User equipment (UE) performance metric calculation including joint triggering

Abstract

A UE (22) is configured to receive, from the network node (16), information of a first resource configuration within a first report configuration and information of a second resource configuration within a second report configuration; perform a beam prediction; calculate one or more performance metrics corresponding to the beam prediction based on a time value, the time value signaling a maximum value of a time gap between at least one occasion of one or more resources in the second resource configuration and at least one corresponding occasion of the one or more resources in the first resource configuration; and report the beam prediction according to the first report configuration and the computed one or more performance metrics corresponding to the beam prediction according to the second report configuration.

Description

[0001] USER EQUIPMENT (UE) PERFORMANCE METRIC CAECUEATION INCLUDING JOINT TRIGGERING FIELD

[0002] The present disclosure relates to wireless communications, and in particular, to user equipment performance metric calculation including joint triggering.

[0003] BACKGROUND

[0004] The Third Generation Partnership Project (3GPP) has developed and is developing standards for Fourth Generation (4G) (also referred to as Long Term Evolution (LTE)) and Fifth Generation (5G) (also referred to as New Radio (NR)) wireless communication systems. Such systems provide, among other features, broadband communication between network nodes (NNs), such as base stations, and mobile user equipments (UE), as well as communication between network nodes and between UEs. The 3GPP is also developing standards for Sixth Generation (6G) wireless communication networks.

[0005] Beam management (BM)

[0006] Beam management procedure

[0007] In high frequency range (FR2), multiple radio frequency (RF) beams may be used to transmit and receive signals at a network node (e.g., gNB) and a UE. For each DL beam from a network node, there is typically an associated best UE Reception (Rx) beam for receiving signals from the downlink (DL) beam. The DL beam and the associated UE Rx beam forms a beam pair. The beam pair can be identified through a so-called beam management process in NR.

[0008] A DL beam is (typically) identified by an associated DL reference signal (RS) transmitted in the beam, either periodically, semi-persistently, or aperiodically. The DL RS for the purpose can be a Synchronization Signal (SS) and Physical Broadcast Channel (PBCH) block (SSB) or a Channel State Information RS (CSI-RS). By measuring all the DL RSs, the UE can determine and report to the network node the best DL beam to use for DL transmissions. The network node can then transmit a burst of DL-RS using the reported best DL beam to let the UE evaluate candidate UE Rx beams.

[0009] Although not explicitly stated in the NR specification, beam management has been divided into three procedures, schematically illustrated in FIG. 1 :

[0010] • P-1 : Purpose is to find a coarse direction for the UE using wide gNB Transmit (TX) beam covering the whole angular sector.• P-2: Purpose is to refine the gNB TX beam by doing a new beam search around the coarse direction found in Pl.

[0011] • P-3: Used for UE that has analog beamforming to let the UE find a suitable UE RX beam.

[0012] More specifically, FIG. 1 shows an example of a beam management procedure. P-1 is expected to utilize beams with rather large beamwidths and where the beam reference signals are transmitted periodically and are shared between all UEs of the cell. Typically, the reference signals to use for P-1 are periodic CSI-RS or SSB. The UE then reports the N best beams to the network node and their corresponding Reference Signal Received Power (RSRP) values. P-2 is expected to use aperiodic / or semi-persistent CSI-RS transmitted in narrow beams around the coarse direction found in P-1. P-3 is expected to use aperiodic or semi-persistent CSI-RSs repeatedly transmitted in one narrow gNB beam. One alternative way is to let the UE determine a suitable UE RX beam based on the periodic SSB transmission. Since each SSB consists of four Orthogonal Frequency Division Multiplexing (OFDM) symbols, a maximum of four UE RX beams can be evaluated during each SSB burst transmission. One benefit of using SSB instead of CSI-RS is that no extra overhead of CSI-RS transmission is needed.

[0013] Reference signal

[0014] Reference signal configurations

[0015] CSI-RS:

[0016] A CSI-RS is transmitted over each transmit (Tx) antenna port at the network node and for different antenna ports. The CSI-RS are multiplexed in time, frequency, and code domain such that the channel between each Tx antenna port at the network node and each receive antenna port at a UE can be measured by the UE. The time-frequency resource used for transmitting CSI-RS is referred to as a CSI-RS resource.

[0017] In NR, the CSI-RS for beam management is defined as a 1- or 2-port CSI-RS resource in a CSI-RS resource set where the field repetition is present. The following three types of CSI-RS transmissions are supported:

[0018] • Periodic CSI-RS: CSI-RS is transmitted periodically in certain slots. This CSI-RS transmission is semi-statically configured using Radio Resource Control (RRC) signaling with parameters such as CSI-RS resource, periodicity, and slot offset.

[0019] • Semi-Persistent CSI-RS: Similar to periodic CSI-RS, resources for semi-persistent CSI-RS transmissions are semi-statically configured using RRC signaling withparameters such as periodicity and slot offset. However, unlike periodic CSI-RS, dynamic signaling is needed to activate and deactivate the CSI-RS transmission.

[0020] • Aperiodic CSI-RS: This is a one-shot CSI-RS transmission that can happen in any slot. Here, one-shot means that CSI-RS transmission only happens once per trigger. The CSI-RS resources (i. e. , the Resource Element (RE) locations which consist of subcarrier locations and OFDM symbol locations) for aperiodic CSI-RS are semi-statically configured. The transmission of aperiodic CSI-RS is triggered by dynamic signaling through Physical Downlink Control Channel (PDCCH) using the CSI request field in UL Downlink Control Information (DCI), in the same DCI where the Uplink (UL) resources for the measurement report are scheduled. Multiple aperiodic CSI-RS resources can be included in a CSI-RS resource set and the triggering of aperiodic CSI-RS is on a resource set basis.

[0021] SSB:

[0022] In NR, an SSB consists of a pair of synchronization signals (SSs), physical broadcast channel (PBCH), and Downlink Demodulation Reference Signals (DMRS) for PBCH. A SSB is mapped to 4 consecutive OFDM symbols in the time domain and 240 contiguous subcarriers (20 RBs) in the frequency domain.

[0023] To support beamforming and beam-sweeping for SSB transmission, in NR, a cell can transmit multiple SSBs in different narrow-beams in a time multiplexed fashion. The transmission of these SSBs is confined to a half frame time interval (5 ms). It is also possible to configure a cell to transmit multiple SSBs in a single wide-beam with multiple repetitions. The design of beamforming parameters for each of the SSBs within a half frame is up to network implementation. The SSBs within a half frame are broadcast periodically from each cell. The periodicity of the half frames with SS / PBCH blocks is referred to as SSB periodicity, which is indicated by SIB1.

[0024] The maximum number of SSBs within a half frame, denoted by L, depends on the frequency band, and the time locations for these L candidate SSBs within a half frame depends on the SCS of the SSBs. The L candidate SSBs within a half frame are indexed in an ascending order in time from 0 to L-l. By successfully detecting PBCH and its associated DMRS, a UE knows the SSB index. A cell does not necessarily transmit SS / PBCH blocks in all L candidate locations in a half frame, and the resource of the unused candidate positions can be used for the transmission of data or control signaling instead. It is up to network implementation to decide which candidate time locations toselect for SSB transmission within a half frame, and which beam to use for each SSB transmission.

[0025] Measurement resource configurations

[0026] In NR, a UE can be configured with N>1 CSI reporting settings (i. e. , alternatively referred to as CSI-ReportConfig), M>1 resource settings (i.e. , alternatively referred to as CSI-ResourceConfig), where each CSI reporting setting is linked to one or more resource setting for channel and / or interference measurement. The CSI framework is modular, meaning that several CSI reporting settings may be associated with the same Resource Setting.

[0027] The measurement resource configurations for beam management are provided to the UE by RRC Information Elements (IES) CSI-ResourceConfigs. One CSI-ResourceConfig contains several NZP-CSI-RS -ResourceSets and / or CSI-SSB-ResourceSets.

[0028] A UE can be configured to perform measurements on CSI-RSs. The RRC information element (IE) NZP-CSI-RS-ResourceSet may be used. ANZP CSI-RS resource set contains the configuration of Ks >1 CSI-RS resources, where the configuration of each CSI-RS resource includes at least: mapping to REs, the number of antenna ports, time-domain behavior, etc. Up to 64 CSI-RS resources can be grouped to a NZP-CSI-RS-ResourceSet. A UE can also be configured to perform measurements on SSBs. The RRC IE CSI-SSB-ResourceSet may be used. Resource sets comprising SSB resources are defined in a similar manner.

[0029] In the case of aperiodic CSI-RS and / or aperiodic CSI reporting, the network node configures the UE with ScCSI triggering states. Each triggering state contains the aperiodic CSI report setting to be triggered along with the associated aperiodic CSI-RS resource sets.

[0030] Periodic and semi-persistent Resource Settings can only comprise a single resource set (i.e. S=l) while S>=1 for aperiodic Resource Settings. This is because in the aperiodic case, one out of the S resource sets comprised in the Resource Setting is indicated by the aperiodic triggering state that triggers a CSI report. The RRC IEs described above are defined in 3GPP 38.331 V18.0.0

[0031] Measurement Reporting

[0032] Three types of CSI reporting are supported in NR as follows:• Periodic CSI Reporting on PUCCH: CSI is reported periodically by a UE. Parameters such as periodicity and slot offset are configured semi-statically by higher layer RRC signaling from the network node to the UE.

[0033] • Semi-Persistent CSI Reporting on PUSCH or PUCCH: similar to periodic CSI reporting, semi-persistent CSI reporting has a periodicity and slot offset which may be semi-statically configured. However, a dynamic trigger from network node to UE may be needed to allow the UE to begin semi-persistent CSI reporting. A dynamic trigger from network node to UE is needed to request the UE to stop the semi-persistent CSI reporting.

[0034] • Aperiodic CSI Reporting on Physical Uplink Shared Channel (PUSCH): This type of CSI reporting involves a single-shot (i. e. , one time) CSI report by a UE which is dynamically triggered by the network node using DCI. Some of the parameters related to the configuration of the aperiodic CSI report is semi-statically configured by RRC, but the triggering is done dynamically via DCI.

[0035] In each CSI reporting setting, the content and time-domain behavior of the report is defined, along with the linkage to the associated Resource Settings. The CSI-ReportConflg IE comprise the following configurations:

[0036] • reportConflgType

[0037] o Defines the time-domain behavior, i.e., periodic CSI reporting, semi-persistent CSI reporting, or aperiodic CSI reporting, along with the periodicity and slot offset of the report for periodic CSI reporting.

[0038] • reportQuantity

[0039] o Defines the reported CSI parameter(s) (i.e., the CSI content), such as PMI, CQI, RI, LI (layer indicator), CRI (CSI-RS resource index) and Ll-RSRP. Only a certain number of combinations are possible (e.g., ‘cri-RI-PMI-CQT is one possible value and ‘cri-RSRP’ is another) and each value of reportQuantity could be said to correspond to a certain CSI mode.

[0040] • codebookConflg

[0041] o Defines the codebook used for PMI reporting, along with possible codebook subset restriction (CBSR). Two “Types” of PMI codebook are defined in NR, Type I CSI and Type II CSI, each codebook type further has two variants each.

[0042] reportF requencyConflgurationo Define the frequency granularity of PMI and CQI (wideband or subband), if reported, along with the CSI reporting band, which is a subset of subbands of the bandwidth part (BWP) which the CSI corresponds to.

[0043] • Measurement restriction in time domain (ON / OFF) for channel and interference respectively.

[0044] For beam management, a UE can be configured to report Ll-RSRP for up to four different CSI-RS / SSB resource indicators. The reported Ll-RSRP value corresponding to the first (best) CRI / SSBRI requires 7 bits, using absolute values, while the others require 4 bits using encoding relative to the first. The report of Ll-SINR for beam management has been supported since 3GPP NR Rel-16.

[0045] AI / ML based spatial beam prediction in NR

[0046] During the 3GPP meeting RANl#109-e, it was agreed to study AI / ML based spatial beam prediction (BM Case 1) for a set A of beams based on measurement results of Set B of beams. The Set B of beams could either be a subset of the Set A of beams, or the set A of beams could consist of different beams compared to the Set B of beams (for example Set A consists of narrow beams and Set B consists of wide beams). The spatial beam prediction could either be made at the gNB side or at the UE side.

