Methods for enhancing mobility robustness by triggering reports based on fulfilment of joint events (associated to measurements and predictions

Abstract

Systems and methods for triggering reports based on fulfillment of joint events associated to measurements and predictions are disclosed. In one embodiment, a method performed by a User Equipment (UE) for triggering a report comprises performing one or more measurements on at least one cell, performing one or more mobility related time-domain predictions on the at least one cell, and triggering transmission of a report comprising the one or more measurements performed on the at least one cell and / or the one or more mobility related time-domain predictions performed on the at least one cell, responsive to both a first condition and a second condition being fulfilled wherein the first condition is associated to the one or more measurements and the second condition is associated to the one or more mobility related time- domain predictions. The method further comprises transmitting the report responsive to the triggering of the transmission of the report.

Description

[0001] METHODS FOR ENHANCING MOBILITY ROBUSTNESS BY TRIGGERING REPORTS BASED ON FULFILMENT OF JOINT EVENTS (ASSOCIATED TO MEASUREMENTS AND PREDICTIONS)

[0002] RELATED APPLICATIONS

[0003] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 680,323, filed August 7, 2024, the disclosure of which is hereby incorporated herein by reference in its entirety.

[0004] TECHNICAL FIELD

[0005] The present disclosure relates to a cellular communications system and, more specifically, event-based triggering of measurement reporting in a cellular communications system.

[0006] BACKGROUND

[0007] In 3rdGeneration Partnership Project (3GPP) systems such as 5thGeneration (5G) New Radio (NR) and 4thGeneration (4G) Long Term Evolution (LTE), event-triggered measurement reporting has been specified. In NR, for example, the User Equipment (UE) receives a measurement configuration wherein each measurement (configured as measld) is associated to a reporting configuration in which an event is configured e.g. A3 event parameters, and a measurement object which indicates a Synchronization Signal (SS) / Physical Broadcast Channel (PBCH) Block (SSB) frequency in which the UE is to search for neighbor cells for comparing their radio measurements with the radio measurements of the Special Cell (SpCell) e.g. the Primary Cell (PCell) (see 3GPP Technical Specification (TS) 38.331 vl5.25.0).

[0008] In the case of an A3 event, for example, the UE considers the event as fulfilled when all Layer 3 (L3) filtered cell measurements fulfill the entering condition of the event for a time to trigger (TTT) i.e. when all L3 filtered cell measurements of at least one neighbor cell (in the SSB frequency of the associated measurement object) for a configured trigger quantity (e.g. Reference Signal Received Power (RSRP)) is an offset better than all L3 filtered cell measurements of the SpCell (e.g. PCell) for the trigger quantity (e.g. RSRP). Figure 1 illustrate an example of the entering condition for an A3 event.

[0009] When the event is considered fulfilled, such as an A3 event from the example above, the UE transmits a Radio Resource Control (RRC) Measurement Report including cell measurements for serving cells and the neighbor cells for which the event has been fulfilled, which are referred to in this context as triggered cells. The UE may also be configured to include L3 filtered beam measurements associated to the cells for which cell measurements are included.

[0010] In summary, the input to the entering condition(s) for an event configured for event- triggered measurement reports consists of cell measurements of an SpCell and / or a neighbor cell, depending on the event which is being configured at the UE, such as a cell level RSRP based on SSB (or Channel State Information Reference Signal (CSI-RS)), a cell level RSRQ based on SSB (or CSI-RS), or a cell level Signal to Interference plus Noise Ratio (SINR) based on SSB (or CSI- RS).

[0011] The Artificial Intelligence (Al) / Machine Learning (ML) for Physical Layer (PHY) Study Item in Rel-18 has led to a work item in Rel-19. However, the scope has been limited to lower layer features, such as Beam Management, which is sometimes referred as intra-cell mobility. Other features, such as L3 handovers, RRC measurements configuration, and reporting of predictions have not been part of the Rel-18.

[0012] In Rel-19, a Study Item to study the usage of Al / ML for L3 Mobility and / or Radio Resource Management (RRM) measurements has been approved, which may lay the foundation of AI / ML for RAN2 features for 6thGeneration (6G) higher layers (see RP -234055, Study on Artificial Intelligence (AI) / Machine Learning (ML) for mobility in NR, 3 GPP TSG RAN Meeting #102, Edinburgh, GB, December 11-15, 2023). In that case, one of the RRC features which will be considered is event-triggered measurement reporting and / or variants of that related to predictions, as indicated by the objective of the study item as follows (emphasis added):

[0013] • Study and evaluate potential benefits and gains of AI / ML aided mobility for network triggered L3-based handover, considering the following aspects: o AI / ML based RRM measurement and event prediction

[0014] ■ Cell-level measurement prediction including intra and inter-frequency (UE sided and NW sided model)

[0015] • Inter-cell Beam-level measurement prediction for L3 Mobility (UE sided and NW sided model) o HO failure / RLF prediction (UE sided model) o Measurement events prediction (UE sided model)

[0016] • Study the need / benefits of any other UE assistance information for the network side model.

[0017] • The evaluation of the AI / ML aided mobility benefits should consider HO performance KPIs and complexity tradeoffs. 3 GPP has started the work and the following agreements are relevant background for the present disclosure (emphasis added):

[0018] RAN2#125bis

[0019] RRM Measurement prediction

[0020] • For cell level measurement prediction model, at least consider the following cases: o Case 1 : To predict beam level results, then generate cell level results based on the predicted beam results; o Case 2: To directly predict cell level results based on cell level results, o Case 3: To directly predict cell level results based on beam level results [...]

[0021] • For temporal domain measurement prediction, we will consider the AI-PHY beam management Case A and Case B from the RANI AI / ML PHY TR and it applies to both beam level and cell level. As baseline we will focus on pure temporal prediction.

[0022] • The following items can be considered as a baseline for the prediction accuracy of the celllevel measurement prediction: o [...] o Temporal prediction: RSRP difference to the actual measurement, measurement reduction rate as one KPI

[0023] Measurement event predictions

[0024] • At least measurement event evaluation based on RRM measurement prediction result will be studied. Direct measurement event prediction is also allowed.

[0025] • [ . ]

[0026] • Start with Event A3 as a baseline.

[0027] • Measurement event prediction study can start after some further progress on RRM measurement prediction has been made.

[0028] RAN2 126

[0029] RLF / HOF prediction

[0030] 1 : Study Indirect: RLF prediction based on the temporal domain serving cell measurement predictions (e.g. SINR).

[0031] 2: Study Direct: Directly RLF prediction by AI / ML models.

[0032] [...] 4: The study should focus on RLF due to T310 expiry (i.e. in-synch / out-of-synch case) as the representative RLF case for direct and indirect prediction.

[0033] 5: HOF prediction is downprioritized in our study. NO simulations / evaluations should be done / submitted

[0034] 6: RLF prediction result is the RLF probability within a time window or at time instance, at least for direct case. FFS on expected RLF time and indirect case.

[0035] 7: No evaluation / simulations are expected for August meeting for RLF

[0036] 8: Simulation assumption specific to RLF will be discussed in August. The assumption is that we will reuse RRM simulation assumptions (where possible).

[0037] SUMMARY

[0038] Systems and methods for triggering reports based on fulfillment of joint events associated to both measurements and predictions are disclosed. In one embodiment, a method performed by a User Equipment (UE) for triggering a report comprises performing one or more measurements on at least one cell, performing one or more mobility related time-domain predictions on the at least one cell, and triggering transmission of a report comprising the one or more measurements performed on the at least one cell and / or the one or more mobility related time-domain predictions performed on the at least one cell, responsive to both a first condition and a second condition being fulfilled wherein the first condition is associated to the one or more measurements and the second condition is associated to the one or more mobility related time-domain predictions. The method further comprises transmitting the report responsive to the triggering of the transmission of the report. In this manner, the UE is enabled to evaluate the triggering conditions for reporting based on both measurements and predictions as input to the evaluation criteria, which in turn enables the UE to reduce the amount of measurement reports being transmitted.

[0039] In one embodiment, the method further comprises determining whether the fist condition and the second condition are fulfilled, wherein triggering transmission of the report comprises triggering transmission of the report responsive to determining that both the first condition and the second condition are fulfilled.

[0040] In one embodiment, both the first condition and the second condition are fulfilled, at the same time or contiguously in time.

[0041] In one embodiment, the first condition is an entry condition of an event which takes as input one or more measurements of a serving cell and / or a neighbor cell, and the second condition is an entry condition of an event which takes as input one or more mobility related time-domain predictions of the serving cell and / or the neighbor cell. In one embodiment, the method further comprises receiving, from a network node, configuration information that configures the UE to perform the one or more measurements and to perform the one or more mobility related time-domain predictions. In one embodiment, the configuration information comprises first configuration information that configures the UE to perform the one or more measurements, the first configuration information comprising information that indicates the first condition having at least one input based on the one or more measurements and second configuration information that configures the UE to perform the one or more mobility related time-domain predictions, the second configuration information comprising information that indicates the second condition having at least one input based on the one or more mobility related time-domain predictions. In one embodiment, the configuration information is comprised in a Radio Resource Control (RRC) message. In another embodiment, the first configuration information and the second configuration information are comprised in a same measurement configuration, further wherein including both the first configuration information and the second configuration in the same measurement configuration is an implicit indication that both the first condition and the second condition need to be fulfilled before triggering the report. In one embodiment, at least one parameter in the first configuration information controls triggering of the second condition. In one embodiment, performing the one or more mobility related time-domain predictions on the at least one cell comprises performing the one or more mobility related timedomain predictions on the at least one cell once the first condition is fulfilled. In one embodiment, the second configuration information further comprises information that configures a period of time during which the one or more mobility related time-domain predictions can be performed.

[0042] In one embodiment, the one or more mobility related time-domain predictions comprise any one or more of the following: a radio link failure prediction in a serving cell of the UE, a radio link failure prediction post handover in a neighbor cell of the UE, a handover failure prediction in a neighbor cell of the UE, a time-domain prediction of a measurement quantity for at least one cell, information related to a future time instance in which the UE ma report to assist the network to make mobility decisions.

[0043] In one embodiment, the at least one cell is a neighbor cell, considered as a triggered cell when the one or more triggering conditions are fulfilled.

[0044] In one embodiment, one of the at least one cell is a neighbor cell and / or a serving cell of the UE.

[0045] In one embodiment, the report is a measurement report and / or a prediction report.

[0046] In one embodiment, if the one or more mobility related time-domain predictions do not fulfill accuracy criteria, transmission of the report is not triggered or transmission of the report is triggered, but the one or more mobility related time-domain predictions are not included in the measurement report or transmission of the report is triggered and an indication is included in the report indicating that the one or more mobility related time-domain predictions do not satisfy the accuracy criteria.

[0047] In one embodiment, the method further comprises determining whether the one or more mobility related time-domain predictions fulfill one or more accuracy criteria. In one embodiment, triggering transmission of the report comprises triggering transmission of the report if the one or more mobility related time-domain predictions fulfill the one or more accuracy criteria and otherwise refraining from triggering transmission of the report. In another embodiment, the one or more mobility related time-domain predictions are not included in the report if the one or more mobility related time-domain predictions fulfill the one or more accuracy. In another embodiment, the one or more mobility related time-domain predictions do not fulfill the one or more accuracy criteria and, responsive thereto, the one or more mobility related time-domain predictions are not included in the report. In one embodiment, the one or more mobility related time-domain predictions do not fulfill the one or more accuracy criteria and, responsive thereto, the report comprises an indication that the one or more mobility related time-domain predictions do not fulfill the one or more accuracy criteria.

[0048] In one embodiment, the one or more mobility related time domain predictions include predicted measurements for the at least one cell during a time period between occurrence of the one or more measurements and occurrence of the one or more mobility related time-domain predictions.

[0049] Corresponding embodiments of a UE are also disclosed. In one embodiment, a UE is adapted to perform one or more measurements on at least one cell, perform one or more mobility related time-domain predictions on the at least one cell, and trigger transmission of a report comprising the one or more measurements performed on the at least one cell and / or the one or more mobility related time-domain predictions performed on the at least one cell, responsive to both a first condition and a second condition being fulfilled wherein the first condition is associated to the one or more measurements and the second condition is associated to the one or more mobility related time-domain predictions. The UE is further adapted to transmit the report responsive to the triggering of the transmission of the report.

[0050] In one embodiment, a UE comprises a communication interface comprising a transmitter and a receiver, and processing circuitry associated with the communication interface. The processing circuitry is configured to cause the UE to perform one or more measurements on at least one cell, perform one or more mobility related time-domain predictions on the at least one cell, and trigger transmission of a report comprising the one or more measurements performed on the at least one cell and / or the one or more mobility related time-domain predictions performed on the at least one cell, responsive to both a first condition and a second condition being fulfilled wherein the first condition is associated to the one or more measurements and the second condition is associated to the one or more mobility related time-domain predictions. The processing circuitry is further configured to cause the UE to transmit the report responsive to the triggering of the transmission of the report.

[0051] Embodiments of a method performed by a network node are also disclosed. In one embodiment, a method performed by a network node for a cellular communications system comprises configuring a UE with a first configuration to perform one or more mobility related measurements, configuring the UE with a second configuration to perform one or more mobility related time-domain predictions, and configuring the UE with a first condition to the one or more measurements and a second condition associated to the one or more mobility related time-domain predictions. The method further comprises receiving, from the UE, one or more messages comprising the one or more mobility related measurements and / or the one or more mobility related time-domain predictions, wherein the one or more messages indicate fulfillment of the both the first condition and the second condition.

[0052] Corresponding embodiments of a network node are also disclosed. In one embodiment, a network node for a cellular communications system is adapted to configure a UE with a first configuration to perform one or more mobility related measurements, configure the UE with a second configuration to perform one or more mobility related time-domain predictions, and configure the UE with a first condition to the one or more measurements and a second condition associated to the one or more mobility related time-domain predictions. The network node is further adapted to receive, from the UE, one or more messages comprising the one or more mobility related measurements and / or the one or more mobility related time-domain predictions, wherein the one or more messages indicate fulfillment of both the first condition and the second condition.

[0053] In one embodiment, a network node for a cellular communications system comprises processing circuitry configured to cause the network node to configure a UE with a first configuration to perform one or more mobility related measurements, configure the UE with a second configuration to perform one or more mobility related time-domain predictions, and configure the UE with a first condition to the one or more measurements and a second condition associated to the one or more mobility related time-domain predictions. The processing circuitry is further configured to cause the network node to receive, from the UE, one or more messages comprising the one or more mobility related measurements and / or the one or more mobility related time-domain predictions, wherein the one or more messages indicate fulfillment of both the first condition and the second condition.

