Measurement reporting method and user equipment

Abstract

The present disclosure provides a measurement reporting method and user equipment. The measurement reporting method comprises: a user equipment (UE) receiving a radio resource control (RRC) message sent by a network side and containing a measurement configuration used for artificial intelligence (AI) / machine learning (ML) model assisted radio resource management (RRM) measurement; saving the received measurement configuration to a UE variable; and, on the basis of the saved measurement configuration, executing a model-assisted measurement prediction, evaluation and reporting process, comprising: if a time difference between a moment at which a predicted measurement event occurs and a moment at which the same measurement event occurs in a predicted measurement report sent to the network side a previous time is greater than a first threshold value in the measurement configuration, and the measurement events are associated with the same cell, triggering a measurement reporting process.

Description

Measurement reporting methods and user equipment Technical Field

[0001] This disclosure relates to the field of wireless communication technology, and more specifically, to a measurement reporting method and corresponding user equipment. Background Technology

[0002] Artificial Intelligence / Machine Learning (AI / ML) represents a significant revolution in computer science and data processing. AI / ML typically refers to processes and algorithms that simulate human intelligence, using the collection, analysis, learning, and deduction of existing data to solve problems in various fields. At the 3GPP RAN plenary meeting in September 2024, a research project on the application of AI / ML in NR mobility was approved (see 3GPP non-patent document RP-242393). This research project focuses on enhancing air interface mobility in Radio Resource Connection (RRC) states, referring to changes in the Primary Cell (PCell) in the NR system. This project studies and evaluates the benefits and gains of AI / ML-assisted, network-triggered Layer 3 handover mobility, primarily considering the following aspects:

[0003] • AI / ML-based Radio Resource Management (RRM) measurement and event prediction. This includes cell-level measurement prediction, encompassing intra-fequency and inter-frequency measurements; Radio Link Failure (RLF) prediction; and handover failure prediction, among others.

[0004] • Research the necessity and benefits of UE auxiliary information in network-side models.

[0005] The impact of AI / ML-assisted mobility on 3GPP specifications.

[0006] In existing NR systems, the UE performs measurements for RRM and reports the obtained measurement results to the network based on the reporting configuration (such as measurement events) configured on the network side. The network side then performs RRC connected-state mobility management and decision-making based on the received measurement results actually performed by the UE.

[0007] This disclosure aims to address the issues of AI / ML-based measurement configuration or measurement reporting in NR networks, and further, to address the problem of reducing unnecessary reporting of measurement results predicted by the UE in systems that enable AI / ML-assisted RRM measurements. Summary of the Invention

[0008] The main objective of this disclosure is to provide a measurement reporting method and user equipment to reduce the unnecessary reporting of measurement results predicted by the UE in a system with AI / ML-assisted RRM measurement.

[0009] According to a first aspect of this disclosure, a measurement reporting method is provided, comprising: a user equipment (UE) receiving a measurement configuration radio resource control (RRC) message sent by a network side, comprising a measurement configuration for radio resource management (RRM) measurements assisted by an artificial intelligence (AI) / machine learning (ML) model; the measurement configuration comprising one or more of the following information: first information indicating whether the RRM measurement model is direct or indirect; second information enabling a mechanism for determining whether to trigger a measurement reporting process based on the time of occurrence of a predicted measurement event; third information indicating a time threshold for the UE to determine whether to trigger a measurement reporting process based on the time of occurrence of a predicted measurement event and the third information; fourth information enabling a mechanism for determining whether to trigger a measurement reporting process based on the probability of occurrence of a predicted measurement event; and fifth information indicating a percentage threshold for the UE to determine whether to trigger a measurement reporting process based on the probability of occurrence of a predicted measurement event and the fifth information; and saving the received measurement configuration to a UE variable; and performing a model-assisted measurement prediction, evaluation, and reporting process based on the saved measurement configuration.

[0010] In the measurement reporting method of the first aspect above, the measurement configuration refers to the configuration associated with model-assisted RRM measurement prediction or the input information of the model, including one or more of the following: measurement identifier, measurement object, measurement report configuration, measurement reference cell, observation window size, prediction window size, model identifier, etc.

[0011] In the measurement reporting method of the first aspect mentioned above, the direct output of the indirect prediction model is to predict the signal quality of the measured cell at a future time, and then to indirectly obtain whether a measurement event occurs at that time based on the signal quality and the measurement event configuration; the output of the direct prediction model is the probability of a measurement event occurring at a future time or time period.

[0012] In the measurement and reporting method of the first aspect mentioned above, the fifth piece of information is a threshold value or a hysteresis parameter.

[0013] In the measurement reporting method of the first aspect above, the execution model-assisted measurement prediction and evaluation includes: if the time difference between the time of occurrence of a predicted measurement event and the time of occurrence of the same measurement event in the last predicted measurement report sent by the UE to the network side is greater than or equal to a first threshold value, and the measurement event is associated with the same or the same group of cells, then a measurement reporting process is triggered.

[0014] In the measurement reporting method of the first aspect above, the execution model-assisted measurement prediction and evaluation includes: if the signal quality measurement value of the measured cell corresponding to a predicted measurement event exceeds or equals a second threshold value compared with the signal quality of the measured cell in the previously predicted measurement report sent to the network side, the UE triggers a measurement reporting process.

[0015] In the measurement reporting method of the first aspect above, the execution model-assisted measurement prediction and evaluation includes: if the difference between the predicted probability of a measurement event occurring and a threshold value TH1 is greater than or equal to another threshold value TH2, the entry condition of the measurement event is considered to be met.

[0016] In the measurement reporting method of the first aspect above, the execution model-assisted measurement prediction and evaluation includes: if the sum of the predicted probability of a measurement event occurring and a threshold value TH3 is less than or equal to another threshold value TH4, it is considered that the departure condition of the measurement event is met.

