WO2025177165A1 - Virtual beam in ai-based beam management - Google Patents

Abstract

According to some embodiments, a method is performed by a wireless device for beam prediction in a wireless network. The method comprises receiving beam configuration information for two sets of beams, a beam set A and a beam set B. Beam set B comprises physical beams and beam set A comprises one or more virtual beams. The beam configuration information comprises data indicating a beam pattern for each of the beams in beam Set A and beam Set B and at least physical time/frequency resources for transmission/reception of the physical beams of beam Set B. The method further comprises measuring one or more beams from beam set B and predicting one or more beams from beam set A based on the measurement of the one or more beams from beam set B.

Description

Virtual Beam in AI-Based Beam Management

TECHNICAL FIELD

[0001] The present disclosure generally relates to communication networks, and more specifically to a virtual beam for artificial intelligence (Al)-based beam management.

BACKGROUND

[0002] One of the key features of new radio (NR), compared to previous generation of wireless networks, is the ability to operate in higher frequencies (e.g., above 10 Gigahertz (GHz)). The available large transmission bandwidths in these frequency ranges may potentially provide large data rates. However, as carrier frequency increases, both pathloss and penetration loss increase. To maintain the coverage at the same level, highly directional beams are used to focus the radio transmitter energy in a particular direction on the receiver. However, large radio antenna arrays - at both receiver and transmitter sides - are needed to create such highly direction beams. [0003] To reduce hardware costs, large antenna arrays for high frequencies use time-domain analog beamforming. The core idea of analog beamforming is to share a single radio frequency chain between many (or potentially all) of the antenna elements. A limitation of analog beamforming is that it is only possible to transmit radio energy using one beam (in one direction) at a given time.

[0004] The above limitation requires the network and user equipment (UE) to perform beam management procedures to establish and maintain suitable transmitter (Tx)Zreceiver (Rx) beampairs. For example, beam management procedures may be used by a transmitter to sweep a geographic area by transmitting reference signals on different candidate beams, during nonoverlapping time intervals, using a predetermined pattern. Thus, by measuring the quality of the reference signals at the receiver side, the best transmit and receive beams may be identified.

[0005] Beam management procedures in NR are defined by a set of layer 1/layer 2 (L1/L2) procedures that establish and maintain suitable beam pairs for both transmitting and receiving data. A beam management procedure may include the following sub-procedures: beam determination, beam measurements, beam reporting, and beam sweeping.

[0006] For downlink transmission from the network to the UE, P1/P2/P3 beam management procedures may be performed according to the NR technical report to overcome the challenges of establishing and maintaining the beam pairs when, for example, a UE moves or a blockage in the environment requires changing the beams. Although these scenarios are not directly mentioned in specifications, there are relevant procedures defined that enable the realization of these scenarios. Examples of such realization are depicted in the corresponding figure of each scenario.

[0007] Figure 1 illustrates synchronization signal block (SSB) beam selection as part of an initial access procedure according to the Pl scenario. The Pl procedure is used to enable UE measurement on different transmission/reception point (TRP) Tx beams to support the selection of TRP Tx beams/UE Rx beam(s). During initial access, for example, the gNB transmits synchronization signal/physical broadcast channel (SS/PBCH) block (SSB) beams in different directions to cover the entire cell. The UE measures signal quality on corresponding SSB signals to detect and select an appropriate SSB beam, as illustrated in Figure 1. Random access is then transmitted on the random access channel (RACH) resources indicated by the selected SSB. The corresponding beam will be used by both the UE and the network to communicate until connected mode beam management is active. The network infers which SSB beam was chosen by the UE without any explicit signaling.

[0008] For beamforming at a TRP, beamforming typically includes an intra/inter-TRP Tx beam sweep from a set of different beams. For beamforming at UE, beamforming typically includes a UE Rx beam sweep from a set of different beams.

[0009] Figure 2 illustrates channel state information reference signal (CSI-RS) Tx beam selection in downlink according to the P2 scenario. The P2 procedure is used to enable UE measurement on different TRP Tx beams to possibly change inter/intra-TRP Tx beam(s). The network may use the SSB beam as an indication of which (narrow) CSI-RS beams to try; that is, the selected SSB beam may be used to define a candidate set of narrow CSI-RS beams for beam management.

[0010] Once CSI-RS is transmitted, the UE measures the reference signal received power (RSRP) and reports the result to the network. If the network receives a CSI-RSRP report from the UE where a new CSI-RS beam is better than the old beam used to transmit physical downlink control channel (PDCCH)Zphysical downlink shared channel (PDSCH), the network updates the serving beam for the UE accordingly, and possibly also modifies the candidate set of CSI-RS beams. The network may also instruct the UE to perform measurements on SSBs. If the network receives a report from the UE where a new SSB beam is better than the previous best SSB beam, a corresponding update of the candidate set of CSI-RS beams for the UE may be motivated.

[0011] The P2 procedure is performed on a possibly smaller set of beams for beam refinement than in Pl. Note that P2 may be a special case of Pl. For example, in connected mode, a gNB configures the UE with different CSI-RSs and transmits each CSI-RS on the corresponding beam. The UE then measures the quality of each CSI-RS beam on its current RX beam and sends feedback about the quality of the measured beams. Thereafter, based on this feedback, the gNB will decide and possibly indicate to the UE which beam will be used in future transmissions. This is shown in Figure 2.

[0012] Figure 3 illustrates UE Rx beam selection for corresponding CSI-RS Tx beam in downlink according to P3 scenario. P3 is used to enable UE measurement on the same TRP Tx beam to change UE Rx beam when the UE uses beamforming. Once in connected mode, the UE is configured with a set of reference signals. Based on measurements, the UE determines which Rx beam is suitable to receive each reference signal in the set. The network then indicates which reference signals are associated with the beam that will be used to transmit PDCCH/PDSCH, and the UE uses the information to adjust its Rx beam when receiving PDCCH/PDSCH.

[0013] In connected mode, P3 may be used by the UE to find the best Rx beam for the corresponding Tx beam. In this case, the gNB keeps one CSI-RS Tx beam at a time, and the UE performs the sweeping and measurements on its own Rx beams for that specific Tx beam. The UE then finds the best corresponding Rx beam based on the measurements and will use it in the future for reception when the gNB indicates the use of that Tx beam.

[0014] For beam management, a UE may be configured to report RSRP or/and signal-to- interference-plus-noise ratio (SINR) for each one of up to four beams, either on CSI-RS or SSB. UE measurement reports may be sent either over physical uplink control channel (PUCCH) or physical uplink shared channel (PUSCH) to the network node, e.g., gNB.

[0015] A CSI-RS is transmitted over each transmit (Tx) antenna port at the network node and for different antenna ports. The CSI-RS is 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 may be measured by the UE. A time-frequency resource used for transmitting CSI-RS is referred to as a CSI-RS resource.

[0016] 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.

[0017] For periodic CSI-RS, CSI-RS is transmitted periodically in certain slots. The CSI-RS transmission is semi-statically configured using radio resource control (RRC) signaling with parameters such as CSI-RS resource, periodicity, and slot offset.

[0018] Semi-persistent CSI-RS is 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 used to activate and deactivate the CSI-RS transmission. [0019] Aperiodic CSI-RS is a one-shot CSI-RS transmission that may 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 orthogonal frequency division multiplexing (OFDM) symbol locations) for aperiodic CSI-RS are semi-statically configured. The transmission of aperiodic CSI-RS is triggered by dynamic signaling through PDCCH using the CSI request field in uplink downlink control information (UL DCI), in the same DCI where the uplink resources for the measurement report are scheduled. Multiple aperiodic CSI-RS resources may be included in a CSI-RS resource set, and the triggering of aperiodic CSI-RS is on a resource set basis.

[0020] In NR, an SSB consists of a pair of synchronization signals (SSs), a physical broadcast channel (PBCH), and a demodulation reference signal (DMRS) for PBCH. An SSB is mapped to four consecutive OFDM symbols in the time domain and 240 contiguous subcarriers (20 resource blocks (RBs)) in the frequency domain.

