Configuration of measurement predictions

Abstract

According to an aspect, there is provided method performed by a user equipment, UE, the method comprising: receiving (102) a first message indicating one or more configuration parameters for generating and / or reporting prediction information for one or more communication resources; and / or receiving (102) a second message indicating meta information relating to a particular prediction environment and / or a particular prediction task.

Description

[0001] Configuration of measurement predictions

[0002] Technical Field

[0003] This disclosure relates to communication networks, and in particular to the reporting of Artificial Intelligence (Al) / Machine Learning (ML)-based measurement predictions to the network by a User Equipment (UE).

[0004] Background

[0005] Serving cell measurements in 5G NR

[0006] In New Radio (NR), the User Equipment (UE) is required to perform serving cell measurements (e.g. Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Signal to Interference and Noise Ratio (SINR)) and include these measurements in Radio Resource Control (RRC) Measurement Reports, when the serving cell configuration includes the field servingCellMO set to the measurement object identifier of the particular serving frequency to be reported (this is described in 3rdGeneration Partnership Project (3GPP) Technical Standard (TS) 38.331 v17.6.0 (2023-09); 3rdGeneration Partnership Project; Technical Specification Group Radio Access Network; NR; Radio Resource Control (RRC) protocol specification (Release 17)). These serving cell measurements are serving cell measurement results i.e. value(s) per cell. In addition, if beam reporting parameters are configured for at least one measurement identifier, the UE also performs beam measurements per beam for the serving cell (this is also described in 3GPP TS 38.331 v17.6.0).

[0007] [38.331]

[0008] 5.5.3 Performing measurements

[0009] 5.5.3.1 General

[0010] [...]

[0011] The UE shall:

[0012] 1> whenever the UE has a measConfig, perform RSRP and RSRQ measurements for each serving cell for which servingCellMO is configured as follows:

[0013] 2> if the reportConfig associated with at least one measld included in the measIdList within VarMeasConfig contains an rsType set to ssb and ssb-ConfigMobility is configured in the measObject indicated by the servingCellMO'.

[0014] 3> if the reportConfig associated with at least one measld included in the measIdList within

[0015] VarMeasConfig contains a reportQuantityRS-Indexes and maxNrofRS-IndexesToReport and contains an rsType set to ssb :

[0016] 4> derive layer 3 filtered RSRP and RSRQ per beam for the serving cell based on SS / PBCH block, as described in 5.5.3.3a;

[0017] 3> derive serving cell measurement results based on SS / PBCH block, as described in 5.5.3.3;

[0018] 2> if the reportConfig associated with at least one measld included in the measIdList within VarMeasConfig contains an rsType set to csi-rs and CSI-RS-ResourceConfigMobility is configured in the measObject indicated by the servingCellMO:

[0019] 3> if the reportConfig associated with at least one measld included in the measIdList within

[0020] VarMeasConfig contains a reportQuantityRS-Indexes and maxNrofRS-IndexesToReport and contains an rsType set to csi-rs:

[0021] 4> derive layer 3 filtered RSRP and RSRQ per beam for the serving cell based on CSI-RS, as described in 5.5.3.3a;

[0022] 3> derive serving cell measurement results based on CSI-RS, as described in 5.5.3.3;

[0023] 1> for each serving cell for which servingCellMO is configured, if the reportConfig associated with at least one measld included in the measIdList within VarMeasConfig contains SINR as trigger quantity and / or reporting quantity:

[0024] 2> if the reportConfig contains rsType set to ssb and ssb-ConfigMobility is configured in the servingCellMO 3> if the reportConfigcontains a reportQuantityRS-Indexes and maxNrofRS-IndexesToReport:

[0025] 4> derive layer 3 filtered SINR per beam for the serving cell based on SS / PBCH block, as described in 5.5.3.3a;

[0026] 3> derive serving cell SINR based on SS / PBCH block, as described in 5.5.3.3;

[0027] 2> if the reportConfig contains rsType set to csi-rs and CSI-RS-ResourceConfigMobility is configured in the servingCellMO:

[0028] 3> if the reportConfigcontains a reportQuantityRS-Indexes and maxNrofRS-IndexesToReport:

[0029] 4> derive layer 3 filtered SINR per beam for the serving cell based on CSI-RS, as described in 5.5.3.3a;

[0030] 3> derive serving cell SINR based on CSI-RS, as described in 5.5.3.3;

[0031] [...]

[0032] Al (Artificial lntelliqence) / ML (Machine Learning) for Mobility Rel-19

[0033] The AI / ML for PHY work in Rel-18 has been limited to lower layer features, such as Beam Management, which is sometimes referred to as intra-cell mobility. Other features, such as Layer 3 (L3) handovers, RRC measurement configuration and reporting of predictions have not been part of Rel-18.

[0034] Hence, a Rel-19 Study Item to study the usage of Al / ML for L3 Mobility and / or Radio Resource Management (RRM) measurements is considered. The study item is RP-234055, 3GPP TSG RAN #101 , Edinburgh, GB, December 11-15, 2023; “Study on Al (Artificial Intelligence) / ML (Machine Learning) for mobility in NR”. The study item includes the following objectives:

[0035] • Study and evaluate potential benefits and gains of AI / ML aided mobility for network triggered L3-based handover, considering the following aspects: o AI / ML based RRM measurement and event prediction

[0036] □ Cell-level measurement prediction including intra and inter-frequency (user equipment (UE) sided and network (NW) sided model)

[0037] • Inter-cell Beam-level measurement prediction for L3 Mobility (UE sided and NW sided model) o Handover (HO) failure / Radio Link Failure (RLF) prediction (UE sided model) o Measurement events prediction (UE sided model)

[0038] • Study the need / benefits of any other UE assistance information for the network side model

[0039] • The evaluation of the AI / ML aided mobility benefits should consider HO performance KPIs and complexity trade offs

[0040] Meta- Learning

[0041] In machine learning literature, a training phase conducts back propagation procedures (e.g., stochastic gradient descent) to tune neurons to enable the optimization of a Neural Network (NN) (or ML model) to generate high accuracy predictions (outputs). However, there are several issues that hinder optimal prediction using ML, for instance, training on one scenario and inferring on a different scenario may result in low accuracy due to the fact that the ML model was exposed to a limited set of training samples (limitation may not only result due to the number of samples, but also result due the distribution of those samples not spanning the whole sample set), or not exposed to enough descriptive information spanning all relevant factors impacting the output. Another issue that could occur due to the input samples characteristics (and the way that the frequency of samples impacts the gradient tuning of the ML model), is catastrophic forgetting, where the NN forgets what it learnt due to unbalanced sample frequency, or other reasons.

[0042] Meta-Learning has been proposed to enable AI / ML algorithms to overcome the above shortcomings, i.e., faster learning (including online learning, allowing the AI / ML algorithms to adapt to dynamically changing distributions of input samples), generalization, and the ability to adapt to environmental (scenario) changes.

[0043] Meta learning is heavily dependent on identifying and using supportive data (meta-data) to optimize the NN. Its purpose is to help the model to quickly adapt to (or learn on) a new task or generalize to a new distribution, based on a few examples. In a way, a meta-learner is learning to extrapolate. The core idea is to learn a feature representation (e.g., learning an embedding network that transforms raw inputs into a representation, which allows similarity comparison between the support set and the query set).

[0044] Meta-learning can be considered as a new training algorithm, a new input feature, or preparation phase of the input data to the targeted ML model.

[0045] Summary

[0046] There currently exist certain challenge(s). A UE needs to be configured to properly report AI / ML based RRM measurement predictions to the network. However, current procedures do not allow the UE to be configured to do so. Therefore, even though the UE is able to generate these predictions, it will not be able to send them to the network in a correct (and standardized) manner.

[0047] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges.

[0048] The present disclosure comprises a method at a User Equipment (UE), in which the UE receives a first message (e.g., an RRCReconfiguration message) indicating one or more configuration parameters for generating and / or reporting prediction information for one or more communication resources (e.g. a cell, beam or other time / frequency resource), and / or receives a second message indicating meta information relating to a particular prediction environment and / or a particular prediction task. The first message and / or the second message may be received from a network, or a network node. The first message is also referred to herein as a configuration message.

[0049] The first message can indicate which predictions (e.g. time-domain predicted measurement quantities such as RSRP, RSRQ, SINR; or one or more communication resources (e.g. cells) that the UE is going to move to in the future, such as a next k-th hop) the UE should generate, and / or can indicate the proper reporting of such predictions (e.g. in a MeasurementReport message, or a report message). The second message may be an indication describing meta information that corresponds to a certain prediction environment (or task). The second message may be an implicit or explicit indicator.

