Systems and methods for indicating downlink reference signal characteristics

Abstract

A method (300) performed by a user equipment, UE (612), for indicating Downlink- Reference Signal, DL-RS, characteristics for transmission using a set of spatial filters includes transmitting (302), to a network node (610), information comprising a second set of DL-RS characteristics to be used for transmission of a third set of DL-RSs. The method includes receiving (304), from the network node, the third set of DL-RSs transmitted using a third set of spatial filters with the second set of DL-RS characteristics, and the third set of spatial filters comprises at least some of a second set of spatial filters. Prediction information is transmitted (306) to the network node, and the prediction information includes at least one predicted spatial filter that is a best spatial filter from a first set of spatial filters. The prediction information is based on a first set of DL-RSs associated with a first set of DL-RS characteristics. The first set of DL-RSs are associated with the first set of spatial filters and the second set of DL-RSs are associated with the second spatial filters.

Description

[0001] SYSTEMS AND METHODS FOR INDICATING DOWNLINK REFERENCE SIGNAL

[0002] CHARACTERISTICS

[0003] TECHNICAL FIELD

[0004] The present disclosure relates, in general, to wireless communications and, more particularly, systems and methods for indicating downlink (DL) reference signal (RS) characteristics.

[0005] BACKGROUND

[0006] Beam Management

[0007] In high frequency range (FR2), multiple Radio Frequency (RF) beams may be used to transmit and receive signals at a gNodeB (gNB) and a User Equipment (UE). For each DL beam from a gNB, there is typically an associated best UE Receive (Rx) beam for receiving signals from the DL beam. The DL beam and the associated UE Rx beam forms a beam pair. The beam pair can be identified through a so-called beam management process in New Radio (NR).

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

[0009] Although not explicitly stated in the NR specification, beam management has been divided into three procedures, which are illustrated in FIGURE 1 :

[0010] • P-1 : Purpose is to find a coarse direction for the UE using wide gNB Tx beam covering the whole angular sector.

[0011] • P-2: Purpose is to refine the gNB Tx beam by doing a new beam search around the coarse direction found in PL • P-3: Used for UE that has analog beamforming to let them find a suitable UE Rx beam.

[0012] P-1 is expected to utilize beams with rather large beamwidths. The beam reference signals are transmitted periodically and are shared between all UEs of the cell. Typically, reference signals to use for P-1 are periodic CSI-RS or SSB. The UE then reports thefbest beams to the gNB and their corresponding Reference Signal Received Power (RSRP) values.

[0013] P-2 is expected to use aperiodic / or semi-persistent CSI-RS transmitted in narrow beams around the coarse direction found in P-1.

[0014] P-3 is expected to use aperiodic or semi-persistent CSI-RSs repeatedly transmitted in one narrow gNB beam. One alternative way is to let the UE determine a suitable UE Rx beam based on the periodic SSB transmission. Since each SSB consists of four Orthogonal Frequency Division Multiplexing (OFDM) symbols, a maximum of four UE Rx beams can be evaluated during each SSB burst transmission. One benefit with using SSB instead of CSI-RS is that no extra overhead of CSI-RS transmission is needed.

[0015] In NR, several signals can be transmitted from different antenna ports of a same base station. These signals can have the same large-scale properties such as Doppler shift / spread, average delay spread, or average delay. These antenna ports are then said to be quasi co-located (QCL).

[0016] If the UE knows that two antenna ports are QCL with respect to a certain parameter (e.g., Doppler spread), the UE can estimate that parameter based on one of the antenna ports and apply that estimate for receiving signal on the other antenna port. For example, there may be a QCL relation between a CSI-RS for tracking RS (TRS) and the Physical Downlink Shared Channel (PDSCH) Demodulation Reference Signal (DMRS). When UE receives the PDSCH DMRS it can use the measurements already made on the TRS to assist the DMRS reception.

[0017] Information about what assumptions can be made regarding QCL is signaled to the UE from the network. In NR, four types of QCL relations between a transmitted source RS and transmitted target RS were defined:

[0018] Type A: {Doppler shift, Doppler spread, average delay, delay spread}

[0019] Type B: {Doppler shift, Doppler spread}

[0020] Type C: {average delay, Doppler shift}

[0021] Type D: {Spatial Rx parameter} QCL type D was introduced in NR to facilitate beam management with analog beamforming and is known as spatial QCL. There is currently no strict definition of spatial QCL, but the understanding is that if two transmitted antenna ports are spatially QCL, the UE can use the same Rx beam to receive them. This is helpful for a UE that uses analog beamforming to receive signals, since the UE needs to adjust its Rx beam in some direction prior to receiving a certain signal. If the UE knows that the signal is spatially QCL with some other signal it has received earlier, then it can safely use the same Rx beam to receive also this signal.

[0022] In NR, the spatial QCL relation for a DL or uplink (UL) signal / channel can be indicated to the UE by using a “beam indication”. The “beam indication” is used to help the UE to find a suitable Rx beam for DL reception, and / or a suitable Tx beam for UL transmission. In NR, the “beam indication” for DL is conveyed to the UE by indicating a transmission configuration indicator (TCI) state to the UE, while in UL the “beam indication” can be conveyed by indicating a DL-RS or UL-RS as spatial relation (in NR Rel-15 / 16) or a TCI state (in NR Rel-17).

[0023] Reference Signal

[0024] CSI-RS

[0025] A CSLRS is transmitted over each transmit (Tx) antenna port at the network node and for different antenna ports. The CSLRS are multiplexed in time, frequency, and code domain such that the channel between each Tx antenna port at the network node and each receive antenna port at a UE can be measured by the UE. The time-frequency resource used for transmitting CSLRS is referred to as a CSLRS resource.

[0026] In NR, the CSLRS for beam management is defined as a 1- or 2-port CSLRS resource in a CSLRS resource set where the Radio Resource Control (RRC) parameter “repetition” present. The following three types of CSLRS transmissions are supported:

[0027] • Periodic CSI-RS: CSI-RS is transmitted periodically in certain slots. This CSLRS transmission is semi-statically configured using RRC signaling with parameters such as CSI-RS resource, periodicity, and slot offset.

[0028] • Semi -Persistent CSI-RS: Similar to periodic CSI-RS, resources for semi-persistent CSI-RS transmissions are semi-statically configured using RRC signaling with parameters such as periodicity and slot offset. However, unlike periodic CSI-RS, dynamic signaling is needed to activate and deactivate the CSI-RS transmission. • Aperiodic CSI-RS: This is a one-shot CSI-RS transmission that can happen in any slot. Here, one-shot means that CSI-RS transmission only happens once per trigger. The CSI-RS resources (i.e., the Resource Element (RE) locations which consist of subcarrier locations and OFDM symbol locations) for aperiodic CSI-RS are semi- statically configured. The transmission of aperiodic CSI-RS is triggered by dynamic signaling through Physical Downlink Control Channel (PDCCH) using the CSI request field in UL Downlink Control Information (DCI), in the same DCI where the UL resources for the measurement report are scheduled. Multiple aperiodic CSI-RS resources can be included in a CSI-RS resource set and the triggering of aperiodic CSI-RS is on a resource set basis.

[0029] Synchronization Signal Block (SSB)

[0030] In NR, an SSB consists of a pair of synchronization signals (SSs), PBCH, and DMRS for PBCH. A SSB is mapped to four consecutive OFDM symbols in the time domain and 240 contiguous subcarriers (twenty Resource Blocks (RBs)) in the frequency domain.

[0031] To support beamforming and beam-sweeping for SSB transmission, in NR, a cell can transmit multiple SSBs in different narrow-beams in a time multiplexed fashion. The transmission of these SSBs is confined to a half frame time interval (5 ms). It is also possible to configure a cell to transmit multiple SSBs in a single wide beam with multiple repetitions. The design of beamforming parameters for each of the SSBs within a half frame is up to network implementation. The SSBs within a half frame are 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 SIB1.

[0032] The maximum number of SSBs within a half frame, denoted by Z, depends on the frequency band, and the time locations for these L candidate SSBs within a half frame depends on the Subcarrier Spacing (SCS) of the SSBs. The Z candidate SSBs within a half frame are indexed in an ascending order in time from 0 to Z-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 Z candidate locations in a half frame, and the resource of the un-used candidate positions can be used for the transmission of data or control signaling instead. It is up to network implementation to decide which candidate time locations to select for SSB transmission within a half frame and which beam to use for each SSB transmission. Measurement Resource Configurations

[0033] In NR, a UE can be configured with N> 1 CSI reporting settings (i.e., CSI-ReportConfig), A >1 resource settings (i.e., CSI-ResourceConfig where each CSI reporting setting is linked to one or more resource setting for channel and / or interference measurement. The CSI framework is modular, meaning that several CSI reporting settings may be associated with the same Resource Setting.

[0034] The measurement resource configurations for beam management are provided to the UE by RRC Information Elements (IES) CSI-ResourceConfigs . One CSI-ResourceConfig contains several NZP-CSI-RS-ResourceSets and / or CSI-SSB-ResourceSets.

[0035] A UE can be configured to perform measurement on CSI-RSs. Here the RRC IE NZP-CSI- RS-Re source Set is used. A NZP CSI-RS resource set contains the configuration of Ks >1 CSI-RS resources, where the configuration of each CSI-RS resource includes at least: mapping to REs, the number of antenna ports, time-domain behavior, etc. Up to 64 CSI-RS resources can be grouped to an NZP -CSI-RS-Re source Set. A UE can also be configured to perform measurements on SSBs. Here, the RRC IE CSI-SSB-ResourceSet is used. Resource sets comprising SSB resources are defined in a similar manner.

[0036] In the case of aperiodic CSI-RS and / or aperiodic CSI reporting, the network node configures the UE with ScCSI triggering states. Each triggering state contains the aperiodic CSI report setting to be triggered along with the associated aperiodic CSI-RS resource sets.

[0037] Periodic and semi-persistent Resource Settings can only comprise a single resource set (i.e., 5=1) while 5 =1 for aperiodic Resource Settings. This is because, in the aperiodic case, one out of the S resource sets comprised in the Resource Setting is indicated by the aperiodic triggering state that triggers a CSI report.

