Network configuration identifier for machine learning models

Abstract

According to some embodiments, a method is performed by a wireless device. The method comprises receiving one or more first network configuration identifiers from a network node. Each of the one or more first network configuration identifiers identifying a particular configuration of the network node. The method further comprises associating the one or more first network configuration identifiers with a machine learning model, the association representing a network configuration used for training the machine learning model. The method may further comprise receiving one or more second network configuration identifiers from a network node, each of the one or more second network configuration identifiers identifying a particular configuration of the network node and comparing the one or more second network configuration identifiers with the one or more first network configuration identifiers to determine whether the machine learning model is suitable to be used for inference.

Description

Network Configuration Identifier for Machine Learning ModelsTECHNICAL FIELD

[0001] The present disclosure generally relates to communication networks, and more specifically to a network configuration identifier for machine learning (ML) models.BACKGROUND

[0002] Artificial intelligence (Al) and machine learning (ML) have been investigated as promising tools to optimize the design of air-interface in wireless communication networks in both academia and industry. 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-line-of-sight (NLOS) conditions to enhance the positioning accuracy; and using reinforcement learning for beam selection at the network side and / or the user equipment (UE) side to reduce the signaling overhead and beam alignment latency; and using deep reinforcement learning to learn an optimal precoding policy for complex multiple-input multiple-output (MIMO) precoding problems.

[0003] Third Generation Partnership Project (3GPP) New Radio (NR) standardization work includes a release 18 study item on AI / ML for NR air interface. The study item explores 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), this study item lays the foundation for future air-interface use cases leveraging AI / ML techniques.

[0004] One important AI / ML physical layer (PHY) use case is the positioning of a target UE. Both positioning approaches below have been shown to be effective in obtaining a target UE location.• Direct AI / ML positioning, where the AI / ML model output is UE location. Direct AI / ML positioning typically refers to radio fingerprinting, where channel observation is used as the input of an AI / ML model.• AI / ML assisted positioning, where the AI / ML model output is a new measurement and / or an enhancement of an existing measurement. The model output can be, for example, LOS / NLOS identification, timing and / or angle measurement, likelihood or reliability of the measurement. The model input is also channel observations.

[0005] When applying the direct and assisted AI / ML positioning to NR wireless communication network, the following cases are further identified for standardization in 3GPP for the NR system.• Case 1: UE-based positioning with UE-side model, direct AI / ML or AI / ML assisted positioning• Case 2a: UE-assisted / location management function (LMF)-based positioning with UE- side model, AI / ML assisted positioning• Case 2b: UE-assisted / LMF-based positioning with LMF-side model, direct AI / ML positioning• Case 3a: next generation radio access network (NG-RAN) node assisted positioning with gNB-side model, AI / ML assisted positioning• Case 3b: NG-RAN node assisted positioning with LMF-side model, direct AI / ML positioning

[0006] For the model inference stage of life cycle management (LCM), for an AI / ML model to work properly, the model inference context needs to be consistent with the model training context. The model training context needs to include all the settings that noticeably affect the model performance and cannot be ignored. A model cannot be activated for inference without checking the model inference context requirement. Specifically, if the model inference context requirement is satisfied, then the model inference can proceed. This is the ideal case where the trained model is fully compatible with the deployment environment.

[0007] If the model inference context requirement is not satisfied, then configuration can be updated to satisfy the model inference context requirement. After that, the model inference can proceed. This may happen if the model is trained for the deployment area, but some of the parameters (e.g., positioning reference signal (PRS)) are not configured the same as in the model training.

[0008] If the model inference context requirement is not satisfied, and the configuration cannot be updated to satisfy the model inference context requirement, then model inference cannot proceed. A notification message (e.g., an error message) can be sent on the failure of model inference due to mismatched context between model training and model inference. This may happen if the deployment area is different from the validity area of the trained model.

[0009] The model inference context requirement includes at least the following of an AI / ML model, which are directly related to the model input of the various positioning cases:• Validity area of the model for inference, e.g., a list of transmission reception points (TRPs) that transmits PRS (for downlink based cases) or receives sounding reference signal (SRS) (for uplink based bases),• Configuration of reference signal transmission and measurement. For models using DL PRS (Case 1, 2a, 2b), this refers to DL PRS configuration for transmission (Case 1, 2a, 2b), and measurement report on PRS (Case 2b). For models using UL SRS (Case 3a, 3b), this refers to UL SRS configuration for transmission (Case 3a, 3b), and measurement report on SRS (Case 3b).

[0010] More context requirements need to be checked for compatibility, e.g., range of signal to noise ratio (SNR) / signal to interference and noise ratio (SINR).

[0011] Furthermore, the observed fingerprint of a location is the composite picture including transmitter effect, radio channel, and receiver effect. To correctly determine a UE location based on fingerprinting, the features of the composite picture need to stay (approximately) the same for different UEs (e.g., positioning reference unit (PRU) and normal UE) at different times (e.g., training data collection, model inference). Thus, it is necessary to ensure consistency in any transmitter behavior (e.g., PRS configuration) and receiver behavior (e.g., measurement type and granularity, at least for Case 2b / 3b) that affect the observed fingerprint.

[0012] For AI / ML based positioning using DL PRS (Case 1, 2a, 2b), the configuration of DL PRS transmission and measurement need to be consistent between model training and model inference.

[0013] The following is a description of TRP information exchange between gNB and LMF. The purpose of the TRP information exchange procedure is to enable the LMF to request the NG- RAN node to provide detailed information for TRPs hosted by the NG-RAN node. This procedure applies only if the NG-RAN node is a gNB.

[0014] The LMF initiates the procedure by sending a TRP INFORMATION REQUEST message. The NG-RAN node responds with a TRP INFORMATION RESPONSE message that contains the requested TRP information.

[0015] The successful operation of this procedure is illustrated in Figure 1 below. Figure 1 is a flow diagram illustrating TRP information exchange procedure with successful operation.

[0016] There are also procedures related to assistance data transfer between target UE and location server.

[0017] Long Term Evolution (LTE) Positioning Protocol (LPP) is used point-to-point between a location server (evolved serving mobile location center (E-SMLC), LMF or secure user plane location (SUPL) location platform (SLP)) and a target device (UE or SUPL enabled terminal(SET)) to position the target device using position-related measurements obtained by one or more reference sources. In the description below, without loss of generality, the location server is assumed to be LMF, and the target device is assumed to be a UE.

[0018] Several procedures are specified to enable the target UE to request assistance data from the location server to assist in positioning, and to enable the location server to transfer assistance data to the target in the absence of a request.

[0019] For example, the assistance data transfer procedure enables the target to request assistance data from the server to assist in positioning. The assistance data transfer procedure is shown in Figure 2. Figure 2 is a flow diagram illustrating LTE Positioning Protocol (LPP) assistance data transfer procedure.

[0020] The assistance data delivery procedure enables the server to provide unsolicited assistance data to the target and is shown in Figure 3. Figure 3 is a flow diagram illustrating LPP assistance data transfer procedure.

