Generating measurement reports based on mobility related time-domain predictions

Abstract

A communication device in a communications network that includes a network node can generate (1620) a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells The report can include information associated with a subset of the 5 plurality of neighbor cells. Generating the report can include selecting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell. Generating the report can further include sorting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell. The communication device can further transmit (1630) the report to the network node.

Description

GENERATING MEASUREMENT REPORTS BASED ON MOBILITY RELATED TIME-DOMAIN PREDICTIONSTECHNICAL FIELD

[0001] The present disclosure is related to wireless communication systems and more particularly to generating measurement reports based on mobility related time-domain predictions. For example, selecting and sorting cells to include in measurement reports which include mobility related time-domain predictions.BACKGROUND

[0002] FIG. 1 illustrates an example of a new radio (NR) network (e.g., a 5th Generation (5G) network) including a 5G core (5GC) network 130, network nodes 120a-b (e.g., 5G base station (gNB)), multiple communication devices 110 (also referred to as user equipment (UE)).

[0003] A UE in NR may be configured by the network to perform measurements (e.g., reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to- interference-plus-noise ratio (SINR)) on one or more neighbor cells associated to a measurement object (e.g., associated to a synchronization signal block (SSB) frequency) and report information in a radio resource control (RRC) measurement report. In some examples, the information includes the measurements on the one or more neighbor cells. In additional or alternative examples, the information includes beam measurement information including beam identifier (e.g., SSB index) or beam measurements (e.g., synchronization signal (SS)-RSRP, SS- RSRQ, and SS-SINR).SUMMARY

[0004] According to some embodiments, a method of operating a communication device in a communications network that includes a network node is provided. The method includes generating a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells. The report includes information associated with a subset of the plurality of neighbor cells. Generating the report includes selecting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell. Generating the report further includes sorting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell. The method further includes transmitting the report to the network node.

[0005] According to additional or alternative embodiments, generating the report comprises generating the report to include the mobility related time-domain prediction of the neighbor cell.

[0006] According to additional or alternative embodiments, generating the report to include the mobility related time-domain prediction of the neighbor cell comprises generating the report to include only the mobility related time-domain prediction of the neighbor cell.

[0007] According to additional or alternative embodiments, generating the report comprises sorting the subset of the plurality of neighbor cells based on at least one of: a trigger quantity; a measurement reporting quantity; a prediction reporting quantity; and a prediction-based trigger quantity.

[0008] According to additional or alternative embodiments, the report comprises a radio resource control, RRC, measurement report.

[0009] According to additional or alternative embodiments, the method further includes receiving configuration information from the network node, the configuration information indicating how to generate the report based on the mobility related time-domain prediction of the neighbor cell. Generating the report comprises generating the report based on the configuration information.

[0010] According to additional or alternative embodiments, transmitting the report comprises at least one of: periodically transmitting the report; transmitting the report in response to an event trigger; semi-persistently transmitting the report; and aperiodically transmitting the report.

[0011] According to additional or alternative embodiments, the mobility related timedomain prediction of the neighbor cell comprises at least one of: a predicted reference signal received power, pRSRP; a predicted reference signal received quality, pRSRQ; and a predicted signal interference-to-noise ratio, pSINR.

[0012] According to additional or alternative embodiments, generating the report based on the mobility related time-domain prediction of the neighbor cell comprises at least one of: determining a latest time-domain prediction per cell; determining a first time-domain prediction per cell; determining a maximum value among time-domain predictions per cell; determining a minimum value among the time-domain predictions per cell; determining an average value of the time-domain predictions per cell; determining a maximum with highest accuracy and / or lowest prediction error among the time-domain predictions per cell; and determining a value associated to a time instance k indicated by the network among the time-domain predictions per cell.

[0013] According to other embodiments, an apparatus for generating measurement reports based on time-domain predictions of a neighbor cell is provided. The apparatus includes a processor and a memory, the memory containing instructions executable by the processor whereby the apparatus is operative to generate a report based on a mobility related time-domainprediction of a neighbor cell of a plurality of neighbor cells, the report including information associated with a subset of the plurality of neighbor cells. The apparatus further operative to transmit the report to the network node. Generating the report comprises selecting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell. Generating the report comprises sorting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell.

[0014] According to additional or alternative embodiments, the apparatus is further operative to perform any of the operations of the above method.

[0015] According to other embodiments, a communication device is provided. The communication device is adapted to perform operations comprising generating a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells, the report including information associated with a subset of the plurality of neighbor cells. The operations further comprising transmitting the report to the network node. Generating the report comprises selecting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell. Generating the report comprises sorting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell.

[0016] According to additional or alternative embodiments, the operations further include any of the operations of the above method.

[0017] According to other embodiments, a computer program is provided. The computer program comprises program code to be executed by processing circuitry of a communication device, whereby execution of the program code causes the communication device to perform operations including generating a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells, the report including information associated with a subset of the plurality of neighbor cells. The operations further include transmitting the report to the network node. Generating the report comprises selecting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell. Generating the report comprises sorting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell.

[0018] According to additional or alternative embodiments, the operations further comprising any of the operations of the above method.

[0019] According to other embodiments, a computer program product is provided. The computer program product comprises a non-transitory storage medium including program code to be executed by processing circuitry of a communication device, whereby execution of the program code causes the communication device to perform operations comprising generating areport based on a mobility related time -domain prediction of a neighbor cell of a plurality of neighbor cells, the report including information associated with a subset of the plurality of neighbor cells. The operations further comprise transmitting the report to the network node. Generating the report comprises selecting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell. Generating the report comprises sorting the subset of the plurality of neighbor cells based on the mobility related timedomain prediction of the neighbor cell.

[0020] According to additional or alternative embodiments, the operations further comprise any of the operations of the above method.

[0021] According to other embodiments, a method of operating a network node in a communications network that includes a communication device is provided. The method includes generating configuration information including instructions to cause the communication device to generate a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells. Generating the configuration information includes generating the instructions to cause the communication device to select a subset of the plurality of neighbor cells to include in the report based on the mobility related time-domain prediction of the neighbor cell. Generating the configuration information further includes generating the instructions to cause the communication device to sort a subset of the neighbor cells included in the report based on the mobility related time-domain prediction of the neighbor cells. The method further includes transmitting the configuration information to the communication device. The method further includes receiving the report from the communication device.

[0022] According to additional or alternative embodiments, generating the configuration information comprises generating the instructions to cause the communication device to generate the report to include the mobility related time-domain prediction of the neighbor cell. Receiving the report comprises receiving the report including the mobility related time-domain prediction of the neighbor cell.

[0023] According to additional or alternative embodiments, generating the configuration information comprises generating the instructions to cause the communication device to generate the report to include only the mobility related time-domain prediction of the neighbor cell. Receiving the report comprises receiving the report including only the mobility related time-domain prediction of the neighbor cell.

[0024] According to additional or alternative embodiments, generating the configuration information comprises generating instructions to cause the communication device to generate the report with a subset of the plurality of neighbor cells sorted based on at least one of: a triggerquantity; a measurement reporting quantity; a prediction reporting quantity; and a predictionbased trigger quantity.

[0025] According to additional or alternative embodiments, the report comprises a radio resource control, RRC, measurement report.

[0026] According to additional or alternative embodiments, receiving the report comprises at least one of: periodically receiving the report; receiving the report in response to an event trigger; semi-persistently receiving the report; and aperiodically receiving the report.

[0027] According to additional or alternative embodiments, the mobility related timedomain prediction of the neighbor cell comprises at least one of: a predicted reference signal received power, pRSRP; a predicted reference signal received quality, pRSRQ; and a predicted signal interference-to-noise ratio, pSINR.

[0028] According to additional or alternative embodiments, generating the configuration information comprises generating instructions to cause the communication device to generate the report based on at least one of: determining a latest time-domain prediction per cell; determining a first time-domain prediction per cell; determining a maximum value among timedomain predictions per cell; determining a minimum value among the time-domain predictions per cell; determining an average value of the time-domain predictions per cell; determining a maximum with highest accuracy and / or lowest prediction error among the time-domain predictions per cell; and determining a value associated to a time instance k indicated by the network among the time-domain predictions per cell.

[0029] According to additional or alternative embodiments, generating the configuration information comprises instructing the communication device to include a ‘best’ detected neighbor cell first in the report, wherein the ‘best’ detected neighbor cell is the cell in a first position after the sorting of detected cells.

[0030] According to other embodiments, an apparatus for enabling generation of measurement reports based on time-domain predictions of a neighbor cell is provided. The apparatus comprises a processor and a memory, the memory containing instructions executable by the processor whereby the apparatus is operative to generate configuration information including instructions to cause the communication device to generate a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells. The apparatus is further operative to transmit the configuration information to the communication device. The apparatus is further operative to receive the report from the communication device. Generating the configuration information comprises generating the instructions to cause the communication device to select a subset of the plurality of neighbor cells to include in the report based on the mobility related time-domain prediction of the neighbor cell. Generating theconfiguration information comprises generating the instructions to cause the communication device to sort a subset of the neighbor cells included in the report based on the mobility related time-domain prediction of the neighbor cells.

[0031] According to additional or alternative embodiments, the apparatus further operative to perform any of the operations the above network node method.

[0032] According to other embodiments, a network node is provided. The network node is adapted to perform operations comprising generating configuration information including instructions to cause the communication device to generate a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells. The operations further include transmitting the configuration information to the communication device. The operations further include receiving the report from the communication device. Generating the configuration information comprises generating the instructions to cause the communication device to select a subset of the plurality of neighbor cells to include in the report based on the mobility related time-domain prediction of the neighbor cell. Generating the configuration information comprises generating the instructions to cause the communication device to sort a subset of the neighbor cells included in the report based on the mobility related time-domain prediction of the neighbor cells.

[0033] According to additional or alternative embodiments, the operations further comprising any of the operations the above network node method.

[0034] According to other embodiments, a computer program is provided. The computer program includes program code to be executed by processing circuitry of a network node, whereby execution of the program code causes the network node to perform operations comprising generating configuration information including instructions to cause the communication device to generate a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells. The operations further including transmitting the configuration information to the communication device. The operations further include receiving the report from the communication device. Generating the configuration information comprises generating the instructions to cause the communication device to select a subset of the plurality of neighbor cells to include in the report based on the mobility related time-domain prediction of the neighbor cell. Generating the configuration information comprises generating the instructions to cause the communication device to sort a subset of the neighbor cells included in the report based on the mobility related time-domain prediction of the neighbor cells.

[0035] According to additional or alternative embodiments, the operations further comprising any of the operations of the above network node method.

[0036] According to other embodiments, a computer program product is provided. The computer program product comprises a non-transitory storage medium including program code to be executed by processing circuitry of a network node, whereby execution of the program code causes the network node to perform operations comprising generating configuration information including instructions to cause the communication device to generate a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells. The operations further comprise transmitting the configuration information to the communication device. The operations further include receiving the report from the communication device. Generating the configuration information comprises generating the instructions to cause the communication device to select a subset of the plurality of neighbor cells to include in the report based on the mobility related time-domain prediction of the neighbor cell. Generating the configuration information comprises generating the instructions to cause the communication device to sort a subset of the neighbor cells included in the report based on the mobility related time-domain prediction of the neighbor cells.

[0037] According to additional or alternative embodiments, the operations further comprising any of the operations of the above network node method.

[0038] According to other embodiments, a non-transitory computer readable medium, host, or system is provided to perform one of the above methods.

[0039] Certain embodiments may provide one or more of the following technical advantages. In some embodiments, the UE can include the mobility related time-domain prediction(s) for the cells expected to be included in the RRC Measurement Report by the network, since the sorting would be specified for all UEs. Thus, the UE includes the mobility related time-domain prediction(s) for the cells expected to be the most likely the network will choose for a mobility related procedure, such as a handover, reconfiguration with sync, a dual connectivity setup, an SCG addition, a carrier aggregation setup, activation or deactivation, a conditional handover configuration, a LTM configuration, etc.

[0040] In some examples, when a trigger quantity is used as sorting quantity for determining the cells for which to include mobility related time-domain prediction(s), one of the advantages of such option is that the predicted values are included opportunistically for the cells for which the UE would anyways report in a legacy RRC Measurement Report i.e. the expected cells included in the report do not change, but the report is enriched for these particular triggered cells.

[0041] In additional or alternative examples, when a measurement reporting quantity is used as sorting quantity for determining the cells for which to include mobility related time -domain prediction(s), one of the advantages of such option is that the predicted values are not onlyincluded but influence the sorting i.e. which cells to include in the RRC Measurement Report, and enables the possibility to include in the RRC Measurement Report cells which shows excellent predicted values in the future (e.g. very high pRSRP for future time instances), despite note being the strongest for the actual measurements.

[0042] In additional or alternative examples, when the prediction-based trigger quantity (pRSRP, pRSRQ, pSINR) is used as sorting quantity and determining the cells to be included in the measurement reports, the UE includes cells with the best time domain predictions and may include the measurements along with the predictions, this enables the network to reply more on the future quality of the cells (deduced form the reported perditions) for the mobility purpose than the quality at the time being deduced from the measurements. In particular if the best cell at the current time is predicted to be a very bad cell in the future time instances, the UE does not include such cells in the triggered report and instead reports the cells with higher quality in the future time instances to be selected at target for mobility purpose.BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this application, illustrate certain non-limiting embodiments of inventive concepts. In the drawings:

[0044] FIG. 1 is a schematic diagram illustrating an example of a 5thgeneration (“5G”) network;

[0045] FIG. 2 is a signal flow diagram illustrating an example of a UE including mobility related time-domain predictions in an RRC measurement report in accordance with some embodiments;

[0046] FIG. 3 is a block diagram illustrating an example of a measurement model in which time-domain prediction of beam measurement information are included in an RRC measurement report in accordance with some embodiments;

[0047] FIG. 4 is a graph illustrating an example of using mobility related time-domain predictions in accordance with some embodiments;

[0048] FIG. 5 is a schematic diagram illustrating an example of time domain cell prediction derivation based on predicted RSRP value in accordance with some embodiments;

[0049] FIG. 6 is a schematic diagram illustrating an example of time domain cell prediction derivation based on Max() in accordance with some embodiments;

[0050] FIG. 7 is a schematic diagram illustrating an example of time domain cell prediction derivation based on Average() in accordance with some embodiments;

[0051] FIGS. 8-9 are flow charts illustrating examples of neural networks used for designing AI / ML models for spatial beam prediction in accordance with some embodiments;

[0052] FIG. 10 is a schematic diagram illustrating an example of an AI / ML model input and output for time domain beam prediction in accordance with some embodiments;

[0053] FIG. 11 is a block diagram illustrating an example of an AI / ML LSTM model used to predict the best beam index for the predicting time instance in accordance with some embodiments;

[0054] FIG .12 is a flow chart illustrating an example of operations of a transformer encoder based neural network in accordance with some embodiments;

[0055] FIG. 13 is a flow chart illustrating an example of operations of a transformer encoder in accordance with some embodiments;

[0056] FIG. 14 is a flow chart illustrating an example of operations of multi head self attention in accordance with some embodiments;

[0057] FIG. 15 is a flow chart illustrating an example of operations of scaled dot-production attention in accordance with some embodiments;

[0058] FIG. 16 is a flow chart illustrating an example of operations performed by a communication device in accordance with some embodiments;

[0059] FIG. 17 is a flow chart illustrating an example of operations performed by a network node in accordance with some embodiments.

[0060] FIG. 18 is a block diagram of a communication system in accordance with some embodiments;

[0061] FIG. 19 is a block diagram of a user equipment in accordance with some embodiments;

[0062] FIG. 20 is a block diagram of a network node in accordance with some embodiments; and

[0063] FIG. 21 is a block diagram of a virtualization environment in accordance with some embodiments.DETAILED DESCRIPTION

[0064] 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, in which examples of embodiments of inventive concepts are shown. Inventive concepts may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough andcomplete, and will fully convey the scope of present inventive concepts to those skilled in the art. It should also be noted that these embodiments are not mutually exclusive. Components from one embodiment may be tacitly assumed to be present / used in another embodiment.

[0065] A UE can include in an RRC Measurement Report the performed measurements on one or more cells up to a maximum number of cells. In case event triggered Measurement Report is configured (e.g., when the fulfillment of an event triggers the transmission of the RRC Measurement Report) the neighbor cells to be included are the so-called triggered cells, for example, the UE includes up to a maximum number of triggered cells in an RRC Measurement Report.

[0066] The “maximum number” of cells can be defined by the parameter “maxReportCells”, defined as an INTEGER (l..maxCellReport), and configured at the UE in the IE ReportConfigNR. In case there are more triggered cells than such a maximum number, the UE needs to determine which cells are to be included, so that the associated information is included. This can be performed via a so-called sorting function for cell measurement information.

[0067] In generating an event-triggered RRC measurement report, sorting is performed by the UE based on the trigger quantity (e.g., RSRP, RSRQ, or SINR). When an event is configured (e.g., event A3 in ReportConfigNR), the UE is also configured with a trigger quantity, to indicate based on which quantity the event shall be considered fulfilled.

