Method for enabling dynamic configuration, measurement and reporting of beams within the channel state information framework

Abstract

A method implemented in a user equipment (UE) configured to communicate with a network node and to determine one or more predictable beams based on one or more first measurements on a first set of beams belonging to the network node is described The method includes receiving a channel state information (CSI) report configuration including configuration information indicating the UE is to perform one or more second measurements on one or more beams of the one or more predictable beams. The configuration information of the CSI report configuration includes one or both of one or more beam identifiers (IDs) and one or more transmission configuration indication (TCI) states associated with the one or more predictable beams. The method also includes performing the one or more second measurements based on the CSI report configuration.

Description

[0001] METHOD FOR ENABLING DYNAMIC CONFIGURATION, MEASUREMENT AND REPORTING OF BEAMS WITHIN THE CHANNEL STATE INFORMATION FRAMEWORK

[0002] TECHNICAL FIELD

[0003] The present disclosure relates to wireless communications, and in particular, to dynamic configuration, measurement and reporting of wireless beams within a channel state information (CSI) framework.

[0004] BACKGROUND

[0005] 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.

[0006] Beam management

[0007] In high frequency range 2 (FR2) communications, multiple RF beams may be used to transmit and receive signals at a network node (e.g., gNB (gNodeB)) and a UE. For each downlink (DL) beam from a network node, there is typically an associated best UE receiver (Rx) beam for receiving signals from the (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, more specifically: P-1 : Purpose is to find a coarse direction for the UE using wide gNB transmitter (TX) beam covering the whole angular sector.

[0010] P-2: Purpose is to refine the gNB TX beam by doing a new beam search around the coarse direction found in P-1.

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

[0012] The term gNB may refer to the network node and is an example of a network node.

[0013] Further, 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 reference signal to use for P-1 include 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.

[0014] P-2 is expected to use aperiodic / or semi-persistent CSI-RS transmitted in narrow beams around the coarse direction found in P-1.

[0015] P-3 is expected to use aperiodic or semi-persistent CSI-RSs repeatedly transmitted in one narrow network node beam. One alternative 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.

[0016] Reference signal

[0017] Reference signal configurations

[0018] CSI-RS:

[0019] A CSI-RS is transmitted over each 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.

[0020] 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:

[0021] • 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. • Semi -Persistent CSI-RS: Similar to periodic CSI-RS, resources for semi-persistent CSI-RS transmissions are semi-statically configured using RRC signaling with parameters such as periodicity and slot offset. However, unlike periodic CSI-RS, dynamic signaling is needed to activate and deactivate the CSI-RS transmission.

[0022] • 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 uplink (UL) downlink control information (DCI), in the same DCI where the 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.

[0023] SSB:

[0024] In NR, an SSB consists of a pair of synchronization signals (SSs), physical broadcast channel (PBCH), and Downlink Demodulation Reference Signal (DMRS) for PBCH. A SSB is mapped to 4 consecutive OFDM symbols in the time domain and 240 contiguous subcarriers (20 resource blocks (RBs)) in the frequency domain.

[0025] 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.

[0026] 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 subcarrier spacing (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 un-used 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 to select for SSB transmission within a half frame, and which beam to use for each SSB transmission.

[0027] Measurement resource configurations

[0028] 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.

[0029] The measurement resource configurations for beam management are provided to the UE by RRC Information Elements (IES) CSI-ResourceConfigs. One CSI- ResourceConfig contains several non-zero power (NZP)-CSI-RS-ResourceSets and / or CSI-SSB-ResourceSets.

[0030] A UE can be configured to perform measurement on CSI-RSs. Here the RRC information element (IE) NZP-CSI-RS-ResourceSet is used. A NZP 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, timedomain behavior, etc. Up to 64 CSI-RS resources can be grouped to an NZP-CSI-RS- ResourceSet. A UE can also be configured to perform measurements on SSBs. Here, the RRC IE CSI- SSB -Re source Set is used. Resource sets comprising SSB resources are defined in a similar manner.

[0031] 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.

[0032] 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.

[0033] The RRC parameters and IEs described above are defined in 3GPP TS 38.331 V18.0.0

[0034] Measurement Reporting

[0035] Three types of CSI reporting are supported in NR as follows: • Periodic CSI Reporting on Physical Uplink Control Channel (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.

[0036] • 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.

[0037] • 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 dynamic.

[0038] 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-ReportConfig IE comprise the following configurations:

[0039] • reportConfigType 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.

[0040] • reportQuantity o Defines the reported CSI parameter(s) (i.e. the CSI content), such as Precoding Matrix Index (PMI), Channel Quality Indication (CQI), Rank Indicator (RI), layer indicator (LI), CSLRS resource index (CRI) and Ll-RSRP. Only a certain number of combinations are possible (e.g. ‘cri-RI-PMI-CQI’ is one possible value and ‘cri-RSRP’ is another) and each value of reportQuantity could be said to correspond to a certain CSI mode.

[0041] • codebookConfig 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] • reportFrequencyConfiguration o Defines 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 LI -RSRP for up to four different CSI-RS / SSB resource indicators. The reported 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. In NR release 16, the report of Layer 1 Signal to Interference plus Noise Ratio (Ll-SINR) for beam management has already been supported.

[0045] The RRC parameters and IES described above are defined in 3GPP TS 38.331 V18.0.0.

[0046] AI / ML based spatial beam prediction in NR

[0047] During a 3 GPP meeting Radio Access Network 1 (RANl)#109-e, it was agreed to study artificial intelligence (Al) and / or machine learning (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 a network side (e.g., a network node such as e.g., a gNB) or at the UE side.

[0048] During the 3 GPP 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. Furthermore, 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.

[0049] During the 3GPP meeting RAN1#115, it was also agreed to capture the following figure 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. In the below, the notation N is used for prediction of top- / V beams while we’ll use the notation K for denoting the prediction of top-K beams.

[0050] FIG. 2 provides an example of 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) among Set 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.

[0051] For both BM-Casel and BM-Case2, UE can report the prediction result to network node based on the output of a UE-side model, or NW can predict the Top-l / N beam(s) based on the reported measurements of Set B for a NW-side model.

[0052] It is noted that as a beam is something that is formed on the network side, the UE can only measure the result of this. This can, for example, be that the narrow beams are measured via CSLRS resources and the wide beams are measured via SSBs at the UE side.

[0053] Set B is different from Set A

[0054] FIG. 3 illustrates a schematic example of the Set A of beams and the Set B of beams. The top illustration shows all the narrow network node beams, which constitutes the Set A of beams, and the lower illustrations shows all the wide network node beams, which constitutes the Set B of beams. In the schematic example of Set A and Set B of beams, Set B is different from Set A. Set B of beams are wide network node beams and the Set A of beams are the narrow network node beams. Set B is a subset of Set A

[0055] FIG. 4 illustrates another example of the Set A of beams and the Set B of beams, wherein Set A contains narrow gNB beams and set B is subset of Set A including some narrows beams from the network node (e.g., gNB). In other words, a schematic example of Set A and Set B of beams is shown, where Set B is a subset of Set A of beams. Both Set B and Set A of beams are the narrow network node beams.

[0056] Beam prediction relation to P-1, P-2, P-3

[0057] The use of an AI / ML model for beam prediction may impact the current P-1 / P-2 / P- 3 procedure in finding the best beam for a UE. A network receiving a best predicted beam from the UE is not sufficient. That is, the beam quality of such best beam to set adequate transmission parameters should be assessed. How the P-1 / P-2 / P-3 procedures are impacted are mainly due to the following beam prediction aspects:

[0058] - If Set B of beams are measured via SSB or CSI-RS

[0059] - If Top-1 beam or Top-K beams are predicted

[0060] - If the P-3 procedure is performed in case of TX-beam prediction

[0061] - If beam quality is assessed via predictions or measurements

[0062] A flowchart showing TX-beam prediction in respect to P-1 / P-2 / P-3 is shown in FIG. 5. Given the flowchart, when performing measurements, the latency in selecting the beam for the data transmission increases. For TX-beam prediction, the lowest latency would be when Set B is SSB, Top-1 beam is predicted, and L1- / RSRP / CQI / SINR is estimated via predictions. In contrast, the highest latency would comprise when Top-K beams are predicted using a set B of CSI-RS, and an additional P-3 procedure is performed to find the best RX-beam.

[0063] Rel-19 status

[0064] In RAN1 116, it was decided that the UE can report Top-K beams to the network node, according to the agreement below. It is for further study the exact number of K. More specifically, the details of the agreement are as follows:

[0065] For UE-sided model, at least for BM-Casel, for content in the report of inference results, support

[0066] • Opt 1 : Beam information on predicted Top K beam(s) among a set of beams.

