Early measurement predictions

Abstract

Systems and methods related to early measurement predictions are disclosed. In one embodiment, a method performed by a wireless device, the method comprises receiving, from a first network node, information that configures the wireless device to perform measurement predictions based on measurements performed by the wireless device while in an idle state 5 and / or while in an inactive state. The method further comprises performing one or more measurement predictions for one or more frequencies and / or one or more cells based on measurements performed by the wireless device while in the idle state and / or while in the inactive state, in accordance with the configuration, wherein the one or more measurement predictions comprise one or more frequency domain measurement predictions and / or one or 0 more spatial domain measurement predictions. The method further comprises transmitting at least a subset of the one or more measurement predictions to the first network node or another network node.

Description

[0001] EARLY MEASUREMENT PREDICTIONS

[0002] RELATED APPLICATIONS

[0003] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 721,142, filed November 15, 2024, the disclosure of which is hereby incorporated herein by reference in its entirety.

[0004] TECHNICAL FIELD

[0005] The present disclosure relates to a wireless communications system and, more specifically, to measurement predictions in a wireless communications system.

[0006] BACKGROUND

[0007] Release- 16 Early Measurements

[0008] In Release (Rel-) 16 of the 3rdGeneration Partnership Project (3GPP) specifications, early measurements were standardized. The User Equipment (UE) can be configured to perform measurements in Radio Resource Control (RRC) Idle state (RRC_IDLE) / RRC Inactive state (RRC INACTIVE) and report the measurement results when transferring to RRC Connected state (RRC CONNECTED). The network can use the measurement results to, e.g., decide which carrier to set up for Carrier Aggregation (CA) or Dual Connectivity (DC).

[0009] The measurements are configured, at least partly, through dedicated signaling (RRC message RRCRelease). The dedicated configuration includes a timer (timer T331, set within measIdleDuration-r 16) for how long the UE is required to perform the measurements, and it may include information about the frequency / -ies on which the UE should perform the measurements. The timer T331 is started by the UE at reception of the configuration. The UE may optionally continue to perform measurements also after the timer T331 has expired.

[0010] Information about early measurement configuration can also be broadcasted in System Information Block (SIB) 11 (SIB11) where this broadcasted information contains e.g. information about the frequency on which the UE should perform the measurements. If the dedicated signaling does not include information about frequencies on which the UE is to perform the measurements, the UE performs the measurements according to the broadcasted configuration in the cell where the UE is currently located. If the UE continues performing the measurements in RRC IDLE / RRC INACTIVE even after T331 has expired (or has been stopped), the UE performs the measurements on the frequency / -ies that is included in the broadcasted configuration (in SIB11) for the cell. When the UE resumes or is being setup to RRC_CONNECTED again, the UE can transmit, in RRCResumeComplete or RRCSetupComplete , an indication that it has measurement results. In RRCResumeComplete, the UE may also transmit the measurement results directly, if the network has requested this in the RRCResume message.

[0011] Figure 1 illustrates the procedure for early measurements. As illustrated, the UE receives an RRCRelease from the network (i.e., from the gNodeB (gNB)), where the RRCRelease includes a configuration for early measurements in the MeasIdleConfig Information Element (IE). The UE responds to the gNB with an RRCReleaseComplete. While in RRC IDLE and RRC INACTIVE, the UE performs measurements in accordance with the received configuration. Optionally, for a UE-triggered RRC resume, the UE transmits an RRCResumeRequest to the gNB. The gNB sends an RRCResume to the UE. The UE responds with an RRCResumeComplete including an indication of a measurement result or the measurement result.

[0012] Release- 18 Early Measurements Enhancements

[0013] In Rel-18, Radio Access Network (RAN) Working Group 2 (RAN2) and RAN Working Group 4 (RAN4) have worked on enhancing the Rel-16 early measurements. For those Rel-16 early measurements, it was agreed that a validity timer X for the measurements can be configured. If the validity timer X is configured, the UE will only transmit measurement results that are newer than the configured timer value. The timer value can be configured either in the RRCRelease message or in SIB11. The UE can report an indication of which validity timer that was applied to the target cell as the cell where the UE resumes may not have information of a configuration made by the cell where the UE was transferred to RRC IDLE / RRC INACTIVE.

[0014] It was also specified that cell reselection measurements can be reported to the network. The UE performs cell reselection measurements when in RRC IDLE / RRC INACTIVE for the purpose of mobility in RRC IDLE / RRC INACTIVE. Reporting these measurement results to the network does not cause any extra overhead for the UE in terms of performing measurements. The network can configure which frequencies the UE should report measurement results for, as is possible also for the rel-16 measurements.

[0015] It is possible for the network to configure a validity timer X also for the cell reselection measurements, in the same way as for the enhanced rel-16 measurements, i.e. in RRCRelease and in SIB11. The UE can report an indication of which validity timer was applied for the reported cell reselection measurements to the target cell. Rel-19: AI / ML for Mobility Study Item

[0016] In Rel-19, a study item for Artificial Intelligence (AI) / Machine Learning (ML) for mobility was agreed with the following objectives.

[0017] The study will focus on mobility enhancement in RRC CONNECTED mode over air interface by following existing mobility framework, i.e., handover decision is always made on the network side. Mobility use cases focus on standalone New Radio (NR) Primary Cell (PCell) change. UE-side and network-side AI / ML model can be both considered, respectively.

[0018] Study and evaluate potential benefits and gains of AI / ML aided mobility for network triggered layer 3 (L3)-based handover, considering the following aspects:

[0019] • AI / ML based Radio Resource Monitoring (RRM) measurement and event prediction, • Cell-level measurement prediction including intra and inter-frequency (UE sided and network (NW) sided model) [RAN2]

[0020] • Inter-cell Beam-level measurement prediction for L3 Mobility (UE sided and NW sided model) [RAN2]

[0021] • Handover (HO) failure / Radio Link Failure (RLF) prediction (UE sided model) [RAN2]

[0022] • Measurement events prediction (UE sided model) [RAN2]

[0023] • Study the need / benefits of any other UE assistance information for the network side model [RAN2]

[0024] • The evaluation of the AI / ML aided mobility benefits should consider HO performance Key Performance Indicators (KPIs) (e.g., Ping-pong HO, HO Failure (HOF) / RLF, Time of stay, Handover interruption, prediction accuracy, and measurement reduction) etc.) and complexity tradeoffs [RAN2]

[0025] • NOTE: Simulation assumption and methodology can leverage 3GPP Technical Report (TR) 38.901, 38.843 and 36.839. And leave the detailed discussion to RAN2.

[0026] • Potential Al mobility specific enhancement should be based on the Rel-19 AI / ML-air interface work item description (WID) general framework (e.g. Life Cycle Management (LCM), performance monitoring, etc.) [RAN2]

[0027] • NOTE: This would only be treated after sufficient progress is made in the Rel-19 AI / ML air interface WID

[0028] • Potential specification impacts of AI / ML aided mobility [RAN2]

[0029] • Evaluate testability, interoperability, and impacts on RRM requirements and performance [RAN4] In Rel-20, the work on AI / ML mobility is likely to continue and the scope will be decided in RAN# 106 in December 2024.

[0030] SUMMARY

[0031] Systems and methods are disclosed herein that relate to early measurement predictions. In one embodiment, a method performed by a wireless device, the method comprises receiving, from a first network node, information that configures the wireless device to perform measurement predictions based on measurements performed by the wireless device while in an idle state and / or while in an inactive state. The method further comprises performing one or more measurement predictions for one or more frequencies and / or one or more cells based on measurements performed by the wireless device while in the idle state and / or while in the inactive state, in accordance with the configuration, wherein the one or more measurement predictions comprise one or more frequency domain measurement predictions and / or one or more spatial domain measurement predictions. The method further comprises transmitting at least a subset of the one or more measurement predictions to the first network node or another network node. By performing and reporting measurement predictions, rather than actual measurements, less power is consumed by the wireless device while in the idle state and / or inactivate state while at the same time providing the ability for the wireless device to provide recent measurement (predictions) upon setting up or resuming a connection.

[0032] In one embodiment, transmitting the at least a subset of the one or more measurement predictions comprises transmitting the at least a subset of the one or more measurement predictions upon resuming a connection of the wireless device or upon setup on a connection of the wireless device.

[0033] In one embodiment, transmitting the at least a subset of the one or more measurement predictions comprises transmitting the at least a subset of the one or more measurement predictions in response to a request from the first network node or another network node, the request being responsive to an indication, by the wireless device, that the measurement predictions are available.

[0034] In one embodiment, the one or more measurement predictions are one or more Artificial Intelligence (AI) / Machine Learning (ML) based measurement predictions.

[0035] In one embodiment, the one or more measurement predictions comprise one or more frequency domain measurement predictions. In one embodiment, the one or more frequency domain measurement predictions comprise one or more predicted beam level measurements of a cell in a second frequency based on actual beam level measurements in a first frequency. In one embodiment, the one or more frequency domain measurement predictions comprise one or more predicted cell level measurements in a second frequency based on actual beam level measurements in a first frequency. In one embodiment, the one or more frequency domain measurement predictions comprise one or more predicted cell level measurements in a second frequency based on actual cell level measurements in a first frequency.

[0036] In one embodiment, the one or more measurement predictions comprise one or more spatial domain measurement predictions. In one embodiment, the one or more spatial domain measurement predictions comprise one or more predicted beam level measurements of a cell in a certain frequency based on actual beam level measurements in the same certain frequency. In one embodiment, the one or more spatial domain measurement predictions comprise one or more predicted cell level measurements in a certain frequency based on actual beam level measurements in the same certain frequency. In one embodiment, the one or more spatial domain measurement predictions comprise one or more predicted cell level measurements in a certain frequency based on actual cell level measurements in the same certain frequency.

[0037] In one embodiment, the one or more measurement predictions comprise one or more cell and / or beam level predicted measurement values.

[0038] In one embodiment, the one or more measurement predictions comprise probability values each representing a probability of a corresponding cell or beam in an associated frequency being a best beam or cell for the wireless device.

[0039] In one embodiment, the one or more measurement predictions comprise one or more best beams for the wireless device.

[0040] In one embodiment, the one or more measurement predictions comprise one or more best beams for the wireless device and associated predicted measurement values.

[0041] In one embodiment, the method further comprises transmitting, to the first network node, capability information related to one or more capabilities of the wireless device related to measurement predictions based on measurements performed by the wireless device while in the idle state and / or while in the inactive state.

[0042] In one embodiment, the method further comprises, transmitting, to the first network node, capability information of the wireless device, the capability information comprising any one or more of the following: wireless device capabilities related to the ability to perform measurement predictions in idle and / or inactive state indicated per frequency, per frequency band, per frequency band combination, or per frequency range; a measurement quantity of early measurement prediction of which the wireless device is capable; a type of measurement prediction of which the wireless device is capable; a maximum amount of measurement predictions of which the wireless is capable; a maximum amount of measurement predictions and measurements the wireless device is capable of totally performing while in the idle state and / or while in the inactive state.

[0043] In one embodiment, the method further comprises transmitting, to the first network node, wireless device assistance information related to measurement predictions based on measurements performed by the wireless device while in the idle state and / or while in the inactive state.

[0044] In one embodiment, the method further comprises transmitting, to the first network node, wireless device assistance information, the wireless device assistance information comprising any one or more of the following: a wireless device speed range in which an associated AI / ML model used to generate the one or more measurement predictions is applicable; mobility scenario categories and / or classes; mobility scenario categories and / or classes for which the AI / ML model used to generate the one or more measurement predictions is applicable; a radio condition in which the AI / ML model used to generate the one or more measurement predictions is applicable; information that indicates an area where measurement predictions based on measurements performed by the wireless device while in the idle state and / or while in the inactive state are applicable.

[0045] In one embodiment, the method further comprises transmitting, to the first network node, wireless device assistance information, the wireless device assistance information comprising any one or more of the following: preferred frequencies for performing measurement predictions based on measurements performed by the wireless device while in the idle state and / or while in the inactive state; preferred number of frequencies for which the measurement predictions are made; estimated time for performing Radio Resource Monitoring, RRM, measurement prediction of a certain frequency; preferred predicted measurement quantity.

[0046] In one embodiment, the received configuration comprises any one or more of the following: an identity of the configuration; a list of carriers or frequencies or frequency band for which the wireless device is to perform measurement predictions; a list of cells for which the wireless device is to perform measurement predictions; an indication per carrier or per cell, indicating that the wireless device is to perform predictions for that frequency or cell; an indication per carrier or per cell, indicating whether the wireless device is to perform measurements or predictions for that frequency or cell; multiple lists of carriers or frequencies or cells; a type of measurement prediction the wireless device is to perform; a measurement quantity of the prediction; an indication, indicating that the wireless device is allowed to report predicted measurement results instead of actual measurement result; an indication that the wireless device is to report both actual measurement results and predicted measurement results; an accuracy threshold or an uncertainty level based on which the wireless device is requested to report the measurement prediction(s); a priority order for the measurement predictions; an area where the wireless device is to perform the measurement predictions; an indication of a time for how old the reported predictions may be; a quality threshold for including measurement predictions of neighboring cells; an indication to report a cause in case the wireless device failed to provide the predictions; an indication for the wireless device to report a cause why a measurement prediction is reported instead of an actual measurement.

[0047] In one embodiment, the wireless device receives the configuration via dedicated signaling, broadcast system information, or a combination thereof.

[0048] In one embodiment, transmitting the at least a subset of the one or more predictions to the first network node or another network node comprises transmitting a report to the fist network node or another network node, the report comprising the at least a subset of the one or more predictions. In one embodiment, the report comprises any one or more of the following: an identity of the configuration associated with the report; prediction results for measurement prediction quantities, as indicated in the configuration; prediction results for measurement prediction quantities, as indicated in the configuration, indicated in priority order; an indication of an accuracy of the measurement predictions; an indication that the measurement predictions are predictions, rather than actual measurements; amount of predicted samples and an amount of measured samples; a time stamp of when the measurement predictions were made or an indication of how old the measurement predictions are; an indication of a length of a prediction window that was used; a cause value indicating a reason for why certain predictions were not performed, or why measurement predictions were reported instead of actual measurements.

[0049] Corresponding embodiments of a wireless device are also disclosed. In one embodiment, a wireless device adapted to is receive, from a first network node, information that configures the wireless device to perform measurement predictions based on measurements performed by the wireless device while in an idle state and / or while in an inactive state. The wireless device is further adapted to perform one or more measurement predictions for one or more frequencies and / or one or more cells based on measurements performed by the wireless device while in the idle state and / or while in the inactive state, in accordance with the configuration, wherein the one or more measurement predictions comprise one or more frequency domain measurement predictions and / or one or more spatial domain measurement predictions. The wireless device is further adapted to transmit at least a subset of the one or more measurement predictions to the first network node or another network node.

[0050] In one embodiment, a wireless device comprises a communication interface comprising a transmitter and a receiver. The wireless device further comprises processing circuitry associated with the communication interface, the processing circuitry configured to cause the wireless device to receive, from a first network node, information that configures the wireless device to perform measurement predictions based on measurements performed by the wireless device while in an idle state and / or while in an inactive state and perform one or more measurement predictions for one or more frequencies and / or one or more cells based on measurements performed by the wireless device while in the idle state and / or while in the inactive state, in accordance with the configuration, wherein the one or more measurement predictions comprise one or more frequency domain measurement predictions and / or one or more spatial domain measurement predictions. The processing circuitry is further configured to cause the wireless device to transmit at least a subset of the one or more measurement predictions to the first network node or another network node.

[0051] Embodiments of a method performed by a network node are also disclosed. In one embodiment, a method performed by a network node comprises transmitting, to a wireless device, information that configures the wireless device to perform measurement predictions, the measurement predictions comprising one or more frequency domain measurement predictions and / or one or more spatial domain measurement predictions based on measurements performed by the wireless device while in the idle state and / or while in the inactive state.

[0052] Corresponding embodiments of a network node are also disclosed. In one embodiment, a network node is adapted to transmit, to a wireless device, information that configures the wireless device to perform measurement predictions, the measurement predictions comprising one or more frequency domain measurement predictions and / or one or more spatial domain measurement predictions based on measurements performed by the wireless device while in the idle state and / or while in the inactive state.

[0053] In one embodiment, a network node comprises processing circuitry configured to causes the network node to transmit, to a wireless device, information that configures the wireless device to perform measurement predictions, the measurement predictions comprising one or more frequency domain measurement predictions and / or one or more spatial domain measurement predictions based on measurements performed by the wireless device while in the idle state and / or while in the inactive state.

[0054] BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain the principles of the disclosure. Figure 1 illustrates the procedure for early measurements in 3rdGeneration Partnership Project (3GPP) New Radio (NR).

