Time window of radio resource management measurement for data collection in artificial intelligence mobility

Abstract

A method performed by a wireless device for collecting radio resource management (RRM) measurements is disclosed. The method includes receiving a RRM measurement configuration that includes first and second sets of measurement configurations, wherein the RRM measurement configuration indicates time requirements for the wireless device to perform a measurement on the first and second sets of measurement configurations. The method further includes collecting one or more measurement samples at one or more collection times indicated by each of the first and second sets of measurement configurations. The method also includes sending a RRM measurement report comprising a first set of measurement data corresponding for the first set of measurement configurations and a second set of measurement data for the second set of measurement configurations, wherein the first and second sets of measurement data includes logged result data related to the one or more collected measurement samples.

Description

TIME WINDOW OF RADIO RESOURCE MANAGEMENT MEASUREMENT FOR DATA COLLECTION IN ARTIFICIAL INTELLIGENCE MOBILITYTECHNICAL FIELD

[0001] The present disclosure relates generally to communications, and more particularly to communication methods and related devices and network nodes configured to utilize artificial intelligence (Al) to conduct radio resource management (RRM) measurements for mobility applications.BACKGROUND

[0002] In 3rd Generation Partnership Project (3GPP) New Radio (NR) standardization work, a new release 19 study item (SI) on artificial intelligence / machine learning (AI / ML) for the New Radio (NR) mobility started in 2024. This study item explored the benefits of augmenting the mobility with features enabling improved support of AI / ML based algorithms for enhanced performance and / or reduced complexity / overhead in such use cases in mobility as RRM measurement prediction, measurement event prediction, and radio link failure / handover failure (RLF / HOF) prediction.

[0003] RRM measurement prediction in this SI includes RRM measurement prediction in the temporal domain, spatial domain, and frequency domain. For temporal-domain prediction, one Al model predicts RRM measurement results for the future time instances, and / or may predict unavailable RRM measurement results for partial time instances in the past and / or present, based on available RRM results measured by the user equipment (UE). For spatial-domain prediction, one Al model predicts RRM measurement results for the beams / cells in Set A according to available RRM measurement results of the beams / cells in Set B. In frequencydomain prediction, one Al model predicts RRM measurement results for the frequency(ies) in Set A according to available RRM measurement results for the frequency(ies) in Set B. Some examples for intra-frequency temporal RRM measurement prediction are shown in diagram 100 of Figure 1, diagram 200 of Figure 2, and diagram 300 of Figure 3. In each of Figures 1, 2, and 3, the illustrated observation window refers to a time window within which the UE collects available RRM measurement samples that serve as inputs to the Al model. As shown in diagram 100, a single observation window precedes a single prediction window, wherein measured RRM samples are collected during the observation window and predicted RRM samples correspond to future time instances within the prediction window. As shown indiagram 200, multiple observation windows and corresponding prediction windows are illustrated, reflecting a scenario in which the UE collects measured RRM samples across multiple discrete observation windows, each associated with a respective prediction window. As shown in diagram 300, a single observation window is shown in which all collected samples are measured RRM samples, with the associated prediction window containing both measured and predicted RRM samples.

[0004] RRM Measurement and Report Configuration

[0005] In Rel-18, the network can send the configuration of RRM measurement and report to the UE via a radio resource control (RRC) message, where for normal periodical RRM measurement report and event-triggered periodical RRM measurement report, the configuration indicates the report periodicity (e.g., 120ms, 240ms, 480ms, ..., 30min) and may indicate partial or all parameters indicated below:• One primary synchronization signal block (SSB) based measurement timing configuration (i.e., smtcl): Periodicity and the timing of the SSBs that the UE must use for RRM measurement. SSB blocks outside of the applicable Synchronization Signal / Physical Broadcast Channel (SS / PBCH) Block Measurement Timing Configuration (SMTC) are not be measured.• One secondary SSB -based measurement timing configuration (i.e., smtc2): Periodicity in smtc2 can only be set to a value strictly shorter than the periodicity indicated by smtcl, and the offset and time duration needs to be from smtcl.• Associated measurement gap for SSB measurement identified by ssb-ConfigMobility:When multiple MeasObjectNR with the same SSB frequency are configured, the network configures the same measurement gap ID in this field for each MeasObjectNR. If this field is absent, then the associated measurement gap is the gap configured via gapFRl, gapFR2, or gapUE.• Associated measurement gap for Channel State Information Reference Signal (CSI-RS) measurement identified by csi-rs-ResourceConfigMobility. If this field is absent, then the associated measurement gap is the gap configured via gapFRl, gapFR2, or gapUE.

[0006] The periodicity ranges which smtcl and smtc2 can be configured are (5 subframes, 10 subframes, 20 subframes, 40 subframes, 80 subframes, 160 subframes), and the duration ranges which smtcl and smtc2 can be configured are (1 subframe, 2 subframes, 3 subframes, 4 subframes, 5 subframes).

[0007] On the indicated ssbFrequency in the same MeasObjectNR, the UE shall not consider SSB in subframes outside the SMTC occasion for SSB-based RRM measurement and CSI-RS-based RRM measurement expect for System Frame Number and Frame Timing Difference (SFTD) measurement. Note that if SS reference signal received power (SS-RSRP) is used for Eayer 1 RSRP (El-RSRP), the measurement time resource(s) restriction by SMTC window duration is not applicable.SUMMARY

[0008] There currently exist certain challenge(s). As part of the work carried out in 3GPP to support mobility procedures with AI / ME techniques, use cases are being investigated where the RAN and / or the UE can infer measurements by means of AI / ME techniques, and where such measurements are used for the purpose of making optimized mobility decisions. However, existing measurements are not always fit to support AI / ML processes.

[0009] Indeed, to develop AI / ML based models that can infer mobility measurements, there is in the first place the need to collect data that can be used to train such models. Such data are made of two components, one comprising of measurements the model will take as inputs to derive the inferred metrics (e.g., predictions on measurements or events to be used to derive mobility actions), the other component being the so called “ground truth”, which is a measured representation of the metric the model will infer. The inputs required should be taken all within a specific time window (e.g., a predefined time window) or at one or more specific point in time, to give an exact representation of the radio environment (as measured by the inputs), based on which the inferred output needs to be calculated. Similarly, the ground truth needs to be measured at a specific point in time, which corresponds to the time for which the inferred output is predicted.

[0010] In one example, training data to derive an AI / ML model able to derive temporal measurement predictions, namely predictions of a measurement at a specific point in time, will need to be made of mobility measurements or events collected at time tO, and a measurement of the ground truth for the inferred measurement at a time tl, e.g., ahead in the future with respect to tO, where tl is the prediction time for which the model will derive the inferred measurement. Current measurement configuration and reporting techniques do not allow to impose such timing constraints towards a UE for the collection of measurements.

[0011] In another example, training data to derive an AI / ML model able to infer frequency measurement predictions, namely prediction of a measurement at the same point in time or at a very close point in time as for the input measurements, but on a different frequency than theinput measurements, will also need input measurements and ground truth to be collected at specific points in time. Namely, both input measurements and ground truth measurements will need to be collected ideally at the same point in time. However, this might be very challenging for the UE, as the measurements are to be taken on different frequencies. The problem is therefore to provide measurement collection configurations to the UE that specify when input measurements and ground truth should be collected and that provide constraints about maximum time windows within which measurements can be collected.

[0012] Once an AI / ML model to infer mobility measurements / events is trained, and to enable such model to infer mobility measurements, the RAN needs to configure the UE with measurements to be collected and reported. As for collection of input measurements for training data collection purposes, such input measurements need to be collected at specific points in time, so that the model can infer output mobility measurements / events at specific points in time, e.g., in the future or for the same point in time as the inputs. The problem in this case is also related to providing to the UE specific measurements configurations for inference input measurement collection that allow for collection and reporting of measurements at specific points in time.

[0013] Finally, once an AI / ML model is operating and inferring mobility measurements / events, there is a need to monitor how such model is performing. In order to do so, the RAN needs to configure the UE to measure the ground truth corresponding to the inferred mobility measurements / events. As for collection of training data, such ground truth needs to be measured at a specific point in time that coincides, or it is close enough to the time for which a mobility measurement / event is predicted.

[0014] In conclusion, the problem solved by the methods in this disclosure is how to provide measurement configurations to the UE that allow the collection of different sets of measurements, where each measurement is configured to be taken at a specific point in time or within specific time windows, so to support collection of training data, collection of inference inputs and collection of performance measurements for AI / ML models supporting mobility optimization.

[0015] In the context of the present disclosure, a predefined time window used in the measurement configurations may interchangeably refer to a specific time window, a collection time window, a fixed time window, and / or a maximum time window. In some embodiments, a predefined time period (e.g., as used in the claims) may correspond to a specified amount of time, a time period, or a time offset configured to define the temporal relationship between different measurement sets or collection windows.

[0016] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges.

[0017] Network transmits a configuration to the UE with two sets of measurement configurations wherein each set may be refereeing one or more of• Set of measurements to be collected as input data for the model training (also called set B)• Set of measurements to be collected as output / label data for the model training (also called set A)

[0018] The configuration implicitly or explicitly indicates the time requirements for the UE to perform the measurements on first and second set. For example, measurements flagged as set A and set B can be an implicit indication of a default time requirements on how the measurements should be performed.

[0019] In some embodiments, the measurements in the first set of configurations should be performed within a time window and the measurements associated to the second set should be performed within a time window (T) in relation to the measurements associated to the first set.

[0020] After receiving the first and second sets of the measurement configurations (associated with set A and set B), the UE performs the measurements. Upon logging the measurements in a report (or an internal storage), the UE includes one or more indications with the measurement samples associated with the time information.

[0021] In some embodiments, the disclosed subject matter includes a method for collecting RRM measurements by a wireless device. For example, the method may include receiving (910), from a network node, a RRM measurement configuration that includes a first set of measurement configurations and a second set of measurement configurations, wherein the RRM measurement configuration implicitly or explicitly indicates time requirements for the wireless device to perform a measurement on the first set and the second set of measurement configurations; collecting one or more measurement samples at one or more collection times indicated by each of the first set of measurement configurations and the second set of measurement configurations; and sending, to the network node, a RRM measurement report comprising a first set of measurement data corresponding for the first set of measurement configurations and a second set of measurement data for the second set of measurement configurations, wherein the first set and the second set of measurement data comprises logged result data related to the one or more collected measurement samples.

