Configuration and reporting of radio link failure (RLF) predictions

Abstract

Methods, apparatuses, and systems for handling predictions of radio link failure, RLF, in a wireless network. An example method, as carried out by a user equipment, UE, comprises the steps of estimating or predicting (120) a likelihood of the UE experiencing RLF, for one or more future time instances or intervals, and reporting (140) the estimate or prediction to the wireless network. The method may further comprise logging (130) the estimate or prediction, before reporting to the wireless network. The wireless network may use the estimate or prediction to adjust one or more parameters in the wireless network, to reduce the possibility of future RLF.

Description

[0001] CONFIGURATION AND REPORTING OF RADIO LINK FAILURE (RLF) PREDICTIONS

[0002] TECHNICAL FIELD

[0003] The present disclosure is generally related to wireless communications and is more particularly related to improved techniques for handling and / or preventing radio link failure (RLF) in communications networks.

[0004] BACKGROUND

[0005] Mobility is an area of features in wireless communications relating to how the terminal, such as a user equipment (UE) in a wireless communication system specified by the 3rdGeneration Partnership Project (3GPP), changes which radio resources it is associated with. One common mobility procedure is the handover procedure in 3GPP systems where the UE is handed over from one cell to another when the UE receives a command (handover command) indicating that the UE shall do so. But there are other similar features as well, such as a conditional handover where the UE receives the handover command before it should be executed, and where the UE receives, with the command, conditions that, when fulfilled, trigger the UE to execute the command. Another example is L1 / L2 triggered mobility (LTM), where the UE also receives a mobility command beforehand but doesn’t execute it until the network sends a MAC control element.

[0006] The concept of mobility can also cover cases when the UE starts and / or stops using additional resources, typically under the control of the network. The UE may for example be able to connect to multiple cells (such as in Carrier Aggregation) or nodes (such as in Dual Connectivity).

[0007] One of the functions provided by the Radio Resource Control (RRC) protocol layer in the UE is detection of and recovery from radio link failures (RLFs) over the air interface (referred to as the Uu interface in 3GPP specifications), while the UE is in RRC CONNECTED mode. The UE monitors the radio link towards the gNB by measuring, in the downlink, the reference signals (e.g. SSB and / or CSI-RS) in the active bandwidth part (BWP). Which exact signal(s) the UE measures depends on the configuration and the deployment scenario.

[0008] According to 3GPP specifications, the UE declares an RLF in the following situations: Expiry of a timer which is started when detecting issues at the physical layer. o For example, timer T310 is started when detecting a certain configured amount of “out-of-sync” indications from the physical layer. When T310 expires, an RLF is declared. If UE is back in sync (as reported by physical layer), the timer T310 is stopped.

[0009] Expiry of a timer started upon triggering a measurement report, when another timer related to radio problems is running. o E.g., timer T312 which is started when triggering a measurement report when T310 is running. Random access problem or failure. RLC failure, e.g. maximum number of retransmissions is reached. Uplink listen-before-talk (LBT) failure.

[0010] When an RLF is detected, the UE should, in principle, try to stay in RRC CONNECTED mode. In a normal case where RLF is detected in the serving cell or during DAPS handover, the UE selects a suitable cell and performs an RRC re-establishment procedure (see, e.g., [2]). In case of a CHO or LTM if an RLF is declared in the source cell, the UE selects a suitable cell and if the cell is a CHO or LTM candidate, that cell is selected, otherwise RRC re-establishment is performed. In the case a suitable cell is not found within a certain time after declaring RLF, the UE enters RRC IDLE mode.

[0011] After detection of the RLF, UE stores radio link failure information in UE variable VarRLF-Report as described in 3GPP TS 38.331 (version 17.6.0.) clause 5.3.10.5 [2], In this failure information, UE may store, for example (this list is not exhaustive, see [2]):

[0012] The cause of the failure, e.g. T310 or T312 expiry, random access problem, etc. This is specified in 3GPP documentation as follows:

[0013] - begin 3GPP standard excerpt - rlf-Cause-rl 6 ENUMERATED {t310-Expiry, randomAccessProblem, rlc-MaxNumRetx, beamFailureRecovery Failure, IbtFailure-rl 6, bh-rlfRecoveryFailure, t312-expiry-r!7, sparel}, -end 3 GPP standard excerpt - Type of the previous handover, ID of the previous cell. Previous measurement results. Time since RLF.

[0014] When requested, the UE provides the information stored in VarRLF-Report to the network (gNB).

[0015] Artificial Intelligence / Machine Learning (AI / ML) for Mobility Rel-19

[0016] A study in 3GPP regarding “AI / ML for PHY” was carried out in Rel-18. It was limited to lower layer features, such as Beam Management, which is sometimes referred to as intra-cell mobility. Other features, such as L3 handovers and RRC measurements predictions, have not been part of the Rel-18.

[0017] Hence, a Rel-19 Study Item to study the usage of Al / ML for L3 Mobility and / or RRM measurements has been approved in RAN plenary meeting #102. The objectives of the study item include [1]: • 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

[0018] ■ Cell-level measurement prediction including intra and inter-frequency (UE sided and NW sided model)

[0019] • 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)

[0020] • Study the need / benefits of any other UE assistance information for the network side model.

[0021] • The evaluation of the AI / ML-aided mobility benefits should consider HO performance KPIs and complexity tradeoffs.

[0022] SUMMARY

[0023] With the introduction of artificial intelligence (IE) / machine learning (ML) tools in the UE, a UE may use these or other models or tools to predict that a radio link failure (RLF) may happen. This may be referred to as a UE-side model for RLF prediction(s) / inference. However, there are no standardized or specified procedures for a UE to perform these predictions, much less to put them to practical use. This means that even if the UE could predict with a large certainty that an RLF is about to happen, there are currently no means to provide this information to a base station or to the network side more generally, which could in the best case use this information to try to avoid the RLF proactively and which could at least use the information to configure the network and the UEs to better avoid failure situations and thus improve network capacity.

[0024] Current mobility procedures rely on configured measurements and actual measurements performed by the UE, which the UE reports to the network, and on UE -provided information regarding events that have already occurred, such as RLF reports providing information regarding the reasons for and statistics of earlier radio link failure(s). If the network can be made aware of possible failures in the future, it could make decisions to impact the network performance and avoid for example RLFs from happening, thus avoiding additional signaling and delays when the UE is trying to recover from such failure(s).

[0025] Embodiments of the techniques, apparatuses, and systems described herein address these problems. According to some of these techniques, the network configures the UE to report RLF predictions. In this configuration the network may include, for example, a threshold for a confidence score or other value indicating how certain the UE (or the UE model) is or the estimated probability of the RLF event happening (in the future). The UE then reports when it predicts RLF will happen with higher probability than threshold indicated in the configuration. The UE may log the RLF predictions, possibly together with the confidence level, and report the result(s) to the network. The message may contain a list of RLF predictions, where each entry of the list corresponds to probability of confidence level of RLF happening during a certain time period or at a certain time occasion. In some embodiments or instances, the UE may additionally log and report the cause for the predicted RLF.

[0026] An example method, according to some embodiments of the presently disclosed techniques, apparatuses, and systems described herein, is a method in a UE for handling predictions of RLF in a wireless network. According to this method, the UE estimates or predicts a likelihood of the UE experiencing RLF, for one or more future time instances or intervals. The UE then reports the estimate or prediction to the wireless network. In some embodiments or instances, the UE logs the estimate or prediction, before reporting to the wireless network.

[0027] A corresponding example method is carried out in a network node, and comprises the step of receiving, from a UE, an estimate or prediction of a likelihood of the UE experiencing RLF, for one or more future time instances or intervals. The network node may forward the estimate or prediction to another network node, in some instances or embodiments, and / or may adjust one or more network parameter based on the estimate or prediction, in some other instances or embodiments.

[0028] Other embodiments described herein include a UE apparatus configured to carry out one or more of the methods described herein, as well as network node apparatuses configured to carry out corresponding methods.

[0029] Using the approaches described herein, a UE may report RLF predictions and the reasons to the network, and the network can take action based on the information it receives from the UE. For example, to avoid a possible upcoming RLF, to adjust and short and long-term network resource usage configuration through configuration. This may result in a reduction in RLFs and improved network operation and efficiency overall.