[0021] There currently exist certain challenges. For example, for UE-side model, one issue is that the network node(s) may voluntarily change NW-side additional condition across training and inference, while such NW-side additional condition can affect the UE-side model inference. If the UE is not notified of such changes in NW-side additional condition, then the UE-side model is likely to fail when performing model inference.

[0022] Specific to AI / ML based positioning, there is a need to ensure consistency in the following aspects of NW-side additional conditions:• TRP / antenna reference point (ARP) locations• TRP / ARP beam antenna information, which provides the relative DL-PRS resource power between PRS resources per angle per TRP.• DL PRS beam information (per-TRP), which provides the spatial directions of DL-PRS resources for TRPs• Mapping between PRS Resource Set and Resource IDs to physical anchor / TRP / ARP locations• Time synchronization information between the TRPs, e.g., between reference TRP and neighbor TRPs.

[0023] The NW-side additional conditions listed above are those that may change without UE knowledge unless signaling is provided to indicate the change.SUMMARY

[0024] As described above, certain challenges currently exist with consistency between machine learning (ML) model training and model inference. Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, particular embodiments assign network configuration identifier(s), which may be used to verify the consistency of network (NW)-side additional conditions between model training and model inference.

[0025] The network configuration identifier(s) may be exchanged between network nodes, for example, between gNB and location management function (LMF), to indicate the change in network side conditions and / or configurations.

[0026] The network configuration identifier(s) are signaled to the user equipment (UE) at training data collection stage and model inference stage. This enables the target UE to verify the consistency of NW-side additional conditions before the UE-side model is activated for model inference.

[0027] In general, in the existing specification TS 37.355 (LPP), some information elements (IES) already exist, which may be optionally used by the location server to provide assistance data to enable legacy UE-based downlink positioning methods (i.e., DL-TDOA, DL-AoD). For example:• NR-DL-PRS-PositioningFrequencyLayer-rl6: This field provides a list of positioning reference signal (PRS) configurations including: subcarrier spacing, PRS resource bandwidth, etc.• nr-TRP-LocationInfo-rl6: This field provides the location coordinates of the transmission reception points (TRPs) and location coordinates of antenna reference points (ARPs) for DL-PRS Resource Set(s) and DL-PRS Resources of the TRPs.• nr-DL-PRS-BeamInfo-rl6: This field provides the spatial directions of DL-PRS Resources for TRPs.• nr-RTD-Info-rl6: This field provides the time synchronization information between the reference TRP and neighbor TRPs.• nr-TRP-BeamAntennaInfo-rl7: This field provides the relative DL-PRS Resource power between PRS resources per angle per TRP.• assistanceDataValidityArea-rl7: This field specifies the network area for which the relevant assistance data is valid.

[0028] For AI / ML based positioning with UE-side model, to support verification of NW additional conditions, the IES above may be signaled from LMF to UE at training data collection stage and model inference stage.

[0029] In particular embodiments, the network configuration identifier(s) may be defined to identify a given set of NW additional conditions. This has the benefit that the IE details do not need to be explicitly signaled, if they are not needed for performing model inference, and only needed for consistency verification.

[0030] According to some embodiments, a method is performed by a wireless device. The method comprises receiving one or more first network configuration identifiers from a network node. Each of the one or more first network configuration identifiers identifying a particular configuration of the network node. The method further comprises associating the one or more first network configuration identifiers with a machine learning model, the association representing a network configuration used for training the machine learning model.

[0031] The method may further comprise receiving one or more second network configuration identifiers from a network node, each of the one or more second network configuration identifiers identifying a particular configuration of the network node and comparing the one or more second network configuration identifiers with the one or more first network configuration identifiers. Based upon the comparison, the method further comprises determining whether the machine learning model is suitable to be used for inference.

[0032] In particular embodiments, the method further comprises, upon determining the machine learning model is not suitable to be used for inference, transmitting a message to the network node indicating that the machine learning model is not suitable to be used for inference. In particular embodiments, the message further includes an indication of which of the one or more second network configuration identifiers do not match the one or more first network configuration identifiers.

[0033] In particular embodiments, a network configuration identifier identifies one or more of: a geographical location of a TRP; a location of an ARP; mobile TRP location information; antenna physical tilt of a TRP / ARP; and antenna physical pointing direction of a TRP / ARP.

[0034] In particular embodiments, a network configuration identifier identifies one or more of: TRP beam antenna information; ARP beam antenna information; downlink PRS beam information; and PRS quasi-colocation information.

[0035] In particular embodiments, a network configuration identifier identifies one or more of: time synchronization information between a TRP and a neighbor TRP; TRP receive / transmit TEG information; TRP receive TEG information; and TRP transmit TEG information.

[0036] In particular embodiments, a network configuration identifier identifies one or more of: a mapping between a PRS resource set and a physical location; PRS bandwidth; and PRS resource transmit power.

[0037] In particular embodiments, determining whether the machine learning model is suitable to be used for inference comprises determining whether a threshold number of the one or more second network configuration identifiers match the one or more first configuration identifiers.

[0038] According to some embodiments, a wireless device comprises processing circuitry operable to perform any of the wireless device methods described above.

[0039] 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.

[0040] According to some embodiments, a method is performed by a network node. The method comprises transmitting one or more first network configuration identifiers to one or more of a UE or a LMF network node. Each of the one or more first network configuration identifiers identifies a particular configuration of the network node to be used for training a machine learning model. The method further comprises transmitting one or more second network configuration identifiers to one or more of the UE or the LMF network node. Each of the one or more second network configuration identifiers identifies a particular configuration of the network node to be used for performing inference with the machine learning model.

[0041] In particular embodiments, the one or more first network configuration identifiers are the same as the one or more second network configuration identifiers.

[0042] In particular embodiments, the method further comprises determining a configuration of the network node has changed, and thus one or more of the second network configuration identifiers are different than the one or more first network configuration identifiers.

[0043] In particular embodiments, the method further comprises receiving a message from the UE indicating that the machine learning model is not suitable to be used for inference. In particular embodiments, the message further includes an indication of which of the one or more second network configuration identifiers do not match the one or more first network configuration identifiers.

[0044] In particular embodiments, determining the configuration of the network node has changed comprises determining one or more configuration parameters has changed by more than a threshold amount.

[0045] According to some embodiments, a network node comprises processing circuitry operable to perform any of the network node methods described above.

[0046] 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.

