WO2025201451A1

WO2025201451A1 - Methods and apparatus for reconfiguring measurement configuration in mobile communications

Info

Publication number: WO2025201451A1
Application number: PCT/CN2025/085317
Authority: WO
Inventors: Ta-Yuan LIU; Per Johan Mikael Johansson; Yao PENG; Yuanyuan Zhang
Original assignee: MediaTek Inc
Current assignee: MediaTek Inc
Priority date: 2024-03-28
Filing date: 2025-03-27
Publication date: 2025-10-02
Anticipated expiration: 2026-09-28
Also published as: WO2025199869A1

Abstract

Various solutions for reconfiguring measurement configuration with respect to an apparatus in mobile communications are described. The apparatus may determine a first set of measurement objects (MOs) and a second set of MOs based on a MO determination model. The first set of MOs may be associated with at least one first occasion to be measured, and the second set of MOs may be associated with at least one second occasion excluded from measurement. The apparatus may transmit a report to a network node for reconfiguring a measurement configuration. The report may be associated with the first set of MOs and the second set of MOs.

Description

METHODS AND APPARATUS FOR RECONFIGURING MEASUREMENT CONFIGURATION IN MOBILE COMMUNICATIONS

CROSS REFERENCE TO RELATED PATENT APPLICATION (S)

The present disclosure is part of a non-provisional application claiming the priority benefits of PCT Application No. PCT/CN2024/084423, filed on 28 March 2024, the content of which herein being incorporated by reference in their entireties.

TECHNICAL FIELD

The present disclosure is generally related to mobile communications and, more particularly, to reconfiguring measurement configuration with respect to apparatus in mobile communications.

BACKGROUND

Unless otherwise indicated herein, approaches described in this section are not prior art to the claims listed below and are not admitted as prior art by inclusion in this section.

In Long-Term Evolution (LTE) or New Radio (NR) mobile communications, a user equipment (UE) may perform measurements on multiple measurement objects (MOs) to detect and monitor beams and cells for various purposes, such as handover, Radio Resource Management (RRM) , and others.

In some scenarios, measuring the MOs may introduce substantial overhead at the UE side. Moreover, when a target MO is not located in the same frequency layer based on the current operating setting, additional measurements may be required. However, due to radio frequency (RF) limitations, the UE may be restricted to measuring the target MO only during the corresponding measurement occasions, which may degrade flexibility and efficiency of the overall network system.

Accordingly, how to reduce overhead at UE side and improve the flexibility and efficiency of the overall network system becomes an important issue in the newly developed wireless communication network. Therefore, there is a need to provide proper schemes to perform measurement and reporting and improve the flexibility and efficiency of the overall network system.

SUMMARY

The following summary is illustrative only and is not intended to be limiting in any way. That is, the following summary is provided to introduce concepts, highlights, benefits and advantages of the novel and non-obvious techniques described herein. Select implementations are further described below in the detailed description. Thus, the following summary is not intended to identify essential features of the claimed subject matter, nor is it intended for use in determining the scope of the claimed subject matter.

An objective of the present disclosure is to propose solutions or schemes that address the aforementioned issues pertaining to reconfiguring measurement configuration with respect to apparatus in mobile communications.

In one aspect, a method may involve an apparatus determining a first set of measurement objects (MOs) and a second set of MOs based on a MO determination model. The first set of MOs may be associated with at least one first occasion to be measured. The second set of MOs may be associated with at least one second occasion excluded from measurement. The method may also involve the apparatus transmitting a report to a network node for reconfiguring a measurement configuration. The report may be associated with the first set of MOs and the second set of MOs.

In one aspect, a method may involve an apparatus receiving a report associated with a first set of MOs and a second set of MOs determined by a MO determination model from a UE. The first set of MOs may be associated with at least one first occasion to be measured. The second set of MOs may be associated with at least one second occasion excluded from measurement. The method may also involve the apparatus determining a measurement configuration according to the report.

In one aspect, an apparatus may comprise a transceiver which, during operation, wirelessly communicates with a wireless network. The apparatus may also comprise a processor communicatively coupled to the transceiver. The processor, during operation, may perform operations comprising determining a first set of MOs and a second set of MOs based on a MO determination model. The first set of MOs may be associated with at least one first occasion to be measured. The second set of MOs may be associated with at least one second occasion excluded from measurement. The processor may further perform operations comprising transmitting, via the transceiver, a report to a network node for reconfiguring a measurement configuration, wherein the report is associated with the first set of MOs and the second set of MOs.

It is noteworthy that, although description provided herein may be in the context of certain radio access technologies, networks and network topologies such as Long-Term Evolution (LTE) , LTE-Advanced, LTE-Advanced Pro, 5th Generation (5G) , New Radio (NR) , Internet-of-Things (IoT) and Narrow Band Internet of Things (NB-IoT) , Industrial Internet of Things (IIoT) , and 6th Generation (6G) , the proposed concepts, schemes and any variation (s) /derivative (s) thereof may be implemented in, for and by other types of radio access technologies, networks and network topologies. Thus, the scope of the present disclosure is not limited to the examples described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of the present disclosure. The drawings illustrate implementations of the disclosure and, together with the description, serve to explain the principles of the disclosure. It is appreciable that the drawings are not necessarily in scale as some components may be shown to be out of proportion than the size in actual implementation in order to clearly illustrate the concept of the present disclosure.

FIG. 1 is a diagram depicting an example scenario under schemes in accordance with implementations of the present disclosure.

FIG. 2A is a diagram depicting an example scenario under schemes in accordance with implementations of the present disclosure.

FIG. 2B is a diagram depicting an example scenario under schemes in accordance with implementations of the present disclosure.