[0056] Figure 2 illustrates an example in which an Artificial Intelligence (AI) / Machine Learning (ML) model uses beam level measurements from beams in Set B (beams denoted by the circles with diagonal hatching) in a cell operating in frequency Fl as input to predict another set of beams referred to as set A (beams denoted by white circles) served by cell Y operating in frequency F2, in accordance with an embodiment of the present disclosure.

[0057] Figure 3 illustrates an example in which an AI / ML model uses beam level measurements from beams in Set B (beams denoted by circles with diagonal hatching) in a cell operating in frequency Fl as input to predict a Top-K beams also referred to as set A (beams denoted by circles with cross-hatching), in accordance with an embodiment of the present disclosure.

[0058] Figure 4 illustrates that, in one sub-option, cell prediction(s) for a Set A of one or more cells operating in target frequency F2 is inferred based on measurements of a Set B of beams served by one or more cells in frequency Fl.

[0059] Figure 5 illustrates that, in one sub-option, the Top-K (here K is equal to 1 i.e., the best cell in target frequency F2) cells in a Set A of one or more cell operating in target frequency F2 is inferred based on beam level measurements of a Set B of beams served by one or more cells in frequency FL

[0060] Figure 6 illustrates that, in one sub-option, the cell prediction(s) for a Set A of one or more cells operating in target frequency F2 is inferred based on measurements of a Set B of cells operating in frequency FL

[0061] Figure 7 illustrates that, in one sub-option, Top-K (here K is equal to 1 i.e., the best cell in target frequency F2) Top K cells in a Set A operating in target frequency F2 are inferred based on beam level measurements of a Set B of cells operating in frequency Fl.

[0062] Figure 8 illustrates an example in which an AI / ML model uses beam level measurements from beams in Set B (beams denoted by circles having diagonal hatching) in a cell operating in a certain frequency as input to predict another set of beams also referred to as set A (beams denoted by white circles) served by the cell Y operating in the same frequency, in accordance with an embodiment of the present disclosure.

[0063] Figure 9 illustrates an example in which an AI / ML model uses beam level measurements from beams in Set B (beams denoted by circles with diagonal hatching) in a cell operating in a certain frequency as input to predict the Top-K beams also referred to as set A (beams denoted by circles with cross-hatching), in accordance with an embodiment of the present disclosure. Figure 10 illustrates that, in one sub-option, the cell predict! on(s) for a Set A of one or more cells is inferred based on measurements of a Set B of beams served by one or more cells in the same frequency.

[0064] Figure 11 illustrates that, in one sub-option, Top-K (here K is equal to 1 i.e., the best cell) cells in a Set A of one or more cells is inferred based on beam level measurements of a Set B of beams served by one or more cells in the same frequency.

[0065] Figure 12 illustrates that, in one sub-option, the cell predict! on(s) for a Set A of one or more cells is inferred based on measurements of a Set B of cells operating in the same frequency.

[0066] Figure 13 illustrates that, in one sub-option, Top-K (here K is equal to 1 i.e., the best cell) Top K cells in a Set A are inferred based on beam level measurements of a Set B of cells.

[0067] Figure 14 illustrates examples of Neural Networks used for designing AI / ML models for spatial / frequency domain beam prediction, in accordance with an exemplary embodiment of the present disclosure.

[0068] Figure 15 illustrates examples of Neural Networks used for designing AI / ML models for spatial / frequency domain cell prediction, in accordance with an embodiment of the present disclosure.

[0069] Figure 16 illustrates an example procedure for configuration and reporting of early predictions in RRC IDLE / RRC INACTIVE, in accordance with example embodiments of the present disclosure.

[0070] Figure 17 shows an example of a communication system in accordance with some embodiments.

[0071] Figure 18 is another example of a communication system according to some embodiments. Figure 19 shows a wireless device, which may be configured to operate in communication system of Figure 17 or in communication system of Figure 18.

[0072] Figure 20 shows a network node in accordance with some embodiments.

[0073] Figure 21 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized.

[0074] DETAILED DESCRIPTION

[0075] The embodiments set forth below represent information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure.

[0076] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0077] There currently exist certain challenge(s). One problem with early measurements is that it consumes additional battery in the User Equipment (UE) to perform additional measurements e.g. on carriers / frequencies not configured for inter-frequency cell reselection and / or measurements with CONNECTED mode requirements (or requirements more strict than IDLE mode measurement requirements), possibly requiring more samples and / or shorter measurement periods. When the UE is in RRC IDLE / RRC INACTIVE, the UE would like to perform as few actions as possible in order to save battery, and this is not possible if the UE needs to perform additional measurements which are only necessary for operations in RRC CONNECTED. In addition, it is usually not known when the UE will resume and the measurement results will actually be needed. As a result, a lot of the measurements may be performed unnecessarily. Also, the source node may not know which frequencies are relevant for the target node to receive measurement results. So, the UE may perform measurements for irrelevant frequencies.

[0078] In addition, the network would like to have results of recently performed measurements. The UE may have been in RRC IDLE / RRC INACTIVE for a long time when it is time to resume, and therefore the measurement results may be too old. If configured, the UE can report cell reselection measurement results, but the measurement results may not be for the frequency that the network is interested in setting up for Carrier Aggregation (CA) or Dual Connectivity (DC). An issue in existing network is that the setting up of CA / DC may occur too late when the need for the CA / DC has already passed, as the traffic may be bursty.

[0079] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. Embodiments of a method performed by a wireless terminal (i.e., a UE) in RRC IDLE or RRC Inactive state are disclosed. In one embodiment, the method performed by the wireless terminal in RRC IDLE or RRC Inactive state comprises:

[0080] • Receiving an early prediction configuration from a network node, e.g. upon transitioning to RRC IDLE or RRC INACTIVE state or in a reconfiguration message or broadcasted in system information broadcast and storing the received early indication.

[0081] • Performing Artificial intelligence (AI) / Machine Learning (ML) based predictions for one or more frequencies / cells (e.g. predicted Reference Signal Received Power (RSRP) value(s) of the frequency / cell), based on the measurements (and / or additional assistance information) performed in RRC IDLE or RRC_ INACTIVE states wherein the predictions can be one or more of frequency domain or spatial domain.

[0082] • Reporting the predictions to the network either as part of early measurement report or as part of a new report.

[0083] Embodiments of a method performed by a UE configured to communicate with a radio access network (RAN) are also disclosed. In one embodiment, the method performed by the UE configured to communication with the RAN comprises one or more of the following:

[0084] • receiving a configuration from the RAN, wherein the configuration is associated with a measurement configuration for performing AI / ML based measurement predictions in RRC IDLE / RRC INACTIVE;

[0085] • performing frequency domain measurement predictions of a quality of an indicated frequency (ies) (e.g., one or more frequencies indicated in the received configuration); • performing spatial domain predictions of a quality of one or more of a set (e.g., a subset of the set) of indicated / non-indicated cells (e.g., indicated or not indicated in the received configuration) in one or more certain frequencies (e.g., all or a subset of the one or more indicated frequencies).

[0086] • sending early measurement prediction results (e.g., including all or a subset of the frequency domain measurement predictions and / or all or a subset of the spatial domain predictions, or information derived therefrom) to the RAN when or after resuming to RRC CONNECTED.

[0087] Certain embodiments may provide one or more of the following technical advantage(s). An advantage of the proposed solution is that performing predictions consumes less battery in the UE than performing actual measurements. With the proposed solution, the UE can, for example, more continuously perform predictions with less battery consumption and thereby send recently performed prediction results to the network when setting up or resuming to RRC CONNECTED. The network can then more quickly and more accurately set up carrier aggregation or dual connectivity. Alternatively, the UE could perform the same amount of measurements but report prediction information of additional carriers and / or cells in a carrier, increasing the understanding in the network.

[0088] Now, a description of further details regarding embodiments of the solution(s) disclosed herein will be provided.

[0089] In the present disclosure, the “predictions” refer to any AI / ML based predictions, namely the results of the inference of the AI / ML engine, that is performed based on the measurements (and / or some additional information), or any non-AL / ML based predictions performed in RRC IDLE or RRC INACTIVE state, and reported to the network, either beside (e.g., with or as part of) the Early measurements or as a separate report.

[0090] Embodiments are disclosed herein related to frequency domain predictions. In this regard, embodiments of solutions are described related to a UE receiving a prediction configuration, and the UE, upon resuming or setting up an RRC connection (i.e., transition from RRC IDLE / INACTIVE to RRC CONNECTED state), transmitting a report to the network including a set of frequency domain predictions for one or more cells per one or more frequencies and / or actual measurements, wherein the frequency domain predictions are conducted based on one or more of the following non-limiting example models:

[0091] 1. Beam level measurements in frequency Fl is the input of the model, and predicted beam level measurements of a cell in F2 is the output of the model. Cell level prediction is derived based on the beam level predictions.

[0092] 2. Beam level measurements in a frequency Fl are the input of the model, and predicted cell level measurements in frequency F2 are the output of the model.

[0093] 3. Cell level measurements in a frequency Fl are the input of the model, and predicted cell level measurements in frequency F2 is the output of the model.

[0094] The above examples are described in the following.

[0095] 1- A Frequency-domain Downlink (DL) beam prediction for a Set of beams

[0096] In one option, the frequency-domain DL beam prediction for a Set of beams is based on measurement results of another set of beams in one or more of other frequency (es).

[0097] Figure 2 illustrates an example of the AI / ML model using the beam level measurements (also referred to as Beam-level RSRP (BRSRP) / Beam-level Reference Signal Received Quality (BRSRQ) / Beam-level Signal to Interference plus Noise Ratio (BSINR)) from beams in Set B (beams denoted by the circles with diagonal hatching) in a cell operating in frequency Fl as input, predicts another set of beams also referred to as set A (beams denoted by white circles) served by the cell Y operating in frequency F2. The output of the AI / ML model could be predicted beam identifiers (IDs) with or without predicted measurement quality values. If the output includes the beam level measurement prediction also referred to as predicted BRSRP (pBRSRP), predicted BRSRQ (pBRSRQ), predicted BSINR (pBSINR), the UE may use the predicted beam level measurement prediction as input to the cell quality derivation procedure to derive the predicted cell quality (CQD) to report to the network.

[0098] Figure 3 illustrates an example of the AI / ML model using the beam level measurements (also referred to as BRSRP / BRSRQ / BSINR) from beams in Set B (beams denoted by circles with diagonal hatching) in a cell operating in frequency Fl as input, predicts Top-K beams also referred to as set A (beams denoted by circles with cross-hatching). The output of the AI / ML model could be predicted beam IDs with or without predicted measurement quality values. If the output includes the beam level measurement prediction also referred to as pBRSRP, pBRSRQ, pBSINR, the UE may use the predicted beam level measurement as input to the cell quality derivation procedure to derive the predicted cell quality to report to the network. In another embodiment the UE includes beam level measurement prediction in the report sent to the network e.g., if requested by the network.

[0099] In one option, to derive one or more Frequency domain DL beam prediction(s) as an output of an AI / ML model, the AI / ML model receives as input one or more of:

[0100] • At least a Layer 1 (Ll)-RSRP measurement based on the input beams also referred to as Set B;

[0101] • At least a Ll-RSRP measurement based on the input beams also referred to Set B and assistance information e.g. beam pattern information and / or a configuration information related to one or more network transmission(s)

[0102] • At least one Ll-RSRP measurement based on the input beams also referred to Set B and the corresponding DL transmit (Tx) and / or receive (Rx) beam ID.

[0103] • At least a Layer 3 (L3)-RSRP measurement based on the input beams also referred to as Set B;

[0104] • At least a L3-RSRP measurement based on the input beams also referred to Set B and assistance information e.g. beam pattern information and / or a configuration information related to one or more network transmission(s)

[0105] • At least one L3-RSRP measurement based on the input beams also referred to Set B and the corresponding DL Tx and / or Rx beam ID.

[0106] • Any combination of the above inputs of the AIML models.

[0107] In one option, the Frequency-domain DL beam prediction comprises a prediction of a measurement quantity of a reference signal, such as a Synchronization Signal (SS) / Physical Broadcast Channel (PBSCH) Block (SSB) or Channel State Information (CSI) Reference Signal (CSI-RS) reference signals. For example, assuming beams transmitting SSBs, and assuming that Set A corresponds to [SSB(l), SSB(2), SSB (3), SSB (4)] in a target frequency (e.g., F2) and that Set B corresponds to [SSB(l), SSB(2), SSB (3), SSB (4)] the prediction that the UE infers (as part of inference phase) may correspond to pBRSRP for SSB (1), to pBRSRP for SSB (2), to pBRSRP for SSB (3), to pBRSRP for SSB (4) served by a target cell (Cell Y) operating in frequency F2, based on the beam level measurements referred as BRSRP for SSB (1), to BRSRP for SSB (2), to BRSRP for SSB (3), to BRSRP for SSB (4) served by a cell (Cell X) operating in frequency Fl.

[0108] In one option, the Frequency-domain DL beam prediction comprises a beam identifier (e.g. SSB index, CSI-RS resource identity, beam ID) derived based on a prediction of a measurement quantity of a reference signal in which the beam is transmitted. For example, the UE predicts an RSRP of a beam in Set A whose beam ID = SSB1 and includes the beam ID=SSB1 in the first message e.g. when the predicted RSRP of that beam is above a threshold or if requested by the network.

[0109] In one option, the one or more Frequency DL beam prediction(s) are one or more outputs of an AI / ML model.

[0110] In one option, a Frequency DL beam prediction corresponds to one or more of:

[0111] • Transmit (Tx) and / or Receive (Rx) Beam ID(s) and / or

[0112] o For example, that may correspond to one or more Reference signal (RS) identifiers transmitted in a spatial direction in a target frequency or beam, such as an SSB Index (or SSB identifier) or a CSI-RS resource identity, and possibly derived based on prediction of measurements on the corresponding RS e.g. SSB ID=X corresponds to a predicted information when the predicted value of SS-RSRP of SSB ID=X is above a threshold.

[0113] • The predicted Ll-RSRP of the N predicted DL Tx and / or Rx beam(s) e.g. top N predicted beams.

[0114] o For example, that may correspond to N predicted RSRP values (Layer 1 RSRP) or other measurement quantities per beam and / or per RS transmitted on a spatial direction in a target frequency or beam, such as SS-RSRP, SS-RSRQ, SS-SINR, CSI-RSRP, CSI-RSRQ, CSI-SINR transmitted in a target frequency.

[0115] • Tx and / or Rx Beam angle(s) and / or the predicted Ll-RSRP of the N predicted DL Tx and / or Rx beams.

[0116] 2- beam level measurements in a frequency Fl are the input of the model, and predicted cell level measurements in frequency F2 are the output of the model

[0117] In one option the frequency domain cell prediction for a Cell Y corresponds to the value of a measurement quantity (also referred to as pRSRP value, a pRSRQ value, a pSINR value) representing the layer 1 or layer 3 measurement quantity of cell Y (or a cell quality or cell measurement result) operating in a target frequency (F2), wherein the frequency-domain cell prediction for cell Y is calculated based on one or more DL beam measurements of a Set B including one or more cells.

[0118] Figure 4 illustrates that, in one sub-option, the cell prediction(s) for a Set A of one or more cells operating in target frequency F2 is inferred based on measurements of a Set B of beams served by one or more cells in frequency Fl. For example, assuming beams transmitting SSBs, and assuming that Set A corresponds to [cell Yl and cell Y2] of one or more cells in frequency F2, and that Set B corresponds to [SSB(l), SSB(2), SSB (3), SSB (4)] the UE derives as prediction information the values of pRSRP for cell Yl, and Cell Y2 based on the SS-RSRP for SSB (1), SS-RSRP for SSB (2), SS-RSRP for SSB (3), SS-RSRP for SSB (4).

[0119] Figure 5 illustrates that, in one sub-option, the Top-K (here K is equal to 1 i.e., the best cell in target frequency F2) cells in a Set A of one or more cell operating in target frequency F2 is inferred based on beam level measurements of a Set B of beams served by one or more cells in frequency Fl. For example, assuming beams transmitting SSBs, and assuming that Set B corresponds to [SSB(l), SSB(2), SSB (3), SSB (4)] of one or more cells in frequency Fl, and that Set A corresponds to [Cell Yl and Cell Y2] the UE derives the Top-K cells (in this example the shown by circles with cross-hatching) based on the prediction of the values of pRSRP for cell Yl, and Cell Y2 based on the AI / ML model inputs including the SS-RSRP for SSB (1), SS-RSRP for SSB (2), SS-RSRP for SSB (3), SS-RSRP for SSB (4).

