Methods and apparatuses for prediction-based communications

Abstract

Disclosed are methods, apparatuses, and systems for communications. One of the methods includes: receiving, from a second node, a request to perform at least one of a radio resource management (RRM) measurement prediction or a measurement event prediction; performing the at least one of the RRM measurement prediction or the measurement event prediction; transmitting, to the second node, an RRM measurement report and a result of the at least one of the RRM measurement prediction or the measurement event prediction, the RRM measurement report being based on one or more RRM measurements performed by the first node; and receiving, from the second node, a notification generated based on a prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction.

Description

METHODS AND APPARATUSES FOR PREDICTION-BASED COMMUNICATIONSCROSS-REFERENCE TO RELATED PATENT APPLICATION

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 683,916, filed on August 16, 2024, entitled “PREDICTION-BASED HANDOVER,” the entirety of which is incorporated by reference herein.Field

[0002] Apparatuses and methods consistent with the present disclosure relate generally to communications, more specifically, methods, systems, and devices for prediction-based communications.Background

[0003] Handover refers to the process of transferring an ongoing communication session of a user equipment (UE) from one cell to another while in a connected state. The primary motivation for handover is to ensure seamless connectivity and uninterrupted service continuity, particularly as the user moves across different coverage areas. To determine whether a handover-triggering event will occur, the UE typically performs signal quality measurements of the serving cell. A handover is generally initiated only after the UE detects that the current signal quality has degraded below a predefined threshold, which inherently introduces latency due to the time required for measurement and evaluation. Also, the handover procedure itself involves multiple signaling and coordination steps between the UE and the network, which can further extend the overall handover completion time. To address these challenges, prediction-based handover may be used.

[0004] One approach in the prediction-based handover is that predictions are performed centrally at the network side. As modern networks are typically designed to support a large number of UEs, executing predictive algorithms centrally at the network side may lead to excessive computational complexity, rendering such an approach impractical at scale. Another approach in the prediction-based handover is that predictions are performed collaboratively at both UE and network sides. Under this configuration, performing the prediction tasks at the UE side becomes a practical approach to reduce computational complexity at the network side. However, the network may not have knowledge on whether the prediction models used for prediction at the UE side can deliver predictions with sufficient accuracy. If these models fail to meet the required prediction performance, the resulting inaccuracies may adversely impact the reliability of the handover decisions made by the network side. On the other hand, even if the prediction models employed by the UE achieve sufficient accuracy such that the prediction results alone are sufficient for the network to make a reliable handover decision, the UE may still send frequent measurement reports to the network, which results in unnecessary signaling overhead and resource waste. Systems and methods that can efficiently manage predictions at the UE side to ensure communication reliability while minimizing signaling overhead are desired.Summary

[0005] According to some embodiments of the present disclosure, there is provided a first node for communication. The first node includes a memory storing an instruction; and a processor configured to execute the instruction stored in the memory to: receive, from a second node, a request to perform at least one of a radio resource management (RRM) measurement prediction or a measurement event prediction; perform the at least one of the RRM measurement prediction or the measurement event prediction; transmit, to the second node, an RRM measurement report and a result of the at least one of the RRM measurement prediction or the measurement event prediction, the RRM measurement report being based on one or more RRM measurements performed by the first node; and receive, from the second node, a notification generated based on a prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction.

[0006] According to some embodiments of the present disclosure, there is provided a first node for communication. The first node includes a memory storing an instruction; and a processor configured to execute the instruction stored in the memory to: receive, from a second node, a request to perform at least one prediction at the first node, the request including at least one target prediction accuracy associated with the at least one prediction to be performed at the first node; perform the at least one prediction; and transmit a result of the at least one prediction, in response to a determination that a prediction accuracy of the at least one prediction meets the target prediction accuracy, or transmit a notification, in response to a determination that the prediction accuracy of the at least one prediction does not meet the target prediction accuracy.

[0007] According to some embodiments of the present disclosure, there is provided a second node for communication. The second node includes a memory storing an instruction; and a processor configured to execute the instruction stored in the memory to: transmit, to a first node, a request to perform at least one of an RRM measurement prediction or a measurement event prediction; receive, from the first node, an RRM measurement report and a result of the at least one of the RRM measurement prediction or the measurement event prediction, the RRM measurement report being based on one or more RRM measurements performed by the first node; determine a prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction; and transmit, to the first node, a notification generated based on the determined prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction.

[0008] According to some embodiments of the present disclosure, there is provided a second node for communication. The second node includes a memory storing an instruction; and a processor configured to execute the instruction stored in the memory to: transmit, to a first node, a request to perform at least one prediction at the first node, the request including at least one target prediction accuracy associated with the at least one prediction to be performed at the first node; and receive, from the first node, a result of the at least one prediction performed at the first node, or a notification indicating that a prediction accuracy of the at least one prediction does not meet the target prediction accuracy.

[0009] According to some embodiments of the present disclosure, there is provided a method for a first node for communication. The method includes: receiving, from a second node, a request to perform at least one of an RRM measurement prediction or a measurement event prediction; performing the at least one of the RRM measurement prediction or the measurement event prediction; transmitting, to the second node, an RRM measurement report and a result of the at least one of the RRM measurement prediction or the measurement event prediction, the RRM measurement report being based on one or more RRM measurements performed by the first node; and receiving, from the second node, a notification generated based on a prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction.

[0010] According to some embodiments of the present disclosure, there is provided a method for a first node for communication. The method includes: receiving, from a second node, a request to perform at least one prediction at the first node, the request including at least one target prediction accuracy associated with the at least one prediction to be performed at the first node; performing the at least one prediction; and transmitting a result of the at least one prediction, in response to a determination that a prediction accuracy of the at least one prediction meets the target prediction accuracy, or transmitting a notification, in response to a determination that the prediction accuracy of the at least one prediction does not meet the target prediction accuracy.

[0011] According to some embodiments of the present disclosure, there is provided a method for a second node for communication. The method includes: transmitting, to a first node, a request to perform at least one of an RRM measurement prediction or a measurement event prediction; receiving, from the first node, an RRM measurement report and a result of the at least one of the RRM measurement prediction or the measurement event prediction, the RRM measurement report being based on one or more RRM measurements performed by the first node; determining a prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction; and transmitting, to the first node, a notification generated based on the determined prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction.

[0012] According to some embodiments of the present disclosure, there is provided a method for a second node for communication. The method includes: transmitting, to a first node, a request to perform at least one prediction at the first node, the request including at least one target prediction accuracy associated with the at least one prediction to be performed at the first node; and receiving, from the first node, a result of the at least one prediction performed at the first node, or a notification indicating that a prediction accuracy of the at least one prediction does not meet the target prediction accuracy.

[0013] According to some embodiments of the present disclosure, there is provided a non-transitory computer-readable medium storing instructions that are executable by one or more processors of a first node for communication, to perform a method. The method includes: receiving, from a second node, a request to perform at least one of an RRM measurement prediction or a measurement event prediction; performing the at least one of the RRM measurement prediction or the measurement event prediction; transmitting, to the second node, an RRM measurement report and a result of the at least one of the RRM measurement prediction or the measurement event prediction, the RRM measurement report being based on one or more RRM measurements performed by the first node; and receiving, from the second node, a notification generated based on a prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction.

[0014] According to some embodiments of the present disclosure, there is provided a non-transitory computer-readable medium storing instructions that are executable by one or more processors of a first node for communication, to perform a method. The method includes: receiving, from a second node, a request to perform at least one prediction at the first node, the request including at least one target prediction accuracy associated with the at least one prediction to be performed at the first node; performing the at least one prediction; and transmitting a result of the at least one prediction, in response to a determination that a prediction accuracy of the at least one prediction meets the target prediction accuracy, or transmitting a notification, in response to a determination that the prediction accuracy of the at least one prediction does not meet the target prediction accuracy.

[0015] According to some embodiments of the present disclosure, there is provided a non-transitory computer-readable medium storing instructions that are executable by one or more processors of a second node for communication, to perform a method. The method includes: transmitting, to a first node, a request to perform at least one of an RRM measurement prediction or a measurement event prediction; receiving, from the first node, from the first node, an RRM measurement report and a result of the at least one of the RRM measurement prediction or the measurement event prediction, the RRM measurement report being based on one or more RRM measurements performed by the first node; determining a prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction; and transmitting, to the first node, a notification generated based on the determined prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction.

[0016] According to some embodiments of the present disclosure, there is provided a non-transitory computer-readable medium storing instructions that are executable by one or more processors of a second node for communication, to perform a method. The method includes: transmitting, to a first node, a request to perform at least one prediction at the first node, the request including at least one target prediction accuracy associated with the at least one prediction to be performed at the first node; and receiving, from the first node, a result of the at least one prediction performed at the first node, or a notification indicating that a prediction accuracy of the at least one prediction does not meet the target prediction accuracy.

[0017] FIG. 1 is a schematic diagram illustrating a communication system in which the network has prediction models, consistent with some embodiments of the present disclosure.

[0018] FIG. 2 is a schematic diagram illustrating a communication system in which both the UE and the network have prediction models, consistent with some embodiments of the present disclosure.

[0019] FIG. 3 is a schematic diagram illustrating a procedure for managing prediction capability of a UE, consistent with some embodiments of the present disclosure.

[0020] FIG. 4 is a schematic diagram illustrating a procedure for managing prediction capability of a UE, consistent with some embodiments of the present disclosure.

[0021] FIG. 5 is a schematic diagram illustrating a procedure for managing prediction capability of a UE, consistent with some embodiments of the present disclosure.

[0022] FIG. 6 is a schematic diagram illustrating a procedure for managing prediction capability of a UE, consistent with some embodiments of the present disclosure.

[0023] FIG. 7 is a schematic diagram illustrating a procedure for managing prediction capability of a UE, consistent with some embodiments of the present disclosure.

[0024] FIG. 8 is a schematic diagram illustrating a procedure for managing prediction capability of a UE, consistent with some embodiments of the present disclosure.

[0025] FIG. 9 is a flow chart illustrating a method for a first node for communication, consistent with some embodiments of the present disclosure.

[0026] FIG. 10 is a flow chart illustrating a method for a first node for communication, consistent with some embodiments of the present disclosure.

[0027] FIG. 11 is a flow chart illustrating a method for a second node for communication, consistent with some embodiments of the present disclosure.

[0028] FIG. 12 is a flow chart illustrating a method for a second node for communication, consistent with some embodiments of the present disclosure.

[0029] FIG. 13 is a block diagram of a node, consistent with some embodiments of the present disclosure.DETAILED DESCRIPTION

[0030] Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings in which the same numbers in different drawings represent the same or similar elements unless otherwise represented.

[0031] The implementations set forth in the following description of exemplary embodiments do not represent all implementations consistent with the present disclosure. Instead, they are merely examples of systems, apparatuses, and methods consistent with aspects related to the present disclosure as recited in the appended claims.

[0032] In the present disclosure, the term “node” is used as a general term that includes, but is not limited to, UE, one or more vehicles, one or more vehicle mounted modules, and one or more network infrastructure nodes such as base stations, core networks, roadside units, repeaters, transponders, wireless routers, controllers, access points, and sub-systems thereof. In the present disclosure, the term “network” and the term “network node” are used interchangeably. In the present disclosure, the term “network” (or “network node”) may be terrestrial network or non-terrestrial network.

[0033] Handover procedure includes a process of transferring an ongoing wireless service and management of a UE from one cell to another cell. As described in the 3rd Generation Partnership Project (3GPP) New Radio (NR) specifications, six events (Events A1-A6) may be considered as triggering events for intra radio access technology (RAT) handover. The intra RAT handover described in the present disclosure may include a handover in which the serving cell and the target cell are of the same RAT. The six events are discussed below.

[0034] Event A1 indicates an event in which received signal quality of the serving cell becomes better than a predefined threshold. The received signal quality metrics may include reference signal received power (RSRP), reference signal received quality (RSRQ), signal to interference and noise power ratio (SINR), and received signal strength indicator (RSSI). Event A2 indicates an event in which received signal quality of the serving cell becomes worse than a threshold. Event A3 indicates an event in which received signal quality of a neighbor cell becomes offset better than that of the serving cell. Event A4 indicates that received signal quality of a neighbor cell becomes better than a threshold. Event A5 indicates received signal quality of the serving cell becomes worse than threshold 1 and received signal quality of a neighbor cell becomes better than threshold 2. The threshold 1 and the threshold 2 may be the same or different from each other. Event A6 indicates an event in which received signal quality of a neighbor cell becomes offset better than that of the serving cell.