[0022] In some embodiments, the method for collecting RRM measurements may be performed at a wireless device that includes processing circuitry and memory coupled with the processing circuitry, wherein the memory includes instructions that when executed by the processing circuitry causes the wireless device to perform the steps and / or operations of the disclosed method. In some embodiments, the disclosed subject matter may include a non-transitory computer readable medium including program code to be executed by processing circuitry of the wireless device whereby execution of the program code causes the program code to perform the steps and / or operations of the disclosed method.

[0023] In some embodiments, the disclosed subject matter includes a method for predicting RRM measurements by a network node. For example, the method may include sending, to a wireless device, a RRM measurement configuration that includes a first set of measurement configurations and a second set of measurement configurations, wherein the RRM measurement configuration implicitly or explicitly indicates time requirements for the wireless device to perform a measurement on the first set and the second set of measurement configurations; receiving, from the wireless device, RRM measurement report that comprises a first set of measurement data for the first set of measurement configurations and a second set of measurement data for the second set of measurement configurations, wherein the first set and the second set of measurement results comprises logged result data related to the one or more measurement samples collected by the wireless device; and utilizing at least one of the first set of measurement data and / or the second set of measurement data to conduct training of at least one artificial intelligence (Al) model configured to infer mobility measurements and / or events associated with the wireless device.

[0024] In some embodiments, the method for predicting RRM measurements may be performed at a network node that includes processing circuitry and memory coupled with the processing circuitry, wherein the memory includes instructions that when executed by the processing circuitry causes the network node to perform the steps and / or operations of the disclosed method. In some embodiments, the disclosed subject matter may include a non-transitory computer readable medium including program code to be executed by processing circuitry of the network node whereby execution of the program code causes the program code to perform the steps and / or operations of the disclosed method.BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a diagram depicting a first example of an intra-frequency temporal RRM measurement prediction;

[0026] Figure 2 is a diagram depicting a second example of an intra-frequency temporal RRM measurement prediction;

[0027] Figure 3 is a diagram depicting a third example of an intra-frequency temporal RRM measurement prediction;

[0028] Figure 4 is a diagram depicting an example of temporal measurement predictions with a fixed measurement collection time and a fixed time between different sets of measurements according to an embodiment described herein;

[0029] Figure 5 is a diagram depicting an example of temporal measurement predictions with a configured time window for measurement collection of each set of measurements and a fixed time between the end of the first set collection window and the beginning of the second set collection window according to an embodiment described herein;

[0030] Figure 6 is a diagram depicting the collection of a first set and second set of measurements within a common time window according to an embodiment described herein;

[0031] Figure 7 is a diagram depicting the collection of a first set measurements within a first time window and collection of a second set of measurements within a second common time window that starts at the termination of the first time window according to an embodiment described herein;

[0032] Figure 8 is a diagram depicting a measurement configuration that indicates a relationship between a plurality of measurement identifiers and a plurality of measurement objects and report configurations according to an embodiment described herein;

[0033] Figure 9 is a flow diagram of a method that performed by a wireless device according to an embodiment described herein;

[0034] Figure 10 is a flow diagram of a method that performed by a network node according to an embodiment described herein;

[0035] Figure 11 is a block diagram of a communication system in accordance with some embodiments;

[0036] Figure 12 is a block diagram of a user device in accordance with some embodiments

[0037] Figure 13 is a block diagram of a network node in accordance with some embodiments;

[0038] Figure 14 is a block diagram of a host computer communicating with a user device in accordance with some embodiments; and

[0039] Figure 15 is a block diagram of a virtualization environment in accordance with some embodiments.DETAILED DESCRIPTION

[0040] The method disclosed herein enables the data collection to be performed in manner that enhances the model training, which in turn improves the model inference accuracy. More precisely, by performing measurements associated to the input of the model (also called ‘set B’) and performing measurements associated with the output / label of the models (also called ‘set A’) within a standard time constraint, the measurements become more correlated and eventually the prediction model becomes more accurate.

[0041] In some embodiments, the disclosed methods apply to radio access networks (RANs) where the RAN collects measurements and information from UEs. The collected measurements and information pertain to the served radio environment to support processes that are able to derive information (e.g., such as measurements, metrics, events, etc.) that help in making mobility decisions for served UEs.

[0042] The techniques used to drive such information may be based on AI / ML or not, e.g., the techniques may be based on rule-based algorithms. In the present disclosure, the description of the methods is made assuming that the techniques used to derive such information are AI / ML based, but that should not limit the applicability of the methods to non AI / ML techniques.

[0043] The methods in this disclosure are described by taking the 5G network as an example. This does not limit the applicability of the methods to other radio access networks where mobility decisions are made via techniques that require the acquisition of specific information (such as measurements and events) at specific points in time, such as in 6G networks.

[0044] In some embodiments, methods for temporal predictions are derived for cases where the RAN acquires specific measurements and events from the UE to derive predictions of measurements at a point in time in the future.

[0045] Collection of Training Data for Temporal Predictions

[0046] In one embodiment, the RAN node serving the UE configures the UE to measure a first set of one or more specific measurements or events and a second set of one or more specific measurements or events at a later point in time.

[0047] In one depending embodiment, the first set of measurements / events shall be measured all at the same time.

[0048] In one depending embodiment, the first set of measurements / events shall be measured within a specific time window.

[0049] In one depending embodiment, the first set of measurements / events shall be measured all at the same time or within a specific window and the first set of measurements / events shall be measured periodically. Namely, at each measurement period, the first set shall be measured at the same time or within a specific time window.

[0050] In one depending embodiment, the second set of one or more specific measurements or events shall be measured all at the same time.

[0051] In one depending embodiment, the second set of one or more specific measurements or events shall be measured within a specific time window.

[0052] In one depending embodiment, the second set of measurements is measured after a specific amount of time after the first set of measurements has been measured.

[0053] In one depending embodiment, measurements for the second set of measurements starts after a specific amount of time after the first set of measurements has been measured.

[0054] In one depending embodiment, measurements for the second set of measurements starts after a specific amount of time after the first set of measurements started to be measured.

[0055] In one depending embodiment, if the first set of measurements is taken periodically, the second set of measurements is also taken periodically. In this case, for each first set of measurement samples (e.g., measured at a measurement period), the second set of measurements is measured as per embodiments above describing the time relation between the first and the second set of measurements.

[0056] Figure 4 and Figure 5 show examples of how the first and second set of measurements may be collected. In particular, Figure 4 depicts a diagram 400, which illustrates an example of temporal measurement predictions with a fixed measurement collection time and a fixed time between different sets of measurements. Specifically, diagram 400 illustrates a first set 410 of measurements collected on Frequency A for Cells 1, 2, and 3. Notably, the measurements collected for Cells 1, 2, and 3 are conducted at the same point in time (i.e., a common fixed collection time). At some time period after the collection time of first set 410, a second set 420 of measurements is collected on Frequency B for Cell 1 at a second collection time.

[0057] Figure 5 depicts a diagram 500, which illustrates an example of temporal measurement predictions with a configured time window for measurement collection of each set of measurements and a fixed time between the end of the first set collection window and the beginning of the second set collection window. Specifically, diagram 500 illustrates a first set 510 of measurements collected on Frequency A for Cells 1, 2, and 3. Notably, the measurements collected for Cells 1, 2, and 3 are conducted during a first time window 511 forcollection. At some time period after the end of the first time window 510, a second set 520 of measurements is collected on Frequency B for Cell 1 during a second time window 522 for collection.

[0058] Collection of Inference Input Data and Performance Monitoring Data for Temporal Predictions

[0059] The methods in this case are equivalent to the methods described above, but with simplified conditions. Namely, for the collection of input measurements and for the collection of performance measurements, a single set of measurements needs to be configured (each for different purposes, namely inference inputs or performance monitoring). In this case, the configurations described in this method are the same as configurations for the collection for training data but where only one set of measurements is configured to be collected and where conditions on measurement collection are for specific points in time. In contrast, the input / performance monitoring measurements should be collected for specific time windows and time window start times within which the set of measurements should be collected.

[0060] In some embodiments, inference input measurements and performance measurements may be configured at the same time.

[0061] In some embodiments, one UE receives one RRM measurement and report configuration from one RAN node (e.g., a network node) via one or more RRC messages, where this configuration indicates at least the first information set and the second information set. Notably, RRM measurement resources in the first information set are the same as RRM measurement resources in the second information set, but the periodicity of RRM measurement time window of the first information set is shorter than the corresponding periodicity of RRM measurement time window of the second information set, and the periodicity of RRM measurement report of the first information set is shorter than the corresponding periodicity of RRM measurement report of the second information set.

[0062] In some embodiments, the time distance (e.g., a time period) between the first information set of specific measurements or events and the second information set of specific measurements or events is equal to the prediction window of the Al model. Notably, the prediction window is how far in the future the Al model infers, given a certain data in input. As used herein, an Al model may include a machine learning model, a neural network model, and / or other similar AI / ML based models.

[0063] The UE can periodically report the RRM measurement results to the RAN node according to the configuration from the RAN node. The network and / or network node uses the reported measurement results corresponding to the first information set to implement RRMmeasurement prediction at the network side, and uses the reported measurement results corresponding to the second information set to monitor the performance of RRM measurement prediction at the network side.

[0064] In some embodiments, methods for frequency predictions are derived for cases where the RAN and / or network node acquires specific measurements and events from the UE to derive predictions of measurements at the same time or at a very close point in time but on a different frequency than the frequency associated with the first measurements.

[0065] Collection of Training Data for Frequency Predictions

[0066] In some embodiments, the RAN node serving the UE configures (e.g., via an RRM measurement configuration) the UE to measure i) a first set of one or more specific measurements or events on one or more specific frequencies and ii) a second set of one or more specific measurements for one or more frequencies different from those for the first set of measurements, where both the first and second sets of measurements should be collected at the same time.