[0017] In the measurement reporting method of the first aspect mentioned above, the execution model-assisted measurement prediction and evaluation is performed on a measurement identifier, a measurement object, a measured cell, or a measured event.

[0018] According to a second aspect of this disclosure, a user equipment is provided, comprising: a processor; and a memory storing instructions; wherein the instructions, when executed by the processor, perform the aforementioned measurement reporting method. Attached Figure Description

[0019] The above and other features of this disclosure will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, wherein:

[0020] Figure 1 is a schematic diagram of the measurement model for RRM measurement in the NR system.

[0021] Figures 2 and 3 are schematic flowcharts illustrating measurement reporting methods with multiple implementation methods or steps.

[0022] Figure 4 shows a block diagram of a user equipment according to an embodiment of the present disclosure. Detailed Implementation

[0023] Other aspects, advantages, and key features of this disclosure will become apparent to those skilled in the art from the following detailed description of exemplary embodiments of the disclosure taken in conjunction with the accompanying drawings.

[0024] In this disclosure, the terms “comprising” and “containing” and their derivatives are meant to include rather than limit; the term “or” is inclusive and may be equivalent to “and” or “and / or”.

[0025] In this specification, the various embodiments described below to illustrate the principles of this disclosure are merely illustrative and should not be construed as limiting the scope of the disclosure in any way. The following description, with reference to the accompanying drawings, is intended to aid in a comprehensive understanding of exemplary embodiments of this disclosure as defined by the claims and their equivalents. The following description includes various specific details to aid understanding, but these details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Furthermore, for clarity and brevity, descriptions of well-known functions and structures have been omitted. Additionally, throughout the drawings, the same reference numerals are used for similar functions and operations.

[0026] The following description uses an NR mobile communication system as an example application environment to illustrate several implementations according to this disclosure. However, it should be noted that this disclosure is not limited to the following implementations, but is applicable to many other wireless communication systems.

[0027] The base station in this disclosure can be any type of base station, including Node B, enhanced base station eNB, 5G communication system base station gNB; or micro base station, pico base station, macro base station, home base station, etc.; the network side generally refers to the base station. The cell can also be a cell under any of the above-mentioned types of base stations. Unless otherwise specified, cell, beam, and transmission point (TRP) can be interchanged, and the base station can also be the central unit (gNB-Central Unit, gNB-CU) or distributed unit (gNB-Distributed Unit, gNB-DU) that makes up the base station. Different embodiments can also be combined, for example, the same variables / parameters / terms in different embodiments can be interpreted in the same way. Cancel, release, delete, clear, and clear can be replaced. Execute, use, and apply can be replaced. Configure and reconfigure can be replaced. Monitor and detect can be replaced. Initiate and trigger can be replaced. If..., if..., and under... circumstances can be replaced, or can be replaced with UE determine....

[0028] The following section will first explain some existing mechanisms involved in this disclosure. It is worth noting that some names in the following description are merely illustrative and not restrictive, and may be used in other ways.

[0029] Artificial Intelligence / Machine Learning (AI / ML)

[0030] In this disclosure, an AI / ML model is used to represent the application of AI / ML technology in the NR air interface. An AI / ML model can also be referred to as an AI / ML function. AI / ML technology can be divided into the following five aspects:

[0031] - AI / ML model training

[0032] The training of an AI / ML model represents the generation of an inference relation (e.g., a function) based on the combination of input and output parameters, which is then used for subsequent inference. Taking the RRM measurement model as an example, this model can be trained on the network side or by the UE. The input parameters of this model are the raw channel data (e.g., actual cell or beam measurement results), and the output parameters are the predicted RRM measurement results reported to the network.

[0033] - AI / ML model transfer

[0034] If an AI / ML model is trained by a network, the trained model can be sent from the network to the user (UE) for inference based on that model. This sending of the model is called AI / ML model transfer.

[0035] - AI / ML model inference

[0036] Taking the RRM measurement model as an example, the process of the UE inputting relevant input information (such as measurement events, measurement results of one or more cells actually measured) into an RRM measurement model and generating the predicted RRM measurement results by using the model is the inference process of the AI / ML model.

[0037] AI / ML model monitoring

[0038] The network or UE needs to monitor the AI / ML model used to determine whether the model is suitable for the current link state or network environment.

[0039] - AI / ML model update

[0040] When the network or UE deems the model no longer applicable, the AI / ML model will be updated.

[0041] RRM measurement

[0042] RRM measurements in connected state are primarily used for mobility management, such as PCell handover. RRM measurements include intra-cell measurements, intra-frequency measurements, inter-cell measurements, and inter-frequency measurements. The NR measurement model is shown in Figure 1.

[0043] -A: Measurement sample of a single beam inside the physical layer.

[0044] - Layer 1 filtering: The Layer 1 (L1) filtering process performed within the physical layer based on a single beam measurement sample.

[0045] -A1: Measurement results of a single beam obtained after layer 1 filtering. This result is reported from the physical layer to the RRC layer (i.e., from L1 to L3).

[0046] - Beam selection / merging: Select / merge several measurements from the beam measurement results reported by the physical layer to obtain the measurement results of the cell.

[0047] -B: Cell measurement results are reported to the RRC layer (i.e., L3).

[0048] - Layer 3 Cell Quality Filtering: The cell measurement results are filtered based on filtering parameters, etc.

[0049] -C: Measurement results, used as input for the evaluation of reporting criteria.

[0050] - Evaluate the reporting criteria: Based on the configuration parameters of the measurement report, evaluate whether it is necessary to trigger the measurement report reporting.

[0051] -D: Report a measurement report containing cell measurement results to the base station over the air interface.

[0052] - Layer 3 beam filtering: Layer 3 (L3) filtering is applied to the measurement results of a single beam.

[0053] -E: Measurement results of a single beam obtained after filtering.