[0054] BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain the principles of the disclosure.

[0056] Figure 1 illustrates an example of the entering condition for an A3 event.

[0057] Figure 2 illustrates an example in which a User Equipment (UE) equipped with an Artificial Intelligence (AI) / Machine Learning (ML) model for Mobility (or Radio Resrouce Management (RRM) measurements) includes time-domain prediction of measurements (e.g. predicted Reference Signal Received Power (pRSRP) values, represented by the stars) for serving and / or triggered cells in a Radio Resource Control (RRC) Measurement report, in accordance with a first 3rdGeneration Partnership Project (3GPP) proposal.

[0058] Figure 3 illustrates an example in which a UE equipped with an AI / ML model for Mobility (or RRM measurements) triggers a report based on time-domain prediction of measurements (e.g. pRSRP values, represented by the stars) for serving and / or triggered cells in an RRC Measurement report in accordance with the second proposal.

[0059] Figure 4 illustrates an example in which the UE includes time-domain predictions of measurement in an RRC Measurement Report, in accordance with the first proposal.

[0060] Figure 5 illustrates an example in which a UE includes measurements in the report of predictions triggered based on the prediction event.

[0061] Figure 6 is a flow chart that illustrates the operation of a UE in accordance with one example embodiment of the present disclosure.

[0062] Figure 7 illustrates a procedure for triggering of a measurement report at the UE, upon fulfilment of both a measurement based event (an event evaluated based on the real measurements performed by the UE) and of a mobility related time domain prediction event (an event evaluated based on the UE predictions), e.g., in accordance with the method of Figure 7.

[0063] Figure 8 is a flow chart that illustrates the operation of a source network node (e.g., a source gNodeB (gNB)) for mobility of a UE in connected state, in accordance with one embodiment of the present disclosure.

[0064] Figure 9 illustrates a scenario in which a UE evaluates criteria of both legacy event condition (e.g., A3 entry event condition, evaluated based on the measurements) and predicted event condition (e.g., predicted A3 event entry condition, evaluated based on the prediction samples) and sends the measurement report to the network only if both criteria are fulfilled for a configured period of Time to Trigger (TTT) and predicted TTT (pTTT).

[0065] Figure 10 illustrates a scenario in which a UE evaluates criteria of both legacy event condition (e.g., A3 entry event condition, evaluated based on the measurements) and predicted event condition (e.g., predicted A3 event entry condition, evaluated based on the prediction). UE does not send the measurement report to the network since the legacy A3 event condition is not fulfilled.

[0066] Figure 11 illustrates a scenario in which a UE evaluates criteria of both legacy event condition (e.g., A3 entry event condition, evaluated based on the measurements) and predicted event condition (e.g., predicted A3 event entry condition, evaluated based on the prediction). UE does not send the measurement report to the network since the prediction based A3 event condition is not fulfilled.

[0067] Figure 12 shows an example of a communication system in accordance with some embodiments.

[0068] Figure 13 shows a UE in accordance with some embodiments.

[0069] Figure 14 shows a network node in accordance with some embodiments.

[0070] Figure 15 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized.

[0071] DETAILED DESCRIPTION

[0072] The embodiments set forth below represent information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure.

[0073] 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

[0074] There currently exist certain challenge(s). As highlighted in the section entitled "Rel-19 Study Item AI / ML for Mobility and Potential for6G” of the Background above, the 3rdGeneration Partnership Project (3 GPP) Release (Rel-) 19 Artificial Intelligence (AI) / Machine Learning (ML) for Mobility study item and initial 3GPP agreements consider a User Equipment (UE) performing time-domain prediction of measurements (temporal domain or time-domain measurement prediction), for measurements (e.g. cell level Reference Signal Received Power (RSRP)) which are currently included in Radio Resource Control (RRC) Measurement Reports (also called Radio Resource Management (RRM) measurements) to assist the network in making mobility decisions, such as handovers.

[0075] In addition, it is also considered that a UE performs measurement event evaluation based on the RRM measurement prediction result, e.g. using the time-domain prediction of measurements as input to entering condition(s) of an event, and the direct measurement event prediction.

[0076] There are no agreements in 3 GPP on further details of how these concepts would be specified but two proposals below may serve as a reference.

[0077] In a first proposal, a UE equipped with an AI / ML model for Mobility (or RRM measurements) is configured to include time-domain predictions of measurements, or, more generally, mobility-related time domain predictions in an RRC Measurement Report. The basic idea is that a UE is equipped with a UE sided AI / ML model for performing mobility related timedomain predictions, such as predicted RSRP (pRSRP), predicted Reference Signal Received Quality (pRSRQ), or predicted Signal to Interference plus Noise Radio (SINR) (pSINR) for one or more serving and / or neighbor cells; these would be produced as inference, which are output of the AI / ML model for an RRM measurements / mobility related functionality. Then, when an RRC Measurement Report is triggered (based on actual Layer 3 (L3) filtered cell level measurements, as defined in 3GPP Technical Specification (TS) 38.331), the UE includes the mobility related time-domain predict! on(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. The UE may also be configured to include the time-domain prediction(s) of measurements in periodic measurement reports. Figure 2 illustrates an example in which the UE includes time-domain prediction of measurements (e.g. pRSRP values, represented by the stars) for serving and / or triggered cells in an RRC Measurement report, in accordance with the first proposal.

[0078] In another proposal, a UE equipped with an AI / ML model for Mobility (or RRM measurements) is configured to predict a future occurrence of an event (i.e., the event fulfillment), possibly based on the time-domain predictions of measurements. Upon predicting the fulfillment of the event ahead in time, the UE transmits a report (e.g., an RRC Measurement Report), which may include the time-domain predictions of measurements for the serving and / or the predicted triggered cells . As in the first proposal, the UE is equipped with a UE sided AI / ML model for performing mobility related time-domain predictions, such as pRSRP, pRSRQ, or pSINR for one or more serving and / or neighbor cells; these would be produced as inference, which are output of the AI / ML model for an RRM measurements / mobility related functionality and used as input to triggering conditions, for triggering the transmission of a report. Figure 3 illustrates an example in which the UE triggers a report based on time-domain prediction of measurements (e.g. pRSRP values, represented by the stars) for serving and / or triggered cells in an RRC Measurement report in accordance with the second proposal.

[0079] The first and second proposals described above aim to assist the network to make more educated mobility decisions by including time-domain predictions of measurements or related to measurements, but certainly have limitations. In the case predictions are reported periodically as in the first proposal, there is an increase in the uplink (UL) signaling and UE power consumption since the UE needs to constantly report time-domain prediction(s) of measurements. In the case of triggering a report based on time-domain prediction(s) of measurements as in the second proposal, such UL signaling is reduced.

[0080] In the first proposal, the time-domain predictions of measurements are reported only when all cell level measurements for a Time to Trigger (TTT) duration triggers an event (for the event triggered reports). In this case, if the thresholds for triggering an event are not properly set, the time-domain predictions of measurement may not arrive soon enough for avoiding e.g. too late handovers and / or a Radio Link failure (RLF). Further, something that may occur relates to the assumption that the threshold for measurement report triggering is properly set, i.e. that when that is triggered, radio conditions are still good enough so the UE is able to transmit a measurement report and / or the network is able to respond to it if needed. What could happen is that the UE performs these predictions and even before triggering a measurement report, a RLF can happen, and the UE is not able to send the reports with measurements and predictions. Figure 4 illustrates an example in which the UE includes time-domain predictions of measurement in an RRC Measurement Report, in accordance with the first proposal.

[0081] In the second proposal, a different solution is proposed in which the UE triggers the report based on fulfilling an event calculated based on the predicted values of the serving and neighboring cells. Figure 5 illustrates an example in which a UE includes measurements in the report of predictions triggered based on the prediction event. Although the solution in the second proposal enables the network to make the mobility decisions based on the prediction values earlier than the actual timing in which an event is fulfilled, the current measurements may not indicate a good quality of the neighboring cells that triggered the report based on the prediction i.e. this report may be transmitted unnecessarily early to the network. In other words, such prediction-based event triggered reports may lead to a too early handover failure as the target might not be suitable at the current time.

[0082] Another concept which 3GPP may discuss and which seems relevant for this discussion is the concept in which an RLF prediction of a Primary Cell (PCell) (and / or a Handover Failure prediction of a neighbor cell) triggers the UE to transmit a report to the network including the RLF prediction(s), so the network can take further actions.

[0083] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. Embodiments of systems and methods are disclosed herein that relate to a UE being configured to trigger a report (e.g. measurement report and / or a prediction report) based on a triggering condition which has as input one or more measurements of a cell (e.g. cell level RSRP) AND one or more mobility related time-domain prediction(s) of the cell, such as: a time-domain prediction of a measurement, e.g. pRSRP, and / or a RLF prediction, and / or a Handover Failure (HOF) prediction. In other words, both measurements and time-domain predictions are input to triggering condition(s) for triggering a report to the network.

[0084] It is worth noting that the triggering condition based on measurements and the time domain predictions (e.g. measurements, events) may be contiguous in time, e.g. at the termination of the time to trigger of the triggering condition based on measurement, the time to trigger or measuring period of the predicted values may start, or they may not be contiguous in time, namely, the triggering condition based on measurements may occur a certain time window before the time to trigger or measuring period of the predictions.

[0085] In one example, the triggering condition may be a measurement (e.g. RSRP) of a neighbor cell becoming a first offset better than a measurement of a PCell AND a time-domain prediction of a measurement (e.g. predicted RSRP) of a neighbor cell becoming a second offset better than a time-domain prediction of a measurement of the PCell. The triggering condition may also be called an entering condition. In other words, a report is triggered to be transmitted by the UE when a neighbor cell becomes a first offset better than a measurement of a PCell AND a time-domain prediction of a measurement (e.g. predicted RSRP) of that neighbor cell becomes a second offset better than a time-domain prediction of a measurement of the PCell. Additionally, the triggering condition may also be a statistical or Auto-Encoding aggregation operation (01) on the neighbor cell predicted samples over ‘p’ Period of time of prediction becomes a third offset better than the same 01 operation but on the PCell’s ‘p’ predicted samples.

[0086] In another example, the triggering condition may be a measurement (e.g. RSRP) of a neighbor cell becoming a first offset better than a measurement of a PCell AND the HOF prediction of the neighbor cell (e.g. probability of a HOF in a future time instance) being lower than a probability threshold. Additionally, the triggering condition maybe measurement of a neighbor cell becoming a first offset better than a measurement of a PCell AND a statistical or Auto-Encoding aggregation of HOF prediction of the neighbor cell (of a multiple of future time instance) being lower than a second probability threshold.

[0087] In another example, the triggering condition may be a measurement (e.g., RSRP) of a PCell becoming below a threshold AND an RLF prediction of that serving cell indicating a probability higher than a second probability threshold. Additionally, the triggering condition maybe measurement of a neighbor cell becoming a first offset better than a measurement of a PCell AND a statistical or Auto-Encoding aggregation of RLF prediction of the neighbor cell (of a multiple of future time instance) being lower than a second probability threshold.

[0088] Notice that such a triggering condition in the method, having as input measurement s) and time-domain predict! on(s), may be modelled as the combination of two joint conditions (or events) wherein a first event (condition) has as input measurement(s), the second event (condition) has time-domain prediction(s) as input, and the triggering condition is considered fulfilled when the first condition (event) is fulfilled AND the second condition (event) is fulfilled (while the first remains fulfilled).

[0089] Figure 6 is a flow chart that illustrates the operation of a UE in accordance with one example embodiment of the present disclosure. This procedure may include any one or more of the following steps:

[0090] Step 600: Receiving a first configuration and a second configuration based on which the UE performs one or more mobility related measurements AND mobility related time domain predictions; o In a dependent embodiment, receiving, as part of the first and / or second configuration, a configuration requesting predicted measurements on the cells involved in the measured and predicted conditions (events) during the time period between the occurrence of first configured measurements and the second mobility related time domain predictions. As a further configuration, these measurements are requested only if the distance in time between first condition (event) and second condition (event) is higher than a specified value.

[0091] Step 602(a): Performing measurements of mobility related resources AND

[0092] Step 602(b): Performing mobility -related time-domain predictions

[0093] Step 602(c) (optional): Receiving an implicit or explicit indication indicating how (under fulfilment of which criteria) the UE transmits the report to the network Step 604: Evaluating a triggering condition having as input both the mobility related measurements and the mobility related time-domain predictions. In other words, the UE determines whether the triggering condition is fulfilled. More specifically, in one example embodiment, the aforementioned triggering condition includes two triggering conditions, referred to as a first condition (or first triggering condition) whose input is based on the mobility related measurement(s) and a second condition (or second triggering condition) whose input is based on the mobility related time-domain prediction(s), and step 604 includes the following: o Step 604(a): Evaluating a first condition whose input is based on the measurement of mobility related resource (as performed in Step 602(a)) (i.e., determining whether the first condition whose input is based on the mobility measurement(s) is fulfilled); AND

[0094] ■ The “first condition” may be configured as part of the first configuration (a reportConfig included in the first configuration) as a reporting / (event triggering) criteria which may comprise of one or multiple subcondition^) (e.g., an entry condition or combination of entry conditions) o Step 604(b): Evaluating a second condition whose input is based on mobility related time-domain predictions of mobility related resource (as performed in Step 602(b)) (i.e., determining whether the second condition whose input is based on the mobility related time-domain prediction(s) is fulfilled);

[0095] ■ The “second condition” may be configured as part of the second configuration (a reportConfig included in the second configuration) as a reporting / (event triggering) criteria which may comprise o one or multiple sub-condition(s) (e.g., an entry condition or combination of entry conditions)

[0096] Step 606: Upon the fulfilment of the first condition (whose input is based on measurement of mobility related resources) AND upon the fulfilment of the second condition (whose input is based on mobility related time domain predictions), while the first condition remains fulfilled, trigger one or more reports (e.g., a measurement report(s), e.g., in a measurement reporting procedure). o In a dependent embodiment, evaluating the distance in time between the first and second condition and, if the UE has been configured to provide predicted measurements between the occurrence of the first condition and the second predicted condition (and if the distance in time of such conditions is higher than the threshold established), include in the measurement report measurement predictions for the cells involved in the conditions, e.g. the triggering cells of the conditions, for points in time that occur between the first and the second condition.

[0097] Step 608: Responsive to triggering of the one or more reports, sending one or more reports (e.g., measurement reports) including the measurement and / or the mobility related time domain prediction(s) of mobility related resources to the network. o Additionally, a report of both measurement and temporally predicted information can be encoded and sent to the network.

