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WO2025183611A1 - Channel state information acquisition for unconventional arrays - Google Patents

Channel state information acquisition for unconventional arrays

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Publication number
WO2025183611A1
WO2025183611A1 PCT/SE2025/050175 SE2025050175W WO2025183611A1 WO 2025183611 A1 WO2025183611 A1 WO 2025183611A1 SE 2025050175 W SE2025050175 W SE 2025050175W WO 2025183611 A1 WO2025183611 A1 WO 2025183611A1
Authority
WO
WIPO (PCT)
Prior art keywords
cmrs
network node
antenna ports
additional information
ports
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/SE2025/050175
Other languages
French (fr)
Inventor
Xinlin ZHANG
Johan WINGES
Siva Muruganathan
Jingya Li
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Telefonaktiebolaget LM Ericsson AB
Original Assignee
Telefonaktiebolaget LM Ericsson AB
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Telefonaktiebolaget LM Ericsson AB filed Critical Telefonaktiebolaget LM Ericsson AB
Publication of WO2025183611A1 publication Critical patent/WO2025183611A1/en
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0048—Allocation of pilot signals, i.e. of signals known to the receiver
    • H04L5/005—Allocation of pilot signals, i.e. of signals known to the receiver of common pilots, i.e. pilots destined for multiple users or terminals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0686—Hybrid systems, i.e. switching and simultaneous transmission
    • H04B7/0695—Hybrid systems, i.e. switching and simultaneous transmission using beam selection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/0001—Arrangements for dividing the transmission path
    • H04L5/0014—Three-dimensional division
    • H04L5/0023—Time-frequency-space
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0053—Allocation of signalling, i.e. of overhead other than pilot signals
    • H04L5/0057—Physical resource allocation for CQI

Definitions

  • Embodiments of the present disclosure are directed to wireless communications and, more particularly, to channel state information (CSI) acquisition for unconventional arrays.
  • CSI channel state information
  • Multi-antenna techniques can significantly increase the data rates and reliability of a wireless communication system. The performance is improved if both the transmitter and the receiver are equipped with multiple antennas, which results in a multiple-input multiple-output (MIMO) communication channel.
  • MIMO multiple-input multiple-output
  • ⁇ [ ⁇ 1, ⁇ 2, ... , ⁇ ] ⁇
  • ⁇ precoding matrix
  • ⁇ serves to beamform each data P110845WO01 PCT APPLICATION 2 of 47 layer towards a user equipment (UE) such that signal to interference plus noise ratio (SINR) is maximized and cross layer interference is minimized at the UE receiver.
  • SINR signal to interference plus noise ratio
  • Spatial multiplexing is achieved because multiple symbols can be transmitted simultaneously in a same time and frequency resource element (RE).
  • RE resource element
  • the precoder matrix ⁇ is chosen to match the characteristics of the NRxNT MIMO channel matrix ⁇ resulting in channel dependent precoding.
  • the precoder ⁇ can be a wideband precoder, i.e., the same over a whole bandwidth, or a subband precoder, i.e., optimized per subband.
  • ⁇ is typically selected from a codebook of precoding matrices by the UE and reported to the gNB in terms of a precoding matrix indicator (PMI).
  • PMI precoding matrix indicator
  • One example method for a UE to select a precoder matrix ⁇ can be to select the ⁇ from a codebook that maximizes the Frobenius norm of the hypothesized equivalent channel: ⁇ ⁇ ⁇ ⁇ ⁇ 2 max ⁇ ⁇ where ⁇ is a channel estimate precoder matrix with index k.
  • a UE In addition to ⁇ feedback, a UE typically also feedsback a rank indicator (RI) and channel quality indicator(s) (CQI) as part of channel state information (CSI) feedback. Given the CSI feedback from the UE, the gNB can determine the transmission parameters to use for data transmissions to the UE.
  • CSI-RS channel state information reference signal
  • CSI-RS channel state information reference signal
  • the antennas with NT antenna ports described above can be either a linear antenna array or two-dimensional (2D) plenary antenna array.
  • a linear antenna array is a special case of a 2D antenna array.
  • the 2D antenna array may be rotated at any angle. In this case, the row and columns may no longer correspond to vertical and horizontal directions.
  • NZP CSI-RS non-zero power channel state information reference signal
  • NZP CSI-RS is configured in terms of NZP CSI- RS resources.
  • NZP may be omitted in the following disclosure.
  • a NZP CSI- RS resource supports up to 32 antenna ports.
  • the antenna ports are also referred to as CSI-RS antenna ports, CSI-RS ports, or antenna ports.
  • Different CSI-RS antenna ports in a CSI-RS resource are allocated with different REs and/or different CDM (code division multiplexing) codes so that the downlink channel associated to each antenna port can be individually measured and estimated.
  • ⁇ 1 ⁇ 2, 1, and 3.
  • ⁇ is the number of REs per RB per CSI-RS port.
  • the CDM codes used can be either length 2 or length 4 time domain orthogonal cover codes (TD-OCC), i.e., TD-OCC2 or TD-OCC4, or length 2 frequency domain OCC (FD-OCC), i.e., FD-OCC2, or both TD-OCC and FD-OCC.
  • the CDM groups are numbered in order of increasing frequency domain allocation first and then increasing time domain allocation.
  • An example of a CSI-RS resource for 32 antenna ports are shown in FIGURE 3, where CSI-RS REs in one RB is shown.
  • FIGURE 3 illustrates an example of a CSI-RS resource for 32 antenna ports with 8 CDM groups.
  • the CDM codes are TD- OCC2 plus FD-OCC2.
  • Each antenna port is mapped to one of the CDM groups.
  • Antenna ports are mapped in CDM group first, then frequency, and then time.
  • antenna ports are multiplexed via CDM codes or sequences.
  • the NR Type I single panel codebook is based on discrete Fourier transform (DFT) beams or precoders and is for cross polarized 2D antenna arrays, where a DFT beam is selected for each MIMO layer. The same DFT beam is applied to antenna ports at both polarizations. A co-phasing factor is applied at antenna ports of one of the two polarizations.
  • DFT discrete Fourier transform
  • ( ⁇ , ⁇ ) and is 2 ⁇ ⁇ b y ⁇ , ⁇ [ ⁇ ⁇ ⁇ 2 ⁇ ( ⁇ 1 ⁇ 1) ⁇ ⁇ ⁇ ⁇ 1 ⁇ 1 ⁇ ...
  • ⁇ ⁇ 1 ⁇ 1 ⁇ ⁇ ] and ⁇ ⁇ [ 1 ⁇ ⁇ 2 ⁇ ⁇ ⁇ 2 ⁇ 2 ... ⁇ ⁇ ⁇ 1 and ⁇ 2 are the f actor in the dimension ⁇ 1 and ⁇ 2, respectively.
  • ⁇ ⁇ ⁇ 2 is a co-phasing factor.
  • DFT beams ⁇ ⁇ , ⁇ ⁇ are also referred to as spatial [ 0023]
  • PDSCH physical downlink shared channel
  • b ased ⁇ structure mean that the CSI-RS antenna ports for a 2D ports need to be indexed in order of increasing along the ⁇ 2 dimension first and then increasing along the ⁇ 1 dimension at a first polarization and repeat the above for the other polarization.
  • An example is shown in FIGURE 4 for a 2D antenna with 32 ports where the CSI-RS port number is given by adding 3000 to the numbers shown in the figure.
  • FIGURE 4 illustrates an example of a mapping of CSI-RS antenna ports to a 2D antenna with 32 ports. P110845WO01 PCT APPLICATION 6 of 47 [0026] In Rel-18, a network energy saving feature was introduced for muting a subset of CSI- RS ports for energy saving purposes.
  • a bitmap with ⁇ 2 ⁇ 1 ⁇ 2 bits are signaled from the network (e.g., gNB) to the UE.
  • the bitmap can have ⁇ unmuted, and the remaining ⁇ ⁇ ⁇ ports are muted. Because the network does not transmit any CSI-RS on the muted ports, the network can save energy by skipping these transmissions on muted ports.
  • the number of unmuted ports ⁇ have to correspond to one of the number of CSI-RS ports (among 2, 4, 8, 12, 16, 24 and 32) supported in NR.
  • bit ⁇ corresponds to antenna port 3000 + i
  • ⁇ m is the number of ports nrofPorts configured for the CSI-RS resources(s) within a NZP- CSI-RS-ResourceSet contained in the CSI-ResourceConfig for channel measurement that corresponds to the CSI-ReportConfig.
  • a bit value 0 in [port-subsetIndicator] indicates that the corresponding antenna port is disabled for the sub-configuration, whereas bit value 1 indicates that the antenna port is enabled and belongs to the antenna port subset for the sub-configuration.
  • port-subsetIndicator is the port muting bitmap and ⁇ ⁇ is the notation used instead of ⁇ ⁇ .
  • all the unmuted ports i.e., those with bits corresponding to value 1 in the bitmap
  • FIGURE 5 An example is shown in FIGURE 5 for a 2D antenna with 32 ports where 8 of the ports are muted.
  • FIGURE 5 illustrates an example of a mapping of CSI-RS antenna ports to a 2D antenna with 32 ports with 8 ports muted.
  • Example use cases include using autoencoders for channel state information (CSI) compression to reduce the feedback overhead and improve channel prediction accuracy; using deep neural networks for classifying line-of-sight (LOS) and non- LOS (NLOS) conditions to enhance the positioning accuracy; using reinforcement learning for beam selection at the network side and/or the UE side to reduce the signaling overhead and beam alignment latency; and using deep reinforcement learning to learn an optimal precoding policy for complex MIMO precoding problems.
  • CSI channel state information
  • LOS line-of-sight
  • NLOS non- LOS
  • FIGURE 6 illustrates a functional framework for AI/ML for NR air interface.
  • FIGURE 6 shows a functional framework that can be used for studying model LCM aspects for different AI for PHY use cases.
  • the general framework consists of the following.
  • Data Collection is a function that provides input data to the Model Training, Management, and Inference functions.
  • P110845WO01 PCT APPLICATION 8 of 47 ⁇ Training Data: Data needed as input for the AI/ML Model Training function.
  • Monitoring Data Data needed as input for the Management of AI/ML models or AI/ML functionalities.
  • Inference Data Data needed as input for the AI/ML Inference function.
  • Model Training is a function that performs AI/ML model training, validation, and testing, which may generate model performance metrics that can be used as part of the model testing procedure.
  • the Model Training function is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on Training Data delivered by a Data Collection function if required.
  • Trained/Updated Model When using a Model Storage function, this is used to deliver trained, validated, and tested AI/ML models to the Model Storage function, or to deliver an updated version of a model to the Model Storage function.
  • Management is a function that oversees the operation (e.g., selection, (de)activation, switching, fallback) and monitoring (e.g., performance) of AI/ML models or AI/ML functionalities. This function is also responsible for making decisions to ensure the proper inference operation based on data received from the Data Collection function and the Inference function.
  • ⁇ Management Instruction Information needed as input to manage the Inference function. Concerning information may include selection/(de)activation/switching of AI/ML models or AI/ML-based functionalities, fallback to non-AI/ML operation (i.e., not relying on inference process), etc.
  • Model Transfer/Delivery Request Used to request model(s) to the Model Storage function.
  • Performance Feedback/Retraining Request Information needed as input for the Model Training function, e.g., for model (re)training or updating purposes.
  • Inference is a function that provides outputs from the process of applying AI/ML models or AI/ML functionalities using the data that is provided by the Data Collection function (i.e., Inference Data) as an input.
  • the Inference function is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on Inference Data delivered by a Data Collection function, if required.
  • Inference Output Data used by the Management function to monitor the performance of AI/ML models or AI/ML functionalities.
  • Model Storage is a function responsible for storing trained/updated models that can be used to perform the Inference function.
  • the Model Storage function in FIGURE 6 is only intended as a reference point (if any) when applicable for protocol terminations, model transfer/delivery, and related processes. It should be stressed that its purpose does not encompass restricting the actual storage locations of models.
  • Model Transfer/Delivery Used to deliver an AI/ML model to the Inference function.
  • the AI/ML models being discussed in the Rel-18 study item on AI/ML for the NR air interface can be categorized into the following two types: One-sided AI/ML model, which can be a UE-sided AI/ML model whose inference is performed entirely at the UE, or a NW-sided AI/ML model whose inference is performed entirely at the network.
  • FIGURE 7 shows a use case of CSI prediction using a one-sided UE-sided AI/ML model, where one or more AI/ML models can be trained and deployed at a UE.
  • a UE is configured by the gNB to measure a set of historical CSI-RSs and then report a predicted CSI for one or multiple future time instances using its AI/ML model(s).
  • FIGURE 7 illustrates an example of the CSI prediction using UE-sided AI model(s).
  • a two-sided AI/ML model refers to paired AI/ML Model(s) over which joint inference is performed across the UE and the network, i.e., the first part of the inference is firstly performed by UE and then the remaining part is performed by gNB, or vice versa.
  • FIGURE 8 shows a use case of autoencoder (AE)-based CSI feedback/report, where an encoder (UE-part of the two-sided AE model) is operated at a UE to compress the estimated wireless channel, and the output of the encoder (the compressed wireless channel information estimates) is reported from the UE to a gNB.
  • AE autoencoder
  • the gNB uses a decoder (network part of the two-sided AE model) to reconstruct the estimated wireless channel information.
  • the two-sided AI/ML model is composed of the encoder at the UE side and the decoder at the base station (i.e., a gNB) side.
  • the code is generated by the encoder and only interpretable by a jointly trained decoder.
  • P110845WO01 PCT APPLICATION 10 of 47 The situation is different from running an AI/ML model in the UE, reporting the output over the air in a fully standardized format, and running a separate AI/ML model at the base station.
  • FIGURE 8 illustrates an autoencoder (AE)-based CSI compression using two-sided AI/ML model use case.
  • AE autoencoder
  • FIGURE 8 illustrates an autoencoder (AE)-based CSI compression using two-sided AI/ML model use case.
  • different levels of collaboration between network nodes and UEs can be considered.
  • One case is no collaboration between network nodes and UEs.
  • a proprietary ML model operating with the existing standard air-interface is applied at one end of the communication chain (e.g., at the UE side), and the model life cycle management (e.g., model selection/training, model monitoring, model retraining, model update) is done at this node without inter-node assistance (e.g., assistance information provided by the network node).
  • model life cycle management e.g., model selection/training, model monitoring, model retraining, model update
  • Another case is limited collaboration between network nodes and UEs for one-sided models.
  • an ML model is operating at one end of the communication chain (e.g., at the UE side), but this node gets assistance from the node(s) at the other end of the communication chain (e.g., a next generation Node B (gNB)) for its AI model life cycle management to some extent (e.g., for training/retraining the AI model, model update, model monitoring, model selection/fallback/switching).