[0035] In addition to Events A1 to A6, two events (Events B1 and B2) may be considered as triggering events for the inter RAT handover. The inter RAT handover described in the present disclosure may include a handover in which the serving cell and the target cell are of the different RATs. Event B1 indicates an event in which received signal quality of an inter RAT neighbor cell becomes better than a threshold. Event B2 indicates an event in which received signal quality of the serving cell becomes worse than threshold 1 and received signal quality of the inter RAT neighbor cell becomes better than threshold 2. The threshold 1 and the threshold 2 may be the same or different from each other.

[0036] To determine whether these events will happen, a UE may perform received signal quality measurement, also known as RRM measurement. Taking the measurement of RSRP as an example, the RSRP measurement may be the Layer-1 filtered measurement, in which the UE measures RSRP on a demodulation reference signal (DMRS) conveyed by the physical broadcast channel (PBCH) or the channel state information (CSI) reference signal (CSI-RS). The UE may further report the measurement results via CSI feedback. The reporting range of the Layer-1 filtered measurement result may be -140 dBm to -40 dBm with 1 dBm resolution. The purpose of Layer-1 filtered measurement may be for beam management and link adaptation.

[0037] The RSRP measurement may also be the Layer-3 filtered measurement, in which the UE measures RSRP on the DMRS conveyed by the PBCH or the CSI-RS. The UE may further report the measurement results via a radio resource control (RRC) message called the measurement report. The reporting range of the Layer-3 filtered measurement result may be -156 dBm to -30 dBm with 1 dBm resolution. The purpose of Layer-3 filtered measurement may be for handover decision.

[0038] The existing handover procedure may suffer from disadvantages. For example, according to the above-noted handover triggering events, a handover procedure may be triggered only after a UE has knowledge that a current wireless service of the UE may be degraded. To determine whether a handover-triggering event will occur, the UE typically performs signal quality measurements of the serving cell. A handover is generally initiated only after the UE detects that the current signal quality has degraded below a predefined threshold, which inherently introduces latency due to the time required for measurement and evaluation. In addition, the handover procedure itself involves multiple signaling and coordination steps between the UE and the network, which can further extend the overall handover completion time. At least some embodiments of the present disclosure address the above-noted disadvantages by providing prediction to assist the operation of handover.

[0039] The prediction can be applied at least in three different aspects and there can be three different types of predictions. The first type of prediction is RRM measurement prediction, in which the UE may predict the quality of future-received signals such as RSRP. The UE may perform RRM measurement prediction based on the present and / or past measurement results of the received signal quality. On the other hand, based on the reports of the present and / or past measurement results of the received signal quality, the network may also predict the quality of future received signals. An example of the network is a gNB or a core network. The second type of prediction is measurement event prediction, in which the UE may predict the future occurrence of the handover events, for example, Events A1-A6 or Events B1-B2 described above. The third type of prediction is radio link failure or handover failure prediction, in which the UE may predict the future occurrences of radio link failure or handover failure.

[0040] The above-noted predictions have advantages. For example, as per existing handover events listed above, the handover procedure may be triggered when the received signal quality degrades. In other words, the handover procedure may be triggered when the performance of communications degrades. The prediction may trigger the handover procedure before the serving cell’s signal quality deteriorates, thereby mitigating potential communication degradation. Also, to accurately capture the handover occasions, the UE typically performs frequent measurements and reports of the received signal quality, which introduces substantial measurement overhead. However, by leveraging predictive mechanisms, the UE can reduce the frequency of real-time measurements while still providing predicted signal quality reports. As a result, the overall measurement overhead can be significantly reduced without compromising handover accuracy.

[0041] To perform predictions, in some embodiments of the present disclosure, various prediction models, for example, artificial intelligence and / or machine learning (AI / ML) models may be used. In this case, for example, one or more AI / ML models may be used at the UE side to perform prediction. Alternatively or in addition, one or more AI / ML models may also be used at the network side to perform prediction. When the prediction accuracy is lower than a threshold, a current AI / ML model may not be feasible, and one or more other AI / ML models may be used. The prediction accuracy can be measured with different metrics, such as the average of the difference between the predicted received signal quality and the actual received signal quality.

[0042] FIG. 1 is a schematic diagram illustrating a communication system in which only the network has prediction models, consistent with some embodiments of the present disclosure. Referring to FIG. 1, a communication system 100 includes a UE 102 and a network 104 (e.g., gNB). The network 104 has one or more AI / ML models while a UE 102 does not have an AI / ML model. As such, prediction tasks, such as the RRM measurement prediction and / or measurement event prediction are performed at the network side. The UE 102 may perform RRM measurement and send the RRM measurement report to the network 104. The UE 102 may be one or more UEs served by the network 104. To perform prediction at the network side, the network 104 may utilize the reports of RRM measurement from each of the one or more UEs. Since the prediction is performed solely at the network side, the network needs to predict the RRM measurement and / or the measurement event for each of its serving UEs. As the network typically supports a large number of UEs, executing prediction algorithms centrally at the network side may lead to excessive computational complexity, rendering such an approach impractical at scale.

[0043] FIG. 2 is a schematic diagram illustrating a communication system in which both the UE and the network have prediction models, consistent with some embodiments of the present disclosure. Referring to FIG. 2, a communication system 200 includes a UE 202 and a network 204 (e.g., gNB). Both the UE 202 and the network 204 have one or more AI / ML models for prediction. Accordingly, prediction tasks, such as RRM measurement prediction and / or measurement event prediction, can be performed at the UE side, or collaboratively at both UE and network sides, depending on system design and computational constraints. The UE 202 may be one or more UEs served by the network 204. Under this configuration, performing the prediction tasks at the UE side becomes a practical approach to reduce computational complexity at the network side. To perform the prediction, the UE 202 may perform RRM measurement and utilize its own RRM measurement results. The UE 202 may also report the prediction results to the network 204 so that the network may make a handover decision. In some embodiments, the UE 202 may also send the RRM measurement report to the network 204 so that the network 204 may also perform prediction. In this case, the network 204 may make a handover decision based on the prediction results received from the UE 202 and the prediction performed by the network 204 itself, thereby increasing accuracy of the handover decision.

[0044] However, the network 204 may not be aware of the one or more AI / ML models used for performing the prediction tasks at the UE 202. For example, the network 204 may not have knowledge on whether the one or more AI / ML models used for prediction at the UE 202 can deliver predictions with sufficient accuracy. If these models fail to meet the required prediction performance, the resulting inaccuracies may adversely impact the reliability of the handover decisions made by the network 204. On the other hand, even if the prediction models employed by the UE achieve sufficient accuracy such that the prediction results alone are sufficient for the network to make a reliable handover decision, the UE may still send frequent measurement reports to the network, which results in unnecessary signaling overhead and resource waste.

[0045] At least some embodiments of the present disclosure address this issue by providing effective schemes for the network to determine the prediction capability and accuracy of the prediction models executed at the UE side. If the network determines that the UE’s prediction results satisfy predefined accuracy criteria, the network may notify the UE that it is permitted to report only the prediction results, omitting the transmission of RRM measurement reports to reduce signaling overhead. On the other hand, if the network determines that the UE’s prediction results do not meet the predefined accuracy criteria, the network may notify the UE to switch to an alternative prediction model, thereby enhancing the accuracy of subsequent predictions.

[0046] Although the methods of the present disclosure are explained in the context of handover, the scope of the present disclosure is not so limited. The methods described in the present disclosure can be applied to any prediction-based communications between a UE and a network, or more generally, between two or more nodes. Although the present disclosure generally refers to AI / ML models for predictions, the prediction models described herein are not limited to AI / ML and may include any type of model, whether currently known or developed in the future, that is capable of performing the intended predictive functions. For example, the prediction models may include, but are not limited to, long short-term memory (LSTM), gated recurrent unit (GRU), multi-layer perception (MLP), convolutional neural network (CNN), k-nearest neighbors (KNN), reinforcement learning (RL), decision trees, random forests, deep neural network (DNN), graph neural network (GNN), Bayesian networks, federated learning aggregators, ensemble model, etc.

[0047] FIG. 3 is a schematic diagram illustrating a procedure for managing prediction capability of a UE, consistent with some embodiments of the present disclosure. Referring to FIG. 3, a procedure 300 is used for managing prediction capability of a UE 302. The UE 302 may be one or more UEs served by a network 304 (e.g., gNB). In some embodiments, the UE 302, or both the UE 302 and the network 304 may have one or more prediction models. Accordingly, prediction tasks, such as RRM measurement prediction and / or measurement event prediction, can be performed at the UE 302, or collaboratively at both UE 302 and network 304, depending on system design and computational constraints.

[0048] Referring to FIG. 3, the procedure 300 may include a step 306 at which the UE 302 receives a request from the network 304. The request may include an instruction to perform an RRM measurement prediction at the UE 302. The request may also include configuration information for the UE 302. The configuration information may include information for configuring at least one of: the RRM measurement, the RRM measurement prediction, an RRM measurement period, an RRM measurement prediction period, a sample period of RRM measurement, a sample period of RRM measurement prediction, a report period of RRM measurement, or a report period of RRM measurement prediction. One or more metrics for the RRM measurement or the RRM measurement prediction may include at least one of: RSRP, RSRQ, RSSI, signal-to-noise ratio (SNR), or SINR of at least one of: a serving cell, or one or more neighboring cells. The one or more neighboring cells can be of the same or different RAT as the serving cell. The reference signal received by the UE 302 may be any reference signal, such as DMRS, CSI-RS, synchronization signal block (SSB), and positioning reference signal (PRS), etc.

[0049] The procedure 300 may include a step 308 at which the UE 302 identifies a prediction model (e.g., AI / ML model) for RRM measurement prediction. The prediction model may be any model, whether currently known or developed in the future, that is capable of performing RRM measurement prediction at a UE. For example, the prediction model may be capable of predicting at least one of: RSRP, RSRQ, RSSI, SNR, or SINR values of at least one of: a serving cell, or one or more neighboring cells, at a future time point.

[0050] The procedure 300 may include a step 310 at which the UE 302 performs RRM measurements, RRM measurement prediction, and reports the RRM measurement results and the RRM measurement prediction results to the network 304. In the present disclosure, the term “RRM measurement result(s)” and the term “RRM measurement report(s)” are used interchangeably. The UE 302 may perform the RRM measurement prediction, for example, by collecting a current RRM measurement result and / or historical RRM measurements results, and feeding the collected data to the prediction model identified for RRM measurement prediction. In some embodiments, the request received from the network 304 may include the beginning time and period (duration) of the RRM measurement prediction, and the UE 302 may perform the RRM measurement prediction based on the beginning time and the period (duration) indicated in the request. In some embodiments, the period for the RRM measurement prediction may include a plurality of sample periods of RRM measurement prediction, and the UE 302 may perform an RRM measurement prediction at each of the plurality of sample periods of RRM measurement prediction.

[0051] Along with the RRM measurement prediction, the UE 302 may also perform RRM measurement. The RRM measurement may be periodic or aperiodic RRM measurements. In some embodiments, the request received from the network 304 may include the beginning time and period (duration) of the RRM measurement, and the UE 302 may perform the RRM measurement based on the beginning time and the period (duration) indicated in the request. In some embodiments, the period of RRM measurement may include a plurality of sample periods of RRM measurement and the UE 302 may perform an RRM measurement at each of the plurality of sample periods of RRM measurement. The UE 302 may measure at least one of: RSRP, RSRQ, RSSI, SNR, or SINR values of at least one of: a serving cell, or one or more neighboring cells. The RRM measurements may be the Layer-1 measurements and / or Layer-3 measurements. Based on the RRM measurements, the UE 302 may generate the RRM measurement report.