[0047] Certain embodiments may provide one or more of the following technical advantages. For example, particular embodiments define network configuration identifier(s) that may be used to verify the consistency of NW-side additional conditions between model training and model inference. The related signaling and procedures are provided, including the signaling between network nodes (e.g., between gNB and LMF), the signaling from network node (e.g., LMF) to UE, and a procedure at the UE to apply the network configuration identifier(s) to verify consistency.BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The present disclosure may be best understood by way of example with reference to the following description and accompanying drawings that are used to illustrate embodiments of the present disclosure. In the drawings:Figure l is a flow diagram illustrating TRP information exchange procedure with successful operation;Figure 2 is a flow diagram illustrating Long Term Evolution (LTE) Positioning Protocol (LPP) assistance data transfer procedure;Figure 3 is a flow diagram illustrating LPP assistance data transfer procedure;Figure 4 shows an example of a communication system, according to certain embodiments;Figure 5 shows a user equipment (UE), according to certain embodiments;Figure 6 shows a network node, according to certain embodiments;Figure 7 is a block diagram of a host, according to certain embodiments;Figure 8 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized;Figure 9 shows a communication diagram of a host communicating via a network node with a UE over a partially wireless connection in accordance with some embodiments;Figure 10 is a flowchart illustrating an example method in a wireless device, according to certain embodiments; andFigure 11 is a flowchart illustrating an example method in a network node, according to certain embodiments.DETAILED DESCRIPTION

[0049] As described above, certain challenges currently exist with consistency between machine learning (ML) model training and model inference. Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, particular embodiments assign network configuration identifier(s), which may be used to verify the consistency of network (NW)-side additional conditions between model training and model inference.

[0050] Particular embodiments are described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0051] As described above, for artificial intelligence (AI) / ML model input, channel measurement is obtained from reference signals, such as a UE measuring downlink reference signals (e.g., downlink (DL) positioning reference signal (PRS)) transmitted from the radio network nodes (such as the transmission reception points (TRPs)), or a radio network node (such as a TRP or a gNB) measuring uplink reference signals (e.g., uplink (UL) sounding reference signal (SRS)) transmitted from a user equipment (UE).

[0052] Additionally, if AI / ML is used to support sidelink (SL) positioning, then the ML data measurement node may be a UE measuring sidelink reference signal (e.g., sidelink (SL) PRS) transmitted by another UE.

[0053] In some embodiments, the non-limiting terms UE or a wireless device are used interchangeably. The UE herein may be any type of wireless device capable of communicating with a network node or another UE over radio signals. The UE may also be a radio communication device, target device, device to device (D2D) UE, machine type UE or UE capable of machine to machine communication (M2M), low-cost and / or low-complexity UE, a sensor equipped with UE, tablet, mobile terminals, smart phone, laptop embedded equipment (LEE), laptop mounted equipment (LME), USB dongles, customer premises equipment (CPE), an Internet of things (loT) device, or a narrowband loT (NB-IOT) device, etc.

[0054] NW configuration identifier may also be denoted by other names, for example, NW consistency identifier, or UE dataset identifier.

[0055] For proper AI / ML based positioning with UE-side model, the NW-side additional conditions need to be consistent between model training and model inference for the given UE-side model. However, the NW-side additional conditions may change without UE knowledge unless signaling is provided to indicate the change.

[0056] For example, DL PRS transmission beam of the TRPs needs to be consistent between model training and model inference for UE-side model (e.g., Case l / 2a), because the model input is obtained using DL PRS from the surrounding TRPs, and the model input can vary significantly if DL PRS is transmitted differently. If the network node(s) decide to change DL PRS beams for one or more TRPs that the UE performs measurement on, the UE should be notified of the change so that the UE can react to the change accordingly, e.g., exclude from model input those TRPs that have changed their DL PRS beams; deactivate the UE-side model; and / or retrain or fine-tune the UE-side model.

[0057] Some embodiments include NW configuration ID on NW additional condition. One or more NW configuration IDs may be defined to provide an identifier of network side additional conditions.

[0058] In a first example, one or more of the following types of NW configuration IDs are provided.

[0059] (A). Network location information ID. This ID covers one or more of the following network side information. For a given TRP ID, one network location information ID is mapped to one set of configurations (A-l) to (A-5), and different network location information IDs are mapped to different configurations in (A-l) to (A-5).1. Geographical coordinates of the TRP2. Antenna reference points (ARP) location information, which provides the relative position of ARP(s) to the TRP3. Mobile TRP Location Information4. Antenna physical tilt of the TRP / ARP5. Antenna physical pointing direction of the TRP / ARP

[0060] (B). Network antenna spatial information ID. This ID covers one or more of the following network side information. For a given TRP ID, one antenna spatial information ID is mapped to one set of configurations (B-l) to (B-3), and different antenna spatial information IDs are mapped to different configurations (B-l) to (B-3).1. TRP / ARP beam antenna information, which provides the relative DL-PRS Resource power between PRS resources per angle per TRP.2. DL PRS beam info (per-TRP), which provides the spatial directions of DL-PRS Resources for TRPs3. PRS quasi-colocation (QCL) information

[0061] (C). Network timing information ID. This ID covers one or more of the following network side information. For a given TRP ID, one network timing information ID is mapped to one set of configurations (C-l) to (C-4), and different network timing information IDs are mapped to different configurations (C-l) to (C-4).• Time synchronization information between the TRPs, e.g., between reference TRP and neighbor TRPs.• TRP RxTx timing error group (TEG) Information• TRP Tx TEG Information• TRP Rx TEG Information

[0062] (D). DL PRS configuration information ID. This ID covers one or more of the following network side information. For a given TRP ID, one DL PRS configuration information ID is mapped to one set of configurations (D-l) to (D-3), and different DL PRS configuration information IDs are mapped to different configurations (D-l) to (D-3).1. Mapping between PRS Resource Set IDs and Resource IDs to physical anchor / TRP / ARP locations2. PRS bandwidth3. PRS Resource Transmit Power

[0063] In another example, one NW configuration ID is provided to cover all aspects that may affect model inference performance of the UE-side model. The aspects that are identified that may affect model inference performance may be determined in 3GPP standardization, for example the standard defines a certain criterion that needs to be consistent for the UE-sided model to work properly. For example, the standard defines that the TRP locations needs to be consistent from UE training to UE inference, thus the TRP locations are the base for creating the identifier. Other aspects that are identified may be based on the UE reported capability, the UE capability may indicate which type of consistency is needed, and the NW may indicate the configuration identifier for such capability.

[0064] In another example, the NW configuration ID is further split into different parts covering the different consistency aspects. For example, considering option A above, the NW may define the Network location information ID as one non-limiting example according to the table below. How the NW allocates the range of L1,L2,L3 bits may be predefined in the standard, or signaled to the UE as part of LPP. The UE may use this information when training the model, for example it might require consistency in the coordinates of the TRP, but a change of tilt might only slightly degrade the performance or can be used by the UE to indicate a more uncertain location estimate.Table 1: Example of how the NW location ID may be split into subparts.

[0065] It is understood by those skilled in the art that the NW configuration IDs (A)-(D) are provided as an example to illustrate the principle. Other variations may be used without departing from the spirit of the methodology. For example, the information may be grouped differently. For example, information listed under (A) and (B) may be combined into one set; or the information listed under (A) is split into two groups. Some information may not be included. For example, "Mobile TRP Location Information" is not included in (A), if only fixed TRPs are considered for AI / ML based positioning. Some information may be added. For example, "Resource Repetition Factor" may be added to (D).

