Data collection for ai-based RLF model

Abstract

According to some embodiments, a method is performed by a wireless device. The method comprises receiving a configuration from a network node. The configuration comprises an indication of assistance data the wireless device is to collect to be used with a radio link failure machine learning model. The method further comprises collecting the assistance information according to the received configuration and transmitting a report including the collected assistance information to the network node.

Description

Data Collection for AI-Based RLF ModelTECHNICAL FIELD

[0001] The present disclosure generally relates to communication networks, and more specifically to data collection for artificial intelligence-based radio link failure training and inference.BACKGROUND

[0002] In fifth generation (5G) wireless networks, a user equipment (UE) may detect radio link failure (RLF). RLF happens when the wireless radio connection between a network and the UE is degraded or lost, typically due to reasons like signal interference, distance or obstacles. The radio link interruption may lead to dropped calls, slow data speed, or complete loss of connectivity. In a 5G network, the UE triggers RLF for the following scenarios.• Scenario#! (‘t310-Expiry’): Timer T310 expiry• Scenario#2-1 (‘beamFailureRecoveryFailure’): Random access problem indication from master cell group (MCG) medium access control (MAC) if the random access procedure was initiated for beam failure recovery• Scenario#2-2 (‘randomAccessProblem’): Random access problem indication from MCG MAC for other cases• Scenario#3 (‘rlc-MaxNumRetx’): Reaching a maximum number of retransmissions from the MCG Radio Link Control (RLC)• Scenario#4 (TbtFailure’): Consistent uplink listen before talk (LBT) failures• Scenario#5 (‘bh-rlfRecoveryFailure’): Reception of a backhaul (BH) RLF indication on backhaul adaptation protocol (BAP) entity• Scenario#6 (‘t312-Expiry’): Timer T312 expiry

[0003] Figure 1 illustrates RLF in Scenario#! and Scenario#6. In Scenario#!, as shown in Figure 1, for the detection of physical layer problems in RRC CONNECTED, one UE starts Timer T310 for its source special cell (SpCell) if receiving N310 consecutive ‘out-of-sync’ indications for that SpCell from lower layers and Timer T304 is running and moreover if any dual active protocol stack (DAPS) bearer is configured, or the UE starts Timer T310 for one SpCell if receiving N310 consecutive ‘out-of-sync’ indications for the SpCell from lower layers while neither Timer T300, T301, T304, T311, T316 nor T319 are running. When the UE receives N311 consecutive ‘in-sync’ indications for the SpCell from lower layers while Timer T310 is running,the UE shall stop Timer T310 for the corresponding SpCell and stop Timer T312 for the corresponding SpCell if runnng. The value range is (0, 50ms, 100ms, 200ms, 500ms, 1000ms, 2000ms) for T310, (1, 2, 3, 4, 5, 6, 7, 8, 10, 20) for n310, (1, 2, 3, 4, 5, 6, 8, 10) for n311, and (0, 50ms, 100ms, 200ms, 300ms, 400ms, 500ms, 1000ms) for T312.

[0004] In Scenario#2-1, the UE triggers RLF report with the cause information of “beamFailureRecoveryFailure” if there is random access problem indication from MCG MAC and moreover the random access procedure was initiated for beam failure recovery. In Scenario#2-2, the UE triggers RLF report with the cause information of “randomAccessProblem” if there is random access problem indication from MCG MAC for other cases.

[0005] In Scenario#3 for reaching a maximum number of retransmissions from the MCG RLC, the UE retransmits the same uplink RLC protocol data unit (PDU) for the maximum number of retransmissions minus one due to the received negative acknowl edement (NACK) for the initial transmission and retransmissions of the PDU. The uplink RLC retransmission may be due to channel interference, transmission errors, network congestion, UE movement or others.

[0006] The Scenario#4 for consistent uplink LBT failures is considered for unlicensed spectrums. The Scenario#5 for the reception of a BH RLF indication on BAP entity is considered for integrated access and backhual (IAB). In Scenario#6 for T312 expiry, UE declares radio link failure and sets the rlf-Cause to t312-Expiry.

[0007] Scenario#! is related to downlink quality. Scenario#2-1 and Scenario #2-2 are related to downlink quality and / or uplink quality. Scenario#3 is related to uplink quality.

[0008] If the network configures the UE to report RLF information, and if the UE has radio link failure information or handover failure information available in VarRLF -Report, and if registered public land mobile network (RPLMN) is included in plmn-IdentityList (or current registered standalone non-public network (SNPN) is included in snpn-IdentityList) stored in VarRLF-Report message in Radio Resource Control (RRC) signaling, then the UE may need to set part of the following information.• timeSinceFailure in VarRLF-Report: the elapsed time since the last radio link failure or handover failure in New Radio (NR) or evolved universal terrestrial radio access (EUTRA)• rlf-Report in the UEInformationResponse message: value of rlf-Report in VarRLF-Report• failedPCellld-EUTRA in the rlf-Report in the UEInformationResponse message: indicate the PCell in which RLF was detected or the source PCell of the failed handover in the VarRLF-Report• measResult-RLF-Report-EUTRA in the rlf-Report in the UEInformationResponse message: value of rlf-Report in VarRLF-Report

[0009] Additionally, the UE needs to discard the rlf-Report from VarRLF-Report upon successful delivery of the UEInformationResponse message confirmed by lower layers.

[0010] Depending on cases and configuration, the RLF report information in VarRLF-Report message may include part of the following information.• snpn-IdentityList: List of equivalent SNPNs (i.e. registered standalone non-public network (SNPN)) if the UE is in SNPN access mode.• plmn-IdentityList: List of equivalent public land mobile networks (EPLMNs) (i.e. RPLMN) if the UE is not in SNPN access mode.• measResultLastServCell: Cell-level reference signal receive power (RSRP), reference signal receive quality (RSRQ), available signal to interference and noise ratio (SINR) of the source primary cell (PCell) (for handover failure) or PCell (for RLF) based on the available synchronization signal / physical broadcast channel (PBCH) block (SSB) and channel state information reference signal (CSLRS) measurements collected up to the moment the UE detected failure.• measResultLastServCell-RSSI: Linear average of the available received signal strength indicator (RS SI) sample value(s) for the frequency of source PCell (for handover failure) or PCell (for RLF) up to the moment the UE detected the failure, if configured.• rsIndexResults in measResultLastServCell : All the available measurement quantities of the source PCell (for handover failure) or PCell (for RLF), ordered such that the highest SSB RSRP is listed first if SSB RSRP measurement results are available, otherwise the highest SSB RSRQ is listed first if SSB RSRQ measurement results are available, otherwise the highest SSB SINR is listed first, based on the available SSB based measurements collected up to the moment the UE detected failure, if SSB-based measurement quantities are available.• rsIndexResults in measResultLastServCell : All the available measurement quantities of the source PCell (for handover failure) or PCell (for RLF), ordered such that the highest CSL RS RSRP is listed first if CSLRS RSRP measurement results are available, otherwise the highest CSLRS RSRQ is listed first if CSLRS RSRQ measurement results are available, otherwise the highest CSLRS SINR is listed first, based on the available CSLRS based measurements collected up to the moment the UE detected failure, if CSLRS-based measurement quantities are available.

[0011] For each configured measObjectNR in which measurements are available, the RLF report information in VarRLF-Report message may include part of the following information.• measResultListNR in measResultNeighCells: All the available measurement quantities of the best measured cells, other than the source PCell (for handover failure) or PCell (for RLF), ordered such that the cell with highest SSB RSRP is listed first if SSB RSRP measurement results are available, otherwise the cell with highest SSB RSRQ is listed first if SSB RSRQ measurement results are available, otherwise the cell with highest SSB SINR is listed first, based on the available SSB based measurements collected up to the moment the UE detected failure, if S SB-based measurement quantities are available. Note for each neighbour cell included, include the optional fields that are available.• measResultListNR in measResultNeighCells: All the available measurement quantities of the best measured cells, other than the source PCell (for handover failure) or PCell (for RLF), ordered such that the cell with highest CSLRS RSRP is listed first if CSLRS RSRP measurement results are available, otherwise the cell with highest CSLRS RSRQ is listed first if CSLRS RSRQ measurement results are available, otherwise the cell with highest CSLRS SINR is listed first, based on the available CSLRS based measurements collected up to the moment the UE detected radio link failure, if CSLRS-based measurement quantities are available. Note for each neighbour cell included, include the optional fields that are available. Note that for ordering the neighboring cells based on the CSLRS measurement quantities, UE includes measurements only for the cells not yet included in measResultListNR in measResultNeighCells to avoid overriding SSB-based ordered measurements.• For each neighbor cell, if any, included in measResultListNR in measResultNeighCells, if the UE supports RLF -Report for conditional handover and if the neighbor cell is one of the candidate cells for which the reconfigurationWithSync is included in the masterCellGroup in the MCG VarConditionalReconfig at the moment of the detected failure:■ choConfig in MeasResult2NR: Execution condition for each measld within condTriggerConfig associated to the neighbor cell within the MCG VarConditionalReconfig.■ firstTriggeredEvent: Execution condition condFirstEvent corresponding to the first entry of choConfig or to the execution condition condSecondEvent corresponding to the second entry of choConfig, whichever execution condition was fulfilled first in time, if the first entry of choConfig corresponds to a fulfilled execution condition at the moment of handover failure, or radio link failure, or if the second entry of choConfig, if available,corresponds to a fulfilled execution condition at the moment of handover failure, or radio link failure.■ timeBetweenEvents: Elapsed time between the point in time of fulfilling the condition in choConfig that was fulfilled first in time, and the point in time of fulfilling the condition in choConfig that was fulfilled second in time, if both the first execution condition corresponding to the first entry and the second execution condition corresponding to the second entry in the choConfig were fulfilled, if the first entry of choConfig corresponds to a fulfilled execution condition at the moment of handover failure, or radio link failure, or if the second entry of choConfig, if available, corresponds to a fulfilled execution condition at the moment of handover failure, or radio link failure.

[0012] The RLF report information in VarRLF-Report message may further include part of the following information.• measResultNeighFreq-RSSI in the measResultNeighFreqList-RSSE Linear average of the available RSSI sample value(s) provided by lower layers for the frequencies other than the frequency of the source PCell (for handover failure) or of the PCell (for RLF), up to the moment the UE detected failure.• measResultListEUTRA in measResultNeighCells: The best measured cells ordered such that the cell with highest RSRP is listed first if RSRP measurement results are available, otherwise the cell with highest RSRQ is listed first, and based on measurements collected up to the moment the UE detected failure. Note for each neighbor cell included, include the optional fields that are available.• c-RNTL C-RNTI used in the source PCell (for handover failure) or PCell (for RLF).• connectionFailureType: rlf• rlf-Cause: The trigger for detecting radio link failure• nrFailedPCellld in failedPCellld: Global cell identity and the tracking area code, if available, and otherwise to the physical cell identity and carrier frequency of the PCell where radio link failure is detected;

[0013] If an RRCReconfiguration message including the reconfigurationWithSync was received before the connection failure, the RLF report information in VarRLF-Report message may further include part of the following information.