[0047] During the 3GPP meeting RANl#109-e, it was also agreed to study AI / ML based temporal (BM case 2) beam prediction for a Set A of beams based on measurement results of Set B of beams, where the Set A of beams and Set B of beams can be the same set of beams or different set of beams. For AI / ML based temporal beam prediction, it was also agreed that the measurement results of K (K>=1) latest measurement instances during a time window T1 of the Set B beams are used for AI / ML model input. Further, it was agreed that one or more beams from the Set A beams will be used as AI / ML model output, where the AI / ML model output should be F predictions for F future time instances, where all F future time instances are located within a time window T2.

[0048] During the 3 GPP meeting RANI# 115, it was agreed to capture the contents of FIG. 2 and description of the two sub-use cases for providing a description of the BM use case as part of the 3GPP TR 38.843 V18.0.0. More specifically, FIG. 2 corresponds to Figure 6.3.1-1 shown in 3GPP TR 38.843 V18.0.0 and provides an example for the inference procedure for beam management for BM-Casel and BM-Case2. Measurements based on Set B of beams are used as model input. In addition, beam ID information may be also provided as input to the AI / ML model. Based on model output (e.g., probability of each beam in Set A to be the Top-1 beam, predicted Ll-RSRPs), Top-l / N beam(s) amongSet A of beams can be predicted and / or potentially with predicted Ll-RSRPs (depending on the labeling). In the evaluation, for BM-Case 1, the measurements of Set B (otherwise stated) are used as model input to predict Top-l / N beams from Set A, and for BM-Case2, the measurements from historic time instance(s) are used as model input for temporal DL beam prediction of beams from Set A. In the evaluation, the cases that Set A and Set B are different (Set B is NOT a subset of Set A), and Set B is a subset of Set A for both BM-Casel and BM-Case2, and case that Set A and Set B are the same for BM-Case2 are considered. And the performance of DL Tx beam prediction and DL Tx-Rx beam pair prediction is evaluated.

[0049] For both BM-Casel and BM-Case2, the UE can report the prediction result to the network node based on the output of a UE-side model, or gNB can predict the Top-l / N beam(s) based on the reported measurements of Set B for a NW-side model. As beam is something that is formed on the NW side, the UE may only measure the result of this. This can for example be that the narrow beams are measured by CSI-RS resources and the wide beams are measured by SSBs at the UE side. This would be how the UE could see the beams.

[0050] Set B is different from Set A

[0051] FIG. 3 shows a schematic example of the Set A of beams and the Set B of beams, where Set B is different from Set A. Set B of beams are wide NW beams, and the Set A of beams are the narrow gNB beams. The term “NW” may refer to network node or network. The top illustration shows all the narrow gNB beams, which constitutes the Set A of beams, and the lower illustrations shows all the wide gNB beams, which constitutes the Set B of beams.

[0052] Set B is a subset of Set A

[0053] FIG. 4 shows another example of the Set A of beams and the Set B of beams, where Set A contains narrow gNB beams and set B is subset of Set A containing some narrows beams from the gNB. Set B may be a subset of Set A of beams. Both Set B and Set A of beams are the narrow gNB beams.

[0054] 3GPP Release 19 (Rel-19) status

[0055] In radio access network 1 (RANI) #116, it was decided that the UE can report Top-K beams to the NW, according to the agreement below. It is for further study the exact number of K. The following is an excerpt associated with RAN1#116.

[0056] For UE-sided model, at least for BM-Casel, for content in the report of inference results, supportOpt 1: Beam information on predicted Top K beam(s) among a set of beams

[0057] • Opt 2: Beam information on predicted Top K beam(s) among a set of beams and RSRP of predicted Top K beam(s) among a set of beams

[0058] • At least K=1 and more, FFS on max value

[0059] • FFS on beam information

[0060] • FFS on the definition of predicted Top K beam(s)

[0061] • FFS on definition of reported RSRP when applicable

[0062] • FFS on other information in the report with potential down selection among the following options

[0063] • Opt 3: Beam information on predicted Top K beam(s) among a set of beams and probability information of predicted Top K beam(s) among a set of beams o FFS on the quantization method of probability information o Probability information is the probability of the beam to be the Top 1 or Top K beam

[0064] • Opt 4: Beam information on predicted Top K beam(s) among a set of beams, RSRP of predicted Top Kbeam(s) among a set of beams, and confidence information of the RSRP

[0065] o FFS on definition of reported RSRP

[0066] o FFS on the definition and quantization method of confidence information

[0067] • Other options are not precluded

[0068] In RANI# 117, for Type I Option 1 performance monitoring for a UE-side AI / ML model, it was agreed that UE send a report including measurements results to NW for calculating the performance metric at NW. For Type I Option 2 performance monitoring for a UE-sided AI / ML model, the UE computes the performance metric and reports it to the NW. Further details of performance monitoring for AI / ML beam management can be found in 3GPP TR 38.843 V18.0.0 Section 7.1.3 and may include the following:

[0069] For BM-Casel and BM-Case2 with a UE-side AI / ML model:

[0070] • Support Type 1 performance monitoring, including the following two options:

[0071] o Option 1 (NW-side performance monitoring):

[0072] ■ UE sends a report to NW (for the calculation of performance metric at NW)• Measurement results from resource set for monitoring, e.g., Ll-RSRP and / or RS index is supported as the content of the report • FFS on other contents

[0073] ■ The report is at least configured / triggered by NW

[0074] ■ Note: this may or may not have additional spec impact o Option 2 (UE-assisted performance monitoring):

[0075] ■ UE calculates performance metric(s)

[0076] • FFS how to report and what to report

[0077] o FFS whether to trigger the report based on event(s) for Option 1 and / or Option 2

[0078] o FFS Type 2 performance monitoring

[0079] Monitoring configuration for AI / ML beam prediction

[0080] In NR Rel-19, the configuration of monitoring is to be done by configuring a separate CSI report configuration for the purpose of monitoring. This report configuration provides both a link to the inference report configuration, and a set of resources for the UE to use for calculating the performance metrics. The corresponding agreement is given below as follows:

[0081] At least for the monitoring Type 1 Option 2 ofUE-side model monitoring (when applicable), support to reuse CSI framework for the configuration for monitoring result report in LI signaling:

[0082] • Dedicated resource set(s) for monitoring and report configuration for monitoring are configured in a dedicated CSI report configuration used for monitoring o The ID of an inference report configuration is configured in the configuration for monitoring to link the inference report configuration and monitoring report configuration

[0083] ■ FFS how to identify the connection between RSs in the resource set(s) for monitoring and Set A beams

[0084] o FFS on whether to support all the combination on time domain behavior of the reportConfigType for infernece report and the reportConfigType for monitoring report o FFS on the timing related issues

[0085] o UE measures the dedicated resource set(s) for monitoring.

[0086] The view on how the monitoring report configuration is done in the current 3GPP NR CSI framework as shown in FIG. 5. That is, an example of linking of monitoring and inference configuration for Rel-19 is shown. The monitoring is linked to an inferenceconfiguration (or vice versa) according to the RANI agreements. How a monitoring and inference configuration is triggered is an open issue. One solution is to activate them in separate messages according to existing standard. However, this can lead to unnecessary high signaling overhead since the two configurations are linked. There is further an issue of whether and how the UE should report an aggregated metric (over N monitoring occasions) when a monitoring report configuration is an aPeriodic configuration. In such configuration, typically the UE would only estimate a one-shot metric. It is an open issue on how to handle this scenario (i.e., how to enable reporting of an aggregated metric for an aPeriodic monitoring report configuration).

[0087] SUMMARY

[0088] Some embodiments advantageously provide methods, systems, and apparatuses for user equipment performance metric calculation including joint triggering.

[0089] One or more embodiments provide methods for jointly triggering the monitoring reporting configuration and the linked inference reporting configuration. Other embodiments provide methods to support first triggering the performance metric calculation via the first (joint) activation message, and the activation of the metric report in a second message. The embodiments may provide one or more of the following:

[0090] For aPeriodic or semi-persistent report configuration, using DCI / MAC-CE to activate both the monitoring resource configuration and the linked inference configuration (or vice versa) within the same DCI / MAC-CE message.

[0091] o Further details include message details that handle when multiple inference configurations are linked with a monitoring configuration (or vice versa).

[0092] o For semi-persistent scenario:

[0093] ■ Using DCI to activate both the inference / monitoring in the same message for PUSCH based reporting.

[0094] ■ Using MAC CE for PUCCH based reporting.

[0095] For semi-persistent resource configuration, using DCI / MAC-CE to activate both periodic inference and the linked periodic monitoring resource configuration within the same DCI / MAC-CE message or vice versa, thus enabling the UE to start calculating a monitoring performance metric

[0096] o In a subsequent aPeriodic report configuration, activating the report of such metricUsing RRC to configure both the periodic inference and monitoring resource configurations.

[0097] o In a subsequent aPeriodic report configuration, activating the report of such metric

[0098] For semi-persistent scenario,

[0099] o using DCI to activate both the inference / monitoring in the same message for PUSCH based reporting

[0100] o Using MAC CE. for PUCCH based reporting

[0101] In one embodiment, the DCI or the MAC CE includes an indication of the reporting configuration that the UE should activate, wherein the indication is an identifier associated to one of reporting configuration including monitoring resources or reporting configuration including inference configuration. If the indication is associated with the reporting configuration including monitoring resources, then the UE should activate both the reporting configuration including monitoring resources and the linked inference configuration. If the indication is associated with the reporting configuration including resources for the inference, then the UE may activate the reporting configuration both for the said reporting configuration including resources for the inference and for the associated monitoring configuration, if there is a monitoring configuration linked to the said inference configuration. Same method applies in case the MAC CE / DCI includes indication of the reporting configuration associated to which the UE should transmit the report, i.e. if the MAC CE includes indication for the reporting of monitoring results, then the UE also transmit a report including inference results (e.g. radio measurement predictions), and vice versa if the MAC CE includes indication for the reporting of inference results.

[0102] The advantages of the embodiments may include using a single DCI / MAC-CE message to both activate the inference and the monitoring resource for enabling the UE to calculate the performance of its AI / ML model. This leads to reduced signaling overhead on the control channel. The advantage is furthermore methods for enabling triggering a specific linked resource / report configuration. Further, the embodiments enable the NW to first start the UE performance metric calculation by initiating periodic monitoring and inference resources, and then activate the report of such metric in an aPeriodic report. This reduces the signaling overhead, since otherwise the UE would have needed to be configured with a periodic report, instead of triggering a report when needed by the NW (e.g. when a large drop in performance is observed).Also, the embodiments enable UE signaling of a Tmaxvalue that determine the maximum time between inference and monitoring resources. This can enable more flexibility at the NW, e.g., at the network nodes, as to how to configure the monitoring and inference resources, and which of such configurations that it can trigger based on UE Tmaxsignalling. The NW may be enabled to configure a subset of beams that are able to be transmitted within the Tmaxtime limit.

[0103] According to one or more embodiments, a method of performance monitoring in a user equipment (UE) that is configured to communicate with a network node is provided. The method includes receiving, from the network node, information of a first resource configuration within a first report configuration and information of a second resource configuration within a second report configuration; performing a beam prediction based on at least one occasion of one or more resources in the first resource configuration; calculating one or more performance metrics corresponding to the beam prediction based on a maximum time value, Tmaxsignaling a maximum time of a time gap between at least one occasion of one or more resources in the second resource configuration and at least one corresponding occasion of the one or more resources in the first resource configuration; and reporting the beam prediction according to the first report configuration and the calculated one or more performance metrics corresponding to the beam prediction according to the second report configuration.

[0104] According to one or more embodiments, the first resource configuration is an inference resource configuration.

[0105] According to one or more embodiments, the second resource configuration is a monitoring resource configuration.

[0106] According to one or more embodiments, a first message received from the network node jointly triggers both the monitoring resource configuration and the inference resource configuration.