[0068] The trigger quantity is configured by the field “aN-ThresholdM”, which is a threshold value associated to the selected trigger quantity (e.g., RSRP, RSRQ, SINR) per RS Type (e.g. SS / PBCH block, CSI-RS) to be used in NR measurement report triggering condition for event number aN (e.g., a3). And if multiple thresholds are defined for event number aN, the thresholds are differentiated by M. In the same eventA5, the network configures the same quantity for the MeasTriggerQuantity of the a5 -Threshold 1 and for the MeasTriggerQuantity of the a5-Threshold2.

[0069] In periodic RRC Measurement Report, sorting is performed by the UE based on RSRP, if RSRP is configured as a reporting quantity. Otherwise, RSRQ is used if RSRP is not configured. Otherwise, SINR is used.

[0070] A similar reasoning is applicable for beam(s) i.e. for SSB indexes and / or CSI-RS resource identifiers to be included, per included triggered cell. Once cells are included by the UE in the RRC Measurement Report, based on the sorting quantity, the UE needs to determine which beams to include for each included cells. That is also determined based on the same sorting quantity.

[0071] A UE can include in an RRC Measurement Report beam measurement information up to a maximum number of beams, for a given cell determined to be included in the RRC Measurement Report. The maximum number of beams may also be denoted as a maximum number of Reference Signal (RS) Indexes, such as SSB indexes and / or CSI-RS resource identifiers, considering that these RS(s) are transmitted in beams (e.g. spatial directions) for a given neighbor cell.

[0072] The “maximum number of beams” is defined by the parameter “maxNrofRS- IndexesToReport”, defined as an INTEGER, and configured at the UE in the IE ReportConfigNR. In case there are more measured / detected beam(s) than such a maximum number, the UE needs to determine which beams (i.e. which RS indexes) are to be included for a given included cell, so that the associated information is included. That is done via a so-called sorting function for beam measurement information.

[0073] Artificial intelligence (“AI”) / machine learning (“ML”) for physical (“PHY”) layer work has been limited to lower layer features, such as Beam Management, which is sometimes referred as intra-cell mobility.

[0074] There currently exist certain challenges. A couple of concepts are being considered for AI / ML aided mobility for network triggered L3-based handover: i) a UE equipped with an AI / ML model for Mobility (or RRM measurements) including mobility related time-domain predictions in an RRC Measurement Report and ii) a UE equipped with an AI / ML model for Mobility (or RRM measurements) predicting the triggering of a measurement event like A3 and triggering a report.

[0075] The basic idea is that a UE is equipped with a UE sided AI / ML model for performing mobility related time-domain predictions, such as predicted RSRP (pRSRP), predicted RSRQ (pRSRQ) or predicted SINR (pSINR). In other words, these pRSRP, pRSRQ and / or pSINR values for one or more serving and / or neighbour cells would be produced as inference, which are output of the AI / ML model for an RRM measurements / mobility related functionality. According to some concepts, when an RRC Measurement Report is triggered (based on actual L3 filtered cell level measurements, as defined in TS 38.331), the UE incudes the mobility related time-domain prediction(s) for the cells included in the RRC Measurement Report. These reports, enriched with mobility related time-domain predictions, may be used by the network for more educated handover decisions. In addition, the UE generates a report if an event is predicted (e.g., predicted A3 -event) to happen (with a certain likelihood) and hence the UE sends a report to the network.

[0076] Thus, the UE includes the mobility related time-domain prediction(s) for the cells determined to be included in the RRC Measurement Report. For that reason, a sorting function for determining which cells to include mobility related time-domain predictions is required.

[0077] FIG. 2 illustrates an example of a UE configured to include mobility related timedomain predictions in an RRC measurement report to assist the network to take more educated mobility decisions.

[0078] In some examples, the UE reports mobility-related time domain predictions (output of the AI / ML models) to the network so the network uses not only the measurements but also the prediction to decide about the mobility (or CA / DC) procedures. However, co-existence of the measurements (i.e., legacy measurements such as RSRP, RSRQ and SINR) and the predictions of the measurements (such as pRSRP, pRSRQ, pSINR) lends itself careful considerations when a limited set of measurements and predictions can be reported to the network. In particular, the problem of how the UE populate and include the measurements and predictions in the report while the total number of measurements and predictions for the detected cells is more than the allowed number of cells in the report is not addressed.

[0079] Various embodiments herein address some of these challenges by a procedure in which a UE determines a subset of ‘X’ detected neighbor cells for which to include mobility related time-domain prediction(s) in a measurement report (e.g. RRC Measurement Report). That is necessary, for example, when there are more detected neighbor cells compared to the maximum number of neighbor cells (e.g., ‘X’) that are allowed to be included in an RRC Measurement Report (i.e., number of detected neighbor cells > ‘X’). FIG. 3 illustrates an example of a measurement model in which the mobility related time-domain predictions are included in an RRC measurement report.

[0080] In some embodiments, the UE determines that the subset of ‘X’ detected neighbor cells are the top ‘X’ detected neighbor cells for which to include mobility related time-domain prediction(s) in a measurement report are sorted according to a sorting quantity which is based on one or more of the following: 1) A trigger quantity the UE is configured with (e.g. RSRP, RSRQ, SINR); 2) A measurement reporting quantity the UE is configured with (e.g. RSRP, RSRQ, SINR) e.g. for periodic measurement report; 3) A prediction (or predicted) reporting quantity the UE is configured with (e.g. predicted RSRP, predicted RSRQ, predicted SINR); and 4) A prediction-based report triggering quantity (e.g., a pRSRP, pRSRQ, pSINR) in case report is triggered by fulfillment of entry condition of an event evaluated based on mobility related time-domain prediction(s).

[0081] In additional or alternative embodiments, the UE is configured with the value ‘X’, within the reporting configuration instance (e.g. IE ReportConfigNR) associated to the RRC Measurement Report which is to be transmitted.

[0082] In additional or alternative embodiments, the UE is configured with the RRC Measurement Report which is one or more of: event-triggered; periodical; aperiodic; and semi- persistent.

[0083] Various embodiments herein describe a UE determining a subset of ‘X’ detected neighbour cells for which to include mobility related time-domain prediction(s) in a measurement report (e.g. an RRC Measurement Report). Such a function is necessary when there are more detected neighbour cells compared to the maximum number of neighbour cells that are allowed to be included in an RRC Measurement Report (denoted ‘X’).

[0084] In some embodiments, the UE determines that the subset of ‘X’ detected neighbour cells are the top (‘best’) ‘X’ detected neighbour cells sorted according to a sorting quantity which is based on one or more of the following: 1) A trigger quantity the UE is configured with (e.g. RSRP, RSRQ, SINR); 2) A measurement reporting quantity the UE is configured with (e.g. RSRP, RSRQ, SINR); 3) A prediction reporting quantity the UE is configured with (e.g. predicted RSRP, predicted RSRQ, predicted SINR); and 4) A prediction-based report triggering quantity, e.g., a pRSRP, pRSRQ, pSINR e.g., in case report is triggered by fulfillment of entry condition of an event evaluated based on mobility related time-domain prediction(s).

[0085] A measurement quantity herein may also be referred to as a quantity derived based on a measurement on a reference signal (RS) or synchronization signal (SS) associated to a network entity such as a cell and / or a beam. A measurement quantity may reflect some property at a given point in time of the radio link the UE is detecting e.g. a cell power in the downlink (DL), cell coverage, cell signal to noise + interference ratio, etc.

[0086] Examples of measurement quantities are: i) Reference Signal Received Power (RSRP); ii) Reference Signal Received Quality (RSRQ); iii) Signal to Interference Noise Ratio (SINR). A measurement quantity may be associated to a reference signal type (e.g. SSB, CSI- RS, Mobility Reference Signal), in case that reference signal type is used for measuring and deriving the cell level measurement quantity. For example, an SS-RSRP is an RSRP measured on an SSB, a CSI-RSRP is an RSRP value measured on a CSI-RS. Or, a cell based or cell level RSRP based on SSB is an RSRP of a cell measured on SSB(s) of that cell.

[0087] In additional or alternative embodiments, the UE includes in the measurement report (e.g. RRC Measurement Report) one or more mobility related time-domain prediction(s) of one or more neighbouring cells wherein the one or more neighbouring cells are up to a number ‘X’ (configured at the UE by a network node e.g., in ReportConfigNR in maxReportCells or anothernew parameter or new field). The UE includes one or more neighbouring cells in decreasing order of the sorting quantity determined by the UE (or configured by the network), which depending on the solution may either be an actual measurement quantity (e.g. RSRP, RSRQ, SINR) or a prediction of a measurement quantity in a future time instance (e.g. predicted RSRP, predicted RSRQ, predicted SINR).

[0088] In some examples, the one or more neighbouring cells corresponds to a subset of ‘X’ detected neighbour cells. In other words, the UE detects at least ‘X’ neighbour cells in an Synchronization Sequence Block (SSB) frequency configured in a measurement object (e.g. IE MeasObjectNR) associated with the reporting configuration instance (e.g. IE ReportConfigNR) and a measurement identifier, wherein the measurement identifier is associated to an RRC Measurement Report which is to be transmitted (e.g. event-triggered, periodic). In one option, the UE receives one or more parameters associated to the one or more mobility-related timedomain prediction(s) which are to be included for neighbour cells in the measurement report. In another option, the UE receives one or more indications of neighbour cells (e.g. in a reporting configuration), such as cell identifiers (e.g. physical cell identifiers) for which to include the one or more mobility-related time-domain prediction(s) in the measurement report.

[0089] The mobility related time-domain prediction(s) may also be called RRM (Radio Resource Management) measurement related time-domain prediction(s), and includes, for example, predicted value(s) or the output of inferences of an AI / ML model for a measurement quantity (RSRP) for a cell in at least one future time instance (in relation to the time at which the RRC Measurement Report is being transmitted).

[0090] The measurement report may be an RRC Measurement Report, as defined in TS 38.331. However, the method is applicable to any sort of measurement reporting in which the size is limited and, in particular, when the UE detects more cells than what maybe included in the measurement report. In that sense, a measurement report may be: i) a LI or Medium Access Control layer (MAC) measurement report (e.g. a CSI report or similar for Lower-Layer triggered Mobility (LTM) measurement reporting); ii) a LI measurement report transmitted on Physical Uplink Control Channel, PUCCH; iii) a LI measurement report transmitted on Physical Uplink Shared Channel PUSCH.

[0091] There may be different options for the UE to determine the sorting quantity, depending on various aspects, such as the trigger for the report e.g. if it is event triggered, periodic, aperiodic, semi-persistent. Also, there may be different manners to define for each of these alternatives 1, 2, 3, 4 what exact quantity is determined by the UE.

[0092] Embodiments associated with an event triggered measurement report are described below. In some embodiments, the UE determines that the subset of ‘X’ detected neighbour cellsare the top ‘X’ detected neighbour cells sorted according to a sorting quantity which is 1) a trigger quantity the UE is configured with (e.g. RSRP, RSRQ, SINR). When the RRC Measurement Report in which the UE is configured to include mobility related time-domain prediction(s) is an event-triggered RRC Measurement Report (e.g. when the measld has an associated IE ReportConfigNR which includes a reportType set to eventTriggered), the UE determines that the sorting quantity is the measurement quantity configured as the trigger quantity. An event-triggered measurement report is referred as the main example, however, the option is applicable whenever there is a form of trigger quantity the UE is configured with, based one which a measurement report is triggered to be transmitted.

[0093] In the case of event-triggered measurement reports, the subset of ‘X’ detected neighbour cells corresponds to a subset of the triggered cells i.e. neighbour cells for which a condition triggering the transmission of the measurement report is fulfilled. These may also be considered applicable cells. It may be the case that the UE detects and measures more neighbour cells compared to the triggered cells.

[0094] In one option, when the reportType is set to eventTriggered, for an NR cell, the UE considers the trigger quantity used in the aN-Threshold (for eventAl, eventA2, eventA4, eventA4Hl and eventA4H2) or in the a5-Threshold2 (for eventA5, eventA5Hl and eventA5H2) or in the aN-Offset (for eventA3, eventA3Hl, eventA3H2 and eventA6) or in the xl-Threshold2 (for eventXl) as the sorting quantity.

[0095] In one option, when the trigger quantity is set to RSRP, the UE determines RSRP as the sorting quantity. Or, when the trigger quantity is set to RSRQ, the UE determines RSRQ as the sorting quantity. Or, when the trigger quantity is set to SINR, the UE determines SINR as the sorting quantity.

[0096] In a first example, the UE is configured to include in an RRC Measurement Report mobility related time-domain prediction(s), such as predicted RSRP and / or predicted RSRQ and / or predicted SINR values, for the cells which are to be included in the RRC Measurement Report. When that is an event-triggered report i.e. reportType in IE ReportConfigNR set to eventTriggered, for an event A3 (entering / entry condition: neighbour cell becomes an offset better than SpCell e.g. PCell), with trigger quantity set to RSRQ (e.g. a3-Offset, of IE MeasTriggerQuantityOffset, set to ‘rsrq’), the entering / entry condition is considered fulfilled when neighbour cell’s RSRQ becomes offset better than the SpCell’s RSRQ) for a time period e g., TTT.

[0097] Assuming that the UE is configured to report a maximum number of cells = 2, but there are 3 triggered cells (i.e. 3 cells fulfilling the entering / entry condition of the event). And, for each of these triggered cells the UE has available one or more mobility related time-domainprediction(s), such as predicted RSRP (denoted pRSRP) and / or predicted RSRQ and / or predicted SINR values, as follows:Triggered Cell A: RSRP(A), RSRQ(A); pRSRP(A, 1), pRSRQ(A, 1);Triggered Cell B: RSRP(B), RSRQ(B); pRSRP(B, 1), pRSRQ(B, 1);Triggered Cell C: RSRP(C), RSRQ(C); pRSRP(C, 1), pRSRQ(C, 1); wherein RSRQ(B) > RSRQ(A) > RSRQ(C). Note that pRSRP(A,k) denotes the predicted RSRP value of cell A in a future time instance indicated by the value k e.g. k-th future time instance.

[0098] Since trigger quantity is determined to be RSRQ, sorting the cells in decreasing RSRQ order leads to the following order:1) Triggered Cell B: RSRP(B), RSRQ(B); pRSRP(B, 1), pRSRQ(B, 1);2) Triggered Cell A: RSRP(A), RSRQ(A); pRSRP(A, 1), pRSRQ(A, 1);3) Triggered Cell C: RSRP(C), RSRQ(C); pRSRP(C, 1), pRSRQ(C, 1);And, since the maximum number of cells = 2, only cells in position 1 and 2 are included in the RRC Measurement Report and these are the cells for which the UE includes the mobility related time-domain prediction(s), such as predicted RSRP (denoted pRSRP) and / or predicted RSRQ and / or predicted SINR values. In this case the ‘best’ cell is the cell in position 1), which is cell B.

[0099] In order words, the UE determines the trigger quantity to be the sorting quantity for determining the cells for which the UE includes the mobility related time-domain prediction(s), such as predicted RSRP (denoted pRSRP) and / or predicted RSRQ and / or predicted SINR values. This does not consider the values of the mobility related time-domain prediction(s), such as predicted RSRP (denoted pRSRP) and / or predicted RSRQ and / or predicted SINR values, for the triggered cells.

[0100] One of the advantages of such option is that the predicted values are included opportunistically for the cells for which the UE would anyways report in a legacy RRC Measurement Report i.e. the expected cells included in the report do not change, but the report is enriched for these particular triggered cells.

[0101] The above example is also valid for other triggering events (measurement events) related to neighbor cells, such as events A3, A4, A5, A6, Bl, B2 as defined in TS 38.331. For example, for an event A4 (entry condition: neighbour cell becomes better than an absolute threshold), with trigger quantity set to RSRQ (e.g. a4-Threshold, of IE MeasTriggerQuantity, set to ‘rsrq’), the entering / entry condition is considered fulfilled when neighbour cell’s RSRQ becomes better that the a4-Threshold) for a time period e.g., TTT.

[0102] A variant of 1): Sorting based on the predicted version of the triggering quantity

[0103] In a variant the UE determines that the subset of ‘X’ detected neighbour cells are the top ‘X’ detected neighbour cells sorted according to a sorting quantity which is predicted version of a trigger quantity (RSRP, RSRQ, SINR) the UE is configured with (i.e., the sorting quantity is predicted RSRP, predicted RSRQ or predicted SINR). When the RRC Measurement Report in which the UE is configured to include mobility related time-domain prediction(s) is an event-triggered RRC Measurement Report (e.g. when the measld has an associated IE ReportConfigNR which includes a reportType set to eventTriggered), the UE determines that the sorting quantity is the predicted version of the measurement quantity that is configured as the trigger quantity.

[0104] In one option, when the trigger quantity is set to RSRP, the UE determines predicted RSRP as the sorting quantity (e.g. when predicted RSRP is also configured as a predicted reporting quantity). Or, when the trigger quantity is set to RSRQ, the UE determines predicted RSRQ as the sorting quantity (e.g. when predicted RSRQ is also configured as a predicted reporting quantity). Or, when the trigger quantity is set to SINR, the UE determines SINR as the sorting quantity (e.g. when predicted SINR is also configured as a predicted reporting quantity).

