A wireless device, a network node and methods for performance monitoring for multiple CSI prediction schemes

Abstract

A method performed by a wireless device capable of performing channel state information, CSI, prediction, the method comprising: − obtaining (110) a performance monitoring configuration for more than one CSI prediction scheme; − determining (120) one or more performance monitoring results based on received reference signals and the performance monitoring configuration; and − reporting (130) the one or more performance monitoring results to a network node.

Description

[0001] A WIRELESS DEVICE, A NETWORK NODE AND METHODS FOR PERFORMANCE MONITORING FOR MULTIPLE CSI PREDICTION SCHEMES

[0002] TECHNICAL FIELD

[0003] The embodiments disclosed herein relate to a wireless device, a network node and methods for performance monitoring for multiple CSI prediction schemes. A corresponding computer program and a computer program carrier are also disclosed.

[0004] BACKGROUND

[0005] Artificial intelligence (Al) and machine learning (ML) have been investigated, 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; and using reinforcement learning for beam selection at the network side and / or the user equipment (UE) side to reduce the 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.

[0006] The Third Generation Partnership Project (3GPP) New Radio (NR) standardization work for release 18 (Rel. 18) included a study item (SI) on AI / ML for the NR air interface and this work will continue in Rel. 19. The works 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 and specifying a few selected use cases (CSI feedback, beam management, and positioning), the works aim to design the mechanisms to accommodate AI / ML into the 3GPP standard.

[0007] An important part of Al development and operation is the lifecycle management (LCM) of the AI / ML model (e.g., model training, model deployment, model inference, model monitoring, model updating) and AI / ML functionality.

[0008] In NR Rel-18 AI / ML for NR air interface study item, the LCM procedure is studied for the case that an AI / ML model has a model ID with associated information and / or for the case that a given functionality is provided by some AI / ML operations. Two types of LCM operations were studied in NR Rel-18, functionality-based LCM, and model-l D-based LCM.

[0009] Functionality refers to an AI / ML-enabled feature / Feature Group (FG) enabled by configuration(s), where config uration(s) is(are) supported based on conditions indicated by UE capability. For example, in the UE-side prediction case, a UE may be capable of predicting up to 10 future CSI prediction instances. In the implementation, the UE may have two models where the first model is a more specific model to predict 5 future CSI instances (and consumes lower power) and the second model is capable of predicting 10 future CSI instance (and consumes more power). A configuration / indication from the NW, such as the NW configures the UE to report 8 future CSI instances, may serve as an indication for the UE to use the second model instead of the first one.

[0010] Correspondingly, functionality-based LCM operates based on, at least, one configuration of an AI / ML-enabled feature / FG or specific configurations of an AI / ML- enabled feature / FG. In functionality-based LCM, the network indicates activation / deactivation / fallback / switching of AI / ML functionality via 3GPP signaling (e.g., Radio Resource Control (RRC), medium access control (MAC) control element (CE), downlink control information (DCI). Models may not be identified at the network, and the UE may perform model-level LCM. This means that that the model currently used by the UE does not necessarily need be reported to the UE. Related to the example above, the UE does not necessarily inform the network that the UE switches from the first to the second model. For functionality identification, there may be either one or more than one functionalities defined within an AI / ML-enabled feature, whereby AI / ML-enabled feature refers to a feature where AI / ML may be used.

[0011] In model-l D-based LCM, models are identified at the network, and the network / UE may activate / deactivate / select / switch individual AI / ML models via model ID. A model may be associated with specific configurations / conditions associated with the UE capability of an AI / ML-enabled feature / FG and additional conditions (e.g., scenarios, sites, and datasets) as determined / identified between the UE side and network side. An AI / ML model identified by a model ID may be logical, and how it maps to physical AI / ML model(s) may be up to implementation.

[0012] Figure 1 is a block diagram illustrating a functional framework for AI / ML for NR air interface. The illustrated example shows a functional framework that can be used for studying model LCM aspects for different Al for physical layer (PHY) use cases. The general framework consists of the following. Data Collection is a function that provides input data to the Model Training, Management, and Inference functions.

[0013] Training Data: Data needed as input for the AI / ML Model Training function.

[0014] Monitoring Data: Data needed as input for the Management of AI / ML models or AI / ML functionalities.

[0015] Inference Data: Data needed as input for the AI / ML Inference function.

[0016] Model Training is a function that performs AI / ML model training, validation, and testing which may generate model performance metrics that can be used as part of the model testing procedure. The Model Training function is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on Training Data delivered by a Data Collection function if required.

[0017] Trained / Updated Model: In case of having a Model Storage function, this is used to deliver trained, validated, and tested AI / ML models to the Model Storage function, or to deliver an updated version of a model to the Model Storage function.

[0018] Management is a function that oversees the operation (e.g., selection, (de)activation, switching, fallback) and monitoring (e.g., performance) of AI / ML models or AI / ML functionalities. This function is also responsible for making decisions to ensure the proper inference operation based on data received from the Data Collection function and the Inference function.

[0019] Management Instruction: Information needed as input to manage the Inference function. Concerning information may include selection / (de)activation / switching of AI / ML models or AI / ML-based functionalities, fallback to non-AI / ML operation (i.e., not relying on inference process), etc.

[0020] Model Transfer / Delivery Request: Used to request model(s) to the Model Storage function.

[0021] Performance Feedback / Retraining Request: Information needed as input for the Model Training function, e.g., for model (re)training or updating purposes.

[0022] Inference is a function that provides outputs from the process of applying AI / ML models or AI / ML functionalities, using the data that is provided by the Data Collection function (i.e., Inference Data) as an input. The Inference function is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on Inference Data delivered by a Data Collection function, if required.

[0023] Inference Output: Data used by the Management function to monitor the performance of AI / ML models or AI / ML functionalities. Model Storage is a function responsible for storing trained / updated models that can be used to perform the Inference function.

[0024] The Model Storage function in Figure 1 is only intended as a reference point (if any) when applicable for protocol terminations, model transfer / delivery, and related processes. It should be stressed that its purpose does not encompass restricting the actual storage locations of models.

[0025] Model Transfer / Delivery: Used to deliver an AI / ML model to the Inference function.

[0026] 3GPP NR Rel. 18 also includes time domain Type II CSI prediction at the UE. 3GPP NR Rel-18 introduces channel measurement resource (CMR) enhancement for Type II CSI prediction at UE (also referred to as Rel-18 Type II CSI). To enable CSI prediction, channel measurements for a sufficient number of time instances are required for extracting the time domain channel property, based on which a future CSI can be predicted. To obtain such measurements, gNB may either configure a legacy periodic (P) or semi-persistent (SP) CSI-RS resource, or an aperiodic (AP) CSI-RS burst according to Rel-18. To be more specific, for AP CSI-RS burst based CSI prediction at a UE, a single burst of Ke{4,8,12} CSI-RS resources may be configured to the UE within a single CSI-RS resource set, which may be aperiodically triggered using a single downlink control information (DCI). The CSI-RS resources are uniformly spaced in time, separated by me{1 ,2} slots, within the resource set.

[0027] For the Rel-18 Type II precoding matrix indicator (PMI) enhancement, a UE may be configured by gNB to report predicted PMIs for N_4e{1,2,4} time slots. The prediction herein is relative to the CSI-RS reference resource. The predicted N_4 PMIs are supposed to reflect the channels with de{1 ,2} slots separation, starting from 6e{0, 1 ,2} slots into the future relative to the CSI-RS reference resource. The spacing d between the N_4 PMIs and offset 5 relative to the CSI-RS reference resource may be configured by the gNB via RRC signaling. The N_4 PMIs are compressed in a beam-frequency-Doppler domain, and the compressed PMI is reported to the gNB in a single CSI report and then transmitted to the network in the CSI-RS reference resource slot.

[0028] 3GPP NR Rel-18 also includes time-domain CSI prediction using UE-side AI / ML model. Rel-18 introduces Al-based UE-side CSI prediction as an Al for PHY use case and was being studied in the study item on AI / ML for the NR air interface. One or more AI / ML models may be trained and deployed at a UE for the Al-based CSI-prediction feature. During model inference, a UE is configured by the gNB to measure a set of historical CSI- RSs and then report a predicted CSI for one or multiple future time instances using its AI / ML model(s).

[0029] There are several methods for model monitoring. Monitoring based on intermediate key performance indicators (KPIs) (e.g., inference accuracy) requires collecting new ground-truth data similar / identical to the training data, which is accurate but has a higher measurement / reporting overhead. Monitoring based on data distribution of input / output data does not require any additional signaling overhead but is less accurate than monitoring based on inference accuracy because it does not retrieve the ground truth. In this disclosure, ground truth refers to CSI obtained by actually measuring the received reference signal (e.g., CSI-RS). For example, the UE may be configured to report the performance of the model by predicting 5 future CSI instances by measuring 6 past CSI. Here, the network may send 11 CSI-RS, where the 5 CSI-RS of those corresponds to the 5 future CSI instances that the UE should predict. The measurement result (not the prediction result) of those 5 CSI-RS are referred to as the ground truth.