[0050] The configuration parameters may comprise one or more of the following parameters:

[0051] One or more parameters indicating to the UE a number of time-domain mobility predictions (e.g. predicted RSRP, predicted next communication resource / cell) the UE is to include in a report. This number may be configured in terms of time units (e.g. seconds, subframes, radio frames, measurement periods, prediction period(s)), or in terms of a number of prediction value(s). One or more parameters indicating an offset (or starting point for the prediction(s)) at which the UE is to report time-domain prediction(s).

[0052] One or more parameters indicating a reporting frequency / period of the predicted measurement samples included in the report (e.g., one measurement prediction sample every X milliseconds).

[0053] One or more parameters indicating a range to report the predicted measurements (e.g. RSRP of SCell-A is between X dBm and Y dBm with confidence Z%).

[0054] One or more parameters indicating that the UE is not to send predictions whose confidence level (or accuracy or prediction error or inference error) is lower than a threshold. This threshold may be configurable.

[0055] One or more parameters indicating that the UE is to send time-domain prediction(s) whose confidence level (or accuracy or prediction error or inference error) is above a threshold. This threshold may be configurable.

[0056] One or more parameters indicating that the predictions are to be reported when a certain event occurs or when a certain procedure takes place.

[0057] The configuration message may indicate one or more types of predicted measurements (e.g. predicted cell-level or beam-level measurement, predicted RSRP, predicted RSRQ, etc.) to be predicted and / or reported, and / or a configuration for the predictions (for example, a configuration by which the predictions are to be generated). This configuration may include, for example, a prediction window length, a granularity of the predictions, a confidence level, etc.

[0058] Each prediction which is to be performed / generated and / or reported may be associated with one or more serving communication resources, e.g. serving cell(s), that the UE is configured with (e.g. Primary cell, SCell(s) of a Master Cell group or of a Secondary Cell Group), and / or one or more neighbour cell(s).

[0059] The UE may receive from the NW an indication on how to use such meta information.

[0060] The present disclosure also comprises a method at a network node (such as a serving or source network node (e.g. gNodeB) serving a UE), wherein the network node sends, to a user equipment, UE, a first message indicating one or more configuration parameters for generating and / or reporting prediction information for one or more communication resources, and / or sends, to a UE, a second message indicating meta information relating to a particular prediction environment and / or a particular prediction task. The first message may configure the UE to generate predictions and to report them to the network (e.g. in a MeasurementReport message).

[0061] Certain embodiments may provide one or more of the following technical advantage(s). The techniques disclosed herein thereby enable the network to configure a UE to generate and report predictions in a proper format. According to a first specific aspect, there is provided a method performed by a user equipment, UE. The method comprises: receiving a first message indicating one or more configuration parameters for generating and / or reporting prediction information for one or more communication resources; and / or receiving a second message indicating meta information relating to a particular prediction environment and / or a particular prediction task.

[0062] According to a second aspect, there is provided a method performed by a network node. The method comprises: sending, to a user equipment, UE, a first message indicating one or more configuration parameters for generating and / or reporting prediction information for one or more communication resources; and / or sending, to a UE, a second message indicating meta information relating to a particular prediction environment and / or a particular prediction task.

[0063] According to a third aspect, there is provided a computer program product comprising a computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, on execution by a suitable computer or processor, the computer or processor is caused to perform the method according to the first aspect, the second aspect, or any embodiments thereof.

[0064] According to a fourth aspect, there is provided a UE configured to perform the method according to the first aspect, or any embodiments thereof.

[0065] According to a fifth aspect, there is provided a UE comprising a processor and a memory, said memory containing instructions executable by said processor whereby said UE is operative to perform the method according to the first aspect, or any embodiments thereof.

[0066] According to a sixth aspect, there is provided a network node configured to perform the method according to the second aspect, or any embodiments thereof.

[0067] According to a seventh aspect, there is provided a network node comprising a processor and a memory, said memory containing instructions executable by said processor whereby said network node is operative to perform the method according to the second aspect, or any embodiments thereof.

[0068] According to an eighth aspect, there is provided a user equipment, UE, that comprises: processing circuitry configured to cause the user equipment to perform any of the steps according to the first aspect, or any embodiments thereof; and power supply circuitry configured to supply power to the processing circuitry.

[0069] According to a ninth aspect, there is provided a radio access network, RAN, node, that comprises: processing circuitry configured to cause the RAN node to perform any of the steps according to the second aspect, or any embodiments thereof; power supply circuitry configured to supply power to the processing circuitry. According to a tenth aspect, there is provided a user equipment, UE, comprising: an antenna configured to send and receive wireless signals; radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; the processing circuitry being configured to perform any of the steps according to the first aspect, or any embodiments thereof; an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry; an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and a battery connected to the processing circuitry and configured to supply power to the UE.

[0070] Brief Description of the Drawings

[0071] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings, in which:

[0072] Fig. 1 is a flow chart illustrating a method in accordance with some embodiments; and

[0073] Fig. 2 is a flow chart illustrating a method in accordance with some embodiments;

[0074] Fig. 3 is a signalling diagram showing a signalling flow for generating and reporting prediction information;

[0075] Fig. 4 shows an example of a communication system in accordance with some embodiments;

[0076] Fig. 5 shows a UE in accordance with some embodiments;

[0077] Fig. 6 shows a RAN network node in accordance with some embodiments;

[0078] Fig. 7 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized.

[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] The techniques described herein are applicable to prediction information for / relating to any type of communication resource, where a communication resource can be any of a cell, beam, or other time / frequency resource, etc. The following description describes embodiments with reference to communication resources in the form of cells, for example as used in 3G, 4G, 5G networks etc. However it will be appreciated that the embodiments in the following description are also applicable to beams and other types of time / frequency resources, and thus for the purposes of this disclosure, the terms ‘cell’, ‘beam’ and ‘time / frequency resource’ can be used interchangeably.

[0082] In the context of the present disclosure, a serving cell corresponds to a cell, or any other network entity the UE is considered to be connected with and / or being served by. For example, for a UE in a connected state (e.g. RRC_CONNECTED), and not configured with Carrier Aggregation (CA) or Multi-Radio Dual Connectivity (MR-DC), there is one serving cell comprising of the primary cell (Pcell). For a UE in a connected state configured with CA / MR-DC, the term ‘serving cells’ is used to denote the set of cells comprising of the Special Cell(s) and all secondary cells.

[0083] In the context of the present disclosure, the serving / neighbor cell may correspond to one or more of:

[0084] • A Radio Access Network (RAN) node

[0085] A gNodeB (gNB)

[0086] • A 6G RAN node

[0087] • A Centralized Unit (CU) gNodeB e.g. a source gNB-CU in case of inter-CU, or simply CU in case of intra-CU.

[0088] • A Distributed Unit (DU) gNodeB

[0089] • A Cloud-RAN centralized unit

[0090] • A remote radio head (RRH) or a remote radio unit (RRU), which may be further connected to a gNB or another RAN node including control functionality

[0091] In the context of the present disclosure, a measurement or predicted measurement / prediction may correspond to one or more of:

[0092] • An RRM measurement, since they assist Radio Resource Management decisions at the network side and / or Layer 3 (L3) or higher layer measurements, since these measurements would be the responsibility of the RRC protocol, also called L3 in the Control Plane RAN protocol stack.

[0093] • A NR measurement and / or an Inter-RAT measurement of E-UTRA frequencies and / or 6G measurements (i.e. performed over the 6G air interface on 6G reference signal(s))

[0094] • A measurement performed on one or more reference signal(s) of a reference signal type e.g. SSB or Channel State Information - Reference Signal (CSI-RS). For example:

[0095] • A measurement which may be associated to a measurement quantity, such as RSRP, RSRQ or SINR. For example, one may say that a “measurement” corresponds to an RSRP value, so that a measurement of a neighbor cell corresponds to an RSRP value of the neighbor cell.

[0096] • A measurement of a cell (which may also be called cell quality or cell measurement result), where the measurement of a cell may be performed based on one or more beam measurements.

[0097] • A measurement which is filtered according to one or more filter parameters configured by the network e.g. a L3 filtered measurement, with a time-domain filter.

[0098] • A measurement quantity, such as an RSRP and / or RSRQ and / or SI NR and / or RSSI value in dB and / or dBm.

[0099] • A cell-based measurement result or cell measurement, wherein a measurement value represents a cell quality e.g. RSRP of a cell, RSRQ of a cell

[0100] • A beam-based measurement result or beam measurement, wherein a measurement value represents a beam quality e.g. RSRP of a beam, RSRQ of a beam, SINR of a beam. A beam-based measurement may also be a RS based measurement when the RS is transmitted on a spatial direction or beam e.g. SSB measurement may correspond to a measurement associated to an SSB index, like an SS-RSRP value; CSI-RS measurement may correspond to a measurement associated to an CSI-RS resource index / identifier, like an CSI-RSRP value.