[0067] • 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.

[0068] • At least K=1 and more, FFS on max value.

[0069] • For Further Study (FFS) on beam information. • FFS on the definition of predicted Top K beam(s).

[0070] • FFS on definition of reported RSRP when applicable.

[0071] • FFS on other information in the report with potential down selection among the following options.

[0072] • 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.

[0073] • Opt 4: Beam information on predicted Top K beam(s) among a set of beams, RSRP of predicted Top K beam(s) among a set of beams, and confidence information of the RSRP. o FFS on definition of reported RSRP. o FFS on the definition and quantization method of confidence information. Other options are not precluded.

[0074] However, to support UE-based temporal / spatial beam predictions, the UE needs to be configured with a set A of beams (to be the output of an inference function e.g. output of the AI / ML model for beam management predict! on(s)) and a set B of beams (to be measured by the UE and used as input to the inference function, e.g., AI / ML model for beam management prediction). One such process is depicted in FIG. 6, where the UE receives the configuration of set A / B according to, and then report Top-K predicted beams accordingly. The Top-K are a subset of the beams in set A of beams. In general, the UE may predict K beams from set A of beams such that the probability of a beam to be a Top- K beam is above a predefined threshold. As such, the value of K may change from time- to-time depending on channel conditions, etc.

[0075] Further, for the P-2 procedure, a preconfigured number of resources may be configured, and the UE performs measurements on the preconfigured number of resources to identify one or more measured beams (identified via their associated resource IDs) and the corresponding Ll-RSRPs or Ll-SINRs. However, in the case of Top-K predicted beams from the beam report, the value of K may change dynamically. Given the dynamically changing value of K, how to configure / signal measurement resources to perform the P-2 procedure using the Top-K predicted beams is an open and unsolved problem. SUMMARY

[0076] Some embodiments advantageously provide methods, systems, and apparatuses for dynamic configuration, measurement and reporting of beams within a CSI framework.

[0077] Some embodiments provide extensions to the existing NR CSI framework to enable a Top-K beam sweep measurement / reporting that can support a dynamic number of K beams in such sweep. New signaling and / or rules are described and may dynamically determine which CSI-RS-Resources in a CSI-RS-ResourceSet that is transmitted by the network node and / or that is to be measured / reported / predicted by the UE. In response to signaling being proposed, the method also includes UE actions for adapting its measurement actions, inference actions and reporting actions according to the signaling.

[0078] One or more embodiments avoid frequent reconfiguration of the number of beams to be measured, enabling efficient measurement of Top-K beams, where K can change based on the UE or network node AI / ML model output. The solution enables dynamic signaling of the number of NZP CSI-RS resources to be measured during the P-2 procedure within the current CSI framework, leading to efficient CSI-RS measurement procedure at the UE. The dynamic signaling of the number of NZP CSI-RS resources to be measured can also result in energy savings at both the UE and the network node.

[0079] According to one aspect, a method implemented in a user equipment (UE) configured to communicate with a network node and to determine one or more predictable beams based on one or more first measurements on a first set of beams belonging to the network node is described. The method includes receiving a channel state information (CSI) report configuration including configuration information indicating the UE is to perform one or more second measurements on one or more beams of the one or more predictable beams. The configuration information of the CSI report configuration includes one or both of one or more beam identifiers (IDs) and one or more transmission configuration indication (TCI) states associated with the one or more predictable beams. The method also includes performing the one or more second measurements based on the CSI report configuration.

[0080] In some embodiments, one or both of the one or more beam IDs and the one or more TCI states associated with the one or more second measurements are dynamically configured by the network node and comprised in a medium access control (MAC) control element (CE). In some other embodiments, one or both of the one or more beam IDs and the one or more TCI states associated with the one or more second measurements follow a UE prediction report associated with the one or more predictable beams.

[0081] In some embodiments, the UE prediction report includes a beam order, and the following of the UE prediction report corresponds to one or more beams being transmitted by the network node in the beam order of the UE prediction report (e.g., if the UE reports a beam order such as beam IDs 3, 6, and 2, the beams in the following round of measurements are transmitted in the order of 3, 6, and 2).

[0082] In some other embodiments, a number of K second measurements are dynamically configured by the network node and comprised in a medium access control (MAC) control element (CE).

[0083] In some embodiments, the number of K second measurements follow a UE prediction report associated with the one or more predictable beams.

[0084] In some other embodiments, the UE prediction report includes an indication indicating the number of K second measurement, and following of the UE prediction report corresponds to the UE performing K second measurements.

[0085] In some embodiments, the method further includes receiving a beam set configuration configuring all beams that are predictable by the UE.

[0086] In some other embodiments, all beams that are predictable by the UE correspond to a set A of beams.

[0087] In some embodiments, the first set of beams correspond to a set B of beams.

[0088] In some other embodiments, the configuration information further indicates one or more channel state information (CSI) reference signal (RS) resources in a CSI-RS resource set received from the network node and measurable by the UE.

[0089] In some other embodiments, the one or more beam IDs comprise one or more of: (A) a CSI-RS resource index (CRI) of the one or more predictable beams; (B) a non-zero power CSI RS resource identifier (NZP-CSI-RS-ResourcelD); (C) and a pair of identifiers including a consistency identifier (consistency ID) and the NZP-CSI-RS-ResourcelD.

[0090] In some embodiments, a number of K second measurements is different on two or more second measurement occasions.

[0091] In some other embodiments, the method further includes determining the one or more predictable beams using an artificial intelligence model, the one or more predictable beams include one or more top-K beams, and the top-K beams have the highest predicted signal quality among the one or more predictable beams.

[0092] In some embodiments, the method further includes transmitting a report including a beam indication indicating a best beam and optionally a signal quality of one or more beams corresponding to the second measurements, the best beam having the highest signal quality among the one or more predictable beams.

[0093] In some other embodiments, the method further includes receiving a data transmission from the network node using the best beam.

[0094] According to another aspect, a user equipment (UE) configured to communicate with a network node and to determine one or more predictable beams based on one or more first measurements on a first set of beams belonging to the network node is described. The UE is configured to perform one or more steps corresponding to one or more of the embodiments of the method implemented in the UE and / or is associated with one or more features that are similar or equal to the features of one or more of the embodiments of the method implemented in the UE.

[0095] According to one aspect, a method implemented in a network node configured to communicate with a user equipment (UE) is described. The UE is configured to determine one or more predictable beams based on one or more first measurements on a first set of beams belonging to the network node. The method includes determining a channel state information (CSI) report configuration including configuration information indicating the UE is to perform one or more second measurements on one or more beams of the one or more predictable beams. The configuration information of the CSI report configuration includes one or both of one or more beam identifiers (IDs) and one or more transmission configuration indication (TCI) states associated with the one or more predictable beams. The method also includes performing one or more actions based on the CSI report configuration.

[0096] In some embodiments, the one or more actions include dynamically configuring one or both of the one or more beam IDs and the one or more TCI states associated with the one or more second measurements. One or both of the one or more beam IDs and the one or more TCI states are included in a medium access control (MAC) control element (CE).

[0097] In some other embodiments, one or both of the one or more beam IDs and the one or more TCI states associated with the one or more second measurements follow a UE prediction report associated with the one or more predictable beams. In some embodiments, the UE prediction report includes a beam order, and the following of the UE prediction report corresponds to one or more beams being transmitted by the network node in the beam order of the UE prediction report.

[0098] In some other embodiments, the method further includes dynamically configuring a number of K second measurements, where the number of K second measurements is included in a medium access control (MAC) control element (CE).

[0099] In some embodiments, the number of K second measurements follow a UE prediction report associated with the one or more predictable beams.

[0100] In some other embodiments, the UE prediction report includes an indication indicating the number of K second measurement, and following of the UE prediction report corresponds to the UE measuring performing K second measurements.

[0101] In some embodiments, the one or more actions include transmitting a beam set configuration configuring all beams that are predictable by the UE.

[0102] In some other embodiments, all beams that are predictable by the UE correspond to a set A of beams.

[0103] In some embodiments, the first set of beams correspond to a set B of beams.

[0104] In some other embodiments, the configuration information further indicates one or more channel state information (CSI) reference signal (RS) resources in a CSI-RS resource set transmitted by the network node and measurable by the UE.

[0105] In some embodiments, the one or more beam IDs comprise one or more of: (A) a CSI-RS resource index (CRI) of the one or more predictable beams; (B) a non-zero power CSI RS resource identifier (NZP-CSI-RS-ResourcelD); and (C) a pair of identifiers including a consistency identifier (consistency ID) and the NZP-CSI-RS-ResourcelD.