  • gNB next generation Node B
  • Another case is joint ML operation between network nodes and UEs for two-sided models. This case assumes that the AI model is split with one part located at the network (NW) side and the other part located at the UE side.
  • the AI model requires joint inference between the network and UE, and the AI model life cycle management involves both ends of a communication chain.
  • large antenna or massive MIMO antenna arrays with many antenna ports can provide significant beamforming gains.
  • high spatial resolution provided by large antenna arrays also enables higher multiple- user MIMO performance.
  • the increased antenna ports will lead to increased resource overhead needed for CSI-RS transmission, thereby less resource utilization for data transmission.
  • the complexity of massive MIMO antenna design is high with many constraints, size, weight, cooling and multiple-bands with multiple overlapping arrays together, ranging from 1 to 5 GHz that all needs to fit inside the same radome.
  • the legacy 3GPP PMI codebooks are designed under the assumption of uniform planar 2D antenna array with equally spaced identical subarrays, which are not suited for unconventional arrays.
  • AI/ML-based models may be used for CSI acquisition for unconventional arrays.
  • how to configure data collection, including channel measurement resources and other types of required information, is not known.
  • how to create ground-truth labels for such applications is also not known.
  • CSI channel state information
  • particular embodiments include a data collection scheme to support training one or multiple artificial intelligence (AI)/machine learning (ML) models that may be used at a user equipment (UE) to generate CSI report for an unconventional array.
  • AI artificial intelligence
  • ML machine learning
  • the collected data may be used for creating training datasets for training one or more one-sided UE-sided AI/ML models.
  • the inference of a one-sided UE-sided model is performed entirely at the UE.
  • the UE uses one of the AI/ML models to generate a CSI report associated to ⁇ ⁇ antenna ports, by measuring ⁇ ⁇ antenna ports, where ⁇ ⁇ is smaller than ⁇ ⁇ .
  • the collected data may be used for creating training datasets for training one or more two-sided AI/ML models.
  • the inference of a two- sided model is jointly performed across the UE and the network node.
  • the UE uses a UE-part of a two-sided model to generate a CSI report associated to P110845WO01 PCT APPLICATION 12 of 47 ⁇ ⁇ antenna ports, by measuring ⁇ ⁇ antenna ports.
  • the network node generates the model input using the received CSI report and passes the model input to the paired network part of the two-sided model to reconstruct the estimated wireless channel information.
  • a method performed by a wireless device comprises receiving, from a network node, a configuration for data collection.
  • the configuration includes at least one or more channel measurement resources (CMRs) and additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a reference signal (RS).
  • CMRs channel measurement resources
  • RS reference signal
  • a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more subset indicators. Each subset indicator identifies a muting pattern that indicates a subset of the first set of antenna ports to be muted for transmitting a RS during model inference.
  • a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more superset indicators. Each superset indicator identifies a muting pattern that indicates which ports of a superset of the first set of antenna ports are to be muted for transmitting a RS during model inference.
  • the additional information comprises an indication of an antenna configuration of the network node.
  • the method further comprises creating ground truth labels for a machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS.
  • the method further comprises performing inference for the machine learning model based on the channel measurements for the one or more CMRs and P110845WO01 PCT APPLICATION 13 of 47 the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS.
  • a wireless device comprises processing circuitry operable to perform any of the methods of the wireless device described above.
  • a computer program product comprising a non-transitory computer readable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the wireless device described above.
  • a method performed by a network node comprises transmitting, to a wireless device, a configuration for data collection.
  • the configuration includes at least one or more CMRs and additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS.
  • the method further comprises receiving a CSI report from the wireless device.
  • the CSI report is based on channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS.
  • a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more subset indicators.
  • Each subset indicator identifies a muting pattern that indicates a subset of the first set of antenna ports to be muted for transmitting a RS during model inference.
  • a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more superset indicators.
  • Each superset indicator identifies a muting pattern that indicates which ports of a superset of the first set of antenna ports are to be muted for transmitting a RS during model inference.
  • the additional information comprises an indication of an antenna configuration of the network node.
  • the method further comprises creating ground truth labels for a machine learning model based on the channel measurements for the one or more CMRs P110845WO01 PCT APPLICATION 14 of 47 and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS.
  • the method further comprises performing inference for the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS.
  • a network node comprises processing circuitry operable to perform any of the network node methods described above.
  • Another computer program product comprises a non-transitory computer readable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the network node described above.
  • FIGURE 1 shows an example of data transmission with spatial multiplexing where the information carrying symbol vector is first multiplied (or precoded) by a precoding matrix
  • FIGURE 2 illustrates a two-dimensional antenna array of cross-polarized antenna elements
  • FIGURE 3 illustrates an example of a channel state information reference signal (CSI- RS) resource for 32 antenna ports with 8 code division multiplexing (CDM) groups
  • CSI- RS channel state information reference signal
  • CDM code division multiplexing
  • FIGURE 4 illustrates an example of a mapping of CSI-RS antenna ports to a two- dimensional antenna with 32 ports
  • FIGURE 5 illustrates an example of a mapping of CSI-RS antenna ports to a two
  • Each configured resource for channel measurement consists of a reference signal (e.g., non-zero power (NZP) channel state information reference signal (CSI-RS)) with ⁇ ⁇ ports, and the antenna array at the network node contains ⁇ antenna ports, where ⁇ ⁇ ⁇ .
  • NZP non-zero power
  • CSI-RS channel state information reference signal
  • the term “unconventional array” describes an antenna array that is not using all its antenna ports or an antenna array that does not have uniformly spaced elements in a two-dimensional (2D) grid.
  • An unconventional array may be achieved in different ways. Below, three examples for unconventional arrays are listed.
  • RS reference signal
  • FIGURE 9 illustrates an example of a 2D uniform planar array (UPA) with 16 ports that has 2 rows, 4 columns, and cross-polarized antenna elements.
  • the network node may configure a UE to report a CSI report for the whole 16 port array, but the network node may only configure the UE with a subset of ports, i.e., 12 ports, for channel measurement to save RS overhead.
  • FIGURE 9 illustrates a 2D antenna array grid where only a subset of all ports is sounded. Ports 3000, 3001, 3008 and 3009 illustrated with dashed lines are not sounded. Thus, the UE needs to generate a CSI report for 16 ports with channel measurement of only 12 ports (thus the array is unconventional compared to UPA, because part of the array is silent for RS transmission).
  • the UE may not be able to generate a good CSI report for the full array with 16 ports.
  • Table 1 Performance loss by measuring a subset of ports for a CSI report with all ports, in a system level simulation in UMi scenario, at 50% resource utilization.
  • CSI-RS transmitted on all Mean user 10% percentile ports, except throughput loss user throughput (%) loss (%) [ 3000,3001,3002,3003] 39 43 [3000,3001,3008,3009] 28 36 [3000,3004,3008,3012] 13 16 [0091] It is expected that AI/ML-based CSI feedback may reduce the performance loss.
  • FIGURE 10 An example AI/ML-based CSI feedback solution is found in FIGURE 10, where a UE-sided model is used to generate a CSI report for ⁇ ⁇ ports, based on the channel measurement of ⁇ ⁇ ports ( ⁇ ⁇ ⁇ ), and potentially with other inputs.
  • FIGURE 10 illustrates an example of a UE-sided model for CSI acquisition by measuring a subset of ports. The channel measurement of ⁇ ⁇ ports is input to the UE-sided model, and the CSI report of ⁇ ⁇ ports is output from the UE-sided model.
  • Some embodiments include antenna muting for energy saving.
  • a subset of antenna ports may be muted (i.e., no CSI-RS transmission nor data transmission (e.g., physical downlink shared channel (PDSCH)) in the muted ports) for saving energy on the network side.
  • FIGURE 11 shows an example of a 2D array with 16 unmuted/active ports from a UPA with 24 ports. The 8 ports shaded in black are muted. The unmuted ports, however, are not uniformly spaced. For example, the spacing between ports 3001 and 3003 is different from the spacing between ports 3003 and 3005.
  • the network node may configure UE with a 16-port CSI-RS resource for channel measurement and configure the UE to report a 16-port CSI.
  • FIGURE 11 illustrates a 2D antenna array grid with muted ports along the 2nd and 5th columns.
  • Table 2 Performance gain by using full eigenvector feedback over legacy Rel-16 eType II codebook for the array in FIGURE 11, in a system level simulation in UMi scenario, at 50% resource utilization.
  • An example of performance gain by using full eigenvector feedback over legacy Rel- 16 eType II codebook for the array in FIGURE 11 is shown in Table 2.
  • FIGURE 12 An example AI/ML-based CSI feedback solution is found in FIGURE 12, where a UE-sided model is used to generate a CSI report for ⁇ ⁇ unmuted ports, based on the channel measurement of ⁇ ports ( ⁇ ⁇ ⁇ ), and potentially with other inputs.
  • FIGURE 12 illustrates an example of two-sided model for CSI acquisition where a subset of antenna port is muted for DL RS transmission and data transmission.
  • the antenna array may be non-uniform due to arrangement of elements.
  • FIGURE 13 illustrates an example of an irregular array antenna layout. It can be expected that applying legacy codebooks, which are designed for uniform arrays, directly on irregular arrays will result performance loss.
  • FIGURE 14 is a flowchart illustrating data collection procedure at the UE for AI/ML modeling training.
  • Step 101 a UE receives from a network node configuration for data collection.
  • the configuration includes at least one or more of the following: channel measurement resource(s), CMR(s); and additional information for associating the configured CMR(s) and the antenna ports at the network node that are used for transmitting reference signals (RSs).
  • the collected data may be used by the UE for model training, as well as other LCM aspects of an AI/ML model, such as inference, monitoring, etc.
  • a subset indicator may be a bitmap of length ⁇ ⁇ , where each bit in the bitmap indicates whether an antenna port is muted or not when the subset indicator is configured during a model inference phase.
  • a subset indicator [1100111111001111] indicates that ports 3002,3003,3010,3011 are muted when this subset indicator is configured during model inference phase.
  • FIGURE 15 illustrates an example UPA with 16 ports (2 vertical ports, 4 horizontal ports, 2 polarizations). The numbers are port indices.
  • each subset indicator is associated with a port muting pattern for RS.
  • UE may obtain channel measurements associated with the unmuted ports from the channel measurements of ⁇ ⁇ ports, by selecting the measurements associated only with the unmuted ports.
  • the UE obtains channel P110845WO01 PCT APPLICATION 20 of 47 measurements associated with the unmuted ports by only measuring the channel on the unmuted ports among the ⁇ ⁇ ports.
  • the obtained channel measurements associated with the unmuted ports may be used to generate ground truth labels and used as input to an AI/ML model.
  • the obtained channel measurements associated with the unmuted ports may be used to generate input to an AI/ML model, while the channel measurements of all ⁇ ⁇ ports may be used to generate ground truth labels.
  • the subset indicator may be dynamically signaled to the UE from the network node.
  • one or more subset indicators may be indicated to the UE from the network node via a ‘CSI request’ field in DCI.
  • one or more subset indicators may be indicated to the UE from the network node via a MAC CE.
  • the UE may perform one of more of the following actions.
  • the UE obtains channel measurements associated with the unmuted ports in the dynamically signaled subset indicator. The obtained channel measurements are then used to generate ground truth labels and used as input to an AI/ML model.
  • the UE obtains channel measurements associated with the unmuted ports in the dynamically signaled subset indicator. The obtained channel measurements associated with the unmuted ports can be used to generate input to an AI/ML model. The channel measurements of all ⁇ ⁇ ports may be used to generate ground truth labels.
  • Case 2 Measuring ⁇ ⁇ ⁇ ports, + superset indicator(s)
  • Case 2-1 Measuring one CMR with ⁇ ⁇ ports
  • the configuration contains one CMR with ⁇ ⁇ ⁇ ports, while the additional information contains at least ⁇ 2 ⁇ 1 superset indicator(s), where each superset indicator identifies a muting pattern for RS sounding.
  • an example of superset indictor is a bitmap of length ⁇ ⁇ , where the bitmap identifies the subset of the antenna ports that are used for sounding the configured 8-port RS.
  • [1100110011001100] may be used to indicate that the [1, 2, 5, 6, 9, 10, 13, 14]-th ports of the antenna array are used for transmitting port [3000,... ,3007] of the configured RS.
  • the above can be used for RS overhead reduction use case if the ground truth can be obtained.
  • the ground truth depends on channel measurement associated with all ⁇ ⁇ ports, but now only ⁇ ⁇ ports are sounded from ⁇ ⁇ of ⁇ ⁇ antenna ports.
  • the ground truth can either be transmitted by the network node to the UE, or it can be generated by the UE based on the measurement of ⁇ ⁇ ports.
  • channel measurement for all ⁇ ports is transmitted by the network node to the UE via DL signaling.
  • the channel measurements for all ⁇ ⁇ ports are obtained by the network by measuring UL RS transmitted by the UE.
  • channel measurement for all ⁇ ports is generated by UE, based on the channel measurement for ⁇ ⁇ ports and the superset indicator(s).
  • channel measurement for all ⁇ ⁇ ports is generated based on the channel measurement for ⁇ ⁇ ports and the correlation between the ⁇ ⁇ ports and the ⁇ ⁇ ports.
  • the obtained channel measurements associated with the unmuted ports i.e., the ⁇ ⁇ ports
  • the obtained channel measurements associated with the unmuted ports may be used to generate input to an AI/ML model, while the channel measurements of all ⁇ ⁇ ports may be used to generate ground truth.
  • the superset indicator may be dynamically signaled to the UE from the network node.
  • one or more superset indicators may be indicated to the UE from the network node via a ‘CSI request’ field in DCI.
  • one or more superset indicators may be indicated to the UE from the network node via a MAC CE.
  • the additional information contains at least ⁇ 2, ⁇ ⁇ P110845WO01 PCT APPLICATION 22 of 47 1 superset indicator(s), where each of the superset indicator(s) identifies a muting pattern for RS transmission.
  • the antenna array illustrated in FIGURE 15 Take the antenna array illustrated in FIGURE 15 as an example to further explain the above embodiment.
  • the first indicator may be [1111111100000000], indicating that the 8 RS ports in the first CMR with port index [3000, ..., 3007] are transmitted on the first half of the antenna array;
  • the second indicator may be [0000000011111111], indicating that the 8 RS ports in the second CMR with port index [3000, ..., 3007] are transmitted on the second half of the antenna array.
  • Step 103 for Case 2-2 [0123]
  • the above may be used for RS overhead reduction use case.
  • the ground truth depends on channel measurement associated with all ⁇ ⁇ ports. In one embodiment, channel measurements associated with all ⁇ ⁇ ports are obtained based on the measurements for the ⁇ CMRs.