[0054] - Beam selection reporting: Select the measurement results of X beams from the measurement results of K beams obtained from point E.

[0055] -F: Report a measurement report containing beam measurement results to the base station over the air interface.

[0056] In RRC connected mode, the network sends measurement configuration (such as that contained in the MeasConfig information element) to the UE via RRC messages (e.g., RRC reconfiguration messages). This measurement configuration can include: measurement object configuration, measurement report configuration, measurement identifier configuration, measurement quantity configuration, measurement interval configuration, etc. The UE stores the measurement configuration in its measurement configuration variable (VarMeasConfig).

[0057] Measurement objects: These are the objects that the UE needs to measure. The network can configure a list of measurement objects containing multiple objects. The measurement object configuration mainly includes: the time-frequency resource location and subcarrier spacing of the reference signal used for measurement, the frequency information of the measurement, and the cell information of the measurement. Measurement objects are identified by measurement object identifiers; each measurement object corresponds to a unique measurement object identifier.

[0058] Reporting configurations: Each measurement object can correspond to one or more reporting configurations, and the network can configure a list of reporting configurations containing multiple measurement report configurations. Measurement report configurations mainly include: reporting criteria, measurement events, reference signal types, and report formats. Measurement reports are identified by a measurement report identifier, and each measurement report corresponds to a unique identifier.

[0059] Measurement identities (Measurement IDs): Used to associate measurement object identifiers (IDs) and measurement report identifiers (IDs). Each measurement identity is associated with one measurement object identifier and one measurement report identifier. The network can be configured to contain a list of multiple measurement identities.

[0060] Quantity configurations: This mainly includes the configuration of the filtering parameters for the measurement.

[0061] Measurement gaps: The time period that the UE may use for measurement.

[0062] The measurement report configuration allows you to configure the type of triggering measurement reports, including periodically triggered measurement reports and event-triggered measurement reports. For event-triggered measurement reports, the network configures the triggering conditions, such as signal quality and time thresholds. When these conditions are met, the UE sends a measurement report to the network. After sending an event-triggered measurement report, the UE can periodically report the measurement report for that event to the base station multiple times. That is, after sending a measurement report triggered by a certain event, the UE starts a periodic reporting timer for the measurement ID associated with that event. After the periodic reporting timer expires, the UE re-reports the measurement report corresponding to the measurement ID associated with that event.

[0063] If the measurement report type is set to eventTriggered, and if the entering condition applies to a measurement event (i.e., one or more cells among all cell measurement results after Layer 3 filtering meet the entering condition of the measurement event), the UE adds the corresponding measurement ID to the UE Variable Measurement Report List (VarMeasReportList), includes the cell (one or more) that triggered the measurement event (or the concerned cell) in the cellTriggeredList, and initiates the measurement report process. If the triggering event is related to neighboring cells, such as Event A3 (neighboring cell has better quality than SpCell and exceeds a threshold), Event A4 (neighboring cell quality exceeds a threshold), Event A5 (SpCell is below threshold 1 and neighboring cell is above threshold 2), then the cells included in the cellsTriggeredList are the neighboring cells.

[0064] The measurement configuration variable and the measurement report list variable are both internal variables of the UE. The measurement configuration variable contains the cumulative configuration of the measurements that the UE will perform. The measurement report list variable contains measurement result information that has met the trigger conditions.

[0065] The UE reports the measured results to the network via RRC messages (Measurement Report Messages). The UE includes the measurement results from the UE Variable Measurement Report List in the RRC message and indicates them to the network. The measurement results generally include at least one or more cell-level measurement results / signal quality or beam-level measurement results / signal quality for one or more cells or frequencies. The measurement results / signal quality are usually characterized by Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), or Received Signal Strength Indicator (RSSI).

[0066] AI / ML-assisted RRM measurement

[0067] In the AI / ML-assisted mobility research project currently being studied by 3GPP, AI / ML-assisted RRM measurement and measurement event prediction are involved. The main objective is to achieve RRM measurement prediction and measurement event prediction in the time, frequency, or spatial domains through inference using pre-trained AI / ML models. This predictive approach reduces the measurement overhead of RRM measurements on the UE and improves mobility performance such as handover robustness.

[0068] For ease of description, AI / ML models are simply referred to as models.

[0069] In current 3GPP discussions, RRM measurement prediction uses the actual measured results as model input and the predicted measurement results as output. For example, for time-domain prediction of RRM measurements, the model input is the actual measured results at time T0, and the output is the predicted measurement results (such as RSRP values) at time T1; for frequency-domain prediction of RRC measurements, the model input is the actual measured results of one or more cells at frequency f1, and the model output is the predicted measurement results of one or more cells at frequency f2. In one possible approach, the model input may also include the predicted RRM measurement results. For example, by inputting the actual measured results and a portion of the predicted measurement results, another predicted measurement result can be obtained.

[0070] In current 3GPP conclusions, measurement event prediction employs two methods: direct and indirect. In the direct method, the model input includes the actual measured RRM (Resonance Rating Scale) results, and the model output is the probability of a measurement event occurring, or more specifically, the probability of a measurement event occurring within a given timeframe or time period. In the indirect method, the model input includes the actual measured RRM results, and the model output is the predicted measurement results (such as RSRP values) of one or more serving cells or one or more neighboring cells. The output of the indirect method can be considered an intermediate output, and the final output is based on this intermediate output to predict the expected occurrence time of a measurement event. Clearly, the model's involvement is not always necessary to reach the final output. In other words, the direct output of the indirect prediction model is the predicted signal quality of the measured cell at a future time, and then, based on this signal quality and the measurement event configuration, it indirectly determines whether a measurement event will occur at that time. The output of the direct prediction model is the probability of a measurement event occurring within a future timeframe or time period.