[0014] For example, if the last successfully executed RRCReconfiguration message including the reconfigurationWithSync concerned an intra NR handover and it was received while connected to the previous PCell to which the UE was connected before connecting to the PCell where radio link failure is detected; and if T311 was not running before entering the PCell in which the radio link failure was detected, o nrPreviousCell in previousPCellld: global cell identity and the tracking area code of the PCell where the last executed RRCReconfiguration message including reconfigurationWithSync was received o lastHO-Type: daps if the last executed RRCReconfiguration message including reconfigurationWithSync was concerning a DAPS handover o lastHO-Type: cho if the last executed RRCReconfiguration message including reconfigurationWithSync was concerning a conditional handover o timeConnFailure: elapsed time since the execution of the last RRCReconfiguration message including the reconfigurationWithSync

[0015] Otherwise, if the last RRCReconfiguration message including the reconfigurationWithSync concerned a handover to NR from E-UTRA and if the UE supports Radio Link Failure Report for Inter-RAT MRO EUTRA o eutraPreviousCell in previousPCellld: global cell identity and the tracking area code of the E-UTRA PCell where the last RRCReconfiguration message including reconfigurationWithSync was received embedded in E-UTRA RRC message MobilityFromEUTRACommand message o timeConnFailure: elapsed time since reception of the last RRCReconfiguration message including the reconfigurationWithSync embedded in E-UTRA RRC message MobilityFromEUTRACommand message

[0016] If configuration of the conditional handover is available in the MCG VarConditionalReconfig at the moment of declaring the RLF o timeSinceCHO-Reconfig: time elapsed between the detection of the RLF, and the reception, in the source PCell, of the last conditionalReconfiguration including the condRRCReconfig message o choCandidateCellList: global cell identity if available, and otherwise to the physical cell identity and carrier frequency of each of all the candidate target cells for conditional handover included in condRRCReconfig within the MCG VarConditionalReconfig at the time of radio link failure, excluding the candidate target cells included in measResultNeighCells

[0017] The RLF report information in VarRLF-Report message may further include part of the following information.• ra-InformationCommon: random-access related information as described in clause 5.7.10.5 in TS 38.331 for Scenario#2-1 and Scenario#2-2• locationAndBandwidth and subcarrierSpacing in bwp-Info: Uplink bandwidth part (BWP) in which the consistent uplink LBT failure was detected for Scenario#4• ssbRLMConfigBitmap and / or csi-rsRLMConfigBitmap in measResultLastServCell: Radio link monitoring configuration of the last serving cell, if available, for Scenario#! and Scenario#6• locationinfo as in 5.3.3.7 in TS 38.331

[0018] The measured quantities are filtered by the layer three (L3) filter as configured in the mobility measurement configuration. The measurements are based on the time domain measurement resource restriction, if configured. Exclude-listed cells are not required to be reported.

[0019] The UE may discard the radio link failure information 48 hours after the radio link failure / handover failure is detected.

[0020] The UE cleans VarRLF-Report if a new RLF is triggered.

[0021] The UE variable VarRLF-Report includes the radio link failure information or handover failure information.

[0022] The random-access related information above includes 1) the absolute frequency of the reference resource block associated to the random access resources used in the random access procedure, 2) location, bandwidth and subcarrier spacing of the associated uplink BWP, 3) frequency start, orthogonal frequency division multiplexing (OFDM), and subcarrier spacing of Message A in the associated two step random access resources if used in the random access processing, and / or 4) frequency start, OFDM and / or subcarrier spacing of Message 1 in the associated four step random access resources.

[0023] The locationinfo above includes detailed location information, Bluetooth measurement results, wireless local area network (WLAN) measurement results, sensor measurement results, and / or sensor motion information. The detailed location information includes location time stamp, location coordinate, velocity estimate, location error, location source, and / or global navigation satellite system (GNSS)-ToD-msec.

[0024] Network-detected RLF is one implementation solution in the existing specifications, and may be triggered, for example, when gNB is unable to detect any NACK or ACK from UEfor physical downlink shared channel (PDSCH), or for example the channel quality of sounding reference signal (SRS) is much lower, or the power headroom (PHR) of SRS is much lower.

[0025] In S. Khunteta and A.K.R. Chavva, “Deep learning based link failure mitigation”, 2017 16thIEEE International Conference on Machine Learning and Applications, 2017, deep learning is used to reduce RLF during handover process via measured RSRP values of the serving cell and the strongest neighbor cell to predict whether the future handover (1-2 sec ahead) will succeed or fail. L. V. Le, L. P. Tung, and B. S. Lin, “Big data and machine learning driven handover management and forecasting”, 2017 IEEE Conference on Standards for Communications and Networking, 2017, uses a clustering method to group cells to trigger early handovers without requesting the measruements of each UE. S. M. Asad Zaidi, M. Manalastas, A. Abu-Dayya, and A. Imran, “ALassisted RLF avoidance for smart en-dc activation”, IEEE Global Communications Conference, 2020, considers RLF prediction via RSRP measurement when UE is switching between 4G cell and 5G cell.

[0026] In K. Boutiba, M. Bagaa, A. Ksentini, “Radio link failure prediction in 5G networks”, IEEE, Feb 2024, CQI, RSRQ and PHR at time t-1, t-2, . . . t-N are used at time t for RLF prediction at time t+p, where p is larger than zero, and the output of the Al model is connectivity or no connectivity. Regarding data collection for Al model training, the collected data includes CQI, PHR and RSRQ when both the connectivity is available and non-available (i.e., RLF), and the data of (CQI, PHR and RSRQ) is labeled by 1 if there is connectivity, or 0 otherwise.

[0027] In network-side Al-based RLF prediction, Al model at the network side uses the measurement reports (which may be from UE) to predict RLF event for mobility issue. For example, when RLF is predicted, the network will trigger an early handover to avoid disconnection from the network.

[0028] 3 GPP Release 19 includes a Study Item for AI / ML for Mobility. Objectives described in the Study Item Description include studying and evaluating 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 and HO failure / RLF prediction (UE sided model). Another objective is to study the need / benefits of any other UE assistance information for the network side model.

[0029] The latest version of the study item description does not explicitly mention network sided models for RLF predictions, but this may become relevant as the study progresses.

[0030] There currently exist certain challenges. For example, the existing 3GPP specifications for 5G networks only consider UE-side RLF to cope with the failures, which is functioning based on UE monitoring including UE internal radio link monitoring timers and constants, randomaccess, MCG RLC retransmission, LBT failure and so on. Therefore, predicting the near failure (i.e., the failure which will hapen soon) or the radio link failure at the network side requires collecting information and measurements associated to the RLF procedure. However, the current data collection framework (e.g., self-optimizing network (SON) / minimization of drive test (MDT) features) does not enable the network to collect sufficient information and measurements required to predict the RLF at the network side.

[0031] When an AI / ML model is placed at the network side, the network needs to receive assistance information from the UE to generate accurate predictions of RLF or HO failure occurrence (i.e., inference), and in response to it trigger further actions, e.g. RRC Reconfiguration (configuring new RLM resources, e.g., new set of SSB or CSLRS beams), and / or handovers, configuration of conditional handover and / or release with redirect towards a given frequency.

[0032] However, the current procedures specified in the standards do not enable the UE to send such assistance information, thus making it not possible to use a network-sided AI / ML model for RLF predictions. Even if the proposed input to be reported by the UE is of an inference of an AI / ML model of RLF reception in the network side, the input may also be used for inference of other mobility related prediction, e.g. new cell the UE is to move to.

[0033] One existing solution in publication for Al-based network-side RLF prediction assumes CQI, RSRQ and power headroom (PHR) measurement report from UE to network and corresponding information of connectivity or a RLF, but the information is very general and not designed for 5G network.SUMMARY

[0034] As described above, certain challenges currently exist with artificial intelligence (AI)- based radio link failure (RLF) modeling. Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, particular embodiments include data collection to support the training / inference of Al models for RLF prediction. The collected data may be from user equipment (UE) to network for the training / inference of Al model(s) of network-side RLF prediction and / or UE-side RLF prediction, or from UE to UE’s server (any logical entity in the operation, administration, and maintenance (0AM) system or outside the Third Generation Partnership Project (3 GPP) network) for the training / inference of Al model(s) of UE- side RLF prediction.

[0035] In particular embodiments, the network sends the configuration information to the UE to perform data collection for training / retraining / inference of one or more Al models that are used for network-side Al-based RLF prediction and / or UE-side Al-based RLF prediction, and theconfiguration information may indicate at least one start condition and / or at least one stop condition (for example time window, triggered event(s), triggered threshold(s), areas, global cell ID(s), tracking area ID(s), frequency information, network ID(s), UE speed, and / or UE location) for data collection at the UE side and / or corresponding data report from the UE to the network, and the configuration information may indicate some specific data for collection besides the preconfigured or the pre-defined data for collection, and the configuration information may be sent from network to UE via Radio Resource Control (RRC) signaling for example as part of RRC Reconfiguration message (e.g., included in the otherConfig), and / or as part of logMeasConfiguration signal, and / or as part of RLF configuration message, and / or as part of RLF report configuration message.

[0036] According to the configuration information, the collected data from UE to network for the Al model training and / or retraining may include RLF event information and its associated channel condition information (e.g., the existing measurement results) for serving cell and / or one or more neighbour cells.

[0037] According to the configuration information, the collected data from UE to network for the Al model training and / or retraining may include 1) the information related to counter N310, counter N311, timer T310, timer T312, out of sync, in of sync, out of service (OOS), in service (IS), random access, retransmission of master cell group (MCG) Radio Link Control (RLC), beam failure detection (BFD), and / or beam failure recovery (BFR); and / or 2) the information of the associated channel conditions regarding serving cell and neighbour cells.

[0038] According to the configuration information and / or pre-configured information and / or the pre-defined information, the collected data from UE to network for the Al model training and / or retraining may be related to the avaiable memory at the UE side and / or may be up to UE implementation. The UE may report to the network the UE capability for data collection reporting from the UE to the network.

[0039] The UE may report the collected data to the network via the existing (or extending) RLF report framework, and / or the existing (or extending) MDT framework, and / or the existing (or extending) UE Assistance Information framework, and / or existing (or extending) RRM reporting framework, and / or new RRC IE(s), and / or new RRC IE element(s). The data collection report from the UE to the network may be associated with the corresponding condition information for data collection.

[0040] The network may use the trained / retrained Al models for network-side RLF prediction / inference, or the network may send the trained / retrained Al models above to the UE for UE-side RLF prediction / infrence. The training unit, which may be at the network or not at thenetwork for the Al models may send the trained / retrained Al models to the gNB at the network side. If the training unit is not inside the network, the network sends the collected data to the training unit for Al model training / retraining for the RLF prediction.

[0041] In some embodiments, the configuration information for data collection may indicate, not only characteristics / starting / stopping conditions, and corresponding report, but also the configurations of a pre-processing agent (for example, latent representation agent (LRA)). The LRA is responsible to convert the network assistance information (to be sent to RLF training / prediction agent) to an efficient form that further assists the RLF training / prediction node to improve its training / prediction.

[0042] In other words, pre-processing agent abstracts the projection space of all input samples and the mapping between input sample and such space. For simplicity and for sake of discussion, it is assumed that the LRA module is built via fully connected neural network (moreover, it can be built by other techniques). Then the output of LRA is abstracted samples that can be used as input to the RLF training / prediction agent to enhance its performance. LRA output will be reported to the RLF training / prediction agent (whether at UE or at gNB sides). There are many advantages of LRA including: the possibility of charting the environment instead of having to generalize to so many environments; the possibility of denoising unnecessary data; conditioning on specific scenario to tune the reported data to be more beneficial to the main RLF training / prediction agent; much less network footprint or required signaling overhead to be conveyed to the RLF training / prediction agent (e.g., at network side) as compared to the common input data; and / or reduce the amount of complexity of RLF training / prediction agent.

[0043] Collected data may be reported from UE to network, or from UE to UE’s server and the corresponding configuration for data collection may be from network to UE or from UE’s server to UE. Network (or UE’s server) may forward the collected data to training / inference unit, and may forward its received data collection configuration to the UE.

[0044] The Al model in particular embodiments may be an Al model at the network side or Al model at the UE side. The network may receive Al model from training unit, and may transfer the Al model to a UE. A UE may receive an Al model from UE’s server, which gets the Al model from training unit internally or externally.

[0045] The collected data may be logged by the UE, and to be reported possibly offline.

[0046] In some embodiments, the UE reports to the network assistance information associated with: i) one or more serving cell(s) the UE is configured with (e.g., primary cell, SCell(s) of a master cell group or of a secondary cell group); ii) one or more neighbor cell(s).

[0047] In some embodiments, the UE may receive from the network a command, e.g., HO command, release with redirect, suspend / release, dual connectivity setup, as result of the network predictions of RLF.

[0048] Some embodiments include a method in a serving or source network node (e.g., gNodeB) serving a UE configuring the UE to report assistance information.

