19. The apparatus of claim 18, wherein, The second sending unit also sends a response to the terminal device to confirm the report.

20. A communication system, comprising: A network device comprising the beam management device as described in claim 18 or 19; as well as Terminal equipment, comprising the beam management device according to any one of claims 1 to 17.

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