[0030] BRIEF DESCRIPTION OF THE FIGURES

[0031] Figure 1 shows an exemplary method (e.g., procedure) for a UE, according to various embodiments of the present disclosure.

[0032] Figure 2 shows an exemplary method (e.g., procedure) for a network node, according to various embodiments of the present disclosure.

[0033] Figure 3 shows a communication system according to various embodiments of the present disclosure.

[0034] Figure 4 shows a UE according to various embodiments of the present disclosure. Figure 5 shows a network node according to various embodiments of the present disclosure.

[0035] Figure 6 shows a host computing system according to various embodiments of the present disclosure.

[0036] Figure 7 is a block diagram of a virtualization environment in which functions implemented by some embodiments of the present disclosure may be virtualized.

[0037] Figure 8 illustrates communication between a host computing system, a network node, and a UE via multiple connections, at least one of which is wireless, according to various embodiments of the present disclosure.

[0038] DETAILED DESCRIPTION

[0039] As discussed above, current mobility procedures rely on configured measurements and measurements performed by the UE, which the UE reports to the network, and on UE-provided information regarding events that have already occurred, such as RLF reports providing information regarding the reasons for and statistics of earlier radio link failure(s). If the network can be made aware of possible failures in the future, it could make decisions to impact the network performance and avoid for example RLFs from happening, thus avoiding additional signaling and delays when the UE is trying to recover from such failure(s).

[0040] Described herein is a framework for handling predictions of RLF, as predicted by a UE using tools based on Artificial Intelligence / Machine Learning (AI / ML) and methods for implementing this framework. According to possible implementations of this approach, a UE receives a configuration from the network which configures the UE to predict (and report) radio link failures and reasons for the failures according to a confidence threshold. The UE then performs the RLF predictions, e.g., based on a UE-sided AI / ML model, according to the received configuration and / or trigger, and logs and possibly stores the predictions for which the confidence level is higher than the threshold.

[0041] This logging and / or storing may include logging and storing the reason for the predicted failure and the times or time occasions of predicted failures. The UE may then report the predictions (and the information) where the confidence level in the prediction is higher than the threshold.

[0042] Thus, an example method, details and variants of which are described in much greater detail below, is carried out by a User Equipment (UE), and comprises logging information pertaining to prediction of a radio link failure and reporting the logged information to the network node, wherein the information may comprise one or more of:

[0043] • Prediction of the radio link failure

[0044] • Probability of the radio link failure

[0045] • Confidence level of the UE AI / ML model on the prediction • Predicted radio link failure cause(s)

[0046] • Probability of the radio link failure based on specific cause(s)

[0047] In some embodiments, the logging of the information pertaining to the prediction of the radio link failure is based on a configuration received from a network node, where the configuration might comprise one or more of the following, for example: o Report configuration o Periodic reporting of the predictions o Event based reporting of the prediction o Combination of event based and periodic reporting

[0048] • Prediction object o The frequency and / or cell identity at which the prediction should be done o Indicating the PCell, PSCell, or MCG, SCG toward which the prediction should be done.

[0049] Thus, for example, the UE may perform prediction on the object (one or more frequency or one or more cells) configured by the network node, and / or reporting a prediction on a prediction object may be performed based on a report configuration, where the UE reports the predicted information pertained to the radio link failure, in various embodiments or instances:

[0050] • periodically, e.g., based on a number of reports and a report interval configured in the report configuration,

[0051] • Event based e.g., based on the fulfillment of one or more event(s) configured in the report configuration, or

[0052] • Combination of event based and periodic reporting, e.g., reporting for a certain number of times if an event / condition is fulfilled.

[0053] Configuring RLF predictions and / or logging / reporting of RLF predictions

[0054] According to various embodiments of the techniques described herein, a UE receives a configuration, from the wireless network, to configure the UE to perform RLF predictions, for example based on an UE-sided AI / ML model, to log the predictions and to report the predictions to the network.

[0055] In an example implementation, the UE receives a configuration message from the network either in dedicated signaling, such as in the information element(s) in RRCSetup, RRCResume or RRCReconfiguration messages, or in broadcast system information such as in SIB1 or in another system information block (SIB) included in the Systeminformation message. The configuration message may contain one or more of the following elements, in various embodiments:

[0056] One or more parameters indicating whether the UE is expected to perform prediction(s) of whether radio link failures (RLF) will happen in future. One or more parameters indicating whether the UE is expected to log and / or report the reason of the predicted RLFs.

[0057] Threshold(s) indicating the confidence score or confidence level for the predictions, that is, if the UE certainty Y% about an RLF is higher that the configured threshold X%, the UE logs and and / or reports the prediction to the network. o The confidence level can, for example, be indicated as a number between 0 and 1 indicating how certain the UE is an RLF would occur. 0 means it is not certain at all and 1 indicates that according to the UE prediction an RLF is certain to happen. Such number can also be expressed as a percentage, e.g., a confidence level / score being Y%. This can be also interpreted as the probability estimate made by the UE for an RLF event happening.

[0058] One or more parameters indicating whether the UE is expected to report RLF for a single time instant or for multiple time instants in the future. o These parameters may include, for example, a list of time instants, where the UE is expected to report a list of its predictions of the confidence levels of an RLF happening at various time instants.

[0059] ■ For example, the UE may be configured to report probability for an RLF event at 1, 2, 3 and 4 seconds after receiving the configuration, or after some other configured time reference. In this example, the UE reports a list with four elements corresponding to the confidence level or probability of an RLF at 1, 2, 3 or 4 seconds in the future, o In an alternative, the network may configure a count N, and the UE reports up to N predictions of the probability of an RLF event in the future, for example, the UE would report a list denoting different times in the future together with the confidence level for an RLF event at that particular time.

[0060] One or more parameters indicating when the UE is expected to perform RLF predictions. For example, the network may want to configure certain time periods deltaT when the UE is expected to perform and report the RLF predictions and other time periods when such RLF predictions are not wanted to be reported. o In one example, deltaT is indicated as an absolute start and end point in time and the UE is expected to perform the measurements between the indicated start and end points, but not otherwise. o In another example, deltaT refers to a time duration or a time window which can be combined with a time reference and / or a time offset, see the next example. o In one example these parameters may include a time reference value tO indicated with some time format, or alternatively an offset value tOff to a known time reference tO. The time reference tO can be for example the time of receiving the configuration. The UE is expected to perform the predictions at the time indicated by the reference tO, or at the time indicated by a reference and the offset, i.e. at time tO+tOff If deltaT is configured, the UE can be expected to perform predictions starting at the reference time tO, or at tO+tOff for the duration indicated by deltaT.

[0061] One or more parameters indicating whether the UE is expected to send the report(s) containing the prediction immediately after the prediction, at a certain time, or periodically, or only when requested (triggered) by the network. o One benefit of this and the previous configuration parameter(s) is to control the frequency at which the UEs performs predictions and reports the predictions. By configuring the UE to report in some time periods and not in other time periods, the possible impact on UE power consumption and the amount of signaling can be controlled and, for example, minimized. o In one example, the possible triggers are explicitly configured. Examples of such triggers are discussed below.

[0062] One or more parameters on whether the UE is expected to send the report(s) containing the prediction on specific prediction object such as serving frequency or serving cell. o In some embodiments or instances, the one or more prediction object(s) indicate to the UE to perform the prediction on a certain configured frequency, e.g., on the serving frequency or neighboring frequencies. o In some embodiments or instances, the one or more prediction object(s) may indicate to the UE to perform the prediction on certain cells, e.g., on the Primary Cell (PCell) or on the Primary Secondary Cell (PSCell) or on the Secondary Cell SCell or any other neighboring cells. o In other embodiments or instances, the one or more prediction object(s) may indicate to the UE to perform the prediction on certain cell groups, e.g., on the master cell group or secondary cell group or on both of the master and secondary cell groups.

[0063] One or more parameters indicating an area which the UE is expected to consider when making predictions. o In one option, the network may request and configure the UE to only predict RLF if the UE is in a specific area, such as certain cells, tracking area, routing area, or PLMN. o In one option, the network may request and configure the UE to only predict and report RLF if the UE is in a specific area and other configured parameters are fulfilled, e.g., when the UE is in a certain cell and the prediction of RLF is above a threshold.

