Use of spatial domain predictions for triggering of events

Abstract

The present disclosure provides a method performed by a user equipment (UE) for performing measurements. The method comprises receiving, from a network node, a configuration message containing a configuration of a measurement event, wherein the measurement event can be triggered based on predictions in combination with measurements. Responsive to the received configuration, the method includes performing measurements on one or more cells and generating predictions for one or more other cells. The method further comprises monitoring whether the configured measurement event is fulfilled based on a combination of the performed measurements and the generated predictions. Upon fulfillment of the measurement event, the method includes transmitting a fulfillment indication to the network node. The predictions may comprise spatial domain predictions, frequency domain predictions, temporal domain predictions, or any combination thereof. The measurement event may comprise event A3 or event A5 configurations that utilize both measured and predicted RRM measurement values for triggering conditions.

Description

Use Of Spatial Domain Predictions For Triggering Of Events Cross-Reference to Related Applications

[0001] This application to or is a (divisional, continuation,continuation-in-part) of or relates to] U.S. Application No. [application No.], titled [title], filed [filing date], which is hereby incorporated by reference in its entirety. Technical Field

[0002] The present disclosure relates to wireless communication systems and artificial intelligence / machine learning techniques for radio resource management, and more particularly to the use of spatial domain predictions in combination with actual measurements for triggering measurement events in user equipment. Background

[0003] To date, the Artificial Intelligence / Machine Learning (AI / ML) for Physical Layer (PHY) work in Rel-18 has been limited to lower layer features such as Beam Management, which is sometimes referred as intra-cell mobility. Other features, such as L3 handovers, RRC measurements configuration and reporting of predictions have not been part of the Rel-18.

[0004] Hence, a Rel-19 Study Item to study the usage of AI / ML for L3 Mobility and / or RRM measurements is considered. The objective of the study item includes: Study and evaluate potential benefits and gains of AI / ML aided mobility for network triggered L3-based handover, considering the following aspects: AI / ML based RRM measurement and event prediction Cell-level measurement prediction including intra and inter-frequency (UE sided and NW sided model) Inter-cell Beam-level measurement prediction for L3 Mobility (UE sided and NW sided model) HO failure / RLF prediction (UE sided model)Measurement events prediction (UE sided model) Study the need / benefits of any other UE assistance information for the network side model. The evaluation of the AI / ML aided mobility benefits should consider HO performance KPIs and complexity tradeoffs. Spatial domain predictions

[0005] As part of the rel-19 study item, spatial, frequency and temporal domain RRM predictions will be studied. When spatial predictions are used, the UE performs measurements of one (set of) cell(s) or beam(s) and predicts measurement results of another (set of) cell(s) or beam(s). The predicted results are for the same time instance and for the same frequency as the input. When frequency predictions are used, the UE performs measurements of (one set) of cell(s) or beam(s) and predicts measurement results of another (set of) cell(s) or beam(s). The predicted results are for the same time instance but for a different frequency as the input. When temporal predictions are used, the UE performs measurements of one (set of) cell(s) or beam(s) and predicts measurement results of the same (set of) of cell(s) or beam(s) at future time instances. Measurement events

[0006] A UE can be configured with different RRC measurement events where an event is that may be triggered when certain criteria are fulfilled. In the Information Element (IE) ReportConfigNR in TS 38.331, specific NR measurement events and / or conditional events are defined, along with the criteria for triggering those events. The RRC Information Element (IE) ReportConfigNR defined in TS 38.331 specifies the following events and triggering conditions as shown below:

[0007] Excerpt from TS 38.331 V18.3.0 (2024-09), begins – ReportConfigNR The IE ReportConfigNR specifies criteria for triggering of an NR measurement reporting event or of a CHO, CPA or CPC event or of an L2 U2N relay measurement reporting event. For events labelled AN with N equal to 1, 2 and so on, measurement reporting events and CHO, CPA or CPC events are based on cell measurement results, which can either be derived based on SS / PBCH block or CSI-RS. Event A1: Serving becomes better than absolute threshold;Event A2: Serving becomes worse than absolute threshold; Event A3: Neighbour becomes amount of offset better than PCell / PSCell; Event A4: Neighbour becomes better than absolute threshold; Event A5: PCell / PSCell becomes worse than absolute threshold1 AND Neighbour / SCell becomes better than another absolute threshold2; Event A6: Neighbour becomes amount of offset better than SCell; Event D1: Distance between UE and a reference location referenceLocation1 becomes larger than configured threshold distanceThreshFromReference1 and distance between UE and a reference location referenceLocation2 becomes shorter than configured threshold distanceThreshFromReference2; Event D2: Distance between UE and the serving cell moving reference location determined based on movingReferenceLocation and its corresponding satellite ephemeris and epoch time broadcast in SIB19 becomes larger than configured threshold distanceThreshFromReference1 and distance between UE and a moving reference location determined based on referenceLocation and its corresponding satellite ephemeris and epoch time for the neighbor cell provided in the associated MeasObjectNR becomes shorter than configured threshold distanceThreshFromReference2; CondEvent A3: Conditional reconfiguration candidate becomes amount of offset better than PCell / PSCell; CondEvent A4: Conditional reconfiguration candidate becomes better than absolute threshold where condEventA4 can also be used for current PSCell (i.e., in case it is configured as candidate PSCell for CondEvent A4 evaluation) for CHO with candidate SCG(s) caseCondEvent A5: PCell / PSCell becomes worse than absolute threshold1 AND Conditional reconfiguration candidate becomes better than another absolute threshold2; CondEvent D1: Distance between UE and a reference location referenceLocation1 becomes larger than configured threshold distanceThreshFromReference1 and distance between UE and a reference location referenceLocation2 of conditional reconfiguration candidate becomes shorter than configured threshold distanceThreshFromReference2; CondEvent D2: Distance between UE and the serving cell moving reference location determined based on movingReferenceLocation and its corresponding satellite ephemeris and epoch time broadcast in SIB19 becomes larger than configured threshold distanceThreshFromReference1 and distance between UE and a moving reference location determined based on referenceLocation and its corresponding satellite ephemeris and epoch time for the conditional reconfiguration candidate provided in the associated MeasObjectNR becomes shorter than configured threshold distanceThreshFromReference2; CondEvent T1: Time measured at UE becomes more than configured threshold t1-Threshold but is less than t1-Threshold + duration; Event X1: Serving L2 U2N Relay UE becomes worse than absolute threshold1 AND NR Cell becomes better than another absolute threshold2; Event X2: Serving L2 U2N Relay UE becomes worse than absolute threshold; For event I1, measurement reporting event is based on CLI measurement results, which can either be derived based on SRS-RSRP or CLI-RSSI.Event I1: Interference becomes higher than absolute threshold; The reporting events concerning Aerial UE altitude are labelled HN with N equal to 1 and 2. Additionally, the reporting events concerning Aerial UE altitude and the neighboring cell measurements simultaneously are labelled AMHN with M equal to 3, 4, 5 and N equal to 1, 2. Event H1: Aerial UE altitude becomes higher than a threshold; Event H2: Aerial UE altitude becomes lower than a threshold; Event A3H1: Neighbour becomes offset better than SpCell and the Aerial UE altitude becomes higher than a threshold; Event A3H2: Neighbour becomes offset better than SpCell and the Aerial UE altitude becomes lower than a threshold; Event A4H1: Neighbour becomes better than threshold1 and the Aerial UE altitude becomes higher than a threshold2; Event A4H2: Neighbour becomes better than threshold1 and the Aerial UE altitude becomes lower than a threshold2; Event A5H1: SpCell becomes worse than threshold1 and neighbour becomes better than threshold2 and the Aerial UE altitude becomes higher than a threshold3; Event A5H2: SpCell becomes worse than threshold1 and neighbour becomes better than threshold2 and the Aerial UE altitude becomes lower than a threshold3. […] from TS 38.331 V18.3.0 (2024-09) ends.