[0021] NR supports beamforming and beam-sweeping for SSB transmission by enabling a cell to transmit multiple SSBs in different narrow-beams multiplexed in time. The transmission of the 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 broadcasted periodically from each cell. The periodicity of the half frames with SS/PBCH blocks is referred to as SSB periodicity, which is indicated by system information block 1 (SIB1).

[0022] The maximum number of SSBs within a half frame, denoted by L, depends on the frequency band, and the time locations for the 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 ascending order in time from 0 to L-l. By successfully detecting PBCH and its associated DMRS, a UE knows the SSB index. A cell does not necessarily transmit SS/PBCH blocks in all L candidate locations in a half frame, and the resource of the unused candidate positions may 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.

[0023] For measurement resource configurations in NR, a UE may be configured with N>1 CSI reporting settings (CSI-ReportConfig) and M>1 resource settings (CSI-ResourceConfig), where each of N and M is an integer. [0024] Each CSI reporting setting is linked to one or more resource settings for channel and/or interference measurement. The CSI framework is modular in the sense that several CSI reporting settings may be associated with the same Resource Setting.

[0025] The measurement resource configurations for beam management are provided to the UE by RRC information element (IE) (CSI-ResourceConfigs). One CSI-ResourceConfig contains several NZP-CSI-RS-ResourceSets and/or CSI-SSB-ResourceSets.

[0026] A UE may be configured to measure CSI-RSs using the RRC IE non-zero power channel state information reference signal (NZP-CSI-RS)-ResourceSet. A NZP CSI-RS resource set contains the configurations of Ks >1 CSI-RS resources. Each CSI-RS resource configuration resource includes at least the following: mapping to REs, the number of antenna ports, and timedomain behavior.

[0027] Up to 64 CSI-RS resources may be grouped together in a NZP-CSI-RS-ResourceSet. A UE may be configured to measure SSBs using the RRC IE CSI-SSB-ResourceSet. Resource sets comprising SSB resources are defined in a similar manner to the CSI-RS resources defined above.

[0028] For aperiodic CSI-RS and/or aperiodic CSI reporting, the network node configures the UE with S_c CSI triggering states. Each triggering state contains the aperiodic CSI report setting to be triggered along with the associated aperiodic CSI-RS resource sets.

[0029] Periodic and semi-persistent resource settings may only comprise a single resource set (i.e., S=l). Aperiodic resource settings may have many resource sets (S>=1), because one out of the S resource sets defined in the resource setting is indicated by the aperiodic triggering state that triggers a CSI report.

[0030] Three types of CSI reporting are supported in NR, as follows.

[0031] One type is periodic CSI reporting on PUCCH. CSI is reported periodically by a UE. Parameters such as periodicity and slot offset are configured semi-statically by higher layer RRC signaling from the network node to the UE.

[0032] Another type is semi-persistent CSI reporting on PUSCH or PUCCH. This is similar to periodic CSI reporting where semi-persistent CSI reporting has a periodicity and slot offset that may be semi-statically configured. However, a dynamic trigger from a network node to UE may be used to enable the UE to begin semi-persistent CSI reporting. A dynamic trigger from the network node to UE is needed to request the UE to stop the semi-persistent CSI reporting.

[0033] A third type is aperiodic CSI reporting on PUSCH. This type of CSI reporting involves a single-shot (i.e., one time) CSI report by a UE that is dynamically triggered by the network node using DCI. Some of the parameters related to the configuration of the aperiodic CSI report are semi-statically configured by RRC, but the triggering is dynamic.

[0034] 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.

[0035] The CSI-ReportConfig IE comprises the following configurations:

• reportConfigType'. Defines the time-domain behavior (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.

• reportQuantity. Defines the reported CSI parameters — the CSI content; for example, the pre-coding matrix indicator (PMI), channel quality indicator (CQI), rank indicator (RI), layer indicator (LI), CSI-RS resource index (CRI) and Ll-RSRP. Only certain combinations are possible; for example, channel-related information - rank indicator - precoding matrix indicator - channel quality indicator (‘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.

• codebookConfig'. Defines the codebook used for PMI reporting, along with possible codebook subset restriction (CBSR). NR supported the following two types of PMI codebooks: Type I CSI and Type II CSI. Additionally, the Type I and Type II codebooks each have two different variants: regular and port selection.

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

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

[0036] For beam management, a UE may be configured to report Ll-RSRP for up to four different CSI-RS/SSB resource indicators. The reported RSRP value corresponding to the first more optimal channel-related information (CRI)/synchronization signal block rank indicator (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 Ll-SINR for beam management has already been supported.

[0037] Third Generation Partnership Project (3GPP) is studying artificial intelligence/machine learning (AI/ML) based spatial beam prediction. The core idea is to predict the “best” or more optimal beam (or beams) from a Set A of beams using measurement results from another Set B of beams. [0038] Set A and Set B of beams have not been defined yet; however, the following two examples illustrate some scenarios that will likely be studied in Release 18.

[0039] In a first example, Set B is a subset of a Set A. For example, Set A is a set of 8 SSB/CSI-

RS beams shown in Figure 5 (both light and dark circles). The UE measures Set B (the 4 beams indicated by dark circles). The AI/ML model should predict the best beam (or beams) in Set A using only measurements from Set B.

[0040] Figure 4 illustrates an example where Set B is a subset of Set A. Figure 4 illustrates a grid-of-beam type radiation pattern: Each row (resp. column) depicts a certain zenith (resp. azimuth) angle from the antenna array. Set A has 8 beams and Set B has 4 beams (indicated by dark circles).

[0041] In a second example, Set A and Set B correspond to two different sets of beams. For example, Set A is a set of 30 narrow CSI-RS beams, and Set B is a set of 8 wide SSB beams. The UE measures beams in Set B and the AI/ML model should predict the best beam(s) from Set A. [0042] Figure 5 illustrates an example where Set A is a set of narrow beams and Set B is a set of wide beams.

[0043] The spatial beam prediction may be performed in the gNB or the UE. 3GPP is studying AI/ML model training both at the network and UE side. Which side that performs the training is expected to impact how data collection is performed, and another agreement is to study the aspect of data collection for beam management. In addition, 3GPP is studying the aspect of model monitoring and the standard impact on AI/ML model inference (e.g., reporting of predicted values).

[0044] The following text is reproduced from TR 38.843 regarding performance monitoring.

For the performance monitoring of BM-Casel and BM-Case2:

- Performance metric(s) with the following alternatives:

- Alt. l: Beam prediction accuracy related KPIs, e.g., Top-K/1 beam prediction accuracy

- Alt.2: Link quality related KPIs, e.g., throughput, Ll-RSRP, Ll-SINR, hypothetical

BLER

- Alt.3: Performance metric based on input/output data distribution of AI/ML

- Alt.4: The Ll-RSRP difference evaluated by comparing measured RSRP and predicted

RSRP

- Benchmark/reference for the performance comparison, including:

- Alt. 1 : The best beam(s) obtained by measuring beams of a set indicated by gNB (e.g.,

Beams from Set A) - Alt.4: Measurements of the predicted best beam(s) corresponding to model output (e.g.,

Comparison between actual Ll-RSRP and predicted RSRP of predicted Top-l/K Beams)

- Signalling/configuration/measurement/report for model monitoring, e.g., signalling aspects related to assistance information (if supported), Reference signals

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

- Typel performance monitoring:

- Configuration/Signalling from gNB to UE for measurement and/or reporting

- UE may have different operations

- Optionl : UE sends reporting to NW (e.g., for the calculation of performance metric at NW)

- Option2: UE calculates performance metric(s), either reports it to NW or reports an event to NW based on the performance metric(s)

- Indication from NW for UE to do LCM operations

- Note: At least the performance and reporting overhead of model monitoring mechanism should be considered

- Type2 performance monitoring (UE-side performance monitoring):

- Indication/request/report from UE to gNB for performance monitoring

- Note: The indication/request/report may be not needed in some case(s)

- Configuration/Signalling from gNB to UE for performance monitoring measurement and/or reporting

- UE calculates performance metric(s), either reports it to NW or reports an event to NW based on the performance metric(s)

- If it is for UE-side model monitoring, UE makes decision(s) of model selection/activation/ deactivation/switching/fallback operation

- Indication from NW to UE to do LCM operation

- UE reporting of beam measurement(s) based on a set of beams indicated by gNB

- Signalling, e.g., RRC -based, Ll-based

- Note: Performance and UE complexity, power consumption should be considered

Mechanism that facilitates the UE to detect whether the functionality/model is suitable or no longer suitable

Table 7.2.3-1 summarizes applicability of various alternatives for performance metric(s) of AI/ML model monitoring for BM-Casel and BM-Case2. Table 7.2.3- 1: Alternatives for Performance metric(s) of AI/ML model monitoring for BM-Case 1 and BM-Case 2

Mote 1 : The above analysis shall not give an indication about whether/which metric is supported or specified. Note2: Monitoring performance of the above alternatives are not addressed in the table.

