WO2023211356A1 - User equipment machine learning functionality monitoring - Google Patents

Abstract

According to some embodiments, a method is performed by a wireless device for monitoring performance of a functionality (e.g., machine learning (ML) model) of the wireless device. The method comprises receiving a configuration from the network node. The configuration comprises a set of parameters for monitoring the functionality and one or more monitoring time occasions. The method further comprises monitoring the functionality based on the received set of parameters during the one or more monitoring time occasions.

Description

USER EQUIPMENT MACHINE LEARNING FUNCTIONALITY MONITORING

TECHNICAL FIELD

[0001] Embodiments of the present disclosure are directed to wireless communications and, more particularly, to user equipment (UE) machine learning (ML) functionality monitoring.

BACKGROUND

[0002] Generally, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given and/or is implied from the context in which it is used. All references to a/an/the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any methods disclosed herein do not have to be performed in the exact order disclosed, unless a step is explicitly described as following or preceding another step and/or where it is implicit that a step must follow or precede another step. Any feature of any of the embodiments disclosed herein may be applied to any other embodiment, wherever appropriate. Likewise, any advantage of any of the embodiments may apply to any other embodiments, and vice versa. Other objectives, features, and advantages of the enclosed embodiments will be apparent from the following description.

[0003] Artificial Intelligence (Al) and Machine Learning (ML) are considered, both in academia and industry, as promising tools to optimize the design of the air-interface in wireless communication networks. Example use cases include: using autoencoders for channel state information (CSI) compression to reduce the feedback overhead and improve channel prediction accuracy; using deep neural networks for classifying line-of-sight (LOS) and non- LOS (NLOS) conditions to enhance positioning accuracy; using reinforcement learning for beam selection at the network side and/or the user equipment (UE) side to reduce signaling overhead and beam alignment latency; and using deep reinforcement learning to learn an optimal precoding policy for complex multiple-input multiple-output (MIMO) precoding problems.

[0004] Third Generation Partnership Project (3 GPP) new radio (NR) Release 18 standardization work includes a study item on AI/ML for the NR air interface. The study item will explore the benefits of augmenting the air-interface with features enabling improved support of AI/ML based algorithms for enhanced performance and/or reduced complexity/overhead. Through studying a few selected use cases (CSI feedback, beam management, and positioning), the study item intends to lay the foundation for future airinterface use cases leveraging AI/ML techniques.

[0005] When applying AI/ML on air interface use cases, different levels of collaboration between network nodes and UEs may be considered. One use case is no collaboration between network nodes and UEs. In this case, a proprietary ML model operating with the existing standard air-interface is applied at one end of the communication chain (e.g., at the UE side), and the model life cycle management (e.g., model selection/training, model monitoring, model retraining, model update) is done at this node without inter-node assistance (e.g., assistance information provided by the network node).

[0006] Another use case is limited collaboration between network nodes and UEs. In this case, a ML model is operating at one end of the communication chain (e.g., at the UE side), but this node gets assistance from the node(s) at the other end of the communication chain (e.g., a next generation Node B (gNB)) for its Al model life cycle management (e.g., for training/retraining the Al model, model update).

[0007] A third use case is joint ML operation between network nodes and UEs. In this case, the Al model may be split with one part located at the network side and the other part located at the UE side. Thus, the Al model includes joint training between the network and UE, and the Al model life cycle management involves both ends of a communication chain.

[0008] Building the Al model, or any machine learning model, includes several development steps where the actual training of the Al model is just one step in a training pipeline. An important part in Al development is the ML model lifecycle management. An example is illustrated in FIGURE 1.

[0009] FIGURE 1 is an illustration of training and inference pipelines, and their interactions within a model lifecycle management procedure. The model lifecycle management typically consists of a training (re-training) pipeline, a deployment stage to make the trained (or retrained) Al model part of the inference pipeline, an inference pipeline, and a drift detection stage that informs about any drifts in the model operations.

[0010] The training (re-training) pipeline may include data ingestion, data pre-processing, model training, model evaluation, and model registration. Data ingestion refers to gathering raw (training) data from a data storage. After data ingestion, there may be a step that controls the validity of the gathered data. [0011] Data pre-processing refers to feature engineering applied to the gathered data, e.g., it may include data normalization and possibly a data transformation required for the input data to the Al model.

[0012] Model training refers to the actual model training steps as previously outlined.

[0013] Model evaluation refers to benchmarking the performance to a model baseline. The iterative steps of model training and model evaluation continues until the acceptable level of performance (as previously exemplified) is achieved.

[0014] Model registration refers to registering the Al model, including any corresponding AI- metadata that provides information on how the Al model was developed, and possibly Al model evaluations performance outcomes.

[0015] The deployment stage makes the trained (or re-trained) Al model part of the inference pipeline.

[0016] The inference pipeline may include data ingestion, data pre-processing, model operational, and data and model monitoring. Data ingestion refers to gathering raw (inference) data from a data storage.

[0017] The data pre-processing stage is typically identical to corresponding processing that occurs in the training pipeline.

[0018] Model operational refers to using the trained and deployed model in an operational mode.

[0019] Data and model monitoring refers to validating that the inference data are from a distribution that aligns well with the training data, as well as monitoring model outputs for detecting any performance, or operational, drifts.

[0020] A drift detection stage informs about any drifts in the model operations.

[0021] The area of handling ML-model performance monitoring within the 3 GPP domain is a new area, particularly when it involves both UE and network.

[0022] There currently exist certain challenges. For example, a UE may not be able to monitor the performance of an ML-function or ML-model because of, for example, limitations in the number of receiver/transmitter chains or number of beams the UE can manage at the same time. This is because the UE is mandated to be able to receive data or monitor physical downlink control channel (PDCCH) with its receiver chains. Also, with respect to transmitting chains, the UE is mandated to transmit in a certain manner by gNB scheduling. [0023] Further, the current UE scheduling by the network may not match particular assumptions required to monitor the performance or required network signals, e.g., particular reference signals (RS) are not available. Similarly, the UE may not be able to collect training data in a sufficient manner when in operation because of the same limitations.

[0024] SUMMARY

[0025] As described above, certain challenges currently exist with user equipment (UE) machine learning (ML) functionality monitoring. Certain aspects of the present disclosure and their embodiments may provide solutions to these or other challenges.

[0026] For example, in particular embodiments a UE indicates to the network a need for performing operations related to monitoring the performance of ML-models/functions and a request to be specially configured to perform this. A special configuration may refer to operational state, receiver configuration, availability of reference signal (RS), etc.

[0027] In some embodiments, the UE collects data for training purposes using the special configuration during configured intervals.

[0028] According to some embodiments, a method in a first node that is communicating with a second node comprises sending a message indicating a need for monitoring the performance of a functionality (e.g., either the ML-model directly or the functionality that the ML-model is part of) to a second node and receiving a confirmation message from the second node.

[0029] In particular embodiments, the method further comprises sending an indication message that the functionality has been monitored to the second node.

[0030] In particular embodiments, the first node is a UE and a second node is network node (e.g., base station).

[0031] In particular embodiments, the message indicating the need for monitoring the performance of a functionality (e.g., either the ML-model directly or the functionality that the ML-model is part of) is a radio resource control (RRC) message, medium access control (MAC) control element (CE), uplink control information (UCI), or scheduling control information (SCI) and includes/indicates at least one of the following information: request for a functionality update, a specific carrier or frequency the message applies to, specific scheduling behaviour desired from the second node, specific signals (e.g., RS) made available by the second node, a functionality ID, a functionality area ID characterizing the purpose of the functionality ID, e.g., channel estimation, decoding, etc., if the functionality can be monitored in discontinuous reception (DRX), non-DRX, RRC_CONNECTED STATE, RRC_INACTIVE STATE or RRC_IDLE STATE, time required to monitor the functionality, a preferred functionality at the second node, and an indication indicating to the second node that the first node needs to monitor an ML-model. The indication may be transmitted within the message (e.g., the RRC message UEAssistancelnformation).

[0032] In particular embodiments, the functionality is an ML-model or is functionality configured that is implemented in part by an ML-model. That the functionality is in part defined by an ML-model may, for example, be seen by the fact that it is possible to monitor/indicate the performance of the functionality by the network. It may also be that the functionality is directly defined in a specification that the functionality may be supported with an ML based approach.

[0033] According to some embodiments, a method is performed by a wireless device for monitoring performance of a functionality (e.g., machine learning (ML) model) of the wireless device. The method comprises receiving a configuration from the network node. The configuration comprises a set of parameters for monitoring the functionality and one or more monitoring time occasions. The method further comprises monitoring the functionality based on the received set of parameters during the one or more monitoring time occasions.

[0034] In particular embodiments, the method further comprises transmitting to the network node a request for the wireless device to monitor the functionality. The request may comprise a request for one or more monitoring time occasions during which the wireless device is to perform measurements related to the functionality. The requested one or more monitoring time occasions may be associated with one or more of: one or more carriers; one or more cells; and one or more beams or beam directions. The requested one or more monitoring time occasions may be periodic or aperiodic. The request to monitor the functionality may comprise one or more of: a functionality identifier (e.g., ML model identifier); a functionality version identifier (e.g., ML model version identifier); and an indication of a type of functionality. The type of functionality may comprise one or more of: channel state information reporting; radio resource management (RRM) measurements; mobility management; beam management; and positioning. The request to monitor the functionality may comprise a request for transmission of one or more reference signals (e.g., positioning reference signal (PRS), channel state information reference signal (CRS-RS), etc.) from the network node to the wireless device. [0035] In particular embodiments, the one or more monitoring time occasions in the received configuration are associated with one or more of: one or more carriers; one or more cells; and one or more beams or beam directions. The one or more monitoring time occasions in the received configuration may be periodic or aperiodic.

[0036] In particular embodiments, the received set of parameters comprises a configuration for transmission of one or more reference signals (e.g., positioning reference signal (PRS), channel state information reference signal (CRS-RS), etc.) from the network node to the wireless device.

[0037] In particular embodiments, the method further comprises reporting results of monitoring the functionality to one or more network nodes.

[0038] In particular embodiments, the method further comprises adjusting one or more parameters of the functionality based on the monitoring of the functionality.

[0039] In particular embodiments, the functionality comprises a ML model.

[0040] According to some embodiments, a wireless device comprises processing circuitry operable to perform any of the methods of the wireless device described above.