[0101] In the context of the present disclosure, the term “ML-model” or “Al-model”, “Model Inference”, “Model Inference function” or “AI / ML model” are used interchangeable. An AI / ML model can be defined, in the context of the present disclosure, as a functionality or be part of a functionality that is deployed / implemented in the UE. An AI / ML model can be defined as a feature or part of a feature that is implemented / supported in a UE, in which it may be called a UE-sided AI / ML model or simply UE-side model. The AI / ML-model may correspond to a function which receives one or more inputs (e.g. measurements, configuration(s)) and provide as outcome one or more prediction(s) / estimates of a certain type (e.g. time-domain and / or spatial domain predictions of beam measurements). In one example, an ML-model may correspond to a function receiving as input the measurement of a reference signal at time instance to (e.g. transmitted in beam-X and / or cell) and provide as outcome the prediction of the reference signal quality and / or measurement in timer tO+T. In another example, an ML-model may correspond to a function receiving as input the measurement of a reference signal X (e.g. transmitted in beam-x of a cell), such as an SSB whose index is ‘x’, and provide as outcome the prediction of other reference signals transmitted in different beams e.g. reference signal Y (e.g. transmitted in beam-x of a cell), such as an SSB whose index is ‘x’.

[0102] The term prediction(s) in the context of the present disclosure may correspond to timedomain predictions: thus, the input of the ML-model may comprise at least one or more measurements at (or starting at) a time instance to (and / or a time interval such as T1 or tO+T 1 , which may comprise one or more samples or measurement time occasions, from 1 to K time occasions) of at least one neighbor and / or serving cell, and the output of the ML-model may comprise one or more predicted measurements at (or starting at) a future time instance e.g. to + T, possibly comprising future time instances within a time window of duration T2 and having F predictions. Further terminology may refer to an “actor”, as a function that receives the output from the Model inference function and triggers or performs corresponding actions. The Actor may trigger actions directed to other entities or to itself. In the context of the present disclosure, one actor may correspond to AI / ML mobility prediction reporting functionality at the UE, and / or the functionality at the UE responsible for generating the data structure to transmit the one or more information derived based on the one or more time-domain predictions. In one example, an ML- model may correspond to a function receiving as input one or more measurements of at least one downlink (DL) Reference Signal (RS) at time instance to (or a time interval starting or ending at tO), after at least one measurement period, (e.g. transmitted in beam-X, SSB-x, CSI-RS resource index x) and provide as output the prediction of the RS measurement(s) in time instance tO+T (or a time interval starting or ending at tO+T, until tO+T+T2). This future time instance tO+T, obtained at tO, may be in different time units such as in number of slots (frames, sub-frames, OFDM symbols, etc.) after the UE has performed the last measurement or targeting a specific slot in time within the future.

[0103] In the context of the present disclosure, the term “beam” may correspond to a spatial direction in which a signal is transmitted (e.g. by a network node) or received (e.g. by the UE), or a spatial filter applied to a signal which is transmitted or received. Thus, transmitting signals different beams could correspond to transmitting signals in different spatial directions. When the text refers to a “beam” it may refer to a beam index and / or a Reference Signal (RS) index or identifier, such as a Synchronization Signal block (SSB) index, or a CSI-RS resource identifier. Thus, predicting a beam or predicting a measurement of a beam may correspond to predicting an SSB, associated to an SSB index. Or, predicting a beam or a measurement of a beam may correspond to predicting a CSI-RS, associated to a CSI-RS resource identifier.

[0104] It may be said that an ML model or Model Inference is a function that provides AI / ML model inference output (e.g. predictions or decisions). The Model inference function may also be responsible for data preparation (e.g. data pre-processing and cleaning, formatting, and transformation) based on Inference Data delivered by a Data Collection function. The output may correspond to the inference output of the AI / ML model produced by a Model Inference function.

[0105] Fig. 1 depicts a method in accordance with particular embodiments. The method may be performed by a User Equipment (UE) or wireless device (e.g. the UE 412 or UE 500 as described later with reference to Figs. 4 and 5 respectively). The UE may perform the method in response to executing suitably formulated computer readable code. The computer readable code may be embodied or stored on a computer readable medium, such as a memory chip, optical disc, or other storage medium. The computer readable medium may be part of a computer program product.

[0106] The method begins at step 102 which comprises the UE receiving a first message indicating one or more configuration parameters for generating and / or reporting prediction information for one or more communication resources, and / or the UE receiving a second message indicating meta information relating to a particular prediction environment and / or a particular prediction task. In some embodiments, the prediction information may comprise time-domain prediction information and / or may comprise at least one prediction of a measurement of a communication resource, such as a cell, beam or time / frequency resource.

[0107] A communication resource can be any of a cell, beam or other time / frequency resource.

[0108] The first message may be a RRC reconfiguration message, a RRC resume message, or an RRC reestablishment message.

[0109] Thus, in some embodiments the method in the UE comprises the UE just receiving the first message, or the UE just receiving the second message. In other embodiments, the UE can receive both the first message and the second message. In this case, the first message can be received: before the second message, after the second message, or at the same time as the second message.

[0110] The configuration parameter(s) in the first message can indicate any one or more of: one or more types of prediction information that the UE is to generate; one or more types of prediction information that the UE is to report; one or more communication resources that the prediction information is to relate to; a quantity of prediction information that the UE is to report; a time or time offset at which the UE is to generate prediction information and / or report prediction information; a frequency or interval at which the UE is to report prediction information; a frequency or interval at which the UE is to generate prediction information; an event or condition that triggers or prevents reporting of the prediction information; a time period in which the prediction information is to be generated and / or reported; a format in which the prediction information is to be reported; and a range of values for the prediction information. In some embodiments, the event or condition that triggers reporting of the prediction information comprises a confidence threshold, and reporting the prediction information is triggered if a confidence associated with generated prediction information exceeds a confidence threshold. In some embodiments, the type of prediction information is one or more of: a predicted cell-level measurement for a cell; a predicted beam-level measurement for a cell; a predicted communication resource-level measurement for a communication resource; a predicted signal strength or quality value for a communication resource; a predicted RSRP of a communication resource; a predicted RSRQ of a communication resource; a predicted RSSI of a communication resource; a predicted SINR of a communication resource; a predicted resource index and / or resource identifier of a communication resource; and a predicted beam index and / or beam identifier of a cell.

[0111] The prediction information may be for a communication resource configured to be serving the UE, or a communication resource not configured to be serving the UE.

[0112] In some embodiments, the method may further comprise, in response to receiving the one or more configuration parameters: transmitting a report message to a network node. The report message comprises the prediction information according to the received one or more configuration parameters. The report message may be a RRC measurement report message, or a UE assistance information message.

[0113] In some embodiments, the method may further comprise generating prediction information according to the received one or more configuration parameters and / or collecting prediction information from one or more sources.

[0114] In some embodiments, the generated prediction information comprises any of: a predicted signal strength or quality value for a communication resource at a future time instance; a predicted identifier for a communication resource at a future time instance; an identifier based on a predicted signal strength or quality value for a communication resource at a future time instance; a predicted RSRP of a communication resource at a future time instance; a predicted RSRQ of a communication resource at a future time instance; a predicted SI NR of a communication resource at a future time instance; a predicted resource index and / or resource identifier of a communication resource at a future time instance; a predicted beam index and / or beam identifier of a cell at a future time instance; a SSB index of a communication resource at a future time instance; a corresponding confidence indicator of a predicted value for a communication resource; a corresponding time duration for which a predicted value fora communication resource is expected to be valid; a corresponding time duration for which a predicted value for a communication resource is expected to satisfy a condition; and a capability of the UE to generate prediction information.

[0115] In some embodiments, the meta information indicated in the second message is for use in a meta learning process of updating a machine learning model for generating prediction information. The second message may further comprise one or more of: information indicating how the meta information was generated; information indicating how meta information is to be generated by the UE; information indicating how the meta information is to be used in the meta learning process; information indicating what the meta information describes; and information indicating what information associated with the meta learning process can be transferred between the UE and a network node.

[0116] Further detail regarding the method in Fig. 1 and other methods performed by the UE is set out below in the section “Embodiments in a UE”.

[0117] Fig. 2 depicts a method in accordance with particular embodiments. The method may be performed by a network node (e.g. the RAN network node 410 or RAN network node 600 as described later with reference to Fig. 4 and 6 respectively). The network node may perform the method in response to executing suitably formulated computer readable code. The computer readable code may be embodied or stored on a computer readable medium, such as a memory chip, optical disc, or other storage medium. The computer readable medium may be part of a computer program product.