[0105] In this example network configures the UE to perform the sorting based on the predicted version of the triggering quantity (instead of using the triggering quantity as a sorting quantity).

[0106] The benefic of this approach is that when a legacy event (e.g., A3 event) is triggered and UE sends the measurement report to the network, the report is sorted based on the prediction value of the radio link quality (in a future time instance). Hence network can make the handover decision based on the future quality of the cells, which is beneficial in particular when the network sends the HO command to the UE with some delay after receiving the measurement report.

[0107] In an example, the UE is configured to include in an RRC Measurement Report mobility related time-domain prediction(s), such as predicted RSRP and / or predicted RSRQ and / or predicted SINR values, for the cells which are to be included in the RRC Measurement Report. When that is an event-triggered report i.e. reportType in IE ReportConfigNR set to eventTriggered, for an event A3 (entering / entry condition: neighbour cell becomes an offset better than SpCell e.g. PCell), with trigger quantity set to RSRQ (e.g. a3-Offset, of IE MeasTriggerQuantityOffset, set to ‘rsrq’), the entering / entry condition is considered fulfilled when neighbour cell’s RSRQ becomes offset better than the SpCell’s RSRQ) for a time period e g., TTT.

[0108] Assuming that the UE is configured to report a maximum number of cells = 2, but there are 3 triggered cells (i.e. 3 cells fulfilling the entering / entry condition of the event). And,for each of these triggered cells the UE has available one or more mobility related time-domain prediction(s), such as predicted RSRP (denoted as pRSRP) and / or predicted RSRQ (denoted as pRSRQ), as follows:Triggered Cell A: RSRP(A), RSRQ(A); pRSRP(A, 1), pRSRQ(A, 1);Triggered Cell B: RSRP(B), RSRQ(B); pRSRP(B, 1), pRSRQ(B, 1);Triggered Cell C: RSRP(C), RSRQ(C); pRSRP(C, 1), pRSRQ(C, 1); with the following inequalities:RSRQ(B) > RSRQ(A) > RSRQ(C) pRSRQ(A,l) > pRSRQ(B,l) > pRSRQ(C,l)

[0109] Note that pRSRP(A,k) denotes the predicted RSRP value of cell A in a future time instance indicated by the value k e.g. k-th future time instance.

[0110] Since trigger quantity is determined to be RSRQ, the sorting quanitity () is the predicted version of pRSRQ, hence sorting the cells in decreasing RSRQ order leads to the following order:1) Cell A: RSRP(A), RSRQ(A); pRSRP(A, 1), pRSRQ(A, 1);2) Cell B: RSRP(B), RSRQ(B); pRSRP(B, 1), pRSRQ(B, 1);3) Cell C: RSRP(C), RSRQ(C); pRSRP(C, 1), pRSRQ(C, 1);And, since the maximum number of cells = 2, only cells in position 1 and 2 are included in the RRC Measurement Report and these are the cells for which the UE includes the mobility related measurement and information, such as measured RSRP, and / or RSRQ and / or SINR values.

[0111] In order words, the UE determines the trigger quantity to be the sorting quantity for determining the cells for which the UE includes the mobility related time-domain prediction(s), such as predicted RSRP (denoted pRSRP) and / or predicted RSRQ and / or predicted SINR values. This does not take into account the values of the mobility related time-domain prediction(s), such as predicted RSRP (denoted pRSRP) and / or predicted RSRQ and / or predicted SINR values, for the triggered cells.

[0112] One of the advantages of such option is that the predicted values are included opportunistically for the cells for which the UE would anyways report in a legacy RRC Measurement Report i.e. the expected cells included in the report do not change, but the report is enriched for these particular triggered cells.

[0113] The above example is also valid for other triggering events, e.g., event A4 (entering / entry condition: neighbour cell becomes better than an absolute threshold), with trigger quantity set to RSRQ (e.g. a4-Threshold, of IE MeasTriggerQuantity, set to ‘rsrq’), the entering / entry condition is considered fulfilled when neighbour cell’s RSRQ becomes better that the a4- Threshold) for a time period e.g., TTT.

[0114] Embodiments associated with a sorting quantity including a prediction reporting quantity (rule-based) are described below. In some embodiments, the UE determines that the subset of ‘X’ detected neighbour cells are the top ‘X’ detected neighbour cells sorted according to a sorting quantity which is based on 3) a prediction reporting quantity the UE is configured with (e.g. predicted RSRP), based on a rule which prioritizes a prediction reporting quantity.

[0115] When the RRC Measurement Report in which the UE is configured to include mobility related time-domain prediction(s) is an event-triggered RRC Measurement Report (e.g. when the measld has an associated IE ReportConfigNR which includes a reportType set to eventTriggered), the UE determines that the sorting quantity is a reporting prediction quantity.

[0116] According to this option, for a given RRC Measurement Report for which the UE needs to include mobility related time-domain prediction(s) (such as pRSRQ), the UE is configured with one or more reporting prediction quantities (prediction based report quantity). That indicates to the UE what predicted quantities (e.g. pRSRP and / or pRSRQ, and / or pSINR) are to be included in an RRC Measurement Report for a cell to be included in the RRC Measurement Report. That also indicates to the UE which prediction quantities are to be inferred by an AI / ML model i.e. which inferences to produce by the AI / ML model, since there are the ones to be included in the RRC Measurement Report. The reporting prediction quantities may be configured in the IE ReportConfigNR, in which an event-triggered RRC Measurement Report is also configured (i.e. reportType set to eventTriggered), to indicate which reporting prediction quantities are to be included in the report, when triggered.

[0117] According to this option, the sorting quantity is one of the configured reporting prediction quantities.

[0118] In one sub-option, when a single reporting prediction quantity is configured, the UE uses that single reporting prediction quantity as the sorting quantity e.g. pSINR.

[0119] In one sub-option, when multiple reporting prediction quantities are configured, and pRSRP is one of them, the UE uses pRSRP as the sorting quantity e.g. pSINR.

[0120] In one sub-option, when multiple reporting prediction quantities are configured, and pRSRP is not one of them, the UE uses pRSRQ as the sorting quantity e.g. pSINR.

[0121] In one sub-option, when multiple reporting prediction quantities are configured, one of them is explicitly indicated to be the sorting quantity.

[0122] In a second example, the UE is configured to include in an RRC Measurement Report mobility related time-domain prediction(s), such as the following reporting prediction quantities predicted RSRP and / or predicted RSRQ, for the cells which are to be included in the RRC Measurement Report. When that is an event-triggered report i.e. reportType in IE ReportConfigNR set to eventTriggered, for an event A3 (entering / entry condition: neighbourcell becomes an offset better than SpCell e.g. PCell), with trigger quantity set to RSRQ (e.g. a3- Offset, of IE MeasTriggerQuantityOffset, set to ‘rsrq’), the entering / entry condition is considered fulfilled when neighbour cell’s RSRQ becomes offset better than the SpCell’s RSRQ).

[0123] Assuming that the UE is configured to report a maximum number of cells = 2, but there 3 triggered cells (i.e. 3 cells fulfilling the entering / entry condition of the event). And, for each of these triggered cells the UE has available predicted RSRP (denoted pRSRP) and predicted RSRQ, as follows:Triggered Cell A: RSRP(A), RSRQ(A); pRSRP(A, 1), pRSRQ(A, 1);Triggered Cell B: RSRP(B), RSRQ(B); pRSRP(B, 1), pRSRQ(B, 1);Triggered Cell C: RSRP(C), RSRQ(C); pRSRP(C, 1), pRSRQ(C, 1); wherein:RSRQ(B) > RSRQ(A) > RSRQ(C), as in the first example pRSRP(C, 1) > pRSRP(B, 1) > pRSRP(A, 1) pRSRQ(C, 1) > pRSRQ(A, 1) > pRSRQ(B, 1)

[0124] As before, pRSRP(A,k) denotes the predicted RSRP value of cell A in a future time instance indicated by the value k e.g. k-th future time instance.

[0125] According to this option, the sorting quantity is one of the configured reporting prediction quantities, in this example: pRSRP and pRSRQ. Considering the sub-option in which when multiple reporting prediction quantities are configured, and pRSRP is one of them, the UE uses pRSRP as the sorting quantity, the UE considers pRSRP and the sorting quantity for the second example. In other words, since pRSRP is configured as one of the reporting prediction quantities, the UE determines pRSRP to be the sorting quantity, and sorting the cells in decreasing pRSRP order leads to the following order:1) Triggered Cell C: RSRP(C), RSRQ(C); pRSRP(C, 1), pRSRQ(C, 1);2) Triggered Cell B: RSRP(B), RSRQ(B); pRSRP(B, 1), pRSRQ(B, 1);3) Triggered Cell A: RSRP(A), RSRQ(A); pRSRP(A, 1), pRSRQ(A, 1);And, since the maximum number of cells = 2, only cells in position 1 and 2 are included in the RRC Measurement Report and these are the cells for which the UE includes the mobility related time-domain prediction(s), such as predicted RSRP (denoted pRSRP) and / or predicted RSRQ, cells C and B.

[0126] In other words, the UE determines one of the reporting prediction quantities to be the sorting quantity for determining the cells for which the UE includes the mobility related timedomain prediction(s), such as predicted RSRP (denoted pRSRP) and / or predicted RSRQ and / or predicted SINR values. The sorting takes into account account the values of the mobility relatedtime-domain prediction(s), such as predicted RSRP (denoted pRSRP) and / or predicted RSRQ and / or predicted SINR values, for the triggered cells.

[0127] One of the advantages of such option is that the predicted values are not only included but influence the sorting i.e. which cells to include in the RRC Measurement Report, and enables the possibility to include in the RRC Measurement Report cells which shows excellent predicted values in the future (e.g. very high pRSRP for future time instances), despite note being the strongest for the actual measurements

[0128] Embodiments associated with sorting quantity including prediction reporting quantity (associated with a trigger quantity) are described below. In some embodiments, the UE determines that the subset of ‘X’ detected neighbour cells are the top ‘X’ detected neighbour cells sorted according to a sorting quantity which is based on 3) a prediction reporting quantity the UE is configured with (e.g. predicted RSRP), associated to a trigger quantity the UE is configured with (e.g. trigger quantity RSRP). In other words, this is as if the UE would determine the sorting quantity to be the trigger quantity, but the sorting would be performed based on the associated predicted quantity.

[0129] When the RRC Measurement Report in which the UE is configured to include mobility related time-domain prediction(s) is an event-triggered RRC Measurement Report (e.g. when the measld has an associated IE ReportConfigNR which includes a reportType set to eventTriggered), the UE determines that the sorting quantity is a reporting prediction quantity associated to a trigger quantity.

[0130] According to this option, for a given RRC Measurement Report for which the UE needs to include mobility related time-domain prediction(s) (such as pRSRQ), the UE is configured with one or more reporting prediction quantities. That indicates to the UE what predicted quantities (e.g. pRSRP and / or pRSRQ, and / or pSINR) are to be included in an RRC Measurement Report for a cell to be included in the RRC Measurement Report. That also indicates to the UE which prediction quantities are to be inferred by an AI / ML model i.e. which inferences to produce by the AI / ML model, since there are the ones to be included in the RRC Measurement Report. The reporting prediction quantities may be configured in the IE ReportConfigNR, in which an event-triggered RRC Measurement Report is also configured (i.e. reportType set to eventTriggered), to indicate which reporting prediction quantities are to be included in the report, when triggered.

[0131] According to this option, the sorting quantity is a reporting prediction quantity associated to a trigger quantity. The association may be that they are of the same type e.g. if RSRP is a measurement quantity, its associated predicted quantity is predicted RSRP. For example:

[0132] When RSRP is set as the trigger quantity, the UE uses pRSRP as the sorting quantity.

[0133] When RSRQ is set as the trigger quantity, the UE uses pRSRQ as the sorting quantity.

[0134] When SINR is set as the trigger quantity, the UE uses pSINR as the sorting quantity.

[0135] In a third example, the UE is configured to include in an RRC Measurement Report mobility related time-domain prediction(s), such as the following reporting prediction quantities predicted RSRP and / or predicted RSRQ, for the cells which are to be included in the RRC Measurement Report. When that is an event-triggered report i.e. reportType in IE ReportConfigNR set to eventTriggered, for an event A3 (entering / entry condition: neighbour cell becomes an offset better than SpCell e.g. PCell), with trigger quantity set to RSRQ (e.g. a3- Offset, of IE MeasTriggerQuantityOffset, set to ‘rsrq’), the entering / entry condition is considered fulfilled when neighbour cell’s RSRQ becomes offset better than the SpCell’s RSRQ).

[0136] Assuming that the UE is configured to report a maximum number of cells = 2, but there 3 triggered cells (i.e. 3 cells fulfilling the entering / entry condition of the event). And, for each of these triggered cells the UE has available predicted RSRP (denoted pRSRP) and predicted RSRQ, as follows:Triggered Cell A: RSRP(A), RSRQ(A); pRSRP(A, 1), pRSRQ(A, 1);Triggered Cell B: RSRP(B), RSRQ(B); pRSRP(B, 1), pRSRQ(B, 1);Triggered Cell C: RSRP(C), RSRQ(C); pRSRP(C, 1), pRSRQ(C, 1); wherein:RSRQ(B) > RSRQ(A) > RSRQ(C), as in the first example pRSRP(C, 1) > pRSRP(B, 1) > pRSRP(A, 1) pRSRQ(C, 1) > pRSRQ(A, 1) > pRSRQ(B, 1)

[0137] As before, pRSRP(A,k) denotes the predicted RSRP value of cell A in a future time instance indicated by the value k e.g. k-th future time instance.

[0138] According to this option, the sorting quantity is the reporting prediction quantity associated with the trigger quantity, in this example: pRSRQ (associated with RSRQ, which is the trigger quantity). In other words, since RSRQ is configured as trigger quantity and one of the reporting prediction quantities is pRSRQ, the UE determines pRSRQ to be the sorting quantity, and sorting the cells in decreasing pRSRQ order leads to the following order:1) Triggered Cell C: RSRP(C), RSRQ(C); pRSRP(C, 1), pRSRQ(C, 1);2) Triggered Cell A: RSRP(A), RSRQ(A); pRSRP(A, 1), pRSRQ(A, 1);3) Triggered Cell B: RSRP(B), RSRQ(B); pRSRP(B, 1), pRSRQ(B, 1);And, since the maximum number of cells = 2, only cells in position 1 and 2 are included in the RRC Measurement Report and these are the cells for which the UE includes the mobility related time-domain prediction(s), such as predicted RSRP (denoted pRSRP) and / or predicted RSRQ, cells C and A.

[0139] In order words, the UE determines one of the reporting prediction quantities to be the sorting quantity for determining the cells for which the UE includes the mobility related time-domain prediction(s), such as predicted RSRP (denoted pRSRP) and / or predicted RSRQ and / or predicted SINR values. The sorting takes into account the values of the mobility related time-domain prediction(s), such as predicted RSRP (denoted pRSRP) and / or predicted RSRQ and / or predicted SINR values, for the triggered cells, but considering that the trigger quantity is what matters the most for the mobility decisions at the network side.

[0140] One of the advantages of such option is that the predicted values are not only included but influence the sorting i.e. which cells to include in the RRC Measurement Report, and enables the possibility to include in the RRC Measurement Report cells which shows excellent predicted values in the future (e.g. very high pRSRP for future time instances), despite note being the strongest for the actual measurements. At the same time, it considers the trigger quantity configured for a given event.

[0141] Embodiments associated with sorting quantity including prediction-based triggering quantity (e.g., pRSRP, pRSRQ, pSINR) are described below. In some embodiments, the UE determines that the subset of ‘X’ detected neighbour cells are the top ‘X’ detected neighbour cells sorted according to a sorting quantity which is based on 4) a prediction-based trigger quantity the UE is configured with (e.g. predicted RSRP). In other words, this is as if the UE would determine the sorting quantity to be the trigger quantity, which is a prediction-based triggering quantity, and the sorting would be performed based on that prediction based triggering quantity.

[0142] In an embodiment the sorting quantity can be the prediction-based trigger quantity, if the prediction of the triggering cells is / are enough accurate e.g., the prediction confidence of the predictions that triggered the report is above a configured confidence score. Otherwise, if the prediction is not enough accurate, the event will not be triggered or the sorting will be done based on the actual measurement of the same quantity e.g., if pRSRP triggered an event but pRSRP confidence / accuracy is not above the confidence / accuracy threshold, the RSRP quantity will be chosen as a sorting quantity.

[0143] When the RRC Measurement Report in which the UE is configured to include mobility related time-domain prediction(s) is an event-triggered Report (e.g. when the measld has an associated IE ReportConfigNR which includes a reportType set to predicted-eventTriggered), the UE determines that the sorting quantity is the trigger quantity which is one of the prediction quantity e.g., pRSRP, pRSRQ, pSINR.

[0144] According to this option, for a given Report for which the UE needs to include mobility related time-domain prediction(s) (such as pRSRP, and / or pRSRQ and / or pSINR), the UE is configured with one or more triggering quantities (e.g., the prediction quantity used in the prediction form of aN-Threshold (for eventAl, eventA2, eventA4, eventA4Hl and eventA4H2) or in the a5-Threshold2 (for eventA5, eventA5Hl and eventA5H2) or in the prediction form of. aN-Offset (for eventA3, eventA3Hl, eventA3H2 and eventA6) or in the prediction form of xl- Threshold2 (for eventXl)).

