Reducing sample during measurement

Abstract

Example embodiments of the present disclosure relate to reducing samples during measurement. A method includes receiving configuration information for beam management, the configuration information indicating a threshold instance number of instances for a triggering event to be reported, and a predicted instance number of instances to be determined based on a reference signal quality prediction, an instance representing that a condition for the event is met; determining, based on a measurement of a reference signal quality associated with a transmission configuration indicator (TCI) state of a beam, an instance of a measured instance number of instances for the beam; and determining, based on a prediction of the reference signal quality associated with the TCI state of the beam, an instance of a predicted instance number of instances for the beam, wherein a sum of the measured instance number and the predicted instance number is equal to or greater than the threshold instance number.

Description

Cross-references to related applications

[0001] This application claims priority and benefit to Indian Patent Application No. 202541040932, filed on April 28, 2025, the contents of which are incorporated herein by reference in their entirety. Technical Field

[0002] Various exemplary embodiments of this disclosure are generally related to the telecommunications field, and more particularly to methods, apparatus, devices, and computer-readable storage media for reducing samples during measurement. Background Technology

[0003] Currently, User Equipment (UE) Initiated Beam Management (UEIBM) has been introduced to enable UEs to notify the network of changes in radio conditions more effectively than traditional CSI reporting by network control. Several events can be used as triggers for UE-initiated beam reporting, including Event-2 based on beam quality measurements. The UE can perform measurements on a new beam and report an event when the quality of the new beam becomes better than a threshold of the current beam's quality. Summary of the Invention

[0004] In a first aspect of this disclosure, a first apparatus is provided. The first apparatus includes: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the first apparatus to at least: receive configuration information for beam management from a network device, the configuration information indicating a threshold number of instances for triggering a report of an event, and a predicted number of instances to be determined based on reference signal quality prediction, the instances indicating that a condition for the event is met; determine instances of a measurement instance number for the beam based on measurements of reference signal quality associated with the beam's Transmission Configuration Indicator (TCI) state; and determine instances of the predicted instance number for the beam based on the predictions of the reference signal quality associated with the beam's TCI state, wherein the sum of the measurement instance number and the predicted instance number is equal to or greater than the threshold instance number.

[0005] In a second aspect of this disclosure, a second apparatus is provided. The second apparatus includes: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the second apparatus to at least: determine a threshold number of instances for triggering a report of an event based on prediction, where an instance represents a condition for the event being met; determine a predicted number of instances to be determined by a terminal device based on reference signal quality prediction; and transmit configuration information for beam management to the terminal device, the configuration information indicating the threshold number of instances and the predicted number of instances.

[0006] In a third aspect of this disclosure, a method is provided. The method includes: receiving configuration information from a network device for beam management, the configuration information indicating a threshold number of instances for triggering a report of an event, and a predicted number of instances to be determined based on reference signal quality prediction, each instance representing a condition for the event being met; determining instances of a measurement instance number for the beam based on measurements of reference signal quality associated with the beam's Transmission Configuration Indicator (TCI) state; and determining instances of the predicted instance number for the beam based on the predictions of the reference signal quality associated with the beam's TCI state, wherein the sum of the measurement instance number and the predicted instance number is equal to or greater than the threshold number of instances.

[0007] In a fourth aspect of this disclosure, a method is provided. The method includes: determining a threshold number of instances for triggering a report of an event based on prediction, where an instance represents a condition for the event being met; determining a predicted number of instances to be determined by a terminal device based on reference signal quality prediction; and transmitting configuration information for beam management to the terminal device, the configuration information indicating the threshold number of instances and the predicted number of instances.

[0008] In a fifth aspect of this disclosure, a first apparatus is provided. The first apparatus includes: means for receiving configuration information for beam management from a network device, the configuration information indicating a threshold number of instances for triggering a report of an event, and a predicted number of instances to be determined based on reference signal quality prediction, each instance representing a condition for the event being met; means for determining instances of a measurement instance number for the beam based on measurements of reference signal quality associated with the beam's Transmission Configuration Indicator (TCI) state; and means for determining instances of the predicted instance number for the beam based on the predictions of the reference signal quality associated with the beam's TCI state, wherein the sum of the measurement instance number and the predicted instance number is equal to or greater than the threshold number of instances.

[0009] In a sixth aspect of this disclosure, a second apparatus is provided. The second apparatus includes components for determining a threshold number of instances for triggering a report of an event based on prediction, where an instance represents a condition for the event being met; components for determining a predicted number of instances to be determined by a terminal device based on reference signal quality prediction; and components for transmitting configuration information for beam management to the terminal device, the configuration information indicating the threshold number of instances and the predicted number of instances.

[0010] In a seventh aspect of this disclosure, a computer-readable medium is provided. The computer-readable medium includes instructions stored thereon for causing a device to perform at least the method according to a third aspect.

[0011] In an eighth aspect of this disclosure, a computer-readable medium is provided. The computer-readable medium includes instructions stored thereon for causing a device to perform at least the method according to the fourth aspect.

[0012] It should be understood that the summary portion is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0013] Some exemplary embodiments will now be described with reference to the accompanying drawings, in which: Figure 1 An example communication environment in which example embodiments of this disclosure may be implemented is shown; Figure 2A A schematic diagram of a general timeline for event 2 detection in a UE that supports detection of event 2 within a time window is shown. Figure 2B A schematic diagram is shown for detecting an instance of event-2 within a time window; Figure 3 The signaling flow of a sample UEIBM procedure triggered by event-2 is shown; Figure 4 A schematic diagram showing the quality variations of different beams is shown; Figure 5 The signaling flow of an example process for predicting instances of event reporting according to some example embodiments of this disclosure is shown; Figure 6 A schematic diagram illustrating the training process of a model for predicting instances that trigger reporting events, according to some example embodiments of the present disclosure; Figure 7 A schematic diagram illustrating an update to uplink resources for reporting events, according to some example embodiments of this disclosure; Figure 8 A schematic diagram illustrating the general concept of some exemplary embodiments according to this disclosure is shown; Figure 9 The signaling flow of an example procedure for reporting events according to some example embodiments of this disclosure is shown; Figure 10 The signaling flow of an example process for predicting instances of event reporting according to some example embodiments of this disclosure is shown; Figure 11 The signaling flow of an example procedure for reporting events according to some example embodiments of this disclosure is shown; Figure 12 The signaling flow of an example procedure for reporting events according to some example embodiments of this disclosure is shown; Figure 13 A flowchart is shown illustrating a method implemented at a first device according to some exemplary embodiments of the present disclosure; Figure 14 A flowchart is shown illustrating a method implemented at a second device according to some example embodiments of the present disclosure; Figure 15 A flowchart is shown illustrating a method implemented at a first device according to some exemplary embodiments of the present disclosure; Figure 16 A flowchart is shown illustrating a method implemented at a second device according to some example embodiments of the present disclosure; Figure 17 A simplified block diagram of a device suitable for implementing example embodiments of the present disclosure is shown; and Figure 18 A block diagram of an example computer-readable medium according to some example embodiments of the present disclosure is shown.

[0014] In all the accompanying drawings, the same or similar reference numerals denote the same or similar elements. Detailed Implementation

[0015] The principles of this disclosure will now be described with reference to some exemplary embodiments. It should be understood that these embodiments are described for illustrative purposes only and to assist those skilled in the art in understanding and implementing this disclosure, and do not imply any limitation on the scope of this disclosure. The embodiments described herein can be implemented in various ways other than those described below.

[0016] In the following description and claims, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0017] References to "an embodiment," "embodiment," "example embodiment," etc., in this disclosure indicate that the described embodiment may include a particular feature, structure, or characteristic, but not every embodiment must include that particular feature, structure, or characteristic. Furthermore, such phrases do not necessarily refer to the same embodiment. Additionally, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is believed that incorporating other embodiments, whether explicitly described or not, to affect such a feature, structure, or characteristic is within the knowledge of those skilled in the art.

[0018] It should be understood that although terms such as "first," "second," etc., preceding nouns may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another, and they do not restrict the order of the nouns. For example, without departing from the scope of the exemplary embodiments, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element. As used herein, the term "and / or" includes any and all combinations of one or more of the listed terms.

[0019] As used herein, “at least one of the following: a list of two or more elements” and “at least one of the following: a list of two or more elements” and similar wording (where the list of two or more elements is connected by “and” or “or”) means at least any one of the elements, or at least any two or more of the elements, or at least all of the elements.

[0020] As used herein, unless explicitly stated otherwise, the execution step “in response to A” does not indicate that the step is performed immediately after “A” occurs, and may include one or more intermediate steps.

[0021] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that, when used herein, the terms “comprising,” “containing,” “having,” “possessing,” “including,” and / or “encompassing” designate the presence of the stated features, elements, and / or components, but do not exclude the presence or addition of one or more other features, elements, components, and / or combinations thereof.

[0022] As used in this application, the term "circuit" may refer to one or more of the following: (a) Hardware circuit implementation only (such as implementation in analog and / or digital circuits only) and (b) A combination of hardware circuitry and software, such as (if applicable): (i) A combination of analog and / or digital hardware circuitry with software / firmware, and (ii) Any part of a hardware processor having software (including digital signal processors), software, and memory, which work together to enable a device such as a mobile phone or server to perform various functions, and (c) (Multiple) hardware circuits and / or (multiple) processors, such as (multiple) microprocessors or a portion thereof, which require software (e.g., firmware) to operate, but may not exist when the software is not required to operate.