[0106] In some other embodiments, a number of K second measurements is different on two or more second measurement occasions.

[0107] In some embodiments, the one or more predictable beams are predicted using an artificial intelligence model, the one or more predictable beams include one or more top-K beams, and the top-K beams have the highest predicted signal quality among the one or more predictable beams.

[0108] In some other embodiments, the one or more actions include receiving a report including a beam indication indicating a best beam and optionally a signal quality of one or more beams corresponding to the second measurements. The best beam have the highest signal quality among the one or more predictable beams. In some embodiments, the one or more actions include transmitting a data transmission to the UE using the best beam.

[0109] According to another aspect, a network node configured to communicate with a user equipment (UE) configured to determine one or more predictable beams based on one or more first measurements on a first set of beams belonging to the network node is described. The network node is configured to perform one or more steps corresponding to one or more of embodiments implemented in the network node and / or is associated with one or more features that are similar or equal to the features of one or more of the embodiments implemented in the network node.

[0110] BRIEF DESCRIPTION OF THE DRAWINGS

[0111] 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:

[0112] FIG. 1 shows example beam management procedures;

[0113] FIG. 2 shows an example inference procedure for beam management;

[0114] FIG. 3 shows a schematic example of the Set A of beams and the Set B of beams;

[0115] FIG. 4 shows another example of the Set A of beams and the Set B of beams;

[0116] FIG. 5 shows TX-beam prediction in respect to P-1 / P-2 / P-3;

[0117] FIG. 6 shows a beam management process;

[0118] FIG. 7 is a schematic diagram of an example network architecture illustrating a communication system according to principles disclosed herein;

[0119] FIG. 8 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;

[0120] FIG. 9 is a flowchart of an example process in a user equipment according to some embodiments of the present disclosure;

[0121] FIG. 10 is a flowchart of an example process in a network node according to some embodiments of the present disclosure;

[0122] FIG. 11 is a flowchart of an example process in a user equipment according to some embodiments of the present disclosure;

[0123] FIG. 12 is a flowchart of an example process in a network node according to some embodiments of the present disclosure; FIG. 13 shows an example of dynamic K for temporal beam prediction according to some embodiments of the present disclosure;

[0124] FIG. 14 shows an example of an NZP CSI-RS resource set with M configured NZP CSI-RS resources with a subset K of an M maximum resources being transmitted according to some embodiments of the present disclosure; and

[0125] FIG. 15 shows example UE reported top-K beams according to some embodiments of the present disclosure.

[0126] DETAILED DESCRIPTION

[0127] Before describing in detail exemplary embodiments, it is noted that the embodiments reside primarily in combinations of apparatus components and processing steps related to dynamic configuration, measurement and reporting of beams within a CSI framework. 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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” used herein may be used to also denote a user equipment (UE) such as a wireless device (WD) or a radio network node.

[0133] 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.

[0134] 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).

[0135] Note that although terminology from one particular wireless system, such as, for example, 3GPP LTE and / or New Radio (NR), may be used in this disclosure, this should not be seen as limiting the scope of the disclosure to only the aforementioned 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.

[0136] 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.

[0137] In some embodiments, the term “belong” or “belonging” may be used and may refer to being transmitted by, e.g., a beam or a set of beams, belonging to a network node. In other words “belong” or “belonging” with respect to a beam or set of beams may refer to the beam or the set of beams being transmitted by the network node or being associated with the network node.

[0138] 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.

[0139] Referring again to the drawing figures, in which like elements are referred to by like reference numerals, there is shown in FIG. 7 a schematic diagram of a communication system 10, according to an embodiment, such as a 3 GPP -type cellular network that may support standards such as LTE and / or NR (5G), which comprises an access network 12, such as a radio access network, and a core network 14. 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.

[0140] 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 and a gNB for NR / NG-RAN.

[0141] A network node 16 (eNB or gNB) is configured to 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.

[0142] 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. 8.

[0143] The communication system 10 includes a network node 16 provided in a communication system 10 and includes hardware 28 enabling it to communicate with the UE 22. The hardware 28 may include 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.

[0144] 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).

[0145] 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. 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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 22 may 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. In some embodiments, the inner workings of the network node 16 and UE 22 may be as shown in FIG. 8 and independently, the surrounding network topology may be that of FIG. 7.

[0150] 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.

[0151] Although FIGS. 7 and 8 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.

[0152] FIG. 9 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 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. User equipment 22 such as via processing circuitry 50 and / or processor 52 and / or radio interface 46 is configured to receive (Block SI 00) a channel state information (CSI) configuration including configuration information on a K number of spatial filters transmitted by the network node 16 and associated to at least one measurement occasion for a measurement the UE 22 is to perform. Optionally, the K number of spatial filters may be different in each measurement occasion.

[0153] In some embodiments, the K number of spatial filters is related to the CSI configuration at the UE 22, the CSI configuration optionally supports M spatial filters, and K is less than or equal to M.

[0154] In some other embodiments, the first or last spatial filters of the K number of spatial filters of the CSI configuration are received by the UE 22.

[0155] In some embodiments, K is determined based on one or more of: (A) reported K beams from the UE 22; (B) KI resources being used from the UE prediction and K2 being used from the network node 16; (C) K being comprised in a Medium Access Control (MAC) Control Element (CE) indication to the UE 22; and (D) a UE implementation to estimate.

[0156] In some embodiments, one or more of: (A) K is larger for future measurement occasions in a second beam management case than a first beam management case; (B) K beams to be measured are selected based on an artificial intelligence and / or machine learning (AI / ML) functionality in the UE 22; (C) the K beams to be measured are selected based on another AI / ML functionality in the network node 16; (D) the method further includes measuring the K beams and reporting the strongest of the K beams to the network node 16; and (E) the method further includes using the measured K beams when performing prediction.

[0157] FIG. 10 is a flowchart of an 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 such as via processing circuitry 36 and / or processor 38 and / or radio interface 30 is configured to transmit (Block S102) a channel state information (CSI) configuration including configuration information on a K number of spatial filters usable by the network node 16 in each measurement occasion. Optionally, the K number of spatial filters are different in each measurement occasion.

[0158] In some embodiments, the K number of spatial filters is related to the CSI configuration at the UE 22, the CSI configuration optionally supports M spatial filters, and K is less than or equal to M.

[0159] In some other embodiments, the first or last spatial filters of the K number of spatial filters of the CSI configuration are to be transmitted to the UE 22.

[0160] In some embodiments, K is determined based on one or more of: (A) reported K beams from the UE 22; (B) KI resources being used from the UE prediction and K2 being used from the network node 16; (C) K being comprised in a Medium Access Control (MAC) Control Element (CE) indication to the UE; and (D) a UE implementation to estimate.

[0161] In some embodiments, one or more of: (A) K is larger for future measurement occasions in a second beam management case than a first beam management case; (B) K beams that are activated are indicated via another beam measurement configuration; (C) the other beam configuration comprises a set A configuration, and the beams are indicated via a channel state information reference signal (CSLR) resource index (CRI); (D) the K beams to be measured are selected based on an artificial intelligence and / or machine learning (AI / ML) functionality in the UE 22; and (E) the K beams to be measured are selected based on another AI / ML functionality in the network node 16.

[0162] FIG. 11 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 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. User equipment 22 such as via processing circuitry 50 and / or processor 52 and / or radio interface 46 is configured to receive (Block SI 04) a channel state information (CSI) report configuration including configuration information indicating the UE is to perform one or more second measurements on one or more beams of the one or more predictable beams. The configuration information of the CSI report configuration includes one or both of one or more beam identifiers (IDs) and one or more transmission configuration indication (TCI) states associated with the one or more predictable beams. The method also includes performing (Block SI 06) the one or more second measurements based on the CSI report configuration.

[0163] In some embodiments, one or both of the one or more beam IDs and the one or more TCI states associated with the one or more second measurements are dynamically configured by the network node 16 and comprised in a medium access control (MAC) control element (CE).

[0164] In some other embodiments, one or both of the one or more beam IDs and the one or more TCI states associated with the one or more second measurements follow a UE prediction report associated with the one or more predictable beams.

[0165] In some embodiments, the UE prediction report includes a beam order, and the following of the UE prediction report corresponds to one or more beams being transmitted by the network node 16 in the beam order of the UE prediction report (e.g., if the UE reports a beam order such as beam IDs 3, 6, and 2, the beams in the following round of measurements are transmitted in the order of 3, 6, and 2).

