User equipment and method performed therein

Abstract

Embodiments herein relate to, for example, a method performed by a UE (10) for handling communication in a wireless communications network. The UE (10) obtains one or more indications indicating a respective performance metric of a computational model used for estimating an instance parameter related to a signalling process. The UE (10) further triggers an action at the UE (10) when a condition is fulfilled, wherein the condition is related to a prediction of one or more upcoming indications taking the obtained one or more indications into account.

Description

[0001] USER EQUIPMENT AND METHOD PERFORMED THEREIN

[0002] TECHNICAL FIELD

[0003] Embodiments herein relate to a user equipment (UE), and a method performed therein for communication. Furthermore, a computer program and a computer readable storage medium are also provided herein. In particular, embodiments herein relate to handling communication, such as handling a computational model, in a wireless communications network.

[0004] BACKGROUND

[0005] In a typical wireless communications network, UEs, also known as wireless communication devices, mobile stations, stations (STA) and / or wireless devices, communicate via for example a Radio Access Network (RAN) with one or more core networks (CN). The RAN covers a geographical area which is divided into service areas or cell areas, with each service area or cell area being served by radio network node such as an access node e.g. a Wi-Fi access point or a radio base station (RBS), which in some networks may also be called, for example, a NodeB, a gNodeB, or an eNodeB. The service area or cell area is a geographical area where radio coverage is provided by the radio network node. The radio network node operates on radio frequencies to communicate over an air interface with the UEs within range of the radio network node. The radio network node communicates over a downlink (DL) to the UE and the UE communicates over an uplink (UL) to the radio network node.

[0006] A Universal Mobile Telecommunications System (UMTS) is a third generation telecommunications network, which evolved from the second generation (2G) Global System for Mobile Communications (GSM). The UMTS terrestrial radio access network (UTRAN) is essentially a RAN using wideband code division multiple access (WCDMA) and / or High-Speed Packet Access (HSPA) for communication with user equipment. In a forum known as the Third Generation Partnership Project (3GPP), telecommunications suppliers propose and agree upon standards for present and future generation networks and UTRAN specifically, and investigate enhanced data rate and radio capacity. In some RANs, e.g. as in UMTS, several radio network nodes may be connected, e.g., by landlines or microwave, to a controller node, such as a radio network controller (RNC) or a base station controller (BSC), which supervises and coordinates various activities of the plural radio network nodes connected thereto. The RNCs are typically connected to one or more core networks.

[0007] Specifications for the Evolved Packet System (EPS) have been completed within the 3GPP and this work continues in the coming 3GPP releases, such as 5G, for example New Radio (NR), and beyond networks. The EPS comprises the Evolved Universal Terrestrial Radio Access Network (E-UTRAN), also known as the Long-Term Evolution (LTE) radio access network, and the Evolved Packet Core (EPC), also known as System Architecture Evolution (SAE) core network. E-UTRAN / LTE is a 3GPP radio access technology wherein the radio network nodes are directly connected to the EPC core network. As such, the Radio Access Network (RAN) of an EPS has an architecture comprising radio network nodes connected directly to one or more core networks.

[0008] With the 5G technologies such as NR, focus is on a set of features such as the use of very many transmit- and receive-antenna elements that makes it possible to utilize beamforming, such as transmit-side and receive-side beamforming. Transmit-side beamforming means that the transmitter can amplify the transmitted signals in a selected direction or directions, while suppressing the transmitted signals in other directions. Similarly, on the receive-side, a receiver can amplify signals from a selected direction or directions, while suppressing unwanted signals from other directions.

[0009] Beam management procedure.

[0010] In high frequency range such as Frequency Range two (FR2), multiple radio frequency (RF) beams may be used to transmit and receive signals at a gNB and a UE. For each DL beam from a gNB, there is typically an associated best UE reception (Rx) beam for receiving signals from the DL beam. The DL beam and the associated UE Rx beam forms a beam pair. The beam pair can be identified through a so-called beam management process in NR.

[0011] A DL beam is, typically, identified by an associated DL reference signal (RS) transmitted in the beam, either periodically, semi-persistently, or aperiodically. The DL RS for the purpose can be a Synchronization Signal (SS) and Physical Broadcast Channel (PBCH) block (SSB) or a Channel State Information RS (CSI-RS). By measuring all the DL RSs, the UE can determine and report to the gNB the best DL beam to use for DL transmissions. The gNB can then transmit a burst of DL-RS in the reported best DL beam to let the UE evaluate candidate UE RX beams.

[0012] Reference signal configurations for different types of reference signals: A CSI-RS is transmitted over each Transmission (Tx) antenna port at the network node and for different antenna ports. The CSI-RS are multiplexed in time, frequency, and code domain such that the channel between each Tx antenna port at the network node and each receive antenna port at a UE can be measured by the UE. The time-frequency resource used for transmitting CSI-RS is referred to as a CSI-RS resource.

[0013] In NR, the CSI-RS for beam management is defined as a 1- or 2-port CSI-RS resource in a CSI-RS resource set where the filed repetition is present. The following three types of CSI-RS transmissions are supported:

[0014] • Periodic CSI-RS: CSI-RS is transmitted periodically in certain slots. This CSI-RS transmission is semi-statically configured using radio resource control (RRC) signalling with parameters such as CSI-RS resource, periodicity, and slot offset.

[0015] • Semi-Persistent CSI-RS: Similar to periodic CSI-RS, resources for semi- persistent CSI-RS transmissions are semi-statically configured using RRC signalling with parameters such as periodicity and slot offset. However, unlike periodic CSI-RS, dynamic signalling is needed to activate and deactivate the CSI- RS transmission.

[0016] • Aperiodic CSI-RS: This is a one-shot CSI-RS transmission that can happen in any slot. Here, one-shot means that CSI-RS transmission only happens once per trigger. The CSI-RS resources, i.e. , the Resource Element (RE) locations which consist of subcarrier locations and orthogonal frequency division multiplexing (OFDM) symbol locations, for aperiodic CSI-RS are semi-statically configured.

[0017] The transmission of aperiodic CSI-RS is triggered by dynamic signalling through Physical Downlink Control Channel (PDCCH) using the CSI request field in UL DCI, in the same DCI where the UL resources for the measurement report are scheduled. Multiple aperiodic CSI-RS resources can be included in a CSI-RS resource set and the triggering of aperiodic CSI-RS is on a resource set basis.

[0018] During the 3GPP meeting RAN1#109-e it was agreed to study Artificial Intelligence (Al) and / or Machine Learning (ML) based spatial beam prediction, the core idea of which is as follows: Predict the “best” beam (or beams) from a Set A of beams using measurement results from another Set B of beams.

[0019] Set A and Set B of beams have not been defined yet (left for future study); however, the following two examples illustrate some scenarios that will likely be studied in Release (Rel) 18: Set B is a subset of a Set A. For example, Set A is a set of 8 SSB / CSI-RS beams shown in Fig. 1 , illustrated as both light and dark circles. The UE measures Set B such as the 4 beams indicated by dark circles. The AI / ML model may then predict the best beam (or beams) in Set A using only measurements from Set B. It is thus shown in Fig. 1 an example where Set B is a subset of Set A. Fig. 1 illustrates a grid-of-beam type radiation pattern: Each row (resp. column) depicts a certain zenith (resp. azimuth) angle from the antenna array. Set A has 8 beams and Set B has 4 beams (indicated by dark circles).

[0020] Set A and Set B correspond to two different sets of beams. For example, Set A is a set of 30 narrow CSI-RS beams, and Set B is a set of 8 wide SSB beams, see Fig. 2. The UE 10 may measure beams in Set B and the AI / ML model may predict the best beam(s) from Set A.