[0038] Beam Prediction

[0039] One example Artificial Intelligence (AI) / Machine Learning (ML) model currently discussed in the Al for air-interface Rel-18 comprises predicting the channel in respect to a beam for a certain time-frequency resource. The expected performance of such predictor depends on several different aspects, for example time / frequency variation of channel due to UE mobility or changes in the environment. Due to the inherent correlation in time, frequency and the spatial domain of the channel, an ML-model can be trained to exploit such correlations. The spatial domain can comprise of different beams, where the correlation properties partly depend on how the gNB antennas forms the different beams, and how UE forms the receiver beams.

[0040] The device can use such prediction ML-model to reduce its measurement related to beamforming. In NR, one can request a device to measure on a set of SSB beams or / and CSI-RS beams. A stationary device typically experiences less variations in beam quality in comparison to a moving device. The stationary device can therefore save battery and reduce the number of beam measurements by instead using an ML model to predict the beam quality without an explicit measurement. It can do this, for example, by measuring a subset of the beams and predicting the rest of the beams. For example, one can with use Al measurements on a subset of beams in order to predict the best beam, which can reduce up to 75% measurement time.

[0041] AI / ML Based Spatial Beam Prediction in NR

[0042] In 3 GPP NR Rel-18 study of AI / ML for Physical Layer (PHY), one aspect was to study AI / ML based spatial beam prediction for a Set A of beams based on measurement results of Set B of beams. The Set B of beams could either be a subset of the Set A of beams, or the Set A of beams could consist of different beams compared to the Set B of beams (for example Set A consists of narrow beams and Set B consists of wide beams). The spatial beam prediction could either be made at the network (NW) side or at the UE side.

[0043] In addition to spatial beam prediction, another aspect was to study AI / ML based temporal beam prediction for a Set A of beams based on measurement results of Set B of beams, where the Set A of beams and Set B of beams can be the same set of beams or different set of beams. For AI / ML based temporal beam prediction, the measurement results of K (K>=P) latest measurement instances during a time window T1 of the Set B beams are used for AI / ML model input. Furthermore, one or more beams from the Set A beams will be used as AI / ML model output, where the AI / ML model output should be F predictions for F future time instances, where all F future time instances are located within a time window T2.

[0044] It is noted that as the DL Tx beam is formed on the NW side in a proprietary manner, the UE can only passively measure the DL Tx beam transmission. This can, for example, be that the narrow beams are measured using CSLRS resources and the wide beams are measured using SSBs at the UE side. This would be how the UE could see the DL Tx beams. FIGURE 2 illustrates a schematic example of the Set A of beams and the Set B of beams where Set B is different from Set A. The top illustration shows all the narrow gNB beams, which constitutes the Set A of beams, and the lower illustrations shows all the wide gNB beams, which constitutes the Set B of beams.

[0045] In another example, the Set B of beams is a subset of Set A of beams. In this example, Set A contains narrow gNB beams and set B is subset of Set A also containing some narrows beams from the gNB.

[0046] The above-mentioned prediction can be based on, for example, Ll-RSRP estimates for each beam.

[0047] In order to facilitate beam prediction, the NW can indicate a beam configuration identifier or beam identifier (ID) to the UE. The core idea of beam IDs is that the NW associates different SSB / CSI-RS beams with different beam IDs. The NW shares the beam IDs with the UE whenever the UE needs to know how the SSB / CSI-RS is beamformed. The UE does not know how the SSB / CSI-RS is beamformed, but it can safely assume that any two reference signals with the same beam ID have been beamformed in the same way. In addition, since no measurements might be performed on Set A of beams during inference, these beams might not be directly associated with a DL-RS, instead, beam IDs can be used to index the Set A of beams instead of DL-RS indexes. In this case, the UE might be configured with a group of Set A beam indexes, and the UE can report one or more of these Set A beam index in a beam prediction report.

[0048] The beam ID is defined in such way that it will assume a certain configuration for the NW precoder and transmission power, enabling the device to build models for predicting the effective channel for a certain beam ID (e.g. associated to an CSI-RS transmission for example).

[0049] FIGURE 3 illustrates an example for designing a beamforming pattern. Specifically, as shown in FIGURE 3, a network node can transmit ten beams, where each beam is configured to be strong in a certain direction. The UE can in such case receive ten different unique beam IDs.

[0050] In 3 GPP TR38.843, AI / ML model Inference is defined as a process of using a trained AI / ML model to produce a set of outputs based on a set of inputs. See, 3GPP TR 38.843 V18.0.0 (2023-12). For example, in FIGURE 2, UE predicts the Top- / beam ID as model output based on the measurement of Set B of beams as model input.

[0051] There currently exist certain challenge(s), however. For example, during AI / ML model training, the UE performs measurements on Set A of beams and Set B of beams and trains the UE- sided model for UE-side beam prediction. The Set A of beams and Set B of beams are transmitted in DL reference signal resources (e.g., NZP CSI-RS resource) during AI / ML model training.

[0052] During inference, the UE performs measurements on Set B of beams to predict one or more beams from Set A (either via spatial and / or time domain beam prediction).

[0053] During AI / ML model training, Set A of beams and Set B of beams may be transmitted in DL reference signal resources with a large bandwidth to ensure sufficiently accurate model training. However, transmission of Set B of beams in DL reference signal resources with such large bandwidth during inference may result in large DL reference signal overhead.

[0054] Depending on UE Al capabilities and design choice, transmission of DL reference signal resources with a large bandwidth might not be needed even during AI / ML training / monitoring.

[0055] However, how to reduce DL reference signal overhead during transmission of set B of beams is an open problem.

[0056] SUMMARY

[0057] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, methods and systems are disclosed for providing, to a UE, information on DL-RS characteristics to be used for AI / ML based spatial filter (or beam) prediction, CSI prediction, and / or positioning.

[0058] According to certain embodiments, a method by a UE, for indicating DL-RS characteristics for transmission using a set of spatial filters, includes transmitting, to a network node, information comprising a second set of DL-RS characteristics to be used for transmission of a third set of DL-RSs. The method includes receiving, from the network node, the third set of DL- RSs transmitted using a third set of spatial filters with the second set of DL-RS characteristics. The third set of spatial filters includes at least some of a second set of spatial filters. Prediction information is transmitted to the network node. The prediction information includes at least one predicted spatial filter that is a best spatial filter from a first set of spatial filters. The prediction information is based on a first set of DL-RSs associated with a first set of DL-RS characteristics. The first set of DL-RSs are associated with the first set of spatial filters and the second set of DL- RSs are associated with the second spatial filters.

[0059] According to certain embodiments, a UE for indicating DL-RS characteristics for transmission using a set of spatial filters is configured to transmit, to a network node, information that includes a second set of DL-RS characteristics to be used for transmission of a third set of DL- RSs. The UE is configured to receive, from the network node, the third set of DL-RSs transmitted using a third set of spatial filters with the second set of DL-RS characteristics. The third set of spatial filters includes at least some of a second set of spatial filters. Prediction information is transmitted to the network node. The prediction information includes at least one predicted spatial filter that is a best spatial filter from a first set of spatial filters. The prediction information is based on a first set of DL-RSs associated with a first set of DL-RS characteristics. The first set of DL- RSs are associated with the first set of spatial filters and the second set of DL-RSs are associated with the second spatial filters.

[0060] According to certain embodiments, a method by a network node for receiving DL-RS characteristics includes receiving, from a UE, information that includes a second set of DL-RS characteristics to be used for transmission of a third set of DL-RSs. The method includes transmitting, to the UE, the third set of DL-RSs transmitted using a third set of spatial filters with the second set of DL-RS characteristics. The third set of spatial filters includes at least some of a second set of spatial filters. Prediction information is received from the UE. The prediction information includes at least one predicted spatial filter that is a best spatial filter from a first set of spatial filters. The prediction information is based on a first set of DL-RSs associated with a first set of DL-RS characteristics. The first set of DL-RSs are associated with the first set of spatial filters and the second set of DL-RSs are associated with the second spatial filters.

[0061] According to certain embodiments, a network for receiving DL-RS characteristics is configured to receive, from a UE, information that includes a second set of DL-RS characteristics to be used for transmission of a third set of DL-RSs. The network node is configured to transmit, to the UE, the third set of DL-RSs transmitted using a third set of spatial filters with the second set of DL-RS characteristics. The third set of spatial filters includes at least some of a second set of spatial filters. Prediction information is received from the UE. The prediction information includes at least one predicted spatial filter that is a best spatial filter from a first set of spatial filters. The prediction information is based on a first set of DL-RSs associated with a first set of DL-RS characteristics. The first set of DL-RSs are associated with the first set of spatial filters and the second set of DL-RSs are associated with the second spatial filters.

[0062] Certain embodiments may provide one or more of the following technical advantage(s). For example, certain embodiments may provide a technical advantage of reducing DL reference signal overhead during transmission of Set B beams. The method can be equally applicable to transmission of set B of beams for training, inference, or monitoring of UE sided model.

[0063] As another example, certain embodiments may provide a technical advantage of improving the consistency between training and inference.

[0064] Other advantages may be readily apparent to one having skill in the art. Certain embodiments may have none, some, or all of the recited advantages.

