Method executed by user equipment, handover preparation method in communication system, and user equipment

Abstract

The present disclosure provides a method executed by a user equipment (UE), a handover preparation method in a communication system, and a UE. The method executed by a UE comprises: a UE for which RRM measurement based on a first RRM configuration is configured detects whether a condition for switching to performing RRM measurement on the basis of a second RRM configuration is satisfied, the first RRM configuration being an RRM measurement configuration in which a model is used, and the second RRM configuration being an RRM measurement configuration in which no model is used; upon detecting that the condition is satisfied, the UE performs an operation of switching to performing the RRM measurement on the basis of the second RRM configuration; and the UE performs the RRM measurement based on the second RRM configuration.

Description

Methods executed by user equipment, handover preparation methods in communication systems, and user equipment Technical Field

[0001] This disclosure relates to the field of wireless communication technology, and more specifically, to a method performed by a user equipment, a handover preparation method in a communication system, and a 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-frequency 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 measurement configuration or measurement report management based on AI / ML in NR networks, and further, to address how the network side uses the obtained measurement reports during cell handover in systems with AI / ML-assisted RRM measurements enabled, and how the UE performs measurement configuration during the transition between AI / ML-assisted RRM measurements and traditional non-AI / ML-assisted RRM measurements. Summary of the Invention

[0008] The main objective of this disclosure is to provide a method executed by a user equipment, a handover preparation method in a communication system, and a user equipment, so as to enable the UE to obtain an effective measurement configuration and start RRM measurement during the transition between AI / ML-assisted RRM measurement and traditional non-AI / ML-assisted RRM measurement in a system with AI / ML-assisted RRM measurement enabled, thereby reducing the RRM measurement interruption time caused by RRM mode transition; in addition, the network side makes the handover preparation process more robust by using AI / ML-assisted RRC measurement results during the handover preparation process.

[0009] According to a first aspect of this disclosure, a method performed by a user equipment (UE) is provided, comprising: a UE configured to perform RRM measurements based on a first RRM configuration detecting whether conditions are met for switching to perform RRM measurements based on a second RRM configuration, wherein the first RRM configuration is an RRM measurement configuration using a model and the second RRM configuration is an RRM measurement configuration not using a model; if the conditions are detected to be met, the UE performs an operation to switch to perform RRM measurements based on the second RRM configuration; and the UE performs RRM measurements based on the second RRM configuration.

[0010] According to a second aspect of this disclosure, a method performed by a user equipment (UE) is provided, comprising: the UE receiving a second RRM configuration from a network side, the second RRM configuration being an RRM measurement configuration without a model; and, for a measurement identifier in the second RRM configuration, if it is determined that there is an ongoing RRM measurement based on a first RRM configuration on the UE, and the RRM measurement is associated with the same measurement object or measurement event as the measurement identifier, the UE stops the RRM measurement based on the second RRM configuration for the measurement identifier, the first RRM configuration being an RRM measurement configuration using a model.

[0011] According to a third aspect of this disclosure, a method performed by a user equipment (UE) is provided, comprising: the UE receiving an RRM measurement-related configuration from a network side, the RRM measurement-related configuration including a correspondence between a first RRM configuration and a second RRM configuration, wherein the first RRM configuration is an RRM measurement configuration using a model, and the second RRM configuration is an RRM measurement configuration not using a model; and the UE performing the measurement mode conversion based on the received RRM measurement-related configuration when it determines that conditions are met for performing a measurement mode conversion between an RRM measurement based on the first RRM configuration and an RRM measurement based on the second RRM configuration.

[0012] According to a fourth aspect of this disclosure, a handover preparation method is provided in a communication system, the communication system having a source cell and a target cell for cell handover, the handover preparation method comprising: the source cell obtaining RRM measurement results using a model; the source cell generating a candidate cell information list based on the obtained RRM measurement results and including it in handover preparation information in a handover request message and sending it to the target cell; and the target cell configuring a handover command in a handover request confirmation message based on the candidate cell information list in the handover preparation information and responding to the handover request confirmation message to the source cell.

[0013] According to a fifth aspect of this disclosure, a user equipment is provided, comprising: a processor; and a memory storing instructions that, when executed by the processor, perform the method described above performed by the user equipment (UE).

[0014] Invention Effects

[0015] According to this disclosure, in a system that enables AI / ML-assisted RRM measurement, the UE can obtain an effective measurement configuration and start RRM measurement during the transition between AI / ML-assisted RRM measurement and traditional non-AI / ML-assisted RRM measurement, thereby reducing the RRM measurement interruption time caused by RRM mode switching; in addition, by using AI / ML-assisted RRM measurement results during the handover preparation process, the network side can make the handover preparation process more robust. Attached Figure Description

[0016] 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:

[0017] Figure 1 is a schematic diagram of the measurement model for RRM measurement in the NR system.