[0021] The spatial beam prediction can be performed in the gNB or the UE - the study item will cover both scenarios.

[0022] During the 3GPP meeting RAN1#110, it was agreed to study AI / ML model training both at the network (NW) side and UE side. Which side that performs the training is expected to impact how data collection is performed, where another agreement is to study the aspect of data collection for beam management. Moreover, it was agreed to study the aspect of model monitoring and the standard impact on AI / ML model inference, e.g., reporting of predicted values.

[0023] The following text is captured in the TR 38.843 v.1.1.0 regarding performance monitoring.

[0024] Performance monitoring:

[0025] For the performance monitoring of BM-Casel and BM-Case2:

[0026] - Performance metric(s) with the following alternatives:

[0027] - Alt.1 : Beam prediction accuracy related KPIs, e.g., Top-K / 1 beam prediction accuracy

[0028] - Alt.2: Link quality related KPIs, e.g., throughput, Ll-RSRP, Ll-SINR, hypothetical BLER

[0029] - Alt.3 : Performance metric based on input / output data distribution of AI / ML

[0030] - Alt.4: The Ll-RSRP difference evaluated by comparing measured RSRP and predicted RSRP

[0031] Benchmark / reference for the performance comparison, including: Alt.1 : The best beam(s) obtained by measuring beams of a set indicated by gNB (e.g., Beams from Set A)

[0032] - Alt.4: Measurements of the predicted best beam(s) corresponding to model output (e.g., Comparison between actual Ll-RSRP and predicted RSRP of predicted Top-l / K Beams)

[0033] - Signalling / configuration / measurement / report for model monitoring, e.g., signalling aspects related to assistance information (if supported), Reference signals

[0034] For BM-Casel and BM-Case2 with a UE-side AI / ML model:

[0035] - Type 1 performance monitoring:

[0036] - Configuration / Signalling from gNB to UE for measurement and / or reporting

[0037] - UE may have different operations

[0038] - Optionl : UE sends reporting to NW (e.g., for the calculation of performance metric at NW)

[0039] - Option2: UE calculates performance metric(s), either reports it to NW or reports an event to NW based on the performance metric(s)

[0040] - Indication from NW for UE to do LCM operations

[0041] - Note: At least the performance and reporting overhead of model monitoring mechanism should be considered

[0042] - Type2 performance monitoring (UE-side performance monitoring):

[0043] - Indication / request / report from UE to gNB for performance monitoring

[0044] - Note: The indication / request / report may be not needed in some case(s)

[0045] - Configuration / Signalling from gNB to UE for performance monitoring measurement and / or reporting

[0046] - UE calculates performance metric(s), either reports it to NW or reports an event to NW based on the performance metric(s)

[0047] - If it is for UE-side model monitoring, UE makes decision(s) of model selection / activation / deactivation / switching / fallback operation

[0048] - Indication from NW to UE to do LCM operation

[0049] - UE reporting of beam measurement(s) based on a set of beams indicated by gNB

[0050] - Signalling, e.g., RRC-based, Ll-based

[0051] - Note: Performance and UE complexity, power consumption should be considered Mechanism that facilitates the UE to detect whether the functionality / model is suitable or no longer suitable

[0052] Table 7.2.3-1 summarizes applicability of various alternatives for performance metric(s) of AI / ML model monitoring for BM-Casel and BM-Case2.

[0053] Table 7.2.3-1 : Alternatives for Performance metric(s) of AI / ML model monitoring for BM-Case 1 and BM-Case 2

[0054] Notel : The above analysis shall not give an indication about whether / which metric is supported or specified.

[0055] Note2: Monitoring performance of the above alternatives are not addressed in the table.

[0056] SUMMARY

[0057] As part of developing embodiments herein one or more problems were first identified. It is a problem that there is a large overhead in signalling, require a lot of RS transmissions resources and also UE measurements for performing performance metric calculations. To achieve sufficient statistics to determine at the NW whether to, for example, activate / deactivate a computational model or not, there is a need to have a lot of samples. Based on the NW received performance metrics, the NW can for example have a threshold on the required accuracy needed for activating the NW / UE-sided model, for example the required accuracy needs to be above 50% for the computational model to be activated, or not deactivated. Then, a performance metric such as beam prediction accuracy above 60% might impact the selection of a set of radio parameters, e.g., how many beams to measure in Top-K, in a certain way, and above 70% in another way. For example, in case of very high accuracy, the NW might not need to perform extra measurements to find the top-1 beam in the BM use case in 3GPP.

[0058] One issue that occurs for the low-accuracy models, is that the NW might spend unnecessary number of resources for the purpose on performance metric calculation on computational models that are performing significantly bad, i.e. a metric below e.g. 50%. For this case, the NW is not interested in activating and using such computational models.

[0059] Moreover, the NW is not interested in understanding whether an underperforming computational model achieves 10% or 20 %, it will anyway not activate such computational model. Note that 50% is an arbitrary selected number, for other models used for Ultra Reliable Low Latency Control (URLLC) traffic, a model might need >90% accuracy to be activated.

[0060] In general, the NW should spend more resources to understand if a model accuracy is 90% or 95%, in comparison to whether a model have 5% or 10% accuracy.

[0061] It is thus a problem how to minimize the resource overhead, signalling overhead and UE battery consumption for performance monitoring procedures, see Fig. 3.

[0062] The object of embodiments herein is to provide a mechanism handling communication, such as handling computational models, in an efficient manner.

[0063] According to an aspect of embodiments herein the object is achieved by providing a method performed by a UE for handling communication in a wireless communications network. The UE obtains one or more indications indicating a respective performance metric of a computational model used for estimating an instance parameter related to a signalling process. The UE further triggers an action at the UE when a condition is fulfilled, wherein the condition is related to a prediction of one or more upcoming indications taking the obtained one or more indications into account.

[0064] It is furthermore provided herein a computer program product comprising instructions, which, when executed on at least one processor, cause the at least one processor to carry out the methods herein, as performed by the UE. It is additionally provided herein a computer-readable storage medium, having stored thereon a computer program product comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out the methods herein, as performed by the UE.

[0065] The object is further achieved by providing a UE configured to perform the method herein. Thus, according to another aspect of embodiments herein the object is achieved by providing a UE for handling communication in a wireless communications network. The UE is configured to obtain one or more indications indicating a respective performance metric of a computational model used for estimating an instance parameter related to a signalling process. The UE is further configured to trigger an action at the UE when a condition is fulfilled, wherein the condition is related to a prediction of one or more upcoming indications taking the obtained one or more indications into account.

[0066] Embodiments herein focus on that the UE detects early how the computational model is performing and may efficiently react to the detection.

[0067] Embodiments herein thus enable the UE to quickly react to performance of UE sided computational models by allowing to trigger an action when obtaining an early indication, such as the one or more indications, of performance transparently at the UE. Thereby embodiments herein handle handling communication, such as implementation of computational models, in an efficient manner.