[0023] This definition of "circuit" applies to all uses of the term in this application (including in any claim). As another example, as used herein, the term "circuit" also encompasses only hardware circuitry or a processor (or multiple processors) or a portion thereof and its accompanying software and / or firmware implementation. The term "circuit" also encompasses, for example and if applicable to a particular claim element, baseband integrated circuits or processor integrated circuits for mobile devices or similar integrated circuits in servers, cellular network devices, or other computing or network devices.

[0024] As used herein, the term "communication network" refers to a network that conforms to any suitable communication standard, such as New Radio (NR), Long Term Evolution (LTE), LTE-A Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), High-Speed ​​Packet Access (HSPA), Narrowband Internet of Things (NB-IoT), etc. Furthermore, communication between terminal devices and network devices in a communication network can be performed according to any suitable generation of communication protocol, including but not limited to first-generation (1G), second-generation (2G), 2.5G, 2.75G, third-generation (3G), fourth-generation (4G), 4.5G, fifth-generation (5G), 5.5G, sixth-generation (6G) communication protocols and / or any other currently known or future-developed protocols. Embodiments of this disclosure can be applied to a variety of communication systems. Given the rapid development of communications, there will certainly be future types of communication technologies and systems that embody the future types of this disclosure. The scope of this disclosure should not be construed as limited to the aforementioned systems.

[0025] As used herein, the term "network device" refers to a node in a communications network through which terminal devices access the network and receive services. Depending on the terminology and technology applied, a network device can refer to a base station (BS) or access point (AP), such as a Node B (NodeB or NB), an evolved Node B (eNodeB or eNB), an NR NB (also known as a gNB), a Remote Radio Unit (RRU), a Radio Header (RH), a Remote Radio Header End (RRH), a relay, an Integrated Access and Backhaul (IAB) node, a low-power node (such as a femtosecond or picosecond), a non-terrestrial network (NTN) or non-terrestrial network device (such as a satellite network device), a low Earth orbit (LEO) satellite and a geostationary Earth orbit (GEO) satellite, a spacecraft network device, etc. In some example embodiments, the Radio Access Network (RAN) split architecture includes a centralized unit (CU) and a distributed unit (DU) at the IAB donor node. The IAB node includes a mobile terminal (IAB-MT) portion that behaves as a UE toward the parent node, and the DU portion of the IAB node behaves as a base station toward the next-hop IAB node.

[0026] The term "terminal device" refers to any terminal device capable of wireless communication. By way of example and not limitation, a terminal device may also be referred to as a communication device, user equipment (UE), subscriber station (SS), portable subscriber station, mobile station (MS), or access terminal (AT). Terminal devices can include, but are not limited to, mobile phones, cellular phones, smartphones, Voice over IP (VoIP) phones, wireless local loop phones, tablets, wearable terminal devices, personal digital assistants (PDAs), portable computers, desktop computers, image capture terminal devices (such as digital cameras), gaming terminal devices, music storage and playback devices, in-vehicle wireless terminal devices, wireless endpoints, mobile stations, laptop embedded devices (LEEs), laptop devices (LMEs), USB dongles, smart devices, wireless customer premises equipment (CPEs), Internet of Things (IoT) devices, watches or other wearable devices, head-mounted displays (HMDs), vehicles, drones, medical devices and applications (e.g., remote surgery), industrial devices and applications (e.g., robots and / or other wireless devices operating in industrial and / or automated processing chain environments), consumer electronics devices, devices operating on commercial and / or industrial wireless networks, etc. The terminal equipment may also correspond to the mobile termination (MT) portion of an IAB node (e.g., a relay node). In the following description, the terms "terminal equipment," "communication equipment," "terminal," "user equipment," and "UE" are used interchangeably.

[0027] As used herein, the terms “resource,” “transmission resource,” “resource block,” “physical resource block” (PRB), “uplink resource,” or “downlink resource” can refer to any resource used to perform communication, such as communication between a terminal device and a network device, including resources in the time domain, frequency domain, spatial domain, code domain, or any other combination of time, frequency, spatial, and / or code domain resources used to enable communication. In the following, unless explicitly stated otherwise, resources in the frequency and time domains will be used as examples of transmission resources used to describe some exemplary embodiments of this disclosure. Note that the exemplary embodiments of this disclosure are equally applicable to other resources in other domains.

[0028] Figure 1 An example communication environment 100 in which exemplary embodiments of the present disclosure can be implemented is shown. In communication environment 100, multiple communication devices, including terminal device 110 and network device 120, can communicate with each other. Figure 1 In the example, terminal device 110 can be a UE, and network device 120 can be a base station serving the UE. The service area of ​​network device 120 can be referred to as cell 102.

[0029] It should be understood that Figure 1The number of devices and their connections shown are for illustrative purposes only and do not imply any limitation. Communication environment 100 may include any suitable number of devices configured to implement the exemplary embodiments of this disclosure. Although not shown, it should be understood that one or more additional devices may be located in cell 102, and one or more additional cells may be deployed in communication environment 100. Note that although shown as a network device, network device 120 may be another device besides a network device. Although illustrated as a terminal device, terminal device 110 may be another device besides a terminal device.

[0030] In the following description, for illustrative purposes, some example embodiments are depicted in which terminal device 110 operates as a UE and network device 120 operates as a base station. However, in some example embodiments, the operations described in connection with the terminal device can be implemented at the network device or other devices, and the operations described in connection with the network device can be implemented at the terminal device or other devices.

[0031] In some example embodiments, the transmission direction from network device 120 to terminal device 110 is referred to as the downlink (DL), and the transmission direction from terminal device 110 to network device 120 is referred to as the uplink (UL). In the DL, network device 120 is a transmitting (TX) device (or transmitter), and terminal device 110 is a receiving (RX) device (or receiver). In the UL, terminal device 110 is a TX device (or transmitter), and network device 120 is an RX device (or receiver).

[0032] Communication in communication environment 100 can be implemented according to any suitable communication protocol, including but not limited to cellular communication protocols, wireless local network communication protocols such as IEEE 802.11, and / or any other currently known or future-developed protocols. Furthermore, communication can utilize any suitable wireless communication technology, including but not limited to: Code Division Multiple Access (CDMA), Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), Frequency Division Duplex (FDD), Time Division Duplex (TDD), Multiple Input Multiple Output (MIMO), Orthogonal Frequency Division Multiple Access (OFDM), Discrete Fourier Transform Extended OFDM (DFT-s-OFDM), and / or any other currently known or future-developed technologies.

[0033] Currently, the UE has several events that trigger beam reporting for UEIBM. For example, the UE can report event-2 when the quality of at least one new beam becomes better than the current beam by a threshold amount. Alternatively or additionally, the UE can report event-1 when the quality of the current beam becomes worse than a dedicated threshold. Alternatively or additionally, the UE can report event-7 when the quality of at least one new beam becomes better than the reference signal derived from the active Transmission Configuration Indicator (TCI) state with the Q-th best quality by a threshold amount.

[0034] The UE can perform the beam reporting procedure using two alternative modes (i.e., Mode A and Mode B). In Mode A, the UE can transmit an indication to the network on the first Physical Uplink Control Channel (PUCCH) to request resources for the second UL channel to carry the beam report. This indication can be in PUCCH format with one or more bits. The UE can then detect an indication in DL Control Information (DCI) format indicating the resources for the second UL channel. The UE can then transmit the beam report to the network on the second UL channel. Mode A is a basic UE capability; that is, all UEs that support UE-initiated or event-driven beam management reporting are expected to support Mode A.

[0035] In Mode B, resources for the second UL channel can be pre-configured for the UE. The UE can transmit a notification to the network on the first PUCCH to inform the network of the second UL channel. The UE can then transmit a beam report on the second channel. It should be noted that this notification is in a separate reporting instance from the beam report.

[0036] In both modes, the UE can transmit an indication on the first PUCCH when the event conditions are met. In Mode A, receiving the first PUCCH at the network may indicate that the UE is requesting uplink resources to transmit a beam report. In Mode B, receiving a notification on the first PUCCH at the network may indicate that the UE will transmit a beam report on uplink resources reserved for beam reports.

[0037] Within the context of the UEIBM Event-2 trigger condition, there are two options: a single measurement or a window-based method, where within a time window W, there are M instances where the quality of a new beam is higher than that of the current beam and the difference reaches a threshold to satisfy the event trigger condition. If, within the time window, the number of Event-2 instances of at least one identical new beam is greater than or equal to the configurable threshold number of instances M, the UE will initiate a beam report. Event-2 instances are used to characterize the determination that a new beam is a target available beam when its quality is superior to that of the current beam and the difference reaches the threshold.

[0038] The following is for reference. Figure 2A and Figure 2BDescribe in detail the basic concepts of event-2 based reporting beam management.

[0039] Figure 2A A schematic diagram 200A illustrates a general timeline for event-2 detection in a UE that supports event-2 detection within a time window. Within the time window configured for the UE, the UE can perform measurements on the new beam and the Channel State Information (CSI)-Reference Signal (CSI-RS) on the current beam. If the quality of the new beam becomes better than the current beam and the difference reaches a threshold, an event-2 instance is identified. If, within the time window, the number of event-2 instances of at least one identical new beam is greater than or equal to the configurable threshold number of instances M, the UE will initiate a beam report. The UE may need to wait for the next PUCCH instance to transmit the beam report to the gNB, and then wait for the UL resource indicated by the DCI format received from the gNB. The UE can then transmit the beam report via an L1 report on the UL resource.