[0145] According to this option, the sorting quantity is the prediction-based triggering quantity that eventually triggered the measurement report. For example:

[0146] When the pRSRP value triggered the measurement report, the UE uses pRSRP as the sorting quantity.

[0147] When the pRSRQ value triggered the measurement report, the UE uses pRSRQ as the sorting quantity.

[0148] When the pSINR value triggered the measurement report, the UE uses pSINR as the sorting quantity.

[0149] In some other example embodiments:

[0150] When the pRSRP value triggered the measurement report and the prediction confidence / accuracy is above a certain threshold, the UE uses pRSRP as the sorting quantity otherwise RSRP will be chosen as sorting quantity.

[0151] When the pRSRQ value triggered the measurement report and the prediction confidence / accuracy is above a certain threshold, the UE uses pRSRQ as the sorting quantity otherwise RSRQ will be chosen as sorting quantity.

[0152] When the pSINR value triggered the measurement report and the prediction confidence / accuracy is above a certain threshold, the UE uses pSINR as the sorting quantity otherwise SINR will be chosen as sorting quantity.

[0153] In a forth example, the UE is configured to trigger and include in a Report mobility related time-domain prediction(s) and or measurements, for the cells which are to be included in the Report upon being triggered upon fulfilment of a prediction-based event. When that is an event-triggered report i.e. reportType in IE ReportConfigNR set to prediction-eventTriggered, for an predicted event A3 (entering / entry condition: the prediction of a quantity (pRSRP, pRSRQ, pSINR) of a neighbour cell becomes an offset better than SpCell e.g. PCell), with trigger quantity set to predicted RSRQ (e.g. a3 -Offset, of IE MeasTriggerQuantityOffset, set topredicted ‘rsrq’), the entering / entry condition is considered fulfilled when neighbour cell’s predicted RSRQ becomes offset better than the SpCell’s RSRQ).

[0154] Assuming that the UE is configured to report a maximum number of cells = 2, but there 3 triggered cells (i.e. 3 cells fulfilling the entering / entry condition of the event). And, for each of these triggered cells the UE has available predicted RSRP (denoted pRSRP) and predicted RSRQ, as follows:Triggered Cell A: RSRP(A), RSRQ(A); pRSRP(A, 1), pRSRQ(A, 1);Triggered Cell B: RSRP(B), RSRQ(B); pRSRP(B, 1), pRSRQ(B, 1);Triggered Cell C: RSRP(C), RSRQ(C); pRSRP(C, 1), pRSRQ(C, 1);Wherein:RSRQ(B) > RSRQ(A) > RSRQ(C), as in the first example pRSRP(C, 1) > pRSRP(B, 1) > pRSRP(A, 1) pRSRQ(C, 1) > pRSRQ(A, 1) > pRSRQ(B, 1)

[0155] As before, pRSRP(A,k) denotes the predicted RSRP value of cell A in a future time instance indicated by the value k e.g. k-th future time instance.

[0156] According to this option, the sorting quantity is the prediction -based trigger quantity, in this example: pRSRQ that fulfilled the triggering condition. Therefore the UE sorts the cells in decreasing pRSRQ order which leads to the following order:1) Triggered Cell C: RSRP(C), RSRQ(C); pRSRP(C, 1), pRSRQ(C, 1);2) Triggered Cell A: RSRP(A), RSRQ(A); pRSRP(A, 1), pRSRQ(A, 1);3) Triggered Cell B: RSRP(B), RSRQ(B); pRSRP(B, 1), pRSRQ(B, 1);And, since the maximum number of cells = 2, only cells in position 1 and 2 are included in the RRC Measurement Report and these are the cells for which the UE includes the mobility related time-domain prediction(s), such as predicted RSRP (denoted pRSRP) and / or predicted RSRQ, cells C and A.

[0157] Note that in on embodiment pRSRQ values may not be included in the report if they are not among the reporting quantity. In other words, the UE sorts the reporting quantities using the prediction-based trigger quantity while the prediction-based trigger quantity is not included in the report.

[0158] In another embodiment the UE only takes into account the prediction quantities of the cells in which the accuracy / confidence of the predictions is above a minimum configured threshold and discard the rest of the cells with predictions of the triggered quantities with lower than threshold accuracy / confidence.

[0159] In another embodiment the UE performs the sorting based on the measurement equivalent quantity of the prediction-based quantity. For example, if the measurement reporttriggering quantity is pRSRP the UE performs sorting based on the legacy measurement quantity of RSRP, or if the measurement report triggering quantity is pRSRQ, the UE performs sorting based on the legacy measurement quantity of RSRQ. In another example if the measurement report triggering quantity is pSINR the UE performs sorting based on the legacy measurement quantity of SINR. This is to avoid missing inclusion of the cells for which the time domain prediction does not exist while the legacy measurements indicate a very high quality. Such cells would be missing in the measurement report, if the UE sorts the cells in the measurement report list based on the prediction-based triggering quantities.

[0160] One of the advantages of option 4) described above is that the triggered prediction values influence the sorting i.e. which cells to include in the RRC Measurement Report and enables the possibility to include in the RRC Measurement Report cells which shows excellent predicted values in the future (e.g. very high pRSRP for future time instances), despite note being the strongest for the actual measurements. At the same time, it considers the trigger quantity configured for a given event.

[0161] Embodiments herein focus on the A3 event, but the same innovations are applicable to Al, A2 and A6 events.

[0162] Event A3 is defined in the RRC TS 38.331 (version 18.1.0) according to the following, wherein the event is fulfilled if all the measurements in the (TTT) period fulfil the entry condition (Inequality A3-1) of the A3 event.

[0163] Therefore, prediction of the A3 event can be seen as applying A3 entry condition(s) on the predicted quantities e.g., predicted RSRP (pRSRP) value associated with a future instance instead of the actual measurements (i.e., using mobility related time-domain prediction(s).

[0164] [t] indicates the future time instance the UE should perform the predictions and evaluate the entry condition(s). Once the UE mobility related time-domain prediction(s) and evaluation of the condition(s) based on the prediction-based triggering quantity indicate fulfilment of the A3 event for a period of time so called Time-To-Trigger (TTT), the UE sends a report to the network including the available measurements and / or predictions. This is schematically shown in the FIG. 4.

[0165] Note that the report sent by the UE upon fulfilment of the entry condition(s) associated to the prediction-based event, the UE sends a report. In an embodiment the report is RRC measurement report but it can be a new report by which the UE sends the prediction(s) to the network. In another embodiment the report can be an enriched / enhanced version of the measurement report including the measurement and predictions (time domain mobility related prediction and / or the spatial or frequency related predictions).

[0166] Embodiments associated with a sorting quantity including a measurement reporting quantity (rule-based) are described below. In some embodiments, the UE determines that the subset of ‘X’ detected neighbour cells are the top ‘X’ detected neighbour cells sorted according to a sorting quantity which is based on 2) A measurement reporting quantity the UE is configured with (e.g. RSRP, RSRQ, SINR).

[0167] In the case of periodical measurement reports, the subset of ‘X’ detected neighbour cells corresponds to a subset of the applicable cells i.e. neighbour cells allowed to be included in the measurement report e.g. explicitly configured in a list, associated to a particular SSB frequency and / or measurement object. These may also be considered applicable cells. It may be the case that the UE detects and measures more neighbour cells compared to the applicable cells.

[0168] When the RRC Measurement Report in which the UE is configured to include mobility related time-domain prediction(s) is a periodical RRC Measurement Report (e.g. when the measld has an associated IE ReportConfigNR which includes a reportType set to PeriodicalReportConfig), the UE determines that the sorting quantity is one of the reporting quantity, according to the following rule: if a single reporting quantity is configured, that is the sorting quantity; else if RSRP is configured, RSRP is the sorting quantity; and else (RSRP is not configured), RSRQ is the sorting quantity. According to this, SINR is determined to be the sorting quantity when it is configured as the single reporting quantity.

[0169] In a fourth example, the UE is configured to include in an RRC Measurement Report mobility related time-domain prediction(s), such as the following reporting prediction quantities predicted RSRP and / or predicted RSRQ, for the cells which are to be included in the RRC Measurement Report. In the example, that is a periodical report so the UE is also configured with one or more reporting quantities i.e. measurement quantities for which the UE is to include measurements, e.g., RSRP and RSRQ. The UE is configured to report a maximum number of cells = 2, but there 3 detected cells.

[0170] For each of these detected cells the UE has available predicted RSRP (denoted pRSRP) and predicted RSRQ, as follows:Triggered Cell A: RSRP(A), RSRQ(A); pRSRP(A, 1), pRSRQ(A, 1);Triggered Cell B: RSRP(B), RSRQ(B); pRSRP(B, 1), pRSRQ(B, 1);Triggered Cell C: RSRP(C), RSRQ(C); pRSRP(C, 1), pRSRQ(C, 1);Wherein:RSRP(C) > RSRP(A) > RSRP(B)RSRQ(B) > RSRQ(A) > RSRQ(C) pRSRP(C, 1) > pRSRP(B, 1) > pRSRP(A, 1) pRSRQ(C, 1) > pRSRQ(A, 1) > pRSRQ(B, 1)

[0171] As before, pRSRP(A,k) denotes the predicted RSRP value of cell A in a future time instance indicated by the value k e.g. k-th future time instance.

[0172] According to this option, the sorting quantity is one of the configured reporting quantities, in this example: RSRP and RSRQ. And, since multiple reporting quantities are configured, and RSRP is configured as one of them, the UE determines RSRP to be the sorting quantity. The UE sorts the cells in decreasing RSRP order which leads to the following order:1) Triggered Cell C: RSRP(C), RSRQ(C); pRSRP(C, 1), pRSRQ(C, 1);2) Triggered Cell A: RSRP(A), RSRQ(A); pRSRP(A, 1), pRSRQ(A, 1);3) Triggered Cell B: RSRP(B), RSRQ(B); pRSRP(B, 1), pRSRQ(B, 1);And, since the maximum number of cells = 2, only cells in position 1 and 2 are included in the RRC Measurement Report and these are the cells for which the UE includes the mobility related time-domain prediction(s), such as predicted RSRP (denoted pRSRP) and / or predicted RSRQ, cells C and A.

[0173] In other words, the UE determines one of the reporting quantities to be the sorting quantity for determining the cells for which the UE includes the mobility related time-domain prediction(s), such as predicted RSRP (denoted pRSRP) and / or predicted RSRQ and / or predicted SINR values.

[0174] One of the advantages of such option is that the predicted values are included opportunistically for the cells for which the UE would anyways report in a legacy RRC Measurement Report i.e. the expected cells included in the report do not change, but the report is enriched for these particular cells.

[0175] Embodiments associated with a sorting quantity including a prediction reporting quantity (rule-based) are described below.

[0176] According to this option, the UE determines that the subset of ‘X’ detected neighbour cells are the top ‘X’ detected neighbour cells sorted according to a sorting quantity which is based on 3) a prediction reporting quantity the UE is configured with (e.g. predicted RSRP), based on a rule which prioritizes a prediction reporting quantity.

[0177] When the RRC Measurement Report in which the UE is configured to include mobility related time-domain prediction(s) is a periodical RRC Measurement Report (e.g. when the measld has an associated IE ReportConfigNR which includes a reportType set to PeriodicalReportConfig), the UE determines that the sorting quantity is a reporting prediction quantity.

[0178] According to this option, for a given RRC Measurement Report for which the UE needs to include mobility related time-domain prediction(s) (such as pRSRQ), the UE is configured with one or more reporting prediction quantities. That indicates to the UE whatpredicted quantities (e.g. pRSRP and / or pRSRQ, and / or pSINR) are to be included in an RRC Measurement Report for a cell to be included in the RRC Measurement Report. That also indicates to the UE which prediction quantities are to be inferred by an AI / ML model i.e. which inferences to produce by the AI / ML model, since there are the ones to be included in the RRC Measurement Report. The reporting prediction quantities may be configured in the IE ReportConfigNR, in which av periodical RRC Measurement Report is also configured (i.e. reportType set to periodical), to indicate which reporting prediction quantities are to be included in the report, when triggered.

[0179] According to this option, the sorting quantity is one of the configured reporting prediction quantities.

[0180] In one sub-option, when a single reporting prediction quantity is configured, the UE uses that single reporting prediction quantity as the sorting quantity e.g. pSINR.

[0181] In one sub-option, when multiple reporting prediction quantities are configured, and pRSRP is one of them, the UE uses pRSRP as the sorting quantity e.g. pSINR.

[0182] In one sub-option, when multiple reporting prediction quantities are configured, and pRSRP is not one of them, the UE uses pRSRQ as the sorting quantity e.g. pSINR.

[0183] In one sub-option, when multiple reporting prediction quantities are configured, one of them is explicitly indicated to be the sorting quantity.

[0184] In a fifth example, the UE is configured to include in an RRC Measurement Report mobility related time-domain prediction(s), such as the following reporting prediction quantities predicted RSRP and / or predicted RSRQ, for the cells which are to be included in the RRC Measurement Report. When that is periodical report the UE transmits an RRC Measurement Report according to a configured periodicity, in which the reporting period is configured at the UE e.g. in the IE ReportConfigNR, in the field.

[0185] Assuming that the UE is configured to report a maximum number of cells = 2, but there 3 triggered cells (i.e. 3 cells fulfilling the entering / entry condition of the event). And, for each of these triggered cells the UE has available predicted RSRP (denoted pRSRP) and predicted RSRQ, as follows:Triggered Cell A: RSRP(A), RSRQ(A); pRSRP(A, 1), pRSRQ(A, 1);Triggered Cell B: RSRP(B), RSRQ(B); pRSRP(B, 1), pRSRQ(B, 1);Triggered Cell C: RSRP(C), RSRQ(C); pRSRP(C, 1), pRSRQ(C, 1);Wherein:RSRQ(B) > RSRQ(A) > RSRQ(C), as in the first example pRSRP(C, 1) > pRSRP(B, 1) > pRSRP(A, 1) pRSRQ(C, 1) > pRSRQ(A, 1) > pRSRQ(B, 1)

[0186] As before, pRSRP(A,k) denotes the predicted RSRP value of cell A in a future time instance indicated by the value k e.g. k-th future time instance.

[0187] According to this option, the sorting quantity is one of the configured reporting prediction quantities, in this example: pRSRP and pRSRQ. Considering the sub-option in which when multiple reporting prediction quantities are configured, and pRSRP is one of them, the UE uses pRSRP as the sorting quantity, the UE considers pRSRP and the sorting quantity for the second example. In other words, since pRSRP is configured as one of the reporting prediction quantities, the UE determines pRSRP to be the sorting quantity, and sorting the cells in decreasing pRSRP order leads to the following order:1) Triggered Cell C: RSRP(C), RSRQ(C); pRSRP(C, 1), pRSRQ(C, 1);2) Triggered Cell B: RSRP(B), RSRQ(B); pRSRP(B, 1), pRSRQ(B, 1);3) Triggered Cell A: RSRP(A), RSRQ(A); pRSRP(A, 1), pRSRQ(A, 1);And, since the maximum number of cells = 2, only cells in position 1 and 2 are included in the RRC Measurement Report and these are the cells for which the UE includes the mobility related time-domain prediction(s), such as predicted RSRP (denoted pRSRP) and / or predicted RSRQ, cells C and B.

[0188] In other words, the UE determines one of the reporting prediction quantities to be the sorting quantity for determining the cells for which the UE includes the mobility related timedomain prediction(s), such as predicted RSRP (denoted pRSRP) and / or predicted RSRQ and / or predicted SINR values. The sorting takes into account account the values of the mobility related time-domain prediction(s), such as predicted RSRP (denoted pRSRP) and / or predicted RSRQ and / or predicted SINR values, for the triggered cells.

[0189] One of the advantages of such option is that the predicted values are not only included but influence the sorting i.e. which cells to include in the RRC Measurement Report, and enables the possibility to include in the RRC Measurement Report cells which shows excellent predicted values in the future (e.g. very high pRSRP for future time instances), despite note being the strongest for the actual measurements.

[0190] In some embodiments, in an aperiodic reporting or semi -persistent reporting, the UE receives a request by the network (e.g. in an RRC message) and, in response to the request, the UE reports one or more measurement results e.g. in an RRC Measurement Report. In the case of aperiodic, a single RRC Measurement Report is transmitted. In the case of semi-persistent the UE transmits multiple RRC Measurement reports after the request e.g. periodically.

[0191] In the case of aperiodic or semi-persistent measurement reports, the subset of ‘X’ detected neighbour cells corresponds to a cell which may be indicated in the request, or a cellassociated to a configuration indication included in the request e.g. a reporting configuration identifier and / or a measurement configuration identifier.

[0192] In aperiodic or semi-persistent measurement reporting, any of the embodiments / options in which the UE determines the sorting quantity to be i) a measurement reporting quantity (rule-based) or ii) prediction reporting quantity (rule-based) may be applied for such a case.

[0193] In another option, the UE determines the sorting quantity to be a quantity indicated in the request message e.g. RSRP, RSRQ, SINR.

