Determining an RTOA reference time for ai / ML based user equipment positioning

Abstract

Systems and methods related to direct or assisted Artificial Intelligence (AI) / Machine Learning (ML) UE positioning are disclosed. In one embodiment, a method performed by a User Equipment (UE) comprises obtaining a downlink (DL) relative time of arrival (RTOA) reference time for a downlink measurement for an AI / ML model input for an AI / ML model related to UE positioning based on DL RTOA measurements, the DL RTOA reference time being an absolute time. The method further comprises using the DL RTOA reference time for the downlink measurement for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on DL RTOA measurements. By using the DL RTOA reference time, timing information is correctly interpreted, regardless of the many variables in AI / ML operation.

Description

DETERMINING AN RTOA REFERENCE TIME FOR AI / ML BASED USER EQUIPMENT POSITIONINGRELATED APPLICATIONS

[0001] This application claims the benefit of provisional patent application serial number 63 / 554,481, filed February 16, 2024, the disclosure of which is hereby incorporated herein by reference in its entirety.TECHNICAL FIELD

[0002] The present disclosure relates to User Equipment (UE) positioning in a cellular communications system and, more specifically, to direct or assisted Artificial Intelligence (AI) / Machine Learning (ML) UE positioning.BACKGROUND

[0003] 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-LOS (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; using deep reinforcement learning to learn an optimal precoding policy for complex Multiple Input Multiple Output (MIMO) precoding problems.

[0004] In 3rdGeneration Partnership Project (3GPP) New Radio (NR) standardization work, the Release 18 study item on AI / ML for NR air interface has been completed. This 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 aims at laying the foundation for future air-interface use cases leveraging AI / ML techniques.

[0005] Building an AI / ML model includes several development steps where the actual training of the Al model is just one step in a training pipeline. An important part in AI / ML developing is the AI / ML model lifecycle management. This is illustrated in Figure 1. The Al model lifecycle management typically consists of• A training (re-training) pipeline,o With data ingestion referring to gathering raw (training) data from a data storage. After data ingestion, there may also be a step that controls the validity of the gathered data. o With data pre-processing referring to some feature engineering applied to the gathered data, e.g., it may include data normalization and possibly a data transformation required for the input data to the AI / ML model. o With the actual model training steps where a model is obtained using the training dataset. o With model evaluation referring to benchmarking the performance to some baseline. The iterative steps of model training and model evaluation continues until the acceptable level of performance is achieved. o With model registration referring to register the AI / ML model, including any corresponding AI / ML-meta data that provides information on how the AI / ML model was developed, and possibly AI / ML model evaluations performance outcomes.• A deployment stage to make the trained (or re-trained) AI / ML model part of the inference pipeline.• An inference pipeline, o With data ingestion referring to gathering raw (inference) data from a data storage. o With data pre-processing stage that is typically identical to corresponding processing that occurs in the training pipeline. o With model operational referring to using the trained and deployed model in an operational mode. o With data & model monitoring referring to validate that the inference data are from a distribution that aligns well with the training data, as well as monitoring model outputs for detecting any performance, or operational, drifts.• A drift detection stage that informs about any drifts in the model operations.

[0006] 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 target UE's 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 AI / ML model.• AI / ML assisted positioning, where the AI / ML model output is new measurement and / or enhancement of 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.

[0007] When applying the direct and assisted AI / ML positioning to NR wireless communication network, the following cases are further identified for investigation:• 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 gNodeB (gNB)-side model, AI / ML assisted positioning• Case 3b: NG-RAN node assisted positioning with LMF-side model, direct AI / ML positioning

[0008] Direct AI / ML positioning is illustrated in Figure 2.

[0009] For Assisted AI / ML positioning, multiple constructions are possible.• Assisted AI / ML positioning with multi-Transmission and Reception Point (TRP) construction, see an example illustrated in Figure 3.• Assisted positioning with single-TRP construction and one model for N TRPs, see an example illustrated in Figure 4.• Assisted positioning with single-TRP construction and N models for N TRPs, see an example illustrated in Figure 5.

[0010] For radio signal based positioning methods, conventional methods rely on a sufficient number of LOS links, typically at least three to five LOS links depending on the positioning method, and whether vertical position is estimated in addition to horizontal position. In a cluttered environment, there is often a low probability of line-of-sight for a radio link between a UE and a TRP. For example, for InF-DH (Indoor Factory with Dense clutter and High base station height (Tx or Rx elevated above the clutter)) environment, the LOS probability ranges from 44.9% in a mildly cluttered environment to only 0.8% in a heavily cluttered environment. Thus, conventional positioning methods struggle to locate a target UE in a heavily cluttered environment. Evaluations show that the 90%-tile positioning accuracy of conventional positioning methods is more than 15 meters in an InF-DH environment with clutter {60%, 6m, 2m}, due to the unavailability ofsufficient LOS links. This motivates the application of AI / ML based positioning in such challenging deployment environments.