[0045] As described above, the AI/ML model for beam prediction may be network-sided or UE-sided (i.e., executed in the gNB or in the UE). If the model is network-sided, the UE makes RSRP (i.e., layer 1 RSRP or Ll-RSRP) and/or SINR (i.e., layer 1 SINR or Ll-SINR) measurements and reports the measurement re suits to the network for input into the AI/ML model .

If the model is UE-sided, the UE both makes the measurements and the AI/ML-model-based prediction, and thus no reporting of the measurements is needed except for the final predicted beam(s).

[0046] A key part of AI/ML-based prediction is data collection. Data collection is performed in several stages of the life-cycle management (LCM).

[0047] First, the model must be trained by collecting measurement data for a large set of UE locations/channel conditions representative of the UE locations/channel conditions that may be encountered during the use of the model (i.e., inference). For each UE, preferably all possible narrow Tx beam directions should be swept, i.e. a fairly large set of beams.

[0048] Second, when using the model for prediction (i.e., inference), measurement data for any UE to predict beams must be collected and fed to the AI/ML model. The set of beams to sweep for a UE is here much smaller than during training because not all narrow beams are swept, only a few wide (or possibly narrow) beams are swept.

[0049] Finally, measurements are needed to monitor that the model functions well, or otherwise disable the model or update it.

[0050] For a network-sided model, all three types of data collection (training, inference, monitoring) follow the same general procedure:

1. The network transmits a signal (e.g. CSI-RS or SSB) using a set of several different Tx beams on the downlink.

2. The UE measures the RSRP (or another quantity, for example, Ll-SINR) of the different transmissions. The UE here typically does Rx beamforming; this beamforming is, however, an implementation detail that it is up to the UE to decide.

3. The UE reports the measured RSRP (or other quantity, for example, Ll-SINR ) values to the network.

[0051] A potential issue with a data-driven approach for learning the gNB TX/RX beam correlations/properties is that different sites/cells may have different antenna/beam configurations. Moreover, even within the same cell, there may be scenarios where antenna/beam configurations are semi-dynamically adjusted to better fit the current traffic load situations.

[0052] There currently exist certain challenges. For example, in beam management according to existing specifications, the network sends SSB information and NZP CSI-RS resource information to the UE for RSRP measurement of each considered beam so that the UE and the gNB are able to find the identifiers (IDs) of the best (e.g., more optimal) one or more measured beams according to RSRP measurement of the measured beams. One of the straightforward approaches is to reuse the existing resource indication (i.e., the indication via NZP CSI-RS resources and SSB index) in the specification for Set A and Set B configurations of Al-based beam management. However, this approach will cause the network to configure NZP CSI-RS resources and/or NZP CSI-RS resource sets that are not measured by UE.

SUMMARY

[0053] As described above, certain challenges currently exist with managing large beam sets. Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, particular embodiments include virtual beams to support beam prediction in artificial intelligence (Al)-based beam management for signaling overhead reduction. Thus, in such embodiments, the network may send configuration information of Set A (i.e., beam set for prediction) that indicates at least one virtual beam and configuration information of Set B (i.e., measured beam set) to UE for Al-based beam prediction, where Set A explicitly or implicitly indicates beam location/direction information in two-dimensional (2D) space (e.g., 2D grid) or three-dimensional (3D) space (e.g., 3D grid) for each beam in Set A, and Set B explicitly or implicitly indicates beam location/direction information in 2D space (e.g., 2D grid) or 3D space (e.g., 3D grid) for each beam in Set B. In some example cases, Set A may or may not include Set B. In particular embodiments, a virtual beam may be a beam that the network does not physically transmit to the UE, for example, in the configured (or pre -configured, or pre-defined) time window (or time instances, or time-frequency window, or time-frequency instances) for prediction.

[0054] According to some embodiments, a method is performed by a wireless device (e.g., UE) for beam prediction in a wireless network. The method comprises receiving beam configuration information for two sets of beams, a beam set A and a beam set B. Beam set B comprises physical beams and beam set A comprises one or more virtual beams (and potentially one or more physical beams). The beam configuration information comprises data indicating a beam pattern for each of the beams in beam Set A and beam Set B and at least physical time/frequency resources for transmission/reception of the physical beams of beam Set B (and beam Set A, if any). The method further comprises measuring one or more beams from beam set B and predicting one or more beams from beam set A based on the measurement of the one or more beams from beam set B.

[0055] In particular embodiments, the method further comprises reporting an indication of the predicted one or more beams from beam set A to a network node . Each of the one or more predicted beams from beam set A may be associated with an expected signal quality that is higher than expected signal qualities of other beams from beam set A. The indication may further comprise a prediction result of each of the one or more predicted beams from beam set A. [0056] In particular embodiments, the beam configuration information associated with the beam set A is configured on a first resource set comprising at least one of a first non-zero power channel state information-reference signal (NZP CSI-RS) resource set and a first synchronization signal block (SSB) resource set. The beam configuration information associated with the beam set B is configured on a second resource set comprising at least one of a second NZP CSI-RS resource set and a second SSB resource set.

[0057] In particular embodiments, the configuration for a virtual beam does not include time/frequency resources for transmission/reception of the beam.

[0058] In particular embodiments, beam set A further comprises one or more physical beams and the beam configuration information comprises physical time/frequency resources for transmission/reception of the physical beams of beam Set A. The one or more predicted beams from beam set A may comprise at least one virtual beam and at least one physical beam.

[0059] In particular embodiments, the method further comprises measuring a physical beam from beam set A and reporting a performance value associated with a predicted value of the beam from beam set A based on a comparison of the predicted value for the beam and the measured value of the beam.

[0060] In particular embodiments, the time/frequency resources for reception of the beam comprise one of channel state information (CSI) resources or synchronization signal block (SSB) resources.

[0061] In particular embodiments, predicting the one or more beams is based on a machine learning model that uses beam set B as input and beam set A as output.

[0062] According to some embodiments, a wireless device comprises processing circuitry operable to perform any of the wireless device methods described above.

[0063] Also disclosed is a computer program product comprising a non-transitory computer readable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the wireless device described above.

[0064] According to some embodiments, a method is performed by a network node (e.g., gNB) for beam prediction in a wireless network. The method comprises transmitting beam configuration information for two sets of beams, a beam set A and a beam set B. Beam set B comprises physical beams and beam set A comprises one or more virtual beams. The beam configuration information comprises data indicating a beam pattern for each of the beams in beam Set A and beam Set B and at least physical time/frequency resources for transmission/reception of the physical beams of beam Set B. The method further comprises transmitting one or more beams from beam set B and receiving a report from the wireless device. The report comprises an indication of a prediction of one or more beams from beam set A based on measurements of one or more beams from beam set B by the wireless device.

[0065] In particular embodiments, the method further comprises transmitting a physical beam from beam set A and receiving a report from the wireless device. The report comprises a performance value associated with a predicted value of the beam from beam set A based on a comparison of the predicted value for the beam and the measured value of the beam.

[0066] According to some embodiments, a network node comprises processing circuitry operable to perform any of the network node methods described above.

[0067] Another computer program product comprises a non-transitory computer readable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the network node described above.

[0068] Certain embodiments may provide one or more of the following technical advantages. For example, particular embodiments reduce signaling overhead for NZP CSI-RS resource configuration in beam management in wireless communication networks.

BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The present disclosure may be best understood by way of example with reference to the following description and accompanying drawings that are used to illustrate embodiments of the present disclosure. In the drawings:

Figure 1 illustrates synchronization signal block (SSB) beam selection as part of an initial access procedure according to the Pl scenario;

Figure 2 illustrates channel state information reference signal (CSI-RS) Tx beam selection in downlink according to the P2 scenario;

Figure 3 illustrates user equipment (UE) Rx beam selection for corresponding CSI-RS Tx beam in downlink according to P3 scenario;

Figure 4 illustrates an example where Set B is a subset of Set A;

Figure 5 illustrates an example where Set A is a set of narrow beams and Set B is a set of wide beams;

Figure 6 illustrates time/frequency diagrams illustrating physical beams and virtual beams, according to particular embodiments;

Figure 7 shows an example of a communication system, according to certain embodiments;

Figure 8 shows a UE, according to certain embodiments; Figure 9 shows a network node, according to certain embodiments;

Figure 10 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized;

Figure 11 is a flowchart illustrating an example method in a wireless device, according to certain embodiments; and

Figure 12 is a flowchart illustrating an example method in a network node, according to certain embodiments.

DETAILED DESCRIPTION

[0070] As described above, certain challenges currently exist with managing large beam sets. Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, particular embodiments include virtual beams to support beam prediction in artificial intelligence (Al)-based beam management for signaling overhead reduction.

[0071] Particular embodiments are described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0072] Some or all of the embodiments and examples described herein are primarily in terms of New Radio (NR), however, embodiments and examples may also be applicable to other networks.

[0073] As used herein, a beam location may be replaced by beam direction. For example, in any of the disclosed embodiments, methods may be with respect to the beam location and/or beam direction.

[0074] A non-zero power channel state information-reference signal (NZP CSI-RS) as used herein may be replaced by other downlink signals (e.g., downlink reference signal, downlink synchronization signal). For example, in any of the disclosed embodiments, a downlink signal may include a NZP CSI-RS or other types of signals.

[0075] In particular embodiments, the network may configure beam Set A, where at least one beam is a virtual beam in the beam Set A, and beam Set B, where all beams in the beam Set B are physical beams. The network may also indicate the UE to perform beam prediction for beam Set A based on at least a measurement in beam Set B. The network may also indicate the UE to report the prediction results (e.g., one or more best (e.g., more optimal) beams, reference signal receive power (RSRPs) of one or more best (e.g., more optimal) beams) to the network.

[0076] In particular embodiments, the network may transmit the beam configuration information of one or more downlink signals and/or the beam configuration information of one or more downlink signal sets to the UE. The network may also configure beam Set A, which indicates beam location/ direction information of each beam in beam Set A. The network may also configure beam Set B, which indicates beam location/direction information of each beam in beam Set B for UE-side beam prediction of Set A via Set B. The network may transmit the configured beam Set A and beam Set B to the UE. Note that the network may indicate the UE to predict one or more best (e.g., more optimal) beams in beam Set A or predict one or more beams with strongest (or higher) signal strength (e.g., RSRP) in beam Set A via beam Set B. However, in particular embodiments, partial or all beams in beam Set A may be virtual beams (i.e., the network does not transmit the virtual beams via downlink signal physically at the specific time instances). In these cases, the network may not send the configuration information of the downlink signal of the virtual beams to the UE. However, the network may need to indicate the beam location information and/or beam direction information of the virtual beam(s) to the UE so that the UE knows the location and/or direction of the virtual beams for beam prediction of beam Set A.

[0077] Figure 6 illustrates time/frequency diagrams illustrating physical beams and virtual beams, according to particular embodiments. In the example shown in Figure 6, the physical beam is the beam with downlink signal transmission (e.g. NZP CSI-RS transmission, synchronization signal block (SSB) transmission) and the virtual beam is the beam without downlink signal transmission.

[0078] In particular embodiments, the network may configure the first NZP CSI-RS resource set as the beam Set A and transmit it to the UE. The network may also configure the second NZP CSI-RS resource set as the beam Set B and transmit it to the UE. In some embodiments, partial or all NZP CSI-RS resources in the first NZP CSI-RS resource set may have the beam location information and/or the beam direction information but have no NZP CSI-RS signal information because the network does not expect the UE to measure them.

[0079] In particular embodiments, the network may configure the first NZP CSI-RS resource set as the beam Set A and transmit it to the UE. The network may also configure the second NZP CSI-RS resource set as the beam Set B and transmit it to the UE. In some embodiments, all NZP CSI-RS resources in the first NZP CSI-RS resource set may have the beam location information and/or the beam direction information and also may have NZP CSI-RS signal information to support the UE to measure these signals in partial time instances but not all predicted time instances In such embodiments, a beam may be a physical beam in one time instance if the UE is able to measure this beam in this time instance, and another beam may be a virtual beam in one time instance if the UE needs to predict this beam in this time instance but is not able to measure this beam in this time instance . Such embodiments may be used to support performance monitoring at the UE side and/or at the network side besides, for example, the Al-model inference because the prediction performance may be compared via predicted results and measured results for some time instances.

[0080] In particular embodiments, the network may configure and transmit the first NZP CSI- RS resource set as the beam Set Ao to the UE at time 0, beam Set Ai to the UE at time 1, Set A_t-i to the UE at time t-1, beam Set A_t to the UE at time t, among other beam Set As between beam Set Ao and beam Set At. The network may configure and transmit the second NZP CSI-RS resource set as the beam Set Bo to the UE at time 0, beam Set Bi to the UE at time 1, beam Set B_t-i to the UE at time t-1, beam Set Bt to the UE at time t, among other beam Set Bs between beam Set Bo and beam Set B_t. The UE may predict the best (e.g., more optimal) beam from the virtual beam and/or physical beam in beam Set A_t configured at the current time t. The UE may predict the best (e.g., more optimal) beam from the virtual beam and/or physical beam in the historical set of beam Set An, where n is between 0 to t-1.

[0081] Figure 7 shows an example of a communication system 100 in accordance with some embodiments. In the example, the communication system 100 includes a telecommunication network 102 that includes an access network 104, such as a radio access network (RAN), and a core network 106, which includes one or more core network nodes 108. The access network 104 includes one or more access network nodes, such as network nodes 110a and 110b (one or more of which may be generally referred to as network nodes 110), or any other similar 3rd Generation Partnership Project (3GPP) access node or non-3GPP access point. The network nodes 110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 112a, 112b, 112c, and 112d (one or more of which may be generally referred to as UEs 112) to the core network 106 over one or more wireless connections.

[0082] Example wireless communications over a wireless connection include transmitting and/or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and/or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 100 may include any number of wired or wireless networks, network nodes, UEs, and/or any other components or systems that may facilitate or participate in the communication of data and/or signals whether via wired or wireless connections. The communication system 100 may include and/or interface with any type of communication, telecommunication, data, cellular, radio network, and/or other similar type of system.

[0083] The UEs 112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and/or operable to communicate wirelessly with the network nodes 110 and other communication devices. Similarly, the network nodes 110 are arranged, capable, configured, and/or operable to communicate directly or indirectly with the UEs 112 and/or with other network nodes or equipment in the telecommunication network 102 to enable and/or provide network access, such as wireless network access, and/or to perform other functions, such as administration in the telecommunication network 102.

[0084] In the depicted example, the core network 106 connects the network nodes 110 to one or more hosts, such as host 116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 106 includes one more core network nodes (e.g., core network node 108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and/or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and/or a User Plane Function (UPF).

[0085] The host 116 may be under the ownership or control of a service provider other than an operator or provider of the access network 104 and/or the telecommunication network 102 and may be operated by the service provider or on behalf of the service provider. The host 116 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio/video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0086] As a whole, the communication system 100 of Figure 7 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and/or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and/or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and/or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[0087] In some examples, the telecommunication network 102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 102. For example, the telecommunications network 102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and/or Massive Machine Type Communication (mMTC)ZMassive loT services to yet further UEs.