[0041] Also disclosed is a computer program product comprising a non-transitory computer readable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the wireless device described above.

[0042] According to some embodiments, a method is performed by a network node for facilitating monitoring a performance of a functionality (e.g. ML model) of a wireless device by the wireless device. The method comprises determining a configuration comprising a set of parameters for monitoring the functionality of the wireless device and one or more monitoring time occasions and transmitting the configuration to the wireless device.

[0043] In particular embodiments, the method further comprises receiving from the wireless device a request for the wireless device to monitor the performance of the functionality of the wireless device.

[0044] In particular embodiments, the method further comprises receiving results of the monitoring from the wireless device.

[0045] In particular embodiments, the method further comprises adjusting one or more parameters of the functionality based on the received results. [0046] According to some embodiments, a network node network node comprises processing circuitry operable to perform any of the network node methods described above.

[0047] Another computer program product comprises a non-transitory computer readable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the network nodes described above.

[0048] Certain embodiments may provide one or more of the following technical advantages. For example, particular embodiments enable a UE to be configured with a set of parameters or in a setting where the UE may monitor the performance of one or multiple ML-models or collect training data for the models. This may, for example, be that the network does not schedule the UE during certain time intervals which facilitates the UE to use the intervals for its receive chain to receive input data for an ML-model to later assess the performance of the ML-model.

BRIEF DESCRIPTION OF THE DRAWINGS

[0049] For a more complete understanding of the disclosed embodiments and their features and advantages, reference is now made to the following description, taken in conjunction with the accompanying drawings, in which:

FIGURE 1 is an illustration of training and inference pipelines, and their interactions within a model lifecycle management procedure;

FIGURE 2 is a sequence diagram illustrating an example of ML-model adjustment time occasions;

FIGURE 3 is s sequence diagram illustrating an example configuration of UE transition to RRC_IDLE or IN_ACTIVE STATE for the UE to monitor ML-model performance;

FIGURE 4 illustrates an example of beam management, according to particular embodiments;

FIGURE 5 illustrates an example communication system, according to certain embodiments;

FIGURE 6 illustrates an example UE, according to certain embodiments;

FIGURE 7 illustrates an example network node, according to certain embodiments;

FIGURE 8 illustrates a block diagram of a host, according to certain embodiments; FIGURE 9 illustrates a virtualization environment in which functions implemented by some embodiments may be virtualized, according to certain embodiments;

FIGURE 10 illustrates a host communicating via a network node with a UE over a partially wireless connection, according to certain embodiments;

FIGURE 11 illustrates a method performed by a wireless device, according to certain embodiments; and

FIGURE 12 illustrates a method performed by a network node, according to certain embodiments.

DETAILED DESCRIPTION

[0050] As described above, certain challenges currently exist with user equipment (UE) machine learning (ML) functionality monitoring. Certain aspects of the present disclosure and their embodiments may provide solutions to these or other challenges. For example, in particular embodiments a UE indicates to the network a need for performing operations related to monitoring the performance of ML-models/functions and a request to be specially configured to perform this. A special configuration may refer to operational state, receiver configuration, availability of reference signal (RS), etc. In some embodiments, the UE collects data for training purposes using the special configuration during configured intervals.

[0051] Particular embodiments are described more fully with reference to the accompanying drawings. Other embodiments, however, are contained within the scope of the subject matter disclosed herein, the disclosed subject matter should not be construed as limited to only the embodiments set forth herein; rather, these embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0052] The terms ML-model and ML functionality may be used interchangeably herein. The concept of ‘node’ may be understood as a UE, a generic network node, gNB, base station, unit within the base station to handle at least some functionality, relay node, core network node, or a core network node that handles at least some ML operations.

[0053] For ease of description, particular embodiments are described with respect to the example use case where the first node is a UE and the second node is a network node, referred to as ‘network’. However, some of the functionalities described below are understood to be applicable to other cases, e.g., those where the first node is a network node and the second node is a UE, or where both the first and second nodes are UEs. The network node may be in various formats depending on the functionality and deployment scenario, including gNB, gNB-CU, gNB-DU, IAB node (or relay node), location server, etc. It is understood that the same methodology may be applied to a wide variety of cases, even though the description focuses on the example use case where the first node is a UE and second node is a gNB.

[0054] As used herein, the terms “ML-model” and “Al-model”, ”AI-based feature” and “ML- based feature” are interchangeable. An AI/ML model may be defined as a functionality or be part of a functionality that is deployed/implemented in a first node. The first node may receive a message from a second node indicating that the functionality is not performing correctly, e.g. prediction error is higher than a pre-defined value, error interval is not in acceptable levels, or prediction accuracy is lower than a pre-defined value.

[0055] Further, an AI/ML model may be defined as a feature or part of a feature that is implemented/supported in a first node. The first node may indicate the feature version to a second node. If the ML-model is updated, the feature version may be changed by the first node. [0056] An ML-model may be viewed as a functionality that is defined in a UE that may receive a message from a network indicating that the functionality is not performing correctly. Further, the functionality may be defined as a feature that may have a feature version that may be indicated from the UE implementing the feature to a network that communicates with the first node. If the functionality is updated, the feature version may be changed by the UE. The ML- model may be implemented by a neural network or other types of similar functions.

[0057] An ML-model may correspond to a function that receives one or more inputs (e.g., measurements) and provide as output one or more decision, estimation, or prediction of a particular type. The ML model may be described from different perspectives.

[0058] In terms of the time/frequency /spatial domain, the output of the ML model may be in a different time instance, or at a different frequency location, or at a different spatial direction, or a combination of time/frequency/space, than those of the model input. In one example (time domain), an ML-model may correspond to a function receiving as input the measurement of a reference signal at time instance tO (e.g., transmitted in beam-X) and provide as output the prediction of the reference signal in time instance tO+T. In another example (spatial domain), an ML-model may correspond to a function receiving as input the measurement of a reference signal X (e.g., transmitted in beam-x), such as a synchronization signal block (SSB) whose index is ‘x’, and provide as output the estimation/prediction of the link quality of other reference signals transmitted in different beams, e.g., reference signal Y (e.g., transmitted in beam-y).

[0059] In terms of model structure, the ML model may be fully contained within the UE or split between the UE and network.

[0060] One example of split structure is a ML model for aid in channel state information (CSI) estimation, where a possible setup of the ML- model is a split model, which comprises a specific sub-ML-model within a UE and a sub-ML-model within the network side that collaborate to generate a desired outcome for the overall ML model. The function of the sub-ML-model at the UE may be to compress a channel input and the function of the sub-ML-model at the network side may be to decompress the received output from the UE.

[0061] Similar examples apply for positioning where the input may be a channel impulse in a form related to a particular reference point in time. The purpose on the network side is to detect different peaks within the impulse response that corresponds to different reception directions of radio signals at the UE side.

[0062] One example of ML contained within the UE is ML enhanced positioning, e.g., an ML model implemented in the UE takes as input multiple sets of measurements (each corresponding to a downlink signal from a different network node) and based on that, derive an estimated position of the UE.

[0063] In terms of utility for physical layer, the ML model may be used for many functions, including channel estimation, line-of-sight (LOS)/non-line-of-sight (NLOS) classification, beam selection, position estimation of the UE, link adaption, etc. For example, an ML-model may be able to aid the UE in channel estimation, which may or may not incorporate interference estimation. The channel estimation may, for example, be for the physical downlink shared channel (PDSCH) and be associated with specific set of reference signal patterns that are transmitted from the network to the UE. The ML-model is then part of the receiver chain within the UE and may not be directly visible within the reference signal pattern that is configured/scheduled to be used between the network and UE.

[0064] Another example of an ML-model for CSI estimation is to predict a suitable channel quality indicator (CQI), precoding matrix indicator (PMI), rank indicator (RI), or similar value into the future. The future may be a certain number of slots after the UE has performed the last measurement or targeting a specific slot in time within the future. [0065] The UE is connected to the network (e.g., it may receive and transmit data and/or control information). The UE may further be in RRC_CONNECTED state and be configured to fulfil a specific function by using an ML-model. The specific function may, for example, be for one of the following examples (also referred to as functionality area). The specific function may include CSI reporting, beam management, and/or radio resource management (RRM) measurement. The RRM measurement may include mobility measurement, i.e., reference signal received power (RSRP), reference signal received quality (RSRQ), reference signal strength indicator (RSSI), but also aspects related to radio link failure, e.g., RLF predictions, and/or the measurement framework defined in §5.5., 3GPP TS 38.331 comprising how the UE performs measurements (e.g., measurement configuration), what triggers measurement reports (e.g., event-triggered reports, periodic reports), and content to be included in measurement reports).

[0066] The specific functions may further include link adaptation, hybrid automatic repeat request (HARQ) transmission, data transmission, data reception, power control, positioning of the UE, random access transmission, and/or energy efficiency (e.g., discontinuous reception (DRX) settings).

[0067] The UE indicates to the network in a message that the UE wants to monitor the performance of ML-functionality in general or a specific ML-model. The UE further indicates specifics around the configuration that the UE would like to be able to monitor the performance of the ML functionality. Such specifics may, for example, be that the UE is configured with a gap on all cells/carriers it is operating on or on a specific cell or carrier. The gap may be configured in downlink so that the UE does not need to receive downlink transmissions for normal data communication for a certain time interval on either all carriers or on specific carriers. The UE may, for example, during the time interval may receive downlink transmission only for the purpose of assessing/calibrating/adjusting the performance of the ML-model it is operating.

[0068] To exemplify this in more detail, if the ML-model at the UE is for predicting the best cell, based on measurements of RSRP, RSRQ, signal to interference and noise ratio (SINR), at a certain future time, the UE may observe and measure the signal quality (e.g., RSRP, RSRQ, SINR) of all candidate cells, including inter-frequency measurements, for a certain period and select the best cell for a future occasion. This may not be feasible if the UE is not configured with a gap from the network side because the number of receiver chains are limited in the UE, and it may thus not be able to use all of them if the UE is at the same time required to receive downlink data using some of its receiver chains. For the purpose of monitoring/calibrating/adjusting the ML model function, additional gaps beyond the measurement gap may be necessary. Furthermore, if the UE model is predicting the coverage, or signal strength associated to a certain inter-frequency cell, the network might need to wakeup the cell if it is deactivated for energy-saving purposes.

