WO2025136195A1

WO2025136195A1 - Methods and apparatuses for supporting network predictions and refinements

Info

Publication number: WO2025136195A1
Application number: PCT/SE2024/051091
Authority: WO
Inventors: Angelo Centonza; Luca LUNARDI; Serban Purge; Vengatanathan KRISHNAMOORTHI; Pablo SOLDATI
Original assignee: Telefonaktiebolaget LM Ericsson AB
Current assignee: Telefonaktiebolaget LM Ericsson AB
Priority date: 2023-12-22
Filing date: 2024-12-17
Publication date: 2025-06-26
Anticipated expiration: 2026-06-22

Abstract

A method performed by a first network node for supporting network predictions. The method comprises initiating transmission of a first message to a second network node, wherein the first message comprises a request for a network prediction. The method further comprises receiving a response to the first message from the second network node.

Description

METHODS AND APPARATUSES FOR SUPPORTING NETWORK PREDICTIONS AND REFINEMENTS

Technical Field

[0001] The present disclosure relates to methods and apparatuses for supporting network predictions, in particular methods and apparatuses for timing communications of network predictions between network nodes.

Background

Ongoing 3GPP discussion for AI/ML in RAN

[0002] As part of the Rel-18 Work Item for Artificial Intelligence/Machine Learning (AI/ML) for New Generation Radio Access Network (NG-RAN), the 3rd Generation Partnership Project (3GPP) RAN3 working group agreed that when a first Radio Access Network (RAN) node sends a request to a second RAN node for some predicted information, the first RAN node can include in the request message, DATA COLLECTION REQUEST, an information element (IE) named Requested Prediction Time. The agreed semantics description for the above IE is the following:

Requested Prediction Time: For one time reporting, it indicates the point in time, measured from reception of the DATA COLLECTION REQUEST message, for which predictions are provided. For periodic reporting, it indicates the points in time, measured from the reception of the DATA COLLECTION REQUEST message and shifted by each reporting period, for which predictions are provided, (unit: second)

The following is how the Requested Prediction Time is expected to be specified in 3 GPP Technical Specification (TS) 38.423 for Release 18 (additional information can be found in 3GPP CR R3-238071, available at https://www.3gpp.Org/ftp/tsg_ran/WG3_Iu/TSGR3_122/Docs//R3-238071.zip):

9.1.3. CC DATA COLLECTION REQUEST

This message is sent by NG-RAN nodei to NG-RAN node2 to initiate the requested information reporting according to the parameters given in the message.

Direction: NG-RAN nodei -> NG-RAN node2

[0003] For completeness, the DATA COLLECTION RESPONSE and the DATA COLLECITON UPDATE messages are reported below, as expected to be specified in TS 38.423 for Rel-18:

9.1.3. DD DATA COLLECTION RESPONSE

This message is sent by NG-RAN node2 to NG-RAN nodei to indicate that the requested information, for all or part of the measurement objects included in the reporting, is successfully initiated.

Direction:

9.1 3.FF DATA COLLECTION UPDATE

This message is sent by NG-RAN node2 to NG-RAN nodei to report the requested information.

Direction:

nodei.

[0004] There currently exist certain challenge(s). [0005] According to the current or published technology, the paradigm according to which predictions are requested and provided is limited, for example:

• A node that receives a prediction referring to a certain point in time in the future (i.e. , a time instant corresponding to the requested prediction time) does not know whether the prediction is still valid or within expected accuracy bounds as the time approaches the requested prediction time. This is a major shortfall in the solution agreed so far in TS 38.423 because a prediction that is generated long before the time for which the prediction was derived, i.e., long before the requested prediction time, has a greater chance of being inaccurate. However, the node providing the prediction could have a much more accurate inferred prediction, compared to a prediction that was made a certain time ago, as the prediction time approaches. The above is true for dynamic systems (such as the 5G System (5GS)) and for models that perform predictions using the current system parameters as inputs both for inference and training data.

• According to the current definition, the requested prediction time is measured from the reception of the DATA COLLECTION REQUEST message, but this is not always the best choice. For instance, the node receiving the predictions may need to use such information for an action that is not correlated to the reception of the DATA COLLECTION REQUEST.

• According to the current definition, the node receiving a request for a prediction should make the prediction available at a specific time determined by the Reporting Periodicity and by the Prediction Time. However, this may not be possible due to issues encountered at the reporting node, such as lack of resources or processing delays. It is currently not possible to account for such events where the requested predictions are not available for reporting.

• According to the current methods, a node that needs to determine when specific events will occur in the network or within its neighbourhood of other nodes or functions would need to request predictions from neighbour nodes that detail how certain metrics are predicted to change in the future. This process encompasses transmission of considerable amount of data consisting of periodic/non periodic input data, output data and feedback information. The process also implies computational costs at the nodes that make data available and that derive predictions. However, these events (i.e., the events for which a node needs to know their future occurrence) may never occur. The process in pace would therefore have consumed considerable signalling and processing resources without delivering any information upon which the node would have acted. The latter creates a problem of inefficient use of resources.

[0006] It is an object of the present disclosure to support improved accuracy in communications of network predictions.

Summary

[0007] Certain aspects of the disclosure and their embodiments may provide solutions to the above or other challenges.

[0008] This disclosure describes techniques for targeting refined predictions concerning metrics or events.

[0009] According to a first method of the present disclosure, a first network node may request a second network node to provide predictions for parameters representing aspects of the network operational status, for a specific point in time in the future. The second network node may provide predictions for such parameters for the requested point in time in the future and additionally, it may provide refinements of the predictions concerning the requested metrics and other aspects of the predictions requested, such as the prediction uncertainty, as the time for which the prediction is requested approaches.

[00010] Accordingly, the first network node is able to receive more and more accurate predictions as the time for which the predicted parameters are requested approaches. This enables the first node to take optimal actions based on the received predictions.

[00011] According to a second method of the present disclosure, the first method may be applied to scenarios where the first network node wants to identify whether the requested predictions reveal that a specific event would occur in the future. As an example, the event may comprise any of the predicted parameters becoming higher/lower/equal to a specific value.

[00012] In some of these embodiments, the event that the first node wants to be predicted may comprise an action or a process, or in general something that is not directly representable by a measurable parameter. As an example, such an event may comprise the deactivation of a cell or the occurrence of a handover.

[00013] While the prediction of the event of interest to the first node may be achieved by means of the first method, it is noted herein that to do so would be at the expense of heavy signalling traffic and high computational power. In some cases, if the event of interest to the first node is not predicted at all, the signalling load and processing power demanded by the first method would be used with no resulting optimization for the system. Thus, it is noted that an enhancement of the first method is achieved by the first network node requesting the second network node to provide predictions for the occurrence of the requested event (this is referred to as the second method). This prediction may comprise expressing a time at which, or after which, or during which the event of interest to the first node is predicted to occur.

[00014] This second method enables saving of signalling load and processing power because predictions are generated only for the events of interest to the requesting node and are signalled only if there is a concrete prediction that the events of interest will be fulfilled according to the specified fulfilment conditions. The first node can therefore take targeted actions based on the occurrence in the future of the predicted events.

[00015] In summary, the present disclosure provides two methods. The first method is based on a first node requesting a time series of prediction for specific parameters or metrics or events. These predictions may comprise the predicted values of the requested parameters or metrics or events for a specific point in time in the future, Tp. In this method, a second network node may provide multiple instances of such predictions, where each instance is generated at successive points in time from the start of the prediction reporting to the time before Tp. This provides the first network node with increasingly accurate predictions of the requested metrics or parameters or events, because the closer in time a prediction is produced to the time Tp, the more accurate the prediction is expected to be.

[00016] The second method comprises the first node requesting the second node to provide a prediction of the occurrence of an event, where an event may be an action, a process or a parameter or metric taking a certain value. This allows the first node to receive targeted predictions for the events upon which the first node will take actions, while avoiding the reporting of predictions for which the first node cannot or should not take any actions and for which the first node would not generate any performance impact on the system.

[00017] Certain embodiments may provide one or more of the following technical advantage(s).

[00018] The techniques of the present disclosure provide the advantage of enhancing the accuracy of the prediction a network node can receive when such predictions are requested for a specific point in time in the future. Additionally, the invention enables the generation and reporting of predictions relative to the occurrence of events. The latter enables to target the effort of prediction inference and signalling only on those events for which network nodes can act/react and therefore only for those cases where actions that can optimize the system performance can be taken.

Brief Description of the Drawings

[00019] For a better understanding of the embodiments of the present disclosure, and to show how it may be put into effect, reference will now be made, by way of example only, to the accompanying drawings, in which:

Fig. 1 is a flow chart illustrating a method in accordance with some embodiments; Fig. 2 is a flow chart illustrating a method in accordance with some embodiments; Fig. 3 is a signaling diagram showing signaling between a first network node and a second network node according to some embodiments;

Fig. 4 is a signaling diagram showing signaling between a first network node and a second network node according to some embodiments;

Fig. 5 is a signaling diagram showing signaling between a first network node and a second network node according to some embodiments;

Fig. 6 is a signaling diagram showing signaling between a first network node and a second network node according to some embodiments;

Fig. 7 shows an example of a communication system in accordance with some embodiments;

Fig. 8 shows a UE in accordance with some embodiments;

Fig. 9 shows a network node in accordance with some embodiments;

Fig. 10 is a block diagram of a host;

Fig. 11 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized; and

Fig. 12 shows a communication diagram of a host communicating via a network node with a UE over a partially wireless connection in accordance with some embodiments.