[0194] In another option, the UE receives the request message which indicates to the UE that the UE is to use as sorting quantity a measurement reporting quantity e.g. RSRP, RSRQ, SINR.

[0195] In another option, the UE receives the request message which indicates to the UE that the UE is to use as sorting quantity a prediction reporting quantity e.g. predicted RSRP, predicted RSRQ, predicted SINR.

[0196] In some cases, the mobility related time-domain prediction(s) are not associated to a measurement quantity, such as when the mobility related time-domain prediction(s) comprise predicted location related information and / or predicted trajectory information and / or predicted mobility target information (cell identifier the UE is likely to move in a future time instance).

[0197] In these cases, the UE may determine the sorting quantity to be associated to one of these metrics, and the UE sorts the cells in decreasing order to likelihood of the cell being a target cell. In other words, the top ‘X’ cells to include in the measurement report are the cells for which predictions indicate that the UE is most likely to move to in a future time instance.

[0198] In one option, the UE receives an explicit configuration to use a measurement quantity as sorting quantity e.g. RSRP, RSRQ, SINR.

[0199] In one option, the UE receives an explicit configuration to use a predicted measurement quantity as sorting quantity e.g. predicted RSRP, predicted RSRQ, predicted SINR.

[0200] In the following it is disclosed different alternatives to include the the subset of ‘X’ detected neighbour cells in the measurement report.

[0201] (same subset of neighbour cells) In one option, the subset of ‘X’ detected neighbour cells for which the UE includes the mobility related time-domain predictions (e.g. pRSRP), are the ‘X’ detected neighbour cells for which the UE includes the measurement quantities (e.g. as configured as a reporting quantity and / or as trigger quantity). Thus, when the sorting function is performed and the ‘X’ cells are selected to be included according to a sorting quantity (top X, indecreasing order according to the sorting quantity), the UE includes the measurements for these cells and one or more available mobility related time-domain predictions.

[0202] (separate subsets of neighbour cells) In another option, the subset of ‘X’ detected neighbour cells for which the UE includes the mobility related time-domain predictions (e.g. pRSRP), are not necessarily the same ‘X’ detected neighbour cells for which the UE includes the measurement quantities (e.g. as configured as a reporting quantity and / or as trigger quantity). In other words, the UE performs a first sorting function (defined according to the method (e.g. based on a prediction of a measurement quantity associated to the trigger quantity, such as pRSRQ) for sorting the cells for which to include the mobility related time-domain predictions (e.g. pRSRP) and a second sorting function for sorting the cells for which to include the measurement quantities (e.g. based on trigger quantity, in the case of an event triggered RRC Measurement Report).

[0203] In one sub-option, the additional sorting function is the same as the first sorting function.

[0204] In another sub-option, the additional sorting function is different than the first sorting function.

[0205] The fact that the subsets are separated (e.g. included in two different ‘lists’ in the RRC Measurement Report) does not preclude that the same cells are included e.g. in case the first and the second sorting function(s) are the same, or in case the results of the sorting leads to the same output even in case the sorting fiinction(s) are different.

[0206] In one sub-option, for this case of two sorting functions and two separated subsets of detected neighbour cells, the UE is configured with two values of maximum number of cells to report: a first value ‘X’ associated to the number of detected neighbour cells for which to include the mobility related time-domain prediction(s) and a second value ‘Y’ associated to the number of detected neighbour cells for which to include the measurement quantities (e.g. configured as reporting quantities).

[0207] Thus, when the first sorting function is performed and the ‘X’ cells are selected to be included according to a sorting quantity (top X, in decreasing order according to the sorting quantity), the UE includes the for these cells the one or more mobility related time-domain predictions.

[0208] And, when the second sorting function is performed and the ‘Y’ cells are selected to be included according to a sorting quantity (top Y, in decreasing order according to the sorting quantity), the UE includes the for these cells the measurement quantities (e.g. configured as reporting quantities).

[0209] In another sub-option, for this case of two sorting functions and two separated subsets of detected neighbour cells, the UE is configured with a single value for the maximum number of cells to report i.e. the value ‘X’ associated to the number of detected neighbour cells for which to include the mobility related time-domain prediction(s) is the same value associated to the number of detected neighbour cells for which to include the measurement quantities (e.g. configured as reporting quantities).

[0210] Thus, when the sorting function is performed and the ‘X’ cells are selected to be included according to a sorting quantity (top X, in decreasing order according to the sorting quantity), the UE includes the measurements for these cells and one or more available mobility related time-domain predictions.

[0211] In some embodiments, the UE determines a subset of ‘X’ detected neighbour cells for which to include mobility related time-domain prediction(s) in a measurement report (e.g. RRC Measurement Report).

[0212] A mobility related time-domain prediction for a neighbour cell may comprise further prediction dimensions. For example, a mobility related time-domain prediction may correspond to a time-domain prediction of a measurement of a cell A, in a future time instance k, which may also be a spatial domain prediction i.e. the time-domain prediction of the measurement of the cell A, in the future time instance k, is derived from a measurement of a cell B (and not from a measurement of cell A). For example, a time-domain prediction of a measurement of a cell A, in a future time instance k, is also a frequency domain prediction i.e. the time-domain prediction of the measurement of the cell A, in the future time instance k, is derived from a measurement of a cell B in another frequency different the frequency (e.g. SSB frequency) of cell A.

[0213] According to the method, in more details, the UE derives a mobility related timedomain prediction(s) which may include one or more of the following:One or more time-domain predictions of or related to UE mobility. For example, when the input to a Al / ML model is one or more values of a certain type, and the output of the ML model are time-domain predictions such as one or more indications, such as values of a certain type at a future time instance (such as predicted measurement values) or within a time window in a future time instance. The values of a certain type may be for example measurement values (e.g. RSRP, RSRQ or SINR, of a cell and / or beam and / or reference signal), UE location(s) such as cells or positions, and those values may be obtained (e.g. measured) by the UE or given.- One of more indications of predicted values of radio measurements, in future time instances (mobility related time-domain predictions), such as predicted RSRP, predicted RSRQ, predicted SINR. These may be associated to: o cell-level layer 3 filtered measurement values, such as RSRP, RSRQ or SINR, associated with a cell identity such as cell global identity (CGI) or PCI and ARFCN o cell level layer 1 radio measurements, such as CSI measurements, associated with a cell identity such as cell global identity (CGI) or PCI and ARFCNo beam-level radio measurements, such as RSRP, RSRQ or SINR associated with a beam such as an S SB or CSI-RS beam identity related to a cell identified with cell global identity (CGI) or PCI and ARFCN- One or more indications of one or more predicted target cells for mobility, each cell identified with a cell global identity (CGI), PCI and ARFCN, or other form of cell identifier (e.g. candidate cell identifier) o In this case, one option is that the UE determines the sorting quantity to be the likelihood of the cell becoming a target cell in a handover. In other words, suppose that there are 3 detected cells and the UE can only include 2 in a measurement report, the UE includes the 2 with highest chances to be an offset better than the PCell in a future time instance.- One or more indications of one or more predicted UE locations, where a UE location may be, for example, a UE position coordinate. o In this case, one option is that the UE determines the sorting quantity to be the likelihood of the cell becoming a target cell in a handover. In other words, suppose that there are 3 detected cells and the UE can only include 2 in a measurement report, the UE includes the 2 with highest chances to be an offset better than the PCell in a future time instance.- One or more indications of one or more predicted UE trajectories, such as a sequence (vector) of UE locations, where a UE location may be, for example, a UE position coordinate or a cell identity such as cell global identity (CGI) or PCI and ARFCN, or a geometric line or path describing the UE movement o In this case, one option is that the UE determines the sorting quantity to be the likelihood of the cell becoming a target cell in a handover. In other words, suppose that there are 3 detected cells and the UE can only include 2 in a measurement report, the UE includes the 2 with highest chances to be an offset better than the PCell in a future time instance.- A confidence value associated with any of the above indications, indicating the confidence of predicted values.- A confidence value associated with any of the above indications, indicating the confidence of the AI / ML model in predicted values.- A validity time information associated with any of the above indications, indicating a time interval in which the predicted values are valid.- A time-domain prediction associated to a Handover failure (HOF) for a neighbour cell which may occur in a future time instance e.g. information about the likelihood of a HOF in case the UE is handed to a particular neighbour cell in a future time instance.- A value derived from one or more mobility related time-domain prediction(s) for a neighbour cell. The value may be derived from predicted measurement quantity values (e.g. pRSRP(A, 1), pRSRP(A, 2), . . . , pRSRP(A, k)) for one or more future time instances, such as i) an average; ii) a maximum value; iii) a minimum value; iv) a subset of values above a threshold.

[0214] When the mobility related time-domain prediction(s) comprises one of more indications of predicted values of radio measurements, in future time instances (mobility related time-domain predictions), such as predicted RSRP, predicted RSRQ, predicted SINR, one may denote that to be a time-domain cell prediction for a cell X.

[0215] In one option the time-domain cell prediction for a cell X corresponds to the value of a measurement quantity (e.g. an RSRP value, an RSRQ value, an SINR value), or rather a predicted value, representing the measurement quantity of cell X at a future time instance ‘f (ora predicted cell quality or predicted cell measurement result) for a future time instance .In one option, the time-domain cell prediction for cell X is calculated based on one or more timedomain DL beam prediction(s) of a Set A of beams of cell X e.g. predicted RSRP values for each beam in the Set A of beams.

[0216] In one option, both the prediction of the measurement quantity and the cell identifier associated to the cell X are included in the first message, for a given future time instance which may also be included in the measurement report.

[0217] For example, the UE obtains the following time-domain DL beam prediction of a Set A of beams of cell X (e.g., predicted best or strongest beam with predicted SS-RSRP value) for the future time instances as follows:1 . Future time instance f=l a. SS-RSRP =x, for the predicted best beam / Top-1 beam among SSB(l), SSB(2), SSB(3) and SSB(4).2. For future time instance f=2; a. SS-RSRP=x*, for the predicted best beam / Top-1 beam among SSB(l), SSB(2), SSB(3) and SSB(4).3. For future time instance f=3 ; a. SS-RSRP=x**, for the predicted best beam / Top-1 beam among SSB(l), SSB(2), SSB(3) and SSB(4).4. For future time instance f=4; a. SS-RSRP=x***, for the predicted best beam / Top-1 beam among SSB(l), SSB(2), SSB(3) and SSB(4). ii. In one option, the UE considers as the predicted RSRP for cell X in time instance f=l to be the predicted SS-RSRP value x of predicted best beam / Top-1 / strongest beam among SSB(l), SSB(2), SSB(3) and SSB(4); the predicted RSRP for cell X in time instance f=2 to be the predicted SS-RSRP value x* of predicted best beam / Top-1 beam among SSB(l), SSB(2), SSB(3) and SSB(4); the predicted RSRP for cell X in time instance f=3 to be the predicted SS-RSRP value x** of predicted best beam / Top-1 beam among SSB(l), SSB(2), SSB(3) and SSB(4); and the predicted RSRP for cell X in time instance f=4 to be the predicted SS-RSRP value x*** of predicted best beam / Top-1 beam among SSB(l), SSB(2), SSB(3) and SSB(4). Thus, what is included in the first message as predicted information may be following:1 . Future time instance f=I -> predicted RSRP for cell X= x;2. Future time instance f=2 predicted RSRP for cell X= x*;3. Future time instance f=3predicted RSRP for cell X= x* * ;4. Future time instance f=4 predicted RSRP for cell X= x***;

[0218] FIG. 5 illustrates an example of time domain cell prediction derivation based on predicted RSRP value of predicted best beam / Top-1 beam of set A beams of Cell X

[0219] For example, the UE obtains the following time-domain DL beam prediction(s) of aSet A of beams of cell X for the future time instances as follows:1 . Future time instance f=l a. SS-RSRP=xl, for SSB(l); SS-RSRP=x2, for SSB(2); SS- RSRP=x3, for SSB(3); SS-RSRP=x4, for SSB(4).2. For future time instance f=2; a. SS-RSRP=xl*, for SSB(l); SS-RSRP=x2*, for SSB(2); SS- RSRP=x3*, for SSB(3); SS-RSRP=x4*, for SSB(4);3. For future time instance f=3; a. SS-RSRP=xI**, for SSB(l); SS-RSRP=x2**, for SSB(2); SS- RSRP=x3**, for SSB(3); SS-RSRP=x4**, for SSB(4);4. For future time instance f=4; a. SS-RSRP=xI***, for SSB(l); SS-RSRP=x2***, for SSB(2); SS-RSRP=x3***, for SSB(3); SS-RSRP=x4***, for SSB(4); ii. In one option, the UE considers as the predicted RSRP for cell X in time instance f=l to be the highest predicted SS-RSRP value out of xl, x2, x3, x4; the predicted RSRP for cell X in time instance f=2 to be the highest predicted SS-RSRP value out of xl*, x2*, x3*, x4*; the predicted RSRP for cell X in time instance f=3 to be the highest predicted SS-RSRP value out of xl**, x2**, x3**, x4**; and the predicted RSRP for cell X in time instance f=4 to be the highest predicted SS-RSRP value out of xl***, x2***, x3***, x4*. Thus, what is included in the measurement report as predicted information may be following:1. Future time instance f=l -> predicted RSRP for cell X= Max (xl, x2, x3, x4)2. Future time instance f=2 predicted RSRP for cell X= Max (xl*, x2*, x3*, x4*);3. Future time instance f=3predicted RSRP for cell X= Max (xl**, x2**, x3**, x4**);4. Future time instance f=4 predicted RSRP for cell X= Max (xl***,

[0220] FIG. 6 illustrates an example of time domain cell prediction derivation based onMax(). i. In one option, the UE considers as the predicted RSRP for cell X in time instance f=l to be an average of the highest predicted SS-RSRP values e.g. highest SS-RSRP value averaged with the SS-RSRP values of the other SSBs whose SS-RSRP is above a threshold, up to a certain configurable value e.g. N. For simplicity this can be denoted as an Average() function, as follows:1. Future time instance f=l -> predicted RSRP for cell X= Average (xl, x2, x3, x4)2. Future time instance f=2 predicted RSRP for cell X= Average (xl*, x2*, x3*, x4*);3. Future time instance f=3predicted RSRP for cell X= Average (xl**, x2**, x3**, x4**);4. Future time instance f=4 predicted RSRP for cell X= Average

[0221] FIG. 7 illustrates an example of time domain cell prediction derivation based one Average().

[0222] In one option the time-domain cell prediction for neighbor cell X corresponds to a cell identifier such as a Cell ID and / or a PCI + SSB frequency, derived based on the predicted value of a measurement quantity (e.g. an RSRP value, an RSRQ value, an SINR value)representing the measurement quantity of cell X at a future time instance (or a predicted cell quality or predicted cell measurement result).

[0223] In one option, for a future time instance ‘f the time-domain cell prediction for neighbor cell X is calculated based on one or more time-domain DL beam prediction(s) of a Set A of beams of cell X e.g. predicted RSRP values for each beam in the Set A of beams.

[0224] In one option, the UE obtains the following time-domain DL beam prediction(s) of a Set A of beams of cell X for the future time instances as follows:1 . Future time instance f=l a. SS-RSRP=xl, for SSB(l); SS-RSRP=x2, for SSB(2); SS- RSRP=x3, for SSB(3); SS-RSRP=x4, for SSB(4).2. For future time instance f=2; a. SS-RSRP=xl*, for SSB(l); SS-RSRP=x2*, for SSB(2); SS- RSRP=x3*, for SSB(3); SS-RSRP=x4*, for SSB(4);3. For future time instance f=3 ; a. SS-RSRP=xl**, for SSB(l); SS-RSRP=x2**, for SSB(2); SS- RSRP=x3**, for SSB(3); SS-RSRP=x4**, for SSB(4);4. For future time instance f=4; a. SS-RSRP=xl***, for SSB(l); SS-RSRP=x2***, for SSB(2); SS-RSRP=x3***, for SSB(3); SS-RSRP=x4***, for SSB(4);

[0225] In one option, the UE considers as the predicted RSRP for cell X in time instance f=l to be the highest predicted SS-RSRP value out of xl, x2, x3, x4; the predicted RSRP for cell X in time instance f=2 to be the highest predicted SS-RSRP value out of xl*, x2*, x3*, x4*; the predicted RSRP for cell X in time instance f=3 to be the highest predicted SS-RSRP value out of xl**, x2**, x3**, x4**; and the predicted RSRP for cell X in time instance f=4 to be the highest predicted SS-RSRP value out of xl***, x2***, x3***, x4*. Thus, the UE calculates the following and determines to include a Cell ID or not as the predicted information as follows:1 . Future time instance f=I -> predicted RSRP for cell X= Max (xl, x2, x3, x4) a. When the predicted RSRP for cell X= Max (xl, x2, x3, x4) > threshold, UE includes the Cell ID of cell X as predicted information for time instance f=l2. Future time instance f=2 predicted RSRP for cell X= Max (xl*, x2*, x3*, x4*); a. When the predicted RSRP for cell X= Max (xl*, x2*, x3*, x4*) > threshold, UE includes the Cell ID of cell X as predicted information for time instance f=23. Future time instance f=3 predicted RSRP for cell X= Max (xl**, x2**, x3**, x4**); a. When the predicted RSRP for cell X= Max (xl**, x2**, x3**, x4**) > threshold, UE includes the Cell ID of cell X as predicted information for time instance f=34. Future time instance f=4 predicted RSRP for cell X= Max (xl***,a. When the predicted RSRP for cell X= Max (xl***, x2***, x3***, x4***) > threshold, UE includes the Cell ID of cell X as predicted information for time instance f=45. Following that logic, for each future time instance to be included in the first message e.g. f=l, f=2, f=3, f=4 the UE may include one or more cell ID(s), wherein the cell ID is included for a future time instance when the predicted value of the measurement quantity of that cell for that time instance fulfills a criterion, e.g., being above a threshold in the examples above, or being an offset better than the predicted measurement quantity of the PCell (something like the entry condition of an Event A3 but based on tiem-domain prediction of a measurement quantity of a cell). An example of a prediction information may be the following: a. Future time instance f=l -> Cell ID=X, cell ID=Y b. Future time instance f=2 Cell ID=X c. Future time instance f=3Cell ID=X d. Future time instance f=4Cell ID=X, cell ID=Z e. That reported information would indicate that the cell whose cell ID=X has a predicted measurement quantity (RSRP) which fulfills the criteria (e.g. being above a threshold) in the future time instances f=l, f=2, f=3, f=4, while the cell with Cell ID=Y only fulfills the criteria in f=l, and the cell with cell ID= z in f=4.