[0049] In some embodiments, the network uses the assistance information received from the UE to generate RLF predictions. Based on the predictions, the network performs actions to mitigate the effects of the RLF (e.g., interference protection, release with redirect of the UE, suspend / release, handover, dual connectivity (DC) setup, etc.).

[0050] According to some embodiments, a method is performed by a wireless device. The method comprises receiving a configuration from a network node. The configuration comprises an indication of assistance data the wireless device is to collect to be used with a radio link failure machine learning model. The method further comprises collecting the assistance information according to the received configuration and transmitting a report including the collected assistance information (e.g., to the network node or a training node).

[0051] In particular embodiments, the method further comprises receiving a command from the network node as a result of a prediction of radio link failure based on the transmitted report.

[0052] In particular embodiments, the method further comprises receiving a reporting configuration from the network node. The reporting configuration comprises an indication of how to report the collected assistance information.

[0053] In particular embodiments, the assistance information comprises one or more of the following: a value of one or more radio link failure related counters; a value of one or more radio link failure related timers; a number of out-of-synchronization events; a number of insynchronization events; a number of retransmissions from a Radio Link Control layer; a number of retransmissions of a random access preamble; Radio Resource Management measurements associated with one or more of a serving cell and one or more neighbor cells; lower layer measurements associated with one or more of a serving cell and one or more neighbor cells; a beam identifier associated with any one or more of the events, timers, counters or measurements; a cell identifier associated with any one or more of the events, timers, counters or measurements; a bandwidth part identifier associated with any one or more of the events, timers, counters or measurements; and frequency information associated with any one or more of the events, timers, counters or measurements.

[0054] In particular embodiments, the configuration information comprises one or more of the following: a start condition for collecting the assistance data; and a stop condition for collecting the assistance data.

[0055] In particular embodiments, the report further comprises an identifier associating the report with the received configuration information.

[0056] According to some embodiments, a wireless device comprises processing circuitry operable to perform any of the wireless device methods described above.

[0057] Also disclosed is a computer program product comprising a non-transitory computer readable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the wireless devices described above.

[0058] According to some embodiments, a method is performed by a network node. The method comprises transmitting a configuration to a wireless device. The configuration comprises an indication of assistance data the wireless device is to collect to be used with a radio link failure machine learning model. The method further comprises receiving a report including the collected assistance information from the wireless device.

[0059] In particular embodiments, the method further comprises transmitting a command to the wireless device as a result of a prediction of radio link failure based on the received report and the radio link failure machine learning model.

[0060] In particular embodiments, the method further comprises transmitting a reporting configuration to the wireless device. The reporting configuration comprises an indication of how to report the collected assistance information to the network node.

[0061] In particular embodiments, the method further comprises performing machine learning model training, inference, or validation using the received assistance information with the machine learning model.

[0062] In particular embodiments, the method further comprises transmitting the received assistance information to another network node for machine learning model training, inference, or validation.

[0063] According to some embodiments, a network node comprises processing circuitry operable to perform any of the network node methods described above.

[0064] Another computer program product comprises a non-transitory computer readable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the network nodes described above.

[0065] Certain embodiments may provide one or more of the following technical advantages. For example, advantages of particular embodiments include 1) reusing existing framework of measurement reporting with minimum effect on specifications, and 2) enabling data collection with different payload for measurement and detection reporting via configurable report embodiments (i.e., different size of reporting embodiments), in particular embodiments, the network may configure the UE to collect and report assistance information for network sided AI / ML model RLF predictions.BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The present disclosure may be best understood by way of example with reference to the following description and accompanying drawings that are used to illustrate embodiments of the present disclosure. In the drawings:Figure 1 illustrates two example radio link failure (RLF) scenarios;Figure 2 is a flow diagram illustrating signalling flow for data collection for RLF interference;Figure 3 illustrates an example pre-processor structure for an autoencoder use case;Figure 4 shows an example of a communication system, according to certain embodiments;Figure 5 shows a user equipment (UE), according to certain embodiments;Figure 6 shows a network node, according to certain embodiments;Figure 7 is a block diagram of a host, according to certain embodiments;Figure 8 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized;Figure 9 shows a communication diagram of a host communicating via a network node with a UE over a partially wireless connection in accordance with some embodiments;Figure 10 is a flowchart illustrating an example method in a wireless device, according to certain embodiments; andFigure 11 is a flowchart illustrating an example method in a network node, according to certain embodiments.DETAILED DESCRIPTION

[0067] As described above, certain challenges currently exist with artificial intelligence (AI)- based radio link failure (RLF) modeling. Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, particular embodiments include data collection to support the training / inference of Al models for RLF prediction.

[0068] Particular embodiments are described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0069] As used herein, “network" may be replaced by “training unit” or “user equipment (UE) server” to support data collection for the training of Al model(s) for UE-side RLF prediction. ’’Network-side" may be replaced by ”UE-side” to support data collection for the training of Al model(s) for UE-side RLF prediction.

[0070] Network-side Al-based RLF prediction means that the network uses Al to predict an RLF event, and UE-side Al-based RLF prediction means that a UE uses Al to predict an RLF event.

[0071] As used herein, 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 multiple-radio dual connectivity (MR-DC), there is one serving cell comprising the primary cell (Pcell). For a UE in a connected state configured with CA / MR-DC the term ‘serving cells’ is used to denote the set of cells comprising the special cell(s) and all secondary cells.

[0072] As used herein, the serving / neighbor cell may correspond to one or more of: a radio access network (RAN) node; a gNodeB (gNB); a sixth generation (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; and / or 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.

[0073] As used herein, a measurement, which is used as input to the RLF training / prediction agent, may correspond to one or more of:• A Radio Resource Management (RRM) measurement, because they assist RRM decisions at the network side and / or layer 3 (L3) or higher layer measurements, because these measurements would be responsibility of the Radio Resource Control (RRC) protocol, also referred to L3 in the control plane RAN protocol stack.• A New Radio (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. synchronization signal block (SSB) or channel state information reference signal (CSI- RS).• A measurement which may be associated to a measurement quantity, such as RSRP, RSRQ, SINR or RSSI. 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 referred to as cell quality or cell measurement result), wherein the measurement of a cell may be performed based on one or more beam measurements.• A measurement that 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 a reference signal (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

[0074] As used herein, the term “ML-model” or “Al-model”, “Model Inference”, “Model Inference function” or “AI / ML model” are used interchangeably. An AI / ML model may be defined as a functionality or be part of a functionality that is deployed / implemented in the network. The AI / ML-model may correspond to a function which receives one or more inputs (e.g., measurements, timers, counters, etc.) and provide as outcome one or more prediction(s) (e.g., radio link failure predictions of service / neighbor cells).

[0075] The term predict! on(s) may correspond to time-domain predictions: thus, the input of the ML-model comprises at least one or more measurements / timer / counters at (or starting at) a time instance tO (and / or a time interval such as T1 or tO+Tl, which may comprise one or more samples or measurement time occasions, from 1 to K time occasions) of at least one neighbor and / or serving cell, and the output of the ML-model comprises one or more predicted radio link failure at a future time instance, e.g. tO + T.

[0076] Further terminology may refer to an “actor”, as a function that receives the output from the Model inference function and triggers or performs corresponding actions. The actor may trigger actions directed to other entities or to itself. In the context of particular embodiments, one actor may correspond to RLF prediction reporting functionality at the network.

[0077] An ML model or Model Inference is a function that provides AI / ML model inference output (e.g., predictions or decisions). The Model inference function may also be responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on Inference Data delivered by a Data Collection function. The output may correspond to the inference output of the AI / ML model produced by a Model Inference function.

[0078] Figure 2 is a flow diagram illustrating signalling flow for data collection for RLF interference.

[0079] Particular embodiments include a method performed by a wireless terminal, referred to as a user equipment (UE), connected to a wireless network (e.g., via a serving cell), comprising receiving a configuration message (e.g., RRC Reconfiguration) based on which the UE collects assistance information as requested by the network and triggers reports including the assistance information. In an embodiment, the received configuration indicates / instructs the UE to collect / report the “available” information, i.e., the UE is not mandated to collect any information, if not available, based on the received configuration.

[0080] In some embodiments, the UE configures the pre-processing agent based on the received configuration message from the network node.

[0081] The configuration message includes assistance data the UE should collect and send to the network, e.g., such as the following:• Status of counters: e.g. values of N310, N311, etc.• Status of timers: expired / not expired and / or value of the timers (e.g.T310, T312). In an embodiment the network indicates the frequency for which the T312 is configured, so the UE monitors the T312 associated to the configured frequencies.• Number of Out Of Synch (OO S) indications received from the lower layers• Number of In Synch (IS) indications received from lower layers• Number of retransmission from master cell group (MCG) and / or secondary cell group (SCG) Radio Link Control (REC)• Number of retransmission of random access preamble• Beam / cell / bandwidth part (BWP) / frequency information (e.g., cell ID, beam ID, downlink BWP index, uplink BWP index) where the OOS and / or IS events occurred• RRM measurements of serving and neighbor cells (e.g. RSRP, RSRQ, SINR, RSSI, etc.)• RRM measurement predictions of serving and neighboring cells (e.g. RSRP, RSRQ, SINR, RSSI, etc.). The configuration may indicate whether the time domain or space domain or frequency domain prediction is needed to be done at the UE.• Lower layer measurements (e g. CSLRSRP, CSLRSRQ, CSLSINR, CSLRSSI, SSB- RSRP, SSB-RSRQ, SSB-SINR, SSB-RSSI). The measurements may include the CSL report used for the layerl / layer2 mobility (LTM) cell switch. The configuration may include a list of one or more frequencies for which the lower layer measurements are required.• Predictions of the lower layer measurements (e.g., CSLRSRP, CSLRSRQ, CSLSINR, CSLRSSI, SSB-RSRP, SSB-RSRQ, SSB-SINR, SSB-RSSI). The measurement predictions may include the CSLreport used for the LTM cell switch. The configuration may include a list of one or more frequencies for which the lower layer measurements are required. The configuration may indicate whether the time domain or space domain or frequency domain prediction is needed to be done at the UE.• One or more parameters indicating the reporting frequency / period of the assistance information samples, that is how frequent the UE shall provide the assistance information in the report. The value may be expressed, e.g., in seconds, milliseconds, subframes, radio frames, time slots, sub-slots, half-slots, orthogonal frequency division multiplexing (OFDM) symbols, etc. For example, if the reporting frequency is once per second, this means that the UE shall send the assistance information in the report at least once per second or that the UE cannot provide assistance information more often than every second.• One or more parameters indicating to report the assistance information when a certain event occurs. This may include certain measurement reporting event occurring such as, e.g. when an A3 or A5 event becomes fulfilled or when some timers (e.g., T310) start. A new event for reporting of assistance information may be defined. The new event may be fulfilled in relation to the status of the assistance data, such as, e.g., when a number of OOS occurs.• In one option, the frequency of which the UE sends the assistance information is dependent on certain conditions where the conditions may be related to the assistance information. The UE may in this option send the assistance information more seldom when, e.g. no OOS is detected and with increasing frequency when the number of OOS increases. There may be UE configuration, where the configuration instructs the UE to send the assistanceinformation after, e.g. OOS has occurred X number of times in a row. Such a configuration leads to the UE sending the assistance information more often when, e.g. the OOS or some other event occurs with higher frequency.• One or more parameters indicating to report the assistance information when a certain procedure take place. This may relate to a certain procedure being executed, so that the UE reports the assistance information when the procedure is executed, e.g., when a mobility procedure is executed or when a state transfer to a certain RRC state, e.g. to RRC INACTIVE or RRC IDLE is executed.• One or more parameters indicating to report the assistance information when a certain data exceed a certain threshold. For example, the UE reports the assistance information when the value of a certain counter (e.g., N310 or N311) reaches or exceed a percentage (X%) of its maximum value.

[0082] The method further comprises, based on the received configuration, including in a report the assistance information related to a subset of the configured one or more serving cells and / or neighboring cells and transmitting the report to a network node.

[0083] In some embodiments, the configuration received by the UE may be included in an RRCReconfiguration message or in any other message used to configure the UE (e.g., an RRC Resume message, or an RRC Reestablishment message).