[0166] In some other embodiments, a number of K second measurements are dynamically configured by the network node 16 and comprised in a medium access control (MAC) control element (CE).

[0167] In some embodiments, the number of K second measurements follow a UE prediction report associated with the one or more predictable beams. In some other embodiments, the UE prediction report includes an indication indicating the number of K second measurement, and following of the UE prediction report corresponds to the UE performing K second measurements.

[0168] In some embodiments, the method further includes receiving a beam set configuration configuring all beams that are predictable by the UE.

[0169] In some other embodiments, all beams that are predictable by the UE correspond to a set A of beams.

[0170] In some embodiments, the first set of beams correspond to a set B of beams.

[0171] In some other embodiments, the configuration information further indicates one or more channel state information (CSI) reference signal (RS) resources in a CSI-RS resource set received from the network node 16 and measurable by the UE.

[0172] In some other embodiments, the one or more beam IDs comprise one or more of: (A) a CSI-RS resource index (CRI) of the one or more predictable beams; (B) a non-zero power CSI RS resource identifier (NZP-CSI-RS-ResourcelD); (C) and a pair of identifiers including a consistency identifier (consistency ID) and the NZP-CSI-RS-ResourcelD.

[0173] In some embodiments, a number of K second measurements is different on two or more second measurement occasions.

[0174] In some other embodiments, the method further includes determining the one or more predictable beams using an artificial intelligence model, the one or more predictable beams include one or more top-K beams, and the top-K beams have the highest predicted signal quality among the one or more predictable beams.

[0175] In some embodiments, the method further includes transmitting a report including a beam indication indicating a best beam and optionally a signal quality of one or more beams corresponding to the second measurements, the best beam having the highest signal quality among the one or more predictable beams.

[0176] In some other embodiments, the method further includes receiving a data transmission from the network node 16 using the best beam.

[0177] FIG. 12 is a flowchart of an 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 such as via processing circuitry 36 and / or processor 38 and / or radio interface 30 is configured to determine (Block S108) a channel state information (CSI) report configuration including configuration information indicating the UE 22 is to perform one or more second measurements on one or more beams of the one or more predictable beams. The configuration information of the CSI report configuration includes one or both of one or more beam identifiers (IDs) and one or more transmission configuration indication (TCI) states associated with the one or more predictable beams. The network node 16 is also configured to perform (Block SI 10) one or more actions based on the CSI report configuration.

[0178] In some embodiments, the one or more actions include dynamically configuring one or both of the one or more beam IDs and the one or more TCI states associated with the one or more second measurements. One or both of the one or more beam IDs and the one or more TCI states are included in a medium access control (MAC) control element (CE).

[0179] In some other embodiments, one or both of the one or more beam IDs and the one or more TCI states associated with the one or more second measurements follow a UE prediction report associated with the one or more predictable beams.

[0180] In some embodiments, the UE prediction report includes a beam order, and the following of the UE prediction report corresponds to one or more beams being transmitted by the network node 16 in the beam order of the UE prediction report.

[0181] In some other embodiments, the method further includes dynamically configuring a number of K second measurements, where the number of K second measurements is included in a medium access control (MAC) control element (CE).

[0182] In some embodiments, the number of K second measurements follow a UE prediction report associated with the one or more predictable beams.

[0183] In some other embodiments, the UE prediction report includes an indication indicating the number of K second measurement, and following of the UE prediction report corresponds to the UE 22 measuring performing K second measurements.

[0184] In some embodiments, the one or more actions include transmitting a beam set configuration configuring all beams that are predictable by the UE 22.

[0185] In some other embodiments, all beams that are predictable by the UE 22 correspond to a set A of beams.

[0186] In some embodiments, the first set of beams correspond to a set B of beams.

[0187] In some other embodiments, the configuration information further indicates one or more channel state information (CSI) reference signal (RS) resources in a CSI-RS resource set transmitted by the network node and measurable by the UE 22. In some embodiments, the one or more beam IDs comprise one or more of: (A) a CSI-RS resource index (CRI) of the one or more predictable beams; (B) a non-zero power CSI RS resource identifier (NZP-CSI-RS-ResourcelD); and (C) a pair of identifiers including a consistency identifier (consistency ID) and the NZP-CSI-RS-ResourcelD.

[0188] In some other embodiments, a number of K second measurements is different on two or more second measurement occasions.

[0189] In some embodiments, the one or more predictable beams are predicted using an artificial intelligence model, the one or more predictable beams include one or more top-K beams, and the top-K beams have the highest predicted signal quality among the one or more predictable beams.

[0190] In some other embodiments, the one or more actions include receiving a report including a beam indication indicating a best beam and optionally a signal quality of one or more beams corresponding to the second measurements. The best beam have the highest signal quality among the one or more predictable beams.

[0191] In some embodiments, the one or more actions include transmitting a data transmission to the UE 22 using the best beam.

[0192] In some other embodiments, the network node 16 transmits the CSI report configuration to the UE.

[0193] 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 dynamic configuration, measurement and reporting of beams within a CSI framework.

[0194] One or more embodiments associated with the network node 16 may provide one or more of the following:

[0195] 1. A method in a network node that provides a UE 22 with configuration information on the K number of spatial filters (beams) that are used by the network node in each measurement occasion, when such K number of spatial filters can be different in each measurement occasion.

[0196] 2. The method or apparatus or system in #1, where the number of K such spatial filters is related to a CSI configuration at the UE 22, where such configuration can support M spatial filters, and where K<=M.

[0197] 3. The method or apparatus or system in #2, where the K first or last spatial filters of a CSI configuration are to be transmitted. a. CSI configuration may comprise of an NZP-CSI-RS-Resource Set. b. Spatial filters are to be used in an NZP-CSI-RS-Resource transmission. The method or apparatus or system in #3, where K is determined via any of the following embodiments: a. The reported K beams from the UE 22. For example, if a UE 22 reports K=4 beams in the prediction. The UE 22 can assume that K=4 spatial filters are transmitted by the network node 16. b. Be based on that KI resources are used from the UE prediction in step #4a, and K2 from the network node 16, hence UE 22 can assume that only K1+K2 resources are transmitted. i. K2 can be configured in a CSI configuration. ii. K2 can be indicated via a Medium Access Control (MAC) Control Element (CE). c. K is Part of a MAC CE indication to the UE 22. i. The network node 16 can, for example, indicate the number of beams that are transmitted in a certain resource SetID. d. based on UE 22 implementation to estimate. i. For example, the network node 16 configures K resources of the M maximum resources. Some of them are intentionally transmitted with zero-power. If the UE 22 detects a zero power of CSI-RS resource with index K+l in the sequence described in previous section, it can assume that K+l,. . . .M are also of zeropower. The method or apparatus or system in any of the above embodiments, where the value of K is larger for future measurement occasions in BM-Case2, for example, the UE 22 is signaled that K increases with a fixed number on each measurement occasion, e.g., increases with 2 according to FIG. 11 in between the temporal beam predictions. The method or apparatus or system in #1, where the K beams that are activated are indicated via another beam measurement configuration. The method or apparatus or system in#6, where the beam configuration can comprise a set A configuration, and the beams are indicated via a CRI. The method or apparatus or system in #1, where the K beams to be measured are selected based on an AI / ML functionality in the UE 22. 9. The method or apparatus or system in #1, where the K beams to be measured are selected based on an AI / ML functionality in the network node 16.

[0198] One or more embodiments associated with the UE 22 may provide one or more of the following:

[0199] 1. A method in a User Equipment (UE) 22 in which the UE 22 receives a CSI configuration including configuration information on a K number of spatial filters (beams and / or spatial directi on(s)) transmitted by the network node 16, associated with at least one measurement occasion for a measurement the UE 22 is to perform, wherein the K number of spatial filters can be different in each measurement occasion.

[0200] 2. The method or apparatus or system in #1, where the number of K such spatial filters is related to a CSI configuration at the UE 22, where such configuration can support M spatial filters, and where K<=M.

[0201] 3. The method or apparatus or system in #2, where the K first or last spatial filters of a CSI configuration (or fewer detected beams, out of the K beams) are received by the UE 22. a. CSI configuration may comprise of an NZP-CSI-RS-Resource Set. b. Spatial filters are to be used in an NZP-CSI-RS-Resource transmission.