[0069] Another example is that the UE is predicting the best downlink beam using ML. The network may configure the UE according to the recommendation (i.e., prediction) from the UE, after the network has received a report indicating the best predicted downlink beam. The UE may, however, not be able to measure in all downlink beam directions after being configured with a certain downlink beam, be it the best or not the best predicted downlink beam. Thus, the UE may request to be configured with a gap in time that enables the UE to measure many different beam directions during a certain time interval. Another request may be that the UE is configured to measure on all available network beams, or a larger number of beams than in normal beam management operation mode.

[0070] In another example, the UE is forecasting a future signal quality value, for example in respect to a certain SSB-beam. The UE might be equipped with multiple models, for example models that use measurements every 20ms to predict future value in 20ms or 40ms ahead. While another model might comprise measuring every 80ms to predict future value in 80ms, 160ms, ...., etc. ahead. The UE in this example may request a change in periodicity of the gNB transmitted synchronization signal blocks (SSBs). For example, the UE may request the gNB to transmit the SSB every 20ms to enable the UE to test such functionality.

[0071] In another example, the UE is operating an ML-model or function that affects downlink scheduling, for example an ML-model to estimate or predict CSI including CQI, PMI, rank; or a channel estimator operation that is connected to a certain demodulation reference signal (DMRS) patterns. The UE may, for such cases, indicate that the network should schedule the UE according to the reported CSI and the assumptions related to deriving the CSI. The assumptions may, for example, be a specific block error rate (BLER) target, e.g., 10%. The report may indicate preferred precoder via an index, CQI and a particular rank. The assistance information then indicates that the UE would like the gNB to schedule it according to the CSI report or parts of the CSI report. Further, it may indicate that it should schedule the UE according to some assumptions the UE makes to derive the CSI report, e.g., the BLER target. This may be during a certain time interval.

[0072] Based on the examples above, in particular embodiments the UE requests to be configured with a ML model monitoring occasion to the network. The request, as earlier described, may be for all carriers, a certain cell group, or all carriers/cells. Based on the request, the network configures the UE with ML-model monitoring time occasions. During such ML- model monitoring time occasions, the UE may adjust and then perform operations so that the UE monitors the performance of the ML-model if it wishes to do so. During an ML-model performance time occasion, the network cannot assume that the UE will respond to a scheduling message, because the UE may turn off functionality to monitor the performance of the ML-model and due to that have tuned its receiver chains to other frequencies or beams as an example. After the UE has finalized monitoring the performance of the ML-model, it may communicate to the network that the UE does not need the configuration of the monitoring time occasions. The ML model monitoring time occasions may be configured periodically by the network.

[0073] Alternatively, the UE may request to be configured with a single occasion of a ML- model monitoring occasion. If the network receives such a request, the network may then configure a ML-model monitoring time occasion. The ML-model monitoring time occasion may be one time occasion, or it may be multiple occasions periodically or aperiodically occurring. Details around what the UE wants in this request may also be included in the ML- model monitoring time occasions request.

[0074] There may further be different lengths of ML-model monitoring time occasions depending on how complicated it is for the UE to monitor/assess/calibrate/adjust the ML-model in question. The request may thus further include the preferred duration or frequency by the UE. The configuration of the ML-model monitoring time occasions from the network may include the length of the ML-model monitoring time occasions. The occasion in time of the ML-model monitoring time occasions is included in the configuration by the network and potentially with the length of the ML-model monitoring time occasions as mentioned.

[0075] FIGURE 2 is a sequence diagram illustrating an example of ML-model adjustment time occasions. A request 22 of an ML-model monitoring occasion message may comprise an RRC message, MAC CE or UCI information sent by the UE to the network. Information about ML- model(s) monitoring occasion 24 from the UE to the network may comprise RRC, MAC CE or LI message. The message may include cells, carriers, cell groups (CGs), frequencies of the monitoring time occasion, the requested length of the monitoring time occasion, and/or the number of monitoring time occasions. The message may further include the ML-model ID and the current ML-model version for which the UE is requesting to monitor the performance.

[0076] The message may further, as described above, include certain scheduling or network behaviours the UE would like the network to follow during the time interval. This may, for example, be that the network refrains from scheduling the UE (i.e., the UE is not to be scheduled or transmit according to the other request information, scheduling according to a certain behaviour, e.g., with a certain BLER target, not requested to report CSI of a certain type or report of CSI of a certain type, to change to certain TCI state or not to change TCI state, to change to a certain or not change SS/PBCH or CSI- RS id for beam management, schedule data with a certain interval, schedule data with certain modulation order (or avoid certain modulation orders), schedule above or below a certain TBS size, schedule a certain number of layers, schedule according to the CSI report or not according to CSI report, schedule with a certain DMRS pattern or not with a certain DMRS pattern, schedule data with TRS or not with TRS, schedule on a certain carrier(s) or not on a certain carrier(s), schedule certain lengths in time and frequencies of resources, schedule data with certain processing time. Schedule in the text should be viewed as either UL or DL scheduling or both, i.e., the UE transmits or receives data or both.

[0077] ML-model monitoring time occasions configuration 24 may comprise an RRC, MAC CE or LI message sent to the UE from the network. Each outlined information above may be combined or be separated fields in an RRC, MAC CE or LI message for configuring an ML- model monitoring time occasions. The LI message may, for example, comprise downlink control information (DCI) format. The message may be sent by RRC, MAC CE or LI signalling. LI signalling may, for example, comprise uplink control information.

[0078] The network configures the UE with ML-model monitoring time occasions. During such ML-model monitoring time occasions, the UE may determine to monitor the ML-model. During an ML-model monitoring time occasion, the network operates according to the configuration details with which the network configured the UE, e.g., if the network configured the UE such that the UE does not need to receive data on a certain carrier, the network operates with that assumption. [0079] After the UE has monitored the ML-model, the UE may communicate to the network a UE release 26 of the configuration of the ML-model monitoring occasions, after which the ML-model monitoring occasion is no longer configured. Note that in FIGURE 2 only a single ML-model monitoring occasion is shown, but if these are configured with periodically multiple of them may follow after each other. Further, if there is an end time set on the ML-model monitoring occasions, the UE does not need to send a release message, rather the configuration will be released after a the time window. The time window may be a single ML-model monitoring occasion.

[0080] In some embodiments, the performance monitoring by the UE may be performed during at least one autonomous gap. In one option, an autonomous gap is used if the UE is configured by the network with an indication indicating that the UE is allowed to perform the ML-model performance monitoring during an autonomous gap. When the UE starts the autonomous gap for performance monitoring of the ML model, the UE also starts a timer Txxx (with a timer value that is hard-coded for this purpose or configured by the network) and performs the ML model performance monitoring while the timer is running. If the timer expires, the UE stops using the autonomous gap and if the ML model performance monitoring is successful, the procedures ends. If the timer expires and if the ML model performance monitoring is not successful, the UE triggers a failure handling procedure, e.g., re-establishment (if security has been established) or transition to RRC_IDLE. In addition, if timer expires, one option is to notify the network that the procedure for monitoring the performance of the ML model is not successful, so the network can take further actions, such as transition the UE to RRC_IDLE or RRC_INACTIVE (so the UE performs another attempt to do performance monitoring of the ML model).

[0081] In some embodiments, the UE may request the network to be sent/configured with nonmonitoring intervals of PDCCH search space (SS), DRX, RRC_IDLE and/or RRC_INACTIVE state to be able to perform performance monitoring of an ML-model. When the UE goes into DRX, RRC_IDLE and/or RRC_INACTIVE state, the UE monitors the performance of the ML-model. The performance monitoring may also be performed while the UE is in non-DRX in RRC_CONNECTED, during non-monitoring intervals of PDCCH search space (SS). When the UE transitions to RRC_CONNECTED state (or when it is transitioning to RRC_CONNECTED, e.g., upon initiation of an RRC resume procedure or an RRC Setup/Establishment procedure) or non-DRX at some point later, the UE informs the network that it has performed performance monitoring its ML-model accordingly. This may be done directly in a message indicating which ML-models have been monitored.

[0082] In one group of embodiments, the special configuration request may include requesting additional measurement resources and configurations. The UE may request the network to make available certain signals to perform measurements or other operations (e.g., tentative synchronization) for the purposes of ML model performance evaluation. For example, the UE may request additional or specific CSLRS resources, TRS resources, PRS transmissions, DMRS configurations, etc. for the purpose of monitoring or otherwise evaluating the ML model performance in the current environment.

[0083] In the above descriptions, the special configuration requests from the UE have been provided in one single step where the UL signalling (e.g., RRC, MAC CE, UCI) contains the description of the currently desired configuration. In an alternative embodiment, the request process may contain two steps. The UE may first provide one or more special configuration descriptions via, e.g., RRC or MAC CE signalling. In one embodiment, the network may return via a similar signalling mechanism a modified list of special configurations it is prepared to support. The UE then later dynamically requests one of the provided descriptions using a short indicator referring, e.g., to an index in the description list, e.g., via UCI signalling. This approach reduces the signalling load for scenarios where the UE may have multiple special configurations of interest and/or the switching between ML model monitoring and regular operation may be frequent.

[0084] In some embodiments, the UE requests and receives a special configuration for ML model monitoring from the network, and subsequently receives a regular configuration to return from the ML model monitoring to regular operation. The second (regular) configuration may be an explicit configuration, providing detailed configuration parameters. Alternatively, it may be a short configuration reset command instructing the UE to return to the configuration prior to model monitoring. At the time of receiving the special (monitoring) configuration, the UE may store parameter values that are changed or affected by the special configuration; the UE may then reinstate these stored values when receiving the configuration reset command.

[0085] In some embodiments, the UE receives the special configuration together with a validity time indicator or a timer setting. The UE will not receive a command for original configuration reinstatement but after the validity time or timer expires the UE (and the network) will revert to regular operation without special support for ML model monitoring. [0086] In some embodiments, if the ML model performance monitoring/verification procedure at the UE was not successful, e.g., the performance evaluation was inconclusive or a relevant testing scenario/data was not encountered, the UE may notify the network about the lack of success. The network may then provide the special configuration once more, immediately or after a delay (specified, configured, or according to the network discretion). The failure message and/or new special configuration message may be short messages not including the details of the requested and granted configurations, referring rather to previous requested and provided configurations.

[0087] In some embodiments, the requests for a special configuration for ML model performance monitoring may be associated with a prohibit timer. For example, after the UE has transmitted a request, it is blocked from further requests for the duration of the timer even if the requested configuration is not granted by the network. The UE may transmit another request after the timer has expired. The requests may be provided using the UE Assistance information (UAI) framework.