[0018] Figure 2 is a schematic flowchart illustrating an example of a method performed by a user equipment according to the present disclosure.

[0019] Figure 3 is a schematic flowchart illustrating another example of the method performed by a user equipment according to the present disclosure.

[0020] Figure 4 is a schematic flowchart illustrating yet another example of the method performed by a user equipment according to the present disclosure.

[0021] Figure 5 is a schematic flowchart illustrating an example of a handover preparation method in the communication system of this disclosure.

[0022] Figure 6 is a block diagram illustrating 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 When the UE determines...

[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 filter 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 discussions, 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 move from the intermediate output to 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. Conversely, 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] Handover preparation

[0073] The NR handover process includes several stages: handover preparation, handover execution, and handover completion. During handover preparation, the source cell makes a handover decision, such as based on the UE's RRM measurement results or network-side load status, to determine if the UE needs to maintain network connectivity through handover. The source cell initiates a handover request message to one or more target cells. The handover request message carries an inter-site RRC message called handover preparation information (HOPreparationInformation). This handover preparation information may include the UE's configuration parameters in the source cell (such as the UE identifier, UE access layer configuration, etc.), access layer contexts for several UEs (such as re-establishment information for RRC connection reconstruction after handover failure, auxiliary information reported by the UE), and RRM configuration. The RRM configuration includes a candidate cell information list (candidateCellInfoList information element), which contains a list of the best cells for each frequency with available measurement information. After receiving the handover request message, the target cell determines whether it can accept the UE's access and sets the corresponding UE configuration based on the UE configuration and information in the handover request message, and sends the configuration information (i.e. the handover command) to the source cell in the Handover request acknowledge message.

[0074] Based on current 3GPP discussions, RRM measurements (predictions) using AI / ML models are referred to as model-based RRC measurements, and their output is the RRC measurement prediction result. RRM measurements without AI / ML models (such as RRM measurements supported in version 18 and earlier) are called traditional RRM measurements, and their output is the measurement result obtained based on actual measurements of historical or current reference signals. As mentioned earlier, model management includes model updates, and in some cases, the model applicability of the UE may change, such as from applicable to inapplicable. Therefore, in order to complete seamless RRM measurements in time when the model is inapplicable, a feasible method is for the UE to autonomously switch to traditional RRM measurements or for the network side to instruct the UE to switch to traditional RRM measurements. How to achieve autonomous UE switching to traditional RRM measurements is one of the issues addressed in this disclosure. Furthermore, for the cell handover preparation process, after introducing model-based RRM measurements, how to utilize the RRM measurement prediction results to perform the handover preparation process is also one of the issues addressed in this disclosure.

[0075] This disclosure addresses at least a portion of the problems related to the handover of UEs between model-based RRM measurements and conventional measurements, as well as how the network side can utilize the results of RRM measurement predictions to perform the handover preparation process. Specific implementation methods are given below.

[0076] 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.

[0077] Figure 2 is a schematic flowchart illustrating an example of a method performed by a user equipment according to the present disclosure, specifically a schematic flowchart illustrating some implementation steps of a method for converting a model-assisted RRM measurement to a conventional measurement method. As shown in Figure 2, this embodiment includes any one or more of the following steps, performed on the UE.

[0078] Optionally, in step 201, the UE configured with model-assisted RRM measurement checks whether the conditions for performing fallback to conventional RRM measurement are met. Conditions for fallback to conventional RRM measurement include, for example, the AI / ML model corresponding to the model-assisted RRM measurement no longer being applicable, or a model prediction error, or the network indicating the use of conventional RRM measurement. For ease of description, the model-assisted RRM measurement configuration will be referred to as the first RRM configuration, and the conventional RRM measurement configuration will be referred to as the second RRM configuration (usually referring to the configuration contained in the measConfig information element of the RRC message).

[0079] Optionally, in step 202, if the above conditions are detected, the UE performs a fallback to the operation of performing RRM measurements based on the second RRM configuration. For example, the operation includes any one or more of the following operations (the implementation steps of each operation are not limited):

[0080] Operation 1: UE suspends RRM measurements based on the first RRM configuration;

[0081] Optionally, if the UE has a second RRM configuration, then for each measurement object or measurement event in the first RRM configuration, if the same measurement object (such as frequency) or measurement event is configured in the second RRM configuration, the UE suspends the RRM measurement based on the first RRM configuration corresponding to that measurement object or measurement event. This suspension operation is also called stopping.