[0118] The method begins at step 202 which comprises the network node sending, to a user equipment, UE, a first message indicating one or more configuration parameters for generating and / or reporting prediction information for one or more communication resources, and / or sending, to a UE, a second message indicating meta information relating to a particular prediction environment and / or a particular prediction task. In some embodiments, the prediction information may comprise time-domain prediction information and / or may comprise at least one prediction of a measurement of a communication resource, such as a cell, beam or time / frequency resource.

[0119] A communication resource can be any of a cell, beam or other time / frequency resource.

[0120] The first message may be a RRC reconfiguration message, a RRC resume message, or an RRC reestablishment message.

[0121] Thus, in some embodiments the method in the network node comprises the network node just sending the first message, or the network node just sending the second message. In other embodiments, the network node can send both the first message and the second message. In this case, the first message can be sent: before the second message, after the second message, or at the same time as the second message.

[0122] The configuration parameter(s) in the first message can indicate any one or more of: one or more types of prediction information that the UE is to generate; one or more types of prediction information that the UE is to report; one or more communication resources that the prediction information is to relate to; a quantity of prediction information that the UE is to report; a time or time offset at which the UE is to generate prediction information and / or report prediction information; a frequency or interval at which the UE is to report prediction information; a frequency or interval at which the UE is to generate prediction information; an event or condition that triggers or prevents reporting of the prediction information; a time period in which the prediction information is to be generated and / or reported; a format in which the prediction information is to be reported; and a range of values for the prediction information. In some embodiments, the event or condition that triggers reporting of the prediction information comprises a confidence threshold, and reporting the prediction information is triggered if a confidence associated with generated prediction information exceeds a confidence threshold. In some embodiments, the type of prediction information is one or more of: a predicted cell-level measurement for a cell; a predicted beam-level measurement for a cell; a predicted communication resource-level measurement for a communication resource; a predicted signal strength or quality value for a communication resource; a predicted RSRP of a communication resource; a predicted RSRQ of a communication resource; a predicted RSSI of a communication resource; a predicted SINR of a communication resource; a predicted resource index and / or resource identifier of a communication resource; and a predicted beam index and / or beam identifier of a cell.

[0123] The prediction information may be for a communication resource configured to be serving the UE, or a communication resource not configured to be serving the UE.

[0124] In some embodiments, the method may further comprise receiving a report message from the UE. The report message comprises the prediction information according to the one or more configuration parameters. The report message may be a RRC measurement report message, or a UE assistance information message.

[0125] In some embodiments, the prediction information comprises any of: a predicted signal strength or quality value for a communication resource at a future time instance; a predicted identifier for a communication resource at a future time instance; an identifier based on a predicted signal strength or quality value for a communication resource at a future time instance; a predicted RSRP of a communication resource at a future time instance; a predicted RSRQ of a communication resource at a future time instance; a predicted SINR of a communication resource at a future time instance; a predicted resource index and / or resource identifier of a communication resource at a future time instance; a predicted beam index and / or beam identifier of a cell at a future time instance; a SSB index of a communication resource at a future time instance; a corresponding confidence indicator of a predicted value for a communication resource; a corresponding time duration for which a predicted value fora communication resource is expected to be valid; a corresponding time duration for which a predicted value for a communication resource is expected to satisfy a condition; and a capability of the UE to generate prediction information.

[0126] In some embodiments, the network node can perform one or more of the following actions based on the received prediction information: configuring a neighbour communication resource as a target communication resource of the UE; triggering a handover to a target communication resource of the UE; configuring a communication resource as a target candidate communication resource for conditional handover; configuring a communication resource as a target communication resource for lower-layer triggered mobility; triggering a communication resource switch; triggering Early Data Forwarding for a neighbour communication resource configured as target communication resource; and configuring a new communication resource as a neighbour communication resource.

[0127] In some embodiments, the meta information indicated in the second message is for use in a meta learning process of updating a machine learning model for generating prediction information. The second message may further comprise one or more of: information indicating how the meta information was generated; information indicating how meta information is to be generated by the UE; information indicating how the meta information is to be used in the meta learning process; information indicating what the meta information describes; and information indicating what information associated with the meta learning process can be transferred between the UE and a network node.

[0128] Further detail regarding the method in Fig. 2 and other methods performed by the network node is set out below in the sections “Embodiments in a Network Node”.

[0129] Embodiments in a UE

[0130] The present disclosure includes a method at a User Equipment (UE). The UE may be connected to a wireless network (e.g. via a serving cell). The method can comprise one or more of:

[0131] • Receiving a configuration message (e.g. RRC Reconfiguration) indicating one or more configuration parameters for generating and / or reporting prediction information for one or more communication resources,

[0132] • Receiving a second message indicating meta information relating to a particular prediction environment and / or a particular prediction task,

[0133] • Based on the received configuration, including in a measurement report (or a report message), prediction information according to the received one or more configuration parameters (for example, one or more measurement prediction(s) of a subset of the configured one or more communication resources (for example, serving cells and / or neighboring cells)), and

[0134] • Transmitting the measurement report to a network node.

[0135] Therefore, in some embodiments, based on the configuration message, the UE may configure measurement predictions for one or more communication resources, and trigger a measurement report including the predicted measurements. Fig. 3 is a signalling diagram showing a signalling flow for configuring prediction information, and generating and reporting prediction information according to such an embodiment.

[0136] In some embodiments, the received configuration may indicate / instruct the UE to collect / report the “available” predictions i.e., the UE is not mandated to “perform” any predictions based on the received configuration.

[0137] The configuration message may include a prediction reporting configuration. This prediction reporting configuration may be part of the IE ReportConfigNR, in the case the predictions are reported together with measurement(s).

[0138] The configuration message may include one or more of the following: o One or more parameters indicating to the UE a quantity of prediction information that the UE is to report. For example, the one or more parameters may indicate a number of timedomain predictions the UE is to include in the report. The number may be configured in terms of time units (e.g. seconds, subframes, radio frames, measurement periods, prediction period(s)), or configured in terms of a number of prediction value(s); o One or more parameters indicating a time offset (or starting point (time) for the prediction(s)) to be performed and / or reported) to begin generating and / or reporting the timedomain prediction(s) (e.g. from which future point in time the UE is to begin performing the prediction(s)); o One or more parameters indicating a frequency or interval at which the UE is to generate prediction information. For example, one or more parameters indicating a reporting frequency / period for the predicted measurement samples. This indicates how frequently the UE shall provide measurement prediction samples in the report (e.g., a measurement prediction sample every X milliseconds); o One of more parameters indicating a range of values for the prediction information, such as a range to report predicted measurements (e.g. RSRP of SCell-A is between X dBm and Y dBm with confidence Z%) o One or more parameters indicating that the UE is not to send the predictions whose confidence level (or accuracy or prediction error or inference error) is lower than a threshold. In other words, one or more parameters indicating an event or condition that prevents reporting of the prediction information. This threshold may be configurable; o One or more parameters indicating that the UE is to send the time-domain prediction(s) whose confidence level (or accuracy or prediction error or inference error) is above a threshold. In other words, one or more parameters indicating an event or condition that triggers the reporting of the prediction information. This threshold may be configurable; o One or more parameters indicating whether the UE is to report a cell identifier (or an equivalent identifier enabling the network to identify a neighbour cell and / or serving cell) indicating the k-th next neighbour cell the UE is going to move to. For example when the UE in cell A indicates that it is moving towards cell B (which is a likely candidate for handover and / or a cell soon to be a triggered cell for an event like A3).

[0139] The configuration message received by the UE may be included in an RRCReconfiguration message, or any other message used to configure the UE (e.g. an RRC Resume message, or an RRC Reestablishment message). This message may include any of the following:

[0140] • Prediction measurements type(s) that the UE shall provide (in other words, one or more types of prediction information that the UE is to generate and / or report). The UE may receive an indication per measurement type indicating whether the UE shall provide predictions of that type. This may be configured using an indication per measurement type where if this indication is included (or where this indication is set to a certain value), the UE is considered configured to provide such predictions: o cell-level or L3 beam-level measurements predictions o cell-level RSRP measurements predictions o cell-level RSRQ measurements predictions o cell-level RSSI measurement predictions o cell-level SINR measurement predictions o any combination of above o RS type for which the prediction is to be performed e.g. SSB, CSI-RS, Mobility Reference Signal (MRS), etc.