[0068] BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Embodiments will now be described in more detail in relation to the enclosed drawings, in which:

[0070] Fig. 1 shows an overview depicting sets of beams according to prior art;

[0071] Fig. 2 shows an overview depicting sets of beams according to prior art;

[0072] Fig. 3 shows an overview depicting accuracy versus required resources;

[0073] Fig. 4 shows an overview depicting a wireless communications network according to embodiments herein;

[0074] Fig. 5 shows a combined signalling scheme and flowchart according to embodiments herein;

[0075] Fig. 6 shows a schematic flowchart depicting a method performed by a UE according to embodiments herein;

[0076] Fig. 7 shows a combined signalling scheme and flowchart according to some embodiments herein;

[0077] Fig. 8 shows a schematic overview depicting performance metrics in time-instances;

[0078] Fig. 9 shows a schematic overview depicting performance metrics in time-instances;

[0079] Fig. 10 shows an overview depicting a wireless communications network according to some embodiments herein;

[0080] Fig. 11 shows a schematic overview depicting performance metrics in time-instances;

[0081] Fig. 12 shows a combined signalling scheme and flowchart according to some embodiments herein;

[0082] Fig. 13 is a block diagram depicting a UE according to embodiments herein; Fig. 14 shows an example of a communication system QQ100 in accordance with some embodiments;

[0083] Fig. 15 shows a UE QQ200 in accordance with some embodiments;

[0084] Fig. 16 shows a network node QQ300 in accordance with some embodiments;

[0085] Fig. 17 is a block diagram of a host QQ400, which may be an embodiment of the host QQ116 of Fig. 14, in accordance with various aspects described herein;

[0086] Fig. 18 is a block diagram illustrating a virtualization environment QQ500 in which functions implemented by some embodiments may be virtualized; and

[0087] Fig. 19 shows a communication diagram of a host QQ602 communicating via a network node QQ604 with a UE QQ606 over a partially wireless connection in accordance with some embodiments.

[0088] DETAILED DESCRIPTION

[0089] Embodiments herein relate to communication networks in general. Fig. 4 is a schematic overview depicting a wireless communications network 1. The wireless communications network 1 comprises one or more RANs and one or more CNs. The wireless communications network 1 may use a number of different technologies, such as Wi-Fi, Long Term Evolution (LTE), LTE-Advanced, NR, Wideband Code Division Multiple Access (WCDMA), Global System for Mobile communications / Enhanced Data rate for GSM Evolution (GSM / EDGE), Worldwide Interoperability for Microwave Access (WiMax), or Ultra Mobile Broadband (UMB), just to mention a few possible implementations.

[0090] In the wireless communications network 1, wireless devices e.g. a user equipment (UE) 10 such as an loT device, an A-loT device, a ZE device, a mobile station, a non-access point (non-AP) STA, a STA, a wireless device and / or a wireless terminal, communicate via one or more Access Networks (AN), e.g. a RAN, to one or more core networks (CN). It should be understood by those skilled in the art that “UE” is a non-limiting term which means any terminal, wireless communication terminal, internet of things (loT) capable device, Machine Type Communication (MTC) device, Device to Device (D2D) terminal, or node e.g. smart phone, laptop, mobile phone, sensor, relay, mobile tablets or even a base station communicating within a cell.

[0091] The wireless communications network 1 comprises a radio network node 12 providing radio coverage over a geographical area, e.g. a first service area, of a first radio access technology (RAT), such as NR, LTE, UMTS, Wi-Fi or similar. The radio network node 12 may be a radio access network node such as radio network controller or an access point such as a wireless local area network (WLAN) access point or an Access Point Station (AP ST A), an access controller, a base station, e.g. a radio base station such as a NodeB, an evolved Node B (eNB, eNodeB), a base transceiver station, Access Point Base Station, base station router, a transmission arrangement of a radio base station, a stand-alone access point or any other network unit capable of serving a UE within the service area served by the radio network node 12 depending e.g. on the first radio access technology and terminology used.

[0092] The UE 10 may use a computational model to indicate an instance parameter such as a signalling strength or a best beam. The UE 10 would in the most probable scenario be configured to calculate a performance metric of the computational model, or actually, the performance metric of the instance parameter from the computational model, based on one or more measurements for a large time-window. The performance metric indicates how accurate the instance parameter is. For such solution, it can imply a high measurement overhead for the UE 10. This might be unnecessary high for computational models with bad prediction performance. One improved solution that can mitigate the measurements at the UE 10 and report early. However, it is questionable how to configure the UE 10 to report an early performance metrics. It is possible that in some situations, the radio network node 12, also referred to as NW, does not support or enable such solution even if it is standardized. For example, when the radio network node 12 cannot spend extra resources associated for such signalling. This would imply unnecessarily high measurement overhead for the UE 10.

[0093] It might be enough that only a limited amount such as one or two samples are needed to determine that the computational model is underperforming and should be deactivated. Hence early detection of an underperforming AI / ML model is utilized herein.

[0094] Embodiments herein provide a UE implementation that triggers an action such as stops measuring reference signals used to estimate a performance metric of the instance parameter of a signalling process, when the UE 10 has determined in what range / value the performance metric will be in. The signalling process may be transmission of a beam, a reference signal and / or a synchronization signal, and the instance parameter may, for example, be a best beam indication or a signal strength of a CSI-RS. The action may comprise stop measuring, and / or perform computational model action such as change, train and / or remove computational model if the UE 10 knows that the performance metric is within a certain reporting value. The UE 10 may, additionally, or alternatively, also turn off RX chains, and / or delete saved measurement data based on the predicted performance metric of the computational model. • The concept “network” may refer to one of a generic network node, such as the radio network node 12, e.g., a gNB (or the corresponding node in a 6G network), a base station, a unit within the base station to handle at least some ML operation, a relay node, a core network node, a core network node that handle at least some ML operations, or a device supporting device to device (D2D) communication.

[0095] • A computational model may refer to an ML-based model, a configuration of an ML-based model, a non-ML-based functionality, or a configuration of a non- ML-based functionality.

[0096] • The terms “ML-model” and “Al-model” are interchangeable. An AI / ML model can be defined as a functionality or be part of a functionality that is deployed / implemented in a first node. This first node can receive a message from a second node indicating that the functionality is not performing correctly. Further, an AI / ML model can be defined as a feature or part of a feature that is implemented / supported in a first node. This first node can indicate the feature version to a second node. If the ML-model is updated, the feature version maybe changed by the first node.

[0097] Fig. 5 is a combined flowchart and signalling scheme according to some embodiments herein.

[0098] Action 501. The radio network node 12 may determine a reporting configuration for the UE 10, wherein the reporting configuration defines a condition.

[0099] Action 502. The radio network node 12 may transmit a configuration indication to the UE 10. The configuration indication may indicate the determined reporting configuration.

[0100] Action 503. The UE 10 may obtain from a computational model an estimate of an instance parameter such as a best beam or a signal strength of a CSI-RS.

[0101] Action 504. The UE 10 further obtains, such as determine, calculates, and / or measures, one or more indications indicating a respective performance metric of the computational model used for estimating the instance parameter related to the signalling process, such as the best beam or the signal strength of a CSI-RS. The one or more indications may be one or more values, such as accuracy values or fractions, indicating the level of accuracy of the instance parameter from the computational model. The UE may measure signal strength of RSs and compare with a result of the computational model to determine an indication. Action 505. The UE 10 may further check whether the condition is fulfilled or not fulfilled. The condition is related to a prediction of one or more upcoming indications taking the obtained one or more indications into account.

[0102] Action 506. The UE 10 then triggers an action at the UE 10 when the condition is fulfilled. The action may comprise disabling the computational model, stopping measuring signals for obtaining the one or more indications, changing the computational model to an alternative model, deleting a memory of one or more parameters such as measurements or computational model(s), and / or training the computational model.

[0103] Example embodiments of a method performed by the UE 10 for handling communication in the wireless communications network will now be described with reference to a flowchart depicted in Fig. 6. The actions do not have to be taken in the order stated below, but may be taken in any suitable order. Optional actions are marked in dashed boxes.