[0071] Typically, the model's input parameters may also include an observation window. In the time domain, the observation window is a time window or a period of time, such as 100ms. Additionally, the model's input parameters may include a prediction window. The user interface (UE) uses the (actually measured) measurements within the observation window to predict the output within the prediction window.

[0072] Based on current 3GPP discussions, for indirect prediction models, if the UE predicts the triggering of a certain measurement event, the UE reports the triggering of the event to the network side. For direct prediction models, if the UE predicts the triggering probability of a certain event, the UE reports the prediction result to the network side. In this model-assisted prediction RRM measurement result reporting mechanism, how to configure the corresponding measurement configuration and how to avoid unnecessary prediction RRM measurement result reporting are among the issues addressed in this disclosure.

[0073] This disclosure addresses at least some of the problems related to the aforementioned measurement configuration for predicted RRM measurements and the avoidance of unnecessary reporting of predicted RRM measurement results. Specific implementation methods are given below. Through the measurement reporting method described in this disclosure, the UE receives configuration information related to model-assisted predicted RRM measurements from the network side and determines whether to trigger the reporting process of model-predicted RRM measurement reports, thus avoiding energy and signaling overhead caused by unnecessary measurement report reporting.

[0074] The following provides a detailed description of specific examples and embodiments related to this invention. Furthermore, as described above, the examples and embodiments described in this disclosure are illustrative examples provided for easy understanding of the invention and are not intended to limit the invention. In the following embodiments, the order of the steps is merely illustrative and not strictly limited; the implementation steps can also be combined and implemented without limitation.

[0075] Figure 2 is a schematic flowchart illustrating some implementation steps of an embodiment of the measurement reporting method. As shown in Figure 2, this embodiment includes any one or more of the following steps.

[0076] Optionally, in step 201, the UE receives an RRC-specific message (e.g., an RRC Reconfiguration message) sent by the network, which contains one or more measurement configurations for model-aided RRM measurements.

[0077] Optionally, the measurement configuration refers to the configuration associated with model-assisted RRM measurement prediction, which may include one or more of the following: measurement ID, measurement object, measurement report configuration, measurement quantity configuration, measurement reference cell, observation window size, prediction window size, number of time instances, model identifier, etc. Optionally, the model identifier is used to identify or recognize a model or functional body on the network side or UE.

[0078] Optionally, the measurement configuration includes first information indicating that the aforementioned RRM measurement model or measurement method is direct (or indirect). This indication can be an explicit signaling indication or an implicit indication, such as implicitly indicating the type of the measurement model or measurement method through another parameter.

[0079] Optionally, the measurement configuration includes second information indicating whether the UE is enabled to determine whether to trigger a measurement reporting process (or whether the measurement event is satisfied, triggered, or predicted) based on the predicted occurrence time of a measurement event. For example, if the second indication information indicates that the UE is enabled to determine whether to trigger a measurement reporting process (or whether the measurement event is satisfied, triggered, or predicted) based on the predicted occurrence time of a measurement event, then when the UE determines whether to trigger a measurement reporting process (or whether the measurement event is satisfied, triggered, or predicted) during model-assisted RRM measurement prediction, it needs to determine whether to trigger a measurement reporting process (or whether the measurement event is satisfied, triggered, or predicted) based on the occurrence time of a predicted measurement event. Optionally, the second information is configured when the UE supports an indirect model. Optionally, the second information is configured / existent only when the first information indicates an indirect model.

[0080] Optionally, the measurement configuration includes third information, which is a time threshold. The UE determines whether to trigger the measurement reporting process (or whether the measurement event is met, triggered, or predicted) based on the predicted time of a measurement event and the time threshold. Optionally, the third information is configured when the UE supports an indirect model. Optionally, the third information is configured / exists only when the first information indicates an indirect model.

[0081] Optionally, the measurement configuration includes a fourth piece of information indicating whether the UE is enabled to determine whether to trigger a measurement reporting process (or whether a measurement event is satisfied, triggered, or predicted) based on the predicted probability (and / or time interval) of a measurement event. For example, if the fourth indication information indicates that the UE is enabled to determine whether to trigger a measurement reporting process (or whether a measurement event is satisfied, triggered, or predicted) based on the predicted probability (and / or time interval) of a measurement event, then when the UE determines whether to trigger a measurement reporting process (or whether a measurement event is satisfied, triggered, or predicted) during model-assisted RRM measurement prediction, it needs to determine whether to trigger a measurement reporting process (or whether a measurement event is satisfied, triggered, or predicted) based on the predicted probability (and / or time interval) of a certain measurement event. Optionally, the fourth piece of information is configured when the UE supports a direct model. Optionally, the fourth piece of information is configured / existent only when the first information indicates a direct model.

[0082] Optionally, the measurement configuration includes fifth information, which is identified as a percentage (e.g., 5%), used by the UE to determine whether to trigger the measurement reporting process or whether the measurement event is met, triggered, or predicted based on the predicted probability (and / or time interval) of the measurement event and the fifth information. Optionally, the fifth information is configured when the UE supports the direct model. Optionally, the fifth information is configured / exists only when the first information indicates the direct model. Optionally, the fifth information is a hysteresis parameter corresponding to a specific measurement event and can be included in the configuration corresponding to that measurement event (e.g., in the reporting configuration).

[0083] Optionally, in step 202, after receiving the RRC proprietary message, the UE saves the measurement configuration therein to a UE variable.

[0084] Optionally, in step 203, the UE performs model-aided measurement prediction and related measurement reporting evaluation based on the received or saved measurement configuration.

[0085] Figure 3 is a schematic flowchart illustrating some implementation steps in an embodiment of the measurement reporting method. This is performed on the UE. As shown in Figure 3, this embodiment includes any one or more of the following steps.

[0086] Optionally, in step 301, the UE performs model-assisted RRM measurement prediction. During this process, the UE obtains the output of a model, which may be the specific time of a measurement event or the signal quality of the measurement object (such as the measured cell) associated with the measurement event. Based on the output, the UE may perform one or more operations, including those described below.