[0084] In some embodiments, the UE includes the assistance information related to serving and / or neighbor cells in report message. The report message may be: (a) an RRC measurement report (e.g., MeasurementReport message) used for the indication of measurement results; (b) an RRC measurement report (e.g., MeasurementReport message) used for the indication of prediction information (not necessarily including actual measurements); and / or (c) a UE assistance information message (e.g., UEAssistancelnformation message) used for the indication of UE assistance information to the network. Any other type of RRC report or / and message used to indicate the prediction information for at least one neighbor cell and / or a serving cell.

[0085] In some embodiments, the assistance information included in the report message are in the form of a list (or vector) including one or more of the following elements: (a) at least a cell identifier (e.g., Cell ID, PCI + SSB frequency) and / or a beam index and / or beam identifier (e.g., SSB index) where the assistance information refer to; (b) at least one element of the assistance data collected by the UE (e.g., counters, timer, number of OOS, number of IS, etc.); and / or at least RRM measurements of serving and neighbor cells (e.g. RSRP, RSRQ, SINR, RSSI).

[0086] In some embodiments, the UE reports to the network a capability indicating which type of assistance information the UE is able to report to the network. For example, the capabilitymay indicate which counters or timers the UE is able to report. In another embodiment, one capability bit may be used to indicate that UE is capable of data collection for the RLF prediction at the network side.

[0087] Some embodiments include a method in a network node (e.g., a serving gNodeB), comprising: transmitting a configuration message to the UE based on which the UE collects the assistance information and triggers a report; receiving the report from the UE including the assistance information; and using the assistance information received from the UE to generate RLF predictions and based on the prediction, performing actions to mitigate the effects of the RLF (e.g., release with redirect of the UE, suspend / release, handover, DC setup, etc.).

[0088] In some embodiments, the configuration message sent to the UE may be a RRCReconfiguration message or any other message used to configure the UE. This message may include configuration of the assistance data to report (e.g., which counters, timers to report and any combination of them) and configuration of the reporting (e.g., periodical or event / procedure based).

[0089] In some embodiments, the network node receives the report from the UE including the assistance information and, possibly, other data (e.g., the actual RRM measurements). The network node may trigger one or more actions based on the received predictions. For example, the network node may: (a) configure a neighbor cell as target cell and trigger a handover to that cell; (b) configure a cell as a target candidate cell for conditional handover; (c) configure a cell as a target cell for LTM; (d) trigger a beam switch; (e) configure a new cell as a neighbor cell; (f) release with redirect of the UE; and / or (g) suspend / release of the UE.

[0090] In one option, the assistance information may be used by a source gNB to indicate to a target candidate gNB, the probability of the UE executing conditional handover (CHO) to the target candidate gNB.

[0091] Some embodiments include transmission of assistance information between network nodes, e.g. between a master node (MN) and a secondary node (SN) or between gNBs, e.g. between a source gNB and a target gNB. The assistance information may be sent in RRC internode messages, in XnAP, NGAP, Fl, El etc.

[0092] In some embodiments, the following contain description of pre-processing agent (LRA). For the sake of discussion, an ML- Autoencoder is an example module of LRA. A potential structure of LRA with Input data, Encoder, Latent-Space, Decoder, and Decoder Output is illustrated in Figure 3.

[0093] Figure 3 illustrates LRA structure for an autoencoder use case. The input of LRA may be configured in similar way (or partly different) as data (measurement) to be sent for RLFprediction agent, which are described above. For example, different types of measurement, event, also, configurations of temporal characteristics of reported data to the RLF predictor.

[0094] The output of LRA, is split into two components. Output-1 represents the Decoder Output (i.e., label in training and e.g., reconstructed data in inference), which has the purpose of guiding the b ackpropagation process (when performing gradient over neurons), such data might not be necessarily used in the RLF prediction but will be used to construct the most efficient latent space which then will be used (individually or together with the other data) as RLF prediction input. The construction of the latency space is done via training the Encoder of the LRA, via selected the right labels, and performing training procedure on both Decoder and Encoder, then obtaining the Encoder output as latent space and input it to the RLF predictor. The selection of such labels can include (but not only): (a) replica of the input, to aim at reconstructing the input in the inference; and / or (b) network or UE related key performance indicator (KPI), for example SINR or throughput, etc.

[0095] Output-2: Latent output (i.e., output of the trained Encoder) which will be sent to the RLF prediction model (e.g., at network side).

[0096] In some embodiments, the network may configure LRA frequency and temporal characteristics of operation, i.e., when to send the Encoder output to the RLF predictor. The network may also configure the latent space size of neurons.

[0097] Some embodiments are related to measurement collections for radio link failure prediction model training / inference. In one embodiment, the network sends a first message (in a non-limiting example an RRCReconfiguration message) to configure the UE to perform data collection for training / update / inference for one or more Al models that are used for network-side Al-based RLF prediction. The first message may comprise configuration for the UE to perform the data collection when one or more of the following conditions are fullfilled at the UE:• Starting of one or more timers related to the RLF detection (such as the T310, T312, T304)• Counter values related to the RLF detection above or below certain thresholds (e.g., value of N310 above a certain threshold, value of N311 above a certain threshold, number of random access preamble retransmission above a certain threshold, number of listen-before- talk (LBT) failures above a certain threshold, number of Radio Resource Control (RRC) retransmissions above a certain threshold, number of retransmissions from the maser cell group (MCG) Radio Link Control (RLC), etc.)

[0098] One or more of the above conditions may be configured by the gNB in the first message.

[0099] The network may also comprise a second message that configures explicitly or implicitly the UE to report the collected data back to the network periodically, aperiodically or semi-persistent, or on gNB-request, or event-triggered. This message may explicitly or implicitly indicate time window and / or start time for RLF information collection at the UE side, and may explicitly or implicitly inidcate when the UE should report the (updated) collection information.

[0100] The network may send another message to the UE to stop the RLF related data collection and / or collected RLF data reporting. The second message may be sent before the first message, or may be sent together with the first message, or may be sent after the first message, or may be sent without the first message. In one embodiment, the data collected by the UE upon fullfilling one or more of the above conditions are reported to the gNB, e.g. periodically, or aperiodically, or in a semi-persistent way, or on gNB-request, or event-triggered.

[0101] In some embodiments, the data collected upon fullfilling one or more of the above conditions are logged and stored by the UE in its internal memory, and reported to the gNB, e.g. periodically, or aperiodically, or in a semi-persistent way, or on gNB-request, or event-triggered. The UE may store and log a plurality of collected data associated to conditions fullfilled at different points in time, and the report may comprise a plurality of collected data associated to conditions fullfilled at different points in time. The data collected by the UE may be different depending on the specific condition fullfilled.

[0102] The collected data sent from UE to network may include all or part of the information below:• Number of RLF• For each RLF (in Scenario#!) (the corresponding cases include that the UE reports data for one or more RLFs in one report) o Time stamp information (e.g., time stamp when RLF event is triggered, time stamp when counter N310 is triggered) o Serving cell ID and / or bandwidth part (BWP) ID o Reason type (e.g., ’t310-Expiry’) o For each out-of-sync in the time window (N310) all or partial following measurements are collected:■ Time stamp or out-of-sync index■ Reference signal receive power (RSRP), reference signal receive quality (RSRQ), reference signal strength indicator (RS SI), signal to interference and noise ratio (SINR), channel state information (CSI) and / or block error rate (BLER) of specific downlink resources (e.g., the downlink resources whichmeasurement / detection results are used for UE’s determination of out-of-sync and / or in-sync) and / or corresponding downlink resource information. The downlink resource information may include the downlink resource information of one or more cells, and may include the cell ID, BWP ID, serving cell indication and / or neighbor cell indication. The downlink resources may be SSB-RS or CSI-RS or other. The measurements collected related to serving cells or neighboring cell may be the latest available before detecting one out- of-sync.■ Serving cell ID and / or BWP ID each triggered T310, all or partial following measurements are collected:■ Time stamp information (e.g., time stamp when T310 is triggered, time stamp when counter N310 is triggered)■ Serving cell ID and / or BWP ID■ For each out-of-sync in the time window (N310):• Time stamp or out-of-sync index• RSRP, RSRQ, RSSI, SINR, CSI and / or BLER of specific downlink resources (e.g., the downlink resources which measurement / detection results are used for UE’s determination of out-of-sync and / or in-sync) and / or corresponding downlink resource information. The downlink resource information may include the downlink resource information of one or more cells, and may include the cell ID, BWP ID, serving cell indication and / or neighbor cell indication. The downlink resources may be SSB-RS or CSI-RS or other. The measurements collected related to serving cells or neighboring cell may be the latest available before triggering T310, and one or more measurements may be collected while T310 timer is running.• Serving cell ID and / or BWP ID■ For each in-sync in the time window T310, all or partial following measurement are collected:• Time stamp information which may be relative or absolute• RSRP, RSRQ, RSSI, SINR, CSI and / or BLER of specific downlink resources (e.g., the downlink resources which measurement / detection results are used for UE’s determination of out-of-sync and / or in-sync)and / or corresponding downlink resource information. The downlink resource information may include the downlink resource information of one or more cells, and may include the cell ID, BWP ID, serving cell indication and / or neighbor cell indication. The downlink resources may be SSB-RS or CSI-RS or other. The measurements collected related to serving cells or neighboring cell may be the latest available before detecting one in-sync.• Serving cell ID and / or BWP ID■ For each out-of-sync in the time window T310 (low priority), all or partial following measurement are collected:• Time stamp information which may be relative or absolute• RSRP, RSRQ, RSSI, SINR, CSI and / or BLER of specific downlink resources (e.g., the downlink resources which measurement / detection results are used for UE’s determination of out-of-sync and / or in-sync) and / or corresponding downlink resource information. The downlink resource information may include the downlink resource information of one or more cells, and may include the cell ID, BWP ID, serving cell indication and / or neighbor cell indication. The downlink resources may be SSB-RS or CSI-RS or other. The measurements collected related to serving cells or neighboring cell may be the latest available before detecting one out-of-sync.• Serving cell ID and / or BWP ID■ For each triggered T312, collected information as above are reported via one of following methods:• Alt 1 source measurement and event flag collected during N310 or T310 or T311 are reported, (in case of heavy overhead for reporting) o Time stamp o Out-of-sync or in-sync flag o RSRP, RSRQ, RSSI, SINR, CSI and / or BLER of specific downlink resources (e.g., the downlink resources which measurement / detection results are used for UE’s determination of out-of-sync and / or in-sync) and / or corresponding downlink resource information. The downlink resource information mayinclude the downlink resource information of one or more cells, and may include the cell ID, BWP ID, serving cell indication and / or neighbor cell indication. The downlink resources may be SSB-RS or CSI-RS or other. The measurements collected related to serving cells or neighboring cell may be the latest available before triggering T312, and one or more measurements may be collected while T312 timer is running. o Serving cell ID and / or BWP ID• Alt 2 source measurements collected during N310 or T310 or T311 are reported, (in medium overhead for reporting case) o Time stamp o RSRP, RSRQ, RSSI, SINR, CSI and / or BLER of specific downlink resources (e.g., the downlink resources which measurement / detection results are used for UE’s determination of out-of-sync and / or in-sync) and / or corresponding downlink resource information. The downlink resource information may include the downlink resource information of one or more cells, and may include the cell ID, BWP ID, serving cell indication and / or neighbor cell indication. The downlink resources may be SSB-RS or CSI-RS or other. The measurements collected related to serving cells or neighboring cell could be the latest available before triggering T312, and one or more measurements may be collected while T312 timer is running. o Serving cell ID and / or BWP ID o Note that all or partial measurement results may be reported with more coarse accuracy.• Alt 3 event flag collected during N310 or T310 or T311 reported, (in limited overhead for reporting case) o Time stamp o Out-of-sync or in-sync flag o Serving cell ID and / or BWP ID■ For each T312 expiry, all or partial following information may be reported.• Time stamp• Cause for T312 expire, such as T310 expiry, trigger of handover, initiate connection re-establishment. o For each T310 expiry, all or partial following information should be reported.■ Time stamp■ Result for T310 expiry, such as UE enter IDLE state or re-establish RRC connection.• For each triggered T310 o Time stamp information (e.g., time stamp when T310 is triggered, time stamp when counter N310 is triggered) o Reason type (e.g., ’t310-triggered’) o Serving cell ID and / or BWP ID o For each out-of-sync in the time window (N310):■ Time stamp or out-of-sync index■ RSRP, RSRQ, RSSI, SINR, CSI and / or BLER of specific downlink resources (e.g., the downlink resources which measurement / detection results are used for UE’s determination of out-of-sync and / or in-sync) and / or corresponding downlink resource information. The downlink resource information may include the downlink resource information of one or more cells, and may include the cell ID, BWP ID, serving cell indication and / or neighbor cell indication. The downlink resources may be SSB-RS or CSLRS or other. The measurements collected related to serving cells or neighboring cell may be the latest available before triggering T310, and one or more measurements may be collected while T310 timer is running.■ Serving cell ID and / or BWP ID o RLF event is triggered or not■ Reason if RLF event is not triggered• If this UE receives N311 consecutive in-sync indications,• If this UE receives RRCReconfiguration with reconfigurationWithSync,• If this UE initiates the connection re-establishment procedure.• If this UE stops T310 if SCG release and moreover the T310 is kept in SCG.• For reaching specific number (or threshold) of out-of-syn from lower layer, or for each out-of- sync indication from lower layer: o Time stamp information o Reason type o Serving cell ID and / or BWP ID o RSRP, RSRQ, RSSI, SINR, CSI and / or BLER of specific downlink resources (e.g., the downlink resources which measurement / detection results are used for UE’s determination of out-of-sync and / or in-sync) and / or corresponding downlink resource information. The downlink resource information may include the downlink resource information of one or more cells, and may include the cell ID, BWP ID, serving cell indication and / or neighbor cell indication. The downlink resources may be SSB-RS or CSI-RS or other.• Number of T310 stops without triggered RLF event• Number of T310 stops with triggered RLF event• Number of T310 starts (without necessarily expiry), where this kind of T310 may be defined as in-running T310, T310 stopping with triggered RLF event and / or T310 stopping without triggered RLF event.• Whether a handover (HO) command was received while T310 was running, and how much time was left to T310 expiry or how much time has run in this case.• Number of N311 stopping T310 (i.e., recovery before RLF)• Number of N310 starts, how N310 develops (before T310 starts), how was N310 when HO command was received) or when UE was moved to IDLE / INACTIVE.• Info on OOS events and IS event: e.g. what beam was not good so that OOS event was sent from lower layers; Same for IS events, for recovering.• BFD and BFR related information for predicting RLF, e.g. timer status, number of BFI(s), whether BFR has happened or not, which TCI states / beams were activated when BFI was sent from lower layers, etc.• For each RLF in Scenario#2 (i.e., random access problem indication from MCG medium access control (MAC)) o Time stamp o Serving cell ID and / or BWP ID and / or beam ID information o Reason type (e.g., ’beamFailureRecoveryFailure’, ’randomAccessProblem’)o RSRP, RSRQ, RSSI, SINR, CSI and / or BLER of specific downlink resources and / or corresponding downlink resource information for a time window. The downlink resource information may include the downlink resource information of one or more cells, and may include cell ID, BWP ID, beam ID, SSB index, CSI-RS index serving cell indication and / or neighbor cell indication. The downlink resources may be SSB- RS or CSI-RS or other. The measurements collected related to serving cells or neighboring cell may be the latest available before initiating the random access, and one or more measurements may be collected while random access is ongoing. The time window may start when random access procedure starts, and may stop when this kind of RLF is triggered. The collected data may correspond to all or part time slots in this time window.• For each RLF in Scenario#3 (i.e., reaching of maximum number of retransmissions from the MCG RLC): o Time stamp o Serving cell ID and / or BWP ID o Reason type (e.g., ’rlc-MaxNumRetx’) o RSRP, RSRQ, RSSI, SINR, CSI and / or BLER of specific downlink resources and / or corresponding downlink resource information for a time window. The downlink resource information may include the downlink resource information of one or more cells, and may include cell ID, BWP ID, beam ID, SSB index, CSI-RS index serving cell indication and / or neighbor cell indication. The downlink resources may be SSB- RS or CSI-RS or other. The measurements collected related to serving cells or neighboring cell may be the latest available before initiating the random access, and one or more measurements may be collected while random access is ongoing. The time window may start when the first transmission from the MCG RLC is failed, and may stop when this kind of RLF is triggered. The collected data may correspond to all or part time slots in this time window.• Trajectory of UE in the previous Tp time-steps (and or future Tf time-steps). o Such UE-trajectory information may be:■ Independent of any other measurement, timers, and counters■ Associated with RLF measurements (e g., RSRP, RSRQ, RSSI, SINR, CSI and / or BLER).■ Associated with RLF timers (e.g., T300, T301, T304, T311, etc.).■ Associated with RLF counters (e.g., N310, N311, etc.)