[0052] The UE 302 then transmits the RRM measurement report and RRM measurement prediction results to the network 304. The UE 302 may transmit the RRM measurement report and RRM measurement prediction results periodically or aperiodically. In some embodiments, the request received from the network 304 may include the beginning time and period (duration) of the report, and the UE 302 may transmit the RRM measurement report and RRM measurement prediction results to the network 304 based on the beginning time and the period (duration) for report indicated in the request. In some embodiments, the report period may include a plurality of sample report periods and the UE 302 may transmit the RRM measurement report and RRM measurement prediction results to the network 304 at each of the plurality of sample report periods. For example, as shown in FIG. 3, a sample report period 312 may be a period of time between two reports sent to the network 304.

[0053] In some embodiments, as shown in FIG. 3, the sample report period of RRM measurement and the sample report period of RRM measurement prediction are the same. In this case, the sample report period of RRM measurement (or the sample report period of RRM measurement prediction) may be indicated in the same field in the request transmitted from the network 304. In some embodiments, the sample report period of RRM measurement and the sample report period of RRM measurement prediction are different (not shown). In this case, the sample report period of RRM measurement and the sample report period of RRM measurement prediction may be indicated in two different fields in the request transmitted from the network 304. In some embodiments, as shown in FIG. 3, the report period of RRM measurement and the report period of RRM measurement prediction are the same. In this case, the report period of RRM measurement (or the report period of RRM measurement prediction) may be indicated in the same field in the request transmitted from the network 304. In some embodiments, the report period of RRM measurement and the report period of RRM measurement prediction are different (not shown). In this case, the report period of RRM measurement and the report period of RRM measurement prediction may be indicated in two different fields in the request transmitted from the network 304.

[0054] Referring to FIG. 3, the procedure 300 may include a step 314 at which the UE 302 receives a notification from the network 304. The notification may be generated by the network 304 based on a prediction accuracy of the RRM measurement prediction performed by the UE 302. For example, the network 304 may determine whether the accuracy of the RRM measurement prediction results received from the UE 302 is acceptable based on comparing with predetermined criteria. If the accuracy of RRM measurement prediction is not acceptable, the network 304 may include an indication in the notification to indicate that the accuracy of prediction is not acceptable or instruction to switch the current prediction model to a new model, thereby ensuring prediction accuracy and the communication reliability. On the other hand, if the accuracy of RRM measurement prediction is acceptable, the network 304 may include an indication that the UE 302 is permitted to transmit RRM measurement prediction results and stop transmit RRM measurement reports, thereby minimizing signaling overhead and resource waste.

[0055] In some embodiments, the UE 302 may also include models for other predictions, for example, models for measurement event prediction. In this case, in addition to the RRM measurement prediction, the UE 302 may also perform measurement event prediction.

[0056] FIG. 4 is a schematic diagram illustrating a procedure for managing prediction capability of a UE, consistent with some embodiments of the present disclosure. Referring to FIG. 4, a procedure 400 is used for managing prediction capability of a UE 402. The UE 402 may be one or more UEs served by a network 404 (e.g., gNB). In some embodiments, the UE 402, or both the UE 402 and the network 404 may have one or more prediction models. Accordingly, prediction tasks, such as RRM measurement prediction and / or measurement event prediction, can be performed at the UE 402, or collaboratively at both UE 402 and network 404, depending on system design and computational constraints.

[0057] Referring to FIG. 4, the procedure 400 may include a step 406 at which the UE 402 receives a request from the network 404. The operations at the step 406 are similar to that of the step 306 of FIG. 3. For the sake of simplicity, the detailed descriptions of the step 406 are omitted here. Also, similar to the procedure 300 of FIG. 3, the procedure 400 may include a step (not shown) at which the UE 402 identifies a prediction model to perform RRM measurement prediction.

[0058] The procedure 400 may include performing RRM measurement during an RRM measurement period (P1) and transmitting the RRM measurement results to the network 404. The solid arrowed lines shown in FIG. 4 indicate performing the RRM measurements and transmitting the RRM measurement results to the network 404. As shown in FIG. 4, the RRM measurement period (P1) includes a plurality of sample periods of RRM measurement and the UE 402 performs an RRM measurement at each of the plurality of sample periods of RRM measurement.

[0059] The procedure 400 may include performing RRM measurement prediction during an RRM measurement prediction period (P2) and transmitting the RRM measurement prediction results to the network 404. The dashed arrowed lines shown in FIG. 4 indicate performing the RRM measurement prediction and transmitting the RRM measurement prediction results to the network 404. As shown in FIG. 4, the RRM measurement prediction period (P2) may include a plurality of sample periods of RRM measurement prediction and the UE 402 may perform an RRM measurement prediction at each of the plurality of sample periods of RRM measurement prediction. As shown in FIG. 4, the RRM measurement results and the RRM measurement prediction results may not be reported at the same time. In this case, the RRM measurement report and the RRM measurement prediction results can be reported in two different messages sent to the network 404.

[0060] Referring to FIG. 4, the procedure 400 may include a step 408 at which the UE 402 receives a notification from the network 404. The operations at the step 408 are similar to the operations at the step 314 of FIG. 3. For the sake of simplicity, the detailed descriptions of the step 408 are omitted here.

[0061] FIG. 5 is a schematic diagram illustrating a procedure for managing prediction capability of a UE, consistent with some embodiments of the present disclosure. Referring to FIG. 5, a procedure 500 is used for managing prediction capability of a UE 502. The UE 502 may be one or more UEs served by a network 504 (e.g., gNB). In some embodiments, the UE 502, or both the UE 502 and the network 504 may have one or more prediction models. Accordingly, prediction tasks, such as RRM measurement prediction and / or measurement event prediction, can be performed at the UE 502, or collaboratively at both UE 502 and network 504, depending on system design and computational constraints.

[0062] Referring to FIG. 5, the procedure 500 may include a step 506 at which the UE 502 receives a request from the network 504. The operations at the step 506 are similar to the operations at the step 306 of FIG. 3. For the sake of simplicity, the detailed descriptions of the operations at the step 506 are omitted here.

[0063] The procedure 500 may include a step 508 at which the UE 502 identifies a prediction model (e.g., AI / ML model) for RRM measurement prediction. The prediction model may be any model, whether currently known or developed in the future, that is capable of performing RRM measurement prediction at a UE. For example, the prediction model may be capable of predicting at least one of: RSRP, RSRQ, RSSI, SNR, or SINR values of at least one of: a serving cell, or one or more neighboring cells, at a future time point.

[0064] The procedure 500 may include a step 510 at which the UE 502 performs RRM measurement and RRM measurement prediction, and reports RRM measurement results and the RRM measurement prediction results to the network 504. The operations at the step 510 are similar to the operations at the step 310 of FIG. 3. For the sake of simplicity, the detailed descriptions of the operations at the step 510 are omitted here.

[0065] The procedure 500 may include a step 514 at which the UE 502 receives a notification from the network 504. The notification may include an indication that the prediction accuracy of the RRM measurement prediction performed at the step 510 satisfies the accuracy requirement and thus, the UE 502 may omit reporting the RRM measurement results and only report the RRM measurement prediction results. In this case, the notification may include information for configuring at least one of: the RRM measurement prediction, an RRM measurement prediction period, a sample period of RRM measurement prediction, or a report period of RRM measurement prediction. The metrics of RRM measurement prediction may be the same as that in the request. The notification may also include the beginning time and / or the length of RRM measurement prediction period.

[0066] The procedure 500 may include a step 516 at which the UE 502 transmits RRM measurement prediction results to the network 504 and omits transmission of the RRM measurement reports, in response to a determination that the notification received from the network 504 indicates that the prediction accuracy of the RRM measurement predictions performed at the UE 502 meets predetermined criteria. In this way, signaling overhead and resource waste are minimized, and the communication efficiency is enhanced. In some embodiments, as shown in FIG. 5, the step 516 may include a plurality of sample report periods for RRM measurement prediction results and the UE 502 may report RRM measurement prediction results at each of the plurality of sample report periods of RRM measurement prediction.

[0067] FIG. 6 is a schematic diagram illustrating a procedure for managing prediction capability of a UE, consistent with some embodiments of the present disclosure. Referring to FIG. 6, a procedure 600 is used for managing prediction capability of a UE 602. The UE 602 may be one or more UEs served by a network 604 (e.g., gNB). In some embodiments, the UE 602, or both the UE 602 and the network 604 may have one or more prediction models. Accordingly, prediction tasks, such as RRM measurement prediction and / or measurement event prediction, can be performed at the UE 602, or collaboratively at both UE 602 and network 604, depending on system design and computational constraints.

[0068] Referring to FIG. 6, the procedure 600 may include a step 606 at which the UE 602 receives a request from the network 604. The request may include an instruction to perform a measurement event prediction at the UE 602. The request may also include configuration information for the UE 602. The configuration information may include information on configuring at least one of: one or more RRM measurements, a measurement event prediction, an RRM measurement period, a measurement event prediction period, a sample period of RRM measurement, a report period of RRM measurement, or a report period of measurement event prediction. The one or more metrics for the RRM measurements may include at least one of: RSRP, RSRQ, RSSI, SNR, or SINR values of at least one of: a serving cell, or one or more neighboring cells. The one or more neighboring cells can be of the same or different RAT as the serving cell. The reference signal received by the UE 602 may be any reference signal, such as DMRS, CSI-RS, SSB, and PRS, etc.

[0069] The procedure 600 may include a step 608 at which the UE 602 identifies a prediction model (e.g., AI / ML model) for measurement event prediction. The prediction model may be any model, whether currently known or developed in the future, that is capable of performing measurement event prediction at a UE. For example, the prediction model may be capable of predicting at least one of Events A1-A6 and / or Event B1-B2 as described above.

[0070] The procedure 600 may include a step 610 at which the UE 602 performs RRM measurement and measurement event prediction, and reports the RRM measurement report and the measurement event prediction results to the network 604. For example, the UE 602 may perform measurement event prediction by collecting a current RRM measurement data, historical RRM measurements data, and / or historical measurement event data, and feeding the data to the prediction model identified for measurement event prediction. In some embodiments, the request received from the network 604 may include the beginning time and period (duration) of the measurement event prediction, and the UE 602 may perform the measurement event prediction based on the beginning time and the period (duration) indicated in the request.

[0071] The UE 602 then transmits the RRM measurement results and measurement event prediction results to the network 604. In some embodiments, the request received from the network 604 may include the beginning time and period (duration) of the report, and the UE 602 may transmit the RRM measurement report and measurement event prediction results to the network 604 based on the beginning time and the period (duration) for report indicated in the request. In some embodiments, the report period may include a plurality of sample report periods and the UE 602 may transmit the RRM measurement report and measurement event prediction results to the network 604 at each of the plurality of sample report periods. For example, as shown in FIG. 6, a sample report period 612 may be a period of time between two reports sent to the network 604.

[0072] The procedure 600 may include a step 614 at which the UE 602 receives a notification from the network 604. The notification may include an indication that the prediction accuracy of the measurement event prediction performed during the step 610 satisfies the predetermined accuracy criteria and thus, the UE 602 may omit reporting the RRM measurement results and only report the measurement event prediction results. In this case, the notification may include information for configuring at least one of: the measurement event prediction, a measurement event prediction period, or a report period of measurement event prediction. The notification may also include the beginning time and / or the length of measurement event prediction.

[0073] The procedure 600 may include a step 616 at which the UE 602 transmits measurement event prediction results to the network 604 and omits transmission of the RRM measurement reports, in response to a determination that the notification received from the network 604 indicates that the prediction accuracy of the measurement event predictions performed at the UE 602 meets predetermined criteria. In this way, signaling overhead and resource waste are minimized, and the communication efficiency is enhanced.

[0074] FIG. 7 is a schematic diagram illustrating a procedure for managing prediction capability of a UE, consistent with some embodiments of the present disclosure, consistent with some embodiments of the present disclosure. Referring to FIG. 7, a procedure 700 is used for managing prediction capability of a UE 702. The UE 702 may be one or more UEs served by a network 704 (e.g., gNB). In some embodiments, the UE 702, or both the UE 702 and the network 704 may have one or more prediction models. Accordingly, prediction tasks, such as RRM measurement prediction and / or measurement event prediction, can be performed at the UE 702, or collaboratively at both UE 702 and network 704, depending on system design and computational constraints.