[0120] In one option, to derive one or more Frequency domain DL cell prediction(s) or the Top-K cells in the target frequency (here F2) as output of an AI / ML model, the AI / ML model receives as input one or more of:

[0121] • At least a Ll-RSRP measurement based on the input beams served by one or more cells also referred to as Set B;

[0122] • At least a Ll-RSRP measurement based on the input beams also referred to Set B and assistance information e.g. beam pattern information and / or a configuration information related to one or more network transmission(s)

[0123] • At least one Ll-RSRP measurement based on the input beams also referred to Set B and the corresponding DL Tx and / or Rx beam ID.

[0124] • At least an L3-RSRP measurement based on the input beams also referred to as Set B; • At least a L3-RSRP measurement based on the input beams also referred to Set B and assistance information e.g. beam pattern information and / or a configuration information related to one or more network transmission(s)

[0125] • At least one L3-RSRP measurement based on the input beams also referred to Set B and the corresponding DL Tx and / or Rx beam ID. • Any combination of the above inputs of the AIML models.

[0126] In one option, the Frequency-domain DL cell level prediction comprises prediction(s) of measurement quantity(es) of one or more reference signal(s), such as an SSB or CSI-RS reference signals. For example, assuming beams transmitting SSBs, and assuming that Set A corresponds to [Cell Yl and Cell Y2] in a target frequency (e.g., F2) and that Set B corresponds to [SSB(l), SSB(2), SSB (3), SSB (4)] of one or more cells in frequency Fl, the prediction that the UE infers (as part of AIML inference phase) may correspond to pRSRP for Cell Yl, to pRSRP for Cell Y2 operating in the target frequency F2, based on the beam level measurements referred as BRSRP for SSB (1), to BRSRP for SSB (2), to BRSRP for SSB (3), to BRSRP for SSB (4) served by one or more cells operating in frequency Fl.

[0127] In one option, the Frequency -domain DL cell level prediction comprises one or more cell identifier(s) (e.g. physical cell identity (PCI) or cell global identity (CGI)) derived based on one or more prediction(s) of measurement quantity(es) of one or more cells. For example, the UE predicts an RSRP of a cell Yl in Set A whose PCI is equal to 100 and includes the PCI=100 in the first message e.g. when the predicted RSRP of the cell Yl is above a configured threshold or if requested by the network.

[0128] In one option, the one or more Frequency cell level measurements prediction(s) are one or more outputs of an AI / ML model.

[0129] 3- Cell level measurements in a frequency Fl are the input of the model, and predicted cell level measurements in frequency F2 are the output of the model

[0130] In one option, the frequency domain cell level prediction for a cell Y corresponds to the value of a measurement quantity (also referred to as pRSRP value, a pRSRQ value, a pSINR value) representing the layer 1 or layer 3 measurement quantity of cell Y (or a cell quality or cell measurement result) operating in a target frequency (here F2), wherein the frequency-domain cell level prediction for cell Y is inferred from on one or more cell level measurements of a Set B including one or more cells.

[0131] Figure 6 illustrates that, in one sub-option, the cell prediction(s) for a Set A of one or more cells operating in target frequency F2 is inferred based on measurements of a Set B of cells operating in frequency FL For example, assuming that Set A corresponds to [Cell Yl and Cell Y2] of one or more cells in frequency F2, and that Set B corresponds to [Cell XI, Cell X2, Cell X3 and Cell X4] the UE derives as prediction information the values of pRSRP for Cell Yl and Cell Y2 based on the RSRP for Cell XI, RSRP for Cell X2, RSRP for Cell X3, RSRP for X4. Figure 7 illustrates that, in one sub-option, Top-K (here K is equal to 1 i.e., the best cell in target frequency F2) Top K cells in a Set A operating in target frequency F2 are inferred based on beam level measurements of a Set B of cells operating in frequency Fl. For example, assuming that Set A corresponds to [Cell Yl and Cell Y2] operating in frequency F2, and that Set B corresponds to [Cell XI, Cell X2, Cell X3, and Cell X4] the UE derives the Top-K cells (in this example the shown by circles with cross-hatching) based on the prediction of the values of pRSRP for Cell Yl, and Cell Y2 based on the AI / ML model inputs including the RSRP for Cell XI, RSRP for Cell X2, RSRP for Cell X3, and RSRP for Cell X4.

[0132] In one option, to derive one or more Frequency domain DL cell prediction(s) or the Top-K cells in the target frequency (here F2) as output of an AI / ML model, the AI / ML model receives as input one or more of:

[0133] • At least a cell level Ll-RSRP measurement associated to the input cells operating in frequency Fl;

[0134] • At least a cell level Ll-RSRP measurement associated to the input cells operating in frequency Fl and assistance information e.g. beam pattern and or beam level measurement information and / or a configuration information related to one or more network transmission(s)

[0135] • At least one cell level Ll-RSRP measurement associated to the input cells operating in frequency Fl, and / or the associated DL Tx and / or Rx beam ID and / or beam level measurements.

[0136] • At least a cell level L3-RSRP measurement associated to the input cells;

[0137] • At least a cell level L3-RSRP measurement associated to the input cells and assistance information e.g. beam level measurements and / or beam pattern information and / or a configuration information related to one or more network transmission(s)

[0138] • At least one cell level L3-RSRP measurement based associated to the input cells and the corresponding DL Tx and / or Rx beam ID and or the beam level measurement information.

[0139] • Any combination of the above inputs of the AIML models.

[0140] In one option, the Frequency-domain DL cell level prediction comprises prediction(s) of measurement quantity(es) of one or more reference signal(s), such as an SSB or CSI-RS reference signals. For example, assuming beams transmitting SSBs, and assuming that Set A corresponds to [Cell Yl and Cell Y2] in a target frequency (here called F2) and that Set B corresponds to [Cell XI, Cell X2, Cell X3, and Cell X4] of one or more cells in frequency Fl, the prediction that the UE infers (as part of AIML inference phase) may correspond to pRSRP for Cell Yl, to pRSRP for Cell Y2 operating in the target frequency F2, based on the cell level measurements referred as RSRP for Cell XI, to RSRP for Cell X2, to RSRP for Cell X3, to RSRP for Cell X4 served by one or more cells operating in frequency Fl.

[0141] In one option, the Frequency -domain DL cell prediction comprises one or more cell identifier(s) (e.g. physical cell identity (PCI) or cell global identity (CGI)) derived based on one or more prediction(s) of measurement quantity(es) of one or more cells. For example, the UE predicts an RSRP of a cell Y1 in Set A whose PCI is equal to 100 and includes the PCI=100 in the first message e.g. when the predicted RSRP of the cell Y1 is above a configured threshold or if requested by the network.

[0142] In one option, the one or more Frequency cell level measurements prediction(s) are one or more outputs of an AI / ML model.

[0143] Embodiments are also disclosed herein related to spatial domain predictions. In this regard, similarly to the frequency domain predictions, the UE, upon resuming or setting up an RRC connection (i.e., transition from RRC IDLE / INACTIVE to RRC CONNECTED state), can transmit a report to the network including a set of spatial domain predictions for one or more cells in the same frequency and / or actual measurements, wherein the spatial domain predictions are conducted based on one or more of the following non-limiting example models:

[0144] 4. Beam level measurements in a certain frequency are the input of the model, and predicted beam level measurements of a cell in the same frequency are the output of the model. Cell level prediction is derived based on the beam level predictions.

[0145] 5. Beam level measurements in a certain frequency are the input of the model, and predicted cell level measurements in the same frequency are the output of the model

[0146] 6. Cell level measurements in a certain frequency are the input of the model, and predicted cell level measurements in the same frequency are the output of the model

[0147] The above examples are described in the following.

[0148] 4- A spatial-domain Downlink (DL) beam prediction for a Set of beams

[0149] In one option, the spatial-domain Downlink (DL) beam prediction for a Set of beams is based on measurement results of another set of beams in the same frequency.

[0150] Figure 8 illustrates an example of the AI / ML model using the beam level measurements (also referred to as BRSRP / BRSRQ / BSINR) from beams in Set B (beams denoted by circles having diagonal hatching) in a cell operating in a certain frequency as input, predicts another set of beams also referred to as set A (beams denoted by white circles) served by the cell Y operating in the same frequency. The output of the AI / ML model could be predicted beam IDs with or without predicted measurement quality values. If the output includes the beam level measurement prediction also referred to as pBRSRP, pBRSRQ, pBSINR, the UE may use the predicted beam level measurement prediction as input to the cell quality derivation procedure to derive the predicted cell quality (CQD) to report to the network.

[0151] Figure 9 illustrates an example of the AI / ML model using the beam level measurements (also referred to as BRSRP / BRSRQ / BSINR) from beams in Set B (beams denoted by circles with diagonal hatching) in a cell operating in a certain frequency as input, predicts Top-K beams also referred to as set A (beams denoted by circles with cross-hatching). The output of the AI / ML model could be predicted beam IDs with or without predicted measurement quality values. If the output includes the beam level measurement prediction also referred to as pBRSRP, pBRSRQ, pBSINR, the UE may use the predicted beam level measurement as input to the cell quality derivation procedure to derive the predicted cell quality to report to the network. In another embodiment the UE includes beam level measurement prediction in the report sent to the network e.g., if requested by the network.

[0152] In one option, to derive one or more Spatial domain DL beam prediction(s) as an output of an AI / ML model, the AI / ML model receives as input one or more of:

[0153] • At least a Ll-RSRP measurement based on the input beams also referred to as Set B; • At least a Ll-RSRP measurement based on the input beams also referred to Set B and assistance information e.g. beam pattern information and / or a configuration information related to one or more network transmission(s)

[0154] • At least one Ll-RSRP measurement based on the input beams also referred to Set B and the corresponding DL Tx and / or Rx beam ID.

[0155] • At least an L3-RSRP measurement based on the input beams also referred to as Set B; • At least a L3-RSRP measurement based on the input beams also referred to Set B and assistance information e.g. beam pattern information and / or a configuration information related to one or more network transmission(s)

[0156] • At least one L3-RSRP measurement based on the input beams also referred to Set B and the corresponding DL Tx and / or Rx beam ID.

[0157] • Any combination of the above inputs of the AIML models.

[0158] In one option, the Spatial-domain DL beam prediction comprises a prediction of a measurement quantity of a reference signal, such as an SSB or CSI-RS reference signals. For example, assuming beams transmitting SSBs, and assuming that Set A corresponds to [SSB(l), SSB(2), SSB (3), SSB (4)] in a target cell Y and that Set B corresponds to [SSB(l), SSB(2), SSB (3), SSB (4)] the prediction that the UE infers (as part of inference phase) may correspond to pBRSRP for SSB (1), to pBRSRP for SSB (2), to pBRSRP for SSB (3), to pBRSRP for SSB (4) served by a target cell (Cell Y), based on the beam level measurements referred as BRSRP for SSB (1), to BRSRP for SSB (2), to BRSRP for SSB (3), to BRSRP for SSB (4) served by a cell (Cell X).

[0159] In one option, the Spatial-domain DL beam prediction comprises a beam identifier (e.g. SSB index, CSI-RS resource identity, beam ID) derived based on a prediction of a measurement quantity of a reference signal in which the beam is transmitted. For example, the UE predicts an RSRP of a beam in Set A whose beam ID = SSB1 and includes the beam ID=SSB1 in the first message e.g. when the predicted RSRP of that beam is above a threshold or if requested by the network.

[0160] In one option, the one or more Spatial DL beam prediction(s) are one or more outputs of an AI / ML model.

[0161] In one option, a Spatial DL beam prediction corresponds to one or more of:

[0162] • Tx and / or Rx Beam ID(s) and / or

[0163] o For example, that may correspond to one or more Reference signal (RS) identifiers transmitted in a spatial direction in a frequency or beam, such as an SSB Index (or SSB identifier) or a CSI-RS resource identity, and possibly derived based on prediction of measurements on the corresponding RS e.g. SSB ID=X corresponds to a predicted information when the predicted value of SS-RSRP of SSB ID=X is above a threshold.

[0164] • The predicted Ll-RSRP of the N predicted DL Tx and / or Rx beam(s) e.g. top N predicted beams.

[0165] o For example, that may correspond to N predicted RSRP values (Layer 1 RSRP) or other measurement quantities per beam and / or per RS transmitted on a spatial direction in a target cell or beam, such as SS-RSRP, SS-RSRQ, SS-SINR, CSI- RSRP, CSI-RSRQ, CSI-SINR transmitted in a target cell.

[0166] • Tx and / or Rx Beam angle(s) and / or the predicted Ll-RSRP of the N predicted DL Tx and / or Rx beams.

[0167] 5- beam level measurements are the input of the model, and predicted cell level measurements in the same frequency are the output of the model

[0168] In one option, the spatial domain cell prediction for a Cell Y corresponds to the value of a measurement quantity (also referred to as pRSRP value, a pRSRQ value, a pSINR value) representing the layer 1 or layer 3 measurement quantity of cell Y (or a cell quality or cell measurement result), wherein the spatial-domain cell prediction for cell Y is calculated based on one or more DL beam measurements of a Set B including one or more cells.

[0169] Figure 10 illustrates that, in one sub-option, the cell predict! on(s) for a Set A of one or more cells is inferred based on measurements of a Set B of beams served by one or more cells in the same frequency. For example, assuming beams transmitting SSBs, and assuming that Set A corresponds to [cell Yl and cell Y2] of one or more cells, and that Set B corresponds to [SSB(l), SSB(2), SSB (3), SSB (4)] the UE derives as prediction information the values of pRSRP for cell Yl, and Cell Y2 based on the SS-RSRP for SSB (1), SS-RSRP for SSB (2), SS-RSRP for SSB (3), SS-RSRP for SSB (4).

[0170] Figure 11 illustrates that, in one sub-option, Top-K (here K is equal to 1 i.e., the best cell) cells in a Set A of one or more cell is inferred based on beam level measurements of a Set B of beams served by one or more cells in the same frequency. For example, assuming beams transmitting SSBs, and assuming that Set B corresponds to [SSB(l), SSB(2), SSB (3), SSB (4)] of one or more cells, and that Set B corresponds to [Cell Yl and Cell Y2] the UE derives the Top-K cells (in this example the shown by circles with cross-hatching) based on the prediction of the values of pRSRP for cell Yl, and Cell Y2 based on the AI / ML model inputs including the SS-RSRP for SSB (1), SS-RSRP for SSB (2), SS-RSRP for SSB (3), SS-RSRP for SSB (4).

[0171] In one option, to derive one or more Spatial domain DL cell prediction(s) or the Top-K cells as output of an AI / ML model, the AI / ML model receives as input one or more of:

[0172] • At least a Ll-RSRP measurement based on the input beams served by one or more cells also referred to as Set B;

[0173] • At least a Ll-RSRP measurement based on the input beams also referred to Set B and assistance information e.g. beam pattern information and / or a configuration information related to one or more network transmission(s)

[0174] • At least one Ll-RSRP measurement based on the input beams also referred to Set B and the corresponding DL Tx and / or Rx beam ID.

[0175] • At least an L3-RSRP measurement based on the input beams also referred to as Set B; • At least a L3-RSRP measurement based on the input beams also referred to Set B and assistance information e.g. beam pattern information and / or a configuration information related to one or more network transmission(s)

[0176] • At least one L3-RSRP measurement based on the input beams also referred to Set B and the corresponding DL Tx and / or Rx beam ID.

[0177] • Any combination of the above inputs of the AIML models. In one option, the Spatial-domain DL cell level prediction comprises prediction(s) of measurement quantity(es) of one or more reference signal(s), such as an SSB or CSI-RS reference signals. For example, assuming beams transmitting SSBs, and assuming that Set A corresponds to [Cell Y1 and Cell Y2] and that Set B corresponds to [SSB(l), SSB(2), SSB (3), SSB (4)] of one or more cells in the same frequency, the prediction that the UE infers (as part of AIML inference phase) may correspond to pRSRP for Cell Y1 and to pRSRP for Cell Y2, based on the beam level measurements referred as BRSRP for SSB (1), to BRSRP for SSB (2), to BRSRP for SSB (3), to BRSRP for SSB (4) served by one or more cells operating in the same frequency.

[0178] In one option, the Spatial-domain DL cell level prediction comprises one or more cell identifier(s) (e.g. physical cell identity (PCI) or cell global identity (CGI)) derived based on one or more prediction(s) of measurement quantity(es) of one or more cells. For example, the UE predicts an RSRP of a cell Y1 in Set A whose PCI is equal to 100 and includes the PCI=100 in the first message e.g. when the predicted RSRP of the cell Y1 is above a configured threshold or if requested by the network.

[0179] In one option, the one or more Spatial cell level measurements prediction(s) are one or more outputs of an AI / ML model.