[0066] To reduce the number of needed identifiers, there needs to be a threshold on what degree of change constitutes a change of identifier. For example, if a TRP is moved Im from its original position, will this motivate a change of identifier that is based on the TRP location? The standard may define a certain predefined threshold on what motivates a change of identifier.

[0067] As one example, in example (A) above, (Network location info ID), geographical coordinates of the TRP have not changed with a certain amount (e.g., summed absolute changed of all relevant TRPs). In example (D) above, (DL PRS configuration info ID), transmit power is within a certain range of x dB.

[0068] The definition of what constitutes a change of identifier may in another embodiment be indicated to the UE. For example, the NW indicates what maximum change of TRP / ARP geographical locations that implies using the same identifier.

[0069] Some embodiments include signaling of NW configuration ID on NW additional condition between network nodes. The NW configuration ID on NW additional condition may be exchanged between gNB and LMF. This may be achieved via the TRP Information Exchange procedure, including the TRP INFORMATION REQUEST message and the TRP INFORMATION RESPONSE message.

[0070] In general, the information exchange between gNB and LMF may be one-time only, e.g., triggered by an event; request and response. Alternatively, the messages may be periodic until explicitly stopped.

[0071] As a non-limiting example, the NW configuration ID(s) may be exchanged between gNB and LMF via the TRP Information IE, which contains information for one TRP within an NG-RAN node. This is illustrated below, assuming that four NW configuration ID(s) are provided.

[0072] Some embodiments include signaling of NW configuration ID on updated NW additional condition between network nodes. The previous section describes a procedure that is initiated by LMF to obtain the NW configuration ID from the gNB node(s) to LMF. Another procedure that is initiated by a gNB towards LMF may be introduced to provide unsolicited information about changes in the NW-side additional conditions. The purpose of this procedure is to notify LMF of changes in previously indicated NW-side additional condition.

[0073] This procedure may be defined so that the gNB may initiate the procedure to send TRP information to LMF, i.e., without LMF making a request first. The gNB may trigger the procedure when the conditions and / or configurations at its TRPs have changed above a threshold to merit sending an update of TRP information to LMF. This procedure is useful because the LMF may not be aware of when the conditions and / or configurations at the TRPs have changed.

[0074] The procedure may compromise a gNB sending a notification message to notify of changes on the NW-additional conditions, after which the LMF may respond by TRP INFORMATION REQUEST message. The updated NW configuration ID are then provided to the LMF in the TRP INFORMATION RESPONSE message.

[0075] In an alternative embodiment, the gNB may directly include the updated NW configuration ID in the notification message.

[0076] Some embodiments include signaling of NW configuration ID on NW additional condition between UE and network nodes. The NW configuration ID on NW additional condition may be exchanged between UE and LMF. This may be achieved via the procedures that provides Assistance Data from LMF to the UE, for example, the Assistance Data Transfer procedure and the Assistance Data Delivery procedure.

[0077] One example IE to provide assistance data for the UE-side AI / ML model is shown below. The integers Nl, N2, N3, N4 represent the number of IDs for each field, minus 1.}

[0078] Some embodiments include NW configuration ID in training data collection and model inference. At training data collection of the UE-side model, for each TRP that DL-PRS measurement data is collected, the one or more NW configuration ID(s), ID_training(j), arerecorded as a part of the metadata, which provides the context information for training data collection. Here j=l ,2, ... J, and J is the total number of NW configuration ID(s).

[0079] When the UE-side model is trained and compiled for model inference, for each TRP that DL-PRS measurement data is collected, the one or more NW configuration ID(s), ID_training(j), are recorded as a part of the metadata of the model.

[0080] At the model inference stage, before the UE-side model can be activated for model inference, for each TRP that DL-PRS measurement data may be used as model input, the UE examines the NW configuration ID(s) to check whether consistency is maintained between training and inference in terms of NW additional condition.

[0081] First, for each TRP that DL-PRS measurement data may be used as model input, the target UE receives the one or more NW configuration ID(s), ID_inference(j), from the LMF.

[0082] Then, for each TRP k that DL-PRS measurement data may be used as model input, the target UE compares ID_inference(k, j ) with ID_training(k, j ), for each j = 1 ,2, ... J, and k is the TRP index or TRP ID. Let TRP set c be a set containing TRP indices or TRP IDs which has maintained consistency between training and inference for the given UE's model. Initialize TRP_set_c to NULL.

[0083] If ID_inference(k, j) = ID_training(k, j) for each j=l,2,... J, then the target UE may decide that consistency is maintained between training and inference in terms of NW-side additional condition for TRP k. Add TRP k to TRP set c.

[0084] Otherwise, if ID_inference(k, j) is different from ID_training(k, j) for any j=l,2,...J, then the target UE may decide that consistency is not maintained between training and inference in terms of NW-side additional condition for TRP k. Do not add TRP k to TRP set c.

[0085] The target UE makes a decision whether to activate the model for inference.

[0086] If there are sufficient number of TRPs in TRP set c, (i.e., sufficient number of TRPs have maintained consistency between training and inference for the given UE's model), then the target UE may decide to activate the model for inference. The DL-PRS from those TRPs in TRP set c can be used to provide model input. Also, the UE may optionally notify the LMF that it has successfully verified the consistency between training and inference in terms of NW-side additional condition. The UE may optionally notify LMF of the TRP set c.

[0087] Otherwise (i.e., insufficient number of TRPs have maintained consistency between training and inference for the given UE model), the target UE may not activate the model for inference. The UE may optionally notify the LMF that the UE has failed to verify the consistency between training and inference in terms of NW-side additional condition. This may be sent as apart of a FAILURE message or sent as an error code in a message. The error reason may be provided as: "inconsistency between training and inference in NW-side additional condition".

[0088] Furthermore, the UE may optionally notify the LMF of the TRP set c and make a request for other candidate TRPs that may be used by the target UE for positioning.

[0089] During inference, the UE may receive assistance information from the LMF that indicates change in the NW-side conditions that impact the applicability of the UE side model. The UE examines the updated NW configuration ID(s) to check whether consistency is maintained between training and inference in terms of NW additional condition. When the training conditions and the inference conditions are no longer consistent, the UE-side model is likely to fail when performing model inference. In this case, the UE notifies the LMF of the need to deactivate the model. This may be sent as a part of a FAILURE message or sent as an error code in a message. The error reason may be provided as: "inconsistency between training and inference in NW-side additional condition" .

[0090] Figure 4 shows an example of a communication system 100 in accordance with some embodiments. In the example, the communication system 100 includes a telecommunication network 102 that includes an access network 104, such as a radio access network (RAN), and a core network 106, which includes one or more core network nodes 108. The access network 104 includes one or more access network nodes, such as network nodes 110a and 110b (one or more of which may be generally referred to as network nodes 110), or any other similar 3rdGeneration Partnership Project (3GPP) access node or non-3GPP access point. The network nodes 110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 112a, 112b, 112c, and 112d (one or more of which may be generally referred to as UEs 112) to the core network 106 over one or more wireless connections.