[0098] Figure 7 illustrates a procedure for triggering of a measurement report at the UE, upon fulfilment of both a measurement based event (an event evaluated based on the real measurements performed by the UE) and of a mobility related time domain prediction event (an event evaluated based on the UE predictions), e.g., in accordance with the method above. As illustrated, a predicted event trigger is evaluated based on measurement predictions and a measurement event trigger is evaluated based on measurements. When both the predicted event trigger and the measurement event trigger are met, the UE sends a measurement reporting including both the measurement prediction(s) and the measurement(s).

[0099] Figure 8 is a flow chart that illustrates the operation of a source network node (e.g., a source gNodeB (gNB)) for mobility of a UE in connected state, in accordance with one embodiment of the present disclosure. As illustrated, the procedure of Figure 8 includes any one or more of the following:

[0100] Step 800(a): Configuring the UE with a first configuration to perform measurement of mobility related resources, e.g., measurements of radio quality such as RSRP, RSRQ, SINR in a given frequency (e.g. per cell, per beam, per RS type like SSB and / or CSI- RS, etc.); AND

[0101] Step 800(b): Configuring the UE with a second configuration to perform mobility related time domain predictions, e.g., o Cell level time domain prediction of RSRP and / or RSRP, and / or SINR, or time domain prediction of RSRP and / or RSRP, and / or SINR at the beam level which are used in cell quality derivation. o Prediction of a radio link failure in a cell (serving or neighboring cell) or predicting a handover failure toward a neighboring cell ■ In a dependent embodiment, when predictions for a failure are derived by the UE, e.g. RLF, HOF, the UE provides also predicted measurements for the cells involved in such events. o Notably, in one embodiment, the first and second configurations can be same / one instance of configuration e.g., pointing to the same measurement object or radio frequency etc.

[0102] Step 800(c) (optional): In a dependent embodiment, configuring the UE with a configuration to perform predicted measurements on the cells involved in the measured and predicted conditions (events) during the time period between the occurrence of first configured measurements and the second mobility related time domain predictions. As a further configuration, these measurements should be performed only if the distance in time between first condition (event) and second condition (event) is higher than a specified value.

[0103] Step 802: Configuring the UE with a triggering condition having as inputs both measurements of mobility related resources (as performed in step 800(a)) (i.e., mobility related measurements) AND mobility related time-domain predictions. More specifically, in one example embodiment, step 802 includes the following: o Step 802(a): Configuring the UE with the first condition e.g., reporting / triggering criteria comprising one or more sub-condition(s) (e.g., an entry condition or combination of entry conditions) whose evaluation input is based on measurements of mobility related resources (as performed in Step 1- a); AND o Step 802(b): Configuring the UE with the second condition e.g., reporting / triggering criteria comprising one or more sub-condition(s) (e.g., an entry condition or combination of entry conditions) whose evaluation input is based on mobility related time domain predict! on(s) (as performed in Step 1-b); Step 804: Receiving one or more messages including mobility related measurements and / or mobility related time domain predictions, where the message indicates the fulfilment of a first condition (whose input is based on measurements of mobility related resources), AND the fulfilment of a second condition (whose input is based on mobility related time domain predictions). o In a dependent embodiment, the network receives in the message predicted measurements on the cells involved in the measured and predicted conditions (events) during the time period between the occurrence of first configured measurements and the second mobility related time domain predictions

[0104] Step 806 (optional): Performing one or more mobility related actions based on the received message(s).

[0105] In a dependent step (e.g., as part of Step 806), the source network node transmits to a target network node or to a candidate network node the mobility related measurements and / or mobility related time domain predictions received from the UE, e.g. in a Handover Request message or in an Access and Mobility Information message. In response to it, the source network node may receive a message including a Handover command for the UE, e.g. RRC Reconfiguration message including a reconfiguration with sync. Alternatively, the target node may use the information to deduce how signal levels have been predicted by the UE after the UE has moved to the target node cell. As an example, this could be due to the source node signaling to the target node information concerning a measured A3 event pointing at a possible handover from the source cell to a cell of the target node and predicted measurements for the target cell decreasing below a given threshold. With this information, the target node may be able to anticipate actions that would need to be taken to maintain the UE in coverage, e.g. beamforming changes, cell shape changes, or the target node may plan for mobility actions aimed at avoiding failures or suboptimal performance at the UE while it is served by the target node.

[0106] Certain embodiments may provide one or more of the following technical advantage(s). Embodiments of the solution(s) disclosed herein may enable the UE to evaluate the measurement triggering based on both measurements and mobility related time-domain predictions (e.g., timedomain predictions of measurements) as input to the evaluation criteria. That would enable the UE to reduce the amount of measurement reports being transmitted i.e. the UE would only transmit a measurement report when measurements of a cell fulfill the entry condition for the report, but also when mobility related time-domain predictions of that cell (e.g. predicted RSRP) indicate that the conditions are likely to remain fulfilled in future time instance(s). For example, in the case of a new A3 event based on both measurements and mobility related time-domain predictions, the UE would only transmit a measurement report (or consider a cell as a triggered cell) when both the measurements are an offset better than the PCell AND the time-domain prediction(s) of the cell (predicted RSRP) are an offset better than the time-domain predict! on(s) of the PCell.

[0107] Embodiments of the solution(s) disclosed herein may enable the network to make a more educated decisions based on the provided measurements and predictions. More precisely the network can configure the UE to send the measurement reports only if the current measurement of for example a neighboring cell is an offset better than the serving cell measurements AND the prediction of the measurements of such neighboring cell is an offset better that the prediction of the serving cell in a given time instance in the future (for a certain period of time e.g., time to trigger associated to the predictions). Moreover, by means of enabling the UE to report predicted measurements for the cells involved in the measured and predicted events, during the time span between the occurrence of the measured and predicted events, the network is able to deduce how signal quality for such cells will evolve. This will enable the network to detect the possibility of, e.g. RLF events that may occur in between the measured and the predicted events, and therefore to take actions to avoid such failures or suboptimal conditions occur.

[0108] Other mobility related time domain prediction(s) information that can be used in prediction-based events in conjunction with the measurement-based events are for example RLF probability or HOF probability (see section below entitled “Mobility Related Time-Domain Predictions” for further details). As described herein, such predictions can be used in conjunction with the measurement-based events to trigger a report. This enables the network to avoid triggering the HO toward the cells at which the probability of post-HO RLF, e.g. RLF shortly after a successfully executed handover, or HOF is high.

[0109] To summarize, one exemplary advantage of the proposed solution(s) is increasing the mobility robustness by triggering mobility procedure toward cells with more stable radio quality (not only during the HO but also after the HO).

[0110] The teachings of certain embodiments may improve, e.g., power consumption.

[0111] As used herein, the term “measurement” or “real / current measurement” is used to refer to a radio measurement, such as the measurements described in the RRC specifications in sub-clause 5.5 that may be configured by the network for a CONNECTED UE and / or measurements defined in 3GPP TS 38.215. These measurements may also be called RRM measurements (since they assist Radio Resource Management decisions at the network side and / or L3 measurements, since they are responsibility of the RRC protocol, also called Layer 3 in the Control Plane Radio Access Network (RAN) protocol stack). In the scope of the present disclosure, having New Radio (NR) as an example, these measurements to be performed by the UE and reported may include at least one of the following:

[0112] - NR measurements;

[0113] - Inter-Radio Access Technology (RAT) measurements of Evolved Universal Terrestrial Radio Access (E-UTRA) frequencies.

[0114] - Measurements defined for 6thGeneration (6G), i.e. based on reference signals and / or synchronization signals defined in 6G air interface. These measurements may be based on different reference signals. In the case of Synchronization Signal (SS) / Physical Broadcast Channel (PBCH) block(s) (SSB(s)), these measurements (or in more general terms, measurement information) are:

[0115] - Measurement results per SS / PBCH block;

[0116] - Measurement results per cell based on SS / PBCH block(s);

[0117] SS / PBCH block(s) indexes.

[0118] In the case of Channel State Information (CSI) Reference Signal (CSI-RS), these measurements (or in more general terms, measurement information) are:

[0119] - Measurement results per CSI-RS resource;

[0120] - Measurement results per cell based on CSI-RS resource(s);

[0121] CSI-RS resource measurement identifiers.

[0122] Each of these measurements may be associated to a measurement quantity, such as RSRP, RSRQ, or SINR. For example, in the description provided herein may refer to a real / current measurement as the cell level or beam level RSRP based on Reference Signal (RS) type SSB, for an NR carrier frequency in the NR RAT. Hence, the term “measurement prediction” is used herein to refer to a prediction of a radio measurement as one of the measurements described above as a real / current measurement. In other words, the present disclosure refers to a prediction of NR or inter-RAT measurements, prediction of measurement results per RS type (i.e., SS / PBCH block or CSI-RS), prediction of cell measurement or beam measurements (e.g., cell level RSRP, cell level RSRQ, cell level SINR, beam level RSRP, beam level RSRQ, beam level SINR).

[0123] According to embodiments of the present disclosure, these real / current measurements (or simply measurements) may be used as input to predictions models so the UE is able to predict, as output of the AI / ML model, mobility information such as radio quality related parameters such as RSRP, RSRQ, SINR in a given frequency in different levels of granularities such as per cell, per beam, per reference signal (RS) type like SSB and / or CSI-RS, list of cells and / or list of beams and / or list of reference signal (RS) type coverage (like SSB identifier coverage or CSI-RS identifier coverage) the UE is moving to, etc. Additionally, as output of the AI / ML model, the UE may be able to predict other time domain prediction(s) information such as RLF probability or HOF probability.

[0124] Even if most of the examples referred in the application are for NR, that may be applied to any system, e.g., in the 6G context, where AI / ML is envisioned to play a more impactful role when it comes to the design of protocols.

[0125] In the description provided herein, the term legacy event is used to refer to the mobility related events existing in the RRC 3GPP TS 38.331 Version 18.1.0. such as A1-A5 events etc., wherein the input to the evaluation of the entering condition and exist conditions for such events is based on the RRM “measurements”.

[0126] In the description provided herein, the term legacy event is used to refer to the mobility related events as predicted A1-A6 an d B1-B2 events etc., wherein the input to the evaluation of the entering condition and exist conditions for such events is fully or partially based on the prediction of RRM measurements which are generated using prediction models e.g., using AI / ML models.

[0127] The description herein uses the term ‘event’, ‘condition’, ‘triggering condition’, ‘triggering criteria’ to refer to the condition (e.g., first condition and / or second condition) that needs to be fulfilled before the UE transmits a report.

[0128] In accordance with embodiments of the present disclosure, a UE is configured (see, e.g., step 600 of Figure 6) to trigger a report based on a triggering condition which has as input one or more measurements of a cell (e.g. cell level RSRP) AND one or more mobility related time-domain prediction(s) of the cell, such as a time-domain prediction of a measurement, e.g. pRSRP, and / or a RLF prediction, and / or a Handover Failure prediction. Optionally, the UE may be configured with the reporting of predicted measurements for the cells involved in the triggering condition based on measurements and in the time-domain predictions, where such measurements are predicted for time instances occurring between the triggering condition based on measurements and in the time-domain predictions.

[0129] In one example, the triggering condition may be a measurement (e.g. RSRP) of a neighbor cell becoming a first offset better than a measurement of a PCell AND a time-domain prediction of a measurement (e.g. predicted RSRP) of a neighbor cell becoming a second offset better than a time-domain prediction of a measurement of the PCell. The triggering condition may also be called an entering condition. In other words, a report is triggered to be transmitted by the UE when a neighbor cell becomes a first offset better than a measurement of a PCell AND a time-domain prediction of a measurement (e.g. predicted RSRP) of that neighbor cell becomes a second offset better than a time-domain prediction of a measurement of the PCell.

[0130] In another example, the triggering condition may be a measurement (e.g. RSRP) of a neighbor cell becoming a first offset better than a measurement of a PCell AND the HOF prediction of the neighbor cell (e.g. probability of a HOF in a future time instance) being lower than a probability threshold.

[0131] In another example, the triggering condition may be a measurement (e.g., RSRP) of a PCell becoming below a threshold AND an RLF prediction of that serving cell indicating a probability higher than a second probability threshold. As illustrated in Figure 6, the procedure performed by the UE may include any of the following steps:

[0132] Step 600: Receiving a configuration (e.g., a first configuration and a second configuration) to perform one or more mobility related measurements AND mobility related time domain predictions; o The first and second configuration(s) may be received in an RRC Reconfiguration message, while the UE is in Connected state (e.g., RRC CONNECTED) within a measurement configuration and / or within a reporting configuration. o The first and / or second configuration(s) may be considered deactivated, i.e. the UE does not perform actions based on it until it activates. Activation may occur upon reception of an activation command, e.g. MAC Control Element (CE). o In one option, the first configuration corresponds to a measurement configuration, e.g. reporting configuration and / or a measurement object configuration. The first configuration may indicate one or more of the following:

[0133] ■ An indication of the first condition which is required to be fulfilled by the UE before the transmission of the report, such as an event identifier corresponding the first condition.

[0134] ■ One or more parameters controlling the first condition, such as:

[0135] • An indication of a trigger quantity associated with the first condition. For example, an indication that RSRP, RSRQ and / or SINR is to be used by the UE as trigger quantity.

[0136] • An indication of a value of a time to trigger (TTT).

[0137] • For example, when the UE is indicated that RSRP is trigger quantity and an event A3 is associated with the first condition, the first condition is fulfilled when “a neighbor cell RSRP is an offset better than the PCell’s RSRP,” e.g. for all cell measurements after L3 filtering during a TTT

[0138] • An indication of a reference signal (RS) type, indicating the RS the UE needs to measure for deriving the measurements used as input to the first condition, e.g. SSB, CSI-RS, Mobility Reference Signal (MRS). In one option, the second configuration corresponds to a prediction or inference configuration, e.g. inference or prediction reporting configuration and / or a prediction or inference object configuration. The second configuration may indicate one or more of the following:

[0139] ■ An indication of the second condition which is required to be fulfilled by the UE before the transmission of the report, such as an event identifier corresponding the second condition, which is to be jointly used with the first condition.

[0140] ■ One or more parameters controlling the second condition, such as:

[0141] • An indication of a prediction trigger quantity associated with the second condition. For example, an indication that pRSRP, pRSRQ and / or pSINR is to be used by the UE as trigger quantity.

[0142] • An indication of a value of a predicted time to trigger (pTTT).

[0143] • For example, when the UE is indicated that pRSRP is prediction trigger quantity, and the following second condition is indicated: “a neighbor cell pRSRP is an offset better than the PCell’s pRSRP,” e.g. for all time-domain prediction(s) of cell measurements after L3 filtering during a pTTT.