[0180] 6- Cell level measurements are the input of the model, and predicted cell level measurements in the same frequency are the output of the model

[0181] In one option, the spatial domain cell level prediction for a cell Y corresponds to the value of a measurement quantity (also referred to as pRSRP value, a pRSRQ value, a pSINR value) representing the layer 1 or layer 3 measurement quantity of cell Y (or a cell quality or cell measurement result), wherein the spatial-domain cell level prediction for cell Y is inferred from on one or more cell level measurements of a Set B including one or more cells.

[0182] Figure 12 illustrates that, in one sub-option, the cell predict! on(s) for a Set A of one or more cells is inferred based on measurements of a Set B of cells operating in the same frequency. For example, assuming that Set A corresponds to [Cell Y1 and Cell Y2] of one or more cells, and that Set B corresponds to [Cell XI, Cell X2, Cell X3 and Cell X4] the UE derives as prediction information the values of pRSRP for Cell Y1 and Cell Y2 based on the RSRP for Cell XI, RSRP for Cell X2, RSRP for Cell X3, RSRP for X4.

[0183] Figure 13 illustrates that, in one sub-option, Top-K (here K is equal to 1 i.e., the best cell) Top K cells in a Set A are inferred based on beam level measurements of a Set B of cells. For example, assuming that Set A corresponds to [Cell Y1 and Cell Y2], and that Set B corresponds to [Cell XI, Cell X2, Cell X3, and Cell X4] the UE derives the Top-K cells (in this example the shown by circles with cross-hatching) based on the prediction of the values of pRSRP for Cell Yl, and Cell Y2 based on the AI / ML model inputs including the RSRP for Cell XI, RSRP for Cell X2, RSRP for Cell X3, and RSRP for Cell X4.

[0184] In one option, to derive one or more Spatial domain DL cell prediction(s) or the Top-K cells as output of an AI / ML model, the AI / ML model receives as input one or more of:

[0185] • At least a cell level Ll-RSRP measurement associated to the input cells;

[0186] • At least a cell level Ll-RSRP measurement associated to the input cells and assistance information e.g. beam pattern and or beam level measurement information and / or a configuration information related to one or more network transmission(s)

[0187] • At least one cell level Ll-RSRP measurement associated to the input cells, and / or the associated DL Tx and / or Rx beam ID and / or beam level measurements.

[0188] • At least a cell level L3-RSRP measurement associated to the input cells;

[0189] • At least a cell level L3-RSRP measurement associated to the input cells and assistance information e.g. beam level measurements and / or beam pattern information and / or a configuration information related to one or more network transmission(s)

[0190] • At least one cell level L3-RSRP measurement based associated to the input cells and the corresponding DL Tx and / or Rx beam ID and or the beam level measurement information.

[0191] • Any combination of the above inputs of the AIML models.

[0192] In one option, the Spatial-domain DL cell level prediction comprises prediction(s) of measurement quantity(es) of one or more reference signal(s), such as an SSB or CSI-RS reference signals. For example, assuming beams transmitting SSBs, and assuming that Set A corresponds to [Cell Yl and Cell Y2] and that Set B corresponds to [Cell XI, Cell X2, Cell X3, and Cell X4] of one or more cells, the prediction that the UE infers (as part of AIML inference phase) may correspond to pRSRP for Cell Yl, to pRSRP for Cell Y2, based on the cell level measurements referred as RSRP for Cell XI, to RSRP for Cell X2, to RSRP for Cell X3, to RSRP for Cell X4 served by one or more cells operating in the same frequency.

[0193] In one option, the Spatial-domain DL cell prediction comprises one or more cell identifier(s) (e.g. physical cell identity (PCI) or cell global identity (CGI)) derived based on one or more prediction(s) of measurement quantity(es) of one or more cells. For example, the UE predicts an RSRP of a cell Yl in Set A whose PCI is equal to 100 and includes the PCI=100 in the first message e.g. when the predicted RSRP of the cell Yl is above a configured threshold or if requested by the network.

[0194] In one option, the one or more Spatial cell level measurements prediction(s) are one or more outputs of an AI / ML model. As described above, an AI / ML model can be designed to produce the beam-level measurement prediction in frequency domain or spatial domain (e.g., in accordance with any of the options described above). Utilizing the predicted beam-level measurement quality(es) and beam IDs generated as output of the AI / ML model inference, a predicted cell-level measurement quality for a cell X can be derived using the approaches described above. An AI / ML model can also be designed to directly predict the cell-level measurement by taking LI and or L3 measurements of a set of beams and or cells as model input. Besides predicted beam-level or / and cell-level measurement quantities and beam / cell IDs, the model may also provide additional information like confidence level of the model output, the validation time of the predicted measurements, etc.

[0195] The designed AI / ML model can be deployed at the UE or at the network side and associated to a beam / cell prediction feature or a Radio Resource Monitoring (RRM) prediction feature. When connecting to a network node, a UE can report its support of the AI / ML model for spatial and / or inter-frequency beam and or cell prediction feature or RRM prediction feature to the network node, via UE capability reporting. In addition, UE can report applicability of its AI / ML model for spatial and / or inter-frequency beam and or cell prediction feature or RRM prediction feature to the network node. The applicability indication can be seen as a dynamic UE capability on conducting predictions under certain network configuration and conditions. Based on the received UE capability and applicability indications, together with other conditions, the network node can make decisions on whether to configure / activate the AI / ML model at the UE or not.

[0196] Below different examples are given on how to design an AI / ML model to achieve the beam / cell-lev el measurement quality prediction in spatial or / and frequency domain i.e., interfrequency prediction.

[0197] For the AI / ML model used for cell prediction, in an example, a neural network-based model is composed of multiple connected neurons. Optionally it contains one or a few input layers, one or a few hidden layers, and one output layer. For the input layer, it takes UE measurements results as the model input, where the beam or cell level measurement results are obtained based on measuring some reference signals, e.g., SSBs and / or CSI-RSs. Optionally, the measurement results would be normalized before input to the hidden layers. The normalization can change the value of the numeric variable in the dataset to a typical scale which improves model training. For the hidden layer(s), it is located between the input and output, in which the function applies weights to the inputs and directs them through an activation function to the output layer. Optionally, activation function can be one of Softmax function, Sigmoid function, ReLU function, Leaky ReLU, tanh function and Maxout. For the output layer, the output can be predicted RSRP values for each beam or cell in a different frequency. Optionally, the output can be the probability values where each value means the probability of the beam / cell in a target frequency to be the best beam / cell.

[0198] In an example for the AI / ML model, the AI / ML model used for spatial and inter-frequency cell level measurement prediction is based on convolutional neural networks, optionally it contains one or a few of input layers, one of a few of convolution layer, one or a few of pooling layer and output layer. For the input layer, it takes UE cell / beam level measurements results as the model input, where the beam level measurement results are obtained based on measuring some reference signals, e.g., SSBs and / or CSI-RSs and cell level measurement results are derived from the beam level measurements using cell quality derivation procedure. Optionally, the beam / cell level measurement results would be normalized before input to the convention layers. The normalization can change the value of the numeric variable in the dataset to a typical scale which improves model training. For the convention layer(s), it is used to extract the feature from the input. It applies a set of learnable filters (known as the kernels) to the input with smaller size than the whole input. These filters and kernels slide over the input data and computes the dot product between kernel weight and the corresponding input. The output of convention layer is referred to as feature maps coming from the input measured RSRP values. For pooling layers, it involves sliding a two-dimensional filter over each channel of feature map and summarizing the features lying within the region covered by the filter. Before the output layer, there can be a fully connected layer to interpret / summarize the features obtained and direct them through activation function to the output layer. For the output layer, the output can be predicted RSRP values for each beam or each cell operating in a target frequency. Optionally, the output can be the probability values where each value means the probability of the beam to be the best beam or best cell in a target frequency.

[0199] In another set of examples, an AI / ML model is designed to predict the beam measurements of one or more beams in the spatial or frequency domain. A predicted cell-level measurement quality for a cell X in spatial or frequency domain (i.e., a cell operating in a frequency different from the cells / beams used as input to the model) can be derived based on the predicted beam-level measurement quality(es) or / and beam IDs generated from the AI / ML model output. In another example an AI / ML model is designed to directly predict the cell level measurements of one or more cells in spatial or frequency domain. Different design options for AI / ML based spatial / frequency domain beam / cell prediction can be considered. Some non-limiting example design options are listed below.

[0200] Option 1) the AI / ML model predicts Top-l / K beam ID(s), where the model takes the (postprocessed) RSRP measurements of the beams in set B as model input and directly outputs the top-l / K beam ID(s) of the beams in Set A which are served by cells in a different frequency or space domain.

[0201] Option 2) the AI / ML model predicts Top-l / K beam ID(s) and the associated predicted RSRP values, where the model takes the (postprocessed) RSRP measurements of the beams in set B as model input and directly outputs the top-l / K beam ID(s) and the predicted RSRP values of these beams in set A which are operating in a different frequency or space domain.

[0202] Option 3) the AI / ML model predicts top-l / K beam ID(s) with or without the associated predicted RSRP values, where the model takes the (postprocessed) RSRP measurements of the beams in set B or / and the assistance information like UE position / location as model input, which are operating in a different frequency or space domain.

[0203] Option 4) the AI / ML model predicts Top-l / K cell ID(s), where the model takes the (postprocessed) RSRP measurements of the beams / cells in set B as model input and directly outputs the top-l / K cell ID(s), denoted as Set A, which are operating in a different frequency or space domain.

[0204] Option 5) the AI / ML model predicts Top-l / K cell ID(s) and the associated predicted RSRP values, where the model takes the (postprocessed) RSRP measurements of the beams / cells in set B as model input and directly outputs the top-l / K cells ID(s) and the predicted RSRP values of these beams in set A, which are operating in a different frequency or space domain.

[0205] Option 6) the AI / ML model predicts top-l / K cell ID(s) with or without the associated predicted RSRP values, where the model takes the (postprocessed) RSRP measurements of the beams / cells in set B or / and the assistance information like UE position / location as model input and directly outputs the top-l / K cells ID(s) and the predicted RSRP values of these beams in set A, which are operating in a different frequency or space domain.

[0206] As an example, the AI / ML model mentioned in the above design options can be based on neural network architectures, e.g., convolutional neural network (CNN), fully connected NN, Residual Networks (ResNet). Figure 14 illustrates examples of Neural Networks used for designing AI / ML models for spatial / frequency domain beam prediction, in accordance with an exemplary embodiment of the present disclosure. Figure 14 shows two exemplary model architectures, where Neural network B (NN B) is a model with higher complexity in comparison to NN A. The number of nodes in the dense layers equals the number of beams in Set A, NSetA. The model input takes RSRP of SSB and / or CSI RS of set B beams (one real value per measured beam, normalized based on min and max values per sampl)e. Normalization is based on scaling the beam RSRP values in dB per sample to yield the range 0.0 to 1.0 for RSRP values for each sample. In case assistance information, such as UE location information, is also used as input to the neural network, that information is concatenated to the RSRP values after being separately scaled by a fixed scaling factor designed to yield values with maximum magnitudes in the order of 1. A softmax cross-entropy function is used to generate the probability of a beam being the strongest beam, used to derive top-l / K beams.

[0207] Figure 15 illustrates examples of Neural Networks used for designing AI / ML models for spatial / frequency domain cell prediction, in accordance with an embodiment of the present disclosure. Figure 15 shows two exemplary model architectures, where Neural network B (NN B) is a model with higher complexity in comparison to NN A. The number of nodes in the dense layers equals the number of cells in Set A, NSetA. The model input takes RSRP values of set B cells (one real value per measured cell which can be layer 1 or Layer 3 filtered RSRP, normalized based on min and max values per sample. Normalization is based on scaling the cell RSRP values in dB per sample to yield the range 0.0 to 1.0 for RSRP values for each sample. In case assistance information, such as UE location information, is also used as input to the neural network, that information is concatenated to the RSRP values after being separately scaled by a fixed scaling factor designed to yield values with maximum magnitudes in the order of 1. A softmax crossentropy function is used to generate the probability of a cell being the strongest beam, used to derive top-l / K beams.

[0208] Embodiments of solutions are described herein that are related to a UE performing AI / ML based early measurement predictions of certain frequencies and / or cells while in RRC IDLE / RRC INACTIVE, and transmitting the prediction results to a network node when resuming to RRC CONNECTED e.g. in RRC Resume Complete or in a UE Assistance Information after or multiplexed with the RRC Resume Complete. Embodiments of the present disclosure also contain the possibility for a network node to configure the details of the predictions to be performed while in RRC IDLE / RRC INACTIVE. The UE may be configured in dedicated RRC messages or receive the configurations broadcasted in system information. Embodiments of solutions related to validity and / or accuracy of the prediction results are also described herein.

[0209] Figure 16 illustrates an example procedure for configuration and reporting of early predictions in RRC IDLE / RRC INACTIVE, in accordance with example embodiments of the present disclosure. Optional steps are represented by dashed lines. As illustrated, initially, the UE may (optionally) transmit information to the network related to UE capabilities and / or UE assistance information (e.g., UE preferences related to AI / ML based frequency or spatial predictions) (steps 1600 and 1602). This information may be transmitted prior to the actual procedure for early measurements / predictions, e.g. in a procedure for UE Attach, UE RRC connection establishment, UE Assistance Information. The UE may (optionally) transmit UE capabilities (e.g., in step 1600) to the network as part of the Attach procedure. The UE capabilities may include, but are not limited to, any one or more of the following:

[0210] - UE capabilities related to the ability to perform early measurement predictions in RRC IDLE / RRC INACTIVE. The UE capability may be indicated per frequency, per frequency band, per frequency band combination, per frequency range etc.

[0211] - The quantity of early measurement prediction the UE is capable of, e.g. prediction of RSRP, RSRQ, SINR, etc.

[0212] - The type of prediction the UE is capable of, e.g. frequency domain prediction or spatial domain prediction or a combination of frequency and spatial domain predictions.

[0213] - The maximum amount of predictions the UE is capable of, e.g. the maximum amount of frequencies the UE is capable of predicting, the maximum amount of cells, the maximum amount of beams, etc.

[0214] - The maximum amount of early measurement predictions and early measurements the UE is capable of totally performing when in RRC IDLE / RRC INACTIVE. In one option the relationship between the amount of measurements and predictions is reported, e.g. the UE may be capable of predicting one frequency for each measured frequency.

[0215] The UE may (optionally) transmit information to the network related to the applicability conditions of the early measurement predictions (e.g., in step 1602). Such applicability conditions may include, but are not limited to, any one or more of the following:

[0216] • The mobility scenarios such as:

[0217] o UE speed range in which the AI / ML model is applicable.

[0218] o Mobility scenario categories / classes such as low / medium / high speed classes identified by the number of cells UE visits in a certain period of time.

[0219] • The radio condition, e.g. range of radio coverage measured based on serving cell RSRP or RSRQ or SINR, in which the AIML model for early measurement predictions is appliable.

[0220] • An area where early measurement predictions are applicable.

[0221] The UE may (optionally) transmit (e.g., in step 1602) information to the network related to preferred / recommended configuration(s) for performing early measurement predictions. The information may comprise one or more of the following:

[0222] - Preferred frequencies (or frequency band, band combination, range etc.) for performing early measurement predictions. This may in one option be implicitly indicated by the UE capabilities. Additionally, the UE may indicate estimated performance / accuracy for each frequency. - Preferred number of frequencies to predict.

[0223] - Estimated time for performing RRM measurement prediction of a certain frequency. - Preferred predicted quantity, such as RSRP, RSRQ, SINR, etc.

[0224] In the main scenario, the network (e.g., network node A which operates Cell A in the illustrated example of Figure 16) transmits a configuration to the UE related to performing AI / ML based early measurement predictions in RRC IDLE / RRC INACTIVE, e.g. in RRCRelease or in system information broadcast (step 1604). The configuration may comprise the configuration of idle / inactive measurement predictions (early measurement predictions) and / or the configuration of reporting of the prediction results. The early measurement predictions may comprise frequency domain predictions or spatial domain predictions. A combination of the different types of predictions may be used in some cases, e.g. frequency domain prediction of one frequency and spatial domain predictions of additional cells in the predicted frequency. The early measurement predictions may comprise predictions of RSRP, RSRQ, SINR etc. of a certain frequency or a certain cell.

[0225] The configuration may comprise one or multiple of the following:

[0226] - An identity of the configuration, e.g. a prediction ID or a measID.

[0227] - A list of carriers / frequencies or frequency band for which the UE should perform measurement predictions (prediction of RSRP, RSRQ, SINR, ...).

[0228] - A list of cells for which the UE should perform measurement predictions (prediction of RSRP, RSRQ, SINR, ...).

[0229] - Alternatively, there may be an indication per carrier or per cell, indicating that the UE shall perform predictions for that frequency or cell, or there may be an indication per carrier or per cell, indicating whether the UE shall perform measurements or predictions for that frequency or cell.