[0088] In some examples, the UEs 112 are configured to transmit and/or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 104. Additionally, a UE may be configured for operating in single- or multi-RAT or multi -standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0089] In the example, the hub 114 communicates with the access network 104 to facilitate indirect communication between one or more UEs (e.g., UE 112c and/or 112d) and network nodes (e.g., network node 110b). In some examples, the hub 114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 114 may be a broadband router enabling access to the core network 106 for the UEs. As another example, the hub 114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 110, or by executable code, script, process, or other instructions in the hub 114. As another example, the hub 114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 114 may be a content source . For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 114 then provides to the UE either directly, after performing local processing, and/or after adding additional local content. In still another example, the hub 114 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices. [0090] The hub 114 may have a constant/persistent or intermitent connection to the network node 110b. The hub 114 may also allow for a different communication scheme and/or schedule between the hub 114 and UEs (e.g., UE 112c and/or 112d), and between the hub 114 and the core network 106. In other examples, the hub 114 is connected to the core network 106 and/or one or more UEs via a wired connection. Moreover, the hub 114 may be configured to connect to an M2M service provider over the access network 104 and/or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 110 while still connected via the hub 114 via a wired or wireless connection. In some embodiments, the hub 114 may be a dedicated hub - that is, a hub whose primary function is to route communications to/from the UEs from/to the network node 110b. In other embodiments, the hub 114 may be a nondedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 110b, but which is additionally capable of operating as a communication start and/or end point for certain data channels.

[0091] Figure 8 shows a UE 200 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle-mounted or vehicle embedded/integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and/or an enhanced MTC (eMTC) UE.

[0092] A UE may support device-to-device (D2D) communication, for example by implementing a 3 GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to- everything (V2X) . In other examples, a UE may not necessarily have a user in the sense of a human user who owns and/or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter). [0093] The UE 200 includes processing circuitry 202 that is operatively coupled via a bus 204 to an input/output interface 206, a power source 208, a memory 210, a communication interface 212, and/or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 7. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0094] The processing circuitry 202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 210. The processing circuitry 202 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field- programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 202 may include multiple central processing units (CPUs).

[0095] In the example, the input/output interface 206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and/or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 200. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0096] In some embodiments, the power source 208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 208 may further include power circuitry for delivering power from the power source 208 itself, and/or an external power source, to the various parts of the UE 200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 208 to make the power suitable for the respective components of the UE 200 to which power is supplied.

[0097] The memory 210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 210 includes one or more application programs 214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 216. The memory 210 may store, for use by the UE 200, any of a variety of various operating systems or combinations of operating systems.

[0098] The memory 210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and/or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 210 may allow the UE 200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 210, which may be or comprise a device-readable storage medium.

[0099] The processing circuitry 202 may be configured to communicate with an access network or other network using the communication interface 212. The communication interface 212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 222. The communication interface 212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 218 and/or a receiver 220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 218 and receiver 220 may be coupled to one or more antennas (e.g., antenna 222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0100] In the illustrated embodiment, communication functions of the communication interface 212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short- range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and/or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol/intemet protocol (TCP/IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0101] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient). [0102] As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0103] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non -limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door/window sensor, a flood/moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or itemtracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and/or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 200 shown in Figure 7.

[0104] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and/or measurements and transmits the results of such monitoring and/or measurements to another UE and/or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and/or reporting on its operational status or other functions associated with its operation.

[0105] In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and/or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0106] Figure 9 shows a network node 300 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and/or operable to communicate directly or indirectly with a UE and/or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NRNodeBs (gNBs)).

[0107] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units and/or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0108] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell/multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and/or Minimization of Drive Tests (MDTs).

[0109] The network node 300 includes a processing circuitry 302, a memory 304, a communication interface 306, and a power source 308. The network node 300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 304 for different RATs) and some components may be reused (e.g., a same antenna 310 may be shared by different RATs). The network node 300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 300.

[0110] The processing circuitry 302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and/or encoded logic operable to provide, either alone or in conjunction with other network node 300 components, such as the memory 304, to provide network node 300 functionality.

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

[0112] The memory 304 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and/or any other volatile or non-volatile, non-transitory device-readable and/or computer-executable memory devices that store information, data, and/or instructions that may be used by the processing circuitry 302. The memory 304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and/or other instructions capable of being executed by the processing circuitry 302 and utilized by the network node 300. The memory 304 may be used to store any calculations made by the processing circuitry 302 and/or any data received via the communication interface 306. In some embodiments, the processing circuitry 302 and memory 304 is integrated.

[0113] The communication interface 306 is used in wired or wireless communication of signaling and/or data between a network node, access network, and/or UE. As illustrated, the communication interface 306 comprises port(s)/terminal(s) 316 to send and receive data, for example to and from a network over a wired connection. The communication interface 306 also includes radio front-end circuitry 318 that may be coupled to, or in certain embodiments a part of, the antenna 310. Radio front-end circuitry 318 comprises filters 320 and amplifiers 322. The radio front-end circuitry 318 may be connected to an antenna 310 and processing circuitry 302. The radio front-end circuitry may be configured to condition signals communicated between antenna 310 and processing circuitry 302. The radio front-end circuitry 318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 320 and/or amplifiers 322. The radio signal may then be transmitted via the antenna 310. Similarly, when receiving data, the antenna 310 may collect radio signals which are then converted into digital data by the radio front-end circuitry 318. The digital data may be passed to the processing circuitry 302. In other embodiments, the communication interface may comprise different components and/or different combinations of components.

[0114] In certain alternative embodiments, the network node 300 does not include separate radio front-end circuitry 318, instead, the processing circuitry 302 includes radio front-end circuitry and is connected to the antenna 310. Similarly, in some embodiments, all or some of the RF transceiver circuitry 312 is part of the communication interface 306. In still other embodiments, the communication interface 306 includes one or more ports or terminals 316, the radio front-end circuitry 318, and the RF transceiver circuitry 312, as part of a radio unit (not shown), and the communication interface 306 communicates with the baseband processing circuitry 314, which is part of a digital unit (not shown).

[0115] The antenna 310 may include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals. The antenna 310 may be coupled to the radio front-end circuitry 318 and may be any type of antenna capable of transmitting and receiving data and/or signals wirelessly. In certain embodiments, the antenna 310 is separate from the network node 300 and connectable to the network node 300 through an interface or port.

[0116] The antenna 310, communication interface 306, and/or the processing circuitry 302 may be configured to perform any receiving operations and/or certain obtaining operations described herein as being performed by the network node. Any information, data and/or signals may be received from a UE, another network node and/or any other network equipment. Similarly, the antenna 310, the communication interface 306, and/or the processing circuitry 302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and/or signals may be transmitted to a UE, another network node and/or any other network equipment.

[0117] The power source 308 provides power to the various components of network node 300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 300 with power for performing the functionality described herein. For example, the network node 300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 308. As a further example, the power source 308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0118] Embodiments of the network node 300 may include additional components beyond those shown in Figure 9 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and/or any functionality necessary to support the subject matter described herein. For example, the network node 300 may include user interface equipment to allow input of information into the network node 300 and to allow output of information from the network node 300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 300.

[0119] Figure 10 is a block diagram illustrating a virtualization environment 500 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 500 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized.

[0120] Applications 502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and/or benefits of some of the embodiments disclosed herein.

[0121] Hardware 504 includes processing circuitry, memory that stores software and/or instructions executable by hardware processing circuitry, and/or other hardware devices as described herein, such as a network interface, input/output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 508a and 508b (one or more of which may be generally referred to as VMs 508), and/or perform any of the functions, features and/or benefits described in relation with some embodiments described herein. The virtualization layer 506 may present a virtual operating platform that appears like networking hardware to the VMs 508.

[0122] The VMs 508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 506. Different embodiments of the instance of a virtual appliance 502 may be implemented on one or more of VMs 508, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0123] In the context of NFV, a VM 508 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 508, and that part of hardware 504 that executes that VM, be it hardware dedicated to that VM and/or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 508 on top of the hardware 504 and corresponds to the application 502.

[0124] Hardware 504 may be implemented in a standalone network node with generic or specific components. Hardware 504 may implement some functions via virtualization. Alternatively, hardware 504 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 510, which, among others, oversees lifecycle management of applications 502. In some embodiments, hardware 504 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 512 which may alternatively be used for communication between hardware nodes and radio units.