[0104] Action 601. The UE 10 may obtain a reporting configuration indicating the condition. The reporting configuration may be signalled from the radio network node 12 and / or preconfigured at the UE 10- Thus, the UE 10 may receive from the radio network node 12 the configuration indication, wherein the configuration indication may indicate the reporting configuration such as a number of times one or more indications are below a threshold before disabling usage of a computational model. The condition may thus be for establishing a performance measure of a computational model.

[0105] Action 602. The UE 10 may obtain an instance parameter related to a signalling process. The instance parameter related to the signalling process may comprise a signal strength or quality of the first beam relative other beams, and / or a signal strength or quality of a reference signal. For example, the UE 10 may obtain a parameter related to signal strength or quality of the first beam relative other beams using a computational model, such as an Al or ML model. As an example, the UE 10 may calculate a best beam or a signal strength in the computational model.

[0106] Action 603. The UE 10 obtains the one or more indications indicating a respective performance metric of the computational model used for estimating the instance parameter related to the signalling process. Hence, the UE 10 may obtain, such as determine, estimate or calculate, from measurements, an indication indicating the performance metric of the instance parameter from the computational model. The indication may be related to a first probability that the instance parameter is correct, also referred to, value of a performance metric. The respective indication may comprise a measured performance metric sample of the computational model. As an example, the performance metric may comprise a probability that the first beam is a best beam out of a number of beams in terms of signal strength or quality, and / or a certain signal strength or quality.

[0107] Action 604. The UE 10 may predict the one or more upcoming indications when the obtained one or more indications are indicating a same value for the one or more upcoming instance parameters. The prediction may take an aggregation of the one or more indications into account. The prediction may combine at least two indications into an aggregated metric (typically the average), which aggregated metric may be compared to a threshold.

[0108] Action 605. The UE 10 triggers an action at the UE 10 when the condition is fulfilled. The condition is related to the prediction of the one or more upcoming indications taking the obtained one or more indications into account. The condition may define that the prediction is within a set interval, or range, to trigger the action. The set interval may be defined by a fraction indicating a number of times the respective indication is below or above a threshold out of a total number of times the one or more indications are obtained. The action may be triggered upon the prediction being in the set interval. The set interval may be associated with a number of times the respective indication is below or above a threshold, and the action may be triggered upon the indication is below or above the threshold the number of times. The action may comprise disabling the computational model, stopping measuring signals for obtaining the one or more indications, changing the computational model to an alternative computational model, deleting a memory of one or more parameters, and / or training or updating the computational model. As an example, upon the obtained one or more indications are below or above the threshold the number of times, the UE 10 may trigger an action at the UE 10, such as disables usage of the computational model, and / or stop measuring, or storing measurements. Deleting one or more parameters may comprise deleting measurements and / or computational models. The triggered action may further comprise reporting to a radio network node, such as the radio network node 12, the condition that is fulfilled, for example, the UE 10 may report in what interval the instance parameter is. Alternatively or additionally, the UE may report the predicted one or more upcoming indications indicating an interval of the performance metric.

[0109] Thus, embodiments may provide one or more of the following remarks: • Reduced UE battery consumptions due to early abortion of performance monitoring metric calculation (model inference) and measuring,

[0110] • Reduce memory consumption at the UE 10 by early removing computational models that have bad performance,

[0111] • Possibility to evaluate the performance of a new computational model for the time instances that does not contribute to the performance monitoring metric calculation. This provides the UE 10 to perform better model Life Cycle Management (LCM) improving the UE possibility to select the best computational model.

[0112] Some embodiments are summarized in Fig. 7, wherein the triggered action is to stop measuring if the performance metric value is predicted to a certain value. Note that the input to the computational model may also be based on a measurement, hence the UE 10 will also stop executing the computational model which leads to even more energy saving.

[0113] Thus, the radio network node 12, see step 100, can for example configure the UE 10 to estimate the beam prediction accuracy, that is the performance metric. The radio network node 12 can for example configure the UE 10 with different resolutions based on the accuracy.

[0114] Table 2: Example of ranges of performance metrics

[0115] The UE 10 performs a performance metric calculation in time instances n, step 110. Assume the UE 10 will measure on 10 time instances, the UE 10 in step 120 checks what would the “final” performance metric comprise if the remaining predications are from an ideal (genie) predictor. The UE 10 will then already in time instance 5 understand, if all 5 have been erroneous and the result will be part of the first range of the performance metric (accuracy below 50%), hence UE 10 can stop measuring, see step 130, (and corresponding model inference) after 5 instances and thereby save battery by not measuring in the final 5 occasions.

[0116] In some embodiments, the UE 10 may store measurements for each measurement instance, such that the UE 10 may re-use the measurements to evaluate other candidate computational models, for example, in case the currently used computational model is not performing well. In case the UE 10 determines that the current computational model is performing well the UE 10 may remove the stored measurements from the memory, which frees up memory space for other potential use cases, since the UE 10 then might not need to evaluate candidate computational models.

[0117] When the UE 10 notices that the current computational model is not performing well, the UE 10 may stop evaluating that computational model and may instead use the stored measurements to evaluate candidate computational models. The UE 10 can then determine which candidate computational model(s) that performs best with regards to the stored measurements and continue evaluating the performance of these computational model(s) for the remaining measurement instances. Thus, implementing a candidate computational model at an early stage.

[0118] In case the UE 10 is not scheduled without DL data during and / or between the measurement instance, the UE 10 may turn off the RX chains that would be used to perform the measurements to save even more power.

[0119] The UE 10 may further report to the radio network node 12, of the performed performance metric calculation after time instance N. The different performance metrics reported may comprise one or more of the following:

[0120] For a classifier o As a list indicating the corresponding ranges e.g. [0,50,75,85,90,99,100]

[0121] ■ Where the list can indicate in which range the UE 10 should report the exact value based on all measurement time instances (N) o A uniform list of ranges with a single value indicating the resolution. For example a value 10 divides the range of 10 uniform distributed bins [0,10,20,... .90,100],

[0122] In case the key performance indicator (KPI) is a regression value: o As a list indicating the mean, median, maximum and / or minimum error values [0-3 dB error, 3-5 dB error, 5dB->inf error],

[0123] ■ Where the list may indicate in which range the UE 10 should report the exact value based on all measurement time instances (N)

[0124] Embodiments herein relate to methods for determining when computational model performance metric is within a certain range. This may trigger the UE 10 to perform the action related to, for example, measuring signal strength or quality. As an example, the UE 10 may not measure on one or more reference signals for the occasions when the UE 10 early can establish in what range the performance metric will fall into. The reference signals transmitted by the radio network node 12 to the UE 10 may comprise at least one of a CSI-RS, an SSB, a primary synchronization signal (PSS), a secondary synchronization signal (SSS), and a cell reference signal (CRS). More specifically, a UE may assess beam qualities via measurements on the SSB, e.g., corresponding to a Synchronization Signal / Physical Broadcast Channel (PBCH) block, in a 5G, e.g., NR, network, or via measurements on the CSI-RS resources in a 5G, e.g., NR, network or a 4G (e.g., LTE) network.

[0125] Detecting when the “final” performance metric, being an example of the one or more upcoming indications, is within a range, see step 120.

[0126] The UE 10 in step 120 checks what would the “final” performance metric comprise if the remaining predications are from an ideal (genie) predictor, see Fig. 8. For a regression problem, an ideal predictor that always predicts the correct value, that is a squared and / or absolute error of 0, or cosine similarity of 1.

[0127] This may, for example, comprise for a classification problem, a predictor that always correctly classifies. For example, the accuracy for a model would in case N=12 in the Figure 9 be as best 75%. Fig. 9 shows an example of calculating the performance metric in time-instance 5. In this case 8 / 12=75% accuracy over all time-instances. Thus, the UE 10 should not stop measuring if the reporting metric is according to “Table 2: Example of ranges”, since the after 6 instances it is below 50%.