[0065] BRIEF DESCRIPTION OF THE DRAWINGS

[0066] For a more complete understanding of the disclosed embodiments and their features and advantages, reference is now made to the following description, taken in conjunction with the accompanying drawings, in which:

[0067] FIGURE 1 illustrates three procedures for beam management;

[0068] FIGURE 2 illustrates a schematic example of the Set A of beams and the Set B of beams where Set B is different from Set A;

[0069] FIGURE 3 illustrates an example for designing a beamforming pattern;

[0070] FIGURE 4 illustrates a flowchart depicting a method performed at the UE, according to certain embodiments;

[0071] FIGURE 5 illustrates an example method by a UE for indicating DL-RS characteristics for transmission using a set of spatial filters, according to certain embodiments;

[0072] FIGURE 6 illustrates an example method performed by a UE for indicating DL-RS characteristics for transmission using a set of spatial filters, according to certain embodiments;

[0073] FIGURE 7 illustrates an example method by a network node for receiving DL-RS characteristics, according to certain embodiments;

[0074] FIGURE 8 illustrates an example method by a network node for receiving DL-RS characteristics, according to certain embodiments

[0075] FIGURE 9 illustrates an example communication system, according to certain embodiments;

[0076] FIGURE 10 illustrates an example UE, according to certain embodiments;

[0077] FIGURE 11 illustrates an example network node, according to certain embodiments; and

[0078] FIGURE 12 illustrates a virtualization environment in which functions implemented by some embodiments may be virtualized, according to certain embodiments. DETAILED DESCRIPTION

[0079] Some of the embodiments contemplated herein will now be 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

[0080] As used herein, ‘node’ can be a network node or a UE. Examples of network nodes are NodeB, base station (BS), multi -standard radio (MSR) radio node such as MSR BS, eNodeB (eNB), gNodeB (gNB), Master eNB (MeNB), Secondary eNB (SeNB), integrated access backhaul (IAB) node, network controller, radio network controller (RNC), base station controller (BSC), relay, donor node controlling relay, base transceiver station (BTS), Central Unit (e.g. in a gNB), Distributed Unit (e.g. in a gNB), Baseband Unit, Centralized Baseband, C-RAN, access point (AP), transmission points, transmission nodes, Remote Radio Unit (RRU), Remote Radio Head (RRH), nodes in distributed antenna system (DAS), core network node (e.g. Mobile Switching Center (MSC), Mobility Management Entity (MME), etc.), Operations & Maintenance (O&M), Operations Support System (OSS), Self-Organizing Network (SON), positioning node (e.g. E- SMLC), etc. The terms network node and radio network node are used interchangeably herein.

[0081] Another example of a node is user equipment (UE), which is a non-limiting term and refers to any type of wireless device communicating with a network node and / or with another UE in a cellular or mobile communication system. Examples of UE are target device, device to device (D2D) UE, vehicular to vehicular (V2V), machine type UE, MTC UE or UE capable of machine to machine (M2M) communication, Personal Digital Assistant (PDA), Tablet, mobile terminals, smart phone, laptop embedded equipment (LEE), laptop mounted equipment (LME), Unified Serial Bus (USB) dongles, etc.

[0082] The term radio access technology (RAT), may refer to any RAT such as, for example, Universal Terrestrial Radio Access Network (UTRA), Evolved Universal Terrestrial Radio Access Network (E-UTRA), narrow band internet of things (NB-IoT), WiFi, Bluetooth, next generation RAT, NR, 4G, 5G, etc. Any of the equipment denoted by the terms node, network node or radio network node may be capable of supporting a single or multiple RATs.

[0083] The term signal or radio signal used herein can be any physical signal or physical channel. Examples of downlink (DL) physical signals are reference signal (RS) such as Primary Synchronization Signal (PSS), Secondary Synchronization Signal (SSS), Channel State Information-Reference Signal (CSI-RS), Demodulation Reference Signal (DMRS) signals in SS / PBCH block (SSB), discovery reference signal (DRS), Cell Specific Reference Signal (CRS), Positioning Reference Signal (PRS), etc. RS may be periodic. For example, RS occasions carrying one or more RSs may occur with certain periodicity (e.g., 20 ms, 40 ms, etc.). The RS may also be aperiodic.

[0084] Each SSB carries New Radio-Primary Synchronization Signal (NR-PSS), New RadioSecondary Synchronization Signal (NR-SSS) and New Radio-Physical Broadcast Channel (NR- PBCH) in four successive symbols. One or multiple Synchronization Signal Blocks (SSBs) are transmitted in one SSB burst which is repeated with certain periodicity such as, for example, 5 ms, 10 ms, 20 ms, 40 ms, 80 ms, and 160 ms. The UE is configured with information about SSB on cells of certain carrier frequency by one or more SS / PBCH block measurement timing configuration (SMTC) configurations. The SMTC configuration comprising parameters such as SMTC periodicity, SMTC occasion length in time or duration, SMTC time offset with regard to reference time (e.g., serving cell’s SFN) etc. Therefore, SMTC occasion may also occur with certain periodicity (e.g., 5 ms, 10 ms, 20 ms, 40 ms, 80 ms, and 160 ms). Examples of uplink (UL) physical signals are reference signals such as Sounding Reference Signals (SRS), Demodulation Reference Signals (DMRS), etc. The term physical channel refers to any channel carrying higher layer information e.g. data, control etc. Examples of physical channels are Physical Broadcast Channel (PBCH), Physical Downlink Control Channel (PDCCH), Physical Downlink Shared Channel (PDSCH), Physical Uplink Shared Channel (PUSCH), Physical Uplink Control Channel (PUCCH), Physical Uplink Shared Channel (PUSCH), Short PUSCH (sPUCCH), Short PDSCH (sPDSCH), Short PUCCH (sPUCCH), Short PUSCH (sPUSCH), MTC PDCCH (MPDCCH), Narrowband PBCH (NPBCH), Narrowband PDCCH (NPDCCH), Narrowband PDSCH (NPDSCH), Narrowband PUSCH (NPUSCH), Enhanced PDCCH (E-PDCCH), etc.

[0085] The term time resource used herein may correspond to any type of physical resource or radio resource expressed in terms of length of time. Examples of time resources are symbol, time slot, subframe, radio frame, transmission time interval (TTI), interleaving time, slot, sub-slot, minislot, system frame number (SFN) cycle, hyper-SFN (H-SFN) cycle, etc.

[0086] Although the terms ‘AI / ML model training’ and ‘Inference’ are used in this disclosure, these terms may not necessarily be captured in 3 GPP specifications. Alternative terminologies such as ‘training phase’ may be used in place of ‘AI / ML model training’. Similarly, terms such as ‘inference phase’ may be used in place of ‘Inference’. Thus, such terms are used interchangeably herein.

[0087] Although the terms ‘Set A beams’ and ‘Set B beams’ are used in this disclosure, these terms may not necessarily be captured in 3 GPP specifications. Alternative terminologies such as ‘first beams set’ or ‘first spatial filters set’ may be used in place of ‘Set A beams’. Similarly, terms such as ‘second beams set’ or ‘second spatial filters set’ may be used in place of ‘Set B beams’. Thus, such terms are used interchangeably herein.

[0088] The DL-RSs referred in this disclosure may be at least one of NZP CSI-RSs and SSBs.

[0089] Although the embodiments described below are written with respect to the use case of AI / ML beam prediction, the embodiments are non-limiting and are equally applicable to the AI / ML uses cases of CSI prediction and positioning.

[0090] According to certain embodiments, methods and systems are disclosed for providing, to a UE, information on DL-RS characteristics to be used for AI / ML based spatial filter (or beam) prediction, CSI prediction, and / or positioning. For example, a second set of DL-RS characteristics are transmitted to a UE for use for transmission of Set B beams during inference, re-training or finetuning of AI / ML model, and model monitoring. The second set of DL-RS characteristics are derived based on measurements of Set A beams and Set B beams received during AI / ML model training using a first set of DL-RS characteristics. The second set of DL-RS characteristics can result in DL-RS overhead reduction during transmission of set B beams during inference, retraining or finetuning of AI / ML model, and model monitoring.

[0091] For example, according to certain embodiments, a method at a UE of transmitting to a network node information on DL-RS characteristics for any one of AI / ML based spatial filter (or beam) prediction, CSI prediction, and positioning, includes:

[0092] • receiving from the network node signaling (e.g., configuration) of a first set of DL-RSs and a second set of DL-RSs to be used during AI / ML model training with a first set of DL-RS characteristics, wherein the first set of DL-RS characteristics include one or more of the following: o DL-RS bandwidth, DL-RS starting Physical Resource Block (PRB), DL-RS end PRB, DL-RS density, DL-RS periodicity, number of DLRS ports, DL-RS time offset, number of DL-RS repetitions, interval between DL-RS repetitions • receiving the first set of DL-RSs transmitted using a first set of spatial filters (e.g., set A beams), and the second set of DL-RSs transmitted using a second set of spatial filters (e.g., set B beams) with the first set of DL-RS characteristics during AI / ML model training.

[0093] • transmitting to the network node information on a second set of DL-RS characteristics to be used for transmission of a third set of DL-RSs during one or more of the following stages: o during inference, during re-training or finetuning of AI / ML model, and during model monitoring wherein the second set of DL-RS characteristics include one or more of the following: o DL-RS bandwidth, DL-RS starting PRB, DL-RS end PRB, DL-RS density, DL-RS periodicity, number of DL-RS ports, DL-RS time offset, number of DL-RS repetitions, interval between DL-RS repetitions

[0094] • receiving the third set of DL-RSs transmitted using a third set of spatial filters (e.g., set B beams that may be same or different from the set B beams used during training) with the second set of DL-RS characteristics during one or more of the following stages: o during inference, during re-training or finetuning of AI / ML model, and during model monitoring

[0095] • transmitting to the network node a prediction of at least one of the following using the measurements on the third set of DL-RSs and the AI / ML model o at least one predicted spatial filter (or beam), at least one predicted CSI, and at least one estimate of positioning.

[0096] In a particular embodiment, the AI / ML model is a UE-sided model.

[0097] In a particular embodiment, the second set of spatial filters is the same as the third set of spatial filters.

[0098] In a particular embodiment, the second set of spatial filters is different from the third set of spatial filters. In a particular embodiment, the transmitting to the network node information on a second set of DL-RS characteristics is via RRC signaling.

[0099] In a particular embodiment, the transmitting to the network node information on a second set of DL-RS characteristics is via UL MAC CE signaling.

[0100] In a particular embodiment, the transmitting to the network node information on a second set of DL-RS characteristics is via a UE assistance information message.

[0101] FIGURE 4 illustrates a flowchart depicting a method 100 performed at the UE, according to certain embodiments. The steps involved are described below. It is noted that some of the steps can be optional. Thus, the method may include more or fewer steps. Additionally, the steps may be performed in any suitable order.

[0102] At step 101, the UE receives, from a network node, signaling of information on a first set of DL-RSs for Set A beams transmission, and information on a second set of DL-RSs for Set B beams transmission to be used during AI / ML model training. In various particular embodiments, the signaling may be one or more of the following:

[0103] • Configuration'. In this case, the UE receives from the network node configuration of the first and the second sets of DL-RSs via RRC signaling.