[0064] One or more parameters indicating a service type or an application which the UE is expected to consider when making predictions. o In one option, the network may request and configure the UE to only predict RLF if the UE starts a specific service, such as a streaming service. o In one option, the network may request and configure the UE to only predict and report RLF if the UE starts a certain application.

[0065] One or more parameters indicating a configuration or feature which the UE is expected to consider when making predictions. o In one example, the network may request and configure the UE to only predict RLF if the UE has a certain other configuration, e.g. a conditional handover configuration or an NR-DC configuration. o In one option, the network may request and configure the UE to compare predictions of RLF depending on if certain features are active. In one example, the UE may compare and report the prediction of RLF if conditional handover is configured compared with if conditional handover is not configured.

[0066] One or more parameters indicating whether the UE is expected to send the report(s) containing the prediction upon fulfillment of one or more events. o In some embodiments or instances, the one or more event(s) can be a probability of radio link failure, e.g., if the UE predicts that radio link failure occurs with a probability above a value indicated in the received configuration, the UE reports the RLF prediction to the network. o In some embodiments or instances, the one or more event(s) can be associated to the confidence level / score of the UE in the predicted RLF, e.g., if the UE predicts that radio link failure is likely to occur with a confidence level / score above a value indicated in the received configuration, it reports the RLF prediction to the network. o In some embodiments or instances, the one or more event(s) can be associated to the RLF related timer and counters e.g., an event associated with the T310 / T312 timers and / or N310 / N311 counters configured at the UE e.g., The UE should send the RLF prediction to the network if the T310 / T312 timers are running at the UE. o In some embodiments or instances, the one or more event(s) can be associated to radio link quality, e.g., an event associated with the RSRP / RSRQ / SINR / RSSI measurements e.g., the UE should send the RLF prediction to the network if the measured RSRP / RSRQ / SINR / RSSI is below / above certain configured threshold. o In one option, the existing measurements, e.g. A3 and A5 can be reused to define when the UE should send the RLF prediction. The UE may, for example, be configured to report RLF prediction only when a certain event is fulfilled.

[0067] One or more parameters indicating which different RLF reasons / causes the UE is expected to consider when making predictions. o As an example, the network may request and configure the UE to only predict RLF happening due to a specific timer expiring, or due to random access error, etc. o This configuration can be, for example, a bit map mapping to the possible RLF reasons as indicated by rlf-Cause field in TS 38.331 (e.g. existing rlf-Cause-rl6). Each bit in the bit map indicates whether a specific reason is to be considered by the UE, and in this way any combination of the possible reasons can be configured.

[0068] ■ Alternatively, the configuration may be just a single bit indicating either all of the reasons (e.g. ‘1’) or then ‘0’ to indicate the reason does not need to be considered and further logged and / or reported at all.

[0069] One or more parameters requesting / configuring the UE to include the RLF cause for the predicted RLF, e.g., what will be the cause of the RLF if it happens in the future. o This can be an optional step and the UE is not limited to include the RLF cause for the predicted RLF only based on this configuration. In some embodiments, the UE may always include the RLF cause for the predicted RLF.

[0070] One or more parameters requesting / configuring the UE to include additional information and measurements beside the RLF prediction. o Some example information can be one or more of RLF timer and constant information such as the value of the T310, T311, T312 timers and N310 and N311 counters as well as the number of RLC retransmission counter. o In some embodiments, the parameters indicate to the UE to include such information at the time of predicting the RLF (e.g., at inference time) o Such parameters in this embodiment can be optional and in an example implementation the UE is not limited to include the RLF timer and constants values for the predicted RLF only based on the parameters in this configuration i.e., in some embodiments the UE may always include the value of the RLF timer and constants for the predicted RLF. o In one alternative, the parameters indicate to the UE to include the number of RLC re-transmission at the time of inference. o In another alternative, the parameters indicate to the UE to include radio link quality measurements such as RSRP / RSRQ / SINR / RSSI values for the serving and neighboring frequencies at the time of inference at the UE.

[0071] One or more parameters indicating how far into the future the UE shall predict RLF. For example, the network may indicate that the UE shall predict 3 seconds into the future. This means that the UE may only perform predictions up to 3 seconds into the future, and / or that the UE would only report RLFs that a predicted to happen up to 3 seconds in the future. If an RLF is expected to happen further into the future than this time, the UE will not report it, and / or not predict it. Performing the RLF predictions

[0072] According to various embodiments, the UE performs predictions of an RLF event happening which may be based on a UE -based AI / ML model according to the configuration it has received from the network. Note that the details of the AI / ML model are unimportant to an understanding of the presently described techniques, which are concerned with providing mechanisms and a framework for reporting RLF event predictions to the network. Generally speaking, however, the AI / ML will operate so as to take, as inputs, measurements and operating conditions of the UE, e.g., as a time series, and apply those measurements and operating conditions to a model that, based on previously learned correlations and relationships, predicts or estimates RLF. Note that in various embodiments the model may be refined, or “trained,” at least in part by the UE itself, based on the UE’s own experiences, or it may be pre-trained, e.g., using a broader set of collected real-world data, before being provided to the UE, e.g., at manufacturing time or via a later download.

[0073] The times or time periods when the UE does the predictions can be configured (as explained above), or they can be up to the UE or up to the UE-based AI / ML model (and the configuration included in the model, if one exists).

[0074] In some embodiments, the UE performs the RLF predictions after each of one or more triggers, which may be specified in a configuration provided to the UE by the network and / or in the model itself. The trigger can be any one or more of the following, in various embodiments or instances:

[0075] • A command received from the network, such as a configuration command (e.g., RRCSetup, RRCResume, RRCReconfiguration RRC messages) or a UE information request, such as UEInformationResponse message.

[0076] • A command received from the network, where the command is a new RRC message defined for this purpose.

[0077] • A command received from the network, where the command is a MAC information element defined for this purpose.

[0078] Note that any of the above commands m may optionally contain configuration information, e.g., the confidence thresholds X% for logging and / or reporting, as were described above.

[0079] Other possible triggers for performing the predicting are:

[0080] • The start of a procedure according to a specification, for example, start of a random access procedure according to 3GPP TS 38.321.

[0081] • A timer, where predictions are performed after the timer expires.

[0082] • Passing or reaching a configured time or time period, or entering a configured time period / window.

[0083] • When the UE is on a certain frequency, certain cell or certain cell group. • When the UE is in a specific area, e.g., in certain cells, tracking area or PLMN.

[0084] • When the prediction of RLF is higher than a threshold in a specific area.

[0085] • When a certain application starts or a certain type of service, e.g. streaming service.

[0086] Logging and reporting of the RLF predictions

[0087] In some embodiments or instances, a UE performs RLF prediction based on a received configuration and sends a report according to the received configuration to the network.

[0088] In other embodiments or instances, a UE performs RLF prediction without any network configuration and reports to the network either immediately, e.g., upon satisfaction of a pre-determined criterion, or upon receiving a network request.

[0089] In some embodiments or instances, for example, the UE may include the RLF prediction information in an immediately initiated procedure such as RRM measurements framework (e.g., measResultsNR) or the UE assistance information framework (e.g. UEAssistancelnformation message), to immediately transmit the RLF prediction to the network upon collecting the prediction and the associated information such as the UE confidence level / score / probability as well as the RLF timers and constants value at the time of prediction.

[0090] In other embodiments or instances, the UE may report the RLF prediction information to the network upon network request. In a non-limiting example, the UE may use a UE information request / response procedure to transmit the RLF prediction and the associated information such as the UE confidence level / score / probability as well as the RLF timers and constants value at the time of prediction. The UE may need to indicate the availability of such predictions to the network to enable the network to fetch the RLF prediction information using the UE information request / response procedure.

[0091] In some embodiments or instances, the UE may report an RLF failure prediction to the network in the event that the confidence level or score of the prediction is more than a specified value, e.g., X%. This value may be provided to the UE in a configuration, as discussed above, or it may be built in to the implementation.