[0107] According to one or more embodiments, the first message triggers the UE to report the beam prediction according to the first report configuration and the calculated one or more performance metrics corresponding to the beam prediction according to the second report configuration.

[0108] According to one or more embodiments, the first message triggers the UE to start calculating one or more performance metrics according to the second report configuration.According to one or more embodiments, a second message is received from a network node where the second message indicates that the UE is to report the calculated one or more performance metrics calculated according to the second report configuration.

[0109] According to one or more embodiments, the first message triggers the UE to report the calculated one or more performance metrics corresponding to the beam prediction according to the second report configuration to the network node.

[0110] According to one or more embodiments, one or more of: the first message activates a periodic inference resource configuration and a periodic monitoring resource configuration for performance metric calculation; the first message further triggers a report of the one or more performance metrics corresponding to the beam prediction according to the second report configuration; the first message lacks an indication for the network node to activate a subsequent report of the one or more performance metrics; and the first message further includes an indication for the network node to activate a subsequent aPeriodic report of the one or more performance metrics.

[0111] According to one or more embodiments, one or more of: the first message activates an aPeriodic inference resource and an aPeriodic monitoring resource; the first message further triggers a report of the one or more performance metrics corresponding to the beam prediction according to the second report configuration; and wherein the first message indicates which resourceSet, among a number of resource sets in the monitoring resource configuration that is received, the indication triggering the UE to predict at least one resource set based on the inference resource configuration.

[0112] According to one or more embodiments, the second message is received where the second message activates an aPeriodic report of the one or more performance metrics corresponding to the beam prediction according to the second report configuration.

[0113] According to one or more embodiments, the first message is received using one or more of downlink control information, DCI, medium access control, MAC, control element, CE, and radio resource control, RRC.

[0114] According to one or more embodiments, the second message is received using DCI, MAC CE, or RRC.

[0115] According to one or more embodiments, when the first message is received using DCI, the monitoring resource configuration and the inference resource configuration are jointly triggered by a Channel State Information, CSI, request field in the DCI.

[0116] According to one or more embodiments, the first message or the second message comprises a reporting configuration identifier associated to the monitoring resourceconfiguration or to the inference resource configuration, the first message triggering the UE to activate the monitoring resource configuration and a linked inference configuration.

[0117] According to one or more embodiments, one or more calculated performance metrics are transmitted for one or more configurations that are activated by the first message or the second message.

[0118] According to one or more embodiments, wherein the second message includes a reference to one of reporting configuration identifier associated to the monitoring resource configuration, or to the inference resource configuration, the second message triggering the UE to transmit one or more reports associated to the monitoring resources configuration and to a linked inference configuration.

[0119] According to one or more embodiments, the inference configuration is an RRC configuration including one or more sets of resources that enable the UE to perform inference according to the inference resource configuration and report inference resource results; and a monitoring resource configuration is another RRC configuration including one or more sets of resources that enable the UE to perform monitoring of inference according to the inference resource configuration, and report monitoring results.

[0120] According to one or more embodiments, wherein Tmaxis one or more of: a part of a Third Generation Partnership Project (3GPP) specification; based on a beam prediction scenario; reported to the network node as part of a UE capability; and reported periodically to the network node.

[0121] According to one or more embodiments, the performance metric comprises at least one or more of: a beam prediction accuracy; a beam prediction Reference Signal Received Power, RSRP; and a probability in best beam selection.

[0122] According to one or more embodiments, a method of performance monitoring in a network node that is configured to communicate with a user equipment, UE, is provided. The method comprises: transmitting, to the UE, information of a first resource configuration within a first report configuration and information of a second resource configuration within a second report configuration; receiving a beam prediction based on at least one occasion of one or more resources in the first resource configuration; receiving, from the UE, one or more calculated performance metrics corresponding to the beam prediction based on a maximum time value, Tmax, signaling a maximum time for a time gap between at least one occasion of one or more resources in the second resource configuration and at least one corresponding occasion of the one or more resources in the first resource configuration; and reporting, to the UE based on the Tmaxsignaling, thebeam prediction according to the first report configuration where one or more calculated performance metrics corresponding to the beam prediction according to the second report configuration are received.

[0123] According to one or more embodiments, the first resource configuration is an inference resource configuration.

[0124] According to one or more embodiments, the second resource configuration is a monitoring resource configuration.

[0125] According to one or more embodiments, a first message is transmitted to the UE to trigger the UE to initiate both the monitoring resource configuration and the inference resource configuration.

[0126] According to one or more embodiments, the first message triggers the UE to report the beam prediction according to the first report configuration and the calculated one or more performance metrics corresponding to the beam prediction according to the second report configuration.

[0127] According to one or more embodiments, the first message triggers the UE to start calculating one or more performance metrics according to the second report configuration.

[0128] According to one or more embodiments, the second message for the UE is configured where the second message indicates that the UE is to report the one or more performance metrics calculated based on the second report configuration.

[0129] According to one or more embodiments, the first message triggers the UE to report the one or more performance metrics corresponding to the beam prediction according to the second report.

[0130] According to one or more embodiments, one or more of: the first message activates a periodic inference resource configuration and a periodic monitoring resource configuration for performance metric calculation; the first message further triggers a report of the one or more performance metrics corresponding to the beam prediction according to the second report configuration; the first message lacks an indication for the network node to activate a subsequent report of the one or more performance metrics; and the first message further includes an indication for the network node to activate a subsequent aPeriodic report of the one or more performance metrics.

[0131] According to one or more embodiments, one or more of: the first message activates an aPeriodic inference resource and an aPeriodic monitoring resource; the first message further configures a report of the one or more performance metrics of at least one configuration; and wherein the first message indicates which resourceSet, among anumber of resource sets in the monitoring resource configuration that is transmitted, the indication triggering the UE to predict at least one resource set based on the inference resource configuration.

[0132] According to one or more embodiments, the second message is transmitted where the second message activates an aPeriodic report of the one or more performance metrics corresponding to the beam prediction according to the second report configuration.

[0133] According to one or more embodiments, the first message is sent using one or more of downlink control information, DCI, medium access control, MAC, control element, CE, and radio resource control, RRC.

[0134] According to one or more embodiments, a second message is transmitted using DCI, MAC CE, or RRC.

[0135] According to one or more embodiments, when the first message is sent using DCI, the monitoring resource configuration and the inference resource configuration are jointly triggered by a Channel State Information, CSI, request field in the DCI.

[0136] According to one or more embodiments, the first message or the second message comprises a reporting configuration identifier associated to the monitoring resource configuration or to the inference resource configuration, the first message triggering the UE to activate the monitoring resource configuration and any linked inference configuration.

[0137] According to one or more embodiments, one or more performance metrics calculated by the UE for one or more configurations that are activated by the first message or the second message are received.

[0138] According to one or more embodiments, the second message includes a reference to one of reporting configuration identifier associated to the monitoring resource configuration, or to the inference resource configuration, the second message triggering the UE to transmit one or more reports associated to the monitoring resources configuration and to a linked inference configuration.

[0139] According to one or more embodiments, the inference configuration is an RRC configuration including one or more sets of resources that enable the UE to perform inference according to the inference resource configuration and report inference resource results; and a monitoring resource configuration is another RRC configuration including one or more sets of resources that enable the UE to perform monitoring of inference according to the inference resource configuration, and report monitoring results.According to one or more embodiments, Tmaxis one or more of: a part of a Third Generation Partnership Project (3GPP) specification; based on a beam prediction scenario; received as part of a UE capability; and received periodically from the UE.

[0140] According to one or more embodiments, one or more performance metrics comprises at least one or more of: a beam prediction accuracy; a beam prediction Reference Signal Received Power, RSRP; and a probability in best beam selection.

[0141] According to one or more embodiments, a user equipment (UE) configured to communicate with a network node is provided. The UE comprises at least processing circuitry that is configured to perform a method described herein.

[0142] According to one or more embodiments, a network node configured to communicate with user equipment (UE) is provided. The network node comprises at least processing circuitry that is configured to perform a method described herein.

[0143] BRIEF DESCRIPTION OF THE DRAWINGS

[0144] A more complete understanding of the present embodiments, and the attendant advantages and features thereof, will be more readily understood by reference to the following detailed description when considered in conjunction with the accompanying drawings wherein:

[0145] FIG. 1 shows examples of beam management procedures;

[0146] FIG. 2 shows an example inference procedure for beam management for BM-Casel and BM-Case2;

[0147] FIG. 3 shows a schematic example of the Set A of beams and the Set B of beams, where Set B is different from Set A;

[0148] FIG. 4 shows another example of the Set A of beams and the Set B of beams; FIG. 5 shows an example 3GPP NR CSI framework;

[0149] FIG. 6 is a schematic diagram of an example network architecture illustrating a communication system according to principles disclosed herein;

[0150] FIG. 7 is a block diagram of a network node in communication with a user equipment over a wireless connection according to some embodiments of the present disclosure;

[0151] FIG. 8 is a schematic diagram of another example network architecture illustrating a communication system according to principles disclosed herein;

[0152] FIG. 9 is a flowchart of an example process in a network node according to some embodiments of the present disclosure;FIG. 10 is a flowchart of an example process in a user equipment according to some embodiments of the present disclosure;

[0153] FIG. 11 is a flowchart of another example process in a network node according to some embodiments of the present disclosure;

[0154] FIG. 12 is a flowchart of another example process in a user equipment according to some embodiments of the present disclosure;

[0155] FIG. 13 shows a first example of inference resource and monitoring resource configuration according to some embodiments of the present disclosure;

[0156] FIG. 14 shows a second example of inference resource and monitoring resource configuration according to some embodiments of the present disclosure;

[0157] FIG. 15 shows a first example where the monitoring resource(s) and inference resource(s) are activated via a first message according to some embodiments of the present disclosure;

[0158] FIG. 16 shows an example of joint trigger of monitoring / inference in a first DCI message according to some embodiments of the present disclosure; and

[0159] FIG. 17 shows an example where first the monitoring resource(s) and inference resource(s) are activated via a first message according to some embodiments of the present disclosure.

[0160] DETAILED DESCRIPTION

[0161] Before describing in detail exemplary embodiments, it is noted that the embodiments reside primarily in combinations of apparatus components and processing steps related to UE performance metric calculation including joint triggering. Accordingly, components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

[0162] As used herein, relational terms, such as “first” and “second,” “top” and “bottom,” and the like, may be used solely to distinguish one entity or element from another entity or element without necessarily requiring or implying any physical or logical relationship or order between such entities or elements. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the concepts described herein. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and / or“including” when used herein, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0163] In embodiments described herein, the joining term, “in communication with” and the like, may be used to indicate electrical or data communication, which may be accomplished by physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling or optical signaling, for example. One having ordinary skill in the art will appreciate that multiple components may interoperate and modifications and variations are possible of achieving the electrical and data communication.

[0164] In some embodiments described herein, the term “coupled,” “connected,” and the like, may be used herein to indicate a connection, although not necessarily directly, and may include wired and / or wireless connections.

[0165] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the concepts described herein. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and / or “including” when used herein, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0166] The term “network node” used herein can be any kind of network node comprised in a radio network which may further comprise any of base station (BS), radio base station, base transceiver station (BTS), base station controller (BSC), radio network controller (RNC), g Node B (gNB), evolved Node B (eNB or eNodeB), Node B, multistandard radio (MSR) radio node such as MSR BS, multi-cell / multicast coordination entity (MCE), relay node, donor node controlling relay, radio access point (AP), transmission points, transmission nodes, Remote Radio Unit (RRU) Remote Radio Head (RRH), a core network node (e.g., mobile management entity (MME), self-organizing network (SON) node, a coordinating node, positioning node, MDT node, etc.), an external node (e.g., 3rd party node, a node external to the current network), nodes in distributed antenna system (DAS), a spectrum access system (SAS) node, an element management system (EMS), etc. The network node may also comprise test equipment. The term “radio node” usedherein may be used to also denote a user equipment (UE) such as a wireless device (WD) or a radio network node.