[0030] Similarly, monitoring based on system performance does not require any additional signaling overhead, however, it can be challenging to identify whether the root cause for bad system performance is due to an inaccurate model or due to some other malfunctioning procedure or hardware. Monitoring based on data distribution may in contrast identify a potential problem in the model by detecting that the dataset observed during inference is not the same as during training. However, it is not trivial to define conditions and measurable data-distribution-based KPIs for sounding a model failure alarm with a good trade-off between model failure detection reliability and accuracy (e.g., low false alarm rate, low missed detection rate, and low latency).

[0031] To ensure reliable / accurate model performance monitoring results, sufficient monitoring data samples may be collected and used to derive the performance monitoring results. Examples of model-output / inference accuracy- based performance monitoring results include intermediate KPI per monitoring data sample, intermediate KPI statistics associated with a monitoring data set, the percentage of monitoring data samples within a monitoring dataset for which the intermediate KPI fulfils a certain condition, a flag indicating whether the model is functioning well or not. Examples of data-drift-based performance monitoring results include monitoring data statistics, the difference between the monitoring data statistics and the data statistics obtained in the model training stage, and a flag indicating whether a data drift is detected or not.

[0032] When implementing model monitoring, the monitoring method may be selected based on UE service requirements. For example, a UE with MBB could start with a low- cost solution (e.g., system performance based), if problems are observed / predicted, then activate an inference accuracy based monitoring method associated with higher complexity. High-complexity and signaling overhead monitoring may be required for certain UE service requirements, such as for emergency localization use cases or UEs with Ultra-Reliable Low-Latency Communication (URLLC) connections.

[0033] For CSI prediction using UE side AI / ML model use case studied in Rel-18 AI / ML for NR air interface study item, at least the following aspects have been proposed for performance monitoring for functionality-based LCM:

[0034] Type 1:

[0035] UE calculates performance metric(s)

[0036] UE reports performance monitoring output that facilitates functionality fallback decisions at the network.

[0037] The performance monitoring metric is a metric that may be used to quantitatively measure the performance of a model. For example, a model may have in a certain performance monitoring occasion an SGCS value of 0.8250 (e.g., by comparing the output of the model and the ground truth). The performance monitoring output, on the other hand, is the output of a categorization of the performance monitoring metric, most likely with one or more thresholds to determine such an output. For example, a threshold can be defined (e.g., via a standard text, NW configuration, or UE implementation), where an SGCS value above 0.8000 corresponds to a performance deemed as acceptable and an SGCS value below 0.8000 corresponds to a performance deemed as unacceptable. Considering the performance monitoring metric of 0.8250 as in the above example, the performance monitoring output in this case will be “acceptable”.

[0038] Functionality fallback decisions at the network corresponds to the fallback operation that may be taken by considering the performance monitoring output from the UE. For example, if the UE reports: “performance of the model is unacceptable”, the NW may then configure the UE with, e-g., non-AI based CSI prediction or disable the CSI prediction. In this particular example, the non-AI based CSI prediction or disabling the CSI prediction is the fallback.

[0039] Network may configure threshold criteria to facilitate UE-side performance monitoring (if needed). As mentioned above, the threshold criteria may be something that directly relates to the performance monitoring metric. For example, if the metric is an SGCS value, than the threshold may be one or more SGCS values that may categorize the SGCS value calculated by the UE into two or more performance monitoring outputs. Network makes the decision(s) of functionality fallback operation (fallback mechanism to legacy CSI reporting).

[0040] Type 2:

[0041] UE reports predicted CSI and / or the corresponding ground truth.

[0042] Network calculates the performance metrics.

[0043] Network makes a decision(s) of functionality fallback operation (a fallback mechanism to legacy CSI reporting).

[0044] Type 3:

[0045] UE calculates the performance metric(s)

[0046] UE reports performance metric(s) to the network

[0047] Network makes a decision(s) of functionality fallback operation (a fallback mechanism to legacy CSI reporting).

[0048] Functionality selection / activation / deactivation / switching as defined for other UE side use cases may be reused, if applicable.

[0049] The study includes configuration and procedure for performance monitoring, such as CSI-RS configuration for performance monitoring. The study may include performance metric including at least intermediate KPI (e.g., NMSE or SGCS) and UE report, including periodic / semi-persistent / aperiodic reporting, and event-driven report.

[0050] A UE may make decisions within the same functionality on model selection, activation, deactivation, and switching operations transparent to the network.

[0051] SUMMARY

[0052] There currently exist certain challenges. For example, because some UEs may be capable of supporting more than one CSI prediction scheme, it is beneficial if the network knows the performance monitoring results of more than one CSI prediction scheme based on the same ground truth. Considering this, performance monitoring configurations, performance monitoring report format, and how to determine the performance monitoring results are needed for multiple performance monitoring.

[0053] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, particular embodiments include performance monitoring configuration and performance monitoring reports for the case of multiple performance monitoring for CSI prediction.

[0054] According to a first aspect, the object is achieved by a method, performed by wireless device capable of performing CSI prediction. The method comprises obtaining a performance monitoring configuration for more than one CSI prediction scheme.

[0055] The method further comprises determining one or more performance monitoring results based on received reference signals and the performance monitoring configuration.

[0056] The method further comprises reporting the one or more performance monitoring results to a network node.

[0057] According to a second aspect, the object is achieved by a wireless device configured to perform the method according to the first aspect.

[0058] According to a third aspect, the object is achieved by a method, performed by a network node.

[0059] The method comprises transmitting a performance monitoring configuration for more than one CSI prediction scheme to a wireless device.

[0060] The method further comprises receiving from the wireless device one or more performance monitoring results based on received reference signals and the performance monitoring configuration.

[0061] According to a further aspect, the object is achieved by a computer program comprising instructions, which when executed by a processor of the wireless device, causes the wireless device to perform actions according to the first aspect above.

[0062] According to a further aspect, the object is achieved by a computer program comprising instructions, which when executed by a processor of the wireless communications node, causes the wireless communications node to perform actions according to the third aspect above.

[0063] According to a further aspect, the object is achieved by a carrier comprising the computer program of the aspects above, wherein the carrier is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer-readable storage medium.

[0064] Since the wireless device determines the one or more performance monitoring results based on the performance monitoring configuration for more than one CSI prediction scheme and reports the one or more performance monitoring results to the network node, the network can optimise the performance monitoring configuration for the CSI prediction schemes faster. Knowing the performance of the plurality of CSI prediction mechanisms, the network may take a necessary action (e.g., initiate model switching, switch the CSI-RS configuration and / or CSI-prediction configuration, etc.) faster. This faster action may, in turn, give the wireless device a more optimized configuration for its CSI prediction. For example, if a model-1 D based LCM is supported, the network may use the (multiple) performance monitoring report from the wireless device to indicate to the wireless device to switch from a first model to a second model, e.g. by indicating the second model’s ID to the wireless device. The indication of the second model’s ID to the wireless device may be performed by configuration of the second model’s ID.

[0065] Embodiments herein enable evaluation of one or more performance monitoring results of more than one CSI prediction scheme based on the same ground truth.

[0066] BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In the figures, features that appear in some embodiments are indicated by dashed lines.

[0068] The various aspects of embodiments disclosed herein, including particular features and advantages thereof, will be readily understood from the following detailed description and the accompanying drawings, in which:

[0069] Figure 1 is a block diagram illustrating a functional framework for AI / ML for NR air interface.

[0070] Figure 2 is a flowchart illustrating a method of a wireless device according to some embodiments disclosed herein,

[0071] Figure 3 is a flowchart illustrating a method of a network node according to some embodiments disclosed herein,

[0072] Figure 4 is a block diagram illustrating a wireless device according to some embodiments disclosed herein,

[0073] Figure 5 is a block diagram illustrating a network node according to some embodiments disclosed herein,

[0074] Figure QQ1 is a block diagram schematically illustrating an example of a communication system QQ100 in accordance with some embodiments, Figure QQ2 is a block diagram schematically illustrating another example of a communication system QQ200 according to some embodiments

[0075] Figure QQ3 is a block diagram schematically illustrating a wireless device according to some embodiments herein,

[0076] Figure QQ4 is a block diagram schematically illustrating a network node according to some embodiments herein,

[0077] Figure QQ5 is a block diagram schematically illustrating a virtualization environment according to some embodiments disclosed herein,

[0078] Figure QQ6 is a block diagram schematically illustrating a host, a network node and a UE and a flow chart of a method.

[0079] DETAILED DESCRIPTION

[0080] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0081] As used herein, the concept of ‘network (NW)’ and / or a gNB may be understood as a generic network node, gNB, base station, unit within the base station, relay node, core network node, a core network node, or a device supporting device-to-device (D2D) communication. The node may be deployed in a fifth generation (5G) network, or a sixth generation (6G) network. Moreover, although the term AI / ML model uses a singular form, it should be well understood that it should not limit the implementation to just one AI / ML model. The UE may either be configured or autonomously switch between the models depending on certain conditions and / or proprietary implementations.