[0011] For radio signal based positioning, timing information is used extensively, since timing information can be converted to distance information in determining the location of a target UE. In Figures 18, 19 20, 21, 22, 23A-23B, and 24, various timing-related information elements (IE) in the existing 3GPP NR specifications are shown with emphasis added via bold, italicized text.SUMMARY

[0012] Systems and methods related to direct or assisted Artificial Intelligence (AI) / Machine Learning (ML) UE positioning are disclosed. In one embodiment, a method performed by a User Equipment (UE) comprises obtaining a downlink (DL) relative time of arrival (RTOA) reference time for a downlink measurement for an AI / ML model input for an AI / ML model related to UE positioning based on DL RTOA measurements, the DL RTOA reference time being an absolute time. The method further comprises using the DL RTOA reference time for the downlink measurement for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on DL RTOA measurements. By using the DL RTOA reference time, timing information is correctly interpreted, regardless of the many variables in AI / ML operation.

[0013] In one embodiment, the DL RTOA reference time is associated to a downlink reference signal.

[0014] In one embodiment, the DL RTOA reference time is associated to a downlink reference signal and transmission and reception point (TRP).

[0015] In one embodiment, the downlink reference signal is a positioning reference signal (PRS), and the DL RTOA reference time is defined as:^DL-RTOA,ref = ?0 +fPRS wherein:• To is the beginning time of System Frame Number, SFN, 0 provided by a SFN Initialization Time associated with a TRP transmitting the PRS; and• fpRS=(10nf+ nsf) x IO’3, where nfand nsfare an SFN and the subframe number of the PRS, respectively.

[0016] In one embodiment, using the DL RTOA reference time for the downlink measurement for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on DL RTOA measurements comprises representing timing information of the AI / ML model input to the AI / ML model based on the DL RTOA reference time.

[0017] In one embodiment, using the DL RTOA reference time for the downlink measurement for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on DL RTOA measurements comprises representing timing information of the downlink measurement for the AI / ML model input to the AI / ML model based on the DL RTOA reference time.

[0018] In one embodiment, using the DL RTOA reference time for the downlink measurement for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on DL RTOA measurements comprises generating one or more model outputs of the AI / ML model such that timing information of the one or more model outputs is relative to DL RTOA reference time. In one embodiment, the method further comprises postprocessing the one or more model outputs including the timing information in accordance with a desired measurement report format. In one embodiment, the method further comprises reporting the one or more post-processed model outputs to a Location Management Function (LMF).

[0019] Corresponding embodiments of a UE are also disclosed. In one embodiment, a UE comprises a communication interface comprising a transmitter and a receiver, and processing circuitry associated with the communication interface. The processing circuitry is configured to cause the UE to obtain a DL RTOA reference time for a downlink measurement for an AI / ML model input for an AI / ML model related to UE positioning based on DL RTOA measurements, the DL RTOA reference time being an absolute time. The processing circuitry is further configured to cause the UE to use the DL RTOA reference time for the downlink measurement for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on DL RTOA measurements.

[0020] Embodiments of a method performed by a network node are also disclosed. In one embodiment, a method performed by a network node comprises determining an uplink (UL) RTOA reference time for an uplink measurement for an AI / ML model input for an AI / ML model related to User Equipment (UE) positioning based on UL RTOA measurements, the UL RTOA reference time being an absolute time. The method further comprises using the UL RTOA reference time for the uplink measurement for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on UL RTOA measurements.

[0021] In one embodiment, the UL RTOA reference time is defined as To + tsRS, where To is a nominal beginning time of System Frame Number, SFN, 0 provided by SFN Initialization Time and tsRS is equal to (10nf + nSf)xl0'3where nf and nSf are an SFN and subframe number of a corresponding SRS used for the uplink measurement, respectively.

[0022] In one embodiment, the method further comprises repeating the steps of determining and using for a plurality of uplink measurements for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on UL RTOA measurements.

[0023] In one embodiment, using the UL RTOA reference time for the uplink measurement for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on UL RTOA measurements comprises representing timing information of the uplink measurement for the AI / ML model input based on the UL RTOA reference time. In one embodiment, the AI / ML model input comprises a Channel Impulse Response (CIR), a Power Delay Profile (PDP), or a Delay Profile (DP).

[0024] In one embodiment, the AI / ML model is for direct AI / ML positioning.

[0025] In one embodiment, the AI / ML model is for assisted AI / ML positioning. In one embodiment, the method further comprises, during inference, reporting one or more outputs of the AI / ML model including associated timing information represented based on UL RTOA reference time to an LMF. In another embodiment, the method further comprises, during inference, postprocessing one or more outputs of the AI / ML model including associated timing information represented based on UL RTOA reference time such that the timing information of the postprocessed outputs is in a desired reporting format. In one embodiment, the method further comprises, during inference, reporting the post-processed outputs to an LMF.

[0026] Corresponding embodiment of a network node are also disclosed. In one embodiment, a network node for a cellular communications system comprises processing circuitry configured to cause the network node to determine an UL RTOA reference time for an uplink measurement for an AI / ML model input for an AI / ML model related to UE positioning based on UL RTOA measurements, the UL RTOA reference time being an absolute time. The processing circuitry is further configured to cause the network node to use the UL RTOA reference time for the uplink measurement for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on UL RTOA measurements.BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain the principles of the disclosure.