[0092] A UE may predict it will experience / detect RLF at some time instant in the future, and the UE may also report this time instant, in some embodiments or instances (e.g. RLF is expected in 4 seconds with confidence level Y% (which is more than the threshold X%). In some embodiments or instances, the UE may predict the confidence level or probability of the UE experiencing RLF at multiple time instances in the future. For example, the UE may predict the confidence level for RLF happening at times x, y and z in the future with confidence X%, Y%, Z% (where the confidence levels are higher than a possibly configured threshold). The UE may then report a list of these results to the network. The specific number of instances, the time between them, and / or other parameters governing this behavior may be provided to the UE by the network, via configuration, in some embodiments, or preprogrammed, in others.

[0093] In some embodiments or instances, the UE provides additional information beside the RLF prediction, where such additional information may be one or more of RLF timers and constant values such as T310 / T311 / T312 timers as well as the value of the N310 and N311 at the time of prediction. The additional measurements and information may also include the number of uplink / downlink RLC retransmission counter value. In an embodiment the UE includes the RLF timer and constant values at the time of predicting the RLF (the value of the RLF timers and constants at AI / ML inference phase) in the report beside the RLF prediction information (prediction and the confidence level / score / probability). Again, parameters governing this behavior may be provided to the UE by the network, via configuration, in some embodiments, or pre-programmed, in others.

[0094] In some embodiments, the UE provides such information based on the received configuration, i.e., the UE provides the RLF timers and constant values as well as the number of RLC re-transmission at the time of inference if the network requests the UE to provide such information to the network.

[0095] Additional information besides the RLF prediction may also comprise the radio link quality measurements such as RSRP / RSRQ / SINR / RSSI values for the serving and neighboring frequencies at the time of inference at the UE. The measurements can be provided in different form of L3 filtered measurements or in the form of Layer 1 unfiltered measurements.

[0096] In one alternative the UE logs and stores the RLF predictions and related information internally, for example in a UE variable. The UE may send the report including the predictions and related information later according to configuration or a triggering condition. These conditions may be any of the triggering conditions described above for reporting - the triggers for performing predictions may be the same as the triggers for logging and / or reporting, or they may differ, in various embodiments or instances.

[0097] In some embodiments or instances, the UE may log the RLF predictions in a UE variable VarRLF- Report.

[0098] In some embodiments or instances, the UE logs the reason or cause for the predicted RLF according to the existing reasons specified in the RRC specification [2] .

[0099] • In some embodiments or instances, the possible causes the UE predicts are configured by the network, i.e. the UE is expected to only report possible RLF and the confidence level of some specific RLF causes.

[0100] • In an alternative, a new RLF reason is defined, e.g., using the sparel value in the existing rlf- cause information element. • In an alternative, a new reason is defined and the reason does not indicate the possible cause for the RLF but indicates that the RLF is a predicted RLF, e.g., UE indicates predictedRLF in rlf- Cause.

[0101] • In an alternative, a new field is defined in RRC ASN.1, which the UE uses to report the predicted RLF causes (optionally / alternatively also configured using the same field).

[0102] In some embodiments the UE logs, in the RLF report message, whether the RLF is a predicted one instead of an earlier, already declared and possibly resolved RLF.

[0103] In some embodiments or instances, the UE sends the reports containing the RLF predictions at a certain configured time occasion, or time period. In one example, the UE is configured to send the reports periodically.

[0104] In some embodiments or instances, the UE sends the reports containing the RLF predictions when it receives a request from the network to do so. For example, the network sends a UEInformationRequest message and the UE is then expected to perform RLF prediction according to configuration (either configured earlier, or within the received UEInformationRequest), and report the results in information contained in UEInformationResponse message.

[0105] UE capability / applicability information signalling on RLF prediction

[0106] In some embodiments, the UE may indicate to the network (e.g., to the serving node or to the core network over the non-access stratum, NAS) whether it is capable of providing a report of predicted RLF or estimated likelihood of RLF. In a variant, the UE may indicate how far in the future it can predict the RLF and what would be the accuracy of such prediction.

[0107] In some embodiments or instances, the UE indicates to the network the applicability of the RLF prediction. The applicability of the RLF prediction model can be dependent on the current setting, radio conditions and network configuration, operating frequencies, etc.

[0108] In some embodiments or instances, the UE indicates to the network node (e.g., serving node) whenever the accuracy of its model goes below or above a certain threshold. Such accuracy thresholds can be configured by the network or can be hard-coded at the UE, in various embodiments or instances.

[0109] In view of the techniques described above, it will be appreciated that the process flow diagram shown in Figure 1 illustrates an example method for handling predictions of RLF in a wireless network, as might be carried out by a UE operating in the wireless network, according to many of these techniques. The method illustrated here is intended to generalize and therefore encompass many, if not all, of the processes described above from the perspective of the UE. Therefore, where terminology used to describe the method shown in Figure 1 differs in some respects from that used above, the former should be understood, where reasonably possible, to be synonymous with and / or encompass the latter.

[0110] The method illustrated in Figure 1 includes, as shown at block 120, estimating or predicting a likelihood of the UE experiencing RLF, for one or more future time instances or intervals. Note that the phrasing “estimating or predicting a likelihood of the UE experiencing RLF” is intended to convey that the UE may formulate and log binary predictions (e.g., “yes, RLF is expected to occur” versus, “no, RLF is not expected to occur”), e.g., using some threshold value for likelihood, or may formulate numerical or categorical estimations of the likelihood of RLF (e.g., “the probability of RLF is 55” or “the probability of RLF is ‘High’ on a scale of ‘Low,’ ‘Medium,’ and ‘High’”), or some combination of these. Of course, a binary or categorical estimate or prediction may be preceded by a numerical one, in some cases, in which cases the numerical value may or may not be recorded for future use, or a prediction / estimation model may directly output a binary or categorical estimate or prediction.

[0111] Skipping ahead to block 140, the method may further comprise reporting the estimate or prediction to the wireless network. As detailed above, various additional information regarding the estimate or prediction may be reported as well. In some embodiments or instances, this reporting may be directly coupled to the estimating / predicting step and / or to a logging step, such that every prediction or estimate is reported. In others, as was described above, separate triggers and / or configurations may apply to each of these steps, e.g., such that not necessarily every logged prediction or estimate is reported, or such that only some predictions or estimates are logged, but every prediction or estimate is reported, etc.

[0112] As shown at block 130, the method further comprises logging the estimate or prediction, for subsequent reporting to the wireless network. Again, as detailed above, various additional information regarding the estimate or prediction may be logged as well.

[0113] In some embodiments or instances, the reporting may be in response to receiving a request, from the wireless network, for a report comprising the estimate or prediction. This request is shown at block 136 in Figure 1. In some embodiments or instances, this is preceded by the UE sending, to the wireless network, an indication that the report comprising the estimate or prediction is available. This is shown at block 133.

[0114] In some embodiments or instances, the reporting is performed in accordance with a reporting configuration received by the UE from the wireless network. The receiving of this configuration is shown at block 110 of Figure 1. This reporting configuration may specify, for example, one or both of periodic reporting of RLF estimates or predictions, for at least a specific interval, and reporting of RLF estimates or predictions in response to one or more specified report triggering events.

[0115] Similarly, the estimating or predicting is performed according to a prediction configuration received from the wireless network. This prediction configuration may be received with or independently of any reporting configuration, in various combinations or instances. The prediction configuration may specify one or more of any of the following, in various examples: a frequency and / or cell identity with respect to which the predicting or estimating should be performed; and an indication of whether the predicting or estimating should be performed with respect to any or each of a primary cell (PCell), primary secondary cell (PSCell), master cell group (MCG), or secondary cell group (SCG) for the UE. Other parameters that might be included in a prediction configuration were discussed in detail above.

[0116] In various embodiments, logging the estimate or prediction may comprise logging any one or more of any of the following, for each of one or more time intervals or time instances: a prediction of RLF; an estimated probability of RLF; a confidence level of the prediction; a predicted cause of RLF; and a probability of RLF for each of two or more specific causes.

[0117] Figure 2 shows an example method in a network node, for handling predictions of RLF. This method complements the UE-side method shown in Figure 1, and thus includes the step of receiving, from a UE, an estimate or prediction of a likelihood of the UE experiencing RLF, for one or more future time instances or intervals. This is shown at block 220. The method may comprise receiving, with the estimate or prediction, for each of one or more time intervals or time instances: a prediction of RLF; an estimated probability of RLF; a confidence level of the prediction; a predicted cause of RLF; and a probability of RLF for each of two or more specific causes.