[0088] The terms RRC_IDLE and RRC_INACTIVE refers to RRC states as defined in TS 38.300 for NR. However, the terms are applicable to any control plane (or RRC) states for which procedures are designed for power savings (e.g., cell selection/ cell reselection, paging monitoring, etc.) rather than continuous data transmissions/ receptions.

[0089] FIGURE 3 is s sequence diagram illustrating an example configuration of UE transition to RRC_IDLE or IN_ACTIVE STATE for the UE to monitor ML-model performance. The UE may send request 32 for monitoring performance of an ML-model to the network node. The network node may send response 34 to the UE to configure the UE to go to RRC_IDLE or IN_ACTIVE STATE.

[0090] The example uses cases above for ML-models performance occasion may implemented as follows. Some embodiments include CSI reporting. The UE is configured with ML-models performance occasion by the network (either periodic or aperiodic). During the ML-models performance occasion the UE may observe whether the scheduled PDSCH transmission matches the previously reported CSI reports or not, for example ,in terms of CQI, rank, precoder and so on. The CQI may, for example, be determined in terms of the BLER target, i.e., the BLER target that the CQI is reported for and the BLER that the scheduled PDSCHs achieve in practice. Given that this is a statistical measurement, multiple PDSCH and CQI reports may be considered. [0091] Further, the reported CQI is valid given a certain RI and PMI is followed as reported by the UE in the CSI report, thus the PDSCH is scheduled accordingly. If there is a large enough difference the UE may conclude that there is something wrong with the CSI estimation, and if the difference is bounded within a certain limited, then the UE may conclude that the CSI estimation is functioning. Some embodiments may include an intermediate bound where the UE is not able to determine whether the CSI estimation is functioning or not. The UE may, for example, compare if the CQI that the UE reports match the BLER targeted that the UE has requested/indicated the gNB to operate with (or the gNB has indicated to the UE that gNB will operate with).

[0092] If the purpose is to study scheduled RI, the network may set the RI according to its best knowledge and the UE may compare the scheduled rank of the PDSCH with the reporting rank in the CSI report. Similarly, the UE may compare the scheduled PMI with the indicated PMI in the CSI report and the scheduled PDSCHs.

[0093] The UE may further use the ML-models performance occasion to collect data for different CSI report and channels and potentially together with its corresponding PDSCH BLER target. The collected measurements may be stored and later sent to the network or a third node, wherein the collected data may be used for training. Further, the UE may collect additional metrics such as downlink and/or uplink packet metrics (e.g., BLER, packet delay, UE power consumption, time/frequency resource utilization).

[0094] Some embodiments include a DMRS pattern. The UE is configured with ML-models performance occasion by the network (either periodic or aperiodic). During the ML-models performance occasion the UE may observe whether the scheduled PDSCH transmission matches the previously reported CSI reports, for example, in terms of CQI, rank, precoder and so on. The UE is further only considering PDSCH scheduled with a DMRS pattern for which the UE is running an ML-model within its receiver, e.g., for channel estimation. The specific DMRS pattern that the UE wants the network to use may be indicated by the UE to the network within the request for the ML-models performance occasion. If there is a large enough difference between the BLER of the PDSCH and the reported CSI by the UE, the UE may conclude that the reception with the DMRS pattern is functioning. Some embodiments include an intermediate bound where it is not possible for the UE to judge whether or not the PDSCH reception is functioning. [0095] The UE may, for example, compare if the CQI that the UE reports matches the BLER targeted that the UE has requested/indicated the gNB to operate with (or the gNB has indicated to the UE that gNB will operate with). Similarly, the UE may compare the scheduled rank (RI) with the indicated rank in the between the CSI report and the scheduled PDSCHs. Another option is that the UE does not do a comparison to the CSI, but rather indicates that the network should use a specific DMRS pattern during a certain time interval and is able based on the scheduling occasion of the DMRS with or without PDSCH the performance of the reception ML function for the DMRS, e.g., the channel estimator. Because the purpose may be for the UE to be able to either monitor or collect training data, the UE may indicate that specifically to the network, and the network can then only send the DMRS without the PDSCH. The purpose is to limit the number of transmissions from the network if it does not have any scheduling to the UE at the moment.

[0096] The UE may also collect information on the DMRS and send this later to the network or a third node for use for training at a later stage.

[0097] Some embodiments include beam management. The UE is configured with a ML- models performance occasion by the network (either periodic or aperiodic). During the occasion the UE may measure the different downlink beams (e.g., signal and/or sequence and/or reference signals and/or synchronization sequences like SSB, SSB burst, PSS, SSS, DRMS, CRS transmitted in the downlink beams) and based on the measurement see (or determine, detect) if the measured results correspond to one or more predictions (e.g., at an earlier point in time) made/performed by the UE on which downlink beam would be the best downlink beam based on for example RSRP, RSRQ, SINR, RS SI. A downlink beam in this respect may, for example, be associated to or correspond to a specific SS/PBCH (SSB) that is identified by an index or an CSI- RS; or a downlink beam may be a spatial direction and/or spatial filtering for transmitting an SSB and/or CSLRS. It may also be a combination of a SS/PBCH and a CSLRS. The UE may further use the ML- models performance occasion to collect data for different SS/PBCH and CSLRS. The collected measurements may be stored and later sent to the network or a third node, wherein the collected data may be used for training. [0098] FIGURE 4 illustrates an example of beam management, according to particular embodiments. The term gap herein may correspond to one or more occasions.

[0099] In one set of embodiments, at a time instance and/or interval tO, the UE performs one or more predictions associated to a first subset of downlink beams (e.g., beam a, beam b). Or, to perform one or more predictions the UE is required to perform measurements on the first subset of downlink beams, e.g., beam a and beam b. The UE may have been configured to perform measurements on or monitor (e.g., if there is more than one beam associated to a TCI state that is activated at the UE) the first subset of beams, e.g., based on CSI measurement configuration (based on fields and/or IES within CSI-MeasConfig, e.g., CSI-ReportConfig). [0100] The UE may perform these measurements and/or predictions and/or monitoring based on the measurements for CSI reporting and/or BFD and/or RLM. According to the method, at a later point in time (tO+T, T<0), or in a later measurement period, the UE is indicated by the network (e.g., gNB) to monitor and/or measure a different subset of downlink beams, with at least one non-overlapping beam, e.g. beam c, upon reception of an RRC message (e.g. RRCReconfiguration) or a MAC Control Element (MAC CE) or a DCI signalling (that may be, e.g., a TCI state activation MAC CE or DCI command, or a MAC CE activating a CSI- Measconfig).

[0101] According to the method the UE, even after tO+T, the UE may be required to perform measurements on the first subset of beams for performance monitoring of the ML-model providing the one or more predictions as the output, e.g., to compare the time domain predictions at tO with actual measurements of beams in the first subset. Thus, the UE relies on one or more methods, e.g., usage of autonomous gaps (occasions) and/or gaps configured by the network to be used at least for that purpose. The usage of these gaps may the triggered by the reception of the message/command that leads the UE to switch from the monitoring of the first subset of beams.

[0102] In some embodiments, the configuration and/or the usage of the gaps is determined based on at least a UE capability. For example, if the UE is capable of a specific type of Rx digital beamforming the UE may not require gaps to measure the first subset of beams at tO+T for ML-model performance monitoring, even if according to the command/ message the UE is supposed to change another subset of beams.

[0103] In some embodiments, the UE reports at least one capability related to the need for gaps for the purpose of ML-model performance monitoring, at least related to the need for gaps for measurements for beam management procedures.

[0104] In some embodiments, the first subset of beams is from a first cell (e.g., SSBs encoding a cell identity of the first cell) and the command/message the UE receives is a handover command (e.g., RRC Reconfiguration including a Reconfiguration with Sync), which indicates the UE to go to another cell (and align with a beam in the target cell indicated in the handover command). According to the method, after the handover, the UE uses the gap configuration and/or autonomous gap to monitor the performance of the ML-model providing as output one or more measurements of the first cell.

[0105] In one option, the gap is configured by the target network node in the handover command; in that case, the target network node that generates the handover command is aware the UE needs to perform the ML-output model performance monitoring, e.g., by reception of an indication from the source network node the UE is connected to, wherein the indication may comprise at least one UE capability related to UE Rx beamforming.

[0106] In other words, gaps or occasions may be used by the UE for ML-model performance monitoring if the UE is not configured to measure a first subset of beams at a time or time period for beam management procedures (e.g., CSI reporting, beam measurements, SS-RSRP, SS-RSRQ, SS-SINR, LI measurements, Radio Link Monitoring, Beam Failure Detection) for the first subset of beams.

[0107] The beam measurements disclosed here are not limited to measurements associated to beam management as configured by CSI measurement configuration, e.g., CSLMeasConfig, but also comprise beam measurements to be performed and/or predicted according to an RRC measurement configuration (i.e., MeasConfig) for cell quality derivation (e.g., RSRP and/or RSRQ or a cell based on beam measurements) and/or measurement reporting.

[0108] Some embodiments include RRM measurement. The UE determines if the frequency to perform measurements on is on the frequency list of configured measurements. If not, the UE requests to be configured with ML-models performance occasion by the network. The UE is configured with a ML-models performance occasion by the network (either periodic or aperiodic). During those occasions the UE can measure the different cells and based on this measurement see if the measured results correspond to the predictions earlier made by the UE on which cell would be the best cells or SS/PBCH in terms for RSRP, RSRQ, SINR, RSSI or similar measurement. The collected measurements may be stored and later sent to the network or a third node, wherein the collected data may be used for training.

[0109] In one set of embodiments, at a time instance and/or interval tO, the UE performs one or more predictions associated to a at least one cell in one or more frequencies, e.g., cell A in frequency fl, such as the predicted RSRP of cell A in frequency fl. Or, to perform one or more predictions the UE is required to perform measurements on the cell of the first frequency, e.g., frequency fl . The UE may have been configured to perform measurements on or monitor that frequency fl but, according to the method, at a later point in time (tO+T, T<0), or in a later measurement period, the UE is indicated by the network (e.g., gNB) to monitor and/or measure a different frequency which does not comprise frequency fl , for example, upon reception of an RRC message (e.g. RRCReconfiguration) or a MAC Control Element (MAC CE) or a DCI signalling.