FIG. 3 is a diagram depicting an example scenario under schemes in accordance with implementations of the present disclosure.

FIG. 4 is a block diagram of an example communication system in accordance with an implementation of the present disclosure.

FIG. 5 is a flowchart of an example process in accordance with an implementation of the present disclosure.

FIG. 6 is a flowchart of an example process in accordance with an implementation of the present disclosure.
DETAILED DESCRIPTION OF PREFERRED IMPLEMENTATIONS

Detailed embodiments and implementations of the claimed subject matters are disclosed herein. However, it shall be understood that the disclosed embodiments and implementations are merely illustrative of the claimed subject matters which may be embodied in various forms. The present disclosure may, however, be embodied in many different forms and should not be construed as limited to the exemplary embodiments and implementations set forth herein. Rather, these exemplary embodiments and implementations are provided so that description of the present disclosure is thorough and complete and will fully convey the scope of the present disclosure to those skilled in the art. In the description below, details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the presented embodiments and implementations.
Overview

Implementations in accordance with the present disclosure relate to various techniques, methods, schemes and/or solutions pertaining to reconfiguring measurement configuration with respect to apparatus in mobile communications. According to the present disclosure, a number of possible solutions may be implemented separately or jointly. That is, although these possible solutions may be described below separately, two or more of these possible solutions may be implemented in one combination or another.

Regarding the present disclosure, a user equipment (UE) may determine a first set of measurement objects (MOs) and a second set of MOs based on a MO determination model. The first set of MOs may be associated with at least one first occasion to be measured. The second set of MOs may be associated with at least one second occasion excluded from measurement. Therefore, based on the results from the MO determination model, the UE's measurement overhead may be reduced by excluding certain occasions (i.e., at least one second occasion) from measurement.

Then, the UE may transmit a report to a network node for reconfiguring a measurement configuration while the report is associated with the first set of MOs and the second set of MOs. After receiving the report, the network node may determine a measurement configuration (e.g., a measurement configuration newer than a previous measurement configuration) and transmit the measurement configuration to the UE. The measurement configuration may indicate a shorter measurement gap and/or less frequent measurement gap.

After receiving the measurement configuration, the UE may apply the measurement configuration configured by the network node for further reducing measurement overhead. Accordingly, the overall network system's measurement flexibility and efficiency may be significantly enhanced. In some cases, the UE may apply another measurement configuration determined by itself for further reducing measurement overhead in an event that the UE determines that the measurement configuration from the network node is inappropriate.

In some cases, MOs may have reference pilot transmissions for measurements and be identified by one or more elements including frequency (e.g., Absolute Radio-Frequency Channel Number (ARFCN) , timing information, type of pilot/symbols, bandwidth, and pattern information) . The measurements may be NR Synchronization Signal/PBCH block (SSB) and/or Channel State Information-Reference Signal (CSI-RS) Radio Resource Management (RRM) measurement of neighbor cells. In some cases, the measurements may be associated with beams detection or measurement.

FIG. 1 illustrates an example scenario 100 under schemes in accordance with implementations of the present disclosure. Scenario 100 involves a network node and a UE, which may be a part of a wireless communication network (e.g., an LTE network, a 5G/NR network, an IoT network or a 6G network) . Scenario 100 illustrates the current network framework. The UE may connect to the network node.

In some embodiments, the network node may transmit a configuration to the UE. The UE may determine a first set of MOs and a second set of MOs based on a MO determination model. Based on the configuration transmitted from the network node, the first set of MOs may be associated with at least one first occasion to be measured. The second set of MOs may be associated with at least one second occasion excluded from measurement (i.e., associated with at least one second occasion that does not need to be measured) .

In some implementations, the MO determination model may include a machine learning model. In particular, the MO determination model may be generated by the UE or the network node based on an artificial intelligence/machine learning (AI/ML) scheme (e.g., a data-driven approach based on the pre-training neural network model) . The UE may use the MO determination model to determine the first set of MOs and the second set of MOs by performing a measurement prediction for a plurality of MOs based on the machine learning model.

More specifically, the UE may perform the measurement prediction for the plurality of MOs to generate a plurality of prediction values based on the machine learning model. In some cases, the UE may determine the first set of MOs while each MO in the first MOs has a specific value (e.g., a highest value or a value over a threshold) . In some cases, the UE may determine the second set of MOs while each MO in the second MOs has corresponding prediction value under a threshold.

Then, the UE may measure the at least one first occasion for the first set of MOs to determine at least one measurement value. The UE may perform a measurement prediction on the at least one second occasion for the second set of MOs to generate at least one prediction value based on a prediction mode (which may be the same as the MO determination model or another machine learning model for measurement prediction) . The UE may transmit a report including the at least one measurement value and/or the at least one prediction value to the network node.

After receiving the report, the network node may determine a measurement configuration including a measurement gap parameter different from a previous measurement gap parameter used for the UE and transmit the measurement configuration to the UE. After receiving the measurement configuration, the UE may: (1) apply the measurement gap parameter configured by the network node; or (2) determine another measurement gap parameter different from the previous measurement gap parameter and apply the measurement gap parameter determined by the UE itself.

In some implementations, the measurement gap parameter may include a measurement gap length (duration) and/or a measurement gap period. In some cases, to reduce the measurement overhead, (1) the measurement gap length may be decreased and/or (2) the measurement gap period may be enlarged. In some cases, the measurement gap length may be increased and the measurement gap period may also be enlarged. The above cases are provided as examples only; the present invention is not limited thereto.