[0008] There currently exist certain challenge(s). Existing measurement events are based on measurements performed in the UE. Performing these measurements consumes energy and battery capacity in the UE. Configuration of too many measurements in the UE leads to rapid draining of the UE battery and also increases a risk that other tasks in the UE may be down-prioritized. Summary

[0009] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. Accordingly, an aspect of the disclosure provides a method for a User Equipment (UE), the method comprising:

[0010] An aspect of the disclosure provides a method performed by a user equipment (UE) for performing measurements. The method comprises: receiving, from a network node, a configuration message containing aconfiguration of a measurement event, wherein the measurement event is triggered based on predictions in combination with measurements; responsive to the received configuration, performing measurements and generating predictions based on the measurements; monitoring whether the configured measurement event is fulfilled based on a combination of the performed measurements and the generated predictions; and upon fulfillment of the measurement event, transmitting a fulfillment indication to the network node.

[0011] In some embodiments, performing measurements includes measuring a quality of at least one beam of a first cell. In specific embodiments, measuring the quality of at least one beam of the first cell includes measuring any one or more of RSRP, RSRQ, SINR, and RSSI of the at least one beam.

[0012] In some embodiments, generating predictions comprises using the measured value of the at least one beam of the first cell to predict a quality of at least one other beam. In specific embodiments, the predicted quality of the at least one other beam comprises a predicted value of any one or more of RSRP, RSRQ, SINR, and RSSI of the at least one other beam.

[0013] The at least one other beam may be comprised within the first cell or within a second cell different than the first cell.

[0014] In some embodiments, generating predictions further comprises using the measured value of the at least one beam of the first cell and the predicted quality of the at least one other beam to predict a quality of the first cell or a second cell different than the first cell.

[0015] In some embodiments, the predictions comprise spatial domain predictions, frequency domain predictions, temporal domain predictions, or any combination thereof.

[0016] In some embodiments, the first cell is a serving cell, and wherein the measurement event comprises a predicted event A3, the predicted event A3 being defined as one of: a predicted quality of a neighbour cell becomes an amount of offset better than measured quality of the serving cell; a measured quality of the neighbour cell becomes an amount of offset better than predicted quality of the serving cell; or a predicted quality of the neighbour cell becomes an amount of offset better than predicted quality of the serving cell.

[0017] In some embodiments, the first cell is a serving cell, and wherein the measurement event comprises a predicted event A5, the predicted event A5 being defined as one of: a measured quality of the serving cell becomes worse than a first absolute threshold and a predicted quality of a neighbour cell becomes better than a second absolute threshold; a predicted quality of the serving cell becomes worse than a first absolute threshold and a measured quality of the neighbour cell becomes better than a second absolute threshold; or a predicted quality of the serving cell becomes worse than a first absolute threshold and a predicted quality of the neighbour cell becomes better than a second absolute threshold.

[0018] In some embodiments, generating predictions comprises using an artificial intelligence model or machine learning model to predict the quality of the at least one other beam based on the measured value of the at least one beam of the first cell.

[0019] In some embodiments, the configuration message comprises an indication of whether the UE is allowed to perform spatial domain predictions instead of performing actual measurements for specific cells or frequencies.

[0020] In some embodiments, the fulfillment indication comprises information indicating whether the measurement event was fulfilled based on measurements, predictions, or a combination of measurements and predictions.

[0021] In some embodiments, the fulfillment indication comprises a confidence score associated with the predicted measurements used to determine fulfillment of the measurement event.

[0022] In some embodiments, the method further comprises transmitting, to the network node, capability information indicating UE capabilities related to spatial domain predictions, including a maximum number of cells or beams that the UE can predict.

[0023] In some embodiments, the configuration message further comprises parameters indicating a relationship between a number of measurements to be performed and a number of predictions to be generated.

[0024] Another aspect of the disclosure provides a user equipment (UE) for performing measurements. The UE comprises: processing circuitry configured to perform any of the steps of any of the embodiments described above; and power supply circuitry configured to supply power to the processing circuitry.

[0025] Another aspect of the disclosure provides a method performed by a network node. The method comprises: transmitting, to a user equipment (UE), a configuration message containing a configuration of a measurement event, where the measurement event can be triggered based on predictions and optionally actual measurements; and subsequently receiving, from the UE, a message indicating fulfillment of the event.

[0026] In some embodiments, the predictions are any one or more of special domain predictions, frequency domain predictions and temporal domain predictions.

[0027] In some embodiments, the configuration message comprises an indication of whether the UE is allowed to perform spatial domain predictions instead of performing actual measurements for specific cells or frequencies.

[0028] In some embodiments, the method further comprises receiving, from the UE, capability information indicating UE capabilities related to spatial domain predictions, including a maximum number of cells or beams that the UE can predict.

[0029] In some embodiments, the configuration message further comprises parameters indicating a relationship between a number of measurements to be performed and a number of predictions to be generated.

[0030] Another aspect of the disclosure provides a network node for performing measurements. The network node comprises processing circuitry configured to perform any of the steps of any of the embodiments described above; and power supply circuitry configured to supply power to the processing circuitry.

[0031] Certain embodiments may provide one or more of the following technical advantage(s). An advantage of the proposed solution is that it reduces the amount of measurements that the UE needs to perform which saves UE battery. The network still gets information from the UE when a certain event is fulfilled, but with less UE capacity being used. Alternatively, more measurements or measurement events can be configured when the UE capacity increases. The teachings of certain embodiments may improve the power consumption of the UE.

[0032] Embodiments of a base station, communication system, and a method in a communication system are also disclosed. Brief Description of the Drawings

[0033] The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain principles of the disclosure.

[0034] FIG.1 illustrates a sequence diagram for configuration and reporting of a measurement event based on measurements and predictions, according to aspects of the present disclosure.