[0141] • A prediction configuration. The UE can be configured with the following to generate predictions: o A time period in which the prediction information is to be generated and / or reported, such as a prediction window length (that is, how far in the future the UE performs the time-domain predictions and / or report the time-domain predictions). The window length can, for instance, be expressed in number of time unit(s) T (e.g., seconds, milliseconds, subframes, radio frames, time slots, sub-slots, half-slots, OFDM symbols, etc.) or a number of predictions N (e.g. for a given prediction period and / or measurement period). For example, the UE can be configured to perform and / or report time-domain predictions of a cell quality (e.g. predicted RSRP) for T=5 seconds in the future, which means that at time t, the UE would provide time-domain predictions of measurements only up until t+5 seconds. This has the benefit that overhead can be saved since it may not be meaningful for the network to get predicted measurements which are too far into the future. The network may alternatively indicate that the UE is to include predictions for at most N samples in the report, e.g. report up to N=20 predictions. This may also limit the UE processing in terms of the effort to compute AI / ML model outputs which are too far ahead in time. The time ‘t’ is related to the window length: □ ‘t’ may be the time in which the UE performs the time-domain prediction(s) e.g. the time in which the UE provides the input to the AI / ML model.

[0142] □ ‘t’ may be the time in which the UE prepares the report (to be transmitted to the network) including the time-domain prediction(s).

[0143] □ ‘t’ may be configurable. o A time or time offset at which the UE is to generate prediction information and / or report prediction information. The UE can be configured with an offset (or starting point for the time-domain predictions) to request UE predictions in the future. With an offset and window length, the UE may predict the measurements results corresponding to the window in the future, starting at the offset value. One of the benefits in configuring an offset is that when a UE performs a time-domain prediction at a time to to include in a report to the network, the UE may use measurements performed at time to, so that measurements at time tO may be included in the report. Thus, there may be no point in including a timedomain prediction for time tO, but for some time ahead indicated by the offset. The time ahead (offset) could be related to the time window for Layer 3 (L3) filtering. The measurement in time tO may also influence samples up to a certain point, so that the network may want to have predictions from that point onwards, but not too close to tO (as this may not provide a lot more information).

[0144] □ In some cases, the window length may be zero. In this case, the network may ask the UE to predict at a certain time according to the offset from a reference time. The reference time can be, for example, the time at which the configuration is received, or a time of triggering of the sending of the prediction results. For example, for a window length of 2 (seconds) and an offset of 5 (seconds), the UE will predict what the measurement would be from 5 to 7 seconds from the reference time. o A reporting frequency / period. The UE may be configured with a parameter indicating how frequently the UE shall provide measurement prediction samples in the report. The value can be expressed e.g., in seconds, milliseconds, subframes, radio frames, time slots, sub-slots, half-slots, OFDM symbols, etc. For example, if the reporting frequency is once per 100 milliseconds, this may mean that the UE shall include in the report one prediction at least every 100 milliseconds (if and when available), or that the UE cannot provide predictions more often than every 100th millisecond. o A confidence level threshold. The UE may be configured to not report or include predictions with a confidence lower than a certain threshold C. In other words, the UE may be configured to only report or include predictions with a confidence higher than a certain threshold. For example, the UE may only report predictions which the UE is at least 80% sure about. This has the benefit that overhead can be saved since the UE doesn’t report predictions which are not sufficiently accurate. In these embodiments, the event or condition that triggers reporting of the prediction information comprises a confidence threshold, and reporting the prediction information is triggered if a confidence associated with generated prediction information exceeds a confidence threshold. o The UE may be configured to report or include predictions when certain events occur. This may include a certain measurement reporting event occurring, such as, for example, when an A3 or A5 event becomes fulfilled. o The UE may be configured to report or include predictions when an event occurs, where the event is a new event used for triggering reporting of AI / ML related information. The fulfilment of the event may depend on different aspects of the AI / ML information being fulfilled as discussed above, e.g. the confidence level being above a certain threshold, or when all samples associated with a window have been collected. o The UE may be configured to report or include predictions when certain procedures take place. This could relate to a certain procedure being executed, such that the UE reports or includes predictions when the procedure is executed, e.g. when a mobility procedure is executed or when a state transfer to a certain RRC state, e.g. RRC_CONNECTED is executed.

[0145] • A format in which the prediction information is to be reported. A predicted measurement (e.g., predicted RSRP value or predicted RSRQ value) can be expressed as a value or a range of values. The following formats can be defined for reporting: o Predicted value equal to X dbm, with Z % confidence. o Predicted value equal to X dBm + / - y dBm, with Z % confidence. o Predicted value between X dBm and Y dBm, with Z % confidence.

[0146] The UE may generate prediction information according to the received one or more configuration parameters and / or collecting prediction information from one or more sources. The UE may then include this prediction information in a measurement report.

[0147] The UE may include in a measurement report, one or more prediction(s) related to serving and / or neighbor cells, beams or other communication resources. The measurement report message can be:

[0148] • An RRC measurement report (e.g. MeasurementReport message) used for the indication of measurement results and the indication of prediction information.

[0149] • An RRC measurement report (e.g. MeasurementReport message) used for the indication of prediction information (not necessarily including actual measurements). • A UE assistance information message (e.g. UEAssistancelnformation message) used for the indication of UE assistance information to the network.

[0150] • Any other type of RRC report or / and message used to indicate the prediction information for at least one neighbor cell and / or a serving cell.

[0151] The predictions included in the measurement report message may be in the form of a list (or vector) including one or more of the following elements:

[0152] • A time stamp or time range, indicating when the predicted measurement quantity (e.g. RSRP, RSRQ) of the serving / neighbour cell is expected to be valid.

[0153] • A cell identifier (e.g. Cell ID, PCI + SSB frequency) derived based on the predicted measurement quantity (e.g. predicted RSRP value) of the serving / neighbor cell.

[0154] • A beam index and / or beam identifier (e.g. SSB index) of the serving / neighbor cell, wherein the beam index is derived based on prediction of a measurement quantity (e.g. RSRP) of a beam.

[0155] • One or more predicted measurement quantities (e.g., predicted RSRP value, predicted RSRQ value) of the serving / neighbor cell. The predicted measurement quantity can be indicated as a value or a range of values (e.g., predicted value equal to X dBm + / - y dBm or predicted value between X dBm and Y dBm).

[0156] • A confidence level of the predicted value of the measurement quantity. This may be reported as a percentage value, or a value between 0 and 1 where 1 means that the UE is fully confident in the prediction, and 0 means that the UE has no confidence in the prediction.

[0157] The UE may report to the network a capability that indicates a number of time-domain predictions the UE is capable of performing, and that it can include in the report. The capability may indicate a maximum length the UE is capable to support, the length being indicated in terms of time units (e.g. seconds, subframes, radio frames, measurement periods, prediction period(s)) or in a number of prediction value(s). For example, when the UE indicates a value ‘3’ it indicates to the network that it is capable of performing time-domain predictions (e.g. of measurements) 3 seconds ahead in time. The reported indication may be used at the network side for the setting of the prediction window and / or the setting of a number of time-domain prediction(s) to be included in the report. More capable UEs would be able to perform time-domain predictions further ahead in time.

[0158] Such a capability related to the number of time-domain predictions the UE is capable to perform, to include in the report, may be a dynamic capability, which differs depending on the applicability of an AI / ML model under a certain situation e.g. depending on the area and / or cell the UE is located when the prediction is being performed, UE speed, etc. In that case, the actual value may differ over time. In that case, the indicated capability is indicated as a maximum value, but the UE is not always capable of achieving that maximum value. In that case, when the UE is configured with a time-window for the time-domain prediction(s), the UE also interprets the configurable as a maximum value, and, depending on whether it is capable or not, and / or whether the AI / ML model is applicable or not at the moment the UE needs to report or perform the timedomain prediction(s), the UE performs the prediction(s) up to the value indicated in the window.

[0159] The UE may report to the network a capability associated with the offset (or starting point for the prediction(s)) to be performed and / or associated) for the start of the time-domain prediction(s). The capability may indicate a maximum starting point the UE is capable to support, the length being indicated in terms of time units (e.g. seconds, subframes, radio frames, measurement periods, prediction period(s)) or in a number of prediction value(s). For example, when the UE indicates a value ‘2’ it indicates to the network that it is capable of performing timedomain predictions (e.g. of measurements) starting from 2 seconds ahead in time of the current measurement being performed. The reported indication may be used at the network side for the setting of the offset parameter (to indicate the starting point in which a time-doimain predictions is required by the network). More capable UEs would be able to perform time-domain predictions starting further ahead in time.

[0160] Such a capability related to the offset may also be a dynamic capability, which differs depending on the applicability of an AI / ML model under a certain situation e.g. depending on the area and / or cell the UE is located when the prediction is being performed, UE speed, etc. In that case, the actual value may differ over time. In that case, the indicated capability is indicated as a minimum value, i.e., regardless of the scenario the UE is able to start predictions at that point, even if in fact, the UE is able to start further ahead in some scenarios e.g. when it is static.