[0104] • Activation'. In this case, the UE receives from the network node control signaling (e.g., one or more MAC CEs) that activates the first and the second set of DL-RSs.

[0105] • Indication'. In this case, the UE receives from the network node layer 1 control signaling (e.g., one or more DCIs) that indicate the first and the second sets of DL- RSs.

[0106] As part of the signaling from the network node, in Step 102, the UE receives a first set of DL-RS characteristics of the first set of DL-RSs and the second set of DL-RSs. In various particular embodiments, the first set of DL-RS characteristics include one or more of the following:

[0107] • DL-RS Bandwidth'. This represents the bandwidth of a DL-RSs, which is defined as the number of PRBs across which the DL-RS resource spans. In one embodiment, the first set and the second set of DL-RSs have the same DL-RS bandwidth. In another embodiment, the first set of DL-RSs have a first DL-RS bandwidth, and the second set of DL-RSs have a second DL-RS bandwidth, wherein the first DLRS bandwidth is different from the second DL-RS bandwidth. • DL-RS StartingPRB'. This represents the first PRB where a DL-RS resource starts in relation to a reference PRB (e.g., a common resource block with #0). In one embodiment, all resources within the first set and the second set of DL-RSs have the same DL-RS starting PRB. In another embodiment, all resources within the first set of DL-RSs have a first DL-RS starting PRB, and all resources within the second set of DL-RSs have a second DL-RS starting PRB, wherein the first DL-RS starting PRB is different from the second DL-RS starting PRB. In some further embodiments, the DL-RS resources within the first set of DL-RSs have resourcespecific starting PRBs (e.g., starting PRB defined per each DL-RS resource), and the DL-RS resources within the second set of DL-RSs have resource-specific starting PRBs (e.g., starting PRB defined per each DL-RS resource).

[0108] • DL-RS end PRB'. This represents the last PRB where a DL-RS resource ends in relation to a reference PRB (e.g., a common resource block with #0). In a particular embodiment, all resources within the first set and the second set of DL-RSs have the same DL-RS end PRB. In another embodiment, all resources within the first set of DL-RSs have a first DL-RS end PRB, and all resources within the second set of DL-RSs have a second DL-RS end PRB, wherein the first DL-RS end PRB is different from the second DL-RS end PRB. In some further embodiments, the DLRS resources within the first set of DL-RSs have resource-specific end PRBs (e.g., end PRB defined per each DL-RS resource), and the DL-RS resources within the second set of DL-RSs have resource-specific end PRBs (e.g., end PRB defined per each DL-RS resource).

[0109] • DL-RS Density. This represents the number of resource elements (REs) per port per PRB occupied by a DL-RS resource. In a particular embodiment, all resources within the first set and the second set of DL-RSs have the same DL-RS density. In another embodiment, all resources within the first set of DL-RSs have a first DLRS density, and all resources within the second set of DL-RSs have a second DLRS density, wherein the first DL-RS density is different from the second DL-RS density. In some further embodiments, the DL-RS resources within the first set of DL-RSs have resource-specific densities (e.g., DL-RS density defined per each DLRS resource), and the DL-RS resources within the second set of DL-RSs have resource-specific DL-RS densities (e.g., DL-RS density defined per each DL-RS resource).

[0110] • DL-RS port. Number of DL-RS ports used in DL-RS.

[0111] • DL-RS Periodicity. This represents the periodicity of a DL RS resource in time domain (e.g., for a periodic DL-RS that is received every T slots, the periodicity is said to be T slots). In a particular embodiment, all resources within the first set and the second set of DL-RSs have the same DL-RS periodicity. In another embodiment, all resources within the first set of DL-RSs have a first DL-RS periodicity, and all resources within the second set of DL-RSs have a second DLRS periodicity, wherein the first DL-RS periodicity is different from the second DL-RS periodicity.

[0112] • DL-RS time Offset. This represents the time offset relative to a common starting slot, e.g. starting slot of a radio frame, of the DL-RS

[0113] • DL-RS Repetition'. This represents number of DL-RS repetition for a DL-RS. In a particular embodiment, all resources within the first set and the second set of DL- RSs have the same DL-RS repetition. In another embodiment, all resources within the first set of DL-RSs have a first DL-RS repetition, and all resources within the second set of DL-RSs have a second DL-RS repetition, wherein the first DL-RS density is different from the second DL-RS density. In some further embodiments, the DL-RS resources within the first set of DL-RSs have resource-specific repetitions (e.g., DL-RS repetition defined per each DL-RS resource), and the DLRS resources within the second set of DL-RSs have resource-specific DL-RS repetitions (e.g., DL-RS repetition defined per each DL-RS resource).

[0114] • DL-RS Interval'. This represents the interval between DL-RS repetition.

[0115] At step 102, the UE receives, from the network node, Set A beams in the first set of DL RSs and Set B beams in the second set of DL RSs with the first set of DL RS characteristics and trains the UE-sided AI / ML model.

[0116] At step 103, the UE determines a second set of DL RS characteristics to be used for Set B beams transmission during inference. In particular embodiments, the second set of DL-RS characteristics include one or more of the following: • DL-RS Bandwidth. This represents the bandwidth of DL-RSs to be used for Set B beams transmission during inference.

[0117] • DL-RS starting PRB'. This represents the first PRB in relation to a reference PRB (e.g., a common resource block with #0) of DL-RSs to be used for Set B beams transmission during inference.

[0118] • DL-RS end PRB'. This represents the end PRB in relation to a reference PRB (e.g., a common resource block with #0) of DL-RSs to be used for Set B beams transmission during inference.

[0119] • DL-RS Density-. This represents the density of DL-RSs to be used for Set B beams transmission during inference.

[0120] • DL-RS Periodicity. This represents the periodicity of DL-RSs to be used for Set B beams transmission during inference.

[0121] • DL-RS Starting Offset. This represents the minimum starting slot relative to a common starting slot known to the UE, e.g. starting slot of a radio frame, of DLRS to be used for Set B beams transmission during inference.

[0122] • DL-RS Repetition'. This represents number of DL-RS repetitions.

[0123] • DL-RS interval'. This represents the time interval between DL-RS repetition such as, for example, the number of slots or number of symbols.

[0124] • DL-RS port. This represents the number of DL-RS ports used in DL-RS.

[0125] The second set of characteristics are determined such that one or more beam(s) predicted using the second set of characteristics is equivalent to a hypothetical beam predicted using the first set of characteristics. In a particular embodiment, the UE evaluates the training loss when using a certain set of first and second set of characteristics. Then, in a particular embodiment, the UE checks if the training loss is within a certain threshold distance. In one non-exclusive example embodiment, when predicting the strongest beam identifier, the UE checks if the log-likelihood loss is within a certain threshold range. In a further particular embodiment, the threshold is configured by the NW, and the NW configures a more relaxed threshold for a battery-constrained device than a high-end smartphone device.

[0126] In a further particular embodiment, based on the determined second set of characteristics, the UE collects measurements and use those to train / retrain / finetune or monitor the AI / ML model. In an alternative embodiment, the UE collects measurements and uses those to train / retrain / finetune or monitor the AI / ML model even without indicating information about the selected second set of characteristics to the NW. This method allows the UE to reduce measurements even if indicating a second set of characteristics is not possible.

[0127] At step 104, the UE signals to the network the second set of DL RS characteristics to be used for Set B beams transmission during UE data collection for one or more LCM stages (e.g., inference, (re)-training / finetuning, monitoring). For instance, in a particular embodiment, the indicated DL RS characteristics improves the consistency between training and inference. In a particular embodiment, the second set of DL-RS characteristics include one or more of DL-RS bandwidth, DL-RS starting RB, DL-RS end RB, DL-RS density, DL-RS periodicity, DL-RS repetition, DL-RS interval.

[0128] In a particular embodiment, the UE signals the second set of DL-RS characteristics to the network via RRC and / or uplink MAC-CE.

[0129] In a particular embodiment, the UE signals the second set of DL-RS characteristics to the network via RRC message such as, for example, an RRC message like UEAssistancelnformation. An example of a UE assistance information message for signaling the second set of characteristics to be used for Set B beams transmission during inference is shown below. The changes required to the UEAssistancelnformation message over what is specified in 3GPP TS 38.331 V18.0.0 are in underlined italics. In this example , the second subset of characteristics are given by:

[0130] • preferredSetB-RsBandwidth-r!9'. This field provides the DL-RS bandwidth preferred by the UE for Set B beams transmission during inference. The parameter ‘maxNrofPhysicalResourceBlocksPlusr is an integer value as specified in 3GPP TS 38.331.

[0131] • preferredSetB-RsStartPRB-r!9'. This field provides the DL-RS starting PRB preferred by the UE for Set B beams transmission during inference.

[0132] • preferredSetB-RsDensity-r 19'. This field provides the DL-RS density preferred by the UE for Set B beams transmission during inference. The values ‘dot5’, ‘one’ and ‘three’ respectively represent DL-RS density values of 0.5 RE / port / PRB, 1 RE / port / PRB, and 3 RE / port / PRB. • preferredSetB-RsPeriodicity-rl9'. This field provides the DL-RS periodicity preferred by the UE for Set B beams transmission during inference. The values ‘slotx’ represents a DL-RS periodicity of x slots.

[0133] UEAssistancelnformation message

[0134] UEAssistancelnformation : := SEQUENCE { criticalExtensions CHOICE { ue Assistance Informat ion UEAssistancelnf ormation-IEs , criticalExtensions Future SEQUENCE { }

[0135] 1

[0136] 1

[0137] AIML-SetBrsPreference-rl9 : := SEQUENCE { preferredSetB-RsBandwidth-r!9

[0138] INTEGER (24. . maxNrofPhysi calResourceBlocksPlusl ) OPTIONAL , preferredSetB-RsStartPRB-r!9

[0139] INTEGER (0. . maxNrofPhysi calResourceBlocksPlusl) OPTIONAL , preferredSetB-RsDensi ty-rl 9 _ ENUMERATED { dot5 , one, three)

[0140] > OPTIONAL , pref erredSetB-Rs Peri odi ci ty-rl 9 ENUMERATED { slots 4 , slots 4, slotsS , slotslO , slots!6 , slots20 , slots32 , slots40 , slots64 , slots80 , slots!60 , slots320, slots640 } OPTIONAL , }

[0141] TAG-UEASSISTANCEINFORMATION-STOP

[0142] ASN1STOP

[0143] In an alternative embodiment, the UE signals the second set of DL-RS characteristics to the network via a MAC CE message.