Detailed Description

[00020] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art. [00021] Figure 1 is a flow chart that depicts a method for supporting network predictions in accordance with particular embodiments. The method of Figure 1 may be performed by a first network node (e.g. the network node 710 or network node 900 as described later with reference to Figures 7 and 9 respectively). The first network node may comprise a Centralized Unit (CU), e.g., a gNB-CU, or a Distributed Unit (DU), e.g., a gNB-DU

[00022] The first network node may be referred to herein as gNBl or NG-RAN nodel. However, the skilled person will appreciate that the techniques disclosed herein relating to the first network node are applicable to any type of network node configured according to any RAT and are not limited to gNBs.

[00023] The method comprises, at step 102, initiating transmission of a first message to a second network node. The first message comprises a request for a network prediction.

[00024] The request for a network prediction may comprise a request for a prediction relating to a specific point in time in the future.

[00025] The request may further comprise a request for one or more intermediary predictions between the current time and the specific point in time in the future. The request may specify a periodicity with which the one or more intermediary predictions should be provided. The request may specify refinements of successive predictions.

[00026] The request may specify that a single prediction relating to the specific point in time should be provided. Optionally, the request may provide intermediary refinements of the prediction.

[00027] The refinements may comprise one or more of: an updated prediction; an updated uncertainty for an earlier prediction; an indication of whether or not an earlier prediction is applicable; and a request to provide updated predictions in the event of retraining or parameter optimization of a Machine Learning model used to generate predictions.

[00028] The first message may request information related to predictions of event fulfilment. The first message may further specify parameters used to define the event. The parameters may include one or more of: time parameters; resource parameters; and performance measurement parameters (e.g., energy cost, UE performance, etc).

[00029] The first message may specify one or more first conditions to be used by the second network node to determine whether or not to start providing or to continue providing predictions and/or refinements. [00030] The first message may specify one or more second conditions. The first message may instruct the second network node not to provide or to stop providing a prediction if one or more of the second conditions is satisfied.

[00031] The first message may be a data collection request message. The data collection request message may be an Xn Application Protocol (XnAP) message or Fl Application Protocol (F1AP) message.

[00032] The method further comprises, at step 104, receiving a response to the first message from the second network node.

[00033] The response from the second network node may be a second message, a third message and/or a fourth message. The second message may be a data collection response message. The third message may be a data collection update message. The fourth message may be a data collection update message or handover acknowledgement message. The response from the second network node may include one or more XnAP messages. The response from the second network node may include one or more Fl AP messages.

[00034] The method may further comprise obtaining user data; and forwarding the user data to a host via the transmission to the network node.

[00035] Figure 2 is a flow chart that depicts a method for supporting network predictions in accordance with particular embodiments. The method of Figure 2 may be performed by a second network node (e.g. the network node 710 or network node 900 as described later with reference to Figures 7 and 9 respectively). The second network node may comprise a Centralized Unit (CU), e.g., a gNB-CU, or a Distributed Unit (DU), e.g., a gNB-DU.

[00036] The second network node may be referred to herein as gNB2 or NG-RAN node2. However, the skilled person will appreciate that the techniques disclosed herein are applicable to any network node and are not limited to gNBs.

[00037] The method comprises, at step 202, receiving a first message from a first network node. The first message comprises a request for a network prediction.

[00038] The first message may be a data collection request message. The data collection request message may be an XnAP message or Fl AP message.

[00039] The method comprises, at step 204, determining a response to the first message.

[00040] The method further comprises, at step 206, initiating transmission of the response to the first network node. [00041] The request for a network prediction may comprise a request for a prediction relating to a specific point in time in the future. The response may comprise providing a prediction relating to the specific point in time.

[00042] The response may further comprise one or more intermediary predictions between the current time and the specific point in time. The intermediary predictions may be provided by the second network node with a periodicity specified in the first message. Each successive prediction may provide refinements.

[00043] The response may comprise a single prediction. Optionally, the response may comprise intermediary refinements of the single prediction. The intermediary refinements may comprise one or more of: an updated prediction; an updated uncertainty for an earlier prediction; and an indication of whether or not an earlier prediction is applicable; a request to provide updated predictions in the event of retraining or parameter optimization of a Machine Learning model used to generate predictions.

[00044] The second network node may provide information related to predictions of event fulfilment. The information may be based on parameters provided by the first network node, the parameters being used to define the event. The parameters may include one or more of: time parameters; resource parameters; and performance measurement parameters.

[00045] The second network node may determine, based on one or more first conditions, whether or not to start providing or to continue providing predictions and/or refinements.

[00046] In some embodiments, the second network node does not provide or stops providing a prediction if one or more second conditions is satisfied. The second conditions may be specified by the first network node.

[00047] The response from the second network node may be a second message, a third message and/or a fourth message. The second message may be a data collection response message. The third message may be a data collection update message. The fourth message may be a data collection update message or handover acknowledgement message.

[00048] The response from the second network node may include one or more XnAP messages. The response from the second network node may include one or more XnAP messages or one or more Fl AP messages.

[00049] The method may further comprise obtaining user data; and forwarding the user data to a host or a user equipment.

[00050] As used herein, a “network node” may be a Radio Access Network (RAN) node, an Operations, Administration, and Management (0AM), a Core Network (CN) node, a Service Management & Orchestration (SMO), a Network Management System (NMS), a NonReal Time RAN Intelligent Controller (Non-RT RIC), a Real-Time RAN Intelligent Controller (RT-RIC), agNB, eNB, en-gNB, ng-eNB, gNB Centralized Unit (gNB-CU), gNB-CU-Control Plane (gNB-CU-CP), gNB-CU-User Plane (gNB-CU-UP), eNB-CU, eNB-CU-CP, eNB-CU- UP, Integrated access and backhaul (IAB) node, lAB-donor DU, lAB-donor-CU, IAB-DU, IAB Mobile Termination (IAB-MT), Open-RAN Centralized Unit (O-CU), O-CU-CP, O-CU- UP, O-DU, Open-RAN Radio Unit (O-RU), O-eNB.

[00051] The techniques proposed herein may comprise one or more of the following:

• a FIRST MESSAGE, sent by a first network node to request a second network node to provide a certain prediction. The FIRST MESSAGE may be a DATA COLLECTION REQUEST XnAP message (the initiating message of the Data Collection Reporting Initiation procedure).

• a SECOND MESSAGE, sent by a second network node to the first network node in response to the FIRST MESSAGE. The SECOND MESSAGE may be a DATA COLLECTION RESPONSE XnAP message or a DATA COLLECTION FAILURE XnAP message (respectively the successful or the unsuccessful terminating message of the Data Collection Reporting Initiation procedure).

• a THIRD MESSAGE, sent by a second network node to the first network node to provide predictions and additional pieces of information associated with the predictions according to the request comprised in the FIRST MESSAGE. The THIRD MESSAGE may be a DATA COLLECTION UPDATE XnAP message (as defined for the Data Collection Reporting procedure).

• a F OURTH MES SAGE, sent by a second network node to the first network node to indicate when a certain event is fulfilled, wherein the time at which the event is fulfilled may be used as reference with respect to which predictions are requested and/or are to be provided, the THIRD MESSAGE may be a DATA COLLECTION UPDATE XnAP message (as defined for the Data Collection Reporting procedure).

[00052] In the description below, the FIRST MESSAGE, SECOND MESSAGE and THIRD MESSAGE may be used interchangeably with the DATA COLLECTION REQUEST message, the DATA COLLECTION RESPONSE message, and the DATA COLLECTION UPDATE message respectively. The first network node and the second network node may be referred to as “first RAN node” or gNBl, and “second RAN node” or gNB2 respectively. The above should not be regarded as limiting in terms of applicability of the methods.

Requested prediction time and periodic reporting of prediction(s) with subsequent refinements

[00053] As discussed above, the current way the requested prediction time is specified implies that the reporting node, gNB2, reports a single value of the requested prediction for the requested prediction time, after the Data Collection Reporting Initiation has been completed. However, the prediction signalled may be far away in time from the requested prediction time. For this reason, the prediction may be out of bounds of expected prediction accuracy, because with the passing of time, inputs and conditions on the basis of which the prediction is calculated may change and with that the prediction would change too.