[0125] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and/or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and/or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0126] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer- readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and/or by end users and a wireless network generally.

[0127] FIGURE 11 is a flowchart illustrating an example method 1100 in a wireless device, according to certain embodiments. In particular embodiments, one or more steps of FIGURE 11 may be performed by UE 200 described with respect to FIGURE 8. The wireless device is capable of beam prediction in a wireless network.

[0128] The method 1100 begins at step 1112, where the wireless device (e.g., UE 200) receives beam configuration information for two sets of beams, a beam set A and a beam set B. Beam set B comprises physical beams and beam set A comprises one or more virtual beams (and potentially one or more physical beams). The beam configuration information comprises data indicating a beam pattern for each of the beams in beam Set A and beam Set B and at least physical time/frequency resources for transmission/reception of the physical beams of beam Set B (and beam Set A, if any).

[0129] For example, the beam configuration information may comprise data (such as an identifier) that explicitly or implicitly indicates a beam pattern of beams in the beam set A and/or may comprise data that explicitly or implicitly indicates a beam pattern of beams in the beam set B and/or may comprise data that explicitly or implicitly indicates a beam pattern of beams in the beam set A and beams in the beam set B.

[0130] In particular embodiments, the beam configuration information associated with the beam set A is configured on a first resource set comprising at least one of a first non-zero power channel state information-reference signal (NZP CSI-RS) resource set and a first synchronization signal block (SSB) resource set. The beam configuration information associated with the beam set B is configured on a second resource set comprising at least one of a second NZP CSI-RS resource set and a second SSB resource set.

[0131] In particular embodiments, the configuration for a virtual beam does not include time/frequency resources for transmission/reception of the beam.

[0132] In particular embodiments, beam set A further comprises one or more physical beams and the beam configuration information comprises physical time/frequency resources for transmission/reception of the physical beams of beam Set A.

[0133] In particular embodiments, the beam configuration information comprises any of the beam configuration information described with respect to the embodiments and examples described herein.

[0134] At step 1114, the wireless device measures one or more beams from beam set B. For example, the network node transmits the physical beams from beam set B on the configured time/frequency resources and the wireless device measures the beams for signal quality, strength, etc. The wireless device may measure the one or more beams according to any of the embodiments and examples described herein.

[0135] In particular embodiments, the time/frequency resources for reception of the beam comprise one of channel state information (CSI) resources or synchronization signal block (SSB) resources.

[0136] At step 116, the wireless device predicts one or more beams from beam set A based on the measurement of the one or more beams from beam set B. In particular embodiments, predicting the one or more beams is based on a machine learning model that uses beam set B as input and beam set A as output. [0137] In particular embodiments, beam set A further comprises one or more physical beams and the beam configuration information comprises physical time/frequency resources for transmission/reception of the physical beams of beam Set A. The one or more predicted beams from beam set A may comprise at least one virtual beam and at least one physical beam.

[0138] In particular embodiments, the wireless device predicts the one or more beams according to any of the embodiments and examples described herein.

[0139] In some embodiments, the method continues step 1122. Some embodiments include the following optional steps for performance monitoring.

[0140] At step 1118, the wireless device measures a physical beam from beam set A. The wireless device may use the measurement for determining a performance of the beam prediction model. For example, the wireless device may both predict the beam from beam set A based on measurements from beam Set B and then actually measure the physical beam. Then the wireless device may compare the predicted beam with the actual measurements from the physical beam to determine an accuracy level of the prediction (i.e., how close is the prediction to the measured value).

[0141] As another example, at a first time instance a beam from beam set A may be a virtual beam and the wireless device may predict values for the beam. At a second time instance, the same beam from beam set A may be a physical beam that the wireless device measures. Then the wireless device can compare the measured value with the previously predicted value to determine an accuracy level of the prediction.

[0142] In some embodiments, the wireless device may do the performance monitoring according to any of the embodiments and examples described herein.

[0143] At step 1120, the wireless device reports a performance value associated with a predicted value of the beam from beam set A based on a comparison of the predicted value for the beam and the measured value of the beam.

[0144] At step 1122, the wireless device reports an indication of the predicted one or more beams from beam set A to a network node. Each of the one or more predicted beams from beam set A may be associated with an expected signal quality that is higher than expected signal qualities of other beams from beam set A. The indication may further comprise a prediction result of each of the one or more predicted beams from beam set A.

[0145] In particular embodiments, the reporting from steps 1120 and 1122 may be combined as a single report. [0146] Modifications, additions, or omissions may be made to method 1100 of FIGURE 11. Additionally, one or more steps in the method of FIGURE 11 may be performed in parallel or in any suitable order.

[0147] FIGURE 12 is a flowchart illustrating an example method 1200 in a network node, according to certain embodiments. In particular embodiments, one or more steps of FIGURE 12 may be performed by network node 300 described with respect to FIGURE 9. The network node is operable to participate in beam prediction in a wireless network.

[0148] The method 1200 begins at step 1212, where the network node (e.g., network node 300) transmitting beam configuration information for two sets of beams, a beam set A and a beam set B. Beam set B comprises physical beams and beam set A comprises one or more virtual beams. The beam configuration information comprises data indicating a beam pattern for each of the beams in beam Set A and beam Set B and at least physical time/frequency resources for transmission/reception of the physical beams of beam Set B.

[0149] The beam configuration information is described in more detail with respect to FIGURE 11 and the embodiments and examples described herein.

[0150] At step 1214, the network node transmits one or more beams from beam set B. The wireless device may receive the beams and use them as input to a beam prediction model.

[0151] At step 1216, the network node receives a report from the wireless device. The report comprises an indication of a prediction of one or more beams from beam set A based on measurements of one or more beams from beam set B by the wireless device.

[0152] At step 1218, the network node may transmit a physical beam from beam set A. The wireless device may receive the beam and use it for validation of a beam prediction model.

[0153] At step 1220, the network node may receive a report from the wireless device. The report comprises a performance value associated with a predicted value of the beam from beam set A based on a comparison of the predicted value for the beam and the measured value of the beam.

[0154] Modifications, additions, or omissions may be made to method 1200 of FIGURE 12. Additionally, one or more steps in the method of FIGURE 12 may be performed in parallel or in any suitable order.

[0155] The foregoing description sets forth numerous specific details. It is understood, however, that embodiments may be practiced without these specific details. In other instances, well-known circuits, structures and techniques have not been shown in detail in order not to obscure the understanding of this description. Those of ordinary skill in the art, with the included descriptions, will be able to implement appropriate functionality without undue experimentation. [0156] References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to implement such feature, structure, or characteristic in connection with other embodiments, whether or not explicitly described.

[0157] Although this disclosure has been described in terms of certain embodiments, alterations and permutations of the embodiments will be apparent to those skilled in the art. Accordingly, the above description of the embodiments does not constrain this disclosure. Other changes, substitutions, and alterations are possible without departing from the scope of this disclosure, as defined by the claims below.

[0158] Some example embodiments are described below.

Group A Embodiments

1. A method performed by a user equipment for beam prediction in wireless communication networks, the method comprising: receiving beam configuration information associated with two sets of beams, wherein the two sets of beams comprise a beam set A and a beam set B, wherein: the beam set A comprises at least one virtual beam; the at least one virtual beam represents a simulated beam that is not physically transmitted to the UE; the beam configuration information associated with the beam set A comprises data indicating a beam location and/or a beam direction of each beam in the beam set A in a two-dimensional (2D) space or a three-dimensional (3D) space; the beam set B comprises physical beams; the beam configuration information associated with the beam set B comprises data indicating the beam location and/or the beam direction of each beam in the beam set B in the 2D space or the 3D space; the beam configuration information associated with the beam set B further comprises measured beam data for the beam set B obtained from measuring the beam set B; receiving a request to perform beam prediction for the beam set A based at least on a measurement on the beam set B; performing the measurement on the beam set B; analyzing the measurements on the beam set B by the ML model; and predicting, based at least on the measurement on the beam set B and by the ML model, one or more beams from the beam set A, wherein each of the one or more beams is associated with an expected signal quality that is higher than signal qualities of other beams from the beam set A.