[0067] In one embodiment, the RAN node serving the UE configures the UE to measure a first set of one or more specific measurements or events on one or more specific frequencies and a second set of one or more specific measurements for frequencies different from those for the first set of measurements, where both first and second sets of measurements should be collected within a fixed time window (e.g., a predefined time window) starting from a specific point in time.

[0068] In one depending embodiment, the first set of measurements / events shall be measured all at the same time, while the second set of measurements shall be measured within a maximum time window (e.g., a predefined time window) from collection of the first set of measurements.

[0069] In one depending embodiment, the first set of measurements / events shall be measured starting from a specific point in time and within a specific time window, while the second set of measurements should be measured within a maximum time window from the termination of the collection window of the first set of measurements.

[0070] In one depending embodiment, the first set of measurements / events shall be measured starting from a specific point in time and within a specific time window, while the second set of measurements should be measured all at the same time, where such time is calculated by a time distance from the start or the end of the collection time window (e.g., a predefined time window) for the first set of measurements.

[0071] In one depending embodiment, the first and / or the second set of measurements can be measured periodically.

[0072] In one depending embodiment, if the first set of measurements is taken periodically, the second set of measurements is also taken periodically. In this case, for each first set of measurement samples (measured at a measurement period) the second set of measurement is measured as per embodiments above describing the time relation between the first and the second set of measurements.

[0073] Figure 6 and Figure 7 show examples of how the first and second set of measurements may be collected. For example, Figure 6 depicts a diagram 600 that illustrates the collection of a first set and second set of measurements within a common time window. In particular, first set 610 of measurements is collected on Frequency A for Cells 1, 2, and 3, whereas the second set 620 of measurements is collected on Frequency B for Cell 4. Notably, first set 610 and second set 620 are collected within the same time window 650 in diagram 600.

[0074] Likewise, Figure 7 depicts a diagram 700 that illustrates the collection of a first set measurements within a first time window and collection of a second set of measurements within a second common time window that starts at the termination of the first time window. In particular, first set 710 of measurements is collected on Frequency A for Cells 1, 2, and 3 during a first time window 751, whereas the second set 720 of measurements is collected on Frequency B for Cell 4 during a second time window 752. Notably, second time window 752 starts at the completion and / or termination of first time window 751 in diagram 700.

[0075] Upon receiving the configuration and performing the measurements, the UE may include time information associated to the performed measurements samples in the report and sent to the network node so the network (and / or network node) can use the time information for the model training as well. In some embodiments, the time information indicates the time difference between two consecutive measurement samples that are required to be performed within a time period. The UE may avoid including the measurements in the report, or include a flag associated to the measurement samples that indicates that the measurement is not performed according to the time requirements requested / expected by the network.

[0076] Example#2-1

[0077] In some embodiments, one UE receives one RRM measurement and report configuration (i.e., MeasConfig) from one RAN node via one RRC message (e.g., RRCReconfiguration message, RRCResume message), and this MeasConfig (see diagram 800 in Figure 8) indicatesi) measObjectNR#l and ReportConfigNR#l are linked to Measld#l, meas0bjectNR#2 and ReportConfigNR#2 are linked to Measld#2, and meas0bjectNR#3 and ReportConfigNR#3 are linked to Measld#3,ii) carrier frequencies for RRM measurement in measObjectNR#l, meas0bjectNR#2, and meas0bjectNR#3 are different, andiii) measurement time windows in measObjectNR#l, meas0bjectNR#2, and meas0bjectNR#3, where measurement time windows may be configured to one of the following options:• Option 1: Measurement time windows in these three measObjectNR are same.• Option 2: Measurement time windows in measObjectNR#l and measObjectNR#2 are same, but measurement time window in measObjectNR#3 starts within a maximum time window from the termination of measurement time windows in measObjectNR#l and measObjectNR#2.• Option 3: Measurement time windows in measObjectNR#l and measObjectNR#2 are same, but measurement time window in measObjectNR#3 starts within a maximum time window from the start of measurement time windows in measObjectNR# 1 and measObjectNR#2 in each measurement period for these measurement time windows.• Option 4: Measurement time windows in measObjectNR# 1 and measObjectNR#2 may be different, and measurement time window in measObjectNR#3 starts within a maximum time window from the termination of the measurement time windows in measObjectNR#l and measObjectNR#2• Option 5: Measurement time windows in measObjectNR#l and measObjectNR#2 are different, and measurement time window in measObjectNR#3 starts within a maximum time window from the start of measurement time windows in measObjectNR# 1 and measObjectNR#2 in each measurement period for these measurement time windows.

[0078] The measurement time windows may be indicated by, for example, SMTC configuration which may be extended to support CSI-RS-based RRM measurement, or other time indication information.

[0079] Upon receiving such configuration, the UE performs the measurements on different frequencies while fulfilling the time requirements configured by the network and / or serving network node. The UE may log and / or include an indication beside the measurement samples, indicating the time information related to the measurement delay. In some embodiments, theUE includes the relative time elapsed between two consecutive measurements samples. The two consecutive samples can be from the same set (e.g., either the first set or the second set) or from different sets (e.g., the first set and the second set). In other embodiments, the UE includes the absolute time at which the measurement sample is performed.

[0080] The UE either fulfills the time requirement or not. In some embodiments, the UE includes the time information only when the UE does not fulfil the time requirement for the measurements. If the UE does not fulfil the time requirements, the UE includes an indication associated with the performed measurement sample indicating that the measurement does not fulfil the time requirements for the data collection on the first and second set of frequencies. In other embodiments, the UE may avoid including and reporting the measurement for which the time requirement is not fulfilled. In some embodiments, the UE includes the relative time elapsed between two consecutive measurements samples. The two consecutive samples can be from the same set (e.g., either the first set or the second set) or from different sets (e.g., the first set and the second set). In other embodiments, the UE includes the absolute time at which the measurement sample is performed.

[0081] Collection of Inference Input Data and Performance Monitoring Data for Frequency Predictions

[0082] The methods in this case are equivalent to the methods above, but with simplified conditions. Input data and performance monitoring data are considered as separate measurement sets. In this case, the configurations described in this method are the same as for the collection for training data. In some embodiments, two sets of measurements are configured to be collected and where conditions on measurement collection are for specific points in time when the input measurements and performance monitoring measurements should be collected at specific time windows and time window start time within which the set of measurements should be collected.

[0083] Example#2-2

[0084] In some embodiments, one UE receives one RRM measurement and report configuration (i.e., MeasConfig) from one RAN node via one or more RRC messages, where this configuration indicates at least the first information set and the second information set, for example:• The measurement frequency set in the first information set is different from the measurement frequency set in the second information set;• The periodicity of measurement time window of the first information set is shorter than the corresponding periodicity of measurement time window of the second information set;• The periodicity of RRM measurement report of the first information set is shorter than the corresponding periodicity of RRM measurement report of the second information set.

[0085] The UE periodically reports the RRM measurement results to the RAN node (e.g., network node) according to the configuration from the RAN node. The network uses the reported measurement results corresponding to the first information set to implement a RRM measurement prediction at the network side, and uses the reported measurement results corresponding to the second information set to monitor the performance of the RRM measurement prediction at the network side.

[0086] In the following information, for example indicated by the MeasConfig, the first information set is the information related to Measld#l , measObjectNR#l , ReportCOnfigNR#l , Measld#2, measObjectNR#2, and ReportCOnfigNR#2, and the second information set is the information related to Measld#3, measObjectNR#3, and ReportCOnfigNR#3.• measObjectNR#l and ReportCOnfigNR#l are linked to Measld#l, measObjectNR#2and ReportCOnfigNR#2 are linked to Measld#2 and measObjectNR#3 and ReportCOnfigNR#3 are linked to Measld#3,• carrier frequencies for RRM measurement in measObjectNR#l, measObjectNR#2 and measObjectNR#3 are different• measurement time windows in measObjectNR#l, measObjectNR#2 and measObjectNR#3 may be configured to:Option 1: The periods of measurement time windows in measObjectNR#l and measObjectNR#2 are same, and moreover shorter than the period of measurement time windows in measObjectNR#3. The measurement time windows in measObjectNR#l and measObjectNR#2 are same or very close.The measurement time windows in measObjectNR#3 are same or very close to partial measurement time windows in measObjectNR#l and measObjectNR#2

[0087] The measurement time windows may be indicated by SMTC configuration and may be indicated by other time indication information. The SMTC configuration may be used for measurement time window configuration for at least one of a SSB-based RRM measurement and a CSI-RS-based RRM measurement.

[0088] Example#2-3

[0089] In some embodiments, one UE receives two RRM measurement and report configurations (i.e., MeasConfig#l and MeasConfig#2) from one RAN node, where MeasConfig#l includes the first information set and is received at time tl and MeasConfig#2 includes the second information set and is received at time t2,• The measurement frequency set in the first information set is different from the measurement frequency set in the second information set;• The measurement in the first information set is periodic or event-triggered periodic, but the measurement in the second information set is aperiodic or event-triggered aperiodic, where the aperiodic may be configured via periodic or event-triggered periodic but the report amount number is set to 1.

[0090] The UE reports the RRM measurement results to the RAN node according to the configuration from the RAN node. The network uses the reported measurement results corresponding to the first information set to implement a RRM measurement prediction at the network side, and uses the reported measurement results corresponding to the second information set to monitor the performance of the RRM measurement prediction at the network side.

[0091] In some embodiments, methods for spatial predictions are derived for cases where the RAN acquires specific measurements and events from the UE to derive predictions of measurements at the same or at a very close point in time but on a different cell or a different beam than the first measurements.

[0092] Collection of Training Data for Spatial Predictions

[0093] In some embodiments, the RAN node serving the UE configures the UE to measure a first set of one or more specific measurements or events on one or more specific cell(s) or beam(s) and a second set of one or more specific measurements or events on one or more specific cell(s) or beam(s) that are different from those for the first set of measurements, where both first and second sets of measurements should be collected at the same time.

[0094] In one embodiment, the RAN node serving the UE configures the UE to measure a first set of one or more specific measurements or events on one or more specific cell(s) or beam(s) and a second set of one or more specific measurements for a cell set or a beam set different from those for the first set of measurements, where both first and second sets of measurements should be collected within a fixed time window starting from a specific point in time.