[0082] Particular examples described herein refer to the AI / ML-based CSI prediction feature because it has both non-AI / ML-based and AI / ML-based mechanisms to support its implementation. In addition, AI / ML-based CSI prediction may support multiple AI / ML models. Further, the UE may also be configured with different CSI-RS configurations and / or CSI prediction configurations which may lead to different performances. Nevertheless, particular embodiments may be extended to different features that have non-AI / ML-based and AI / ML-based mechanisms, support multiple AI / ML models, and / or support different inference configurations for its implementation.

[0083] In addition, the term “model ID” may be a logical ID that may infer to similar ID, e.g., pairing ID, dataset ID, etc. Further, the term “training” may need to also be understood as a generic term and may represent training, retraining, or fine-tuning. In general, embodiments disclosed herein relate to a method performed in a UE capable of conducting CSI prediction. The method may comprise transmitting a UE capability report on supporting CSI prediction and its related performance monitoring; receiving performance monitoring configurations for more than one CSI prediction scheme; determining the one or more performance monitoring result based on the received reference signals and performance monitoring configurations; and reporting the one or more performance monitoring results based on the predefined / configured performance monitoring report format.

[0084] In particular embodiments, the capability report further includes the number of simultaneous performance monitoring operations that may be conducted by the UE.

[0085] In particular embodiments, the more than one CSI prediction schemes include at least one of:

[0086] • two different CSI prediction mechanisms, e.g., non-AI / ML-based CSI prediction and at least one AI / ML-based CSI prediction model.

[0087] • at least two AI / ML-based CSI prediction models.

[0088] • at least one CSI prediction mechanism with two different performance monitoring configurations.

[0089] In particular embodiments, one or more parameters in the CSI report configurations apply for all configured CSI prediction mechanisms.

[0090] In particular embodiments, more than one performance monitoring results are reported in a single performance monitoring report.

[0091] In particular embodiments, the performance monitoring result for two CSI prediction mechanisms is determined by the performance difference between a first CSI prediction mechanism and a second CSI prediction mechanism. The first CSI prediction mechanism may be the non-AI / ML-based CSI prediction, and the second CSI prediction mechanism may be the AI / ML-based CSI prediction. The first CSI prediction mechanism may be the default / fallback CSI prediction mechanism. The first CSI prediction mechanism may be the CSI prediction mechanism configured with index 0.

[0092] In particular embodiments, the performance monitoring results for the more than one CSI prediction mechanism are reported as separate values inside one performance monitoring result report.

[0093] In particular embodiments, whether the UE may be configured with a single or multiple performance monitoring result report for the more than one CSI prediction mechanism is determined by the UE capability report. In particular embodiments, the more than one performance monitoring results are reported in more than one performance monitoring report.

[0094] Figure 2 is a flowchart of method actions performed by a wireless device, such as a UE, and illustrates some embodiments disclosed herein. The wireless device is capable of performing CSI prediction.

[0095] The methods comprises one or more of the following actions, which actions may be taken in any suitable order.

[0096] Action 100:

[0097] The wireless device may transmit a capability report to the network node. The capability report includes an indication of a capability of the wireless device for performance monitoring for CSI prediction.

[0098] The first CSI prediction scheme may be a non-artificial intelligence / machine learning-based CSI prediction, and the second CSI prediction scheme may be an artificial intelligence / machine learning-based CSI prediction.

[0099] In some embodiments disclosed herein the first CSI prediction scheme is a default / fallback CSI prediction mechanism.

[0100] The UE may conduct simultaneous performance monitoring of more than one CSI prediction scheme. The UE capability report may include information on whether the UE supports simultaneous performance monitoring for multiple CSI prediction schemes. In that case, the indication of the capability of the wireless device for performance monitoring for CSI prediction, included in the capability report, may comprise a number of simultaneous operations of performance monitoring of CSI prediction schemes that may be performed by the wireless device.

[0101] In some embodiments disclosed herein the one or more parameters in the performance monitoring configurations apply for all of the more than one CSI prediction schemes. The more than one CSI prediction schemes may be configured.

[0102] The capability report may include information on a time required by the wireless device 400 to conduct one or more performance monitoring operations for CSI prediction.

[0103] Action 110:

[0104] The wireless device obtains a performance monitoring configuration for more than one CSI prediction scheme.

[0105] The more than one CSI prediction schemes may comprise an artificial intelligence / machine learning-based CSI prediction scheme and a non-artificial intelligence / machine learning-based CSI prediction scheme, or a first artificial intelligence / machine learning-based CSI prediction scheme and a second artificial intelligence / machine learning-based CSI prediction scheme.

[0106] Whether the wireless device is configured with a single or multiple performance monitoring reports for the more than one CSI prediction scheme may be determined by the capability report.

[0107] In some embodiments herein the performance monitoring configuration is obtained from the network node 500 via RRC signaling, or DCI signaling, or a combination of both.

[0108] Action 120:

[0109] The wireless device determines one or more performance monitoring results based on received reference signals and the performance monitoring configuration.

[0110] The one or more performance monitoring results may be based on one or more performance monitoring metrics. A performance monitoring metric is a kind of KPI that may be used to determine the performance monitoring result. For example, squared generalized cosine similarity (SGCS) is often used as the performance metric during 3GPP discussions. In one example, based on this SGCS value (calculated by comparing the predicted channels vs the actual channel), the wireless device may then determine the one or more performance monitoring results, e.g., “acceptable” or “non-acceptable”. Or, in another example, the one or more performance monitoring results may be in terms of the “quantized version” of the SGCS. E.g., the report may be in a 2-bit report (i.e. , 4 possible codepoints 00, 01, 10, and 11), where a report of 00 may refer to as an SGCS value lower than a first SGCS threshold, a report of 01 may refer to as an SGCS value higher than a first SGCS threshold but lower than a second SGCS threshold, a report of 10 may refer to an SGCS value higher than the second SGCS threshold but lower than the third SGCS threshold, and 11 may refer to as an SGCS value higher than the third SGCS threshold.

[0111] In some embodiments disclosed herein the one or more performance monitoring results for two CSI prediction schemes is determined by a performance difference between a first CSI prediction scheme and a second CSI prediction scheme. For example, a single performance monitoring result for the two CSI prediction schemes may be determined by the performance difference between the first CSI prediction scheme and the second CSI prediction scheme.

[0112] For example, a performance monitoring result for two CSI prediction schemes may be determined by a difference between a first performance monitoring result or metric of a first CSI prediction scheme and a second performance monitoring result or metric of a second CSI prediction scheme.

[0113] The first CSI prediction scheme may be an artificial intelligence / machine learningbased CSI prediction scheme serving as a reference or baseline for performance monitoring.

[0114] In some embodiments disclosed herein a first ground truth used to determine a first performance monitoring result of a first CSI prediction scheme is the same, at least in part, to a second ground truth used to determine a second performance monitoring result of a second CSI prediction scheme.

[0115] In some embodiments disclosed herein the wireless device determines for which CSI prediction schemes to perform performance monitoring based on the obtained performance monitoring configuration.

[0116] Action 130:

[0117] The wireless device reports the one or more performance monitoring results to a network node. The one or more performance monitoring results may be reported based on the predefined / configured performance monitoring report format defined by the configuration received in action 100.

[0118] Since the wireless device determines the one or more performance monitoring results based on the performance monitoring configuration and reports the one or more performance monitoring results to the network node, the network can optimise the performance monitoring configuration for the CSI prediction schemes faster. Knowing the performance of the plurality of CSI prediction mechanisms, the network may take a necessary action (e.g., initiate model switching, switch the CSI-RS configuration and / or CSI-prediction configuration, etc.) faster. This faster action may, in turn, give the wireless device a more optimized configuration for its CSI prediction.

[0119] In some embodiments herein more than one of the one or more performance monitoring results are reported in a single performance monitoring report.

[0120] The one or more performance monitoring results for the more than one CSI prediction scheme may be reported as separate values inside the single performance monitoring report. This report may also be referred to as a performance monitoring result report.

[0121] In some embodiments herein more than one of the one or more performance monitoring results is reported in more than one performance monitoring report. Embodiments of the above methods will now be described in more detail and with the wireless device exemplified as a UE. On the UE side, particular embodiments above may be summarized in the following steps which correspond to the actions above having corresponding numerals.

[0122] • Step 100 Transmitting UE capability report on supporting CSI prediction and its related performance monitoring.

[0123] • Step 110 Receiving performance monitoring configurations for more than one CSI prediction scheme.

[0124] • Step 120 Determining the one or more performance monitoring results based on the received reference signals and performance monitoring configurations.

[0125] • Step 130 Reporting the one or more performance monitoring results based on the predefined / configured performance monitoring report format.