[0040] Figure 2B Schematic diagram 200B illustrates the detection of an event-2 instance within a time window. The time window can be configured by the gNB for the UE. Figure 2B As shown, there can be two different new beams, namely beam A and beam B, and the threshold instance number M configured for event-2 is 4. The UE can perform measurements multiple times on the new beam and the current beam to determine the event-2 instance. Figure 2B As shown, there are 4 Event-2 instances for beam B and 2 Event-2 instances for beam A within the time window. The number of Event-2 instances measured for beam B is equal to the threshold instance number M. Therefore, the UE can determine to report Event-2 for beam B.

[0041] Now for reference Figure 3 Describes the signaling exchanged between the UE and the gNB. Figure 3 Signaling flow 300 of a sample procedure for UEIBM triggered by event-2 is shown.

[0042] At block 301, gNB 320 can transmit configuration information to UE 310. The configuration information can indicate the pre-configured threshold number M and time window W for event 2 instances. For example, the threshold number M for event 2 instances can be 5, and the time window W can be 50ms. At block 303, after receiving the configuration information, UE 310 can perform multiple measurements on the beam reference signal (RS) from gNB 320. UE 310 can determine that there are 3 instances, and therefore event 2 conditions are not met. At block 305, within the time window W, UE 310 can continue to perform measurements on the RS from gNB 320. When UE 310 has detected 5 instances, UE 310 can determine that event 2 conditions are met. At block 307, based on the determination that event 2 conditions are met, UE 310 can transmit an event 2 report to gNB 320.

[0043] In this disclosure, the event-2 instance can be counted per new beam. In other words, the event-2 instance can be counted separately for different beams.

[0044] As mentioned above, the number of threshold instances M can be a key parameter for UE IBM. When M is set to a lower value, it is easier to meet the event triggering conditions. The UE may suffer from the ping-pong effect, i.e., rapid switching between different beams, which can lead to a degradation in communication quality. When M is set to a higher value, the UE may have a lower ping-pong probability compared to when M is set to a lower value, because the UE may have relatively more time to measure the beam quality. Therefore, this mechanism is robust and may not trigger beam reporting due to small-scale fading fluctuations in the channel. However, this may come at the cost of higher reporting latency and increased power consumption at the UE, as the UE must measure and evaluate a higher number of samples to determine whether the reporting event conditions are met.

[0045] In scenarios where beam quality varies significantly in the network, the UE may require more time and more measurement instances to accurately report events. Conversely, in scenarios with low variation, a smaller number of instances may be sufficient for the UE to determine which event triggered the reporting. For better understanding, refer to... Figure 4 This will be described.

[0046] Figure 4 A schematic diagram 400 shows the quality variations of different beams. For example... Figure 4 As shown, within the configured time window, the channel fluctuates continuously, and the beam quality may change significantly over time. Therefore, a small number of threshold instances may not be sufficient for the UE to accurately assess whether a new beam is superior to the current beam.

[0047] Therefore, a solution is needed to reduce the number of measurement samples during event-triggered beam reporting while maintaining high accuracy in reporting events to avoid ping-pong effects or retransmissions due to poor beam quality.

[0048] According to some example embodiments of this disclosure, a solution is provided for reducing the number of measurement instances triggered by beam management events. In the solution provided in this disclosure, a first device receives configuration information for beam management from a network device. The configuration information indicates a threshold number of instances for triggering event reporting, and an instance represents a condition for the event being met. The first device determines the number of measurement instances for the beam based on measurements of RS quality associated with the beam's TCI state. Then, the first device determines the number of predicted instances for the beam based on predictions of RS quality associated with the beam's TCI state. If the sum of the number of measurement instances and the number of predicted instances is equal to or greater than the threshold number of instances, the first device transmits a report of the event for the beam to the network device.

[0049] Using the solution provided in this disclosure, the UE can perform predictions on beam quality and determine the reporting of trigger events without performing measurements to obtain a threshold number of instances to trigger events. Therefore, reporting latency and energy consumption can be reduced.

[0050] The exemplary embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0051] Figure 5 Signaling flow 500 of an example process for predicting instances of event reporting according to some example embodiments of this disclosure is shown. Figure 5 This relates to a first device 510 and a second device 520. The first device 510 may include or may be included in... Figure 1 The terminal device 110 is included, and the second device 520 may include or may be included in the terminal device 110. Figure 1 In network device 120, the first device 510 is capable of predicting instances of reports used to trigger an event. The first device 510 is capable of determining the number of instances of reports used to trigger an event (e.g., event-2).

[0052] As shown in signaling stream 500, in some example embodiments, the first device 510 may transmit (502) an indication of its ability to predict instances to the second device 520. Accordingly, the second device 520 may receive (504) an indication from the first device 510. For example, the first device 510 may transmit a capability message to the second device 520 to notify the second device 520 that the first device 510 is capable of predicting instances for triggering event reporting. The second device 520 can then know that the first device 510 can determine the number of instances by prediction rather than measurement of the reference signal quality.

[0053] Then, the second device 520 transmits (506) configuration information for beam management to the first device 510. The configuration information includes a threshold number of instances for triggering the reporting of events. This instance indicates that the conditions for the event have been met. Accordingly, the first device 510 can receive (508) the configuration information from the second device 520.

[0054] For example, the configuration information may indicate the number of threshold instances M. If the first device 510 obtains a measurement with a quality better than the reference signal of the current beam being used by the first device 510 and the difference reaches the threshold, the first device 510 may determine one instance for triggering the report of event-2. Within the time window configured for the first device 510, the first device 510 may determine multiple instances.

[0055] Based on the configuration information, the first device 510 determines (512) the number of measurement instances for a beam based on the measurement of the reference signal quality associated with the TCI state of the beam. For example, the reference signal quality can be measured by RSRP.

[0056] For example, the first device 510 can perform multiple measurements on the reference signal and obtain the reference signal quality associated with the TCI state of the beam. If a measurement result indicates that the reference signal quality is better than the current beam and the difference reaches a threshold, the first device 510 can determine one instance from the number of measurement instances for that beam.

[0057] In some example embodiments, the first device 510 may determine the number of predicted instances based on the characteristics of the communication channel between the first device 510 and the second device 520. For example, the first device 510 may determine a value for the number of predicted instances P, which indicates that the first device 510 is capable of predicting P instances. The first device 510 may determine the number of predicted instances by using an AI / ML model based on the characteristics of the channel between the first device 510 and the second device 520. Alternatively, the first device 510 may calculate the number of predicted instances by using a dedicated algorithm. The dedicated algorithm may be pre-configured for the first device 510.

[0058] After determining the number of measurement instances for the beam, the first device 510 determines (514) the number of predicted instances for the beam based on the prediction of the reference signal quality associated with the TCI state of the beam. For example, the reference signal quality may be the reference signal received power (RSRP), the reference signal received quality (RSRQ), etc. For example, the first device 510 can perform a prediction of the quality of a reference signal on the beam. If the first device 510 is configured with a threshold number of instances M and the first device 510 has determined the number of predicted instances P, the first device can perform a measurement of the reference signal quality. Once the first device 510 has determined (MP) instances based on at least one measurement of the reference signal quality, the first device 510 can decide to perform a prediction of the reference signal quality instead of continuing with actual measurements. Then, the first device 510 can determine the number of predicted instances for the beam based on the prediction.

[0059] In some example embodiments, the first device 510 may perform predictions of the quality of a reference signal on the beam by using an AI / ML model. For example, the AI / ML model may be a reinforcement learning (RL) model trained to predict instances used to trigger events.

[0060] In some example embodiments, training data can be used to train AI / ML models. The training data may include at least one of the following: one or more historical threshold instance numbers indicated to the first device, RSRP measured by the first device 510, the location of the first device 510, corresponding identifiers of one or more beams that have historically triggered event reports, or other characteristics of the communication channel between the first device and the network device. Additionally, the training data may also include other parameters of the first device 510 or the channel between the first device 510 and the second device 520.

[0061] In some example embodiments, the first device 510 may perform predictions using an AI / ML model. The predictions may be performed by the first device 510 based on inference inputs including at least one of the following: a measurement of the quality of a reference signal on the beam, a reference signal associated with the TCI state of the beam, a threshold instance number, a time window for determining instances of at least the threshold instance number, a threshold signal quality for determining whether a condition is met, the location of the first device 510, or other characteristics of the communication channel. Additionally, the inference inputs may also include other parameters of the first device 510 or the channel between the first device 510 and the second device 520.

[0062] Now for reference Figure 6 Describe the details of training the RL model used for prediction. Figure 6 A schematic diagram of a training process 600 for an RL model for predicting instances that trigger event reports, according to some example embodiments of this disclosure, is shown. Figure 6 In the example, event-2 is shown as an example rather than any limitation.

[0063] like Figure 6As shown, the RL model represented by agent 620 is trained to predict the beam used to trigger the reporting of an event. This prediction can be triggered before the first device 510 declares the actual configured number of threshold instances M required to trigger the event. In other words, before the first device 510 obtains the number of threshold instances using actual measurements, the first device 510 may be able to predict the optimal beam that can trigger event -2. Agent 620 can be trained based on at least one of the following: characteristics of the current channel, the measurement reference signal quality of the new beam or the current beam, the RSRP difference between the new beam and the current beam, or the difference between the RSRP of the new beam and the threshold RSRP configured by the network. Additionally, agent 620 can be further trained based on at least one of the location of the first device 510 or the identifier of the beam that historically triggered the reporting of events. Additionally, agent 620 can be further trained based on other characteristics of the channel between the first device 510 and the second device 520. The training dataset can be state 610 or included in state 610. State 610 may include information required to train agent 620.