[0118] In some embodiments, the method may further comprise adjusting one or more network parameters, based on the estimate or prediction. This adjusting, which is shown at block 230, may be based on many such estimates or predictions, in some embodiments.

[0119] In some embodiments or instances, the method may comprise forwarding the estimate or prediction to another network node, e.g., to an Operations, Administration, and Management (0AM) node. This is shown at block 240 of Figure 2.

[0120] In some embodiments or instances, the method may comprise sending, to the UE, a request for a report, the estimate or prediction being received in response to said request. This sending of the request is shown at block 216. In some embodiments or instances, the method may comprise receiving from the UE, an indication that the report comprising the estimate or prediction is available, prior to sending said request. This is shown at block 213. In some embodiments or instances, the method may comprise sending, to the UE, a reporting configuration according to which the estimate or prediction is received. This is shown at block 210. The reporting configuration may specify one or both of the following, for example: periodic reporting of RLF estimates or predictions, for at least a specific interval; and reporting of RLF estimates or predictions in response to one or more specified report triggering events.

[0121] Similarly, the method may comprise sending the UE a prediction configuration. This may be sent with or separately from sending any reporting configuration. The prediction configuration may specify one or more of any of the following, for example: a frequency and / or cell identity with respect to which the predicting or estimating should be performed; and an indication of whether the predicting or estimating should be performed with respect to any or each of a primary cell (PCell), primary secondary cell (PSCell), master cell group (MCG), or secondary cell group (SCG) for the UE.

[0122] Figure 3 shows an example of a communication system 300 in accordance with some embodiments. This provides a context for the techniques described herein, which can be implemented by devices operating in such a communication system 300. In this example, communication system 300 includes telecommunication network 302 that includes access network 304 (e.g., RAN) and a core network 306, which includes one or more core network nodes 308. Access network 304 includes one or more access network nodes, such as network nodes 3 lOa-b (one or more of which may be generally referred to as network nodes 310), or any other similar 4GPP access node or non-3GPP access point. Network nodes 310 facilitate direct or indirect connection of UEs, such as by connecting UEs 312a-d (one or more of which may be generally referred to as UEs 312) to core network 306 over one or more wireless connections.

[0123] 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, communication system 300 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. Communication system 300 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0124] UEs 312 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with network nodes 310 and other communication devices. Similarly, network nodes 310 are arranged, capable, configured, and / or operable to communicate directly or indirectly with UEs 312 and / or with other network nodes or equipment in telecommunication network 302 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in telecommunication network 302.

[0125] In the depicted example, core network 306 connects network nodes 310 to one or more hosts, such as host 316. 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. Core network 306 includes one or more core network nodes (e.g., 308) 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 core network node 308. 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 Deconcealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0126] Host 316 may be under the ownership or control of a service provider other than an operator or provider of access network 304 and / or telecommunication network 302, and may be operated by the service provider or on behalf of the service provider. Host 316 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.

[0127] As a whole, communication system 300 of Figure 3 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 3G, 4G, 5G, 6G standards, or any applicable future generation standard (e.g., 7G); 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.

[0128] In some examples, telecommunication network 302 is a cellular network that implements 4GPP standardized features. Accordingly, telecommunication network 302 may support network slicing to provide different logical networks to different devices that are connected to telecommunication network 302. For example, telecommunication network 302 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs.

[0129] In some examples, UEs 312 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to access network 304 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from access network 304. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e., being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0130] In the example, hub 314 communicates with access network 304 to facilitate indirect communication between one or more UEs (e.g., UE 312c and / or 312d) and network nodes (e.g., network node 310b). In some examples, hub 314 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, hub 314 may be a broadband router enabling access to core network 306 for the UEs. As another example, hub 314 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 310, or by executable code, script, process, or other instructions in hub 314. As another example, hub 314 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, hub 314 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, hub 314 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which hub 314 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, hub 314 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices.

[0131] Hub 314 may have a constant / persistent or intermittent connection to network node 310b. Hub 314 may also allow for a different communication scheme and / or schedule between hub 314 and UEs (e.g., UE 312c and / or 312d), and between hub 314 and core network 306. In other examples, hub 314 is connected to core network 306 and / or one or more UEs via a wired connection. Moreover, hub 314 may be configured to connect to an M2M service provider over access network 304 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with network nodes 310 while still connected via hub 314 via a wired or wireless connection. In some embodiments, hub 314 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to network node 310b. In other embodiments, hub 314 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 310b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0132] Figure 4 shows a UE 400 in accordance with some embodiments. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage 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-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by 4GPP, including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0133] A UE may support device-to-device (D2D) communication, for example by implementing a 4GPP 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).

[0134] UE 400 includes processing circuitry 402 that is operatively coupled via bus 404 to input / output interface 406, power source 408, memory 410, communication interface 412, and possibly other components not explicitly shown. Certain UEs may utilize all or a subset of the components shown in Figure 4. 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.

[0135] Processing circuitry 402 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 memory 410. Processing circuitry 402 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, processing circuitry 402 may include multiple central processing units (CPUs).

[0136] In the example, input / output interface 406 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 UE 400. 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.

[0137] In some embodiments, power source 408 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. Power source 408 may further include power circuitry for delivering power from power source 408 itself, and / or an external power source, to the various parts of UE 400 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging power source 408. Power circuitry may perform any formatting, converting, or other modification to the power from power source 408 to make the power suitable for the respective components of UE 400 to which power is supplied.

[0138] Memory 410 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable readonly memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, memory 410 includes one or more application programs 414, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 416. Memory 410 may store, for use by UE 400, any of a variety of various operating systems or combinations of operating systems. Memory 410 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini -dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ Memory 410 may allow UE 400 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 memory 410, which may be or comprise a device-readable storage medium.

[0139] Processing circuitry 402 may be configured to communicate with an access network or other network using communication interface 412. Communication interface 412 may comprise one or more communication subsystems and may include or be communicatively coupled to antenna 422. Communication interface 412 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 transmitter 418 and / or receiver 420 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, transmitter 418 and receiver 420 may be coupled to one or more antennas (e.g., 422) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0140] In the illustrated embodiment, communication functions of communication interface 412 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, nearfield communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0141] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 412, 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 8 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., an alert is sent when moisture is detected), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0142] 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.

[0143] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (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. AUE 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 UE 400 shown in Figure 4.

[0144] 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 4GPP context be referred to as an MTC device. As one particular example, the UE may implement the 4GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation. 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.

[0145] Figure 5 shows a network node 500 in accordance with some embodiments. Examples of network nodes include, but are not limited to, access points (e.g., radio access points) and base stations (e.g., radio base stations, Node Bs, eNBs, and gNBs).

[0146] 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 and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0147] Other examples of network nodes include multiple transmission point (multi-TRP) 6G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0148] Network node 500 includes processing circuitry 502, memory 504, communication interface 506, and power source 508. Network node 500 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which network node 500 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, network node 500 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 504 for different RATs) and some components may be reused (e.g., a same antenna 510 may be shared by different RATs). Network node 500 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 500, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 500.

[0149] Processing circuitry 502 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node 500 components, such as memory 504, to provide network node 500 functionality.

[0150] In some embodiments, processing circuitry 502 includes a system on a chip (SOC). In some embodiments, processing circuitry 502 includes one or more of radio frequency (RF) transceiver circuitry 512 and baseband processing circuitry 514. In some embodiments, RF transceiver circuitry 512 and baseband processing circuitry 514 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 512 and baseband processing circuitry 514 may be on the same chip or set of chips, boards, or units.