[0161] Referring now to the meta information, the meta information may help improve a UE meta learning process. That is, the meta information may be for use in a meta learning process of updating a machine learning model for generating prediction information.

[0162] The meta information may be associated with a different class of training, so using a set of meta information values [A] to predict in environment [A’] will increase accuracy of predictions, in comparison to using the same meta information values [A] to predict in a different environment [B’].

[0163] The received indication may describe meta information that corresponds to a certain prediction environment (or task). The meta information may differ in different aspects, such as how is it generated, what is it describing, how is it used in a training process, and what is to be transferred between a NW node and the UE.

[0164] The meta-information may be generated:

[0165] • By using a hierarchical iteration that utilizes the loss of un-seen data predictions (using the trained model prediction output), to enhance the back-propagation (or adaptation process) procedure in the second outer iteration of ML model. In other words, creating an ML model is that is capable to generalize for unseen data, as described in “Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks” by Chelsea Finn, Pieter Abbeel and Sergey Levine.

[0166] • By using a dimensionality reduction methodology, e.g., Principal Component Analysis (PCA) or encoder part of auto encoder, on general or categorical information that describes the environment.

[0167] The meta-information may be an implicit or explicit indicator that describes:

[0168] • UE-contextual information, surrounding building contextual information, relevant location with respect to serving and neighbor nodes, neighbor traffic / load contextual information, or contextual information about a category of aggressor / interferer.

[0169] • Statistical (or information theoretic) characteristics of the input features of the model to be trained.

[0170] • A new method of guiding the loss function.

[0171] The meta-information can be used in ML model as follows:

[0172] • As an output cost function, e.g., to give a value of the loss if a certain input satisfies certain criteria. Hence, it biases the training and inference process of the UE model. Correspondingly, it influences the gradient update process.

[0173] • As an input, for example, as an independent new feature, as an input to the preprocessing phase of the training / inference of the data.

[0174] • To describe the multi-task learning, i.e., identify new labels (guiding tasks) in parallel to the agreed upon main label (SINR, L3-RSRP, etc), to bias the training in a desired direction.

[0175] In the context of signalling of meta-information, it is possible to transfer the following information between NW nodes and UE nodes:

[0176] • Descriptive information on the loss between an unseen label and a trained ML prediction.

[0177] • A statistical relation (values of functions) between original input of ML model.

[0178] • Different contextual information (as described above).

[0179] • Indicators on what guiding task should be considered.

[0180] • An output of a dimensionality reduction algorithm. Embodiments in a Network Node

[0181] The present disclosure includes a method at a network node (e.g. a serving gNobeB), the method comprising one or more of:

[0182] • Transmitting a configuration message (e.g. a first message as described above) to a UE,

[0183] • Receiving a measurement report (e.g. a report message as described above) from the UE, and

[0184] • Transmitting, to the UE, an indication (e.g. a second message as described above) describing meta information that corresponds to a certain prediction environment (or task).

[0185] The configuration message sent to the UE may be a RRCReconfiguration message, or any other message used to configure the UE. This message may include any of the following:

[0186] • Prediction measurement types (e.g., cell-level or L3 beam-level measurements predictions, cell-level RSRP measurements predictions, cell-level RSRQ, RSSI, SINR and any combination of them)

[0187] • A prediction configuration (e.g., prediction window length, window offset, reporting frequency, confidence level threshold, event configuration)

[0188] • A prediction reporting format (e.g., single value or range of values)

[0189] The network node may receive the measurement report from the UE including the prediction information and, possibly, the actual measurements. The network node may trigger one or more actions based on the received predictions, for instance:

[0190] • Configuring a neighbor cell as target cell and trigger a handover to that cell.

[0191] • Configuring a cell as a target candidate cell for conditional handover.

[0192] • Configuring a cell as a target cell for LTM.

[0193] • Triggering a beam switch.

[0194] • Triggering Early Data Forwarding for a neighbor cell configured as target cell.

[0195] • Configuring a new cell as a neighbour cell.

[0196] Fig. 4 shows an example of a communication system 400 in accordance with some embodiments. In the example, the communication system 400 includes a telecommunication network 402 that includes an access network 404, such as a radio access network (RAN), and a core network 406, which includes one or more core network nodes 408. The access network 404 includes one or more access network nodes, such as access network nodes 410a and 410b (which are interchangeably referred to as RAN network nodes 410 herein), or any other similar 3rdGeneration Partnership Project (3GPP) access node or non-3GPP access point (AP). Moreover, as will be appreciated by those of skill in the art, a RAN network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network 402 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 402 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network 402, including one or more network nodes 410 and / or core network nodes 408.

[0197] Examples of an ORAN network node include an open radio unit (0-Rll), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O- Cll user plane (O-CU-UP), a RAN intelligent controller (RIC) (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an A1 , F1 , W1 , E1 , E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an O-2 interface defined by the O-RAN Alliance or comparable technologies.

[0198] The access network nodes 410 facilitate direct or indirect connection of wireless devices (also referred to interchangeably herein as user equipment (UE)), such as by connecting UEs 412a, 412b, 412c, and 412d (one or more of which may be generally referred to as UEs 412) to the core network 406 over one or more wireless connections. The access network nodes 410 may be, for example, access points (APs) (e.g. radio access points), base stations (BSs) (e.g. radio base stations, Node Bs, evolved Node Bs (eNBs) and New Radio (NR) NodeBs (gNBs)).

[0199] Unless otherwise indicated, the general term ‘network node’ as used herein refers to access network nodes 410 and core network nodes 408. 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 400 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 400 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0200] The wireless devices / UEs 412 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 410 and other communication devices. Similarly, the access network nodes 410 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 412 and / or with other network nodes or equipment in the telecommunication network 402 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 402.

[0201] In the depicted example, the core network 406 connects the access network nodes 410 to one or more hosts, such as host 416. 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 406 includes one more core network nodes (e.g. core network node 408) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the wireless devices / UEs, access network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 408. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0202] The host 416 may be under the ownership or control of a service provider other than an operator or provider of the access network 404 and / or the telecommunication network 402, and may be operated by the service provider or on behalf of the service provider. The host 416 may host a variety of applications to provide one or more services. Examples of such applications include the provision of live and / or pre-recorded audio / video content, data collection services, for example, retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0203] As a whole, the communication system 400 of Fig. 4 enables connectivity between the wireless devices / 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 2ndGeneration (2G), 3rdGeneration (3G), 4thGeneration (4G), 5thGeneration (5G) standards, or any applicable future generation standard (e.g. 6thGeneration (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.

[0204] In some examples, the telecommunication network 402 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 402 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 402. For example, the telecommunications network 402 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 Internet of Things (loT) services to yet further UEs.

[0205] In some examples, the UEs 412 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 404 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 404. Additionally, a UE may be configured for operating in single- or multi-radio access technology (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- UTRA (UMTS Terrestrial Radio Access) Network) New Radio - Dual Connectivity (EN-DC).

[0206] In the example illustrated in Fig. 4, the hub 414 communicates with the access network 404 to facilitate indirect communication between one or more UEs (e.g. UE 412c and / or 412d) and access network nodes (e.g. access network node 410b). In some examples, the hub 414 may be a controller, router, a content source and analytics node, or any of the other communication devices described herein regarding UEs. For example, the hub 414 may be a broadband router enabling access to the core network 406 for the UEs. As another example, the hub 414 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 410, or by executable code, script, process, or other instructions in the hub 414. As another example, the hub 414 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 414 may be a content source. For example, for a UE that is a Virtual Reality VR headset, display, loudspeaker or other media delivery device, the hub 414 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 414 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 414 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy Internet of Things (loT) devices.

[0207] The hub 414 may have a constant / persistent or intermittent connection to the network node 410b. The hub 414 may also allow for a different communication scheme and / or schedule between the hub 414 and UEs (e.g. UE 412c and / or 412d), and between the hub 414 and the core network 406. In other examples, the hub 414 is connected to the core network 406 and / or one or more UEs via a wired connection. Moreover, the hub 414 may be configured to connect to a Machine-to-Machine (M2M) service provider over the access network 404 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 410 while still connected via the hub 414 via a wired or wireless connection. In some embodiments, the hub 414 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 410b. In other embodiments, the hub 414 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 410b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0208] Fig. 5 shows a wireless device or UE 500 in accordance with some embodiments.

[0209] 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 wireless device / UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless camera, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-loT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0210] A wireless device / 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).

[0211] The UE 500 includes processing circuitry 502 that is operatively coupled via a bus 504 to an input / output interface 506, a power source 508, a memory 510, a communication interface 512, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Fig. 5. 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.