[00054] Figure 3 is a signaling diagram showing signaling between a first network node and a second network node according to some embodiments. More particularly, Figure 3 illustrates periodic reporting of prediction(s) with a request comprising a Requested Prediction Time (tp), and a request for prediction refinements. Thus, in the scenario exemplified in Figure 3, a first RAN node (gNBl) requests a second RAN node (gNB2) to provide periodic reporting of prediction(s) with subsequent refinement(s). Namely, the requesting node (gNBl) does not request a single prediction for a specified prediction time, but it requests a series of predictions, where all the prediction values of the series refer to the same requested prediction time and where each prediction is generated at a specific time interval while approaching the occurrence of the requested prediction time. It should be noted that each message reporting such predictions may carry various prediction series, where each series provides prediction values towards one prediction time, but where the prediction time for each series is different. The request comprises a requested periodicity (value T), a requested prediction time (value t_p), and one or more configuration parameter(s) for reporting of prediction(s) refinements.

[00055] The request for obtaining refinements of predictions may be implicit or explicit.

[00056] The request for refinements and/or the corresponding reporting may refer only (or also) to parameters (or characteristics) of predictions, e.g., uncertainty of prediction(s) due to a large prediction window. For example, a subsequent reporting may be used to provide an update of the uncertainty of a previously provided prediction(s). In another example, an update of the reporting can be used to indicate that previously provided prediction(s) is(are) no longer applicable.

Configuration parameter(s) for reporting of prediction(s) refinements

[00057] The requesting node, gNB 1 , may include in the request for predictions at a specific prediction time one or more parameters determining the criteria according to which prediction refinement values shall be reported. Configuration parameter(s) for reporting of prediction(s) refinements may be one or more of the following:

• A certain periodicity (e.g., T_sub), according to which update(s) (or refinement(s)) of the prediction(s) for a specific prediction time are requested o T_Sub may be different compared to the reporting periodicity T (if T is defined) o T_Sub may coincide with the reporting periodicity T. In this case the same reporting periodicity T can be reused and Tsub may not be signalled. o Tsub may be a multiple of T o T may be a multiple of Tsub

• an indication, e.g., a flag, indicating that update(s) (or refinement(s)) of the predictions for a specific prediction time is(are) requested

• a number of update(s) (or refinement(s)) of the predictions for a specific prediction time that the reporting node shall generate towards the requesting node

• a time interval during which update(s) (or refinement(s)) of the predictions for a specific prediction time is(are) requested

• uncertainty of each refined prediction(s) value

• conditions to be fulfilled for reporting of updates of the predictions for a specific prediction time. In one example: updates should be provided if the delta in predicted value(s) compared to previously provided prediction(s) is(are) larger than X (or smaller than Y, or not larger than Z, or not smaller than W, or a combination of the above). In another example: updates should be provided until the uncertainty of the prediction is above a threshold (or within a certain range). In another example: updates should be provided until the RRC state of a UE is a certain RRC state (e.g., RRC CONNECTED).

[00058] As for the previous scenarios, the solution may be applicable to the case when predictions are requested starting from the time when a certain event is fulfilled, and the event is triggered after the DATA COLLECTION REQUEST message is received (e.g., the event may be a handover occurring after signalling of the DATA COLLECTION REQUEST).

[00059] Figure 4 is a signaling diagram showing signaling between a first network node and a second network node according to some embodiments. More particularly, Figure 4 illustrates periodic reporting of prediction(s) with a request comprising a Requested Prediction Time (tp), and a request for prediction refinements. The event starting from which prediction are requested occurs after the DATA COLLECTION REQUEST.

[00060] As shown in Figure 4, it may be requested to provide an update of an earlier prediction if any model refinements (e.g., retraining, parameter optimization) have been performed in the period between the initial prediction and the requested prediction time, provided that there is sufficient time to generate a prediction and a corresponding DATA COLLECTION UPDATE message to send the new prediction over. This may be indicated by gNBl as a condition upon which prediction refinements shall be provided. Namely, if a prediction model is changed at gNB2 before the prediction time is expired and after a prediction for the requested prediction time is provided, gNB2 shall generate a new prediction for the requested prediction time and by means of the new model, and signal such prediction to gNBl . [00061] One example of how the details about prediction refinements can be outlined in a response message from gNB2 to gNBl is described below, where the DATA COLLECTION RESPONSE message has been taken as an example and modified:

9.1.3. CC DATA COLLECTION REQUEST

Direction: NG-RAN nodei —> NG-RAN node2.

.2.3. MM Prediction Refinement Configuration This IE contains additional conditions for the refinement of predictions requested for a specific prediction time.

[00062] As it can be seen, the conditions according to which refinement predictions are provided can be described in a container IE which is sent by gNBl as part of the DATA COLLECTION REQUEST message.

Requested prediction time and one-time reporting of prediction(s) with subsequent refinements

[00063] A further scenario of the solution is exemplified in Figure 5. Figure 5 illustrates one-time reporting of prediction(s) with a request comprising a Requested Prediction Time (tp), and a request for prediction refinements. As shown in Figure 5, a first RAN node (gNBl) requests to a second RAN node (gNB2) to provide one-time reporting of predictions with subsequent refinement(s). This use case is similar to the one above, but with the difference that the reporting node provides to the requesting node predictions concerning a single prediction time in the future. The request may comprise a requested prediction time (value t_p), and one or more configuration parameter(s) for reporting of prediction(s) refinements, where the one or more configuration parameters are the same as those defined above with the difference that the time periodicity Tsub is independent of any other periodicity T, because there is no reporting periodicity as T (as the reporting is “one off’). In one variant, the onetime reporting is implicitly indicated by the absence of the Requested Periodicity (T) in the request message; in another variant, the one-time reporting is explicitly indicated by a special value of the Requested Periodicity (e.g., T=0) in the request message. [00064] The reporting of predictions refinements may be configured according to one or more of the options (where applicable) as described for the previous scenario.

Request and report of information related to prediction of event fulfillment

[00065] In the previous embodiment, the reporting node, gNB2, refines its predictions ahead of the occurrence of the requested prediction time, so that gNBl can receive the most accurate prediction possible. However, for cases where the predictions are used by gNBl to determine whether a specific event may occur, this technique may result in heavy signalling loads especially for cases where the predicted event(s) do not occur.

[00066] As an example, gNBl may request a Predicted Radio Resource Status for a specific Requested Prediction Time, so to be able to detect whether the PRB utilization of a neighbour cell goes below a certain threshold. The latter may be a triggering event to, for example, offload UEs from a source cell towards such neighbour cell and trigger source cell deactivation. However, with the solution described in the previous embodiment, the reporting node may provide refinements of the requested prediction, but the prediction may reveal that PRB utilization at the neighbour cell never goes below the desired threshold, namely the event gNBl wants to identify does not occur. This will cause repetitive calculation of refinement predictions and signalling of such refinements, without revealing the event upon which gNBl should act.

[00067] In order to further improve the solution in the previous embodiment, a further scenario of the solution is exemplified in Figure 6. Figure 6 illustrates the request and report of information related to prediction of event fulfillment at gNB2 according to some embodiments. As shown in Figure 6, a first RAN node (gNBl) requests a second RAN node (gNB2) to provide information related to predictions of event fulfillment. The request optionally comprises other reporting related configuration parameters (such as a requested periodicity (value T) or configuration parameter(s) for reporting of prediction(s) refinements). In one variant, one-time reporting is requested, and this may be indicated implicitly or explicitly (as in the previous scenarios).

[00068] To continue with the example used above, it is now assumed that gNBl requests gNB2 to provide a prediction of the event “Total DL PRB utilization in cell A of gNB2 is higher than threshold X”. It is further assumed that at time te the gNB2 predicts that at a certain time in the future (tpe) “The Total DL PRB utilization in cell A of gNB2 will become higher than threshold X”. The statement “The Total DL PRB utilization in cell A of gNB2 is higher than threshold X” defines an event (e.g., an event associated to predicted resource utilization), and the “time in the future for which the gNB2 determines that the Total DL PRB utilization in cell A of gNB2 will become higher than threshold X” is the information related to prediction of event fulfillment being requested. The gNBl can also signal to gNB2 parameters used for the definition of the event, such as the object(s) to which the predicted event refers to (in the example: “cell A of gNB2”), the type of measurement to which the predicted event refers to (in the example: “Total DL PRB utilization”), and the criterion (or criteria) to which the fulfillment of the predicted event refers to (in the example: the threshold X). In alternative embodiments, one or more of the parameters above (the object(s), the type(s) of measurement(s), and the criterion(criteria)) may be implicit.

[00069] In one embodiment, the request sent by gNBl to gNB2 in the FIRST MESSAGE comprises one or more event identities (or identifiers, or indexes or pointers) to identify the one or more events for which information related to prediction of event fulfillment is requested. In one case, the event identity is determined by the requesting node (i.e., gNBl), in another case it is determined by the reporting node (i.e., gNB2), in yet another case it is determined by a combination of elements some of which are determined by the requesting node (i.e., gNBl) and others by the reporting node (i.e., gNB2). In a possible example of implementation, the identity of the event is determined by the combination of the NG-RAN nodel Measurement ID allocated by the gNBl and the NG-RAN node 2 Measurement ID allocated by the gNB2 as per the Data Collection Request Initiation XnAP procedure and used in the HANDOVER REQUEST XnAP message. In yet another embodiment the event ID is an index for which every value corresponds to a preconfigured event description known to both gNBl and gNB2.