[0075] Referring to FIG. 7, the procedure 700 may include a step 706 at which the UE 702 receives a request from the network 704. The request includes a target prediction accuracy for the RRM measurement prediction to be performed at the UE 702. The network 704 may determine the target prediction accuracy for the RRM measurement prediction at the UE 702 based on various factors, such as the historical RRM measurement prediction data, the type and capabilities of the UE 702, as well as the characteristics of the environment in which the UE 702 is located. The request may also include an instruction to perform an RRM measurement prediction at the UE 702. The request may also include configuration information for the UE 702. The configuration information may include information on configuring at least one of: an RRM measurement prediction, an RRM measurement prediction period, a sample period of RRM measurement prediction, a report period of RRM measurement prediction, or a sample report period of RRM measurement prediction. One or more metrics for the RRM measurement prediction may include at least one of: RSRP, RSRQ, RSSI, SNR, or SINR values of at least one of: a serving cell, or one or more neighboring cells. The one or more neighboring cells can be of the same or different RAT as the serving cell. The reference signal received by the UE 702 may be any reference signal, such as DMRS, CSI-RS, SSB, and PRS, etc.

[0076] The procedure 700 may include a step 708 at which the UE 702 identifies a prediction model (e.g., AI / ML model) for RRM measurement prediction. The prediction model may be any model, whether currently known or developed in the future, that is capable of performing RRM measurement prediction at a UE. For example, the prediction model may be capable of predicting at least one of: RSRP, RSRQ, RSSI, SNR, or SINR values of at least one of: a serving cell, or one or more neighboring cells.

[0077] The procedure 700 may include a step 710 at which the UE 702 performs RRM measurement prediction. In some embodiments, the request received from the network 704 may include the beginning time and period (duration) of the RRM measurement prediction, and the UE 702 may perform the RRM measurement prediction based on the beginning time and the period (duration) indicated in the request. In some embodiments, the period for the RRM measurement prediction may include a plurality of sample periods of RRM measurement prediction, and the UE 702 may perform an RRM measurement prediction at each of the plurality of sample periods of RRM measurement prediction. The UE 702 may determine the accuracy of the RRM measurement prediction results, and compare the accuracy with the target prediction accuracy received from the network 704. If the UE 702 determines that the RRM measurement prediction meets the target prediction accuracy, the UE 702 may report the RRM measurement prediction results to the network 704. In some embodiments, the request received from the network 704 may include the beginning time and period (duration) of the report, and the UE 702 may transmit the RRM measurement prediction results to the network 704 based on the beginning time and the period (duration) for report indicated in the request. In some embodiments, the report period may include a plurality of sample report periods and the UE 702 may transmit the RRM measurement prediction results to the network 704 at each of the plurality of sample report periods. For example, as shown in FIG. 7, a sample report period 712 may be a period of time between two reports sent to the network 704.

[0078] If the UE 702 determines that the RRM measurement prediction does not meet the target prediction accuracy, at a step 714, the UE 702 may send a notification to the network 704 to notify that the prediction fails to meet the target prediction accuracy. In some embodiments, if the UE 702 determines that the RRM measurement prediction does not meet the target prediction accuracy, the UE 702 may further switch the current prediction model to a new prediction model. Although FIG. 7 illustrates steps 710 and 714 at least somewhat together, they may be mutually exclusive and not necessarily executed in a sequential manner.

[0079] FIG. 8 is a schematic diagram illustrating a procedure for managing prediction capability of a UE, consistent with some embodiments of the present disclosure. Referring to FIG. 8, a procedure 800 is used for managing prediction capability of a UE 802. The UE 802 may be one or more UEs served by a network 804 (e.g., gNB). In some embodiments, the UE 802, or both the UE 802 and the network 804 may have one or more prediction models. Accordingly, prediction tasks, such as RRM measurement prediction and / or measurement event prediction, can be performed at the UE 802, or collaboratively at both UE 802 and network 804, depending on system design and computational constraints.

[0080] Referring to FIG. 8, the procedure 800 may include a step 806 at which the UE 802 receives a request from the network 804. The request includes a target prediction accuracy for measurement event prediction to be performed at the UE 802. The network 804 may determine the target prediction accuracy for measurement event prediction based on various factors, such as the historical RRM measurement reports, historical measurement event prediction data, the type and capabilities of the UE 802, as well as the characteristics of the environment in which the UE 802 is located. The request may also include an instruction to perform measurement event prediction at the UE 802. The request may also include configuration information for the UE 802. In an embodiment, the request includes an instruction to perform a measurement event prediction, and the configuration information includes information on configuring at least one of: a measurement event prediction, a measurement event prediction period, or a report period of measurement event prediction.

[0081] The procedure 800 may include a step 808 at which the UE 802 identifies the prediction model (e.g., AI / ML model) for the measurement event predictions to be performed at the UE 802. For example, the prediction model may be any model, whether currently known or developed in the future, that is capable of performing measurement event prediction at a UE. For example, the prediction model may be capable of predicting at least one of the Events A1-A6 or the Event B1-B2 as described above. The request may or may not indicate which measurement event to be predicted. The request may indicate occasions / resources for the UE to report prediction result. The request may indicate a measurement event prediction period. The request may also indicate whether the UE 802 is to perform a one-step measurement event prediction or a two-step measurement event prediction. For the two-step measurement event prediction, the UE may perform the RRM measurement prediction first, and then use the results of the RRM measurement prediction to perform the measurement event prediction. In this case, the UE 802 also include a prediction model for RRM measurement prediction. For the one-step measurement event prediction, the UE may directly perform the measurement event prediction.

[0082] The procedure 800 may include a step 810 at which the UE 802 performs the measurement event prediction and reports the prediction results to the network 804, if the UE 802 determines that the prediction results meet the one or more target prediction accuracies. In some embodiments, along with the measurement event prediction, the UE 802 may also perform RRM measurement and / or RRM measurement prediction.

[0083] If the UE 802 determines that the measurement event prediction performed at the UE does not meet the target prediction accuracy, at a step 812, the UE 802 may send a notification to the network 804 that the prediction accuracy is not acceptable. In some embodiments, if the UE 802 determines that the measurement event prediction results do not meet the target prediction accuracy, the UE 802 may further switch the current prediction model to a new prediction model. Although FIG. 8 illustrates steps 810 and 812 somewhat together, they may be mutually exclusive and not necessarily executed in a sequential manner.

[0084] FIG. 9 is a flow chart illustrating a method for a first node for communication, consistent with some embodiments of the present disclosure. The first node may include at least one UE, such as the UE 302 of FIG. 3, the UE 402 of FIG. 4, the UE 502 of FIG. 5, or the UE 602 of FIG. 6.

[0085] Referring to FIG. 9, a method 900 includes a step 902 of receiving, from a second node, a request to perform at least one of a RRM measurement prediction or a measurement event prediction. The second node may include at least one of: a base station, a core network, a road-side unit, a repeater, a transponder, a wireless router, a controller, or an access point. The second node may be a network node (terrestrial or non-terrestrial) to which the first node is connected, such as the network 304 of FIG. 3, the network 404 of FIG. 4, the network 504 of FIG. 5, or the network 604 of FIG. 6. In some embodiments, the request may include information for configuring at least one of: one or more RRM measurements, the RRM measurement prediction, the measurement event prediction, an RRM measurement period, an RRM measurement prediction period, a measurement event prediction period, a sample period of RRM measurement, a sample period of RRM measurement prediction, a report period of RRM measurement, a report period of RRM measurement prediction, or a report period of measurement event prediction. In some embodiments, one or more metrics for the one or more RRM measurements or the RRM measurement prediction comprise at least one of: RSRP, RSRQ, RSSI, SNR, or SINR values of at least one of: a serving cell, or one or more neighboring cells.

[0086] The method 900 includes a step 904 of performing the at least one of the RRM measurement prediction or the measurement event prediction. For example, as shown in FIG. 5, the UE 502 performs the RRM measurement prediction using a prediction model (e.g., AI / ML model) for RRM measurement prediction. For another example, as shown in FIG. 6, the UE 602 performs the measurement event prediction using a prediction model (e.g., AI / ML model) for measurement event prediction. In some embodiments, the UE 502 and / or the UE 602 may perform both the RRM measurement prediction and the measurement event prediction. In some embodiments, the RRM measurement period includes a plurality of sample periods of RRM measurement, and the method 900 may further include performing an RRM measurement at each of the plurality of sample periods of RRM measurement. For example, as shown in FIG. 5, at the step 510, the UE 502 may perform an RRM measurement at each of the plurality of sample periods of RRM measurement. For another example, as shown in FIG. 6, at the step 610, the UE 602 may perform an RRM measurement at each of the plurality of sample periods of RRM measurement. In some embodiments, the RRM measurement prediction period includes a plurality of sample periods of RRM measurement prediction, and the method 900 may further include performing an RRM measurement prediction at each of the plurality of sample periods of RRM measurement prediction. For example, as shown in FIG. 5, at the step 510, the UE 502 may perform an RRM measurement prediction at each of the plurality of sample periods of RRM measurement prediction.

[0087] The method 900 includes a step 906 of transmitting, to the second node, an RRM measurement report and a result of the at least one of the RRM measurement prediction or the measurement event prediction, the RRM measurement report being based on one or more RRM measurements performed by the first node. For example, as shown in FIG. 5, at the step 510, the UE 502 transmits, to the network 504, an RRM measurement report and a result of the RRM measurement prediction. For another example, as shown in FIG. 6, at the step 610, the UE 602 transmits, to the network 604, an RRM measurement report and a result of the measurement event prediction.

[0088] In some embodiments, for example, as shown in FIG. 5, the report period of RRM measurement includes a plurality of sample report periods of RRM measurement, and the method 900 includes transmitting, at each of the plurality of sample report periods of RRM measurement, a corresponding one of a plurality of RRM measurements obtained at the plurality of sample periods of RRM measurement. In some embodiments, for example, as shown in FIG. 5, the report period of RRM measurement prediction includes a plurality of sample report periods of RRM measurement prediction, and the method 900 may further include transmitting, at each of the plurality of sample report periods of RRM measurement prediction, a corresponding one of a plurality of RRM measurement prediction results obtained at the plurality of sample periods of RRM measurement prediction.

[0089] The method 900 includes a step 908 of receiving, from the second node, a notification generated based on a prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction. For example, as shown in FIG. 5, at the step 514, the UE 502 receives a notification from the network 504. For another example, as shown in FIG. 6, at the step 614, the UE 602 receives a notification from the network 604. In some embodiments, the notification may include an instruction to change one or more prediction models used for the at least one of the RRM measurement prediction or the measurement event prediction to one or more new prediction models. In some embodiments, the notification may include at least one of: one or more events to be predicted; one or more resources for the first node to report the result of the at least one of the RRM measurement prediction or the measurement event prediction; or at least one period for the at least one of the RRM measurement prediction or the measurement event prediction.

[0090] The method 900 may include a step (not shown) of transmitting the result of the at least one of the RRM measurement prediction or the measurement event prediction, and halting transmission of the RRM measurement report, based on the notification received from the second node. For example, as shown in FIG. 5, based on the notification received from the network 504, at the step 516, the UE 502 only transmits the RRM measurement prediction results, and halts transmission of the RRM measurement report. For another example, as shown in FIG. 6, based on the notification received from the network 604, at the step 616, the UE 602 only transmits the measurement event prediction results, and halts transmission of the RRM measurement report.

[0091] FIG. 10 is a flow chart illustrating a method 1000 for a first node for communication, consistent with some embodiments of the present disclosure. The first node may include at least one UE, such as the UE 702 of FIG. 7, or the UE 802 of FIG. 8.

[0092] Referring to FIG. 10, the method 1000 includes a step 1002 of receiving, from a second node, a request to perform at least one prediction at the first node, the request including at least one target prediction accuracy associated with the at least one prediction to be performed at the first node. The second node may include at least one of: a base station, a core network, a road-side unit, a repeater, a transponder, a wireless router, a controller, or an access point. The second node may be a network node (terrestrial or non-terrestrial) to which the first node is connected, such as the network 704 of FIG. 7, or the network 804 of FIG. 8.