2. The method of the embodiment 1, further comprising reporting a prediction result comprising the predicted one or more beams to a network node.

3. The method of the embodiment 2, wherein the prediction result further comprises the expected signal quality of each of the predicted one or more beams.

4. The method of any one of the embodiments 1-3, wherein, if the beam set A comprises a non-virtual beam: the beam location and/or the beam direction of the non-virtual beam is transmitted to the UE via a downlink signal comprising a non-zero power channel state information-reference signal (NZP CSI-RS) or a synchronization signal block (SSB); and the non-virtual beam is transmitted to the UE via the downlink signal transmission.

5. The method of any one of the embodiments 1-4, wherein: the beam configuration information associated with the beam set A is configured on a first resource set comprising a first non-zero power channel state information-reference signal (NZP CSI-RS) resource set; and the beam configuration information associated with the beam set B is configured on a second resource set comprising a second NZP CSI-RS resource set.

6. The method of any one of the embodiments 1-5, further comprising: comparing a predicted result with an expected result, wherein: the expected result comprising the measured beam data associated with the beam set B; and the predicted result comprising the predicted one or more beams from the beam set A; determining a performance of the ML model based at least on the comparison; and transmitting feedback indicating the performance of the ML model to the network node.

7. The method of any one of the embodiments 1-6, wherein the predicted one or more beams comprise the at least one virtual beam and/or at least one non-virtual beam.

8. A method performed by a wireless device, the method comprising:

- any of the wireless device steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above.

9. The method of the previous embodiment, further comprising one or more additional wireless device steps, features or functions described above.

10. The method of any of the previous embodiments, further comprising:

- providing user data; and

- forwarding the user data to a host computer via the transmission to the base station.

Group B Embodiments

11. A method performed by a network node for managing beam prediction for a user equipment (UE) in wireless communication networks, the method comprising: configuring two sets of beams comprising a beam set A and a beam set B, wherein: the beam set A comprises at least one virtual beam; the at least one virtual beam represents a simulated beam that is not physically transmitted to the UE; the beam set B comprises physical beams; transmitting beam configuration information associated with the two sets of beams, wherein: the beam configuration information associated with the beam set A comprises data indicating a beam location and/or a beam direction of each beam in the beam set A in a two-dimensional (2D) space or a three-dimensional (3D) space; the beam configuration information associated with the beam set B comprises data indicating the beam location and/or the beam direction of each beam in the beam set B in the 2D space or the 3D space; the beam configuration information associated with the beam set B further comprises measured beam data for the beam set B obtained from measuring the beam set B; receiving a prediction result from the UE, the prediction result comprising one or more predicted beams from beam set A, wherein each of the one or more predicted beams is associated with an expected signal quality higher than other beams in the beam set A; and adjusting at least one beamforming parameter based at least on the prediction result, wherein the at least one beamforming parameter comprises the beam direction and/or the beam location of a subsequent beam to be transmitted to the UE.

12. The method of the embodiment 11, wherein, if the beam set A comprises a non-virtual beam: the beam location and/or the beam direction of the non-virtual beam is transmitted to the UE via a downlink signal comprising a non-zero power channel state information-reference signal (NZP CSI-RS) or a synchronization signal block (SSB); and the non-virtual beam is transmitted to the UE via the downlink signal transmission.

13. The method of any one of the embodiments 11-12, wherein: the beam configuration information associated with the beam set A is configured on a first resource set comprising a first non-zero power channel state information-reference signal (NZP CSI-RS) resource set; and the beam configuration information associated with the beam set B is configured on a second resource set comprising a second NZP CSI-RS resource set.

14. The method of any one of the embodiments 11-13, further comprising: receiving feedback from the UE regarding performance of the ML model based at least on a comparison between the prediction result and an expected result, wherein the expected result comprises the measured beam data associated with the beam set B; and refining the ML model based at least on the feedback, comprising updating weight and bias values of a neural network of the ML model.

15. The method of any one of the embodiments 11-14, wherein the predicted one or more beams comprise the at least one virtual beam and/or at least one non-virtual beam.

16. A method performed by a base station, the method comprising:

- any of the steps, features, or functions described above with respect to base station, either alone or in combination with other steps, features, or functions described above.

17. The method of the previous embodiment, further comprising one or more additional base station steps, features or functions described above.

18. The method of any of the previous embodiments, further comprising:

- obtaining user data; and

- forwarding the user data to a host computer or a wireless device.

Group C Embodiments

19. A user equipment for beam prediction in wireless communication networks, comprising: processing circuitry configured to perform any of the steps of any of the Group A embodiments; and power supply circuitry configured to supply power to the processing circuitry.

20. A network node for managing beam prediction for a user equipment (UE) in wireless communication networks, the network node comprising: processing circuitry configured to perform any of the steps of any of the Group B embodiments; power supply circuitry configured to supply power to the processing circuitry.

21. A user equipment (UE) for beam prediction in wireless communication networks, the UE comprising: an antenna configured to send and receive wireless signals; radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; the processing circuitry being configured to perform any of the steps of any of the Group A embodiments; an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry; an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and a battery connected to the processing circuitry and configured to supply power to the UE. 22. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a cellular network for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UE being configured to perform any of the steps of any of the Group A embodiments to receive the user data from the host.

23. The host of the previous embodiment, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data to the UE from the host.

24. The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

25. A method implemented by a host operating in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; and initiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the UE performs any of the operations of any of the Group A embodiments to receive the user data from the host.

26. The method of the previous embodiment, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the UE.

27. The method of the previous embodiment, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application.

28. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a cellular network for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UE being configured to perform any of the steps of any of the Group A embodiments to transmit the user data to the host.

29. The host of the previous embodiment, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data from the UE to the host.

30. The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

31. A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: at the host, receiving user data transmitted to the host via the network node by the UE, wherein the UE performs any of the steps of any of the Group A embodiments to transmit the user data to the host.

32. The method of the previous embodiment, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the UE.

33. The method of the previous embodiment, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application.

34. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a network node in a cellular network for transmission to a user equipment (UE), the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE.

35. The host of the previous embodiment, wherein: the processing circuitry of the host is configured to execute a host application that provides the user data; and the UE comprises processing circuitry configured to execute a client application associated with the host application to receive the transmission of user data from the host.

36. A method implemented in a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; and initiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the network node performs any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE.

37. The method of the previous embodiment, further comprising, at the network node, transmitting the user data provided by the host for the UE.

38. The method of any of the previous 2 embodiments, wherein the user data is provided at the host by executing a host application that interacts with a client application executing on the UE, the client application being associated with the host application. 39. A communication system configured to provide an over-the-top service, the communication system comprising: a host comprising: processing circuitry configured to provide user data for a user equipment (UE), the user data being associated with the over-the-top service; and a network interface configured to initiate transmission of the user data toward a cellular network node for transmission to the UE, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE.

40. The communication system of the previous embodiment, further comprising: the network node; and/or the user equipment.

41. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to initiate receipt of user data; and a network interface configured to receive the user data from a network node in a cellular network, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to receive the user data from a user equipment (UE) for the host.

42. The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

43. The host of the any of the previous 2 embodiments, wherein the initiating receipt of the user data comprises requesting the user data.

44. A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: at the host, initiating receipt of user data from the UE, the user data originating from a transmission which the network node has received from the UE, wherein the network node performs any of the steps of any of the Group B embodiments to receive the user data from the UE for the host.

45. The method of the previous embodiment, further comprising at the network node, transmitting the received user data to the host.

Claims

Claims

1. A method performed by a wireless device for beam prediction in a wireless network, the method comprising: receiving (1112) beam configuration information for two sets of beams, a beam set A and a beam set B, wherein beam set B comprises physical beams and beam set A comprises one or more virtual beams, and the beam configuration information comprises data indicating a beam pattern for each of the beams in beam Set A and beam Set B and at least physical time/frequency resources for transmission/reception of the physical beams of beam Set B; measuring (1114) one or more beams from beam set B; and predicting (1116) one or more beams from beam set A based on the measurement of the one or more beams from beam set B.