For example, the UE receives a configuration from the network node. The configuration indicates a plurality of MOs, occasion (s) for measurement (referred to as measurement occasion (s) ) and occasion (s) for prediction (referred to as prediction occasion (s) ) . The UE performs a measurement prediction to the MOs to generate a plurality of prediction values based on a machine learning model (i.e., the UE inputs previous information, such as previous measurement samples, of the plurality of MOs into the machine learning model for outputting the corresponding plurality of prediction values) .

In this example, each of the prediction value includes a Reference Signal Receiving Powers (RSRPs) corresponding to one of the MOs. Then, the UE selects (i.e., determines) the MO (s) having predicted RSRP (s) over a threshold as a first set of MOs. The UE selects (i.e., determines) the MO (s) having predicted RSRP (s) under the threshold as a second set of MOs.

Then, the UE actually measures the MO (s) in the first set of MOs on the measurement occasion (s) to generate a first measurement. Regarding the MO (s) in the second set of MOs on the prediction occasion (s) , the corresponding predicted RSRP (s) is (are) used as a second measurement. Regarding the purpose of measurements, the UE transmits a first report to the network node while the report includes the first measurement (actual measurement of MOs) and the second measurement (predicted measurement of MOs) . Regarding the purpose of measurement configuration changes, the UE transmits a second report to the network node while the second report indicates a requirement of changing the measurement configuration.

After receiving at least one of the first report and the second report, the network node determines a measurement configuration including a measurement gap parameter different from a previous measurement gap parameter used for the UE and transmits the measurement configuration to the UE. In this example, the measurement gap parameter includes a measurement gap length shorter than a previous measurement gap length and/or a measurement gap period larger than a previous measurement gap period.

After receiving the measurement configuration, the UE applies the measurement gap parameter configured by the network node when the UE determines the measurement gap parameter is applicable. Otherwise, the UE determines another measurement gap parameter different from the previous measurement gap parameter and applies the measurement gap parameter determined by the UE itself.

In some cases, the UE may determine (i.e., select) MO (s) by the prediction values for the plurality of MOs and prioritizes the MO (s) with the highest prediction value (s) . The value (s) may represent the quality of the cells in the MO.

In some cases, the UE may exclude (or deprioritize) MO (s) for which the prediction value (s) is (are) below a criterion/apre-defined or configured threshold. In some cases, the UE may exclude (or deprioritize) MO (s) whose predictions are expected to have high accuracy (e.g., the MO (s) has (have) a high correlation with previous measurement samples) .

In some implementations, the MO determination model may include a model using MO correlation information. In particular, the UE may use the MO correlation information to determine (i.e., to select) which MO (s) need to be actually measured and which MO (s) need to be predicted.

In some cases, the MO correlation information may include geographical information between a first MO and a second MO. The geographical information may indicate high correlation (e.g., located close to each other) , and the UE may determine to measure one of the first and second MOs and predict the other, as the two MOs may experience similar penetration loss. In some cases, the geographical information may indicate low correlation when one is associated with an indoor base station and the other is associated with an outdoor base station. In some cases, the MO correlation information may indicate high correlation when reference pilot transmissions for the first MO and the second MO are transmitted from the same transmission point.

In some cases, the UE may use the geographical information to prioritize MO (s) for selection. For example, the geographical information in which an MO is predicted to exceed a criterion, a predefined threshold, or a configured threshold may be stored and utilized. In some cases, the geographical information may be incorporated as part of the input to an AI/ML scheme.

In some cases, the UE may use the MO correlation information to determine a degree to which the UE may rely on prediction and the degree to which it needs to perform actual measurements. For example, the higher the MO correlation between two MOs, the more the UE can rely on prediction.

In some cases, the MO correlation information may be transmitted from the network node to the UE. In some cases, the MO correlation information may be learned separately (e.g., learned per cell, and/or learned with multiple cells) .

In some cases, the MO correlation information may include typical Type-of-Service (ToS) time for cells of a certain MO. The ToS time may be used to determine how often the UE needs to actually measure to consolidate a certain prediction. For example, the UE performs measurements at a lower frequency for the MO associated with a longer ToS, whereas a higher measurement frequency is applied for the MO associated with a shorter ToS.

In some cases, location-related information (e.g., tracking area (TA) for both UE and network node side, round-trip time (RTT) information, etc. ) may be provided to derive the MO correlation information. In some cases, the location-related information may be derived based on UE-based enhanced cell identification (eCID) positioning, with the network node providing additional TA information to the UE via RRC.

In some cases, the UE may use the geographical information to prioritize MO (s) for selection. For example, geographical areas in which an MO is predicted to exceed a criterion, a predefined threshold, or a configured threshold may be stored and utilized. In some cases, geographical information may be incorporated as part of the input to an AI/ML scheme.

In some cases, the geographical information may encompass not only commonly used geo-coordinate formats but also cellular or wireless-specific formats, such as a cell ID, optionally supplemented with RTT information or other relevant parameters.

In some embodiments, the network node may transmit a configuration to the UE. The configuration may configure a plurality of first occasions for a first set of MOs and a plurality of second occasions for a second set of MOs. In some cases, each of the first set of MOs and the second set of MOs may include one or more MOs. After receiving the configuration, the UE may: (1) measure the first set of MOs on the plurality of first occasions to determine a first measurement, and (2) predict a second measurement of the second set of MOs on the plurality of second occasions.

Then, the UE may determine a report associated with the first measurement and/or the second measurement to the network node. The network node may receive the report. The UE and the network node may perform subsequent action (s) based on the report. For example, a previous configuration includes a measurement gap configuration which indicates a longer measurement gap length and/or more frequent measurement gap period. After obtaining the report, the UE and/or the network node determines a new measurement gap configuration based on the first measurement and the second measurement. The new measurement gap configuration indicates a shorter measurement gap length and/or less frequent measurement gap period. The UE applies the new measurement gap configuration, thereby reducing overhead and improving flexibility and efficiency of the overall network system.