[0167] In some embodiments, the non-limiting terms wireless device (WD) or a user equipment (UE) are used interchangeably. The UE herein can be any type of user equipment capable of communicating with a network node or another UE over radio signals, such as a wireless device (WD). The UE may also be a radio communication device, target device, device to device (D2D) UE, machine type UE or UE capable of machine to machine communication (M2M), low-cost and / or low-complexity UE, a sensor equipped with UE, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, Customer Premises Equipment (CPE), an Internet of Things (loT) device, or a Narrowband loT (NB-IOT) device etc.

[0168] Also, in some embodiments the generic term “radio network node” is used. It can be any kind of a radio network node which may comprise any of base station, radio base station, base transceiver station, base station controller, network controller, RNC, evolved Node B (eNB), Node B, gNB, Multi-cell / multicast Coordination Entity (MCE), relay node, access point, radio access point, Remote Radio Unit (RRU) Remote Radio Head (RRH).

[0169] Note that although terminology from one particular wireless system, such as, for example, 3GPP LTE and / or New Radio (NR) and / or 6G, may be used in this disclosure, this should not be seen as limiting the scope of the disclosure to only the aforementioned system. It is contemplated that other 3GPP systems may make use of the concepts and arrangements disclosed herein. For example, a disclosure relating to NR may also be implementable in a 6G system and / or an LTE system, a disclosure relating to 6G may also be implementable in a NR and / or LTE system, and a disclosure relating to LTE may also be implementable in a NR and / or 6G system. Other wireless systems, including without limitation Wide Band Code Division Multiple Access (WCDMA), Worldwide Interoperability for Microwave Access (WiMax), Ultra Mobile Broadband (UMB) and Global System for Mobile Communications (GSM), may also benefit from exploiting the ideas covered within this disclosure.

[0170] Note further, that functions described herein as being performed by a user equipment or a network node may be distributed over a plurality of user equipments and / or network nodes. In other words, it is contemplated that the functions of the network node and user equipment described herein are not limited to performance by a single physical device and, in fact, can be distributed among several physical devices.Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0171] Referring again to the drawing figures, in which like elements are referred to by like reference numerals, there is shown in FIG. 6 a schematic diagram of a communication system 10, according to an embodiment, such as a 3GPP-type cellular network that may support standards such as LTE and / or NR (5G) and / or 6G, which comprises an access network 12, such as a radio access network, and a core network 14. The core network 14 includes one or more network nodes 15. The access network 12 comprises a plurality of network nodes 16a, 16b, 16c (referred to collectively as network nodes 16), such as NBs, eNBs, gNBs or other types of wireless access points, each defining a corresponding coverage area 18a, 18b, 18c (referred to collectively as coverage areas 18). Each network node 16a, 16b, 16c is connectable to the core network 14 over a wired or wireless connection 20. A first user equipment (UE) 22a located in coverage area 18a is configured to wirelessly connect to, or be paged by, the corresponding network node 16a. A second UE 22b in coverage area 18b is wirelessly connectable to the corresponding network node 16b. While a plurality of UEs 22a, 22b (collectively referred to as user equipments 22) are illustrated in this example, the disclosed embodiments are equally applicable to a situation where a sole UE is in the coverage area or where a sole UE is connecting to the corresponding network node 16. Note that although only two UEs 22 and three network nodes 16 are shown for convenience, the communication system may include many more UEs 22 and network nodes 16.

[0172] As one example, in certain embodiments, access network 12 may contain some access network nodes 16 that support 3 GPP radio access technologies (RAT), such as LTE or NR, while other access network nodes 16 support (or the same access network nodes 16 additionally support) non-3GPP RATs, such as Wi-Fi or a proprietary RAT. As another example, communication system 10 may support multiple generations of related communication standards (e.g., 4G, 5G and 6G 3GPP communication standards) and, as a result, may include an access network 12 and / or a core network 14 that supports multiple different standard generations or may include multiple access networks 12 and / or multiple core networks 14 with individual networks supporting different standards generations.Also, it is contemplated that a UE 22 can be in simultaneous communication and / or configured to separately communicate with more than one network node 16 and more than one type of network node 16. For example, a UE 22 can have dual connectivity with a network node 16 that supports LTE and the same or a different network node 16 that supports NR. As an example, UE 22 can be in communication with an eNB for LTE / E-UTRAN, a gNB for NR / NG-RAN (i.e. being configured for multiradio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC) and / or Wi-Fi.

[0173] A network node 16 is configured to include a node management unit 24 which is configured to perform any step and / or task and / or process and / or method and / or feature described in the present disclosure, e.g., network node functions. A user equipment 22 is configured to include a UE management unit 26 which is configured to perform any step and / or task and / or process and / or method and / or feature described in the present disclosure, e.g., UE functions.

[0174] Example implementations, in accordance with an embodiment, of the UE 22 and network node 16 discussed in the preceding paragraphs will now be described with reference to FIG. 7.

[0175] The communication system 10 includes a network node 16 provided in a communication system 10 and including hardware 28 enabling it to communicate with the UE 22. The hardware 28 may include a communication interface 29 comprising a radio interface 30 for setting up and maintaining at least a wireless connection 32 with a UE 22 located in a coverage area 18 served by the network node 16. The radio interface 30 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and / or one or more RF transceivers. The radio interface 30 includes an array of antennas 34 to radiate and receive signal(s) carrying electromagnetic waves.

[0176] In the embodiment shown, the hardware 28 of the network node 16 further includes processing circuitry 36. The processing circuitry 36 may include a processor 38 and a memory 40. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry 36 may comprise integrated circuitry for processing and / or control, e.g., one or more processors and / or processor cores and / or FPGAs (Field Programmable Gate Array) and / or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor 38 may be configured to access (e.g., write to and / or read from) the memory 40, which may comprise any kind of volatile and / or nonvolatile memory, e.g., cache and / or buffer memory and / or RAM(Random Access Memory) and / or ROM (Read-Only Memory) and / or optical memory and / or EPROM (Erasable Programmable Read-Only Memory).

[0177] Thus, the network node 16 further has software 42 stored internally in, for example, memory 40, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the network node 16 via an external connection. The software 42 may be executable by the processing circuitry 36. The processing circuitry 36 may be configured to control any of the methods and / or processes described herein and / or to cause such methods, and / or processes to be performed, e.g., by network node 16.

[0178] Processor 38 corresponds to one or more processors 38 for performing network node 16 functions described herein. The memory 40 is configured to store data, programmatic software code and / or other information described herein. In some embodiments, the software 42 may include instructions that, when executed by the processor 38 and / or processing circuitry 36, causes the processor 38 and / or processing circuitry 36 to perform the processes described herein with respect to network node 16. For example, processing circuitry 36 of the network node 16 may include node management unit 24 which is configured to perform any step and / or task and / or process and / or method and / or feature described in the present disclosure, e.g., network node functions.

[0179] The network node 16 may be composed of multiple distinct network entities (e.g., aNodeB entity and a RNC entity, or a BTS entity and a BSC entity, etc.), which may each have or utilize their own respective physical components. In certain scenarios in which the network node 16 comprises multiple such entities (e.g., BTS and BSC), one or more of the separate entities may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 16 may be configured to support multiple radio access technologies (RATs).

[0180] In such embodiments, some components may be duplicated (e.g., separate memories 40 or portions of memory 40 for different RATs) and some components may be reused (e.g., a same antenna may be shared by different RATs). The network node 16 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 16, for example GSM, WCDMA, LTE, NR, Wi-Fi (e.g., according to an IEEE 802.11 family standard), 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 othercomponents within network node 16.

[0181] In certain alternative embodiments, network node 16 may be capable of wireless communication but does not include separate radio front-end circuitry, instead, the processing circuitry 36 includes radio front-end circuitry and is connected to the antenna 34. Similarly, in some embodiments, all or some of the RF receivers, transmitters and / or transceivers are part of the radio interface 30. In still other embodiments, the communication interface 29 includes one or more ports or terminals, the radio interface 30, and the RF receiver, transmitter and / or transceiver, and the communication interface 31 communicates with baseband processing circuitry, which is part of a digital unit (not shown).

[0182] The antenna 34 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 34 may be coupled to the radio front-end circuitry in radio interface 30 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 34 is separate from the network node 16 and connectable to the network node 16 through one or more interfaces or ports.

[0183] Network node 15 can include one or more components described above with respect to network node 16, e.g., communication interface 29, radio interface 30, antenna 34, ports, processing circuitry 36, processor 38, memory 40 and software 42. These elements of network node 15 can be arranged such that network node 15 can perform various core network functions. Network node 15 can communicate wirelessly or via a wired connection with network nodes 16 via communication link 59.

[0184] The communication system 10 further includes the UE 22 already referred to. The UE 22 may have hardware 44 that may include a radio interface 46 configured to set up and maintain a wireless connection 32 with a network node 16 serving a coverage area 18 in which the UE 22 is currently located. The radio interface 46 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and / or one or more RF transceivers. The radio interface 46 includes an array of antennas 48 to radiate and receive signal(s) carrying electromagnetic waves.

[0185] Communication functions of the radio interface 46 may include cellular communication, Wi-Fi communication (e.g., according to an IEEE 802.11 family standard), 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 positioningsystem (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented 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.

[0186] The hardware 44 of the UE 22 further includes processing circuitry 50. The processing circuitry 50 may include a processor 52 and memory 54. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry 50 may comprise integrated circuitry for processing and / or control, e.g., one or more processors and / or processor cores and / or FPGAs (Field Programmable Gate Array) and / or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor 52 may be configured to access (e.g., write to and / or read from) memory 54, which may comprise any kind of volatile and / or nonvolatile memory, e.g., cache and / or buffer memory and / or RAM (Random Access Memory) and / or ROM (Read-Only Memory) and / or optical memory and / or EPROM (Erasable Programmable Read-Only Memory).

[0187] Thus, the UE 22 may further comprise software 56, which is stored in, for example, memory 54 at the UE 22, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the UE 22. The software 56 may be executable by the processing circuitry 50. The software 56 may include a client application 58. The client application 58 may be operable to provide a service to a human or non-human user via the UE 22.

[0188] The processing circuitry 50 may be configured to control any of the methods and / or processes described herein and / or to cause such methods, and / or processes to be performed, e.g., by UE 22. The processor 52 corresponds to one or more processors 52 for performing UE 22 functions described herein. The UE 22 includes memory 54 that is configured to store data, programmatic software code and / or other information described herein. In some embodiments, the software 56 and / or the client application 58 may include instructions that, when executed by the processor 52 and / or processing circuitry 50, causes the processor 52 and / or processing circuitry 50 to perform the processes described herein with respect to UE 22. For example, the processing circuitry 50 of the user equipment 22may include UE management unit 26 which is configured to perform any step and / or task and / or process and / or method and / or feature described in the present disclosure, e.g., UE functions.

[0189] In some embodiments, the inner workings of the network node 16 and UE 22 may be as shown in FIG. 7 and independently, the surrounding network topology may be that of FIG. 6.

[0190] The wireless connection 32 between the UE 22 and the network node 16 is in accordance with the teachings of the embodiments described throughout this disclosure. More precisely, the teachings of some of these embodiments may improve the data rate, latency, and / or power consumption and thereby provide benefits such as reduced user waiting time, relaxed restriction on file size, better responsiveness, extended battery lifetime, etc. In some embodiments, 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.

[0191] Although FIGS. 6 and 7 show various “units” such as node management unit 24 and UE management unit 26 as being within a respective processor, it is contemplated that these units may be implemented such that a portion of the unit is stored in a corresponding memory within the processing circuitry. In other words, the units may be implemented in hardware or in a combination of hardware and software within the processing circuitry.

[0192] FIG. 8 is another example of a communication system 10 according to some embodiments. As used herein, the communication system 10 of FIG. 8 includes multiple access points (APs) 60 (with four example APs 60a, 60b, 60c, and 60d being depicted) and multiple wireless devices, referred to in the context of communication system 10 of FIG. 8 as stations (STAs) 62 (referred to individually as STA 62a, STA 62b, STA 62c, STA 62d, and STA 62e). STA 62a is served by AP 60a in a first basic service set (BSS) 64a. STA 62b and STA 62c are served by AP 60b in a second BSS, BSS 64b. STA 62d is served by AP 60c in a third BSS, BSS 64c. STA 62e is served by AP 60d in a fourth BSS, BSS 64d. Stations 62 may be non-AP STAs and correspond to various kinds of wireless devices, for example, user terminals, such as mobile or stationary computing devices like smartphones, laptop computers, desktop computers, tablet computers, gaming devices, head-mounted displays (HMDs) for Augmented Reality (AR) or Virtual Reality (VR), or the like, including UEs 22 that are shown and described with respect to FIGS. 6 and 7. In other words, in some embodiment, STA 62 is a UE 22. Further, stations 62 could, for example,correspond to other kinds of equipment like smart home devices, printers, multimedia devices, data storage devices, or the like.