[0144] • An indication of a predicted reference signal (RS) type, indicating the RS the UE needs to assume when deriving the time-domain prediction(s) of measurements used as input to the second condition, e.g. SSB, CSI-RS, MRS.

[0145] • An indication of a value of probability of the predicted event being fulfilled. As an example, the UE may consider the event as being fulfilled if the probability of the event being fulfilled is higher than the indicated value for a predicted time to trigger TTT amount of time.

[0146] • An indication of an accuracy / confidence level threshold of the prediction. As an example, the prediction quantity (e.g., pRSRP) is relevant for the evaluation of the second condition only if its accuracy / confidence level is higher than the threshold.

[0147] • ML configurations of the second configuration corresponding to the second condition: o Length of historical values of measurements (e.g., RSRP), could be could observation window, used as input for the model responsible for the second condition. o Period of the predict samples of measurement (e.g., pRSRP) that will be used for the second trigger, and which target neighbor cell. o Statistical operation on the group of samples outputted from predicted range of samples. This could be index, e.g., 0 == averaging, 1 == 90%, 2 == 10%, etc. o The ML algorithm parameters that are considered in such predictions:

[0148] ■ Regularization parameter (e.g., LI, or L2 norm)

[0149] ■ Architecture (e.g., width and depths of Neural- Network)

[0150] ■ Model type (e.g., Deep Neural Network (DNN), Convolutional Neural Network (CNN), Rotation invariant Sheaf Neural Network (RisNN), Long Short-Term Memory (LSTM), or mix of them) In one option, the first and second configurations correspond to measurement configuration which includes a prediction or inference configuration. In one option, the first and the second configuration may be included in the same MeasConfig configuration, where including them in the same MeasConfig configuration serves as an indication that both configurations need to be fulfilled before triggering the report. The UE may at the same time be configured with configurations for only reporting of measurement events or reporting of prediction events. These configurations may be configured in separate MeasConfig configurations with separate measld and where the separate MeasConfig indicates that the UE is configured with both reporting of only measurement event and / or prediction event and with the combination of measurement event and prediction event. In one option, at least one parameter in the first configuration controls the triggering of the second condition.

[0151] ■ In one example, the UE receives in the first configuration an indication of a first condition, e.g. an indication that the condition is “a neighbor cell quantity is an offset better than the PCell’s quantity”. Then, based on that, the UE determines that the second condition is “a neighbor cell predicted quantity is an offset better than the PCell’s predicted quantity”, so that the report is triggered when “the neighbor cell quantity is an offset better than the PCell’s quantity” AND “the neighbor cell predicted quantity is an offset better than the PCell’s predicted quantity”.

[0152] ■ In one example, the UE receives in the first configuration an indication of a trigger quantity of the first condition, e.g. an indication that RSRP is to be used, so that the condition is “a neighbor cell RSRP is an offset better than the PCell’s RSRP”. Then, based on that, the UE determines that the second condition is “a neighbor cell predicted RSRP is an offset better than the PCell’s predicted RSRP”, so that the report is triggered when “the neighbor cell RSRP is an offset better than the PCell’ s RSRP” AND “the neighbor cell predicted RSRP is an offset better than the PCell’s predicted RSRP”. o In another option, the UE might receive configuration on a “joint” or “encoded” reporting of both measurement and predicted measurement of the cells. Such configuration, might include:

[0153] ■ Indication related to which algorithm should be used, e.g., IndO == ML Auto-Encoding algorithm, Indi == conventional compression algorithm.

[0154] ■ Parameter related to properties of the encoding or compressing algorithm. o In another option, the UE might additionally receive a configuration requesting predicted measurements on the cells involved in the measured and predicted conditions (events) during the time period between the occurrence of first configured measurements and the second mobility related time domain predictions. As a further configuration, these measurements are requested only if the distance in time between first condition (event) and second condition (event) is higher than a specified value

[0155] Step 602(a): Performing measurements of mobility related resources; AND o In an embodiment, measurements on the mobility related resources can be performed when one or more certain conditions are met e.g., the second condition is fulfilled. o In one embodiment, the measurements correspond to cell based RSRP and / or cell based RSRQ and / or cell based SINR. o In one embodiment, the UE is configured to perform the measurement based on a measurement configuration including a measurement identifier (e.g., measID) associated to a reporting configuration and to a measurement object. o In one option, the UE performs the one or more mobility related measurements based on the first configuration (e.g. a measurement configuration). That may indicate one or more of: i) a measurement object and / or a reporting configuration. o In one option, a mobility related resource may correspond to a cell, a beam, or an entity the UE detects associated to a reference signal (RS) based on which the UE perform measurements, or a reference signal or a synchronization signal (e.g., SSB). o In one option, the measurements of mobility related resources may correspond to a cell based RSRP on SSB(s), cell based RSRQ on SSB(s), cell based SINR on SSB(s); o In one option, the measurements of mobility related resources may correspond to a beam based RSRP on SSB(s), beam based RSRQ on SSB(s), beam based SINR on SSB(s); o In one option these measurements of mobility related resources are L3 filtered before the evaluation of the triggering condition.

[0156] Step 602(b): Performing mobility related time-domain predictions o In an embodiment, the UE performs the mobility-related time domain predictions when the first condition is fulfilled e.g., the entering condition(s) of the first condition is / are fulfilled. In other words, the report is transmitted when both the first and the second conditions are fulfilled, but the second condition’s input (i.e., mobility related time-domain predict! on(s)) are not produced as output until the first condition is fulfilled. That could make sense in case the AI / ML model which infers the mobility-related time-domain prediction requires a latest time instance as input, to produce the value(s) in future time instances in starting from that value. In one embodiment, mobility related time domain prediction(s) can be performed within a period of time configured by the network e.g., prediction time to trigger (pTTT). The UE starts a timer pTTT upon fulfilment of the entry condition for the first condition that is evaluated based on the measurements. In one option, the UE performs the one or more time-domain mobility related prediction(s) based on the second configuration (e.g., inference or prediction configuration). A mobility -related time-domain prediction(s) may be associated to a prediction in a future time instance for a cell, beams, or other entity. That may correspond to one or more of the following:

[0157] ■ An RLF prediction in a serving cell e.g. probability or other likelihood metric that a RLF may occur in a future time instance;

[0158] • The RLF prediction may be based on the temporal domain serving cell measurement predictions (e.g., SINR), also called time-domain prediction of an SINR of an SpCell (e.g., PCell, PSCell).

[0159] • The RLF prediction may be a direct output from an AI / ML model for an RLF prediction.

[0160] • The RLF prediction may correspond to an output or result from an AI / ML model, where the output (result) may correspond to the RLF probability within a time window or at time instance.

[0161] ■ An RLF prediction post HO in a neighbor cell e.g. probability or other likelihood metric that a RLF may occur in a future time instance shortly after a successful HO to the neighbor cell;

[0162] • The RLF prediction post HO may be based on the temporal domain neighbor cell measurement predictions (e.g., SINR), also called time-domain prediction of an SINR of a neighbor cell, after a handover would have happened.

[0163] • The RLF prediction post HO may be a direct output from an AI / ML model for an RLF prediction.

[0164] • The RLF prediction post HO may correspond to an output or result from an AI / ML model, where the output (result) may correspond to the RLF probability within a time window or at time instance (e.g., after the HO). ■ A HOF prediction in a neighbor cell e.g. when performing the handover from the serving cell to the neighbor cell;

[0165] ■ A time-domain prediction of a measurement quantity, such as a predicted RSRP (pRSRP), predicted RSRQ (pRSRQ), predicted SINR (pSINR).

[0166] ■ Other information related to a future time instance in which the UE may report to assist the network to take mobility decisions. o In one option, the UE starts performing mobility-related time-domain prediction(s) for a cell when the first condition is fulfilled for that same cell. o Optionally, and additionally to the steps above, the UE evaluates the distance in time between the first and second condition and, if the UE has been configured to provide predicted measurements between the occurrence of the first condition and the second predicted condition (and if the distance in time of such conditions is higher than the threshold established), include in the measurement report measurement prediction for the cells involved in the conditions, e.g. the triggering cells of the conditions, for points in time that occur between the first and the second condition

[0167] Step 602(c) (optional): receiving an implicit or explicit indication indicating how (under fulfilment of which criteria) the UE transmits the report to the network o In an embodiment, based on the received indication, the UE transmits the report only if first and the second conditions are jointly fulfilled. o In an embodiment, based on the received indication, the UE transmits the report even if only one of the two conditions is fulfilled. o In an embodiment, based on the received indication, the UE transmits the report both if the first and the second conditions are jointly fulfilled and the UE also transmits the reports if only one of the two conditions is fulfilled. o In an embodiment, the indication can be implicit, e.g., the structure of the information element received by the UE indicates whether the configured events should be jointly fulfilled to trigger the measurement report or not. In another embodiment the variable / (internal storage) in which the UE stores the event conditions indicates whether the UE should send the measurement report upon fulfilment of joint events or the measurement report can be send even upon fulfilment of one of the events criteria. o In one embodiment, the UE receives a measurement configuration with a measurement identifier associated to a reporting configuration. In that reporting configuration the first condition (associated to measurements) and second condition (associated to mobility related time domain prediction(s)) are configured. o In one embodiment, the UE receives a measurement configuration with a measurement identifier associated to two reporting configuration(s), a first and a second reporting configuration(s). The first reporting configuration is associated to the first condition (associated to measurements) and the second reporting configuration is associated to the second conditions (associated to the time domain prediction(s)). The fact that the measld is associated to the two reporting configurations, e.g. in the same MeasIdToAddMod like structure, the UE knows the report is transmitted when both conditions associated to both events in both reporting configurations shall be fulfilled.

[0168] Step 604: Evaluating a triggering condition having as input both the mobility related measurements and the mobility related time-domain predictions. In other words, the UE determines whether the triggering condition is fulfilled. More specifically, in one example embodiment, the aforementioned triggering condition includes two triggering conditions, referred to as a first condition (or first triggering condition) whose input is based on the mobility related measurement(s) and a second condition (or second triggering condition) whose input is based on the mobility related time-domain prediction(s), and step 604 includes the following: o Step 604(a): Evaluating the first condition (e.g., evaluating the reporting criteria in the first conditions) whose input(s) is / are based on one or more of measurement s) of mobility related resource (as performed in Step 602(a)) (i.e., determining whether the first condition whose input is based on the mobility measurement(s) is fulfilled); AND

[0169] ■ In an embodiment, evaluating the criteria can be e.g., evaluating entry condition of an A3 event i.e., a neighboring cell becomes an offset (a relative threshold) better than the serving cell for a certain period of time so called time to trigger (TTT).

[0170] ■ In another embodiment, evaluating the criteria can be e.g., evaluating entry condition of an A4 event i.e., a neighboring cell becomes better than a threshold (an absolute threshold) for a certain period of time so called time to trigger (TTT).

[0171] ■ In another embodiment, evaluating the criteria can be e.g., evaluating entry condition of an A5 event i.e., a neighboring cell becomes better than a threshold (an absolute threshold) and serving cell becomes worse than a threshold for a certain period of time so called time to trigger (TTT).

[0172] ■ In an embodiment, the measurements may be partial measurements i.e., a combination of measurements and spatial / frequency domain predictions e.g., cell quality measurements can be a linear average / combination of the measurements of some beams (e.g., SSB or CSI-RS beams) and predictions of some other beams (SSB or CSI-RS beams) Step 604(b): Evaluating the second condition (e.g., evaluating the reporting / triggering criteria in the second conditions) whose input(s) is / are based on one or more of mobility related time domain prediction(s) (as performed in Step 602(b)) (i.e., determining whether the second condition whose input is based on the mobility related time-domain prediction(s) is fulfilled);

[0173] ■ In this step, the UE evaluates whether the mobility-related time-domain prediction(s) for one or more cells fulfil the condition. The exact UE action may vary depending on what the mobility-related time-domain prediction(s) corresponds to, e.g., whether these are time-domain predictions of measurements (like pRSRP), or time-domain predictions of a RLF (RLF predictions), or time-domain predictions of a HOF (HOF predictions), etc. Different types of mobility related time-domain predictions and their implications are provided in further details below.

[0174] ■ In an embodiment, evaluating the reporting / triggering criteria can be e.g., evaluating entry condition related to the condition of an A3 event but using time-domain prediction(s) of measurement(s) i.e., time domain prediction of a neighboring cell becomes an offset (a relative threshold) better than the time domain prediction of serving cell quality for a certain period of time so called prediction time to trigger (pTTT). ■ In another embodiment, evaluating the reporting / triggering criteria can be e.g., evaluating entry condition related to the condition of an A4 event but using time-domain prediction(s) of measurements i.e., mobility related time domain prediction(s) of measurements of a neighboring cell becomes better than a threshold (an absolute threshold) for a certain period of time so called prediction time to trigger (pTTT).

[0175] ■ In another embodiment, evaluating the reporting / triggering criteria can be e.g., evaluating entry condition related to an A5 event i.e., mobility related time domain prediction(s) of a neighboring cell becomes better than a threshold (an absolute threshold) and mobility related time domain prediction of the measurement of the serving cell becomes worse than a threshold for a certain period of time so called prediction time to trigger (pTTT).

[0176] ■ In an embodiment, the measurements may be partial measurements i.e., a combination of measurements and spatial / frequency domain predictions e.g., cell quality measurements can be a linear average / combination of the measurements of some beams (SSB or CSI- RS beams)

[0177] ■ In a different embodiment, evaluating the second condition comprises evaluation based on the inputs based on performing other types of mobility related time domain predictions depending on the received configuration in Step 600. Some non-limiting examples, of time domain predictions are as following

[0178] • Predicting the probability of a radio link failure in the serving cell associated to a future time instance

[0179] • Predicting the probability of a radio link failure in one or more of neighboring cell(s), e.g., if the UE is handed over to such neighboring cells(s). The neighboring cells can be among the cells that are configured as part of the received second configuration. Notably, in one embodiment, the first and second configurations can be same / one instance of configuration i.e., triggering a report if the prediction of RLF in the source .

[0180] • Predicting the probability of a handover failure in one or more of neighboring cell(s) in case of executing a handover toward such a neighboring cell(s). The neighboring cells can be among the cells that are configured as part of the received second configuration. Notably, in one embodiment, the first and second configurations can be same / one instance of configuration.