In some cases, after determining the new measurement gap, the UE may transmit the new measurement gap configuration to the network node. The new measurement gap configuration may include a periodicity, an offset and a duration. The UE may apply the new measurement gap without receiving any acknowledgement (ACK) from the network node as long as the new measurement gap configuration has less measurement opportunities than the previous measurement gap configuration configured by the network node. In some cases, the UE may transmit a UE capability to the network node based on measurement gap patterns the UE may support (based on a capability of AI/ML) , and the network node may provide a measurement gap configuration to the UE.

In some cases, after the transmission of the report, the UE and the network node may perform triggering events based on the first measurement and/or the second measurement. For example, the triggering events include measurement report events, such as A1 to A6, B1, B2 events defined in 3rd Generation Partnership Project (3GPP) specification, for mobility management, handover, and cell reselection.

In some implementations, the configuration may include: (1) the plurality of first occasions and a first signal type (e.g., Synchronization Signal Block (SSB) , Channe State Information-Reference signal (CSI-RS) , etc. ) for the first set of MOs, and (2) the plurality of second occasions and a second signal type (e.g., SSB, CSI-RS, etc. ) for the second set of MOs. In some cases, the configuration may further include a plurality of actual occasions and an actual signal type for the second set of MOs.

In some implementations, the configuration may include: (1) a first association between the plurality of first occasions and the plurality of second occasions, and (2) a second association between the first set of MOs and the second set of MOs. In some implementations, based on the associations, the second measurement of the second set of MOs may be predicted with respect to at least one of temporal domain, frequency domain and spatial domain.

In some cases, the second association between the first set of MOs and the second set of MOs may indicate that the first set of MOs and the second set of MOs may be the same. The first association between the plurality of first occasions (i.e., the measurement associated occasions) and the plurality of second occasions (i.e., the prediction associated occasions) may indicate an arrangement (e.g., periods and/or offsets) of the plurality of first occasions and the plurality of second occasions within the same set of MOs. In these cases, the UE may predict the second measurement of the second set of MOs with respect to temporal domain. In other words, the UE may perform a temporal domain prediction to determine the second measurement of the second set of MOs.

FIG. 2A illustrates an example scenario 200A under schemes in accordance with implementations of the present disclosure. For example, the configuration includes: (1) a plurality of measurement occasions (with reference signal (RSs) ) and a plurality of prediction occasions (with virtual RSs which are the RSs need to be predicted on the prediction occasions) within the same MO MO1, and (2) periods and/or offsets of the measurement occasions and the prediction occasions. The UE measures the measurement occasions to determine a first measurement. The UE predicts a second measurement of the prediction occasions.

In some cases, the second association between the first set of MOs and the second set of MOs may indicate: (1) that the first set of MOs and the second set of MOs may be different and (2) offset (s) between the first set of MOs and the second set of MOs. The first association between the plurality of first occasions (i.e., the measurement associated occasions) and the plurality of second occasions (i.e., the prediction associated occasions) may indicate an arrangement (e.g., periods and/or offsets) of the plurality of first occasions within the first set of MOs and an arrangement (e.g., periods and/or offsets) of the plurality of second occasions within the second set of MOs. In these cases, the UE may predict the second measurement of the second set of MOs with respect to temporal domain and frequency domain. In other words, the UE may perform a temporal domain and frequency domain prediction to determine the second measurement of the second set of MOs.

FIG. 2B illustrates an example scenario 200B under schemes in accordance with implementations of the present disclosure. For example, the configuration includes: (1) a plurality of measurement occasions (with RSs) within MOs MO1 and MO2, (2) a plurality of prediction occasions (with virtual RSs which are the RSs need to be predicted on the prediction occasions) within MO MO2, (3) a frequency offset between MOs MO1 and MO2, and (4) periods and/or offsets of the measurement occasions and the prediction occasions. The UE measures the measurement occasions to determine a first measurement. The UE predicts a second measurement of the prediction occasions.

Based on the mentioned examples, the UE uses previous measurement samples of MO (s) to predict future measurement results of MO (s) (i.e., the UE uses statistics scheme or AI/ML scheme to predict future measurement results) . Therefore, with the assistance of prediction, the UE does not need to actually measure MOs (e.g., prediction occasions in FIGs. 2A or 2B) for each cycle. As a result, the measurement overhead can be significantly reduced and/or the measurement gap period can be enlarged which reduces the need of measurement gap.

In some cases, each occasion of the plurality of first occasions and the plurality of second occasions may take one or multiple beams into account. In particular, the UE may use measurement samples of other MOs to predict measurement results of target MO (s) , which may include the use of AI/ML spatial domain prediction.

More specifically, the AI/ML spatial domain prediction may be used to predict the result of a certain MO based on the measurement of the same and/or other MOs with spatial correlation. For example, the AI/ML spatial domain prediction is used to predict the measurement of MO with certain transmitter (Tx) (or receiver (Rx) ) beam by the measurement of other Tx (or Rx) beams.

In some cases, the UE may use measurement samples of other MOs to predict measurement results of target MO (s) , which may include the use of AI/ML inter-frequency prediction. In particular, the AI/ML inter-frequency prediction may be used to predict the result of certain MO based on the measurement of other MOs with different frequencies. The correlation between MOs with different frequencies may come from geographical dependence. For example, two different frequency carriers are transmitted from co-located network node.

In some implementations, the UE may utilize correlation information between the predicted MO and other MOs, for instance, the degree to which the MOs exhibit similar or differing penetration loss.

In some cases, the correlation information between two MOs may be low when one is associated with an indoor network node and the other is associated with an outdoor network node.