[0103] In some embodiments, the network may use the information below from the UE for the training / retraining / inference of Al models for RLF prediction, where the report information from UE to network may be collected by the network within a given detection time window, and / or may be collected periodically.• Number of RLF• For each RLF o Time stamp o RSRP, RSRQ, SINR and / or RS SI of serving cell and / or serving cell ID o RSRP, RSRQ, SINR, and / or RSSI of serving beam and / or serving beam ID o RSRP, RSRQ, SINR and / or RSSI of one or more neighbor cells and corresponding neighbor cell IDs o BLER threshold for sync-out and BLER threshold for sync-in o RLF reason type (e.g. ’t310-Expiry’, ’beamFailureRecoveryFailure’, ’randomAccessProblem’, ’rlc-MaxNumRetx’, TbtFailure’, ’bh-rlfRecoveryFailure’, ’ t312-Expiry’, or others)

[0104] In some embodiments, the UE reports to the network the information for data collection, by using, and possibly extending, one or more existing framework:• MDT (Minimization of Drive Tests) framework• Measurement Report framework• UE Assistance Information framework

[0105] In some embodiments, the report message transmitted by the UE to the gNB consists of the one or more collected data associated to one condition according to which the UE performed the data collection. In some embodiments, the report message transmitted by the UE to the gNB comprises the one or more collected data associated to multiple conditions according to which the UE performed the data collection, wherein the multiple conditions may be fullfilled at different points in time. In some embodiments, the report message may comprise a list of collected data, wherein each entry in the list consists of the data collected by the UE associated to one condition according to which the UE the UE performed the data collection.

[0106] In some embodiments, the network may send the trained Al models based on the data collection above to the UE for UE-side RLF prediction.

[0107] In some embodiments, the traing unit in the network may send the trained Al models, which are trained based on the data collection above, to the gNB for network-side RLF prediction.

[0108] Some embodiments include configuration of the measurement collection for radio link failure prediction. In one embodiment, the UE receives an indication from the network to collect the measurements and information pertaining to RLF or near failure scenarios (e.g., when the RLF timer was running but stopped before detection of the radio link failure).

[0109] The indication indicates one or more configuration(s) / conditions(s) for data collection for the sake of AI / ML model training for RLF prediction.

[0110] In some embodiments, the indication indicates to the UE a set of conditions / thresholds for logging the RLF related supervision timers and constants.[OHl] In some embodiments, the indication indicates the UE to log RLF related information in a report if the RLF supervision timers e.g., T310 and or T312 are above certain configured threshold. In a non-limiting example, the UE logs information about the RLF in the report that the timer T310 running value is above 80 percent of a value (e.g., the T310 value set as part of RLF- TimersAndConstants) for which the timer expires.

[0112] The information and measurements to be logged upon fulfilment of this condition are listed above.

[0113] In some embodiments, the indication indicates the UE to log RLF related information in a report if the RLF constants, e.g., N310 and or N311, are above certain configured threshold. In a non-limiting example, the UE logs information about the RLF in the report that the counter N310 running value is above 80 percent of a value (e.g., the N310 value set as part of RLF- TimersAndConstants) for which the T310 starts running.

[0114] The information and measurements to be logged upon fulfilment of this condition are listed above.

[0115] In some embodiments, the configuration includes an area scope in which the UE should perform the measurements if one or more of the configured condition / thresholds are fulfilled.

[0116] In some embodiments, the area in which the UE can perform the logging of the RLF related information may be a set of cells identified by the cell global identity or by the physical cell identity and the frequency information, e.g., absolute radio frequency channel number (ARFCN).

[0117] In some embodiments, the area in which the UE can perform the logging of the RLF related information may be a set of tracking area identifier.

[0118] In some embodiments, the area in which the UE can perform the logging of the RLF related information may be a set of public land mobile networks (PLMNs).

[0119] In some embodiments, the area in which the UE may perform the logging of the RLF related information may be based on specific network type, such as high speed train related network or based on non-public network identities such as PNI-NPN or SNPN identities.

[0120] In some embodiments, the area in which the UE can perform the logging of the RLF related information may be any combination of the above parameters.

[0121] The area scope identifies an area, the area that UE is allowed to perform the logging and reporting to the network. In a variant the area in which the UE can perform reporting may be different from the area in which the UE performed logging and collecting the data for the AI / ML based RLF prediction model training.

[0122] The indications sent by the network to the UE may also include conditions associated to the speed or location of the UE. For example, a portion of the configuration may instruct the UE to perform data collection for the RLF prediction model training if the UE is in a specific mobility state, e.g., UE performs the data collection if the UE is in high mobility state and stops the data collection upon changing the mobility state. In another example, the UE performs the data collection if the UE speed is above an absolute speed, e.g., if the UE moves at any speed above V km / h (or meter / second). The UE stops logging if the absolute UE mobility does not fulfil the configured conditions.

[0123] In some embodiments, the configuration to instruct the UE to perform data collection for the RLF prediction model training, includes a supervision timer. The supervision timer identifies the time period in which the UE is performing the data collection. Once the timer expires, the UE stops data collection.

[0124] In some embodiments, the configuration is sent to the UE as part of RRC Reconfiguration message.

[0125] In some embodiments, the configuration is sent to the UE as part of logMeasConfiguration signal.

[0126] In some embodiments, data collection for RLF prediction model training at the network may be for network-sided model and UE-sided model.

[0127] The information to be logged by the UE, and to be reported possibly offline (like MDT) may be:• Info on T310 triggers such as: how many times in aPCell T310 started (without necessarily expiry); whether a HO command was received while timer T310 was running (and possibly how much time was left to expiry, or how much time has run); how many times the N311 made T3210 stop (recovery before RLF);• Info on N310: when T310 starts it means N310 reaches it maximum values, i.e., indication of T310 start = indication of max N310. But, when N310 increments without T310 starting means OOS events are happening, but not reaching its maximum,; so the UE may log and report how many times N310 in PCell event started; how N310 develops (before T310 starts); how was N310 when HO command was received) or when UE was moved to IDLE / INACTIVE.• Info on OOS events and IS event: In principle the UE may be more granular about the OSS events, e.g. what beam was not good so that OOS event was sent from lower layers; Same for IS events, for recovering.• Info on N311• Info BFD and BFR related input may also be relevant for predicting RLF, e.g. timer status, number of BFI(s), whether BFR has happened, which transmission configuration indicator (TCI) states / beams were activated when BFI was sent from lower layers, etc.

[0128] For the inference of RLF (prediction) on the network, example information reported to support the network to predict RLF (input to inference on network side) and for the network perform actions in response (e.g., release with redirect, suspend / release, or handover(s), DC setup, etc., e.g,. UE reports RLF related info, network predictions, UE receives a command, e.g. handover.