[0181] ■ In a different embodiment, evaluating the second condition comprises evaluating the accuracy of the one or more of the mobility related time domain predictions. The accuracy of a mobility related time domain prediction can be based on procedures at the UE for the AIML model monitoring, based for example on previous time domain predictions and ground truth evaluation based on measurements of mobility related resources. The accuracy of the one or more mobility related time domain predictions can be considered enough, if it satisfies accuracy criteria defined by the UE implementation or configured by the gNB. For example, the gNB may configure the UE with an accuracy criteria, e.g. a threshold, when configuring the UE with the second condition for the mobility related time domain predictions.

[0182] ■ In a different embodiment, evaluating the second condition comprises evaluating the probability of the event being fulfilled. The prediction event may be configured related to a probability number. The UE evaluates prediction event and monitors whether the probability of the event being fulfilled is above the configured probability number. If the probability of the event being fulfilled has been above the configured probability number for a time to trigger (TTT) amount of time, the UE considers the event as being fulfilled.

[0183] ■ In an embodiment, the report is transmitted when both the first and the second conditions are fulfilled, but the second condition is evaluated when the first condition is fulfilled. In other words, the UE does not evaluate whether mobility-related time-domain prediction(s) of one or more cells fulfil the second condition, until measurement s) of the one or more cells fulfil the first condition.

[0184] ■ In an option, the fulfilment of the second condition is produced as a direct output from an AI / ML model. In other words, the prediction that the second condition is going to be fulfilled in a future time instance is not necessarily based on the time-domain prediction(s) of measurements the UE uses as input to the condition, but the model itself indicated when the model is fulfilled and / or not fulfilled, e.g. periodically.

[0185] Step 606: Upon the fulfilment triggering condition (e.g., upon fulfillment of the first condition based on a reporting / triggering criteria according to one or multiple subcondition^), e.g. an entry condition or combination of entry conditions whose input is based on measurement or partial measurements of mobility related resources AND upon the fulfilment of the second condition based on a reporting / (event triggering) criteria according to one or multiple sub-condition(s), e.g. an entry condition or combination of entry conditions whose input is based on mobility related time domain predictions), trigger one or more reports (e.g., trigger a measurement report and / or trigger a prediction report). o In one embodiment, in case the accuracy of the one or more mobility related time domain predictions is not enough (e.g., does not satisfy one or more predefined or configured accuracy requirements), the measurement report is not triggered. In another embodiment, the measurement report is triggered, but the one or more mobility related time domain predictions are not included in the measurement report, i.e. the UE only includes the one or more measurements included according to the first condition. In another embodiment, the measurement report is triggered, and the UE includes or not includes the one or more mobility related time domain prediction(s) and it also includes an indication indicating that the one or more mobility related time domain predictions do not satisfy the accuracy criteria. o In one embodiment, the prediction event is triggered only if the probability of the event being fulfilled is above a certain number which may have been configured by the network. The probability may need to be above the certain number for a time to trigger (TTT) amount of time before being considered as fulfilled. If the probability of the prediction event is below the certain number, the UE does not trigger the report.

[0186] Step 608: Sending the (triggered) one or more reports (e.g., one or more measurement and / or prediction reports) including the measurement and / or prediction of mobility related resources. o In an embodiment, the content of the report depends upon the fulfilment of the events. o In another embodiment, based on configuration from network, UE should report an encoded value of both measurement and predicted measurement of the cells. o In another embodiment, upon evaluating the distance in time between the first and second condition and, if the UE has been configured to provide predicted measurements between the occurrence of the first condition and the second predicted condition (and if the distance in time of such conditions is higher than the threshold established), include in the measurement report measurement predictions for the cells involved in the conditions, e.g. the triggering cells of the conditions, for points in time that occur between the first and the second condition.

[0187] In regard to UE capabilities, prior to the steps described above in relation to Figure 6, the UE may have indicated (e.g., to the network such as to a RAN node such as, e.g., a gNB) support for performing the process of Figure 6 by signaling of UE capability (es). The UE capability may, e.g., indicate:

[0188] Support for reporting of measurements and prediction reports. o In one option, there is a UE capability for the support or reporting of measurements and prediction reports together. o In another option, indicating support for both the measurements and for reporting predictions means that the UE is capable of reporting measurements and predictions together.

[0189] - The UE capabilities may be further detailed, e.g. support for reporting of measurements together with prediction reports per frequency, per frequency band, per frequency band combination, per frequency range etc.

[0190] Example scenarios based on the solution(s) described herein

[0191] A) Triggering the measurement report when both conditions are fulfilled.

[0192] In one scenario shown in Figure 9, the UE evaluates criteria of both legacy event condition (e.g., A3 entry event condition, evaluated based on the measurements) and predicted event condition (e.g., predicted A3 event entry condition, evaluated based on the prediction samples) and sends the measurement report to the network only if both criteria are fulfilled for a configured period of TTT and pTTT. More specifically, the UE is configured (e.g., in step 600 of Figure 6) with an event-triggered reporting, which in particular uses two conditions - a first condition (which may include multiple sub-conditions) to trigger an event / report that is to be evaluated based on the measurements and a second condition (which may include multiple sub-conditions) to trigger an event / report that is to be evaluated based on the mobility related time domain prediction(s). In addition, the UE is configured (e.g., in step 602(c)) by the network to send the measurement report when both conditions (evaluated based on the measurements and mobility related time domain predict! on(s)) are jointly fulfilled.

[0193] In a given time, the UE determines (e.g., in steps 604 and 606 of Figure 6) that entry conditions configured based on the first condition (to be evaluated based on the measurements or partial measurements) is fulfilled for at least a configured period of time so-called TTT, AND the entry condition configured based on the second condition (to be evaluated based on the mobility related time domain predict! on(s)) is fulfilled for at least a configured period of time so-called pTTT.

[0194] In one option, the TTT and pTTT are two different parameters (which may be set to different values). In another option, the pTTT takes the same value of the TTT and a single parameter TTT is configured. In another option, pTTT is set in number of future time instances. In another option, pTTT is set as an offset, e.g. in ms, with respect to TTT. Notably same or different values for other mobility control parameters such as event specific offset (A3 offset) and cell individual offset (CIO) can be configured for the evaluation of the first and second conditions.

[0195] In one implementation, the UE only monitors the first entry condition evaluated based on the legacy measurements or partial measurements and performs the predictions related to the entire time period of pTTT only when the first entry condition was fulfilled for the entire period of TTT i.e., the UE does not need to run a second timer pTTT and monitor predictions over time and it performs all the required predictions samples at the same time. This requires the UE to be capable of predicting the measurement samples in different future time instances.

[0196] In another implementation, the UE starts the timer pTTT once the first entry condition is fulfilled and the timer TTT is started. This method enables the UE to use a model which is able to only predict the future samples for only one future sample. This approach enables the UE to continuously monitor the prediction samples which are inferred once in a time (similar to the measurement samples) and evaluate both entry conditions based on the measurement and predictions while the timers (TTT and pTTT) are running.

[0197] In such a scenario, the UE triggers the measurement reporting procedure. As part of measurement reporting procedure, the UE includes the measurements as well as time domain predictions in the measurement report.

[0198] In a set of embodiments, the UE triggers the measurement report when both the first condition and the second condition(s) are configured as triggered. The UE may perform a parallel process in which both condition(s) are monitored in parallel as follows: - If the first condition is fulfilled for a cell for all measurements after layer 3 filtering taken during a corresponding TTT, the UE considers the first condition to be fulfilled;

[0199] - If the second condition is fulfilled for a cell for all time-domain prediction(s) taken during a corresponding pTTT, the UE considers the second condition to be fulfilled;

[0200] - If both conditions are considered as fulfilled, initiate a reporting procedure.

[0201] In one embodiment, the first condition is associated to a measurement identifier associated to a first reporting configuration, and the second condition is associated to a prediction identifier associated to a second reporting configuration. The UE performs a parallel process in which both condition(s) are monitored in parallel as follows:

[0202] - If the condition associated to the measurement identifier is fulfilled for a cell for all measurements after layer 3 filtering taken during a corresponding TTT, the UE considers the condition associated to that measurement identifier to be fulfilled;

[0203] - If the condition associated to the prediction identifier is fulfilled for a cell for all timedomain prediction(s) taken during a corresponding pTTT, the UE considers the condition associated to that prediction identifier to be fulfilled;

[0204] - If the condition associated to the measurement identifier is fulfilled AND If the condition associated to the prediction identifier (associated to the measurement identifier) is fulfilled: o The UE initiates a reporting procedure.

[0205] The entry conditions for the event-based reporting configuration can be evaluated based on absolute thresholds e.g., aN-Threshold in (for eventAl, eventA2, eventA4, eventA4Hl and eventA4H2) or in the a5-Threshold2 (for eventA5, eventA5Hl and eventA5H2) for the measurement based events and aN-Threshold-pr ediction (for prediction based eventAl, eventA2, eventA4, eventA4Hl and eventA4H2) or in the a5-Threshold2-prediction (for eventA5, eventA5Hl and eventA5H2) for the prediction based events. For such report triggering conditions the measurements and predictions are compared against absolute thresholds.

[0206] In another example, the entry conditions for triggering conditions can be based on the relative thresholds, for example in case of A3 event wherein the radio measurement of at least one neighboring cells should be an offset better that the serving cells radio quality (for example aN- Offset in (for eventA3, eventA3Hl, eventA3H2 and e\'entA6 ) and the neighboring cell’s time domain prediction should be an offset (aN-Offset-predictiori) better than the serving cell’s radio prediction. For such report triggering conditions the measurements and predictions are compared against relative thresholds.

[0207] B) Triggering the measurement report when conditions are not jointly fulfilled. In one scenario shown in Figure 10 and Figure 11, the UE is configured (e.g., in step 600 of Figure 6) with the event -based reporting, which includes in particular two sets of events - a first set of events to be evaluated based on the legacy measurements and a second set of events to be evaluated based on the time domain prediction(s). In addition, the network instructs (implicitly or explicitly) (e.g., in step 602(c) Figure 6) the UE how to send the measurement reports: e.g., only if both conditions (evaluated based on the measurements and predictions of the measurements) are jointly fulfilled, or even if one of the set of events is fulfilled UE sends the report.

[0208] In Figure 10, it is shown that in a scenario that the network instructs the UE to send the report only when both triggering events (in this example A3 event and predicted A3 event) are fulfilled the UE does not send the measurements reports since the entry condition of the first set of events (in this example A3 event) is not fulfilled. In other words, in Figure 10, the UE evaluates criteria of both legacy event condition (e.g., A3 entry event condition, evaluated based on the measurements) and predicted event condition (e.g., predicted A3 event entry condition, evaluated based on the prediction). UE does not send the measurement report to the network since the legacy A3 event condition is not fulfilled. This will avoid ping pong HO or too early HO failure. Figure 11 illustrate another scenario in which the predictions samples do not fulfil the entry condition of the predicted A3 event and hence the UE may not trigger the measurement report to the network. In other words, in Figure 11, the UE evaluates criteria of both legacy event condition (e.g., A3 entry event condition, evaluated based on the measurements) AND predicted event condition (e.g., predicted A3 event entry condition, evaluated based on the prediction). UE does not send the measurement report to the network since the prediction based A3 event condition is not fulfilled. This will avoid any failure in reception of the HO command by the UE.

[0209] In some embodiments, the network (e.g., a network node such as, e.g., a RAN node such as, e.g., a gNB or 6G base station) may configure the UE to anyhow report the measurements and / or predictions to the network even if one of the events is not fulfilled. In such a scenario, the content of the report may be dependent to the triggering event. For example, if only the event associated with the measurement (in this example A3 event) is fulfilled, the UE may only include the measurements. In another example, if the event associated with the prediction (in this example predicted A3 event) is fulfilled, then the UE includes only prediction samples in the report. In another embodiment the UE may include all the available measurement and predictions in the report list. In another example, the UE is configured with a measurement configuration for both the combination of the measurement event and the prediction event and with measurement configuration for only measurement event and / or prediction event. The separate measurement configurations indicate that the UE may trigger both reports for both the combination of events and for stand-alone reports.

[0210] The UE may also include an indication indicating which set of events (events associated to the measurements and / or events associated to the prediction samples are fulfilled.

[0211] In some embodiments, the method comprises the UE monitoring the first condition (for measurements) and the second condition (for time-domain prediction(s)) (see, e.g., steps 604(a) and 604(b) of Figure 6), wherein the first and the second condition(s) are of the same or different type(s). For example:

[0212] - First condition: neighbor cell measurement(s) become better than a first threshold for a TTT duration;

[0213] Second condition: neighbor cell mobility related time-domain prediction(s) become an offset better than a second threshold for a pTTT duration;

[0214] - NOTE: in one option, the same parameters are applicable (e.g., TTT=pTTT and / or first threshold = second threshold); in another option, the parameters are different.

[0215] In some embodiments, the method comprises the UE monitoring the first condition (for measurements) and the second condition (for mobility related time-domain prediction(s)) (see, e.g., steps 604(a) and 604(b) of Figure 6), wherein the first and the second condition(s) are of the same type, and the second condition is associated to the fulfillment of such an event in a future time instance. For example, if the first condition is associated to an event A4, the second is associated to the fulfillment of an event A4, e.g. for the same cell.

[0216] - First condition: neighbor cell measurement(s) become better than a first threshold for a TTT duration;

[0217] Second condition: the first condition would be fulfilled in a future time instance.

[0218] The method of Figure 6 may be extended to the joint events both based on the mobility related time-domain predictions. In this regard, in one example variant, the UE is configured (e.g., in step 600 of Figure 6) to evaluate two triggering conditions for a report, wherein both triggering conditions are evaluated based on the mobility related time domain predictions. In an example,

[0219] - The first condition is fulfilled if mobility related time domain prediction(s) fulfil the entry condition of the first condition. In some non-limiting examples: o the first condition is fulfilled if the probability of RLF is above a certain threshold for a certain period of time. o In another example the first condition is fulfilled if the probability of no-RLF is below a certain threshold for a certain period of time. o In another example the first condition is fulfilled if the probability of post-HO RLF toward a target cell is below a certain threshold for a certain period of time.

[0220] - The second condition is fulfilled if mobility related time domain prediction(s) fulfil the entry condition of the first condition. In some non-limiting examples: o the second condition is fulfilled if the probability of mobility procedure failure is below a certain threshold for a certain period of time. o In another example the second condition is fulfilled if the probability of successful mobility procedure is above a certain threshold for a certain period of time. o In another example the second condition is fulfilled if the probability of post- HO RLF toward a target cell is below a certain threshold for a certain period of time. o In another option, the second condition will be fulfilled if the aggregated value of range of predicted measurement is below another certain threshold (different than the threshold of instantaneous prediction).

[0221] In the above example, an (event based) report will be triggered by the UE if both first (triggering) condition and (second triggering condition) which are evaluated based on mobility related time domain predictions (e.g., RLF probability or HOF probability) are fulfilled.