[0093] The method 1000 includes a step 1004 of performing the at least one prediction. For example, as shown in FIG. 7, the UE 702 performs the RRM measurement prediction using a prediction model (e.g., AI / ML model) for RRM measurement prediction. For another example, as shown in FIG. 8, the UE 802 performs the measurement event prediction using a prediction model (e.g., AI / ML model) for measurement event prediction. In some embodiments, the UE 702 and / or the UE 802 may perform both the RRM measurement prediction and the measurement event prediction. In some embodiments, the RRM measurement period includes a plurality of sample periods of RRM measurement, and the method 1000 may further include performing an RRM measurement at each of the plurality of sample periods of RRM measurement. For example, as shown in FIG. 7, at the step 710, the UE 702 may perform an RRM measurement at each of the plurality of sample periods of RRM measurement.

[0094] In some embodiments, for example, as shown in FIG. 7, the at least one prediction may include an RRM measurement prediction, and the target prediction accuracy is a target prediction accuracy of the RRM measurement prediction, and the method 1000 may further include performing the RRM measurement prediction using a prediction model that satisfies the target prediction accuracy of the RRM measurement prediction; and transmitting, to the second node, a result of the RRM measurement prediction. In some embodiments, the at least one prediction may include an RRM measurement prediction, and the target prediction accuracy is a target prediction accuracy of the RRM measurement prediction, and the method 1000 may further include transmitting, to the second node, an indication of failure to identify a prediction model that satisfies the target prediction accuracy of the RRM measurement prediction.

[0095] In some embodiments, for example, as shown in FIG. 8, the at least one prediction may include a measurement event prediction and the target prediction accuracy is a target prediction accuracy of the measurement event prediction, and the request may include information for configuring at least one of: the measurement event prediction, a measurement event prediction period, or a report period of the measurement event prediction. In this case, the method 1000 may further include performing the measurement event prediction using a prediction model that satisfies the target prediction accuracy of the measurement event prediction; and transmitting, to the second node, a result of the measurement event prediction.

[0096] In some embodiments, the at least one prediction may include an RRM measurement prediction and a measurement event prediction, and the request may include an indication whether a two-step measurement event prediction or a one-step measurement event prediction is to be performed. In the two-step measurement event prediction, the RRM measurement prediction is performed first, and a result of the RRM measurement prediction is used for the measurement event prediction. In the one-step measurement event prediction, the measurement event prediction is performed without first performing the RRM measurement prediction.

[0097] The method 1000 includes a step 1006 of transmitting a result of the at least one prediction, in response to a determination that a prediction accuracy of the at least one prediction meets the target prediction accuracy, or transmitting a notification, in response to a determination that the prediction accuracy of the at least one prediction does not meet the target prediction accuracy. For example, as shown in FIG. 7, the UE 702 transmits the RRM measurement prediction results, in response to a determination that a prediction accuracy of the RRM measurement prediction meets the target prediction accuracy of RRM measurement prediction; and transmits a notification, in response to a determination that the prediction accuracy of the RRM measurement prediction does not meet the target prediction accuracy of RRM measurement prediction. For another example, as shown in FIG. 8, the UE 802 transmits the measurement event prediction results, in response to a determination that a prediction accuracy of the measurement event prediction meets the target prediction accuracy of measurement event prediction; and transmits a notification, in response to a determination that the prediction accuracy of the measurement event prediction does not meet the target prediction accuracy of measurement event prediction.

[0098] FIG. 11 is a flow chart illustrating a method 1100 for a second node for communication, consistent with some embodiments of the present disclosure. The second node may include at least one of: a base station, a core network, a road-side unit, a repeater, a transponder, a wireless router, a controller, or an access point. The second node may be a network node (terrestrial or non-terrestrial), such as the network 304 of FIG. 3, the network 404 of FIG. 4, the network 504 of FIG. 5, or the network 604 of FIG. 6.

[0099] Referring to FIG. 11, the method 1100 includes a step 1102 of transmitting, to a first node, a request to perform at least one of an RRM measurement prediction or a measurement event prediction. The first node may include at least one UE that is connected to the second node. In some embodiments, the request may include information on configuring at least one of: the one or more RRM measurements, the RRM measurement prediction, the measurement event prediction, an RRM measurement period, an RRM measurement prediction period, a measurement event prediction period, a sample period of RRM measurement, a sample period of RRM measurement prediction, a report period of RRM measurement, a report period of RRM measurement prediction, or a report period of measurement event prediction. In some embodiments, one or more metrics for the one or more RRM measurements or the RRM measurement prediction comprise at least one of: RSRP, RSRQ, RSSI, SNR, or SINR of at least one of: a serving cell, or one or more neighboring cells.

[0100] The method 1100 includes a step 1104 of receiving, from the first node, an RRM measurement report and a result of the at least one of the RRM measurement prediction or the measurement event prediction, the RRM measurement report being based on one or more RRM measurements performed by the first node. For example, as shown in FIG. 5, the network 504 receives from the UE 502, an RRM measurement report and the RRM measurement prediction results. For another example, as shown in FIG. 6, the network 604 receives from the UE 602, an RRM measurement report and the measurement event prediction results. In some embodiments, the network 504 and / or the network 604 may receive the RRM measurement report, the RRM measurement prediction results, and the measurement event prediction results.

[0101] The method 1100 includes a step 1106 of determining a prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction. For example, as shown in FIG. 5, the network 504 determines a prediction accuracy of the RRM measurement prediction. For another example, as shown in FIG. 6, the network 604 determines a prediction accuracy of the measurement event prediction.

[0102] The method 1100 includes a step 1108 of transmitting, to the first node, a notification generated based on the determined prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction. For example, as shown in FIG. 5, the network 504 transmits, to the UE 502, a notification generated based on the determined prediction accuracy of the RRM measurement prediction. For another example, as shown in FIG. 6, the network 604 transmits to the UE 602, a notification generated based on the determined prediction accuracy of the measurement event prediction. In some embodiments, the notification may include an instruction to change one or more prediction models used for the at least one of the RRM measurement prediction or the measurement event prediction to one or more new prediction models. In some embodiments, the notification may include at least one of: one or more events to be predicted; one or more resources for the first node to report the result of the at least one of the RRM measurement prediction or the measurement event prediction; or at least one period for the at least one of the RRM measurement prediction or the measurement event prediction.

[0103] In some embodiments, the method 1100 may further include a step (not shown) of receiving the result of the at least one of the RRM measurement prediction or the measurement event prediction, without receiving the RRM measurement report, upon transmission of the notification to the first node. For example, as shown in FIG. 5, the network 504 only receives the RRM measurement prediction results, without receiving the RRM measurement report, upon transmission of the notification to the first node. For another example, as shown in FIG. 6, the network 604 only receives the measurement prediction event results, without receiving the RRM measurement report, upon transmission of the notification to the first node.

[0104] FIG. 12 is a flow chart illustrating a method 1200 for a second node for communication, consistent with some embodiments of the present disclosure. The second node may include at least one of: a base station, a core network, a road-side unit, a repeater, a transponder, a wireless router, a controller, or an access point. The second node may be a network node (terrestrial or non-terrestrial), such as the network 704 of FIG. 7 or the network 804 of FIG. 8.

[0105] Referring to FIG. 12, the method 1200 includes a step 1202 of transmitting, to a first node, a request to perform at least one prediction at the first node, the request including at least one target prediction accuracy associated with the at least one prediction to be performed at the first node. The first node may include at least one UE that is connected to the second node. In some embodiments, the at least one prediction includes an RRM measurement prediction, and the request may include information for configuring at least one of: the RRM measurement prediction, an RRM measurement prediction period, a sample period of RRM measurement prediction, or a report period of RRM measurement prediction. In some embodiments, the at least one prediction includes a measurement event prediction, and the request may include information for configuring at least one of: the measurement event prediction, a measurement event prediction period, or a report period of measurement event prediction.

[0106] In some embodiments, the at least one prediction may include an RRM measurement prediction and a measurement event prediction, the at least one target prediction accuracy may include a target prediction accuracy for the RRM measurement prediction and a target prediction accuracy for the measurement event prediction, and the request may include an indication whether a two-step measurement event prediction or a one-step measurement event prediction is to be performed. In the two-step measurement event prediction, the RRM measurement prediction is performed first, and a result of the RRM measurement prediction is used for the measurement event prediction. In the one-step measurement event prediction, the measurement event prediction is performed without first performing the RRM measurement prediction.

[0107] The method 1200 includes a step 1204 of receiving, from the first node, a result of the at least one prediction performed at the first node, or a notification indicating that a prediction accuracy of the at least one prediction does not meet the target prediction accuracy. For example, as shown in FIG. 7, the network 704 may receive from the UE 702, the RRM measurement prediction results, or the notification that the prediction accuracy of RRM measurement prediction does not meet the target prediction accuracy of the RRM measurement prediction. For another example, as shown in FIG. 8, the network 804 may receive from the UE 802, the measurement event prediction results, or the notification that the prediction accuracy of the measurement event prediction does not meet the target prediction accuracy of the measurement event prediction.

[0108] FIG. 13 is a block diagram of a node 1300, consistent with some embodiments of the present disclosure. In some embodiments, the node 1300 may be a UE that includes prediction models and performs predictions. For example, the node 1300 may be the UE 302 of FIG. 3, the UE 402 of FIG. 4, the UE 502 of FIG. 5, the UE 602 of FIG. 6, the UE 702 of FIG. 7, or the UE 802 of FIG. 8. In some embodiments, the node 1300 may be a network node that serves a UE. For example, the node 1300 may be the network 304 of FIG. 3, the network 404 of FIG. 4, the network 504 of FIG. 5, the network 604 of FIG. 6, the network 704 of FIG. 7, or the network 804 of FIG. 8. In some embodiments, the node 1300 may be a node that performs the method 900, the method 1000, the method 1100, or the method 1200. The node 1300 may take any form, including but not limited to, a computer, a system including at least one computer, a vehicle, a component mounted in a vehicle, a portable computer, a wireless terminal including a mobile phone, a wireless handheld device, or wireless personal device, or any other form.

[0109] Referring to FIG. 1300, the node 1300 may include antenna 1302 that may be used for transmission or reception of electromagnetic signals to / from one or more other nodes. The antenna 1302 may include one or more antenna elements and may enable different input-output antenna configurations, for example, multiple input multiple output (MIMO) configuration, multiple input single output (MISO) configuration, and single input multiple output (SIMO) configuration. In some embodiments, the antenna 1302 may include multiple (e.g., tens or hundreds) antenna elements and may enable multi-antenna functions such as beamforming. In some embodiments, the antenna 1302 is a single antenna.

[0110] The node 1300 may include a transceiver 1304 that is coupled to the antenna 1302. The transceiver 1304 may be a wireless transceiver at the node 1300 and may communicate bi-directionally with one or more other nodes. For example, the transceiver 1304 may receive / transmit wireless signals from / to a base station via downlink / uplink communication. The transceiver 1304 may also receive / transmit wireless signals from / to another node unit via sidelink communication. The transceiver 1304 may include a modem to modulate the packets and provide the modulated packets to the antenna 1302 for transmission, and to demodulate packets received from the antenna 1302.

[0111] The node 1300 may include a memory 1306. The memory 1306 may be any type of computer-readable storage medium including volatile or non-volatile memory devices, or a combination thereof. The computer-readable storage medium includes, but is not limited to, non-transitory computer storage media. A non-transitory storage medium may be accessed by a general purpose or special purpose computer. Examples of non-transitory storage medium include, but are not limited to, a portable computer diskette, a hard disk, solid state drive, random access memory (RAM), read-only memory (ROM), an erasable programmable read-only memory (EPROM), electrically erasable programmable ROM (EEPROM), a digital versatile disk (DVD), flash memory, compact disk (CD) ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, etc. A non-transitory medium may be used to carry or store desired program code means (e.g., instructions and / or data structures) and may be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. In some examples, the software / program code may be transmitted from a remote source (e.g., a website, a server, etc.) using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave. In such examples, the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are within the scope of the definition of medium. The memory 1306 may also be a cloud-based remote (cloud) memory device. Combinations of the above examples are also within the scope of computer-readable medium.

[0112] The memory 1306 may store information related to identities of node 1300 and the signals and / or data received by antenna 1302. The memory 1306 may also store post-processing signals and / or data. The memory 1306 may also store computer-readable program instructions, mathematical models, and algorithms that are used in signal processing in receiver 1304 and computations in a processor 1308 of the node 1300. For example, the memory 1306 may store prediction algorithms, such as AI / ML models for predictions (e.g., RRM measurement prediction, measurement event prediction, etc.). The memory 1306 may further store computer-readable program instructions for execution by processor 1308 to operate the node 1300 to perform various functions described in this disclosure. In some examples, the memory 1306 may include a basic input / output system (BIOS) which may control basic hardware or software operation such as the interaction with peripheral components or devices.