[0126] Step 100 may comprise transmitting UE capability report on supporting more than one CSI prediction mechanism and its related performance monitoring.

[0127] In Step 100, the UE transmits its UE capability report on supporting CSI prediction and its related performance monitoring. In its implementation, the UE may be capable of supporting more than one CSI prediction scheme. Note that here, supporting more than one CSI prediction scheme may not necessarily be interpreted as supporting simultaneous operations of multiple CSI prediction schemes. Rather, one execution instance may contain one CSI prediction inference operation. In addition, the simultaneous CSI prediction operations, if supported, may also be done only for a certain LCM aspect. For example, the UE may conduct simultaneous performance monitoring of more than one CSI prediction scheme but may not support simultaneous inference of more than one CSI prediction scheme.

[0128] As mentioned above, performance monitoring of more than one CSI prediction scheme may be done separately in different time instances or be done simultaneously in one-time instance. In some embodiments, the UE capability report may include information on whether the UE supports simultaneous performance monitoring for multiple CSI prediction schemes. For example, a more capable UE may support simultaneous performance monitoring for multiple CSI prediction schemes.

[0129] In some embodiments, the capability report may further include information on the number of simultaneous performance monitoring that can be conducted by the UE.

[0130] In some embodiments, the capability report may further contain information on the time required by the UE to conduct one or more performance monitoring operations. The time required to conduct performance monitoring operations may also be tied with the number of simultaneous performance monitoring that need to be done by the UE. For example, a less capable UE may report t1, t2, and t3 as the time required to conduct a single performance monitoring, two simultaneous performance monitoring, and three simultaneous performance monitoring, respectively. On the other hand, a more capable UE may report t4, t5, and t6 as the time required to conduct a single performance monitoring, two simultaneous performance monitoring, and three simultaneous performance monitoring, respectively, where t4 < t1 , t5 < t2, and t6 < t3.

[0131] In some embodiments, the UE capability report may include information on whether the UE is capable of storing the performance monitoring dataset. Having this capability, the UE may conduct performance monitoring to more than one CSI prediction scheme under one ground truth (monitoring dataset) but report the performance monitoring results in a different time instance. In some embodiments, the capability report may further include the time duration in which the UE is able to store the monitoring dataset.

[0132] Step 110 comprises receiving performance monitoring configurations for more than one CSI prediction schemes. In Step 110, the UE receives performance monitoring configurations for more than one CSI prediction scheme.

[0133] In one case, the network may want to compare the performance of the non-AI / ML- based CSI prediction and the AI / ML-based CSI prediction under the same CSI-RS configurations and CSI prediction configuration. Performance monitoring of multiple CSI prediction schemes may include two different CSI prediction mechanisms, i.e. , non-AI / ML- based CSI prediction and AI / ML-based CSI prediction.

[0134] In some embodiments, the UE may be configured with at least one from a plurality of CSI prediction mechanisms in its performance monitoring configurations.

[0135] In one example embodiment, a new parameter, e.g., monitoredPred, may be introduced, where the parameter may have values of {non-AI, A / }. The UE may then be configured with at least one of the values. When the UE is configured with two values, the UE conducts multiple performance monitoring. In this example, parameters configured in the performance monitoring configurations other than monitoredPred (e.g., monitoring periodicity, measurement occasions, prediction window, prediction occasion, prediction horizon, etc.), may be used for both non-AI / ML-based CSI prediction and the AI / ML-based CSI prediction.

[0136] In another example embodiment, at least one parameter may be configured inside the non-AI and Al parameters. For example, the UE may be configured with a separate performance monitoring periodicity for the non-AI and Al CSI prediction mechanisms. In an example implementation, the periodicity of the performance monitoring of the first and the second CSI prediction mechanisms may be related. For example, the performance monitoring periodicity of the first CSI prediction mechanism may be an integer multiplication of the performance monitoring periodicity of the second CSI prediction mechanism.

[0137] In another example embodiment, the determination on which performance monitoring should be conducted by the UE is derived from the parameter that is configured for the UE. For example, the UE may be configured with at least one of monitoringFreq_non-AI and monitoringFreq_AI. The UE should conduct performance monitoring for the non-AI / ML-based CSI prediction mechanism, AI / ML-based CSI prediction mechanism, and both non-AI / ML- and AI / ML-based CSI prediction mechanisms, when the UE is configured with monitoringFreq_non-AI, monitoringFreq_AI, and both monitoringFreq_non-AI and monitoringFreq_AI, respectively. In another example, both monitoringFreq_non-AI and monitoringFreq_AI even when the UE is not expected to conduct performance monitoring for at least one of the CSI prediction mechanisms. Here, a value of None may be used inside the monitoringFreq_non-AI and monitoringFreq_AI parameters to indicate that the UE is not expected to conduct performance monitoring for the respected CSI prediction mechanism.

[0138] In some embodiments, the UE may be configured with at least one model, from a plurality of AI / ML models supported by the UE, in its performance monitoring configurations. The identification of the model may be done, e.g., via model ID.

[0139] In one example embodiment, a new parameter, e.g., monitoredPred, may be introduced, where the parameter may have values of {modellD_0, modellD_1, ... , modellD_N-1}, where N is the number of models supported by the UE in its UE capability report. The UE may then be configured with at least one of the values. When the UE is configured with more than one value, the UE conducts multiple performance monitoring. In this example, parameters configured in the performance monitoring configurations other than monitoredPred (e.g., monitoring periodicity, measurement occasions, prediction window, prediction occasion, prediction horizon, etc.), may be used for all configured models.

[0140] In another example embodiment, at least one parameter may be configured inside the modellD_n parameter. For example, the UE may be configured with a separate performance monitoring periodicity for each model ID.

[0141] In another example embodiment, the UE may need to be configured with performance monitoring configurations for all of the supported models. A value of None in one of the configured parameters (e.g., monitoring periodicity) may be used to indicate that the performance of the respected model does not need to be monitored by the UE. The UE may need to conduct multiple performance monitoring when more than one model is configured with a value other than None in its performance monitoring parameters (e.g., performance monitoring frequency).

[0142] In an example of implementation, the periodicity of the performance monitoring of the first and the second CSI prediction models may be related. For example, the performance monitoring periodicity of the first CSI prediction model may be an integer multiplication of the performance monitoring periodicity of the second CSI prediction model.

[0143] Some embodiments may include a combination of the two embodiments above. For example, the parameter monitoredPred may have values of {non-M, modellD_0, modellD_1, ... , modellD_N-1}. The same applies to the examples mentioned in the embodiments.

[0144] In some embodiments, the UE may be configured with one or more parameters containing more than one value to conduct performance monitoring.

[0145] In one example embodiment, the UE may be configured with more than one value in the number of CSI-RS measurement occasions that should be used by the UE for its performance monitoring. For example, a parameter measurementoccasions which has a possible value of, e.g., {4, 5, 6, 8}. The UE may then be configured with more than one value in that parameter, e.g., {4, 6}. Configured with these values, the UE should conduct performance monitoring for the case of the CSI prediction model(s) in the case of the UE has 4 and 6 CSI-RS measurement inputs. Note that this should only serve as an example. Another variant may be, additionally, or optionally, measurementWindowDuration (i.e., the length of the measurement window), firstMeasurementOccasions (i.e., the earliest CSI-RS measurement occasion used for performance metric calculation), measurementGap (the gap between two consecutive measurement occasions), etc.

[0146] In some embodiments, the configuration of the multiple performance monitoring may be in the RRC configuration. In some embodiments, the UE may be further indicated via DCI. For example, the UE may be configured with two values in the monitoredPred parameters in the RRC configuration. The UE, however, only conducts multiple performance monitoring when the UE is further indicated by the DCI to conduct performance monitoring for both CSI prediction mechanisms. A bitmap-based indication may be used. For example, the indication may consist of two bits where the first bit indicates whether to conduct performance monitoring for non-AI / ML-based CSI prediction and the second bit indicates whether to conduct performance monitoring for AI / ML-based CSI prediction. When the UE is indicated, e.g., with 11, the UE conducts multiple performance monitoring, i.e. , to both non-AI / ML-based CSI prediction and AI / ML-based CSI prediction.

[0147] In some embodiments, the UE may be configured with the performance monitoring metric that should be used by the UE to calculate its performance monitoring results. For example, the UE may be configured with monitoringKPI parameters which may include, for example, a value of {GCS, NMSE, SGCS}. For the case of performance monitoring for more than one CSI prediction scheme, the monitoringKPI parameter may also include, for example, a value of {diffGCS, diffNMSE, diffSGCS}. In another example, the KPI that should be used by the UE to calculate its performance monitoring result is predetermined in the standard text, e.g., using SGCS. Here, the monitoringKPI parameter may instead have a value of {absolute, differential .

[0148] Step 120 comprises determining the one or more performance monitoring results. In Step 120, the UE may determine the one or more performance monitoring results based on the CSI-RS received for performance monitoring and the performance monitoring configurations.