[0035] FIG.2 is a flowchart illustrating a method implemented in a User Equipment for configuration and reporting of an event based on measurements and predictions, according to an embodiment.

[0036] FIG.3 is a flowchart illustrating a method implemented in a network node for configuration and reporting of an event based on measurements and predictions, according to an embodiment.

[0037] FIG.4 shows a communication system in accordance with some embodiments of the present disclosure.

[0038] FIG.5 shows a User Equipment in accordance with some embodiments of the present disclosure.

[0039] FIG.6 shows a network node in accordance with some embodiments of the present disclosure.

[0040] FIG.7 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized. Detailed Description

[0041] The embodiments set forth below represent information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure.

[0042] At least some of the following abbreviations and terms may be used in this disclosure. 2D Two Dimensional 3GPP Third Generation Partnership Project 5G Fifth GenerationAAS Antenna Array System AoA Angle of Arrival AoD Angle of Departure ASIC Application Specific Integrated Circuit BF Beamforming BLER Block Error Rate BW Beamwidth CPU Central Processing Unit CSI Channel State Information dB Decibel DCI Downlink Control Information DFT Discrete Fourier Transform DSP Digital Signal Processor eNB Enhanced or Evolved Node B FIR Finite Impulse Response FPGA Field Programmable Gate Array gNB New Radio Base Station ICC Information Carrying Capacity IIR Infinite Impulse Response LTE Long Term Evolution MIMO Multiple Input Multiple Output MME Mobility Management Entity MMSE Minimum Mean Square Error MTC Machine Type Communication NR New Radio OTT Over-the-Top PBCH Physical Broadcast Channel PDCCH Physical Downlink Control Channel PDSCH Physical Downlink Shared ChannelP-GW Packet Data Network Gateway RAM Random Access Memory ROM Read Only Memory RRC Radio Resource Control RRH Remote Radio Head SCEF Service Capability Exposure Function SINR Signal to Interference plus Noise Ratio TBS Transmission Block Size UE User Equipment ULA Uniform Linear Array URA Uniform Rectangular Array

[0043] Radio Node: As used herein, a “radio node” is either a radio access node or a wireless device.

[0044] Radio Access Node: As used herein, a “radio access node” or “radio network node” is any node in a radio access network of a cellular communications network that operates to wirelessly transmit and / or receive signals. Some examples of a radio access node include, but are not limited to, a base station (e.g., a New Radio (NR) base station (gNB) in a Third Generation Partnership Project (3GPP) Fifth Generation (5G) NR network or an enhanced or evolved Node B (eNB) in a 3GPP Long Term Evolution (LTE) network), a high-power or macro base station, a low-power base station (e.g., a micro base station, a pico base station, a home eNB, or the like), and a relay node.

[0045] Core Network Node: As used herein, a “core network node” is any type of node in a core network. Some examples of a core network node include, e.g., a Mobility Management Entity (MME), a Packet Data Network Gateway (P-GW), a Service Capability Exposure Function (SCEF), or the like.

[0046] Wireless Device: As used herein, a “wireless device” is any type of device that has access to (i.e., is served by) a cellular communications network by wirelesslytransmitting (and / or receiving) signals to (and / or from) a radio access node. Some examples of a wireless device include, but are not limited to, a User Equipment device (UE) in a 3GPP network and a Machine Type Communication (MTC) device.

[0047] Network Node: As used herein, a “network node” is any node that is either part of the radio access network or the core network of a cellular communications network / system.

[0048] Cell: As used herein, a “cell” is a combination of radio resources (such as, for example, antenna port allocation, time and frequency) that a wireless device may use to exchange radio signals with a radio access node, which may be referred to as a host node or a serving node of the cell. However, it is important to note that beams may be used instead of cells, particularly with respect to 5G NR. As such, it should be appreciated that the techniques described herein are equally applicable to both cells and beams.

[0049] Note that references in this disclosure to various technical standards (such as 3GPP TS 38.211 V15.1.0 (2018-03) and 3GPP TS 38.214 V15.1.0 (2018-03), for example) should be understood to refer to the specific version(s) of such standard(s) that is(were) current at the time the present application was filed, and may also refer to applicable counterparts and successors of such versions.

[0050] The description herein focuses on a 3GPP cellular communications system and, as such, 3GPP terminology or terminology similar to 3GPP terminology is oftentimes used. However, the concepts disclosed herein are not limited to a 3GPP system.

[0051] In the context of this 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), 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 stateconfigured 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.

[0052] In the context of this disclosure, the serving / neighbor cell may correspond to one or more of: A Radio Access Network (RAN) node A gNodeB (gNB) A 6G RAN node A Centralized Unit gNodeB e.g. a source gNB-CU in case of inter-CU, or simply CU in case of intra-CU. A distributed Unit gNodeB A Cloud-RAN centralized unit 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. A group of beams sharing the same group identity.

[0053] In the context of this disclosure, a measurement may correspond to one or more of: 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 responsibility of the RRC protocol, also called L3 in the Control Plane RAN protocol stack. 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)) 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: A measurement which may be associated with 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. A measurement of a cell (which may also be called cell quality or cell measurement result), wherein the measurement of a cell may be performed based on one or more beam measurements. 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 filtered. A measurement quantity, such as an RSRP and / or RSRQ and / or SINR and / or RSSI value in dB and / or dBm. 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 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 an 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

[0054] In the context of this disclosure, the term “ML-model” or “AI-model”, “Model Inference”, “Model Inference function” or “AI / ML model” are used interchangeably. An AI / ML model can be defined, in the context of this 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 output one or more predictions / estimates of a certain type (e.g. time-domain and / or spatial domain predictions of beam measurements).

[0055] In this disclosure, the term prediction is mainly used for spatial domain predictions. That means that measurements are performed on one cell and measurement results of another cell are predicted. In one 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 output the prediction of other reference signals transmitted in different beams e.g. reference signal Y (e.g. transmitted in beam-y of a cell), such as an SSB whose index is ‘y’. In another use case, the spatial domain prediction means that measurements are performed on partial beams of one cell and measurement results (e.g. cell level measurement results of this cell, or partial or all other beams of this cell) are predicted. In another use case, the spatial domain prediction means that measurements are performed on partial beams of more cells and measurement results (e.g. cell level measurement results of these two cells, or partial or all other beams of these two cells) are predicted.

[0056] The input to the prediction(s) may be beam measurements, cell level measurements, any of the measurements listed above, but other types of input may also be used such as positioning information, UE speed, network topology etc. The output of the prediction(s) is typically cell level measurements of a cell on another frequency.