[0226] An AI / ML model can be designed to realize the beam-level measurement prediction in time-domain. Utilizing the predicted beam-level measurement quality(ies) or / and beam IDs generated from the AI / ML model output, 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 output the predicted cell-level measurement by taking L3 measurements of a set of beams 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.

[0227] The designed AI / ML model can be deployed at the UE and associated to a beam prediction feature or functionality or a RRM prediction feature or functionality. When connecting to a network node, a UE can report its support of the AI / ML model for the beam prediction feature or RRM prediction feature to the network node via UE capability reporting.Based on the received UE capability, together with other conditions, the network node can make decisions on whether to configure / active the AI / ML model at the UE or not.

[0228] Examples on how to design an AI / ML model to achieve the beam / cell-level measurement quality prediction time domain are described below (e.g., examples of how mobility related time-domain prediction(s) may be derived).

[0229] For the AI / ML model used for cell prediction, in an example, it is composed of multiple connected neurons. Optionally it contains one or a few of input layer, one or a few ofhidden layer, and one output layer. For the input layer, it takes UE measurements results as the model input, where the 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 improve 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. Optionally, the output can be the probability values where each value means the probability of the beam to be the best beam.

[0230] In an example for the AI / ML model, the AI / ML model used for cell 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 measurements results as the model input, where the 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 convention layers. The normalization can change the value of the numeric variable in the dataset to a typical scale which improve 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 fdters 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 as feature maps coming from the input measured RSRP values. For pooling layers, it involves sliding a two-dimensional fdter over each channel of feature map and summarizing the features lying within the region covered by the filter. Before the ouput layer, there can be a fully connected layer to interpret / summarize the features obtained and directs them through activation function to the output layer. For the output layer, the output can be predicted RSRP values for each beam. Optionally, the output can be the probability values where each value means the probability of the beam to be the best beam.

[0231] In another set of examples, an AI / ML model is designed to realize the beam-level measurement prediction in the spatial domain. A predicted cell-level measurement quality for a cell X can be derived based on the predicted beam-level measurement quality(ies) or / and beam IDs generated from the AI / ML model output. Different design options for AI / ML based spatial domain beam prediction can be considered. Three example design options are described below.

[0232] 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.

[0233] 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.

[0234] Option 3) the AI / ML model predicts top-l / K beam ID(s) with / 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 as model input.

[0235] As an example, the AI / ML model mentioned in the above three design options can be based on neural network architectures, e.g., convolutional neural network (CNN), fully connected NN, Residual Networks (ResNet). FIGS. 8-9 illustrate two examples of model architectures, where Neural network B (900) is a model with higher complexity in comparison to NN A (800). As illustrated in FIG. 8, NN A (800) includes dense layers 810, 840, a rectified linear unit (ReLU) layer 820, dropout layer 830, and cross-entropy loss function layer 850. As illustrated in FIG. 9, NN B (900) includes dense layers 910, 950, 990, ReLU layer 920, 960, dropout layers 930, 970, concatenate layers 940, 980, and cross-entropy loss function layer 995.

[0236] The number of nodes in the dense layers 810, 910 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 sample. 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.

[0237] In another set of examples, an AI / ML model is designed to realize the beam-level measurement prediction in the time domain (which includes spatial and time domain prediction as a special case). FIG. 10 illustrates an example of model input and model output selection when designing an AI / ML model for time domain beam prediction. The AI / ML model inputs are the Ll-RSRPs measured from 5 consecutive time instances. So, the observation duration Tl=5*40ms=200ms. Prediction is at the time instance immediately following the last observation window time instance, and a prediction at 160ms ahead for comparison. Hence thetime duration for the best beam evaluation is T2= 40 ms or 160 ms. An example of the AI / ML model is described as the following.

[0238] As an example illustrated in FIG. 11, the AI / ML model can be designed based on recurrent neural network (RNN), long short term memory network (LSTM), or transformer network architectures. An example model architecture can consist of two LSTM layers, a Dropout layer, Dense-Relu and a Dense-softmax, which are sequentially connected. The AI / ML model input size is (Nip, 5), where Nip is the number of Ll-RSRPs measured at each of the five time instance. For model ouput, a categorical cross entropy loss function is used to generate a softmax output vector, whose size is equal to the number of beams contained in the set A, based on which the top-l / K beams with the highest probability can be generated for each prediction time instance.

[0239] As another example, the AI / ML model mentioned in the above design options can be based on transformer encoder architecture. The encoder is composed of a stack of identical layers where each layer has some sub-layers. FIG. 12 illustrates an example of a transformer encoder based NN 1200 that receives normalized RSRPs as input. The transformer encoder based NN can include Dense layers 1220, 1260, a positional embedding layer 1230, a transformer encoder layer 1300, dropout layer 1250, and a loss function 1270. FIG. 13 illustrates an example of architecture for the transformer encoder 1300. Within the transformer encoder 1300, there is a sub-layer called multi-head self-attention 1400 where normalized RSRPs would be inputted to several (e.g., h) linear layers 1340, 1360, layer norms 1330, 1380, a rectified linear unit (ReLU) 1350 and concatenate layers 1320, 1370 to extend the dimension with more information explored, and association between these extended samples can be learned by scaled dot-product attention followed by concatenation and linear fully connected neural network.

[0240] FIGS. 14-15 illustrate the example of multi-head self-attention 1400 and scaled dotproduct attention 1500.

[0241] In the following it is disclosed the UE actions when there are multiple time-domain predicted measurement values for multiple ’k’ time instances in the future for a cell for a given mobility related time-domain prediction e.g. when the AI / ML model at the UE derives as inference and / or outputs the values: pRSRP(A,l), pRSRP(A,2), pRSRP(A,3), ... ., pRSRP(A,k), wherein pRSRP(A,k) denotes the predicted RSRP value of cell A in a future time instance indicated by the value k e.g. k-th future time instance.

[0242] In the case in which the UE determines the sorting quantity to be e.g. 3) a prediction reporting quantity the UE is configured with, the UE determines a value (e.g. representativevalue) derived from at least one of the multiple available values of mobility related time-domain prediction(s) to sort the cells.

[0243] For example, let us assume the following scenario:Cell A: RSRP(A), RSRQ(A); pRSRP(A, 1), pRSRQ(A, 1); pRSRP(A, 2), pRSRQ(A, 2); pRSRP(A, 3), pRSRQ(A, 3);Cell B: RSRP(B), RSRQ(B); pRSRP(B, 1), pRSRQ(B, 1); pRSRP(B, 2), pRSRQ(B, 2); pRSRP(B, 3), pRSRQ(B, 3);Cell C: RSRP(C), RSRQ(C); pRSRP(C, 1), pRSRQ(C, 1); pRSRP(C, 2), pRSRQ(C, 2); pRSRP(C, 3), pRSRQ(C, 3);

[0244] As before, pRSRP(A,k) denotes the predicted RSRP value of cell A in a future time instance indicated by the value k e.g. k-th future time instance.

[0245] The UE determines a value per cell, of the sorting quantity, to sort the cells, wherein the value (representative value) is associated to at least one of the multiple values of a prediction reporting quantity (e.g. predicted RSRQ values) in the multiple future time instances. The value of the sorting quantity per cell to be used is determined by the UE to be one of the following:- i) determining the latest time-domain prediction per cell to be the value used by the UE in the sorting;- ii) determining the first time-domain prediction per cell to be the value used by the UE in the sorting;- iii) determining the maximum value among the time-domain prediction(s) per cell to be the value used by the UE in the sorting- iv) determining the minimum value among the time-domain prediction(s) per cell to be the value used by the UE in the sorting;- v) determining an average value of the time-domain prediction(s) per cell to be the value used by the UE in the sorting;- vi) determining the maximum with highest accuracy and / or lowest prediction error among the time-domain prediction(s) per cell to be the value used by the UE in the sorting;- vii) determining the value associated to a time instance k indicated by the network among the time-domain prediction(s) per cell to be the value used by the UE in the sorting.

[0246] For example, the UE determines predicted RSRQ as sorting quantity and option i) is used to determine the representative value to be used for the actual sorting. In other words, the UE determines the latest time-domain prediction per cell to be the value used by the UE in the sorting. Thus, this is the input to the sorting function:Cell A: pRSRQ(A, 3);Cell B: pRSRQ(B, 3);Cell C: pRSRQ(C, 3);Wherein pRSRQ(C, 3) > pRSRQ(B, 3) > pRSRQ(A, 3).

[0247] In that case, when maximum number of cells for which the UE includes mobility related time-domain predictions is X=2, the UE would include in the measurement report cells C and B, since values pRSRQ(C, 3) > pRSRQ(B, 3) > pRSRQ(A, 3) were used for the sorting.

[0248] In one option, the UE includes in the measurement report, for the included cell(s), only the latest time-domain prediction per cell. In another option, the UE also includes the other values available for other time instances.

[0249] For example, let us assume the UE determines predicted RSRQ as sorting quantity and option ii) is used to determine the value to be used for sorting. In other words, the UE determines the first time-domain prediction per cell to be the value used by the UE in the sorting. Thus, this is the input to the sorting function:Cell A: pRSRQ(A, 1);Cell B: pRSRQ(B, 1);Cell C: pRSRQ(C, 1);Wherein pRSRQ(B, 1) > pRSRQ(A, 1) > pRSRQ(C, 1).

[0250] In that case, when maximum number of cells for which the UE includes mobility related time-domain predictions is X=2, the UE would include in the measurement report cells B and A. In one option, the UE includes in the measurement report, for the included cell(s), only the first time-domain prediction per cell. In another option, the UE also includes the other values available for other time instances.

[0251] For example, let us assume the UE determines predicted RSRQ as sorting quantity and option iv) is used to determine the value to be used for sorting i.e. iv) the UE determines an average value of the time-domain prediction(s) per cell to be the value used by the UE in the sorting; Thus, this is the input to the sorting function:Cell A: average (pRSRQ(A, 1); pRSRQ(A, 2); pRSRQ(A, 3))Cell B: average (pRSRQ(B, 1); pRSRQ(B, 2); pRSRQ(B, 3))Cell C: average (pRSRQ(C, 1); pRSRQ(C, 2); pRSRQ(C, 3))Wherein average (pRSRQ(A, 1); pRSRQ(A, 2); pRSRQ(A, 3)) > average (pRSRQ(B, 1); pRSRQ(B, 2); pRSRQ(B, 3)) > average (pRSRQ(C, 1); pRSRQ(C, 2); pRSRQ(C, 3)).

[0252] In that case, when max number of cells to report mobility related time-domain predictions X=2, the UE would include in the measurement report cells A and B.

[0253] The benefit here is that this works for the case in which the AI / ML model produces a different number of values for future time instances for the different cells. This is also an advantage of options iii) and v).

[0254] In one option, the UE derives the same number of values for future time instances per cell which are considering in the sorting, to be included in the measurement report.

[0255] In one option, the UE includes in the measurement report, for the included cell(s), only the representative values according to one or more of the options above (e.g. i), ii), iii), . . . , vii)) per cell. In another option, the UE also includes the other values available for other time instances.

[0256] In another option, the UE derives different number of values for future time instances for at least one cell which is considered in the sorting, to be included in the measurement report.

[0257] For example, let us assume the following scenario:Cell A: RSRP(A), RSRQ(A); pRSRP(A, 1), pRSRQ(A, 1);Cell B: RSRP(B), RSRQ(B); pRSRP(B, 1), pRSRQ(B, 1); pRSRP(B, 2), pRSRQ(B, 2); pRSRP(B, 3), pRSRQ(B, 3);Cell C: RSRP(C), RSRQ(C); pRSRP(C, 1), pRSRQ(C, 1); pRSRP(C, 2), pRSRQ(C, 2);

[0258] As before, pRSRP(A,k) denotes the predicted RSRP value of cell A in a future time instance indicated by the value k e.g. k-th future time instance. As one can see, there is 1 predicted value for cell A, for pRSRP and pRSRQ; 3 for cel B for pRSRP and pRSRQ; and 2 for cell C.

[0259] For example, let us assume the UE determines predicted RSRQ as sorting quantity and option i) is used to determine the value to be used for sorting. In other words, the UE determines the latest time-domain prediction per cell to be the value used by the UE in the sorting.

[0260] In one variant, the latest value is the latest per cell i.e. for cell A: pRSRQ (A, 1), for cell B: pRSRQ (B,3) and for cell C: pRSRQ(C,2). Thus, when pRSRQ (A,l) > pRSRQ(C,2) > pRSRQ (B,3) the sorting is as follows:1) Cell A2) Cell C3) Cell B

[0261] In another variant, the latest value is the latest which is common for all cells i.e. for cell A: pRSRQ (A,l), for cell B: pRSRQ (B, 1) and for cell C: pRSRQ(C,l), sine all the cells have the first prediction (k=l) as the latest prediction. Thus, when pRSRQ (A,l) > pRSRQ(B,l) > pRSRQ (C, 1) the sorting is as follows:1) Cell A2) Cell B3) Cell C.

[0262] Operations of a communication device 1900 (implemented using the structure of FIG. 19) will now be discussed with reference to the flow chart of FIG. 10 according to someembodiments of inventive concepts. For example, modules may be stored in memory 1910 of FIG. 19, and these modules may provide instructions so that when the instructions of a module are executed by respective communication device processing circuitry 1902, communication device 1900 performs respective operations of the flow chart.

[0263] FIG. 16 illustrates an example of operations performed by a communication device in a communications network that includes a network node.

[0264] At block 1610, processing circuitry 1902 receives, via communication interface 1912, configuration information.

[0265] At block 1620, processing circuitry 1902 generates a report based on a mobility related time-domain prediction of a neighbor cell. In some embodiments, generating the report comprises generating the report based on the configuration information.

[0266] In additional or alternative embodiments, generating the report includes selecting (1622) the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell.

[0267] In additional or alternative embodiments, generating the report includes sorting (1624) the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell.

[0268] In additional or alternative embodiments, generating the report includes generating the report to include the mobility related time-domain prediction of the neighbor cell. In some examples, generating the report to include the mobility related time-domain prediction of the neighbor cell includes generating the report to include only the mobility related time-domain prediction of the neighbor cell.

[0269] In additional or alternative embodiments, generating the report based on the mobility related time-domain prediction of the neighbor cell includes at least one of: determining a latest time-domain prediction per cell; determining a first time-domain prediction per cell; determining a maximum value among time-domain predictions per cell; determining a minimum value among the time-domain predictions per cell; determining an average value of the time-domain predictions per cell; determining a maximum with highest accuracy and / or lowest prediction error among the time-domain predictions per cell; and determining a value associated to a time instance k indicated by the network among the time-domain predictions per cell.

[0270] At block 1630, processing circuitry 1902 transmits, via communication interface 1912, the report.

[0271] In some embodiments, generating the report includes sorting the subset of the plurality of neighbor cells based on at least one of: a trigger quantity; a measurement reporting quantity; a prediction reporting quantity; and a prediction-based trigger quantity.

[0272] In additional or alternative embodiments, transmitting the report includes at least one of: periodically transmitting the report; transmitting the report in response to an event trigger; semi -persistently transmitting the report; and aperiodically transmitting the report.

[0273] In additional or alternative embodiments, the mobility related time-domain prediction of the neighbor cell includes at least one of: a predicted reference signal received power, pRSRP; a predicted reference signal received quality, pRSRQ; and a predicted signal interference-to-noise ratio, pSINR.

[0274] In additional or alternative embodiments, the report includes a radio resource control, RRC, measurement report.

[0275] Various operations from the flow chart of FIG. 16 may be optional with respect to some embodiments of communication devices and related methods.

[0276] Operations of a network node 1900 (implemented using the structure of FIG. 20) will now be discussed with reference to the flow chart of FIG. 11 according to some embodiments of inventive concepts. For example, modules may be stored in memory 2004 of FIG. 20, and these modules may provide instructions so that when the instructions of a module are executed by respective network node processing circuitry 2002, network node 2000 performs respective operations of the flow chart.