[0113] The computer-readable program instructions of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of one or more programming languages, including an object-oriented programming language, and conventional procedural programming languages. The computer-readable program instructions may execute entirely on a computing device as a stand-alone software package, or partly on a first computing device and partly on a second computing device remote from the first computing device. In the latter scenario, the second, remote computing device may be connected to the first computing device through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0114] The processor 1308 may include a hardware device with processing capabilities. The processor 1308 may include at least one of a general-purpose processor, a digital signal processor (DSP), a central processing unit (CPU), a graphical processing unit (GPU), a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or other programmable logic device. Examples of the general-purpose processor include, but are not limited to, a microprocessor, any conventional processor, a controller, a microcontroller, or a state machine. In some embodiments, the processor 1308 may be implemented using a combination of devices (e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration). The processor 1308 may perform predictions (e.g., RRM measurement prediction, measurement event prediction, etc.) using the prediction algorithms (e.g., AI / ML models) stored in the memory 1306. The processor 1308 may receive, from transceiver 1304, downlink / uplink signals or sidelink signals and further process the signals. The processor 1308 may also receive, from transceiver 1304, data packets and further process the packets. In some embodiments, the processor 1308 may be configured to operate a memory using a memory controller. In some embodiments, a memory controller may be integrated into the processor 1308. The processor 1308 may be configured to execute computer-readable instructions stored in a memory (e.g., the memory 1306) to cause the node 1300 to perform various functions.

[0115] The node 1300 may include a global positioning system (GPS) 1310. The GPS 1310 may be used for enabling location-based services or other services based on a geographical position of the node 1300 and / or synchronization among nodes. The GPS 1310 may receive global navigation satellite systems (GNSS) signals from a single satellite or a plurality of satellite signals via the antenna 1302 and provide a geographical position of the node 1300 (e.g., coordinates of the node 1300). In some embodiments, the GPS 1310 is omitted. In some embodiments, a timer is included.

[0116] The node 1300 may include an input / output (I / O) device 1312 that may be used to communicate a result of signal processing and computation to a user or another device. The I / O device 1312 may include a user interface including a display and an input device to transmit a user command to processor 1308. The display may be configured to display a status of signal reception at the node 1300, the data stored at memory 1306, a status of signal processing, and a result of computation, etc. The display may include, but is not limited to, a cathode ray tube (CRT), a liquid crystal display (LCD), a light-emitting diode (LED), a gas plasma display, a touch screen, or other image projection devices for displaying information to a user. The input device may be any type of computer hardware equipment used to receive data and control signals from a user. The input device may include, but is not limited to, a keyboard, a mouse, a scanner, a digital camera, a joystick, a trackball, cursor direction keys, a touchscreen monitor, or audio / video commanders, etc.

[0117] The node 1300 may further include a machine interface 1314, such as an electrical bus that connects the transceiver 1304, the memory 1306, the processor 1308, the GPS 1310, and the I / O device 1312.

[0118] In some embodiments, the node 1300 may be a first node for communication. The processor 1308 may be configured or programmed to execute the instructions stored in the memory 1306 to: receive, from a second node, a request to perform at least one of an RRM measurement prediction or a measurement event prediction; perform the at least one of the RRM measurement prediction or the measurement event prediction; transmit, to the second node, an RRM measurement report and a result of the at least one of the RRM measurement prediction or the measurement event prediction, the RRM measurement report being based on one or more RRM measurements performed by the first node; and receive, from the second node, a notification generated based on a prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction.

[0119] In some embodiments, the node 1300 may be a first node for communication. The processor 1308 may be configured or programmed to execute the instructions stored in the memory 1306 to: receive, from a second node, a request to perform at least one prediction at the first node, the request including at least one target prediction accuracy associated with the at least one prediction to be performed at the first node; perform the at least one prediction; and transmit a result of the at least one prediction, in response to a determination that a prediction accuracy of the at least one prediction meets the target prediction accuracy, or transmit a notification, in response to a determination that the prediction accuracy of the at least one prediction does not meet the target prediction accuracy.

[0120] In some embodiments, the node 1300 may be a second node for communication. The processor 1308 may be configured or programmed to execute the instructions stored in the memory 1306 to: transmit, to a first node, a request to perform at least one of an RRM measurement prediction or a measurement event prediction; receive, from the first node, an RRM measurement report and a result of the at least one of the RRM measurement prediction or the measurement event prediction, the RRM measurement report being based on one or more RRM measurements performed by the first node; determine a prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction; and transmit, to the first node, a notification generated based on the determined prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction.

[0121] In some embodiments, the node 1300 may be a second node for communication. The processor 1308 may be configured or programmed to execute the instructions stored in the memory 1306 to: transmit, to a first node, a request to perform at least one prediction at the first node, the request including at least one target prediction accuracy associated with the at least one prediction to be performed at the first node; and receive, from the first node, a result of the at least one prediction performed at the first node, or a notification indicating that a prediction accuracy of the at least one prediction does not meet the target prediction accuracy.

[0122] The embodiments disclosed in the present disclosure can be applied to any radio access technologies, for example, 3GPP 4G technology, 5G technology, or future 3GPP radio technology generations such as 6G, 7G, etc.

[0123] While the examples in this disclosure relate to 3GPP technologies, embodiments described in this disclosure could be used for non-3GPP technologies, for example, IEEE and its 802.11 variants, Wi-Fi, WiMAX, etc.

[0124] As used in this disclosure, use of the term “or” in a list of items indicates an inclusive list. The list of items may be prefaced by a phrase such as “at least one of” or “one or more of.” For example, a list of at least one of A, B, or C includes A or B or C or AB (i.e., A and B) or AC or BC or ABC (i.e., A and B and C). Also, as used in this disclosure, prefacing a list of conditions with the phrase “based on” shall not be construed as “based only on” the set of conditions and rather shall be construed as “based at least in part on” the set of conditions. For example, an outcome described as “based on condition A” may be based on both a condition A and a condition B without departing from the scope of this disclosure.

[0125] In this specification, the terms “comprise,” “include,” or “contain” may be used interchangeably and have the same meaning and are to be construed as inclusive and open-ended. The terms “comprise,” “include,” or “contain” may be used before a list of elements and indicate that at least all of the listed elements within the list exist but other elements that are not in the list may also be present. For example, if A comprises B and C, both {B, C} and {B, C, D} are within the scope of A.

[0126] The present disclosure, in connection with the accompanied drawings, describes example configurations that are not representative of all the examples that may be implemented or all configurations that are within the scope of this disclosure. The term “exemplary” should not be construed as “preferred” or “advantageous compared to other examples” but rather “an illustration, an instance or an example.” By reading this disclosure, including the description of the embodiments and the drawings, it will be appreciated by a person of ordinary skills in the art that the technology disclosed herein may be implemented using alternative embodiments. The person of ordinary skill in the art would appreciate that the embodiments, or certain features of the embodiments described herein, may be combined to arrive at yet other embodiments for practicing the technology described in the present disclosure. Thus, the disclosure is not limited to the examples and designs described herein but is to be accorded the broadest scope consistent with the principles and novel features disclosed herein.

[0127] The flowcharts and block diagrams in the figures illustrate examples of the architecture, functionality, and operation of possible implementations of systems, methods, and devices according to various embodiments. It should be noted that, in some alternative implementations, the functions noted in blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. Likewise, additional steps may be included in such methods, and certain steps may be omitted or combined, in methods consistent with various embodiments.

[0128] It is understood that the described embodiments are not mutually exclusive, and elements, components, materials, or steps described in connection with one example embodiment may be combined with, or eliminated from, other embodiments in suitable ways to accomplish desired design objectives.

[0129] Reference herein to “some embodiments” or “some exemplary embodiments” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment. The appearance of the phrases “one embodiment” “some embodiments” or “another embodiment” in various places in the present disclosure do not all necessarily refer to the same embodiment, nor are separate or alternative embodiments necessarily mutually exclusive of other embodiments.

[0130] Additionally, the articles “a” and “an” as used in the present disclosure and the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form.

[0131] Unless explicitly stated otherwise, each numerical value and range should be interpreted as being approximate as if the word “about” or “approximately” preceded the value of the value or range.

[0132] Although the elements in the following method claims, if any, are recited in a particular sequence, unless the claim recitations otherwise imply a particular sequence for implementing some or all of those elements, those elements are not necessarily intended to be limited to being implemented in that particular sequence.

[0133] It is appreciated that certain features of the present disclosure, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the specification, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination or as suitable in any other described embodiment of the specification. Certain features described in the context of various embodiments are not essential features of those embodiments, unless noted as such.

[0134] It will be further understood that various modifications, alternatives, and variations in the details, materials, and arrangements of the parts which have been described and illustrated in order to explain the nature of described embodiments may be made by those skilled in the art without departing from the scope. Accordingly, the following claims embrace all such alternatives, modifications, and variations that fall within the terms of the claims.

[0135] Clause 1: A first node for communication, the first node comprising: a memory storing an instruction; and a processor configured to execute the instruction stored in the memory to: receive, from a second node, a request to perform at least one of a radio resource management (RRM) measurement prediction or a measurement event prediction; perform the at least one of the RRM measurement prediction or the measurement event prediction; transmit, to the second node, an RRM measurement report and a result of the at least one of the RRM measurement prediction or the measurement event prediction, the RRM measurement report being based on one or more RRM measurements performed by the first node; and receive, from the second node, a notification generated based on a prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction.

[0136] Clause 2: The first node of clause 1, wherein the first node comprises at least one user equipment (UE).

[0137] Clause 3: The first node of clause 1, wherein the second node comprises a network node to which the first node is connected.

[0138] Clause 4: The first node of clause 1, wherein the request comprises information on configuring at least one of: the one or more RRM measurements, the RRM measurement prediction, the measurement event prediction, an RRM measurement period, an RRM measurement prediction period, a measurement event prediction period, a sample period of RRM measurement, a sample period of RRM measurement prediction, a report period of RRM measurement, a report period of RRM measurement prediction, or a report period of measurement event prediction.

[0139] Clause 5: The first node of clause 4, wherein one or more metrics for the one or more RRM measurements or the RRM measurement prediction comprise at least one of: reference signal received power (RSRP), reference signal received quality (RSRQ), received signal strength indicator (RSSI), signal-to-noise ratio (SNR), or signal-to-interference-plus-noise ratio (SINR) of at least one of: a serving cell, or one or more neighboring cells.

[0140] Clause 6: The first node of clause 4, wherein the RRM measurement period comprises a plurality of sample periods of RRM measurement, and the processor is configured to execute the instruction stored in the memory to: perform an RRM measurement at each of the plurality of sample periods of RRM measurement.

[0141] Clause 7: The first node of clause 6, wherein the report period of RRM measurement comprises a plurality of sample report periods of RRM measurement, and the processor is configured to execute the instruction stored in the memory to: transmit, at each of the plurality of sample report periods of RRM measurement, a corresponding one of a plurality of RRM measurements obtained at the plurality of sample periods of RRM measurement.

[0142] Clause 8: The first node of clause 4, wherein the RRM measurement prediction period comprises a plurality of sample periods of RRM measurement prediction, and the processor is configured to execute the instruction stored in the memory to: perform an RRM measurement prediction at each of the plurality of sample periods of RRM measurement prediction.

[0143] Clause 9: The first node of clause 8, wherein the report period of RRM measurement prediction comprises a plurality of sample report periods of RRM measurement prediction, and the processor is configured to execute the instruction stored in the memory to: transmit, at each of the plurality of sample report periods of RRM measurement prediction, a corresponding one of a plurality of RRM measurement prediction results obtained at the plurality of sample periods of RRM measurement prediction.

[0144] Clause 10: The first node of clause 1, wherein the RRM measurement prediction is performed by using at least one artificial intelligence or machine learning (AI / ML) algorithm.

[0145] Clause 11: The first node of clause 1, wherein the measurement event prediction is performed by using at least one AI / ML algorithm.