[0151] Memory 504 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non- transitory device -readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by processing circuitry 502. Memory 504 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 (collectively denoted computer program product 504a) capable of being executed by processing circuitry 502 and utilized by network node 500. Memory 504 may be used to store any calculations made by processing circuitry 502 and / or any data received via communication interface 506. In some embodiments, processing circuitry 502 and memory 504 is integrated. Communication interface 506 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, communication interface 506 comprises port(s) / terminal(s) 516 to send and receive data, for example to and from a network over a wired connection. Communication interface 506 also includes radio front-end circuitry 518 that may be coupled to, or in certain embodiments a part of, antenna 510. Radio front-end circuitry 518 comprises filters 520 and amplifiers 522. Radio front-end circuitry 518 may be connected to antenna 510 and processing circuitry 502. The radio front-end circuitry may be configmed to condition signals communicated between antenna 510 and processing circuitry 502. Radio front-end circuitry 518 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. Radio front-end circuitry 518 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 520 and / or amplifiers 522. The radio signal may then be transmitted via antenna 510. Similarly, when receiving data, antenna 510 may collect radio signals which are then converted into digital data by radio front-end circuitry 518. The digital data may be passed to processing circuitry 502. In other embodiments, the communication interface may comprise different components and / or different combinations of components. in certain alternative embodiments, network node 500 does not include separate radio front-end circuitry 518, instead, processing circuitry 502 includes radio front-end circuitry and is connected to antenna 510. Similarly, in some embodiments, all or some of RF transceiver circuitry 512 is part of communication interface 506. In still other embodiments, communication interface 506 includes one or more ports or terminals 516, radio front-end circuitry 518, and RF transceiver circuitry 512, as part of a radio unit (not shown), and communication interface 506 communicates with the baseband processing circuitry 514, which is part of a digital unit (not shown).

[0152] Antenna 510 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. Antenna 510 may be coupled to radio front-end circuitry 518 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, antenna 510 is separate from network node 500 and connectable to network node 500 through an interface or port.

[0153] Antenna 510, communication interface 506, and / or processing circuitry 502 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, antenna 510, communication interface 506, and / or processing circuitry 502 may be configmed to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment. Power source 508 provides power to the various components of network node 500 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). Power source 508 may further comprise, or be coupled to, power management circuitry to supply the components of network node 500 with power for performing the functionality described herein. For example, network node 500 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of power source 508. As a further example, power source 508 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.

[0154] Embodiments of network node 500 may include additional components beyond those shown in Figure 5 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, network node 500 may include user interface equipment to allow input of information into network node 500 and to allow output of information from network node 500. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for network node 500.

[0155] Figure 6 is a block diagram of a host 600, which may be an embodiment of host 316 of Figure 3, in accordance with various aspects described herein. Host 600 may be or comprise various combinations hardware and / or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. Host 600 may provide one or more services to one or more UEs.

[0156] Host 600 includes processing circuitry 602 that is operatively coupled via a bus 604 to an input / output interface 606, a network interface 608, a power source 610, and a memory 612. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as Figures 4 and 5, such that the descriptions thereof are generally applicable to the corresponding components of host 600.

[0157] Memory 612 may include one or more computer programs including one or more host application programs 614 and data 616, which may include user data, e.g., data generated by a UE for host 600 or data generated by host 600 for a UE. Embodiments of host 600 may utilize only a subset or all of the components shown. Host application programs 614 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). Host application programs 614 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, host 600 may select and / or indicate a different host for over-the-top services for a UE. Host application programs 614 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real- Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.

[0158] Figure 7 is a block diagram illustrating a virtualization environment 700 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 700 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized.

[0159] Applications 702 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 700 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0160] Hardware 704 includes processing circuitry, memory that stores software and / or instructions (collectively denoted computer program product 704a) executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 706 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 708a-b (one or more of which may be generally referred to as VMs 708), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 706 may present a virtual operating platform that appears like networking hardware to VMs 708.

[0161] VMs 708 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 706. Different embodiments of the instance of a virtual appliance 702 may be implemented on one or more of VMs 708, and the implementations may differ. 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.

[0162] In the context of NFV, each VM 708 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 VMs 708, and that part of hardware 704 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 708 on top of hardware 704 and corresponds to application 702.

[0163] Hardware 704 may be implemented in a standalone network node with generic or specific components. Hardware 704 may implement some functions via virtualization. Alternatively, hardware 704 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 710, which, among others, oversees lifecycle management of applications 702. In some embodiments, hardware 704 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 control system 712 which may alternatively be used for communication between hardware nodes and radio units.

[0164] Figure 8 shows a communication diagram of a host 802 communicating via a network node 804 with a UE 806 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE 312a of Figure 3 and / or UE 400 of Figure 4), network node (such as network node 310a of Figure 3 and / or network node 500 of Figure 5), and host (such as host 316 of Figure 3 and / or host 600 of Figure 6) discussed in the preceding paragraphs will now be described with reference to Figure 8.

[0165] Like host 600, embodiments of host 802 include hardware, such as a communication interface, processing circuitry, and memory. Host 802 also includes software, which is stored in or accessible by host 802 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as UE 806 connecting via an over-the-top (OTT) connection 850 extending between UE 806 and host 802. In providing the service to the remote user, a host application may provide user data which is transmitted using OTT connection 850. Network node 804 includes hardware enabling it to communicate with host 802 and UE 806. Connection 860 may be direct or pass through a core network (like core network 306 of Figure 3) and / or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.

[0166] UE 806 includes hardware and software, which is stored in or accessible by UE 806 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE 806 with the support of host 802. In host 802, an executing host application may communicate with the executing client application via OTT connection 850 terminating at UE 806 and host 802. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. OTT connection 850 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through OTT connection 850.

[0167] OTT connection 850 may extend via a connection 860 between host 802 and network node 804 and via wireless connection 870 between network node 804 and UE 806 to provide the connection between host 802 and UE 806. Connection 860 and wireless connection 870, over which OTT connection 850 may be provided, have been drawn abstractly to illustrate the communication between host 802 and UE 806 via network node 804, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0168] As an example of transmitting data via OTT connection 850, in step 808, host 802 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with UE 806. In other embodiments, the user data is associated with a UE 806 that shares data with host 802 without explicit human interaction. In step 810, host 802 initiates a transmission carrying the user data towards UE 806. Host 802 may initiate the transmission responsive to a request transmitted by UE 806. The request may be caused by human interaction with UE 806 or by operation of the client application executing on UE 806. The transmission may pass via network node 804, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 812, network node 804 transmits to UE 806 the user data that was carried in the transmission that host 802 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 814, UE 806 receives the user data carried in the transmission, which may be performed by a client application executed on UE 806 associated with the host application executed by host 802.

[0169] In some examples, UE 806 executes a client application which provides user data to host 802. The user data may be provided in reaction or response to the data received from host 802. Accordingly, in step 816, UE 806 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input / output interface of UE 806. Regardless of the specific manner in which the user data was provided, UE 806 initiates, in step 818, transmission of the user data towards host 802 via network node 804. In step 820, in accordance with the teachings of the embodiments described throughout this disclosure, network node 804 receives user data from UE 806 and initiates transmission of the received user data towards host 802. In step 822, host 802 receives the user data carried in the transmission initiated by UE 806.

[0170] One or more of the various embodiments improve the performance of OTT services provided to UE 806 using OTT connection 850, in which wireless connection 870 forms the last segment. More precisely, embodiments can reduce and / or prevent undesired recovery actions by UEs. For example, due to the conditions for considering an LTM cell switch procedure successful (causing UE to stop a supervision timer), undesired recovery actions due to supervision timer expiration are prevented at the UE. This is especially an issue in the scenarios where LTM cell switch needs to be performed without a RA procedure (i.e., “RACH-less”). Preventing undesired recovery actions makes LTM RACH-less solutions more efficient, which reduces the delay to access an LTM candidate cell. Embodiments can facilitate predictable UE behavior in LTM execution failures and can reduce and / or eliminate ambiguity for UE actions in the event of LTM failures that are concurrent other failures such as radio link failure (RLF). By improving operation of UEs and RANs in this manner, embodiments increase the value of OTT services delivered to / from the UE via the RAN, to both end users and service providers.

[0171] In an example scenario, factory status information may be collected and analyzed by host 802. As another example, host 802 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, host 802 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, host 802 may store surveillance video uploaded by a UE. As another example, host 802 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, host 802 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and / or transmitting data.

[0172] In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring OTT connection 850 between host 802 and UE 806, in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of host 802 and / or UE 806. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which OTT connection 850 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of OTT connection 850 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of network node 804. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by host 802. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using OTT connection 850 while monitoring propagation times, errors, etc.