[00070] The information related to prediction of event fulfillment which the gNBl requests to gNB2 can be implicit in the request, or explicit. The same goes for the objects and data that are used in the definition of the event. Therefore, if we consider the example provided above on “predicted Total DL PRB utilization”, in one option, the gNBl can explicitly indicate in the request to the gNB2 to report “time”, and “predicted Total DL PRB utilization for cell A” when the criterion “predicted Total DL PRB utilization for cell A is equal or higher than the Threshold” is fulfilled. In alternative, the need for reporting of “time” is implicit in the request and/or is specified in a normative text of a 3GPP Technical Specification (e.g., in TS 38.423). [00071] In one option, together with the information related to prediction of event fulfillment the gNB2 sends to the gNBl the identity of the event to which the information related to prediction of event fulfillment refers to. The gNBl may have requested to obtain the identity of the event explicitly.

[00072] Various options are described below for information related to prediction of event fulfillment. It has to be understood that any one or any combination of those can appear in the reporting from gNB2 to gNBl, upon request from gNBl.

[00073] In the examples below the event that is taken into account is the prediction that resource utilization for a given cell or SSB area is equal, below or above a certain threshold. However, such example can be extended to cases where the metric for which the prediction to be equal, above or below a threshold can be any parameter gNB2 can report to gNBl. For example, such parameter(s) may comprise one or more of:

• The Energy Cost for gNB2. As a depending embodiment, the Energy Cost may be calculated for a cell, an SSB area or a sub-part of gNB2;

• The UE performance for a UE served by a given cell of gNB2. In this case the event fulfilment may be that one or more of the metrics describing the UE performance at gNB2 is equal, higher or lower than a given threshold; and

• The trajectory of a given UE served by gNB2 is equal to a given trajectory (e.g. expressed as a sequence of cells, or a sequence of SSB areas, or a sequence of areas served by specific reference signals). In an alternative of this embodiment, the event may consist of the UE entering/exiting a specific cell, or the UE entering/exiting a specific areas, such as a specific tracking area.

[00074] In addition to the above, the condition that would trigger the reporting of a prediction may not be a threshold but an event itself that may not be defined by specific parameters that are reported by gNB2 to gNBl, but mutually intelligible by both nodes, such as a cell turn off event, or activation/ deactivation of certain energy/power modes, etc. In this case, the prediction is related to the occurrence of the event itself and it provides information about the time when the event will occur.

[00075] In one option, information related to prediction of event fulfillment is a time in the future for which the gNB2 determines that certain condition(s) pertaining to predicted resource utilization will be fulfilled. Resource utilization can refer to “radio resource utilization”, or “transport network resource utilization”, or “hardware utilization”, or “coverage state of a cell”, or “coverage state of an SSB”, or “energy consumption”, or “energy cost”, or “energy cost per energy source type”, or any of the load metrics defined for Resource Status Reporting in 3GPP TS 38.423, 3GPP TS 38.473, 3GPP TS 37.483.

• In one example, the gNBl requests to gNB2 to report the time in the future for which the gNB2 determines that the condition = “Predicted Total PRB utilization in cell A is higher than X” will be fulfilled.

• In another example, the gNBl requests to gNB2 to report the time in the future for which the gNB2 determines that the condition = “Predicted TN Available Capacity in gNB2 is lower than X” will be fulfilled.

• In another example, the gNBl requests to gNB2 to report time in the future for which the gNB2 determines that the condition = “Predicted Energy Cost for gNB2 is higher than X” (or “Predicted Energy Cost for gNB2 is lower than Y”) will be fulfilled.

• In another example, the gNBl requests to gNB2 to report time in the future for which the gNB2 determines that the condition = “Predicted Energy Cost for gNB2 for energy source type T is higher than X” (or “Predicted Energy Cost for gNB2 for energy source type is lower than Y”) will be fulfilled.

[00076] In one option, information related to prediction of event fulfillment is a time interval in the future during which the gNB2 determines that certain condition(s) pertaining to predicted resource utilization will be fulfilled. Resource utilization can refer to “radio resource utilization”, or “transport network resource utilization”, or “hardware utilization”, or “coverage state of a cell”, or “coverage state of an SSB”, or “energy consumption”, or “energy cost”, or “energy cost per energy source type”, or any of the load metrics defined for Resource Status Reporting in 3GPP TS 38.423, 3GPP TS 38.473, 3GPP TS 37.483.

[00077] In one variant, fulfillment of predicted resource utilization refers to statistical information derived from the resource utilization, such as the maximum of a set of values, the minimum of a set of values, the average of a set of values, the variance of a set of values, the standard deviation of a set of values, a weighted average of the values.

• In one example, the gNBl requests to gNB2 to report the time interval in the future during which the gNB2 determines that the condition = “Predicted Energy Cost for gNB2 is higher than X” (or “Predicted Energy Cost for gNB2 is lower than Y”) will be fulfilled.

• In another example, the gNBl requests to gNB2 to report time interval in the future during which the gNB determines that the condition = “Predicted Energy Cost for gNB2 for energy source type T is higher than X” (or “Predicted Energy Cost for gNB2 for energy source type is lower than Y”) will be fulfilled. [00078] In one option, information related to prediction of event fulfillment is a time in the future at which (or a time interval in the future during which) the gNB2 predicts that certain mobility related events will occur. For example, the gNB2 reports to the gNBl the time interval in the future during which a handover from a first cell of gNB2 to a second cell of the gNB3 will occur. Other complementary information, such as the probability for the event to occur can be reported as well. Similar reporting of mobility event can relate to handover from a first cell of gNB2 to a third cell of a gNB3, or from a first cell of gNB2 to a third cell of gNBl. This can further extend to mobility between SSB beams.

[00079] In one option, information related to prediction of event fulfillment is a time in the future at which (or a time interval in the future during which) the gNB2 predicts that certain energy saving related action will initiate (or will terminate).

[00080] In one option, information related to prediction of event fulfillment is a time in the future at which (or a time interval in the future during which) the gNB2 predicts an event that may not be defined by specific parameters that are reported by gNB2 to gNBl, but mutually intelligible events, such as cell turn off, activation/deactivation of certain energy/power modes, etc. In this case, the prediction relates to the occurrence of the event itself without any specific thresholds associated to it.

[00081] In one option, information related to prediction of event fulfillment is a time in the future at which (or a time interval in the future during which) the gNB2 predicts that coverage modification (of cells and/or SSB beams) will initiate (or will terminate) due to energy saving related action(s).

[00082] In one option, information related to prediction of event fulfillment is a time in the future at which (or a time interval in the future during which) the gNB2 predicts that certain Coverage and Capacity Optimization (CCO) related action (e.g., cells and/or SSB beams coverage modification) will occur.

[00083] In one option, information related to prediction of event fulfillment is a time in the future at which (or a time interval in the future during which) the gNB2 predicts that certain RAN Visible QoE (RVQoE) metric (or RVQoE score, or RVQoE value) will be above / below a threshold, or will be at certain levels (or certain values). In one option, the RVQoE metrics (or score, or value) refers to an individual UE. In another option, the RVQoE metrics (or score, or value) refers to a group of UEs (e.g., all the UEs handed over from gNBl to gNB2 in a certain time interval). The prediction on RVQoE can be combined with other parameters/metrics (e.g., with Energy Cost as in the following examples). • In one example, the gNBl requests to gNB2 to report the time in the future at which the gNB2 predicts that “Buffer Level status for UE1 will be below X”

• In another example, the gNBl requests to gNB2 to report the time in the future when the gNB2 predicts that “Average Buffer Level status for all UEs in the cell A will be below X” with an “Energy cost lower than X” will be fulfilled.

[00084] In one variant, the information related to prediction of event fulfillment is a time left until the event is predicted to be fulfilled. The event is to be intended as one of the events described above (predicted resource utilization, predicted energy cost, etc.)

[00085] In one variant, the information related to prediction of event fulfillment is an indication (e.g., a flag) indicating whether (or not) within a certain time the event is predicted to be fulfilled. The event is to be intended as one of the events described above (predicted resource utilization, predicted energy cost, etc.).

[00086] In one option, information related to prediction of event fulfillment is an accuracy of the predicted event (or an accuracy associated with the predicted event).

[00087] In one option, information related to prediction of event fulfillment is an uncertainty of the predicted event (or an uncertainty associated with the predicted event).

[00088] In one option, information related to prediction of event fulfillment is a validity of the predicted event (or a validity associated with the predicted event).

[00089] In one embodiment, when the requested information related to prediction of event fulfillment:

• is a time in the future, or

• is an indication (e.g., a flag) indicating whether (or not) within a certain time a certain event is predicted to be fulfilled, or

• is a time interval in the future, or

• is an amount of time left to the predicted fulfillment of the event; then the existing parameter requested prediction time described in the preceding options of the solution is reused, to indicate a reference time interval, within which the gNB2 is expected to provide the information related to prediction of event fulfillment.