[0277] FIG. 17 illustrates an example of operations performed by a network node in a communications network that includes a communication device.

[0278] At block 1710, processing circuitry 2002 generates configuration information to cause a communication device to generate a report based on a mobility related time -domain prediction of a neighbor cell. In some embodiments, generating the configuration information includes generating the instructions to cause the communication device to select a subset of the plurality of neighbor cells to include in the report based on the mobility related time-domain prediction of the neighbor cell.

[0279] In additional or alternative embodiments, generating the configuration information includes generating the instructions to cause the communication device to sort a subset of the neighbor cells included in the report based on the mobility related time-domain prediction of the neighbor cells.

[0280] In additional or alternative embodiments, generating the configuration information includes generating instructions to cause the communication device to generate the report with a subset of the plurality of neighbor cells sorted based on at least one of: a trigger quantity; a measurement reporting quantity; a prediction reporting quantity; and a prediction-based trigger quantity.

[0281] In additional or alternative embodiments, generating the configuration information includes generating instructions to cause the communication device to generate the report based on at least one of: determining a latest time-domain prediction per cell; determining a first timedomain prediction per cell; determining a maximum value among time-domain predictions per cell; determining a minimum value among the time-domain predictions per cell; determining an average value of the time-domain predictions per cell; determining a maximum with highest accuracy and / or lowest prediction error among the time-domain predictions per cell; and determining a value associated to a time instance k indicated by the network among the timedomain predictions per cell.

[0282] In additional or alternative embodiments, generating the configuration information includes instructing the communication device to include a ‘best’ detected neighbor cell first in the report, wherein the ‘best’ detected neighbor cell is the cell in a first position after the sorting of detected cells.

[0283] At block 1720, processing circuitry 2002 transmits, via communication interface 2006, the configuration information to the communication device.

[0284] At block 1730, processing circuitry 2002 receives, via communication interface 2006, the report. In some embodiments, receiving the report includes receiving the report including the mobility related time-domain prediction of the neighbor cell. In additional or alternative embodiments, receiving the report includes receiving the report including only the mobility related time-domain prediction of the neighbor cell.

[0285] In additional or alternative embodiments, receiving the report includes at least one of: periodically receiving the report; receiving the report in response to an event trigger; semi- persistently receiving the report; and aperiodically receiving the report.

[0286] In additional or alternative embodiments, the mobility related time-domain prediction of the neighbor cell includes at least one of: a predicted reference signal received power, pRSRP; a predicted reference signal received quality, pRSRQ; and a predicted signal interference-to-noise ratio, pSINR.

[0287] In additional or alternative embodiments, the report includes a radio resource control, RRC, measurement report.

[0288] Various operations from the flow chart of FIG. 17 may be optional with respect to some embodiments of communication devices and related methods.

[0289] Example Embodiments are provided below.

[0290] Embodiment 1. A method at a User Equipment (UE) for determining a subset of ‘X’ detected neighbor cells for which to include mobility related time-domain prediction(s) in a measurement report, the method comprising:receiving a message from a network node including a configuration indicating to the UE to include in the measurement report the one or more mobility related time-domain prediction(s) of the subset of ‘X‘ detected neighbor cells; including in the measurement report, before transmitting the measurement report, the one or more mobility related time-domain prediction(s) for the subset of ‘X‘ detected neighbor cells in decreasing order of a sorting quantity, wherein the sorting quantity is determined to be at least one of: a trigger quantity the UE is configured with; a measurement reporting quantity the UE is configured with; a prediction reporting quantity the UE is configured with; and a prediction-based trigger quantity e.g., pRSRP, pRSRQ, pSINRthe UE is configured with; and transmitting a report.

[0291] In some examples, the report is the measurement report. In additional or alternative examples, the report can include measurements only, a combination of measurement(s) and prediction(s), or only the prediction(s).

[0292] Embodiment 2. The method of Embodiment 1, wherein the ‘best’ detected neighbor cell is included first in the measurement report, wherein the ‘best’ detected neighbor cell is the cell in the first position after the sorting of the ‘X’ detected cells based on the sorting quantity (e.g. the best detected cell is the cell with strongest radio measurement for the sorting quantity).

[0293] Embodiment 3. The method of any of Embodiments 1-2, wherein the sorting quantity is determined to be the trigger quantity the UE is configured with when the measurement report is triggered by the fulfillment of an event (i.e. in the case of event-triggered measurement report) with entering conditions based on one or more measurements according to a measurement quantity (e.g. RSRP, RSRQ, SINR).

[0294] Embodiment 4. The method of any of Embodiments 1-3, wherein the sorting quantity is determined to be the measurement reporting quantity the UE is configured with when the measurement report is triggered to be transmitted periodically (i.e. in the case of periodic measurement report) as follows: when a single reporting quantity is configured, that reporting quantity is the sorting quantity; when a first measurement quantity (e.g. RSRP) is configured as a measurement reporting quantity, that first measurement reporting quantity is the sorting quantity; when a first measurement quantity (e.g. RSRP) is NOT configured as a measurement reporting quantity, a second measurement reporting quantity (e.g. RSRQ) is the sorting quantity.

[0295] Embodiment 5. The method of any of Embodiments 1-4, wherein the sorting quantity is determined to be the prediction reporting quantity the UE is configured with which is associated to a trigger quantity the UE is configured with.

[0296] Embodiment 6. The method of Embodiment 5, wherein: when the UE is configured with RSRP as a trigger quantity for an event-based measurement report, determining predicted RSRP to be the sorting quantity; when the UE is configured with RSRQ a trigger quantity for an event-based measurement report, determining predicted RSRQ to be the sorting quantity; when the UE is configured with SINR a trigger quantity for an event-based measurement report, determining predicted SINR to be the sorting quantity.

[0297] Embodiment ?. The method of any of Embodiments 5-6, wherein: when the UE is configured with RSRP as a trigger quantity for an event-based measurement report, and the UE is configured with the prediction reporting quantity predicted RSRP, determining predicted RSRP to be the sorting quantity. when the UE is configured with RSRQ a trigger quantity for an event-based measurement report, and the UE is configured with the prediction reporting quantity predicted RSRQ, determining predicted RSRQ to be the sorting quantity. when the UE is configured with SINR a trigger quantity for an event-based measurement report, and the UE is configured with the prediction reporting quantity predicted SINR, determining predicted SINR to be the sorting quantity.

[0298] Embodiment s. The method of any of Embodiments 5-7, wherein: when the UE is configured with RSRP as a trigger quantity for an event-based measurement report, and the UE is NOT configured with the prediction reporting quantity predicted RSRP, determining predicted RSRQ to be the sorting quantity, when predicted RSRQ is configured as prediction reporting quantity.

[0299] Embodiment 9. The method of any of Embodiments 1-8, wherein the sorting quantity is determined to be the prediction reporting quantity the UE is configured with when the measurement report is triggered to be transmitted periodically (i.e. in the case of periodic measurement report) as follows: when a single prediction reporting quantity is configured, that reporting quantity is the sorting quantity; when a first prediction quantity (e.g. predicted RSRP) is configured as a prediction reporting quantity, that first prediction quantity is the sorting quantity;when a first prediction quantity (e.g. predicted RSRP) is NOT configured as a prediction reporting quantity, a second prediction reporting quantity (e.g. predicted RSRQ) is the sorting quantity.

[0300] Embodiment 10. The method of any of Embodiments 1-9, wherein the measurement report is triggered to be transmitted based on one or more of the following criteria: periodically; event-triggered; semi-persistent; and periodic (e.g. based on a request from the network).

[0301] Embodiment 11. The method of any of Embodiments 1-10, wherein when the sorting quantity is determined to be the prediction reporting quantity the UE is configured with and the UE has available multiple time-domain prediction(s) for that sorting quantity, determining a value (representative value used for the sorting) derived from at least one of the multiple predictions to sort the cells

[0302] Embodiment 12. The method of Embodiment 11, wherein determining the value (representative value used for the sorting) derived from at least one of the multiple timed- domain prediction(s) to sort the cells comprises: determining the latest time-domain prediction per cell to be the value used by the UE in the sorting; determining the first time-domain prediction per cell to be the value used by the UE in the sorting; determining the maximum value among the time-domain prediction(s) per cell to be the value used by the UE in the sorting; determining the minimum value among the time -domain prediction(s) per cell to be the value used by the UE in the sorting; determining an average value of the time-domain prediction(s) per cell to be the value used by the UE in the sorting; determining the maximum with highest accuracy and / or lowest prediction error among the time-domain prediction(s) per cell to be the value used by the UE in the sorting; determining the value associated to a time instance k indicated by the network among the time-domain prediction(s) per cell to be the value used by the UE in the sorting.

[0303] Embodiment 13. The method of any of Embodiments 1-12, wherein the subset of ‘X’ detected neighbor cells, determined based on the sorting quantity, for which the UE includes the mobility related time-domain predictions (e.g. pRSRP) corresponds to the ‘X’ detected neighbor cells for which the UE includes the measurement quantities.

[0304] Embodiment 14. The method of any of Embodiments 1-13, determined based on the sorting quantity, wherein the subset of ‘X’ detected neighbor cells for which the UE includes the mobility related time-domain predictions (e.g. pRSRP), are not necessarily the same ‘X’detected neighbor cells for which the UE includes the measurement quantities (e.g. as configured as a reporting quantity and / or as trigger quantity).

[0305] Embodiment 15. The method of any of Embodiments 1-14, wherein the UE performs a first sorting function for sorting the cells for which to include the mobility related time-domain predictions (e.g. pRSRP) and a further second sorting function for sorting the cells for which to include the measurement quantities (e.g. based on trigger quantity, in the case of an event triggered RRC Measurement Report).

[0306] Embodiment 16. The method of Embodiment 15, wherein the further second sorting function is the same as the first sorting function, or different.

[0307] Embodiment 17. The method of any of Embodiments 1-16, wherein the sorting quantity is determined to be the prediction based measurement report triggering quantity the UE is configured with when the report is triggered to be transmitted based on a configured one or more events to be evaluated based on the mobility related time-domain predictions (e.g. pRSRP, pRSRQ or pSINR), so called predicted events (i.e. in the case of event based reporting).

[0308] Additional Embodiments are described below.

[0309] Embodiment Al. A method of operating a communication device in a communications network that includes a network node, the method comprising: generating (1620) a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells, the report including information associated with a subset of the plurality of neighbor cells; and transmitting (1630) the report to the network node.

[0310] Embodiment A2. The method of Embodiment Al, wherein generating the report comprises selecting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell.

[0311] Embodiment A3. The method of any of Embodiments A 1-2, wherein generating the report comprises sorting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell.

[0312] Embodiment A4. The method of any of Embodiments Al-3, wherein generating the report comprises generating the report to include the mobility related time-domain prediction of the neighbor cell.

[0313] Embodiment A5. The method of Embodiment A4, wherein generating the report to include the mobility related time-domain prediction of the neighbor cell comprises generating the report to include only the mobility related time-domain prediction of the neighbor cell.

[0314] Embodiment A6. The method of any of Embodiments Al-5, wherein generating the report comprises sorting the subset of the plurality of neighbor cells based on at least one of:a trigger quantity; a measurement reporting quantity; a prediction reporting quantity; and a prediction-based trigger quantity.

[0315] Embodiment A7. The method of any of Embodiments A 1-6, wherein the report comprises a radio resource control, RRC, measurement report.

[0316] Embodiment A8. The method of any of Embodiments A 1-7, further comprising: receiving (1610) configuration information from the network node, the configuration information indicating how to generate the report based on the mobility related time-domain prediction of the neighbor cell, wherein generating the report comprises generating the report based on the configuration information.

[0317] Embodiment A9. The method of any of Embodiments A 1-8, wherein transmitting the report comprises at least one of: periodically transmitting the report; transmitting the report in response to an event trigger; semi-persistently transmitting the report; and aperiodically transmitting the report.

[0318] Embodiment A 10. The method of any of Embodiments A 1-9, wherein the mobility related time-domain prediction of the neighbor cell comprises at least one of: a predicted reference signal received power, pRSRP; a predicted reference signal received quality, pRSRQ; and a predicted signal interference-to-noise ratio, pSINR.

[0319] Embodiment Al 1. The method of any of Embodiments 1-10, wherein generating the report based on the mobility related time-domain prediction of the neighbor cell comprises at least one of: determining a latest time-domain prediction per cell; determining a first time -domain prediction per cell; determining a maximum value among time-domain predictions per cell; determining a minimum value among the time-domain predictions per cell; determining an average value of the time-domain predictions per cell; determining a maximum with highest accuracy and / or lowest prediction error among the time-domain predictions per cell; and determining a value associated to a time instance k indicated by the network among the time-domain predictions per cell.

[0320] Embodiment A12. A method of operating a network node in a communications network that includes a communication device, the method comprising: generating (1710) configuration information including instructions to cause the communication device to generate a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells; transmitting (1720) the configuration information to the communication device; and receiving (1730) the report from the communication device.

[0321] Embodiment A13. The method of Embodiment A12, wherein generating the configuration information comprises generating the instructions to cause the communication device to select a subset of the plurality of neighbor cells to include in the report based on the mobility related time-domain prediction of the neighbor cell.

[0322] Embodiment A14. The method of any of Embodiments A12-13, wherein generating the configuration information comprises generating the instructions to cause the communication device to sort a subset of the neighbor cells included in the report based on the mobility related time-domain prediction of the neighbor cells.

[0323] Embodiment A15. The method of any of Embodiments A12-14, wherein generating the configuration information comprises generating the instructions to cause the communication device to generate the report to include the mobility related time-domain prediction of the neighbor cell, and wherein receiving the report comprises receiving the report including the mobility related time-domain prediction of the neighbor cell.

[0324] Embodiment A16. The method of Embodiment A15, wherein generating the configuration information comprises generating the instructions to cause the communication device to generate the report to include only the mobility related time-domain prediction of the neighbor cell, and wherein receiving the report comprises receiving the report including only the mobility related time-domain prediction of the neighbor cell.

[0325] Embodiment A17. The method of any of Embodiments A12-16, wherein generating the configuration information comprises generating instructions to cause the communication device to generate the report with a subset of the plurality of neighbor cells sorted based on at least one of: a trigger quantity; a measurement reporting quantity; a prediction reporting quantity; and a prediction-based trigger quantity.

[0326] Embodiment A18. The method of any of Embodiments A12-17, wherein the report comprises a radio resource control, RRC, measurement report.

[0327] Embodiment A 19. The method of any of Embodiments A 12- 18, wherein receiving the report comprises at least one of: periodically receiving the report; receiving the report in response to an event trigger; semi-persistently receiving the report; and aperiodically receiving the report.

[0328] Embodiment A20. The method of any of Embodiments A 12- 19, wherein the mobility related time-domain prediction of the neighbor cell comprises at least one of: a predicted reference signal received power, pRSRP; a predicted reference signal received quality, pRSRQ; and a predicted signal interference-to-noise ratio, pSINR.

[0329] Embodiment A21. The method of any of Embodiments A 12-20, wherein generating the configuration information comprises generating instructions to cause the communication device to generate the report based on at least one of: determining a latest time-domain prediction per cell; determining a first time -domain prediction per cell; determining a maximum value among time-domain predictions per cell; determining a minimum value among the time-domain predictions per cell; determining an average value of the time-domain predictions per cell; determining a maximum with highest accuracy and / or lowest prediction error among the time-domain predictions per cell; and determining a value associated to a time instance k indicated by the network among the time-domain predictions per cell.

[0330] Embodiment A22. The method of any of Embodiments 12-21, wherein generating the configuration information comprises instructing the communication device to include a ‘best’ detected neighbor cell first in the report, wherein the ‘best’ detected neighbor cell is the cell in a first position after the sorting of detected cells.

[0331] Embodiment A23. A communication device (1900) adapted to perform operations comprising: generating (1620) a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells, the report including information associated with a subset of the plurality of neighbor cells; and transmitting (1630) the report to the network node.

[0332] Embodiment A24. The communication device of Embodiment A23, the operations further comprising any of the operations of Embodiments A2-11.

[0333] Embodiment A25. A computer program comprising program code to be executed by processing circuitry (1902) of a communication device (1900), whereby execution of the program code causes the communication device to perform operations comprising: generating (1620) a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells, the report including information associated with a subset of the plurality of neighbor cells; and transmitting (1630) the report to the network node.

[0334] Embodiment A26. The computer program of Embodiment A25, the operations further comprising any of the operations of Embodiments A2- 11.

[0335] Embodiment 28. A computer program product comprising a non-transitory storage medium (1910) including program code to be executed by processing circuitry (1902) of a communication device (1900), whereby execution of the program code causes the communication device to perform operations comprising: generating (1620) a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells, the report including information associated with a subset of the plurality of neighbor cells; and transmitting (1630) the report to the network node.

[0336] Embodiment A28. The computer program product of Embodiment A27, further comprising any of the operations of Embodiments A2- 11.

[0337] Embodiment A29. A network node (2000) adapted to perform operations comprising: generating (1710) configuration information including instructions to cause the communication device to generate a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells; transmitting (1720) the configuration information to the communication device; and receiving (1730) the report from the communication device.