[0202] 4. The method or apparatus or system in #3, where K is determined via any of the following embodiments: a. The reported K beams from the UE 22. For example, if a UE 22 reports K=4 beams in the prediction. The UE 22 can assume that K=4 spatial filters are transmitted by the network node 16. b. Be based on that KI resources are used from the UE 22 prediction in step #4a, and K2 from the network node 16, hence UE 22 can assume that only K1+K2 resources are transmitted. i. K2 can be configured in a CSI configuration. ii. K2 can be indicated via a MAC CE. c. K is Part of a MAC CE indication to the UE 22. i. The network node 16 can for example indicate the number of beams that are transmitted in a certain resourceSetID . d. based on UE 22 implementation to estimate. i. For example, the network node 16 configures K resources of the M maximum resources. Some of them are intentionally transmitted with zero-power. If the UE 22 detects a zero power of CSI-RS resource with index K+l in the sequence described in previous section, it can assume that K+l,. . . .M are also of zeropower.

[0203] 5. The method or apparatus or system in any of the above embodiments, where the value of K is larger for future measurement occasions in BM-Case2, for example the UE 22 is signaled with that K increases with a fixed number on each measurement occasions, e.g., increases with 2 according to FIG. 11 in between the temporal beam predictions.

[0204] 6. The method or apparatus or system in #1, where the K beams to be measured are selected based on an AI / ML functionality in the UE 22.

[0205] 7. The method or apparatus or system in #1, where the K beams to be measured are selected based on an AI / ML functionality in the network node 16.

[0206] 8. The method or apparatus or system in #1, where the UE 22 measures the K beams and reports the strongest to the network node 16.

[0207] 9. The method or apparatus or system in #8, where the UE 22 uses the measured K beams when predicting.

[0208] One or more embodiments facilitate new signaling that enable measurement of beams in the P-2 procedure when the number of beams in the resource set used for channel measurement in the P-2 procedure varies dynamically.

[0209] One example scenario where the number of beams in the P-2 procedure varies over time arises with temporal beam prediction, where the uncertainty of the prediction is typically larger when predicting further in time. FIG. 11 shows a simplified example of dynamic K for temporal beam prediction, where K increases with each Top-K beam sweep due to higher prediction uncertainty further in time. At time t=tO (step S200), the UE 22 performs CSI measurements and predicts, using a UE 22 sided AI / ML model, Top-K (e.g., the K beams that have the highest probability of being the top beam). At time t=tl (step S202), the UE 22 reports, in a single beam prediction report, the following:

[0210] • UE prediction of Top-2 (i.e., K=2) beams corresponding to time t=t2.

[0211] • UE prediction of Top-4 (i.e., K=4) beams corresponding to time t=t4.

[0212] • UE prediction of Top-6 (i.e., K=6) beams corresponding to time t=t6.

[0213] At time t=t2 (step S204), the network node 16 sweeps the K=2 beams reported in the beam prediction report in a first P-2 procedure. The UE 22 reports the best beam among the swept K=2 beams corresponding to the P-2 procedure at time t= t2. The network node 16 may perform necessary beam switching (e.g., via switching / updating of beam for data transmission using a Transmission Configuration Indication (TCI) state indication, e.g., as defined in 3GPP TS 38.214 V18.2.0). At time t=t3 (step S206), the UE 22 receives data transmission from the network using the best beam identified in the P-2 procedure at time t= t2.

[0214] At time t=t4 (step S208), the N network node 16 W sweeps the K=4 beams reported in the beam prediction report in a second P-2 procedure. The UE 22 reports the best beam among the swept K=4 beams corresponding to the P-2 procedure at time t=t4. The network node 16 may perform necessary beam switching (e.g., via switching / updating of beam for data transmission using a TCI state indication as defined in 3GPP TS 38.214 V18.2.0). At time t=t5 (step S210), the UE 22 receives data transmission from the network using the best beam identified in the P-2 procedure at time t= t4.

[0215] At time t=t6 (step S212), the network node 16 sweeps the K=6 beams reported in the beam prediction report in a second P2 procedure. The UE 22 reports the best beam among the swept K=6 beams corresponding to the P2 procedure at time t=t6. The network node 16 may perform necessary beam switching (e.g., via switching / updating of beam for data transmission using a TCI state indication as defined in 3GPP TS 38.214 V18.2.0). At time t=t7 (step S214), the UE 22 receives data transmission from the network using the best beam identified in P2 procedure at time t= t6.

[0216] The above procedure may be continued in a new iteration. This invention disclosure addresses the issue of performing the P-2 procedure using a single CSI reporting configuration and a single NZP CSI-RS resource set for the cases when K varies dynamically.

[0217] Definition of top-K beams: The Top-K beams can comprise of beams that are selected based on the output of an AI / ML-based model, for example K beams that are predicted to be strongest by the model but may not be measured by the UE 22. Furthermore, a 'Top-K beam sweep' is defined such that the UE 22 sends a measurement report on such K predicted beams.

[0218] Embodiment 1 - Via dynamic CSI-RS resource set

[0219] In this embodiment, the UE 22 is configured with a new parameter (e.g., RRC parameter), where the new parameter indicates to the UE 22 that CSI-RSs (or other DL- RSs) are to be transmitted in only a subset of the number of CSI-RS resources as shown in FIG. 12. In other words, an example of an NZP CSI-RS resource set is shown with M configured NZP CSI-RS resources with a subset K of the M being transmitted. In some embodiments, the number of NZP CSI-RS resources in which CSI-RSs are transmitted (and possibly the exact resources) is indicated to the UE 22 by the network node 16. In some other embodiments, the number of NZP CSI-RS resources in which CSI-RSs are transmitted is determined according to beam prediction report configuration by the UE 22 during the model inference phase (e.g., the number of NZP CSI-RS resources in which CSI-RSs are transmitted is determined by the Top-K beams determined by the UE 22 sided AI / ML beam prediction model and reported as part of beam prediction report).

[0220] Further, in this embodiment, the set B beams may be a subset of set A beams. In this case, the UE 22 may assume that only the non-measured beams (i.e. beams part of set A and not part of set B) are transmitted in the Top-K beam measurement. The network node 16 can, in another related embodiment, include a flag indicating whether beams that are part of set B can be part of the Top-K measurement.

[0221] The UE 22 can be configured by the network node 16 with a new parameter that enables CSI-RSs to be transmitted in a subset of the number NZP CSI-RS resource sets in the resource set. This parameter can be configured as part of the existing NZP-CSI-RS- ResourceSet information element as shown below (changes over the existing NZP-CSI- RS-ResourceSet information element in 3GPP TS 38.331 V18.0.0 are shown in italics below).

[0222] An example information element (e.g., NZP-CSI-RS-ResourceSet information element) is as follows:

[0223] - ASN1 START

[0224] - TAG-NZP-CSI-RS-RESOURCESET-START

[0225] NZP-CSI-RS-ResourceSet ::= SEQUENCE { nzp-CSI-ResourceSetld NZP-CSI-RS-ResourceSetld, nzp-CSI-RS-Resources SEQUENCE (SIZE ( I ..maxNrofNZP-CSI-RS-

[0226] ResourcesPerSet)) OF NZP-CSI-RS-Resourceld, repetition ENUMERATED { on, off } OPTIONAL, - Need S aperiodicTriggeringOffset INTEGER(0..6) OPTIONAL, — Need S trs-Info ENUMERATED {true} OPTIONAL, - Need R

[0227] [[ aperiodicTriggeringOffset-rl6 INTEGER(0..31) OPTIONAL —

[0228] Need S ]]

[0229] [[ isDynamicSize-r 19 ENUMERATED {on, off } OPTIONAL, - NeedS

[0230] ]]

[0231] - TAG-NZP-CSI-RS-RESOURCESET-STOP — ASN1STOP

[0232] Table 1. NZP-CSI-RS-ResourceSet field descriptions.