[0149] In some embodiments, the performance metric results are the absolute values of the (intermediate) KPI, e.g., SGCS, NMSE, etc., of all CSI prediction schemes configured to be monitored. In one example, the UE is required to use the absolute value by a predetermined standard text. In another example, the UE may be configured to use this absolute value, e.g., the UE may be configured with SGCS or NMSE in the monitoringKPI parameter. Note that the term absolute should not necessarily be the exact value of the performance metric but may also be the quantized version of the exact value. The thresholds of the quantization may be predetermined in the standard or be configured by the network. Note also that the thresholds may be predetermined or configured differently for each CSI prediction scheme.

[0150] In some embodiments, the performance metric result is the relative value of the performance metric of one CSI prediction scheme toward another CSI prediction scheme. In one example, the UE uses the relative value by a predetermined standard text. In another example, the UE may be configured to use this relative value, e.g., the UE may be configured with diffSGCS or diffNMSE in the monitoringKPI parameter. When a relative value is used, one of the CSI prediction schemes may serve as the baseline.

[0151] For example, for a UE configured to monitor non-AI / ML-based CSI prediction and AI / ML-based CSI prediction, the performance monitoring metric of the non-AI / ML-based CSI prediction may serve as the baseline. The performance monitoring result may be the gap between the AI / ML-based CSI prediction to the non-AI / ML-based CSI prediction. Note that the reported gap should not necessarily be the exact value of the gap but may also be the quantized version of the gap.

[0152] In another example, if the UE is configured with more than one AI / ML-based CSI prediction model but is not configured with non-AI / ML-based CSI prediction, the baseline may be the performance metric of AI / ML-based CSI prediction with modellD_0. Alternatively, the model ID configured with a lower index in the performance monitoring configuration may serve as the baseline. For example, the UE may be configured with {model ID_2, modellD_1}. In this scenario, the performance of modellD_2 may serve as the baseline.

[0153] In another example, for the UE configured with multiple CSI prediction configurations (e.g., multiple configurations of the number of CSI-RS measurement occasions for monitoring), the performance of CSI prediction using the configuration with a lower index may serve as the baseline. For example, if the UE is configured with a value of {4, 6} for its measurementoccasions parameter in the CSI prediction performance monitoring configuration, the performance of using 4 measurement occasions may serve as the baseline. On the other hand, if the UE is configured with a value of {6, 4}, the performance of CSI prediction with 6 measurement occasions may serve as the baseline. Alternatively, the performance of CSI prediction using a configuration that theoretically leads to lower performance may serve as the baseline. For example, in the above example, using 4 measurement occasions theoretically leads to lower performance compared to using 6 measurement occasions. Therefore, the performance of CSI prediction with 4 measurement occasions may serve as the baseline regardless of whether the UE is configured with a value of {4, 6} or {6, 4}.

[0154] In yet another example, the CSI prediction scheme currently used for inference may serve as the baseline.

[0155] Note that in the above, two CSI prediction schemes are mainly used as an example. This, however, may be extended to more than two CSI prediction scheme cases. For example, in the case of a first, a second, and a third CSI prediction scheme configured to be monitored by the UE, the first CSI prediction scheme may serve as the baseline. The reported performance metric is then the performance metric gap between the second CSI prediction scheme and the third CSI prediction scheme toward the first CSI prediction scheme. Note also that the UE may be configured with only one CSI prediction scheme to be monitored. In this case, the UE may instead report the absolute value of the performance metric. The threshold values for the case of the absolute value are reported and the threshold values for the case of the relative values are reported may be predetermined differently in the standard text or be configured by the network.

[0156] Step 130 comprises reporting the one or more performance monitoring results. In Step 130, the UE may report the one or more performance monitoring results based on the predefined / configured performance monitoring report format.

[0157] In some embodiments, the one or more performance monitoring results are reported in a single performance monitoring report. In one example, one performance monitoring report may contain one performance monitoring result. The performance monitoring result may come from, e.g., the performance gap between two CSI prediction schemes described in the previous step.

[0158] In another example, one performance monitoring report may contain more than one performance monitoring result. This applies, for example, when the UE is configured to conduct performance monitoring for more than one different CSI prediction scheme where the performance monitoring result is the absolute value of each CSI prediction scheme performance; or is configured to monitor more than two CSI prediction schemes.

[0159] As mentioned in Step 100, the number of simultaneous performance monitoring that can be done by the UE may depend on the UE’s capability. In one embodiment, multiple performance monitoring results may be reported in more than one performance monitoring report. Using this approach, the UE may instead of doing simultaneous performance monitoring for more than one CSI prediction scheme, conduct sequential performance monitoring operations. Note that the CSI-RS used to calculate the more than one CSI prediction scheme coming from the same (time, frequency, spatial) resources.

[0160] Figure 3 illustrates example methods performed by a network node. The actions on the network side may be the mirror of the above actions / steps performed by the wireless device / UE.

[0161] The methods comprises one or more of the following actions, which actions may be taken in any suitable order.

[0162] Action 301: The network node receives a capability report from the wireless device. The capability report includes an indication of the capability of the wireless device for performance monitoring for CSI prediction.

[0163] The capability report may comprise a number of simultaneous performance monitoring operations that may be performed by the wireless device.

[0164] Action 302:

[0165] The network node transmits a performance monitoring configuration for more than one CSI prediction scheme to the wireless device.

[0166] Action 303:

[0167] The network node receives from the wireless device one or more performance monitoring results based on received reference signals and the performance monitoring configuration.

[0168] The more than one CSI prediction schemes may comprise the artificial intelligence / machine learning-based CSI prediction scheme and the non-artificial intelligence / machine learning-based CSI prediction scheme.

[0169] Figure 4 shows an example of a wireless device 400. The wireless device 400 is capable of performing CSI prediction.

[0170] In some embodiments herein the wireless device 400 is adapted to transmit the capability report to the network node.

[0171] The wireless device 400 is adapted to obtain the performance monitoring configuration for the more than one CSI prediction scheme.

[0172] The wireless device 400 is further adapted to determine one or more performance monitoring results based on received reference signals and the performance monitoring configuration. The wireless device 400 may be adapted to determine the one or more performance monitoring result for two CSI prediction schemes by the performance difference between the first CSI prediction scheme and the second CSI prediction scheme.

[0173] The wireless device 400 is further adapted to report the one or more performance monitoring results to the network node. In some embodiments herein the wireless device 400 is adapted to report more than one of the one or more performance monitoring results in a single performance monitoring report. The wireless device 400 may be further adapted to report performance monitoring results for the more than one CSI prediction scheme as separate values inside the single performance monitoring report.

[0174] Figure 5 shows an example of a network node 500.

[0175] In some embodiments herein the network node 500 is adapted to receiving the capability report from the wireless device 400. The capability report includes the indication of the capability of the wireless device 400 for performance monitoring for CSI prediction.

[0176] The network node 500 is adapted to transmit the performance monitoring configuration for the more than one CSI prediction scheme to the wireless device 400.

[0177] The network node 500 is further adapted to receive from the wireless device 400 one or more performance monitoring results based on received reference signals and the performance monitoring configuration.

[0178] The wireless device 400 and the network node 500 may comprise a respective input and output interface, IF, 406, 506 configured to communicate with each other, see Figure 4 and Figure 5. The input and output interface may comprise a receiver (not shown) and a transmitter (not shown).

[0179] The embodiments herein may be implemented through a respective processor or one or more processors, such as the respective processor 404 and 504, of a processing circuitry in the wireless device 400 and the network node 500 and depicted in Figures 4-5 together with computer program code for performing the functions and actions of the embodiments herein. The program code mentioned above may also be provided as a computer program product, for instance in the form of a data carrier carrying computer program code for performing the embodiments herein when being loaded into the respective wireless device 400 and the network node 500. One such carrier may be in the form of a CD ROM disc. It is however feasible with other data carriers such as a memory stick. The computer program code may furthermore be provided as pure program code on a server and downloaded to the respective wireless device 400 and network node 500.

[0180] The wireless device 400 and the network node 500 may further comprise a respective memory 402 and 502 comprising one or more memory units. The memory comprises instructions executable by the processor in the wireless device 400 and the network node 500.

[0181] Each respective memory 402 and 502 is arranged to be used to store e.g. information, data, configurations, and applications to perform the methods herein when being executed in the respective wireless device 400 and the network node 500.

[0182] In some embodiments, a respective computer program 403 and 503 comprising computer readable code units, which, when executed by the respective processor 404, 504, cause the respective wireless device 400 and network node 111 to perform the actions above.

[0183] In some embodiments, a respective carrier 405 and 505 comprises the respective computer program, wherein the carrier 405, 505 is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer-readable storage medium.

[0184] Those skilled in the art will also appreciate that the units in the units described above may refer to a combination of analog and digital circuits, and / or one or more processors configured with software and / or firmware, e.g. stored in the respective wireless device 400 and network node 111 described above. One or more of these processors, as well as the other digital hardware, may be included in a single Application- Specific Integrated Circuitry (ASIC), or several processors and various digital hardware may be distributed among several separate components, whether individually packaged or assembled into a system-on-a-chip (SoC).