In some cases, the correlation information may be high when the reference pilot transmissions for the MOs are transmitted from the same transmission point.

In some cases, the UE may utilize the correlation information to determine the extent to which prediction may be relied upon and the extent to which actual measurements may be required.

In some cases, the correlation information may be provided by the network node to the UE by signaling. The correlation information may be obtained separately, e.g., per cell, and/or obtained with multiple cells.

In some cases, the correlation information may include an indoor/outdoor indicator. The correlation information between two MOs that belong to different values (e.g., one is associated with indoor and the other is associated with outdoor) may be assumed to be low (e.g., zero) .

In some cases, the correlation information may include typical ToS time for cells of a certain MO. The ToS time may be used to determine how often the UE needs to actually measure to consolidate a certain prediction. For example, the UE performs measurements at a lower frequency for the MO associated with a longer ToS, whereas a higher measurement frequency is applied for the MO associated with a shorter ToS.

In some cases, the location-related information (e.g., tracking area (TA) for both UE and network node side, round-trip time (RTT) information, etc. ) may be provided to derive MO correlation. In some cases, the location-related information may be derived based on UE-based enhanced cell identification (eCID) positioning, with the network node providing additional TA information to the UE via RRC.

In some cases, the UE may utilize geographical area information to prioritize MO (s) for selection. For example, geographical areas in which an MO is predicted to exceed a criterion, a predefined threshold, or a configured threshold may be stored and utilized. In some cases, geographical information may be incorporated as part of the input to an AI/ML scheme.

Regarding the previous AL/ML models, each of the AI/ML models may be determined (i.e., trained) by the network node and/or the UE based on network information (e.g., parameters and/or data) and corresponding measurements. Accordingly, the network node and/or the UE may put some related network information (e.g., previous measurement samples such as previous RSRP samples) associated with an MO to the AI/ML models to output the predicted measurements associated with the MO. In other words, the network node and/or the UE may use the AI/ML models to determine (i.e., obtain, predict, etc. ) the measurements according to related network information.

FIG. 3 illustrates an example scenario 300 under schemes in accordance with implementations of the present disclosure. In some implementations, the UE may transmit a UE capability report to inform the network node of the capability of UE measurement prediction. The capability report may include one or more elements including AI/ML capability, temporal domain prediction capability, spatial domain prediction capability, inter-frequency prediction capability, available measurement gap length, available measurement gap period, measurement reduction ratio, etc. In some cases, the measurement reduction ratio indicator represents the potential reduction in measurements relative to a UE that lacks measurement prediction capability.

Then, the network node may transmit a measurement configuration to the UE. The measurement configuration may include configuration (s) of two different sets of MOs. The first set may indicate the MO (s) needed to be measured. The configuration may include measurement occasion (s) and measurement information for each MO in the first set (e.g., reference/pilot signal, SSB, SSB Measurement Timing Configuration (SMTC) , time and frequency resource (s) that the reference/pilot signal is transmitted, etc. ) . The second set may indicate the MO (s) that the UE may use prediction instead of actual measurements. The configuration of the second set may include the prediction occasion and the type of virtual reference/pilot signal (e.g., SSB, CSI-RS, etc. ) .

In some cases, the measurement configuration may include a measurement gap configuration. In some cases, the measurement configuration may include an association between the measurement occasions (i.e., the occasions for the first set and the second set may be configured) . In some cases, the first set and the second set of MOs may be the same, for example, for temporal domain prediction. In some cases, the measurement configuration may include an association between the occasions in the first set of MOs and the occasions in the second set of MOs, for example, for the temporal and frequency domains prediction. In some cases, the measurement configuration may include an association between the first set of MOs and the second set of MOs for measurement and prediction, for example, for frequency domain prediction. In some cases, for each occasion associated with the first set of MOs and the second set of MOs, one or multiple beams in spatial domain may be considered.

After receiving the measurement configuration, the UE may select the first set of MOs and the second set of MOs from a plurality of MOs by a MO determination model. The MO determination model may include a model using MO correlation information associated with the MOs or a machine learning model for generating prediction values (e.g., RSRP value) of the MOs.

Then, the UE may perform measurement on the occasions configured for the first set of MOs based on the configuration of the first set of MOs, the association between the first set of MOs and the second set of MOs, and the measurement gap configuration. The UE may perform prediction on the occasions configured for the second set of MOs based on the configuration of the second set of MOs, the association between the first set of MOs and the second set of MOs, and the measurement gap configuration.

Then, the UE may determine a report including results of measurement and/or prediction and transmit the report to the network node. In some cases, in an event that the UE determines a new measurement gap, the UE may transmit a change measurement gap request to the network node.
Illustrative Implementations

FIG. 4 illustrates an example communication system 400 having an example communication apparatus 410 and an example network apparatus 420 in accordance with an implementation of the present disclosure. Each of communication apparatus 410 and network apparatus 420 may perform various functions to implement schemes, techniques, processes and methods described herein pertaining to reconfiguring measurement configuration with respect to UE and network apparatus in mobile communications, including scenarios/schemes described above as well as processes 500 and 600 described below.