[0144] In a particular embodiment, UE performs the Step 104 to report the second set of DL RS characteristics to be used for Set B beams transmission during inference when the UE-sided model training is completed.

[0145] In an alternative embodiment, UE reports the second set of DL RS characteristics during the training processing and updates the DL RS characteristics once the model training is completed. For example, UE reports a preferred second DL RS characteristics in the middle of model training (e.g., 50% of model training). This could speed up the model training, which reduces the DL RS overhead used for model training. Once the model training is done, UE only needs to send the updates of the second DL RS characteristics instead of the whole second DL RS characteristics, which could also reduce the reporting overhead.

[0146] At step 105, the UE receives from the network node signaling of a third set of DL-RSs based on the second set of DL-RS characteristics for Set B beams transmission. The signaling can be one or more of the following:

[0147] • Configuration'. In this case, the UE receives from the network node configuration of the first and the second sets of DL-RSs via RRC signaling.

[0148] • Activation'. In this case, the UE receives from the network node control signaling (e.g., one or more MAC CEs) that activate the first and the second set of DL-RSs.

[0149] • Indication'. In this case, the UE receives from the network node layer 1 control signaling (e.g., one or more DCIs) that indicate the first and the second sets of DL- RSs.

[0150] At step 106, the UE receives Set B beams in the third set of DL-RSs based on the second set of DL-RS characteristics and predicts one or more beams.

[0151] At step 107, the UE reports the predicted one or more beams to the network node.

[0152] FIGURE 5 illustrates an example method by a UE for indicating DL-RS characteristics for transmission using a set of spatial filters, according to certain embodiments. In the illustrated embodiment, the method includes a first receiving step at 202, a second receiving step at 204, a first transmitting step at 206, a third receiving step at 208, and a second transmitting step at 210. For example, at step 202, the UE may receive, from a network node, information indicating a first set of DL-RSs and a second set of DL-RSs to be used during a training phase with a first set of DL-RS characteristics. At step 204, for example, during the training phase, the UE may receive the first set of DL-RSs transmitted using a first set of spatial filters and the second set of DL-RSs transmitted using a second set of spatial filters with the first set of DL-RS characteristics. At step 206, for example, the UE may transmit, to the network node, information on, indicating, and / or associated with a second set of DL-RS characteristics to be used for transmission of a third set of DL-RSs. At step 208, for example, during at least one of an inference phase, a re-training phase, and a monitoring phase, the UE may receive the third set of DL-RSs transmitted using a third set of spatial filters with the second set of DL-RS characteristics. At step 210, for example, the UE may transmit, to the network node, prediction information comprising at least one of: at least one predicted spatial filter, at least one predicted CSI, and at least one estimate of positioning.

[0153] FIGURE 6 illustrates an example method 300 performed by a UE for indicating DL-RS characteristics for transmission using a set of spatial filters, according to certain embodiments. In the illustrated embodiment, the method begins at step 302 when the UE transmits, to a network node, information that includes a second set of DL-RS characteristics to be used for transmission of a third set of DL-RSs. At step 304, the UE receives, from the network node, the third set of DL-RSs transmitted using a third set of spatial filters with the second set of DL-RS characteristics. The third set of spatial filters comprises at least some of a second set of spatial filters. At step 306, the UE transmits, to the network node, prediction information comprising at least one predicted spatial filter that is a best spatial filter from a first set of spatial filters. The prediction information is based on a first set of DL-RSs associated with a first set of DL-RS characteristics. The first set of DL-RSs are associated with the first set of spatial filters and the second set of DL-RSs are associated with the second spatial filters.

[0154] In a particular embodiment, the prediction information includes at least one of: at least one predicted CSI, and at least one estimate of positioning.

[0155] In a particular embodiment, the first set of DL-RS characteristics and / or the second set of DL-RS characteristics include at least one of: DL-RS bandwidth, DL-RS starting PRB, DL-RS end PRB, DL-RS density, DL-RS periodicity, number of DL-RS ports, DL-RS time offset, number of DL-RS repetitions, and interval between DL-RS repetitions. In various particular embodiments, one or more values associated with the first set of DL-RS characteristics may be different than or the same as the one or more values associated with the second set of DL-RS characteristics.

[0156] In a particular embodiment, the first set of DL-RSs and the second set of DL-RSs are received during a training phase, and / or the third set of DL-RS are received during at least one of an inference phase, a re-training phase, and a monitoring phase.

[0157] In a particular embodiment, the UE trains an AI / ML model using at least one measurement of the first set of DL-RSs and the second set of DL-RSs transmitted to the UE with the first set of DL-RS characteristics.

[0158] In a further particular embodiment, based on the at least one measurement of the first set of DL-RSs and the second set of DL-RSs transmitted to the UE with the first set of DL-RS characteristics, the UE determines the second set of DL-RS characteristics to be used for transmission of the third set of DL-RSs.

[0159] In a particular embodiment, the UE performs the at least one measurement of the first set of DL-RSs and the second set of DL-RSs based on the first set of DL-RS characteristics. The UE trains, retrains, finetunes, or monitors the AI / ML model based on the at least one measurement.

[0160] In a particular embodiment, the prediction information is determined based on at least one of: at least one measurement performed on the third set of DL-RSs and an AI / ML, model.

[0161] In a particular embodiment, the second set of spatial filters is the same as the third set of spatial filters.

[0162] In a particular embodiment, the second set of spatial filters is a subset of the first set of spatial filters.

[0163] In a particular embodiment, the second set of spatial filters is different from the first set of spatial filters.

[0164] In a particular embodiment, the information including the second set of DL-RS characteristics is transmitted to the network node via RRC signaling.

[0165] In a further particular embodiment, the information comprising the second set of DL-RS characteristics is transmitted in a UEAssistancelnformation message and / or an uplink MAC-CE message.

[0166] In a particular embodiment, the information including the second set of DL-RS characteristics is transmitted to the network node during a training phase or an inference phase associated with the AI / ML model.

[0167] FIGURE 7 illustrates an example method by a network node for receiving DL-RS characteristics, according to certain embodiments. In the illustrated embodiment, the method includes a first transmitting step at 402, a second transmitting step at 404, a first receiving step at 406, a third transmitting step at 408, and a second receiving step at 4110. For example, at step 402, the network node may transmit, to a UE, information indicating a first set of DL-RSs and a second set of DL-RSs to be used during a training phase with a first set of DL-RS characteristics. At step 404, for example, the network node may transmit the first set of DL-RSs transmitted using a first set of spatial filters and the second set of DL-RSs transmitted using a second set of spatial filters with the first set of DL-RS characteristics. At step 404, for example, the network node may receive, from the UE, information on, indicating, and / or associated with a second set of DL-RS characteristics to be used for transmission of a third set of DL-RSs. At step 404, for example, the network node may transmit the third set of DL-RSs transmitted using a third set of spatial filters with the second set of DL-RS characteristics. At step 404, for example, the network node may receive, from the UE, prediction information comprising at least one of: at least one predicted spatial filter, at least one predicted CSI, and at least one estimate of positioning.

[0168] FIGURE 8 illustrates an example method 500 by a network node for receiving DL-RS characteristics, according to certain embodiments. In the illustrated embodiment, the method begins at step 502 when the network node receives, from a UE, information including a second set of DL-RS characteristics to be used for transmission of a third set of DL-RSs. At step 504, the network node transmits, to the UE, the third set of DL-RSs transmitted using a third set of spatial filters with the second set of DL-RS characteristics, and the third set of spatial filters comprises at least some of a second set of spatial filters. At step 506, the network node receives, from the UE, prediction information including at least one predicted spatial filter that is a best spatial filter from a first set of spatial filters. The prediction information is based on a first set of DL-RSs associated with a first set of DL-RS characteristics. The first set of DL-RSs are associated with the first set of spatial filters and the second set of DL-RSs are associated with the second spatial filters.

[0169] In a particular embodiment, the prediction information comprises at least one of: at least one predicted CSI, and at least one estimate of positioning.

[0170] In a particular embodiment, the first set of DL-RS characteristics and / or the second set of DL-RS characteristics include at least one of: DL-RS bandwidth, DL-RS starting PRB, DL-RS end PRB, DL-RS density, DL-RS periodicity, number of DL-RS ports, DL-RS time offset, number of DL-RS repetitions, and interval between DL-RS repetitions. In various particular embodiments, one or more values associated with the first set of DL-RS characteristics may be different than or the same as the one or more values associated with the second set of DL-RS characteristics.

[0171] In a particular embodiment, the first set of DL-RSs and the second set of DL-RSs are transmitted to the UE during a training phase, and / or the third set of DL-RS are transmitted to the UE during at least one of an inference phase, a re-training phase, and a monitoring phase.

[0172] In a particular embodiment, the second set of DL-RS characteristics are based on the at least one measurement of the first set of DL-RSs and the second set of DL-RSs transmitted to the UE with a first set of DL-RS characteristics during a training phase of the AI / ML model. In a particular embodiment, the prediction information is based on at least one of: at least one measurement performed on the third set of DL-RS by the UE; and the AI / ML model.

[0173] In a particular embodiment, the second set of spatial filters is the same as the third set of spatial filters.

[0174] In a particular embodiment, the second set of spatial filters is a subset of the first set of spatial filters.

[0175] In a particular embodiment, the second set of spatial filters is different from the first set of spatial filters.

[0176] In a particular embodiment, the information comprising the second set of DL-RS characteristics is received from the UE via RRC signaling.

[0177] In a further particular embodiment, the information comprising the second set of DL-RS characteristics is transmitted in a UEAssistancelnformation message and / or an uplink MAC-CE message.

[0178] In a particular embodiment, the information comprising the second set of DL-RS characteristics is received from the UE during a training phase or an inference phase associated with the AI / ML model.