[0110] A typical use case is a handover wherein the UE is measuring cells in frequency fl , but after the UE accesses the target, the UE operates according to a new measurement configuration (MeasConfig) for which fl is not configured to be measured. According to the method the UE, even after tO+T, the UE may be required to perform measurements on the at least one cell in frequency fl for performance monitoring of the ML-model providing the one or more predictions as the output e.g., to compare the time domain predictions at tO with actual measurements of the at least one cell in the frequency fl.

[0111] Thus, the UE relies on one or more methods, e.g., usage of autonomous gaps (occasions) and/or gaps configured by the network to be used at least for that purpose. The usage of these gaps may the triggered by the reception of the message/command that leads the UE to switch from one cell to another (in the case of the handover) or a change of measurement configuration even if the UE stays in the same cell but the frequency fl is removed from the list of frequencies to be measured (e.g., it is not in the list of measurement objects the UE is configured to measure).

[0112] Some embodiments include positioning. For example, for UE ML-model for positioning, the UE may use another positioning method to verify the positioning accuracy of the ML model from time to time. In one example, positioning based on non-RAT information can be used, if the UE has access to non-RAT signal such as GNSS, WiFi, Bluetooth, camera, or sensor. The UE may, for example, trigger a request to perform a UE ML-model verification upon retrieving its position via a non-RAT signal GNSS, WiFi, Bluetooth, camera, or sensor.

[0113] The UE is configured with a ML-models performance occasion by the network (either periodic or aperiodic). A RAT based positioning method (e.g., DL-TDOA, DL-AoD) may be used to verify the positioning accuracy of the UL ML model. If the difference is above a threshold, where the difference is between the estimated position provided by the UE ML model and the estimated position provided by the alternative method, then then the UE may determine that its ML model is not working properly. The UE may further request as part of the ML-models performance occasion to be provided with details to perform Time of Arrival positioning, which may be either opportunity to transmit a PRACH during some occasions or details for a TA type of positioning.

[0114] Further, the UE may use the ML-models performance occasion to collect measurements on, for example, PRS or signals as a process of collecting training data. The collected measurements may be stored and later sent to the network or a third node, wherein the collected data may be used for training.

[0115] Some embodiments include handling of ML functionality performance. If the UE finds that the ML function is not functioning within the performance bounds, the UE may perform one or more of the following actions.

[0116] The UE may request additional ML-models performance occasion to by the network. The UE may use the additional ML-models performance occasion to re-evaluate the performance.

[0117] The UE may indicate to the network that the ML functionality associated with CSI reporting is no longer supported. The UE may indicate to the network the ML functionality associated with CSI reporting ML function is not functioning in a good manner. A definition of a good manner used by the UE may be indicated as a warning to the network that the functionality may not work as intended but can still be used, if the network wishes to do so. [0118] The UE may indicate to a third node that the ML function is not functioning. The third node may be a node that, for example, tracks how well deployed ML functionality works in the UEs in live networks or can handle initiation of ML-models for the UE.

[0119] Some embodiments include parameter adaptation with the ML model operation status. With the UE ML model operation status being monitored, some configuration/operation parameters may change according to the status. In the simplest manner, the status refers to two levels: (a) ML model works properly; (b) ML model is not working properly. More levels can be defined to indicate a series of operation status of the ML model, and the same principle applies.

[0120] In some embodiments, the parameter values are predefined or specified, i.e., not configured by signalling.

[0121] One example is the CSI computation delay used to generate A-CSI. Here the UE ML model function is to support A-CSI generation. If it is determined that the UE ML model is working properly, then the CSI computation delay is reduced, i.e., the values of Zi, Z2, Z3 for CSI computation delay requirement is reduced. For example, {ZI,ML=10 symbols, Z2,ML= 20 symbols, Z3,ML= 10 symbols} are used, instead of the existing {Zi=22 symbols, Z2 = 40 symbols, Z3 = 20 symbols} for CSI computation delay requirement 2 for p=0. The reduction is due to the capability of UE ML model to generate A-CSI feedback more quickly, compared to the existing methods. The reduced values of Zi, Z2, Z3 due to ML may be specified (i.e., without signaling from gNB or UE), and applied by the gNB and UE in determining the timing requirement for sending A-CSI. If it is determined that the UE ML model is working properly, then the existing, longer, CSI computation delay is applied.

[0122] Another example is the evaluation period for beam failure detection (BFD) and/or candidate beam detection (CBD) used in link recovery procedure. Here the UE ML model function is to support BFD and/or CBD. If it is determined that the UE ML model is working properly, then a reduced set of evaluation period for BFD and/or CBD are applied. The reduced evaluation period can be defined for SSB-based and/or CSLRS based link beam management. If it is determined that the UE ML model is working properly, then the existing, longer, evaluation period for BFD and CBD are applied.

[0123] In another example, the parameter values are configured by signalling (either RRC signalling or MAC CE). One example is, the UE ML model is used to support link recovery procedure, including beam failure detection, candidate beam selection. If it is determined that the UE ML model is working properly, then the gNB may configure the UE with a larger set of candidate beam list (e.g., candidateBeamRSList or Candida teBeamRSListExt or candidateBeamRSSCellList). Then more SRS indices and/or periodic CSLRS resource configuration indices are included as candidates, and the UE ML model can work on a wider selection to derive the best beam for link recovery. If it is determined that the UE ML model is working properly, then the gNB may configure the UE with a smaller set of candidate beam list (e.g., candidateBeamRSList or candidateBeamRSListExt or candidateBeamRSSCellList). This allows the UE to deal with a smaller selection to derive the best beam for link recovery within the given evaluation time duration.

[0124] Even though the examples herein focus mainly on the signalling aspects over the Uu interface, the same methodologies may be applied for supporting model update/adjustment using signalling between different UEs over the PC5 interference. In that case, sidelink related physical signals/channels and procedures may be used and enhanced to support the model update related signalling between UEs. Examples of these signals/channels/procedures include PC5 connection establishment procedure, sidelink control information (SCI), physical sidelink control channel (PSCCH), physical sidelink feedback channel (PSFCH).

[0125] FIGURE 5 illustrates an example of a communication system 100 in accordance with some embodiments. In the example, the communication system 100 includes a telecommunication network 102 that includes an access network 104, such as a radio access network (RAN), and a core network 106, which includes one or more core network nodes 108. The access network 104 includes one or more access network nodes, such as network nodes 110a and 110b (one or more of which may be generally referred to as network nodes 110), or any other similar 3rd Generation Partnership Project (3GPP) access node or non-3GPP access point. The network nodes 110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 112a, 112b, 112c, and 112d (one or more of which may be generally referred to as UEs 112) to the core network 106 over one or more wireless connections.

[0126] 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 100 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 100 may include and/or interface with any type of communication, telecommunication, data, cellular, radio network, and/or other similar type of system.

[0127] The UEs 112 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 110 and other communication devices. Similarly, the network nodes 110 are arranged, capable, configured, and/or operable to communicate directly or indirectly with the UEs 112 and/or with other network nodes or equipment in the telecommunication network 102 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 102.

[0128] In the depicted example, the core network 106 connects the network nodes 110 to one or more hosts, such as host 116. 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 106 includes one more core network nodes (e.g., core network node 108) 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 108. 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).

[0129] The host 116 may be under the ownership or control of a service provider other than an operator or provider of the access network 104 and/or the telecommunication network 102, and may be operated by the service provider or on behalf of the service provider. The host 116 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio/video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0130] As a whole, the communication system 100 of 1FIGURE 5 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.

[0131] In some examples, the telecommunication network 102 is a cellular network that implements 3 GPP standardized features. Accordingly, the telecommunications network 102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 102. For example, the telecommunications network 102 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.

[0132] In some examples, the UEs 112 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 104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 104. 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).

[0133] In the example, the hub 114 communicates with the access network 104 to facilitate indirect communication between one or more UEs (e.g., UE 112c and/or 112d) and network nodes (e.g., network node 110b). In some examples, the hub 114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 114 may be a broadband router enabling access to the core network 106 for the UEs. As another example, the hub 114 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 110, or by executable code, script, process, or other instructions in the hub 114. As another example, the hub 114 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 114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 114 then provides to the UE either directly, after performing local processing, and/or after adding additional local content. In still another example, the hub 114 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices.

[0134] The hub 114 may have a constant/persistent or intermittent connection to the network node 110b. The hub 114 may also allow for a different communication scheme and/or schedule between the hub 114 and UEs (e.g., UE 112c and/or 112d), and between the hub 114 and the core network 106. In other examples, the hub 114 is connected to the core network 106 and/or one or more UEs via a wired connection. Moreover, the hub 114 may be configured to connect to an M2M service provider over the access network 104 and/or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 110 while still connected via the hub 114 via a wired or wireless connection. In some embodiments, the hub 114 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 110b. In other embodiments, the hub 114 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 110b, but which is additionally capable of operating as a communication start and/or end point for certain data channels.

[0135] FIGURE 6 shows a UE 200 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-mounted or vehicle embedded/integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and/or an enhanced MTC (eMTC) UE.

[0136] A UE may support device-to-device (D2D) communication, for example by implementing a 3 GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle- to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and/or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0137] The UE 200 includes processing circuitry 202 that is operatively coupled via a bus 204 to an input/output interface 206, a power source 208, a memory 210, a communication interface 212, and/or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in FIGURE 6. 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.

[0138] The processing circuitry 202 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 210. The processing circuitry 202 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 202 may include multiple central processing units (CPUs).

[0139] In the example, the input/output interface 206 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 200. 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. [0140] In some embodiments, the power source 208 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 208 may further include power circuitry for delivering power from the power source 208 itself, and/or an external power source, to the various parts of the UE 200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 208 to make the power suitable for the respective components of the UE 200 to which power is supplied.

[0141] The memory 210 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 210 includes one or more application programs 214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 216. The memory 210 may store, for use by the UE 200, any of a variety of various operating systems or combinations of operating systems. [0142] The memory 210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and/or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 210 may allow the UE 200 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 210, which may be or comprise a device-readable storage medium. [0143] The processing circuitry 202 may be configured to communicate with an access network or other network using the communication interface 212. The communication interface 212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 222. The communication interface 212 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 218 and/or a receiver 220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 218 and receiver 220 may be coupled to one or more antennas (e.g., antenna 222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0144] In the illustrated embodiment, communication functions of the communication interface 212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and/or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol/intemet protocol (TCP/IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0145] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 212, 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). [0146] 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.