[0173] The foregoing merely illustrates the principles of the disclosure. Various modifications and alterations to the described embodiments will be apparent to those skilled in the art in view of the teachings herein. It will thus be appreciated that those skilled in the art will be able to devise numerous systems, arrangements, and procedures that, although not explicitly shown or described herein, embody the principles of the disclosure and can be thus within the spirit and scope of the disclosure. Various embodiments can be used together with one another, as well as interchangeably therewith, as should be understood by those having ordinary skill in the art.

[0174] The term unit, as used herein, can have conventional meaning in the field of electronics, electrical devices and / or electronic devices and can include, for example, electrical and / or electronic circuitry, devices, modules, processors, memories, logic solid state and / or discrete devices, computer programs or instructions for carrying out respective tasks, procedures, computations, outputs, and / or displaying functions, and so on, as such as those that are described herein.

[0175] Any appropriate steps, methods, features, functions, or benefits disclosed herein may be performed through one or more functional units or modules of one or more virtual apparatuses. Each virtual apparatus may comprise a number of these functional units. These functional units may be implemented via processing circuitry, which may include one or more microprocessor or microcontrollers, as well as other digital hardware, which may include Digital Signal Processor (DSPs), special-purpose digital logic, and the like. The processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory such as Read Only Memory (ROM), Random Access Memory (RAM), cache memory, flash memory devices, optical storage devices, etc. Program code stored in memory includes program instructions for executing one or more telecommunications and / or data communications protocols as well as instructions for carrying out one or more of the techniques described herein. In some implementations, the processing circuitry may be used to cause the respective functional unit to perform corresponding functions according to one or more embodiments of the present disclosure.

[0176] As described herein, device and / or apparatus can be represented by a semiconductor chip, a chipset, or a (hardware) module comprising such chip or chipset; this, however, does not exclude the possibility that a functionality of a device or apparatus, instead of being hardware implemented, be implemented as a software module such as a computer program or a computer program product comprising executable software code portions for execution or being run on a processor. Furthermore, functionality of a device or apparatus can be implemented by any combination of hardware and software. A device or apparatus can also be regarded as an assembly of multiple devices and / or apparatuses, whether functionally in cooperation with or independently of each other. Moreover, devices and apparatuses can be implemented in a distributed fashion throughout a system, so long as the functionality of the device or apparatus is preserved. Such and similar principles are considered as known to a skilled person.

[0177] Furthermore, functions described herein as being performed by a wireless device or a network node may be distributed over a plurality of wireless devices and / or network nodes. In other words, it is contemplated that the functions of the network node and wireless device described herein are not limited to performance by a single physical device and, in fact, can be distributed among several physical devices.

[0178] In addition, certain terms used in the present disclosure, including the specification, drawings and embodiments thereof, can be used synonymously in certain instances, including, but not limited to, e.g. , data and information. It should be understood that, while these words and / or other words that can be synonymous to one another, can be used synonymously herein, that there can be instances when such words can be intended to not be used synonymously. Further, to the extent that the prior art knowledge has not been explicitly incorporated by reference herein above, it is explicitly incorporated herein in its entirety. All publications referenced are incorporated herein by reference in their entireties.

[0179] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0180] In addition, certain terms used in the present disclosure, including the specification and drawings, can be used synonymously in certain instances (e.g., “data” and “information”). It should be understood, that although these terms (and / or other terms that can be synonymous to one another) can be used synonymously herein, there can be instances when such words can be intended to not be used synonymously.

[0181] EXAMPLE EMBODIMENTS

[0182] Embodiments of the techniques, apparatuses, and systems described herein include, but are not limited to, the following enumerated examples:

[0183] 1. A method, in a user equipment, UE, for handling predictions of radio link failure, RLF, in a wireless network, the method comprising: estimating or predicting a likelihood of the UE experiencing RLF, for one or more future time instances or intervals; and logging the estimate or prediction, for subsequent reporting to the wireless network.

[0184] 2. The method of example embodiment 1, wherein the method further comprises reporting the logged estimate or prediction to the wireless network.

[0185] 3. The method of example embodiment 2, wherein said reporting is in response to receiving a request, from the wireless network, for a report comprising the estimate or prediction.

[0186] 4. The method of example embodiment 3, wherein the method comprises sending, to the wireless network, an indication that the report comprising the estimate or prediction is available, prior to receiving said request.

[0187] 5. The method of example embodiment 2, wherein the reporting is performed in accordance with a reporting configuration received by the UE from the wireless network.

[0188] 6. The method of example embodiment 5, wherein the reporting configuration specifies one or both of: periodic reporting of RLF estimates or predictions, for at least a specific interval; and reporting of RLF estimates or predictions in response to one or more specified report triggering events.

[0189] 7. The method of any one of example embodiments 1-6, wherein said estimating or predicting is performed according to a prediction configuration received from the wireless network. 8. The method of example embodiment 7, wherein the prediction configuration specifies one or more of any of the following: a frequency and / or cell identity with respect to which the predicting or estimating should be performed; an indication of whether the predicting or estimating should be performed with respect to any or each of a primary cell (PCell), primary secondary cell (PSCell), master cell group (MCG), or secondary cell group (SCG) for the UE.

[0190] 9. The method of any one of example embodiments 1-8, wherein logging the estimate or prediction comprises logging any one or more of any of the following, for each of one or more time intervals or time instances: a prediction of RLF; an estimated probability of RLF; a confidence level of the prediction; a predicted cause of RLF; and a probability of RLF for each of two or more specific causes.

[0191] 10. A method, in a network node, for handling predictions of radio link failure, RLF, in a wireless network, the method comprising: receiving, from a UE, an estimate or prediction of a likelihood of the UE experiencing RLF, for one or more future time instances or intervals.

[0192] 11. The method of example embodiment 10, further comprising adjusting one or more network parameter, based on the estimate or prediction.

[0193] 12. The method of example embodiment 10 or 11, further comprising forwarding the estimate or prediction to another network node.

[0194] 13. The method of any one of example embodiments 10-12, further comprising sending, to the UE, a request for a report, the estimate or prediction being received in response to said request.

[0195] 14. The method of example embodiment 13, wherein the method comprises receiving, from the UE, an indication that the report comprising the estimate or prediction is available, prior to sending said request.

[0196] 5. The method of any one of example embodiments 10-14, wherein the method comprises sending, to the UE, a reporting configuration according to which the estimate or prediction is received. 16. The method of example embodiment 15, wherein the reporting configuration specifies one or both of: periodic reporting of RLF estimates or predictions, for at least a specific interval; and reporting of RLF estimates or predictions in response to one or more specified report triggering events.

[0197] 17. The method of any one of example embodiments 10-16, wherein the method comprises sending, to the UE, a prediction configuration.

[0198] 18. The method of example embodiment 17, wherein the prediction configuration specifies one or more of any of the following: a frequency and / or cell identity with respect to which the predicting or estimating should be performed; an indication of whether the predicting or estimating should be performed with respect to any or each of a primary cell (PCell), primary secondary cell (PSCell), master cell group (MCG), or secondary cell group (SCG) for the UE.

[0199] 19. The method of any one of example embodiments 10-18, wherein the method comprises receiving, with the estimate or prediction, for each of one or more time intervals or time instances: a prediction of RLF; an estimated probability of RLF; a confidence level of the prediction; a predicted cause of RLF; and a probability of RLF for each of two or more specific causes.

[0200] 20. A user equipment, UE, comprising: communication interface circuitry configured to communicate with a RAN node via at least one serving cell; and processing circuitry operably coupled to the communication interface circuitry, wherein the processing circuitry and communication interface circuitry are configured to: estimate or predict a likelihood of the UE experiencing RLF, for one or more future time instances or intervals; and log the estimate or prediction, for subsequent reporting to the wireless network.

[0201] 21. The UE of example embodiment 20, wherein the processing circuitry and communication interface circuitry are further configured to report the logged information to the wireless network. 22. The UE of example embodiment 20 or 21, wherein the processing circuitry and communication interface circuitry are configured to carry out a method according to any of example embodiments 3- 9.

[0202] 23. A user equipment, UE, being adapted to: estimate or predict a likelihood of the UE experiencing RLF, for one or more future time instances or intervals; and log the estimate or prediction, for subsequent reporting to the wireless network.