[0087] Optionally, in one operation 1, if the output is the first output obtained since the model was configured or activated, that is, the first prediction of the occurrence of the measurement event, then the UE triggers a measurement reporting process (or considers the measurement event to have been triggered or predicted). Optionally, for a measurement event, a measured cell, or a measurement identifier, when the occurrence of the measurement event is predicted for the first time, the UE triggers a measurement reporting process (or considers the measurement event to have been triggered or predicted).

[0088] Optionally, in one operation 2, if the time gap between the predicted occurrence time of the measurement event and the occurrence time of the measurement event in the last (i.e., most recent) predicted measurement report sent by the UE to the network side is greater than or equal to a time threshold (called the first threshold), then the UE triggers a measurement reporting process (or considers the measurement event to have been triggered or predicted). If the measurement event associated with triggering the measurement reporting process and the predicted measurement event in the last predicted measurement report sent to the network side are the same measurement event, such as both being A3, and / or the associated measurement event and the predicted measurement event in the last predicted measurement report sent to the network side are for the same measured cell (neighboring cell), then operation 2 is executed. Optionally, operation 2 is performed for a measurement event or a measured cell. For example, the UE triggers and sends a predicted measurement report to the network side at time T1, the content of which indicates that a neighboring cell A will trigger a measurement event E1 at a future time T2 and that the predicted signal quality of cell A at time T2 is RSRP1. When the UE predicts at another time T3 that neighboring cell A will trigger the same measurement event E1 at a future time T4, the UE determines that if the time gap between T2 and T4 exceeds or equals a time threshold (first threshold), the UE triggers a new measurement reporting process (or considers the measurement event to have been triggered or predicted), and reports a measurement report to the network containing the content that neighboring cell A will trigger measurement event E1 at a future time T4. Optionally, if the UE determines that the time gap between T2 and T4 is less than or equal to a time threshold (first threshold), the UE does not trigger a measurement reporting process for this predicted measurement result (or considers the measurement event not to have been triggered or predicted), that is, it does not report a measurement report to the network containing the content that neighboring cell A will trigger measurement event E1 at a future time T4. Optionally, the time threshold (first threshold) mentioned in operation 2 is configured to the UE by the network side, as described in the second information above. Optionally, the time threshold can also be determined by the UE's model itself.

[0089] Optionally, in one operation 3, if the signal quality measurement value of the measured cell corresponding to the predicted measurement event exceeds or equals a signal quality threshold (called the second threshold value) in the predicted measurement report sent by the UUE to the network side in the last (i.e., most recent) prediction, then the UE triggers a measurement reporting process (or considers the measurement event to have been triggered or predicted). If the measurement event associated with triggering the measurement reporting process and the predicted measurement event in the last predicted measurement report sent to the network side are the same measurement event, such as both being A3, then operation 3 is executed. For example, the UE triggers and sends a predicted measurement report to the network side at time T1. The content of the measurement report indicates that a neighboring cell A (at a future time T2) will trigger a measurement event E1 and the predicted signal quality of cell A at time T2 is RSRP1. If, at another time T3, the UE predicts that neighboring cell A (at a future time T4) will trigger the same measurement event E1 and that the predicted signal quality of cell A at time T4 is RSRP2, and the UE determines that if the difference between RSRP1 and RSRP2 exceeds or equals a signal quality threshold (a second threshold), the UE triggers a new measurement reporting process (or considers the measurement event to have been triggered or predicted), and reports a measurement report to the network containing the content that neighboring cell A (at a future time T4) will trigger measurement event E1 and the predicted measurement value is RSRP2. Optionally, if the UE determines that the difference between RSRP1 and RSRP2 exceeds or equals a signal quality threshold (a second threshold), the UE does not trigger a measurement reporting process for this predicted measurement result (or considers the measurement event not to have been triggered or predicted), i.e., it does not report a measurement report to the network containing the content that neighboring cell A (at a future time T4) will trigger measurement event E1 and the predicted measurement result is RSRP2. Optionally, the signal quality threshold (second threshold) mentioned in operation 3 is configured to the UE by the network side and is expressed in dB or dBm.

[0090] Optionally, in one operation 4, it is equivalent to combining operations 2 and 3. If the time gap between the predicted occurrence time of the measurement event and the occurrence time of the measurement event in the last (i.e., most recent) predicted measurement report sent by the UE to the network side is greater than or equal to a time threshold (first threshold), or if the signal quality measurement value of the measured cell corresponding to the predicted measurement event exceeds or equals a signal quality threshold (second threshold) in the last (i.e., most recent) predicted measurement report sent by the UE to the network side, then the UE triggers a measurement reporting process (or considers the measurement event to have been triggered or predicted).

[0091] Optionally, the time of occurrence of the measurement event described in operations 2 to 4 is a moment (time point), that is, not a period of time.

[0092] Optionally, triggering a measurement event refers to the fulfillment of entry conditions for one or more measured cells within a certain period (e.g., a time period called timeToTrgger). If all measurements after Layer 3 filtering within this time period satisfy the entry conditions for the measurement event, then the measurement event can be considered triggered, or the measurement event is satisfied / triggered. The entry conditions are configured by the network side and are specific to the measurement event, such as signal quality thresholds, signal quality offsets, etc. The time period (timeToTrgger information element) is configured by the network side and is specific to the measurement event.