[0147] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door/window sensor, a flood/moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. 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 200 shown in FIGURE 6.

[0148] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and/or measurements, and transmits the results of such monitoring and/or measurements to another UE and/or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and/or reporting on its operational status or other functions associated with its operation. [0149] 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.

[0150] FIGURE 7 shows a network node 300 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)).

[0151] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units and/or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0152] 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).

[0153] The network node 300 includes a processing circuitry 302, a memory 304, a communication interface 306, and a power source 308. The network node 300 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 300 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 300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 304 for different RATs) and some components may be reused (e.g., a same antenna 310 may be shared by different RATs). The network node 300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 300, 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 300.

[0154] The processing circuitry 302 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 300 components, such as the memory 304, to provide network node 300 functionality.

[0155] In some embodiments, the processing circuitry 302 includes a system on a chip (SOC). In some embodiments, the processing circuitry 302 includes one or more of radio frequency (RF) transceiver circuitry 312 and baseband processing circuitry 314. In some embodiments, the radio frequency (RF) transceiver circuitry 312 and the baseband processing circuitry 314 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 312 and baseband processing circuitry 314 may be on the same chip or set of chips, boards, or units.

[0156] The memory 304 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 302. The memory 304 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 302 and utilized by the network node 300. The memory 304 may be used to store any calculations made by the processing circuitry 302 and/or any data received via the communication interface 306. In some embodiments, the processing circuitry 302 and memory 304 is integrated.

[0157] The communication interface 306 is used in wired or wireless communication of signaling and/or data between a network node, access network, and/or UE. As illustrated, the communication interface 306 comprises port(s)/terminal(s) 316 to send and receive data, for example to and from a network over a wired connection. The communication interface 306 also includes radio front-end circuitry 318 that may be coupled to, or in certain embodiments a part of, the antenna 310. Radio front-end circuitry 318 comprises filters 320 and amplifiers 322. The radio front-end circuitry 318 may be connected to an antenna 310 and processing circuitry 302. The radio front-end circuitry may be configured to condition signals communicated between antenna 310 and processing circuitry 302. The radio front-end circuitry 318 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 318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 320 and/or amplifiers 322. The radio signal may then be transmitted via the antenna 310. Similarly, when receiving data, the antenna 310 may collect radio signals which are then converted into digital data by the radio front-end circuitry 318. The digital data may be passed to the processing circuitry 302. In other embodiments, the communication interface may comprise different components and/or different combinations of components.

[0158] In certain alternative embodiments, the network node 300 does not include separate radio front-end circuitry 318, instead, the processing circuitry 302 includes radio front-end circuitry and is connected to the antenna 310. Similarly, in some embodiments, all or some of the RF transceiver circuitry 312 is part of the communication interface 306. In still other embodiments, the communication interface 306 includes one or more ports or terminals 316, the radio front-end circuitry 318, and the RF transceiver circuitry 312, as part of a radio unit (not shown), and the communication interface 306 communicates with the baseband processing circuitry 314, which is part of a digital unit (not shown).

[0159] The antenna 310 may include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals. The antenna 310 may be coupled to the radio front-end circuitry 318 and may be any type of antenna capable of transmitting and receiving data and/or signals wirelessly. In certain embodiments, the antenna 310 is separate from the network node 300 and connectable to the network node 300 through an interface or port.

[0160] The antenna 310, communication interface 306, and/or the processing circuitry 302 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 310, the communication interface 306, and/or the processing circuitry 302 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.

[0161] The power source 308 provides power to the various components of network node 300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 300 with power for performing the functionality described herein. For example, the network node 300 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 308. As a further example, the power source 308 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.

[0162] Embodiments of the network node 300 may include additional components beyond those shown in FIGURE 7 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 300 may include user interface equipment to allow input of information into the network node 300 and to allow output of information from the network node 300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 300.

[0163] FIGURE 8 is a block diagram of a host 400, which may be an embodiment of the host 116 of FIGURE 5, in accordance with various aspects described herein. As used herein, the host 400 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 400 may provide one or more services to one or more UEs.

[0164] The host 400 includes processing circuitry 402 that is operatively coupled via a bus 404 to an input/output interface 406, a network interface 408, a power source 410, and a memory 412. 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 3 and 4, such that the descriptions thereof are generally applicable to the corresponding components of host 400.

[0165] The memory 412 may include one or more computer programs including one or more host application programs 414 and data 416, which may include user data, e.g., data generated by a UE for the host 400 or data generated by the host 400 for a UE. Embodiments of the host 400 may utilize only a subset or all of the components shown. The host application programs 414 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs 414 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 400 may select and/or indicate a different host for over-the-top services for a UE. The host application programs 414 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. [0166] FIGURE 9 is a block diagram illustrating a virtualization environment 500 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 500 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.

[0167] Applications 502 (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.

[0168] Hardware 504 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 506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 508a and 508b (one or more of which may be generally referred to as VMs 508), and/or perform any of the functions, features and/or benefits described in relation with some embodiments described herein. The virtualization layer 506 may present a virtual operating platform that appears like networking hardware to the VMs 508.

[0169] The VMs 508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 506. Different embodiments of the instance of a virtual appliance 502 may be implemented on one or more of VMs 508, 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.

[0170] In the context of NFV, a VM 508 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 508, and that part of hardware 504 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 508 on top of the hardware 504 and corresponds to the application 502.

[0171] Hardware 504 may be implemented in a standalone network node with generic or specific components. Hardware 504 may implement some functions via virtualization. Alternatively, hardware 504 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 510, which, among others, oversees lifecycle management of applications 502. In some embodiments, hardware 504 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 512 which may alternatively be used for communication between hardware nodes and radio units.

[0172] FIGURE 10 shows a communication diagram of a host 602 communicating via a network node 604 with a UE 606 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE 112a of FIGURE 5 and/or UE 200 of FIGURE 6), network node (such as network node 110a of FIGURE 5 and/or network node 300 of FIGURE 7), and host (such as host 116 of FIGURE 5 and/or host 400 of FIGURE 8) discussed in the preceding paragraphs will now be described with reference to FIGURE 10.

[0173] Like host 400, embodiments of host 602 include hardware, such as a communication interface, processing circuitry, and memory. The host 602 also includes software, which is stored in or accessible by the host 602 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 606 connecting via an over-the-top (OTT) connection 650 extending between the UE 606 and host 602. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 650.

[0174] The network node 604 includes hardware enabling it to communicate with the host 602 and UE 606. The connection 660 may be direct or pass through a core network (like core network 106 of FIGURE 5) 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.

[0175] The UE 606 includes hardware and software, which is stored in or accessible by UE 606 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 606 with the support of the host 602. In the host 602, an executing host application may communicate with the executing client application via the OTT connection 650 terminating at the UE 606 and host 602. 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 650 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 650.

[0176] The OTT connection 650 may extend via a connection 660 between the host 602 and the network node 604 and via a wireless connection 670 between the network node 604 and the UE 606 to provide the connection between the host 602 and the UE 606. The connection 660 and wireless connection 670, over which the OTT connection 650 may be provided, have been drawn abstractly to illustrate the communication between the host 602 and the UE 606 via the network node 604, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0177] As an example of transmitting data via the OTT connection 650, in step 608, the host 602 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 606. In other embodiments, the user data is associated with a UE 606 that shares data with the host 602 without explicit human interaction. In step 610, the host 602 initiates a transmission carrying the user data towards the UE 606. The host 602 may initiate the transmission responsive to a request transmitted by the UE 606. The request may be caused by human interaction with the UE 606 or by operation of the client application executing on the UE 606. The transmission may pass via the network node 604, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 612, the network node 604 transmits to the UE 606 the user data that was carried in the transmission that the host 602 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 614, the UE 606 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 606 associated with the host application executed by the host 602.

[0178] In some examples, the UE 606 executes a client application which provides user data to the host 602. The user data may be provided in reaction or response to the data received from the host 602. Accordingly, in step 616, the UE 606 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 606. Regardless of the specific manner in which the user data was provided, the UE 606 initiates, in step 618, transmission of the user data towards the host 602 via the network node 604. In step 620, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 604 receives user data from the UE 606 and initiates transmission of the received user data towards the host 602. In step 622, the host 602 receives the user data carried in the transmission initiated by the UE 606.

[0179] One or more of the various embodiments improve the performance of OTT services provided to the UE 606 using the OTT connection 650, in which the wireless connection 670 forms the last segment. More precisely, the teachings of these embodiments may improve the delay to directly activate an SCell by RRC and power consumption of user equipment and thereby provide benefits such as reduced user waiting time and extended battery lifetime.

[0180] In an example scenario, factory status information may be collected and analyzed by the host 602. As another example, the host 602 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 602 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 602 may store surveillance video uploaded by a UE. As another example, the host 602 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 602 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.

[0181] 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 650 between the host 602 and UE 606, 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 602 and/or UE 606. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 650 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 650 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node 604. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by the host 602. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 650 while monitoring propagation times, errors, etc.

[0182] FIGURE 11 is a flowchart illustrating an example method in a wireless device, according to certain embodiments. In particular embodiments, one or more steps of FIGURE 11 may be performed by UE 200 described with respect to FIGURE 6. The wireless device is capable of monitoring performance of a functionality (e.g., ML model) of the wireless device. [0183] The method may begin at step 1112, where the wireless device (e.g., UE 200) transmits to a network node (e.g., network node 300) a request for the wireless device to monitor the functionality. The request may comprise a request for one or more monitoring time occasions during which the wireless device is to perform measurements related to the functionality. The requested one or more monitoring time occasions may be associated with one or more of: one or more carriers; one or more cells; and one or more beams or beam directions. The requested one or more monitoring time occasions may be periodic or aperiodic. [0184] The request to monitor the functionality may comprise one or more of: a functionality identifier (e.g., ML model identifier); a functionality version identifier (e.g., ML model version identifier); and an indication of a type of functionality. The type of functionality may comprise one or more of: channel state information reporting; radio resource management (RRM) measurements; mobility management; beam management; and positioning.

[0185] The request to monitor the functionality may comprise a request for transmission of one or more reference signals (e.g., positioning reference signal (PRS), channel state information reference signal (CRS-RS), etc.) from the network node to the wireless device.

[0186] The request may comprise any of the requests described with respect to the embodiments and examples described herein (e.g., described with respect to FIGURES 2-4).