[0095] In one depending embodiment, the first set of measurements / events shall be measured all at the same time, while the second set of measurements shall be measured within a maximum time window from collection of the first set of measurements.

[0096] In one depending embodiment, the first set of measurements / events shall be measured starting from a specific point in time and within a specific time window, while the second set of measurements should be measured within a maximum time window from the termination of the collection window of the first set of measurements.

[0097] In one depending embodiment, the first set of measurements / events shall be measured starting from a specific point in time and within a specific time window, while the second set of measurements should be measured all at the same time, where such time is calculated by a time distance from the start or the end of the collection time window for the first set of measurements.

[0098] In one depending embodiment, the first and / or the second set of measurements can be measured periodically.

[0099] In one depending embodiment, if the first set of measurements is taken periodically, the second set of measurements is also taken periodically. In this case, for each first set of measurement (measured at a measurement period) the second set of measurement is measured as per embodiments above describing the time relation between the first and the second set of measurements.

[0100] Example#3-1

[0101] One UE receives one RRM measurement and report configuration (i.e., MeasConfig) from one RAN node via one RRC message (e.g., RRCReconfiguration message, RRCResume message), and this MeasConfig indicatesmeasObjectNR#l and ReportCOnfigNR#l are linked to Measld#lmeasObjectNR#l includesOption 1 : one cell list and one measurement time window configurationOption 2: one cell list A associated with one measurement time window information and one cell list B associated with one measurement time window informationOption 2-1: Measurement time windows for cell list A and cell list B are the same Option 2-2: Measurement time window for cell list A starts within a maximum time window from the termination of measurement time window for cell list B

[0102] The measurement time windows may be indicated by smtc configuration and may be indicated by other time indication information. The smtc configuration may be used for measurement time window configuration for at least one of SSB-based RRM measurement andCSI-RS-based RRM measurement. The other time indication information may be used for measurement time window configuration for at least one of SSB-based RRM measurement and CSI-RS-based RRM measurement.

[0103] Example#3-2

[0104] One UE receives one RRM measurement and report configuration (i.e., MeasConfig) from one RAN node via one RRC message (e.g., RRCReconfiguration message, RRCResume message), and this MeasConfig indicatesmeasObjectNR#l and ReportConfigNR#l are linked to Measld#lmeasObjectNR#l or ReportConfigNR#l includesOption 1: one beam list for each cell in one cell list and one measurement time window configuration, where one cell may have its individual measurement time window configuration or each group of cells in the cell list have its individual measurement time window configuration.Option 2: one beam list A associated with one measurement time window information and one beam list B associated with one measurement time window information for each cell in one cell listOption 2- 1 : Measurement time windows for beam list A and beam list B in the same cell are sameOption 2-2: Measurement time window for beam list A in one cell starts within a maximum time window from the termination of measurement time window for beam list B in the same cell

[0105] The measurement time windows may be indicated by smtc configuration and may be indicated by other time indication information. The smtc configuration may be used for measurement time window configuration for at least one of SSB-based RRM measurement and CSI-RS-based RRM measurement. The other time indication information may be used for measurement time window configuration for at least one of SSB-based RRM measurement and CSI-RS-based RRM measurement.

[0106] Example#3-3

[0107] One UE receives one RRM measurement and report configuration (i.e., MeasConfig) from one RAN node via one RRC message (e.g., RRCReconfiguration message, RRCResume message), and this MeasConfig indicatesmeasObjectNR#l and ReportConfigNR#l are linked to Measld#lOption 1 : one beam list in one cell list and one measurement time window configuration, where one cell may have its individual measurement time window configuration or each group ofcells in the cell list have its individual measurement time window configuration or each group of beams in the cell list have its individual measurement time window.Option 2: one beam list A associated with one measurement time window information and one beam list B associated with one measurement time window information in one cell list Option 2- 1 : Measurement time windows for beam list A and beam list B in the same cell list are sameOption 2-2: Measurement time window for beam list A in one cell list starts within a maximum time window from the termination of measurement time window for beam list B in the same cell list

[0108] The measurement time windows may be indicated by smtc configuration and may be indicated by other time indication information. The smtc configuration may be used for measurement time window configuration for at least one of SSB-based RRM measurement and CSI-RS-based RRM measurement. The other time indication information may be used for measurement time window configuration for at least one of SSB-based RRM measurement and CSI-RS-based RRM measurement.

[0109] Collection of Inference Input Data and Performance Monitoring Data:

[0110] The methods in this case are equivalent to the methods above, but with simplified conditions. Namely, input data and performance monitoring data may be considered as two sets of measurements, i.e., one set for inference input data and the other for the performance monitoring data since the sets of cells / beams to measure are different. In this case the configurations described in this method are the same as for the collection for training data but where only one set of measurements is configured to be collected and where conditions on measurement collection are for specific points in time when the input / performance monitoring measurements should be collected or specific time windows and time window start time within which the set of measurements should be collected.

[0111] Example#3-4

[0112] One UE receives one RRM measurement and report configuration (i.e., MeasConfig) from one RAN node via one or more RRC messages, where this configuration indicates at least the first information set and the second information set, for example The measurement beam set in the first information set is different from the measurement beam set in the second information set;The periodicity of measurement time window of the first information set is shorter than the corresponding periodicity of measurement time window of the second information set;The periodicity of RRM measurement report of the first information set is shorter than the corresponding periodicity of RRM measurement report of the second information set.

[0113] The UE periodically reports the RRM measurement results to the RAN node according to the configuration from the RAN node. The network uses the reported measurement results corresponding to the first information set to implement RRM measurement prediction at the network side, and uses the reported measurement results corresponding to the second information set to monitor the performance of RRM measurement prediction at the network side.

[0114] In the following information for example indicated by the MeasConfig, the first information set is the information related to Measld#l, measObjectNR#l, and ReportConfigNR#l, and the second information set is the information related to Measld#2, measObjectNR#2, ReportCOnfigNR#2.measObjectNR#l and ReportCOnfigNR#l are linked to Measld#l, and measObjectNR#2and ReportCOnfigNR#2 are linked to Measld#2beam lists for RRM measurement in measObjectNR#l / ReportCOnfigNR#l and measObjectNR#2 / ReportCOnfigNR#2 are differentmeasurement time windows in measObjectNR#l and measObjectNR#2 may be configured to Option 1: The period of measurement time windows in measObjectNR#l is shorter than the period of measurement time windows in measObjectNR#2.The measurement time window in measObjectNR#2 are same or very close to partial measurement time window in measObjectNR#l.

[0115] The measurement time windows may be indicated by smtc configuration and may be indicated by other time indication information. The smtc configuration may be used for measurement time window configuration for at least one of SSB-based RRM measurement and CSI-RS-based RRM measurement. The other time indication information may be used for measurement time window configuration for at least one of SSB-based RRM measurement and CSI-RS-based RRM measurement.

[0116] Example#3-5

[0117] One UE receives two RRM measurement and report configurations (i.e., MeasConfig#l and MeasConfig#2) from one RAN node, where MeasConfig#l includes the first information set and is received at time tl and MeasConfig#2 includes the second information set and is received at time t2,The measurement beam set in the first information set is different from the measurement beam set in the second information set;The measurement in the first information set is periodic or event-triggered periodic, but the measurement in the second information set is aperiodic or event-triggered aperiodic, where the aperiodic may be configured via periodic or event-triggered periodic but the report amount number is set to 1.

[0118] The UE reports the RRM measurement results to the RAN node according to the configuration from the RAN node. The network uses the reported measurement results corresponding to the first information set to implement RRM measurement prediction at the network side, and uses the reported measurement results corresponding to the second information set to monitor the performance of RRM measurement prediction at the network side.

[0119] Operations of a wireless device 1300 (implemented using the structure of Figure 13) will now be discussed with reference to the flow chart of Figure 9 according to some embodiments of inventive concepts. For example, modules may be stored in memory 1310 of Figure 13, and these modules may provide instructions so that when the instructions of a module are executed by respective wireless device processing circuitry 1302, wireless device 1300 (e.g., a UE) performs respective operations of the flow chart depicting an exemplary method 900.

[0120] Figure 9 illustrates an example of operations performed by a UE. Various operations from the method 900 shown in the flow chart of Figure 9 may be optional with respect to some embodiments of communication devices and related methods.

[0121] In step 910, method 900 includes receiving, from a network node, a RRM measurement configuration that includes a first set of measurement configurations and a second set of measurement configurations. Notably, the RRM measurement configuration implicitly or explicitly indicates time requirements for the wireless device to perform a measurement on the first set and the second set of measurement configurations.

[0122] In step 920, method 900 includes collecting (920) one or more measurement samples at one or more collection times indicated by each of the first set of measurement configurations and the second set of measurement configurations.

[0123] In step 930, method 900 includes sending, to the network node, a RRM measurement report comprising (and / or containing) a first set of measurement data corresponding for the first set of measurement configurations and a second set of measurement data for the second set of measurement configurations, wherein the first set and the second set of measurement data comprises logged result data related to the one or more collected measurement samples.

[0124] In some embodiments, the first set of measurement configurations indicates the first set of measurement data, which is to be collected as output and / or label data for Al model training and performance monitoring, and the second set of measurement configurations indicates the second set of measurement data, which is to be collected as inference input data for Al model training. In some embodiments, the first set of measurement configurations and the second set of measurement configurations respectively indicate time requirements for the wireless device to collect the one or more measurement samples. In some embodiments, the one or more collection times include at least one fixed collection time point and / or at least one collection time window. In some embodiments, the at least one collection time window includes a first collection time window and a second collection time window, wherein the one or more measurement samples for the first set of measurement configurations are collected within the first collection time window and the one or more measurement samples for the second set of measurement configurations are collected within the second collection time window.

[0125] In some embodiments, the one or more measurement samples are periodically collected over a respective at least one periodic collection time point and / or at least one periodic collection time window. In some embodiments, the first set of measurement data is collected i) after a specified amount of time after a start of the collection of the one or more measurement samples corresponding to the second set of measurement data, or ii) after a specified amount of time after an end of the collection of the one or more measurement samples corresponding to the second set of measurement data. In some embodiments, the one or more measurement samples corresponding to the first set of measurement data is collected on one or more first frequencies, and the one or more measurement samples corresponding to the second set of measurement data is collected on one or more second frequencies, wherein the one or more second frequencies differ from the one or more first frequencies.