[0093] When multiple cells are involved in a single measurement event or measurement identifier, the judgment described in operations 2-4 above is performed on these multiple involved cells. Optionally, in the judgment, when the prediction information corresponding to at least one cell satisfies the time threshold (first threshold) or signal quality threshold (second threshold), the UE triggers a measurement reporting process to report the new predicted measurement result to the network side. That is, if, for at least one involved cell, the time gap between the predicted occurrence time of the measurement event and the occurrence time of the measurement event in the UE's last (i.e., most recent) predicted measurement report to the network side is greater than or equal to a time threshold, or if the signal quality measurement value of the measured cell corresponding to the predicted measurement event exceeds or equals a signal quality threshold in the UE's last (i.e., most recent) predicted measurement report to the network side, then the UE triggers a measurement reporting process (or considers the measurement event to have been triggered or predicted). Optionally, in another approach, when the prediction information corresponding to all cells involved in the measurement event / measurement identifier meets the time threshold or signal quality threshold, the UE triggers a measurement reporting process to report the new predicted measurement results to the network side. That is, if for each involved cell, the time gap between the predicted occurrence time of the measurement event and the occurrence time of the measurement event in the UE's last (i.e., most recent) predicted measurement report to the network side is greater than or equal to a time threshold, or if the signal quality measurement value of the measured cell corresponding to the predicted measurement event exceeds or equals a signal quality threshold in the UE's last (i.e., most recent) predicted measurement report to the network side, then the UE triggers a measurement reporting process (or considers the measurement event to have been triggered or predicted).

[0094] Optionally, operations 1 to 4 above are performed when the model is indirect.

[0095] Optionally, in one operation 5, if the difference between the predicted probability of the measurement event occurring and the probability of the measurement event occurring in the last (i.e., most recent) predicted measurement report sent by the UE to the network side is greater than or equal to a (percentage) threshold value (referred to as the third threshold value), then the UE triggers a measurement reporting process or the UE believes that the measurement event has been predicted or the UE believes that the measurement event has been triggered. Operation 5 is executed when the measurement event associated with triggering the measurement reporting process and the measurement event predicted in the last predicted measurement report sent to the network side are the same measurement event, such as both being A3, and / or the associated measurement event and the measurement event predicted in the last predicted measurement report sent to the network side are for the same measured cell (neighboring cell). Optionally, operation 5 is performed for a measurement event or a measured cell. For example, the UE triggers and sends a predicted measurement report to the network side at time T1, the content of which indicates that the probability of a neighboring cell A triggering a measurement event E1 within a future time period Duration1 is P1. When the UE predicts at another time T3 that the probability of neighboring cell A triggering the same measurement event E1 in a future time period Duration 2 is P2, the UE determines that if the difference between P1 and P2 exceeds or equals a threshold (third threshold), the UE triggers a new measurement reporting process (or considers the measurement event to have been triggered or predicted), and reports a measurement report to the network containing the content that neighboring cell A will trigger measurement event E1 (with a probability of P2) in a future time period Duration 2. Optionally, in addition to determining that the difference between P1 and P2 exceeds or equals a threshold (third threshold), the determination may also include another determination condition, namely, that Duration 1 and Duration 2 overlap in the time domain, or that the time difference between Duration 1 and Duration 2 exceeds or equals another time threshold (called the fourth threshold) (for example, the time difference may be the difference between the start time or the end time of these two time periods), or that the overlap between Duration 1 and Duration 2 in the time domain is relatively large, etc. Optionally, otherwise, if the UE determines that the difference between P1 and P2 is less than or equal to the threshold value (third threshold value), the UE will not trigger the measurement reporting process for this predicted measurement result (or consider that the measurement event has not been triggered or predicted), that is, it will not report a measurement report to the network containing the content that the neighboring cell A will trigger measurement event E1 (probability is P2) within a future time period Duration2. Optionally, the percentage threshold value (third threshold value) in operation 5 or the threshold value (fourth threshold value) in the Duration-related judgment is configured to the UE by the network side.

[0096] Optionally, in operation 6, if the difference between the predicted probability of the measurement event occurring and a (percentage) threshold (referred to as the sixth threshold) is greater than or equal to a percentage threshold (referred to as the fifth threshold), then the UE considers that the entry condition for the measurement event has been met, or the UE considers that the measurement event has been triggered, or the UE considers that the measurement event has been predicted. Optionally, operation 6 is performed for a measurement event or a measured cell. For example, if the UE predicts that the probability of a measured cell A triggering measurement event E1 within a future time period Duration is P, and the UE determines that the inequality P - Hys1 > Threshold1 holds, then the UE considers that the entry condition for the measurement event has been met, or the UE considers that the measurement event has been triggered or predicted. Here, Hys1 is the aforementioned sixth threshold, and Threshold1 is the aforementioned fifth threshold. Optionally, the percentage threshold (fifth threshold or sixth threshold) mentioned in operation 6 is configured to the UE by the network side. Alternatively, the sixth threshold value can also be regarded as a hysteresis parameter.

[0097] Optionally, in operation 7, if the sum of the predicted probability of the measurement event occurring and a (percentage) threshold (referred to as the eighth threshold) is less than or equal to a percentage threshold (referred to as the seventh threshold), then the UE considers the departure condition of the measurement event met, or the UE considers the measurement event not to be triggered, or the UE considers the measurement event not to be predicted. Optionally, operation 7 is performed for a measurement event or a measured cell. For example, if the UE predicts that the probability of a measured cell A triggering measurement event E1 within a future time period Duration is P, and the UE determines that the inequality P + Hys2 < Threshold2 holds, then the UE considers the departure condition of the measurement event met, or the UE considers the measurement event not to be triggered or not to be predicted. Here, Hys2 is the aforementioned eighth threshold, and Threshold2 is the aforementioned seventh threshold. Optionally, the percentage threshold in operation 7 is configured to the UE by the network side. Optionally, the eighth threshold can also be considered as a hysteresis parameter. Optionally, the sixth threshold value and the eighth threshold value are the same parameter; the seventh threshold value and the fifth threshold value are the same parameter.