[0222] Upon fulfillment of both conditions the UE sends a report to the network and include the relevant information for example the available measurements and / or mobility related time domain prediction(s) information.

[0223] As described above with respect to Figure 8, embodiments of a method of operation of a source network node (e.g., source gNB) for mobility of a UE in connected state are also disclosed. As shown in Figure 8, the method performed by the source network node may include any one or more of the following:

[0224] Step 800(a): Configuring a UE to perform measurement of mobility related resources, such as measurements of radio quality such as RSRP, RSRQ, SINR in a given frequency (e.g. per cell, per beam, per RS type like SSB and / or CSLRS, etc.); AND Step 800(b): Configuring a UE to perform predictions of mobility related resources, such as predictions of radio quality such as RSRP, RSRQ, SINR in a given frequency (e.g. per cell, per beam, per RS type like SSB and / or CSLRS, etc.); o Sending an implicit or explicit indication to the UE indicating how the UE should send the report to the network ■ In an embodiment the network (implicitly or explicitly) indicates that the UE should send the report only if event based reporting criteria configured as part of the two configuration should be jointly fulfilled.

[0225] ■ In an embodiment the network (implicitly or explicitly) indicates that the UE should send the report even if only one of the event based reporting criteria configured as part of one of two configurations is fulfilled.

[0226] Step 800(c) (optional): In a dependent embodiment, configuring the UE with a configuration to perform predicted measurements on the cells involved in the measured and predicted conditions (events) during the time period between the occurrence of first configured measurements and the second mobility related time domain predictions. As a further configuration, these measurements should be performed only if the distance in time between first condition (event) and second condition (event) is higher than a specified value.

[0227] Step 802: Configuring the UE with a triggering condition having as inputs both measurements of mobility related resources (as performed in step 800(a)) (i.e., mobility related measurements) AND mobility related time-domain predictions. More specifically, in one example embodiment, step 802 includes the following: o Step 802(a): Configuring a UE to evaluate reporting criteria according to a configured condition (entry condition) whose input is based on measurements of mobility related resources (as performed in 800(a)); AND o Step 802(b): Configuring a UE to evaluate reporting criteria according to a configured condition (entry condition) whose input is based on predictions of mobility related resources (as performed in Step 800(b));

[0228] Step 804: Receiving one or more messages including measurement and / or predictions of mobility related information resources by a UE, where the message indicates the fulfilment of a first condition (whose input is based on measurements of mobility related resources), AND the fulfilment of a second condition (whose input is based on predictions of mobility related resources). In a dependent embodiment, the network receives in the message predicted measurements on the cells involved in the measured and predicted conditions (events) during the time period between the occurrence of first configured measurements and the second mobility related time domain predictions.

[0229] Step 806 (optional): Performing one or more mobility related actions based on the received message(s). In addition, embodiments of a method for a first (source) network node to transfer the measurement results and / or predictions to a second (target) network node are also disclosed. The information may be transferred jointly or separately in the network message. The transfer of the information may be done in network messages such as XnAP, NGAP, RRC inter-node message, F1AP, E1AP etc. The information may, e.g., be used by a target node for AI / ML training purposes.

[0230] In regard to information to include in the report(s) when triggered, in accordance with some embodiments of the present disclosure, the UE determines what to include in the report when that is triggered to be transmitted e.g.. when both the first and the second conditions are fulfilled.

[0231] In one option, the UE uses both measurements and time domain prediction of a cell to trigger a report, and when that is triggered, the UE includes in the report the one or more measurements for at least one cell. The UE may receive from the network, in a reporting configuration, an indication to include the one or more measurements of a cell to be reported, e.g. an indication of one or more reporting quantities such as RSRP, RSRQ and / or SINR.

[0232] In one option, the UE uses both measurements and time domain prediction of a cell to trigger a report, and when that is triggered, the UE includes in the report the one or more timedomain predict! on(s) of the cell. The UE may receive from the network, in a reporting configuration, an indication to include the one or more time-domain prediction(s) of a cell to be reported e.g. an indication of one or more predicted reporting quantities such as predicted RSRP, predicted RSRQ and / or predicted SINR, or any other predicted mobility-related metrics, such as predicted RLF probability in serving / neighbor cell or HOF probability. The UE may also receive from the network, e.g. as part of the said reporting configuration, an indication to include the accuracy of the reported one or more mobility related time domain predictions.

[0233] In one option, the UE uses both measurements and time domain prediction of a cell to trigger a report, and when that is triggered the UE includes in the report both the one or more timedomain predict! on(s) of the cell AND the one or more measurements for at least one cell.

[0234] - The UE may include based on the reporting configuration, measurement quantities associated to the one or more time-domain prediction(s) of a cell, e.g. the predicted RSRP, predicted RSRQ and / or predicted SINR, or any other predicted mobility -related metrics, such as predicted RLF probability in serving / neighbor cell or HOF probability.

[0235] - The UE may include, based on the received reporting configuration, measurement quantities associated to the one or more measurements of a cell to be reported, e.g. the measured RSRP, RSRQ and / or SINR. - The UE may include, based on the received reporting configuration, the measurement quantities BOTH associated to the one or more measurements of a cell to be reported AND to the one or more time-domain prediction(s) of a cell to be reported.

[0236] - The UE may include an accuracy indication associated to reported one or more timedomain predictions. The indication can be expressed in percentage, or it can be a flag indicating whether the reported one or more time-domain predictions are considered to be accurate or not. In one embodiment the accuracy indication is only included if the UE is configured by the gNB in the reporting configuration to report the accuracy indication. In another embodiment, the accuracy indication is always included.

[0237] - In one option, the UE may include the estimated probability of the event being fulfilled. The UE may be configured to transmit the report only if the probability is higher than a configured number and if the probability is higher than the configured number and the UE sends the report, the UE may include the estimated probability in the report. The estimated probability may be a single (possibly averaged) number or a list of numbers where each probability may be related to a certain time in the future.

[0238] In one option, according to some of the above embodiments, the measurement report only includes the one or more measurements of a cell, and it does not include the one or more time domain predictions, e.g. in case the one or more time domain predictions are not considered to be accurate. In such a case, the UE may include in the measurement report an indication indicating the reason why the one or more time domain prediction(s)s are not included in the measurement report. The indication can be for example a flag indicating that the accuracy criteria were not satisfied, or that the AIML model failed to generate the output.

[0239] In another option, to reduce reporting overhead, network decides to configure UE with encoding scheme of both measurement and predicted measurement reports. In this situation (assumed the network configured UE to use ML Auto-Encoding for merging the reports) UE should report the output of such Auto-Encoder, the accuracy of such autoencoder, the size of output neuron, every N occasions UE is to send a sample of the original labels used to train the autoencoding or (nonML) compression algorithm.

[0240] In one option, the UE includes in the report predicted measurements for cell level signals (e.g., RSRP, RSRQ, SINR) for the cells involved in the conditions (events) triggering the report. Namely, such cells are the cells for which the measurements taken by the UE fulfilled the conditions for the first event and the cells for which mobility related predictions were derived as part of the second event. Such predicted measurements should be derived for points in time that are in between the occurrence of the first measured event and the occurrence of the second predicted measurement / event.

[0241] The Network may have configured the UE with both a request to generate / report such prediction, as well with conditions based on which such predicted measurements should be reported. The criteria based on which the UE should report such measurements can be based on the time elapsed between the occurrence of the first measured condition (event) and the time at which the second condition (event) is predicted. If this elapsed time is equal or higher than what configured by the network, the UE shall include in the report the predicted cell level measurements.

[0242] In regard to mobility related time-domain prediction(s), the UE performs one or more mobility related time-domain predictions(s) (e.g., in step 602(b) of Figure 6) on at least one cell, and triggers the transmission of a report including the one or more mobility related time-domain predictions(s) for the at least one cell WHEN a first condition AND a second condition are both fulfilled.

[0243] According to embodiments of the present disclosure, the so-called mobility related timedomain prediction for a 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 timedomain 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.

[0244] According to at least some embodiments, in more detail, the UE derives or performs a mobility related time-domain prediction(s) which may include one or more of the following:

[0245] 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 ARFCN o beam-level radio measurements, such as RSRP, RSRQ or SINR associated with a beam such as an SSB or CSI-RS beam identity related to a cell identified with cell global identity (CGI) or PCI and ARFCN o For example, in the case of a new event with joint conditions, the UE triggers a report when a neighbor cell’s RSRQ (e.g. RSRQ as trigger quantity) is an offset better than the PCell’s RSRQ AND the neighbor cell’s pRSRQ in one or more future time instances (e.g. pRSRQ as prediction trigger quantity) is an offset better than the PCell’s pRSRQ in the one or more future time instances. o In one option, when mobility-related time-domain prediction(s) correspond to a prediction value of radio measurement s), such as pRSRP, pRSRQ, pSINR, the UE is configured with the second condition which may indicate one or more of the following parameters which control the fulfillment of the second condition:

[0246] ■ A prediction trigger quantity, e.g. pRSRP, pRSRQ, pSINR

[0247] ■ Time to trigger related to the future time instances

[0248] ■ Offset(s)

[0249] ■ Absolute threshold(s)

[0250] - 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 For example, in the case of a new event with joint conditions, the UE triggers a report when a neighbor cell’s RSRQ (e.g. RSRQ as trigger quantity) is an offset better than the PCell’s RSRQ AND the AI / ML model output indicates this is a likely target cell the UE is moving to in a handover in a future time instance.

[0251] - One or more indications of one or more predicted UE locations, where a UE location may be, for example, a UE position coordinate. o For example, in the case of a new event with joint conditions, the UE triggers a report when a neighbor cell’s RSRQ (e.g. RSRQ as trigger quantity) is an offset better than the PCell’s RSRQ AND the AI / ML model output indicates a location and / or region associated to the coverage of that neighbor cell. o In other words, the measurements are indicating the UE has better coverage / quality (e.g. RSRP, RSRQ) in the neighbor cell than the PCell, and the second condition is indicating that the UE is moving in the future to an area in which the UE is expected to have better coverage / quality (RSRP, RSRQ) forthat neighbor cell; in that sense, the UE may be configured with a “region” or “area” (e.g. in terms of positioning, GPS location) so that the second condition is fulfilled when the location prediction(s) indicate that the UE is going to enters that region in a future time instance e.g. that the UE is entering deeper into the coverage of the neighbor cell in a future time instance.

[0252] - 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 other words, the measurements are indicating the UE has better coverage / quality (e.g. RSRP, RSRQ) in the neighbor cell than the PCell, and the second condition is indicating that the UE is moving in the future towards a certain “direction” in which the UE is expected to enter an area in which it will have better coverage / quality (RSRP, RSRQ) for that neighbor cell; in that sense, the UE may be configured with a “region” associated to one or more trajectories (e.g. in terms of positioning, GPS location) so that the second condition is fulfilled when the trajectory predict! on(s) indicate that the UE is moving towards a particular region in a future time instance e.g. that the UE is entering deeper into the coverage of the neighbor cell in a future time instance.

[0253] - A confidence value associated with any of the above indications, indicating the confidence of predicted values.

[0254] - A confidence value associated with any of the above indications, indicating the confidence of the AI / ML model in predicted values.

[0255] - A validity time information associated with any of the above indications, indicating a time interval in which the predicted values are valid.

[0256] - A time-domain prediction associated to a Handover failure (HOF) for a neighbor 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 neighbor cell in a future time instance. - A time-domain prediction associated to a Radio Link Failure for a serving cell (e.g., PCell, PSCell) which may occur in a future time instance.

[0257] - A value derived from one or more mobility related time-domain prediction(s) for a neighbor 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. An RLF prediction in a serving cell e.g. probability or other likelihood metric that a RLF may occur in a future time instance; o The RLF prediction may be based on the temporal domain serving cell measurement predictions (e.g., SINR), also called time-domain prediction of an SINR of an SpCell (e.g., PCell, PSCell). o The RLF prediction may be a direct output from an AI / ML model for an RLF prediction. o The RLF prediction may correspond to an output or result from an AI / ML model, where the output (result) may correspond to the RLF probability within a time window or at time instance.

[0258] - An RLF prediction post HO in a neighbor cell e.g. probability or other likelihood metric that a RLF may occur in a future time instance shortly after a successful HO to the neighbor cell; o The RLF prediction post HO may be based on the temporal domain neighbor cell measurement predictions (e.g., SINR), also called time-domain prediction of an SINR of a neighbor cell, after a handover would have happened. o The RLF prediction post HO may be a direct output from an AI / ML model for an RLF prediction. o The RLF prediction post HO may correspond to an output or result from an AI / ML model, where the output (result) may correspond to the RLF probability within a time window or at time instance (e.g., after the HO).

[0259] Figure 12 shows an example of a communication system 1200 in accordance with some embodiments.

[0260] In the example, the communication system 1200 includes a telecommunication network 1202 that includes an access network 1204, such as a Radio Access Network (RAN), and a core network 1206, which includes one or more core network nodes 1208. The access network 1204 includes one or more access network nodes, such as network nodes 1210A and 1210B (one or more of which may be generally referred to as network nodes 1210), or any other similar Third Generation Partnership Project (3GPP) access nodes or non-3GPP Access Points (APs). 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 1202 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 1202 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 the telecommunication network 1202, including one or more network nodes 1210 and / or core network nodes 1208.

[0261] 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 O-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes 1210 facilitate direct or indirect connection of User Equipment (UE), such as by connecting UEs 1212A, 1212B, 1212C, and 1212D (one or more of which may be generally referred to as UEs 1212) to the core network 1206 over one or more wireless connections.

[0262] 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 1200 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 1200 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0263] The UEs 1212 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 1210 and other communication devices. Similarly, the network nodes 1210 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 1212 and / or with other network nodes or equipment in the telecommunication network 1202 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 1202.

[0264] In the depicted example, the core network 1206 connects the network nodes 1210 to one or more hosts, such as host 1216. 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 1206 includes one more core network nodes (e.g., core network node 1208) 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 1208. 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).

[0265] The host 1216 may be under the ownership or control of a service provider other than an operator or provider of the access network 1204 and / or the telecommunication network 1202, and may be operated by the service provider or on behalf of the service provider. The host 1216 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.

[0266] As a whole, the communication system 1200 of Figure 12 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 1200 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 Second, Third, Fourth, or Fifth Generation (2G, 3G, 4G, or 5G) standards, or any applicable future generation standard (e.g., Sixth Generation (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.

[0267] In some examples, the telecommunication network 1202 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunication network 1202 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 1202. For example, the telecommunication network 1202 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing enhanced Mobile Broadband (eMBB) services to other UEs, and / or massive Machine Type Communication (mMTC) / massive Internet of Things (loT) services to yet further UEs.