  • the UE may obtain the channel measurements for all ⁇ ⁇ ports by aggregating the measurements based on the two CMRs.
  • the obtained channel measurements associated with the unmuted ports i.e., the ⁇ ⁇ ,1 and ⁇ ⁇ ,2 ports, may be used to generate input to an AI/ML model, while the channel measurements of all ⁇ ⁇ ports may be used to generate ground truth.
  • Step 103 for Case 3 P110845WO01 PCT APPLICATION 23 of 47 [0127] The above can be used for the RS overhead reduction use case.
  • the obtained channel measurements associated with the ⁇ ⁇ ports may be used to generate input to an AI/ML model, while the channel measurements of all ⁇ ⁇ ports may be used to generate ground truth.
  • the additional information may further contain information for identifying antenna array layout and/or antenna numbering/ordering at the network node. Such information may also be used for generating input data to an AI/ML model and/or for generating ground truth.
  • such information is contained in an associated codebook configuration. For example, the NR Type I, Type II, enhanced Type II codebooks, etc.
  • codebook parameter ⁇ 1 and ⁇ 2 which can be used to identify the number of ports along a first dimension and a second dimension for a UPA structure.
  • codebooks may be configured in data collection configuration.
  • array layout and/or antenna numbering/ordering is configured/signaled explicitly to the UE. For example, (relative) coordinates of the antenna elements in the array, correlation of the antenna elements of the array, etc.
  • FIGURE 16 illustrates an example of a communication system 100 in accordance with some embodiments.
  • the communication system 100 includes a telecommunication network 102 that includes an access network 104, such as a radio access network (RAN), and a core network 106, which includes one or more core network nodes 108.
  • an access network 104 such as a radio access network (RAN)
  • RAN radio access network
  • core network 106 which includes one or more core network nodes 108.
  • the access network 104 includes one or more access network nodes, such as network nodes 110a and 110b (one or more of which may be generally referred to as network nodes 110), or any other similar 3rd Generation Partnership Project (3GPP) access node or non-3GPP access point.
  • the network nodes 110 facilitate direct or indirect connection of user equipment (UE), P110845WO01 PCT APPLICATION 24 of 47 such as by connecting UEs 112a, 112b, 112c, and 112d (one or more of which may be generally referred to as UEs 112) to the core network 106 over one or more wireless connections.
  • UE user equipment
  • P110845WO01 PCT APPLICATION 24 of 47 such as by connecting UEs 112a, 112b, 112c, and 112d (one or more of which may be generally referred to as UEs 112) to the core network 106 over one or more wireless connections.
  • 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.
  • the communication system 100 may include any number of wired or wireless networks, network nodes, UEs, and/or any other components or systems that may facilitate or participate in the communication of data and/or signals whether via wired or wireless connections.
  • the communication system 100 may include and/or interface with any type of communication, telecommunication, data, cellular, radio network, and/or other similar type of system.
  • the UEs 112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and/or operable to communicate wirelessly with the network nodes 110 and other communication devices.
  • the network nodes 110 are arranged, capable, configured, and/or operable to communicate directly or indirectly with the UEs 112 and/or with other network nodes or equipment in the telecommunication network 102 to enable and/or provide network access, such as wireless network access, and/or to perform other functions, such as administration in the telecommunication network 102.
  • the core network 106 connects the network nodes 110 to one or more hosts, such as host 116. These connections may be direct or indirect via one or more intermediary networks or devices.
  • the core network 106 includes one more core network nodes (e.g., core network node 108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and/or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 108.
  • Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and/or a User Plane Function (UPF).
  • MSC Mobile Switching Center
  • MME Mobility Management Entity
  • HSS Home Subscriber Server
  • AMF Access and Mobility Management Function
  • SMF Session Management Function
  • AUSF Authentication Server Function
  • SIDF Subscription Identifier De-concealing function
  • UDM Unified Data Management
  • SEPP Security Edge Protection Proxy
  • NEF Network Exposure Function
  • UPF User Plane Function
  • the host 116 may be under the ownership or control of a service provider other than an operator or provider of the access network 104 and/or the telecommunication network 102, and may be operated by the service provider or on behalf of the service provider.
  • the host 116 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio/video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.
  • the communication system 100 of FIGURE 16 enables connectivity between the UEs, network nodes, and hosts.
  • 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.
  • GSM Global System for Mobile Communications
  • UMTS Universal Mobile Telecommunications System
  • LTE Long Term Evolution
  • the telecommunication network 102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 102. For example, the telecommunications network 102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and/or Massive Machine Type Communication (mMTC)/Massive IoT services to yet further UEs.
  • URLLC Ultra Reliable Low Latency Communication
  • eMBB Enhanced Mobile Broadband
  • mMTC Massive Machine Type Communication
  • the UEs 112 are configured to transmit and/or receive information without direct human interaction.
  • a UE may be designed to transmit information to the access network 104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 104.
  • a UE may be configured for operating in single- or multi-RAT or multi-standard mode.
  • a P110845WO01 PCT APPLICATION 26 of 47 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).
  • MR-DC multi-radio dual connectivity
  • the hub 114 communicates with the access network 104 to facilitate indirect communication between one or more UEs (e.g., UE 112c and/or 112d) and network nodes (e.g., network node 110b).
  • the hub 114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs.
  • the hub 114 may be a broadband router enabling access to the core network 106 for the UEs.
  • the hub 114 may be a controller that sends commands or instructions to one or more actuators in the UEs.
  • Commands or instructions may be received from the UEs, network nodes 110, or by executable code, script, process, or other instructions in the hub 114.
  • the hub 114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data.
  • the hub 114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 114 then provides to the UE either directly, after performing local processing, and/or after adding additional local content.
  • the hub 114 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy IoT devices.
  • the hub 114 may have a constant/persistent or intermittent connection to the network node 110b.
  • the hub 114 may also allow for a different communication scheme and/or schedule between the hub 114 and UEs (e.g., UE 112c and/or 112d), and between the hub 114 and the core network 106.
  • the hub 114 is connected to the core network 106 and/or one or more UEs via a wired connection.
  • the hub 114 may be configured to connect to an M2M service provider over the access network 104 and/or to another UE over a direct connection.
  • UEs may establish a wireless connection with the network nodes 110 while still connected via the hub 114 via a wired or wireless connection.
  • the hub 114 may be a dedicated hub – that is, a hub whose primary function is to route communications to/from the UEs from/to the network node 110b.
  • the hub 114 may be a non-dedicated hub – that is, a device which is capable of operating to route communications between the UEs and network node 110b, but which is P110845WO01 PCT APPLICATION 27 of 47 additionally capable of operating as a communication start and/or end point for certain data channels.
  • FIGURE 17 shows a UE 200 in accordance with some embodiments.
  • 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.
  • 3GPP 3rd Generation Partnership Project
  • NB-IoT narrow band internet of things
  • MTC machine type communication
  • eMTC enhanced MTC
  • a UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle- to-everything (V2X).
  • D2D device-to-device
  • DSRC Dedicated Short-Range Communication
  • V2V vehicle-to-vehicle
  • V2I vehicle-to-infrastructure
  • V2X vehicle- to-everything
  • a UE may not necessarily have a user in the sense of a human user who owns and/or operates the relevant device.
  • 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).
  • 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).
  • the UE 200 includes processing circuitry 202 that is operatively coupled via a bus 204 to an input/output interface 206, a power source 208, a memory 210, a communication interface 212, and/or any other component, or any combination thereof.
  • Certain UEs may utilize all or a subset of the components shown in FIGURE 17. The level of integration between the components may vary from one UE to another UE.
  • certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.
  • P110845WO01 PCT APPLICATION 28 of 47 The processing circuitry 202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 210.
  • Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof.
  • An input device may allow a user to capture information into the UE 200.
  • Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like.
  • the presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user.
  • a sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof.
  • An output device may use the same type of interface port as an input device.
  • a Universal Serial Bus (USB) port may be used to provide an input device and an output device.
  • the power source 208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used.
  • the power source 208 may further include power circuitry for delivering power from the power source 208 itself, and/or an external power source, to the various parts of the UE 200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 208.
  • Power circuitry may perform any formatting, converting, or other modification to the power from the power source 208 to make the power suitable for the respective components of the UE 200 to which power is supplied.
  • the memory 210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth.
  • the memory 210 includes one or more application programs 214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 216.
  • the memory 210 may store, for use by the UE 200, any of a variety of various operating systems or combinations of operating systems.
  • the memory 210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and/or ISIM, other memory, or any combination thereof.
  • RAID redundant array of independent disks
  • HD-DVD high-density digital versatile disc
  • HDDS holographic digital data storage
  • DIMM external mini-dual in-line memory module
  • SDRAM synchronous dynamic random access memory
  • SDRAM synchronous dynamic random access memory
  • the UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’
  • the memory 210 may allow the UE 200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data.
  • An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 210, which may be or comprise a device-readable storage medium.
  • the processing circuitry 202 may be configured to communicate with an access network or other network using the communication interface 212.
  • the communication interface 212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 222.
  • the communication interface 212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network).
  • Each transceiver may include a transmitter 218 and/or a receiver 220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth).
  • the transmitter 218 and receiver 220 may be P110845WO01 PCT APPLICATION 30 of 47 coupled to one or more antennas (e.g., antenna 222) and may share circuit components, software or firmware, or alternatively be implemented separately.
  • communication functions of the communication interface 212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof.
  • GPS global positioning system
  • 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.
  • a UE may provide an output of data captured by its sensors, through its communication interface 212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE.
  • 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.
  • 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.
  • a UE when in the form of an Internet of Things (IoT) 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.
  • IoT Internet of Things
  • Non-limiting examples of such an IoT device are a device which is or which is embedded in: a connected refrigerator or freezer, P110845WO01 PCT APPLICATION 31 of 47 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 item-tracking 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
  • a UE in the form of an IoT device comprises circuitry and/or software in dependence of the intended application of the IoT device in addition to other components as described in relation to the UE 200 shown in FIGURE 17.
  • a UE may represent a machine or other device that performs monitoring and/or measurements, and transmits the results of such monitoring and/or measurements to another UE and/or a network node.
  • the UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device.
  • the UE may implement the 3GPP NB-IoT standard.
  • 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.
  • 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.
  • the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed.
  • FIGURE 18 shows a network node 300 in accordance with some embodiments.
  • 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, P110845WO01 PCT APPLICATION 32 of 47 in a telecommunication network.
  • 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)).
  • APs access points
  • BSs base stations
  • Node Bs evolved Node Bs
  • gNBs NR NodeBs
  • 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).
  • DAS distributed antenna system
  • network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell/multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and/or Minimization of Drive Tests (MDTs).
  • MSR multi-standard radio
  • RNCs radio network controllers
  • BSCs base station controllers
  • BTSs base transceiver stations
  • OFDM Operation and Maintenance
  • OSS Operations Support System
  • SON Self-Organizing Network
  • positioning nodes e.g., Evolved Serving Mobile Location Centers (E-SMLCs)
  • the network node 300 includes a processing circuitry 302, a memory 304, a communication interface 306, and a power source 308.
  • the network node 300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components.
  • the network node 300 comprises multiple separate components (e.g., BTS and BSC components)
  • one or more of the separate components may be shared among several network nodes.
  • a single RNC may control multiple NodeBs.
  • each unique NodeB and RNC pair may in some instances be considered a single separate network node.
  • the network node 300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 304 for different RATs) and some components may be reused (e.g., a same antenna 310 may be shared by different RATs).
  • the network node 300 may also include multiple sets of the various illustrated P110845WO01 PCT APPLICATION 33 of 47 components for different wireless technologies integrated into network node 300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 300.
  • RFID Radio Frequency Identification
  • the processing circuitry 302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and/or encoded logic operable to provide, either alone or in conjunction with other network node 300 components, such as the memory 304, to provide network node 300 functionality.
  • the processing circuitry 302 includes a system on a chip (SOC).
  • the processing circuitry 302 includes one or more of radio frequency (RF) transceiver circuitry 312 and baseband processing circuitry 314.
  • RF radio frequency
  • the radio frequency (RF) transceiver circuitry 312 and the baseband processing circuitry 314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 312 and baseband processing circuitry 314 may be on the same chip or set of chips, boards, or units.
  • the memory 304 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and/or any other volatile or non-volatile, non-transitory device-readable and/or computer-executable memory devices that store information, data, and/or instructions that may be used by the processing circuitry 302.
  • 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-
  • the memory 304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and/or other instructions capable of being executed by the processing circuitry 302 and utilized by the network node 300.
  • the memory 304 may be used to store any calculations made by the processing circuitry 302 and/or any data received via the communication interface 306.
  • the processing circuitry 302 and memory 304 is integrated.
  • P110845WO01 PCT APPLICATION 34 of 47 [0165]
  • the communication interface 306 is used in wired or wireless communication of signaling and/or data between a network node, access network, and/or UE.
  • the communication interface 306 comprises port(s)/terminal(s) 316 to send and receive data, for example to and from a network over a wired connection.
  • the communication interface 306 also includes radio front-end circuitry 318 that may be coupled to, or in certain embodiments a part of, the antenna 310.
  • Radio front-end circuitry 318 comprises filters 320 and amplifiers 322.
  • the radio front-end circuitry 318 may be connected to an antenna 310 and processing circuitry 302.
  • the radio front-end circuitry may be configured to condition signals communicated between antenna 310 and processing circuitry 302.
  • the radio front-end circuitry 318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection.
  • the radio front-end circuitry 318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 320 and/or amplifiers 322. The radio signal may then be transmitted via the antenna 310. Similarly, when receiving data, the antenna 310 may collect radio signals which are then converted into digital data by the radio front-end circuitry 318. The digital data may be passed to the processing circuitry 302. In other embodiments, the communication interface may comprise different components and/or different combinations of components. [0166] In certain alternative embodiments, the network node 300 does not include separate radio front-end circuitry 318, instead, the processing circuitry 302 includes radio front-end circuitry and is connected to the antenna 310.
  • the RF transceiver circuitry 312 is part of the communication interface 306.
  • the communication interface 306 includes one or more ports or terminals 316, the radio front-end circuitry 318, and the RF transceiver circuitry 312, as part of a radio unit (not shown), and the communication interface 306 communicates with the baseband processing circuitry 314, which is part of a digital unit (not shown).
  • the antenna 310 may include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals.
  • the antenna 310 may be coupled to the radio front-end circuitry 318 and may be any type of antenna capable of transmitting and receiving data and/or signals wirelessly.
  • the antenna 310 is separate from the network node 300 and connectable to the network node 300 through an interface or port.
  • the antenna 310, communication interface 306, and/or the processing circuitry 302 may be configured to perform any receiving operations and/or certain obtaining operations P110845WO01 PCT APPLICATION 35 of 47 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.