[0185] When using the word "comprise" or “comprising” it shall be interpreted as nonlimiting, i.e. meaning "consist at least of".

[0186] The embodiments herein are not limited to the above described preferred embodiments. Various alternatives, modifications and equivalents may be used. Figure QQ1 shows an example of a communication system QQ100 in accordance with some embodiments. In the example, the communication system QQ100 includes a telecommunication network QQ102 that includes an access network QQ104, such as a radio access network (RAN), and a core network QQ106, which includes one or more core network nodes QQ108. The access network QQ104 includes one or more access network nodes, such as network nodes QQ110a and QQ110b (one or more of which may be generally referred to as network nodes QQ110), or any other similar 3rd Generation Partnership Project (3GPP) access node or non-3GPP access point. The network nodes QQ110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs QQ112a, QQ112b, QQ112c, and QQ112d (one or more of which may be generally referred to as UEs QQ112) to the core network QQ106 over one or more wireless connections.

[0187] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system QQ100 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system QQ100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0188] The UEs QQ112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes QQ110 and other communication devices. Similarly, the network nodes QQ110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs QQ112 and / or with other network nodes or equipment in the telecommunication network QQ102 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network QQ102.

[0189] In the depicted example, the core network QQ106 connects the network nodes QQ110 to one or more hosts, such as host QQ116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network QQ106 includes one more core network nodes (e.g., core network node QQ108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node QQ108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (ALISF), Subscription Identifier Deconcealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0190] The host QQ116 may be under the ownership or control of a service provider other than an operator or provider of the access network QQ104 and / or the telecommunication network QQ102, and may be operated by the service provider or on behalf of the service provider. The host QQ116 may host a variety of applications to provide one or more service. Examples of such applications include live and 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.

[0191] As a whole, the communication system QQ100 of Figure QQ1 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.

[0192] In some examples, the telecommunication network QQ102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network QQ102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network QQ102. For example, the telecommunications network QQ102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs.

[0193] In some examples, the UEs QQ112 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network QQ104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network QQ104. Additionally, a UE may be configured for operating in single- or multi- RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0194] In the example, the hub QQ114 communicates with the access network QQ104 to facilitate indirect communication between one or more UEs (e.g., UE QQ112c and / or QQ112d) and network nodes (e.g., network node QQ110b). In some examples, the hub QQ114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub QQ114 may be a broadband router enabling access to the core network QQ106 for the UEs. As another example, the hub QQ114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes QQ110, or by executable code, script, process, or other instructions in the hub QQ114. As another example, the hub QQ114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub QQ114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub QQ114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub QQ114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub QQ114 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices.

[0195] The hub QQ114 may have a constant / persistent or intermittent connection to the network node QQ110b. The hub QQ114 may also allow for a different communication scheme and / or schedule between the hub QQ114 and UEs (e.g., UE QQ112c and / or QQ112d), and between the hub QQ114 and the core network QQ106. In other examples, the hub QQ114 is connected to the core network QQ106 and / or one or more UEs via a wired connection. Moreover, the hub QQ114 may be configured to connect to an M2M service provider over the access network QQ104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes QQ110 while still connected via the hub QQ114 via a wired or wireless connection. In some embodiments, the hub QQ114 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node QQ110b. In other embodiments, the hub QQ114 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node QQ110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0196] Figure QQ2 shows a UE QQ200 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop- embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-loT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0197] A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0198] The UE QQ200 includes processing circuitry QQ202 that is operatively coupled via a bus QQ204 to an input / output interface QQ206, a power source QQ208, a memory QQ210, a communication interface QQ212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure QQ2. 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.

[0199] The processing circuitry QQ202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory QQ210. The processing circuitry QQ202 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry QQ202 may include multiple central processing units (CPUs).

[0200] In the example, the input / output interface QQ206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE QQ200. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0201] In some embodiments, the power source QQ208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source QQ208 may further include power circuitry for delivering power from the power source QQ208 itself, and / or an external power source, to the various parts of the UE QQ200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source QQ208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source QQ208 to make the power suitable for the respective components of the UE QQ200 to which power is supplied.

[0202] The memory QQ210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory QQ210 includes one or more application programs QQ214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data QQ216. The memory QQ210 may store, for use by the UE QQ200, any of a variety of various operating systems or combinations of operating systems.

[0203] The memory QQ210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual 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 (eUlCC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory QQ210 may allow the UE QQ200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory QQ210, which may be or comprise a device-readable storage medium.

[0204] The processing circuitry QQ202 may be configured to communicate with an access network or other network using the communication interface QQ212. The communication interface QQ212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna QQ222. The communication interface QQ212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter QQ218 and / or a receiver QQ220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter QQ218 and receiver QQ220 may be coupled to one or more antennas (e.g., antenna QQ222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0205] In the illustrated embodiment, communication functions of the communication interface QQ212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0206] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface QQ212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0207] 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.

[0208] 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 QQ200 shown in Figure QQ2.

[0209] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-loT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0210] 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.

[0211] Figure QQ3 shows a network node QQ300 in accordance with some embodiments.

[0212] 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)).

[0213] 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).

[0214] 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).

[0215] The network node QQ300 includes a processing circuitry QQ302, a memory QQ304, a communication interface QQ306, and a power source QQ308. The network node QQ300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node QQ300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node QQ300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory QQ304 for different RATs) and some components may be reused (e.g., a same antenna QQ310 may be shared by different RATs). The network node QQ300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node QQ300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node QQ300.

[0216] The processing circuitry QQ302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node QQ300 components, such as the memory QQ304, to provide network node QQ300 functionality.

[0217] In some embodiments, the processing circuitry QQ302 includes a system on a chip (SOC). In some embodiments, the processing circuitry QQ302 includes one or more of radio frequency (RF) transceiver circuitry QQ312 and baseband processing circuitry QQ314. In some embodiments, the radio frequency (RF) transceiver circuitry QQ312 and the baseband processing circuitry QQ314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry QQ312 and baseband processing circuitry QQ314 may be on the same chip or set of chips, boards, or units.

[0218] The memory QQ304 may comprise any form of volatile or non-volatile computer- readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device- readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry QQ302. The memory QQ304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry QQ302 and utilized by the network node QQ300. The memory QQ304 may be used to store any calculations made by the processing circuitry QQ302 and / or any data received via the communication interface QQ306. In some embodiments, the processing circuitry QQ302 and memory QQ304 is integrated. The communication interface QQ306 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 QQ306 comprises port(s) / terminal(s) QQ316 to send and receive data, for example to and from a network over a wired connection. The communication interface QQ306 also includes radio front-end circuitry QQ318 that may be coupled to, or in certain embodiments a part of, the antenna QQ310. Radio front-end circuitry QQ318 comprises filters QQ320 and amplifiers QQ322. The radio front-end circuitry QQ318 may be connected to an antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry may be configured to condition signals communicated between antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry QQ318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry QQ318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters QQ320 and / or amplifiers QQ322. The radio signal may then be transmitted via the antenna QQ310. Similarly, when receiving data, the antenna QQ310 may collect radio signals which are then converted into digital data by the radio front-end circuitry QQ318. The digital data may be passed to the processing circuitry QQ302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0219] In certain alternative embodiments, the network node QQ300 does not include separate radio front-end circuitry QQ318, instead, the processing circuitry QQ302 includes radio front-end circuitry and is connected to the antenna QQ310. Similarly, in some embodiments, all or some of the RF transceiver circuitry QQ312 is part of the communication interface QQ306. In still other embodiments, the communication interface QQ306 includes one or more ports or terminals QQ316, the radio front-end circuitry QQ318, and the RF transceiver circuitry QQ312, as part of a radio unit (not shown), and the communication interface QQ306 communicates with the baseband processing circuitry QQ314, which is part of a digital unit (not shown).

[0220] The antenna QQ310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna QQ310 may be coupled to the radio front-end circuitry QQ318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna QQ310 is separate from the network node QQ300 and connectable to the network node QQ300 through an interface or port. The antenna QQ310, communication interface QQ306, and / or the processing circuitry QQ302 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna QQ310, the communication interface QQ306, and / or the processing circuitry QQ302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0221] The power source QQ308 provides power to the various components of network node QQ300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source QQ308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node QQ300 with power for performing the functionality described herein. For example, the network node QQ300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source QQ308. As a further example, the power source QQ308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0222] Embodiments of the network node QQ300 may include additional components beyond those shown in Figure QQ3 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node QQ300 may include user interface equipment to allow input of information into the network node QQ300 and to allow output of information from the network node QQ300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node QQ300.