[0233] In one embodiment, when the parameter isDynamicSize is ‘on’, the number NZP- CSI-RS resources within the resource set (i.e., the NZP CSI-RS resource set which has the parameter isDynamicSize configured) in which CSI-RSs are transmitted is determined using one or more of the following methods:

[0234] • The NZP CSI-RS resource set which has the parameter isDynamicSize configured is linked or associated with a CSI reporting configuration used for a beam prediction report reported by a UE 22 using a UE sided AI / ML model (e.g., similar to a UE beam prediction report at time t=ti shown in FIG. 11). Then, for a P-2 procedure performed at a given time t, the UE 22 knows how many beams were reported in the latest instance of the beam prediction report, and hence the UE 22 measures that many NZP CSI-RS resources in the NZP CSI-RS resource set which has the parameter isDynamicSize configured during the P-2 procedure. For instance, referring to the example in FIG. 11 : o For time t=t2, the number of beams reported in the beam prediction report is K=2. Hence, during the P-2 procedure at time t=t2, the UE 22 measures K=2 NZP CSI-RS resources (e.g., the first K=2 NZP CSI-RS resources) in the NZP CSI-RS resource set which has the parameter isDynamicSize configured. The UE 22 then reports the best beam (e.g., the NZP CSI-RS resources among the first K=2 NZP CSI-RS resources that have the highest LI -SINR) to the network node 16. o For time t=t4, the number of beams reported in the beam prediction report is K=4. Hence, during the P-2 procedure at time t=t4, the UE 22 measures K=4 NZP CSI-RS resources (e.g., the first K=4 NZP CSI-RS resources) in the NZP CSI-RS resource set which has the parameter isDynamicSize configured. The UE 22 then reports the best beam (e.g., the NZP CSI-RS resources among the first K=4 NZP CSI-RS resources that have the highest Ll-SINR) to the network node 16. o For time t=te, the number of beams reported in the beam prediction report is K=6. Hence, during the P-2 procedure at time t=te, the UE 22 measures K=6 NZP CSI-RS resources (e.g., the first K=6 NZP CSI-RS resources) in the NZP CSI-RS resource set which has the parameter isDynamicSize configured. The UE 22 then reports the best beam (e.g., the NZP CSI-RS resources among the first K=6 NZP CSI-RS resources that have the highest Ll-SINR) to the network node 16.

[0235] • The UE 22 measures the received signal power (e.g., Ll-RSRP) on the NZP CSI- RS resources in the NZP CSI-RS resource set which has the parameter isDynamicSize configured. In one embodiment, if an NZP CSI-RS resource has a measured received signal power above a threshold, then the UE 22 assumes that CSI-RS is transmitted in that NZP CSI-RS resource. In this embodiment, the UE 22 assumes that CSLRSs are transmitted on a subset of resources that have a received signal power above the threshold.

[0236] In one embodiment, the UE 22 may be configured to report periodic, semi- persistent or aperiodic beam reports during P-2 procedure based on measurement of the resource set (i.e., the NZP CSI-RS resource set which has the parameter isDynamicSize configured). The UE 22 is then according to previous embodiments configured with “SEQUENCE (SIZE (L.maxNrofNZP-CSLRS-ResourcesPerSet)) OF NZP-CSI-RS- Resourceld,”. If the parameter isDynamicSize is turned on, the UE 22 can expect that CSL RSs are transmitted on NZP CSI-RS resources corresponding to only the first K resource IDs out of M total resource IDs in the resource set. In an alternative non -limiting example, the UE 22 can expect that CSLRSs are transmitted on NZP CSI-RS resources corresponding to the last K resource IDs out of M total IDs in the resource set. In another alternative embodiment, NZP CSI-RS resources corresponding to which K resource IDs out of M total IDs in which CSLRSs are transmitted are pre-defined according to some other pre-defined rule.

[0237] Moreover, in one aspect of the above embodiment, the UE 22 may benefit from understanding which beam is part of the Top-K beam sweep. When UE 22 knows the identity of the beam in the Top-K beam sweep, UE 22 can use such indication and corresponding measurement as an input to further predictions. In a related embodiment, the standard provides the assumption that RS-resource IDs are transmitted in the sequence of the UE reported Top-K report. The UE 22 can, for example, assume that the NZP-CSL RS-Resource is using the same spatial filter as the strongest beam predicted by the UE 22, which is indicated via a CRI of a set A resourceSet configuration.

[0238] The number K may, in another embodiment, be based on two parts:

[0239] • A first part of K is defined as follows. Let the NZP CSLRS resource set which has the parameter isDynamicSize configured be linked with a CSI reporting configuration used for a beam prediction report reported by a UE 22 using a UE sided AI / ML model. In this case, a first part of K is given by KI, wherein KI is the number of beams report by the UE 22 in the latest instance of the beam prediction report associated with the linked CSI reporting configuration;

[0240] • A second part of K is given by K2 wherein K2 is signaled from the network node 16 to the UE 22, Then, UE 22 can assume that CSLRSs are transmitted on NZP CSLRS resources corresponding to only K1+K2 resources (i.e., K=K1+K2).

[0241] In a detailed embodiment, the number K2 could be a parameter with a fixed value signaled by the network node 16 to the UE 22 when configuring the UE 22 with the NZP CSLRS resource set which has the parameter isDynamicSize configured (e.g., in either the information element corresponding to the NZP CSLRS resource set or in a new information element). In yet another detailed embodiment, the value of K2 could be signaled to the UE 22 by the network node 16 via a MAC CE.

[0242] The network node 16 can also configure K2 beams that were not part of the UE training setup, i.e. “new or temporary” beams that the UE 22 cannot predict. The network node 16 can in this case indicate to the UE 22 that these beams are not part of set A or B, UE 22 can for example use such information to include the new beams in the model.

[0243] The number K may, in another embodiment, be indicated as part of a MAC CE indication to the UE 22. The network node 16 can for example indicate the number of beams that are transmitted in a certain resourceSetID (that are dynamic), in the upcoming reports. The network node 16 could for example signal such value of K to the UE 22 based on the historical reports of the UE 22. The MAC CE could furthermore be used to identify which beams are transmitted on each measurement occasion at the UE 22. The network node 16 could for example indicate a CRI in respect to a configuration of set A, or a mapping list indicting which beam ID that is transmitted in each NZP-CSLRS-Resource, or indicate the beam ID in a similar method via the pair {consistency ID, NZP-CSLRS- ResourcelD}. The number K, in another embodiment, may be indicated in a RRC configuration to the UE 22, and then MAC CE is used to configure which of the candidate Ks that are valid for the UE 22. The network node 16 may thereafter activate the candidate K value in a DCI signaling. For example, the RRC configures K=l,2,3,4,5,. . . .12. Then MAC CE indicates that K=2,4 is valid, and the DCI then indicates in a single bit 1 / 0 if the UE 22 can receive 2 or 4 beams.

[0244] The number K may, in another embodiment, be based on UE 22 implementation to estimate. For example, the network node 16 signals K resources out of the M maximum resources. Some of them are intentionally transmitted with zero-power. If the UE 22 detects a zero power on CSI-RS resource with index K+l in the sequence described in previous embodiments, the UE 22 can assume that CSI-RS resources with indices K+l,. . . .M are also of zero-power. This can, in some embodiments, imply some extra effort at the UE 22 to estimate that a zero-power RS have been transmitted to the network node 16.

[0245] If semi-persistent NZP CSI-RS is used, the number K could in another embodiment be indicated in the MAC CE that activates the NZP CSI-RS resource set.

[0246] In another embodiment, the UE 22 is configured with a beam prediction reporting configuration (e.g. a reporting configuration for reporting beam prediction) associated with multiple resource configuration(s), e.g., a list, wherein each element is associated with a resource configuration identifier, and each resource configuration includes the configuration of a CSI-RS resource set (possibly with different number of CSI-RS resources). The UE 22 further receives a command (e.g. MAC CE and / or DCI) to indicate which of the CSI-RS resources are to be associated with the beam prediction reporting configuration. In one option, the beam prediction reporting configuration includes an initial resource configuration which is to be considered the one 'activated’, i.e., to be used by the UE 22 until the UE 22 further receives a command.

[0247] Embodiment 2 - Via set A configuration and with new signaling that provides UE with which beams that are activated.

[0248] In this embodiment, as shown in FIG. 13, the network node 16 configures the resources for an entire set A, for example as an NZP-CSI-RS-ResourceSet. However, only some of the beams are activated. The network node 16 can then first signal periodic, aperiodic or semi-persistent transmission of such set A, where the network node 16 secondly only activates part of the resources. This enables UEs 22 to measure the Top-K beams, since the network node 16 can activate and signal such activation decision to the UE 22 prior to each set A transmission. Moreover, since all activated beams can be indicated to the UE 22 in one embodiment, this enables UE 22a to also measure extra beams if it desires, e.g., Top-K beams for UE 22b. Mechanisms for supporting signaling to the UE 22 is disclosed in the subsections below, mainly comprising MAC CE or DCI.

[0249] The following is an example information element.