  • the antenna 310, the communication interface 306, and/or the processing circuitry 302 may be configured to perform any transmitting operations described herein as being performed by the network node.
  • the power source 308 provides power to the various components of network node 300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component).
  • the power source 308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 300 with power for performing the functionality described herein.
  • the network node 300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 308.
  • the power source 308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry.
  • the battery may provide backup power should the external power source fail.
  • Embodiments of the network node 300 may include additional components beyond those shown in FIGURE 18 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.
  • the network node 300 may include user interface equipment to allow input of information into the network node 300 and to allow output of information from the network node 300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 300.
  • FIGURE 19 is a flowchart illustrating an example method 1900 in a wireless device, according to certain embodiments. In particular embodiments, one or more steps of FIGURE 19 may be performed by UE 200 described with respect to FIGURE 17.
  • the method may begin at step 1910, where the wireless device (e.g., UE 200) receives, from a network node (e.g., network node 300), a configuration for data collection.
  • the configuration includes at least one or more channel measurement resources (CMRs) and additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a reference signal (RS).
  • CMRs channel measurement resources
  • RS reference signal
  • a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more subset indicators.
  • Each subset indicator identifies a muting pattern that indicates a subset of the first set of antenna ports to be muted for transmitting a RS during model inference.
  • Examples are described in more detail above, such as for the case 1 and 3 examples described with respect to FIGURE 14.
  • a subset indicator may be a bitmap of length ⁇ ⁇ , where each bit in the bitmap indicates whether an antenna port is muted or not when the subset indicator is configured during a model inference phase.
  • the subset indicator may be beneficial for overhead reduction and/or port muting use cases, as described above.
  • a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more superset indicators.
  • Each superset indicator identifies a muting pattern that indicates which ports of a superset of the first set of antenna ports are to be muted for transmitting a RS during model inference.
  • the indication of which ports to mute may comprise an indication of the muted ports, or an indication of the unmuted ports.
  • port muting may apply for particular operations, such a model inference, but not for other operations, such as data collection.
  • the additional information comprises an indication of an antenna configuration of the network node. Examples are described in more detail above, such as for the case 4 example described with respect to FIGURE 14. [0181] At step 1914, the wireless device performs channel measurements for the one or more CMRs.
  • the wireless device may perform the channel measurements according to any of the embodiments and examples described herein. [0182] The following steps describe operations the wireless device may perform based on the CMRs and the additional information. P110845WO01 PCT APPLICATION 37 of 47 [0183] At step 1916, the wireless device may create ground truth labels for a machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. The wireless device may create ground truth labels according to any of the embodiments and examples described herein.
  • the wireless device may perform inference for the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS.
  • the wireless device may perform inference according to any of the embodiments and examples described herein.
  • the wireless device may monitor performance of the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS.
  • the wireless device may monitor performance according to any of the embodiments and examples described herein.
  • FIGURE 20 is a flowchart illustrating an example method 2000 in a network node, according to certain embodiments. In particular embodiments, one or more steps of FIGURE 20 may be performed by network node 300 described with respect to FIGURE 18. [0188] The method begins at step 2012, where the network node (e.g., network node 300) transmits, to a wireless device, a configuration for data collection.
  • the network node e.g., network node 300
  • the configuration includes at least one or more CMRs and additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS.
  • the configuration is described in more detail with respect to FGIURE 19 and with respect to the embodiments and examples described herein.
  • the network node receives a CSI report from the wireless device.
  • the CSI report is based on channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS.
  • the network node may create ground truth labels for a machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS.
  • the network node may create ground truth labels according to any of the embodiments and examples described herein.
  • the network node may perform inference for the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS.
  • the network node may perform inference according to any of the embodiments and examples described herein.
  • the network node may monitor performance of the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS.
  • the network node may monitor performance according to any of the embodiments and examples described herein.
  • Modifications, additions, or omissions may be made to method 2000 of FIGURE 20. Additionally, one or more steps in the method of FIGURE 20 may be performed in parallel or in any suitable order. [0194]
  • the foregoing description sets forth numerous specific details.
  • a method performed by a wireless device comprising: ⁇ receiving from a network node configuration for data collection, including at least one or more of channel measurement resource(s), CMR(s), and additional information for associating the configured CMR(s) and antenna ports at the network node that are used for transmitting reference signals (RSs); ⁇ performing channel measurement based on the configured CMR(s); ⁇ creating ground truth labels according to the channel measurement and the additional information for associating the CMR(s) and the antenna ports at the network node; and ⁇ training an AI/ML neural network according to the created ground truth labels. 2 .
  • a method performed by a wireless device comprising: ⁇ any of the wireless device steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above. 3 .
  • the method of the previous embodiment further comprising one or more additional wireless device steps, features or functions described above.
  • 4 The method of any of the previous two embodiments, further comprising: ⁇ providing user data; and ⁇ forwarding the user data to a host computer via the transmission to the base station.
  • Group B Embodiments 5
  • a method performed by a base station comprising: ⁇ transmitting to a wireless device configuration for data collection, including at P110845WO01 PCT APPLICATION 40 of 47 least one or more of channel measurement resource(s), CMR(s), and additional information for associating the configured CMR(s) and antenna ports at the network node that are used for transmitting reference signals (RSs).
  • RSs reference signals
  • a mobile terminal comprising: ⁇ circuitry configured to perform any of the steps of any of the Group A embodiments; and ⁇ power supply circuitry configured to supply power to the wireless device. 1 0.
  • a base station comprising: ⁇ processing circuitry configured to perform any of the steps of any of the Group B embodiments; ⁇ power supply circuitry configured to supply power to the wireless device. 1 1.
  • a user equipment comprising: ⁇ an antenna configured to send and receive wireless signals; ⁇ radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; ⁇ the processing circuitry being configured to perform any of the steps of any of the Group A embodiments; P110845WO01 PCT APPLICATION 41 of 47 ⁇ 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. 12.
  • a communication system including a host computer comprising: ⁇ processing circuitry configured to provide user data; and ⁇ a communication interface configured to forward the user data to a cellular network for transmission to a user equipment (UE), ⁇ wherein the cellular network comprises a base station having a radio interface and processing circuitry, the base station’s processing circuitry configured to perform any of the steps of any of the Group B embodiments. 13.
  • the communication system of the pervious embodiment further including the base station.

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Abstract

According to some embodiments, a method performed by a wireless device comprises receiving, from a network node, a configuration for data collection. The configuration includes at least one or more channel measurement resources (CMRs) and additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a reference signal (RS). The method further comprises performing channel measurements for the one or more CMRs.

Description

CHANNEL STATE INFORMATION ACQUISITION FOR UNCONVENTIONAL ARRAYS TECHNICAL FIELD [0001] Embodiments of the present disclosure are directed to wireless communications and, more particularly, to channel state information (CSI) acquisition for unconventional arrays. BACKGROUND [0002] Generally, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given and/or is implied from the context in which it is used. All references to a/an/the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any methods disclosed herein do not have to be performed in the exact order disclosed, unless a step is explicitly described as following or preceding another step and/or where it is implicit that a step must follow or precede another step. Any feature of any of the embodiments disclosed herein may be applied to any other embodiment, wherever appropriate. Likewise, any advantage of any of the embodiments may apply to any other embodiments, and vice versa. Other objectives, features, and advantages of the enclosed embodiments will be apparent from the following description. [0003] Multi-antenna techniques can significantly increase the data rates and reliability of a wireless communication system. The performance is improved if both the transmitter and the receiver are equipped with multiple antennas, which results in a multiple-input multiple-output (MIMO) communication channel. Such systems and/or related techniques are commonly referred to as MIMO systems. [0004] A core component of New Radio (NR) is the support of MIMO related techniques. NR supports up to 8-layer spatial multiplexing for up to 32 transmit antenna ports at the gNB with channel dependent precoding. [0005] FIGURE 1 shows an example of data transmission with spatial multiplexing where the information carrying symbol vector ^^ = [^^1, ^^2, … , ^^^^] ^^ is first multiplied (or precoded) by a precoding matrix ^^ ∈ ^^^^^^×^^ before being sent over NT antenna ports. Each symbol in s is associated to a data layer and r is the number of data layers or rank, which is a property of the wireless channel between the transmitter and the receiver. ^^ serves to beamform each data P110845WO01 PCT APPLICATION 2 of 47 layer towards a user equipment (UE) such that signal to interference plus noise ratio (SINR) is maximized and cross layer interference is minimized at the UE receiver. Spatial multiplexing is achieved because multiple symbols can be transmitted simultaneously in a same time and frequency resource element (RE). [0006] The received NR x 1 signal vector ^^ at the UE quipped with NR receive antennas can be expressed as ^^ = ^^^^^^ + ^^ [0007] where ^^ ∈ ^^^^^^×^^^^ is the MIMO channel between the transmit and receive antennas, and e is a noise plus interference vector due to receiver noise and interference. [0008] The precoder matrix ^^ is chosen to match the characteristics of the NRxNT MIMO channel matrix ^^ resulting in channel dependent precoding. The precoder ^^ can be a wideband precoder, i.e., the same over a whole bandwidth, or a subband precoder, i.e., optimized per subband. ^^ is typically selected from a codebook of precoding matrices by the UE and reported to the gNB in terms of a precoding matrix indicator (PMI). [0009] One example method for a UE to select a precoder matrix ^^ can be to select the ^^^^ from a codebook that maximizes the Frobenius norm of the hypothesized equivalent channel: ^^ ‖ ^ ^^^^‖ 2 max ^ ^^ where ^^ is a channel estimate precoder matrix with index k. [0010] In addition to ^^ feedback, a UE typically also feedsback a rank indicator (RI) and channel quality indicator(s) (CQI) as part of channel state information (CSI) feedback. Given the CSI feedback from the UE, the gNB can determine the transmission parameters to use for data transmissions to the UE. [0011] For channel estimation purpose, a channel state information reference signal (CSI-RS) is typically transmitted to the UE. [0012] The antennas with NT antenna ports described above can be either a linear antenna array or two-dimensional (2D) plenary antenna array. A linear antenna array is a special case of a 2D antenna array. A 2D antenna array can be described by ^^ℎ columns, corresponding to the horizontal dimension, ^^^^ rows, corresponding to the vertical dimension, and ^^^^ polarizations. The total number of antenna ports is thus ^^ = ^^ℎ^^^^^^^^. An example of a cross polarized (i.e., ^^^^ = 2) antenna array with (^^ℎ,^^^^) = (4,4) is illustrated in FIGURE 2. P110845WO01 PCT APPLICATION 3 of 47 [0013] FIGURE 2 illustrates a two-dimensional antenna array of cross-polarized antenna elements (^^^^ = 2), with ^^ℎ = 4 horizontal antenna elements and ^^^^ = 4 vertical antenna elements. The 2D antenna array may be rotated at any angle. In this case, the row and columns may no longer correspond to vertical and horizontal directions. To reflect this more general case in NR, a 2D antenna array is simply defined by a number of antenna ports in each of two dimensions, i.e., ^^1 and ^^2, and ^^^^ is always 2. Thus, the total number of antenna ports is ^^ = 2^^1^^2. [0014] The concept of an antenna port is non-limiting in the sense that it can refer to any virtualization (e.g., linear mapping) of the physical antenna elements. For example, pairs of physical sub-elements could be fed the same signal, and thus share the same virtualized antenna port. [0015] In NR, for downlink channel measurement by a UE, a reference signal is transmitted at each antenna port. The reference signal is referred to as non-zero power channel state information reference signal (NZP CSI-RS). NZP CSI-RS is configured in terms of NZP CSI- RS resources. For simplicity, “NZP” may be omitted in the following disclosure. A NZP CSI- RS resource supports up to 32 antenna ports. The antenna ports are also referred to as CSI-RS antenna ports, CSI-RS ports, or antenna ports. Different CSI-RS antenna ports in a CSI-RS resource are allocated with different REs and/or different CDM (code division multiplexing) codes so that the downlink channel associated to each antenna port can be individually measured and estimated. [0016] Three densities are supported, i.e., ^^ = ½, 1, and 3. ^^ is the number of REs per RB per CSI-RS port. The parameter ^^ = ½ means one RE per port in every other RBs, e.g., in even or odd numbered RBs. ^^ = 3 is only supported for single port CSI-RS resource. [0017] In a CSI-RS resource, there can be multiple CDM groups. A CDM group consists of 2, 4 or 8 REs, corresponding to length ^^ = 2, 4, or 8 CDM codes, respectively. The CDM codes used can be either length 2 or length 4 time domain orthogonal cover codes (TD-OCC), i.e., TD-OCC2 or TD-OCC4, or length 2 frequency domain OCC (FD-OCC), i.e., FD-OCC2, or both TD-OCC and FD-OCC. The CDM groups are numbered in order of increasing frequency domain allocation first and then increasing time domain allocation. An example of a CSI-RS resource for 32 antenna ports are shown in FIGURE 3, where CSI-RS REs in one RB is shown. P110845WO01 PCT APPLICATION 4 of 47 [0018] FIGURE 3 illustrates an example of a CSI-RS resource for 32 antenna ports with 8 CDM groups. In this case, there are 8 CDM groups each with 4 REs. The CDM codes are TD- OCC2 plus FD-OCC2. [0019] Each antenna port is mapped to one of the CDM groups. Antenna ports are mapped in CDM group first, then frequency, and then time. Within each CDM group, antenna ports are multiplexed via CDM codes or sequences. CSI-RS antenna ports are numbered according to ^^ = 3000 + ^^ + ^^^^; ^^ = 0,1, ... ,^^⁄ ^^ − 1 ^^ = 0,1, ... , ^^ − 1; where ^^ is the CDM code index given in ^^ ∈ {1,2,4,8} is the CDM group size, and ^^ is the number of CSI-RS ports. [0020] The NR Type I single panel codebook is based on discrete Fourier transform (DFT) beams or precoders and is for cross polarized 2D antenna arrays, where a DFT beam is selected for each MIMO layer. The same DFT beam is applied to antenna ports at both polarizations. A co-phasing factor is applied at antenna ports of one of the two polarizations. The details of Type I single panel codebook can be found in Third Generation Partnership Project (3GPP) TS38.214 V18.1.0 section 5.2.2.2.1. [0021] For example, for a CSI-RS resource with ^^CSI-RS = 2^^1^^2 antenna ports, rank 1 precoding matrix for codebook mode 1 is given by ^^ = 1 ^^^^, √^^CSI-RS [ ^^ ^^^^^^^^,^^], ^^ = 0,1, … ,^^1^^1 − 1; ^^ = 0,1, … ,^^2^^2 − 1. where (^^,^^) and is 2^^^^ ^^ by ^^^,^^ = [^^ ^^ ^^2^^^^(^^ 1 −1) ^ ^^ ^^ ^^1^^1^^^^ ... ^^ ^^1^^1 ^^^^] and ^^^^ = [1 ^^ ^^ 2^^^^ ^^2^^2 ... ^^ ^^ ^^1 and ^^2 are the factor in the dimension ^^1 and ^^2, respectively. ^^^^ = ^^ ^^^^^^⁄ 2 is a co-phasing factor. The supported (^^1,^^2)and (^^1,^^2) are given in Table 2 of 3GPP TS38.214, which is copied below: P110845WO01 PCT APPLICATION 5 of 47 Table 5.2.2.2.1-2: Supported configurations of (^^1,^^2)and (^^1,^^2) Number of CSI-RS antenna ports, ^^ (^^^^,^^^^) (^^^^,^^^^) CSI-RS 4 (2,1) (4,1) 8 (2,2) (4,4) (4,1) (4,1) 12 (3,2) (4,4) (6,1) (4,1) 16 (4,2) (4,4) (8,1) (4,1) (4,3) (4,4) 24 (6,2) (4,4) (12,1) (4,1) (4,4) (4,4) 32 (8,2) (4,4) (16,1) (4,1) [0022] A type I single panel codebook based precoding matrix is a two-stage precoder and can be expressed as ^^ = ^^1^^2 where ^^1 contains the selected DFT beams and ^^2 contains co- 1 ^^ ng factors. For the above rank 1 precoding matrix, ^^ ^^,^^ 0 1 = √^^CSI-RS [ 0 , 1 phasi ] ^^2 = [ ^^^^]. DFT beams {^^^^,^^} are also referred to