[0126] In some embodiments, the one or more measurement samples corresponding to the first set of measurement data is collected on one or more first cells or beams, and the one or more measurement samples corresponding to the second set of measurement data is collected on one or more second cells or beams, wherein the one or more second cells or beams differ from the one or more first cells or beams. In some embodiments, the one or more measurement samples corresponding to the first and second sets of measurement data are collected i) at a same time or ii) within a fixed time window starting from a specific point in time. In some embodiments, the one or more measurement samples corresponding to the first set of measurement data is collected within a predefined time window that starts at a predefined timeperiod after a termination of the collection of the one or more measurement samples corresponding to the second set of measurement data. In some embodiments, the one or more measurement samples corresponding to the first set of measurement data is collected within a predefined time window that starts at a predefined time period after a start of the collection of the one or more measurement samples corresponding to the second set of measurement data. In some embodiments, the first set and the second set of measurement data is utilized by the network node to train one or more Al models, and at least the first set of measurement data is utilized by the network node to monitor the performance of the one or more Al models.

[0127] Operations of a network node 1400 (implemented using the structure of Figure 14) will now be discussed with reference to the flow chart of Figure 10 according to some embodiments of inventive concepts. For example, modules may be stored in memory 1404 of Figure 14, and these modules may provide instructions so that when the instructions of a module are executed by respective network node processing circuitry 1402, network node 1400 performs respective operations of the flow chart depicting an exemplary method 1000.

[0128] Figure 10 illustrates an example of operations performed by a network node (e.g., a RAN node, eNodeB, etc.). Various operations from the method 1000 shown in the flow chart of Figure 10 may be optional with respect to some embodiments of communication devices and related methods.

[0129] In step 1010, method 1000 includes sending, to a wireless device, a RRM measurement configuration that includes a first set of measurement configurations and a second set of measurement configurations. Notably, the RRM measurement configuration implicitly or explicitly indicates time requirements for the wireless device to perform a measurement on the first set and the second set of measurement configurations.

[0130] In step 1020, method 1000 includes receiving, from the wireless device, RRM measurement report that comprises (and / or contains) a first set of measurement data for the first set of measurement configurations and a second set of measurement data for the second set of measurement configurations, wherein the first set and the second set of measurement results comprises logged result data related to the one or more measurement samples collected by the wireless device.

[0131] In step 1030, method 1000 includes utilizing at least one of the first set of measurement data and / or the second set of measurement data to conduct training of at least one Al model configured to infer mobility measurements and / or events associated with the wireless device.

[0132] In some embodiments, the method further compromises utilizing at least one of the first set of measurement data and / or the second set of measurement data to monitor a performance of the at least one Al model. In some embodiments, the one or more measurement samples are collected by the wireless device at one or more collection times indicated by each of the first set of measurement configurations and the second set of measurement configurations. In some embodiments, the first set of measurement configurations indicates the first set of measurement data, which is to be collected as output and / or label data for Al model training and performance monitoring, and the second set of measurement configurations indicates the second set of measurement data, which is to be collected as inference input data for Al model training.

[0133] In some embodiments, the first set of measurement configurations and the second set of measurement configurations respectively indicate time requirements for the wireless device to collect the one or more measurement samples. In some embodiments, the one or more collection times include at least one fixed collection time point and / or at least one collection time window. In some embodiments, the at least one collection time window includes a first collection time window and a second collection time window, wherein the one or more measurement samples for the first set of measurement configurations are collected within the first collection time window and the one or more measurement samples for the second set of measurement configurations are collected within the second collection time window.

[0134] Figure 11 shows an example of a communication system 1100 in accordance with some embodiments. In the example, the communication system 1100 includes a telecommunications network 1102 that includes an access network 1104, such as a radio access network (RAN), and a core network 1106, which includes one or more core network nodes 1108. The access network 1104 includes one or more access network nodes or base stations of various types, access network nodes 1110A and 1110B are depicted (which may be collectively referred to as network nodes 1110), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points (APs). Some embodiments of the access network 1104 may include more than one access network technology. The network nodes 1110 of access network 1104 facilitate direct or indirect connection of wireless devices, also referred to as user equipments (UEs), such as by connecting UEs 1112A, 1112B, 1112C, and 1112D (one or more of which may be generally referred to as UEs 1112) to the core network 1106 over one or more wireless connections.

[0135] Moreover, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, itwill be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunications network 1102 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a network node in the telecommunications network 1102 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other network nodes to implement one or more functionalities of any network node in the telecommunications network 1102, including one or more access network nodes 1110 and / or core network nodes 1108.

[0136] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). An ORAN network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an Al, Fl, Wl, El, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN network node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an O-2 interface defined by the O-RAN Alliance or comparable technologies.

[0137] The network nodes 1110 facilitate direct or indirect connection of one or more UEs 1112 to the core network 1106 over one or more wireless connections. Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 1100 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 1100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0138] The UEs 1112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 1110 and other communication devices. Similarly, the network nodes 1108, 1110 are arranged, capable, configured, and / or operable to communicate directly or indirectly (e.g., via other devices of telecommunications network 1102) with the UEs 1112 and / or with other network nodes or equipment in the telecommunications network 1102 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunications network 1102. More specifically, UEs 1112 may send messages, data, and / or other signals to network nodes 1108, 1110 or other elements of the telecommunications network 1102 by transmitting such signals to the relevant device directly without the signals passing through any intervening devices or by transmitting such signals to the relevant device indirectly through an intervening device (or multiple intervening devices) that then transmit the signal to the relevant device. Similarly, network nodes 1108, 1110 may send messages, data, and other signals to UEs 11122, other network nodes 1108, 1110, and other devices in telecommunications network 1102 directly or indirectly. As one specific example, a core network node 108 may transmit a particular message to a UE 1112 by transmitting the message to an access network node 1110 that will then transmit the message to the intended UE 1112. Similarly, a core network node 108 may receive a particular message from a UE 1112 by receiving the message from an access network node 1110 that itself received the message from the UE 1112.

[0139] In the depicted example, the core network 1106 connects elements of the access network 1104 (e.g., one or more of the network nodes 1110) to one or more host computing systems, such as host 1116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 1106 includes one or more core network nodes (e.g., core network node 1108) of various types, one or more of which may be generally referred to as network nodes 1108. Network nodes 1108 are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, access network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 1108. Example core network nodes provide functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDE), Unified DataManagement (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0140] The host 1116 may be under the ownership or control of a service provider other than an operator or provider of the access network 1104 and / or the telecommunications network 1102. The host 1116 may be operated by the service provider or on behalf of the service provider. The host 1116 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0141] As a whole, the communication system 1100 of Figure 11 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 1100 may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (Wi-Fi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (Wi-Max), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, Li-Fi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox. Moreover, the communication system 1100 may be configured to support multiple different standards, protocols, or other rule sets, with individual components supporting all of the relevant rule sets or with different components or sub-systems within the communication system 1100 supporting different standards, protocols, or rule sets.

[0142] As one example, in certain embodiments, access network 1104 may comprise (and / or contain) some access network nodes 1110 that support 3GPP radio access technologies (RAT), such as LTE or NR, while other access network nodes 1110 support (or the same access network nodes 1110 additionally support) non-3GPP RATs, such as Wi-Fi or a proprietary RAT. As another example, telecommunications network 1102 may support multiple generations of related communication standards (e.g., 4G and 5G 3GPP communication standards) and, as a result, may include an access network 104 and / or a core network 106 that supports multiple different standard generations or may include multiple access networks 104and / or multiple core networks 106 with individual networks 104, 106 supporting different standard generations.

[0143] Telecommunications network 1102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunications network 1102. For example, the telecommunications network 1102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs.

[0144] In some examples, one or more of the UEs 1112 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 1104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 1104. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e., being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0145] In the example, the hub 1114 communicates with the access network 1104 to facilitate indirect communication between one or more UEs (e.g., UE 1112C and / or 1112D) and network nodes (e.g., network node 1110B). In some examples, the hub 1114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 1114 may be a broadband router enabling access to the core network 1106 for the UEs. As another example, the hub 1114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 1110, or by executable code, script, process, or other instructions in the hub 1114.

[0146] As another example, the hub 1114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 1114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 1114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 1114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub1114 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0147] The hub 1114 may have a constant / persistent or intermittent connection to the network node 1110B. The hub 1114 may also allow for a different communication scheme and / or schedule between the hub 1114 and UEs (e.g., UE 1112C and / or 1112D), and between the hub 1114 and the core network 1106. In other examples, the hub 1114 is connected to the core network 1106 and / or one or more UEs via a wired connection. Moreover, the hub 1114 may be configured to connect to a machine-to-machine (M2M) service provider over the access network 1104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 1110 while still connected via the hub 1114 via a wired or wireless connection. In some embodiments, the hub 1114 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 1110B. In other embodiments, the hub 1114 may be a nondedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 1110B, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0148] Figure 12 is another example of a communication system 1200 according to some embodiments. As used herein, the communication system 1200 includes multiple access points (APs) 1210 (with four exemplary APs 1210A, 1210B, 1210C, and 1210D being depicted) and multiple wireless devices, referred to in the context of communication system 1200 as stations (STAs) 1212 (referred to individually as STA 1212A, STA 1212B, STA 1212C, STA 1212D, and STA 1212E). STA 1212A is served by AP 1210A in a first basic service set (BSS) 1220A. STA 1210B and STA 1210C are served by AP 1210B in a second BSS, BSS 1220B. STA 1212D is served by AP 1210C in a third BSS, BSS 1220C. STA 1212E is served by AP 1210D in a fourth BSS, BSS 1220D. Stations 1212 may be non-AP STAs and correspond to various kinds of wireless devices, for example, user terminals, such as mobile or stationary computing devices like smartphones, laptop computers, desktop computers, tablet computers, gaming devices, head-mounted displays (HMDs) for Augmented Reality (AR) or Virtual Reality (VR), or the like. Further, stations 1212 could, for example, correspond to other kinds of equipment like smart home devices, printers, multimedia devices, data storage devices, or the like.