[0179] FIGURE 9 shows an example of a communication system 600 in accordance with some embodiments. In the example, the communication system 600 includes a telecommunication network 602 that includes an access network 604, such as a radio access network (RAN), and a core network 606, which includes one or more core network nodes 608. The access network 604 includes one or more access network nodes, such as network nodes 610a and 610b (one or more of which may be generally referred to as network nodes 610), or any other similar 3rd Generation Partnership Project (3 GPP) access node or non-3GPP access point. The network nodes 610 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 612a, 612b, 612c, and 612d (one or more of which may be generally referred to as UEs 612) to the core network 606 over one or more wireless connections.

[0180] 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 600 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 600 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0181] The UEs 612 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 610 and other communication devices. Similarly, the network nodes 610 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 612 and / or with other network nodes or equipment in the telecommunication network 602 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 602.

[0182] In the depicted example, the core network 606 connects the network nodes 610 to one or more hosts, such as host 616. 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 606 includes one more core network nodes (e.g., core network node 608) 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 608. 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).

[0183] The host 616 may be under the ownership or control of a service provider other than an operator or provider of the access network 604 and / or the telecommunication network 602 and may be operated by the service provider or on behalf of the service provider. The host 616 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.

[0184] As a whole, the communication system 600 of FIGURE 9 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.

[0185] In some examples, the telecommunication network 602 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 602 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 602. For example, the telecommunications network 602 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs.

[0186] In some examples, the UEs 612 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 604 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 604. 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).

[0187] In the example, the hub 614 communicates with the access network 604 to facilitate indirect communication between one or more UEs (e.g., UE 612c and / or 612d) and network nodes (e.g., network node 610b). In some examples, the hub 614 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 614 may be a broadband router enabling access to the core network 606 for the UEs. As another example, the hub 614 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 610, or by executable code, script, process, or other instructions in the hub 614. As another example, the hub 614 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 614 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 614 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 614 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 614 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.

[0188] The hub 614 may have a constant / persistent or intermittent connection to the network node 610b. The hub 614 may also allow for a different communication scheme and / or schedule between the hub 614 and UEs (e.g., UE 612c and / or 612d), and between the hub 614 and the core network 606. In other examples, the hub 614 is connected to the core network 606 and / or one or more UEs via a wired connection. Moreover, the hub 614 may be configured to connect to an M2M service provider over the access network 604 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 610 while still connected via the hub 614 via a wired or wireless connection. In some embodiments, the hub 614 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 610b. In other embodiments, the hub 614 may be a nondedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 610b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0189] FIGURE 10 shows a UE 700, which may be an embodiment of the UE 112 of FIGURE 9, 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.

[0190] 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), orvehicle-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).

[0191] The UE 700 includes processing circuitry 702 that is operatively coupled via a bus 704 to an input / output interface 706, a power source 708, a memory 710, a communication interface 712, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in FIGURE 10. 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.

[0192] The processing circuitry 702 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 710. The processing circuitry 702 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 702 may include multiple central processing units (CPUs). In the example, the input / output interface 706 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 700. 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.

[0193] In some embodiments, the power source 708 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 708 may further include power circuitry for delivering power from the power source 708 itself, and / or an external power source, to the various parts of the UE 700 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 708. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 708 to make the power suitable for the respective components of the UE 700 to which power is supplied.

[0194] The memory 710 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 710 includes one or more application programs 714, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 716. The memory 710 may store, for use by the UE 700, any of a variety of various operating systems or combinations of operating systems. The memory 710 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 710 may allow the UE 700 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 710, which may be or comprise a device-readable storage medium.

[0195] The processing circuitry 702 may be configured to communicate with an access network or other network using the communication interface 712. The communication interface 712 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 722. The communication interface 712 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 718 and / or a receiver 720 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 718 and receiver 720 may be coupled to one or more antennas (e.g., antenna 722) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0196] In the illustrated embodiment, communication functions of the communication interface 712 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 / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0197] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 712, 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).

[0198] 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.

[0199] 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 700 shown in FIGURE 10.

[0200] 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 3 GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3 GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0201] 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.

[0202] FIGURE 11 shows a network node 800, which may be an embodiment of the network node 110 of FIGURE 4, in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)).

[0203] 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).

[0204] 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 / multi cast 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).

[0205] The network node 800 includes a processing circuitry 802, a memory 804, a communication interface 806, and a power source 808. The network node 800 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 800 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 800 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 804 for different RATs) and some components may be reused (e.g., a same antenna 810 may be shared by different RATs). The network node 800 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 800, 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 800.

[0206] The processing circuitry 802 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 800 components, such as the memory 804, to provide network node 800 functionality.

[0207] In some embodiments, the processing circuitry 802 includes a system on a chip (SOC). In some embodiments, the processing circuitry 802 includes one or more of radio frequency (RF) transceiver circuitry 812 and baseband processing circuitry 814. In some embodiments, the radio frequency (RF) transceiver circuitry 812 and the baseband processing circuitry 814 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 812 and baseband processing circuitry 814 may be on the same chip or set of chips, boards, or units.

[0208] The memory 804 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 802. The memory 804 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 802 and utilized by the network node 800. The memory 804 may be used to store any calculations made by the processing circuitry 802 and / or any data received via the communication interface 806. In some embodiments, the processing circuitry 802 and memory 804 is integrated.

[0209] The communication interface 806 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 806 comprises port(s) / terminal(s) 816 to send and receive data, for example to and from a network over a wired connection. The communication interface 806 also includes radio frontend circuitry 818 that may be coupled to, or in certain embodiments a part of, the antenna 810. Radio front-end circuitry 818 comprises filters 820 and amplifiers 822. The radio front-end circuitry 818 may be connected to an antenna 810 and processing circuitry 802. The radio frontend circuitry may be configured to condition signals communicated between antenna 810 and processing circuitry 802. The radio front-end circuitry 818 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 818 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 820 and / or amplifiers 822. The radio signal may then be transmitted via the antenna 810. Similarly, when receiving data, the antenna 810 may collect radio signals which are then converted into digital data by the radio front-end circuitry 818. The digital data may be passed to the processing circuitry 802. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0210] In certain alternative embodiments, the network node 800 does not include separate radio front-end circuitry 818, instead, the processing circuitry 802 includes radio front-end circuitry and is connected to the antenna 810. Similarly, in some embodiments, all or some of the RF transceiver circuitry 812 is part of the communication interface 806. In still other embodiments, the communication interface 806 includes one or more ports or terminals 816, the radio front-end circuitry 818, and the RF transceiver circuitry 812, as part of a radio unit (not shown), and the communication interface 806 communicates with the baseband processing circuitry 814, which is part of a digital unit (not shown).

[0211] The antenna 810 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 810 may be coupled to the radio front-end circuitry 818 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 810 is separate from the network node 800 and connectable to the network node 800 through an interface or port.

[0212] The antenna 810, communication interface 806, and / or the processing circuitry 802 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 810, the communication interface 806, and / or the processing circuitry 802 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.

[0213] The power source 808 provides power to the various components of network node 800 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 808 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 800 with power for performing the functionality described herein. For example, the network node 800 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 808. As a further example, the power source 808 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.

[0214] Embodiments of the network node 800 may include additional components beyond those shown in FIGURE 11 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 800 may include user interface equipment to allow input of information into the network node 800 and to allow output of information from the network node 800. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 800.

[0215] FIGURE 12 is a block diagram illustrating a virtualization environment 900 in which functions implemented by some embodiments may be virtualized.

[0216] 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 900 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.

[0217] Applications 902 (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.

[0218] Hardware 904 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 906 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 908a and 908b (one or more of which may be generally referred to as VMs 908), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 906 may present a virtual operating platform that appears like networking hardware to the VMs 908.

[0219] The VMs 908 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 906. Different embodiments of the instance of a virtual appliance 902 may be implemented on one or more of VMs 908, 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.

[0220] In the context of NFV, a VM 908 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 908, and that part of hardware 904 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 908 on top of the hardware 904 and corresponds to the application 902.

[0221] Hardware 904 may be implemented in a standalone network node with generic or specific components. Hardware 904 may implement some functions via virtualization. Alternatively, hardware 904 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 910, which, among others, oversees lifecycle management of applications 902. In some embodiments, hardware 904 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 912 which may alternatively be used for communication between hardware nodes and radio units.

[0222] 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.

[0223] 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 functionalities 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.

[0224] EXAMPLE EMBODIMENTS

[0225] Group A Example Embodiments

[0226] Example Embodiment Al . A method performed by a user equipment for indicating DLRS characteristics for transmission of a set of spatial filters, the method comprising: any of the user equipment steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above.

[0227] Example Embodiment A2. The method of the previous embodiment, further comprising one or more additional user equipment steps, features or functions described above.

[0228] Example Embodiment A3. 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 network node.

[0229] Group B Example Embodiments

[0230] Example Embodiment Bl. A method performed by a network node for receiving DLRS characteristics for transmission of a set of spatial filters, the method comprising: any of the network node steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above.

[0231] Example Embodiment B2. The method of the previous embodiment, further comprising one or more additional network node steps, features or functions described above.

[0232] Example Embodiment B3. The method of any of the previous embodiments, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment.

[0233] Group C Example Embodiments Example Embodiment Cl. A method performed by a user equipment (UE) for indicating Downlink-Reference Signal (DL-RS) characteristics for transmission of a set of spatial filters, the method comprising at least one of receiving, from a network node, information indicating a first set of DL-RSs and a second set of DL-RSs to be used with a first set of DL-RS characteristics; receiving the first set of DL-RSs transmitted using a first set of spatial filters and the second set of DL-RSs transmitted using a second set of spatial filters with the first set of DLRS characteristics; transmitting, to the network node, information on, indicating, and / or associated with a second set of DL-RS characteristics to be used for transmission of a third set of DL-RSs; receiving the third set of DL-RSs transmitted using a third set of spatial filters with the second set of DL-RS characteristics; transmitting, to the network node, prediction information comprising at least one of at least one predicted spatial filter, at least one predicted CSI, and at least one estimate of positioning.

[0234] Example Embodiment C2. The method of Example Embodiment Cl, wherein the first set of DL-RS characteristics comprises at least one of DL-RS bandwidth, DL-RS starting PRB, DL-RS end PRB, DL-RS density, DL-RS periodicity, number of DL-RS ports, DL-RS time offset, number of DL-RS repetitions, and interval between DL-RS repetitions.