[0128] According to some embodiments the computational model is used for Beam prediction.

[0129] For beam prediction described in the 3GPP TR 38.843 v.1.1.0. The UE 10 may predict the best spatial beam by measuring on the set B of beams and predict set A of beams, according to Fig. 10. In case the UE 10 predicts the strongest beam in set A, and subsequentially obtains the same result while measuring, the samples can be seen as a successful one. Then, given that the UE 10 repeats this 7 times in the example, the UE 10 can obtain a prediction accuracy of [1 / 7, 2 / 7, 3 / 7, 4 / 7, 5 / 7, 6 / 7, 7 / 7]%. Note that if the report comprises, for this simple example, a range according to Table 2, the UE 10 could stop measuring after 4-time instances if all such instances have resulted in errors, since the UE 10 will then report an accuracy in range of 0-50%. To further reduce on number of measurements, the UE 10 may utilize an additional computational model, such as an Al or ML model, that would calculate the likelihood that the computational model is underperforming based on observing a limited number of measurements instances, for example, below number of measurement instances in an observation window. If the likelihood that the computational model underperforms is high, the UE 10 may stop the measurements even earlier and save on energy. This additional computational model may, for example, predict the channel variations in future time-instances. In case the UE 10 predicts a very static channel, e.g. via using external sensors such as inertial measurement units such as accelerometers and gyroscopes, the UE measurement and the corresponding beam prediction error in time instance 1 and 7 in Fig. 10 would be very similar. The errors are hence very similar in time instance 1 and 7, which could be predicted by the additional computational model also referred to as an AI / ML channel variation predictor.

[0130] According to some embodiments the computational model is used for CSI prediction.

[0131] For CSI prediction described in the 3GPP TR 38.843 v.1.1.0, the UE 10 may estimate the performance of the computational model by measuring a channel in a first time instance, and then predict the channel in a second time instance that is also measured at the UE 10. Based on the measured and predicted channel, the UE 10 may calculate a performance metric such as a Squared Generalized Cosine Similarity (SGCS) score of the computational model, see Fig. 11. In case the NW, such as the radio network node 12, has configured a certain range according to the Table 3, the UE 10 may understand in what bin the final performance metric will be earlier than in the N time windows, given the best the UE can achieve is SGCS if 1. Hence the UE 10 could stop measuring before time instance 10.

[0132] Table 3

[0133] Embodiments herein relate to method for triggering an action at the UE 10 when determining a performance metric at an early stage, see Fig. 12.

[0134] The radio network node 12, see action 1201 , can for example configure the UE 10 to perform performance metric calculation based on N time instances. The UE 10 performs performance metric calculation in time instances n, action 1202. The UE 10 in action 1203 checks if performance metric is known to be a certain value, within a certain range. The UE 10 may then, if yes, stop measuring, action 1204.

[0135] As mentioned in Action 605, and exemplified in actions 1204-1205, the UE 10 may perform one or more of the following actions when the UE 10 detects that the final performance metric of the computational model is within a certain range:

[0136] In some embodiments, when the UE 10 has more than one candidate computational model, the UE 10 may store one or more measurements for each measurement instance, such that the UE 10 can re-use the one or more measurements to evaluate candidate computational models, for example, in case the currently used computational model is not performing well. The UE 10 may store RSRP measurements or raw channel measurements of the Set B of beams, as well as the top best beam or top k best beams from set A of beams (including potential performance metric, like RSRP or similar for those beam(s)).

[0137] As an example, the UE 10 may stop measuring any new reference signals when the UE 10 has determined in what range the “final” performance metric would comprise.

[0138] The UE 10 may have stored measurement data for one or more measurement instance, and in case the UE 10 determines that the “final” performance metric for the currently used computational model is good, the UE 10 may delete the stored measurement data from the memory, which frees up memory space for other potential use cases. This can be performed since the UE 10 then probably does not need the stored measurement data to evaluate other candidate computational models.

[0139] Furthermore, in case the UE 10 is not scheduled with other DL data / signals during and / or between the measurement instances for the computational model monitoring, the UE 10 may turn off the RX chains that would be used to perform the measurements to save even more power.

[0140] Additionally, or alternatively, when the computational model is indicated to be part of a low-accuracy region of the performance metric, the UE 10 may expect that the radio network node 12 is not interested in using the predictions from said computational model in a subsequent time step. Hence, the UE 10 may then, when it has predicted, e.g., estimated, that the model accuracy is within a low accuracy region of the performance metrics, deactivate such computational model, see option 1205a. For example, by removing it from the main memory, this would free memory at the UE 10 for other purposes. The UE 10 may in a related embodiment then also activate another computational model for the same feature as the previous computational model, e.g., activate another beam prediction model that the UE has stored, see option 1205b. The UE 10 can determine which other candidate model(s) to activate by evaluating it with respect to the stored measurement data. The UE 10 may then select the best computational model among the available computational models. In another alternative, to save on computation and processing, the UE 10 may evaluate the available computational models subsequently and activate the first computational model that fulfills a certain performance criterion. The UE 10 may store the performed measurement data for the purpose of finetuning and updating the underperforming model. If the training / retraining of the computational model is located in an over the top (OTT) server, the UE 10 can signal those measurement data to the OTT server. The measurement data can be sent on user plane, transparent to the RAN.

[0141] The decision on removing / changing / training computational model may also be based on historical Information on previous monitoring procedures. For example, the radio network node 12, may configure multiple performance monitoring procedures to understand its performance over time. The UE 10 may then, for example, decide to deactivate the model in case the model has been part of the lowest accuracy range more than N times.

[0142] Additionally, or alternatively, the UE 10 may, when determined in what range the performance metric is, activate another candidate computational model to evaluate such computational model for the prediction task. The UE 10 may in this case deactivate the current computational model and activate another computational model. The UE 10 may then test if the new computational model is better than the previous computational model for the remainder of the samples, option 1205c. This would allow the UE 10 to evaluate a second computational model within the same performance metric calculation time duration. In case the second computational model is better than the first computational model, the UE 10 may select to continue having the second computational model activated, or vice versa.

[0143] The UE 10 may have stored measurement data for one or more measurement instance, and when the UE 10 notices that the current computational model is not performing well, the UE 10 may stop evaluating that computational model and instead uses the stored measurements to evaluate candidate computational model(s). The UE 10 may then determine which other candidate computational model(s) that performs best with regards to the stored measurement data and continue evaluating the performance of that computational model(s) for the remaining measurement instances. The UE 10 may in case of functionality-based life cycle management (LCM), i.e., when the computational model is transparent to the radio network node 12, report the performance metric for the best candidate computational model, since the radio network node 12 is anyway not aware of which exact computational model that is used at the UE 10, action 1206. In case of model-ID based LCM, the UE 10 may include an indication, such as ID, of which computational model that produced the performance metric. The UE 10 may signal to the radio network node 12 that it has found a computational model that is better in the later time instances of the performance calculation, action 1207. This information could be used by the radio network node 12 to configure another performance metric calculation occasion for the UE to understand whether its new computational models work adequate.

[0144] Fig. 13 shows a block diagram depicting the UE 10 for handling communication in the wireless communications network.

[0145] The UE 10 may comprise processing circuitry 1301 , e.g. one or more processors, configured to perform the methods herein. The UE 10 and / or the processing circuitry 1301 is configured to obtain the one or more indications indicating the respective performance metric of the computational model used for estimating the instance parameter related to the signalling process.