[0149] Each of STAs 1212 may connect through a radio link to one of APs 1210. For example, depending on location or channel conditions experienced by a given STA 1212, the STA may select an appropriate AP and BSS for establishing the radio link. The radio link may be based on one or more orthogonal frequency-division multiplexing (OFDM) carriers from afrequency spectrum that is shared on the basis of a contention-based mechanism, e.g., an unlicensed or license exempt band like 2.4 GHz Industrial, Scientific, and Medical (ISM) band, the 5 GHz band, the 6 GHz band, or the 60 GHz band.

[0150] Each AP 1210 may provide data connectivity to STAs 1212 connected to a particular AP 1210. As illustrated, APs 1210 may be connected to a data network 1230. In this way, APs 1210 may also provide data connectivity between STAs 1212 and other entities, e.g., to one or more servers, service providers, data sources, data sinks, user terminals, or the like. Accordingly, the radio link established between a given STA 1212 and its serving AP 1210 may be used for providing various kinds of services to STA 1212, e.g., a voice service, a multimedia service, or other data service. Such services may be based on applications that are executed on STA 1212 and / or on a device linked to STA 1212. By way of example, Figure 12 illustrates an application service platform 1232 provided in data network 1230. The application(s) executed on STA 1212 and / or on one or more other devices linked to STA 1212 may use the radio link for data communication with one or more other STA 1212 and / or the application service platform 1232, thereby enabling utilization of the corresponding service(s) at STA 1212.

[0151] Figure 13 shows a wireless device 1300, which may be configured to operate in communication system 1100 of Figure 11 or in communication system 1200 of Figure 120. The wireless device 1300 may be alternatively referred to as a UE 1300, like a UE 1112 within the context of communication system 1100, or as a station (STA) 1300 or as a non-access-point station (non-AP STA) 1300, like a STA 1212 within the context of the communication system 1200, in accordance with respective embodiments. As used herein, a wireless device refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other wireless devices. Examples of a wireless device include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (FEE), laptopmounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, and wireless terminal. Other examples include any type of UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0152] A wireless device 1300 may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, wireless device 1300 may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, wireless device 1300 may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, wireless device 1300 may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0153] In particular embodiments, wireless device 1300 includes processing circuitry 1302 that is operatively coupled via a bus 1304 to an input / output interface 1306, a power source 1308, a memory 1310, a communication interface 1312, and / or any other component, or any combination thereof. Certain embodiments of wireless device 1300 may include all or a subset of the components shown in Figure 13. The level of integration between the components may vary from one embodiment of wireless device 1300 to another. In general, in a particular embodiment of wireless device 1300, processing circuitry 1302, input / output interface 1306, power source 1308, memory 1310, and communication interface 1312 may, in whole or in part, represent or include physical components common to or shared by one or more of the other elements of wireless device 1300. Further, certain embodiments of wireless devices 1300 may comprise (and / or contain) multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0154] The processing circuitry 1302 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 1310. The processing circuitry 1302 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 1302 may include multiple central processing units (CPUs).

[0155] In the example, the input / output interface 1306 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display,a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into wireless device 1300. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0156] In some embodiments, the power source 1308 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used to supply power to circuitry or to charge an associated battery. The power source 1308 may further include power circuitry for delivering power from the power source 1308 itself, and / or an external power source, to the various parts of wireless device 1300 via input circuitry or an interface such as an electrical power cable. Power source 1308 may perform any formatting, converting, or other modification to make accessible power suitable for the respective components of the wireless device 1300 to which power is supplied.

[0157] The memory 1310 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 1310 includes one or more programs 1314, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1316. The memory 1310 may store, for use by wireless device 1300, any of a variety of various operating systems or combinations of operating systems.

[0158] The memory 1310 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module(DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a Universal Subscriber Identity Module (USIM) and / or Internet Protocol Multimedia Services Identity Module (ISIM), other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 1310 may allow wireless device 1300 to access instructions, programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 1310, which may be or comprise a device -readable storage medium.

[0159] The processing circuitry 1302 may be configured to communicate with an access network or other network via or using the communication interface 1312. The communication interface 1312 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 1322. The communication interface 1312 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another wireless device or a network node in an access network). Each transceiver may include a transmitter 1318 and / or a receiver 1320 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 1318 and receiver 1320 may be coupled to one or more antennas (e.g., antenna 1322) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0160] In the illustrated embodiment, communication functions of the communication interface 1312 may include cellular communication, Wi-Fi communication (e.g., according to an IEEE 802.11 family standard), LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM),Quick User Datagram Protocol Internet Connection (QUIC), Hypertext Transfer Protocol (HTTP), and so forth.

[0161] In particular embodiments, wireless device 1300 may provide an output of data captured via a sensor, through its communication interface 1312, via a wireless connection to a network node, and / or in any appropriate manner. Data captured by sensors of a wireless device 1300 can be communicated through a wireless connection to a network node via another wireless device 1300. In particular embodiments, such output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0162] As another example, wireless device 1300 comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, wireless device 1300 may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0163] Wireless device 1300, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. In particular embodiments, wireless device 1300 represents an loT device that comprises circuitry and / or software in dependence of the intended application of the loT devicein addition to other components as described in relation to the example embodiment of wireless device 1300 shown in Figure 13.

[0164] As yet another specific example, in an loT scenario, wireless device 1300 may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another wireless device and / or a network node. Wireless device 1300 may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, wireless device 1300 may implement the 3GPP NB-IoT standard. In other scenarios, wireless device 1300 may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0165] In practice, any number of wireless devices 1300 may be used together with respect to a single use case. For example, a first wireless device 1300 might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second wireless device 1300 that is a remote controller operating the drone. When a user makes changes from the remote controller, the first wireless device 1300 may adjust the throttle on the drone (e.g., by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second wireless device 1300 can also include more than one of the functionalities described above. For example, wireless device 1300 might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0166] Figure 14 shows a network node 1400 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunications network. In accordance with respective embodiments, network node 1400 may be configured to operate in communication system 1100 of Figure 11, like network nodes 1108 or 1110, or in communication system 1200 of Figure 12, like an AP 1210 or a station 1212. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (e.g., gNBs)), O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU).

[0167] Network nodes 1400 may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. Network node 1400 may be a relay node or a relay donor nodecontrolling a relay. Network nodes 1400 may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0168] Other examples of network nodes 1400 include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0169] In particular embodiments, network node 1400 includes a processing circuitry 1402, a memory 1404, a communication interface 1406, and a power source 1408. In general, in a particular embodiment of network node 1400, processing circuitry 1402, memory 1404, communication interface 1406, and power source 1408 may, in whole or in part, represent or include physical components common to or shared by one or more of the other elements of network node 1400.

[0170] The network node 1400 may be composed of multiple distinct network entities (e.g., a NodeB entity and a RNC entity, or a BTS entity and a BSC entity, etc.), which may each have or utilize their own respective physical components. In certain scenarios in which the network node 1400 comprises multiple such entities (e.g., BTS and BSC), one or more of the separate entities may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 1400 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memories 1404 or portions of memory 1404 for different RATs) and some components may be reused (e.g., a same antenna 1410 may be shared by different RATs). The network node 1400 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1400, for example GSM, WCDMA, LTE, NR, Wi-Fi (e.g., according to an IEEE 802.11 family standard), Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may beintegrated into the same or different chip or set of chips and other components within network node 1400.

[0171] The processing circuitry 1402 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other components, such as the memory 1404, to provide network node 1400 functionality.

[0172] In some embodiments, the processing circuitry 1402 includes a system on a chip (SOC). In some embodiments, the processing circuitry 1402 includes one or more of radio frequency (RF) transceiver circuitry 1412 and baseband processing circuitry 1414. In some embodiments, the RF transceiver circuitry 1412 and the baseband processing circuitry 1414 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 1412 and baseband processing circuitry 1414 may be on the same chip or set of chips, boards, or units.

[0173] The memory 1404 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device -readable and / or computerexecutable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 1402. The memory 1404 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 1402 and utilized by the network node 1400. The memory 1404 may be used to store any calculations made by the processing circuitry 1402 and / or any data received via the communication interface 1406. In some embodiments, the processing circuitry 1402 and memory 1404 is integrated.

[0174] The communication interface 1406 is used in wired or wireless communication of signaling and / or data with UEs, other network nodes, and / or any other network equipment. In the illustrated embodiment, communication interface 1406 comprises port(s) / terminal(s) 1416 to send and receive data, for example to and from a network over a wired connection. In particular embodiments, network node 1300 may be capable of wireless communication andcommunication interface 1406 may also include radio front-end circuitry 1418 that may be coupled to, or in certain embodiments a part of, an antenna 1410. Particular embodiments of radio front-end circuitry 1418 include filter(s) 1420 and amplifier(s) 1422. The radio front-end circuitry 1418 may be connected to an antenna 1410 and processing circuitry 1402. The radio front-end circuitry may be configured to condition signals communicated between antenna 1410 and processing circuitry 1402. The radio front-end circuitry 1418 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio frontend circuitry 1418 may convert the digital data into a radio signal(s) having the appropriate channel and bandwidth parameters using a combination of filters 1420 and / or amplifiers 1422. The radio signal(s) may then be transmitted via the antenna 1410. Similarly, when receiving data, the antenna 1410 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1418. The digital data may be passed to the processing circuitry 1402. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0175] In certain alternative embodiments, network node 1400 may be capable of wireless communication but does not include separate radio front-end circuitry 1418, instead, the processing circuitry 1402 includes radio front-end circuitry and is connected to the antenna 1410. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1412 is part of the communication interface 1406. In still other embodiments, the communication interface 1406 includes one or more ports or terminals 1416, the radio front-end circuitry 1418, and the RF transceiver circuitry 1412, as part of a radio unit (not shown), and the communication interface 1406 communicates with the baseband processing circuitry 1414, which is part of a digital unit (not shown).

[0176] The antenna 1410 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 1410 may be coupled to the radio frontend circuitry 1418 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 1410 is separate from the network node 1400 and connectable to the network node 1400 through one or more interfaces or ports.