[0064] Agent 620 can then perform a prediction based on the information provided by state 610. Based on this prediction, agent 620 can determine to perform action 630, in this example, action 630 being the triggering of early event -2. For example, agent 620 can determine whether to report the event based on the prediction and perform action 630 accordingly. Action 630 is performed in environment 640. Environment 640 can be a downlink path. Therefore, environment 640 can respond to action 630 or provide feedback to action 630. Agent 620 can receive a penalty or reward from environment 640 via its response to action 630. For example, a penalty could include a ping-pong effect due to frequent reporting of the event, or a Hybrid Automatic Repeat Request (HARQ) retransmission. And a reward could be the opposite, i.e., a reduction in the ping-pong effect or HARQ retransmissions. After being penalized or rewarded, agent 620 can receive an updated state 610 for the next iteration of the training process.

[0065] Now return to the reference Figure 5 If the sum of the number of measured instances and the number of predicted instances is equal to or greater than the threshold number of instances, the first device 510 transmits (516) a report of events for the beam to the second device 520. Accordingly, the second device 520 may receive (518) a report from the first device 510.

[0066] In some example embodiments, the report may indicate a portion of the threshold instance number based on a prediction of the reference signal quality associated with the TCI state of the beam. For example, the first device 510 may be configured with a threshold instance number M, and the first device 510 may determine a measured instance number and a predicted instance number P. The first device 510 may notify the second device 520 that a portion of the threshold instance number M instances exist based on the prediction. In other words, the first device 510 may indicate to the second device 520, based on the prediction, that a report of the event has been partially triggered.

[0067] In some example embodiments, the first device 510 may transmit a report to the second device 520 using uplink resources available after determining the predicted number of instances. For example, after the prediction of the reference signal quality, if the sum of the measured number of instances and the predicted number of instances is equal to or greater than a threshold number of instances, the first device 510 may immediately determine that a triggering condition has been met and trigger an event. The first device 510 may transmit an indication that the event has been triggered on the next available PUCCH.

[0068] In some example embodiments, based on beam-based event reporting, the second device 520 may update (522) the threshold instance number for triggering event reporting based on the beam-based event reporting. For example, the first device 510 may indicate to the second device 520 that the number of instances is determined by prediction, and the second device 520 may optimize the value of the threshold instance number for evaluating future event triggering.

[0069] Then, the second device 520 can transmit (524) an indication of the updated number of threshold instances to the first device 510. Accordingly, the first device 510 can receive (526) an indication of the updated number of threshold instances from the second device 520. The first device can use the updated number of threshold instances for future event reporting. For example, a reduced number of threshold instances M can be updated to the first device 510, so that the first device may need to perform fewer measurements to determine the triggering of the event the next time it determines the instance used to trigger the reporting of the event.

[0070] Additionally, in some example embodiments, the second device 520 may also update the uplink resource configuration for reporting events based on the uplink resources used for receiving reports. For better understanding, reference will now be made to... Figure 7 Describes the update of uplink resources.

[0071] Figure 7 A schematic diagram 700 illustrates the use of uplink resources to report events according to some example embodiments of this disclosure. For example... Figure 7As shown, the first device 510 can be configured with a threshold instance number of 5 and a prediction instance number of 2. The first device 510 can perform measurements and determine 3 instances based on measurements of the reference signal quality, wherein the quality of the same new beam is better than the current beam or the indicated beam and the difference reaches a threshold. Then, the first device 510 can determine that it can accurately predict two more instances where the same new beam is better than the current or indicated beam and the difference reaches a threshold. Based on this determination, the first device 510 can transmit a trigger indication on the next PUCCH at time instance t0. The first device 510 can transmit the indication on the PUCCH at time instance t1 without waiting for the time window to end. The second device 520 can update the uplink resources for reporting events based on reports and resources for receiving reports. For example, the second device 520 can configure the PUCCH to the first device 510 at time instance t0 within the time window. Therefore, the first device 510 may not need to wait for the time window and does not need to wait for t1; the first device 510 can transmit the report at t0. The updated uplink resources enable the first device 510 to report events earlier and reduce the latency required for reporting events.

[0072] Now for reference Figure 8 Describe a summary implementation of the solution provided in this disclosure. Figure 8 A schematic diagram 800 illustrates a general concept according to some example embodiments of the present disclosure. For example... Figure 8 As shown, the first device 510 can be configured with a time window (e.g., 50ms), a threshold instance number M=5, and a prediction instance number P=2.

[0073] Within this time window, the first device 510 can perform measurements of the reference signal quality associated with the TCI state of the beam. For example, the first device 510 can perform actual measurements on the reference signals transmitted on beams A, B, and C. Based on the actual measurements, beam B can be determined as the optimal beam based on three instances (i.e., instances 811, 812, and 813). The first device 510 can then immediately perform predictions on beam B using an RL-based predictor to obtain the remaining P=2 instances (i.e., instances 821 and 822). If the predicted instances indicate that beam B is the optimal beam, the first device 510 can determine that the sum of the number of measured instances and the number of predicted instances equals the threshold number of instances M. After this determination, the first device 510 can report the early event 2 triggering on the next PUCCH instead of performing actual measurements and reporting event 2 triggering.

[0074] Additionally, the first device 510 may include the number of instances it has already used to predict triggering events in the report. The second device 520 may be enabled to optimize the threshold instance number M based on the report for evaluating future event occurrences. Thereafter, the second device 520 may reconfigure the updated threshold instance number M to the first device 510.

[0075] Figure 9 Signaling flow 900 of an example procedure for reporting events according to some example embodiments of this disclosure is shown. Figure 9 This involves UE 910 and gNB 920. UE 910 can be, or may be included in, [the list of UEs]. Figure 5 In the first device 510, and gNB920 may be or can be included Figure 5 In the second device 520. UE 910 can be considered as Figure 1 The example of terminal device 110 in the text, and gNB 920 can be regarded as Figure 1 Example of network device 120. UE 910 may be able to predict instances used to trigger reports for events.

[0076] like Figure 9 As shown, UE 910 can predict instances, while gNB 920 cannot. At 901, UE 910 can transmit a message to gNB 920 indicating its ability to predict instances. At 903, UE 910 can develop an RL-based model for prediction. Training data for the RL-based model may include the historical threshold number of instances M, the RSRP value measured by UE 910, the location of UE 910, the IDs of one or more beams that historically triggered the event, or other characteristics of the communication channel between UE 910 and gNB 920. Penalties and rewards for the RL-based model may include HARQ retransmissions and the ping-pong effect due to multiple reports.

[0077] At 905, gNB 920 can configure UEIBM configuration to UE 910, including the threshold instance number M. For example, the threshold instance number M can be 5. At 907, UE 910 can determine the predicted instance number P based on the current situation. For example, UE 910 can determine the predicted instance number P=2 for M=5.

[0078] At block 908, UE 910 can perform actual measurements on the reference signal transmitted from gNB 920. For example, UE 910 can perform three instances of measurement on the reference signal. At block 909, gNB 920 can transmit the reference signal to UE 910, and UE 910 can perform measurements on the reference signal. At block 911, UE 910 can determine one instance based on the measurement at block 909. Actions 913 to 919 are similar to 909 and 911. In summary, UE 910 can determine the number of measurement instances to be three based on the measurements.

[0079] At point 921, UE 910 can perform predictions for the remaining P instances using the RL-based model developed at point 903. In this case, UE 910 can perform predictions for the remaining 2 instances. The inference inputs to the RL-based model can include the quality of the reference signal measured on the beam, the reference signal associated with the TCI state of the beam, the threshold instance number M, the time window for determining the instances at least the threshold instance number, the location of UE 910, and other characteristics of the communication channel. The output of the inference can be a prediction of the optimal beam that can trigger the event.

[0080] At 923, based on the prediction at 921, if the sum of the measured instance number and the predicted instance number is equal to or greater than the threshold instance number, UE 910 can immediately trigger an event report to gNB 920. At 925, UE 910 can transmit the event report to gNB 920 on the next PUCCH. The report may indicate that a portion of the instances was determined based on the prediction. At 927, gNB 920 can optimize the threshold instance number M and the PUCCH used for event reporting based on the report. At 929, gNB 920 can transmit an updated UEIBM configuration to UE 910.

[0081] According to some example embodiments of this disclosure, a solution is provided for reducing the number of measurement instances triggered by beam management events. In the solution provided in this disclosure, a first device receives configuration information for beam management from a network device. The configuration information indicates a threshold number of instances for triggering event reporting, and a predicted number of instances to be determined based on RS quality prediction. An instance represents a condition for an event being met. The first device determines the number of measurement instances for a beam based on measurements of RS quality on that beam. Then, the first device determines the number of predicted instances for that beam based on predictions of RS quality on that beam. The sum of the number of measurement instances and the number of predicted instances is equal to or greater than the threshold number of instances.

[0082] Using the solution provided in this disclosure, the UE can perform predictions on beam quality and determine the reporting of trigger events without performing measurements to obtain a threshold number of instances to trigger events. Therefore, reporting latency and energy consumption can be reduced.