[0203] 24. The UE of example embodiment 23, being further adapted to perform operations corresponding to any of the methods of example embodiments 2-9.

[0204] 25. Anon-transitory, computer-readable medium storing computer-executable instructions that, when executed by processing circuitry of a user equipment, UE, configure the UE to perform operations corresponding to any of the methods of example embodiments 2-9.

[0205] 26. A computer program product comprising computer-executable instructions that, when executed by processing circuitry of a user equipment, UE, configure the UE to perform operations corresponding to any of the methods of example embodiments 2-9.

[0206] 27. A network node, the network node comprising: communication interface circuitry configured to communicate with one or more user equipments, UEs, via at least one serving cell; and processing circuitry operably coupled to the communication interface circuitry, wherein the processing circuitry and communication interface circuitry are configured to carry out a method according to any of example embodiments 10-19.

[0207] 28. A network node, the network node being adapted to carry out a method according to any of example embodiments 10-19.

[0208] 29. A computer program product comprising computer-executable instructions that, when executed by processing circuitry of a network node, configure the network node to perform operations corresponding to any of the methods of example embodiments 10-19. REFERENCES

[0209] 1. RP-234055, 3GPP TSG RAN #101, Edinburgh, GB, December 11-15, 2023; Study on Al (Artificial Intelligence) / ML (Machine Learning) for mobility in NR.

[0210] 2. 3GPP TS 38.331 V17.6.0 (2023-09); 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; NR; Radio Resource Control (RRC) protocol specification (Release 17)

[0211] ABBREVIATIONS

[0212] Al Artificial Intelligence

[0213] CHO Conditional Handover

[0214] CSI Channel State Information

[0215] CSI-RS Channel State Information - Reference Signals

[0216] DC Dual Connectivity

[0217] DCCH Dedicated Control Channel

[0218] DL Downlink

[0219] EDF Early Data Forwarding gNB gNodeB

[0220] HOF Handover Failure

[0221] LTM L1 / L2 Triggered Mobility

[0222] ML Machine Learning

[0223] MR Measurement Report

[0224] PCell Primary Cell

[0225] RAN Radio Access Network

[0226] RLF Radio Link Failure

[0227] RSRP Reference Signal Received Power

[0228] RSRQ Reference Signal Received Quality

[0229] SINR Signal to Interference and Noise Ratio

[0230] UE User Equipment

Claims

CLAIMS1. A method, in a user equipment, UE, for handling predictions of radio link failure, RLF, in a wireless network, the method comprising: estimating or predicting (120) a likelihood of the UE experiencing RLF, for one or more future time instances or intervals; and reporting (140) the estimate or prediction to the wireless network.

2. The method of claim 1, wherein the method further comprises logging (130) the estimate or prediction, before reporting to the wireless network.

3. The method of claim 1 or 2, wherein said reporting (140) is in response to receiving a request, from the wireless network, for a report comprising the estimate or prediction.

4. The method of claim 3, wherein the method comprises sending (133), to the wireless network, an indication that the report comprising the estimate or prediction is available, prior to receiving said request.

5. The method of claim 1 or 2, wherein the reporting (140) is performed in accordance with a reporting configuration received by the UE from the wireless network.

6. The method of any one of claims 1-5, wherein the method further comprises receiving, from the wireless network, a reporting configuration and / or a prediction configuration.

7. The method of claim 5 or 6, wherein the reporting configuration specifies one or both of: periodic reporting of RLF estimates or predictions, for at least a specific interval; and reporting of RLF estimates or predictions in response to one or more specified report triggering events.

8. The method of any one of claims 1-7, wherein said estimating or predicting (120) is performed according to a prediction configuration received from the wireless network.

9. The method of claim 8, wherein the prediction configuration specifies one or more of any of the following: a frequency and / or cell identity with respect to which the predicting or estimating should be performed;an indication of whether the predicting or estimating should be performed with respect to any or each of a primary cell (PCell), primary secondary cell (PSCell), master cell group (MCG), or secondary cell group (SCG) for the UE.

10. The method of any one of claims 1-9, wherein reporting (140) the estimate or prediction to the wireless network comprises reporting either that RLF is expected to occur or that RLF is not expected to occur.

11. The method of any one of claims 1-10, wherein logging (130) the estimate or prediction comprises logging any one or more of any of the following, for each of one or more time intervals or time instances: a prediction of RLF; an estimated probability of RLF; a confidence level of the prediction; a predicted cause of RLF; and a probability of RLF for each of two or more specific causes.

12. A method, in a network node, for handling predictions of radio link failure, RLF, in a wireless network, the method comprising: receiving (220), from a UE, an estimate or prediction of a likelihood of the UE experiencing RLF, for one or more future time instances or intervals.

13. The method of claim 12, further comprising adjusting (230) one or more network parameter, based on the estimate or prediction.

14. The method of claim 12 or 13, further comprising forwarding (240) the estimate or prediction to another network node.

15. The method of any one of claims 12-14, further comprising sending (216), to the UE, a request for a report, the estimate or prediction being received in response to said request.

16. The method of claim 15, wherein the method comprises receiving (213), from the UE, an indication that the report comprising the estimate or prediction is available, prior to sending said request.

17. The method of any one of claims 12-16, wherein the method comprises sending (210), to the UE, a reporting configuration according to which the estimate or prediction is received.

18. The method of claim 17, wherein the reporting configuration specifies one or both of: periodic reporting of RLF estimates or predictions, for at least a specific interval; and reporting of RLF estimates or predictions in response to one or more specified report triggering events.

19. The method of any one of claims 12-18, wherein the method comprises sending (210), to the UE, a prediction configuration.

20. The method of claim 19, wherein the prediction configuration specifies one or more of any of the following: a frequency and / or cell identity with respect to which the predicting or estimating should be performed; an indication of whether the predicting or estimating should be performed with respect to any or each of a primary cell (PCell), primary secondary cell (PSCell), master cell group (MCG), or secondary cell group (SCG) for the UE.

21. The method of any one of claims 12-20, wherein the method comprises receiving, with the estimate or prediction, for each of one or more time intervals or time instances: a prediction of RLF; an estimated probability of RLF; a confidence level of the prediction; a predicted cause of RLF; and a probability of RLF for each of two or more specific causes.

22. A user equipment, UE, (400), comprising: communication interface circuitry (412) configured to communicate with a RAN node via at least one serving cell; and processing circuitry (402) operably coupled to the communication interface circuitry, wherein the processing circuitry and communication interface circuitry are configured to: estimate or predict a likelihood of the UE experiencing RLF, for one or more future time instances or intervals; and report the estimate or prediction to the wireless network.

23. The UE (400) of claim 22, wherein the processing circuitry (402) and communication interface circuitry (412) are further configured to log the estimate or prediction, before reporting to the wireless network.

24. The UE (400) of claim 22 or 23, wherein the processing circuitry (402) and communication interface circuitry (412) are configured to carry out a method according to any of claims 3-11.

25. Auser equipment, UE, (400), being adapted to: estimate or predict a likelihood of the UE experiencing RLF, for one or more future time instances or intervals; and report the estimate or prediction to the wireless network.

26. The UE (400) of claim 25, being further adapted to perform operations corresponding to any of the methods of claims 2-11.

27. Anon-transitory, computer-readable medium (410) storing computer-executable instructions that, when executed by processing circuitry of a user equipment, UE, configure the UE to perform operations corresponding to any of the methods of claims 1-11.

28. A computer program product (414) comprising computer-executable instructions that, when executed by processing circuitry of a user equipment, UE, configure the UE to perform operations corresponding to any of the methods of claims 1-11.

29. A network node (500), the network node (500) comprising: communication interface circuitry (506) configured to communicate with one or more user equipments, UEs, via at least one serving cell; and processing circuitry ((502) operably coupled to the communication interface circuitry, wherein the processing circuitry and communication interface circuitry are configured to carry out a method according to any of claims 12-21.

30. A network node (500), the network node (500) being adapted to carry out a method according to any of claims 12-21.

31. A computer program product (504a) comprising computer-executable instructions that, when executed by processing circuitry of a network node, configure the network node to perform operations corresponding to any of the methods of claims 12-21.

32. Anon-transitory computer-readable medium (504) comprising, stored thereupon, the computer program product of claim 31.

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