[0082] Operation 2: The UE considers the function / model corresponding to the RRM measurement based on the first RRM configuration to be deactivated or inapplicable.

[0083] Optionally, if the UE has a second RRM configuration, then for each measurement object or measurement event in the first RRM configuration, if the same measurement object (such as frequency) or measurement event is configured in the second RRM configuration, the UE considers that the function / model corresponding to the RRM measurement based on the first RRM configuration for that measurement object or measurement event is deactivated or inapplicable.

[0084] Operation 3: If the UE has a second RRM configuration, for each measurement identifier in the second RRM configuration, restore (or initiate) the RRM measurement based on the corresponding second RRM configuration. Optionally, restoring the RRM measurement based on the corresponding second RRM configuration means that if there is the same measurement object (e.g., frequency) or measurement event configuration (e.g., event A3) in the first RRM configuration, the UE restores the RRM measurement based on the configuration corresponding to the measurement identifier associated with the same measurement object or measurement event in the second RRM configuration. Optionally, this operation is performed when the first RRM measurement function / model is inactive or invalid, that is, if there is the same measurement object or measurement event configuration in the first RRM configuration and the corresponding RRM measurement based on the first RRM configuration is inactive or invalid, the UE restores the RRM measurement based on the configuration corresponding to the measurement identifier in the second RRM configuration. In another description, if the UE has a second RRM configuration, for each measurement object or measurement event in the first RRM measurement, if the same measurement object (such as frequency) or measurement event is configured in the second RRM configuration, then the UE resumes the RRM measurement based on the measurement identifier corresponding to the same measurement or associated with the measurement event in the second RRM configuration.

[0085] Optionally, in step 203, the UE performs an RRM measurement based on the second RRM configuration. Optionally, the UE performs an RRM measurement based on the second RRM configuration on the measurement identifier.

[0086] Optionally, prior to step 201, the UE may also receive 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.

[0087] 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.

[0088] Optionally, prior to step 201, the UE may also receive an RRC-specific message (e.g., an RRC Reconfiguration message) sent by the network, which contains traditional RRM measurement configurations (such as the configurations contained in the measConfig information element).

[0089] Optionally, the measurement mode switching operation in step 202 (or 201) is performed when the measurement mode switching is enabled. For example, when the UE supports the function of autonomously switching measurement modes from RRM measurement based on a first RRM configuration to RRM measurement based on a second RRM configuration, the network side enables the measurement mode switching function through RRC configuration. When the measurement mode switching condition in step 201 is met, if the measurement mode switching function is enabled / configured by the network side, the UE performs the operation described in step 202 to realize the measurement mode switching.

[0090] Figure 3 is a schematic flowchart illustrating another example of the method performed by a user equipment according to the present disclosure, specifically illustrating some implementation steps of a method for converting RRM measurements based on a second RRM configuration to RRM measurements based on a first RRM configuration. As shown in Figure 3, this embodiment includes any one or more of the following steps.

[0091] Optionally, in step 301, the UE receives the second RRM configuration from the network side.

[0092] In step 302, if the UE determines that there is an ongoing RRM measurement based on the first RRM configuration on the UE for a measurement identifier in the second RRM configuration and that the measurement is associated with the same measurement object or measurement event as the measurement identifier, then the UE suspends the RRM measurement based on the second RRM configuration for that measurement identifier.

[0093] Optionally, the ongoing RRM measurement based on the first RRM configuration refers to the RRM measurement based on the first RRM configuration being in progress or the RRM measurement model / function based on the first RRM configuration being active (or applicable or effective) with respect to the aforementioned measurement object or measurement event.

[0094] Figure 4 is a schematic flowchart illustrating yet another example of the method performed by a user equipment according to the present disclosure, specifically illustrating some implementation steps of a method for converting the measurement mode of RRM measurement based on a second RRM configuration and RRM measurement based on a first RRM configuration to an associated RRC configuration. As shown in Figure 4, this embodiment includes any one or more of the following steps.

[0095] Optionally, in step 401, the UE receives an RRM measurement-related configuration (referred to as the third RRM association configuration for convenience) from the network side. This configuration contains the correspondence between the measurement object / measurement event / measurement identifier between the first RRM configuration and the second RRM configuration.

[0096] Optionally, in step 402, when the UE determines that the conditions for performing RRM measurement mode conversion are met, it performs measurement mode conversion based on the received third RRM association configuration for RRM measurement between the first RRM configuration and the second RRM configuration.

[0097] Optionally, step 402 can be applied to the measurement mode conversion implementation method corresponding to Figure 2 or Figure 3.