[0083] Figure 10 Signaling flow 1000 of an example process for predicting instances of event reporting according to some example embodiments of this disclosure is shown. Figure 10 This relates to a first device 510 and a second device 520. The first device 510 may be, or may be included in, a second device. Figure 1 The terminal device 110 is included, and the second device 520 may be or can be included. Figure 1 In network device 120, the first device 510 and the second device 520 are capable of predicting instances used to trigger reports for events. Figure 10 In this process, both the first device 510 and the second device 520 are able to determine the number of instances of a report used to trigger an event (e.g., event-2).

[0084] In signaling stream 1000, in some example embodiments, the first device 510 may transmit (1002) an indication of the ability to predict instances to the second device 520. Accordingly, the second device 520 may receive (1004) the indication from the first device 510. The second device 520 may transmit (1006) an indication of the ability to predict instances to the first device 510. Accordingly, the first device 510 may receive (1008) the indication from the second device 520.

[0085] For example, the first device 510 can transmit a capability report to the second device 520 to inform the second device 520 that the first device 510 is able to predict instances of reports used to trigger events. The second device 520 can then know that the first device 510 can determine the number of instances by prediction rather than measurement of the reference signal quality. Similarly, the second device 520 can transmit a capability message to the first device 510 to indicate that it is capable of prediction.

[0086] Then, the second device 520 determines (1012) the number of threshold instances for triggering the report of the event based on the prediction. An instance indicates that a condition for the event is met. For example, an instance of event-2 could indicate that the quality of the new beam is better than that of the current beam and the difference reaches a threshold.

[0087] In some example embodiments, the second device 520 can perform the prediction of the threshold instance number by using an AI / ML model. The training of the AI / ML model used to predict the threshold instance number is similar to that of the reference model. Figure 6 A description of the model training process.

[0088] In some example embodiments, prediction may be performed based on at least one of the following: characteristics of a first communication channel between the first device and the network device, one or more historical threshold instances indicated to a terminal device, a threshold instance number indicated to another terminal device having communication channel characteristics matching the communication channel characteristics of the first communication channel, or a threshold instance number indicated to another terminal device near the terminal device. Additionally, prediction may also be performed based on other parameters of the first device 510 and the second device 520 or other parameters of the channel between the first device 510 and the second device 520.

[0089] Then, the second device 520 can determine (1014) the predicted number of instances to be determined by the first device based on reference signal quality prediction. For example, the second device 520 can determine the predicted number of instances based on the characteristics of the communication channel between the first device and the network device. Additionally, the predicted number of instances can be further determined based on other parameters of the first device 510 and the second device 520. The second device 520 can determine the predicted number of instances by using an AI / ML model. Alternatively, the second device 520 can determine the predicted number of instances by using a specific algorithm.

[0090] In some example embodiments, the second device 520 may determine (1016) a confidence threshold for the prediction of the reference signal quality based on the capabilities of the first device 510. For example, the second device 520 may determine the confidence threshold using an AI / ML model.

[0091] The second device 520 transmits (1018) configuration information for beam management to the first device 510. The configuration information indicates the number of threshold instances and the number of predicted instances. Accordingly, the first device 510 receives (1022) configuration information from the second device 520. In some example embodiments, the configuration information may also indicate a confidence threshold determined by the second device 520. Alternatively, the confidence threshold may be pre-configured to the first device 510.

[0092] Actions 1024 and 1026 are the same as actions 512 and 514, and are omitted here. The first device 510 can then determine the prediction confidence for the number of instances predicted. If the prediction confidence for the number of instances predicted is below a confidence threshold, the first device 510 can re-execute (1028) the prediction. For example, the first device 510 can attempt to derive a prediction with better confidence by extending the prediction time or executing the prediction multiple times. Additionally, the first device 510 can determine that the prediction has failed after multiple re-executions or after a certain time period.

[0093] Alternatively, if the prediction confidence for the number of instances is higher than a confidence threshold, the first device 510 may transmit an event report (1032) to the second device 520. Accordingly, the second device 520 may receive (1034) a report from the first device 510.

[0094] Actions 1036 to 1040 and Figure 5 Actions 522 to 526 are the same and are omitted here. Additionally, the second device 520 can further update the uplink resource configuration, as described above. Figure 7 As described.

[0095] Figure 11 Signaling flow 1100 of an example process for reporting events according to some example embodiments of this disclosure is shown. Figure 11 This involves UE 910 and gNB 920. UE 910 can be or is included in... Figure 5 The first device 510 is included, and gNB920 can be included. Figure 5 In the second device 520. UE 910 can be considered as Figure 1 The example of terminal device 110 in the text, and gNB 920 can be regarded as Figure 1 Example of network device 120. Both UE 910 and gNB 920 are able to predict instances used to trigger reports for events.

[0096] like Figure 11 As shown, both UE 910 and gNB 920 are capable of prediction. At 1101, UE 910 can transmit capability messages to gNB 920. And at 1103, gNB can also transmit capability messages to UE 910. Capability messages can indicate the capabilities of the prediction instance. At 1105, UE 910 can develop RL-based models, as described above. Figure 9 As described at 903 in [the document]. At 1107, gNB 920 can predict the number of threshold instances M, the number of predicted instances P, and the confidence threshold C. For example, gNB 920 can perform the prediction using an AI / ML model. At 1109, gNB 920 can configure an optimized number of threshold instances M and the number of predicted instances P to UE 910 in the UEIBM configuration based on the prediction of gNB 920. Additionally, gNB 920 can also configure the confidence threshold C to UE 910. Alternatively, the confidence threshold C can be pre-configured to UE 910. The confidence threshold C can indicate the minimum probability that UE 910 may need to ensure the correctness of the prediction in the prediction.

[0097] At 1111, UE 910 can configure the number of received threshold instances M and the number of predicted instances P. Additionally, UE 910 can also configure the confidence threshold C.

[0098] Actions 1113 to 1125 and Figure 9 Actions 909 to 921 are the same and are omitted here. At 1127, UE910 can calculate the prediction confidence and compare it with the confidence threshold C received from gNB920.

[0099] If the prediction confidence is below the confidence threshold C, the signaling flow proceeds to 1129. At 1129, UE 910 can return to 1125 and perform the prediction again. If the prediction confidence is greater than or equal to the confidence threshold C, the signaling flow advances to 1131. At 1131, UE 910 can trigger an event report. Then, at 1133, UE 910 can transmit the UEIBM early event trigger to gNB 920 on the next available PUCCH.

[0100] At 1135, gNB 920 can optimize the threshold instance number M and the predicted instance number P. For example, gNB 920 can perform optimization by using an internal AI / ML model or algorithm. At 1137, gNB 920 can transmit the updated UEIBM configuration to UE 910 to indicate the updated threshold instance number M and predicted instance number P to UE 910.

[0101] Additionally, the gNB 920 can further update the uplink resource configuration, as described above. Figure 7 As described.

[0102] Figure 12 Signaling flow 1200 of an example process for reporting events according to some example embodiments of this disclosure is shown. Figure 12 This involves UE 910 and gNB 920. UE 910 can be or is included in... Figure 5 The first device 510 is included, and gNB920 can be included. Figure 5 In the second device 520. UE 910 can be considered as Figure 1 The example of terminal device 110 in the text, and gNB 920 can be regarded as Figure 1 Example of network device 120. gNB 920 may be able to predict the instance used to trigger the event report, while UE 910 may not be able to predict the instance.

[0103] like Figure 12As shown, gNB 920 may be able to predict, while UE 910 may not. At 1201, gNB 920 can predict the number of threshold instances M, as described above at 1107. At 1203, gNB 920 can transmit the UEIBM configuration to UE 910. This configuration can indicate the number of threshold instances M predicted by gNB 920. For example, the number of threshold instances M predicted by gNB 920 could be 3 instances.

[0104] Actions 1205 to 1215 and Figure 9 Actions 909 to 919 are the same and are omitted here. For example, UE 910 can determine three instances based on measurements of the reference signal quality. At 1217, UE 910 can determine that the triggering condition is met and trigger an event report on the next available PUCCH. At 1219, UE 910 can transmit the UEIBM early event trigger to gNB 920. Actions 1221 and 1223 are the same as... Figure 9 Actions 927 and 929 are the same, and their descriptions are omitted here. Additionally, gNB 920 can further update the uplink resource configuration, as described above. Figure 7 As described.

[0105] Figure 13 A flowchart of an example method 1300 implemented at a first device according to some example embodiments of the present disclosure is shown. For the purposes of discussion, [the following will be discussed]. Figure 5 Method 1300 is described by the angle of the first device 510 in the middle.

[0106] At block 1310, the first device 510 receives configuration information for beam management from a network device, the configuration information indicating a threshold number of instances for triggering an event report, and an instance indicating that a condition for the event has been met.

[0107] At block 1320, the first device 510 determines the number of measurement instances for the beam based on measurements of the reference signal quality associated with the beam's transmission configuration indicator (TCI) status.

[0108] At block 1330, the first device 510 determines the number of prediction instances for the beam based on the prediction of the reference signal quality associated with the TCI state of the beam.

[0109] At box 1340, the first device 510 transmits a report of events for the beam to the network device, wherein the sum of the number of measured instances and the number of predicted instances is equal to or greater than the threshold number of instances.

[0110] In some example embodiments, a portion of the reported number of instances indicating the threshold instance count is based on a prediction of the quality of the reference signal associated with the TCI state of the beam.