Communication apparatus 410 may be a part of an electronic apparatus, which may be a UE such as a portable or mobile apparatus, a wearable apparatus, a wireless communication apparatus or a computing apparatus. For instance, communication apparatus 410 may be implemented in a smartphone, a smartwatch, a personal digital assistant, a digital camera, or a computing equipment such as a tablet computer, a laptop computer or a notebook computer. Communication apparatus 410 may also be a part of a machine type apparatus, which may be an IoT, NB-IoT, or IIoT apparatus such as an immobile or a stationary apparatus, a home apparatus, a wire communication apparatus or a computing apparatus. For instance, communication apparatus 410 may be implemented in a smart thermostat, a smart fridge, a smart door lock, a wireless speaker or a home control center. Alternatively, communication apparatus 410 may be implemented in the form of one or more integrated-circuit (IC) chips such as, for example and without limitation, one or more single-core processors, one or more multi-core processors, one or more reduced-instruction set computing (RISC) processors, or one or more complex-instruction-set-computing (CISC) processors. Communication apparatus 410 may include at least some of those components shown in FIG. 4 such as a processor 412, for example. Communication apparatus 410 may further include one or more other components not pertinent to the proposed scheme of the present disclosure (e.g., internal power supply, display device and/or user interface device) , and, thus, such component (s) of communication apparatus 410 are neither shown in FIG. 4 nor described below in the interest of simplicity and brevity.

Network apparatus 420 may be a part of a network apparatus, which may be a network node such as a satellite, a base station, a small cell, a router or a gateway. For instance, network apparatus 420 may be implemented in an eNodeB in an LTE network, in a gNB in a 5G/NR, IoT, NB-IoT or IIoT network or in a satellite or base station in a 6G network. Alternatively, network apparatus 420 may be implemented in the form of one or more IC chips such as, for example and without limitation, one or more single-core processors, one or more multi-core processors, or one or more RISC or CISC processors. Network apparatus 420 may include at least some of those components shown in FIG. 4 such as a processor 422, for example. Network apparatus 420 may further include one or more other components not pertinent to the proposed scheme of the present disclosure (e.g., internal power supply, display device and/or user interface device) , and, thus, such component (s) of network apparatus 420 are neither shown in FIG. 4 nor described below in the interest of simplicity and brevity.

In one aspect, each of processor 412 and processor 422 may be implemented in the form of one or more single-core processors, one or more multi-core processors, or one or more CISC processors. That is, even though a singular term “a processor” is used herein to refer to processor 412 and processor 422, each of processor 412 and processor 422 may include multiple processors in some implementations and a single processor in other implementations in accordance with the present disclosure. In another aspect, each of processor 412 and processor 422 may be implemented in the form of hardware (and, optionally, firmware) with electronic components including, for example and without limitation, one or more transistors, one or more diodes, one or more capacitors, one or more resistors, one or more inductors, one or more memristors and/or one or more varactors that are configured and arranged to achieve specific purposes in accordance with the present disclosure. In other words, in at least some implementations, each of processor 412 and processor 422 is a special-purpose machine specifically designed, arranged and configured to perform specific tasks including reconfiguring measurement configuration in a device (e.g., as represented by communication apparatus 410) and a network (e.g., as represented by network apparatus 420) in accordance with various implementations of the present disclosure.

In some implementations, communication apparatus 410 may also include a transceiver 416 coupled to processor 412 and capable of wirelessly transmitting and receiving data. In other words, processor 412 may transceive the data such as configuration, message, signal, information, indicator, etc. via transceiver 416. In some implementations, communication apparatus 410 may further include a memory 414 coupled to processor 412 and capable of being accessed by processor 412 and storing data therein. In some implementations, network apparatus 420 may also include a transceiver 426 coupled to processor 422 and capable of wirelessly transmitting and receiving data. In other words, processor 422 may transceive the data such as configuration, message, signal, information, indicator, etc. via transceiver 426. In some implementations, network apparatus 420 may further include a memory 424 coupled to processor 422 and capable of being accessed by processor 422 and storing data therein. Accordingly, communication apparatus 410 and network apparatus 420 may wirelessly communicate with each other via transceiver 416 and transceiver 426, respectively. To aid better understanding, the following description of the operations, functionalities and capabilities of each of communication apparatus 410 and network apparatus 420 is provided in the context of a mobile communication environment in which communication apparatus 410 is implemented in or as a communication apparatus or a UE and network apparatus 420 is implemented in or as a network node of a communication network.

In some implementations, each of memory 414 and memory 424 may include a type of random-access memory (RAM) such as dynamic RAM (DRAM) , static RAM (SRAM) , thyristor RAM (T-RAM) and/or zero-capacitor RAM (Z-RAM) . Alternatively, or additionally, each of memory 414 and memory 424 may include a type of read-only memory (ROM) such as mask ROM, programmable ROM (PROM) , erasable programmable ROM (EPROM) and/or electrically erasable programmable ROM (EEPROM) . Alternatively, or additionally, each of memory 414 and memory 424 may include a type of non-volatile random-access memory (NVRAM) such as flash memory, solid-state memory, ferroelectric RAM (FeRAM) , magnetoresistive RAM (MRAM) and/or phase-change memory.
Illustrative Processes

FIG. 5 illustrates an example process 500 in accordance with an implementation of the present disclosure. Process 500 may be an example implementation of above scenarios/schemes, whether partially or completely, with respect to reconfiguring measurement configuration of the present disclosure. Process 500 may represent an aspect of implementation of features of communication apparatus 410. Process 500 may include one or more operations, actions, or functions as illustrated by one or more of blocks 510 and 520. Although illustrated as discrete blocks, various blocks of process 500 may be divided into additional blocks, combined into fewer blocks, or eliminated, depending on the desired implementation. Moreover, the blocks of process 500 may be executed in the order shown in FIG. 5 or, alternatively, in a different order. Process 500 may be implemented by communication apparatus 410 or any suitable UE or machine type devices. Solely for illustrative purposes and without limitation, process 500 is described below in the context of communication apparatus 410. Process 500 may begin at block 510.

At block 510, process 500 may involve processor 412 of communication apparatus 410 determining a first set of MOs and a second set of MOs based on a MO determination model. The first set of MOs may be associated with at least one first occasion to be measured. The second set of MOs may be associated with at least one second occasion excluded from measurement. Process 500 may proceed from block 510 to block 520.