[0250] NZP-CSI-RS-ResourceSet information element

[0251] - ASN1 START

[0252] - TAG-NZP-CSLRS-RESOURCESET-START

[0253] NZP-CSI-RS-ResourceSet ::= SEQUENCE { nzp-CSI-ResourceSetld NZP-CSI-RS-ResourceSetld, nzp-CSI-RS-Resources SEQUENCE (SIZE ( I ..maxNrofNZP-CSI-RS-

[0254] ResourcesPerSet)) OF NZP-CSI-RS-Resourceld, repetition ENUMERATED { on, off } OPTIONAL, - Need S aperiodicTriggeringOffset INTEGER(0..6) OPTIONAL, — Need S isSetA

[0255] ENUMERATED { on, off } OPTIONAL, - Need S trs-Info ENUMERATED {true} OPTIONAL, - Need R

[0256] [[ aperiodicTriggeringOffset-rl6 INTEGER(0..31) OPTIONAL —

[0257] Need S

[0258] ]]

[0259] }

[0260] - TAG-NZP-CSLRS-RESOURCESET-STOP

[0261] - ASN1STOP

[0262] Table 2. NZP-CSI-RS-ResourceSet field descriptions.

[0263] Based on UE reported top-K beams

[0264] In this embodiment, the NZP CSLRS resource set which has the parameter isSetA configured is linked or associated with a CSI reporting configuration used for a beam prediction report reported by a UE 22 using a UE sided AI / ML model (e.g., similar to a UE beam prediction report at time t=ti shown in FIG. 11). Then, for a P-2 procedure performed at a given time t, the UE 22 knows how many beams were reported in the latest instance of the beam prediction report, and hence the UE 22 measures that many NZP CSLRS resources in the NZP CSLRS resource set which has the parameter isSetA configured during the P-2 procedure. For instance, referring to the example in FIG. 11 :

[0265] • For time t=t2, the number of beams reported in the beam prediction report is K=2. Hence, during the P-2 procedure at time t=t2, the UE 22 measures K=2 NZP CSLRS resources (e.g., the first K=2 NZP CSLRS resources) in the NZP CSI-RS resource set which has the parameter isSetA configured; the UE 22 then reports the best beam (e.g., the NZP CSI-RS resources among the first K=2 NZP CSI-RS resources that have the highest Ll-SINR) to the network node 16.

[0266] • For time t=t4, the number of beams reported in the beam prediction report is K=4. Hence, during the P-2 procedure at time t=t4, the UE 22 measures K=4 NZP CSI-RS resources (e.g., the first K=4 NZP CSI-RS resources) in the NZP CSI-RS resource set which has the parameter isSetA configured; the UE 22 then reports the best beam (e.g., the NZP CSI-RS resources among the first K=4 NZP CSI-RS resources that have the highest Ll-SINR) to the network node 16.

[0267] • For time t=te, the number of beams reported in the beam prediction report is K=6. Hence, during the P-2 procedure at time t=te, the UE 22 measures K=6 NZP CSI-RS resources (e.g., the first K=6 NZP CSI-RS resources) in the NZP CSI-RS resource set which has the parameter isSetA configured; the UE 22 then reports the best beam (e.g., the NZP CSI-RS resources among the first K=6 NZP CSI-RS resources that have the highest Ll-SINR) to the network node 16.

[0268] For example, if a UE 22 reports CRI of beam 1,3, 5, 6 in the beam prediction report. The UE 22 can assume that CSLRSs are transmitted in the NZP CSI-RS resources associated with the reported CRIs in the resource set.

[0269] MAC CE

[0270] The MAC CE indicates one or more indices (e.g., NZP CSI-RS resource Ids) of active beams.

[0271] MAC CE + PCI

[0272] For example, the network node 16 first configures a set of candidate values of beam sets to be used via MAC CE. For example, the network node 16 configures a beam set where one set may have the active beams according to set A (or subset of setA), and another according to set B. Next, the network node 16 toggles between the two values via a single bit in the DCI, e.g. 0=set A beams, l=set B beams.

[0273] The following is a nonlimiting list of example embodiments.

[0274] Embodiment Al . A method implemented in a user equipment (UE) that is configured to communicate with a network node, the method comprising: receiving a channel state information (CSI) configuration including configuration information on a K number of spatial filters transmitted by the network node and associated to at least one measurement occasion for a measurement the UE is to perform, the K number of spatial filters optionally being different in each measurement occasion.

[0275] Embodiment A2. The method of Embodiment Al, wherein the K number of spatial filters is related to the CSI configuration at the UE, the CSI configuration optionally supporting M spatial filters, K being less than or equal to M.

[0276] Embodiment A3. The method of any one of Embodiments Al and A2, wherein the first or last spatial filters of the K number of spatial filters of the CSI configuration are received by the UE.

[0277] Embodiment A4. The method of any one f Embodiments A1-A3, wherein K is determined based on one or more of: reported K beams from the UE;

[0278] KI resources being used from the UE prediction and K2 being used from the network node;

[0279] K being comprised in a Medium Access Control (MAC) Control Element (CE) indication to the UE; and a UE implementation to estimate.

[0280] Embodiment A5. The method of any one of Embodiments A1-A4, wherein one or more of:

[0281] K is larger for future measurement occasions in a second beam management case than a first beam management case;

[0282] K beams to be measured are selected based on an artificial intelligence and / or machine learning (AI / ML) functionality in the UE; the K beams to be measured are selected based on another AI / ML functionality in the network node; the method further includes measuring the K beams and reporting the strongest of the K beams to the network node; and the method further includes using the measured K beams when performing prediction.

[0283] Embodiment Bl. A user equipment (UE) that is configured to communicate with a network node, the UE being configured to, and / or comprising a radio interface and / or processing circuitry configured to: receive a channel state information (CSI) configuration including configuration information on a K number of spatial filters transmitted by the network node and associated to at least one measurement occasion for a measurement the UE is to perform, the K number of spatial filters optionally being different in each measurement occasion.

[0284] Embodiment B2. The UE of Embodiment Bl, wherein the K number of spatial filters is related to the CSI configuration at the UE, the CSI configuration optionally supporting M spatial filters, K being less than or equal to M.

[0285] Embodiment B3. The UE of any one of Embodiments Bl and B2, wherein the first or last spatial filters of the K number of spatial filters of the CSI configuration are received by the UE.

[0286] Embodiment B4. The UE of any one f Embodiments B 1-B3, wherein K is determined based on one or more of: reported K beams from the UE;

[0287] KI resources being used from the UE prediction and K2 being used from the network node;

[0288] K being comprised in a Medium Access Control (MAC) Control Element (CE) indication to the UE; and a UE implementation to estimate.

[0289] Embodiment B5. The UE of any one of Embodiments B 1-B4, wherein one or more of:

[0290] K is larger for future measurement occasions in a second beam management case than a first beam management case;

[0291] K beams to be measured are selected based on an artificial intelligence and / or machine learning (AI / ML) functionality in the UE; the K beams to be measured are selected based on another AI / ML functionality in the network node; the UE is further configured to measure the K beams and report the strongest of the K beams to the network node; and the UE is further configured to use the measured K beams when performing prediction.

[0292] Embodiment Cl . A method implemented in a network node that is configured to communicate with a user equipment (UE), the method comprising: transmitting a channel state information (CSI) configuration including configuration information on a K number of spatial filters usable by the network node in each measurement occasion, the K number of spatial filters optionally being different in each measurement occasion.

[0293] Embodiment C2. The method of Embodiment Cl, wherein the K number of spatial filters is related to the CSI configuration at the UE, the CSI configuration optionally supporting M spatial filters, K being less than or equal to M.

[0294] Embodiment C3. The method of any one of Embodiments Cl and C2, wherein the first or last spatial filters of the K number of spatial filters of the CSI configuration are to be transmitted to the UE.

[0295] Embodiment C4. The method of any one of Embodiments C1-C3, wherein K is determined based on one or more of: reported K beams from the UE;

[0296] KI resources being used from the UE prediction and K2 being used from the network node;

[0297] K being comprised in a Medium Access Control (MAC) Control Element (CE) indication to the UE; and a UE implementation to estimate.

[0298] Embodiment C5. The method of any one of Embodiments C1-C4, wherein one or more of:

[0299] K is larger for future measurement occasions in a second beam management case than a first beam management case;

[0300] K beams that are activated are indicated via another beam measurement configuration; the other beam configuration comprises a set A configuration, and the beams are indicated via a channel state information reference signal (CSI-R) resource index (CRI); the K beams to be measured are selected based on an artificial intelligence and / or machine learning (AI / ML) functionality in the UE; and the K beams to be measured are selected based on another AI / ML functionality in the network node.

[0301] Embodiment DI . A network node that is configured to communicate with a user equipment (UE), the network node being configured to, and / or comprising a radio interface and / or processing circuitry configured to: transmit a channel state information (CSI) configuration including configuration information on a K number of spatial filters usable by the network node in each measurement occasion, the K number of spatial filters optionally being different in each measurement occasion.