[0091] 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.

[0092] 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 thenetwork 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.

[0093] 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).

[0094] 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.

[0095] As a whole, the communication system 100 of Figure 4 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 asthe 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.

[0096] In some examples, the telecommunication network 102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 102. For example, the telecommunications network 102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC)ZMassive loT services to yet further UEs.

[0097] In some examples, the UEs 112 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 104. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi -radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0098] In the example, the hub 114 communicates with the access network 104 to facilitate indirect communication between one or more UEs (e.g., UE 112c and / or 112d) and network nodes (e.g., network node 110b). In some examples, the hub 114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 114 may be a broadband router enabling access to the core network 106 for the UEs. As another example, the hub 114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 110, or by executable code, script, process, or other instructions in the hub 114. As another example, the hub 114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 114 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices.

[0099] The hub 114 may have a constant / persistent or intermitent connection to the network node 110b. The hub 114 may also allow for a different communication scheme and / or schedule between the hub 114 and UEs (e.g., UE 112c and / or 112d), and between the hub 114 and the core network 106. In other examples, the hub 114 is connected to the core network 106 and / or one or more UEs via a wired connection. Moreover, the hub 114 may be configured to connect to an M2M service provider over the access network 104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 110 while still connected via the hub 114 via a wired or wireless connection. In some embodiments, the hub 114 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 110b. In other embodiments, the hub 114 may be a nondedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0100] Figure 5 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.

[0101] 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).

[0102] 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 2. 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.

[0103] 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).

[0104] 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.

[0105] 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. Powercircuitry 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.

[0106] 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.

[0107] 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.

[0108] The processing circuitry 202 may be configured to communicate with an access network or other network using the communication interface 212. The communication interface 212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 222. The communication interface 212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 218 and / or a receiver 220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 218 and receiver 220 may be coupled to one or moreantennas (e.g., antenna 222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0109] In the illustrated embodiment, communication functions of the communication interface 212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short- range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / intemet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0110] 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), [oni] 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.

[0112] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoringdevice, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or itemtracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 200 shown in Figure 2.

[0113] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0114] 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.

[0115] Figure 6 shows a network node 300 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NRNodeBs (gNBs)).

[0116] 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).

[0117] 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).

[0118] The network node 300 includes a processing circuitry 302, a memory 304, a communication interface 306, and a power source 308. The network node 300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 304 for different RATs) and some components may be reused (e.g., a same antenna 310 may be shared by different RATs). The network node 300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 300.

[0119] 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 operableto provide, either alone or in conjunction with other network node 300 components, such as the memory 304, to provide network node 300 functionality.

[0120] 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.

[0121] 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.

[0122] 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 fdters 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 signalmay 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.

[0123] 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).

[0124] 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.

[0125] The antenna 310, communication interface 306, and / or the processing circuitry 302 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 310, the communication interface 306, and / or the processing circuitry 302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0126] 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 maycomprise 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.

[0127] Embodiments of the network node 300 may include additional components beyond those shown in Figure 6 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.

[0128] Figure 7 is a block diagram of a host 400, which may be an embodiment of the host 116 of Figure 4, in accordance with various aspects described herein. As used herein, the host 400 may be or comprise various combinations hardware and / or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host 400 may provide one or more services to one or more UEs.

[0129] The host 400 includes processing circuitry 402 that is operatively coupled via a bus 404 to an input / output interface 406, a network interface 408, a power source 410, and a memory 412. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as Figures 10 and 3, such that the descriptions thereof are generally applicable to the corresponding components of host 400.

[0130] The memory 412 may include one or more computer programs including one or more host application programs 414 and data 416, which may include user data, e.g., data generated by a UE for the host 400 or data generated by the host 400 for a UE. Embodiments of the host 400 may utilize only a subset or all of the components shown. The host application programs 414 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs 414 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host 400 may select and / or indicate a differenthost for over-the-top services for a UE. The host application programs 414 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.

[0131] Figure 8 is a block diagram illustrating a virtualization environment 500 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 500 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized.

[0132] Applications 502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0133] Hardware 504 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 508a and 508b (one or more of which may be generally referred to as VMs 508), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 506 may present a virtual operating platform that appears like networking hardware to the VMs 508.

[0134] The VMs 508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 506. Different embodiments of the instance of a virtual appliance 502 may be implemented on one or more of VMs 508, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used toconsolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0135] In the context of NFV, a VM 508 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 508, and that part of hardware 504 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 508 on top of the hardware 504 and corresponds to the application 502.

[0136] Hardware 504 may be implemented in a standalone network node with generic or specific components. Hardware 504 may implement some functions via virtualization. Alternatively, hardware 504 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 510, which, among others, oversees lifecycle management of applications 502. In some embodiments, hardware 504 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 512 which may alternatively be used for communication between hardware nodes and radio units.

[0137] Figure 9 shows a communication diagram of a host 602 communicating via a network node 604 with a UE 606 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE 112a of Figure 4 and / or UE 200 of Figure 5), network node (such as network node 110a of Figure 4 and / or network node 300 of Figure 6), and host (such as host 116 of Figure 4 and / or host 400 of Figure 7) discussed in the preceding paragraphs will now be described with reference to Figure 9.

[0138] Like host 400, embodiments of host 602 include hardware, such as a communication interface, processing circuitry, and memory. The host 602 also includes software, which is stored in or accessible by the host 602 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE 606 connecting via an over-the-top (OTT) connection 650 extending between the UE 606 and host602. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 650.

[0139] The network node 604 includes hardware enabling it to communicate with the host 602 and UE 606. The connection 660 may be direct or pass through a core network (like core network 106 of Figure 4) and / or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.

[0140] The UE 606 includes hardware and software, which is stored in or accessible by UE 606 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE 606 with the support of the host 602. In the host 602, an executing host application may communicate with the executing client application via the OTT connection 650 terminating at the UE 606 and host 602. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. The OTT connection 650 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection 650.

[0141] The OTT connection 650 may extend via a connection 660 between the host 602 and the network node 604 and via a wireless connection 670 between the network node 604 and the UE 606 to provide the connection between the host 602 and the UE 606. The connection 660 and wireless connection 670, over which the OTT connection 650 may be provided, have been drawn abstractly to illustrate the communication between the host 602 and the UE 606 via the network node 604, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0142] As an example of transmitting data via the OTT connection 650, in step 608, the host 602 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE 606. In other embodiments, the user data is associated with a UE 606 that shares data with the host 602 without explicit human interaction. In step 610, the host 602 initiates a transmission carrying the user data towards the UE 606. The host 602 may initiate the transmission responsive to a request transmitted by the UE 606. The request may be caused by human interaction with the UE 606 or by operation of the client application executing on the UE 606. The transmission may pass via the network node 604, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 612, the network node 604 transmits to the UE 606 the user datathat was carried in the transmission that the host 602 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 614, the UE 606 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 606 associated with the host application executed by the host 602.