[0268] In some examples, the UEs 1212 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 1204 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 1204. Additionally, a UE may be configured for operating in single- or multi -Radio Access Technology (RAT) or multi-standard mode. For example, a UE may operate with any one or combination of WiFi, New Radio (NR), and LTE, i.e. being configured for Multi-Radio Dual Connectivity (MR-DC), such as Evolved UMTS Terrestrial RAN (E-UTRAN) NR - Dual Connectivity (EN-DC).

[0269] In the example, a hub 1214 communicates with the access network 1204 to facilitate indirect communication between one or more UEs (e.g., UE 1212C and / or 1212D) and network nodes (e.g., network node 1210B). In some examples, the hub 1214 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 1214 may be a broadband router enabling access to the core network 1206 for the UEs. As another example, the hub 1214 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 1210, or by executable code, script, process, or other instructions in the hub 1214. As another example, the hub 1214 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 1214 may be a content source. For example, for a UE that is a Virtual Reality (VR) headset, display, loudspeaker or other media delivery device, the hub 1214 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 1214 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 1214 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0270] The hub 1214 may have a constant / persistent or intermittent connection to the network node 1210B. The hub 1214 may also allow for a different communication scheme and / or schedule between the hub 1214 and UEs (e.g., UE 1212C and / or 1212D), and between the hub 1214 and the core network 1206. In other examples, the hub 1214 is connected to the core network 1206 and / or one or more UEs via a wired connection. Moreover, the hub 1214 may be configured to connect to a Machine-to-Machine (M2M) service provider over the access network 1204 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 1210 while still connected via the hub 1214 via a wired or wireless connection. In some embodiments, the hub 1214 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 1210B. In other embodiments, the hub 1214 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and the network node 1210B, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0271] Figure 13 shows a UE 1300 in accordance with some embodiments. 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 Internet Protocol (VoIP) phone, wireless local loop phone, desktop computer, Personal Digital Assistant (PDA), wireless camera, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, Laptop Embedded Equipment (LEE), Laptop Mounted Equipment (LME), smart device, wireless Customer Premise Equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3 GPP, including a Narrowband Internet of Things (NB-IoT) UE, a Machine Type Communication (MTC) UE, and / or an enhanced MTC (eMTC) UE. A UE may support Device-to-Device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), or Vehicle-to-Everything (V2X). In other examples, 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).

[0272] The UE 1300 includes processing circuitry 1302 that is operatively coupled via a bus 1304 to an input / output interface 1306, a power source 1308, memory 1310, a communication interface 1312, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 13. The level of integration between the components 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.

[0273] The processing circuitry 1302 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 1310. The processing circuitry 1302 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general purpose processors, such as a microprocessor or Digital Signal Processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 1302 may include multiple Central Processing Units (CPUs).

[0274] In the example, the input / output interface 1306 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 1300. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0275] In some embodiments, the power source 1308 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 1308 may further include power circuitry for delivering power from the power source 1308 itself, and / or an external power source, to the various parts of the UE 1300 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 1308. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 1308 to make the power suitable for the respective components of the UE 1300 to which power is supplied.

[0276] The memory 1310 may be or be configured to include memory such as Random Access Memory (RAM), Read Only Memory (ROM), Programmable ROM (PROM), Erasable PROM (EPROM), Electrically EPROM (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 1310 includes one or more application programs 1314, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1316. The memory 1310 may store, for use by the UE 1300, any of a variety of various operating systems or combinations of operating systems.

[0277] The memory 1310 may be configured to include a number of physical drive units, such as Redundant Array of Independent Disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, High Density Digital Versatile Disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, Holographic Digital Data Storage (HDDS) optical disc drive, external mini Dual In-line Memory Module (DIMM), Synchronous Dynamic RAM (SDRAM), external micro-DIMM SDRAM, smartcard memory such as a tamper resistant module in the form of a Universal Integrated Circuit Card (UICC) including one or more Subscriber Identity Modules (SIMs), such as a Universal SIM (USIM) and / or Internet Protocol Multimedia Services Identity Module (ISIM), other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as a ‘SIM card.’ The memory 1310 may allow the UE 1300 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 1310, which may be or comprise a device-readable storage medium.

[0278] The processing circuitry 1302 may be configured to communicate with an access network or other network using the communication interface 1312. The communication interface 1312 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 1322. The communication interface 1312 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 1318 and / or a receiver 1320 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 1318 and receiver 1320 may be coupled to one or more antennas (e.g., the antenna 1322) and may share circuit components, software, or firmware, or alternatively be implemented separately.

[0279] In the illustrated embodiment, communication functions of the communication interface 1312 may include cellular communication, WiFi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, NFC, location-based communication such as the use of the Global Positioning System (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband CDMA (WCDMA), GSM, LTE, NR, UMTS, WiMax, Ethernet, Transmission Control Protocol / Internet Protocol (TCP / IP), Synchronous Optical Networking (SONET), Asynchronous Transfer Mode (ATM), Quick User Datagram Protocol Internet Connection (QUIC), Hypertext Transfer Protocol (HTTP), and so forth.

[0280] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 1312, 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).

[0281] 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.

[0282] A UE, when in the form of an 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 television, 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 head-mounted display for Augmented Reality (AR) or VR, 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 comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 1300 shown in Figure 13.

[0283] 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 3 GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship, an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0284] 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.

[0285] Figure 14 shows a network node 1400 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged, and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment in a telecommunication network. Examples of network nodes include, but are not limited to, APs (e.g., radio APs), Base Stations (BSs) (e.g., radio BSs, Node Bs, evolved Node Bs (eNBs), NR Node Bs (gNBs)), and O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU).

[0286] 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 RRUs 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).

[0287] Other examples of network nodes include multiple Transmission Point (multi-TRP) 5G access nodes, Multi -Standard Radio (MSR) equipment such as MSRBSs, network controllers such as Radio Network Controllers (RNCs) or BS 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).

[0288] The network node 1400 includes processing circuitry 1402, memory 1404, a communication interface 1406, and a power source 1408. The network node 1400 may be composed of multiple physically separate components (e.g., a NodeB component and an 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 1400 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 1400 may be configured to support multiple RATs. In such embodiments, some components may be duplicated (e.g., separate memory 1404 for different RATs) and some components may be reused (e.g., a same antenna 1410 may be shared by different RATs). The network node 1400 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1400, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, Long Range Wide Area Network (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 the network node 1400.

[0289] The processing circuitry 1402 may comprise a combination of one or more of a microprocessor, controller, microcontroller, CPU, DSP, ASIC, FPGA, 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 1400 components, such as the memory 1404, to provide network node 1400 functionality.

[0290] In some embodiments, the processing circuitry 1402 includes a System on a Chip (SOC). In some embodiments, the processing circuitry 1402 includes one or more of Radio Frequency (RF) transceiver circuitry 1412 and baseband processing circuitry 1414. In some embodiments, the RF transceiver circuitry 1412 and the baseband processing circuitry 1414 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of the RF transceiver circuitry 1412 and the baseband processing circuitry 1414 may be on the same chip or set of chips, boards, or units.

[0291] The memory 1404 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid state memory, remotely mounted memory, magnetic media, optical media, RAM, 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 1402. The memory 1404 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 1402 and utilized by the network node 1400. The memory 1404 may be used to store any calculations made by the processing circuitry 1402 and / or any data received via the communication interface 1406. In some embodiments, the processing circuitry 1402 and the memory 1404 are integrated.

[0292] The communication interface 1406 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 1406 comprises port(s) / terminal(s) 1416 to send and receive data, for example to and from a network over a wired connection. The communication interface 1406 also includes radio front-end circuitry 1418 that may be coupled to, or in certain embodiments a part of, the antenna 1410. The radio front-end circuitry 1418 comprises filters 1420 and amplifiers 1422. The radio front-end circuitry 1418 may be connected to the antenna 1410 and the processing circuitry 1402. The radio front-end circuitry 1418 may be configured to condition signals communicated between the antenna 1410 and the processing circuitry 1402. The radio front-end circuitry 1418 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 1418 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of the filters 1420 and / or the amplifiers 1422. The radio signal may then be transmitted via the antenna 1410. Similarly, when receiving data, the antenna 1410 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1418. The digital data may be passed to the processing circuitry 1402. In other embodiments, the communication interface 1406 may comprise different components and / or different combinations of components.

[0293] In certain alternative embodiments, the network node 1400 does not include separate radio front-end circuitry 1418; instead, the processing circuitry 1402 includes radio front-end circuitry and is connected to the antenna 1410. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1412 is part of the communication interface 1406. In still other embodiments, the communication interface 1406 includes the one or more ports or terminals 1416, the radio front-end circuitry 1418, and the RF transceiver circuitry 1412 as part of a radio unit (not shown), and the communication interface 1406 communicates with the baseband processing circuitry 1414, which is part of a digital unit (not shown).

[0294] The antenna 1410 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 1410 may be coupled to the radio front-end circuitry 1418 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 1410 is separate from the network node 1400 and connectable to the network node 1400 through an interface or port.

[0295] The antenna 1410, the communication interface 1406, and / or the processing circuitry 1402 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node 1400. Any information, data, and / or signals may be received from a UE, another network node, and / or any other network equipment. Similarly, the antenna 1410, the communication interface 1406, and / or the processing circuitry 1402 may be configured to perform any transmitting operations described herein as being performed by the network node 1400. Any information, data, and / or signals may be transmitted to a UE, another network node, and / or any other network equipment.

[0296] The power source 1408 provides power to the various components of the network node 1400 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 1408 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1400 with power for performing the functionality described herein. For example, the network node 1400 may be connectable to an external power source (e.g., the power grid or an electricity outlet) via input circuitry or an interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 1408. As a further example, the power source 1408 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0297] Embodiments of the network node 1400 may include additional components beyond those shown in Figure 14 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 1400 may include user interface equipment to allow input of information into the network node 1400 and to allow output of information from the network node 1400. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1400. In some embodiments providing a core network node, such as core network node 108 of FIG. 12, some components, such as the radio front-end circuitry 1418 and the RF transceiver circuitry 1412 may be omitted.

[0298] Figure 15 is a block diagram illustrating a virtualization environment 1500 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices, and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more Virtual Machines (VMs) implemented in one or more virtualization environments 1500 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, a UE, a core network node, or a 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 1500 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an 0-2 interface. Virtualization may facilitate distributed implementations of a network node, a UE, a core network node, or a host.

[0299] Applications 1502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1500 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0300] Hardware 1504 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, an input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1506 (also referred to as hypervisors or Virtual Machine Monitors (VMMs)), provide VMs 1508 A and 1508B (one or more of which may be generally referred to as VMs 1508), and / or perform any of the functions, features, and / or benefits described in relation with some embodiments described herein. The virtualization layer 1506 may present a virtual operating platform that appears like networking hardware to the VMs 1508.

[0301] The VMs 1508 comprise virtual processing, virtual memory, virtual networking, or interface and virtual storage, and may be run by a corresponding virtualization layer 1506. Different embodiments of the instance of a virtual appliance 1502 may be implemented on one or more of VMs 1508, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as Network Function Virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers and customer premise equipment.

[0302] In the context of NFV, a VM 1508 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 1508, and that part of the hardware 1504 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 1508 on top of the hardware 1504 and corresponds to the application 1502. The hardware 1504 may be implemented in a standalone network node with generic or specific components. The hardware 1504 may implement some functions via virtualization. Alternatively, the hardware 1504 may be part of a larger cluster of hardware (e.g., such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 1510, which, among others, oversees lifecycle management of the applications 1502. In some embodiments, the hardware 1504 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 1512 which may alternatively be used for communication between hardware nodes and radio units.

[0303] 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.

[0304] 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.

[0305] Those skilled in the art will recognize improvements and modifications to the embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein.

[0306] Some exemplary embodiments of the present disclosure are as follows:

[0307] Group A Embodiments

[0308] Embodiment 1 : A method performed by a User Equipment, UE, (1400) for triggering a report, the method comprising: performing (602(a)) one or more measurements on at least one cell; performing (602(b)) one or more mobility related time-domain predictions on the at least one cell; triggering (606) transmission of a report comprising the one or more measurements performed on the at least one cell and / or the one or more mobility related time-domain predictions performed on the at least one cell, responsive to a triggering condition(s) being fulfilled wherein the triggering condition(s) has inputs based on both the one or more measurements and the one or more mobility related time-domain predictions; and transmitting the report (e.g., measurement report, prediction report, etc.) responsive to the triggering (606) of the transmission of the report.

[0309] Embodiment 2: The method of embodiment 1, further comprising: determining (604) whether the triggering condition(s) is fulfilled; wherein triggering (606) transmission of the report comprises triggering (606) transmission of the report responsive to determining (604) that the triggering condition(s) is fulfilled.

[0310] Embodiment 3: The method of embodiment 1 or 2, wherein the triggering condition(s) comprises a first triggering condition associated to (e.g., based on) the one or more measurements and a second triggering condition associated to (e.g., based on) the one or more mobility related time-domain predictions.

[0311] Embodiment 4: The method of embodiment 3, wherein the triggering condition(s) is fulfilled when both the first triggering condition and the second triggering condition are fulfilled (e.g., at the same time or contiguously in time). Embodiment 5: The method of any of embodiments 3 or 4, wherein the first condition is an entry condition of an event which takes as input one or more measurements of a serving cell and / or a neighbor cell, and the second condition is an entry condition of an event which takes as input one or more mobility related time-domain predictions of the serving cell and / or the neighbor cell.

[0312] Embodiment 6: The method of any of embodiments 1 to 5, wherein the at least one cell is a neighbor cell, considered as a triggered cell when the triggering condition(s) is fulfilled.

[0313] Embodiment 7: The method of any of embodiments 1 to 5, wherein one of the at least one cell is a neighbor cell and / or a serving cell (e.g. PCell, PSCell, SCell of the MCG, SCell of the SCG) ofthe UE.

[0314] Embodiment 8: The method of any of embodiments 1 to 7, wherein the report is a measurement report and / or a prediction report.

[0315] Embodiment 9: The method of any of embodiments 1 to 8, wherein if the one or more mobility related time-domain predictions do not fulfill accuracy criteria: transmission of the report (e.g., measurement report) is not triggered; or transmission of the report (e.g., measurement report) is triggered, but the one or more mobility related time-domain predictions are not included in the measurement report; or transmission of the report is triggered and an indication is included in the report (e.g., measurement report) indicating that the one or more mobility related time-domain predictions do not satisfy the accuracy criteria.