[0146] The UE 10 and / or the processing circuitry 1301 is configured to trigger the action at the UE 10 when the condition is fulfilled, wherein the condition is related to the prediction of the one or more upcoming indications taking the obtained one or more indications into account.

[0147] The UE 10 and / or the processing circuitry 1301 may be configured to predict the one or more upcoming indications when the obtained one or more indications are indicating the same value for the one or more upcoming instance parameters.

[0148] The indication may comprise a measured performance metric sample of the computational model.

[0149] The UE 10 and / or the processing circuitry 1301 may be configured to obtain the reporting configuration indicating the condition.

[0150] The prediction may take into account the aggregation of the one or more indications.

[0151] The condition may define that the prediction is within a set interval to trigger the action. The set interval may be defined by the fraction indicating the number of times the respective indication is below or above a threshold out of a total number of times the one or more indications are obtained.

[0152] The action may comprise disabling the computational model, stopping measuring signals for obtaining the one or more indications, changing the computational model to an alternative computational model, deleting a memory of one or more parameters, and / or training or updating the computational model.

[0153] The action may further comprise reporting to the radio network node 12 the condition that is fulfilled. The UE 10 may report the interval the instance parameter is in and / or the predicted one or more upcoming indications.

[0154] The instance parameter related to the signalling process may comprise a signal strength or quality of the first beam relative other beams, and / or a signal strength or quality of a reference signal.

[0155] The UE 10 further comprises a memory 1305. The memory comprises one or more units to be used to store data on, such as indications, computational model, performance metrics, reconfiguration, applications to perform the methods disclosed herein when being executed, and similar. The UE 10 comprises a communication interface 1306 comprising transmitter, receiver, transceiver and / or one or more antennas. Thus, it is herein provided the UE 10 for handling communication in a wireless communications network, wherein the UE 10 comprises processing circuitry and a memory, said memory comprising instructions executable by said processing circuitry whereby said UE 10 is operative to perform any of the methods herein.

[0156] The methods according to the embodiments described herein for the UE 10 are respectively implemented by means of e g. a computer program product 1307 or a computer program product, comprising instructions, i.e. , software code portions, which, when executed on at least one processor, cause the at least one processor to carry out the actions described herein, as performed by the UE 10. The computer program product 1307 may be stored on a computer-readable storage medium 1308, e g. a universal serial bus (USB) stick, a disc or similar. The computer-readable storage medium 1308, having stored thereon the computer program product, may comprise the instructions which, when executed on at least one processor, cause the at least one processor to carry out the actions described herein, as performed by the UE 10. In some embodiments, the computer-readable storage medium may be a non-transitory or transitory computer- readable storage medium. As will be readily understood by those familiar with communications design, that functions means or modules may be implemented using digital logic and / or one or more microcontrollers, microprocessors, or other digital hardware. In some embodiments, several or all of the various functions may be implemented together, such as in a single application-specific integrated circuit (ASIC), or in two or more separate devices with appropriate hardware and / or software interfaces between them. Several of the functions may be implemented on a processor shared with other functional components of a radio network node, for example.

[0157] Alternatively, several of the functional elements of the processing means discussed may be provided through the use of dedicated hardware, while others are provided with hardware for executing software, in association with the appropriate software or firmware. Thus, the term “processor” or “controller” as used herein does not exclusively refer to hardware capable of executing software and may implicitly include, without limitation, digital signal processor (DSP) hardware, read-only memory (ROM) for storing software, random-access memory for storing software and / or program or application data, and non-volatile memory. Other hardware, conventional and / or custom, may also be included. Designers of communications receivers will appreciate the cost, performance, and maintenance trade-offs inherent in these design choices.

[0158] Fig. 14 shows an example of a communication system QQ100 in accordance with some embodiments.

[0159] In the example, the communication system QQ100 includes a telecommunication network QQ102 that includes an access network QQ104, such as a radio access network (RAN), and a core network QQ106, which includes one or more core network nodes QQ108. The access network QQ104 includes one or more access network nodes, such as network nodes QQ110a and QQ110b (one or more of which may be generally referred to as network nodes QQ110) being examples of the radio network node 12, or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points. Moreover, as will be appreciated by those of skill in the art, a network node, being examples of the entities herein, 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 QQ102 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network QQ102 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 QQ102, including one or more network nodes QQ110 and / or core network nodes QQ108.

[0160] Examples of an ORAN network node include an open radio unit (0-Rll), an open distributed unit (0-Dll), 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 A1 , F1 , W1, E1, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an 0-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes QQ110 facilitate direct or indirect connection of the user equipment (UE) 10, such as by connecting UEs QQ112a, QQ112b, QQ112c, and QQ112d (one or more of which may be generally referred to as UEs QQ112) to the core network QQ106 over one or more wireless connections.

[0161] 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 QQ100 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 QQ100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0162] The UEs QQ112 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 QQ110 and other communication devices. Similarly, the network nodes QQ110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs QQ112 and / or with other network nodes or equipment in the telecommunication network QQ102 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 QQ102.

[0163] In the depicted example, the core network QQ106 connects the network nodes QQ110 to one or more hosts, such as host QQ116. 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 QQ106 includes one more core network nodes (e.g., core network node QQ108) such as network node 15 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 QQ108. 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).

[0164] The host QQ116 may be under the ownership or control of a service provider other than an operator or provider of the access network QQ104 and / or the telecommunication network QQ102, and may be operated by the service provider or on behalf of the service provider. The host QQ116 may host a variety of applications to provide one or more service. Examples of such applications include live and prerecorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0165] As a whole, the communication system QQ100 of Fig. 14 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.

[0166] In some examples, the telecommunication network QQ102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network QQ102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network QQ102. For example, the telecommunications network QQ102 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.

[0167] In some examples, the UEs QQ112 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 QQ104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network QQ104. 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).

[0168] In the example, the hub QQ114 communicates with the access network QQ104 to facilitate indirect communication between one or more UEs (e.g., UE QQ112c and / or QQ112d) and network nodes (e.g., network node QQ110b). In some examples, the hub QQ114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub QQ114 may be a broadband router enabling access to the core network QQ106 for the UEs. As another example, the hub QQ114 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 QQ110, or by executable code, script, process, or other instructions in the hub QQ114. As another example, the hub QQ114 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 QQ114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub QQ114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub QQ114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub QQ114 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0169] The hub QQ114 may have a constant / persistent or intermittent connection to the network node QQ110b. The hub QQ114 may also allow for a different communication scheme and / or schedule between the hub QQ114 and UEs (e.g., UE QQ112c and / or QQ112d), and between the hub QQ114 and the core network QQ106. In other examples, the hub QQ114 is connected to the core network QQ106 and / or one or more UEs via a wired connection. Moreover, the hub QQ114 may be configured to connect to an M2M service provider over the access network QQ104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes QQ110 while still connected via the hub QQ114 via a wired or wireless connection. In some embodiments, the hub QQ114 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 QQ110b. In other embodiments, the hub QQ114 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node QQ110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0170] Figure 15 shows a UE QQ200 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 cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-loT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0171] A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0172] The UE QQ200 includes processing circuitry QQ202 that is operatively coupled via a bus QQ204 to an input / output interface QQ206, a power source QQ208, a memory QQ210, a communication interface QQ212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 15. 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.

[0173] The processing circuitry QQ202 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 QQ210. The processing circuitry QQ202 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 QQ202 may include multiple central processing units (CPUs).

[0174] In the example, the input / output interface QQ206 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 QQ200. 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.