[00090] A possible use case can be the following: the gNBl plans an energy saving action (e.g., turning off one of its cells), and before executing that, it wants to collect information on whether the predicted load (or traffic) at one of its neighboring nodes will become excessive, or whether the energy cost of one of its neighboring nodes will become too high. A possible outcome could be that if the gNB2 indicates to gNBl that its load (e.g., the predicted number of RRC connected user is above a threshold value, or the energy cost at the gNB2 is too high, the gNBl will not execute (or it will postpone) the planned energy saving action. The requested prediction time included in the request in this case can be seen as a period of time that relates to time interval from the planning and the execution (e.g., it could be 90% of this time interval). The gNBl can then send a request to the gNB2, wherein the request includes a requested prediction time and it is intended that, within this time, the gNB2 needs to inform (report) to the gNBl one of the following:

• a time in the future for which the gNB2 predicts that a certain load metric (or more load metrics, or the energy cost) will surpass a certain threshold (for example, gNB2 should report if it expects the number of RRC connected users to be higher than 100, or if it expects the number of total PRB utilization to be higher than 50%).

• an indication (e.g., a flag) indicating whether (or not) within the requested prediction time at least one of the above conditions on load is predicted to happen.

• a time interval in the future during which at least one of the above conditions on load is predicted to happen.

[00091] In one embodiment, the information related to prediction of event fulfillment is requested to be provided before a certain timer running at the gNBl expires. For example, the gNB2 is requested to provide information related to prediction of event fulfillment before a timer used by the gNBl for keeping the UE context expires.

[00092] In one embodiment, the information related to prediction of event fulfillment is requested to be provided before a certain timer running at the gNB2 expires. For example, the gNB2 is requested to provide information related to prediction of event fulfillment before or within the time interval during which the UE Trajectory is measured at gNB2 for a UE handed over from gNBl to gNB2 reaches the end.

[00093] In one embodiment, the gNBl indicates in the FIRST MESSAGE to the gNB2 one or more conditions to be used by the gNB2 to determine whether to continue (or to stop) the reporting of information related to prediction of event fulfillment.

[00094] In one embodiment, the gNB2 continues to report to gNBl information related to prediction of event fulfillment after the first reporting. One or more conditions can be considered by the gNB2 for determining whether and for how long the reporting can continue, as detailed here below. • In one case, reporting of information related to prediction of event fulfillment continues until a certain Reporting duration specified in the request by gNBl has passed and stops after that. o For example, if the information related to prediction of event fulfillment is (or at least comprises) a time interval related to the predicted event, the gNB2 continues to report refinements (and/or updates) of the time interval until a certain Reporting duration specified by gNBl has passed and stops after that

• In one case, reporting of information related to prediction of event fulfillment continues until a certain number of reports have been sent.

• In one case, reporting of information related to prediction of event fulfillment continues until an RLF is detected by the UE associated to the predicted event fulfillment.

• In one case, reporting of information related to prediction of event fulfillment continues until the UE associated to the predicted event fulfillment transit from a certain RRC state to another RRC state (e g., from RRC CONNECTED to RRC INACTIVE or from RRC CONNECTED to RRC IDLE).

[00095] In another embodiment of the solution applied to the energy saving use-case, gNB 1 may request gNB2 for reporting of information related to prediction of event fulfillment where gNB2 is requested to report the predicted time at which the energy cost parameter may be above or below a threshold. gNBl may configure multiple reporting configurations with different thresholds with the intention of obtaining a snapshot of different points in time at which the thresholds may be met. Such a set of requests may be done by sending several separate configurations, or one single configuration which specifies the multiple thresholds for which reporting is to be performed.

[00096] In another embodiment, gNBl may request gNB2 to report information related to prediction of event fulfillment in addition to a time-window or a period of time from the point in time when the event will be fulfilled as part of the request. gNB2 may be expected to report the predicted time of event fulfillment in addition to a time-window (starting from the point in time at which the event is fulfilled) of predicted values for the Energy Cost metric (assuming this as the metric of interest). gNBl upon reception of the event fulfillment and how the Energy Cost of the neighbor gNB may be predicted to change after the event fulfillment can plan energy saving actions in line with the predictions.

[00097] One example of how some of the embodiments above may be implemented is shown below, taking the Data Collection Request and Data Collection Update messages signalled over the Xn interface into account. In the example below the case of SSB Area DL/UL Total PRB usage and Energy Cost events is taken into account.

9.1.3. CC DATA COLLECTION REQUEST

Direction: NG-RAN nodei -> NG-RAN node2.

9.1.3.FF DATA COLLECTION UPDATE

This message is sent by NG-RAN node2 to NG-RAN nodel to report the requested information.

Direction:

nodei.

9.2.3.00 Event Prediction Configuration

The Event Prediction Configuration IE contains a list of events for which a fulfilment prediction is requested.

9.2.3.000 Event Prediction Information

The Event Prediction Information IE contains information about the event for which a fulfilment prediction is requested.

9.2.3. PP Event Prediction Reporting

The Event Prediction Reporting IE contains a list of events for which a fulfilment prediction is provided.

Applicability of the methods

[00098] The methods described in the previous scenarios may apply:

• between RAN nodes

• between a gNB-DU and a gNB-CU

• between a gNB-CU-CP and a gNB-CU-UP

• between a RAN node and a CN node

• between a RAN node and an 0AM node

• between of of a gNB-DU, or a gNB-CU-CP, or a gNB-CU-UP and an 0AM node

[00099] Figure 7 shows an example of a communication system 700 in accordance with some embodiments.

[000100] In the example, the communication system 700 includes a telecommunication network 702 that includes an access network 704, such as a radio access network (RAN), and a core network 706, which includes one or more core network nodes 708. The access network 704 includes one or more access network nodes, such as network nodes 710a and 710b (one or more of which may be generally referred to as network nodes 710), or any other similar 3^rd Generation Partnership Project (3GPP) access nodes or non-3GPP access points. Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network 702 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 702 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network 702, including one or more network nodes 710 and/or core network nodes 708.

[000101] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O- CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an Al, Fl, Wl, El, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an O-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes 710 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 712a, 712b, 712c, and 712d (one or more of which may be generally referred to as UEs 712) to the core network 706 over one or more wireless connections.

[000102] Example wireless communications over a wireless connection include transmitting and/or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and/or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 700 may include any number of wired or wireless networks, network nodes, UEs, and/or any other components or systems that may facilitate or participate in the communication of data and/or signals whether via wired or wireless connections. The communication system 700 may include and/or interface with any type of communication, telecommunication, data, cellular, radio network, and/or other similar type of system. [000103] The UEs 712 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and/or operable to communicate wirelessly with the network nodes 710 and other communication devices. Similarly, the network nodes 710 are arranged, capable, configured, and/or operable to communicate directly or indirectly with the UEs 712 and/or with other network nodes or equipment in the telecommunication network 702 to enable and/or provide network access, such as wireless network access, and/or to perform other functions, such as administration in the telecommunication network 702.

[000104] In the depicted example, the core network 706 connects the network nodes 710 to one or more hosts, such as host 716. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 706 includes one more core network nodes (e.g., core network node 708) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and/or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 708. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and/or a User Plane Function (UPF).

[000105] The host 716 may be under the ownership or control of a service provider other than an operator or provider of the access network 704 and/or the telecommunication network 702, and may be operated by the service provider or on behalf of the service provider. The host 716 may host a variety of applications to provide one or more services. Examples of such applications include the provision of live and/or pre-recorded audio/video content, data collection services, for example, 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.

[000106] As a whole, the communication system 700 of Figure 7 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and/or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and/or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and/or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[000107] In some examples, the telecommunication network 702 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 702 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 702. For example, the telecommunications network 702 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.

[000108] In some examples, the UEs 712 are configured to transmit and/or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 704 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 704. 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).

[000109] In the example illustrated in Figure 7, the hub 714 communicates with the access network 704 to facilitate indirect communication between one or more UEs (e.g., UE 712c and/or 712d) and network nodes (e.g., network node 710b). In some examples, the hub 714 may be a controller, router, a content source and analytics node, or any of the other communication devices described herein regarding UEs. For example, the hub 714 may be a broadband router enabling access to the core network 706 for the UEs. As another example, the hub 714 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 710, or by executable code, script, process, or other instructions in the hub 714. As another example, the hub 714 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 714 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 714 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 714 then provides to the UE either directly, after performing local processing, and/or after adding additional local content. In still another example, the hub 714 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices. [000110] The hub 714 may have a constant/persistent or intermittent connection to the network node 710b. The hub 714 may also allow for a different communication scheme and/or schedule between the hub 714 and UEs (e.g., UE 712c and/or 712d), and between the hub 714 and the core network 706. In other examples, the hub 714 is connected to the core network 706 and/or one or more UEs via a wired connection. Moreover, the hub 714 may be configured to connect to an M2M service provider over the access network 704 and/or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 710 while still connected via the hub 714 via a wired or wireless connection. In some embodiments, the hub 714 may be a dedicated hub - that is, a hub whose primary function is to route communications to/from the UEs from/to the network node 710b. In other embodiments, the hub 714 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 710b, but which is additionally capable of operating as a communication start and/or end point for certain data channels.