[0129] Examples of Reported information may include:• Status of N310, N311, T310, OOS events, IS events, beam information related to IS and OOS events, measurements, etc.• RRM measurements• Lower layer measurements

[0130] Regarding triggers and reporting:• This needs to be sent in critical times, close to an RLF but with enough time for the network to act and possibly predict an RLF• Measurement report, e.g. include in MR information enabling network models to predict RLF, such as some of the above. In this case, there is no need to change the triggers of a measurement report like A3, etc.• An alternative is to define new “events” for triggering MR and / or for a new report, based on the RLF information used as input to the inference at the network side. For example: o UE sends a report when T310 starts (some parameters to avoid too much uplink, some filtering, TTT, etc.)o UE sends a report when N310 reaches X% of its max value o UE sends a report when N311 reaches X% of its max value

[0131] In some embodiments, the configuration information for data collecation might indicate, not only characteristics / starting / stopping conditions, and corresponding report, but also the configurations of a pre-processing agent (latent representation agent (LRA)). The LRA is responsible to convert the network assistant information (to be sent to RLF prediction agent) to an efficient form that further help the RLF prediction node to improve its prediction. In other words, it abstracts the projection space of all input samples and the mapping between input sample and such space. For simplicity and for sake of discussion, assume that this LRA module is built via fully connected neural network (moreover, it can be built by other techniques). Then the output of LRA is abstracted samples that can be used as input to the RLF predictor to enhance its performance. LRA output will be reported to the RLF prediction agent (whether at UE or at gNB sides). There are many advantages of LRA including: the possibility of charting the environment instead of having to generalize to so many environments; the possibility of denoising unnecessary data; conditioning on specific scenario to tune the reported data to be more beneficial to the main RLF prediction agent; much less network footprint or required signaling overhead to be conveyed to the RLF prediction agent (e.g., at network side) in compared to the common input data; and / or reduce the amount of complexity of RLF prediction agent.

[0132] The input of LRA, may be similar or part of the input of the targeted RLF prediction agent.

[0133] The output of LRA includes two components: (a) latent output which is sent to the network side model (or the RLF prediction agent); and (b) labels or b ackpropagation guidance label, such labels will not be used in the RLF prediction but will be used to construct the most efficient latent space. Such labels may include: replica of the input, network or UE related key performance indicator (KPI), for example SINR or throughput, etc.

[0134] The network may configure LRA frequency and temporal characteristics of operation.

[0135]

[0136]

[0137] Figure 4 shows an example of a communication system 100 in accordance with some embodiments. In the example, the communication system 100 includes a telecommunication network 102 that includes an access network 104, such as a radio access network (RAN), and a core network 106, which includes one or more core network nodes 108. The access network 104 includes one or more access network nodes, such as network nodes 110a and 110b (one or more of which may be generally referred to as network nodes 110), or any other similar 3rdGenerationPartnership Project (3 GPP) access node or non-3GPP access point. The network nodes 110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 112a, 112b, 112c, and 112d (one or more of which may be generally referred to as UEs 112) to the core network 106 over one or more wireless connections.

[0138] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 100 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0139] The UEs 112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 110 and other communication devices. Similarly, the network nodes 110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 112 and / or with other network nodes or equipment in the telecommunication network 102 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 102.

[0140] In the depicted example, the core network 106 connects the network nodes 110 to one or more hosts, such as host 116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 106 includes one more core network nodes (e.g., core network node 108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0141] The host 116 may be under the ownership or control of a service provider other than an operator or provider of the access network 104 and / or the telecommunication network 102, and may be operated by the service provider or on behalf of the service provider. The host 116 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0142] As a whole, the communication system 100 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.

[0143] In some examples, the telecommunication network 102 is a cellular network that implements 3 GPP standardized features. Accordingly, the telecommunications network 102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 102. For example, the telecommunications network 102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs.

[0144] In some examples, the UEs 112 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 104. Additionally, a UE may be configured for operating in single- or multi -RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0145] In the example, the hub 114 communicates with the access network 104 to facilitate indirect communication between one or more UEs (e.g., UE 112c and / or 112d) and network nodes (e.g., network node 110b). In some examples, the hub 114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 114 may be a broadband router enabling access to the core network 106 for the UEs. As another example, the hub 114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 110, or by executable code, script, process, or other instructions in the hub 114. As another example, the hub 114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 114 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices.

[0146] The hub 114 may have a constant / persistent or intermittent connection to the network node 110b. The hub 114 may also allow for a different communication scheme and / or schedule between the hub 114 and UEs (e.g., UE 112c and / or 112d), and between the hub 114 and the core network 106. In other examples, the hub 114 is connected to the core network 106 and / or one or more UEs via a wired connection. Moreover, the hub 114 may be configured to connect to an M2M service provider over the access network 104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 110 while still connected via the hub 114 via a wired or wireless connection. In some embodiments, the hub 114 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 110b. In other embodiments, the hub 114 may be a nondedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0147] Figure 5 shows a UE 200 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, musicstorage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3 GPP), including a narrow band internet of things (NB-IoT) LIE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0148] A UE may support device-to-device (D2D) communication, for example by implementing a 3 GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to- everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0149] The UE 200 includes processing circuitry 202 that is operatively coupled via a bus 204 to an input / output interface 206, a power source 208, a memory 210, a communication interface 212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 2. 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.

[0150] The processing circuitry 202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 210. The processing circuitry 202 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field- programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 202 may include multiple central processing units (CPUs).

[0151] In the example, the input / output interface 206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof.An input device may allow a user to capture information into the UE 200. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0152] In some embodiments, the power source 208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 208 may further include power circuitry for delivering power from the power source 208 itself, and / or an external power source, to the various parts of the UE 200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 208 to make the power suitable for the respective components of the UE 200 to which power is supplied.

[0153] The memory 210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 210 includes one or more application programs 214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 216. The memory 210 may store, for use by the UE 200, any of a variety of various operating systems or combinations of operating systems.

[0154] The memory 210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, orany combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 210 may allow the UE 200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 210, which may be or comprise a device-readable storage medium.

[0155] The processing circuitry 202 may be configured to communicate with an access network or other network using the communication interface 212. The communication interface 212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 222. The communication interface 212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 218 and / or a receiver 220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 218 and receiver 220 may be coupled to one or more antennas (e.g., antenna 222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0156] In the illustrated embodiment, communication functions of the communication interface 212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short- range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / intemet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0157] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), inresponse 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).

[0158] 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.

[0159] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or itemtracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 200 shown in Figure 2.

[0160] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3 GPP 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.

[0161] 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 speedinformation (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.

[0162] Figure 6 shows a network node 300 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NRNodeBs (gNBs)).

[0163] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0164] 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).

[0165] The network node 300 includes a processing circuitry 302, a memory 304, a communication interface 306, and a power source 308. The network node 300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may beshared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 304 for different RATs) and some components may be reused (e.g., a same antenna 310 may be shared by different RATs). The network node 300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 300.

[0166] The processing circuitry 302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node 300 components, such as the memory 304, to provide network node 300 functionality.

[0167] In some embodiments, the processing circuitry 302 includes a system on a chip (SOC). In some embodiments, the processing circuitry 302 includes one or more of radio frequency (RF) transceiver circuitry 312 and baseband processing circuitry 314. In some embodiments, the radio frequency (RF) transceiver circuitry 312 and the baseband processing circuitry 314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 312 and baseband processing circuitry 314 may be on the same chip or set of chips, boards, or units.

[0168] The memory 304 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 302. The memory 304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 302 and utilized by the networknode 300. The memory 304 may be used to store any calculations made by the processing circuitry 302 and / or any data received via the communication interface 306. In some embodiments, the processing circuitry 302 and memory 304 is integrated.

[0169] The communication interface 306 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 306 comprises port(s) / terminal(s) 316 to send and receive data, for example to and from a network over a wired connection. The communication interface 306 also includes radio front-end circuitry 318 that may be coupled to, or in certain embodiments a part of, the antenna 310. Radio front-end circuitry 318 comprises filters 320 and amplifiers 322. The radio front-end circuitry 318 may be connected to an antenna 310 and processing circuitry 302. The radio front-end circuitry may be configured to condition signals communicated between antenna 310 and processing circuitry 302. The radio front-end circuitry 318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 320 and / or amplifiers 322. The radio signal may then be transmitted via the antenna 310. Similarly, when receiving data, the antenna 310 may collect radio signals which are then converted into digital data by the radio front-end circuitry 318. The digital data may be passed to the processing circuitry 302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0170] In certain alternative embodiments, the network node 300 does not include separate radio front-end circuitry 318, instead, the processing circuitry 302 includes radio front-end circuitry and is connected to the antenna 310. Similarly, in some embodiments, all or some of the RF transceiver circuitry 312 is part of the communication interface 306. In still other embodiments, the communication interface 306 includes one or more ports or terminals 316, the radio front-end circuitry 318, and the RF transceiver circuitry 312, as part of a radio unit (not shown), and the communication interface 306 communicates with the baseband processing circuitry 314, which is part of a digital unit (not shown).

[0171] The antenna 310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 310 may be coupled to the radio front-end circuitry 318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 310 is separate from the network node 300 and connectable to the network node 300 through an interface or port.

[0172] The antenna 310, communication interface 306, and / or the processing circuitry 302 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 310, the communication interface 306, and / or the processing circuitry 302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0173] The power source 308 provides power to the various components of network node 300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 300 with power for performing the functionality described herein. For example, the network node 300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 308. As a further example, the power source 308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0174] Embodiments of the network node 300 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 300 may include user interface equipment to allow input of information into the network node 300 and to allow output of information from the network node 300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 300.

[0175] Figure 7 is a block diagram of a host 400, which may be an embodiment of the host 116 of Figure 4, in accordance with various aspects described herein. As used herein, the host 400 may be or comprise various combinations hardware and / or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host 400 may provide one or more services to one or more UEs.

[0176] The host 400 includes processing circuitry 402 that is operatively coupled via a bus 404 to an input / output interface 406, a network interface 408, a power source 410, and a memory412. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as Figures 10 and 3, such that the descriptions thereof are generally applicable to the corresponding components of host 400.

[0177] The memory 412 may include one or more computer programs including one or more host application programs 414 and data 416, which may include user data, e.g., data generated by a UE for the host 400 or data generated by the host 400 for a UE. Embodiments of the host 400 may utilize only a subset or all of the components shown. The host application programs 414 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs 414 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host 400 may select and / or indicate a different host for over-the-top services for a UE. The host application programs 414 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.

[0178] Figure 8 is a block diagram illustrating a virtualization environment 500 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 500 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized.

[0179] Applications 502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in thevirtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0180] Hardware 504 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 508a and 508b (one or more of which may be generally referred to as VMs 508), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 506 may present a virtual operating platform that appears like networking hardware to the VMs 508.

[0181] The VMs 508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 506. Different embodiments of the instance of a virtual appliance 502 may be implemented on one or more of VMs 508, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0182] In the context of NFV, a VM 508 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 508, and that part of hardware 504 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 508 on top of the hardware 504 and corresponds to the application 502.

[0183] Hardware 504 may be implemented in a standalone network node with generic or specific components. Hardware 504 may implement some functions via virtualization. Alternatively, hardware 504 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 510, which, among others, oversees lifecycle management of applications 502. In some embodiments, hardware 504 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate networkinterfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 512 which may alternatively be used for communication between hardware nodes and radio units.

[0184] Figure 9 shows a communication diagram of a host 602 communicating via a network node 604 with a UE 606 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE 112a of Figure 4 and / or UE 200 of Figure 5), network node (such as network node 110a of Figure 4 and / or network node 300 of Figure 6), and host (such as host 116 of Figure 4 and / or host 400 of Figure 7) discussed in the preceding paragraphs will now be described with reference to Figure 9.

[0185] Like host 400, embodiments of host 602 include hardware, such as a communication interface, processing circuitry, and memory. The host 602 also includes software, which is stored in or accessible by the host 602 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE 606 connecting via an over-the-top (OTT) connection 650 extending between the UE 606 and host 602. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 650.

[0186] The network node 604 includes hardware enabling it to communicate with the host 602 and UE 606. The connection 660 may be direct or pass through a core network (like core network 106 of Figure 4) and / or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.

[0187] The UE 606 includes hardware and software, which is stored in or accessible by UE 606 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE 606 with the support of the host 602. In the host 602, an executing host application may communicate with the executing client application via the OTT connection 650 terminating at the UE 606 and host 602. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. The OTT connection 650 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection 650.