[0193] Each of STAs 62 may connect through a radio link to one of APs 60. For example, depending on location or channel conditions experienced by a given STA 62, the STA may select an appropriate AP and BSS for establishing the radio link. The radio link may be based on one or more orthogonal frequency-division multiplexing (OFDM) carriers from a frequency spectrum that is shared on the basis of a contention-based mechanism, e.g., an unlicensed or license exempt band like 2.4 GHz Industrial, Scientific, and Medical (ISM) band, the 5 GHz band, the 6 GHz band, or the 60 GHz band.

[0194] Each AP 60 may provide data connectivity to STAs 62 connected to a particular AP 60. As illustrated, APs 60 may be connected to a data network 66. In this way, APs 60 may also provide data connectivity between STAs 62 and other entities, e.g., to one or more servers, service providers, data sources, data sinks, user terminals, or the like.

[0195] Accordingly, the radio link established between a given STA 62 and its serving AP 60 may be used for providing various kinds of services to STA 62, e.g., a voice service, a multimedia service, or other data service. Such services may be based on applications that are executed on STA 62 and / or on a device linked to STA 62. By way of example, FIG. 8 illustrates an application service platform 68 provided in data network 66. The application(s) executed on STA 62 and / or on one or more other devices linked to STA 62 may use the radio link for data communication with one or more other STA 62 and / or the application service platform 68, thereby enabling utilization of the corresponding service(s) at STA 62.

[0196] FIG. 9 is a flowchart of an example process in a network node 16. One or more blocks described herein may be performed by one or more elements of network node 16 such as by one or more of processing circuitry 36 (including the node management unit 24), processor 38, and / or radio interface 30. Network node 16 such as via processing circuitry 36 and / or processor 38 and / or radio interface 30 is configured to configure (Block SI 00) the UE 22 to activate one or more monitoring occasions of an artificial intelligence and / or machine learning (AI / ML) model by transmitting in a first message a joint triggering of an inference resource configuration and a monitoring resource configuration. The configuring of the UE 22 triggers the UE 22 and / or the network node 16 to calculate one or more performance metrics of the AI / ML model.

[0197] In some embodiments, one or both of: (A) the first message triggers the UE 22 to report information usable by the network node 16 to calculate the one or moreperformance metrics; and (B) the information includes one or more measurements on the monitoring resource configuration and one or more predictions according to an inference configuration.

[0198] In some other embodiments, the first message triggers the UE 22 to start calculating a performance metric of the AI / ML model.

[0199] In some embodiments, the method further includes configuring a second message for the UE 22, where the second message indicates that the UE 22 is to report the one or more performance metrics calculated based on the resource configuration from the first message.

[0200] In some other embodiments, the first message triggers the UE 22 to report the one or more performance metrics to the network node 16.

[0201] In some embodiments, one or more of: (A) the first message activates a periodic inference resource configuration and a periodic monitoring resource configuration for performance metric calculation; (B) the first message further configures a report of the one or more performance metrics of one or both of the periodic inference resource configuration and the periodic monitoring resource configuration; (C) the first message further indicates that the network node 16 may not activate a subsequent report of the one or more performance metrics; and (D) the first message further indicates that the network node 16 may activate a subsequent aPeriodic report of the one or more performance metrics.

[0202] In some other embodiments, one or more of: (A) the first message activates an aPeriodic inference resource and an aPeriodic monitoring resource; (B) the first message further configures a report of the one or more performance metrics of at least one configuration; (C) the method further includes indicating in the first message which resourceSet, among a number of resource sets in the monitoring resource configuration that is transmitted, the indication triggering the UE 22 to predict at least one resource set based on the inference resource configuration.

[0203] In some embodiments, the method further includes transmitting a second message that activates an aPeriodic report of the one or more performance metrics calculated as configured in the first message.

[0204] In some other embodiments, the first message is sent using one or mor of downlink control information (DCI), medium access control (MAC) control element (CE), and radio resource control (RRC).In some embodiments, when the first message is sent using DCI, the monitoring resource configuration and the inference resource configuration are jointly triggered by the “CSI request” field in the DCI.

[0205] In some other embodiments, the first message comprises a reference to a reporting configuration identifier associated to the monitoring resource configuration or to the inference resource configuration, where the first message triggers the UE 22 to activate the monitoring resources configuration and any linked inference configuration.

[0206] In some embodiments, the method further includes transmitting a second message using DCI, MAC CE, or RRC.

[0207] In some other embodiments, the method further includes receiving one or more performance metrics calculated by the UE 22 for one or more configurations that are activated by the first second message or the second message.

[0208] In some embodiments, the method further includes transmitting a second message including a reference to one of reporting configuration identifier associated to the monitoring resource configuration, or to the inference resource configuration. The second message triggers the UE 22 to transmit one or more reports associated to the monitoring resources configuration and to a linked inference configuration.

[0209] In some other embodiments, one or more results of monitoring and results of inference are included in the same or different uplink message.

[0210] In some embodiments, an inference configuration is an RRC configuration including one or more sets of resources that enable the UE 22 to perform inference and report related results, and a monitoring configuration is another RRC configuration including one or more sets of resources that enable the UE 22 to perform monitoring of inference performed according to the inference configuration, and report the related monitoring results.

[0211] In some other embodiments, a time distance between a monitoring and inference resource occasion is one or more of: (A) a maximum time value, denoted Tmaxe Tmax(B) part of a Third Generation Partnership Project (3GPP) specification; (C) based on a beam prediction scenario; (D) reported as part of a UE capability; and (E) reported periodically by the UE 22.

[0212] In some embodiments, the periodicity of monitoring and inference resource occasion is a value of Pi.

[0213] FIG. 10 is a flowchart of an example process in a user equipment 22 according to some embodiments of the present disclosure. One or more blocks described herein may beperformed by one or more elements of user equipment 22 such as by one or more of processing circuitry 50 (including the UE management unit 26), processor 52, and / or radio interface 46. User equipment 22 such as via processing circuitry 50 and / or processor 52 and / or radio interface 46 is configured to configure (Block SI 02) the UE 22 to activate one or more monitoring occasions of an artificial intelligence and / or machine learning (AI / ML) model by receiving in a first message a joint triggering of an inference resource configuration and a monitoring resource configuration. The configuring of the UE 22 triggers the UE 22 and / or the network node 16 to calculate one or more performance metrics of the AI / ML model.

[0214] In some embodiments, the method includes one or more steps corresponding to and / or complementary to the steps of the method embodiments corresponding to the network node 16.

[0215] FIG. 11 is a flowchart of another example process in a user equipment 22 according to some embodiments of the present disclosure. One or more blocks described herein may be performed by one or more elements of user equipment 22 such as by one or more of processing circuitry 50 (including the UE management unit 26), processor 52, and / or radio interface 46. UE 22 is configured to receive (Block SI 04), from network node 16, information of a first resource configuration within a first report configuration and information of a second resource configuration within a second report configuration. UE 22 is configured to perform (Block SI 06) a beam prediction based on an at least one occasion of one or more resources in the first resource configuration. UE 22 is configured to calculate (Block SI 08) one or more performance metrics corresponding to the beam prediction based on a maximum time value, Tmax, signaling a maximum time for a time gap between at least one occasion of one or more resources in the second resource configuration and at least one corresponding occasion of the one or more resources in the first resource configuration. UE 22 is configured to report (Block SI 10) the beam prediction according to the first report configuration and the calculated one or more performance metrics corresponding to the beam prediction according to the second report configuration.

[0216] In some embodiments, the first resource configuration is an inference resource configuration.

[0217] In some embodiments, the second resource configuration is a monitoring resource configuration.In some embodiments, the second resource configuration is a monitoring resource configuration.

[0218] In some embodiments, a first message received from the network node jointly triggers both the monitoring resource configuration and the inference resource configuration.

[0219] In one or more embodiments, the first message triggers the UE 22 to report the beam prediction according to the first report configuration and the calculated one or more performance metrics corresponding to the beam prediction according to the second report configuration.

[0220] In one or more embodiments, the first message triggers the UE 22 to start calculating one or more performance metrics according to the second report configuration.

[0221] In some embodiments, a second message is received from the network node where the second message indicates that the UE 22 is to report the calculated one or more performance metrics calculated according to the second report configuration.

[0222] In some embodiments, the first message triggers the UE 22 to report the calculated one or more performance metrics corresponding to the beam prediction according to the second report configuration to the network node 16.

[0223] In some embodiments, one or more of: the first message activates a periodic inference resource configuration and a periodic monitoring resource configuration for performance metric calculation; the first message further triggers a report of the one or more performance metrics corresponding to the beam prediction according to the second report configuration; the first message lacks an indication for the network node 16 to activate a subsequent report of the one or more performance metrics; and the first message further includes an indication for the network node 16 to activate a subsequent aPeriodic report of the one or more performance metrics.

[0224] In some embodiments, one or more of: the first message activates an aPeriodic inference resource and an aPeriodic monitoring resource; the first message further triggers a report of the one or more performance metrics corresponding to the beam prediction according to the second report configuration; and wherein the first message indicates which resourceSet, among a number of resource sets in the monitoring resource configuration that is received, the indication triggering the UE 22 to predict at least one resource set based on the inference resource configuration.In some embodiments, the second message is received where the second message activates an aPeriodic report of the one or more performance metrics corresponding to the beam prediction according to the second report configuration.

[0225] In some embodiments, the first message is received using one or more of downlink control information, DCI, medium access control, MAC, control element, CE, and radio resource control, RRC.

[0226] In some embodiments, the second message is received using DCI, MAC CE, or RRC.

[0227] In some embodiments, when the first message is received using DCI, the monitoring resource configuration and the inference resource configuration are jointly triggered by a Channel State Information, CSI, request field in the DCI.

[0228] In some embodiments, the first message or the second message comprises a reporting configuration identifier associated to the monitoring resource configuration or to the inference resource configuration, the first message triggering the UE 22 to activate the monitoring resource configuration and a linked inference configuration.

[0229] In some embodiments, one or more calculated performance metrics for one or more configurations that are activated by the first message or the second message is performed are transmitted.

[0230] In some embodiments, the second message includes a reference to one of reporting configuration identifier associated to the monitoring resource configuration, or to the inference resource configuration, the second message triggering the UE (22) to transmit one or more reports associated to the monitoring resources configuration and to a linked inference configuration.

[0231] In some embodiments, the inference configuration is an RRC configuration including one or more sets of resources that enable the UE (22) to perform inference according to the inference resource configuration and report inference resource results; and a monitoring resource configuration is another RRC configuration including one or more sets of resources that enable the UE (22) to perform monitoring of inference according to the inference resource configuration, and report monitoring results.

[0232] In some embodiments, Tmaxis one or more of: a part of a Third Generation Partnership Project (3GPP) specification; based on a beam prediction scenario; reported to the network node (16) as part of a UE (22) capability; and reported periodically to the network node (16).

[0233] In some embodiments, the performance metric comprises at least one or more of:a beam prediction accuracy; a beam prediction Reference Signal Received Power, RSRP; and a probability in best beam selection.