[0146] Clause 12: The first node of clause 1, wherein the notification comprises an instruction to change one or more prediction models used for the at least one of the RRM measurement prediction or the measurement event prediction to one or more new prediction models.

[0147] Clause 13: The first node of clause 1, wherein the processor is configured to execute the instruction stored in the memory to: transmit the result of the at least one of the RRM measurement prediction or the measurement event prediction, and stop transmitting the RRM measurement report, based on the notification received from the second node.

[0148] Clause 14: The first node of clause 1, wherein the notification comprises at least one of: one or more events to be predicted; one or more resources for the first node to report the result of the at least one of the RRM measurement prediction or the measurement event prediction; or at least one period for the at least one of the RRM measurement prediction or the measurement event prediction.

[0149] Clause 15: A first node for communication, the first node comprising: a memory storing an instruction; and a processor configured to execute the instruction stored in the memory to: receive, from a second node, a request to perform at least one prediction at the first node, the request including at least one target prediction accuracy associated with the at least one prediction to be performed at the first node; perform the at least one prediction; and transmit a result of the at least one prediction, in response to a determination that a prediction accuracy of the at least one prediction meets the target prediction accuracy, or transmit a notification, in response to a determination that the prediction accuracy of the at least one prediction does not meet the target prediction accuracy.

[0150] Clause 16: The first node of clause 15, wherein the at least one prediction comprises a radio resource management (RRM) measurement prediction and the target prediction accuracy is a target prediction accuracy of the RRM measurement prediction, and the processor is configured to execute the instruction stored in the memory to: perform the RRM measurement prediction using a prediction model that satisfies the target prediction accuracy of the RRM measurement prediction; and transmit, to the second node, a result of the RRM measurement prediction.

[0151] Clause 17: The first node of clause 15, wherein the at least one prediction comprises an RRM measurement prediction, and the target prediction accuracy is a target prediction accuracy of the RRM measurement prediction, and the processor is configured to execute the instruction stored in the memory to: transmit, to the second node, an indication of failure to identify a prediction model that satisfies the target prediction accuracy of the RRM measurement prediction.

[0152] Clause 18: The first node of clause 15, wherein the at least one prediction comprises a measurement event prediction and the target prediction accuracy is a target prediction accuracy of the measurement event prediction, and the request comprises information for configuring at least one of: the measurement event prediction, a measurement event prediction period, or a report period of the measurement event prediction.

[0153] Clause 19: The first node of clause 18, wherein the processor is configured to execute the instruction stored in the memory to: perform the measurement event prediction using a prediction model that satisfies the target prediction accuracy of the measurement event prediction; and transmit, to the second node, a result of the measurement event prediction.

[0154] Clause 20: The first node of clause 15, wherein the at least one prediction comprises an RRM measurement prediction and a measurement event prediction, and the request comprises an indication whether a two-step measurement event prediction or a one-step measurement event prediction is to be performed, wherein in the two-step measurement event prediction, the RRM measurement prediction is performed first, and a result of the RRM measurement prediction is used for the measurement event prediction, and wherein in the one-step measurement event prediction, the measurement event prediction is performed without first performing the RRM measurement prediction.

[0155] Clause 21: A second node for communication, the second node comprising: a memory storing an instruction; and a processor configured to execute the instruction stored in the memory to: transmit, to a first node, a request to perform at least one of a radio resource management (RRM) measurement prediction or a measurement event prediction; receive, from the first node, an RRM measurement report and a result of the at least one of the RRM measurement prediction or the measurement event prediction, the RRM measurement report being based on one or more RRM measurements performed by the first node; determine a prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction; and transmit, to the first node, a notification generated based on the determined prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction.

[0156] Clause 22: The second node of clause 21, wherein the first node comprises at least one user equipment (UE).

[0157] Clause 23: The second node of clause 21, wherein the second node comprises a network node to which the first node is connected.

[0158] Clause 24: The second node of clause 21, wherein the request comprises information on configuring at least one of: the one or more RRM measurements, the RRM measurement prediction, the measurement event prediction, an RRM measurement period, an RRM measurement prediction period, a measurement event prediction period, a sample period of RRM measurement, a sample period of RRM measurement prediction, a report period of RRM measurement, a report period of RRM measurement prediction, or a report period of measurement event prediction.

[0159] Clause 25: The second node of clause 24, wherein one or more metrics for the one or more RRM measurements or the RRM measurement prediction comprise at least one of: reference signal received power (RSRP), reference signal received quality (RSRQ), received signal strength indicator (RSSI), signal-to-noise ratio (SNR), or signal-to-interference-plus-noise ratio (SINR) of at least one of: a serving cell, or one or more neighboring cells.

[0160] Clause 26: The second node of clause 21, wherein the notification comprises an instruction to change one or more prediction models used for the at least one of the RRM measurement prediction or the measurement event prediction to one or more new prediction models.

[0161] Clause 27: The second node of clause 21, wherein the processor is configured to execute the instruction stored in the memory to: receive the result of the at least one of the RRM measurement prediction or the measurement event prediction, without receiving the RRM measurement report, upon transmission of the notification to the first node.

[0162] Clause 28: The second node of clause 21, wherein the notification comprises at least one of: one or more events to be predicted; one or more resources for the first node to report the result of the at least one of the RRM measurement prediction or the measurement event prediction; or at least one period for the at least one of the RRM measurement prediction or the measurement event prediction.

[0163] Clause 29: A second node for communication, the second node comprising: a memory storing an instruction; and a processor configured to execute the instruction stored in the memory to: transmit, to a first node, a request to perform at least one prediction at the first node, the request including at least one target prediction accuracy associated with the at least one prediction to be performed at the first node; and receive, from the first node, a result of the at least one prediction performed at the first node, or a notification indicating that a prediction accuracy of the at least one prediction does not meet the target prediction accuracy.

[0164] Clause 30: The second node of clause 29, wherein the at least one prediction comprises a radio resource management (RRM) measurement prediction, and the request comprises information for configuring at least one of: the RRM measurement prediction, an RRM measurement prediction period, a sample period of RRM measurement prediction, or a report period of RRM measurement prediction.

[0165] Clause 31: The second node of clause 29, wherein the at least one prediction comprises a measurement event prediction, and the request comprises information for configuring at least one of: the measurement event prediction, a measurement event prediction period, or a report period of measurement event prediction.

[0166] Clause 32: The second node of clause 29, wherein the at least one prediction comprises an RRM measurement prediction and a measurement event prediction, the at least one target prediction accuracy comprises a target prediction accuracy for the RRM measurement prediction and a target prediction accuracy for the measurement event prediction, and the request comprises an indication whether a two-step measurement event prediction or a one-step measurement event prediction is to be performed, wherein in the two-step measurement event prediction, the RRM measurement prediction is performed first, and a result of the RRM measurement prediction is used for the measurement event prediction, and wherein in the one-step measurement event prediction, the measurement event prediction is performed without first performing the RRM measurement prediction.

[0167] Clause 33: A method for a first node for communication, the method comprising: receiving, from a second node, a request to perform at least one of a radio resource management (RRM) measurement prediction or a measurement event prediction; performing the at least one of the RRM measurement prediction or the measurement event prediction; transmitting, to the second node, an RRM measurement report and a result of the at least one of the RRM measurement prediction or the measurement event prediction, the RRM measurement report being based on one or more RRM measurements performed by the first node; and receiving, from the second node, a notification generated based on a prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction.

[0168] Clause 34: The method of clause 33, wherein the first node comprises at least one user equipment (UE).

[0169] Clause 35: The method of clause 33, wherein the second node comprises a network node to which the first node is connected.

[0170] Clause 36: The method of clause 33, wherein the request comprises information on configuring at least one of: the one or more RRM measurements, the RRM measurement prediction, the measurement event prediction, an RRM measurement period, an RRM measurement prediction period, a measurement event prediction period, a sample period of RRM measurement, a sample period of RRM measurement prediction, a report period of RRM measurement, a report period of RRM measurement prediction, or a report period of measurement event prediction.

[0171] Clause 37: The method of clause 36, wherein one or more metrics for the one or more RRM measurements or the RRM measurement prediction comprise at least one of: reference signal received power (RSRP), reference signal received quality (RSRQ), received signal strength indicator (RSSI), signal-to-noise ratio (SNR), or signal-to-interference-plus-noise ratio (SINR) of at least one of: a serving cell, or one or more neighboring cells.

[0172] Clause 38: The method of clause 36, wherein the RRM measurement period comprises a plurality of sample periods of RRM measurement, and the method further comprises: performing an RRM measurement at each of the plurality of sample periods of RRM measurement.

[0173] Clause 39: The method of clause 38, wherein the report period of RRM measurement comprises a plurality of sample report periods of RRM measurement, and the method further comprises: transmitting, at each of the plurality of sample report periods of RRM measurement, a corresponding one of a plurality of RRM measurements obtained at the plurality of sample periods of RRM measurement.

[0174] Clause 40: The method of clause 36, the RRM measurement prediction period comprises a plurality of sample periods of RRM measurement prediction, and the method further comprises: performing an RRM measurement prediction at each of the plurality of sample periods of RRM measurement prediction.

[0175] Clause 41: The method of clause 40, wherein the report period of RRM measurement prediction comprises a plurality of sample report periods of RRM measurement prediction, and the method further comprises: transmitting, at each of the plurality of sample report periods of RRM measurement prediction, a corresponding one of a plurality of RRM measurement prediction results obtained at the plurality of sample periods of RRM measurement prediction.

[0176] Clause 42: The method of clause 33, wherein the RRM measurement prediction is performed by using at least one artificial intelligence or machine learning (AI / ML) algorithm.

[0177] Clause 43: The method of clause 33, wherein the measurement event prediction is performed by using an AI / ML algorithm.

[0178] Clause 44: The method of clause 33, wherein the notification comprises an instruction to change one or more prediction models used for the at least one of the RRM measurement prediction or the measurement event prediction to one or more new prediction models.

[0179] Clause 45: The method of clause 33, wherein the method further comprises: transmitting the result of the at least one of the RRM measurement prediction or the measurement event prediction, and halting transmission of the RRM measurement report, based on the notification received from the second node.

[0180] Clause 46: The method of clause 33, wherein the notification comprises at least one of: one or more events to be predicted; one or more resources for the first node to report the result of the at least one of the RRM measurement prediction or the measurement event prediction; or at least one period for the at least one of the RRM measurement prediction or the measurement event prediction.

[0181] Clause 47: A method for a first node for communication, the method comprising: receiving, from a second node, a request to perform at least one prediction at the first node, the request including at least one target prediction accuracy associated with the at least one prediction to be performed at the first node; performing the at least one prediction; and transmitting a result of the at least one prediction, in response to a determination that a prediction accuracy of the at least one prediction meets the target prediction accuracy, or transmitting a notification, in response to a determination that the prediction accuracy of the at least one prediction does not meet the target prediction accuracy.

[0182] Clause 48: The method of clause 47, wherein the at least one prediction comprises a radio resource management (RRM) measurement prediction and the target prediction accuracy is a target prediction accuracy of the RRM measurement prediction, and the method further comprises: performing the RRM measurement prediction using a prediction model that satisfies the target prediction accuracy of the RRM measurement prediction; and transmitting, to the second node, a result of the RRM measurement prediction.

[0183] Clause 49: The method of clause 47, wherein the at least one prediction comprises an RRM measurement prediction, and the target prediction accuracy is a target prediction accuracy of the RRM measurement prediction, and the method further comprises: transmitting, to the second node, an indication of failure to identify a prediction model that satisfies the target prediction accuracy of the RRM measurement prediction.

[0184] Clause 50: The method of clause 47, wherein the at least one prediction comprises a measurement event prediction and the target prediction accuracy is a target prediction accuracy of the measurement event prediction, and the request comprises information for configuring at least one of: the measurement event prediction, a measurement event prediction period, or a report period of the measurement event prediction.

[0185] Clause 51: The method of clause 50, wherein the method further comprises: performing the measurement event prediction using a prediction model that satisfies the target prediction accuracy of the measurement event prediction; and transmitting, to the second node, a result of the measurement event prediction.