[0223] Figure QQ4 is a block diagram of a host QQ400, which may be an embodiment of the host QQ116 of Figure QQ1, in accordance with various aspects described herein. As used herein, the host QQ400 may be or comprise various combinations hardware and / or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host QQ400 may provide one or more services to one or more UEs. The host QQ400 includes processing circuitry QQ402 that is operatively coupled via a bus QQ404 to an input / output interface QQ406, a network interface QQ408, a power source QQ410, and a memory QQ412. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as Figures QQ2 and QQ3, such that the descriptions thereof are generally applicable to the corresponding components of host QQ400.

[0224] The memory QQ412 may include one or more computer programs including one or more host application programs QQ414 and data QQ416, which may include user data, e.g., data generated by a UE for the host QQ400 or data generated by the host QQ400 for a UE. Embodiments of the host QQ400 may utilize only a subset or all of the components shown. The host application programs QQ414 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAG, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs QQ414 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host QQ400 may select and / or indicate a different host for over-the-top services for a UE. The host application programs QQ414 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.

[0225] Figure QQ5 is a block diagram illustrating a virtualization environment QQ500 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments QQ500 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized.

[0226] Applications QQ502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0227] Hardware QQ504 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers QQ506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs QQ508a and QQ508b (one or more of which may be generally referred to as VMs QQ508), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer QQ506 may present a virtual operating platform that appears like networking hardware to the VMs QQ508.

[0228] The VMs QQ508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer QQ506. Different embodiments of the instance of a virtual appliance QQ502 may be implemented on one or more of VMs QQ508, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0229] In the context of NFV, a VM QQ508 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs QQ508, and that part of hardware QQ504 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs QQ508 on top of the hardware QQ504 and corresponds to the application QQ502.

[0230] Hardware QQ504 may be implemented in a standalone network node with generic or specific components. Hardware QQ504 may implement some functions via virtualization. Alternatively, hardware QQ504 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration QQ510, which, among others, oversees lifecycle management of applications QQ502. In some embodiments, hardware QQ504 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system QQ512 which may alternatively be used for communication between hardware nodes and radio units.

[0231] Figure QQ6 shows a communication diagram of a host QQ602 communicating via a network node QQ604 with a UE QQ606 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE QQ112a of Figure QQ1 and / or UE QQ200 of Figure QQ2), network node (such as network node QQ110a of Figure QQ1 and / or network node QQ300 of Figure QQ3), and host (such as host QQ116 of Figure QQ1 and / or host QQ400 of Figure QQ4) discussed in the preceding paragraphs will now be described with reference to Figure QQ6.

[0232] Like host QQ400, embodiments of host QQ602 include hardware, such as a communication interface, processing circuitry, and memory. The host QQ602 also includes software, which is stored in or accessible by the host QQ602 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE QQ606 connecting via an over-the-top (OTT) connection QQ650 extending between the UE QQ606 and host QQ602. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection QQ650.

[0233] The network node QQ604 includes hardware enabling it to communicate with the host QQ602 and UE QQ606. The connection QQ660 may be direct or pass through a core network (like core network QQ106 of Figure QQ1) 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.

[0234] The UE QQ606 includes hardware and software, which is stored in or accessible by UE QQ606 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE QQ606 with the support of the host QQ602. In the host QQ602, an executing host application may communicate with the executing client application via the OTT connection QQ650 terminating at the UE QQ606 and host QQ602. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. The OTT connection QQ650 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection QQ650.

[0235] The OTT connection QQ650 may extend via a connection QQ660 between the host QQ602 and the network node QQ604 and via a wireless connection QQ670 between the network node QQ604 and the UE QQ606 to provide the connection between the host QQ602 and the UE QQ606. The connection QQ660 and wireless connection QQ670, over which the OTT connection QQ650 may be provided, have been drawn abstractly to illustrate the communication between the host QQ602 and the UE QQ606 via the network node QQ604, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0236] As an example of transmitting data via the OTT connection QQ650, in step QQ608, the host QQ602 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE QQ606. In other embodiments, the user data is associated with a UE QQ606 that shares data with the host QQ602 without explicit human interaction. In step QQ610, the host QQ602 initiates a transmission carrying the user data towards the UE QQ606. The host QQ602 may initiate the transmission responsive to a request transmitted by the UE QQ606. The request may be caused by human interaction with the UE QQ606 or by operation of the client application executing on the UE QQ606. The transmission may pass via the network node QQ604, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step QQ612, the network node QQ604 transmits to the UE QQ606 the user data that was carried in the transmission that the host QQ602 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step QQ614, the UE QQ606 receives the user data carried in the transmission, which may be performed by a client application executed on the UE QQ606 associated with the host application executed by the host QQ602.

[0237] In some examples, the UE QQ606 executes a client application which provides user data to the host QQ602. The user data may be provided in reaction or response to the data received from the host QQ602. Accordingly, in step QQ616, the UE QQ606 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input / output interface of the UE QQ606. Regardless of the specific manner in which the user data was provided, the UE QQ606 initiates, in step QQ618, transmission of the user data towards the host QQ602 via the network node QQ604. In step QQ620, in accordance with the teachings of the embodiments described throughout this disclosure, the network node QQ604 receives user data from the UE QQ606 and initiates transmission of the received user data towards the host QQ602. In step QQ622, the host QQ602 receives the user data carried in the transmission initiated by the UE QQ606.

[0238] In an example scenario, factory status information may be collected and analyzed by the host QQ602. As another example, the host QQ602 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host QQ602 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host QQ602 may store surveillance video uploaded by a UE. As another example, the host QQ602 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, the host QQ602 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and / or transmitting data.

[0239] In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection QQ650 between the host QQ602 and UE QQ606, in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host QQ602 and / or UE QQ606. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection QQ650 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection QQ650 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node QQ604. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by the host QQ602. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection QQ650 while monitoring propagation times, errors, etc.

[0240] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0241] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

[0242] EMBODIMENTS

[0243] 1. A method performed by a wireless device capable of performing channel state information (CSI) prediction, the method comprising:

[0244] - obtaining a performance monitoring configuration for more than one CSI prediction scheme;

[0245] - determining performance monitoring results based on received reference signals and the more than one performance monitoring configurations; and

[0246] - reporting the performance monitoring results to a network node.

[0247] 2. The method of the previous embodiment, further comprising transmitting a capability report to the network node, wherein the capability report includes an indication of a capability of the wireless device for performance monitoring for CSI prediction.

[0248] 3. The method of the previous embodiment, wherein the capability report comprises a number of simultaneous performance monitoring operations that may be performed by the wireless device.

[0249] 4. The method of any one of the previous embodiments, wherein the more than one CSI prediction schemes comprise an artificial intelligence / machine learning-based CSI prediction scheme and a non-artificial intelligence / machine learning-based CSI prediction scheme.

[0250] 5. A method performed by a wireless device, the method comprising:

[0251] - any of the wireless device steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above.

[0252] 6. The method of the previous embodiment, further comprising one or more additional wireless device steps, features or functions described above.

[0253] 7. The method of any of the previous two embodiments, further comprising:

[0254] - providing user data; and

[0255] - forwarding the user data to a host computer via the transmission to the base station.

[0256] 8. A method performed by a base station, the method comprising:

[0257] - transmitting a performance monitoring configuration for more than one CSI prediction scheme to a wireless device; and

[0258] - receiving from the wireless device performance monitoring results based on received reference signals and the more than one performance monitoring configurations.

[0259] 9. The method of the previous embodiment, further comprising receiving a capability report from the wireless device, wherein the capability report includes an indication of a capability of the wireless device for performance monitoring for CSI prediction.

[0260] 10. The method of the previous embodiment, wherein the capability report comprises a number of simultaneous performance monitoring operations that may be performed by the wireless device..

[0261] 11. The method of any one of the previous embodiments, wherein the more than one CSI prediction schemes comprise an artificial intelligence / machine learning-based CSI prediction scheme and a non-artificial intelligence / machine learning-based CSI prediction scheme.

[0262] 12. A method performed by a base station, the method comprising:

[0263] - any of the steps, features, or functions described above with respect to base stations, either alone or in combination with other steps, features, or functions described above.

[0264] 13. The method of the previous embodiment, further comprising one or more additional base station steps, features or functions described above.

[0265] 14. The method of any of the previous embodiments, further comprising:

[0266] - obtaining user data; and

[0267] - forwarding the user data to a host computer or a wireless device.

[0268] 15. A mobile terminal comprising:

[0269] - processing circuitry configured to perform any of the steps of any of the Group A embodiments; and

[0270] - power supply circuitry configured to supply power to the wireless device. A base station comprising:

[0271] - processing circuitry configured to perform any of the steps of any of the Group B embodiments;

[0272] - power supply circuitry configured to supply power to the base station. A user equipment (UE) comprising:

[0273] - an antenna configured to send and receive wireless signals;

[0274] - 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;

[0275] - the processing circuitry being configured to perform any of the steps of any of the Group A embodiments;

[0276] - 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;

[0277] - 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

[0278] - a battery connected to the processing circuitry and configured to supply power to the UE. A communication system including a host computer comprising:

[0279] - processing circuitry configured to provide user data; and

[0280] - a communication interface configured to forward the user data to a cellular network for transmission to a user equipment (UE),

[0281] - 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

[0282] - the UE comprises processing circuitry configured to execute a client application associated with the host application.