2. The method of claim 1, further comprising reporting (1122) an indication of the predicted one or more beams from beam set A to a network node.

3. The method of any one of claims 1 -2, wherein each of the one or more predicted beams from beam set A is associated with an expected signal quality that is higher than expected signal qualities of other beams from beam set A.

4. The method of any one of claims 2-3, wherein reporting the indication further comprises reporting a prediction result of each of the one or more predicted beams from beam set A.

5. The method of any one of claims 1-4, wherein the beam configuration information associated with the beam set A is configured on a first resource set comprising at least one of a first non-zero power channel state information-reference signal (NZP CSI-RS) resource set and a first synchronization signal block (SSB) resource set, and the beam configuration information associated with the beam set B is configured on a second resource set comprising at least one of a second NZP CSI-RS resource set and a second SSB resource set.

6. The method of any one of claims 1-5, wherein the configuration for a virtual beam does not include time/frequency resources for transmission/reception of the beam.

7. The method of any one of claims 1-6, wherein beam set A further comprises one or more physical beams and the beam configuration information comprises physical time/frequency resources for transmission/reception of the physical beams of beam Set A.

8. The method of claim 7, wherein the one or more predicted beams from beam set A comprise at least one virtual beam and at least one physical beam.

9. The method of any one of claims 7-8, further comprising: measuring (1118) a physical beam from beam set A; and reporting (1120) a performance value associated with a predicted value of the beam from beam set A based on a comparison of the predicted value for the beam and the measured value of the beam.

10. The method of any one of claims 1-9, wherein the time/frequency resources for reception of the beam comprise one of channel state information (CSI) resources or synchronization signal block (SSB) resources.

11. The method of any one of claims 1-10, wherein predicting the one or more beams is based on a machine learning model that uses beam set B as input and beam set A as output.

12. A wireless device (200) capable of beam prediction in a wireless network, the wireless device comprising processing circuitry (202) operable to: receive beam configuration information for two sets of beams, a beam set A and a beam set B, wherein beam set B comprises physical beams and beam set A comprises one or more virtual beams, and the beam configuration information comprises data indicating a beam pattern for each of the beams in beam Set A and beam Set B and at least physical time/frequency resources for transmission/reception of the physical beams of beam Set B; measure one or more beams from beam set B; and predict one or more beams from beam set A based on the measurement of the one or more beams from beam set B.

13. The wireless device of claim 12, the processing circuitry further operable to report an indication of the predicted one or more beams from beam set A to a network node.

14. The wireless device of any one of claims 12-13, wherein each of the one or more predicted beams from beam set A is associated with an expected signal quality that is higher than expected signal qualities of other beams from beam set A.

15. The wireless device of any one of claims 13-14, wherein reporting the indication further comprises reporting a prediction result of each of the one or more predicted beams from beam set A.

16. The wireless device of any one of claims 12-15, wherein the beam configuration information associated with the beam set A is configured on a first resource set comprising at least one of a first non-zero power channel state information-reference signal (NZP CSI-RS) resource set and a first synchronization signal block (SSB) resource set, and the beam configuration information associated with the beam set B is configured on a second resource set comprising at least one of a second NZP CSI-RS resource set and a second SSB resource set.

17. The wireless device of any one of claims 12-16, wherein beam set A further comprises one or more physical beams and the beam configuration information comprises physical time/frequency resources for transmission/reception of the physical beams of beam Set A.

18. The wireless device of claim 17, the processing circuitry further operable to: measure a physical beam from beam set A; and report a performance value associated with a predicted value of the beam from beam set A based on a comparison of the predicted value for the beam and the measured value of the beam.

19. A method performed by a network node for beam prediction in a wireless network, the method comprising: transmitting (1212) beam configuration information for two sets of beams, a beam set A and a beam set B, wherein beam set B comprises physical beams and beam set A comprises one or more virtual beams, and the beam configuration information comprises data indicating a beam pattern for each of the beams in beam Set A and beam Set B and at least physical time/frequency resources for transmission/reception of the physical beams of beam Set B; transmitting (1214) one or more beams from beam set B; and receiving (1216) a report from the wireless device, the report comprising an indication of a prediction of one or more beams from beam set A based on measurements of one or more beams from beam set B by the wireless device.

20. The method of claim 19, wherein each of the one or more predicted beams from beam set A is associated with an expected signal quality that is higher than expected signal qualities of other beams from beam set A.

21. The method of any one of claims 19-20, wherein the report further comprises a prediction result of each of the one or more predicted beams from beam set A.

22. The method of any one of claims 19-21, wherein the beam configuration information associated with the beam set A is configured on a first resource set comprising at least one of a first non-zero power channel state information-reference signal (NZP CSI-RS) resource set and a first synchronization signal block (SSB) resource set, and the beam configuration information associated with the beam set B is configured on a second resource set comprising ate least one of a second NZP CSI-RS resource set and a second SSB resource set.

23. The method of any one of claims 19-22, wherein the configuration for a virtual beam does not include time/frequency resources for transmission/reception of the beam.

24. The method of any one of claims 19-23, wherein beam set A further comprises one or more physical beams and the beam configuration information comprises physical time/frequency resources for transmission/reception of the physical beams of beam Set A

25. The method of claim 24, wherein the one or more predicted beams from beam set A comprise at least one virtual beam and at least one physical beam.

26. The method of any one of claims 24-25, further comprising: transmitting (1218) a physical beam from beam set A; and receiving (1220) a report from the wireless device, the report comprising a performance value associated with a predicted value of the beam from beam set A based on a comparison of the predicted value for the beam and the measured value of the beam.

27. The method of any one of claims 19-26, wherein the time/frequency resources for reception of the beam comprise one of channel state information (CSI) resources or synchronization signal block (SSB) resources.

28. The method of any one of claims 19-27, wherein the predicted the one or more beams are predicted based on a machine learning model that uses beam set B as input and beam set A as output.

29. A network node (300) capable of beam prediction in a wireless network, the network node comprising processing circuitry (302) operable to: transmit beam configuration information for two sets of beams, a beam set A and a beam set B, wherein beam set B comprises physical beams and beam set A comprises one or more virtual beams, and the beam configuration information comprises data indicating a beam pattern for each of the beams in beam Set A and beam Set B and at least physical time/frequency resources for transmission/reception of the physical beams of beam Set B; transmit one or more beams from beam set B; and receive a report from the wireless device, the report comprising an indication of a prediction of one or more beams from beam set A based on measurements of one or more beams from beam set B by the wireless device.

30. The method of claim 29, wherein each of the one or more predicted beams from beam set A is associated with an expected signal quality that is higher than expected signal qualities of other beams from beam set A.

31. The method of any one of claims 29-30, wherein the report further comprises a prediction result of each of the one or more predicted beams from beam set A.

32. The method of any one of claims 29-31, wherein the beam configuration information associated with the beam set A is configured on a first resource set comprising at least one of a first non-zero power channel state information-reference signal (NZP CSI-RS) resource set and a first synchronization signal block (SSB) resource set, and the beam configuration information associated with the beam set B is configured on a second resource set comprising ate least one of a second NZP CSI-RS resource set and a second SSB resource set.

33. The method of any one of claims 29-32, wherein the configuration for a virtual beam does not include time/frequency resources for transmission/reception of the beam.

34. The method of any one of claims 29-33, wherein beam set A further comprises one or more physical beams and the beam configuration information comprises physical time/frequency resources for transmission/reception of the physical beams of beam Set A

35. The method of claim 34, wherein the one or more predicted beams from beam set A comprise at least one virtual beam and at least one physical beam.

36. The method of any one of claims 34-35, the processing circuitry further operable to: transmit a physical beam from beam set A; and receive a report from the wireless device, the report comprising a performance value associated with a predicted value of the beam from beam set A based on a comparison of the predicted value for the beam and the measured value of the beam.

37. The method of any one of claims 29-36, wherein the time/frequency resources for reception of the beam comprise one of channel state information (CSI) resources or synchronization signal block (SSB) resources.

38. The method of any one of claims 29-37, wherein the predicted the one or more beams are predicted based on a machine learning model that uses beam set B as input and beam set A as output.