[0188] The OTT connection 650 may extend via a connection 660 between the host 602 and the network node 604 and via a wireless connection 670 between the network node 604 and the UE 606 to provide the connection between the host 602 and the UE 606. The connection 660 and wireless connection 670, over which the OTT connection 650 may be provided, have been drawn abstractly to illustrate the communication between the host 602 and the UE 606 via the network node 604, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0189] As an example of transmitting data via the OTT connection 650, in step 608, the host 602 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE 606. In other embodiments, the user data is associated with a UE 606 that shares data with the host 602 without explicit human interaction. In step 610, the host 602 initiates a transmission carrying the user data towards the UE 606. The host 602 may initiate the transmission responsive to a request transmitted by the UE 606. The request may be caused by human interaction with the UE 606 or by operation of the client application executing on the UE 606. The transmission may pass via the network node 604, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 612, the network node 604 transmits to the UE 606 the user data that was carried in the transmission that the host 602 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 614, the UE 606 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 606 associated with the host application executed by the host 602.

[0190] In some examples, the UE 606 executes a client application which provides user data to the host 602. The user data may be provided in reaction or response to the data received from the host 602. Accordingly, in step 616, the UE 606 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input / output interface of the UE 606. Regardless of the specific manner in which the user data was provided, the UE 606 initiates, in step 618, transmission of the user data towards the host 602 via the network node 604. In step 620, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 604 receives user data from the UE 606 and initiates transmission of the received user data towards the host 602. In step 622, the host 602 receives the user data carried in the transmission initiated by the UE 606.

[0191] One or more of the various embodiments improve the performance of OTT services provided to the UE 606 using the OTT connection 650, in which the wireless connection 670 formsthe last segment. More precisely, the teachings of these embodiments may improve the data rate and latency and thereby provide benefits such as reduced user waiting time, better responsiveness, and better QoE.

[0192] In an example scenario, factory status information may be collected and analyzed by the host 602. As another example, the host 602 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 602 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 602 may store surveillance video uploaded by a UE. As another example, the host 602 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, the host 602 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and / or transmitting data.

[0193] In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 650 between the host 602 and UE 606, in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host 602 and / or UE 606. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 650 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 650 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node 604. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by the host 602. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 650 while monitoring propagation times, errors, etc.

[0194] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understoodthat 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.

[0195] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer- readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

[0196] Figure 10 is a flowchart illustrating an example method 1000 in a wireless device, according to certain embodiments. In particular embodiments, one or more steps of Figure 10 may be performed by UE 200 described with respect to Figure 5.

[0197] The method may begin at step 1014, where the wireless device (e.g., UE 200) receives a configuration from a network node. The configuration comprises an indication of assistance data the wireless device is to collect to be used with a radio link failure machine learning model.

[0198] In particular embodiments, the assistance information comprises one or more of the following: a value of one or more radio link failure related counters (e.g., counter N310, counter N311); a value of one or more radio link failure related timers (e.g., timer T310, timer T312); a number of out-of-synchronization events; a number of in-synchronization events; a number of retransmissions from a Radio Link Control layer; a number of retransmissions of a random access preamble; Radio Resource Management measurements (e.g., RSRP, RSRQ, RSSI, SINR, CSI and / or BLER) associated with one or more of a serving cell and one or more neighbor cells; lower layer measurements associated with one or more of a serving cell and one or more neighbor cells; a beam identifier associated with any one or more of the events, timers, counters or measurements; a cell identifier associated with any one or more of the events, timers, counters or measurements; a bandwidth part identifier associated with any one or more of the events, timers, counters or measurements; and frequency information associated with any one or more of the events, timers, counters or measurements. In particular embodiments, the assistance information comprises any of the assistance information described with respect to the embodiments and examples described herein.

[0199] In particular embodiments, the wireless device may receive the configuration via RRC configuration.

[0200] At step 1016, the wireless device may receive a reporting configuration from the network node. The reporting configuration comprises an indication of how to report the collected assistance information (e.g., triggering event(s), report format, where to send the report, periodicity, etc.).

[0201] In particular embodiments, the configuration information comprises one or more of the following: a start condition for collecting the assistance data; and a stop condition for collecting the assistance data. In particular embodiments, the configuration information comprises any of the report configuration information described with respect to the embodiments and examples described herein.

[0202] At step 1018, the wireless device collects the assistance information according to the received configuration. The wireless device may collect the assistance information according to any of the embodiments and examples described herein.

[0203] At step 1020, the wireless device transmits a report including the collected assistance information. In particular embodiments, the report further comprises an identifier associating the report with the received configuration information.

[0204] In some embodiments, the wireless device may transmit the report to the network node.For example, the RLF ML model may be a network-side ML model. The network node may usethe information in the report as input for training or inference, or as ground truth for training or for validation.

[0205] In some embodiments the wireless device may transmit the report to an ML server. For example, the RLF model may be a UE-side ML model. The ML server may use the information in the report as input for training or inference, or as ground truth for training or for validation.

[0206] In particular embodiments, the wireless device may transmit the report via RRC message. The wireless device may transmit the report according to any of the examples and embodiments described herein.

[0207] At step 1022, the wireless device may receive a command from the network node as a result of a prediction of radio link failure based on the transmitted report. For example, the wireless device may receive a handover command if the network node predicts a high likelihood of an upcoming RLF. In particular embodiments, the wireless device may receive a command according to any of the embodiments and examples described herein.

[0208] Modifications, additions, or omissions may be made to method 1000 of Figure 10. Additionally, one or more steps in the method of Figure 10 may be performed in parallel or in any suitable order.

[0209] Figure 11 is a flowchart illustrating an example method 1100 in a network node, according to certain embodiments. In particular embodiments, one or more steps of Figure 11 may be performed by network node 300 described with respect to Figure 6.

[0210] The method may begin at step 1114, where the network node (e.g., network node 300) transmits a configuration to a wireless device. The configuration comprises an indication of assistance data the wireless device is to collect to be used with a radio link failure machine learning model. The configuration is described in more detail with respect to Figure 10 and with respect to the embodiments and examples described herein.

[0211] At step 1116, the network node may transmit a reporting configuration to the wireless device. The reporting configuration comprises an indication of how to report the collected assistance information to the network node. The reporting configuration is described in more detail with respect to Figure 10 and with respect to the embodiments and examples described herein.

[0212] At step 1118, the network node may receive a report including the collected assistance information from the wireless device. The report is described in more detail with respect to Figure 10 and with respect to the embodiments and examples described herein.

[0213] At step 1120, the network node may perform machine learning model training, inference, or validation using the received assistance information with the machine learning model.The network node may use the received assistance information according any of the embodiments and examples described herein.

[0214] At step 1122, the network node may transmit the received assistance information to another network node for machine learning model training, inference, or validation. For example, the network node may not perform the machine learning interactions on its own, but may send the assistance information to another network node responsible for machine learning model training, inference, or validation.

[0215] At step 1124, the network node may transmit a command (e.g., handover command) to the wireless device as a result of a prediction of radio link failure based on the received report and the radio link failure machine learning model.

[0216] Modifications, additions, or omissions may be made to method 1100 of Figure 11. Additionally, one or more steps in the method of Figure 11 may be performed in parallel or in any suitable order.

[0217] The foregoing description sets forth numerous specific details. It is understood, however, that embodiments may be practiced without these specific details. In other instances, well-known circuits, structures and techniques have not been shown in detail in order not to obscure the understanding of this description. Those of ordinary skill in the art, with the included descriptions, will be able to implement appropriate functionality without undue experimentation.

[0218] References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to implement such feature, structure, or characteristic in connection with other embodiments, whether or not explicitly described.

[0219] Although this disclosure has been described in terms of certain embodiments, alterations and permutations of the embodiments will be apparent to those skilled in the art. Accordingly, the above description of the embodiments does not constrain this disclosure. Other changes, substitutions, and alterations are possible without departing from the scope of this disclosure, as defined by the claims below.

[0220] Some example embodiments are described below.Group A EmbodimentsA method performed by a wireless device, the method comprising:- receiving a configuration from a network node, the configuration comprising an indication of assistance data the wireless device is to collect to use as input to a radio link failure machine learning model;- collecting the assistance information according to the received configuration; and- transmitting a report including the collected assistance information to the network node. The method of the previous embodiment, wherein the configuration comprises one or more of the following:- one or more parameters indicating the wireless device which assistance data the wireless device should collect and send to the network node;- Status of counters;- Status of timers;- Number of Out Of Synch (OOS) events;- Number of In Synch (IS) events;- Number of retransmissions from MCG RLC;- Number of retransmissions of random access preamble;- Beam / cell / BWP / frequency information where the OOS or IS events occurred;- RRM measurements of serving and neighbor cells;- Lower layer measurements;- One or more parameters indicating the reporting frequency / period of the assistance information;- One or more parameters indicating to report the assistance information when a certain event occurs or when a certain procedure take place;- One or more parameters indicating to report the assistance information when a certain data exceed a certain threshold; and- One or more parameters indicating the preprocessing agent parameters and / or temporal operational characteristics. The method of any one of the previous embodiments, further comprising receiving a command from the network node as a result of a prediction of radio link failure based on the transmitted report.4. The method of any one of the previous embodiments, wherein the configuration comprises any of the example configurations described above herein.5. A method performed by a wireless device, the method comprising:- any of the wireless device steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above.6. The method of the previous embodiment, further comprising one or more additional wireless device steps, features or functions described above.7. The method of any of the previous two embodiments, further comprising:- providing user data; and- forwarding the user data to a host computer via the transmission to the base station.Group B Embodiments8. A method performed by a base station, the method comprising:- determining a configuration for a wireless device, the configuration comprising an indication of assistance data the wireless device is to collect to use as input to a radio link failure machine learning model;- transmitting the configuration to the wireless device; and- receiving a report from the wireless device, the report including collected assistance information based on the configuration.9. The method of the previous embodiment, wherein the configuration comprises one or more of the following:- one or more parameters indicating the wireless device which assistance data the wireless device should collect and send to the network node;- Status of counters;- Status of timers;- Number of Out Of Synch (OOS) events;- Number of In Synch (IS) events;- Number of retransmissions from MCG RLC;- Number of retransmissions of random access preamble;- Beam / cell / BWP / frequency information where the OOS or IS events occurred;- RRM measurements of serving and neighbor cells;- Lower layer measurements;- One or more parameters indicating the reporting frequency / period of the assistance information;- One or more parameters indicating to report the assistance information when a certain event occurs or when a certain procedure take place;- One or more parameters indicating to report the assistance information when a certain data exceed a certain threshold; and- One or more parameters indicating the preprocessing agent parameters and / or temporal operational characteristics.10. The method of any one of the previous two embodiments, further comprising transmitting a command to the wireless device, the command determined as a result of a prediction of radio link failure based on the received report.11. The method of any one of the previous three embodiments, wherein the configuration comprises any of the example configurations described above herein.12. A method performed by a base station, the method comprising:- any of the steps, features, or functions described above with respect to base stations, either alone or in combination with other steps, features, or functions described above.13. The method of the previous embodiment, further comprising one or more additional base station steps, features or functions described above.14. The method of any of the previous embodiments, further comprising:- obtaining user data; and- forwarding the user data to a host computer or a wireless device.Group C Embodimentsobile terminal comprising:- processing circuitry configured to perform any of the steps of any of the Group A embodiments; and- power supply circuitry configured to supply power to the wireless device. ase station comprising:- processing circuitry configured to perform any of the steps of any of the Group B embodiments;- power supply circuitry configured to supply power to the wireless device. ser equipment (UE) comprising:- an antenna configured to send and receive wireless signals;- radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry;- the processing circuitry being configured to perform any of the steps of any of the Group A embodiments;- an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry;- an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and- a battery connected to the processing circuitry and configured to supply power to the UE. mmunication system including a host computer comprising:- processing circuitry configured to provide user data; and- a communication interface configured to forward the user data to a cellular network for transmission to a user equipment (UE),- wherein the cellular network comprises a base station having a radio interface and processing circuitry, the base station’s processing circuitry configured to perform any of the steps of any of the Group B embodiments. communication system of the pervious embodiment further including the base station.The communication system of the previous 2 embodiments, further including the UE, wherein the UE is configured to communicate with the base station. The communication system of the previous 3 embodiments, wherein:- the processing circuitry of the host computer is configured to execute a host application, thereby providing the user data; and- the UE comprises processing circuitry configured to execute a client application associated with the host application. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:- at the host computer, providing user data; and- at the host computer, initiating a transmission carrying the user data to the UE via a cellular network comprising the base station, wherein the base station performs any of the steps of any of the Group B embodiments. The method of the previous embodiment, further comprising, at the base station, transmitting the user data. The method of the previous 2 embodiments, wherein the user data is provided at the host computer by executing a host application, the method further comprising, at the UE, executing a client application associated with the host application. A user equipment (UE) configured to communicate with a base station, the UE comprising a radio interface and processing circuitry configured to performs any of the previous 3 embodiments. A communication system including a host computer comprising:- processing circuitry configured to provide user data; and- a communication interface configured to forward user data to a cellular network for transmission to a user equipment (UE),- wherein the UE comprises a radio interface and processing circuitry, the UE’s components configured to perform any of the steps of any of the Group A embodiments.The communication system of the previous embodiment, wherein the cellular network further includes a base station configured to communicate with the UE. The communication system of the previous 2 embodiments, wherein:- the processing circuitry of the host computer is configured to execute a host application, thereby providing the user data; and- the UE’s processing circuitry is configured to execute a client application associated with the host application. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:- at the host computer, providing user data; and- at the host computer, initiating a transmission carrying the user data to the UE via a cellular network comprising the base station, wherein the UE performs any of the steps of any of the Group A embodiments. The method of the previous embodiment, further comprising at the UE, receiving the user data from the base station. A communication system including a host computer comprising:- communication interface configured to receive user data originating from a transmission from a user equipment (UE) to a base station,- wherein the UE comprises a radio interface and processing circuitry, the UE’s processing circuitry configured to perform any of the steps of any of the Group A embodiments. The communication system of the previous embodiment, further including the UE. The communication system of the previous 2 embodiments, further including the base station, wherein the base station comprises a radio interface configured to communicate with the UE and a communication interface configured to forward to the host computer the user data carried by a transmission from the UE to the base station.The communication system of the previous 3 embodiments, wherein:- the processing circuitry of the host computer is configured to execute a host application; and- the UE’s processing circuitry is configured to execute a client application associated with the host application, thereby providing the user data. The communication system of the previous 4 embodiments, wherein:- the processing circuitry of the host computer is configured to execute a host application, thereby providing request data; and- the UE’s processing circuitry is configured to execute a client application associated with the host application, thereby providing the user data in response to the request data. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:- at the host computer, receiving user data transmitted to the base station from the UE, wherein the UE performs any of the steps of any of the Group A embodiments. The method of the previous embodiment, further comprising, at the UE, providing the user data to the base station. The method of the previous 2 embodiments, further comprising:- at the UE, executing a client application, thereby providing the user data to be transmitted; and- at the host computer, executing a host application associated with the client application. The method of the previous 3 embodiments, further comprising:- at the UE, executing a client application; and- at the UE, receiving input data to the client application, the input data being provided at the host computer by executing a host application associated with the client application,- wherein the user data to be transmitted is provided by the client application in response to the input data.A communication system including a host computer comprising a communication interface configured to receive user data originating from a transmission from a user equipment (UE) to a base station, wherein the base station comprises a radio interface and processing circuitry, the base station’s processing circuitry configured to perform any of the steps of any of the Group B embodiments. The communication system of the previous embodiment further including the base station. The communication system of the previous 2 embodiments, further including the UE, wherein the UE is configured to communicate with the base station. The communication system of the previous 3 embodiments, wherein:- the processing circuitry of the host computer is configured to execute a host application;- the UE is configured to execute a client application associated with the host application, thereby providing the user data to be received by the host computer. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:- at the host computer, receiving, from the base station, user data originating from a transmission which the base station has received from the UE, wherein the UE performs any of the steps of any of the Group A embodiments. The method of the previous embodiment, further comprising at the base station, receiving the user data from the UE. The method of the previous 2 embodiments, further comprising at the base station, initiating a transmission of the received user data to the host computer.