[0235] Example Embodiment C3. The method of any one of Example Embodiments Cl to C2, wherein the second set of DL-RS comprises at least one of DL-RS bandwidth, DL-RS starting PRB, DL-RS end PRB, DL-RS density, DL-RS periodicity, number of DL-RS ports, DL-RS time offset, number of DL-RS repetitions, and interval between DL-RS repetitions.

[0236] Example Embodiment C4. The method of any one of Example Embodiments Cl to C3, wherein at least one of the first set of DL-RSs and the second set of DL-RSs are received during a training phase, the third set of DL-RS are received during at least one of an inference phase, a re-training phase, and a monitoring phase.

[0237] Example Embodiment C5. The method of any one of Example Embodiments Cl to C4, comprising determining the second set of DL-RS characteristics to be used for transmission of a third set of DL-RSs.

[0238] Example Embodiment C6. The method of any one of Example Embodiments Cl to C5, wherein the prediction information is determined based on at least one of at least one measurement performed on the third set of DL-RS; and an AI / ML model. Example Embodiment C7. The method of Example Embodiment C6, wherein at least one of: the training phase comprises AI / ML model training, the inference phase comprises Inference associated with an AI / ML model, the re-training phase comprises model re-training and / or finetuning, the monitoring phase comprises model monitoring.

[0239] Example Embodiment C8. The method of any one of Example Embodiments C6 to C7, wherein the AI / ML model is a UE-sided model.

[0240] Example Embodiment C9. The method of any one of Example Embodiments C6 to C8, comprising training the AI / ML model using the first set of DL-RSs and the second set of DL-RSs.

[0241] Example Embodiment CIO. The method of any one of Example Embodiments C6 to C9, comprising: performing the at least one measurement based on the set of DL-RS characteristics; and training, retraining, finetuning, or monitoring the AI / ML model based on the at least one measurement.

[0242] Example Embodiment C 11. The method of any one of Example Embodiments C 1 to C 10, wherein: the first set of spatial filters comprises set A beams, and the second set of spatial filters comprises set B beams.

[0243] Example Embodiment C12. The method of any one of Example Embodiments Cl to Cl 1, wherein the second set of spatial filters is the same as the third set of spatial filters.

[0244] Example Embodiment C 13. The method of any one of Example Embodiments C 1 to C 11 , wherein the second set of spatial filters is different from the third set of spatial filters.

[0245] Example Embodiment C14. The method of any one of Example Embodiments Cl to C13, wherein the second set of spatial filters is a subset of the first set of spatial filters.

[0246] Example Embodiment Cl 5. The method of any one of Example Embodiments Cl to Cl 3, wherein the second set of spatial filters is different from the first set of spatial filters.

[0247] Example Embodiment C16. The method of any one of Example Embodiment Cl to C15, wherein the information on, indicating, and / or associated with the second set of DL-RS characteristics is transmitted to the network node via at least one of: RRC signaling, UL MAC CE signaling, a UE assistance information message.

[0248] Example Embodiment Cl 7. The method of any one of Example Embodiments Cl to Cl 6, wherein the information on, indicating, and / or associated with the second set of DL-RS characteristics is transmitted to the network node during the training phase. Example Embodiment Cl 8. The method of any one of Example Embodiments Cl to Cl 6, wherein the information on, indicating, and / or associated with the second set of DL-RS characteristics is transmitted to the network node during the inference phase.

[0249] Example Embodiment Cl 9. The method of any one of Example Embodiments Cl to Cl 8, wherein the information indicating the first set of DL-RSs and the second set of DL-RSs is received from the network node via at least one of: RRC signaling, DL MAC CE signaling, DCI.

[0250] Example Embodiment C20. The method of Example Embodiments Cl to C19, further comprising: providing user data; and forwarding the user data to a host via the transmission to the network node.

[0251] Example Embodiment C21. A user equipment comprising processing circuitry configured to perform any of the methods of Example Embodiments Cl to C20.

[0252] Example Embodiment C22. A user equipment configured to perform any of the methods of Example Embodiments Cl to C20.

[0253] Example Embodiment C23. A wireless device comprising processing circuitry configured to perform any of the methods of Example Embodiments Cl to C20.

[0254] Example Embodiment C24. A computer program comprising instructions which when executed on a computer perform any of the methods of Example Embodiments Cl to C20.

[0255] Example Embodiment C25. A computer program product comprising computer program, the computer program comprising instructions which when executed on a computer perform any of the methods of Example Embodiments Cl to C20.

[0256] Example Embodiment C26. A non-transitory computer readable medium storing instructions which when executed by a computer perform any of the methods of Example Embodiments Cl to C20.

[0257] Group D Example Embodiments

[0258] Example Embodiment DI. A method performed by a network node for receiving Downlink-Reference Signal (DL-RS) characteristics, the method comprising at least one of: transmitting, to a User Equipment (UE), information indicating a first set of DL-RSs and a second set of DL-RSs to be used during a training phase with a first set of DL-RS characteristics; transmitting the first set of DL-RSs transmitted using a first set of spatial filters and the second set of DL-RSs transmitted using a second set of spatial filters with the first set of DL-RS characteristics; receiving, from the UE, information on, indicating, and / or associated with a second set of DL-RS characteristics to be used for transmission of a third set of DL-RSs; transmitting the third set of DL-RSs transmitted using a third set of spatial filters with the second set of DL-RS characteristics; receiving, from the UE, prediction information comprising at least one of: at least one predicted spatial filter, at least one predicted CSI, and at least one estimate of positioning.

[0259] Example Embodiment D2. The method of Example Embodiment DI, wherein the first set of DL-RS characteristics comprises at least one of: DL-RS bandwidth, DL-RS starting PRB, DL-RS end PRB, DL-RS density, DL-RS periodicity, number of DL-RS ports, DL-RS time offset, number of DL-RS repetitions, and interval between DL-RS repetitions.

[0260] Example Embodiment D3. The method of any one of Example Embodiments DI to D2, wherein the second set of DL-RS comprises at least one of: DL-RS bandwidth, DL-RS starting PRB, DL-RS end PRB, DL-RS density, DL-RS periodicity, number of DL-RS ports, DL-RS time offset, number of DL-RS repetitions, and interval between DL-RS repetitions.

[0261] Example Embodiment D4. The method of any one of Example Embodiments DI to D3, wherein at least one of: the first set of DL-RSs and the second set of DL-RSs are transmitted during a training phase, the third set of DL-RS are transmitted during at least one of an inference phase, a re-training phase, and a monitoring phase.

[0262] Example Embodiment D5. The method of any one of Example Embodiments DI to D4, wherein the prediction information is determined based on at least one of: at least one measurement performed on the third set of DL-RS; and an AI / ML model.

[0263] Example Embodiment D6. The method of any one of Example Embodiments D4 to D5, wherein at least one of: the training phase comprises AI / ML model training, the inference phase comprises Inference associated with an AI / ML model, the re-training phase comprises model retraining and / or finetuning, the monitoring phase comprises model monitoring.

[0264] Example Embodiment D7. The method of any one of Example Embodiments D5 to D6, wherein the AI / ML model is a UE-sided model.

[0265] Example Embodiment D8. The method of any one of Example Embodiments DI to D7, wherein: the first set of spatial filters comprises set A beams, and the second set of spatial filters comprises set B beams.

[0266] Example Embodiment D9. The method of any one of Example Embodiments DI to D8, wherein the second set of spatial filters is the same as the third set of spatial filters. Example Embodiment DIO. The method of any one of Example Embodiments DI to D8, wherein the second set of spatial filters is different from the third set of spatial filters.

[0267] Example Embodiment DI 1. The method of any one of Example Embodiments DI to DIO, wherein the second set of spatial filters is a subset of the first set of spatial filters.

[0268] Example Embodiment D12. The method of any one of Example Embodiments DI to DIO, wherein the second set of spatial filters is different from the first set of spatial filters.

[0269] Example Embodiment D13. The method of any one of Example Embodiment DI to DI 2, wherein the information on, indicating, and / or associated with the second set of DL-RS characteristics is received from the UE via at least one of: RRC signaling, UL MAC CE signaling, a UE assistance information message.

[0270] Example Embodiment D14. The method of any one of Example Embodiments DI to D13, wherein the information on, indicating, and / or associated with the second set of DL-RS characteristics is received from the UE during a training phase.

[0271] Example Embodiment DI 5. The method of any one of Example Embodiments DI to D13, wherein the information on, indicating, and / or associated with the second set of DL-RS characteristics is received from the UE during the inference phase.

[0272] Example Embodiment DI 6. The method of any one of Example Embodiments DI to DI 5, wherein the information indicating the first set of DL-RSs and the second set of DL-RSs is transmitted to the UE via at least one of: RRC signaling, DL MAC CE signaling, DCI.

[0273] Example Embodiment DI 7. The method of any of the previous Example Embodiments, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment.

[0274] Example Embodiment DI 8. A network node comprising processing circuitry configured to perform any of the methods of Example Embodiments DI to DI 7.

[0275] Example Embodiment DI 9. A network node configured to perform any of the methods of Example Embodiments DI to DI 7.

[0276] Example Embodiment D20. A computer program comprising instructions which when executed on a computer perform any of the methods of Example Embodiments DI to DI 7.

[0277] Example Embodiment D21. A computer program product comprising computer program, the computer program comprising instructions which when executed on a computer perform any of the methods of Example Embodiments DI to DI 7. Example Embodiment D22. A non-transitory computer readable medium storing instructions which when executed by a computer perform any of the methods of Example Embodiments DI to DI 7.

[0278] Group E Example Embodiments

[0279] Example Embodiment El. A user equipment for indicating DL-RS characteristics for transmission of a set of spatial filters, the UE comprising: processing circuitry configured to perform any of the steps of any of the Group A and C Example Embodiments; and power supply circuitry configured to supply power to the processing circuitry.

[0280] Example Embodiment E2. A network node for receiving DL-RS characteristics for transmission of a set of spatial filters, the network node comprising: processing circuitry configured to perform any of the steps of any of the Group B and D Example Embodiments; power supply circuitry configured to supply power to the processing circuitry.