[0143] In some examples, the UE 606 executes a client application which provides user data to the host 602. The user data may be provided in reaction or response to the data received from the host 602. Accordingly, in step 616, the UE 606 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input / output interface of the UE 606. Regardless of the specific manner in which the user data was provided, the UE 606 initiates, in step 618, transmission of the user data towards the host 602 via the network node 604. In step 620, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 604 receives user data from the UE 606 and initiates transmission of the received user data towards the host 602. In step 622, the host 602 receives the user data carried in the transmission initiated by the UE 606.

[0144] One or more of the various embodiments improve the performance of OTT services provided to the UE 606 using the OTT connection 650, in which the wireless connection 670 forms the last segment. More precisely, the teachings of these embodiments may improve the data rate and latency and thereby provide benefits such as reduced user waiting time, better responsiveness, and better QoE.

[0145] In an example scenario, factory status information may be collected and analyzed by the host 602. As another example, the host 602 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 602 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 602 may store surveillance video uploaded by a UE. As another example, the host 602 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, the host 602 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and / or transmitting data.

[0146] In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 650between the host 602 and UE 606, in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host 602 and / or UE 606. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 650 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 650 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node 604. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by the host 602. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 650 while monitoring propagation times, errors, etc.

[0147] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0148] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer- readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

[0149] Figure 10 is a flowchart illustrating an example method 1000 in a wireless device, according to certain embodiments. In particular embodiments, one or more steps of Figure 10 may be performed by UE 200 described with respect to Figure 5.

[0150] The method begins at step 1012, where the wireless device (e.g., UE 200) receives one or more first network configuration identifiers from a network node. Each of the one or more first network configuration identifiers identifying a particular configuration of the network node.

[0151] In particular embodiments, a network configuration identifier identifies one or more of: a geographical location of a TRP; a location of an ARP; mobile TRP location information; antenna physical tilt of a TRP / ARP; and antenna physical pointing direction of a TRP / ARP.

[0152] In particular embodiments, a network configuration identifier identifies one or more of: TRP beam antenna information; ARP beam antenna information; downlink PRS beam information; and PRS quasi-colocation information.

[0153] In particular embodiments, a network configuration identifier identifies one or more of: time synchronization information between a TRP and a neighbor TRP; TRP receive / transmit TEG information; TRP receive TEG information; and TRP transmit TEG information.

[0154] In particular embodiments, a network configuration identifier identifies one or more of: a mapping between a PRS resource set and a physical location; PRS bandwidth; and PRS resource transmit power.

[0155] Additional examples of network configuration identifiers are described in more detail with respect to the embodiments and examples described above.

[0156] At step 1014, the wireless device associates the one or more first network configuration identifiers with a machine learning model . The association represents a network configuration used fortraining the machine learning model.

[0157] At step 1016, the wireless device receives one or more second network configuration identifiers from a network node. Each of the one or more second network configuration identifiers identifies a particular configuration of the network node.

[0158] At step 1018, the wireless device compares the one or more second network configuration identifiers with the one or more first network configuration identifiers.

[0159] Based upon the comparison, at step 1020, the wireless device determines whether the machine learning model is suitable to be used for inference. For example, if the first and second identifiers match, then the network configuration used for training the machine learning model matches the current network configuration and thus the machine learning model may be used for inference.

[0160] In particular embodiments, determining whether the machine learning model is suitable to be used for inference comprises determining whether a threshold number of the one or more second network configuration identifiers match the one or more first configuration identifiers.

[0161] In particular embodiments, the wireless device may determine whether the machine learning model is suitable to be used for inference based on any of the embodiments and / or examples described herein.

[0162] At step 1022, upon determining the machine learning model is not suitable to be used for inference, the wireless device transmits a message to the network node indicating that the machine learning model is not suitable to be used for inference. In particular embodiments, the message further includes an indication of which of the one or more second network configuration identifiers do not match the one or more first network configuration identifiers.

[0163] Modifications, additions, or omissions may be made to method 1000 of Figure 10. Additionally, one or more steps in the method of Figure 10 may be performed in parallel or in any suitable order.

[0164] Figure 11 is a flowchart illustrating an example method 1100 in a network node, according to certain embodiments. In particular embodiments, one or more steps of Figure 11 may be performed by network node 300 described with respect to Figure 6.

[0165] The method may begin at step 1112, where the network node (e.g., network node 300) transmits one or more first network configuration identifiers to one or more of a UE or a LMF network node. Each of the one or more first network configuration identifiers identifies a particular configuration of the network node to be used fortraining a machine learning model.

[0166] At step 1114, the network node may determine that a configuration of the network node has changed. In this case, one or more of the second network configuration identifiers in thenext step are different than the one or more first network configuration identifiers. Otherwise, the one or more first network configuration identifiers are the same as the one or more second network configuration identifiers in the next step.

[0167] In particular embodiments, determining the configuration of the network node has changed comprises determining one or more configuration parameters has changed by more than a threshold amount. Other examples of determining the configuration of the network node has changed are described with respect to the embodiments and examples described above.

[0168] At step 1116, the network node transmits one or more second network configuration identifiers to one or more of the UE or the LMF network node. Each of the one or more second network configuration identifiers identifies a particular configuration of the network node to be used for performing inference with the machine learning model.

[0169] The wireless device will compare the one or more second network configuration identifiers to the one or more first network configuration identifiers to determine if a machine learning model at the wireless device can be used for inference.

[0170] At step 1118, the network node receives a message from the UE indicating that the machine learning model is not suitable to be used for inference. In particular embodiments, the message further includes an indication of which of the one or more second network configuration identifiers do not match the one or more first network configuration identifiers.