[0234] FIG. 12 is a flowchart of another example process in a network node 16 according to some embodiments of the present disclosure. One or more blocks described herein may be performed by one or more elements of network node 16 such as by one or more of processing circuitry 36 (including the node management unit 24), processor 38, and / or radio interface 30. Network node 16 is configured to transmit (Block SI 12), to the UE 22, information of a first resource configuration within a first report configuration and information of a second resource configuration within a second report configuration. Network node 16 is configured to receive (Block SI 14) a beam prediction based on at least one occasion of one or more resources in the first resource configuration. Network node 16 is configured to receive (Block SI 16), from the UE 22, one or more calculated performance metrics corresponding to the beam prediction based on a maximum time value, Tmax, signaling a maximum time for a time gap between at least one occasion of one or more resources in the second resource configuration and at least one corresponding occasion of the one or more resources in the first resource configuration. Network node 16 is configured to report (Block SI 18), to the UE 22 based in the, Tmax, signaling, the beam prediction according to the first report configuration, where the received one or more calculated performance metrics correspond to the beam prediction according to the second report configuration.

[0235] In one or more embodiments, the first resource configuration is an inference resource configuration.

[0236] In some embodiments, the second resource configuration is a monitoring resource configuration.

[0237] In some embodiments, a first message is transmitted to the UE 22 to trigger the UE 22 to initiate both the monitoring resource configuration and the inference resource configuration.

[0238] In one or more embodiments, the first message triggers the UE 22 to report the beam prediction according to the first report configuration and the calculated one or more performance metrics corresponding to the beam prediction according to the second report configuration.

[0239] In one or more embodiments, the first message triggers the UE 22 to start calculating one or more performance metrics according to the second report configuration.In some embodiments, a second message for the UE 22 is configured where the second message indicates that the UE 22 is to report the one or more performance metrics calculated based on the second report configuration.

[0240] In one or more embodiments, the first message triggers the UE 22 to report the one or more performance metrics corresponding to the beam prediction according to the second report.

[0241] In one or more embodiments, the network node 16 is configured to configure the second message for the UE 22, the second message indicating that the UE 22 is to report the one or more performance metrics calculated based on the second report configuration.

[0242] In one or more embodiments, the first message triggers the UE 22 to report the one or more performance metrics to the network node.

[0243] In one or more embodiments, one or more of: the first message activates a periodic inference resource configuration and a periodic monitoring resource configuration for performance metric calculation; the first message further triggers a report of the one or more performance metrics corresponding to the beam prediction according to the second report configuration; the first message lacks an indication for the network node (16) to activate a subsequent report of the one or more performance metrics; and the first message further includes an indication for the network node (16) to activate a subsequent aPeriodic report of the one or more performance metrics.

[0244] In one or more embodiments, one or more of: the first message activates an aPeriodic inference resource and an aPeriodic monitoring resource; the first message further configures a report of the one or more performance metrics of at least one configuration; and wherein the first message indicates which resourceSet, among a number of resource sets in the monitoring resource configuration that is transmitted, the indication triggering the UE 22 to predict at least one resource set based on the inference resource configuration.

[0245] In one or more embodiments, the network node 16 is configured to transmit the second message that activates an aPeriodic report of the one or more performance metrics corresponding to the beam prediction according to the second report configuration.

[0246] In one or more embodiments, the first message is sent using one or more of downlink control information, DCI, medium access control, MAC, control element, CE, and radio resource control, RRC.

[0247] In one or more embodiments, the network node 16 is configured to transmit a second message using DCI, MAC CE, or RRC.In one or more embodiments, when the first message is sent using DCI, the monitoring resource configuration and the inference resource configuration are jointly triggered by a Channel State Information, CSI, request field in the DCI.

[0248] In one or more embodiments, the first message or the second message comprises an identifier of the reporting configuration identifier associated to the monitoring resource configuration or to the inference resource configuration, the first message triggering the UE 22 to activate the monitoring resources configuration and any linked inference configuration.

[0249] In one or more embodiments, the network node 16 is configured to receive one or more performance metrics calculated by the UE 22 for one or more configurations that are activated by the first second message or the second message.

[0250] In one or more embodiments, the second message includes a reference to a reporting configuration identifier associated to the monitoring resource configuration, or to the inference resource configuration, the second message triggering the UE 22 to transmit one or more reports associated to the monitoring resources configuration and to a linked inference configuration.

[0251] In one or more embodiments, the inference configuration is an RRC configuration including one or more sets of resources that enable the UE 22 to perform inference according to the inference resource configuration and report inference resource results; and a monitoring configuration is another RRC configuration including one or more sets of resources that enable the UE 22 to perform monitoring of inference according to the inference resource configuration, and report monitoring results.

[0252] In one or more embodiments, Tmaxis one or more of: a part of a Third Generation Partnership Project (3GPP) specification; based on a beam prediction scenario; reported as part of a UE capability; and reported periodically by the UE 22.

[0253] In one or more embodiments, the performance metric comprises at least one or more of: a beam prediction accuracy; a beam prediction Reference Signal Received Power, RSRP; and a probability in best beam selection.

[0254] Having described the general process flow of arrangements of the disclosure and having provided examples of hardware and software arrangements for implementing the processes and functions of the disclosure, the sections below provide details and examples of arrangements for UE 22 performance metric calculation including j oint triggering.

[0255] In some embodiments, the term “network node” may be referred to as “NW” or refer to a network. In some other embodiments, the UE 22 receives from the network node16 configuration of both inference and monitoring resource configurations. The UE 22 receives information regarding resources for monitoring within a monitoring report configuration. The UE 22 receives information regarding resources for inference within the inference report configuration that is linked to the monitoring report configuration.

[0256] In one embodiment, the inference and monitoring resource configurations both have semi-persistent resource type. In another embodiment, the inference and monitoring resource configurations both have periodic resource type. In yet another embodiment, the monitoring resource configuration has semi-persistent resource type while the inference resource configuration has periodic resource type.

[0257] FIG. 13 shows a first example of inference resource configuration and monitoring resource configuration. Each black rectangle may represent a set of reference signals for inference representing set B of beams. Each rectangle with hatching may represent a set of reference signals for monitoring. The set of reference signals for inference are henceforth referred to as inference resource(s), and the set of reference signals for monitoring are henceforth referred to as monitoring resource(s).

[0258] In the example of FIG. 13, the inference resource(s) and the monitoring resource are configured with periodicity Po. In one embodiment, in each period, the maximum time gap between an occasion of the inference resource(s) and the corresponding occasion of the monitoring resource(s) configuration is limited to a maximum time value of Tmax. For instance, Tmaxthe maximum time gap between inference resource(s) occasion x and monitoring resource(s) occasion x. According to this embodiment, the UE 22 performs AI / ML based beam prediction using an occasion of the inference resource(s) and then computes the performance metrics using the corresponding occasion of the monitoring resource(s).

[0259] For example, the UE 22 performs AI / ML based beam prediction using inference resource(s) occasion x, then computes the performance metrics using monitoring resource(s) occasion x. A Tmaxvalue that is too large may introduce a mismatch between the beam prediction results using the inference resource(s) and measurements performed using the monitoring resource(s) which will result in errors in calculating performance metrics. Hence, to limit such errors, in some embodiments, the maximum Tmaxvalue is prespecified in 3GPP specifications. In some embodiments, the value of Tmaxmay depend on whether the beam prediction is spatial beam prediction (e.g., BM-Case 1) or temporal beam prediction (e.g., BM-Case 2). In another embodiment, the maximum value °f ^max may be reported as part of UE capability. In another embodiment, Tmaxisreported by the UE 22 with a certain periodicity, in case the UE 22 experienced channel is very dynamic, e.g. indoor, the Tmaxmay be a low value in comparison to when the UE 22 is outdoors. The network node 16 can use this dynamic signaling to understand when and what monitoring resources that it can configure. For example, if Tmaxis low, the network node 16 cannot configure a full beam sweep.

[0260] FIG. 14 shows a second example of inference resource and monitoring resource configuration where the periodicity P of the monitoring resource(s) are larger than the periodicity Poof the inference resource(s). This means the monitoring resource(s) occur less frequently compared to the inference resource(s) and performance metrics are only computed in periods where the monitoring resource(s) occur. In some embodiments, the periodicity P is an integer multiple of periodicity Po(e.g., P = 2P0in the example of FIG. 14). In the example of FIG. 14, the UE 22 performs the following actions:

[0261] • the UE 22 performs AI / ML based beam prediction using inference resource(s) occasion x, and computes the corresponding performance metrics using monitoring resource(s) occasion x;

[0262] • the UE 22 performs AI / ML based beam prediction using inference resource(s) occasion y, and no performance metric computation is done in this occasion as there is no corresponding monitoring resource(s) in occasion y;

[0263] • the UE 22 performs AI / ML based beam prediction using inference resource(s) occasion z, and computes the corresponding performance metrics using monitoring resource(s) occasion z;

[0264] The above actions are repeated for the remining occasions according to FIG. 14. FIG. 15 shows a first example where the monitoring resource(s) and inference resource(s) are activated via a first message. Here, it is assumed both the monitoring resource(s) and inference resource(s) are semi-persistent resources. In this example, the first message jointly actives both the monitoring resource(s) and the inference resource(s). In one embodiment, the first message is a MAC CE. In an alternative embodiment, the second message is a DCI. The UE 22 then receives a second message that activates performance monitoring reporting. After the second message is received the UE 22 sends performance monitoring report and the performing monitoring report is sent periodically. In the example of FIG. 15, the performance monitoring report is sent after every 4thmonitoring occasion. In some embodiments, the second message is a MAC CE that activates semi-persistent reporting of performance monitoring in a PUCCH resource. Inanother embodiment, the second message is a DCI that activates semi-persistent reporting of performance monitoring in PUSCH resources.

[0265] FIG. 16 shows another example of joint trigger of monitoring / inference in a first DCI message. Inference resources and monitoring resources are shown. Further, the joint trigger of monitoring / inference in the first DCI message and triggers of aPeriodic report in the second DCI message are shown. In addition, a performance metric report may be transmitted (e.g., by UE 22).

[0266] In another example shown in FIG. 17, first the monitoring resource(s) and inference resource(s) are activated via a first message. Here, it is assumed both the monitoring resource(s) and inference resource(s) are semi-persistent resources. In this example, the first message jointly actives both the monitoring resource(s) and the inference resource(s). In one embodiment, the first message is a MAC CE. In an alternative embodiment, the second message is a DCI. In some embodiments, the UE 22 starts estimating a model performance metric after activation of the monitoring resource(s) and inference resource(s) via the first message. Next, the UE 22 receives a second message (e.g., a DCI) that triggers an aperiodic performance monitoring report.

[0267] Joint activation on DCI or MAC CE

[0268] In one embodiment, when the UE 22 receives activation / trigger to activate / trigger the monitoring report configuration, the UE 22 can assume that the linked inference report configuration is also activated / triggered. For instance, the UE 22 may receive from the network node 16 an indication in a single bit if the UE 22 can assume that the linked inference report is also triggered. In this case, the first and / or second message can include an indication of the reporting configuration that is activated / triggered (first message) or for which the report should be transmitted to the gNB (second message). The indication can be an identifier of a reporting configuration (e.g., an identifier of a CSI reporting configuration) associated to the monitoring configuration or the inference configuration. In case that is associated to the monitoring configuration, then the UE 22 determines that also the inference configuration is activated (if the indication is included in the first message) and that also the results of the inference (e.g., radio measurement predictions) should be transmitted (if the indication is included in the second message). Depending on the inference and monitoring configuration, even though the activation and / or the reporting is triggered jointly for the monitoring and inference configuration, the inference results and the monitoring results may be transmitted in the same or different uplink message.In case of multiple inference configurations that are linked, then the UE 22 is further indicated via a number of bits in the same first or second message which of the linked inference configurations that are triggered. The linked inference configuration could for example be indicated via listing the linked configuration in

[0269] ascending / descending report configuration ID order. In this case, the first or second message may include an identifier of the reporting configuration associated to the monitoring configuration, and one or more identifiers associated to different linked inference configurations. In another method, the first and second message may include only one or more identifiers associated to the inference configurations, and the UE 22 may then activate (in case of the first message) both the said inference configurations and the one linked monitoring configuration, and report (in case of second message) results both for the said inference configurations and for the one linked monitoring configuration.

[0270] In another embodiment, the network node 16 could for example activate the inference report, and a single bit indicating if the linked monitoring report is also activated. In case of multiple monitoring report configurations ,the network node 16 can allocate one or more bits describing which of the linked monitoring report that should be activated.