[000111] Figure 8 shows a UE 800 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless camera, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded/integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and/or an enhanced MTC (eMTC) UE. [000112] A UE may support device-to-device (D2D) communication, for example by implementing a 3 GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle- to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and/or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[000113] The UE 800 includes processing circuitry 802 that is operatively coupled via a bus 804 to an input/output interface 806, a power source 808, a memory 810, a communication interface 812, and/or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 8. 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.

[000114] The processing circuitry 802 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 810. The processing circuitry 802 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general -purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 802 may include multiple central processing units (CPUs). The processing circuitry 802 may be operable to provide, either alone or in conjunction with other UE 800 components, such as the memory 810, UE 800 functionality.

[000115] In the example, the input/output interface 806 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and/or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 800. 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.

[000116] In some embodiments, the power source 808 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 808 may further include power circuitry for delivering power from the power source 808 itself, and/or an external power source, to the various parts of the UE 800 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 808. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 808 to make the power suitable for the respective components of the UE 800 to which power is supplied.

[000117] The memory 810 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 810 includes one or more application programs 814, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 816. The memory 810 may store, for use by the UE 800, any of a variety of various operating systems or combinations of operating systems.

[000118] The memory 810 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD- DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and/or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 810 may allow the UE 800 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 810, which may be or comprise a device-readable storage medium.

[000119] The processing circuitry 802 may be configured to communicate with an access network or other network using the communication interface 812. The communication interface 812 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 822. The communication interface 812 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 818 and/or a receiver 820 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 818 and receiver 820 may be coupled to one or more antennas (e.g., antenna 822) and may share circuit components, software or firmware, or alternatively be implemented separately.

[000120] In some embodiments, communication functions of the communication interface 812 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and/or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol/intemet protocol (TCP/IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth. [000121] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 812, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[000122] 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 controls a robotic arm performing a medical procedure according to the received input.

[000123] 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 devices which are or which are 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. A UE in the form of an loT device comprises circuitry and/or software in dependence on the intended application of the loT device in addition to other components as described in relation to the UE 800 shown in Figure 8.

[000124] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and/or measurements, and transmits the results of such monitoring and/or measurements to another UE and/or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and/or reporting on its operational status or other functions associated with its operation.

[000125] 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.

[000126] Figure 9 shows a network node 900 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and/or operable to communicate directly or indirectly with a UE and/or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node (e g., O-RU, O-DU, O-CU).

[000127] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and/or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[000128] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi -standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell/multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and/or Minimization of Drive Tests (MDTs).

[000129] The network node 900 includes processing circuitry 902, a memory 904, a communication interface 906, and a power source 908, and/or any other component, or any combination thereof. The network node 900 may be composed of multiple physically separate components (e.g., aNodeB component and aRNC component, or aBTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 900 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 900 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 904 for different RATs) and some components may be reused (e.g., a same antenna 910 may be shared by different RATs). The network node 900 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 900, 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 900.

[000130] The processing circuitry 902 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 900 components, such as the memory 904, network node 900 functionality. For example, the processing circuitry 902 may be configured to cause the network node to perform the methods as described with reference to Figure 1 and/or Figure 2.

[000131] In some embodiments, the processing circuitry 902 includes a system on a chip (SOC). In some embodiments, the processing circuitry 902 includes one or more of radio frequency (RF) transceiver circuitry 912 and baseband processing circuitry 914. In some embodiments, the radio frequency (RF) transceiver circuitry 912 and the baseband processing circuitry 914 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 912 and baseband processing circuitry 914 may be on the same chip or set of chips, boards, or units. [000132] The memory 904 may comprise any form of volatile or non-volatile computer- readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and/or any other volatile or non-volatile, non-transitory device-readable and/or computerexecutable memory devices that store information, data, and/or instructions that may be used by the processing circuitry 902. The memory 904 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and/or other instructions capable of being executed by the processing circuitry 902 and utilized by the network node 900. The memory 904 may be used to store any calculations made by the processing circuitry 902 and/or any data received via the communication interface 906. In some embodiments, the processing circuitry 902 and memory 904 is integrated.

[000133] The communication interface 906 is used in wired or wireless communication of signaling and/or data between a network node, access network, and/or UE. As illustrated, the communication interface 906 comprises port(s)/terminal(s) 916 to send and receive data, for example to and from a network over a wired connection. The communication interface 906 also includes radio front-end circuitry 918 that may be coupled to, or in certain embodiments a part of, the antenna 910. Radio front-end circuitry 918 comprises filters 920 and amplifiers 922. The radio front-end circuitry 918 may be connected to an antenna 910 and processing circuitry 902. The radio front-end circuitry may be configured to condition signals communicated between antenna 910 and processing circuitry 902. The radio front-end circuitry 918 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 918 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 920 and/or amplifiers 922. The radio signal may then be transmitted via the antenna 910. Similarly, when receiving data, the antenna 910 may collect radio signals which are then converted into digital data by the radio front-end circuitry 918. The digital data may be passed to the processing circuitry 902. In other embodiments, the communication interface may comprise different components and/or different combinations of components.

[000134] In certain alternative embodiments, the network node 900 does not include separate radio front-end circuitry 918, instead, the processing circuitry 902 includes radio front-end circuitry and is connected to the antenna 910. Similarly, in some embodiments, all or some of the RF transceiver circuitry 912 is part of the communication interface 906. In still other embodiments, the communication interface 906 includes one or more ports or terminals 916, the radio front-end circuitry 918, and the RF transceiver circuitry 912, as part of a radio unit (not shown), and the communication interface 906 communicates with the baseband processing circuitry 914, which is part of a digital unit (not shown).

[000135] The antenna 910 may include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals. The antenna 910 may be coupled to the radio front-end circuitry 918 and may be any type of antenna capable of transmitting and receiving data and/or signals wirelessly. In certain embodiments, the antenna 910 is separate from the network node 900 and connectable to the network node 900 through an interface or port.

[000136] The antenna 910, communication interface 906, and/or the processing circuitry 902 may be configured to perform any receiving operations and/or certain obtaining operations described herein as being performed by the network node. Any information, data and/or signals may be received from a UE, another network node and/or any other network equipment. Similarly, the antenna 910, the communication interface 906, and/or the processing circuitry 902 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and/or signals may be transmitted to a UE, another network node and/or any other network equipment.

[000137] The power source 908 provides power to the various components of network node 900 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 908 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 900 with power for performing the functionality described herein. For example, the network node 900 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 908. As a further example, the power source 908 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.

[000138] Embodiments of the network node 900 may include additional components beyond those shown in Figure 9 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and/or any functionality necessary to support the subject matter described herein. For example, the network node 900 may include user interface equipment to allow input of information into the network node 900 and to allow output of information from the network node 900. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 900.

[000139] Figure 10 is a block diagram of a host 1000, which may be an embodiment of the host 716 of Figure 7, in accordance with various aspects described herein. As used herein, the host 1000 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. The host 1000 may provide one or more services to one or more UEs.

[000140] The host 1000 includes processing circuitry 1002 that is operatively coupled via a bus 1004 to an input/output interface 1006, a network interface 1008, a power source 1010, and a memory 1012. 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 8 and 9, such that the descriptions thereof are generally applicable to the corresponding components of host 1000.

[000141] The memory 1012 may include one or more computer programs including one or more host application programs 1014 and data 1016, which may include user data, e.g., data generated by a UE for the host 1000 or data generated by the host 1000 for a UE. Embodiments of the host 1000 may utilize only a subset or all of the components shown. The host application programs 1014 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., FL AC, 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). The host application programs 1014 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, the host 1000 may select and/or indicate a different host for over-the-top services for a UE. The host application programs 1014 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.

[000142] Figure 11 is a block diagram illustrating a virtualization environment 1100 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 1100 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 1100 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface.

[000143] Applications 1102 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and/or benefits of some of the embodiments disclosed herein.

[000144] Hardware 1104 includes processing circuitry, memory that stores software and/or instructions executable by hardware processing circuitry, and/or other hardware devices as described herein, such as a network interface, input/output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1106 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 1108a and 1108b (one or more of which may be generally referred to as VMs 1108), and/or perform any of the functions, features and/or benefits described in relation with some embodiments described herein. The virtualization layer 1106 may present a virtual operating platform that appears like networking hardware to the VMs 1108. [000145] The VMs 1108 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 1106. Different embodiments of the instance of a virtual appliance 1102 may be implemented on one or more of VMs 1108, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[000146] In the context of NFV, a VM 1108 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 1108, and that part of hardware 1104 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 1108 on top of the hardware 1104 and corresponds to the application 1102.