[0175] In some embodiments, the power source QQ208 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 QQ208 may further include power circuitry for delivering power from the power source QQ208 itself, and / or an external power source, to the various parts of the UE QQ200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source QQ208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source QQ208 to make the power suitable for the respective components of the UE QQ200 to which power is supplied.

[0176] The memory QQ210 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 QQ210 includes one or more application programs QQ214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data QQ216. The memory QQ210 may store, for use by the UE QQ200, any of a variety of various operating systems or combinations of operating systems.

[0177] The memory QQ210 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 inline 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 (IIICC) including one or more subscriber identity modules (SIMs), such as a IISIM and / or ISIM, other memory, or any combination thereof. The IIICC may for example be an embedded IIICC (elllCC), integrated IIICC (illlCC) or a removable IIICC commonly known as ‘SIM card.’ The memory QQ210 may allow the UE QQ200 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 QQ210, which may be or comprise a device-readable storage medium.

[0178] The processing circuitry QQ202 may be configured to communicate with an access network or other network using the communication interface QQ212. The communication interface QQ212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna QQ222. The communication interface QQ212 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 QQ218 and / or a receiver QQ220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter QQ218 and receiver QQ220 may be coupled to one or more antennas (e.g., antenna QQ222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0179] In the illustrated embodiment, communication functions of the communication interface QQ212 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 / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0180] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface QQ212, 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).

[0181] As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0182] 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. Nonlimiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE QQ200 shown in Figure 15.

[0183] 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-loT 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.

[0184] 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.

[0185] Figure 16 shows a network node QQ300 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).

[0186] 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).

[0187] 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).

[0188] The network node QQ300 includes a processing circuitry QQ302, a memory QQ304, a communication interface QQ306, and a power source QQ308. The network node QQ300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node QQ300 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 QQ300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory QQ304 for different RATs) and some components may be reused (e.g., a same antenna QQ310 may be shared by different RATs). The network node QQ300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node QQ300, 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 QQ300.

[0189] The processing circuitry QQ302 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 QQ300 components, such as the memory QQ304, to provide network node QQ300 functionality.

[0190] In some embodiments, the processing circuitry QQ302 includes a system on a chip (SOC). In some embodiments, the processing circuitry QQ302 includes one or more of radio frequency (RF) transceiver circuitry QQ312 and baseband processing circuitry QQ314. In some embodiments, the radio frequency (RF) transceiver circuitry QQ312 and the baseband processing circuitry QQ314 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 QQ312 and baseband processing circuitry QQ314 may be on the same chip or set of chips, boards, or units.

[0191] The memory QQ304 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry QQ302. The memory QQ304 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 QQ302 and utilized by the network node QQ300. The memory QQ304 may be used to store any calculations made by the processing circuitry QQ302 and / or any data received via the communication interface QQ306. In some embodiments, the processing circuitry QQ302 and memory QQ304 is integrated.

[0192] The communication interface QQ306 is used in wired or wireless communication of signalling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface QQ306 comprises port(s) / terminal(s) QQ316 to send and receive data, for example to and from a network over a wired connection. The communication interface QQ306 also includes radio frontend circuitry QQ318 that may be coupled to, or in certain embodiments a part of, the antenna QQ310. Radio front-end circuitry QQ318 comprises filters QQ320 and amplifiers QQ322. The radio front-end circuitry QQ318 may be connected to an antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry may be configured to condition signals communicated between antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry QQ318 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 QQ318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters QQ320 and / or amplifiers QQ322. The radio signal may then be transmitted via the antenna QQ310. Similarly, when receiving data, the antenna QQ310 may collect radio signals which are then converted into digital data by the radio front-end circuitry QQ318. The digital data may be passed to the processing circuitry QQ302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0193] In certain alternative embodiments, the network node QQ300 does not include separate radio front-end circuitry QQ318, instead, the processing circuitry QQ302 includes radio front-end circuitry and is connected to the antenna QQ310. Similarly, in some embodiments, all or some of the RF transceiver circuitry QQ312 is part of the communication interface QQ306. In still other embodiments, the communication interface QQ306 includes one or more ports or terminals QQ316, the radio front-end circuitry QQ318, and the RF transceiver circuitry QQ312, as part of a radio unit (not shown), and the communication interface QQ306 communicates with the baseband processing circuitry QQ314, which is part of a digital unit (not shown).

[0194] The antenna QQ310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna QQ310 may be coupled to the radio front-end circuitry QQ318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna QQ310 is separate from the network node QQ300 and connectable to the network node QQ300 through an interface or port.

[0195] The antenna QQ310, communication interface QQ306, and / or the processing circuitry QQ302 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 QQ310, the communication interface QQ306, and / or the processing circuitry QQ302 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.

[0196] The power source QQ308 provides power to the various components of network node QQ300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source QQ308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node QQ300 with power for performing the functionality described herein. For example, the network node QQ300 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 QQ308. As a further example, the power source QQ308 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.

[0197] Embodiments of the network node QQ300 may include additional components beyond those shown in Figure 16 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 QQ300 may include user interface equipment to allow input of information into the network node QQ300 and to allow output of information from the network node QQ300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node QQ300.

[0198] Figure 17 is a block diagram of a host QQ400, which may be an embodiment of the host QQ116 of Figure 14, in accordance with various aspects described herein. As used herein, the host QQ400 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 QQ400 may provide one or more services to one or more UEs.

[0199] The host QQ400 includes processing circuitry QQ402 that is operatively coupled via a bus QQ404 to an input / output interface QQ406, a network interface QQ408, a power source QQ410, and a memory QQ412. 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 15 and 16, such that the descriptions thereof are generally applicable to the corresponding components of host QQ400. The memory QQ412 may include one or more computer programs including one or more host application programs QQ414 and data QQ416, which may include user data, e.g., data generated by a UE for the host QQ400 or data generated by the host QQ400 for a UE. Embodiments of the host QQ400 may utilize only a subset or all of the components shown. The host application programs QQ414 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (WC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAG, 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 QQ414 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 QQ400 may select and / or indicate a different host for over-the-top services for a UE. The host application programs QQ414 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.

[0200] Figure 18 is a block diagram illustrating a virtualization environment QQ500 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 QQ500 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 QQ500 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an 0-2 interface. Applications QQ502 (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.

[0201] Hardware QQ504 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 QQ506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs QQ508a and QQ508b (one or more of which may be generally referred to as VMs QQ508), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer QQ506 may present a virtual operating platform that appears like networking hardware to the VMs QQ508.

[0202] The VMs QQ508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer QQ506. Different embodiments of the instance of a virtual appliance QQ502 may be implemented on one or more of VMs QQ508, 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.

[0203] In the context of NFV, a VM QQ508 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, nonvirtualized machine. Each of the VMs QQ508, and that part of hardware QQ504 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 QQ508 on top of the hardware QQ504 and corresponds to the application QQ502.

[0204] Hardware QQ504 may be implemented in a standalone network node with generic or specific components. Hardware QQ504 may implement some functions via virtualization. Alternatively, hardware QQ504 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 QQ510, which, among others, oversees lifecycle management of applications QQ502. In some embodiments, hardware QQ504 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 signalling can be provided with the use of a control system QQ512 which may alternatively be used for communication between hardware nodes and radio units.

[0205] Figure 19 shows a communication diagram of a host QQ602 communicating via a network node QQ604 with a UE QQ606 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE QQ112a of Figure 14 and / or UE QQ200 of Figure 15), network node (such as network node QQ110a of Figure 14 and / or network node QQ300 of Figure 16), and host (such as host QQ116 of Figure 14 and / or host QQ400 of Figure 17) discussed in the preceding paragraphs will now be described with reference to Figure 19.