[0098] Optionally, in operation 6 or 7, the content / definition of the measurement event includes the probability that the predicted measurement value satisfies a condition higher than a threshold value. Optionally, the predicted measurement value satisfying the condition indicates that the predicted measurement value of the measured cell or the predicted cell satisfies an inequality (e.g., the signal quality of the predicted cell is better than that of the reference cell by an offset).

[0099] Optionally, in operation 8, if the difference between the predicted probability of the measurement event occurring and a (percentage) threshold (referred to as the tenth threshold) is greater than or equal to a percentage threshold (referred to as the ninth threshold), then the UE considers that the departure condition of the measurement event has been met, or the UE considers that the measurement event has not been triggered, or the UE considers that the measurement event has not been predicted. Optionally, operation 8 is performed for a measurement event or a measured cell. For example, if the UE predicts that the probability of a measured cell A triggering measurement event E1 within a future time period Duration is P, and the UE determines that the inequality P - Hys3 > Threshold3 holds, then the UE considers that the departure condition of the measurement event has been met, or the UE considers that the measurement event has not been triggered or has not been predicted. Here, Hys3 is the aforementioned tenth threshold, and Threshold3 is the aforementioned ninth threshold. Optionally, the tenth threshold can also be considered as a hysteresis parameter.

[0100] Optionally, in operation 9, if the sum of the predicted probability of the measurement event occurring and a (percentage) threshold (referred to as the twelfth threshold) is less than or equal to a percentage threshold (referred to as the eleventh threshold), then the UE considers the entry condition for the measurement event to be met, or the UE considers the measurement event to be triggered, or the UE considers the measurement event to be predicted. Optionally, operation 9 is performed for a measurement event or a measured cell. For example, if the UE predicts that the probability of a measured cell A triggering measurement event E1 within a future time period Duration is P, and the UE determines that the inequality P + Hys4 < Threshold4 holds, then the UE considers the entry condition for the measurement event to be met, or the UE considers the measurement event to be triggered or predicted. Here, Hys4 is the aforementioned twelfth threshold, and Threshold4 is the aforementioned eleventh threshold. Optionally, the twelfth threshold can also be considered as a hysteresis parameter. Optionally, the tenth threshold value and the twelfth threshold value are the same parameter; the eleventh threshold value and the ninth threshold value are the same parameter.

[0101] Optionally, in operation 8 or 9, the content / definition of the measurement event includes the probability that the predicted measurement value meets a condition being below a threshold. Optionally, the predicted measurement value meeting the condition indicates that the predicted measurement value of the measured cell or the predicted cell meets an inequality (e.g., the signal quality of the predicted cell is better than that of the reference cell by an offset).

[0102] Optionally, when the UE determines, based on operation 6 or operation 9, that the entry condition of a measurement event is met and the duration of the met condition exceeds a certain period of time (the configured timeToTrigger), the UE will add an item to the UE variable used for measurement reporting to include the corresponding measurement result and trigger a measurement reporting process.

[0103] Optionally, when the UE determines that the departure condition of a measurement event is met based on operation 7 or operation 8, the UE will move the item containing the corresponding measurement result in the UE variable used for measurement reporting, or trigger a measurement reporting process.

[0104] Optionally, operations 5 to 9 above are performed when the model is direct.

[0105] Optionally, the above operations / behaviors are performed on each measurement identifier.

[0106] Optionally, the above operation / behavior is performed on each measured cell. Optionally, the measured cell refers to a neighboring cell, so the above operation can also be performed on a neighboring cell related to a measurement event.

[0107] Optionally, the above operations are performed on RRM measurement configurations (such as measurement identifiers, report configurations, or measurement objects) when the reporting type is configured to be triggered by an event.

[0108] Optionally, the measurement result may include at least one or more of the following: the predicted link / signal quality of the measured cell, the time information of the predicted measurement event (such as the predicted time of the measurement event, the predicted time period of the measurement event), the prediction window information (prediction window start or end point or window length), the observation window information (observation window start or end point or window length), the probability of the predicted measurement event, and the predicted or actual measurement value of the reference cell (such as the serving cell or PCell) corresponding to the measurement event.

[0109] Optionally, the signal quality is RSRP, or RSRQ, or RSSI, or SINR.

[0110] Optionally, the aforementioned threshold values ​​are configured to the UE by the network side via RRC messages. Optionally, the aforementioned threshold values ​​can also be determined by the UE itself.

[0111] Optionally, in the foregoing operations, triggering a measurement reporting process refers to triggering a process for reporting the measurement results of one or more measured cells that satisfy the triggering of the measurement reporting process described in the operation.

[0112] Optionally, the measured cell in this disclosure can include the serving cell or the neighboring cell being measured; in model-based measurement prediction, the measured cell can also be called the predicted cell or the predicted measured cell, etc. Optionally, in a measurement event, the reference cell generally refers to the serving cell such as PCell, but this disclosure does not restrict it, that is, the reference cell can also be one or more other non-serving cells / neighboring cells, such as the cell identification information of the reference cell corresponding to a measurement event being configured and informed to the UE by the network side.

[0113] Optionally, in step 302, the UE performs a report of the predicted measurement results. Optionally, the report includes at least the following: for each triggered measurement event, each triggered measured cell, or each measurement identifier (or measurement object), the predicted output is included in an RRC message for prediction reporting and sent to the network side. The predicted output, as described above, may be one or more of the following: the predicted signal quality of the measured cell, the type of measurement event, the predicted time (or time period) of the measurement event, the predicted probability of the measurement event, an indication of whether the predicted probability of the measurement event is greater than, equal to, or less than a threshold value, the measurement results of the relevant cells actually measured, and the duration for which the measurement event is satisfied.

[0114] Figure 4 is a block diagram illustrating a user equipment 10 according to an embodiment of the present disclosure. As shown in Figure 4, the user equipment 10 includes a processor 101 and a memory 102. The processor 101 may include, for example, a microprocessor, a microcontroller, an embedded processor, etc. The memory 102 may include, for example, volatile memory (such as random access memory, RAM), a hard disk drive (HDD), non-volatile memory (such as flash memory), or other memory. Program instructions are stored on the memory 102. When executed by the processor 101, these instructions can perform the measurement reporting method described in detail in this disclosure for the user equipment described above.