as spatial [0023] According to 3GPP NR specification, precoded physical downlink shared channel (PDSCH) signals ^^ = [^^1, ^^2, … , ^^^^] ^^ by ^^ (i.e., ^^^^) are equivalent to corresponding symbols transmitted on the CSI-RS antenna ports 3000, … , 3000 + ^^^^^^^^−^^^^ − 1 as given by ^^(3000) ^^1 ⋯ = ^^ . based ^^ structure mean that the CSI-RS antenna ports for a 2D ports need to be indexed in order of increasing along the ^^2 dimension first and then increasing along the ^^1 dimension at a first polarization and repeat the above for the other polarization. An example is shown in FIGURE 4 for a 2D antenna with 32 ports where the CSI-RS port number is given by adding 3000 to the numbers shown in the figure. [0025] FIGURE 4 illustrates an example of a mapping of CSI-RS antenna ports to a 2D antenna with 32 ports. P110845WO01 PCT APPLICATION 6 of 47 [0026] In Rel-18, a network energy saving feature was introduced for muting a subset of CSI- RS ports for energy saving purposes. Consider an NZP CSI-RS resource configured for channel measurement with ^^^^^^^^−^^^^ ports. According to this feature, a bitmap with ^^^^^^^^−^^^^ = 2^^1^^2 bits are signaled from the network (e.g., gNB) to the UE. The bitmap can have ^^ unmuted, and the remaining ^^^^^^^^−^^^^ − ^^ ports are muted. Because the network does not transmit any CSI-RS on the muted ports, the network can save energy by skipping these transmissions on muted ports. In this feature, the number of unmuted ports ^^ have to correspond to one of the number of CSI-RS ports (among 2, 4, 8, 12, 16, 24 and 32) supported in NR. [0027] For example, when ^^^^^^^^−^^^^ = 32 ports, then the possible values for port muting are ^^ ∈ {2, 4, 8, 12, 16, 24}. The reason for this restriction in Rel-18 is that a ^^ port NR Type I single panel codebook can be used for CSI calculation/computation at the UE when ^^^^^^^^−^^^^ − ^^ ports are muted. [0028] The port numbering when ^^^^^^^^−^^^^ − ^^ ports are muted is given in 3GPP TS 38.214 as: “Each sub-configuration can be configured with an antenna port subset using the higher layer bitmap parameter [port-subsetIndicator] which contains the bit sequence ^^0,^^1, ... ,^^^^^^−1, where ^^0 is the MSB and ^^^^m−1 is the LSB, bit ^^^^ corresponds to antenna port 3000 + i, and ^^m is the number of ports nrofPorts configured for the CSI-RS resources(s) within a NZP- CSI-RS-ResourceSet contained in the CSI-ResourceConfig for channel measurement that corresponds to the CSI-ReportConfig. A bit value 0 in [port-subsetIndicator] indicates that the corresponding antenna port is disabled for the sub-configuration, whereas bit value 1 indicates that the antenna port is enabled and belongs to the antenna port subset for the sub-configuration. For the derivation of PMI, antenna ports corresponding to all bits with value of 1 in [port- subsetIndicator] are mapped to consecutive antenna ports starting at CSI-RS antenna port 3000 in increasing order of the bit position in [port-subsetIndicator].” [0029] In the 3GPP specification text above, port-subsetIndicator is the port muting bitmap and ^^^^ is the notation used instead of ^^^^^^^^−^^^^. As per the last sentence in the above 3GPP specification text, all the unmuted ports (i.e., those with bits corresponding to value 1 in the bitmap) are mapped to consecutive antenna ports. [0030] An example is shown in FIGURE 5 for a 2D antenna with 32 ports where 8 of the ports are muted. The CSI-RS port number is this example is given by adding 3000 to the numbers shown in the figure. P110845WO01 PCT APPLICATION 7 of 47 [0031] FIGURE 5 illustrates an example of a mapping of CSI-RS antenna ports to a 2D antenna with 32 ports with 8 ports muted. [0032] According to 3GPP TS 38.214 V18.1.0, precoded PDSCH signals ^^ = [^^1, ^^2, … , ^^^^] ^^ by ^^ (i.e., ^^^^) are equivalent to corresponding symbols transmitted on the ^^(3000) ^^1 ports 3000, … , 3000 + ^^ − 1 as ⋯ = ^^ . [0033] The above is based on the ports are given consecutives indices. [0034] Artificial Intelligence (AI) and Machine Learning (ML) have been investigated, both in academia and industry, as promising tools to optimize the design of the air-interface in wireless communication networks. Example use cases include using autoencoders for channel state information (CSI) compression to reduce the feedback overhead and improve channel prediction accuracy; using deep neural networks for classifying line-of-sight (LOS) and non- LOS (NLOS) conditions to enhance the positioning accuracy; using reinforcement learning for beam selection at the network side and/or the UE side to reduce the signaling overhead and beam alignment latency; and using deep reinforcement learning to learn an optimal precoding policy for complex MIMO precoding problems. [0035] In 3GPP NR standardization work, a release 18 study item on AI/ML for the NR air interface explored the benefits of augmenting the air-interface with features enabling improved support of AI/ML based algorithms for enhanced performance and/or reduced complexity/overhead. Through studying a few selected use cases (CSI feedback, beam management, and positioning), the study item lays a foundation for future air-interface use cases leveraging AI/ML techniques. [0036] An important part of AI development and operation is the lifecycle management (LCM) of the AI/ML model (e.g., data collection, model training, model deployment, model inference, model monitoring, model updating)^and AI/ML functionality. [0037] FIGURE 6 illustrates a functional framework for AI/ML for NR air interface. FIGURE 6 shows a functional framework that can be used for studying model LCM aspects for different AI for PHY use cases.^The general framework consists of the following. [0038] Data Collection is a function that provides input data to the Model Training, Management, and Inference functions. P110845WO01 PCT APPLICATION 8 of 47 ^ Training Data: Data needed as input for the AI/ML Model Training function. ^ Monitoring Data: Data needed as input for the Management of AI/ML models or AI/ML functionalities. ^ Inference Data: Data needed as input for the AI/ML Inference function. [0039] Model Training is a function that performs AI/ML model training, validation, and testing, which may generate model performance metrics that can be used as part of the model testing procedure. The Model Training function is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on Training Data delivered by a Data Collection function if required. ^ Trained/Updated Model: When using a Model Storage function, this is used to deliver trained, validated, and tested AI/ML models to the Model Storage function, or to deliver an updated version of a model to the Model Storage function. [0040] Management is a function that oversees the operation (e.g., selection, (de)activation, switching, fallback) and monitoring (e.g., performance) of AI/ML models or AI/ML functionalities. This function is also responsible for making decisions to ensure the proper inference operation based on data received from the Data Collection function and the Inference function. ^ Management Instruction: Information needed as input to manage the Inference function. Concerning information may include selection/(de)activation/switching of AI/ML models or AI/ML-based functionalities, fallback to non-AI/ML operation (i.e., not relying on inference process), etc. ^ Model Transfer/Delivery Request: Used to request model(s) to the Model Storage function. ^ Performance Feedback/Retraining Request: Information needed as input for the Model Training function, e.g., for model (re)training or updating purposes. [0041] Inference is a function that provides outputs from the process of applying AI/ML models or AI/ML functionalities using the data that is provided by the Data Collection function (i.e., Inference Data) as an input. The Inference function is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on Inference Data delivered by a Data Collection function, if required. P110845WO01 PCT APPLICATION 9 of 47 ^ Inference Output: Data used by the Management function to monitor the performance of AI/ML models or AI/ML functionalities. [0042] Model Storage is a function responsible for storing trained/updated models that can be used to perform the Inference function. The Model Storage function in FIGURE 6 is only intended as a reference point (if any) when applicable for protocol terminations, model transfer/delivery, and related processes. It should be stressed that its purpose does not encompass restricting the actual storage locations of models. Therefore, the specification impact of all data/information/instruction flows (i.e., the arrows in FIGURE 6) to/from this function should be studied case by case. ^ Model Transfer/Delivery: Used to deliver an AI/ML model to the Inference function. [0043] The AI/ML models being discussed in the Rel-18 study item on AI/ML for the NR air interface can be categorized into the following two types: One-sided AI/ML model, which can be a UE-sided AI/ML model whose inference is performed entirely at the UE, or a NW-sided AI/ML model whose inference is performed entirely at the network. [0044] FIGURE 7 shows a use case of CSI prediction using a one-sided UE-sided AI/ML model, where one or more AI/ML models can be trained and deployed at a UE. During model inference, a UE is configured by the gNB to measure a set of historical CSI-RSs and then report a predicted CSI for one or multiple future time instances using its AI/ML model(s). [0045] FIGURE 7 illustrates an example of the CSI prediction using UE-sided AI model(s). [0046] A two-sided AI/ML model refers to paired AI/ML Model(s) over which joint inference is performed across the UE and the network, i.e., the first part of the inference is firstly performed by UE and then the remaining part is performed by gNB, or vice versa. [0047] As an example of a two-sided AI/ML model, FIGURE 8 shows a use case of autoencoder (AE)-based CSI feedback/report, where an encoder (UE-part of the two-sided AE model) is operated at a UE to compress the estimated wireless channel, and the output of the encoder (the compressed wireless channel information estimates) is reported from the UE to a gNB. [0048] The gNB uses a decoder (network part of the two-sided AE model) to reconstruct the estimated wireless channel information. Here, the two-sided AI/ML model is composed of the encoder at the UE side and the decoder at the base station (i.e., a gNB) side. For a two-sided model, the code is generated by the encoder and only interpretable by a jointly trained decoder. P110845WO01 PCT APPLICATION 10 of 47 The situation is different from running an AI/ML model in the UE, reporting the output over the air in a fully standardized format, and running a separate AI/ML model at the base station. [0049] FIGURE 8 illustrates an autoencoder (AE)-based CSI compression using two-sided AI/ML model use case. [0050] When applying AI/ML on air interface use cases, different levels of collaboration between network nodes and UEs can be considered. [0051] One case is no collaboration between network nodes and UEs. In this case, a proprietary ML model operating with the existing standard air-interface is applied at one end of the communication chain (e.g., at the UE side), and the model life cycle management (e.g., model selection/training, model monitoring, model retraining, model update) is done at this node without inter-node assistance (e.g., assistance information provided by the network node). [0052] Another case is limited collaboration between network nodes and UEs for one-sided models. In this case, an ML model is operating at one end of the communication chain (e.g., at the UE side), but this node gets assistance from the node(s) at the other end of the communication chain (e.g., a next generation Node B (gNB)) for its AI model life cycle management to some extent (e.g., for training/retraining the AI model, model update, model monitoring, model selection/fallback/switching). [0053] Another case is joint ML operation between network nodes and UEs for two-sided models. This case assumes that the AI model is split with one part located at the network (NW) side and the other part located at the UE side. Thus, the AI model requires joint inference between the network and UE, and the AI model life cycle management involves both ends of a communication chain. [0054] There currently exist certain challenges. For example, large antenna or massive MIMO antenna arrays with many antenna ports can provide significant beamforming gains. In addition, high spatial resolution provided by large antenna arrays also enables higher multiple- user MIMO performance. However, if following the legacy CSI feedback design in NR, the increased antenna ports will lead to increased resource overhead needed for CSI-RS transmission, thereby less resource utilization for data transmission. [0055] In addition, the complexity of massive MIMO antenna design is high with many constraints, size, weight, cooling and multiple-bands with multiple overlapping arrays together, ranging from 1 to 5 GHz that all needs to fit inside the same radome. All these can result in P110845WO01 PCT APPLICATION 11 of 47 unconventional array design, which adds an additional complexity for the CSI feedback (e.g., PMI codebook) design. [0056] There are at least two problems: how to reduce the channel estimation overhead for large antenna ports and how to make the CSI feedback agnostic to the antenna port array layout so that the network has more freedom to design new products that are more cost effective and/or energy efficient and/or non-planar and/or with lower sidelobes and/or can have more bands, etc. [0057] In light of the above, unconventional antenna arrays may be more common in the future, due to flexible deployment, overhead saving, energy saving, etc. The legacy 3GPP PMI codebooks are designed under the assumption of uniform planar 2D antenna array with equally spaced identical subarrays, which are not suited for unconventional arrays. Alternatively, AI/ML-based models may be used for CSI acquisition for unconventional arrays. However, how to configure data collection, including channel measurement resources and other types of required information, is not known. Also, how to create ground-truth labels for such applications is also not known. SUMMARY [0058] As described above, certain challenges currently exist with channel state information (CSI) acquisition for unconventional arrays. Certain aspects of the present disclosure and their embodiments may provide solutions to these or other challenges. For example, particular embodiments include a data collection scheme to support training one or multiple artificial intelligence (AI)/machine learning (ML) models that may be used at a user equipment (UE) to generate CSI report for an unconventional array. [0059] To solve problem one described above, the collected data may be used for creating training datasets for training one or more one-sided UE-sided AI/ML models. The inference of a one-sided UE-sided model is performed entirely at the UE. During the model inference, the UE uses one of the AI/ML models to generate a CSI report associated to ^^^^ antenna ports, by measuring ^^^^^^ antenna ports, where ^^^^^^ is smaller than ^^^^. [0060] To solve problem two described above, the collected data may be used for creating training datasets for training one or more two-sided AI/ML models. The inference of a two- sided model is jointly performed across the UE and the network node. During the model inference, the UE uses a UE-part of a two-sided model to generate a CSI report associated to P110845WO01 PCT APPLICATION 12 of 47 ^^^^^^ antenna ports, by measuring ^^^^^^ antenna ports. The network node generates the model input using the received CSI report and passes the model input to the paired network part of the two-sided model to reconstruct the estimated wireless channel information. [0061] Particular embodiments provide solutions for how to configure channel measurement resources, how to define UE behavior for creating ground-truth data samples, and how to create training dataset for different use cases using the collected ground-truth data samples. [0062] In general, particular embodiments include configuration for data collection, including configuring channel measurement resources and additional information for different types of unconventional arrays, and methods for creating ground truth labeling. [0063] According to some embodiments, a method performed by a wireless device comprises receiving, from a network node, a configuration for data collection. The configuration includes at least one or more channel measurement resources (CMRs) and additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a reference signal (RS). The method further comprises performing channel measurements for the one or more CMRs. [0064] In particular embodiments, a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more subset indicators. Each subset indicator identifies a muting pattern that indicates a subset of the first set of antenna ports to be muted for transmitting a RS during model inference. [0065] In particular embodiments, a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more superset indicators. Each superset indicator identifies a muting pattern that indicates which ports of a superset of the first set of antenna ports are to be muted for transmitting a RS during model inference. [0066] In particular embodiments, the additional information comprises an indication of an antenna configuration of the network node. [0067] In particular embodiments, the method further comprises creating ground truth labels for a machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. [0068] In particular embodiments, the method further comprises performing inference for the machine learning model based on the channel measurements for the one or more CMRs and P110845WO01 PCT APPLICATION 13 of 47 the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. [0069] In particular embodiments, the method further comprises monitoring performance of the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. [0070] According to some embodiments, a wireless device comprises processing circuitry operable to perform any of the methods of the wireless device described above. [0071] Also disclosed is a computer program product comprising a non-transitory computer readable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the wireless device described above. [0072] According to some embodiments, a method performed by a network node comprises transmitting, to a wireless device, a configuration for data collection. The configuration includes at least one or more CMRs and additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. The method further comprises receiving a CSI report from the wireless device. The CSI report is based on channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. [0073] In