[000147] Hardware 1104 may be implemented in a standalone network node with generic or specific components. Hardware 1104 may implement some functions via virtualization. Alternatively, hardware 1104 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 1110, which, among others, oversees lifecycle management of applications 1102. In some embodiments, hardware 1104 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 1112 which may alternatively be used for communication between hardware nodes and radio units.

[000148] Figure 12 shows a communication diagram of a host 1202 communicating via a network node 1204 with a UE 1206 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE 712a of Figure 7 and/or UE 800 of Figure 8), network node (such as network node 710a of Figure 7 and/or network node 900 of Figure 9), and host (such as host 716 of Figure 7 and/or host 1000 of Figure 10) discussed in the preceding paragraphs will now be described with reference to Figure 12.

[000149] Like host 1000, embodiments of host 1202 include hardware, such as a communication interface, processing circuitry, and memory. The host 1202 also includes software, which is stored in or accessible by the host 1202 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 the UE 1206 connecting via an over-the-top (OTT) connection 1250 extending between the UE 1206 and host 1202. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 1250. [000150] The network node 1204 includes hardware enabling it to communicate with the host 1202 and UE 1206. The connection 1260 may be direct or pass through a core network (like core network 706 of Figure 7) 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.

[000151] The UE 1206 includes hardware and software, which is stored in or accessible by UE 1206 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 1206 with the support of the host 1202. In the host 1202, an executing host application may communicate with the executing client application via the OTT connection 1250 terminating at the UE 1206 and host 1202. 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. The OTT connection 1250 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 the OTT connection 1250.

[000152] The OTT connection 1250 may extend via a connection 1260 between the host 1202 and the network node 1204 and via a wireless connection 1270 between the network node 1204 and the UE 1206 to provide the connection between the host 1202 and the UE 1206. The connection 1260 and wireless connection 1270, over which the OTT connection 1250 may be provided, have been drawn abstractly to illustrate the communication between the host 1202 and the UE 1206 via the network node 1204, without explicit reference to any intermediary devices and the precise routing of messages via these devices. [000153] As an example of transmiting data via the OTT connection 1250, in step 1208, the host 1202 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 the UE 1206. In other embodiments, the user data is associated with a UE 1206 that shares data with the host 1202 without explicit human interaction. In step 1210, the host 1202 initiates a transmission carrying the user data towards the UE 1206. The host 1202 may initiate the transmission responsive to a request transmitted by the UE 1206. The request may be caused by human interaction with the UE 1206 or by operation of the client application executing on the UE 1206. The transmission may pass via the network node 1204, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 1212, the network node 1204 transmits to the UE 1206 the user data that was carried in the transmission that the host 1202 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 1214, the UE 1206 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 1206 associated with the host application executed by the host 1202.

[000154] In some examples, the UE 1206 executes a client application which provides user data to the host 1202. The user data may be provided in reaction or response to the data received from the host 1202. Accordingly, in step 1216, the UE 1206 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 the UE 1206. Regardless of the specific manner in which the user data was provided, the UE 1206 initiates, in step 1218, transmission of the user data towards the host 1202 via the network node 1204. In step 1220, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 1204 receives user data from the UE 1206 and initiates transmission of the received user data towards the host 1202. In step 1222, the host 1202 receives the user data carried in the transmission initiated by the UE 1206.

[000155] One or more of the various embodiments improve the performance of OTT services provided to the UE 1206 using the OTT connection 1250, in which the wireless connection 1270 forms the last segment. More precisely, the teachings of these embodiments may improve the efficient handling of network predictions and thereby provide benefits such as reduced processing resources and/or signalling resources, and or improved predictions.

[000156] In an example scenario, factory status information may be collected and analyzed by the host 1202. As another example, the host 1202 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 1202 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 1202 may store surveillance video uploaded by a UE. As another example, the host 1202 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, the host 1202 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.

[000157] 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 the OTT connection 1250 between the host 1202 and UE 1206, 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 the host 1202 and/or UE 1206. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 1250 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 the OTT connection 1250 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node 1204. 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 the host 1202. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 1250 while monitoring propagation times, errors, etc.

[000158] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and/or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and/or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[000159] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer- readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer- readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and/or by end users and a wireless network generally.

The following numbered statements provide additional information on the disclosure.

1. A method performed by a first network node for supporting network predictions, the method comprising: initiating transmission of a first message to a second network node, wherein the first message comprises a request for a network prediction; and receiving a response to the first message from the second network node.

2. The method of statement 1, wherein the request for a network prediction comprises a request for a prediction relating to a specific point in time in the future.

3. The method of statement 2, wherein the request further comprises a request for one or more intermediary predictions between the current time and the specific point in time in the future.

4. The method of statement 3, wherein the request specifies a periodicity with which the one or more intermediary predictions should be provided.

5. The method of any of embodiments 3 and 4, wherein the request specifies refinements of successive predictions.

6. The method of statement 2, wherein the request specifies that a single prediction relating to the specific point in time should be provided, optionally with intermediary refinements of the prediction provided.

7. The method of statement 5 or 6, wherein the refinements comprise one or more of: an updated prediction; an updated uncertainty for an earlier prediction; an indication of whether or not an earlier prediction is applicable; and a request to provide updated predictions in the event of retraining or parameter optimization of a Machine Learning, ML, model used to generate predictions.

8. The method of any preceding statement, wherein the first message requests information related to predictions of event fulfilment.

9. The method of statement 8, wherein the first message further specifies parameters used to define the event.

10. The method of statement 9, wherein the parameters include one or more of: time parameters; resource parameters; and performance measurement parameters.

11. The method of any of statements8 to 10, wherein the first message specifies one or more first conditions to be used by the second network node to determine whether or not to start providing or to continue providing predictions and/or refinements. The method of any preceding statement, wherein the first message further specifies one or more second conditions, and instructs the second network node not to provide or to stop providing a prediction if one or more of the second conditions is satisfied. The method of any preceding statement, wherein the first message is a data collection request message. The method of statement 13, wherein the data collection request message is an Xn Application Protocol, XnAP, message or Fl Application Protocol, Fl AP, message. The method of any preceding statement, wherein the response from the second network node is a second message, a third message and/or a fourth message. The method of statement 15, wherein the second message is a data collection response message, wherein the third message is a data collection update message and/or wherein the fourth message is a data collection update message or handover acknowledgement message. The method of any of statements 15 and 16, wherein the response from the second network node includes one or more Xn Application Protocol, XnAP, messages. The method of any of statements 15 and 16, wherein the response from the second network node includes one or more Fl Application Protocol, Fl AP, messages. The method of any of the previous statements, further comprising: obtaining user data; and forwarding the user data to a host via the transmission to the network node. A method performed by a second network node for supporting network predictions, the method comprising: receiving a first message from a first network node, wherein the first message comprises a request for a network prediction; determining a response to the first message; and initiating transmission of the response to the first network node. The method of statement 20, wherein the request for a network prediction comprises a request for a prediction relating to a specific point in time in the future, and wherein the response comprises providing a prediction relating to the specific point in time. The method of statement 21, wherein the response further comprises one or more intermediary predictions between the current time and the specific point in time. The method of statement 22, wherein the intermediary predictions are provided by the second network node with a periodicity specified in the first message. The method of any of statements 22 and 23, wherein each successive prediction provides refinements. The method of statement 21, wherein the response comprises a single prediction, optionally wherein the response comprises intermediary refinements of the prediction. The method of any of statements 24 and 25, wherein the intermediary refinements comprise one or more of: an updated prediction; an updated uncertainty for an earlier prediction; and an indication of whether or not an earlier prediction is applicable; a request to provide updated predictions in the event of retraining or parameter optimization of a Machine Learning, ML, model used to generate predictions. The method of any of statements 20 to 26, wherein the second network node provides information related to predictions of event fulfillment. The method of statement 27, wherein the information is based on parameters provided by the first network node, the parameters being used to define the event. The method of statement 28, wherein the parameters include one or more of: time parameters; resource parameters; and performance measurement parameters. The method any of statements 27 to 29, wherein the second network node determines, based on one or more first conditions, whether or not to start providing or to continue providing predictions and/or refinements. The method of any of statements 20 to 30, wherein the second network node does not provide or stops providing a prediction if one or more second conditions is satisfied. The method of statement 31, wherein the second conditions are specified by the first network node. 33. The method of any of statements 20 to 32, wherein the first message is a data collection request message.

34. The method of statement 33, wherein the data collection request message is an Xn Application Protocol, XnAP, message or Fl Application Protocol, Fl AP, message.

35. The method of any of statements 20 to 34, wherein the response from the second network node is a second message, a third message and/or a fourth message.

36. The method of statement 35, wherein the second message is a data collection response message, wherein the third message is a data collection update message and/or wherein the fourth message is a data collection update message or handover acknowledgement message.

37. The method of any of statements 35 and 36, wherein the response from the second network node includes one or more Xn Application Protocol, XnAP, messages.

38. The method of any of statements 35 and 36, wherein the response from the second network node includes one or more Xn Application Protocol, XnAP, messages or one or more Fl Application Protocol, Fl AP, messages.