At block 520, process 500 may involve processor 412 of communication apparatus 410 transmitting a report to a network node for reconfiguring a measurement configuration. The report may be associated with the first set of MOs and the second set of MOs.

In some implementations, the MO determination model may include a model using MO correlation information or a machine learning model.

In some implementations, the MO determination model may include the machine learning model. Process 500 may further involve processor 412 of communication apparatus 410 determining the first set of MOs and the second set of MOs by performing a measurement prediction for a plurality of MOs based on the machine learning model.

In some implementations, process 500 may further involve processor 412 of communication apparatus 410 performing the measurement prediction for the plurality of MOs to generate a plurality of prediction values based on the machine learning model. Process 500 may further involve processor 412 of communication apparatus 410 determining the first set of MOs while each MO in the first MOs has corresponding prediction value over a threshold.

In some implementations, process 500 may further involve processor 412 of communication apparatus 410 performing the measurement prediction for the plurality of MOs to generate a plurality of prediction values based on the machine learning model. Process 500 may further involve processor 412 of communication apparatus 410 determining the second set of MOs while each MO in the second MOs has corresponding prediction value under a threshold.

In some implementations, process 500 may further involve processor 412 of communication apparatus 410 performing the measurement prediction for the plurality of MOs to generate a plurality of prediction values based on the machine learning model. Process 500 may further involve processor 412 of communication apparatus 410 determining the second set of MOs while each MO in the second MOs has corresponding prediction value with prediction accuracy over a threshold.

In some implementations, at least one prediction value corresponding the second set of MOs may be generated in an event of performing the measurement prediction for the plurality of MOs based on the machine learning model, and the report may include the at least one prediction value corresponding the second set of MOs.

In some implementations, process 500 may further involve processor 412 of communication apparatus 410 measuring the at least one first occasion for the first set of MOs to determine at least one measurement value. Process 500 may further involve processor 412 of communication apparatus 410 performing a measurement prediction on the at least one second occasion for the second set of MOs to generate at least one prediction value based on a prediction model.

In some implementations, the report may include the at least one measurement value and the at least one prediction value.

In some implementations, process 500 may further involve processor 412 of communication apparatus 410 receiving the measurement configuration including a measurement gap parameter different from a previous measurement gap parameter. Process 500 may further involve processor 412 of communication apparatus 410 applying the measurement gap parameter.

In some implementations, process 500 may further involve processor 412 of communication apparatus 410 determining a measurement gap parameter different from a previous measurement gap parameter. Process 500 may further involve processor 412 of communication apparatus 410 applying the measurement gap parameter.

In some implementations, the MO determination model may include the model using MO correlation information. Process 500 may further involve processor 412 of communication apparatus 410 receiving the MO correlation information from the network node.

FIG. 6 illustrates an example process 600 in accordance with an implementation of the present disclosure. Process 600 may be an example implementation of above scenarios/schemes, whether partially or completely, with respect to reconfiguring measurement configuration of the present disclosure. Process 600 may represent an aspect of implementation of features of network apparatus 420. Process 600 may include one or more operations, actions, or functions as illustrated by one or more of blocks 610 to 620. Although illustrated as discrete blocks, various blocks of process 600 may be divided into additional blocks, combined into fewer blocks, or eliminated, depending on the desired implementation. Moreover, the blocks of process 600 may be executed in the order shown in FIG. 6 or, alternatively, in a different order. Process 600 may be implemented by network apparatus 420 or any suitable network device or machine type devices. Solely for illustrative purposes and without limitation, process 600 is described below in the context of network apparatus 420. Process 600 may begin at block 610.

At block 610, process 600 may involve processor 422 of network apparatus 420 receiving a report associated with a first set of MOs and a second set of MOs determined by a MO determination model from a UE. The first set of MOs may be associated with at least one first occasion to be measured. The second set of MOs may be associated with at least one second occasion excluded from measurement. Process 600 may proceed from block 610 to block 620.

At block 620, process 600 may involve processor 422 of network apparatus 420 determining a measurement configuration according to the report.

In some implementations, process 600 may further involve processor 422 of network apparatus 420 transmitting the measurement configuration to the UE.

In some implementations, the measurement configuration includes a measurement gap parameter different from a previous measurement gap parameter.

In some implementations, the report may include at least one measurement value and at least one prediction value.

In some implementations, the at least one measurement value may be generated by measuring the at least one first occasion for the first set of MOs, and the at least one prediction value may be generated by performing a measurement prediction on the at least one second occasion for the second set of MOs based on a prediction model.
Additional Notes

The herein-described subject matter sometimes illustrates different components contained within, or connected with, different other components. It is to be understood that such depicted architectures are merely examples, and that in fact many other architectures can be implemented which achieve the same functionality. In a conceptual sense, any arrangement of components to achieve the same functionality is effectively "associated" such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as "associated with" each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. Likewise, any two components so associated can also be viewed as being "operably connected" , or "operably coupled" , to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being "operably couplable" , to each other to achieve the desired functionality. Specific examples of operably couplable include but are not limited to physically mateable and/or physically interacting components and/or wirelessly interactable and/or wirelessly interacting components and/or logically interacting and/or logically interactable components.

Further, with respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity.