[0171] Modifications, additions, or omissions may be made to method 1100 of Figure 11. Additionally, one or more steps in the method of Figure 11 may be performed in parallel or in any suitable order.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Some example embodiments are described below.Group A Embodiments1. A method performed by a wireless device, the method comprising:- obtaining one or more associations between a machine learning model and an associated training configuration identifier, the training configuration identifier representing network conditions used when training the associated machine learning model;- receiving a configuration identifier associated with current network conditions; and- determining whether a machine learning model may be used for inferences based on its associated training configuration identifier and the configuration identifier associated with current network conditions.2. The method of the previous embodiment, wherein the configuration identifier represents any of the conditions described in the embodiments and examples above.3. 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.4. The method of the previous embodiment, further comprising one or more additional wireless device steps, features or functions described above.5. 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 Embodiments6. A method performed by a base station, the method comprising: a. transmitting one or more associations between a machine learning model and an associated training configuration identifier to a wireless device, the training configuration identifier representing network conditions used when training the associated machine learning model; and b. transmitting a configuration identifier associated with current network conditions; to the wireless device.7. The method of the previous embodiment, wherein the configuration identifier represents any of the conditions described in the embodiments and examples above.8. The method of any one of the previous embodiments, further comprising transmitting the one or more associations between a machine learning model and an associated training configuration identifier to a location management function (LMF).9. The method of any one of the previous embodiments, further comprising transmitting the configuration identifier associated with current network conditions to a location management function (LMF).10. 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.11. The method of the previous embodiment, further comprising one or more additional base station steps, features or functions described above.12. 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 Embodiments13. A mobile terminal comprising:- processing circuitry configured to perform any of the steps of any of the Group Aembodiments; and power supply circuitry configured to supply power to the wireless device. 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. 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;- 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. 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. The communication system of the pervious embodiment further including the base station. The communication system of the previous 2 embodiments, further including the UE, wherein the UE is configured to communicate with the base station.The communication system of the previous 3 embodiments, wherein:- the processing circuitry of the host computer is configured to execute a host application, thereby providing the user data; and- the UE comprises processing circuitry configured to execute a client application associated with the host application. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:- at the host computer, providing user data; and- at the host computer, initiating a transmission carrying the user data to the UE via a cellular network comprising the base station, wherein the base station performs any of the steps of any of the Group B embodiments. The method of the previous embodiment, further comprising, at the base station, transmitting the user data. The method of the previous 2 embodiments, wherein the user data is provided at the host computer by executing a host application, the method further comprising, at the UE, executing a client application associated with the host application. A user equipment (UE) configured to communicate with a base station, the UE comprising a radio interface and processing circuitry configured to performs any of the previous 3 embodiments. A communication system including a host computer comprising:- processing circuitry configured to provide user data; and- a communication interface configured to forward user data to a cellular network for transmission to a user equipment (UE),- wherein the UE comprises a radio interface and processing circuitry, the UE’s components configured to perform any of the steps of any of the Group A embodiments. The communication system of the previous embodiment, wherein the cellular networkfurther includes a base station configured to communicate with the UE. The communication system of the previous 2 embodiments, wherein:- the processing circuitry of the host computer is configured to execute a host application, thereby providing the user data; and- the UE’s processing circuitry is configured to execute a client application associated with the host application. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:- at the host computer, providing user data; and- at the host computer, initiating a transmission carrying the user data to the UE via a cellular network comprising the base station, wherein the UE performs any of the steps of any of the Group A embodiments. The method of the previous embodiment, further comprising at the UE, receiving the user data from the base station. A communication system including a host computer comprising:- communication interface configured to receive user data originating from a transmission from a user equipment (UE) to a base station,- wherein the UE comprises a radio interface and processing circuitry, the UE’s processing circuitry configured to perform any of the steps of any of the Group A embodiments. The communication system of the previous embodiment, further including the UE. The communication system of the previous 2 embodiments, further including the base station, wherein the base station comprises a radio interface configured to communicate with the UE and a communication interface configured to forward to the host computer the user data carried by a transmission from the UE to the base station. The communication system of the previous 3 embodiments, wherein:- the processing circuitry of the host computer is configured to execute a hostapplication; and the UE’s processing circuitry is configured to execute a client application associated with the host application, thereby providing the user data. The communication system of the previous 4 embodiments, wherein:- the processing circuitry of the host computer is configured to execute a host application, thereby providing request data; and- the UE’s processing circuitry is configured to execute a client application associated with the host application, thereby providing the user data in response to the request data. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:- at the host computer, receiving user data transmitted to the base station from the UE, wherein the UE performs any of the steps of any of the Group A embodiments. The method of the previous embodiment, further comprising, at the UE, providing the user data to the base station. The method of the previous 2 embodiments, further comprising:- at the UE, executing a client application, thereby providing the user data to be transmitted; and- at the host computer, executing a host application associated with the client application. The method of the previous 3 embodiments, further comprising:- at the UE, executing a client application; and- at the UE, receiving input data to the client application, the input data being provided at the host computer by executing a host application associated with the client application,- wherein the user data to be transmitted is provided by the client application in response to the input data. A communication system including a host computer comprising a communication interfaceconfigured to receive user data originating from a transmission from a user equipment (UE) to a base station, wherein the base station comprises 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. The communication system of the previous embodiment further including the base station. The communication system of the previous 2 embodiments, further including the UE, wherein the UE is configured to communicate with the base station. The communication system of the previous 3 embodiments, wherein:- the processing circuitry of the host computer is configured to execute a host application;- the UE is configured to execute a client application associated with the host application, thereby providing the user data to be received by the host computer. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:- at the host computer, receiving, from the base station, user data originating from a transmission which the base station has received from the UE, wherein the UE performs any of the steps of any of the Group A embodiments. The method of the previous embodiment, further comprising at the base station, receiving the user data from the UE. The method of the previous 2 embodiments, further comprising at the base station, initiating a transmission of the received user data to the host computer.

Claims

CLAIMS1. A method performed by a wireless device, the method comprising: receiving (1012) one or more first network configuration identifiers from a network node, each of the one or more first network configuration identifiers identifying a particular configuration of the network node; and associating (1014) the one or more first network configuration identifiers with a machine learning model, the association representing a network configuration used for training the machine learning model.

2. The method of claim 1, further comprising: receiving (1016) one or more second network configuration identifiers from a network node, each of the one or more second network configuration identifiers identifying a particular configuration of the network node; comparing (1018) the one or more second network configuration identifiers with the one or more first network configuration identifiers; and based upon the comparison, determining (1020) whether the machine learning model is suitable to be used for inference.

3. The method of any one of claims 1-2, further comprising, upon determining the machine learning model is not suitable to be used for inference, transmitting (1022) a message to the network node indicating that the machine learning model is not suitable to be used for inference.

4. The method of claim 3, wherein the message further includes an indication of which of the one or more second network configuration identifiers do not match the one or more first network configuration identifiers.

5. The method of any one of claims 1-4, wherein a network configuration identifier identifies one or more of: a geographical location of a transmission reception point (TRP); a location of an antenna reference point (ARP); mobile TRP location information; antenna physical tilt of a TRP / ARP; andantenna physical pointing direction of a TRP / ARP.

6. The method of any one of claims 1-5, wherein a network configuration identifier identifies one or more of: transmission reception point (TRP) beam antenna information; antenna reference point (ARP) beam antenna information; downlink positioning reference signal (PRS) beam information; andPRS quasi-colocation information.

7. The method of any one of claims 1-6, wherein a network configuration identifier identifies one or more of: time synchronization information between a transmission reception point (TRP) and a neighbor TRP;TRP receive / transmit timing error group (TEG) information;TRP receive TEG information; andTRP transmit TEG information.

8. The method of any one of claims 1-7, wherein a network configuration identifier identifies one or more of: a mapping between a positioning reference signal (PRS) resource set and a physical location;PRS bandwidth; andPRS resource transmit power.

9. The method of claim 2, wherein determining whether the machine learning model is suitable to be used for inference comprises determining whether a threshold number of the one or more second network configuration identifiers match the one or more first configuration identifiers.