[0338] Embodiment A30. The network node of Embodiment A29, the operations further comprising any of the operations of Embodiments A 13-22.

[0339] Embodiment A31. A computer program comprising program code to be executed by processing circuitry (2002) of a network node (2000), whereby execution of the program code causes the network node to perform operations comprising:generating (1710) configuration information including instructions to cause the communication device to generate a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells; transmitting (1720) the configuration information to the communication device; and receiving (1730) the report from the communication device.

[0340] Embodiment A32. The computer program of Embodiment A31, the operations further comprising any of the operations of Embodiments A 13-22.

[0341] Embodiment A33. A computer program product comprising a non-transitory storage medium (2004) including program code to be executed by processing circuitry (2002) of a network node (2000), whereby execution of the program code causes the network node to perform operations comprising: generating (1710) configuration information including instructions to cause the communication device to generate a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells; transmitting (1720) the configuration information to the communication device; and receiving (1730) the report from the communication device.

[0342] Embodiment A34. The computer program product of Embodiment A33, further comprising any of the operations of Embodiments A 13-22.

[0343] FIG. 18 shows an example of a communication system 1800 in accordance with some embodiments.

[0344] In the example, the communication system 1800 includes a telecommunication network 1802 that includes an access network 1804, such as a radio access network (RAN), and a core network 1806, which includes one or more core network nodes 1808. The access network 1804 includes one or more access network nodes, such as network nodes 1810a and 1810b (one or more of which may be generally referred to as network nodes 1810), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points. Moreover, as will be appreciated by those of skill in the art, 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 telecommunication network 1802 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 1802 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 nodes to implement one or more functionalities of any node in thetelecommunication network 1802, including one or more network nodes 1810 and / or core network nodes 1808.

[0345] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU- CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The 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 access 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 0-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes 1810 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 1812a, 1812b, 1812c, and 1812d (one or more of which may be generally referred to as UEs 1812) to the core network 1806 over one or more wireless connections.

[0346] 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 1800 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 1800 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0347] The UEs 1812 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 1810 and other communication devices. Similarly, the network nodes 1810 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 1812 and / or with other network nodes or equipment in the telecommunication network 1802 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 1802.

[0348] In the depicted example, the core network 1806 connects the network nodes 1810 to one or more host computing systems, such as host 1816. 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 1806 includes one more core network nodes (e.g., core network node 1808) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 1808. Example core network nodes include 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).

[0349] The host 1816 may be under the ownership or control of a service provider other than an operator or provider of the access network 1804 and / or the telecommunication network 1802. The host 1816 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.

[0350] As a whole, the communication system 1800 of FIG. 18 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 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 (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[0351] In some examples, the telecommunication network 1802 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 1802 maysupport network slicing to provide different logical networks to different devices that are connected to the telecommunication network 1802. For example, the telecommunications network 1802 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)ZMassive loT services to yet further UEs.

[0352] In some examples, the UEs 1812 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 1804 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 1804. 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).

[0353] In the example, the hub 1814 communicates with the access network 1804 to facilitate indirect communication between one or more UEs (e.g., UE 1812c and / or 1812d) and network nodes (e.g., network node 1810b). In some examples, the hub 1814 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 1814 may be a broadband router enabling access to the core network 1806 for the UEs. As another example, the hub 1814 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 1810, or by executable code, script, process, or other instructions in the hub 1814. As another example, the hub 1814 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 1814 may be a content source. For example, for a UE that is a VR device, display, loudspeaker, or other media delivery device, the hub 1814 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 1814 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 1814 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0354] The hub 1814 may have a constant / persistent or intermittent connection to the network node 1810b. The hub 1814 may also allow for a different communication scheme and / or schedule between the hub 1814 and UEs (e.g., UE 1812c and / or 1812d), and between the hub 1814 and the core network 1806. In other examples, the hub 1814 is connected to the core network 1806 and / or one or more UEs via a wired connection. Moreover, the hub 1814 may be configured to connectto an M2M service provider over the access network 1804 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 1810 while still connected via the hub 1814 via a wired or wireless connection. In some embodiments, the hub 1814 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 1810b. In other embodiments, the hub 1814 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 1810b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0355] FIG. 19 shows a UE 1900 in accordance with some embodiments. The UE 1900 presents additional details of some embodiments of the UE 1812 of Figure 1. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE 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 / playback device, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), an Augmented Reality (AR) or Virtual Reality (VR) device, wireless customer-premise equipment (CPE), vehicle, vehicle -mounted or vehicle embedded / integrated wireless device, etc. Other examples include any 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.

[0356] A UE may support device-to-device (D2D) communication, for example by implementing a 3 GPP 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, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE 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, a UE 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).

[0357] The UE 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 UEs may utilize all or a subset of the components shown in FIG. 19. The level of integration between thecomponents may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0358] 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).

[0359] 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 the UE 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.

[0360] 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. 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 the UE 1900 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 1908. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 1908 to make the power suitable for the respective components of the UE 1900 to which power is supplied.

[0361] 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 application 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 the UE 1900, any of a variety of various operating systems or combinations of operating systems.

[0362] 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 the UE 1900 to access instructions, application 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.

[0363] The processing circuitry 1902 may be configured to communicate with an access network or other network 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 UE 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.

[0364] In the illustrated embodiment, communication functions of the communication interface 1912 may include cellular communication, Wi-Fi communication, LPWANcommunication, 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 in 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.

[0365] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 1912, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The 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).

[0366] As another example, a UE 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, the UE 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.

[0367] A UE, 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, city 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. A UE in the form of an loT device comprisescircuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 1900 shown in FIG. 19.

[0368] As yet another specific example, in an loT scenario, a UE 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 UE and / or a network node. The UE 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, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE 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.

[0369] In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE 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 UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0370] FIG. 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 telecommunication network. 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).

[0371] Base stations 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. A base station may be a relay node or a relay donor node controlling a relay. A network node 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).

[0372] Other examples of network nodes 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).

[0373] The network node 2000 includes a processing circuitry 2002, a memory 2004, a communication interface 2006, and a power source 2008. The network node 2000 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 2000 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components 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 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, WiFi, 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.

[0374] 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 network node 2000 components, such as the memory 2004, to provide network node 2000 functionality.

[0375] 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 radio frequency (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 anddigital 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.

[0376] 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.

[0377] The communication interface 2006 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the 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. The communication interface 2006 also includes radio front-end circuitry 2018 that may be coupled to, or in certain embodiments a part of, the antenna 2010. Radio front-end circuitry 2018 comprises filters 2020 and amplifiers 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 frontend circuitry 2018 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 2020 and / or amplifiers 2022. The radio signal 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.

[0378] In certain alternative embodiments, the network node 2000 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 ofthe 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).

[0379] 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 an interface or port.

[0380] The antenna 2010, communication interface 2006, and / or the processing circuitry 2002 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. 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 any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0381] 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.

[0382] Embodiments of the network node 2000 may include additional components beyond those shown in FIG. 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. In some embodiments providing a core network node, such as core network node 108 of FIG. 18, some components, such as the radio front-end circuitry 2018 and the RF transceiver circuitry 2012 may be omitted.

[0383] FIG. 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 a network node, UE, core network node, or host. Further, in embodiments in which the 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. Virtualization may facilitate distributed implementations of a network node, UE, core network node, or host.

[0384] 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 Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0385] 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 VMs 2108a and 2108b (one or more of which may be generally 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 the VMs 2108.

[0386] The VMs 2108 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding 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.

[0387] In the context of NFV, a VM 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 VMs 2108 on top of the hardware 2104 and corresponds to the application 2102.

[0388] 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.

[0389] Although the computing devices described herein (e.g., UEs, network nodes) 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.

[0390] 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.

Claims

CLAIMSWhat is claimed is:

1. A method of operating a communication device in a communications network that includes a network node, the method comprising: generating (1620) a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells, the report including information associated with a subset of the plurality of neighbor cells; and transmitting (1630) the report to the network node, wherein generating the report comprises selecting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell, and wherein generating the report comprises sorting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell.

2. The method of Claim 1, wherein generating the report comprises generating the report to include the mobility related time-domain prediction of the neighbor cell.

3. The method of Claim 2, wherein generating the report to include the mobility related timedomain prediction of the neighbor cell comprises generating the report to include only the mobility related time-domain prediction of the neighbor cell.

4. The method of any of Claims 1-3, wherein generating the report comprises sorting the subset of the plurality of neighbor cells based on at least one of: a trigger quantity; a measurement reporting quantity; a prediction reporting quantity; and a prediction-based trigger quantity.

5. The method of any of Claims 1-4, wherein the report comprises a radio resource control, RRC, measurement report.

6. The method of any of Claims 1-5, further comprising: receiving (1610) configuration information from the network node, the configuration information indicating how to generate the report based on the mobility related time-domainprediction of the neighbor cell, wherein generating the report comprises generating the report based on the configuration information.

7. The method of any of Claims 1-6, wherein transmitting the report comprises at least one of: periodically transmitting the report; transmitting the report in response to an event trigger; semi-persistently transmitting the report; and aperiodically transmitting the report.

8. The method of any of Claims 1-7, wherein the mobility related time-domain prediction of the neighbor cell comprises at least one of: a predicted reference signal received power, pRSRP; a predicted reference signal received quality, pRSRQ; and a predicted signal interference-to-noise ratio, pSINR.

9. The method of any of Claims 1-8, wherein generating the report based on the mobility related time-domain prediction of the neighbor cell comprises at least one of: determining a latest time-domain prediction per cell; determining a first time -domain prediction per cell; determining a maximum value among time-domain predictions per cell; determining a minimum value among the time-domain predictions per cell; determining an average value of the time-domain predictions per cell; determining a maximum with highest accuracy and / or lowest prediction error among the time-domain predictions per cell; and determining a value associated to a time instance k indicated by the network among the time-domain predictions per cell.

10. A method of operating a network node in a communications network that includes a communication device, the method comprising: generating (1710) configuration information including instructions to cause the communication device to generate a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells; transmitting (1720) the configuration information to the communication device; andreceiving (1730) the report from the communication device, wherein generating the configuration information comprises generating the instructions to cause the communication device to select a subset of the plurality of neighbor cells to include in the report based on the mobility related time-domain prediction of the neighbor cell, and wherein generating the configuration information comprises generating the instructions to cause the communication device to sort a subset of the neighbor cells included in the report based on the mobility related time-domain prediction of the neighbor cells.

11. The method of Claim 10, wherein generating the configuration information comprises generating the instructions to cause the communication device to generate the report to include the mobility related time-domain prediction of the neighbor cell, and wherein receiving the report comprises receiving the report including the mobility related time-domain prediction of the neighbor cell.

12. The method of Claim 11, wherein generating the configuration information comprises generating the instructions to cause the communication device to generate the report to include only the mobility related time-domain prediction of the neighbor cell, and wherein receiving the report comprises receiving the report including only the mobility related time-domain prediction of the neighbor cell.

13. The method of any of Claims 10-12, wherein generating the configuration information comprises generating instructions to cause the communication device to generate the report with a subset of the plurality of neighbor cells sorted based on at least one of: a trigger quantity; a measurement reporting quantity; a prediction reporting quantity; and a prediction-based trigger quantity.

14. The method of any of Claims 10-13, wherein the report comprises a radio resource control, RRC, measurement report.

15. The method of any of Claims 10-14, wherein receiving the report comprises at least one of: periodically receiving the report; receiving the report in response to an event trigger;semi-persistently receiving the report; and aperiodically receiving the report.

16. The method of any of Claims 10-15, wherein the mobility related time-domain prediction of the neighbor cell comprises at least one of: a predicted reference signal received power, pRSRP; a predicted reference signal received quality, pRSRQ; and a predicted signal interference-to-noise ratio, pSINR.

17. The method of any of Claims 10-16, wherein generating the configuration information comprises generating instructions to cause the communication device to generate the report based on at least one of: determining a latest time-domain prediction per cell; determining a first time -domain prediction per cell; determining a maximum value among time-domain predictions per cell; determining a minimum value among the time-domain predictions per cell; determining an average value of the time-domain predictions per cell; determining a maximum with highest accuracy and / or lowest prediction error among the time-domain predictions per cell; and determining a value associated to a time instance k indicated by the network among the time-domain predictions per cell.

18. The method of any of Claims 10-17, wherein generating the configuration information comprises instructing the communication device to include a ‘best’ detected neighbor cell first in the report, wherein the ‘best’ detected neighbor cell is the cell in a first position after the sorting of detected cells.

19. An apparatus (1300) for generating measurement reports based on time-domain predictions of a neighbor cell comprising a processor and a memory, the memory containing instructions executable by the processor whereby the apparatus is operative to: generate (1620) a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells, the report including information associated with a subset of the plurality of neighbor cells; and transmit (1630) the report to the network node, wherein generating the report comprises selecting the subset of the plurality of neighborcells based on the mobility related time-domain prediction of the neighbor cell, and wherein generating the report comprises sorting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell.

20. The apparatus of Claim 19, further operative to perform any of the operations of Claims 2- 9.

21. A communication device (1900) adapted to perform operations comprising: generating (1620) a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells, the report including information associated with a subset of the plurality of neighbor cells; and transmitting (1630) the report to the network node, wherein generating the report comprises selecting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell, and wherein generating the report comprises sorting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell.

22. The communication device of Claim 21, the operations further comprising any of the operations of Claims 2-9.

23. A computer program comprising program code to be executed by processing circuitry (1902) of a communication device (1900), whereby execution of the program code causes the communication device to perform operations comprising: generating (1620) a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells, the report including information associated with a subset of the plurality of neighbor cells; and transmitting (1630) the report to the network node, wherein generating the report comprises selecting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell, and wherein generating the report comprises sorting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell.

24. The computer program of Claim 23, the operations further comprising any of the operations of Claims 2-9.

25. A computer program product comprising a non-transitory storage medium (1910) including program code to be executed by processing circuitry (1902) of a communication device (1900), whereby execution of the program code causes the communication device to perform operations comprising: generating (1620) a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells, the report including information associated with a subset of the plurality of neighbor cells; and transmitting (1630) the report to the network node, wherein generating the report comprises selecting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell, and wherein generating the report comprises sorting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell.

26. The computer program product of Claim 25, further comprising any of the operations of Claims 2-9.

27. An apparatus (1400) for enabling generation of measurement reports based on timedomain predictions of a neighbor cell comprising a processor and a memory, the memory containing instructions executable by the processor whereby the apparatus is operative to: generate (1710) configuration information including instructions to cause the communication device to generate a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells; transmit (1720) the configuration information to the communication device; and receive (1730) the report from the communication device, wherein generating the configuration information comprises generating the instructions to cause the communication device to select a subset of the plurality of neighbor cells to include in the report based on the mobility related time-domain prediction of the neighbor cell, and wherein generating the configuration information comprises generating the instructions to cause the communication device to sort a subset of the neighbor cells included in the report based on the mobility related time-domain prediction of the neighbor cells.

28. The apparatus of Claim 27, further operative to perform any of the operations of Claims 11-18.

29. A network node (2000) adapted to perform operations comprising: generating (1710) configuration information including instructions to cause the communication device to generate a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells; transmitting (1720) the configuration information to the communication device; and receiving (1730) the report from the communication device, wherein generating the configuration information comprises generating the instructions to cause the communication device to select a subset of the plurality of neighbor cells to include in the report based on the mobility related time-domain prediction of the neighbor cell, and wherein generating the configuration information comprises generating the instructions to cause the communication device to sort a subset of the neighbor cells included in the report based on the mobility related time-domain prediction of the neighbor cells.

30. The network node of Claim 29, the operations further comprising any of the operations of Claims 11-18.

31. A computer program comprising program code to be executed by processing circuitry (2002) of a network node (2000), whereby execution of the program code causes the network node to perform operations comprising: generating (1710) configuration information including instructions to cause the communication device to generate a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells; transmitting (1720) the configuration information to the communication device; and receiving (1730) the report from the communication device, wherein generating the configuration information comprises generating the instructions to cause the communication device to select a subset of the plurality of neighbor cells to include in the report based on the mobility related time-domain prediction of the neighbor cell, and wherein generating the configuration information comprises generating the instructions to cause the communication device to sort a subset of the neighbor cells included in the report based on the mobility related time-domain prediction of the neighbor cells.

32. The computer program of Claim 31, the operations further comprising any of the operations of Claims 11-18.

33. A computer program product comprising a non-transitory storage medium (2004)including program code to be executed by processing circuitry (2002) of a network node (2000), whereby execution of the program code causes the network node to perform operations comprising: generating (1710) configuration information including instructions to cause the communication device to generate a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells; transmitting (1720) the configuration information to the communication device; and receiving (1730) the report from the communication device, wherein generating the configuration information comprises generating the instructions to cause the communication device to select a subset of the plurality of neighbor cells to include in the report based on the mobility related time-domain prediction of the neighbor cell, and wherein generating the configuration information comprises generating the instructions to cause the communication device to sort a subset of the neighbor cells included in the report based on the mobility related time-domain prediction of the neighbor cells.

34. The computer program product of Claim 33, further comprising any of the operations of Claims 11-18.

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