[0212] The processing circuitry 502 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 510. The processing circuitry 502 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 502 may include multiple central processing units (CPUs). The processing circuitry 502 may be operable to provide, either alone or in conjunction with other UE 500 components, such as the memory 510, to provide UE 500 functionality. For example, the processing circuitry 502 may be configured to cause the UE 502 to perform the methods as described with reference to Fig. 1.

[0213] In the example, the input / output interface 506 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 500. 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.

[0214] In some embodiments, the power source 508 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 508 may further include power circuitry for delivering power from the power source 508 itself, and / or an external power source, to the various parts of the UE 500 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 508. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 508 to make the power suitable for the respective components of the UE 500 to which power is supplied.

[0215] The memory 510 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 510 includes one or more application programs 514, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 516. The memory 510 may store, for use by the UE 500, any of a variety of various operating systems or combinations of operating systems.

[0216] The memory 510 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 Universal Subscriber Identity Module (USIM) and / or integrated SIM (ISIM), other memory, or any combination thereof. The UICC may for example be an embedded IIICC (elllCC), integrated IIICC (illlCC) or a removable IIICC commonly known as ‘SIM card.’ The memory 510 may allow the UE 500 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to offload 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 510, which may be or comprise a device- readable storage medium.

[0217] The processing circuitry 502 may be configured to communicate with an access network or other network using the communication interface 512. The communication interface 512 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 522. The communication interface 512 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 518 and / or a receiver 520 appropriate to provide network communications (e.g. optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 518 and receiver 520 may be coupled to one or more antennas (e.g. antenna 522) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0218] In some embodiments, communication functions of the communication interface 512 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) or other Global Navigation Satellite System (GNSS) 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, 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.

[0219] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 512, 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). As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or controls a robotic arm performing a medical procedure according to the received input.

[0220] A UE, when in the form of an loT device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are devices which are or which are embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or VR, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence on the intended application of the loT device in addition to other components as described in relation to the UE 500 shown in Fig. 5.

[0221] 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.

[0222] 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.

[0223] Fig. 6 shows an access network node 600 or RAN network node 600 in accordance with some embodiments.

[0224] As used herein, access network node or RAN network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other RAN network nodes or equipment or core network nodes, in a telecommunication network. Examples of access network nodes include, but are not limited to, access network nodes such as APs (e.g. radio access points), base stations (BSs) (e.g. radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), Open RAN (O-RAN) nodes or components of an O- RAN node (e.g., O-RU, O-DU, O-CU)..

[0225] 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 RAN network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node), and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0226] Other examples of access 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).

[0227] The RAN network node 600 includes processing circuitry 602, a memory 604, a communication interface 606, and a power source 608, and / or any other component, or any combination thereof. The RAN network node 600 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 RAN network node 600 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 RAN network node 600 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g. separate memory 604 for different RATs) and some components may be reused (e.g. a same antenna 610 may be shared by different RATs). The RAN network node 600 may also include multiple sets of the various illustrated components for different wireless technologies integrated into RAN network node 600, 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 RAN network node 600.

[0228] The processing circuitry 602 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 RAN network node 600 components, such as the memory 604, to provide network node 600 functionality. For example, the processing circuitry 602 may be configured to cause the RAN network node to perform the methods as described with reference to Fig. 2.

[0229] In some embodiments, the processing circuitry 602 includes a system on a chip (SOC). In some embodiments, the processing circuitry 602 includes one or more of radio frequency (RF) transceiver circuitry 612 and baseband processing circuitry 614. In some embodiments, the radio frequency (RF) transceiver circuitry 612 and the baseband processing circuitry 614 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 612 and baseband processing circuitry 614 may be on the same chip or set of chips, boards, or units.

[0230] The memory 604 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 602. The memory 604 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 602 and utilized by the RAN network node 600. The memory 604 may be used to store any calculations made by the processing circuitry 602 and / or any data received via the communication interface 606. In some embodiments, the processing circuitry 602 and memory 604 is integrated.

[0231] The communication interface 606 is used in wired or wireless communication of signalling and / or data between network nodes, the access network, the core network, and / or a UE. As illustrated, the communication interface 606 comprises port(s) / terminal(s) 616 to send and receive data, for example to and from a network over a wired connection.

[0232] The communication interface 606 also includes radio front-end circuitry 618 that may be coupled to, or in certain embodiments a part of, the antenna 610. Radio front-end circuitry 618 comprises filters 620 and amplifiers 622. The radio front-end circuitry 618 may be connected to an antenna 610 and processing circuitry 602. The radio front-end circuitry may be configured to condition signals communicated between antenna 610 and processing circuitry 602. The radio front-end circuitry 618 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 618 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 620 and / or amplifiers 622. The radio signal may then be transmitted via the antenna 610. Similarly, when receiving data, the antenna 610 may collect radio signals which are then converted into digital data by the radio front-end circuitry 618. The digital data may be passed to the processing circuitry 602. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0233] In certain alternative embodiments, the access network node 600 does not include separate radio front-end circuitry 618, instead, the processing circuitry 602 includes radio front-end circuitry and is connected to the antenna 610. Similarly, in some embodiments, all or some of the RF transceiver circuitry 612 is part of the communication interface 606. In still other embodiments, the communication interface 606 includes one or more ports or terminals 616, the radio front-end circuitry 618, and the RF transceiver circuitry 612, as part of a radio unit (not shown), and the communication interface 606 communicates with the baseband processing circuitry 614, which is part of a digital unit (not shown).

[0234] The antenna 610 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 610 may be coupled to the radio front-end circuitry 618 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 610 is separate from the network node 600 and connectable to the RAN network node 600 through an interface or port. The antenna 610, communication interface 606, and / or the processing circuitry 602 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 610, the communication interface 606, and / or the processing circuitry 602 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.

[0235] The power source 608 provides power to the various components of RAN network node 600 in a form suitable for the respective components (e.g. at a voltage and current level needed for each respective component). The power source 608 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 600 with power for performing the functionality described herein. For example, the RAN network node 600 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 608. As a further example, the power source 608 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.

[0236] Embodiments of the RAN network node 600 may include additional components beyond those shown in Fig. 6 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 RAN network node 600 may include user interface equipment to allow input of information into the RAN network node 600 and to allow output of information from the RAN network node 600. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the RAN network node 600.

[0237] Fig. 7 is a block diagram illustrating a virtualization environment 700 in which functions implemented by some embodiments may be virtualized.

[0238] In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 700 hosted by one or more of hardware nodes, such as a hardware computing device that operates as an access network node, a wireless device / UE, a core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g. a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 700 includes components defined by the Open-RAN (O-RAN) Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an 0-2 interface.

[0239] Applications 702 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 700 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0240] Hardware 704 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 706 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 708a and 708b (one or more of which may be generally referred to as VMs 708), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 706 may present a virtual operating platform that appears like networking hardware to the VMs 708.

[0241] The VMs 708 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 706. Different embodiments of the instance of a virtual appliance 702 may be implemented on one or more of VMs 708, and the implementations may 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.

[0242] In the context of NFV, a VM 708 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 708, and that part of hardware 704 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 708 on top of the hardware 704 and corresponds to the application 702.

[0243] Hardware 704 may be implemented in a standalone network node with generic or specific components. Hardware 704 may implement some functions via virtualization. Alternatively, hardware 704 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 710, which, among others, oversees lifecycle management of applications 702. In some embodiments, hardware 704 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signalling can be provided with the use of a control system 712 which may alternatively be used for communication between hardware nodes and radio units.

[0244] Although the computing devices described herein (e.g. UEs, RAN network nodes, core network node, 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.

[0245] 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.

[0246] The foregoing merely illustrates the principles of the disclosure. Various modifications and alterations to the described embodiments will be apparent to those skilled in the art in view of the teachings herein. It will thus be appreciated that those skilled in the art will be able to devise numerous systems, arrangements, and procedures that, although not explicitly shown or described herein, embody the principles of the disclosure and can be thus within the scope of the disclosure. Various exemplary embodiments can be used together with one another, as well as interchangeably therewith, as should be understood by those having ordinary skill in the art.

Claims

Claims1. A method performed by a user equipment, UE, the method comprising: receiving (102) a first message indicating one or more configuration parameters for generating and / or reporting prediction information for one or more communication resources; and / or receiving (102) a second message indicating meta information relating to a particular prediction environment and / or a particular prediction task.

2. The method of claim 1 , wherein the prediction information comprises time-domain prediction information.

3. The method of claim 1 or 2, wherein the prediction information comprises at least one prediction of a measurement of a communication resource.