[0281] Example Embodiment E3. A user equipment (LIE) for indicating DL-RS characteristics for transmission of a set of spatial filters, 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 and C Example 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.

[0282] Example Embodiment E4. 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 and C Example Embodiments to receive the user data from the host. Example Embodiment E5. The host of the previous Example 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.

[0283] Example Embodiment E6. The host of the previous 2 Example 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.

[0284] Example Embodiment E7. 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.

[0285] Example Embodiment E8. The method of the previous Example 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.

[0286] Example Embodiment E9. The method of the previous Example 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.

[0287] Example Embodiment E10. 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 and C Example Embodiments to transmit the user data to the host.

[0288] Example Embodiment El 1. The host of the previous Example 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.

[0289] Example Embodiment E12. The host of the previous 2 Example 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.

[0290] Example Embodiment El 3. 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 and C Example Embodiments to transmit the user data to the host.

[0291] Example Embodiment E14. The method of the previous Example 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.

[0292] Example Embodiment El 5. The method of the previous Example 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.

[0293] Example Embodiment El 6. 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 and D Example Embodiments to transmit the user data from the host to the UE.

[0294] Example Embodiment El 7. The host of the previous Example 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.

[0295] Example Embodiment El 8. 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 and D Example Embodiments to transmit the user data from the host to the UE. Example Embodiment E19. The method of the previous Example Embodiment, further comprising, at the network node, transmitting the user data provided by the host for the UE.

[0296] Example Embodiment E20. The method of any of the previous 2 Example 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.

[0297] Example Embodiment E21. 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 and D Example Embodiments to transmit the user data from the host to the UE.

[0298] Example Embodiment E22. The communication system of the previous Example Embodiment, further comprising: the network node; and / or the user equipment.

[0299] Example Embodiment E23. 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 and D Example Embodiments to receive the user data from a user equipment (UE) for the host.

[0300] Example Embodiment E24. The host of the previous 2 Example 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.

[0301] Example Embodiment E25. The host of the any of the previous 2 Example Embodiments, wherein the initiating receipt of the user data comprises requesting the user data.

[0302] Example Embodiment E26. 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 and D Example Embodiments to receive the user data from the UE for the host. Example Embodiment E27. The method of the previous Example Embodiment, further comprising at the network node, transmitting the received user data to the host.

Claims

CLAIMS1. A method (300) performed by a user equipment, UE (612), for indicating Downlink- Reference Signal, DL-RS, characteristics for transmission using a set of spatial filters, the method comprising: transmitting (302), to a network node (610), information comprising a second set of DLRS characteristics to be used for transmission of a third set of DL-RSs; receiving (304), from the network node, the third set of DL-RSs transmitted using a third set of spatial filters with the second set of DL-RS characteristics, wherein the third set of spatial filters comprises at least some of a second set of spatial filters; and transmitting (306), to the network node, prediction information comprising at least one predicted spatial filter that is a best spatial filter from a first set of spatial filters, wherein the prediction information is based on a first set of DL-RSs associated with a first set of DL-RS characteristics, and wherein the first set of DL-RSs are associated with the first set of spatial filters and the second set of DL-RSs are associated with the second spatial filters.

2. The method of Claim 1, wherein the prediction information comprises at least one of at least one predicted CSI, and at least one estimate of positioning.

3. The method of any one of Claims 1 to 2, wherein the first set of DL-RS characteristics and / or the second set of DL-RS characteristics comprise at least one ofDL-RS bandwidth,DL-RS starting PRB,DL-RS end PRB,DL-RS density,DL-RS periodicity, number of DL-RS ports,DL-RS time offset, number of DL-RS repetitions, and interval between DL-RS repetitions.

4. The method of any one of Claims 1 to 3, wherein at least one of the first set of DL-RSs and the second set of DL-RSs are received during a training phase,the third set of DL-RS are received during at least one of an inference phase, a re-training phase, and a monitoring phase.

5. The method of any one of Claims 1 to 4, comprising training an Artificial Intelligence / Machine Learning, AI / ML, model using at least one measurement of the first set of DL-RS s and the second set of DL-RS s transmitted to the UE with the first set of DL-RS characteristics.

6. The method of Claim 5, comprising: based on the at least one measurement of the first set of DL-RSs and the second set of DL- RSs transmitted to the UE with the first set of DL-RS characteristics, determining the second set of DL-RS characteristics to be used for transmission of the third set of DL-RSs.

7. The method of any one of Claims 5 to 6, comprising: performing the at least one measurement of the first set of DL-RSs and the second set of DL-RSs based on the first set of DL-RS characteristics; and training, retraining, finetuning, or monitoring the AI / ML model based on the at least one measurement.

8. The method of any one of Claims 1 to 7, wherein the prediction information is determined based on at least one of: at least one measurement performed on the third set of DL-RSs; and an Artificial Intelligence / Machine Learning, AI / ML, model.

9. The method of any one of Claims 1 to 8, wherein the second set of spatial filters is the same as the third set of spatial filters.

10. The method of any one of Claims 1 to 9, wherein the second set of spatial filters is a subset of the first set of spatial filters.

11. The method of any one of Claims 1 to 9, wherein the second set of spatial filters is different from the first set of spatial filters.

12. The method of any one of Claim 1 to 11, wherein the information comprising the second set of DL-RS characteristics is transmitted to the network node viaRRC signaling.

13. The method of Claim 12, wherein the information comprising the second set of DL-RS characteristics is transmitted in a UEAssistancelnformation message and / or an uplink MAC-CE message.

14. The method of any one of Claims 1 to 13, wherein the information comprising the second set of DL-RS characteristics is transmitted to the network node during a training phase or an inference phase associated with the AI / ML model.

15. A method (500) performed by a network node (610) for receiving Downlink-Reference Signal (DL-RS) characteristics, the method comprising: receiving (502), from a User Equipment, UE, (612) information comprising a second set of DL-RS characteristics to be used for transmission of a third set of DL-RSs; transmitting (504), to the UE, the third set of DL-RSs transmitted using a third set of spatial filters with the second set of DL-RS characteristics, wherein the third set of spatial filters comprises at least some of a second set of spatial filters; and receiving (506), from the UE, prediction information comprising at least one predicted spatial filter that is a best spatial filter from a first set of spatial filters, wherein the prediction information is based on a first set of DL-RSs associated with a first set of DL-RS characteristics, and wherein the first set of DL-RSs are associated with the first set of spatial filters and the second set of DL-RSs are associated with the second spatial filters.

16. The method of Claim 15, wherein the prediction information comprises at least one of: at least one predicted CSI, and at least one estimate of positioning.

17. The method of any one of Claims 15 to 16, wherein the first set of DL-RS characteristics and / or the second set of DL-RS characteristics comprise at least one of:DL-RS bandwidth,DL-RS starting PRB,DL-RS end PRB,DL-RS density,DL-RS periodicity, number of DL-RS ports,DL-RS time offset, number of DL-RS repetitions, and interval between DL-RS repetitions.

18. The method of any one of Claims 15 to 17, wherein at least one of: the first set of DL-RSs and the second set of DL-RSs are transmitted to the UE during a training phase, and the third set of DL-RS are transmitted to the UE during at least one of an inference phase, a re-training phase, and a monitoring phase.

19. The method of any one of Claims 15 to 18, wherein the second set of DL-RS characteristics are based on the at least one measurement of the first set of DL-RSs and the second set of DL-RSs transmitted to the UE with a first set of DL-RS characteristics during a training phase of the AI / ML model.

20. The method of any one of Claims 15 to 19, wherein the prediction information is based on at least one of: at least one measurement performed on the third set of DL-RS by the UE; and the AI / ML model.

21. The method of any one of Claims 15 to 20, wherein the second set of spatial filters is the same as the third set of spatial filters.

22. The method of any one of Claims 15 to 21, wherein the second set of spatial filters is a subset of the first set of spatial filters.

23. The method of any one of Claims 15 to 21, wherein the second set of spatial filters is different from the first set of spatial filters.

24. The method of any one of Claim 15 to 23, wherein the information comprising the second set of DL-RS characteristics is received from the UE via RRC signaling.

25. The method of Claim 24, wherein the information comprising the second set of DL-RS characteristics is transmitted in a UEAssistancelnformation message and / or an uplink MAC-CE message.

26. The method of any one of Claims 15 to 25, wherein the information comprising the second set of DL-RS characteristics is received from the UE during a training phase or an inference phase associated with the AI / ML model.

27. A user equipment, UE, (612) for indicating Downlink-Reference Signal, DL-RS, characteristics for transmission using a set of spatial filters, the UE configured to: transmit (302), to a network node (610), information comprising a second set of DL-RS characteristics to be used for transmission of a third set of DL-RSs; receive (304), from the network node, the third set of DL-RSs transmitted using a third set of spatial filters with the second set of DL-RS characteristics, wherein the third set of spatial filters comprises at least some of a second set of spatial filters; and transmit (306), to the network node, prediction information comprising at least one predicted spatial filter that is a best spatial filter from a first set of spatial filters, wherein the prediction information is based on a first set of DL-RSs associated with a first set of DL-RS characteristics, and wherein the first set of DL-RSs are associated with the first set of spatial filters and the second set of DL-RSs are associated with the second spatial filters.

28. The UE of Claim 27 configured to perform any of the methods of Claims 2 to 14.

29. A network node (610) for receiving Downlink-Reference Signal (DL-RS) characteristics, the network node configured to: receive (502), from a User Equipment, UE, (612) information comprising a second set of DL-RS characteristics to be used for transmission of a third set of DL-RSs; transmit (504), to the UE, the third set of DL-RSs transmitted using a third set of spatial filters with the second set of DL-RS characteristics, wherein the third set of spatial filters comprises at least some of a second set of spatial filters; and receive (506), from the UE, prediction information comprising at least one predicted spatial filter that is a best spatial filter from a first set of spatial filters, wherein the prediction information is based on a first set of DL-RSs associated with a first set of DL-RS characteristics, and wherein the first set of DL-RSs are associated with the first set of spatial filters and the second set of DL- RSs are associated with the second spatial filters.

30. The network node of Claim 29, configured to perform any of the methods of Claims 16 to 26.

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