[0111] In some example embodiments, method 1300 further includes: performing a prediction of the quality of a reference signal on the beam by using an artificial intelligence / machine learning (AI / ML) model.

[0112] In some example embodiments, the prediction of the reference signal quality on the beam is performed based on at least one of the following: a measurement of the reference signal quality on the beam, a reference signal associated with the TCI state of the beam, a threshold instance number, a time window for determining instances of at least the threshold instance number, a threshold signal quality for determining whether a condition is met, the location of the first device, or the characteristics of the communication channel between the first device and the network device.

[0113] In some example embodiments, method 1300 further includes transmitting a report of events for the beam to a network device using uplink resources available after the number of predicted instances has been determined.

[0114] In some example embodiments, method 1300 further includes transmitting to a network device an indication of the first device's ability to predict instances.

[0115] In some example embodiments, method 1300 further includes determining the number of predicted instances based on the characteristics of the communication channel between the first device and the network device.

[0116] In some example embodiments, the AI / ML model is trained using training data, which includes at least one of the following: one or more historical threshold instance numbers indicated to the first device, reference signal received power (RSRP) measured by the first device, the location of the first device, the corresponding identifiers of one or more beams that have historically triggered events, or the characteristics of the communication channel between the first device and the network device.

[0117] In some example embodiments, method 1300 further includes receiving an indication from a network device of the number of updated event triggering threshold instances.

[0118] Figure 14 A flowchart of an example method 1400 implemented at a second device according to some example embodiments of the present disclosure is shown. For the purposes of discussion, [the following will be discussed]. Figure 5 Method 1400 is described by the angle of the second device 520 in the middle.

[0119] At box 1410, the second device 520 transmits configuration information to the terminal device, the configuration information indicating the threshold number of instances for triggering the reporting of an event, an instance indicating that a condition for the event has been met.

[0120] At box 1420, the second device 520 receives a report of events related to the beam from the terminal device, the report indicating a portion of the threshold instance number based on a prediction of the quality of the reference signal on the beam.

[0121] In some example embodiments, method 1400 further includes: updating a threshold number of instances used to trigger an event report based on the reporting of events for the beam; and transmitting an indication of the updated threshold number of instances to a terminal device.

[0122] In some example embodiments, method 1400 further includes updating the uplink resource configuration for reporting events based on the uplink resources used for receiving reports.

[0123] In some example embodiments, method 1400 further includes receiving from a terminal device an indication of the first device's ability to predict instances.

[0124] Figure 15 A flowchart of an example method 1500 implemented at a first device according to some example embodiments of the present disclosure is shown. For the purposes of discussion, [the following will be discussed]. Figure 5 Method 1500 is described by the angle of the first device 510 in the middle.

[0125] In block 1510, the first device 510 receives configuration information for beam management from a network device. The configuration information indicates a threshold number of instances for triggering an event report and a predicted number of instances to be determined based on reference signal quality prediction. An instance represents a condition for an event being met.

[0126] At block 1520, the first device 510 determines the number of measurement instances for the beam based on measurements of the reference signal quality associated with the beam's transmission configuration indicator (TCI) status.

[0127] At block 1530, the first device 510 determines the number of predicted instances for the beam based on the prediction of the reference signal quality associated with the TCI state of the beam, wherein the sum of the number of measured instances and the number of predicted instances is equal to or greater than a threshold number of instances.

[0128] In some example embodiments, method 1500 further includes: transmitting a report of events for the beam to a network device based on determining that the prediction confidence for the number of predicted instances is higher than a confidence threshold; and re-performing the prediction based on determining that the prediction confidence for the number of predicted instances is lower than a confidence threshold.

[0129] In some example embodiments, method 1500 further includes: performing a prediction of the reference signal quality associated with the TCI state of the beam by using an artificial intelligence / machine learning (AI / ML) model.

[0130] In some example embodiments, the prediction of the reference signal quality on the beam is performed based on at least one of the following: measurement of the reference signal quality on the beam, reference signal associated with the TCI state of the beam, number of threshold instances, time window for determining instances of at least the number of threshold instances, threshold signal quality for determining whether a condition is met, location of the first device, or characteristics of the communication channel between the first device and the network device.

[0131] In some example embodiments, method 1500 further includes transmitting a report of events for the beam to a network device using uplink resources available after the number of predicted instances has been determined.

[0132] In some example embodiments, method 1500 further includes transmitting to a network device an indication of the first device's ability to predict instances.

[0133] In some example embodiments, the AI / ML model is trained using training data, which includes at least one of the following: one or more historical threshold instance numbers indicated to the first device, reference signal received power (RSRP) measured by the first device, the location of the first device, the corresponding identifiers of one or more beams that have historically triggered events, or the characteristics of the communication channel between the first device and the network device.

[0134] In some example embodiments, method 1500 further includes receiving an indication from a network device of an updated threshold number of instances.

[0135] In some example embodiments, method 1500 further includes receiving from the network device an indication of the network device's ability to predict a threshold number of instances.

[0136] Figure 16 A flowchart of an example method 1600 implemented at a second device according to some example embodiments of the present disclosure is shown. For the purposes of discussion, [the following will be discussed]. Figure 5 Method 1600 is described by the angle of the second device 520 in the middle.

[0137] In box 1610, the second device 520 determines a threshold number of instances for triggering a report of an event based on prediction, where an instance represents a condition for the event being met.

[0138] At box 1620, the second device 520 determines the number of predicted instances to be determined by the terminal device based on reference signal quality prediction.

[0139] At frame 1630, the second device 520 transmits configuration information for beam management to the terminal device, the configuration information indicating the number of threshold instances and the number of predicted instances.

[0140] In some example embodiments, method 1600 further includes: performing a prediction of a threshold number of instances by using an artificial intelligence / machine learning AI / ML model.

[0141] In some example embodiments, the prediction of the threshold instance number is performed based on at least one of the following: characteristics of a first communication channel between a first device and a network device, one or more historical threshold instance numbers indicated to a terminal device, a threshold instance number indicated to another terminal device, the other terminal device having communication channel characteristics that match the characteristics of the first communication channel, or a threshold instance number indicated to another terminal device near the terminal device.

[0142] In some example embodiments, method 1600 further includes receiving from a terminal device an indication of the first device's ability to predict instances.

[0143] In some example embodiments, method 1600 further includes: determining a confidence threshold for the reference signal quality prediction based on the capabilities of the terminal device, wherein configuration information further indicates the confidence threshold.

[0144] In some example embodiments, method 1600 further includes: receiving a report of an event for the beam from a terminal device; updating the number of threshold instances used to trigger the event based on the report of the event for the beam; and transmitting an indication of the updated number of threshold instances to the terminal device.

[0145] In some example embodiments, method 1600 further includes: receiving a report of an event for a beam on uplink resources; and updating the uplink resource configuration for reporting the event based on the uplink resources used to receive the report.

[0146] In some example embodiments, method 1600 further includes transmitting to a terminal device an indication of the network device's ability to predict the number of threshold instances.

[0147] In some example embodiments, a first means capable of performing any one of method 1300 (e.g., Figure 5 The first device 510 may include components for performing the corresponding operations of method 1300. The components may be implemented in any suitable form. For example, the components may be implemented in a circuit or software module. The first device may be implemented as or included in... Figure 5 In the first device 510.

[0148] In some example embodiments, the first apparatus includes: components for receiving configuration information for beam management from a network device, the configuration information indicating a threshold number of instances for triggering an event report, and instances indicating that a condition for the event is met; components for determining an instance of a measurement instance number for the beam based on measurements of reference signal quality associated with the beam's Transmission Configuration Indicator (TCI) state; components for determining an instance of a predicted instance number for the beam based on predictions of reference signal quality associated with the beam's TCI state; and components for transmitting an event report for the beam to the network device, wherein the sum of the measurement instance number and the predicted instance number is equal to or greater than the threshold instance number.

[0149] In some example embodiments, a portion of the reported number of instances indicating the threshold instance count is based on a prediction of the quality of the reference signal associated with the TCI state of the beam.

[0150] In some example embodiments, the first device further includes a component for performing a prediction of the quality of a reference signal on the beam using an artificial intelligence / machine learning (AI / ML) model.

[0151] In some example embodiments, the prediction of the reference signal quality on the beam is performed based on at least one of the following: measurement of the reference signal quality on the beam, reference signal associated with the TCI state of the beam, number of threshold instances, time window for determining instances of at least the number of threshold instances, threshold signal quality for determining whether a condition is met, location of the first device, and characteristics of the communication channel between the first device and the network device.

[0152] In some example embodiments, the first apparatus further includes a component for transmitting a report of events for the beam to a network device using uplink resources available after determining the predicted number of instances.

[0153] In some example embodiments, the first device further includes a component for transmitting to a network device an indication of the first device's ability to predict instances.

[0154] In some example embodiments, the first apparatus further includes a component for determining the predicted number of instances based on the characteristics of the communication channel between the first apparatus and the network device.

[0155] In some example embodiments, the AI / ML model is trained using training data, which includes at least one of the following: one or more historical threshold instance numbers indicated to the first device, the reference signal received power (RSRP) measured by the first device, the location of the first device, the corresponding identifiers of one or more beams that have historically triggered events, and the characteristics of the communication channel between the first device and the network device.

[0156] In some example embodiments, the first apparatus further includes a component for receiving an indication from a network device of the number of updated event triggering threshold instances.