10. The method of any one of claims 1-9, wherein the network node comprises one of a radio access network (RAN) network node or a location management function (LMF) network node.

11. A wireless device (200) comprising processing circuitry (202), the processing circuitryoperable to: receive one or more first network configuration identifiers from a network node (300), each of the one or more first network configuration identifiers identifying a particular configuration of the network node; and associate the one or more first network configuration identifiers with a machine learning model, the association representing a network configuration used for training the machine learning model.

12. The wireless device of claim 11, the processing circuitry further operable to: receive one or more second network configuration identifiers from a network node, each of the one or more second network configuration identifiers identifying a particular configuration of the network node; compare the one or more second network configuration identifiers with the one or more first network configuration identifiers; and based upon the comparison, determine whether the machine learning model is suitable to be used for inference.

13. The wireless device of any one of claims 11-12, the processing circuitry further operable to, upon determining the machine learning model is not suitable to be used for inference, transmit a message to the network node indicating that the machine learning model is not suitable to be used for inference.

14. The wireless device of claim 13, wherein the message further includes an indication of which of the one or more second network configuration identifiers do not match the one or more first network configuration identifiers.

15. The wireless device of any one of claims 11-14, wherein a network configuration identifier identifies one or more of: a geographical location of a transmission reception point (TRP); a location of an antenna reference point (ARP); mobile TRP location information; antenna physical tilt of a TRP / ARP; and antenna physical pointing direction of a TRP / ARP.

16. The wireless device of any one of claims 11-15, wherein a network configuration identifier identifies one or more of: transmission reception point (TRP) beam antenna information; antenna reference point (ARP) beam antenna information; downlink positioning reference signal (PRS) beam information; andPRS quasi-colocation information.

17. The wireless device of any one of claims 11-16, wherein a network configuration identifier identifies one or more of: time synchronization information between a transmission reception point (TRP) and a neighbor TRP;TRP receive / transmit timing error group (TEG) information;TRP receive TEG information; andTRP transmit TEG information.

18. The wireless device of any one of claims 11-17, wherein a network configuration identifier identifies one or more of: a mapping between a positioning reference signal (PRS) resource set and a physical location;PRS bandwidth; andPRS resource transmit power.

19. The wireless device of claim 12, wherein the processing circuitry is operable to determine whether the machine learning model is suitable to be used for inference by determining whether a threshold number of the one or more second network configuration identifiers match the one or more first configuration identifiers.

20. The wireless device of any one of claims 11-19, wherein the network node comprises one of a radio access network (RAN) network node or a location management function (LMF) network node.

21. A method performed by a network node, the method comprising: transmitting (1112) one or more first network configuration identifiers to one or more of a user equipment (UE) or a location management function (LMF) network node, each of the one ormore first network configuration identifiers identifying a particular configuration of the network node to be used for training a machine learning model; and transmitting (1116) one or more second network configuration identifiers to one or more of the UE or the LMF network node, each of the one or more second network configuration identifiers identifying a particular configuration of the network node to be used for performing inference with the machine learning model.

22. The method of claim 21, wherein the one or more first network configuration identifiers are the same as the one or more second network configuration identifiers.

23. The method of claim 21, further comprising determining (1114) a configuration of the network node has changed, and wherein one or more of the second network configuration identifiers are different than the one or more first network configuration identifiers.

24. The method of claim 23, further comprising receiving (1118) a message from the UE indicating that the machine learning model is not suitable to be used for inference.

25. The method of claim 24, wherein the message further includes an indication of which of the one or more second network configuration identifiers do not match the one or more first network configuration identifiers.

26. The method of any one of claims 21-25, wherein a network configuration identifier identifies one or more of: a geographical location of a transmission reception point (TRP); a location of an antenna reference point (ARP); mobile TRP location information; antenna physical tilt of a TRP / ARP; and antenna physical pointing direction of a TRP / ARP.

27. The method of any one of claims 21-26, wherein a network configuration identifier identifies one or more of: transmission reception point (TRP) beam antenna information; antenna reference point (ARP) beam antenna information; downlink positioning reference signal (PRS) beam information; andPRS quasi-colocation information.

28. The method of any one of claims 21-27, wherein a network configuration identifier identifies one or more of: time synchronization information between a transmission reception point (TRP) and a neighbor TRP;TRP receive / transmit timing error group (TEG) information;TRP receive TEG information; andTRP transmit TEG information.

29. The method of any one of claims 21-28, wherein a network configuration identifier identifies one or more of: a mapping between a positioning reference signal (PRS) resource set and a physical location;PRS bandwidth; andPRS resource transmit power.

30. The method of claim 23, wherein determining the configuration of the network node has changed comprises determining one or more configuration parameters has changed by more than a threshold amount.

31. A network node (300) comprising processing circuitry (302), the processing circuitry operable to: transmit one or more first network configuration identifiers to one or more of a user equipment (UE) (200) or a location management function (LMF) network node, each of the one or more first network configuration identifiers identifying a particular configuration of the network node to be used for training a machine learning model; and transmit one or more second network configuration identifiers to one or more of the UE or the LMF network node, each of the one or more second network configuration identifiers identifying a particular configuration of the network node to be used for performing inference with the machine learning model.

32. The network node of claim 31, wherein the one or more first network configuration identifiers are the same as the one or more second network configuration identifiers.

33. The network node of claim 31, the processing circuitry further operable to determine a configuration of the network node has changed, and wherein one or more of the second network configuration identifiers are different than the one or more first network configuration identifiers.

34. The network node of claim 33, the processing circuitry further operable to receive a message from the UE indicating that the machine learning model is not suitable to be used for inference.

35. The network node of claim 34, wherein the message further includes an indication of which of the one or more second network configuration identifiers do not match the one or more first network configuration identifiers.

36. The network node of any one of claims 31-35, wherein a network configuration identifier identifies one or more of: a geographical location of a transmission reception point (TRP); a location of an antenna reference point (ARP); mobile TRP location information; antenna physical tilt of a TRP / ARP; and antenna physical pointing direction of a TRP / ARP.

37. The network node of any one of claims 31-36, wherein a network configuration identifier identifies one or more of: transmission reception point (TRP) beam antenna information; antenna reference point (ARP) beam antenna information; downlink positioning reference signal (PRS) beam information; andPRS quasi-colocation information.

38. The network node of any one of claims 31-37, wherein a network configuration identifier identifies one or more of: time synchronization information between a transmission reception point (TRP) and a neighbor TRP;TRP receive / transmit timing error group (TEG) information;TRP receive TEG information; andTRP transmit TEG information.

39. The network node of any one of claims 31-38, wherein a network configuration identifier identifies one or more of: a mapping between a positioning reference signal (PRS) resource set and a physical location;PRS bandwidth; andPRS resource transmit power.

40. The network node of claim 33, wherein determining the configuration of the network node has changed comprises determining one or more configuration parameters has changed by more than a threshold amount.

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