[0230] - There may be multiple lists of carriers / frequencies or cells, e.g. per RAT (Radio Access Technology) such as 6G, NR or LTE.

[0231] - The configuration may comprise the type of prediction the UE should perform, e.g.

[0232] frequency or spatial domain predictions.

[0233] - The quantity of the prediction, e.g. whether the prediction result should be RSRP, RSRQ or SINR or multiple quantities, e.g. both RSRP and RSRQ.

[0234] - In one option, the configuration contains an indication, indicating that the UE is allowed to report predicted measurement results instead of actual measurement result.

[0235] - The configuration may be included in measIdleConfig or in a new field in the RRC message or in system information broadcast. - One option is that the UE may measure less and save power and report the same amount as specified today. In some scenarios the network may be fine with existing number of reported carriers, but it may accept some of these to be predicted instead, so that the UE can save power.

[0236] - One option is that the UE may measure the same as required today but enable the reporting of more results. In some scenarios the network may want to have information about more carriers than what the UE is able to measure. Thus, this mode can be configured, so that based on the measurements, the UE can predict more carriers.

[0237] - In one option, the UE only performs the early measurements; however, in system information of the cell the UE is camping and trying to resume, the UE is indicated that it shall also include prediction(s) of one or more carriers.

[0238] - In one option, the UE only perform the early measurements; however, in RRC Resume the UE receives the pool for the early measurements to be included in Resume Complete, but in addition, it also receives a pool (based on UE capability) for the UE to perform frequency domain prediction(s) and includes in the early measurement reports.

[0239] - In one option, the UE is configured to report both actual measurement results and predicted measurement results. The configuration may indicate that certain frequencies / cells can be predicted, and other frequencies / cells should be measured. The configuration may further indicate that the results may contain a mixture of actual measurements and predictions. In this option, the configuration may comprise the amount of predicted samples and the amount of measured samples. It could e.g., be indicated that every second sample may be predicted and every second sample may be measured or that two out of three samples can be predicted. The amount of predicted samples and the amount of measured samples may e.g. be indicated per cell, per frequency, per frequency band, per frequency range, per bandcombination, per network slice, etc.

[0240] - In one option, the network may configure an accuracy threshold (or an uncertainty level) based on which the UE is requested to report early measurement prediction(s).

[0241] - Priority order for the predictions. The priority may be explicitly indicated by a dedicated indication, e.g. an integer 1 ... 16, where 1 is the highest priority and 16 is the lower priority. The priority may be implicitly indicated, e.g. by the order in which the frequencies or cells are listed, and where e.g. the first listed frequency / cell has the highest priority. In one option, the priority is up to UE implementation. - In one option, the configuration may include an area indicating an area where the UE may perform prediction of measurements, i.e. a validity area for the predictions. The area may comprise a list of cells, list of tracking areas, list of PLMNs etc.

[0242] - In one option, the configuration may comprise an indication of a time for how old the reported predictions may be. If the prediction results are older than the indicated time, the UE should not report the predictions. In a variant, the UE is configured with a validity timer, indicating how long prediction results are valid and the UE should only report valid prediction results.

[0243] - In an embodiment the configuration may include the quality threshold instructing the UE to include the early measurements predictions of neighboring cells only if the predictions indicate quality better than the configured threshold e.g., the predicted RSRP is greater than the RSRP threshold set as part of the quality threshold. In an embodiment the quality threshold can be the same quality threshold used as part of early measurements configuration or in another embodiment the UE receives a different quality threshold configuration for the early measurement predictions.

[0244] - The configuration may indicate to the UE to report the cause in case it failed to provide the predictions. For instance, the AI / ML model is not applicable to the current scenario, or the accuracy / uncertainty criterion based on which prediction should have been provided was not fulfilled, or that RRM predictions are not available / not supported. The cause provided can relate to all cells or all frequencies, or a certain cell or frequency, or a certain bandcombination, a certain network slice, etc.

[0245] - In one option, the network can request the UE to report measurement prediction of a configured measurement object that was originally configured to collect the actual measurement, if the actual measurement is not available. The network can configure the UE to report the cause why the measurement prediction is reported instead of the actual measurement.

[0246] The configuration may be provided to the UE in a dedicated RRC message, e.g. RRCRelease or some similar message, and / or in broadcasted system information, e.g. SIB11. If both are configured, the dedicated configuration may override the broadcasted information, the dedicated configuration may override the broadcasted information for a certain amount of time, the broadcasted information may override the dedicated configuration after a certain amount of time, or the broadcasted configuration may always override the dedicated configuration. In one option, there is an indication in system information, indicating whether the broadcasted information overrides the dedicated configuration. - In one option, a new timer is introduced, indicating how long a dedicated configuration for prediction of measurements is valid. After the timer has expired, the UE should use broadcasted configuration instead.

[0247] - In one option, the broadcasted information overrides the dedicated information in case the broadcasted information indicates that the UE is required to perform actual measurements, i.e. predicted measurements are not accepted or applicable in that cell / area.

[0248] - In one option, the broadcasted information includes an indication that predicted measurement results are allowed (or not allowed) in that cell / area.

[0249] - In one option, the broadcasted information includes an indication that predicted measurement results are allowed in that cell / area under certain conditions. The conditions may, e.g., comprise that the predictions were made within a certain area, e.g. within a validity area for the predictions.

[0250] The UE performs predictions (e.g. predicted RSRP value(s) and measurements (e.g. RSRP measurements) based on the received configuration (and / or additional assistance information) when in RRC IDLE / RRC INACTIVE, wherein the predictions include one or more frequency domain predictions and / or one or more spatial domain predictions in accordance with any of the embodiments described herein (step 1606).

[0251] In one option, the UE performs one or more frequency domain predictions(s) (e.g. predicted RSRP, cell ID of a cell in a given frequency which is not the frequency of the cell the UE is camping) for a second carrier frequency, by performing measurements on a first carrier frequency e.g. the frequency of the cell the UE is camping. In other words, the UE uses the measurements of the frequency of the cell the UE is camping as input to an AI / ML function which produces as output one or more frequency domain prediction(s).

[0252] In another option, the UE performs one or more frequency domain predictions(s) (e.g. predicted RSRP, cell ID of a cell in a given frequency which is not the frequency of the cell the UE is camping) for a second carrier frequency, by performing measurements on a first carrier frequency e.g. according to early measurement requirements and / or CONNECTED mode measurement requirements. In other words, the UE performs fewer early measurements (e.g. in a first carrier frequency) and use them as input to an AI / ML function which produces as output one or more frequency domain prediction(s)

[0253] In another option, the UE performs one or more frequency domain predictions(s) (e.g. predicted RSRP, cell ID of a cell in a given frequency which is not the frequency of the cell the UE is camping), by performing measurements which anyway are performed by the UE for cell reselection evaluations e.g. cell the UE is camping and some other candidate cells in the same frequency and / or in another frequency. In other words, cell reselection measurements are used as input to an AI / ML function which produces as output one or more frequency domain predict on(s).

[0254] When the UE resumes or is setup in a cell (same cell or different cell than the cell where the UE was transferred to RRC IDLE / RRC INACTIVE), the UE may indicate the availability of (early) prediction results to a corresponding network node and possibly transmit the (early) prediction results to the network node (e.g., if requested by the network node) or the UE may transmit the (early) prediction results to a corresponding network node in or as part of an early measurement report (step 1608). For example, the UE may indicate the availability of (early) prediction results to the network node, e.g. in an RRC message such as RRCSetupComplete, RRCResumeComplete, or the like. As another example, the UE report the IDLE / INACTIVE state predictions to the network in a UE information request / response procedure wherein the network requests the UE to include the early predictions (collected in RRC IDLE / INACTIVE states) in the UE Information Response message and the UE then sends the UE Information Response message to the network node.

[0255] One particular example is shown in Figure 16 in which the UE transmits an RRCResumeRequest to a network node B operating a Cell B, the network node B retrieves the UE context of the UE from Network Node A (steps 1608-2 and 1608-3), the Network Node B transmits an RRCResume message to the UE (step 1608-4), and the UE transmits an RRCResumeComplete message to the Network Node B (step 1608-5), where the RRCResumeComplete message includes an indication of the availability of the (early) prediction results or the actual (early) prediction results. The early prediction results include at least some of the frequency domain predictions and / or at least some of the spatial domain predictions.

[0256] The network node may request the UE to report the prediction results, e.g., in response to the UE transmitting an indication of the availability of the prediction results. The network node may explicitly request (e.g., a dedicated / separate request flag e.g., IdleModePredictionReq) the UE to include the predictions beside the early measurements in the send report to the network, or network node wants the UE to report only the early measurements even if the predictions is available at the UE. Alternatively, if the network configures the existing idleModeMeasurementReq the UE includes the IDLE / INACTIVE mode predictions in the report beside the IDLE / INACTIVE (early) measurements. Alternatively, the UE may directly report the prediction results.

[0257] The UE transmits the prediction results in a report to the network (e.g., in step 1608). The report may comprise one or more of the following:

[0258] - An identity of the configuration associated with the report. - Prediction results for the measurement / prediction quantities (e.g., RSRP, RSRQ, SINR, etc.), as indicated in the configuration. The prediction results may be sent in lists per frequency and / or per cell. In other options, the result may e.g. be indicated per frequency band, per band-combination, per network slice, etc.

[0259] o In an embodiment the UE includes the frequency domain predictions for a number of cells per frequency for a number of frequencies in the report.

[0260] o In another embodiment the report may include spatial domain predictions for a number of cells in one or more frequencies for which the UE performed the actual measurements for at least one cell operating in that frequency.

[0261] - The results may be indicated in priority order.

[0262] - The results may comprise an indication of the accuracy of the predictions.

[0263] - In one option, the measurement results are reported together with an indication, indicating whether the measurement result is an actual measurement result or a predicted measurement result. This indication is particularly needed in case the configuration contains an indication that predictions are allowed, i.e. not an exact configuration of which frequencies that should be measured and which frequencies that should be predicted. In one alternative, the indication may indicate that the result is based on predictions and if the indication is not included, the result is based on actual measurements.

[0264] - The amount of predicted samples and the amount of measured samples. As an example, the UE may not have been able to perform as large amount of measurements as indicated by the network and the UE may then indicate the actual amount of measurements performed and / or the actual amount of predicted samples.

[0265] - In one option, the report comprises a time stamp of when the predictions were made or an indication of how old the predictions are. Alternatively, the UE may report a validity time that was applied to the predictions or an indication that a validity time has been applied. - Information of the age of the predictions may be reported by the UE to the network if explicitly requested by the network e.g., when the network configures the request flags such as validatedMeasurementsReq or validatedPredictionsReq. Some non-limiting examples of such information are described in the following:

[0266] o the information related to the time window in which the prediction is performed; o the time difference between prediction and measurements e.g., elapsed time since performing / collecting early measurements until the predict! ons / inference; o time since prediction until reporting the prediction to the network e.g., the UE logs the time elapsed since performing the inference and collecting the prediction until sending the report to the network e.g., as part RRC Resume or UE information Response signals.

[0267] - In one option, the report comprises an indication of the length of the prediction window that was used. The UE may also report the length of the observation window.

[0268] - The report may comprise a cause value indicating a reason for why certain predictions were not performed, or why predictions were reported instead of actual measurements. Alternatively, an indication may be included, indicating that the RRM results are predictions instead of actual measurements.

[0269] From the network perspective, initially, the network may receive information from the UE related to UE capabilities and / or UE preferences related to AI / ML based frequency or spatial predictions (e.g., in step 1600 and / or step 1602 of Figure 16). This information may be transmitted prior to the actual procedure for early measurements / predictions, e.g. in a procedure for UE Attach, UE RRC connection establishment, UE Assistance Information.

[0270] The network may (optionally) receive UE capabilities from the UE (e.g., in step 1600) as part of the Attach procedure. The UE capabilities may be updated to comprise:

[0271] - UE capabilities related to the ability to perform early measurement predictions in RRC IDLE / RRC INACTIVE. The UE capability may be indicated per frequency, per frequency band, per frequency band combination, per frequency range etc.

[0272] - The quantity of early measurement prediction the UE is capable of, e.g. prediction of RSRP, RSRQ, SINR, etc.

[0273] - The type of prediction the UE is capable of, e.g. frequency domain prediction or spatial domain prediction or a combination of frequency and spatial domain predictions.

[0274] - The maximum amount of predictions the UE is capable of, e.g. the maximum amount of frequencies the UE is capable of predicting, the maximum amount of cells, the maximum amount of beams, etc.

[0275] - The maximum amount of early measurement predictions and early measurements the UE is capable of totally performing when in RRC IDLE / RRC INACTIVE. In one option the relationship between the amount of measurements and predictions is reported, e.g. the UE may be capable of predicting one frequency for each measured frequency.

[0276] The network may (optionally) receive information from the UE (e.g., in step 1602) related to the applicability conditions of the early measurement predictions. Such applicability conditions may include but not limited to:

[0277] • The mobility scenarios such as:

[0278] o UE speed range in which the AI / ML model is applicable. o Mobility scenario categories / classes such as low / medium / high speed classes identified by the number of cells UE visits in a certain period of time.

[0279] • The radio condition, e.g. range of radio coverage measured based on serving cell RSRP or RSRQ or SINR, in which the AIML model for early measurement predictions is appliable.

[0280] • An area where early measurement predictions are applicable.

[0281] The network may (optionally) receive information from the UE (e.g., in step 1602) related to preferred / recommended configuration(s) for performing early measurement predictions. The information may comprise one or more of the following:

[0282] - Preferred frequencies (or frequency band, band combination, range etc.) for performing early measurement predictions. This may in one option be implicitly indicated by the UE capabilities. Additionally, the UE may indicate estimated performance / accuracy for each frequency.

[0283] - Preferred number of frequencies to predict.

[0284] - Estimated time for performing RRM measurement prediction of a certain frequency. - Preferred predicted quantity, such as RSRP, RSRQ, SINR, etc.

[0285] In the main scenario, the network transmits a configuration to the UE related to performing AI / ML based early measurement predictions in RRC IDLE / RRC INACTIVE, e.g. in RRCRelease or in system information broadcast (see, e.g., step 1604). The configuration may comprise the configuration of idle / inactive measurement predictions (early measurement predictions) and / or the configuration of reporting of the prediction results. The early measurement predictions may comprise frequency domain predictions or spatial domain predictions. A combination of the different types of predictions may be used in some cases, e.g. frequency domain prediction of one frequency and spatial domain predictions of additional cells in the predicted frequency. The early measurement predictions may comprise predictions of RSRP, RSRQ, SINR etc. of a certain frequency or a certain cell.

[0286] The configuration may comprise one or multiple of the following:

[0287] - An identity of the configuration, e.g. a prediction ID or a measID.

[0288] - A list of carriers / frequencies or frequency band for which the UE should perform measurement predictions (prediction of RSRP, RSRQ, SINR, ...).

[0289] - A list of cells for which the UE should perform measurement predictions (prediction of RSRP, RSRQ, SINR, ...).

[0290] - Alternatively, there may be an indication per carrier or per cell, indicating that the UE shall perform predictions for that frequency or cell, or there may be an indication per carrier or per cell, indicating whether the UE shall perform measurements or predictions for that frequency or cell.

[0291] - There may be multiple lists of carriers / frequencies or cells, e.g. per RAT (Radio Access Technology) such as 6G, NR or LTE.

[0292] - The configuration may comprise the type of prediction the UE should perform, e.g.

[0293] frequency or spatial domain predictions.

[0294] - The quantity of the prediction, e.g. whether the prediction result should be RSRP, RSRQ or SINR or multiple quantities, e.g. both RSRP and RSRQ.

[0295] - In one option, the configuration contains an indication, indicating that the UE is allowed to report predicted measurement results instead of actual measurement result.

[0296] - The configuration may be included in measIdleConfig or in a new field in the RRC message or in system information broadcast.

[0297] - One option is that the UE may measure less and save power and report the same amount as specified today. In some scenarios the network may be fine with existing number of reported carriers, but it may accept some of these to be predicted instead, so that the UE can save power.

[0298] - One option is that the UE may measure the same as required today but enable the reporting of more results. In some scenarios the network may want to have information about more carriers than what the UE is able to measure. Thus, this mode can be configured, so that based on the measurements, the UE can predict more carriers.

[0299] - In one option, the UE only performs the early measurements; however, in system information of the cell the UE is camping and trying to resume, the UE is indicated that it shall also include prediction(s) of one or more carriers.

[0300] - In one option, the UE only perform the early measurements; however, in RRC Resume the UE receives the pool for the early measurements to be included in Resume Complete, but in addition, it also receives a pool (based on UE capability) for the UE to perform frequency domain prediction(s) and includes in the early measurement reports.