[0057] The focus of this disclosure pertains to spatial domain predictions. However, the ordinarily skilled reader will recognize that the techniques disclosed herein can be readily adapted to performing frequency domain and time domain predictions. Techniques are disclosed herein that provide systems and methods for using spatial domain predictions in combination with actual measurements for triggering measurement events in user equipment. In some embodiments, a UE receives a configuration message from a network node containing a configuration of a measurement event that can be triggered based on predictions and actual measurements. The UE may perform measurements and generate spatial domain predictions, then monitor whether the configured event is fulfilled based on the combination of measured and predicted values. For example, event A3 may be extended to include scenarios where predicted RRMmeasurements of a neighbor cell become an amount of offset better than measured RRM values of a serving cell, or where measured quality of a neighbor becomes better than predicted quality of the serving cell. Similarly, event A5 may be configured such that measured quality of a serving cell becomes worse than a first threshold while predicted quality of a neighbor cell becomes better than a second threshold. Upon fulfillment of the measurement event based on this combination of measurements and predictions, the UE transmits a fulfillment indication to the network node. This approach may reduce the number of measurements the UE needs to perform, thereby conserving battery power while still providing the network with information about measurement events.Solution Overview

[0058] This disclosure contains possibilities for the network to configure a UE with AI model input related to spatial domain predictions to be used for triggering of a measurement event. This disclosure includes the UE replacing some of the measurements of serving cell or of neighboring cell with predictions. The measurements and predictions are used together to determine whether a certain event is fulfilled.

[0059] The focus of this disclosure is on spatial domain predictions, but frequency domain and temporal domain predictions can be used, if desired.

[0060] The focus of this disclosure is on event A3 and event A5, but the disclosed methods can be adapted for any measurement event. Configuration and reporting aspects and possible updates of performance requirements are also part of this disclosure. Configuration and reporting of measurement event based on spatial domain predictions (and / or measurements)

[0061] Figure 1 is a flowchart illustrating a procedure for configuration and reporting of an event (such as, for example, an event A3) based on measurements and predictions. In the illustrated embodiment, the predictions are in the spatial domain,but frequency domain and temporal domain predictions can also be performed in a corresponding manner. Referring to FIG.1, the procedure includes the following steps.

[0062] Step 1 (at 100): a network node (e.g. a gNB) sends a configuration message (e.g. RRCReconfiguration) to the UE. The configuration message contains a configuration of a measurement event, which can be triggered based on predictions and actual measurement values.

[0063] Step 2 (at 102): The UE may optionally transmit an acknowledgement message (e.g. RRCReconfigurationComplete) of the configuration to the network node. The acknowledgement message may include applicability information of the configuration(s) received from the network.

[0064] Step 3 (at 104): Responsive to the received configuration, the UE performs measurements and computes predictions. In the illustrated embodiment, the predictions are computed in the spatial domain, but frequency domain and / or temporal domain predictions can be computed, if desired.

[0065] Step 4 (at 106): The UE monitors whether the measurements and predictions satisfy the condition for triggering the configured event.

[0066] Step 5 (at 108): Upon fulfillment of the measurement event, transmitting fulfillment of the event to the network, e.g. in an RRC message, such as MeasurementReport or similar.

[0067] As may be seen in FIG.1, events are triggered based on a combination of both measurements and predictions. For this purpose, the conventional event definitions specified in 38.331 V18.3.0 (2024-09) and described above may be extended to include both measurements and computed predictions. Thus, for example, eventA3 may be extended to encompass any one or more of the following trigger conditions: The RRM prediction(s) (e.g., predicted RSRP, RSRQ, SINR etc) of a neighbour becomes amount of offset better than the RRM measurement (e.g., measured RSRP, RSRQ, SINR etc) of PCell / PSCell.The measured quality (e.g. measured RSRP, RSRQ, SINR etc) of a neighbour becomes an amount of offset better than the predicted quality ( e.g. predicted RSRP, RSRQ, SINR etc) of PCell / PSCell. The predicted quality (e.g. predicted RSRP, RSRQ, SINR) of a neighbour becomes amount of offset better than the predicted quality (e.g., predicted RSRP, RSRQ, SINR) of the PCell / PSCell.

[0068] In another example, eventA5 may be extended to encompass any one or more of the following trigger conditions: RRM measurements (e.g., measured RSRP, RSRQ, SINR etc.) of serving cell (PCell / PSCell) becomes worse than absolute threshold1 AND predicted RRM measurement values (e.g., predicted RSRP or RSRQ or SINR) of Neighbour / SCell becomes better than another absolute threshold2. RRM predictions (e.g., predicted RSRP, RSRQ, SINR etc) of serving cell (PCell / PSCell) becomes worse than absolute threshold1 AND RRM measurements (e.g., RSRP, RSRQ, SINR) of Neighbour / SCell becomes better than another absolute threshold2. PCell / PSCell prediction(s) become(s) worse than absolute threshold1 AND Neighbour / SCell prediction(s) become(s) better than another absolute threshold2.

[0069] In some embodiments, these extended trigger conditions are used to define a new type of event, namely a predicted event, such as a predicted eventA3 or a predicted eventA5, for example. The new type of event could comprise any of the options described below. The configuration may comprise offset, hysteresis, threshold, TTT (time to trigger) TTT etc. for the predicted event.

[0070] In some embodiments, the UE may initially transmit information to a network node related to UE capabilities for spatial domain predictions. The UE capability information may include any one or more of: UE capabilities related to amount of spatial predictions, e.g. the maximum amount of cells or beams that the UE can predict.UE capabilities related to the relation between amount of spatial predictions and actual measurements, e.g. the UE can predict one cell for one measured cell, or the UE can predict one cell for x number of measured cells. UE capabilities related to the predicted and measured cells / beams / etc., e.g. the maximum number of cells / beams that the UE can totally predict and measure.

[0071] The UE may also transmit UE assistance information to the network, before or after the message comprising the configuration (e.g. RRCReconfiguration), the UE assistance information may include any one or more of: Preferred cells / beams to be measured, preferred cells / beams to be predicted. Preferred configuration for the prediction. Model performance per cell / beam. A measure of distance between inference distribution and training distribution. This could be based on any agreed-on metric, like Jenson-Shannon distance. Latent variable representing Network-side additional condition(s) or configurations (e.g. associated IDs) for which the UE’s AI functionality(ies) is applicable.

[0072] The UE may also request, from the network, prediction assistance information, such as any one or more of : Tx Power. Updated synch signal. Neighbor Cell location information. Load (in case of event dependent on SINR). Extra reference signal on specific resource element (RE) to enable better prediction. Network-side additional conditions (e.g. associated IDs). Candidate cell(s) which the UE needs to implement spatial prediction. Measurement cell(s) for which the UE needs to perform measurements for the implementation of spatial prediction.Set A and Set B information of neighbour cells.

[0073] Figure 2 is a flowchart illustrating an example method implemented in a User Equipment (UE), for configuration and reporting of an event based on measurements and predictions. In the illustrated embodiment, the predictions are in the spatial domain, but frequency domain and temporal domain predictions can also be performed in a corresponding manner. Referring to FIG.2, the method includes the following steps.