[0206] Like host QQ400, embodiments of host QQ602 include hardware, such as a communication interface, processing circuitry, and memory. The host QQ602 also includes software, which is stored in or accessible by the host QQ602 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 QQ606 connecting via an over-the-top (OTT) connection QQ650 extending between the UE QQ606 and host QQ602. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection QQ650.

[0207] The network node QQ604 includes hardware enabling it to communicate with the host QQ602 and UE QQ606. The connection QQ660 may be direct or pass through a core network (like core network QQ106 of Figure 14) 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.

[0208] The UE QQ606 includes hardware and software, which is stored in or accessible by UE QQ606 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 QQ606 with the support of the host QQ602. In the host QQ602, an executing host application may communicate with the executing client application via the OTT connection QQ650 terminating at the UE QQ606 and host QQ602. 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 QQ650 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 QQ650.

[0209] The OTT connection QQ650 may extend via a connection QQ660 between the host QQ602 and the network node QQ604 and via a wireless connection QQ670 between the network node QQ604 and the UE QQ606 to provide the connection between the host QQ602 and the UE QQ606. The connection QQ660 and wireless connection QQ670, over which the OTT connection QQ650 may be provided, have been drawn abstractly to illustrate the communication between the host QQ602 and the UE QQ606 via the network node QQ604, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0210] As an example of transmitting data via the OTT connection QQ650, in step QQ608, the host QQ602 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 QQ606. In other embodiments, the user data is associated with a UE QQ606 that shares data with the host QQ602 without explicit human interaction. In step QQ610, the host QQ602 initiates a transmission carrying the user data towards the UE QQ606. The host QQ602 may initiate the transmission responsive to a request transmitted by the UE QQ606. The request may be caused by human interaction with the UE QQ606 or by operation of the client application executing on the UE QQ606. The transmission may pass via the network node QQ604, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step QQ612, the network node QQ604 transmits to the UE QQ606 the user data that was carried in the transmission that the host QQ602 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step QQ614, the UE QQ606 receives the user data carried in the transmission, which may be performed by a client application executed on the UE QQ606 associated with the host application executed by the host QQ602.

[0211] In some examples, the UE QQ606 executes a client application which provides user data to the host QQ602. The user data may be provided in reaction or response to the data received from the host QQ602. Accordingly, in step QQ616, the UE QQ606 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 QQ606. Regardless of the specific manner in which the user data was provided, the UE QQ606 initiates, in step QQ618, transmission of the user data towards the host QQ602 via the network node QQ604. In step QQ620, in accordance with the teachings of the embodiments described throughout this disclosure, the network node QQ604 receives user data from the UE QQ606 and initiates transmission of the received user data towards the host QQ602. In step QQ622, the host QQ602 receives the user data carried in the transmission initiated by the UE QQ606.

[0212] One or more of the various embodiments improve the performance of OTT services provided to the UE QQ606 using the OTT connection QQ650, in which the wireless connection QQ670 forms the last segment. More precisely, the teachings of these embodiments may improve handling of computational models and thereby provide benefits such as reduced user waiting time, better responsiveness, and / or extended battery lifetime.

[0213] In an example scenario, factory status information may be collected and analyzed by the host QQ602. As another example, the host QQ602 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host QQ602 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host QQ602 may store surveillance video uploaded by a UE. As another example, the host QQ602 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 QQ602 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.

[0214] 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 QQ650 between the host QQ602 and UE QQ606, 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 QQ602 and / or UE QQ606. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection QQ650 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 QQ650 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node QQ604. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signalling that facilitates measurements of throughput, propagation times, latency and the like, by the host QQ602. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection QQ650 while monitoring propagation times, errors, etc.

[0215] 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. 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.

[0216] It will be appreciated that the foregoing description and the accompanying drawings represent non-limiting examples of the methods and apparatus taught herein. As such, the apparatus and techniques taught herein are not limited by the foregoing description and accompanying drawings. Instead, the embodiments herein are limited only by the following claims and their legal equivalents.

Claims

CLAIMS1. A method performed by a user equipment, UE, (10) for handling communication in a wireless communications network, comprising: obtaining (603) one or more indications indicating a respective performance metric of a computational model used for estimating an instance parameter related to a signalling process; and- triggering (605) an action at the UE (10) when a condition is fulfilled, wherein the condition is related to a prediction of one or more upcoming indications taking the obtained one or more indications into account.

2. The method according to claim 1, further comprising predicting (604) the one or more upcoming indications when the obtained one or more indications are indicating a same value for the one or more upcoming instance parameters.

3. The method according to any of the claims 1-2, where respective indication comprises a measured performance metric sample of the computational model.

4. The method according to any of the claims 1-3, further comprising obtaining (601) a reporting configuration indicating the condition.

5. The method according to any of the claims 1-4, wherein the prediction is taking into account an aggregation of the one or more indications.

6. The method according to any of the claims 1-5, wherein the condition defines that the prediction is within a set interval to trigger the action.

7. The method according to claim 6, wherein the set interval is defined by a fraction indicating a number of times the respective indication is below or above a threshold out of a total number of times the one or more indications are obtained.

8. The method according to any of the claims 1-7, wherein the action comprises disabling the computational model, stopping measuring signals for obtainingthe one or more indications, changing the computational model to an alternative computational model, deleting a memory of one or more parameters, and / or training or updating the computational model.

9. The method according to claim 8, wherein the action further comprises reporting to a radio network node (12) the condition that is fulfilled.

10. The method according to any of the claims 1-9, wherein the instance parameter related to the signalling process comprises a signal strength or quality of the first beam relative other beams, and / or a signal strength or quality of a reference signal.

11. A user equipment, UE, (10) for handling communication in a wireless communications network, wherein the UE (10) is configured to obtain one or more indications indicating a respective performance metric of a computational model used for estimating an instance parameter related to a signalling process; and trigger an action at the UE (10) when a condition is fulfilled, wherein the condition is related to a prediction of one or more upcoming indications taking the obtained one or more indications into account.

12. The UE (10) according to claim 11 , wherein the UE is configured to predict the one or more upcoming indications when the obtained one or more indications are indicating a same value for the one or more upcoming instance parameters.

13. The UE according to any of the claims 11-12, where respective indication comprises a measured performance metric sample of the computational model.

14. The UE according to any of the claims 11-13, wherein the UE is configured to obtain a reporting configuration indicating the condition.

15. The UE according to any of the claims 11-14, wherein the prediction is taking into account an aggregation of the one or more indications.

16. The UE according to any of the claims 11-15, wherein the condition defines that the prediction is within a set interval to trigger the action.

17. The UE (10) according to claim 16, wherein the set interval is defined by a fraction indicating a number of times the respective indication is below or above a threshold out of a total number of times the one or more indications are obtained.

18. The UE (10) according to any of the claims 11-17, wherein the action comprises disabling the computational model, stopping measuring signals for obtaining the one or more indications, changing the computational model to an alternative computational model, deleting a memory of one or more parameters, and / or training or updating the computational model.

19. The UE (10) according to the claim 18, wherein the action further comprises reporting to a radio network node (12) the condition that is fulfilled.

20. The UE (10) according to any of the claims 11-19, wherein the instance parameter related to the signalling process comprises a signal strength or quality of the first beam relative other beams, and / or a signal strength or quality of a reference signal.

21. A computer program product comprising instructions, which, when executed on at least one processor, cause the at least one processor to carry out the method according to any of the claims 1-10, as performed by the UE.

22. A computer-readable storage medium, having stored thereon a computer program product comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out the method according to any of the claims 1-10, as performed by the UE.

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