[0177] The antenna 1410, communication interface 1406, and / or the processing circuitry 1402 may be configured to perform some or all of the receiving operations and / or obtaining operations described herein as being performed by the network node 1400. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 1410, the communication interface 1406, and / or theprocessing circuitry 1402 may be configured to perform some or all of the transmitting or sending operations described herein as being performed by the network node 1400. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0178] The power source 1408 provides power to the various components of network node 1400 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 1408 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1400 with power for performing the functionality described herein. For example, the network node 1400 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 1408. As a further example, the power source 1408 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0179] Embodiments of the network node 1400 may include additional components beyond those shown in Figure 14 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 1400 may include user interface equipment to allow input of information into the network node 1400 and to allow output of information from the network node 1400. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1400.

[0180] Figure 15 is a block diagram illustrating a virtualization environment 1500 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 1500 hosted by one or more of hardware nodes, such as a hardware computing device that operates as an access network node, UE, core network node, or host. Further, in embodiments in which a virtual node does not require radio connectivity(e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 1500 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an 0-2 interface.

[0181] Applications 1502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1500 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0182] Hardware 1504 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VM 1508A and VM 1508B (which may be collectively referred to as VMs 1508), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 1506 may present a virtual operating platform that appears like networking hardware to one or more of the VMs 1508.

[0183] The VMs 1508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by virtualization layer 1506. Different embodiments of the instance of a virtual appliance 1502 may be implemented on one or more of VMs 1508, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0184] In the context of NFV, each of the VMs 1508 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, nonvirtualized machine. Each of the VMs 1508, and that part of hardware 1504 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more of the VMs 1508 on top of the hardware 1504 and corresponds to an application 1502.

[0185] Hardware 1504 may be implemented in a standalone network node with generic or specific components. Hardware 1504 may implement some functions via virtualization.Alternatively, hardware 1504 may be part of a larger cluster of hardware (e.g., such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 1510, which, among others, oversees lifecycle management of applications 1502. In some embodiments, hardware 1504 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 1512 which may alternatively be used for communication between hardware nodes and radio units.

[0186] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0187] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate ordiscrete device -readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.EMBODIMENTS1. A method performed by a wireless device (QQ300) for collecting radio resource management, RRM, measurements, the method comprising:receiving (910), from a network node (QQ400), a RRM measurement configuration that includes a first set of measurement configurations and a second set of measurement configurations, wherein the RRM measurement configuration implicitly or explicitly indicates time requirements for the wireless device to perform a measurement on the first set and the second set of measurement configurations;collecting (920) one or more measurement samples at one or more collection times indicated by each of the first set of measurement configurations and the second set of measurement configurations;sending (930), to the network node, a RRM measurement report containing a first set of measurement data corresponding for the first set of measurement configurations and a second set of measurement data for the second set of measurement configurations, wherein the first set and the second set of measurement data comprises logged result data related to the one or more collected measurement samples.2. The method of embodiment 1, wherein the first set of measurement configurations indicates the first set of measurement data, which is to be collected as output and / or label data for artificial intelligence (Al) model training and performance monitoring, and the second set of measurement configurations indicates the second set of measurement data, which is to be collected as inference input data for Al model training.3. The method of any of embodiments 1-2, wherein the first set of measurement configurations and the second set of measurement configurations respectively indicate time requirements for the wireless device to collect the one or more measurement samples.4. The method of any of embodiments 1-3, wherein the one or more collection times include at least one fixed collection time point and / or at least one collection time window.5. The method of any of embodiments 1-4, wherein the at least one collection time window includes a first collection time window and a second collection time window, wherein the one or more measurement samples for the first set of measurement configurations are collected within the first collection time window and the one or more measurement samples for thesecond set of measurement configurations are collected within the second collection time window.6. The method of any of embodiments 1-5, wherein the one or more measurement samples are periodically collected over a respective at least one periodic collection time point and / or at least one periodic collection time window.7. The method of any of embodiments 1-6, wherein the first set of measurement data is collected i) after a specified amount of time after a start of the collection of the one or more measurement samples corresponding to the second set of measurement data, or ii) after a specified amount of time after an end of the collection of the one or more measurement samples corresponding to the second set of measurement data.8. The method of any of embodiments 1-7, wherein the one or more measurement samples corresponding to the first set of measurement data is collected on one or more first frequencies, and the one or more measurement samples corresponding to the second set of measurement data is collected on one or more second frequencies, wherein the one or more second frequencies differ from the one or more first frequencies.9. The method of any of embodiments 1-8, wherein the one or more measurement samples corresponding to the first set of measurement data is collected on one or more first cells or beams, and the one or more measurement samples corresponding to the second set of measurement data is collected on one or more second cells or beams, wherein the one or more second cells or beams differ from the one or more first cells or beams.10. The method of any of embodiments 1-9, wherein the one or more measurement samples corresponding to the first and second sets of measurement data are collected i) at a same time or ii) within a fixed time window starting from a specific point in time.11. The method of any of embodiments 1-10, wherein the one or more measurement samples corresponding to the first set of measurement data is collected within a predefined time window that starts at a predefined time period after a termination of the collection of the one or more measurement samples corresponding to the second set of measurement data.12. The method of any of embodiments 1-11, wherein the one or more measurement samples corresponding to the first set of measurement data is collected within a predefined time window that starts at a predefined time period after a start of the collection of the one or more measurement samples corresponding to the second set of measurement data.13. The method of any of embodiments 1-12, wherein the first set and the second set of measurement data is utilized by the network node to train one or more Al models, and at least the first set of measurement data is utilized by the network node to monitor the performance of the one or more Al models.14. A wireless device (QQ300) comprising:processing circuitry (QQ302); andmemory (QQ310) coupled with the processing circuitry, wherein the memory includes instructions that when executed by the processing circuitry causes the wireless device to perform operations comprising:receiving (910), from a network node (QQ400), a RRM measurement configuration that includes a first set of measurement configurations and a second set of measurement configurations, wherein the RRM measurement configuration implicitly or explicitly indicates time requirements for the wireless device to perform a measurement on the first set and the second set of measurement configurations;collecting (920) one or more measurement samples at one or more collection times indicated by each of the first set of measurement configurations and the second set of measurement configurations;sending (930), to the network node, a RRM measurement report containing a first set of measurement data corresponding for the first set of measurement configurations and a second set of measurement data for the second set of measurement configurations, wherein the first set and the second set of measurement data comprises logged result data related to the one or more collected measurement samples.15. The wireless device of embodiment 14, wherein the operations further comprise any of the operations of embodiments 2-13.16. A method performed by a network node (QQ400) for predicting radio resource management, RRM, measurements, the method comprising:sending (1010), to a wireless device (QQ300), a RRM measurement configuration that includes a first set of measurement configurations and a second set of measurement configurations, wherein the RRM measurement configuration implicitly or explicitly indicates time requirements for the wireless device to perform a measurement on the first set and the second set of measurement configurations;receiving (1020), from the wireless device, RRM measurement report that contains a first set of measurement data for the first set of measurement configurations and a second set of measurement data for the second set of measurement configurations, wherein the first set and the second set of measurement results comprises logged result data related to the one or more measurement samples collected by the wireless device; andutilizing (1030) at least one of the first set of measurement data and / or the second set of measurement data to conduct training of at least one artificial intelligence (Al) model configured to infer mobility measurements and / or events associated with the wireless device.17. The method of embodiment 16, further comprising utilizing at least one of the first set of measurement data and / or the second set of measurement data to monitor a performance of the at least one Al model.18. The method any of embodiments 16-17, wherein the one or more measurement samples are collected by the wireless device at one or more collection times indicated by each of the first set of measurement configurations and the second set of measurement configurations.19. The method of any of embodiments 16-18, wherein the first set of measurement configurations indicates the first set of measurement data, which is to be collected as output and / or label data for Al model training and performance monitoring, and the second set of measurement configurations indicates the second set of measurement data, which is to be collected as inference input data for Al model training.20. The method of any of embodiments 16-19, wherein the first set of measurement configurations and the second set of measurement configurations respectively indicate time requirements for the wireless device to collect the one or more measurement samples.21. The method of any of embodiments 16-20, wherein the one or more collection times include at least one fixed collection time point and / or at least one collection time window.22. The method of any of embodiments 16-21, wherein the at least one collection time window includes a first collection time window and a second collection time window, wherein the one or more measurement samples for the first set of measurement configurations are collected within the first collection time window and the one or more measurement samples for the second set of measurement configurations are collected within the second collection time window.23. A network node (QQ402) comprising:processing circuitry (QQ402); andmemory (QQ404) coupled with the processing circuitry, wherein the memory includes instructions that when executed by the processing circuitry causes the first network node to perform operations comprising:sending (1010), to a wireless device (QQ300), a RRM measurement configuration that includes a first set of measurement configurations and a second set of measurement configurations, wherein the RRM measurement configuration implicitly or explicitly indicates time requirements for the wireless device to perform a measurement on the first set and the second set of measurement configurations;receiving (1020), from the wireless device, RRM measurement report that contains a first set of measurement data for the first set of measurement configurations and a second set of measurement data for the second set of measurement configurations, wherein the first set and the second set of measurement results comprises logged result data related to the one or more measurement samples collected by the wireless device; andutilizing (1030) at least one of the first set of measurement data and / or the second set of measurement data to conduct training of at least one artificial intelligence (Al) model configured to infer mobility measurements and / or events associated with the wireless device.24. The network node of embodiment 23, wherein the operations further comprise any of the operations of embodiments 17-22.25. A non-transitory computer readable medium including program code to be executed by processing circuitry (QQ302) of a wireless device (QQ300) whereby execution of the program code causes the program code to perform operations comprising:receiving (910), from a network node, a RRM measurement configuration that includesa first set of measurement configurations and a second set of measurement configurations, wherein the RRM measurement configuration implicitly or explicitly indicates time requirements for the wireless device to perform a measurement on the first set and the second set of measurement configurations;collecting (920) one or more measurement samples at one or more collection times indicated by each of the first set of measurement configurations and the second set of measurement configurations;sending (930), to the network node, a RRM measurement report containing a first set of measurement data corresponding for the first set of measurement configurations and a second set of measurement data for the second set of measurement configurations, wherein the first set and the second set of measurement data comprises logged result data related to the one or more collected measurement samples.26. The non-transitory computer readable medium of embodiment 25, wherein the operations further comprise any of the operations of embodiments 2-13.27. A non-transitory computer readable medium including program code to be executed by processing circuitry (QQ402) of a network node (QQ402) whereby execution of the program code causes the program code to perform operations comprising:sending (1010), to a wireless device, a RRM measurement configuration that includes a first set of measurement configurations and a second set of measurement configurations, wherein the RRM measurement configuration implicitly or explicitly indicates time requirements for the wireless device to perform a measurement on the first set and the second set of measurement configurations;receiving (1020), from the wireless device, RRM measurement report that contains a first set of measurement data for the first set of measurement configurations and a second set of measurement data for the second set of measurement configurations, wherein the first set and the second set of measurement results comprises logged result data related to the one or more measurement samples collected by the wireless device; andutilizing (1030) at least one of the first set of measurement data and / or the second set of measurement data to conduct training of at least one artificial intelligence (Al) model configured to infer mobility measurements and / or events associated with the wireless device.28. The non-transitory computer readable medium of embodiment 27, wherein the operationsfurther comprise any of the operations of embodiments 17-22.