[0187] At step 1114, the wireless device receives a configuration from the network node. The configuration comprises a set of parameters for monitoring the functionality and one or more monitoring time occasions. The one or more monitoring time occasions are times reserved for the wireless device to perform the monitoring as to not interfere with normal operation of the wireless device.

[0188] In particular embodiments, the one or more monitoring time occasions in the received configuration are associated with one or more of: one or more carriers; one or more cells; and one or more beams or beam directions. The one or more monitoring time occasions in the received configuration may be periodic or aperiodic.

[0189] In particular embodiments, the received set of parameters comprises a configuration for transmission of one or more reference signals (e.g., positioning reference signal (PRS), channel state information reference signal (CRS-RS), etc.) from the network node to the wireless device.

[0190] At step 1116, the wireless device monitors the functionality based on the received set of parameters during the one or more monitoring time occasions. For example, the wireless device may monitor reference signals during the one or more monitoring time occasions. The wireless device may compare the measured results with output from the functionality to assess the performance of the functionality.

[0191] The wireless device may monitor the functionality according to any of the embodiments and examples described herein (e.g., described with respect to FIGURES 2-4).

[0192] At step 1118, the wireless device may report results of monitoring the functionality to one or more network nodes. For example, the wireless device may indicate to the network node whether the functionality is performing within an expected performance range. The wireless device may report results according to any of the embodiments and examples described herein (e.g., described with respect to FIGURES 2-4).

[0193] At step 1120, the wireless device may adjust one or more parameters of the functionality based on the monitoring of the functionality. For example, the wireless device may adjust parameters of an ML model, or the wireless device may perform retraining of an ML model. The retraining may use the monitoring results. In some embodiments, the wireless device may adjust one or more parameters according to any of the embodiments and examples described herein (e.g., described with respect to FIGURES 2-4).

[0194] Modifications, additions, or omissions may be made to method 1100 of FIGURE 11. Additionally, one or more steps in the method of FIGURE 11 may be performed in parallel or in any suitable order.

[0195] FIGURE 12 is a flowchart illustrating an example method in a network node, according to certain embodiments. In particular embodiments, one or more steps of FIGURE 12 may be performed by network node 300 described with respect to FIGURE 7. The network node is capable of facilitating monitoring a performance of a functionality (e.g. ML model) of a wireless device by the wireless device.

[0196] The method may begin at step 1212, where the network node (e.g., network node 300) receives from a wireless device (e.g., wireless device 200) a request for the wireless device to monitor the performance of the functionality of the wireless device. The request is described in more detail above with respect to FIGURE 11.

[0197] At step 1214, the network node determines a configuration comprising a set of parameters for monitoring the functionality of the wireless device and one or more monitoring time occasions and transmitting the configuration to the wireless device. The network node may base the configuration on a requested configuration from the wireless device, or the network node may modify a requested configuration based on available resources. The network node may determine the configuration based on any of the embodiments and examples described herein (e.g., described with respect to FIGURES 2-4).

[0198] At step 1216, the network node transmits the configuration to the wireless device. The wireless device then uses the configuration to perform the monitoring of the functionality. [0199] At step 1218, the network node may receive results of the monitoring from the wireless device. The network node may use the results to adjust one or more parameters of the functionality based on the received results at step 1220.

[0200] Modifications, additions, or omissions may be made to method 1200 of FIGURE 12. Additionally, one or more steps in the method of FIGURE 12 may be performed in parallel or in any suitable order.

[0201] Modifications, additions, or omissions may be made to the methods disclosed herein without departing from the scope of the invention. The methods may include more, fewer, or other steps. Additionally, steps may be performed in any suitable order.

[0202] The foregoing description sets forth numerous specific details. It is understood, however, that embodiments may be practiced without these specific details. In other instances, well-known circuits, structures and techniques have not been shown in detail in order not to obscure the understanding of this description. Those of ordinary skill in the art, with the included descriptions, will be able to implement appropriate functionality without undue experimentation.

[0203] References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to implement such feature, structure, or characteristic in connection with other embodiments, whether or not explicitly described.

[0204] Although this disclosure has been described in terms of certain embodiments, alterations and permutations of the embodiments will be apparent to those skilled in the art. Accordingly, the above description of the embodiments does not constrain this disclosure. Other changes, substitutions, and alterations are possible without departing from the scope of this disclosure, as defined by the claims below.

[0205] Some example embodiments are described below.

Group A Example Embodiments

1. A method performed by a wireless device, the method comprising:

- sending a message indicating a need of monitoring performance of machine learning (M L) functionality to a network node; and receiving a confirmation message from the network node. The method of embodiment 1, further comprising sending an indication message that the functionality has been monitored to the network node. The method of any one of embodiments 1-2, wherein the message indicates at least one of:

- a request for a functionality update,

- a specific carrier or frequency it applies to,

- a specific scheduling behaviour wished from the second node,

- specific signals (e.g., RS) made available by the second node,

- a functionality ID,

- a functionality area ID characterizing the purpose of the functionality ID, e.g., channel estimation, decoding, etc.,

- if the functionality can be monitored in DRX, non-DRX, RRC_CONNECTED STATE, RRCJN ACTIVE STATE or RRCJDLE STATE,

- a time required to monitor the functionality,

- a preferred functionality at the second node,

- an indication indicating to the second node that the first node needs to monitor an ML-model, wherein the indication is transmitted within the message (e.g., the RRC message UEAssistancelnformation). A method performed by a wireless device, the method comprising:

- any of the wireless device steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above. The method of the previous embodiment, further comprising one or more additional wireless device steps, features or functions described above. The method of any of the previous embodiments, further comprising:

- providing user data; and

- forwarding the user data to a host computer via the transmission to the base station.

•up B Example Embodiments

7. A method performed by a base station, the method comprising:

- receiving a message indicating a need of monitoring performance of machine learning (M L) functionality from a wireless device; and

- transmitting a confirmation message to the wireless node.

8. The method of embodiment 7, further comprising receiving an indication message that the functionality has been monitored.

9. The method of any one of embodiments 7-8, wherein the message indicates at least one of:

- a request for a functionality update,

- a specific carrier or frequency it applies to,

- a specific scheduling behaviour wished from the second node,

- specific signals (e.g., RS) made available by the second node,

- a functionality ID,

- a functionality area ID characterizing the purpose of the functionality ID, e.g., channel estimation, decoding, etc.,

- if the functionality can be monitored in DRX, non-DRX, RRC_CONNECTED STATE, RRCJN ACTIVE STATE or RRCJDLE STATE,

- a time required to monitor the functionality,

- a preferred functionality at the second node,

- an indication indicating to the second node that the first node needs to monitor an ML-model, wherein the indication is transmitted within the message (e.g., the RRC message UEAssistancelnformation).

10. A method performed by a base station, the method comprising:

- any of the steps, features, or functions described above with respect to base station, either alone or in combination with other steps, features, or functions described above.

11. The method of the previous embodiment, further comprising one or more additional base station steps, features or functions described above.

12. The method of any of the previous embodiments, further comprising:

- obtaining user data; and

- forwarding the user data to a host computer or a wireless device.

IUD C Example Embodiments

13. A mobile terminal comprising:

- processing circuitry configured to perform any of the steps of any of the Group A embodiments; and

- power supply circuitry configured to supply power to the wireless device.

14. A base station comprising:

- processing circuitry configured to perform any of the steps of any of the Group B embodiments;

- power supply circuitry configured to supply power to the wireless device.

15. A user equipment (UE) comprising:

- an antenna configured to send and receive wireless signals;

- radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry;

- the processing circuitry being configured to perform any of the steps of any of the Group A embodiments;

- an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry;

- an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and

- a battery connected to the processing circuitry and configured to supply power to the UE.

16. A communication system including a host computer comprising:

- processing circuitry configured to provide user data; and - a communication interface configured to forward the user data to a cellular network for transmission to a user equipment (UE),

- wherein the cellular network comprises a base station having a radio interface and processing circuitry, the base station’s processing circuitry configured to perform any of the steps of any of the Group B embodiments. The communication system of the pervious embodiment further including the base station. The communication system of the previous 2 embodiments, further including the UE, wherein the UE is configured to communicate with the base station. The communication system of the previous 3 embodiments, wherein:

- the processing circuitry of the host computer is configured to execute a host application, thereby providing the user data; and

- the UE comprises processing circuitry configured to execute a client application associated with the host application. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:

- at the host computer, providing user data; and

- at the host computer, initiating a transmission carrying the user data to the UE via a cellular network comprising the base station, wherein the base station performs any of the steps of any of the Group B embodiments. The method of the previous embodiment, further comprising, at the base station, transmitting the user data. The method of the previous 2 embodiments, wherein the user data is provided at the host computer by executing a host application, the method further comprising, at the UE, executing a client application associated with the host application. A user equipment (UE) configured to communicate with a base station, the UE comprising a radio interface and processing circuitry configured to performs any of the previous 3 embodiments.

24. A communication system including a host computer comprising:

- processing circuitry configured to provide user data; and

- a communication interface configured to forward user data to a cellular network for transmission to a user equipment (UE),

- wherein the UE comprises a radio interface and processing circuitry, the UE’s components configured to perform any of the steps of any of the Group A embodiments.

25. The communication system of the previous embodiment, wherein the cellular network further includes a base station configured to communicate with the UE.

26. The communication system of the previous 2 embodiments, wherein:

- the processing circuitry of the host computer is configured to execute a host application, thereby providing the user data; and

- the UE’s processing circuitry is configured to execute a client application associated with the host application.

27. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:

- at the host computer, providing user data; and

- at the host computer, initiating a transmission carrying the user data to the UE via a cellular network comprising the base station, wherein the UE performs any of the steps of any of the Group A embodiments.

28. The method of the previous embodiment, further comprising at the UE, receiving the user data from the base station.

29. A communication system including a host computer comprising:

- communication interface configured to receive user data originating from a transmission from a user equipment (UE) to a base station,

- wherein the UE comprises a radio interface and processing circuitry, the UE’s processing circuitry configured to perform any of the steps of any of the Group A embodiments.

30. The communication system of the previous embodiment, further including the UE.

31. The communication system of the previous 2 embodiments, further including the base station, wherein the base station comprises a radio interface configured to communicate with the UE and a communication interface configured to forward to the host computer the user data carried by a transmission from the UE to the base station.

32. The communication system of the previous 3 embodiments, wherein:

- the processing circuitry of the host computer is configured to execute a host application; and

- the UE’s processing circuitry is configured to execute a client application associated with the host application, thereby providing the user data.