[0271] In another embodiment, when the network node 16 activate a semi-persistent resource configuration, the UE 22 is assumed to calculate the monitoring metric for each monitoring occasion. The network node 16 can in a related embodiment indicate in the message whether it may collect the monitoring metric in a subsequent aPeriodic report, or if the activation is purely for the UE 22 to get an understanding of its model performance. This indication that it is UE-only metric calculation can reduce the monitoring overhead at the UE 22, since the UE 22 only need to perform such monitoring when needed (for the case when network node 16 does not need to collect the metric). This indication that the it is UE-only metric calculation can reduce the monitoring overhead at the UE 22, since the UE 22 only need to perform such monitoring when needed (for the case when network node 16 does not need to collect the metric).

[0272] In case of aPeriodic triggering of the monitoring resource configuration, the network node 16 indicates as part of the DCI message which resourceSet among a number of resourceSets that are activated. The UE 22 then calculates the performance metric based on the activated resourceSet. Also, in one related embodiment, the UE 22 provides the inference results in respect to the resourceSet that is activated.

[0273] RRC aspects:The network node 16 can configure in an RRC message if the linked report / resource inference configuration is activated jointly with the MAC CE or DCI activation of a monitoring report configuration.

[0274] Performance metric determination

[0275] The UE 22 use AI / ML model inference using the measured inference resources and the measured monitoring resources to compute a performance metric. The performance metric can comprise of:

[0276] A beam prediction accuracy, e.g. top-l / K accuracy

[0277] A beam prediction RSRP accuracy

[0278] A probability in best beam selection

[0279] In more detail, the beam prediction accuracy metrics can be any of the following: The beam prediction accuracy is a ratio of

[0280]

[0281] where Npis the number of the reported inference result(s) linked with performance monitoring instance(s) to (out of N) for which, the following statement holds:

[0282] • Option 1 (Top-1 / 1): the Top-1 beam with largest measured value of the resource set(s) for monitoring is Top-1 predicted beam

[0283] • Option 2 (1 / Top-K): the Top-1 beam with largest measured value of Ll-RSRP of the resource set(s) for monitoring is one of the Top-K predicted beams

[0284] o K >1, or K is equal to the number of reported predicted beam configured by inference report configuration

[0285] • Option 3a (Top-K / M): the Top-K predicted beams are among Top M beam(s) with largest M measured value(s) of Ll-RSRP(s) of the resource set(s) for monitoring

[0286] • Option 3b (Top-K / M): at least one of the Top-K predicted beams is among Top M beam(s) with largest M measured value(s) of Ll-RSRP(s) of the resource set(s) for monitoring

[0287] o M is configured

[0288] • Option 4 (best of Top-K / 1 with margin): The beam with largest measured value of LI -RSRP of Top-K predicted beams is within a margin X dB of largest measured value of LI -RSRP of the resource set(s) for monitoring

[0289] o K=1 or K is equal to the number of reported predicted beam configured by inference report configuration,

[0290] o X is network node configured.The inference configuration comprises configuring the set B resources to the UE 22, enabling the UE 22 to predict a set A of resources. The set A of resources, or a subset of the set A resources, are then transmitted according to the monitoring resource configuration.

[0291] The embodiments may comprise the following main scenarios:

[0292] 1. When the network node 16 activates the resource and report configuration in the first message.

[0293] 2. When the network node 16 activates the resource in the first message, and the report configuration in second message.

[0294] Embodiments further include details on the configuration of the monitoring and inference resources. The following is a list of examples.

[0295] Example 1. A method to configure a UE 22 to activate one or more monitoring occasions of an AI / ML model by transmitting in a first message a joint triggering of an inference resource configuration and a monitoring resource configuration, thus enabling the UE 22 or network node 16 to calculate one or more performance metrics of an AI / ML model.

[0296] Example 2. Where the first message can activate that UE 22 should report information for performance metric calculation at the network node 16, for example, measurements on the monitoring resource configuration, and predictions according to the inference configuration.

[0297] Example 3. Where the first message can activate that UE 22 should start calculating a performance metric of its AI / ML model;

[0298] Example 4. Where a second message is configured to the UE 22, indicating that the UE 22 should report the performance metric calculated based on the resource configuration from the first message;

[0299] Example 5. Where the first message can activate that UE 22 should report the performance metric to the network node 16;

[0300] Example 6. Where the first message activates a periodic inference resource configuration and a periodic monitoring resource configuration for performance metric calculation,

[0301] a. Method above), where the first message further configures a report of the performance metric of above configuration;

[0302] b. Method above), where the first message further indicates that the network node 16 may not activate a subsequent report of the performance metric;c. Method above), where the first message further indicates that the network node 16 may activate a subsequent aPeriodic report of the performance metric.

[0303] Example 7. Where the first message activates an aPeriodic inference resource and an aPeriodic monitoring resource,

[0304] a. Method above, where the first message further configures a report of the performance metric of above configuration.

[0305] b. Method above, where the network node 16 further indicate in the message which resourceSet, among a number of resource sets in the monitoring resource configuration that is transmitted, and the UE 22 the UE 22 predicts such resource set based on its inference resource configuration.

[0306] Example 8. Where the second message activates an aPeriodic report of the performance metric calculated as configured in the first message.

[0307] Example 9. Where the first message is sent using DCI, MAC CE or RRC. Example 10. Where the first message, in case of sent using DCI; the monitoring and inference resource configuration are jointly triggered by the “CSI request” field in DCI.

[0308] Example 11. Where the first message contains a reference to one of reporting configuration identifier associated to monitoring resource configuration, or to inference resource configuration, and in response the UE 22 activates both the monitoring resources configuration and the linked, if any, inference configuration.

[0309] Example 12. Where the second message (if present) is sent using DCI, MAC CE or RRC.

[0310] Example 13. Where the UE 22 reports the calculated performance metrics for the configurations that are activated by the first or second message.

[0311] Example 14. Where the second message contains a reference to one of reporting configuration identifier associated to monitoring resource configuration, or to inference resource configuration, and in response the UE 22 transmits reports both associated to the monitoring resources configuration (report including performance metrics) and to the linked, if any, inference configuration (report including radio measurement predictions).

[0312] Example 15. The method where the results of the monitoring and results of the inference are included in the same or different uplink message.

[0313] Example 16. The method wherein the inference configuration is an RRC configuration including one or more sets of resources that enable the UE 22 to perform the inference and report related results such as the radio measurement predictions, and themonitoring configuration is an RRC configuration including one or more sets of resources that enable the UE 22 to perform monitoring of the inference performed according to the said inference configuration, and report the related monitoring results (performance metrics).

[0314] Example 17. The method in Example 1, where the time distance between the monitoring and inference resource occasion is a maximum time value, denoted Tmaxwhere Tmaxis part of 3GPP specification, possibly dependent on the beam prediction scenario (spatial or temporal beam prediction).

[0315] b. Is reported as part of the UE capability.

[0316] c. Is reported periodically by the UE 22, e.g. based on the UE 22 experienced channel. A high scattering indoor channel may have a lower value than a low scattering outdoor channel.

[0317] Example 18. The method in Example 1, where the periodicity of the monitoring and inference resource occasion is a value of Pi, that is for example part of the monitoring report configuration.

[0318] Some Additional Examples

[0319] Example Al. A method in a network node configured to communicate with a user equipment (UE), the method comprising: configuring the UE to activate one or more monitoring occasions of an artificial intelligence and / or machine learning (AI / ML) model by transmitting in a first message a joint triggering of an inference resource configuration and a monitoring resource configuration, the configuring of the UE triggering the UE and / or the network node to calculate one or more performance metrics of the AI / ML model.

[0320] Example A2. The method of Example Al, wherein one or both of: the first message triggers the UE to report information usable by the network node to calculate the one or more performance metrics; and the information includes one or more measurements on the monitoring resource configuration and one or more predictions according to an inference configuration.

[0321] Example A3. The method of any one of Examples Al and A2, wherein the first message triggers the UE to start calculating a performance metric of the AI / ML model.

[0322] Example A4. The method of any one of Examples Al -A3, wherein the method further includes: configuring a second message for the UE, the second message indicating that the UE is to report the one or more performance metrics calculated based on the resource configuration from the first message.Example A5. The method of any one of Examples A1-A4, wherein the first message triggers the UE to report the one or more performance metrics to the network node.

[0323] Example A6. The method of any one of Examples A1-A5, wherein one or more of: the first message activates a periodic inference resource configuration and a periodic monitoring resource configuration for performance metric calculation the first message further configures a report of the one or more performance metrics of one or both of the periodic inference resource configuration and the periodic monitoring resource configuration; the first message further indicates that the network node may not activate a subsequent report of the one or more performance metrics; and the first message further indicates that the network node may activate a subsequent aPeriodic report of the one or more performance metrics.

[0324] Example A7. The method of any one of Examples A1-A6, wherein one or more of: the first message activates an aPeriodic inference resource and an aPeriodic monitoring resource; the first message further configures a report of the one or more performance metrics of at least one configuration; the method further includes indicating in the first message which resourceSet, among a number of resource sets in the monitoring resource configuration that is transmitted, the indication triggering the UE to predict at least one resource set based on the inference resource configuration.

[0325] Example A8. The method of any one of Examples A1-A7, wherein the method further includes: transmitting a second message that activates an aPeriodic report of the one or more performance metrics calculated as configured in the first message.

[0326] Example A9. The method of any one of Examples A1-A8, wherein the first message is sent using one or mor of downlink control information (DCI), medium access control (MAC) control element (CE), and radio resource control (RRC).

[0327] Example A10. The method of Example A9, wherein when the first message is sent using DCI, the monitoring resource configuration and the inference resource configuration are jointly triggered by the “CSI request” field in the DCI.

[0328] Example All. The method of any one of Examples A1-A10, wherein the first message comprises a reference to a reporting configuration identifier associated to the monitoring resource configuration or to the inference resource configuration, the first message triggering the UE to activate the monitoring resources configuration and any linked inference configuration.Example A12. The method of any one of Examples Al-All, wherein the method further includes transmitting a second message using DCI, MAC CE, or RRC.

[0329] Example A13. The method of any one of Examples A1-A12, wherein the method further includes: receiving one or more performance metrics calculated by the UE for one or more configurations that are activated by the first second message or the second message.

[0330] Example A14. The method of any one of Examples A1-A13, wherein the method further includes transmitting a second message including a reference to a reporting configuration identifier associated to the monitoring resource configuration, or to the inference resource configuration, the second message triggering the UE to transmit one or more reports associated to the monitoring resources configuration and to a linked inference configuration.

[0331] Example A15. The method of any one of Examples A1-A14, wherein one or more results of monitoring and results of inference are included in the same or different uplink message.

[0332] Example Al 6. The method of any one of Examples Al -Al 5, wherein an inference configuration is an RRC configuration including one or more sets of resources that enable the UE to perform inference and report related results, and a monitoring configuration is another RRC configuration including one or more sets of resources that enable the UE to perform monitoring of inference performed according to the inference configuration, and report the related monitoring results.

[0333] Example A17. The method of any one of Examples A1-A16, wherein a time distance between a monitoring and inference resource occasion is one or more of: a maximum time value, denoted T max here T max part of a Third Generation Partnership Project (3GPP) specification; based on a beam prediction scenario; reported as part of a UE capability; and reported periodically by the UE.

[0334] Example Al 8. The method of any one of Examples Al -Al 7, wherein the periodicity of monitoring and inference resource occasion is a value of Pl.

[0335] Example Bl. A network node configured to communicate with a user equipment (UE), the network node configured to, and / or comprising a radio interface and / or processing circuitry configured to perform one or more steps corresponding to one or more of Examples Al -Al 8.

[0336] Example Cl. A method in a user equipment (UE) configured to communicate with a network node, the method comprising: configuring the UE to activate one or moremonitoring occasions of an artificial intelligence and / or machine learning (AI / ML) model by receiving in a first message a joint triggering of an inference resource configuration and a monitoring resource configuration, the configuring of the UE triggering the UE and / or the network node to calculate one or more performance metrics of the AI / ML model.

[0337] Example C2. The method of Example Cl, wherein the method further includes one or more steps corresponding or complementary to any one of Examples A2-A18.