Claims

CLAIMS1. A method performed by a wireless device (1300) for collecting radio resource management, RRM, measurements, the method comprising:receiving (910), from a network node (1400), a RRM measurement configuration that includes a first set of measurement configurations and a second set of measurement configurations, wherein the RRM measurement configuration implicitly or explicitly indicates time requirements for the wireless device to perform a measurement on the first set and the second set of measurement configurations;collecting (920) one or more measurement samples at one or more collection times indicated by each of the first set of measurement configurations and the second set of measurement configurations;sending (930), to the network node, a RRM measurement report comprising a first set of measurement data corresponding for the first set of measurement configurations and a second set of measurement data for the second set of measurement configurations, wherein the first set and the second set of measurement data comprises logged result data related to the one or more collected measurement samples.

2. The method of claim 1, wherein the first set of measurement configurations indicates the first set of measurement data, which is to be collected as output and / or label data for artificial intelligence (Al) model training and performance monitoring, and the second set of measurement configurations indicates the second set of measurement data, which is to be collected as inference input data for Al model training.

3. The method of any of claims 1-2, wherein the first set of measurement configurations and the second set of measurement configurations respectively indicate time requirements for the wireless device to collect the one or more measurement samples.

4. The method of any of claims 1-3, wherein the one or more collection times include at least one fixed collection time point and / or at least one collection time window.

5. The method of any of claims 1-4, wherein the at least one collection time window includes a first collection time window and a second collection time window, wherein the one or more measurement samples for the first set of measurement configurations are collected within the first collection time window and the one or more measurement samples for thesecond set of measurement configurations are collected within the second collection time window.

6. The method of any of claims 1-5, wherein the one or more measurement samples are periodically collected over a respective at least one periodic collection time point and / or at least one periodic collection time window.

7. The method of any of claims 1-6, wherein the first set of measurement data is collected i) after a specified amount of time after a start of the collection of the one or more measurement samples corresponding to the second set of measurement data, or ii) after a specified amount of time after an end of the collection of the one or more measurement samples corresponding to the second set of measurement data.

8. The method of any of claims 1-7, wherein the one or more measurement samples corresponding to the first set of measurement data is collected on one or more first frequencies, and the one or more measurement samples corresponding to the second set of measurement data is collected on one or more second frequencies, wherein the one or more second frequencies differ from the one or more first frequencies.

9. The method of any of claims 1-8, wherein the one or more measurement samples corresponding to the first set of measurement data is collected on one or more first cells or beams, and the one or more measurement samples corresponding to the second set of measurement data is collected on one or more second cells or beams, wherein the one or more second cells or beams differ from the one or more first cells or beams.

10. The method of any of claims 1-9, wherein the one or more measurement samples corresponding to the first and second sets of measurement data are collected i) at a same time or ii) within a fixed time window starting from a specific point in time.

11. The method of any of claims 1-10, wherein the one or more measurement samples corresponding to the first set of measurement data is collected within a predefined time window that starts at a predefined time period after a termination of the collection of the one or more measurement samples corresponding to the second set of measurement data.

12. The method of any of claims 1-11, wherein the one or more measurement samples corresponding to the first set of measurement data is collected within a predefined time window that starts at a predefined time period after a start of the collection of the one or more measurement samples corresponding to the second set of measurement data.

13. The method of any of claims 1-12, wherein the first set and the second set of measurement data is utilized by the network node to train one or more Al models, and at least the first set of measurement data is utilized by the network node to monitor the performance of the one or more Al models.

14. A wireless device (1300) comprising:processing circuitry (1302); andmemory (1310) coupled with the processing circuitry, wherein the memory includes instructions that when executed by the processing circuitry causes the wireless device to perform operations comprising:receiving (910), from a network node (1400), a RRM measurement configuration that includes a first set of measurement configurations and a second set of measurement configurations, wherein the RRM measurement configuration implicitly or explicitly indicates time requirements for the wireless device to perform a measurement on the first set and the second set of measurement configurations;collecting (920) one or more measurement samples at one or more collection times indicated by each of the first set of measurement configurations and the second set of measurement configurations;sending (930), to the network node, a RRM measurement report comprising a first set of measurement data corresponding for the first set of measurement configurations and a second set of measurement data for the second set of measurement configurations, wherein the first set and the second set of measurement data comprises logged result data related to the one or more collected measurement samples.

15. The wireless device of claim 14, wherein the operations further comprise any of the operations of claims 2-13.

16. A method performed by a network node (1400) for predicting radio resource management, RRM, measurements, the method comprising:sending (1010), to a wireless device (1300), a RRM measurement configuration that includes a first set of measurement configurations and a second set of measurement configurations, wherein the RRM measurement configuration implicitly or explicitly indicates time requirements for the wireless device to perform a measurement on the first set and the second set of measurement configurations;receiving (1020), from the wireless device, RRM measurement report that comprises a first set of measurement data for the first set of measurement configurations and a second set of measurement data for the second set of measurement configurations, wherein the first set and the second set of measurement results comprises logged result data related to the one or more measurement samples collected by the wireless device; andutilizing (1030) at least one of the first set of measurement data and / or the second set of measurement data to conduct training of at least one artificial intelligence (Al) model configured to infer mobility measurements and / or events associated with the wireless device.

17. The method of claim 16, further comprising utilizing at least one of the first set of measurement data and / or the second set of measurement data to monitor a performance of the at least one Al model.

18. The method any of claims 16-17, wherein the one or more measurement samples are collected by the wireless device at one or more collection times indicated by each of the first set of measurement configurations and the second set of measurement configurations.

19. The method of any of claims 16-18, wherein the first set of measurement configurations indicates the first set of measurement data, which is to be collected as output and / or label data for Al model training and performance monitoring, and the second set of measurement configurations indicates the second set of measurement data, which is to be collected as inference input data for Al model training.

20. The method of any of claims 16-19, wherein the first set of measurement configurations and the second set of measurement configurations respectively indicate time requirements for the wireless device to collect the one or more measurement samples.

21. The method of any of claims 16-20, wherein the one or more collection times include at least one fixed collection time point and / or at least one collection time window.

22. The method of any of claims 16-21, wherein the at least one collection time window includes a first collection time window and a second collection time window, wherein the one or more measurement samples for the first set of measurement configurations are collected within the first collection time window and the one or more measurement samples for the second set of measurement configurations are collected within the second collection time window.

23. A network node (1400) comprising:processing circuitry (1402); andmemory (1404) coupled with the processing circuitry, wherein the memory includes instructions that when executed by the processing circuitry causes the network node to perform operations comprising:sending (1010), to a wireless device (1300), a RRM measurement configuration that includes a first set of measurement configurations and a second set of measurement configurations, wherein the RRM measurement configuration implicitly or explicitly indicates time requirements for the wireless device to perform a measurement on the first set and the second set of measurement configurations;receiving (1020), from the wireless device, RRM measurement report that comprises a first set of measurement data for the first set of measurement configurations and a second set of measurement data for the second set of measurement configurations, wherein the first set and the second set of measurement results comprises logged result data related to the one or more measurement samples collected by the wireless device; andutilizing (1030) at least one of the first set of measurement data and / or the second set of measurement data to conduct training of at least one artificial intelligence (Al) model configured to infer mobility measurements and / or events associated with the wireless device.

24. The network node of claim 23, wherein the operations further comprise any of the operations of claims 17-22.

25. A non-transitory computer readable medium including program code to be executed by processing circuitry (1302) of a wireless device (1300) whereby execution of the program code causes the program code to perform operations comprising:receiving (910), from a network node, a RRM measurement configuration that includesa first set of measurement configurations and a second set of measurement configurations, wherein the RRM measurement configuration implicitly or explicitly indicates time requirements for the wireless device to perform a measurement on the first set and the second set of measurement configurations;collecting (920) one or more measurement samples at one or more collection times indicated by each of the first set of measurement configurations and the second set of measurement configurations;sending (930), to the network node, a RRM measurement report comprising a first set of measurement data corresponding for the first set of measurement configurations and a second set of measurement data for the second set of measurement configurations, wherein the first set and the second set of measurement data comprises logged result data related to the one or more collected measurement samples.

26. The non-transitory computer readable medium of claim 25, wherein the operations further comprise any of the operations of claims 2-13.

27. A non-transitory computer readable medium including program code to be executed by processing circuitry (1402) of a network node (1400) whereby execution of the program code causes the program code to perform operations comprising:sending (1010), to a wireless device, a RRM measurement configuration that includes a first set of measurement configurations and a second set of measurement configurations, wherein the RRM measurement configuration implicitly or explicitly indicates time requirements for the wireless device to perform a measurement on the first set and the second set of measurement configurations;receiving (1020), from the wireless device, RRM measurement report that comprises a first set of measurement data for the first set of measurement configurations and a second set of measurement data for the second set of measurement configurations, wherein the first set and the second set of measurement results comprises logged result data related to the one or more measurement samples collected by the wireless device; andutilizing (1030) at least one of the first set of measurement data and / or the second set of measurement data to conduct training of at least one artificial intelligence (Al) model configured to infer mobility measurements and / or events associated with the wireless device.

28. The non-transitory computer readable medium of claim 27, wherein the operations furthercomprise any of the operations of claims 17-22.

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