[0074] Step 1 (200): receiving a configuration message, such as, for example RRCReconfiguration, from a network node, the configuration message contains a configuration of a measurement event, where the measurement event can be triggered based on predictions and measurements.

[0075] Optional Step 2 (at 202): The UE may transmit an acknowledgment message, such as RRCReconfigurationComplete for example, of the configuration to the network node. The acknowledge message may include applicability information of the configurations from the network.

[0076] Step 3 (204): Responsive to the received configuration, the UE performs measurements and generates predictions. In the illustrated embodiment, the predictions are in the spatial domain, but frequency domain and temporal domain predictions can also be performed.

[0077] Step 4 (206): The UE monitors whether the configured event is fulfilled, or equivalently, that the trigger condition(s) for the configured event is(are) satisfied.

[0078] Step 5 (208): Upon fulfillment of the measurement event, transmitting an indication that the configured event is fulfilled to the network node. For example, the UE may transmit, to the network node, an RRC message such as MeasurementReport containing the indication that the configured event is fulfilled.

[0079] In some embodiments, the configuration message may comprise the configuration of a predicted eventA3, as referred to above, and described in further details below:

[0080] Predicted EventA3, option 1: The RRM prediction(s) (e.g., predicted RSRP, RSRQ, SINR etc) of a neighbour becomes amount of offset better than the RRM measurement (e.g., measured RSRP, RSRQ, SINR etc) of PCell / PSCell. This means that the UE measures the quality of the PCell or PSCell, and generates spatial prediction(s) of a neighbour cell. The UE then compares the RRM prediction(s) of the neighbour cell with the measured quality of the serving PCell / PSCell. When the event triggering condition of the event is fulfilled for a given period of time (e.g., time to trigger), the event is fulfilled and the corresponding fulfillment indication is transmitted (FIG.2 at 208) to the network node.

[0081] Predicted EventA3, option 2: The measured quality (e.g. measured RSRP, RSRQ, SINR etc) of a neighbour becomes an amount of offset better than the predicted quality ( e.g. predicted RSRP, RSRQ, SINR etc) of PCell / PSCell. In this option, the UE measures the quality of the neighbour cell, and compares it with the predicted quality of the serving cell. In this case, spatial domain prediction is used to predict the quality of the serving cell. The UE may for example use measurements of a different cell to predict the quality of the serving cell. Alternatively, the UE may measure the quality of some beams of the serving cell, and predict the quality of other beams of the same serving cell. In a further alternative, the UE may measure the quality of some beams of the serving cell, predict the quality of other beams of the serving cell, and then derive a predicted quality of the serving cell as a whole based on both the measured and predicted quality values. As a yet further alternative, the UE may measure the quality of some beams of the serving cell and generate a predicted quality of the serving cell based only on the measured quality value(s) .

[0082] Predicted EventA3, option 3: The predicted quality (e.g. predicted RSRP, RSRQ, SINR) of a neighbour becomes amount of offset better than the predicted quality (e.g., predicted RSRP, RSRQ, SINR) of the serving cell (PCell / PSCell). In this option, the UE may predict the quality of a neighbouring cell, and compare the predicted quality of the neighbour cell with the predicted quality of the serving cell. For example, the UE may perform measurements of a different cell and use themeasurements as input to the ML-model to generate spatial domain predictions of the serving cell and / or the neighbour cell. Alternatively, the UE may measure the quality of some beams of the serving cell and neighbor cell, and predict the quality of other beams of the same cells, based on the measured quality values. In a further alternative, the UE may measure the quality of some beams of the serving cell and / or neighbour cell, predict the quality of other beams of the serving cell and / or neighbour cell, and then derive a predicted quality of the serving cell and / or neighbour cell as a whole based on both the measured and predicted quality values. As a yet further alternative, the UE may measure the quality of some beams of the serving cell and / or neighbour cell and generate a predicted quality of the serving cell and / or neighbour cell based only on the measured quality value(s).

[0083] In some embodiments, the configuration message may include the configuration of a predicted eventA5, as referred to above, and described in further details below:

[0084] Predicted EventA5, option 1: Measured quality (e.g., measured RSRP, RSRQ, SINR etc.) of serving PCell / PSCell becomes worse than absolute threshold1 AND predicted quality (e.g., predicted RSRP or RSRQ or SINR) of Neighbour / SCell becomes better than another absolute threshold2. In this option, the UE measures the quality of the serving cell (e.g. a PCell, PSCell, or another cell), and generates spatial domain predictions of a neighbour cell based on the measured values. The UE then compares the predicted quality of the neighbour cell with the measured quality of the serving cell. When the event is fulfilled i.e., the predicted quality of the neighbour cell is better than threshold2 and the measured quality of the serving PCell / PSCell is worse than threshold1, the event is triggered.

[0085] Predicted EventA5, option 1A: Spatial prediction at cell level: For example, the UE may be configured to perform quality measurements on a neighboring frequency, and detects one or more neighboring cells in that frequency. The UE uses the measurements of the neighboring cells in the neighboring frequency as inputs to predict the quality of the detected one or more cells in that neighboring frequency.

[0086] Predicted EventA5, option 1B Spatial prediction at beam level: For example, the UE may be configured to perform measurements on a neighboring frequency, and detects one or more reference signals (e.g. SSB or CSI-RS reference signals) of one or more neighbor cells in the neighboring frequency. The UE uses the measurements of the SSB or CSI-RS resources of the detected cell(s) to predict the quality of one or more reference signals (e.g. SSB or CSI-RS reference signals) in that neighboring frequency. The UE uses the predicted quality of the detected one or more reference signals in the neighboring frequency to predict the cell quality of the neighboring cell, and thus evaluate the A5 entry condition based on the predicted cell quality.

[0087] In the above embodiments, the network may use SMTC configuration to steer the UE to perform the measurements for target reference signals. The network may also indicate which other reference signals should be predicted by the UE.

[0088] Predicted EventA5, option 2: predicted quality (e.g., predicted RSRP, RSRQ, SINR etc) of a serving cell (e.g. a PCell or a PSCell) becomes worse than absolute threshold1 AND measured quality (e.g., measured RSRP, RSRQ, SINR etc) of a Neighbour / SCell becomes better than another absolute threshold2. In this option, the UE predicts the quality of the PCell or PSCell, and performs measurements of a neighbouring cell. The UE then compares the measured quality of the neighbour cell with the predicted quality of the serving cell (PCell / PSCell). When the trigger condition of the event is fulfilled (e.g., the quality of the measured neighbour cell is better than threshold2 and the quality of the predicted PCell / PSCell is worse than threshold1), the event is triggered. The UE may for example use measurements of a different cell to predict the quality of the serving cell. Alternatively, the UE may measure some beams of the serving cell and predict other beams of the serving cell. In a further alternative, the UE may measure the quality of some beams, predict the quality of other beams, and derive the quality of the serving cell based on the measured and predicted quality values.