[0316] Embodiment 10: The method of any of embodiments 1 to 8, further comprising determining whether the one or more mobility related time-domain predictions fulfill one or more (e.g., predefined or configured) accuracy criteria.

[0317] Embodiment 11 : The method of embodiment 10, wherein triggering (606) transmission of the report comprises triggering (606) transmission of the report if the one or more mobility related time-domain predictions fulfill the one or more accuracy criteria and otherwise refraining from triggering transmission of the report.

[0318] Embodiment 12: The method of embodiment 10, wherein the one or more mobility related time-domain predictions are not included in the report if the one or more mobility related timedomain predictions fulfill the one or more accuracy.

[0319] Embodiment 13: The method of embodiment 10, wherein the one or more mobility related time-domain predictions do not fulfill the one or more accuracy criteria and, responsive thereto, the one or more mobility related time-domain predictions are not included in the report.

[0320] Embodiment 14: The method of embodiment 10, wherein the one or more mobility related time-domain predictions do not fulfill the one or more accuracy criteria and, responsive thereto, the report comprises an indication that the one or more mobility related time-domain predictions do not fulfill the one or more accuracy criteria.

[0321] Embodiment 15: The method of any of embodiments 1 to 14, wherein the one or more mobility related time domain predictions include predicted measurements for the at least one cell during a time period between occurrence of the one or more measurements and occurrence of the one or more mobility related time-domain predictions.

[0322] Group B Embodiments

[0323] Embodiment 16: A method performed by a network node, the method comprising: configuring (800(a)) a User Equipment, UE, with a first configuration to perform one or more mobility related measurements; configuring (800(b)) the UE with a second configuration to perform one or more mobility related time-domain predictions; configuring (802) the UE with a triggering condition(s) having as inputs both the one or more mobility related measurements and the one or more mobility related time-domain predictions; and receiving (804), from the UE, one or more messages (e.g., one or more reports) comprising the one or more mobility related measurements and / or the one or more mobility related time-domain predictions, wherein the one or more messages indicate (e.g., implicitly or implicitly) fulfillment of the triggering condition(s).

[0324] Embodiment 17: The method of embodiment 16, wherein the triggering condition(s) comprises a first triggering condition associated to (e.g., based on) the one or more mobility related measurements and a second triggering condition associated to (e.g., based on) the one or more mobility related time-domain predictions.

[0325] Embodiment 18: The method of embodiment 17, wherein the triggering condition(s) is fulfilled when both the first triggering condition and the second triggering condition are fulfilled (e.g., at the same time or contiguously in time).

[0326] Embodiment 19: The method of any of embodiments 17 or 18, wherein the first condition is an entry condition of an event which takes as input one or more measurements of a serving cell and / or a neighbor cell, and the second condition is an entry condition of an event which takes as input one or more mobility related time-domain predictions of the serving cell and / or the neighbor cell.

[0327] Embodiment 20: The method of any of embodiments 16 to 19, wherein the at least one cell is a neighbor cell, considered as a triggered cell when the triggering condition(s) is fulfilled.

[0328] Embodiment 21 : The method of any of embodiments 16 to 19, wherein one of the at least one cell is a neighbor cell and / or a serving cell (e.g. PCell, PSCell, SCell of the MCG, SCell of the SCG) of the UE. Embodiment 22: The method of any of embodiments 16 to 21, wherein the one or more messages comprise a measurement report and / or a prediction report.

[0329] Embodiment 23: The method of any of embodiments 16 to 22, wherein if the one or more mobility related time-domain predictions do not fulfill accuracy criteria: transmission of the one or more messages (e.g., report(s) such as, e.g., measurement report(s)) is(are) not triggered; or transmission of the one or more messages (e.g., report(s) such as, e.g., measurement report(s)) is(are) triggered, but the one or more mobility related time-domain predictions are not included in one or more messages; or transmission of the one or more messages (e.g., report(s) such as, e.g., measurement report(s)) is(are) triggered and an indication is included in the one or more messages indicating that the one or more mobility related time-domain predictions do not satisfy the accuracy criteria.

[0330] Embodiment 24: The method of any of embodiments 16 to 23, wherein the one or more mobility related time domain predictions include predicted measurements for the at least one cell during a time period between occurrence of the one or more measurements and occurrence of the one or more mobility related time-domain predictions.

[0331] Group C Embodiments

[0332] Embodiment 25: A user equipment comprising: processing circuitry configured to perform any of the steps of any of the Group A embodiments; and power supply circuitry configured to supply power to the processing circuitry.

[0333] Embodiment 26: A network node comprising: processing circuitry configured to perform any of the steps of any of the Group B embodiments; power supply circuitry configured to supply power to the processing circuitry.

[0334] Embodiment 27: A user equipment (UE) comprising: an antenna configured to send and receive wireless signals; radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; the processing circuitry being configured to perform any of the steps of any of the Group A embodiments; an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry; an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and a battery connected to the processing circuitry and configured to supply power to the UE.

Claims

CLAIMS1. A method performed by a User Equipment, UE, for triggering a report, the method comprising: performing (602(a)) one or more measurements on at least one cell; performing (602(b)) one or more mobility related time-domain predictions on the at least one cell; triggering (606) transmission of a report comprising the one or more measurements performed on the at least one cell and / or the one or more mobility related time-domain predictions performed on the at least one cell, responsive to both a first condition and a second condition being fulfilled wherein the first condition is associated to the one or more measurements and the second condition is associated to the one or more mobility related time-domain predictions; and transmitting the report responsive to the triggering (606) of the transmission of the report.

2. The method of claim 1, further comprising: determining (604) whether the fist condition and the second condition are fulfilled; wherein triggering (606) transmission of the report comprises triggering (606) transmission of the report responsive to determining (604) that both the first condition and the second condition are fulfilled.

3. The method of claim 1 or 2, wherein both the first condition and the second condition are fulfilled, at the same time or contiguously in time.

4. The method of any of claims 1 to 3, wherein the first condition is an entry condition of an event which takes as input one or more measurements of a serving cell and / or a neighbor cell, and the second condition is an entry condition of an event which takes as input one or more mobility related time-domain predictions of the serving cell and / or the neighbor cell.

5. The method of any of claims 1 to 4, further comprising receiving (600), from a network node, configuration information that configures the UE to perform the one or more measurements and to perform the one or more mobility related time-domain predictions.

6. The method of claim 5, wherein the configuration information comprises:first configuration information that configures the UE to perform the one or more measurements, the first configuration information comprising information that indicates the first condition having at least one input based on the one or more measurements; and second configuration information that configures the UE to perform the one or more mobility related time-domain predictions, the second configuration information comprising information that indicates the second condition having at least one input based on the one or more mobility related time-domain predictions.

7. The method of claim 6, wherein the configuration information is comprised in a Radio Resource Control, RRC, message.

8. The method of claim 6, wherein the first configuration information and the second configuration information are comprised in a same measurement configuration, further wherein including both the first configuration information and the second configuration in the same measurement configuration is an implicit indication that both the first condition and the second condition need to be fulfilled before triggering the report.

9. The method of any of embodiments 6 to 8, wherein at least one parameter in the first configuration information controls triggering of the second condition.

10. The method of any of embodiments 6 to 9, wherein performing (602(b)) the one or more mobility related time-domain predictions on the at least one cell comprises performing (602(b)) the one or more mobility related time-domain predictions on the at least one cell once the first condition is fulfilled.

11. The method of any of claims 6 to 10, wherein the second configuration information further comprises information that configures a period of time during which the one or more mobility related time-domain predictions can be performed.

12. The method of any of claims 1 to 11, wherein the one or more mobility related time-domain predictions comprise any one or more of the following: a radio link failure prediction in a serving cell of the UE; a radio link failure prediction post handover in a neighbor cell of the UE; a handover failure prediction in a neighbor cell of the UE;a time-domain prediction of a measurement quantity for at least one cell; information related to a future time instance in which the UE ma report to assist the network to make mobility decisions.

13. The method of any of claims 1 to 12, wherein the at least one cell is a neighbor cell, considered as a triggered cell when the one or more triggering conditions are fulfilled.

14. The method of any of claims 1 to 12, wherein one of the at least one cell is a neighbor cell and / or a serving cell of the UE.

15. The method of any of claims 1 to 14, wherein the report is a measurement report and / or a prediction report.

16. The method of any of claims 1 to 15, wherein if the one or more mobility related timedomain predictions do not fulfill accuracy criteria: transmission of the report is not triggered; or transmission of the report is triggered, but the one or more mobility related time-domain predictions are not included in the measurement report; or transmission of the report is triggered and an indication is included in the report indicating that the one or more mobility related time-domain predictions do not satisfy the accuracy criteria.

17. The method of any of claims 1 to 15, further comprising determining whether the one or more mobility related time-domain predictions fulfill one or more accuracy criteria.

18. The method of claim 17, wherein triggering (606) transmission of the report comprises triggering (606) transmission of the report if the one or more mobility related time-domain predictions fulfill the one or more accuracy criteria and otherwise refraining from triggering transmission of the report.

19. The method of claim 17, wherein the one or more mobility related time-domain predictions are not included in the report if the one or more mobility related time-domain predictions fulfill the one or more accuracy.

20. The method of claim 17, wherein the one or more mobility related time-domain predictions do not fulfill the one or more accuracy criteria and, responsive thereto, the one or more mobility related time-domain predictions are not included in the report.

21. The method of claim 17, wherein the one or more mobility related time-domain predictions do not fulfill the one or more accuracy criteria and, responsive thereto, the report comprises an indication that the one or more mobility related time-domain predictions do not fulfill the one or more accuracy criteria.

22. The method of any of claims 1 to 21, wherein the one or more mobility related time domain predictions include predicted measurements for the at least one cell during a time period between occurrence of the one or more measurements and occurrence of the one or more mobility related time-domain predictions.

23. A User Equipment, UE, (1300) adapted to : perform (602(a)) one or more measurements on at least one cell; perform (602(b)) one or more mobility related time-domain predictions on the at least one cell; trigger (606) transmission of a report comprising the one or more measurements performed on the at least one cell and / or the one or more mobility related time-domain predictions performed on the at least one cell, responsive to both a first condition and a second condition being fulfilled wherein the first condition is associated to the one or more measurements and the second condition is associated to the one or more mobility related time-domain predictions; and transmit the report responsive to the triggering (606) of the transmission of the report.

24. The UE (1300) of claim 23, further adapted to perform the method of any of claims 2 to 23.

25. A User Equipment, UE, (1400) comprising: a communication interface (1312) comprising a transmitter (1318) and a receiver (1320); and processing circuitry (1302) associated with the communication interface (1312), the processing circuitry (1312) configured to cause the UE (1300) to: perform (602(a)) one or more measurements on at least one cell;perform (602(b)) one or more mobility related time-domain predictions on the at least one cell; trigger (606) transmission of a report comprising the one or more measurements performed on the at least one cell and / or the one or more mobility related time-domain predictions performed on the at least one cell, responsive to both a first condition and a second condition being fulfilled wherein the first condition is associated to the one or more measurements and the second condition is associated to the one or more mobility related time-domain predictions; and transmit the report responsive to the triggering (606) of the transmission of the report.

26. The UE (1300) of claim 25, wherein the processing circuitry (1302) is further configured to cause the UE (1300) to perform the method of any of claims 2 to 23.

27. A method performed by a network node for a cellular communications system, the method comprising: configuring (800(a)) a User Equipment, UE, with a first configuration to perform one or more mobility related measurements; configuring (800(b)) the UE with a second configuration to perform one or more mobility related time-domain predictions; configuring (802) the UE with a first condition to the one or more measurements and a second condition associated to the one or more mobility related time-domain predictions; and receiving (804), from the UE, one or more messages comprising the one or more mobility related measurements and / or the one or more mobility related time-domain predictions, wherein the one or more messages indicate fulfillment of the both the first condition and the second condition.

28. The method of claim 27, wherein the one or more triggering conditions are fulfilled when both the first triggering condition and the second triggering condition are fulfilled at the same time or contiguously in time.

29. The method of claim 27 or 28, wherein the first condition is an entry condition of an event which takes as input one or more measurements of a serving cell and / or a neighbor cell, and the second condition is an entry condition of an event which takes as input one or more mobility related time-domain predictions of the serving cell and / or the neighbor cell.

30. The method of any of claims 27 to 29, wherein the at least one cell is a neighbor cell, considered as a triggered cell when both the first condition and the second condition are fulfilled.

31. The method of any of claims 27 to 29, wherein one of the at least one cell is a neighbor cell and / or a serving cell of the UE.

32. The method of any of claims 27 to 31, wherein the one or more messages comprise a measurement report and / or a prediction report.

33. The method of any of claims 27 to 32, wherein if the one or more mobility related timedomain predictions do not fulfill accuracy criteria: transmission of the one or more messages are not triggered; or transmission of the one or more messages are triggered, but the one or more mobility related time-domain predictions are not included in one or more messages; or transmission of the one or more messages are triggered and an indication is included in the one or more messages indicating that the one or more mobility related time-domain predictions do not satisfy the accuracy criteria.

34. The method of any of claims 27 to 33, wherein the one or more mobility related time domain predictions include predicted measurements for the at least one cell during a time period between occurrence of the one or more measurements and occurrence of the one or more mobility related time-domain predictions.

35. A network node for a cellular communications system, the network node adapted to: configure (800(a)) a User Equipment, UE, with a first configuration to perform one or more mobility related measurements; configure (800(b)) the UE with a second configuration to perform one or more mobility related time-domain predictions; configure (802) the UE with a first condition to the one or more measurements and a second condition associated to the one or more mobility related time-domain predictions; and receive (804), from the UE, one or more messages comprising the one or more mobility related measurements and / or the one or more mobility related time-domain predictions, wherein the one or more messages indicate fulfillment of both the first condition and the second condition.

36. The network node of claim 35, further adapted to perform the method of any of claims 28 to 34.

37. A network node (1400) for a cellular communications system, the network node comprising processing circuitry (1402) configured to cause the network node (1400) to: configure (800(a)) a User Equipment, UE, with a first configuration to perform one or more mobility related measurements; configure (800(b)) the UE with a second configuration to perform one or more mobility related time-domain predictions; configure (802) the UE with a first condition to the one or more measurements and a second condition associated to the one or more mobility related time-domain predictions; and receive (804), from the UE, one or more messages comprising the one or more mobility related measurements and / or the one or more mobility related time-domain predictions, wherein the one or more messages indicate fulfillment of both the first condition and the second condition.

38. The network node (1400) of claim 35, wherein the processing circuitry (1402) is further configured to cause the network node (1400) to perform the method of any of claims 28 to 34.

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