Claims

CLAIMS1. A method performed by a wireless device, the method comprising: receiving (1014) a configuration from a network node, the configuration comprising an indication of assistance data the wireless device is to collect to be used with a radio link failure machine learning model; collecting (1018) the assistance information according to the received configuration; and transmitting (1020) a report including the collected assistance information.

2. The method of claim 1, further comprising receiving (1022) a command from the network node as a result of a prediction of radio link failure based on the transmitted report.

3. The method of any one of claims 1-2, further comprising receiving (1016) a reporting configuration from the network node, the reporting configuration comprising an indication of how to report the collected assistance information.

4. The method of any one of claims 1-3, wherein the assistance information comprises one or more of the following: a value of one or more radio link failure related counters; and a value of one or more radio link failure related timers.

5. The method of any one of claims 1-4, wherein the assistance information comprises one or more of the following: a number of out-of-synchronization events; and a number of in-synchronization events.

6. The method of any one of claims 1-5, wherein the assistance information comprises one or more of the following: a number of retransmissions from a Radio Link Control layer; a number of retransmissions of a random access preamble.

7. The method of any one of claims 1-6, wherein the assistance information comprises one or more of the following:Radio Resource Management measurements associated with one or more of a serving celland one or more neighbor cells; and lower layer measurements associated with one or more of a serving cell and one or more neighbor cells.

8. The method of any one of claims 1-7, wherein the assistance information comprises one or more of the following: a beam identifier associated with any one or more of the events, timers, counters or measurements; a cell identifier associated with any one or more of the events, timers, counters or measurements; a bandwidth part identifier associated with any one or more of the events, timers, counters or measurements; and frequency information associated with any one or more of the events, timers, counters or measurements.

9. The method of any one of claims 1-8, wherein the configuration information comprises one or more of the following: a start condition for collecting the assistance data; and a stop condition for collecting the assistance data.

10. The method of any one of claims 1-9, wherein the report further comprises an identifier associating the report with the received configuration information.

11. A wireless device (200) comprising processing circuitry (202), the processing circuitry operable to: receive a configuration from a network node (300), the configuration comprising an indication of assistance data the wireless device is to collect to be sued with a radio link failure machine learning model; collect the assistance information according to the received configuration; and transmit a report including the collected assistance information.

12. The wireless device of claim 11, the processing circuitry further operable to receive a command from the network node as a result of a prediction of radio link failure based on the transmitted report.

13. The wireless device of any one of claims 11-12, the processing circuitry further operable to receive a reporting configuration from the network node, the reporting configuration comprising an indication of how to report the collected assistance information.

14. The wireless device of any one of claims 11-31, wherein the assistance information comprises one or more of the following: a value of one or more radio link failure related counters; and a value of one or more radio link failure related timers.

15. The wireless device of any one of claims 11-14, wherein the assistance information comprises one or more of the following: a number of out-of-synchronization events; and a number of in-synchronization events.

16. The wireless device of any one of claims 11-15, wherein the assistance information comprises one or more of the following: a number of retransmissions from a Radio Link Control layer; a number of retransmissions of a random access preamble.

17. The wireless device of any one of claims 11-16, wherein the assistance information comprises one or more of the following:Radio Resource Management measurements associated with one or more of a serving cell and one or more neighbor cells; and lower layer measurements associated with one or more of a serving cell and one or more neighbor cells.

18. The wireless device of any one of claims 11-17, wherein the assistance information comprises one or more of the following: a beam identifier associated with any one or more of the events, timers, counters or measurements; a cell identifier associated with any one or more of the events, timers, counters or measurements; a bandwidth part identifier associated with any one or more of the events, timers, countersor measurements; and frequency information associated with any one or more of the events, timers, counters or measurements.

19. The wireless device of any one of claims 11-18, wherein the configuration information comprises one or more of the following: a start condition for collecting the assistance data; and a stop condition for collecting the assistance data.

20. The wireless device of any one of claims 11-19, wherein the report further comprises an identifier associating the report with the received configuration information.

21. A method performed by a network node, the method comprising: transmitting (1114) a configuration to a wireless device, the configuration comprising an indication of assistance data the wireless device is to collect to be used with a radio link failure machine learning model; and receiving (1118) a report including the collected assistance information from the wireless device.

22. The method of claim 21, further comprising transmitting (1124) a command to the wireless device as a result of a prediction of radio link failure based on the received report and the radio link failure machine learning model.

23. The method of any one of claims 21-22, further comprising transmitting (1116) a reporting configuration to the wireless device, the reporting configuration comprising an indication of how to report the collected assistance information to the network node.

24. The method of any one of claims 21-23, further comprising performing (1120) machine learning model training, inference, or validation using the received assistance information with the machine learning model.

25. The method of any one of claims 21-23, further comprising transmitting (1122) the received assistance information to another network node for machine learning model training, inference, or validation.

26. The method of any one of claims 21-25, wherein the assistance information comprises one or more of the following: a value of one or more radio link failure related counters; a value of one or more radio link failure related timers; a number of out-of-synchronization events; a number of in-synchronization events; a number of retransmissions from a Radio Link Control layer; and a number of retransmissions of a random access preamble.

27. The method of any one of claims 21-26, wherein the assistance information comprises one or more of the following:Radio Resource Management measurements associated with one or more of a serving cell and one or more neighbor cells; and lower layer measurements associated with one or more of a serving cell and one or more neighbor cells;28. The method of any one of claims 21-27, wherein the assistance information comprises one or more of the following: a beam identifier associated with any one or more of the events, timers, counters or measurements; a cell identifier associated with any one or more of the events, timers, counters or measurements; a bandwidth part identifier associated with any one or more of the events, timers, counters or measurements; and frequency information associated with any one or more of the events, timers, counters or measurements.

29. The method of any one of claims 21-28, wherein the configuration information comprises one or more of the following: a start condition for collecting the assistance data; and a stop condition for collecting the assistance data.

30. The method of any one of claims 21-29, wherein the report further comprises anidentifier associating the report with the configuration information.

31. A network node (300) comprising processing circuitry (302), the processing circuitry operable to: transmit a configuration to a wireless device (200), the configuration comprising an indication of assistance data the wireless device is to collect to be used with a radio link failure machine learning model; and receive a report including the collected assistance information from the wireless device.

32. The network node of claim 31, the processing circuitry further operable to transmit a command to the wireless device as a result of a prediction of radio link failure based on the received report and the radio link failure machine learning model.

33. The network node of any one of claims 31-32, the processing circuitry further operable to transmit a reporting configuration to the wireless device, the reporting configuration comprising an indication of how to report the collected assistance information to the network node.

34. The network node of any one of claims 31-33, the processing circuitry further operable to perform machine learning model training, inference, or validation using the received assistance information with the machine learning model.

35. The network node of any one of claims 31-33, the processing circuitry further operable to transmit the received assistance information to another network node for machine learning model training, inference, or validation.

36. The network node of any one of claims 31-35, wherein the assistance information comprises one or more of the following: a value of one or more radio link failure related counters; a value of one or more radio link failure related timers; a number of out-of-synchronization events; a number of in-synchronization events; a number of retransmissions from a Radio Link Control layer; and a number of retransmissions of a random access preamble.

37. The network node of any one of claims 31-36, wherein the assistance information comprises one or more of the following:Radio Resource Management measurements associated with one or more of a serving cell and one or more neighbor cells; and lower layer measurements associated with one or more of a serving cell and one or more neighbor cells;38. The network node of any one of claims 31-37, wherein the assistance information comprises one or more of the following: a beam identifier associated with any one or more of the events, timers, counters or measurements; a cell identifier associated with any one or more of the events, timers, counters or measurements; a bandwidth part identifier associated with any one or more of the events, timers, counters or measurements; and frequency information associated with any one or more of the events, timers, counters or measurements.

39. The network node of any one of claims 31-38, wherein the configuration information comprises one or more of the following: a start condition for collecting the assistance data; and a stop condition for collecting the assistance data.

40. The network node of any one of claims 31-39, wherein the report further comprises an identifier associating the report with the configuration information.

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