[0098] For example, the UE receives a first RRM configuration, a second RRM configuration, and a third RRM association configuration. The first RRM configuration includes the measurement configurations associated with measurement identifiers 1, 2, and 3; the second RRM configuration includes the measurement configurations associated with measurement identifiers 4 and 5; and the third RRM association configuration includes the association of measurement identifier 1 with measurement identifier 5. When the UE performs RRM measurements based on the first RRM configuration, it performs model-assisted measurements on measurement identifiers 1 to 3 (optionally, the UE does not perform RRM measurements based on the second RRM configuration at this time). If the UE determines that the model / function corresponding to measurement identifier 1 needs to be rolled back (i.e., measurement mode conversion) (e.g., because the model / function corresponding to measurement identifier 1 becomes inapplicable, predicts incorrectly, or is deactivated), then according to the association relationship between measurement identifiers 1 and 5 in the third RRM association configuration, the UE initiates RRM measurements on measurement identifier 5 based on the second RRM configuration. Generally, the measurement objects or measurement events associated with measurement identifier 1 and measurement identifier 5 are the same. In this way, the UE can promptly initiate RRM measurements based on the second RRM configuration for the same measurement object or measurement event when RRM measurements based on the first RRC configuration are not applicable, thereby ensuring the continuity of measurement.

[0099] According to the method performed by the user equipment (UE) as described above in this disclosure, in a system where AI / ML-assisted RRM measurement is enabled, the UE can obtain an effective measurement configuration and start RRM measurement during the transition between AI / ML-assisted RRM measurement and traditional non-AI / ML-assisted RRM measurement, thereby reducing the RRM measurement interruption time caused by RRM mode switching.

[0100] Figure 5 is a schematic flowchart illustrating an example of a handover preparation method in the communication system of this disclosure, specifically a schematic flowchart illustrating some implementation steps of a handover preparation method in a model-assisted RRM measurement-enabled network. As shown in Figure 5, this embodiment includes any one or more of the following steps, which are performed in the source and target cells of the handover process on the network side.

[0101] Step 501: The source cell being switched obtains model-aided RRM measurement results.

[0102] Optionally, the RRM measurement results are obtained from the UE, that is, the measurement results obtained by the UE based on the model-assisted RRM measurement configuration are reported to the network side.

[0103] Optionally, the RRM measurement results are generated by the network itself. In a model-assisted RRM measurement, the training and inference of the model are performed on the network side. In this case, the network side can autonomously generate predicted RRM measurement results based on some actual / traditional measurement data reported by the UE and on the trained model.

[0104] Optionally, the measurement results include the measurement signal values ​​of the measurement object or one or more cells corresponding to each measurement identifier, such as one or more of the following: Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal-to-Interference-Noise Ratio (SINR).

[0105] Step 502: Based on the obtained model-assisted RRM measurement results, the source cell generates a candidate cell information list (such as the candidateCellInfoList information element) and sends it to the target cell in the handover preparation information RRC message in the handover request message.

[0106] Optionally, the handover preparation information message contains two candidate cell information lists: one candidate cell information list is obtained based on conventionally performed RRM measurements, and the other candidate cell information list is obtained based on model-assisted RRM prediction measurements. Each candidate cell information list contains one or more frequency lists, as well as one or more best cell lists for each frequency and the measured signal values ​​for each cell.

[0107] Optionally, the handover preparation information message includes a candidate cell information list, which includes both candidate cell information obtained from measurement results based on model-assisted RRM prediction and candidate cell information obtained from traditionally performed RRM measurement results.

[0108] Optionally, the candidate cell information list includes model-aided RRM measurement results, i.e., predicted measurement results, such as the predicted measurement signal value for one (or more) frequencies, one (or more) cells, or one (or more) beams.

[0109] Optionally, the candidate cell information list includes indication information to indicate that the corresponding measurement result is a model-aided predicted measurement value. For example, the indication information may indicate one (or more) frequencies, one (or more) cells, or one (or more) beams; thus, it indicates that the measurement result corresponding to that one (or more) frequency, one (or more) cell, or one (or more) beam is a model-aided predicted measurement value.

[0110] Optionally, the candidate cell information list includes model-aided prediction information to indicate prediction-related information corresponding to the corresponding measurement results, such as prediction time windows, probability of occurrence, and prediction observation windows. For example, the prediction-related information indicates one (or more) frequencies, one (or more) cells, or one (or more) beams; thus, it indicates the prediction-related information corresponding to the measurement results of that one (or more) frequency, or one (or more) cell, or one (or more) beam.