[0186] Clause 52: The method of clause 47, wherein the at least one prediction comprises an RRM measurement prediction and a measurement event prediction, and the request comprises an indication whether a two-step measurement event prediction or a one-step measurement event prediction is to be performed, wherein in the two-step measurement event prediction, the RRM measurement prediction is performed first, and a result of the RRM measurement prediction is used for the measurement event prediction, and wherein in the one-step measurement event prediction, the measurement event prediction is performed without first performing the RRM measurement prediction.

[0187] Clause 53: A method for a second node for communication, the method comprising: transmitting, to a first node, a request to perform at least one of a radio resource management (RRM) measurement prediction or a measurement event prediction; receiving, from the first node, an RRM measurement report and a result of the at least one of the RRM measurement prediction or the measurement event prediction, the RRM measurement report being based on one or more RRM measurements performed by the first node; determining a prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction; and transmitting, to the first node, a notification generated based on the determined prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction.

[0188] Clause 54: The method of clause 53, wherein the first node comprises at least one user equipment (UE).

[0189] Clause 55: The method of clause 53, wherein the second node comprises a network node to which the first node is connected.

[0190] Clause 56: The method of clause 53, wherein the request comprises information on configuring at least one of: the one or more RRM measurements, the RRM measurement prediction, the measurement event prediction, an RRM measurement period, an RRM measurement prediction period, a measurement event prediction period, a sample period of RRM measurement, a sample period of RRM measurement prediction, a report period of RRM measurement, a report period of RRM measurement prediction, or a report period of measurement event prediction.

[0191] Clause 57: The method of clause 56, wherein one or more metrics for the one or more RRM measurements or the RRM measurement prediction comprise at least one of: reference signal received power (RSRP), reference signal received quality (RSRQ), received signal strength indicator (RSSI), signal-to-noise ratio (SNR), or signal-to-interference-plus-noise ratio (SINR) of at least one of: a serving cell, or one or more neighboring cells.

[0192] Clause 58: The method of clause 53, wherein the notification comprises an instruction to change one or more prediction models used for the at least one of the RRM measurement prediction or the measurement event prediction to one or more new prediction models.

[0193] Clause 59: The method of clause 53, wherein the method further comprises: receiving the result of the at least one of the RRM measurement prediction or the measurement event prediction, without receiving the RRM measurement report, upon transmission of the notification to the first node.

[0194] Clause 60: The method of clause 53, wherein the notification comprises at least one of: one or more events to be predicted; one or more resources for the first node to report the result of the at least one of the RRM measurement prediction or the measurement event prediction; or at least one period for the at least one of the RRM measurement prediction or the measurement event prediction.

[0195] Clause 61: A method for a second node for communication, the method comprising: transmitting, to a first node, a request to perform at least one prediction at the first node, the request including at least one target prediction accuracy associated with the at least one prediction to be performed at the first node; and receiving, from the first node, a result of the at least one prediction performed at the first node, or a notification indicating that a prediction accuracy of the at least one prediction does not meet the target prediction accuracy.

[0196] Clause 62: The method of clause 61, wherein the at least one prediction comprises a radio resource management (RRM) measurement prediction, and the request comprises information for configuring at least one of: the RRM measurement prediction, an RRM measurement prediction period, a sample period of RRM measurement prediction, or a report period of RRM measurement prediction.

[0197] Clause 63: The method of clause 61, wherein the at least one prediction comprises a measurement event prediction, and the request comprises information for configuring at least one of: the measurement event prediction, a measurement event prediction period, or a report period of measurement event prediction.

[0198] Clause 64: The method of clause 61, wherein the at least one prediction comprises an RRM measurement prediction and a measurement event prediction, the at least one target prediction accuracy comprises a target prediction accuracy for the RRM measurement prediction and a target prediction accuracy for the measurement event prediction, and the request comprises an indication whether a two-step measurement event prediction or a one-step measurement event prediction is to be performed, wherein in the two-step measurement event prediction, the RRM measurement prediction is performed first, and a result of the RRM measurement prediction is used for the measurement event prediction, and wherein in the one-step measurement event prediction, the measurement event prediction is performed without first performing the RRM measurement prediction.

[0199] Clause 65: A non-transitory computer-readable medium storing instructions that are executable by one or more processors of a first node for communication, to perform a method, the method comprising: receiving, from a second node, a request to perform at least one of a radio resource management (RRM) measurement prediction or a measurement event prediction; performing the at least one of the RRM measurement prediction or the measurement event prediction; transmitting, to the second node, an RRM measurement report and a result of the at least one of the RRM measurement prediction or the measurement event prediction, the RRM measurement report being based on one or more RRM measurements performed by the first node; and receiving, from the second node, a notification generated based on a prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction.

[0200] Clause 66: A non-transitory computer-readable medium storing instructions that are executable by one or more processors of a first node for communication, to perform a method, the method comprising: receiving, from a second node, a request to perform at least one prediction at the first node, the request including at least one target prediction accuracy associated with the at least one prediction to be performed at the first node; performing the at least one prediction; and transmitting a result of the at least one prediction, in response to a determination that a prediction accuracy of the at least one prediction meets the target prediction accuracy, or transmitting a notification, in response to a determination that the prediction accuracy of the at least one prediction does not meet the target prediction accuracy.

[0201] Clause 67: A non-transitory computer-readable medium storing instructions that are executable by one or more processors of a second node for communication, to perform a method, the method comprising: transmitting, to a first node, a request to perform at least one of a radio resource management (RRM) measurement prediction or a measurement event prediction; receiving, from the first node, an RRM measurement report and a result of the at least one of the RRM measurement prediction or the measurement event prediction, the RRM measurement report being based on one or more RRM measurements performed by the first node; determining a prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction; and transmitting, to the first node, a notification generated based on the determined prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction.

[0202] Clause 68: A non-transitory computer-readable medium storing instructions that are executable by one or more processors of a second node for communication, to perform a method, the method comprising: transmitting, to a first node, a request to perform at least one prediction at the first node, the request including at least one target prediction accuracy associated with the at least one prediction to be performed at the first node; and receiving, from the first node, a result of the at least one prediction performed at the first node, or a notification indicating that a prediction accuracy of the at least one prediction does not meet the target prediction accuracy.

Claims

1. A first node for communication, the first node comprising: a memory storing an instruction; and a processor configured to execute the instruction stored in the memory to: receive, from a second node, a request to perform at least one of a radio resource management (RRM) measurement prediction or a measurement event prediction; perform the at least one of the RRM measurement prediction or the measurement event prediction; transmit, to the second node, an RRM measurement report and a result of the at least one of the RRM measurement prediction or the measurement event prediction, the RRM measurement report being based on one or more RRM measurements performed by the first node; and receive, from the second node, a notification generated based on a prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction.

2. The first node of claim 1, wherein the first node comprises at least one user equipment (UE).

3. The first node of claim 1, wherein the second node comprises a network node to which the first node is connected.

4. The first node of claim 1, wherein the request comprises information on configuring at least one of: the one or more RRM measurements, the RRM measurement prediction, the measurement event prediction, an RRM measurement period, an RRM measurement prediction period, a measurement event prediction period, a sample period of RRM measurement, a sample period of RRM measurement prediction, a report period of RRM measurement, a report period of RRM measurement prediction, or a report period of measurement event prediction.

5. The first node of claim 4, wherein one or more metrics for the one or more RRM measurements or the RRM measurement prediction comprise at least one of: reference signal received power (RSRP), reference signal received quality (RSRQ), received signal strength indicator (RSSI), signal-to-noise ratio (SNR), or signal-to-interference-plus-noise ratio (SINR) of at least one of: a serving cell, or one or more neighboring cells.

6. The first node of claim 4, wherein the RRM measurement period comprises a plurality of sample periods of RRM measurement, and the processor is configured to execute the instruction stored in the memory to: perform an RRM measurement at each of the plurality of sample periods of RRM measurement.

7. The first node of claim 6, wherein the report period of RRM measurement comprises a plurality of sample report periods of RRM measurement, and the processor is configured to execute the instruction stored in the memory to: transmit, at each of the plurality of sample report periods of RRM measurement, a corresponding one of a plurality of RRM measurements obtained at the plurality of sample periods of RRM measurement.

8. The first node of claim 4, wherein the RRM measurement prediction period comprises a plurality of sample periods of RRM measurement prediction, and the processor is configured to execute the instruction stored in the memory to: perform an RRM measurement prediction at each of the plurality of sample periods of RRM measurement prediction.

9. The first node of claim 8, wherein the report period of RRM measurement prediction comprises a plurality of sample report periods of RRM measurement prediction, and the processor is configured to execute the instruction stored in the memory to: transmit, at each of the plurality of sample report periods of RRM measurement prediction, a corresponding one of a plurality of RRM measurement prediction results obtained at the plurality of sample periods of RRM measurement prediction.

10. The first node of claim 1, wherein the notification comprises an instruction to change one or more prediction models used for the at least one of the RRM measurement prediction or the measurement event prediction to one or more new prediction models.

11. The first node of claim 1, wherein the processor is configured to execute the instruction stored in the memory to: transmit the result of the at least one of the RRM measurement prediction or the measurement event prediction, and stop transmitting the RRM measurement report, based on the notification received from the second node.

12. The first node of claim 1, wherein the notification comprises at least one of: one or more events to be predicted; one or more resources for the first node to report the result of the at least one of the RRM measurement prediction or the measurement event prediction; or at least one period for the at least one of the RRM measurement prediction or the measurement event prediction.

13. A first node for communication, the first node comprising: a memory storing an instruction; and a processor configured to execute the instruction stored in the memory to: receive, from a second node, a request to perform at least one prediction at the first node, the request including at least one target prediction accuracy associated with the at least one prediction to be performed at the first node; perform the at least one prediction; and transmit a result of the at least one prediction, in response to a determination that a prediction accuracy of the at least one prediction meets the target prediction accuracy, or transmit a notification, in response to a determination that the prediction accuracy of the at least one prediction does not meet the target prediction accuracy.

14. The first node of claim 13, wherein the at least one prediction comprises a radio resource management (RRM) measurement prediction and the target prediction accuracy is a target prediction accuracy of the RRM measurement prediction, and the processor is configured to execute the instruction stored in the memory to: perform the RRM measurement prediction using a prediction model that satisfies the target prediction accuracy of the RRM measurement prediction; and transmit, to the second node, a result of the RRM measurement prediction.

15. The first node of claim 13, wherein the at least one prediction comprises an RRM measurement prediction, and the target prediction accuracy is a target prediction accuracy of the RRM measurement prediction, and the processor is configured to execute the instruction stored in the memory to: transmit, to the second node, an indication of failure to identify a prediction model that satisfies the target prediction accuracy of the RRM measurement prediction.

16. The first node of claim 13, wherein the at least one prediction comprises a measurement event prediction and the target prediction accuracy is a target prediction accuracy of the measurement event prediction, and the request comprises information for configuring at least one of: the measurement event prediction, a measurement event prediction period, or a report period of the measurement event prediction.

17. The first node of claim 16, wherein the processor is configured to execute the instruction stored in the memory to: perform the measurement event prediction using a prediction model that satisfies the target prediction accuracy of the measurement event prediction; and transmit, to the second node, a result of the measurement event prediction.

18. The first node of claim 13, wherein the at least one prediction comprises an RRM measurement prediction and a measurement event prediction, and the request comprises an indication whether a two-step measurement event prediction or a one-step measurement event prediction is to be performed, wherein in the two-step measurement event prediction, the RRM measurement prediction is performed first, and a result of the RRM measurement prediction is used for the measurement event prediction, and wherein in the one-step measurement event prediction, the measurement event prediction is performed without first performing the RRM measurement prediction.

19. A method for a first node for communication, the method comprising: receiving, from a second node, a request to perform at least one of a radio resource management (RRM) measurement prediction or a measurement event prediction; performing the at least one of the RRM measurement prediction or the measurement event prediction; transmitting, to the second node, an RRM measurement report and a result of the at least one of the RRM measurement prediction or the measurement event prediction, the RRM measurement report being based on one or more RRM measurements performed by the first node; and receiving, from the second node, a notification generated based on a prediction accuracy of the at least one of the RRM measurement prediction or the measurement event prediction.

20. A non-transitory computer-readable medium storing instructions that are executable by one or more processors of a first node for communication, to perform the method of claim 19.

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