[0301] - In one option, the UE is configured to report both actual measurement results and predicted measurement results. The configuration may indicate that certain frequencies / cells can be predicted and other frequencies / cells should be measured. The configuration may further indicate that the results may contain a mixture of actual measurements and predictions. In this option, the configuration may comprise the amount of predicted samples and the amount of measured samples. It could e.g., be indicated that every second sample may be predicted and every second sample may be measured or that two out of three samples can be predicted. The amount of predicted samples and the amount of measured samples may e.g. be indicated per cell, per frequency, per frequency band, per frequency range, per bandcombination, per network slice, etc.

[0302] - In one option, the network may configure an accuracy threshold (or an uncertainty level) based on which the UE is requested to report early measurement prediction(s).

[0303] - Priority order for the predictions. The priority may be explicitly indicated by a dedicated indication, e.g. an integer 1 ... 16, where 1 is the highest priority and 16 is the lower priority. The priority may be implicitly indicated, e.g. by the order in which the frequencies or cells are listed, and where e.g. the first listed frequency / cell has the highest priority. In one option, the priority is up to UE implementation.

[0304] - In one option, the configuration may include an area indicating an area where the UE may perform prediction of measurements, i.e. a validity area for the predictions. The area may comprise a list of cells, list of tracking areas, list of PLMNs etc.

[0305] - In one option, the configuration may comprise an indication of a time for how old the reported predictions may be. If the prediction results are older than the indicated time, the UE should not report the predictions. In a variant, the UE is configured with a validity timer, indicating how long prediction results are valid and the UE should only report valid prediction results.

[0306] - In an embodiment the configuration may include the quality threshold instructing the UE to include the early measurements predictions of neighboring cells only if the predictions indicate quality better than the configured threshold e.g., the predicted RSRP is greater than the RSRP threshold set as part of the quality threshold. In an embodiment the quality threshold can be the same quality threshold used as part of early measurements configuration or in another embodiment the UE receives a different quality threshold configuration for the early measurement predictions.

[0307] - The configuration may indicate to the UE to report the cause in case it failed to provide the predictions. For instance, the AI / ML model is not applicable to the current scenario, or the accuracy / uncertainty criterion based on which prediction should have been provided was not fulfilled, or that RRM predictions are not available / not supported. The cause provided can relate to all cells or all frequencies, or a certain cell or frequency, or a certain bandcombination, a certain network slice, etc.

[0308] - In one option, the network can request the UE to report measurement prediction of a configured measurement object that was originally configured to collect the actual measurement, if the actual measurement is not available. The network can configure the UE to report the cause why the measurement prediction is reported instead of the actual measurement.

[0309] The configuration may be provided to the UE in dedicated RRC message, e.g. RRCRelease or correspondingly, and / or in system information broadcast, e.g. SIB11. If both are configured, the dedicated configuration may override the broadcasted information, the dedicated configuration may override the broadcasted information for a certain amount of time, the broadcasted information may override the dedicated configuration after a certain amount of time or the broadcasted configuration may always override the dedicated configuration. In one option, there is an indication in system information, indicating whether the broadcasted information overrides the dedicated configuration.

[0310] - In one option, a new timer is introduced, indicating how long a dedicated configuration for prediction of measurements is valid. After the timer has expired, the UE should use broadcasted configuration instead.

[0311] - In one option, the broadcasted information overrides the dedicated information in case the broadcasted information indicates that the UE is required to perform actual measurements, i.e. predicted measurements are not accepted or applicable in that cell / area.

[0312] - In one option, the broadcasted information includes an indication that predicted measurement results are allowed (or not allowed) in that cell / area.

[0313] - In one option, the broadcasted information includes an indication that predicted measurement results are allowed in that cell / area under certain conditions. The conditions may, e.g., comprise that the predictions were made within a certain area, e.g. within a validity area for the predictions.

[0314] When the UE resumes or is setup in a cell (same cell or different cell than the cell where the UE was transferred to RRC IDLE / RRC INACTIVE), the network may receive an indication from the UE (e.g., in step 1608 such as, e.g., in step 1608-5), indicating the availability of (early) prediction results, e.g. in an RRC message such as RRCSetupComplete, RRCResumeComplete. Another example of the signaling solution to report the IDLE / INACTIVE state prediction is UE information request / response procedure wherein the network requests the UE to include the early predictions (collected in RRC IDLE / INACTIVE states) in the UE Information Response message and sends to the network node.

[0315] A network node where the UE resumes may request a previous network node to transmit the UE context and the previous network node may transmit the UE context to the new serving network node. The network node may request the UE to report the prediction results. The network node may explicitly request (e.g., a dedicated / separate request flag e.g., IdleModePredictionReq) the UE to include the predictions beside the early measurements in the send report to the network, or network node wants the UE to report only the early measurements even if the predictions is available at the UE. Alternatively, if the network configures the existing idleModeMeasurementReq the UE includes the IDLE / INACTIVE mode predictions in the report beside the IDLE / INACTIVE (early) measurements. Alternatively, the UE may directly report the prediction results.

[0316] The network receives the prediction results in a report from the UE. The report may comprise one or multiple of the following:

[0317] - An identity of the configuration associated with the report.

[0318] - Prediction results for the measurement / prediction quantities (e.g., RSRP, RSRQ, SINR, etc.), as indicated in the configuration. The prediction results may be sent in lists per frequency and / or per cell. In other options, the result may e.g. be indicated per frequency band, per band-combination, per network slice, etc.

[0319] o In an embodiment the UE includes the frequency domain predictions for a number of cells per frequency for a number of frequencies in the report.

[0320] o In another embodiment the report may include spatial domain predictions for a number of cells in one or more frequencies for which the UE performed the actual measurements for at least one cell operating in that frequency.

[0321] - The results may be indicated in priority order.

[0322] - The results may comprise an indication of the accuracy of the predictions.

[0323] - In one option, the measurement results are reported together with an indication, indicating whether the measurement result is an actual measurement result or a predicted measurement result. This indication is particularly needed in case the configuration contained an indication that predictions are allowed, i.e. not an exact configuration of which frequencies that should be measured and which frequencies that should be predicted. In one alternative, the indication may indicate that the result is based on predictions and if the indication is not included, the result is based on actual measurements.

[0324] - The amount of predicted samples and the amount of measured samples. As an example, the UE may not have been able to perform as large amount of measurements as indicated by the network and the UE may then indicate the actual amount of measurements performed and / or the actual amount of predicted samples. - In one option, the report comprises a time stamp of when the predictions were made or an indication of how old the predictions are. Alternatively, the UE may report a validity time that was applied to the predictions or an indication that a validity time has been applied. - Information of the age of the predictions may be reported by the UE to the network if explicitly requested by the network e.g., when the network configures the request flags such as validatedMeasurementsReq or validatedPredictionsReq. Some non-limiting examples of such information are described in the following:

[0325] o the information related to the time window in which the prediction is performed; o the time difference between prediction and measurements e.g., elapsed time since performing / collecting early measurements until the predict! ons / inference;

[0326] o time since prediction until reporting the prediction to the network e.g., the UE logs the time elapsed since performing the inference and collecting the prediction until sending the report to the network e.g., as part RRC Resume or UE information Response signals.

[0327] - In one option, the report comprises an indication of the length of the prediction window that was used. The UE may also report the length of the observation window.

[0328] - The report may comprise a cause value indicating a reason for why certain predictions were not performed, or why predictions were reported instead of actual measurements. Alternatively, an indication may be included, indicating that the RRM results are predictions instead of actual measurements.

[0329] Figure 17 shows an example of a communication system 1700 in accordance with some embodiments. The UE described above may be one of the UEs 1712 of Figure 17. The network nodes described above may be network nodes 1710 of Figure 17.

[0330] In the example, the communication system 1700 includes a telecommunications network 1702 that includes an access network 1704, such as a radio access network (RAN), and a core network 1706, which includes one or more core network nodes 1708. The access network 1704 includes one or more access network nodes or base stations of various types, access network nodes 1710A and 1710B are depicted (which may be collectively referred to as network nodes 1710), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points (APs). Some embodiments of the access network 1704 may include more than one access network technology. The network nodes 1710 of access network 1704 facilitate direct or indirect connection of wireless devices, also referred to as user equipments (UEs), such as by connecting UEs 1712A, 1712B, 1712C, and 1712D (one or more of which may be generally referred to as UEs 1712) to the core network 1706 over one or more wireless connections. 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, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunications network 1702 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a network node in the telecommunications network 1702 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 1702, including one or more access network nodes 1710 and / or core network nodes 1708.

[0331] 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 anon-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.

[0332] The network nodes 1710 facilitate direct or indirect connection of one or more UEs 1712 to the core network 1706 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 1700 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 1700 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0333] The UEs 1712 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 1710 and other communication devices. Similarly, the network nodes 1708, 1710 are arranged, capable, configured, and / or operable to communicate directly or indirectly (e.g., via other devices of telecommunications network 1702) with the UEs 1712 and / or with other network nodes or equipment in the telecommunications network 1702 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 1702. More specifically, UEs 1712 may send messages, data, and / or other signals to network nodes 1708, 1710 or other elements of the telecommunications network 1702 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 1708, 1710 may send messages, data, and other signals to UEs 17122, other network nodes 1708, 1710, and other devices in telecommunications network 1702 directly or indirectly. As one specific example, a core network node 108 may transmit a particular message to a UE 1712 by transmitting the message to an access network node 1710 that will then transmit the message to the intended UE 1712. Similarly, a core network node 108 may receive a particular message from a UE 1712 by receiving the message from an access network node 1710 that itself received the message from the UE 1712.

[0334] In the depicted example, the core network 1706 connects elements of the access network 1704 (e.g., one or more of the network nodes 1710) to one or more host computing systems, such as host 1716. 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 1706 includes one or more core network nodes (e.g., core network node 1708) of various types, one or more of which may be generally referred to as network nodes 1708. Network nodes 1708 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 1708. 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 (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0335] The host 1716 may be under the ownership or control of a service provider other than an operator or provider of the access network 1704 and / or the telecommunications network 1702. The host 1716 may be operated by the service provider or on behalf of the service provider. The host 1716 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.

[0336] As a whole, the communication system 1700 of Figure 17 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 1700 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 1700 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 1700 supporting different standards, protocols, or rule sets.

[0337] As one example, in certain embodiments, access network 1704 may contain some access network nodes 1710 that support 3GPP radio access technologies (RAT), such as LTE or NR, while other access network nodes 1710 support (or the same access network nodes 1710 additionally support) non-3GPP RATs, such as Wi-Fi or a proprietary RAT. As another example, telecommunications network 1702 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 104 and / or multiple core networks 106 with individual networks 104, 106 supporting different standard generations. Telecommunications network 1702 may support network slicing to provide different logical networks to different devices that are connected to the telecommunications network 1702. For example, the telecommunications network 1702 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.

[0338] In some examples, one or more of the UEs 1712 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 1704 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 1704. 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).

[0339] In the example, the hub 1714 communicates with the access network 1704 to facilitate indirect communication between one or more UEs (e.g., UE 1712C and / or 1712D) and network nodes (e.g., network node 1710B). In some examples, the hub 1714 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 1714 may be a broadband router enabling access to the core network 1706 for the UEs. As another example, the hub 1714 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 1710, or by executable code, script, process, or other instructions in the hub 1714.

[0340] As another example, the hub 1714 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 1714 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 1714 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 1714 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 1714 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0341] The hub 1714 may have a constant / persistent or intermittent connection to the network node 1710B. The hub 1714 may also allow for a different communication scheme and / or schedule between the hub 1714 and UEs (e.g., UE 1712C and / or 1712D), and between the hub 1714 and the core network 1706. In other examples, the hub 1714 is connected to the core network 1706 and / or one or more UEs via a wired connection. Moreover, the hub 1714 may be configured to connect to an M2M service provider over the access network 1704 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 1710 while still connected via the hub 1714 via a wired or wireless connection. In some embodiments, the hub 1714 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 1710B. In other embodiments, the hub 1714 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 1710B, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0342] Figure 18 is another example of a communication system 1800 according to some embodiments. As used herein, the communication system 1800 includes multiple access points (APs) 1810 (with four exemplary APs 1810A, 1810B, 1810C, and 1810D being depicted) and multiple wireless devices, referred to in the context of communication system 1800 as stations (STAs) 1812 (referred to individually as STA 1812A, STA 1812B, STA 1812C, STA 1812D, and STA 1812E). STA 1812A is served by AP 1810A in a first basic service set (BSS) 1820A. STA 1812B and STA 1812C are served by AP 1810B in a second BSS, BSS 1820B. STA 1812D is served by AP 1810C in a third BSS, BSS 1820C. STA 1812E is served by AP 1810D in a fourth BSS, BSS 1820D. Stations 1812 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, headmounted displays (HMDs) for Augmented Reality (AR) or Virtual Reality (VR), or the like. Further, stations 1812 could, for example, correspond to other kinds of equipment like smart home devices, printers, multimedia devices, data storage devices, or the like.

[0343] Each of STAs 1812 may connect through a radio link to one of APs 1810. For example, depending on location or channel conditions experienced by a given STA 1812, 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 a frequency 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.

[0344] Each AP 1810 may provide data connectivity to STAs 1812 connected to a particular AP 1810. As illustrated, APs 1810 may be connected to a data network 1830. In this way, APs 1810 may also provide data connectivity between STAs 1812 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 1812 and its serving AP 1810 may be used for providing various kinds of services to STA 1812, e.g., a voice service, a multimedia service, or other data service. Such services may be based on applications that are executed on STA 1812 and / or on a device linked to STA 1812. By way of example, Figure 18 illustrates an application service platform 1832 provided in data network 1830. The application(s) executed on STA 1812 and / or on one or more other devices linked to STA 1812 may use the radio link for data communication with one or more other STA 1812 and / or the application service platform 1832, thereby enabling utilization of the corresponding service(s) at STA 1812.

[0345] Figure 19 shows a wireless device 1900, which may be configured to operate in communication system 1700 of Figure 17 or in communication system 1800 of Figure 18. The wireless device 1900 may be alternatively referred to as a UE 1900, like a UE 1712 within the context of communication system 1700, or as a station (STA) 1900 or as anon-access-point station (non-AP STA) 1900, like a STA 1812 within the context of the communication system 1800, 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 (LEE), laptop-mounted 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.

[0346] A wireless device 1900 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 1900 may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, wireless device 1900 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 1900 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).

[0347] In particular embodiments, wireless device 1900 includes processing circuitry 1902 that is operatively coupled via a bus 1904 to an input / output interface 1906, a power source 1908, a memory 1910, a communication interface 1912, and / or any other component, or any combination thereof. Certain embodiments of wireless device 1900 may include all or a subset of the components shown in Figure 19. The level of integration between the components may vary from one embodiment of wireless device 1900 to another. In general, in a particular embodiment of wireless device 1900, processing circuitry 1902, input / output interface 1906, power source 1908, memory 1910, and communication interface 1912 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 1900. Further, certain embodiments of wireless devices 1900 may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0348] The processing circuitry 1902 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 1910. The processing circuitry 1902 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 1902 may include multiple central processing units (CPUs).

[0349] In the example, the input / output interface 1906 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 1900. 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.

[0350] In some embodiments, the power source 1908 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 1908 may further include power circuitry for delivering power from the power source 1908 itself, and / or an external power source, to the various parts of wireless device 1900 via input circuitry or an interface such as an electrical power cable. Power source 1908 may perform any formatting, converting, or other modification to make accessible power suitable for the respective components of the wireless device 1900 to which power is supplied.

[0351] The memory 1910 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 1910 includes one or more programs 1914, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1916. The memory 1910 may store, for use by wireless device 1900, any of a variety of various operating systems or combinations of operating systems.

[0352] The memory 1910 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 USIM and / or 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 1910 may allow wireless device 1900 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 1910, which may be or comprise a device-readable storage medium. The processing circuitry 1902 may be configured to communicate with an access network or other network via or using the communication interface 1912. The communication interface 1912 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 1922. The communication interface 1912 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 1918 and / or a receiver 1920 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 1918 and receiver 1920 may be coupled to one or more antennas (e.g., antenna 1922) and may share circuit components, software, or firmware, or alternatively be implemented separately.

[0353] In the illustrated embodiment, communication functions of the communication interface 1912 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 / intemet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0354] In particular embodiments, wireless device 1900 may provide an output of data captured via a sensor, through its communication interface 1912, via a wireless connection to a network node, and / or in any appropriate manner. Data captured by sensors of a wireless device 1900 can be communicated through a wireless connection to a network node via another wireless device 1900. 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).

[0355] As another example, wireless device 1900 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 1900 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.

[0356] Wireless device 1900, 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 1900 represents an loT device that comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the example embodiment of wireless device 1900 shown in Figure 19.