[0089] Predicted EventA5, option 3: Predicted PCell / PSCell becomes worse than absolute threshold1 AND predicted Neighbour / SCell becomes better than another absolute threshold2. In this option, the UE predicts the quality of the PCell or PSCell and predicts the quality of a neighbour cell. The UE then compares the predicted quality of the neighbour cell with the predicted quality of the serving PCell / PSCell. When the quality of the predicted neighbour cell is better than threshold2 and the quality of the predicted PCell / PSCell is worse than threshold1, the event is triggered. The UE may for example use measurements of a different cell to predict the quality of the serving cell or of the neighbour cell. Alternatively, the UE may measure some beams of the serving cell or of the neighbour cell, and predict the quality of other beams of the serving cell or of the neighbour cell based on the measured quality values. In a further alternative, the UE may measure the quality of some beams, predict the quality of other beams, and then predict the quality of the serving cell or of the neighbour cell based on the measured and predicted quality values. In a yet further alternative, the UE may measure the quality of some beams, and then predict the quality of the serving cell or of the neighbour cell based on the measured quality values.

[0090] In another option, the configuration may include the configuration of any other type of event, in a similar way as described above for event A3 or event A5.

[0091] In an embodiment, the UE performs a combination of measurements and predictions for a number of cells, where some samples are measured and some samples are predicted. In any of the options above, the predicted quality may be based on a combination of predicted samples and measured samples.

[0092] The configuration of any of these events, may be included in RRC message reportConfig or any other suitable configuration message.

[0093] In an embodiment, the configuration may comprise an indication, indicating whether the UE is allowed to perform spatial domain prediction instead of performing actual measurements. This may be indicated for each type of event or for a specific subcase of an event such as the three cases listed above. Alternatively, this may be indicated e.g. for each measObject, where the UE is allowed to perform spatial domainpredictions for a certain carrier, or in another example the UE is not allowed to perform spatial domain predictions for a certain carrier. This means that there could be triggered cells in that carrier as part of the event evaluation that were not measured, but were predicted. The indication may be indicated per frequency, frequency band, band combination, frequency range, etc.

[0094] In an embodiment, the network indicates a list of cells for which the UE is allowed to perform spatial predictions, or in another example the UE is not allowed to perform spatial predictions. In some variants, the network may indicate cells for which the UE shall perform predictions or cells which the UE shall measure. Lack of an indication of that the UE shall measure, may mean that the UE is allowed to predict.

[0095] In an embodiment, the amount of predicted samples and the amount of measured samples may be indicated. It could e.g., be indicated that every second sample may be predicted and every second sample may be measured or that two out of three samples can be predicted. As an alternative, the UE can be configured with a parameter indicating how frequently the UE needs to perform actual measurements.

[0096] In an embodiment, the maximum number of predictions or minimum number of measured cells may be indicated.

[0097] In an embodiment, the UE may receive an indication, indicating that predictions are allowed if the accuracy is above a certain level.

[0098] In an embodiment, the relationship between actual measurements and predictions may be indicated by priorities, where a higher priority means that for example a certain cell has a higher priority related to actual measurements whereas other cells may be predicted.

[0099] In another option, the configuration may comprise an indication of whether using prediction results to replace measurement results for measurement event detection and triggering is allowed, if one or more conditions are met, e.g. if there are available prediction results which meet the configured or predefined performance requirements.

[0100] In an embodiment, the configuration of a measurement event partly or fullybased on spatial domain predictions may imply that the UE requirement on the number of cells / beams that the UE is required to measure may increase. A new minimum number indicating the total number of measured and predicted cells / beams that the UE is required to measure and predict may be defined. Alternatively, a new requirement for the total number of cells / beams that the UE is required to perform spatial predictions for is defined, for the case that the UE has the required UE capabilities for spatial domain predictions. In an embodiment, the UE requirement on the number of cells / beams that the UE is required to measure remains the same and the UE capacity increases when some of the cells / beams can be predicted.

[0101] In the case of eventA3 or predicted eventA3, the report indicatingfulfillment of the event may include one or more of the following: The fulfillment of the event, e.g. based on: a) The predicted quality of a neighbour becomes amount of offset better thanPCell / PSCell. b) The quality of a neighbour becomes amount of offset better than thepredicted quality of the PCell / PSCell. c) The predicted quality of a neighbour becomes amount of offset better thanthe predicted quality of the PCell / PSCell. The variance of the prediction of neighbor cell, which may be defined as : The loss orthe neighbor cell. In some embodiments, this may be represented as a loss of neighbour cell prediction, for example the Mean Absolute Error could be a measure of loss, defined as the mean of absolute different between prediction and groundtruth. Alternatively loss of accuracy of the prediction of the neighbor cell may be represented as the Accuracy of event A3 of neighbour cell prediction, such as the number of correctly predicted A3divided by total number of predictions: Assistant information used in prediction, such as TA, location, speed, etc.. Meta information used in prediction, such as assistant information, or reasoning, relations, and proposition among features of the model. Correlation between partial set of inference distribution compared to training sample distribution.

[0102] In the case of eventA5 or predicted eventA5, the report indicatingfulfillment of the event may include one or more of the following: fulfillment of the event, e.g. based on: a) PCell / PSCell becomes worse than absolute threshold1 AND predictedNeighbour / SCell becomes better than another absolute threshold2. b) Predicted PCell / PSCell becomes worse than absolute threshold1 ANDNeighbour / SCell becomes better than another absolute threshold2. c) Predicted PCell / PSCell becomes worse than absolute threshold1 ANDpredicted Neighbour / SCell becomes better than another absolute threshold2. The variance of the prediction of neighbor cell, which may be defined as :The loss orof the neighbor cell. In some embodiments, this may be represented as a loss of neighbour cell prediction, for example the Mean Absolute Error could be a measure of loss, defined as the mean of absolute different between prediction and groundtruth. Alternatively loss of accuracy of the prediction of the neighbor cell may be represented as the Accuracy of event A5 of neighbour cell prediction, such as the number of correctly predicted A5divided by total number of predictions: Assistant information used in prediction, such as TA, location, speed, etc.. Meta information used in prediction, such as assistant information, or reasoning, relations, and proposition among features of the model.Correlation between partial set of inference distribution compared to training sample distribution.

[0103] Alternatively, the report may indicate fulfillment of any other type of event or predicted event, where part of the fulfilment is based on spatial domain predictions.

[0104] In an embodiment, the predicted event may be based on a combination of predictions and measurements. The amount of prediction and the amount of measurements may be indicated to the network.

[0105] In an embodiment, the UE may indicate whether the event is fulfilled based on measurements or based on predictions or based on a combination of measurements and predictions.