particular embodiments, a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more subset indicators. Each subset indicator identifies a muting pattern that indicates a subset of the first set of antenna ports to be muted for transmitting a RS during model inference. [0074] In particular embodiments, a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more superset indicators. Each superset indicator identifies a muting pattern that indicates which ports of a superset of the first set of antenna ports are to be muted for transmitting a RS during model inference. [0075] In particular embodiments, the additional information comprises an indication of an antenna configuration of the network node. [0076] In particular embodiments, the method further comprises creating ground truth labels for a machine learning model based on the channel measurements for the one or more CMRs P110845WO01 PCT APPLICATION 14 of 47 and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. [0077] In particular embodiments, the method further comprises performing inference for the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. [0078] In particular embodiments, the method further comprises monitoring performance of the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. [0079] According to some embodiments, a network node comprises processing circuitry operable to perform any of the network node methods described above. [0080] Another computer program product comprises a non-transitory computer readable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the network node described above. [0081] Certain embodiments may provide one or more of the following technical advantages. For example, particular embodiments include a framework and configuration for data collection that enables training and monitoring an AI/ML model for CSI acquisition for unconventional arrays. BRIEF DESCRIPTION OF THE DRAWINGS [0082] 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: FIGURE 1 shows an example of data transmission with spatial multiplexing where the information carrying symbol vector is first multiplied (or precoded) by a precoding matrix; FIGURE 2 illustrates a two-dimensional antenna array of cross-polarized antenna elements; FIGURE 3 illustrates an example of a channel state information reference signal (CSI- RS) resource for 32 antenna ports with 8 code division multiplexing (CDM) groups; P110845WO01 PCT APPLICATION 15 of 47 FIGURE 4 illustrates an example of a mapping of CSI-RS antenna ports to a two- dimensional antenna with 32 ports; FIGURE 5 illustrates an example of a mapping of CSI-RS antenna ports to a two- dimensional antenna with 32 ports with 8 ports muted; FIGURE 6 illustrates a functional framework for artificial intelligence (AI)/machine learning (ML) for New Radio (NR) air interface; FIGURE 7 illustrates an example of CSI prediction using user equipment (UE)-side artificial intelligence (AI) model(s); FIGURE 8 illustrates an autoencoder (AE)-based CSI compression using two-sided AI/ML model use case; FIGURE 9 illustrates a two-dimensional (2D) antenna array grid where only a subset of all ports is sounded; FIGURE 10 illustrates an example of a UE-sided model for CSI acquisition by measuring a subset of ports; FIGURE 11 shows an example of a 2D array with 16 unmuted/active ports from a uniform planar array (UPA) with 24 ports; FIGURE 12 illustrates an example of two-sided model for CSI acquisition where a subset of antenna port is muted for downlink reference signal transmission and data transmission; FIGURE 13 illustrates an example of an irregular array antenna layout; FIGURE 14 is a flowchart illustrating steps of particular embodiments; FIGURE 15 illustrates an example UPA with 16 ports; FIGURE 16 illustrates an example communication system, according to certain embodiments; FIGURE 17 illustrates an example user equipment (UE), according to certain embodiments; FIGURE 18 illustrates an example network node, according to certain embodiments; FIGURE 19 illustrates a method performed by a wireless device, according to certain embodiments; and FIGURE 20 illustrates a method performed by a network node, according to certain embodiments. P110845WO01 PCT APPLICATION 16 of 47 DETAILED DESCRIPTION [0083] As described above, certain challenges currently exist with channel state information (CSI) acquisition for unconventional arrays. Certain aspects of the present disclosure and their embodiments may provide solutions to these or other challenges. For example, particular embodiments include a data collection scheme to support training one or multiple artificial intelligence (AI)/machine learning (ML) models that may be used at a user equipment (UE) to generate CSI report for an unconventional array. [0084] Particular embodiments are described more fully with reference to the accompanying drawings. Other embodiments, however, are contained within the scope of the subject matter disclosed herein, the disclosed subject matter should not be construed as limited to only the embodiments set forth herein; rather, these embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art. [0085] Each configured resource for channel measurement consists of a reference signal (e.g., non-zero power (NZP) channel state information reference signal (CSI-RS)) with ^^^^^^ ports, and the antenna array at the network node contains ^^^^ antenna ports, where ^^^^ ≥ ^^^^^^. [0086] As used herein, the term “unconventional array” describes an antenna array that is not using all its antenna ports or an antenna array that does not have uniformly spaced elements in a two-dimensional (2D) grid. An unconventional array may be achieved in different ways. Below, three examples for unconventional arrays are listed. [0087] Some embodiments include reference signal (RS) overhead reduction by sounding over a subset of antenna ports. [0088] FIGURE 9 illustrates an example of a 2D uniform planar array (UPA) with 16 ports that has 2 rows, 4 columns, and cross-polarized antenna elements. The network node may configure a UE to report a CSI report for the whole 16 port array, but the network node may only configure the UE with a subset of ports, i.e., 12 ports, for channel measurement to save RS overhead. [0089] FIGURE 9 illustrates a 2D antenna array grid where only a subset of all ports is sounded. Ports 3000, 3001, 3008 and 3009 illustrated with dashed lines are not sounded. Thus, the UE needs to generate a CSI report for 16 ports with channel measurement of only 12 ports (thus the array is unconventional compared to UPA, because part of the array is silent for RS transmission). If the UE does not know the channel measurements for the remaining 4 ports P110845WO01 PCT APPLICATION 17 of 47 without RS sounding, the UE may not be able to generate a good CSI report for the full array with 16 ports. [0090] An example of performance loss by measuring a subset of ports, compared to measuring all ports, for a CSI report for all ports, is illustrated in Table 1. These are system level simulation results, where Rel-16 eType II codebook is used. Channel measurements of the muted ports are padded with zeros. As can be seen, the performance loss can be as high as 39% and 43%. Table 1: Performance loss by measuring a subset of ports for a CSI report with all ports, in a system level simulation in UMi scenario, at 50% resource utilization. CSI-RS transmitted on all Mean user 10% percentile ports, except throughput loss user throughput (%) loss (%) [3000,3001,3002,3003] 39 43 [3000,3001,3008,3009] 28 36 [3000,3004,3008,3012] 13 16 [0091] It is expected that AI/ML-based CSI feedback may reduce the performance loss. An example AI/ML-based CSI feedback solution is found in FIGURE 10, where a UE-sided model is used to generate a CSI report for ^^^^ ports, based on the channel measurement of ^^^^^^ ports (^^^^^^ < ^^^^), and potentially with other inputs. FIGURE 10 illustrates an example of a UE-sided model for CSI acquisition by measuring a subset of ports. The channel measurement of ^^^^^^ ports is input to the UE-sided model, and the CSI report of ^^^^ ports is output from the UE-sided model. [0093] Some embodiments include antenna muting for energy saving. In this scenario, a subset of antenna ports may be muted (i.e., no CSI-RS transmission nor data transmission (e.g., physical downlink shared channel (PDSCH)) in the muted ports) for saving energy on the network side. FIGURE 11 shows an example of a 2D array with 16 unmuted/active ports from a UPA with 24 ports. The 8 ports shaded in black are muted. The unmuted ports, however, are not uniformly spaced. For example, the spacing between ports 3001 and 3003 is different from the spacing between ports 3003 and 3005. [0094] In this example, the network node may configure UE with a 16-port CSI-RS resource for channel measurement and configure the UE to report a 16-port CSI. P110845WO01 PCT APPLICATION 18 of 47 [0095] FIGURE 11 illustrates a 2D antenna array grid with muted ports along the 2nd and 5th columns. Table 2: Performance gain by using full eigenvector feedback over legacy Rel-16 eType II codebook for the array in FIGURE 11, in a system level simulation in UMi scenario, at 50% resource utilization. paramCombination-r16 Performance Performance gain for mean gain for 5th user throughput percentile user (%) throughput (%) 2 45 98 6 17 41 [0096] An example of performance gain by using full eigenvector feedback over legacy Rel- 16 eType II codebook for the array in FIGURE 11 is shown in Table 2. These are system level simulation results, where for full eigenvector feedback, it is assumed that UE feeds back full eigenvectors in antenna-frequency domain, without any quantization. This is served as an upper bound. Compared to the upper bound, two schemes using the Rel-16 eType II codebook with parameter combination 2 and 6 are used. Thus, by using full eigenvector feedback, the potential gain may be as high as 45% for mean user throughput, and 98% for 5th percentile user throughput. [0097] Using AI/ML-based CSI feedback may achieve such potential performance gain. An example AI/ML-based CSI feedback solution is found in FIGURE 12, where a UE-sided model is used to generate a CSI report for ^^^^^^ unmuted ports, based on the channel measurement of ^^^^^^ ports (^^^^^^ < ^^^^), and potentially with other inputs. [0098] FIGURE 12 illustrates an example of two-sided model for CSI acquisition where a subset of antenna port is muted for DL RS transmission and data transmission. [0099] For an irregular array, the antenna array may be non-uniform due to arrangement of elements. An example is shown in FIGURE 13. [0100] FIGURE 13 illustrates an example of an irregular array antenna layout. It can be expected that applying legacy codebooks, which are designed for uniform arrays, directly on irregular arrays will result performance loss. Thus, using AI/ML based CSI feedback scheme may improve the performance. A similar two-sided AI/ML model as shown in FIGURE 12 P110845WO01 PCT APPLICATION 19 of 47 may be used, with the modification that all Tx antenna ports will transmit RS in DL, i.e., ^^^^ = ^^^^^^. [0101] Particular embodiments include data collection methods for AI/ML based CSI acquisition for unconventional arrays. [0102] FIGURE 14 is a flowchart illustrating data collection procedure at the UE for AI/ML modeling training. [0103] In Step 101, a UE receives from a network node configuration for data collection. In one embodiment, the configuration includes at least one or more of the following: channel measurement resource(s), CMR(s); and additional information for associating the configured CMR(s) and the antenna ports at the network node that are used for transmitting reference signals (RSs). [0104] The collected data may be used by the UE for model training, as well as other LCM aspects of an AI/ML model, such as inference, monitoring, etc. Case 1: Measuring ^^^^^^ = ^^^^ ports, + subset indicator(s) [0105] In one embodiment, the configuration contains a CMR with ^^^^^^ = ^^^^ ports, while the additional information contains at least ^^1 ≥ 1 subset indicators, where each subset indicator identifies a muting pattern for RS transmission. [0106] Take the antenna array illustrated in FIGURE 15 as an example to further explain the above embodiment. In this case, ^^^^^^ = ^^^^ = 16. One example of a subset indicator may be a bitmap of length ^^^^, where each bit in the bitmap indicates whether an antenna port is muted or not when the subset indicator is configured during a model inference phase. For example, a subset indicator [1100111111001111] indicates that ports 3002,3003,3010,3011 are muted when this subset indicator is configured during model inference phase. [0107] FIGURE 15 illustrates an example UPA with 16 ports (2 vertical ports, 4 horizontal ports, 2 polarizations). The numbers are port indices. The illustrated example may be used for RS (e.g., CSI-RS) overhead reduction and/or port muting use cases. [0108] Step 103: For both port muting and RS (e.g., CSI-RS) overhead reduction use cases, each subset indicator is associated with a port muting pattern for RS. For a given subset indicator, UE may obtain channel measurements associated with the unmuted ports from the channel measurements of ^^^^^^ ports, by selecting the measurements associated only with the unmuted ports. In another embodiment, for a given subset indicator, the UE obtains channel P110845WO01 PCT APPLICATION 20 of 47 measurements associated with the unmuted ports by only measuring the channel on the unmuted ports among the ^^^^^^ ports. [0109] For port muting use case, the obtained channel measurements associated with the unmuted ports may be used to generate ground truth labels and used as input to an AI/ML model. [0110] For RS overhead reduction use case, the obtained channel measurements associated with the unmuted ports may be used to generate input to an AI/ML model, while the channel measurements of all ^^^^ ports may be used to generate ground truth labels. [0111] In one embodiment, the subset indicator may be dynamically signaled to the UE from the network node. In one example, one or more subset indicators may be indicated to the UE from the network node via a ‘CSI request’ field in DCI. In another example, one or more subset indicators may be indicated to the UE from the network node via a MAC CE. [0112] When a new subset indicator is dynamically signaled to the UE from the network, the UE may perform one of more of the following actions. For the port muting use case, the UE obtains channel measurements associated with the unmuted ports in the dynamically signaled subset indicator. The obtained channel measurements are then used to generate ground truth labels and used as input to an AI/ML model. For RS overhead reduction use case, the UE obtains channel measurements associated with the unmuted ports in the dynamically signaled subset indicator. The obtained channel measurements associated with the unmuted ports can be used to generate input to an AI/ML model. The channel measurements of all ^^^^ ports may be used to generate ground truth labels. Case 2: Measuring ^^^^^^ < ^^^^ ports, + superset indicator(s) Case 2-1: Measuring one CMR with ^^^^^^ ports [0113] In one embodiment, the configuration contains one CMR with ^^^^^^ < ^^^^ ports, while the additional information contains at least ^^2 ≥ 1 superset indicator(s), where each superset indicator identifies a muting pattern for RS sounding. [0114] Take the antenna array illustrated in FIGURE 15 as an example to further explain the above embodiment. One example is that ^^^^^^ = 8 while ^^^^ = 16. Then, an example of superset indictor is a bitmap of length ^^^^, where the bitmap identifies the subset of the antenna ports that are used for sounding the configured 8-port RS. For example, [1100110011001100] may be used to indicate that the [1, 2, 5, 6, 9, 10, 13, 14]-th ports of the antenna array are used for transmitting port [3000,… ,3007] of the configured RS. P110845WO01 PCT APPLICATION 21 of 47 Step 103 for Case 2-1: [0115] The above can be used for RS overhead reduction use case if the ground truth can be obtained. The ground truth depends on channel measurement associated with all ^^^^ ports, but now only ^^^^^^ ports are sounded from ^^^^^^ of ^^^^ antenna ports. To solve this, the ground truth can either be transmitted by the network node to the UE, or it can be generated by the UE based on the measurement of ^^^^ ports. [0116] In one embodiment, channel measurement for all ^^^^ ports is transmitted by the network node to the UE via DL signaling. In some embodiments, the channel measurements for all ^^^^ ports are obtained by the network by measuring UL RS transmitted by the UE. [0117] In another embodiment, channel measurement for all ^^^^ ports is generated by UE, based on the channel measurement for ^^^^^^ ports and the superset indicator(s). In some embodiments, channel measurement for all ^^^^ ports is generated based on the channel measurement for ^^^^^^ ports and the correlation between the ^^^^^^ ports and the ^^^^ ports. [0118] Once the ground truth has been obtained, for the RS overhead reduction use case, the obtained channel measurements associated with the unmuted ports, i.e., the ^^^^^^ ports, may be used to generate input to an AI/ML model, while the channel measurements of all ^^^^ ports may be used to generate ground truth. [0119] In one embodiment, the superset indicator may be dynamically signaled to the UE from the network node. In one example, one or more superset indicators may be indicated to the UE from the network node via a ‘CSI request’ field in DCI. In another example, one or more superset indicators may be indicated to the UE from the network node via a MAC CE. [0120] When a new superset indicator is dynamically signaled to the UE from the network, the UE may perform one of more of the following actions: channel measurement for all ^^^^ ports is generated by UE, based on the channel measurement for ^^^^^^ ports and the dynamically indicated superset indicator; and for RS overhead reduction use case, the obtained channel measurements associated with the unmuted ports, i.e., the ^^^^^^ ports, may be used to generate input to an AI/ML model, while the channel measurements of all ^^^^ ports may be used to generate ground truth Case 2-2: Measuring ^^ > 1 CMR with ^^^^^^ ports [0121] In one embodiment, the configuration contains ^^ > 1 CMRs, with ^^^^^^,^^ < ^^^^ ports for CMR ^^, for ^^ = 1, … , ^^. For CMR ^^, the additional information contains at least ^^2,^^ ≥ P110845WO01 PCT APPLICATION 22 of 47 1 superset indicator(s), where each of the superset indicator(s) identifies a muting pattern for RS transmission. [0122] Take the antenna array illustrated in FIGURE 15 as an example to further explain the above embodiment. One example is that two CMRs are configured, where they are configured with ^^^^^^,1 = ^^^^^^,2 = 8 ports, while ^^^^ = 16. Further, the first