[0357] As yet another specific example, in an loT scenario, wireless device 1900 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 1900 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 1900 may implement the 3GPP NB-IoT standard. In other scenarios, wireless device 1900 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.

[0358] In practice, any number of wireless devices 1900 may be used together with respect to a single use case. For example, a first wireless device 1900 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 1900 that is a remote controller operating the drone. When a user makes changes from the remote controller, the first wireless device 1900 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 1900 can also include more than one of the functionalities described above. For example, wireless device 1900 might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0359] Figure 20 shows a network node 2000 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 2000 may be configured to operate in communication system 1700 of Figure 17, like network nodes 1708 or 1710, or in communication system 1800 of Figure 18, like an AP 1810 or a station 1812. 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 (gNBs)), O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU).

[0360] Network nodes 2000 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 2000 may be a relay node or a relay donor node controlling a relay. Network nodes 2000 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).

[0361] Other examples of network nodes 2000 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).

[0362] In particular embodiments, network node 2000 includes a processing circuitry 2002, a memory 2004, a communication interface 2006, and a power source 2008. In general, in a particular embodiment of network node 2000, processing circuitry 2002, memory 2004, communication interface 2006, and power source 2008 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 2000.

[0363] The network node 2000 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 2000 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 2000 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memories 2004 or portions of memory 2004 for different RATs) and some components may be reused (e.g., a same antenna 2010 may be shared by different RATs). The network node 2000 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 2000, 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 be integrated into the same or different chip or set of chips and other components within network node 2000.

[0364] The processing circuitry 2002 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 2004, to provide network node 2000 functionality.

[0365] In some embodiments, the processing circuitry 2002 includes a system on a chip (SOC). In some embodiments, the processing circuitry 2002 includes one or more of radio frequency (RF) transceiver circuitry 2012 and baseband processing circuitry 2014. In some embodiments, the RF transceiver circuitry 2012 and the baseband processing circuitry 2014 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 2012 and baseband processing circuitry 2014 may be on the same chip or set of chips, boards, or units.

[0366] The memory 2004 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 computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 2002. The memory 2004 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 2002 and utilized by the network node 2000. The memory 2004 may be used to store any calculations made by the processing circuitry 2002 and / or any data received via the communication interface 2006. In some embodiments, the processing circuitry 2002 and memory 2004 is integrated.

[0367] The communication interface 2006 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 2006 comprises port(s) / terminal(s) 2016 to send and receive data, for example to and from a network over a wired connection. In particular embodiments, network node 1900 may be capable of wireless communication and communication interface 2006 may also include radio front-end circuitry 2018 that may be coupled to, or in certain embodiments a part of, an antenna 2010. Particular embodiments of radio front-end circuitry 2018 include filter(s) 2020 and amplifier(s) 2022. The radio front-end circuitry 2018 may be connected to an antenna 2010 and processing circuitry 2002. The radio front-end circuitry may be configured to condition signals communicated between antenna 2010 and processing circuitry 2002. The radio front-end circuitry 2018 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 2018 may convert the digital data into a radio signal(s) having the appropriate channel and bandwidth parameters using a combination of filters 2020 and / or amplifiers 2022. The radio signal(s) may then be transmitted via the antenna 2010. Similarly, when receiving data, the antenna 2010 may collect radio signals which are then converted into digital data by the radio front-end circuitry 2018. The digital data may be passed to the processing circuitry 2002. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0368] In certain alternative embodiments, network node 2000 may be capable of wireless communication but does not include separate radio front-end circuitry 2018, instead, the processing circuitry 2002 includes radio front-end circuitry and is connected to the antenna 2010. Similarly, in some embodiments, all or some of the RF transceiver circuitry 2012 is part of the communication interface 2006. In still other embodiments, the communication interface 2006 includes one or more ports or terminals 2016, the radio front-end circuitry 2018, and the RF transceiver circuitry 2012, as part of a radio unit (not shown), and the communication interface 2006 communicates with the baseband processing circuitry 2014, which is part of a digital unit (not shown).

[0369] The antenna 2010 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 2010 may be coupled to the radio front-end circuitry 2018 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 2010 is separate from the network node 2000 and connectable to the network node 2000 through one or more interfaces or ports.

[0370] The antenna 2010, communication interface 2006, and / or the processing circuitry 2002 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 2000. Any information, data, and / or signals may be received from a UE, another network node, and / or any other network equipment. Similarly, the antenna 2010, the communication interface 2006, and / or the processing circuitry 2002 may be configured to perform some or all of the transmitting or sending operations described herein as being performed by the network node 2000. Any information, data and / or signals may be transmitted to a UE, another network node, and / or any other network equipment.

[0371] The power source 2008 provides power to the various components of network node 2000 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 2008 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 2000 with power for performing the functionality described herein. For example, the network node 2000 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 2008. As a further example, the power source 2008 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.

[0372] Embodiments of the network node 2000 may include additional components beyond those shown in Figure 20 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 2000 may include user interface equipment to allow input of information into the network node 2000 and to allow output of information from the network node 2000. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 2000. Figure 21 is a block diagram illustrating a virtualization environment 2100 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 2100 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 2100 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface.

[0373] Applications 2102 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 2100 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0374] Hardware 2104 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 2106 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VM 2108A and VM 2108B (which may be collectively referred to as VMs 2108), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 2106 may present a virtual operating platform that appears like networking hardware to one or more of the VMs 2108.

[0375] The VMs 2108 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by virtualization layer 2106. Different embodiments of the instance of a virtual appliance 2102 may be implemented on one or more of VMs 2108, 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.

[0376] In the context of NFV, each of the VMs 2108 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 2108, and that part of hardware 2104 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 2108 on top of the hardware 2104 and corresponds to an application 2102.

[0377] Hardware 2104 may be implemented in a standalone network node with generic or specific components. Hardware 2104 may implement some functions via virtualization. Alternatively, hardware 2104 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 2110, which, among others, oversees lifecycle management of applications 2102. In some embodiments, hardware 2104 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 2112 which may alternatively be used for communication between hardware nodes and radio units.

[0378] 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.

[0379] 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 or discrete 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.

[0380] Those skilled in the art will recognize improvements and modifications to the embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein.

[0381] Some exemplary embodiments of the present disclosure are as follows:

[0382] Group A Embodiments

[0383] Embodiment 1: A method performed by a wireless device, the method comprising: receiving (1604), from a first network node, information that configures the wireless device to perform early predictions; performing (1606) one or more predictions for one or more frequencies and / or one or more cells based on measurements performed by the wireless device while in an idle state and / or while in an inactive state, in accordance with the configuration, wherein the one or more predictions comprise one or more frequency domain predictions and / or one or more spatial domain predictions; and transmitting (1608) at least a subset of the one or more predictions to the first network node or another network node.

[0384] Embodiment 2: The method of embodiment 1, wherein transmitting (1608) the at least a subset of the one or more predictions comprises transmitting (1608) the at least a subset of the one or more predictions upon resuming a connection of the wireless device or upon setup on a connection of the wireless device.

[0385] Embodiment 3: The method of embodiment 1 or 2, wherein the one or more predictions are one or more Artificial Intelligence, Al, / Machine Learning, ML, based predictions.

[0386] Embodiment 4: The method of any of embodiments 1 to 3, wherein the one or more predictions comprise one or more frequency domain predictions.

[0387] Embodiment 5 : The method of embodiment 4, wherein the one or more frequency domain predictions comprise one or more predicted beam level measurements of a cell in a second frequency based on actual beam level measurements in a first frequency.

[0388] Embodiment 6: The method of embodiment 4, wherein the one or more frequency domain predictions comprise one or more predicted cell level measurements in a second frequency based on actual beam level measurements in a first frequency.

[0389] Embodiment 7 : The method of embodiment 4, wherein the one or more frequency domain predictions comprise one or more predicted cell level measurements in a second frequency based on actual cell level measurements in a first frequency.

[0390] Embodiment 8: The method of any of embodiments 1 to 7, wherein the one or more predictions comprise one or more spatial domain predictions.

[0391] Embodiment 9: The method of embodiment 8, wherein the one or more spatial domain predictions comprise one or more predicted beam level measurements of a cell in a certain frequency based on actual beam level measurements in the same certain frequency.

[0392] Embodiment 10: The method of embodiment 8, wherein the one or more spatial domain predictions comprise one or more predicted cell level measurements in a certain frequency based on actual beam level measurements in the same certain frequency.

[0393] Embodiment 11 : The method of embodiment 8, wherein the one or more spatial domain predictions comprise one or more predicted cell level measurements in a certain frequency based on actual cell level measurements in the same certain frequency.

[0394] Embodiment 12: The method of any of embodiments 1 to 11, wherein the one or more predictions comprise one or more cell and / or beam level predicted measurement values.

[0395] Embodiment 13: The method of any of embodiments 1 to 11, wherein the one or more predictions comprise probability values each representing a probability of a corresponding cell or beam in an associated frequency being a best beam or cell for the wireless device.

[0396] Embodiment 14: The method of any of embodiments 1 to 11, wherein the one or more predictions comprise one or more best beams for the wireless device. Embodiment 15: The method of any of embodiments 1 to 11, wherein the one or more predictions comprise one or more best beams for the wireless device and associated predicted measurement values (e.g., predicted BRSRP, predicted BRSRQ, predicted BSINR, or the like).

[0397] Embodiment 16: The method of any of embodiments 1 to 15, further comprising transmitting (1600), to the first network node, capability information related to one or more capabilities of the wireless device related to early predictions.

[0398] Embodiment 17: The method of any of embodiments 1 to 15, further comprising transmitting (1600), to the first network node, capability information of the wireless device, the capability information comprising any one or more of the following:

[0399] - wireless device capabilities related to the ability to perform early measurement predictions in idle and / or inactive state (e.g., indicated per frequency, per frequency band, per frequency band combination, or per frequency range);

[0400] - a quantity of early measurement prediction the wireless device is capable of (e.g. prediction of RSRP, RSRQ, SINR, and / or the like);

[0401] - a type of prediction the wireless device is capable of (e.g. frequency domain prediction or spatial domain prediction or a combination of frequency and spatial domain predictions); - a maximum amount of predictions the wireless is capable of (e.g. a maximum amount of frequencies the wireless device is capable of predicting, a maximum amount of cells is capable of predicting, a maximum amount of beams is capable of predicting, etc.);

[0402] - a maximum amount of early measurement predictions and early measurements the wireless device is capable of totally performing when in idle and / or inactive state.

[0403] Embodiment 18: The method of any of embodiments 1 to 17, further comprising transmitting (1602), to the first network node, wireless device assistance information related to early predictions.

[0404] Embodiment 19: The method of any of embodiments 1 to 17, further comprising transmitting (1602), to the first network node, wireless device assistance information, the wireless device assistance information comprising any one or more of the following:

[0405] • a wireless device speed range in which an associated AI / ML model used to generate the one or more predictions is applicable;

[0406] • mobility scenario categories / classes (e.g., low / medium / high speed classes identified by the number of cells UE visits in a certain period of time);

[0407] • mobility scenario categories / classes (e.g., low / medium / high speed classes identified by the number of cells UE visits in a certain period of time) for which the AI / ML model used to generate the one or more predictions is applicable; • a radio condition (e.g., range of radio coverage measured based on serving cell RSRP or RSRQ or SINR) in which the AI / ML model used to generate the one or more predictions is applicable;

[0408] • information that indicates an area where early measurement predictions are applicable. Embodiment 20: The method of any of embodiments 1 to 17, further comprising transmitting (1602), to the first network node, wireless device assistance information, the wireless device assistance information comprising any one or more of the following:

[0409] - Preferred frequencies (or frequency band, band combination, range etc.) for performing early measurement predictions;

[0410] - Preferred number of frequencies to predict;

[0411] - Estimated time for performing RRM measurement prediction of a certain frequency; - Preferred predicted quantity (e.g., RSRP, RSRQ, or SINR).

[0412] Embodiment 21: The method of any of embodiments 1 to 20, wherein the received configuration comprises any one or more of the following:

[0413] - An identity of the configuration (e.g. a prediction ID or a measID);

[0414] - A list of carriers / frequencies or frequency band for which the wireless device is to perform measurement predictions;

[0415] - A list of cells for which the wireless device is to perform measurement predictions;

[0416] - an indication per carrier or per cell, indicating that the wireless device is to perform predictions for that frequency or cell;

[0417] - an indication per carrier or per cell, indicating whether the wireless device is to perform measurements or predictions for that frequency or cell;

[0418] - multiple lists of carriers / frequencies or cells (e.g. per RAT);

[0419] - a type of prediction the wireless device is to perform (e.g. frequency or spatial domain predictions);

[0420] - a quantity of the prediction (e.g. whether the prediction result should be RSRP, RSRQ or SINR or multiple quantities, e.g. both RSRP and RSRQ);

[0421] - an indication, indicating that the wireless device is allowed to report predicted measurement results instead of actual measurement result;

[0422] - an indication that the wireless device is to report both actual measurement results and predicted measurement results;

[0423] - an accuracy threshold (or an uncertainty level) based on which the wireless device is requested to report early measurement prediction(s);

[0424] - a priority order for the predictions; - an area where the wireless device is to perform prediction of measurements, i.e. a validity area for the predictions;

[0425] - an indication of a time for how old the reported predictions may be;

[0426] - a quality threshold for including the predictions of neighboring cells;

[0427] - an indication to report a cause in case it failed to provide the predictions;

[0428] - an indication for the wireless device to report a cause why a measurement prediction is reported instead of an actual measurement.

[0429] Embodiment 22: The method of any of embodiments 1 to 21, wherein the wireless device receives the configuration via dedicated signaling, broadcast system information, or a combination thereof.

[0430] Embodiment 23: The method of any of embodiments 1 to 22, wherein transmitting (1608) the at least a subset of the one or more predictions to the first network node or another network node comprises transmitting (1608) a report to the fist network node or another network node, the report comprising the at least a subset of the one or more predictions.

[0431] Embodiment 24: The method of embodiment 23, wherein the report comprises:

[0432] - an identity of the configuration associated with the report;

[0433] - prediction results (i.e., the at least a subset of the one or more predictions) for measurement / prediction quantities, as indicated in the configuration.

[0434] - prediction results (i.e., the at least a subset of the one or more predictions) for measurement / prediction quantities, as indicated in the configuration, indicated in priority order;

[0435] - an indication of an accuracy of the predictions;

[0436] - an indication that the predictions are predictions, rather than actual measurements;

[0437] - amount of predicted samples and an amount of measured samples;

[0438] - a time stamp of when the predictions were made or an indication of how old the predictions are;

[0439] - an indication of a length of a prediction window that was used;

[0440] - a cause value indicating a reason for why certain predictions were not performed, or why predictions were reported instead of actual measurements.

[0441] Embodiment 25: The method of any of the previous embodiments, further comprising: providing user data; and forwarding the user data to a host via the transmission to the network node. Group B Embodiments

[0442] Embodiment 26: A method performed by a network node, the method comprising: transmitting (1604), to a wireless device, information that configures the wireless device to perform early predictions, the early predictions comprising one or more frequency domain predictions and / or one or more spatial domain predictions based on measurements performed by the wireless device while in an idle state and / or while in an inactive state.

[0443] Embodiment 27: The method of embodiment 26, wherein the early predictions are Artificial Intelligence, Al, / Machine Learning, ML, based predictions.

[0444] Embodiment 28: The method of embodiment 26 or 27, wherein the early predictions comprise one or more frequency domain predictions.

[0445] Embodiment 29: The method of embodiment 28, wherein the one or more frequency domain predictions comprise one or more predicted beam level measurements of a cell in a second frequency based on actual beam level measurements in a first frequency.

[0446] Embodiment 30: The method of embodiment 28, wherein the one or more frequency domain predictions comprise one or more predicted cell level measurements in a second frequency based on actual beam level measurements in a first frequency.

[0447] Embodiment 31: The method of embodiment 28, wherein the one or more frequency domain predictions comprise one or more predicted cell level measurements in a second frequency based on actual cell level measurements in a first frequency.

[0448] Embodiment 32: The method of any of embodiments 26 to 31, wherein the early predictions comprise one or more spatial domain predictions.

[0449] Embodiment 33: The method of embodiment 32, wherein the one or more spatial domain predictions comprise one or more predicted beam level measurements of a cell in a certain frequency based on actual beam level measurements in the same certain frequency.

[0450] Embodiment 34: The method of embodiment 32, wherein the one or more spatial domain predictions comprise one or more predicted cell level measurements in a certain frequency based on actual beam level measurements in the same certain frequency.

[0451] Embodiment 35: The method of embodiment 32, wherein the one or more spatial domain predictions comprise one or more predicted cell level measurements in a certain frequency based on actual cell level measurements in the same certain frequency.