[0157] In some example embodiments, a second means capable of performing any one of method 1400 (e.g., Figure 5 The second device 520 may include components for performing the corresponding operations of method 1400. The components may be implemented in any suitable form. For example, the components may be implemented in a circuit or software module. The second device may be implemented as or included in... Figure 5 In the second device 520.

[0158] In some example embodiments, the second apparatus includes components for transmitting configuration information to a terminal device, the configuration information indicating a threshold number of instances for triggering a report of an event, each instance representing a condition for the event being met; and components for receiving a report of an event for the beam from the terminal device, the report indicating a portion of the threshold number of instances based on a prediction of the quality of a reference signal on the beam.

[0159] In some example embodiments, the second apparatus further includes: a component for updating a threshold number of instances for triggering an event report based on reports of events for the beam; and a component for transmitting an indication of the updated threshold number of instances to a terminal device.

[0160] In some example embodiments, the second apparatus further includes a component for updating the uplink resource configuration for reporting events based on the uplink resources used for receiving reports.

[0161] In some example embodiments, the second device further includes a component for receiving from a terminal device an indication of the first device's ability to predict instances.

[0162] In some example embodiments, a first means capable of performing any one of method 1500 (e.g., Figure 5 The first device 510 may include components for performing the corresponding operations of method 1500. The components may be implemented in any suitable form. For example, the components may be implemented in a circuit or software module. The first device may be implemented as or included in... Figure 5 In the first device 510.

[0163] In some example embodiments, the first apparatus includes: components for receiving configuration information for beam management from a network device, the configuration information indicating a threshold number of instances for triggering an event report, and a predicted number of instances to be determined based on reference signal quality prediction, each instance representing a condition for the event being met; components for determining a number of measured instances for the beam based on measurements of reference signal quality associated with the beam's Transmission Configuration Indicator (TCI) state; and components for determining a predicted number of instances for the beam based on predictions of reference signal quality associated with the beam's TCI state, wherein the sum of the measured instance number and the predicted instance number is equal to or greater than the threshold number of instances.

[0164] In some example embodiments, the first apparatus further includes: a component for transmitting a report of an event for the beam to a network device based on determining that the prediction confidence of an instance for the predicted number of instances is higher than a confidence threshold; and a component for re-performing the prediction based on determining that the prediction confidence of an instance for the predicted number of instances is lower than a confidence threshold.

[0165] In some example embodiments, the first device further includes a component for performing a prediction of the reference signal quality associated with the TCI state of the beam by using an artificial intelligence / machine learning (AI / ML) model.

[0166] In some example embodiments, the prediction of the reference signal quality on the beam is performed based on at least one of the following: measurement of the reference signal quality on the beam, reference signal associated with the TCI state of the beam, number of threshold instances, time window for determining instances of at least the number of threshold instances, threshold signal quality for determining whether a condition is met, location of the first device, and characteristics of the communication channel between the first device and the network device.

[0167] In some example embodiments, the first apparatus further includes a component for transmitting a report of events for the beam to a network device using uplink resources available after determining the predicted number of instances.

[0168] In some example embodiments, the first device further includes a component for transmitting to a network device an indication of the first device's ability to predict instances.

[0169] In some example embodiments, the AI / ML model is trained using training data, which includes at least one of the following: the number of one or more historical threshold instances indicated to the first device, the reference signal received power (RSRP) measured by the first device, the location of the first device, the corresponding identifiers of one or more beams that have historically triggered events, and the characteristics of the communication channel between the first device and the network device.

[0170] In some example embodiments, the first apparatus further includes a component for receiving an indication from the network device of an updated threshold number of instances.

[0171] In some example embodiments, the first apparatus further includes a component for receiving from the network device an indication of the network device's ability to predict a threshold number of instances.

[0172] In some example embodiments, a second means capable of performing any one of method 1600 (e.g., Figure 5 The second device 520 may include components for performing the corresponding operations of method 1600. The components may be implemented in any suitable form. For example, the components may be implemented in a circuit or software module. The second device may be implemented as or included in... Figure 5 In the second device 520.

[0173] In some example embodiments, the second apparatus includes: a component for determining a threshold number of instances for triggering a report of an event based on prediction, wherein an instance represents a condition for the event being met; a component for determining a predicted number of instances to be determined by a terminal device based on a reference signal quality prediction; and a component for transmitting configuration information for beam management to the terminal device, wherein the configuration information indicates the threshold number of instances and the predicted number of instances.

[0174] In some example embodiments, the second device further includes a component for performing a prediction of a threshold number of instances by using an artificial intelligence / machine learning (AI / ML) model.

[0175] In some example embodiments, the prediction of the threshold instance number is performed based on at least one of the following: characteristics of a first communication channel between a first device and a network device, one or more historical threshold instance numbers indicated to a terminal device, a threshold instance number indicated to another terminal device, the other terminal device having communication channel characteristics that match the characteristics of the first communication channel, or a threshold instance number indicated to another terminal device near the terminal device.

[0176] In some example embodiments, the second device further includes a component for receiving from a terminal device an indication of the first device's ability to predict instances.

[0177] In some example embodiments, the second apparatus further includes a component for determining a confidence threshold for a reference signal quality prediction based on the capabilities of the terminal device, wherein configuration information further indicates the confidence threshold.

[0178] In some example embodiments, the second apparatus further includes: components for receiving reports of beam events from a terminal device; components for updating the number of threshold instances for triggering events based on the reports of beam events; and components for transmitting an indication of the updated number of threshold instances to the terminal device.

[0179] In some example embodiments, the second apparatus further includes: components for receiving reports of events related to the beam on uplink resources; and components for updating the uplink resource configuration for reporting events based on the uplink resources used for receiving reports.

[0180] In some example embodiments, the second apparatus further includes a component for transmitting to a terminal device an indication of the network device's ability to predict the number of threshold instances.

[0181] Figure 17 This is a simplified block diagram of a device 1700 suitable for implementing an example embodiment of the present disclosure. The device 1700 can be provided to implement a communication device, such as... Figure 1 The terminal device 110 or network device 120 shown. As shown, device 1700 includes one or more processors 1710, one or more memories 1720 coupled to processor 1710, and one or more communication modules 1740 coupled to processor 1710.

[0182] Communication module 1740 is used for bidirectional communication. Communication module 1740 has one or more communication interfaces to facilitate communication with one or more other modules or devices. The communication interface can represent any interface required for communication with other network elements. In some example embodiments, communication module 1740 may include at least one antenna.

[0183] As a non-limiting example, processor 1710 can be any type suitable for a local technology network and can include one or more of the following: general-purpose computer, special-purpose computer, microprocessor, digital signal processor (DSP), and processor based on a multi-core processor architecture. Device 1700 can have multiple processors, such as application-specific integrated circuit chips that are time-dependent on a clock of a synchronous main processor.

[0184] Memory 1720 may include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, read-only memory (ROM) 1724, electrically programmable read-only memory (EPROM), flash memory, hard disk, optical disc (CD), digital video disc (DVD), optical disc, laser disc, and other magnetic and / or optical storage. Examples of volatile memories include, but are not limited to, random access memory (RAM) 1722 and other volatile memories that will not persist for the duration of a power outage.

[0185] Computer program 1730 includes computer-executable instructions that are executed by an associated processor 1710. The instructions of program 1730 may include instructions for performing operations / actions of some example embodiments of this disclosure. Program 1730 may be stored in memory (e.g., ROM 1724). Processor 1710 can perform any suitable actions and processes by loading program 1730 into RAM 1722.

[0186] Example embodiments of this disclosure can be implemented by program 1730, enabling device 1700 to execute as shown in Figures 2 to 3. Figure 16 Any process discussed in this disclosure. Exemplary embodiments of this disclosure may also be implemented by hardware or by a combination of software and hardware.

[0187] In some example embodiments, program 1730 may be tangibly contained in a computer-readable medium, which may be included in device 1700 (such as memory 1720) or other storage device accessible by device 1700. Device 1700 may load program 1730 from the computer-readable medium into RAM 1722 for execution. In some example embodiments, the computer-readable medium may include any type of non-transitory storage medium, such as ROM, EPROM, flash memory, hard disk, CD, DVD, etc. As used herein, the term "non-transitory" is a limitation on the medium itself (i.e., tangible, not tactile), rather than a limitation on the persistence of data storage (e.g., RAM and ROM).

[0188] Figure 18 An example of a computer-readable medium 1800 is shown, which may be in the form of a CD, DVD, or other optical storage disc. Program 1730 is stored on the computer-readable medium 1800.

[0189] Generally, the various embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of this disclosure are illustrated and described as block diagrams, flowcharts, or using some other graphical representation, it should be understood that, as non-limiting examples, the blocks, apparatuses, systems, techniques, or methods described herein can be implemented in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0190] Some exemplary embodiments of this disclosure also provide at least one computer program product tangibly stored on a computer-readable medium, such as a non-transitory computer-readable medium. The computer program product includes computer-executable instructions that execute in a device on a target physical or virtual processor, such as those included in a program module, to perform any of the methods described above. Typically, a program module includes routines, programs, libraries, objects, classes, components, data structures, etc., that perform a particular task or implement a particular abstract data type. In various embodiments, the functionality of a program module can be combined or split among program modules as needed. The machine-executable instructions for a program module can execute within a local or distributed device. In a distributed device, the program module can reside in both local and remote storage media.

[0191] Program code used to perform the methods of this disclosure may be written in any combination of one or more programming languages. The program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a stand-alone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0192] In the context of this disclosure, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, etc.