Moreover, it will be understood by those skilled in the art that, in general, terms used herein, and especially in the appended claims, e.g., bodies of the appended claims, are generally intended as “open” terms, e.g., the term “including” should be interpreted as “including but not limited to, ” the term “having” should be interpreted as “having at least, ” the term “includes” should be interpreted as “includes but is not limited to, ” etc. It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases "at least one" and "one or more" to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles "a" or "an" limits any particular claim containing such introduced claim recitation to implementations containing only one such recitation, even when the same claim includes the introductory phrases "one or more" or "at least one" and indefinite articles such as "a" or "an, " e.g., “a” and/or “an” should be interpreted to mean “at least one” or “one or more; ” the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should be interpreted to mean at least the recited number, e.g., the bare recitation of "two recitations, " without other modifiers, means at least two recitations, or two or more recitations. Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc. ” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention, e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc. In those instances where a convention analogous to “at least one of A, B, or C, etc. ” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention, e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc. It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B. ”

From the foregoing, it will be appreciated that various implementations of the present disclosure have been described herein for purposes of illustration, and that various modifications may be made without departing from the scope and spirit of the present disclosure. Accordingly, the various implementations disclosed herein are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

Claims

A method, comprising:

determining, by a processor of an apparatus, a first set of measurement objects (MOs) and a second set of MOs based on a MO determination model, wherein the first set of MOs is associated with at least one first occasion to be measured, and the second set of MOs is associated with at least one second occasion excluded from measurement; and

transmitting, by the processor, a report to a network node for reconfiguring a measurement configuration, wherein the report is associated with the first set of MOs and the second set of MOs.
The method of Claim 1, wherein the MO determination model includes a model using MO correlation information or a machine learning model.
The method of Claim 2, wherein the MO determination model includes the machine learning model, and determining the first set of MOs and the second set of MOs further comprises:

determining, by the processor, the first set of MOs and the second set of MOs by performing a measurement prediction for a plurality of MOs based on the machine learning model.
The method of Claim 3, wherein determining the first set of MOs and the second set of MOs by performing the measurement prediction for the plurality of MOs based on the machine learning model further comprising:

performing, by the processor, the measurement prediction for the plurality of MOs to generate a plurality of prediction values based on the machine learning model; and

determining, by the processor, the first set of MOs while each MO in the first MOs has corresponding prediction value over a threshold.
The method of Claim 3, wherein determining the first set of MOs and the second set of MOs by performing the measurement prediction for the plurality of MOs based on the machine learning model further comprising:

performing, by the processor, the measurement prediction for the plurality of MOs to generate a plurality of prediction values based on the machine learning model; and

determining, by the processor, the second set of MOs while each MO in the second MOs has corresponding prediction value under a threshold.
The method of Claim 3, wherein determining the first set of MOs and the second set of MOs by performing the measurement prediction for the plurality of MOs based on the machine learning model further comprising:

performing, by the processor, the measurement prediction for the plurality of MOs to generate a plurality of prediction values based on the machine learning model; and

determining, by the processor, the second set of MOs while each MO in the second MOs has corresponding prediction value with prediction accuracy over a threshold.
The method of Claim 3, wherein at least one prediction value corresponding the second set of MOs are generated in an event of performing the measurement prediction for the plurality of MOs based on the machine learning model, and the report includes the at least one prediction value corresponding the second set of MOs.
The method of Claim 1, further comprising:

measuring, by the processor, the at least one first occasion for the first set of MOs to determine at least one measurement value; and

performing, by the processor, a measurement prediction on the at least one second occasion for the second set of MOs to generate at least one prediction value based on a prediction model.
The method of Claim 8, wherein the report includes the at least one measurement value and the at least one prediction value.
The method of Claim 1, further comprising:

receiving, by the processor, the measurement configuration including a measurement gap parameter different from a previous measurement gap parameter; and

applying, by the processor, the measurement gap parameter.
The method of Claim 1, further comprising:

determining, by the processor, a measurement gap parameter different from a previous measurement gap parameter; and

applying, by the processor, the measurement gap parameter.
The method of Claim 2, wherein the MO determination model includes the model using MO correlation information, and the method further comprises:

receiving, by the processor, the MO correlation information from the network node.
A method, comprising:

receiving, by a processor of an apparatus, a report associated with a first set of measurement objects (MOs) and a second set of MOs determined by a MO determination model from a user equipment (UE) , wherein the first set of MOs is associated with at least one first occasion to be measured, and the second set of MOs is associated with at least one second occasion excluded from measurement; and

determining, by the processor, a measurement configuration according to the report.
The method of Claim 13, further comprising:

transmitting, by the processor, the measurement configuration to the UE.
The method of Claim 13, wherein the measurement configuration includes a measurement gap parameter different from a previous measurement gap parameter.
The method of Claim 13, wherein the report includes at least one measurement value and at least one prediction value.
The method of Claim 16, wherein the at least one measurement value is generated by measuring the at least one first occasion for the first set of MOs, and the at least one prediction value is generated by performing a measurement prediction on the at least one second occasion for the second set of MOs based on a prediction model.
An apparatus, comprising:

a transceiver which, during operation, wirelessly communicates with a wireless network; and

a processor communicatively coupled to the transceiver such that, during operation, the processor performs operations comprising:

determining a first set of measurement objects (MOs) and a second set of MOs based on a MO determination model, wherein the first set of MOs is associated with at least one first occasion to be measured, and the second set of MOs is associated with at least one second occasion excluded from measurement; and

transmitting, via the transceiver, a report to a network node for reconfiguring a measurement configuration, wherein the report is associated with the first set of MOs and the second set of MOs.
The apparatus of Claim 18, wherein, during operation, the processor further performs operations comprising:

measuring the at least one first occasion for the first set of MOs to determine at least one measurement value; and

performing a measurement prediction on the at least one second occasion for the second set of MOs to generate at least one prediction value based on a prediction model.
The apparatus of Claim 18, wherein, during operation, the processor further performs operations comprising:

obtaining a measurement gap parameter different from a previous measurement gap parameter; and

applying the measurement gap parameter.