[0106] The UE may indicate to the network a confidence score of the predicted event.

[0107] In some embodiments, the UE may indicate that it was not able to perform predictions according to the received configuration. This may include e.g. signaling a cause value for not being able to perform predictions, e.g. that the accuracy does not fulfil the requirements.

[0108] Figure 3 is a flowchart illustrating an example method implemented in a network node, such as a gNB, for configuration and reporting of an event based on measurements and predictions. In the illustrated embodiment, the predictions are in the spatial domain, but frequency domain and temporal domain predictions can also be performed in a corresponding manner. Referring to FIG.3, the procedure includes the following steps.

[0109] Step 1 (300): Transmitting a configuration message, e.g. RRCReconfiguration, to a UE. The message contains a configuration of a measurement event, where the measurement event can be triggered based on predictions in combination with actual measurements.

[0110] Step 2 (at 302): The network node may optionally receive an acknowledge message, e.g. RRCReconfigurationComplete, of the configuration from a UE. The acknowledge message may include applicability information of the configuration from the network.

[0111] Step 3 (at 304): The network node may subsequently receiving a message indicating fulfillment of the event from the UE, e.g. in an RRC message such as an RRC MeasurementReport or similar.

[0112] In the illustrated embodiment, the predictions are in the spatial domain, but frequency domain and temporal domain predictions can also be performed in a corresponding manner.

[0001] Figure 4 shows an example of a communication system 400 in accordance with some embodiments.

[0002] 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 network nodes 410a and 410b (one or more of which may be generally referred to as network nodes 410), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points. Moreover, as will be appreciated by those of skill in the art, a network node 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.

[0003] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near- real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an 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. The network nodes 410 facilitate direct or indirect connection of 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.

[0004] 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.

[0005] The 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 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.

[0006] In the depicted example, the core network 406 connects the network nodes 410 to one or more host computing systems, 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 UEs, 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).

[0007] 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. The host 416 may host a variety of applications to provide one or more service. Examples of such applications include live and pre- recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analyticsfunctionality, 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.

[0008] As a whole, the communication system 400 of Figure 4 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[0009] 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 IoT services to yet further UEs.

[0010] 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- RAT or multi-standard mode. For example, a UE may operate with any one orcombination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio – Dual Connectivity (EN-DC).

[0011] In the example, 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 network nodes (e.g., network node 410b). In some examples, the hub 414 may be a controller, router, content source and analytics, 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 VR device, 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 IoT devices.

[0012] 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 an M2M service provider over the access network 404 and / or to another UE over a direct connection. In somescenarios, 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.

[0013] Figure 5 shows a UE 500 in accordance with some embodiments. The UE 500 presents additional details of some embodiments of the UE 412 of Figure 1. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage / playback device, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop- embedded equipment (LEE), laptop-mounted equipment (LME), an Augmented Reality (AR) or Virtual Reality (VR) device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0014] A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user(e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0015] 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 Figure 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.

[0016] 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).

[0017] 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 mayinclude 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.

[0018] 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.

[0019] 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.

[0020] 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 opticaldisc drive, holographic digital data storage (HDDS) optical disc drive, external mini- dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 510 may allow the UE 500 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 510, which may be or comprise a device- readable storage medium.

[0021] 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.

[0022] In the illustrated embodiment, 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 globalpositioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0023] 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).

[0024] As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0025] A UE, when in the form of an Internet of Things (IoT) 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 IoT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a homesecurity 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 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 IoT device comprises circuitry and / or software in dependence of the intended application of the IoT device in addition to other components as described in relation to the UE 500 shown in Figure 5.

[0026] As yet another specific example, in an IoT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0027] 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.

[0028] Figure 6 shows a network node 600 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU).

[0029] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0030] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E- SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0031] The network node 600 includes a processing circuitry 602, a memory 604, a communication interface 606, and a power source 608. The network node 600 may becomposed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 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 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 network node 600 may also include multiple sets of the various illustrated components for different wireless technologies integrated into 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 network node 600.

[0032] 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 network node 600 components, such as the memory 604, to provide network node 600 functionality.

[0033] 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.

[0034] 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 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.

[0035] The communication interface 606 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 606 comprises port(s) / terminal(s) 616 to send and receive data, for example to and from a network over a wired connection. 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 orUEs 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.

[0036] In certain alternative embodiments, the 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).

[0037] 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 network node 600 through an interface or port.

[0038] 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 anytransmitting 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.

[0039] The power source 608 provides power to the various components of 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 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.

[0040] Embodiments of the network node 600 may include additional components beyond those shown in Figure 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 network node 600 may include user interface equipment to allow input of information into the network node 600 and to allow output of information from the network node 600. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 600. In some embodiments providing a core network node, such as core network node 108 of FIG.4, some components, such as the radio front-end circuitry 618 and the RF transceiver circuitry 612 may be omitted.

[0041] Figure 7 is a block diagram illustrating a virtualization environment 700 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices whichmay 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 a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 700 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface. Virtualization may facilitate distributed implementations of a network node, UE, core network node, or host.

[0042] 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 Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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 signaling can be provided with the use of a control system712 which may alternatively be used for communication between hardware nodes and radio units.

[0047] Although the computing devices described herein (e.g., UEs, network nodes) 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.

[0048] 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 ina 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.EMBODIMENTS Group A Embodiments 1. A method performed by a user equipment for performing measurements, the method comprising: receiving, from a network node, a configuration message containing a configuration of a measurement event, wherein the measurement event can be triggered based on predictions and optionally actual measurements; responsive to the received configuration, performing measurements and / or predictions; and upon fulfillment of the measurement event, transmitting a fulfillment indication to the network node.

[0114] 2. A method as defined in embodiment 1, wherein the predictions are any one or more of special domain predictions, Frequency domain predictions and temporal domain predictions.

[0115] 3. A method as defined in embodiment 1, wherein the measurement event configuration comprises the configuration of an event A3, and wherein the event A3 is defined as one of: predicted RRM measurements (e.g., predicted RSRP, RSRQ, SINR) of a neighbour cell becomes amount of offset better than the RRM measurement (e.g., RSRP, RSRQ, SINR) of serving cell (PCell / PSCell). a quality i.e., RRM measurement (e.g., RSRP, RSRQ, SINR) of a neighbour becomes amount of offset better than the predicted quality i.e., predicted RRM measurement (e.g., RSRP, RSRQ, SINR) of the serving cell (PCell / PSCell). a predicted quality i.e., predicted RRM measurement (e.g., RSRP, RSRQ, SINR) of a neighbour becomes amount of offset better than the predicted quality i.e., predicted RRM measurement (e.g., RSRP, RSRQ, SINR) of the serving cell (PCell / PSCell).