[0283] 22. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:

[0284] - at the host computer, providing user data; and

[0285] - 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.

[0286] 23. The method of the previous embodiment, further comprising, at the base station, transmitting the user data.

[0287] 24. 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.

[0288] 25. 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.

[0289] 26. A communication system including a host computer comprising:

[0290] - processing circuitry configured to provide user data; and

[0291] - a communication interface configured to forward user data to a cellular network for transmission to a user equipment (UE),

[0292] - 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.

[0293] 27. The communication system of the previous embodiment, wherein the cellular network further includes a base station configured to communicate with the UE.

[0294] 28. The communication system of the previous 2 embodiments, wherein:

[0295] - the processing circuitry of the host computer is configured to execute a host application, thereby providing the user data; and

[0296] - the UE’s processing circuitry is configured to execute a client application associated with the host application.

[0297] 29. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:

[0298] - at the host computer, providing user data; and

[0299] - 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.

[0300] 30. The method of the previous embodiment, further comprising at the UE, receiving the user data from the base station.

[0301] 31. A communication system including a host computer comprising:

[0302] - communication interface configured to receive user data originating from a transmission from a user equipment (UE) to a base station,

[0303] - 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.

[0304] 32. The communication system of the previous embodiment, further including the UE.

[0305] 33. 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.

[0306] 34. The communication system of the previous 3 embodiments, wherein:

[0307] - the processing circuitry of the host computer is configured to execute a host application; and

[0308] - the UE’s processing circuitry is configured to execute a client application associated with the host application, thereby providing the user data.

[0309] 35. The communication system of the previous 4 embodiments, wherein:

[0310] - the processing circuitry of the host computer is configured to execute a host application, thereby providing request data; and

[0311] - 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.

[0312] 36. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:

[0313] - 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.

[0314] 37. The method of the previous embodiment, further comprising, at the UE, providing the user data to the base station.

[0315] 38. The method of the previous 2 embodiments, further comprising:

[0316] - at the UE, executing a client application, thereby providing the user data to be transmitted; and

[0317] - at the host computer, executing a host application associated with the client application.

[0318] 39. The method of the previous 3 embodiments, further comprising:

[0319] - at the UE, executing a client application; and

[0320] - 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,

[0321] - wherein the user data to be transmitted is provided by the client application in response to the input data.

[0322] 40. 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.

[0323] 41. The communication system of the previous embodiment further including the base station.

[0324] 42. The communication system of the previous 2 embodiments, further including the UE, wherein the UE is configured to communicate with the base station.

[0325] 43. The communication system of the previous 3 embodiments, wherein: - the processing circuitry of the host computer is configured to execute a host application;

[0326] - 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. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:

[0327] - 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. The method of the previous embodiment, further comprising at the base station, receiving the user data from the UE. 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

CLAIMS1. A method performed by a wireless device (400) capable of performing channel state information, CSI, prediction, the method comprising:- obtaining (110) a performance monitoring configuration for more than one CSI prediction schemes;- determining (120) one or more performance monitoring results based on received reference signals and the performance monitoring configuration; and- reporting (130) the one or more performance monitoring results to a network node (500).

2. The method of claim 1 , further comprising transmitting (100) a capability report to the network node (500), wherein the capability report includes an indication of a capability of the wireless device (400) for performance monitoring for CSI prediction.

3. The method of claim 2, wherein the indication of the capability of the wireless device (400) for performance monitoring for CSI prediction, included in the capability report, comprises a number of simultaneous operations of performance monitoring of CSI prediction schemes that may be performed by the wireless device (400).

4. The method of any one of the claims 2-3, wherein the capability report includes information on a time required by the wireless device (400) to conduct one or more performance monitoring operations for CSI prediction.

5. The method of any one of the claims 1-4, wherein the more than one CSI prediction schemes comprise an artificial intelligence / machine learning-based CSI prediction scheme and a non-artificial intelligence / machine learning-based CSI prediction scheme, or a first artificial intelligence / machine learning-based CSI prediction scheme and a second artificial intelligence / machine learning-based CSI prediction scheme.

6. The method of any one of the claims 1-5, wherein one or more parameters in the performance monitoring configuration apply for all of the more than one CSI prediction schemes.

7. The method of any one of the claims 1-6, wherein more than one of the one or more performance monitoring results are reported in a single performance monitoring report.

8. The method of claim 6, wherein the one or more performance monitoring results for two CSI prediction schemes is determined by a performance difference between a first CSI prediction scheme and a second CSI prediction scheme.

9. The method of claim 7 or 8, wherein the first CSI prediction scheme is a non-artificial intelligence / machine learning-based CSI prediction, and the second CSI prediction scheme is an artificial intelligence / machine learning-based CSI prediction.

10. The method of any one of the claims 7-9, wherein the first CSI prediction scheme is a default / fallback CSI prediction scheme.

11. The method of any one of the claims 8-10, wherein the first CSI prediction scheme is an artificial intelligence / machine learning-based CSI prediction scheme serving as a reference or baseline for performance monitoring.

12. The method of claim 7, wherein the one or more performance monitoring results for the more than one CSI prediction scheme are reported as separate values inside the single performance monitoring report.

13. The method of any one of the claims 2 or 3-12 when dependent on claim 2, wherein whether the wireless device (400) is configured with a single or multiple performance monitoring reports for the more than one CSI prediction scheme is determined by the capability report.

14. The method of any one of the claims 1-13, wherein more than one performance monitoring result of the one or more performance monitoring results is reported in more than one performance monitoring report.

15. The method of any one of the claims 1-14, wherein the performance monitoring configuration is obtained from the network node (500) via Radio Resource Control, RRC, signaling, or Downlink Control Information, DCI, signaling, or a combination of both.

16. The method of any one of the claims 1-15, wherein a first ground truth used to determine a first performance monitoring result of a first CSI prediction scheme is the same, at least in part, to a second ground truth used to determine a second performance monitoring result of a second CSI prediction scheme.

17. The method of any one of the claims 1-16, further comprising determining for which CSI prediction schemes to perform performance monitoring based on the obtained performance monitoring configuration.

18. A method performed by a network node (500), the method comprising:- Transmitting (302) a performance monitoring configuration for a more than one CSI prediction schemes to a wireless device (400); and- Receiving (303) from the wireless device (400) one or more performance monitoring results based on received reference signals and the performance monitoring configuration.

19. The method of claim 18, further comprising receiving (301) a capability report from the wireless device (400), wherein the capability report includes an indication of a capability of the wireless device (400) for performance monitoring for CSI prediction.

20. The method of claim 19, wherein the capability report comprises a number of simultaneous performance monitoring operations that may be performed by the wireless device (400).

21. The method of any one of the claims 19-20, wherein the received capability report includes information on a time required by the wireless device (400) to conduct one or more performance monitoring operations for CSI prediction.

22. The method of any one of claims 18-21, wherein the more than one CSI prediction schemes comprise an artificial intelligence / machine learning-based CSI prediction scheme and a non-artificial intelligence / machine learning-based CSI prediction scheme, or a first artificial intelligence / machine learning-based CSI prediction scheme and a second artificial intelligence / machine learning-based CSI prediction scheme.

23. The method of any one of the claims 18-22, wherein the performance monitoring configuration is transmitted via Radio Resource Control, RRC, signaling, or Downlink Control Information, DCI, signaling, or a combination of both.

24. The method of any one of the claims 18-23, wherein the performance monitoring configuration configures for which schemes the UE shall perform performance monitoring.

25. A wireless device (400) capable of performing channel state information, CSI, prediction and adapted to:- obtain a performance monitoring configuration for more than one CSI prediction scheme;- determine performance monitoring results based on received reference signals and the performance monitoring configuration; and- report the performance monitoring results to a network node (500).

26. The wireless device (400) of claim 25, wherein the wireless device (400) is further adapted to perform the method of any of the claims 2-17.

27. A network node (500) adapted to:- transmit a performance monitoring configuration for more than one CSI prediction scheme to a wireless device (400); and- receive from the wireless device (400) performance monitoring results based on received reference signals and the performance monitoring configuration.

28. The network node (500) of claim 27, further adapted to perform the method of any of the claims 18-24.

29. A computer program (403), comprising computer readable code units which when executed on a processor of a wireless device (400) causes the wireless device (400) to perform the method according to any one of claims 1-17.

30. A computer program (503), comprising computer readable code units which when executed on a processor of a network node (500) causes the network node (500) to perform the method according to any of the claims 18-24.

31. A carrier (405, 505) comprising the computer program (403, 503) according to claim 29 or 30, wherein the carrier (405, 505) is one of an electronic signal, an optical signal, a radio signal and a computer readable medium.

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