4. The method of any preceding claim, wherein the one or more configuration parameters indicate one or more of: one or more types of prediction information that the UE is to generate; one or more types of prediction information that the UE is to report; one or more communication resources that the prediction information is to relate to; a quantity of prediction information that the UE is to report; a time or time offset at which the UE is to generate prediction information and / or report prediction information; a frequency or interval at which the UE is to report prediction information; a frequency or interval at which the UE is to generate prediction information; an event or condition that triggers or prevents reporting of the prediction information; a time period in which the prediction information is to be generated and / or reported; a format in which the prediction information is to be reported; and a range of values for the prediction information.

5. The method of claim 4, wherein the event or condition that triggers reporting of the prediction information comprises a confidence threshold, wherein reporting the prediction information is triggered if a confidence associated with generated prediction information exceeds a confidence threshold.

6. The method of claim 4 or 5, wherein the type of prediction information is one or more of: a predicted cell-level measurement for a cell; a predicted beam-level measurement for a cell; a predicted communication resource-level measurement for a communication resource; a predicted signal strength or quality value for a communication resource; a predicted Reference Signal Received Power, RSRP, of a communication resource; a predicted Reference Signal Received Quality, RSRQ, of a communication resource; a predicted Received Signal Strength Indicator, RSSI, of a communication resource; a predicted Signal to Interference plus Noise Ratio, SINR, of a communication resource; a predicted resource index and / or resource identifier of a communication resource; and a predicted beam index and / or beam identifier of a cell.

7. The method of any preceding claim, wherein the prediction information is for a communication resource configured to be serving the UE, or a communication resource not configured to be serving the UE.

8. The method of any preceding claim, wherein the first message is a Radio Resource Control, RRC, reconfiguration message, a RRC resume message, or an RRC reestablishment message.

9. The method of any preceding claim, wherein the method further comprises, in response to receiving the one or more configuration parameters: transmitting, to a network node, a report message comprising the prediction information according to the received one or more configuration parameters.

10. The method of any preceding claim, wherein the method further comprises: generating prediction information according to the received one or more configuration parameters and / or collecting prediction information from one or more sources.

11. The method of claim 9 or 10, wherein the report message is a Radio Resource Control, RRC, measurement report message, or a UE assistance information message.

12. The method of any preceding claim, wherein the generated prediction information comprises: a predicted signal strength or quality value for a communication resource at a future time instance;a predicted identifier for a communication resource at a future time instance; an identifier based on a predicted signal strength or quality value for a communication resource at a future time instance; a predicted Reference Signal Received Power, RSRP, of a communication resource at a future time instance; a predicted Reference Signal Received Quality, RSRQ, of a communication resource at a future time instance; a predicted Signal to Interference plus Noise Ratio, SI NR, of a communication resource at a future time instance; a predicted resource index and / or resource identifier of a communication resource at a future time instance; a predicted beam index and / or beam identifier of a cell at a future time instance; a Synchronization Signal Block, SSB, index of a communication resource at a future time instance; a corresponding confidence indicator of a predicted value for a communication resource; a corresponding time duration for which a predicted value for a communication resource is expected to be valid; a corresponding time duration for which a predicted value for a communication resource is expected to satisfy a condition; and a capability of the UE to generate prediction information.

13. The method of any preceding claim, wherein the meta information is for use in a meta learning process of updating a machine learning model for generating prediction information.

14. The method of claim 13, wherein the second message further comprises one or more of: information indicating how the meta information was generated; information indicating how meta information is to be generated by the UE; information indicating how the meta information is to be used in the meta learning process; information indicating what the meta information describes; and information indicating what information associated with the meta learning process can be transferred between the UE and a network node.

15. The method of any preceding claim, wherein a communication resource is any of a cell, beam or other time / frequency resource.

16. A method performed by a network node, the method comprising: sending (202), to a user equipment, UE, a first message indicating one or more configuration parameters for generating and / or reporting prediction information for one or more communication resources; and / or sending (202), to a UE, a second message indicating meta information relating to a particular prediction environment and / or a particular prediction task.

17. The method of claim 16, wherein the prediction information comprises time-domain prediction information.

18. The method of claim 16 or 17, wherein the prediction information comprises at least one prediction of a measurement of a communications resource.

19. The method of any of claims 16-18, wherein the one or more configuration parameters indicate one or more of: one or more types of prediction information that the UE is to generate; one or more types of prediction information that the UE is to report; one or more communications resources that the prediction information is to relate to; a quantity of prediction information that the UE is to report; a time or time offset at which the UE is to generate prediction information and / or report prediction information; a frequency or interval at which the UE is to report prediction information; a frequency or interval at which the UE is to generate prediction information; an event or condition that triggers or prevents reporting of the prediction information; a time period in which the prediction information is to be generated and / or reported; a format in which the prediction information is to be reported; and a range of values for the prediction information.

20. The method of claim 19, wherein the event or condition that triggers reporting of the prediction information comprises a confidence threshold, wherein reporting the prediction information is triggered if a confidence associated with generated prediction information exceeds a confidence threshold.

21. The method of claim 19 or 20, wherein the type of prediction information is one or more of: a predicted cell-level measurement for a cell;a predicted beam-level measurement for a cell; a predicted communication resource-level measurement for a communication resource; a predicted signal strength or quality value for a communication resource; a predicted Reference Signal Received Power, RSRP, of a communication resource; a predicted Reference Signal Received Quality, RSRQ, of a communication resource; a predicted Received Signal Strength Indicator, RSSI, of a communication resource; a predicted Signal to Interference plus Noise Ratio, SINR, of a communication resource; a predicted resource index and / or resource identifier of a communication resource; and a predicted beam index and / or beam identifier of a cell.

22. The method of any of claims 16-21 , wherein the prediction information is for a communication resource configured to be serving the UE, or a communication resource not configured to be serving the UE.

23. The method of any of claims 16-22, wherein the first message is a Radio Resource Control, RRC, reconfiguration message, a RRC resume message, or an RRC reestablishment message.

24. The method of any of claims 16-23, wherein the method further comprises: receiving, from the UE, a report message comprising the prediction information according to the one or more configuration parameters.

25. The method of claim 24, wherein the method further comprises, based on the received prediction information, performing one or more of the following actions: configuring a neighbour communication resource as a target communication resource of the UE; triggering a handover to a target communication resource of the UE; configuring a communication resource as a target candidate communication resource for conditional handover; configuring a communication resource as a target communication resource for lower-layer triggered mobility; triggering a communication resource switch; triggering Early Data Forwarding for a neighbour communication resource configured as target communication resource; and configuring a new communication resource as a neighbour communication resource.

26. The method of claim 24 or 25, wherein the report message is a Radio Resource Control, RRC, measurement report message, or a UE assistance information message.

27. The method of any of claims 16-26, wherein the prediction information comprises: a predicted signal strength or quality value for a communication resource at a future time instance; a predicted identifier for a communication resource at a future time instance; an identifier based on a predicted signal strength or quality value for a communication resource at a future time instance; a predicted Reference Signal Received Power, RSRP, of a communication resource at a future time instance; a predicted Reference Signal Received Quality, RSRQ, of a communication resource at a future time instance; a predicted Signal to Interference plus Noise Ratio, SI NR, of a communication resource at a future time instance; a predicted resource index and / or resource identifier of a communication resource at a future time instance; a predicted beam index and / or beam identifier of a cell at a future time instance; a Synchronization Signal Block, SSB, index of a communication resource at a future time instance; a corresponding confidence indicator of a predicted value for a communication resource; a corresponding time duration for which a predicted value for a communication resource is expected to be valid; a corresponding time duration for which a predicted value for a communication resource is expected to satisfy a condition; and a capability of the UE to generate prediction information.

28. The method of any of claims 16-27, wherein the meta information is for use in a meta learning process of updating a machine learning model for generating prediction information.

29. The method of claim 28, wherein the second message further comprises one or more of: information indicating how the meta information was generated; information indicating how meta information is to be generated by the UE; information indicating how the meta information is to be used in the meta learning process; information indicating what the meta information describes; andinformation indicating what information associated with the meta learning process can be transferred between the UE and a network node.

30. The method of any of claims 16-29, wherein a communication resource is any of a cell, beam or other time / frequency resource.

31. A computer program product comprising a computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, on execution by a suitable computer or processor, the computer or processor is caused to perform the method of any of claims 1-30.

32. A user equipment, UE, configured to perform the method of any of claims 1-15.

33. A user equipment, UE, comprising a processor and a memory, said memory containing instructions executable by said processor whereby said UE is operative to perform the method of any of claims 1-15.

34. A radio access network, RAN, node, configured to perform the method of any of claims 16- 30.

35. A radio access network, RAN, node comprising a processor and a memory, said memory containing instructions executable by said processor whereby said RAN node is operative to perform the method of any of claims 16-30.

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