[0452] Embodiment 36: The method of any of embodiments 26 to 35, wherein the early predictions comprise one or more cell and / or beam level predicted measurement values. Embodiment 37: The method of any of embodiments 26 to 35, wherein the early predictions comprise probability values each representing a probability of a corresponding cell or beam in an associated frequency being a best beam or cell for the wireless device.

[0453] Embodiment 38: The method of any of embodiments 26 to 35, wherein the one or more predictions comprise one or more best beams for the wireless device.

[0454] Embodiment 39: The method of any of embodiments 26 to 35, wherein the one or more predictions comprise one or more best beams for the wireless device and associated predicted measurement values (e.g., predicted BRSRP, predicted BRSRQ, predicted BSINR, or the like).

[0455] Embodiment 40: The method of any of embodiments 26 to 39, further comprising receiving (1600), from the wireless device, capability information related to one or more capabilities of the wireless device related to early predictions.

[0456] Embodiment 41 : The method of any of embodiments 26 to 40, further comprising receiving (1600), from the wireless device, capability information of the wireless device, the capability information comprising any one or more of the following:

[0457] - wireless device capabilities related to the ability to perform early measurement predictions in idle and / or inactive state (e.g., indicated per frequency, per frequency band, per frequency band combination, or per frequency range);

[0458] - a quantity of early measurement prediction the wireless device is capable of (e.g. prediction of RSRP, RSRQ, SINR, and / or the like);

[0459] - a type of prediction the wireless device is capable of (e.g. frequency domain prediction or spatial domain prediction or a combination of frequency and spatial domain predictions); - a maximum amount of predictions the wireless is capable of (e.g. a maximum amount of frequencies the wireless device is capable of predicting, a maximum amount of cells is capable of predicting, a maximum amount of beams is capable of predicting, etc.);

[0460] - a maximum amount of early measurement predictions and early measurements the wireless device is capable of totally performing when in idle and / or inactive state.

[0461] Embodiment 42: The method of any of embodiments 26 to 41, further comprising receiving (1602), from the wireless device, wireless device assistance information related to early predictions.

[0462] Embodiment 43: The method of any of embodiments 26 to 41, further comprising receiving (1602), from the wireless device, wireless device assistance information, the wireless device assistance information comprising any one or more of the following:

[0463] • a wireless device speed range in which an associated AI / ML model used to generate the one or more predictions is applicable; • mobility scenario categories / classes (e.g., low / medium / high speed classes identified by the number of cells UE visits in a certain period of time);

[0464] • mobility scenario categories / classes (e.g., low / medium / high speed classes identified by the number of cells UE visits in a certain period of time) for which the AI / ML model used to generate the one or more predictions is applicable;

[0465] • a radio condition (e.g., range of radio coverage measured based on serving cell RSRP or RSRQ or SINR) in which the AI / ML model used to generate the one or more predictions is applicable;

[0466] • information that indicates an area where early measurement predictions are applicable. Embodiment 44: The method of any of embodiments 26 to 41, further comprising receiving (1602), from the wireless device, wireless device assistance information, the wireless device assistance information comprising any one or more of the following:

[0467] - Preferred frequencies (or frequency band, band combination, range etc.) for performing early measurement predictions;

[0468] - Preferred number of frequencies to predict;

[0469] - Estimated time for performing RRM measurement prediction of a certain frequency; - Preferred predicted quantity (e.g., RSRP, RSRQ, or SINR).

[0470] Embodiment 45: The method of any of embodiments 26 to 44, wherein the configuration comprises any one or more of the following:

[0471] - An identity of the configuration (e.g. a prediction ID or a measID);

[0472] - A list of carriers / frequencies or frequency band for which the wireless device is to perform measurement predictions;

[0473] - A list of cells for which the wireless device is to perform measurement predictions;

[0474] - an indication per carrier or per cell, indicating that the wireless device is to perform predictions for that frequency or cell;

[0475] - an indication per carrier or per cell, indicating whether the wireless device is to perform measurements or predictions for that frequency or cell;

[0476] - multiple lists of carriers / frequencies or cells (e.g. per RAT);

[0477] - a type of prediction the wireless device is to perform (e.g. frequency or spatial domain predictions);

[0478] - a quantity of the prediction (e.g. whether the prediction result should be RSRP, RSRQ or SINR or multiple quantities, e.g. both RSRP and RSRQ);

[0479] - an indication, indicating that the wireless device is allowed to report predicted measurement results instead of actual measurement result; - an indication that the wireless device is to report both actual measurement results and predicted measurement results;

[0480] - an accuracy threshold (or an uncertainty level) based on which the wireless device is requested to report early measurement prediction(s);

[0481] - a priority order for the predictions;

[0482] - an area where the wireless device is to perform prediction of measurements, i.e. a validity area for the predictions;

[0483] - an indication of a time for how old the reported predictions may be;

[0484] - a quality threshold for including the predictions of neighboring cells;

[0485] - an indication to report a cause in case it failed to provide the predictions;

[0486] - an indication for the wireless device to report a cause why a measurement prediction is reported instead of an actual measurement.

[0487] Embodiment 46: The method of any of embodiments 26 to 45, wherein the configuration is transmitted to the wireless device via dedicated signaling, broadcast system information, or a combination thereof.

[0488] Embodiment 47 : The method of any of embodiments 26 to 46, further comprising receiving (1608), from the wireless device, one or more predictions based on the configuration.

[0489] Embodiment 48: The method of embodiment 47, wherein the one or more predictions are received in a report, the report comprising:

[0490] - an identity of the configuration associated with the report;

[0491] - prediction results (i.e., the at least a subset of the one or more predictions) for measurement / prediction quantities, as indicated in the configuration.

[0492] - prediction results (i.e., the at least a subset of the one or more predictions) for measurement / prediction quantities, as indicated in the configuration, indicated in priority order;

[0493] - an indication of an accuracy of the predictions;

[0494] - an indication that the predictions are predictions, rather than actual measurements;

[0495] - amount of predicted samples and an amount of measured samples;

[0496] - a time stamp of when the predictions were made or an indication of how old the predictions are;

[0497] - an indication of a length of a prediction window that was used;

[0498] - a cause value indicating a reason for why certain predictions were not performed, or why predictions were reported instead of actual measurements. Embodiment 49: The method of any of the previous embodiments, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment.

[0499] Group C Embodiments

[0500] Embodiment 50: A wireless device comprising: processing circuitry configured to perform any of the operations of any of the Group A embodiments; and a power source configured to supply power to the processing circuitry.

[0501] Embodiment 51 : A network node comprising: processing circuitry configured to perform any of the operations of any of the Group B embodiments; and a power source circuitry configured to supply power to the processing circuitry.

[0502] Embodiment 52: A wireless device comprising: one or more antennas; communication interface connected to the one or more antennas and to processing circuitry; the processing circuitry being configured to perform any of the operations of any of the Group A embodiments; an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry; an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and a power source connected to the processing circuitry and configured to supply power to the UE.

Claims

CLAIMS1. A method performed by a wireless device, the method comprising:receiving (1604), from a first network node, information that configures the wireless device to perform measurement predictions based on measurements performed by the wireless device while in an idle state and / or while in an inactive state;performing (1606) one or more measurement predictions for one or more frequencies and / or one or more cells based on measurements performed by the wireless device while in the idle state and / or while in the inactive state, in accordance with the configuration, wherein the one or more measurement predictions comprise one or more frequency domain measurement predictions and / or one or more spatial domain measurement predictions; andtransmitting (1608) at least a subset of the one or more measurement predictions to the first network node or another network node.

2. The method of claim 1 , wherein transmitting (1608) the at least a subset of the one or more measurement predictions comprises transmitting (1608) the at least a subset of the one or more measurement predictions upon resuming a connection of the wireless device or upon setup on a connection of the wireless device.

3. The method of claim 1 , wherein transmitting (1608) the at least a subset of the one or more measurement predictions comprises transmitting (1608) the at least a subset of the one or more measurement predictions in response to a request from the first network node or another network node, the request being responsive to an indication, by the wireless device, that the measurement predictions are available.

4. The method of any of claims 1 to 3, wherein the one or more measurement predictions are one or more Artificial Intelligence, Al, / Machine Learning, ML, based measurement predictions.

5. The method of any of claims 1 to 4, wherein the one or more measurement predictions comprise one or more frequency domain measurement predictions.

6. The method of claim 5, wherein the one or more frequency domain measurement predictions comprise one or more predicted beam level measurements of a cell in a second frequency based on actual beam level measurements in a first frequency.

7. The method of claim 5, wherein the one or more frequency domain measurement predictions comprise one or more predicted cell level measurements in a second frequency based on actual beam level measurements in a first frequency.

8. The method of claim 5, wherein the one or more frequency domain measurement predictions comprise one or more predicted cell level measurements in a second frequency based on actual cell level measurements in a first frequency.

9. The method of any of claims 1 to 8, wherein the one or more measurement predictions comprise one or more spatial domain measurement predictions.

10. The method of claim 9, wherein the one or more spatial domain measurement predictions comprise one or more predicted beam level measurements of a cell in a certain frequency based on actual beam level measurements in the same certain frequency.

11. The method of claim 9, wherein the one or more spatial domain measurement predictions comprise one or more predicted cell level measurements in a certain frequency based on actual beam level measurements in the same certain frequency.

12. The method of claim 9, wherein the one or more spatial domain measurement predictions comprise one or more predicted cell level measurements in a certain frequency based on actual cell level measurements in the same certain frequency.

13. The method of any of claims 1 to 12, wherein the one or more measurement predictions comprise one or more cell and / or beam level predicted measurement values.

14. The method of any of claims 1 to 12, wherein the one or more measurement predictions comprise probability values each representing a probability of a corresponding cell or beam in an associated frequency being a best beam or cell for the wireless device.

15. The method of any of claims 1 to 12, wherein the one or more measurement predictions comprise one or more best beams for the wireless device.

16. The method of any of claims 1 to 12, wherein the one or more measurement predictionscomprise one or more best beams for the wireless device and associated predicted measurement values.

17. The method of any of claims 1 to 16, further comprising transmitting (1600), to the first network node, capability information related to one or more capabilities of the wireless device related to measurement predictions based on measurements performed by the wireless device while in the idle state and / or while in the inactive state.

18. The method of any of claims 1 to 16, further comprising transmitting (1600), to the first network node, capability information of the wireless device, the capability information comprising any one or more of the following:- wireless device capabilities related to the ability to perform measurement predictions in idle and / or inactive state indicated per frequency, per frequency band, per frequency band combination, or per frequency range;- a measurement quantity of early measurement prediction of which the wireless device is capable;- a type of measurement prediction of which the wireless device is capable;- a maximum amount of measurement predictions of which the wireless is capable;- a maximum amount of measurement predictions and measurements the wireless device is capable of totally performing while in the idle state and / or while in the inactive state.

19. The method of any of claims 1 to 18, further comprising transmitting (1602), to the first network node, wireless device assistance information related to measurement predictions based on measurements performed by the wireless device while in the idle state and / or while in the inactive state.

20. The method of any of claims 1 to 18, further comprising transmitting (1602), to the first network node, wireless device assistance information, the wireless device assistance information comprising any one or more of the following:• a wireless device speed range in which an associated Artificial Intelligence, Al, / Machine Learning, ML, model used to generate the one or more measurement predictions is applicable;• mobility scenario categories and / or classes;• mobility scenario categories and / or classes for which the AI / ML model used to generate the one or more measurement predictions is applicable;• a radio condition in which the AI / ML model used to generate the one or more measurement predictions is applicable;• information that indicates an area where measurement predictions based on measurements performed by the wireless device while in the idle state and / or while in the inactive state are applicable.

21. The method of any of claims 1 to 18, further comprising transmitting (1602), to the first network node, wireless device assistance information, the wireless device assistance information comprising any one or more of the following:- preferred frequencies for performing measurement predictions based on measurements performed by the wireless device while in the idle state and / or while in the inactive state;- preferred number of frequencies for which the measurement predictions are made; estimated time for performing Radio Resource Monitoring, RRM, measurement prediction of a certain frequency;- preferred predicted measurement quantity.

22. The method of any of claims 1 to 21, wherein the received configuration comprises any one or more of the following:- an identity of the configuration;- a list of carriers or frequencies or frequency band for which the wireless device is to perform measurement predictions;- a list of cells for which the wireless device is to perform measurement predictions;- an indication per carrier or per cell, indicating that the wireless device is to perform predictions for that frequency or cell;- an indication per carrier or per cell, indicating whether the wireless device is to perform measurements or predictions for that frequency or cell;- multiple lists of carriers or frequencies or cells;- a type of measurement prediction the wireless device is to perform;- a measurement quantity of the prediction;- an indication, indicating that the wireless device is allowed to report predicted measurement results instead of actual measurement result;- an indication that the wireless device is to report both actual measurement results and predicted measurement results;- an accuracy threshold or an uncertainty level based on which the wireless device is requested to report the measurement predict! on(s);- a priority order for the measurement predictions;- an area where the wireless device is to perform the measurement predictions;- an indication of a time for how old the reported predictions may be;- a quality threshold for including measurement predictions of neighboring cells;- an indication to report a cause in case the wireless device failed to provide the predictions; - an indication for the wireless device to report a cause why a measurement prediction is reported instead of an actual measurement.

23. The method of any of claims 1 to 22, wherein the wireless device receives the configuration via dedicated signaling, broadcast system information, or a combination thereof.

24. The method of any of claims 1 to 23, wherein transmitting (1608) the at least a subset of the one or more predictions to the first network node or another network node comprises transmitting (1608) a report to the fist network node or another network node, the report comprising the at least a subset of the one or more predictions.

25. The method of claim 24, wherein the report comprises any one or more of the following:- an identity of the configuration associated with the report;- prediction results for measurement prediction quantities, as indicated in the configuration; - prediction results for measurement prediction quantities, as indicated in the configuration, indicated in priority order;- an indication of an accuracy of the measurement predictions;- an indication that the measurement predictions are predictions, rather than actual measurements;- amount of predicted samples and an amount of measured samples;- a time stamp of when the measurement predictions were made or an indication of how old the measurement predictions are;- an indication of a length of a prediction window that was used;- a cause value indicating a reason for why certain predictions were not performed, or why measurement predictions were reported instead of actual measurements.

26. A wireless device adapted to:receive (1604), from a first network node, information that configures the wireless device to perform measurement predictions based on measurements performed by the wireless device while in an idle state and / or while in an inactive state;perform (1606) one or more measurement predictions for one or more frequencies and / or one or more cells based on measurements performed by the wireless device while in the idle state and / or while in the inactive state, in accordance with the configuration, wherein the one or more measurement predictions comprise one or more frequency domain measurement predictions and / or one or more spatial domain measurement predictions; andtransmit (1608) at least a subset of the one or more measurement predictions to the first network node or another network node.

27. The wireless device of claim 26, further adapted to perform the method of any of claims 2 to 25.

28. A wireless device (1900), comprising:a communication interface (1912) comprising a transmitter (1918) and a receiver (1920); andprocessing circuitry (1902) associated with the communication interface (1912), the processing circuitry (1902) configured to cause the wireless device (1900) to:receive (1604), from a first network node, information that configures the wireless device to perform measurement predictions based on measurements performed by the wireless device while in an idle state and / or while in an inactive state;perform (1606) one or more measurement predictions for one or more frequencies and / or one or more cells based on measurements performed by the wireless device while in the idle state and / or while in the inactive state, in accordance with the configuration, wherein the one or more measurement predictions comprise one or more frequency domain measurement predictions and / or one or more spatial domain measurement predictions; and transmit (1608) at least a subset of the one or more measurement predictions to the first network node or another network node.

29. The wireless device of claim 28, wherein the processing circuitry (1902) is further configured to cause the wireless device (1900) to perform the method of any of claims 2 to 25.

30. A method performed by a network node, the method comprising:transmitting (1604), to a wireless device, information that configures the wireless device to perform measurement predictions, the measurement predictions comprising one or more frequency domain measurement predictions and / or one or more spatial domain measurement predictions based on measurements performed by the wireless device while in the idle state and / or while in the inactive state.

31. A network node adapted to :transmit (1604), to a wireless device, information that configures the wireless device to perform measurement predictions, the measurement predictions comprising one or more frequency domain measurement predictions and / or one or more spatial domain measurement predictions based on measurements performed by the wireless device while in the idle state and / or while in the inactive state.

32. A network node (2000) comprising processing circuitry (2002) configured to causes the network node (2000) to:transmit (1604), to a wireless device, information that configures the wireless device to perform measurement predictions, the measurement predictions comprising one or more frequency domain measurement predictions and / or one or more spatial domain measurement predictions based on measurements performed by the wireless device while in the idle state and / or while in the inactive state.

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