[0338] Example DI. A user equipment (UE) configured to communicate with a network node, the UE configured to, and / or comprising a radio interface and / or processing circuitry configured to perform one or more steps corresponding to one or both of Examples Cl and C2.

[0339] As will be appreciated by one of skill in the art, the concepts described herein may be embodied as a method, data processing system, computer program product and / or computer storage media storing an executable computer program. Accordingly, the concepts described herein may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects all generally referred to herein as a “circuit” or “module.” Any process, step, action and / or functionality described herein may be performed by, and / or associated to, a corresponding module, which may be implemented in software and / or firmware and / or hardware. Furthermore, the disclosure may take the form of a computer program product on a tangible computer usable storage medium having computer program code embodied in the medium that can be executed by a computer. Any suitable tangible computer readable medium may be utilized including hard disks, CD-ROMs, electronic storage devices, optical storage devices, or magnetic storage devices.

[0340] Some embodiments are described herein with reference to flowchart illustrations and / or block diagrams of methods, systems and computer program products. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer (to thereby create a special purpose computer), special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.These computer program instructions may also be stored in a computer readable memory or storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instruction means which implement the function / act specified in the flowchart and / or block diagram block or blocks.

[0341] The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0342] It is to be understood that the functions / acts noted in the blocks may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality / acts involved. Although some of the diagrams include arrows on communication paths to show a primary direction of communication, it is to be understood that communication may occur in the opposite direction to the depicted arrows.

[0343] Computer program code for carrying out operations of the concepts described herein may be written in an object oriented programming language such as Python, Java® or C++. However, the computer program code for carrying out operations of the disclosure may also be written in conventional procedural programming languages, such as the "C" programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer. In the latter scenario, the remote computer may be connected to the user's computer through a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0344] Many different embodiments have been disclosed herein, in connection with the above description and the drawings. It will be understood that it would be unduly repetitious and obfuscating to literally describe and illustrate every combination and subcombination of these embodiments. Accordingly, all embodiments can be combined inany way and / or combination, and the present specification, including the drawings, shall be construed to constitute a complete written description of all combinations and subcombinations of the embodiments described herein, and of the manner and process of making and using them, and shall support claims to any such combination or subcombination.

[0345] It will be appreciated by persons skilled in the art that the embodiments described herein are not limited to what has been particularly shown and described herein above. In addition, unless mention was made above to the contrary, it should be noted that all of the accompanying drawings are not to scale. A variety of modifications and variations are possible in light of the above teachings without departing from the scope of the following claims.

Claims

What is claimed is:

1. A method of performance monitoring in a user equipment, UE, (22) configured to communicate with a network node (16), the method comprising:receiving, from the network node (16), information of a first resource configuration within a first report configuration and information of a second resource configuration within a second report configuration (SI 04);performing a beam prediction based on at least one occasion of one or more resources in the first resource configuration (SI 06);calculating one or more performance metrics corresponding to the beam prediction based on a maximum time value, Tmax, signaling a maximum time for a time gap between at least one occasion of one or more resources in the second resource configuration and at least one corresponding occasion of the one or more resources in the first resource configuration (SI 08); andreporting the beam prediction according to the first report configuration and the calculated one or more performance metrics corresponding to the beam prediction according to the second report configuration (SI 10).

2. The method of Claim 1, wherein the first resource configuration is an inference resource configuration.

3. The method of Claims 1 or 2, wherein the second resource configuration is a monitoring resource configuration.

4. The method of Claim 3, wherein a first message received from the network node (16) jointly triggers both the monitoring resource configuration and the inference resource configuration.

5. The method of Claim 4, wherein the first message triggers the UE (22) to report the beam prediction according to the first report configuration and the calculated one or more performance metrics corresponding to the beam prediction according to the second report configuration.

6. The method of Claim 3, wherein a first message triggers the UE (22) to start calculating one or more performance metrics according to the second report configuration.

7. The method of any one of Claims 1-6, further comprising:receiving from the network node (16) a second message indicating that the UE (22) is to report the calculated one or more performance metrics calculated according to the second report configuration.

8. The method of any one of Claims 4-7, wherein the first message triggers the UE (22) to report the calculated one or more performance metrics corresponding to the beam prediction according to the second report configuration to the network node (16).

9. The method of any one of Claims 4-8, wherein one or more of:the first message activates a periodic inference resource configuration and a periodic monitoring resource configuration for the performance metric calculation;the first message further triggers a report of the one or more performance metrics corresponding to the beam prediction according to the second report configuration;the first message lacks an indication for the network node (16) to activate a subsequent report of the one or more performance metrics; andthe first message further includes an indication for the network node (16) to activate a subsequent aPeriodic report of the one or more performance metrics.

10. The method of any one of Claims 4-8, wherein one or more of:the first message activates an aPeriodic inference resource and an aPeriodic monitoring resource;the first message further triggers a report of the one or more performance metrics corresponding to the beam prediction according to the second report configuration; and wherein the first message indicates which resourceSet, among a number of resource sets in the monitoring resource configuration that is received, the indication triggering the UE (22) to predict at least one resource set based on the inference resource configuration.

11. The method of any one of Claims 7-10, further comprising receiving the second message that activates an aPeriodic report of the one or more performance metrics corresponding to the beam prediction according to the second report configuration.

12. The method of any one of Claims 4-11, wherein the first message is received using one or more of downlink control information, DCI, medium access control, MAC, control element, CE, and radio resource control, RRC.

13. The method of any one of Claims 7-12, further comprising receiving the second message using DCI, MAC CE, or RRC.

14. The method of Claim 4-13, wherein when the first message is received using DCI, the monitoring resource configuration and the inference resource configuration are jointly triggered by a Channel State Information, CSI, request field in the DCI.

15. The method of any one of Claims 7-14, wherein the first message or the second message comprises a reporting configuration identifier associated to the monitoring resource configuration or to the inference resource configuration, the first message triggering the UE (22) to activate the monitoring resource configuration and a linked inference configuration.

16. The method of any one of Claims 7-15, further comprising transmitting one or more calculated performance metrics for one or more configurations that are activated by the first message or the second message.

17. The method of any one of Claims 7-15, wherein the second message includes a reference to one of reporting configuration identifier associated to the monitoring resource configuration, or to the inference resource configuration, the second message triggering the UE (22) to transmit one or more reports associated to the monitoring resources configuration and to a linked inference configuration.

18. The method of any one of Claims 1-17, wherein the inference configuration is an RRC configuration including one or more sets of resources that enablethe UE (22) to perform inference according to the inference resource configuration and report inference resource results; anda monitoring resource configuration is another RRC configuration including one or more sets of resources that enable the UE (22) to perform monitoring of inference according to the inference resource configuration, and report monitoring results.

19. The method of any one of Claims 1-18, wherein Tmaxis one or more of: a part of a Third Generation Partnership Project (3GPP) specification;based on a beam prediction scenario;reported to the network node (16) as part of a UE (22) capability; and reported periodically to the network node (16).

20. The method of any one of Claims 1-19, wherein the performance metric comprises at least one or more of:a beam prediction accuracy;a beam prediction Reference Signal Received Power, RSRP; anda probability in best beam selection.

21. A method of performance monitoring in a network node (16) configured to communicate with a user equipment, UE (22), the method comprising:transmitting, to the UE (22), information of a first resource configuration within a first report configuration and information of a second resource configuration within a second report configuration (SI 12);receiving a beam prediction based on at least one occasion of one or more resources in the first resource configuration (SI 14);receiving, from the UE (22), one or more calculated performance metrics corresponding to the beam prediction based on a maximum time value, Tmax, signaling a maximum time for a time gap between at least one occasion of one or more resources in the second resource configuration and at least one corresponding occasion of the one or more resources in the first resource configuration (SI 16); andreporting, to the UE (22) based on the Tmaxsignaling, the beam prediction according to the first report configuration, the received one or more calculated performance metrics corresponding to the beam prediction according to the second report configuration (SI 18).

22. The method of Claim 21 , wherein the first resource configuration is an inference resource configuration.

23. The method of any one of Claims 21 or 22, wherein the second resource configuration is a monitoring resource configuration.

24. The method of Claim 23, further comprising transmitting a first message to the UE (22) to trigger the UE (22) to initiate both the monitoring resource configuration and the inference resource configuration.

25. The method of any one of Claims 24, wherein the first message triggers the UE (22) to report the beam prediction according to the first report configuration and the calculated one or more performance metrics corresponding to the beam prediction according to the second report configuration.

26. The method of Claim 23, wherein the first message triggers the UE (22) to start calculating one or more performance metrics according to the second report configuration.

27. The method of any one of Claims 21-26, further comprising: configuring a second message for the UE (22), the second message indicating that the UE (22) is to report the one or more performance metrics calculated based on the second report configuration.

28. The method of any one of Claims 24-27, wherein the first message triggers the UE (22) to report the one or more performance metrics corresponding to the beam prediction according to the second report.

29. The method of any one of Claims 24-28, wherein one or more of:the first message activates a periodic inference resource configuration and a periodic monitoring resource configuration for performance metric calculation;the first message further triggers a report of the one or more performance metrics corresponding to the beam prediction according to the second report configuration;the first message lacks an indication for the network node (16) to activate a subsequent report of the one or more performance metrics; andthe first message further includes an indication for the network node (16) to activate a subsequent aPeriodic report of the one or more performance metrics.

30. The method of any one of Claims 24-28, wherein one or more of:the first message activates an aPeriodic inference resource and an aPeriodic monitoring resource;the first message further configures a report of the one or more performance metrics of at least one configuration; andwherein the first message indicates which resourceSet, among a number of resource sets in the monitoring resource configuration that is transmitted, the indication triggering the UE (22) to predict at least one resource set based on the inference resource configuration.

31. The method of any one of Claims 27-30, further comprising transmitting the second message that activates an aPeriodic report of the one or more performance metrics corresponding to the beam prediction according to the second report configuration.

32. The method of any one of Claims 24-31, wherein the first message is sent using one or more of downlink control information, DCI, medium access control, MAC, control element, CE, and radio resource control, RRC.

33. The method of any one of Claims 27-32, further comprising transmitting the second message using DCI, MAC CE, or RRC.

34. The method of Claim 24-31, wherein when the first message is sent using DCI, the monitoring resource configuration and the inference resource configuration are jointly triggered by a Channel State Information, CSI, request field in the DCI.

35. The method of any one of Claims 27-34, wherein the first message or the second message comprises a reporting configuration identifier associated to the monitoring resource configuration or to the inference resource configuration, the firstmessage triggering the UE (22) to activate the monitoring resource configuration and any linked inference configuration.

36. The method of any one of Claims 27-35, further comprising receiving one or more performance metrics calculated by the UE (22) for one or more configurations that are activated by the first message or the second message.

37. The method of any one of Claims 27-36, wherein the second message includes a reference to one of reporting configuration identifier associated to the monitoring resource configuration, or to the inference resource configuration, the second message triggering the UE (22) to transmit one or more reports associated to the monitoring resources configuration and to a linked inference configuration.

38. The method of any one of Claims 27-37, wherein the inference configuration is an RRC configuration including one or more sets of resources that enable the UE (22) to perform inference according to the inference resource configuration and report inference configuration results; and a monitoring resource configuration is another RRC configuration including one or more sets of resources that enable the UE (22) to perform monitoring of inference according to the inference resource configuration, and report monitoring results.

39. The method of any one of Claims 21-38, wherein Tmaxis one or more of: a part of a Third Generation Partnership Project (3GPP) specification;based on a beam prediction scenario;received as part of a UE (22) capability; andreceived periodically from the UE (22).

40. The method of any one of Claims 21-39, wherein one or more performance metrics comprises at least one or more of:a beam prediction accuracy;a beam prediction Reference Signal Received Power, RSRP; anda probability in best beam selection.

41. A user equipment, UE (22), configured to communicate with a network node (16), the UE (22) comprising at least processing circuitry that is configured to perform a method according to any one of Claims 1-20.

42. A network node (16) configured to communicate with user equipment, UE (22), the network node (16) comprising at least processing circuitry that is configured to perform a method according to any one of Claims 21-40.

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