[0302] Embodiment D2. The network node of Embodiment DI, wherein the K number of spatial filters is related to the CSI configuration at the UE, the CSI configuration optionally supporting M spatial filters, K being less than or equal to M.

[0303] Embodiment D3. The network node of any one of Embodiments DI and D2, wherein the first or last spatial filters of the K number of spatial filters of the CSI configuration are to be transmitted to the UE.

[0304] Embodiment D4. The network node of any one of Embodiments D1-D3, wherein K is determined based on one or more of: reported K beams from the UE;

[0305] KI resources being used from the UE prediction and K2 being used from the network node;

[0306] K being comprised in a Medium Access Control (MAC) Control Element (CE) indication to the UE; and a UE implementation to estimate.

[0307] Embodiment D5. The network node of any one of Embodiments D1-D4, wherein one or more of:

[0308] K is larger for future measurement occasions in a second beam management case than a first beam management case;

[0309] K beams that are activated are indicated via another beam measurement configuration; the other beam configuration comprises a set A configuration, and the beams are indicated via a channel state information reference signal (CSI-R) resource index (CRI); the K beams to be measured are selected based on an artificial intelligence and / or machine learning (AI / ML) functionality in the UE; and the K beams to be measured are selected based on another AI / ML functionality in the network node.

[0310] 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.

[0311] 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.

[0312] 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.

[0313] 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.

[0314] 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.

[0315] 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).

[0316] 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 in any 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.

[0317] 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 and the following claims.

Claims

What is claimed is:

1. A method implemented in a user equipment, UE (22), configured to communicate with a network node (16) and to determine one or more predictable beams based on one or more first measurements on a first set of beams belonging to the network node (16), the method comprising: receiving (SI 04) a channel state information, CSI, report configuration including configuration information indicating the UE (22) is to perform one or more second measurements on one or more beams of the one or more predictable beams, the configuration information of the CSI report configuration including one or both of one or more beam identifiers, IDs, and one or more transmission configuration indication, TCI, states associated with the one or more predictable beams; and performing (SI 06) the one or more second measurements based on the CSI report configuration.

2. The method of Claim 1, wherein one or both of the one or more beam IDs and the one or more TCI states associated with the one or more second measurements are dynamically configured by the network node (16) and comprised in a medium access control, MAC, control element, CE.

3. The method of any one of Claims 1 and 2, wherein one or both of the one or more beam IDs and the one or more TCI states associated with the one or more second measurements follow a UE prediction report associated with the one or more predictable beams.

4. The method of Claim 3, wherein the UE prediction report includes a beam order, and the following of the UE prediction report corresponds to one or more beams being transmitted by the network node (16) in the beam order of the UE prediction report.

5. The method of any one of Claims 1-4, wherein a number of K second measurements are dynamically configured by the network node (16) and comprised in a medium access control, MAC, control element, CE.

6. The method of Claim 5, wherein the number of K second measurements follow a UE prediction report associated with the one or more predictable beams.

7. The method of Claim 6, wherein the UE prediction report includes an indication indicating the number of K second measurement, and following of the UE prediction report corresponds to the UE (22) performing K second measurements.

8. The method of any one of Claims 1-7, wherein the method further includes: receiving a beam set configuration configuring all beams that are predictable by the UE (22).

9. The method of Claim 8, wherein all beams that are predictable by the UE (22) correspond to a set A of beams.

10. The method of any one of Claims 1-9, wherein the first set of beams correspond to a set B of beams.

11. The method of any one of Claims 1-10, wherein the configuration information further indicates one or more channel state information, CSI, reference signal, RS, resources in a CSI-RS resource set received from the network node (16) and measurable by the UE (22).

12. The method of any one of Claims 1-11, wherein the one or more beam IDs comprise one or more of: a CSI-RS resource index, CRI, of the one or more predictable beams; a non-zero power CSI RS resource identifier, NZP-CSI-RS-ResourcelD; and a pair of identifiers including a consistency identifier, consistency ID, and the NZP- CSI-RS-ResourcelD.

13. The method of any one of Claims 1-12, wherein a number of K second measurements is different on two or more second measurement occasions.

14. The method of any one of Claims 1-13, wherein the method further includes:determining the one or more predictable beams using an artificial intelligence model, the one or more predictable beams include one or more top-K beams, and the topic beams have the highest predicted signal quality among the one or more predictable beams.

15. The method of any one of Claims 1-14, wherein the method further includes: transmitting a report including a beam indication indicating a best beam and optionally a signal quality of one or more beams corresponding to the second measurements, the best beam having the highest signal quality among the one or more predictable beams.

16. The method of Claim 15, wherein the method further includes: receiving a data transmission from the network node (16) using the best beam.

17. A user equipment, UE (22), configured to communicate with a network node (16) and to determine one or more predictable beams based on one or more first measurements on a first set of beams belonging to the network node (16), the UE (22) being configured to perform one or more steps corresponding to one or more of Claims 1- 16 and / or being associated with one or more features that are similar or equal to the features of one or more of Claims 1-16.

18. A method implemented in a network node (16) configured to communicate with a user equipment, UE (22), the UE (22) being configured to determine one or more predictable beams based on one or more first measurements on a first set of beams belonging to the network node (16), the method comprising: determining (SI 08) a channel state information, CSI, report configuration including configuration information indicating the UE (22) is to perform one or more second measurements on one or more beams of the one or more predictable beams, the configuration information of the CSI report configuration including one or both of one or more beam identifiers, IDs, and one or more transmission configuration indication, TCI, states associated with the one or more predictable beams; and performing (SI 10) one or more actions based on the CSI report configuration.

19. The method of Claim 18, wherein the one or more actions include: dynamically configuring one or both of the one or more beam IDs and the one or more TCI states associated with the one or more second measurements. One or both of the one or more beam IDs and the one or more TCI states being comprised in a medium access control, MAC, control element, CE.

20. The method of any one of Claims 18 and 19, wherein one or both of the one or more beam IDs and the one or more TCI states associated with the one or more second measurements follow a UE prediction report associated with the one or more predictable beams.

21. The method of Claim 20, wherein the UE prediction report includes a beam order, and the following of the UE prediction report corresponds to one or more beams being transmitted by the network node (16) in the beam order of the UE prediction report.

22. The method of any one of Claims 18-21, wherein the method further includes: dynamically configuring a number of K second measurements, the number of K second measurements being comprised in a medium access control, MAC, control element, CE.

23. The method of Claim 22, wherein the number of K second measurements follow a UE prediction report associated with the one or more predictable beams.

24. The method of Claim 23, wherein the UE prediction report includes an indication indicating the number of K second measurement, and following of the UE prediction report corresponds to the UE (22) measuring performing K second measurements.

25. The method of any one of Claims 18-24, wherein the one or more actions include: transmitting a beam set configuration configuring all beams that are predictable by the UE (22).

26. The method of Claim 25, wherein all beams that are predictable by the UE (22) correspond to a set A of beams.

27. The method of any one of Claims 18-26, wherein the first set of beams correspond to a set B of beams.

28. The method of any one of Claims 18-27, wherein the configuration information further indicates one or more channel state information, CSI, reference signal, RS, resources in a CSI-RS resource set transmitted by the network node (16) and measurable by the UE (22).

29. The method of any one of Claims 18-28, wherein the one or more beam IDs comprise one or more of: a CSI-RS resource index, CRI, of the one or more predictable beams; a non-zero power CSI RS resource identifier, NZP-CSI-RS-ResourcelD; and a pair of identifiers including a consistency identifier, consistency ID, and the NZP- CSI-RS-ResourcelD.

30. The method of any one of Claims 18-29, wherein a number of K second measurements is different on two or more second measurement occasions.

31. The method of any one of Claims 18-30, wherein the one or more predictable beams are predicted using an artificial intelligence model, the one or more predictable beams include one or more top-K beams, and the top-K beams have the highest predicted signal quality among the one or more predictable beams.

32. The method of any one of Claims 18-31, wherein the one or more actions include: receiving a report including a beam indication indicating a best beam and optionally a signal quality of one or more beams corresponding to the second measurements, the best beam having the highest signal quality among the one or more predictable beams.

33. The method of Claim 32, wherein the one or more actions include:transmitting a data transmission to the UE (22) using the best beam.

34. A network node (16) configured to communicate with a user equipment, UE (22), configured to determine one or more predictable beams based on one or more first measurements on a first set of beams belonging to the network node (16), the network node (16) being configured to perform one or more steps corresponding to one or more of Claims 18-33 and / or being associated with one or more features that are similar or equal to the features of one or more of Claims 18-33.

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