and the second CMR may be configured with ^^2,1 = 1 and ^^2,2 = 1 superset indicator, respectively. For example, the first indicator may be [1111111100000000], indicating that the 8 RS ports in the first CMR with port index [3000, …, 3007] are transmitted on the first half of the antenna array; the second indicator may be [0000000011111111], indicating that the 8 RS ports in the second CMR with port index [3000, …, 3007] are transmitted on the second half of the antenna array. Step 103 for Case 2-2: [0123] The above may be used for RS overhead reduction use case. The ground truth depends on channel measurement associated with all ^^^^ ports. In one embodiment, channel measurements associated with all ^^^^ ports are obtained based on the measurements for the ^^ CMRs. [0124] Continuing with the above example, by combining the two CMRs with ^^^^^^,1 = ^^^^^^,2 = 8 ports, where the CMRs are sounded from non-overlapping parts of the antenna array, then the UE may obtain the channel measurements for all ^^^^ ports by aggregating the measurements based on the two CMRs. [0125] Once the ground truth has been obtained, for the RS overhead reduction use case, the obtained channel measurements associated with the unmuted ports, i.e., the ^^^^^^,1and ^^^^^^,2 ports, may be used to generate input to an AI/ML model, while the channel measurements of all ^^^^ ports may be used to generate ground truth. Case 3: Measuring both ^^^^^^ = ^^^^ ports and ^^^^^^ < ^^^^ ports, + optional superset indicator(s) [0126] In one embodiment, a CMR with ^^^^^^ = ^^^^ ports and one or multiple CMRs with ^^^^^^ < ^^^^, optionally additional information is also configured, which contains at least ^^3 ≥ 1 superset indicators associated with each CMR that has ^^^^^^ < ^^^^ ports, where each superset indicator identifies a muting pattern for RS transmission. Step 103 for Case 3: P110845WO01 PCT APPLICATION 23 of 47 [0127] The above can be used for the RS overhead reduction use case. The obtained channel measurements associated with the ^^^^^^ ports may be used to generate input to an AI/ML model, while the channel measurements of all ^^^^ ports may be used to generate ground truth. [0128] For Case 1, Case 2-1, Case 2-2, and Case 3, the additional information may further contain information for identifying antenna array layout and/or antenna numbering/ordering at the network node. Such information may also be used for generating input data to an AI/ML model and/or for generating ground truth. [0129] In one embodiment, such information is contained in an associated codebook configuration. For example, the NR Type I, Type II, enhanced Type II codebooks, etc. contain codebook parameter ^^1 and ^^2, which can be used to identify the number of ports along a first dimension and a second dimension for a UPA structure. One of such codebooks may be configured in data collection configuration. [0130] In another embodiment, array layout and/or antenna numbering/ordering is configured/signaled explicitly to the UE. For example, (relative) coordinates of the antenna elements in the array, correlation of the antenna elements of the array, etc. Case 4: Measuring ^^^^^^ = ^^^^ ports, + information identifying array layout and/or antenna numbering/ordering [0131] In one embodiment, a CMR with ^^^^^^ = ^^^^ ports is configured to the UE. Additional information is also configured to the UE, which contains information identifying array layout and/or antenna numbering/ordering. Step 103 for Case 4: [0132] The above may be used for the CSI acquisition for irregular array use case. The obtained channel measurements for all ^^^^ ports and/or the additional information may be used to generate ground truth and/or input to an AI/ML model. [0133] FIGURE 16 illustrates an example of a communication system 100 in accordance with some embodiments. In the example, the communication system 100 includes a telecommunication network 102 that includes an access network 104, such as a radio access network (RAN), and a core network 106, which includes one or more core network nodes 108. The access network 104 includes one or more access network nodes, such as network nodes 110a and 110b (one or more of which may be generally referred to as network nodes 110), or any other similar 3rd Generation Partnership Project (3GPP) access node or non-3GPP access point. The network nodes 110 facilitate direct or indirect connection of user equipment (UE), P110845WO01 PCT APPLICATION 24 of 47 such as by connecting UEs 112a, 112b, 112c, and 112d (one or more of which may be generally referred to as UEs 112) to the core network 106 over one or more wireless connections. [0134] Example wireless communications over a wireless connection include transmitting and/or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and/or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 100 may include any number of wired or wireless networks, network nodes, UEs, and/or any other components or systems that may facilitate or participate in the communication of data and/or signals whether via wired or wireless connections. The communication system 100 may include and/or interface with any type of communication, telecommunication, data, cellular, radio network, and/or other similar type of system. [0135] The UEs 112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and/or operable to communicate wirelessly with the network nodes 110 and other communication devices. Similarly, the network nodes 110 are arranged, capable, configured, and/or operable to communicate directly or indirectly with the UEs 112 and/or with other network nodes or equipment in the telecommunication network 102 to enable and/or provide network access, such as wireless network access, and/or to perform other functions, such as administration in the telecommunication network 102. [0136] In the depicted example, the core network 106 connects the network nodes 110 to one or more hosts, such as host 116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 106 includes one more core network nodes (e.g., core network node 108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and/or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and/or a User Plane Function (UPF). P110845WO01 PCT APPLICATION 25 of 47 [0137] The host 116 may be under the ownership or control of a service provider other than an operator or provider of the access network 104 and/or the telecommunication network 102, and may be operated by the service provider or on behalf of the service provider. The host 116 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio/video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server. [0138] As a whole, the communication system 100 of FIGURE 16 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. [0139] In some examples, the telecommunication network 102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 102. For example, the telecommunications network 102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and/or Massive Machine Type Communication (mMTC)/Massive IoT services to yet further UEs. [0140] In some examples, the UEs 112 are configured to transmit and/or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 104. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a P110845WO01 PCT APPLICATION 26 of 47 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). [0141] In the example, the hub 114 communicates with the access network 104 to facilitate indirect communication between one or more UEs (e.g., UE 112c and/or 112d) and network nodes (e.g., network node 110b). In some examples, the hub 114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 114 may be a broadband router enabling access to the core network 106 for the UEs. As another example, the hub 114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 110, or by executable code, script, process, or other instructions in the hub 114. As another example, the hub 114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 114 then provides to the UE either directly, after performing local processing, and/or after adding additional local content. In still another example, the hub 114 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy IoT devices. [0142] The hub 114 may have a constant/persistent or intermittent connection to the network node 110b. The hub 114 may also allow for a different communication scheme and/or schedule between the hub 114 and UEs (e.g., UE 112c and/or 112d), and between the hub 114 and the core network 106. In other examples, the hub 114 is connected to the core network 106 and/or one or more UEs via a wired connection. Moreover, the hub 114 may be configured to connect to an M2M service provider over the access network 104 and/or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 110 while still connected via the hub 114 via a wired or wireless connection. In some embodiments, the hub 114 may be a dedicated hub – that is, a hub whose primary function is to route communications to/from the UEs from/to the network node 110b. In other embodiments, the hub 114 may be a non-dedicated hub – that is, a device which is capable of operating to route communications between the UEs and network node 110b, but which is P110845WO01 PCT APPLICATION 27 of 47 additionally capable of operating as a communication start and/or end point for certain data channels. [0143] FIGURE 17 shows a UE 200 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle-mounted or vehicle embedded/integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and/or an enhanced MTC (eMTC) UE. [0144] A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle- to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and/or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter). [0145] The UE 200 includes processing circuitry 202 that is operatively coupled via a bus 204 to an input/output interface 206, a power source 208, a memory 210, a communication interface 212, and/or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in FIGURE 17. 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. P110845WO01 PCT APPLICATION 28 of 47 [0146] The processing circuitry 202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 210. The processing circuitry 202 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 202 may include multiple central processing units (CPUs). [0147] In the example, the input/output interface 206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and/or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 200. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device. [0148] In some embodiments, the power source 208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 208 may further include power circuitry for delivering power from the power source 208 itself, and/or an external power source, to the various parts of the UE 200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 208 to make the power suitable for the respective components of the UE 200 to which power is supplied. P110845WO01 PCT APPLICATION 29 of 47 [0149] The memory 210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 210 includes one or more application programs 214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 216. The memory 210 may store, for use by the UE 200, any of a variety of various operating systems or combinations of operating systems. [0150] The memory 210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and/or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 210 may allow the UE 200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 210, which may be or comprise a device-readable storage medium. [0151] The processing circuitry 202 may be configured to communicate with an access network or other network using the communication interface 212. The communication interface 212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 222. The communication interface 212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 218 and/or a receiver 220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 218 and receiver 220 may be P110845WO01 PCT APPLICATION 30 of 47 coupled to one or more antennas (e.g., antenna 222) and may share circuit components, software or firmware, or alternatively be implemented separately. [0152] In the illustrated embodiment, communication functions of the communication interface 212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and/or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol/internet protocol (TCP/IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth. [0153] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient). [0154] 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. [0155] A UE, when in the form of an Internet of Things (IoT) 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 IoT device are a device which is or which is embedded in: a connected refrigerator or freezer, P110845WO01 PCT APPLICATION 31 of 47 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 item-tracking 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 IoT device comprises circuitry and/or software in dependence of the intended application of the IoT device in addition to other components as described in relation to the UE 200 shown in FIGURE 17. [0156] As yet another specific example, in an IoT scenario, a UE may represent a machine or other device that performs monitoring and/or measurements, and transmits the results of such monitoring and/or measurements to another UE and/or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and/or reporting on its operational status or other functions associated with its operation. [0157] 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. [0158] FIGURE 18 shows a network node 300 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and/or operable to communicate directly or indirectly with a UE and/or with other network nodes or equipment, P110845WO01 PCT APPLICATION 32 of 47 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)). [0159] 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). [0160] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell/multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and/or Minimization of Drive Tests (MDTs). [0161] The network node 300 includes a processing circuitry 302, a memory 304, a communication interface 306, and a power source 308. The network node 300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 304 for different RATs) and some components may be reused (e.g., a same antenna 310 may be shared by different RATs). The network node 300 may also include multiple sets of the various illustrated P110845WO01 PCT APPLICATION 33 of 47 components for different wireless technologies integrated into network node 300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 300. [0162] The processing circuitry 302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and/or encoded logic operable to provide, either alone or in conjunction with other network node 300 components, such as the memory 304, to provide network node 300 functionality. [0163] In some embodiments, the processing circuitry 302 includes a system on a chip (SOC). In some embodiments, the processing circuitry 302 includes one or more of radio frequency (RF) transceiver circuitry 312 and baseband processing circuitry 314. In some embodiments, the radio frequency (RF) transceiver circuitry 312 and the baseband processing circuitry 314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 312 and baseband processing circuitry 314 may be on the same chip or set of chips, boards, or units. [0164] The memory 304 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and/or any other volatile or non-volatile, non-transitory device-readable and/or computer-executable memory devices that store information, data, and/or instructions that may be used by the processing circuitry 302. The memory 304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and/or other instructions capable of being executed by the processing circuitry 302 and utilized by the network node 300. The memory 304 may be used to store any calculations made by the processing circuitry 302 and/or any data received via the communication interface 306. In some embodiments, the processing circuitry 302 and memory 304 is integrated. P110845WO01 PCT APPLICATION 34 of 47 [0165] The communication interface 306 is used in wired or wireless communication of signaling and/or data between a network node, access network, and/or UE. As illustrated, the communication interface 306 comprises port(s)/terminal(s) 316 to send and receive data, for example to and from a network over a wired connection. The communication interface 306 also includes radio front-end circuitry 318 that may be coupled to, or in certain embodiments a part of, the antenna 310. Radio front-end circuitry 318 comprises filters 320 and amplifiers 322. The radio front-end circuitry 318 may be connected to an antenna 310 and processing circuitry 302. The radio front-end circuitry may be configured to condition signals communicated between antenna 310 and processing circuitry 302. The radio front-end circuitry 318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 320 and/or amplifiers 322. The radio signal may then be transmitted via the antenna 310. Similarly, when receiving data, the antenna 310 may collect radio signals which are then converted into digital data by the radio front-end circuitry 318. The digital data may be passed to the processing circuitry 302. In other embodiments, the communication interface may comprise different components and/or different combinations of components. [0166] In certain alternative embodiments, the network node 300 does not include separate radio front-end circuitry 318, instead, the processing circuitry 302 includes radio front-end circuitry and is connected to the antenna 310. Similarly, in some embodiments, all or some of the RF transceiver circuitry 312 is part of the communication interface 306. In still other embodiments, the communication interface 306 includes one or more ports or terminals 316, the radio front-end circuitry 318, and the RF transceiver circuitry 312, as part of a radio unit (not shown), and the communication interface 306 communicates with the baseband processing circuitry 314, which is part of a digital unit (not shown). [0167] The antenna 310 may include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals. The antenna 310 may be coupled to the radio front-end circuitry 318 and may be any type of antenna capable of transmitting and receiving data and/or signals wirelessly. In certain embodiments, the antenna 310 is separate from the network node 300 and connectable to the network node 300 through an interface or port. [0168] The antenna 310, communication interface 306, and/or the processing circuitry 302 may be configured to perform any receiving operations and/or certain obtaining operations P110845WO01 PCT APPLICATION 35 of 47 described herein as being performed by the network node. Any information, data