39. The method of any of statements 20 to 38, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment.

40. A first network node for supporting network predictions, comprising: processing circuitry configured to cause the first network node to perform any of the steps of any of statements 1 to 19; and power supply circuitry configured to supply power to the processing circuitry.

41. A second network node for supporting network predictions, the second network node comprising: processing circuitry configured to cause the second network node to perform any of the steps of any of statements 20 to 39; power supply circuitry configured to supply power to the processing circuitry.

42. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a network node in a cellular network for transmission to a user equipment (UE), the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of statements 1 to 19 or statements 20 to 39 to transmit the user data from the host to the UE.

43. The host of statement 42, wherein: the processing circuitry of the host is configured to execute a host application that provides the user data; and the UE comprises processing circuitry configured to execute a client application associated with the host application to receive the transmission of user data from the host.

44. A method implemented in a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; and initiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the network node performs any of the operations of any of statements 1 to 19 or statements 20 to 39 to transmit the user data from the host to the UE.

45. The method of statement 44, further comprising, at the network node, transmitting the user data provided by the host for the UE.

46. The method of any of statements 44 and 45, wherein the user data is provided at the host by executing a host application that interacts with a client application executing on the UE, the client application being associated with the host application.

47. A communication system configured to provide an over-the-top (OTT) service, the communication system comprising: a host comprising: processing circuitry configured to provide user data for a user equipment (UE), the user data being associated with the over-the-top service; and a network interface configured to initiate transmission of the user data toward a cellular network node for transmission to the UE, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of statements 1 to 19 or statements 20 to 39 to transmit the user data from the host to the UE.

48. The communication system of statement 47, further comprising: the network node; and/or the UE.

49. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to initiate receipt of user data; and a network interface configured to receive the user data from a network node in a cellular network, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of statements 1 to 19 or statements 20 to 39 to receive the user data from a user equipment (UE) for the host.

50. The host of statements 48 and 49, wherein: the processing circuitry of the host is configured to execute a host application that receives the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

51. The host of the any of statements 49 and 50, wherein the initiating receipt of the user data comprises requesting the user data.

52. A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: at the host, initiating receipt of user data from the UE, the user data originating from a transmission which the network node has received from the UE, wherein the network node performs any of the steps of any statements 1 to 19 or statements 20 to 39 to receive the user data from the UE for the host.

53. The method of the previous embodiment, further comprising at the network node, transmitting the received user data to the host.

54. The host of statement 53, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data to the UE from the host.

55. The host of statements 53 and 54, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

56. The method of statement 55, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the host application.

57. The method of statement 56, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application.

58. The host of statement 57, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data from the UE to the host.

59. The host of statements 57 and 58, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

60. The method of statement 59, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the UE.

61. The method of statements 59 and 60, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application.

Claims

1. A method performed by a first network node (710, 900) for supporting network predictions, the method comprising: initiating (102) transmission of a first message to a second network node (710, 900), wherein the first message comprises a request for a network prediction; and receiving (104) a response to the first message from the second network node (710, 900).

2. The method of claim 1, wherein the request for a network prediction comprises a request for a prediction relating to a specific point in time in the future.

3. The method of claim 2, wherein the request further comprises a request for one or more intermediary predictions between the current time and the specific point in time in the future.

4. The method of claim 3, wherein the request specifies a periodicity with which the one or more intermediary predictions should be provided.

5. The method of any of claims 3 and 4, wherein the request specifies refinements of successive predictions.

6. The method of claim 2, wherein the request specifies that a single prediction relating to the specific point in time should be provided, optionally with intermediary refinements of the prediction provided.

7. The method of claims 5 or 6, wherein the refinements comprise one or more of: an updated prediction; an updated uncertainty for an earlier prediction; an indication of whether or not an earlier prediction is applicable; and a request to provide updated predictions in the event of retraining or parameter optimization of a Machine Learning, ML, model used to generate predictions.

8. The method of any preceding embodiment, wherein the first message requests information related to predictions of event fulfilment.

9. The method of claim 8, wherein the first message further specifies parameters used to define the event.

10. The method of claim 9, wherein the parameters include one or more of: time parameters; resource parameters; and performance measurement parameters.

1 1 . The method of any of claims 8 to 10, wherein the first message specifies one or more first conditions to be used by the second network node (710, 900) to determine whether or not to start providing or to continue providing predictions and/or refinements.

12. The method of any preceding claim, wherein the first message further specifies one or more second conditions, and instructs the second network node (710, 900) not to provide or to stop providing a prediction if one or more of the second conditions is satisfied.

13. The method of any preceding claim, wherein the first message is a data collection request message.

14. The method of claim 13, wherein the data collection request message is an Xn Application Protocol, XnAP, message or Fl Application Protocol, F1AP, message.

15. The method of any preceding claim, wherein the response from the second network node (710, 900) is a second message, a third message and/or a fourth message.

16. The method of claim 15, wherein the second message is a data collection response message, wherein the third message is a data collection update message and/or wherein the fourth message is a data collection update message or handover acknowledgement message.

17. The method of any of claims 15 and 16, wherein the response from the second network node (710, 900) includes one or more Xn Application Protocol, XnAP, messages.

18. The method of any of claims 15 and 16, wherein the response from the second network node (710, 900) includes one or more Fl Application Protocol, F1AP, messages.

19. A method performed by a second network node (710, 900) for supporting network predictions, the method comprising: receiving (202) a first message from a first network node (710, 900), wherein the first message comprises a request for a network prediction; determining (204) a response to the first message; and initiating (206) transmission of the response to the first network node (710, 900).

20. The method of claim 19, wherein the request for a network prediction comprises a request for a prediction relating to a specific point in time in the future, and wherein the response comprises providing a prediction relating to the specific point in time.

21. The method of claim 20, wherein the response further comprises one or more intermediary predictions between the current time and the specific point in time.

22. The method of claim 21 , wherein the intermediary predictions are provided by the second network node (710, 900) with a periodicity specified in the first message.

23. The method of any of claims 21 and 22, wherein each successive prediction provides refinements.

24. The method of claim 20, wherein the response comprises a single prediction, optionally wherein the response comprises intermediary refinements of the prediction.

25. The method of any of claims 23 and 24, wherein the intermediary refinements comprise one or more of: an updated prediction; an updated uncertainty for an earlier prediction; and an indication of whether or not an earlier prediction is applicable; a request to provide updated predictions in the event of retraining or parameter optimization of a Machine Learning, ML, model used to generate predictions.

26. The method of any of claims 19 to 25, wherein the second network node (710, 900) provides information related to predictions of event fulfillment.

27. The method of claim 26, wherein the information is based on parameters provided by the first network node (710, 900), the parameters being used to define the event.

28. The method of claim 27, wherein the parameters include one or more of: time parameters; resource parameters; and performance measurement parameters.

29. The method any of claims 26 to 28, wherein the second network node (710, 900) determines, based on one or more first conditions, whether or not to start providing or to continue providing predictions and/or refinements.

30. The method of any of claims 19 to 29, wherein the second network node (710, 900) does not provide or stops providing a prediction if one or more second conditions is satisfied.

31. The method of claim 30, wherein the second conditions are specified by the first network node (710, 900).

32. The method of any of claims 19 to 31, wherein the first message is a data collection request message.

33. The method of claim 32, wherein the data collection request message is an Xn Application Protocol, XnAP, message or Fl Application Protocol, F1AP, message.

34. The method of any of claims 19 to 33, wherein the response from the second network node (710, 900) is a second message, a third message and/or a fourth message.

35. The method of claim 34, wherein the second message is a data collection response message, wherein the third message is a data collection update message and/or wherein the fourth message is a data collection update message or handover acknowledgement message.

36. The method of any of claims 34 and 35, wherein the response from the second network node (710, 900) includes one or more Xn Application Protocol, XnAP, messages or one or more Fl Application Protocol, F1AP, messages.

37. A first network node (710, 900) for supporting network predictions, comprising: processing circuitry (902) configured to cause the first network node (710, 900) to: initiate (102) transmission of a first message to a second network node (710, 900), wherein the first message comprises a request for a network prediction; and receive (104) a response to the first message from the second network node (710, 900); and power supply circuitry (908) configured to supply power to the processing circuitry (902).

38. A second network node (710, 900) for supporting network predictions, the second network node (710, 900) comprising: processing circuitry (902) configured to cause the second network node (710, 900) to: receive (202) a first message from a first network node (710, 900), wherein the first message comprises a request for a network prediction; determine (204) a response to the first message; and initiate (206) transmission of the response to the first network node (710, 900); and power supply circuitry (908) configured to supply power to the processing circuitry (902).

39. A communication system (700) comprising at least one of: the first network node (710, 900) and second network node (710, 900) of claims 37 to 38.

40. The communication system (700) of claim 39, further comprising at least one User Equipment, UE (712).

41. A computer program comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out a method according to any of claims 1 to 36.

42. A computer program product comprising non-transitory computer readable media having stored thereon a computer program according to claim 41.