33. The communication system of the previous 4 embodiments, wherein:

- the processing circuitry of the host computer is configured to execute a host application, thereby providing request data; and

- the UE’s processing circuitry is configured to execute a client application associated with the host application, thereby providing the user data in response to the request data.

34. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:

- at the host computer, receiving user data transmitted to the base station from the UE, wherein the UE performs any of the steps of any of the Group A embodiments.

35. The method of the previous embodiment, further comprising, at the UE, providing the user data to the base station.

36. The method of the previous 2 embodiments, further comprising:

- at the UE, executing a client application, thereby providing the user data to be transmitted; and at the host computer, executing a host application associated with the client application.

37. The method of the previous 3 embodiments, further comprising:

- at the UE, executing a client application; and

- at the UE, receiving input data to the client application, the input data being provided at the host computer by executing a host application associated with the client application,

- wherein the user data to be transmitted is provided by the client application in response to the input data.

38. A communication system including a host computer comprising a communication interface configured to receive user data originating from a transmission from a user equipment (UE) to a base station, wherein the base station comprises a radio interface and processing circuitry, the base station’s processing circuitry configured to perform any of the steps of any of the Group B embodiments.

39. The communication system of the previous embodiment further including the base station.

40. The communication system of the previous 2 embodiments, further including the UE, wherein the UE is configured to communicate with the base station.

41. The communication system of the previous 3 embodiments, wherein:

- the processing circuitry of the host computer is configured to execute a host application;

- the UE is configured to execute a client application associated with the host application, thereby providing the user data to be received by the host computer.

42. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:

- at the host computer, receiving, from the base station, user data originating from a transmission which the base station has received from the UE, wherein the UE performs any of the steps of any of the Group A embodiments. 43. The method of the previous embodiment, further comprising at the base station, receiving the user data from the UE. 44. The method of the previous 2 embodiments, further comprising at the base station, initiating a transmission of the received user data to the host computer.

Claims

CLAIMS:

1. A method performed by a wireless device for monitoring performance of a functionality of the wireless device, the method comprising: receiving (1114) a configuration from a network node, the configuration comprising a set of parameters for monitoring the functionality and one or more monitoring time occasions; and monitoring (1116) the functionality based on the received set of parameters during the one or more monitoring time occasions.

2. The method of claim 1, further comprising transmitting (1112) to the network node a request for the wireless device to monitor the functionality.

3. The method of claim 2, wherein the request to monitor the functionality comprises a request for one or more monitoring time occasions during which the wireless device is to perform measurements related to the functionality.

4. The method of claim 3, wherein the requested one or more monitoring time occasions are associated with one or more of: one or more carriers; one or more cells; and one or more beams or beam directions.

5. The method of any one of claims 3-4, wherein the requested one or more monitoring time occasions are periodic or aperiodic.

6. The method of any one of claims 2-4, wherein the request to monitor the functionality comprises one or more of: a functionality identifier; a functionality version identifier; and an indication of a type of functionality.

7. The method of claim 6, wherein the type of functionality comprises one or more of: channel state information reporting; radio resource management (RRM) measurements; mobility management; beam management; and positioning.

8. The method of any one of claims 2-7, wherein the request to monitor the functionality comprises a request for transmission of one or more reference signals from the network node to the wireless device.

9. The method of any one of claims 1-8, wherein the one or more monitoring time occasions in the received configuration are associated with one or more of: one or more carriers; one or more cells; and one or more beams or beam directions.

10. The method of any one of claims 1-9, wherein the one or more monitoring time occasions in the received configuration are periodic or aperiodic.

11. The method of any one of claims 1-10, wherein the received set of parameters comprises a configuration for transmission of one or more reference signals from the network node to the wireless device.

12. The method of any one of claims 1-11, further comprising reporting (1118) results of monitoring the functionality to one or more network nodes.

13. The method of any one of claims 1-12, further comprising adjusting (1120) one or more parameters of the functionality based on the monitoring of the functionality.

14. The method of any one of claims 1-13, wherein the functionality comprises a machine learning (ML) model.

15. A wireless device (200) capable of monitoring a performance of a functionality of the wireless device, the wireless device comprising processing circuitry (202) operable to: receive a configuration from a network node (300), the configuration comprising a set of parameters for monitoring the functionality and one or more monitoring time occasions; and monitor the functionality based on the received set of parameters during the one or more monitoring time occasions.

16. The wireless device of claim 15, the processing circuitry further operable to transmit to the network node a request for the wireless device to monitor the functionality.

17. The wireless device of claim 16, wherein the request to monitor the functionality comprises a request for one or more monitoring time occasions during which the wireless device is to perform measurements related to the functionality.

18. The wireless device of claim 17, wherein the requested one or more monitoring time occasions are associated with one or more of: one or more carriers; one or more cells; and one or more beams or beam directions.

19. The wireless device of any one of claims 17-18, wherein the requested one or more monitoring time occasions are periodic or aperiodic.

20. The wireless device of any one of claims 16-19, wherein the request to monitor the functionality comprises one or more of: a functionality identifier; a functionality version identifier; and an indication of a type of functionality.

21. The wireless device of claim 20, wherein the type of functionality comprises one or more of: channel state information reporting; radio resource management (RRM) measurements; mobility management; beam management; and positioning.

22. The wireless device of any one of claims 16-21, wherein the request to monitor the functionality comprises a request for transmission of one or more reference signals from the network node to the wireless device.

23. The wireless device of any one of claims 15-22, wherein the one or more monitoring time occasions in the received configuration are associated with one or more of: one or more carriers; one or more cells; and one or more beams or beam directions.

24. The wireless device of any one of claims 15-23 wherein the one or more monitoring time occasions in the received configuration are periodic or aperiodic.

25. The wireless device of any one of claims 15-24, wherein the received set of parameters comprises a configuration for transmission of one or more reference signals from the network node to the wireless device.

26. The wireless device of any one of claims 15-25, the processing circuitry further operable to report results of monitoring the functionality to one or more network nodes.

27. The wireless device of any one of claims 15-26, the processing circuitry further operable to adjust one or more parameters of the functionality based on the monitoring of the functionality.

28. The wireless device of any one of claims 15-27, wherein the functionality comprises a machine learning (ML) model.

29. A method performed by a network node for facilitating monitoring a performance of a functionality of a wireless device by the wireless device, the method comprising: determining (1214) a configuration comprising a set of parameters for monitoring a performance of a functionality of a wireless device and one or more monitoring time occasions; and transmitting (1216) the configuration to the wireless device.

30. The method of claim 29, further comprising receiving (1212) from the wireless device a request for the wireless device to monitor the performance of the functionality of the wireless device.

31. The method of claim 30, wherein the request to monitor the the performance of the functionality comprises a request for one or more monitoring time occasions during which the wireless device is to perform measurements related to the functionality.

32. The method of claim 31, wherein the requested one or more monitoring time occasions are associated with one or more of: one or more carriers; one or more cells; and one or more beams or beam directions.

33. The method of any one of claims 31-32, wherein the requested one or more monitoring time occasions are periodic or aperiodic.

34. The method of any one of claims 30-33, wherein the request to monitor the performance of the functionality comprises one or more of: a functionality identifier; a functionality version identifier; and an indication of a type of functionality.

35. The method of claim 34, wherein the type of functionality comprises one or more of: channel state information reporting; radio resource management (RRM) measurements; mobility management; beam management; and positioning.

36. The method of any one of claims 30-35, wherein the request to monitor the performance of the functionality comprises a request for transmission of one or more reference signals from the network node to the wireless device.

37. The method of any one of claims 29-36, wherein the determined one or more monitoring time occasions are associated with one or more of: one or more carriers; one or more cells; and one or more beams or beam directions.

38. The method of any one of claims 29-37, wherein the determined one or more monitoring time occasions are periodic or aperiodic.

39. The method of any one of claims 29-38, wherein the determined set of parameters comprises a configuration for transmission of one or more reference signals from the network node to the wireless device.

40. The method of any one of claims 29-39, further comprising receiving (1218) results of the monitoring from the wireless device.

41. The method of claim 40, further comprising adjusting (1220) one or more parameters of the functionality based on the received results.

42. The method of any one of claims 29-41, wherein the functionality comprises a machine learning (ML) model.

43. A network node (300) capable of facilitating monitoring a performance of a functionality of a wireless device (200) by the wireless device, the network node comprising processing circuitry (302) operable to: determine a configuration comprising a set of parameters for monitoring a performance of a functionality of a wireless device and one or more monitoring time occasions; and transmit the configuration to the wireless device.

44. The network node of claim 43, the processing circuitry further operable to receive from the wireless device a request for the wireless device to monitor the performance of the functionality of the wireless device.

45. The network node of claim 43, wherein the request to monitor the the performance of the functionality comprises a request for one or more monitoring time occasions during which the wireless device is to perform measurements related to the functionality.

46. The network node of claim 45, wherein the requested one or more monitoring time occasions are associated with one or more of: one or more carriers; one or more cells; and one or more beams or beam directions.

47. The network node of any one of claims 45-46, wherein the requested one or more monitoring time occasions are periodic or aperiodic.

48. The network node of any one of claims 44-47, wherein the request to monitor the performance of the functionality comprises one or more of: a functionality identifier; a functionality version identifier; and an indication of a type of functionality.

49. The network node of claim 48, wherein the type of functionality comprises one or more of: channel state information reporting; radio resource management (RRM) measurements; mobility management; beam management; and positioning.

50. The network node of any one of claims 44-49, wherein the request to monitor the performance of the functionality comprises a request for transmission of one or more reference signals from the network node to the wireless device.

51. The network node of any one of claims 43-50, wherein the determined one or more monitoring time occasions are associated with one or more of: one or more carriers; one or more cells; and one or more beams or beam directions.

52. The network node of any one of claims 43-51, wherein the determined one or more monitoring time occasions are periodic or aperiodic.

53. The network node of any one of claims 43-52, wherein the determined set of parameters comprises a configuration for transmission of one or more reference signals from the network node to the wireless device.

54. The network node of any one of claims 43-43, the processing circuitry further operable to receive results of the monitoring from the wireless device.

55. The network node of claim 54, the processing circuitry further operable to adjust one or more parameters of the functionality based on the received results.

56. The network node of any one of claims 43-55, wherein the functionality comprises a machine learning (ML) model.

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