and/or signals may be received from a UE, another network node and/or any other network equipment. Similarly, the antenna 310, the communication interface 306, and/or the processing circuitry 302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and/or signals may be transmitted to a UE, another network node and/or any other network equipment. [0169] The power source 308 provides power to the various components of network node 300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 300 with power for performing the functionality described herein. For example, the network node 300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 308. As a further example, the power source 308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail. [0170] Embodiments of the network node 300 may include additional components beyond those shown in FIGURE 18 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and/or any functionality necessary to support the subject matter described herein. For example, the network node 300 may include user interface equipment to allow input of information into the network node 300 and to allow output of information from the network node 300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 300. [0171] FIGURE 19 is a flowchart illustrating an example method 1900 in a wireless device, according to certain embodiments. In particular embodiments, one or more steps of FIGURE 19 may be performed by UE 200 described with respect to FIGURE 17. [0172] The method may begin at step 1910, where the wireless device (e.g., UE 200) receives, from a network node (e.g., network node 300), a configuration for data collection. The configuration includes at least one or more channel measurement resources (CMRs) and additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a reference signal (RS). P110845WO01 PCT APPLICATION 36 of 47 [0173] In particular embodiments, a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more subset indicators. Each subset indicator identifies a muting pattern that indicates a subset of the first set of antenna ports to be muted for transmitting a RS during model inference. [0174] Examples are described in more detail above, such as for the case 1 and 3 examples described with respect to FIGURE 14. In that particular example, the configuration contains a CMR with ^^^^^^ = ^^^^ ports, while the additional information contains at least ^^1 ≥ 1 subset indicators, where each subset indicator identifies a muting pattern for RS transmission. A subset indicator may be a bitmap of length ^^^^, where each bit in the bitmap indicates whether an antenna port is muted or not when the subset indicator is configured during a model inference phase. [0175] In particular embodiments, the subset indicator may be beneficial for overhead reduction and/or port muting use cases, as described above. [0176] In particular embodiments, a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more superset indicators. Each superset indicator identifies a muting pattern that indicates which ports of a superset of the first set of antenna ports are to be muted for transmitting a RS during model inference. [0177] Examples are described in more detail above, such as for the case 2 and 3 examples described with respect to FIGURE 14. [0178] In particular embodiments, the indication of which ports to mute may comprise an indication of the muted ports, or an indication of the unmuted ports. [0179] In particular embodiments, port muting may apply for particular operations, such a model inference, but not for other operations, such as data collection. [0180] In particular embodiments, the additional information comprises an indication of an antenna configuration of the network node. Examples are described in more detail above, such as for the case 4 example described with respect to FIGURE 14. [0181] At step 1914, the wireless device performs channel measurements for the one or more CMRs. The wireless device may perform the channel measurements according to any of the embodiments and examples described herein. [0182] The following steps describe operations the wireless device may perform based on the CMRs and the additional information. P110845WO01 PCT APPLICATION 37 of 47 [0183] At step 1916, the wireless device may create ground truth labels for a machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. The wireless device may create ground truth labels according to any of the embodiments and examples described herein. [0184] At step 1918, the wireless device may perform inference for the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. The wireless device may perform inference according to any of the embodiments and examples described herein. [0185] At step 1920, the wireless device may monitor performance of the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. The wireless device may monitor performance according to any of the embodiments and examples described herein. [0186] Modifications, additions, or omissions may be made to method 1900 of FIGURE 19. Additionally, one or more steps in the method of FIGURE 19 may be performed in parallel or in any suitable order. [0187] FIGURE 20 is a flowchart illustrating an example method 2000 in a network node, according to certain embodiments. In particular embodiments, one or more steps of FIGURE 20 may be performed by network node 300 described with respect to FIGURE 18. [0188] The method begins at step 2012, where the network node (e.g., network node 300) transmits, to a wireless device, a configuration for data collection. The configuration includes at least one or more CMRs and additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. The configuration is described in more detail with respect to FGIURE 19 and with respect to the embodiments and examples described herein. [0189] At step 2014, the network node receives a CSI report from the wireless device. The CSI report is based on channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. P110845WO01 PCT APPLICATION 38 of 47 [0190] At step 2016, the network node may create ground truth labels for a machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. The network node may create ground truth labels according to any of the embodiments and examples described herein. [0191] At step 2018, the network node may perform inference for the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. The network node may perform inference according to any of the embodiments and examples described herein. [0192] At step 2020, the network node may monitor performance of the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. The network node may monitor performance according to any of the embodiments and examples described herein. [0193] Modifications, additions, or omissions may be made to method 2000 of FIGURE 20. Additionally, one or more steps in the method of FIGURE 20 may be performed in parallel or in any suitable order. [0194] The foregoing description sets forth numerous specific details. It is understood, however, that embodiments may be practiced without these specific details. In other instances, well-known circuits, structures and techniques have not been shown in detail in order not to obscure the understanding of this description. Those of ordinary skill in the art, with the included descriptions, will be able to implement appropriate functionality without undue experimentation. [0195] References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to implement such feature, structure, or characteristic in connection with other embodiments, whether or not explicitly described. P110845WO01 PCT APPLICATION 39 of 47 [0196] Although this disclosure has been described in terms of certain embodiments, alterations and permutations of the embodiments will be apparent to those skilled in the art. Accordingly, the above description of the embodiments does not constrain this disclosure. Other changes, substitutions, and alterations are possible without departing from the scope of this disclosure, as defined by the claims below. [0197] Some example embodiments are described below. Group A Embodiments 1. A method performed by a wireless device, the method comprising: − receiving from a network node configuration for data collection, including at least one or more of channel measurement resource(s), CMR(s), and additional information for associating the configured CMR(s) and antenna ports at the network node that are used for transmitting reference signals (RSs); − performing channel measurement based on the configured CMR(s); − creating ground truth labels according to the channel measurement and the additional information for associating the CMR(s) and the antenna ports at the network node; and − training an AI/ML neural network according to the created ground truth labels. 2. A method performed by a wireless device, the method comprising: − any of the wireless device steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above. 3. The method of the previous embodiment, further comprising one or more additional wireless device steps, features or functions described above. 4. The method of any of the previous two embodiments, further comprising: − providing user data; and − forwarding the user data to a host computer via the transmission to the base station. Group B Embodiments 5. A method performed by a base station, the method comprising: − transmitting to a wireless device configuration for data collection, including at P110845WO01 PCT APPLICATION 40 of 47 least one or more of channel measurement resource(s), CMR(s), and additional information for associating the configured CMR(s) and antenna ports at the network node that are used for transmitting reference signals (RSs). 6. A method performed by a base station, the method comprising: − any of the steps, features, or functions described above with respect to base stations, either alone or in combination with other steps, features, or functions described above. 7. The method of the previous embodiment, further comprising one or more additional base station steps, features or functions described above. 8. The method of any of the previous embodiments, further comprising: − obtaining user data; and − forwarding the user data to a host computer or a wireless device. Group C Embodiments 9. A mobile terminal comprising: − circuitry configured to perform any of the steps of any of the Group A embodiments; and − power supply circuitry configured to supply power to the wireless device. 10. A base station comprising: − processing circuitry configured to perform any of the steps of any of the Group B embodiments; − power supply circuitry configured to supply power to the wireless device. 11. A user equipment (UE) comprising: − an antenna configured to send and receive wireless signals; − radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; − the processing circuitry being configured to perform any of the steps of any of the Group A embodiments; P110845WO01 PCT APPLICATION 41 of 47 − 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. 12. A communication system including a host computer comprising: − processing circuitry configured to provide user data; and − a communication interface configured to forward the user data to a cellular network for transmission to a user equipment (UE), − wherein the cellular network comprises a base station having a radio interface and processing circuitry, the base station’s processing circuitry configured to perform any of the steps of any of the Group B embodiments. 13. The communication system of the pervious embodiment further including the base station. 14. The communication system of the previous 2 embodiments, further including the UE, wherein the UE is configured to communicate with the base station.

Claims

P110845WO01 PCT APPLICATION 42 of 47 CLAIMS: 1. A method performed by a wireless device, the method comprising: receiving (1912), from a network node, a configuration for data collection, the configuration including at least one or more channel measurement resources, CMRs, and additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a reference signal, RS; and performing (1914) channel measurements for the one or more CMRs; 2. The method of claim 1, wherein a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more subset indicators, each subset indicator identifying a muting pattern that indicates a subset of the first set of antenna ports to be muted for transmitting a RS during model inference. 3. The method of claim 1, wherein a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more superset indicators, each superset indicator identifying a muting pattern that indicates which ports of a superset of the first set of antenna ports are to be muted for transmitting a RS during model inference. 4. The method of any one of claims 1-3, wherein the additional information comprises an indication of an antenna configuration of the network node. 5. The method of any one of claims 1-4, further comprising creating (1916) ground truth labels for a machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. 6. The method of any one of claims 1-5, further comprising performing (1918) inference for the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. P110845WO01 PCT APPLICATION 43 of 47 7. The method of any one of claims 1-6, further comprising monitoring (1920) performance of the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. 8. A wireless device (200) comprising processing circuitry (202), the processing circuitry operable to: receive from a network node, a configuration for data collection, the configuration including at least one or more channel measurement resources, CMRs, and additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a reference signal, RS; and perform channel measurements for the one or more CMRs; 9. The wireless device of claim 8, wherein a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more subset indicators, each subset indicator identifying a muting pattern that indicates a subset of the first set of antenna ports to be muted for transmitting a RS during model inference. 10. The wireless device of claim 8, wherein a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more superset indicators, each superset indicator identifying a muting pattern that indicates which ports of a superset of the first set of antenna ports are to be muted for transmitting a RS during model inference. 11. The wireless device of any one of claims 8-10, wherein the additional information comprises an indication of an antenna configuration of the network node. 12. The wireless device of any one of claims 8-11, the processing circuitry further operable to create ground truth labels for a machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. P110845WO01 PCT APPLICATION 44 of 47 13. The wireless device of any one of claims 8-12, the processing circuitry further operable to perform inference for the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. 14. The wireless device of any one of claims 8-13, the processing circuitry further operable to monitor performance of the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. 15. A method performed by a network node, the method comprising: transmitting (2012), to a wireless device, a configuration for data collection, the configuration including at least one or more channel measurement resources, CMRs, and additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a reference signal, RS; and receiving (2014) a channel state information, CSI, report from the wireless device, the CSI report based on channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. 16. The method of claim 15, wherein a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more subset indicators, each subset indicator identifying a muting pattern that indicates a subset of the first set of antenna ports to be muted for transmitting a RS during model inference. 17. The method of claim 15, wherein a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more superset indicators, each superset indicator identifying a muting pattern that indicates which ports of a superset of the first set of antenna ports are to be muted for transmitting a RS during model inference. P110845WO01 PCT APPLICATION 45 of 47 18. The method of any one of claims 15-17, wherein the additional information comprises an indication of an antenna configuration of the network node. 19. The method of any one of claims 15-18, further comprising creating (2016) ground truth labels for a machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. 20. The method of any one of claims 15-19, further comprising performing (2018) inference for the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. 21. The method of any one of claims 15-20, further comprising monitoring (2020) performance of the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. 22. A network node (300) comprising processing circuitry (302), the processing circuitry operable to: transmit, to a wireless device, a configuration for data collection, the configuration including at least one or more channel measurement resources, CMRs, and additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a reference signal, RS; and receive a CSI report from the wireless device, the CSI report based on channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. 23. The network node of claim 22, wherein a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more subset indicators, each subset indicator identifying a muting pattern that indicates a subset of the first set of antenna ports to be muted for transmitting a RS during model inference. P110845WO01 PCT APPLICATION 46 of 47 24. The network node of claim 22, wherein a CMR of the one or more CMRs comprises a first set of antenna ports and the additional information comprises one or more superset indicators, each superset indicator identifying a muting pattern that indicates which ports of a superset of the first set of antenna ports are to be muted for transmitting a RS during model inference. 25. The network node of any one of claims 22-24, wherein the additional information comprises an indication of an antenna configuration of the network node. 26. The network node of any one of claims 22-25, the processing circuitry further operable to create ground truth labels for a machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. 27. The network node of any one of claims 22-26, the processing circuitry further operable to perform inference for the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS. 28. The network node of any one of claims 22-27, the processing circuitry further operable to monitoring performance of the machine learning model based on the channel measurements for the one or more CMRs and the additional information for associating the one or more CMRs with antenna ports at the network node that are used for transmitting a RS.
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