[0115] A program running on a device according to this disclosure may be a program that enables a computer to perform the functions of embodiments of this disclosure by controlling a central processing unit (CPU). The program or the information processed by the program may be temporarily stored in volatile memory (such as random access memory RAM), hard disk drive (HDD), non-volatile memory (such as flash memory), or other memory systems.

[0116] Programs used to implement the functions of the embodiments of this disclosure can be recorded on a computer-readable recording medium. The corresponding functions can be implemented by causing a computer system to read and execute the programs recorded on the recording medium. The term "computer system" here can refer to a computer system embedded in the device, and may include an operating system or hardware (such as peripheral devices). "Computer-readable recording medium" can be a semiconductor recording medium, an optical recording medium, a magnetic recording medium, a short-time dynamic storage program recording medium, or any other computer-readable recording medium.

[0117] Various features or functional modules of the devices used in the above embodiments can be implemented or executed by circuits (e.g., monolithic or multi-chip integrated circuits). Circuits designed to perform the functions described in this specification may include general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination of the above devices. A general-purpose processor may be a microprocessor, or any existing processor, controller, microcontroller, or state machine. The circuits described above may be digital circuits or analog circuits. In cases where advancements in semiconductor technology have led to new integrated circuit technologies that replace existing integrated circuits, one or more embodiments of this disclosure may also be implemented using these new integrated circuit technologies.

[0118] Furthermore, this disclosure is not limited to the embodiments described above. Although various examples of the embodiments have been described, this disclosure is not limited thereto. Fixed or non-mobile electronic devices installed indoors or outdoors can be used as terminal devices or communication devices, such as AV equipment, kitchen equipment, cleaning equipment, air conditioners, office equipment, vending machines, and other household appliances.

[0119] As described above, embodiments of this disclosure have been described in detail with reference to the accompanying drawings. However, the specific structure is not limited to the above embodiments, and this disclosure also includes any design modifications that do not depart from the spirit of this disclosure. Furthermore, various modifications can be made to this disclosure within the scope of the claims, and embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included within the technical scope of this disclosure. In addition, components with the same effects described in the above embodiments can be substituted for each other.

Claims

1. A measurement reporting method performed by a user equipment (UE), comprising: The user equipment (UE) receives a measurement configuration radio resource control (RRC) message sent by the network side, which contains radio resource management (RRM) measurements assisted by artificial intelligence (AI / machine learning) models. The measurement configuration includes one or more of the following information: The first piece of information indicates whether the RRM measurement model is direct or indirect; The second piece of information is used to enable a mechanism to determine whether to trigger the measurement reporting process based on the time when the predicted measurement event occurs. The third information indicates a time threshold value, which the UE uses to determine whether to trigger the measurement reporting process based on the predicted time of a measurement event and the third information. The fourth piece of information is used to enable a mechanism that determines whether to trigger the measurement reporting process based on the predicted probability of the measurement event occurring. The fifth piece of information indicates a percentage threshold value, which the UE uses to determine whether to trigger the measurement reporting process based on the predicted probability of the measurement event occurring and the fifth piece of information. as well as, Save the received measurement configuration to a UE variable; and The model-aided measurement prediction, evaluation, and reporting process is performed based on the saved measurement configuration.

2. The measurement reporting method according to claim 1, wherein, The measurement configuration refers to the configuration associated with model-assisted RRM measurement prediction or the input information or output configuration of the model, including one or more of the following: measurement identifier, measurement object, measurement report configuration, measurement reference cell, observation window size, prediction window size, model identifier, etc.

3. The measurement reporting method according to claim 1, wherein, In the indirect prediction model, the direct output of the model is to predict the signal quality of the measured cell at a future time. Then, based on the signal quality and the measurement event configuration, information on whether a measurement event has occurred at that time is indirectly obtained. In a direct prediction model, the output of the model is the probability of a measured event occurring at a future time or within a time period.

4. The measurement reporting method according to claim 1, wherein, The fifth piece of information is either a threshold value or a hysteresis parameter.

5. The measurement reporting method according to claim 1, wherein, The execution model-assisted measurement prediction and evaluation include: If the time difference between the predicted occurrence time of a measurement event and the occurrence time of the same measurement event in the last predicted measurement report sent by the UE to the network side is greater than or equal to a first threshold value, and the measurement events are associated with the same cell or the same group of cells, then a measurement reporting process is triggered.

6. The measurement reporting method according to claim 1, wherein, The execution model-assisted measurement prediction and evaluation include: If the signal quality measurement value of the measured cell corresponding to a predicted measurement event exceeds or equals a second threshold value compared with the signal quality of the measured cell in the previously predicted measurement report sent to the network side, the UE triggers a measurement reporting process.

7. The measurement reporting method according to claim 1, wherein, The execution model-assisted measurement prediction and evaluation include: If the difference between the predicted probability of a measurement event and a threshold value TH1 is greater than or equal to another threshold value TH2, the entry condition of the measurement event is considered to be met.

8. The measurement reporting method according to claim 1, wherein, The execution model-assisted measurement prediction and evaluation include: If the sum of the predicted probability of a measurement event and a threshold value TH3 is less than or equal to another threshold value TH4, the departure condition of the measurement event is considered to be met.

9. The measurement reporting method according to claim 1, wherein, The execution model-assisted measurement prediction and evaluation is performed on a measurement identifier, a measurement object, a measured cell, or a measured event.

10. A user equipment (UE), comprising: processor; as well as Memory, which stores instructions; The instructions are executed by the processor to perform the measurement reporting method according to any one of claims 1 to 9.

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