[0193] Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable media can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any suitable combination thereof. More specific examples of computer-readable storage media will include electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable optical disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0194] Furthermore, although operations are described in a specific order, this should not be construed as requiring that such operations be performed in the specific order shown or sequentially, or that all shown operations be performed to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the discussion above, these details should not be construed as limiting the scope of this disclosure, but rather as descriptions of features that may be specific to particular embodiments. Unless explicitly stated otherwise, certain features described in the context of a single embodiment may also be implemented in combination in a single embodiment. Conversely, unless explicitly stated otherwise, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0195] Although this disclosure has been described in language specific to structural features and / or methodological actions, it should be understood that the disclosure as defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are disclosed as exemplary forms for implementing the claims.

[0196] Program code used to perform the methods of this disclosure may be written in any combination of one or more programming languages. The program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a stand-alone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0197] In the context of this disclosure, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, etc.

[0198] Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable media can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any suitable combination thereof. More specific examples of computer-readable storage media will include electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable optical disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0199] Furthermore, although operations are described in a specific order, this should not be construed as requiring that such operations be performed in the specific order shown or sequentially, or that all shown operations be performed to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the discussion above, these details should not be construed as limiting the scope of this disclosure, but rather as descriptions of features that may be specific to particular embodiments. Unless explicitly stated otherwise, certain features described in the context of a single embodiment may also be implemented in combination in a single embodiment. Conversely, unless explicitly stated otherwise, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0200] Although this disclosure has been described in language specific to structural features and / or methodological actions, it should be understood that the disclosure as defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are disclosed as exemplary forms for implementing the claims.

[0201] Furthermore, the various embodiments of this disclosure can be described with reference to the following terms, and their features can be combined in any reasonable manner.

[0202] Clause 1. A first means for communication, comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the first means to at least: receive configuration information for beam management from a network device, the configuration information indicating a threshold number of instances for triggering a report of an event, and a predicted number of instances to be determined based on a reference signal quality prediction, the instances representing conditions for the event being met; determine an instance of a measurement number of instances for the beam based on measurements of the reference signal quality associated with the beam's Transmission Configuration Indicator (TCI) state; and determine an instance of the predicted number of instances for the beam based on the predictions of the reference signal quality associated with the beam's TCI state, wherein the sum of the measurement number of instances and the predicted number of instances is equal to or greater than the threshold number of instances.

[0203] Clause 2. The first apparatus according to Clause 1, wherein the configuration information further indicates a confidence threshold for the prediction of the reference signal quality, and wherein the first apparatus is configured to: transmit a report of the event for the beam to the network device based on determining that the prediction confidence for instances of the predicted number of instances is higher than the confidence threshold; and re-perform the prediction based on determining that the prediction confidence for instances of the predicted number of instances is lower than the confidence threshold.

[0204] Clause 3. The first apparatus according to Clause 1, wherein the first apparatus is configured to: perform the prediction of the reference signal quality associated with the TCI state of the beam by using an artificial intelligence / machine learning (AI / ML) model.

[0205] Clause 4. The first apparatus according to Clause 3, wherein the prediction of the reference signal quality on the beam is performed based on at least one of: the measurement of the reference signal quality on the beam, the reference signal associated with the TCI state of the beam, the number of threshold instances, a time window for determining instances of at least the number of threshold instances, the threshold signal quality for determining whether the condition is met, the location of the first apparatus, or the characteristics of the communication channel between the first apparatus and the network device.

[0206] Clause 5. The first means according to any one of Clauses 1 to 4, wherein the first means is configured to: transmit a report of the event for the beam to the network device by using uplink resources available after determining the predicted number of instances.

[0207] Clause 6. The first means according to any one of Clauses 1 to 4, wherein the first means is configured to: transmit to the network device an indication of the first means's ability to predict the instance.

[0208] Clause 7. The first device according to Clause 3, wherein the AI / ML model is trained using training data, the training data including at least one of the following: one or more historical threshold instance numbers indicated to the first device, reference signal received power (RSRP) measured by the first device, the location of the first device, the corresponding identifiers of one or more beams that historically triggered reports of the event, or the characteristics of the communication channel between the first device and the network device.

[0209] Clause 8. The first means according to any one of Clauses 1 to 4, wherein the first means is configured to: receive from the network device an indication of an updated threshold instance number.

[0210] Clause 9. The first means according to any one of Clauses 1 to 4, wherein the first means is configured to: receive from the network device an indication of the network device's ability to predict the threshold number of instances.

[0211] Clause 10. A second means for communication, comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the second means to at least: determine a threshold number of instances for triggering a report of an event based on prediction, the instances representing conditions for the event being met; determine a predicted number of instances to be determined by a terminal device based on reference signal quality prediction; and transmit configuration information for beam management to the terminal device, the configuration information indicating the threshold number of instances and the predicted number of instances.

[0212] Clause 11. The second apparatus according to Clause 10, wherein the second apparatus is configured to: perform the prediction of the threshold number of instances by using an artificial intelligence / machine learning AI / ML model.

[0213] Clause 12. The second apparatus according to Clause 11, wherein the prediction of the threshold instance number of instances is performed based on at least one of the following: characteristics of a first communication channel between the first apparatus and the network device, one or more historical threshold instance numbers indicated to the terminal device, a threshold instance number indicated to another terminal device having communication channel characteristics that match the characteristics of the first communication channel, or a threshold instance number indicated to another terminal device near the terminal device.

[0214] Clause 13. A second device according to any one of Clauses 10 to 12, wherein the second device is further configured to: receive from the terminal device an indication of the first device's ability to predict the instance.

[0215] Clause 14. A method for communication, comprising: receiving configuration information for beam management from a network device, the configuration information indicating a threshold number of instances for triggering a report of an event, and a predicted number of instances to be determined based on a reference signal quality prediction, the instances representing conditions for the event being met; determining instances of a measurement number for the beam based on measurements of the reference signal quality associated with a transmission configuration indicator (TCI) state of the beam; and determining instances of the predicted number of instances for the beam based on the prediction of the reference signal quality associated with the TCI state of the beam, wherein the sum of the measurement number and the predicted number of instances is equal to or greater than the threshold number of instances.

[0216] Clause 15. A method for communication, comprising: determining a threshold number of instances for triggering a report of an event based on prediction, wherein an instance represents a condition for the event being met; determining a predicted number of instances to be determined by a terminal device based on a reference signal quality prediction; and transmitting configuration information for beam management to the terminal device, the configuration information indicating the threshold number of instances and the predicted number of instances.

Claims

1. A first device for communication, comprising: At least one processor; as well as At least one memory storing instructions that, when executed by the at least one processor, cause the first device to at least: The network device receives configuration information for beam management, which indicates a threshold number of instances for triggering event reporting and a predicted number of instances to be determined based on reference signal quality prediction, where each instance represents a condition for the event being met. The number of measurement instances for the beam is determined based on the measurement of the reference signal quality associated with the beam's transmission configuration indicator (TCI) status. as well as The number of prediction instances for the beam is determined based on the prediction of the reference signal quality associated with the TCI state of the beam, wherein the sum of the number of measurement instances and the number of prediction instances is equal to or greater than the threshold number of instances.

2. The first apparatus of claim 1, wherein the configuration information further indicates a confidence threshold for the quality prediction of the reference signal, and wherein the first apparatus is configured such that: Based on the determination that the prediction confidence of instances for the predicted number of instances is higher than the confidence threshold, a report of the event for the beam is transmitted to the network device; and If the prediction confidence for any instance of the predicted number of instances is determined to be lower than the confidence threshold, the prediction is re-executed.

3. The first device according to claim 1, wherein the first device is configured to: The prediction of the reference signal quality associated with the TCI state of the beam is performed by using an artificial intelligence / machine learning (AI / ML) model.

4. The first apparatus of claim 3, wherein the prediction of the reference signal quality on the beam is performed based on at least one of the following: The measurement of the quality of the reference signal on the beam, The reference signal associated with the TCI state of the beam. The number of threshold instances, The time window used to determine the number of instances at least according to the threshold. The quality of the threshold signal used to determine whether the condition is met. The position of the first device, or Characteristics of the communication channel between the first device and the network device.

5. The first device according to claim 1, wherein the first device is configured to: The event report for the beam is transmitted to the network device using uplink resources available after the predicted number of instances has been determined.

6. The first device according to claim 1, wherein the first device is configured to: The network device is transmitted an indication instructing the first device to predict the instance.

7. The first apparatus of claim 3, wherein the AI / ML model is trained using training data, the training data comprising at least one of the following: The number of one or more historical threshold instances indicated to the first device. The reference signal received power RSRP measured by the first device, The position of the first device. The corresponding identifier of one or more beams that historically triggered the report of the event, or Characteristics of the communication channel between the first device and the network device.

8. The first device according to claim 1, wherein the first device is configured to: Receive an indication of the updated threshold number of instances from the network device.

9. The first device according to claim 1, wherein the first device is configured to: The network device receives an indication of its ability to predict the threshold number of instances.

10. A second means for communication, comprising: At least one processor; as well as At least one memory storing instructions that, when executed by the at least one processor, cause the second device to at least: A threshold number of instances is determined based on predictions to trigger a report for the event, where an instance represents a condition for the event being met. The number of predicted instances to be determined by the terminal device is based on the reference signal quality prediction. The terminal device is transmitted configuration information for beam management, the configuration information indicating the number of threshold instances and the number of predicted instances.

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