[0116] 4 A method as defined in embodiment 1, wherein the measurement event configuration comprises the configuration of an event A5, and wherein the event A5 is defined as one of: RRM measurements (e.g., RSRP, RSRQ, SINR) of serving cell (PCell / PSCell) becomes worse than absolute threshold1 AND predicted RRM measurements (e.g., predicted RSRP or RSRQ or SINR) of Neighbour / SCell becomes better than another absolute threshold2. predicted RRM measurements (e.g., predicted RSRP or RSRQ or SINR) of serving cell (PCell / PSCell) becomes worse than absolute threshold1 AND RRM measurements (e.g., RSRP, RSRQ, SINR) of Neighbour / SCell becomes better than another absolute threshold2. predicted PCell / PSCell becomes worse than absolute threshold1 AND predicted Neighbour / SCell becomes better than another absolute threshold2. Group B Embodiments

[0117] 5 A method performed by a network node, the method comprising: transmitting, to a user equipment (UE), a configuration message containing a configuration of a measurement event, where the measurement event can be triggered based on predictions and optionally actual measurements; and subsequently receiving, from the UE, a message indicating fulfillment of the event.

[0118] 6. A method as defined in embodiment 5, wherein the predictions are any one or more of special domain predictions, Frequency domain predictions and temporal domain predictions. Group C Embodiments

[0119] 7. A user equipment for performing measurements, comprising: processing circuitry configured to perform any of the steps of any of the Group A embodiments; andpower supply circuitry configured to supply power to the processing circuitry.

[0120] 8. A network node for performing measurements, the network node comprising: processing circuitry configured to perform any of the steps of any of the Group B embodiments; power supply circuitry configured to supply power to the processing circuitry.

[0121] 9. A user equipment (UE) for performing measurements, the UE comprising: an antenna configured to send and receive wireless signals; radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; the processing circuitry being configured to perform any of the steps of any of the Group A embodiments; an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry; an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and a battery connected to the processing circuitry and configured to supply power to the UE.

[0122] While processes in the figures may show a particular order of operations performed by certain embodiments of the present disclosure, it should be understood that such order is representative, and that alternative embodiments may perform the operations in a different order, combine certain operations, overlap certain operations, etc.

[0123] Those skilled in the art will recognize improvements and modifications to the embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein.

Claims

Claims What is claimed is:

1. A method performed by a user equipment (UE) for performing measurements, the method comprising: receiving, from a network node, a configuration message containing a configuration of a measurement event, wherein the measurement event is triggered based on predictions in combination with measurements; responsive to the received configuration, performing measurements and generating predictions based on the measurements; monitoring whether the configured measurement event is fulfilled based on a combination of the performed measurements and the generated predictions; and upon fulfillment of the measurement event, transmitting a fulfillment indication to the network node.

2. The method of claim 1, wherein performing measurements comprises measuring a quality of at least one beam of a first cell.

3. The method of claim 2, wherein measuring the quality of at least one beam of the first cell comprises measuring any one or more of RSRP, RSRQ, SINR, and RSSI of the at least one beam.

4. The method of claim 2, wherein generating predictions comprises using the measured value of the at least one beam of the first cell to predict a quality of at least one other beam.

5. The method of claim 4, wherein the predicted quality of the at least one other beam comprises a predicted value of any one or more of RSRP, RSRQ, SINR, and RSSI of the at least one other beam.

6. The method of claim 4, wherein the at least one other beam is comprised within the first cell or within a second cell different than the first cell.

7. The method of claim 4, wherein generating predictions further comprises using the measured value of the at least one beam of the first cell and the predicted quality of the at least one other beam to predict a quality of the first cell or a second cell different than the first cell.

8. The method of any one of claims 1 to 7, wherein the predictions comprise spatial domain predictions, frequency domain predictions, temporal domain predictions, or any combination thereof.

9. The method of claim 2, wherein the first cell is a serving cell, and wherein the measurement event comprises a predicted event A3, the predicted event A3 being defined as one of: a predicted quality of a neighbour cell becomes an amount of offset better than measured quality of the serving cell; a measured quality of the neighbour cell becomes an amount of offset better than predicted quality of the serving cell; or a predicted quality of the neighbour cell becomes an amount of offset better than predicted quality of the serving cell.

10. The method of claim 2, wherein the first cell is a serving cell, and wherein the measurement event comprises a predicted event A5, the predicted event A5 being defined as one of:a measured quality of the serving cell becomes worse than a first absolute threshold and a predicted quality of a neighbour cell becomes better than a second absolute threshold; a predicted quality of the serving cell becomes worse than a first absolute threshold and a measured quality of the neighbour cell becomes better than a second absolute threshold; or a predicted quality of the serving cell becomes worse than a first absolute threshold and a predicted quality of the neighbour cell becomes better than a second absolute threshold.

11. The method of claim 4, wherein generating predictions comprises using an artificial intelligence model or machine learning model to predict the quality of the at least one other beam based on the measured value of the at least one beam of the first cell.

12. The method of claim 1, wherein the configuration message comprises an indication of whether the UE is allowed to perform spatial domain predictions instead of performing actual measurements for specific cells or frequencies.

13. The method of claim 1, wherein the fulfillment indication comprises information indicating whether the measurement event was fulfilled based on measurements, predictions, or a combination of measurements and predictions.

14. The method of claim 1, wherein the fulfillment indication comprises a confidence score associated with the predicted measurements used to determine fulfillment of the measurement event.

15. The method of claim 1, further comprising: transmitting, to the network node, capability information indicating UE capabilities related to spatial domain predictions, including a maximum number of cells or beams that the UE can predict.

16. The method of claim 1, wherein the configuration message further comprises parameters indicating a relationship between a number of measurements to be performed and a number of predictions to be generated.

17. A user equipment for performing measurements, comprising: processing circuitry configured to perform any of the steps of any of claims 1 to 16; and power supply circuitry configured to supply power to the processing circuitry.

18. A method performed by a network node, the method comprising: transmitting, to a user equipment (UE), a configuration message containing a configuration of a measurement event, where the measurement event can be triggered based on predictions and optionally actual measurements; and subsequently receiving, from the UE, a message indicating fulfillment of the event.

19. The method of claim 18, wherein the predictions are any one or more of special domain predictions, frequency domain predictions and temporal domain predictions.

20. The method of claim 18, wherein the configuration message comprises an indication of whether the UE is allowed to perform spatial domain predictions instead of performing actual measurements for specific cells or frequencies.

21. The method of claim 18, further comprising: receiving, from the UE, capability information indicating UE capabilities related to spatial domain predictions, including a maximum number of cells or beams that the UE can predict.

22. The method of claim 18, wherein the configuration message further comprises parameters indicating a relationship between a number of measurements to be performed and a number of predictions to be generated.

23. A network node for performing measurements, comprising: processing circuitry configured to perform any of the steps of any of claims 18 to 22; and power supply circuitry configured to supply power to the processing circuitry.

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