[0111] Optionally, in step 503: the target cell may configure the handover command in the handover request confirmation message based on the candidate cell information list in the handover preparation information, such as the selection of a secondary cell or the configuration of RRM measurements, and may also be used for context preparation for cell reconstruction. The target cell then responds to the source cell with the handover request confirmation message.

[0112] According to the handover preparation method in the communication system disclosed herein, the network side can make the handover preparation process more robust by using AI / ML-assisted RRC measurement results during the handover preparation process.

[0113] Figure 6 is a block diagram illustrating a user equipment 60 according to an embodiment of the present disclosure. As shown in Figure 6, the user equipment 60 includes a processor 601 and a memory 602. The processor 601 may include, for example, a microprocessor, a microcontroller, an embedded processor, etc. The memory 602 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 602. When executed by the processor 601, these instructions can perform the methods described in detail in this disclosure within the user equipment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 method executed by a user equipment (UE), comprising: The UE configured with RRM measurement based on the first RRM configuration checks whether the conditions for switching to perform RRM measurement based on the second RRM configuration are met. The first RRM configuration is an RRM measurement configuration that uses a model, and the second RRM configuration is an RRM measurement configuration that does not use a model. If the condition is detected to be met, the UE performs a switch to perform RRM measurements based on the second RRM configuration; as well as The UE performs RRM measurements based on the second RRM configuration.

2. The method according to claim 1, wherein, The operation includes at least one of the following: Stop RRM measurements based on the first RRM configuration; It is assumed that the model corresponding to the RRM measurement based on the first RRM configuration is deactivated or inapplicable; If the UE has the second RRM configuration, for each measurement identifier in the second RRM configuration, resume or start RRM measurement based on the corresponding second RRM configuration.

3. The method according to claim 1 or 2, wherein, If the condition is detected to be met, the UE performs the operation of switching to perform RRM measurement based on the second RRM configuration, provided that the transition from RRM measurement based on the first RRM configuration to RRM measurement based on the second RRM configuration is enabled.

4. A method performed by a user equipment (UE), comprising: The UE receives a second RRM configuration from the network side, which is an RRM measurement configuration without using the model. If the UE determines that there is an ongoing RRM measurement based on the first RRM configuration and that the RRM measurement is associated with the same measurement object or measurement event as the measurement object or measurement event associated with the measurement identifier in the second RRM configuration, the UE stops the RRM measurement based on the second RRM configuration for the measurement identifier. The first RRM configuration is an RRM measurement configuration that uses a model.

5. The method according to claim 4, wherein, If an RRM measurement based on the first RRM configuration is in progress for the measured object or measurement event, or if the model corresponding to the RRM measurement based on the first RRM configuration is active, then the UE determines that there is an ongoing RRM measurement based on the first RRM configuration.

6. A method performed by a user equipment (UE), comprising: The UE receives RRM measurement-related configuration from the network side. The RRM measurement-related configuration includes a correspondence between at least one of the measurement object, measurement event, and measurement identifier between the first RRM configuration and the second RRM configuration. The first RRM configuration is an RRM measurement configuration that uses a model, and the second RRM configuration is an RRM measurement configuration that does not use a model. as well as When the UE determines that the conditions for performing a measurement mode conversion between an RRM measurement based on the first RRM configuration and an RRM measurement based on the second RRM configuration are met, it performs the measurement mode conversion based on the received RRM measurement-related configuration.

7. A handover preparation method in a communication system, the communication system comprising a source cell and a target cell for cell handover. The handover preparation method includes: The source cell obtained RRM measurement results using the model; The source cell generates a candidate cell information list based on the obtained RRM measurement results and sends it to the target cell in the handover preparation information in the handover request message; as well as The target cell configures the handover command in the handover request confirmation message based on the candidate cell information list in the handover preparation information, and responds to the handover request confirmation message to the source cell.

8. The handover preparation method according to claim 7, wherein, The handover preparation information includes two candidate cell information lists: one candidate cell information list is obtained based on RRM measurement results without using the model, and the other candidate cell information list is obtained based on RRM measurement results with the model used. or The handover preparation information includes a candidate cell information list, which is obtained based on RRM measurement results using the model and RRM measurement results not using the model.

9. The handover preparation method according to claim 7, wherein, The candidate cell information list includes at least one of the following: The RRM measurement results of the model were used; Indication information is used to indicate that the corresponding RRM measurement result is a predicted measurement value using the model; The model's prediction information is used to indicate the prediction-related information corresponding to the corresponding RRM measurement results.

10. A user equipment, comprising: processor; as well as Memory, which stores instructions The instructions, when executed by the processor, perform the method according to any one of claims 1 to 6.

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