[0028] Figure 1 illustrates an Artificial Intelligence (AI) / Machine Learning (ML) model Lifecyle Management (LCM).

[0029] Figure 2 illustrates direct AI / ML User Equipment (UE) positioning.

[0030] Figure 3 illustrates assisted AI / ML UE positioning with multi-Transmission and Reception Point (TRP) construction.

[0031] Figure 4 illustrates assisted AI / ML UE positioning with a single TRP construction and one model for N TRPs.

[0032] Figure 5 illustrates assisted AI / ML UE positioning with single TRP construction and N models for N TRPs.

[0033] Figure 6 illustrates downlink (DL) Relative Time of Arrival (RTOA) Reference Time (TDL-RTOA,ref) for three Positioning Reference Signal (PRS) resources received at a UE, and the DL RTOA for each PRS, in accordance with an embodiment of the present disclosure.

[0034] Figure 7 illustrates postprocessing of the AI / ML model output of an assisted AI / ML model to generate timing information required by legacy positioning method, in accordance with an embodiment of the present disclosure.

[0035] Figure 8 is an illustration of DL RTOA, which is produced at the AI / ML model output for assisted positioning. With the knowledge of reference time (TDL-RTOA,ref) for each PRS resource, the Reference Signal Time Difference (RSTD) between a given TRP and a reference TRP can be calculated and reported, in accordance with an embodiment of the present disclosure.

[0036] Figure 9 is an illustration of timing information of three multi-path channel measurements, in accordance with an embodiment of the present disclosure.

[0037] Figure 10 illustrates the operation of a UE and a network node, in accordance with one example embodiment of the present disclosure.

[0038] Figure 11 illustrates the operation of a network node (e.g., a Radio Access Network (RAN) node such as, e.g., a gNB), in accordance with one example embodiment of the present disclosure /

[0039] Figure 12 shows an example of a communication system in which embodiments of the present disclosure may be implemented.

[0040] Figure 13 shows a UE in accordance with some embodiments.

[0041] Figure 14 shows a network node in accordance with some embodiments.

[0042] Figure 15 is a block diagram of a host, which may be an embodiment of the host ofFigure 12, in accordance with various aspects described herein.

[0043] Figure 16 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized.

[0044] Figure 17 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.

[0045] Figures 18, 19, 20, 21, 22, 23 A, 23B, and 24 illustrate various timing-related information elements (IE) in the existing 3rdGeneration Partnership Project (3GPP) New Radio (NR) specifications.DETAILED DESCRIPTION

[0046] The embodiments set forth below represent information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure.

[0047] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0048] There currently exist certain challenge(s). In 3rdGeneration Partnership Project (3 GPP) New Radio (NR) specifications, for positioning methods based on measurements of downlink (DL) reference signals, currently, no reference time is defined independent of a reference Transmission and Reception Point (TRP). The reference TRP designation may vary between UEs and vary from time to time. Consequently, the clock time of received DL reference signal is not known.

[0049] When Artificial Intelligence (AI) / Machine Learning (ML) based positioning is used, timing information is embedded in model input. This is true for both direct and assisted AI / ML positioning. Moreover, timing information is the most typical model output of AI / ML assisted positioning.

[0050] For model training, training data is collected from a wide range of different User Equipments (UEs) and / or NR base stations (i.e., gNodeBs, gNBs), and likely over a wide range of time instances. Similarly, model inference is performed by a multitude of different UEs and / or gNBs at a wide range of different time instances.

[0051] If the reference time is not known as an absolute time (i.e., clock time), it is not possible to correctly extract the timing information of the wireless channels. Therefore, without such reference time, it is not clear how to correctly collect training data samples for an AI / ML model, whenever timing information is collected as part of the training data, for data corresponding to model input as well as data corresponding to model output. Moreover, during model inference, it is not clear how to provide model input to the model, or how to use the model output, whenevertiming information is involved. Thus, there is a need to provide reference time in absolute time (or clock time) to support training data collection, model training, and model inference for AI / ML based positioning.

[0052] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. In this disclosure, embodiments of systems and methods are disclosed that include any one or more of the following aspects:• Introduce the concept of DL Relative Time of Arrival (RTOA) reference time in terms of absolute time (i.e., clock time).• Location Management Function (LMF) provides DL RTOA reference time to a UE as a type of assistance data. This ensures that the UE has the knowledge of reference time in terms of absolute time (i.e., clock time).• Use DL RTOA reference time in AI / ML model output generation of assisted method in UE (Case 2a), so that the timing information in the AI / ML model output is relative to a pre-defined clock time• Use DL RTOA reference time in the representation of timing information of AI / ML model input (e.g., Channel Impulse Response (CIR), Power Delay Profile (PDP), or Delay Profile (DP)) for both direct and assisted AI / ML positioning in UE (Case 1, 2a, 2b). This is applicable to training data collection and model inference.• Use uplink (UL) RTOA reference time in the representation of timing information in model input (e.g., CIR, PDP, or DP) for both direct and assisted in AI / ML positioning on the network side (Case 3a, 3b). This is applicable to training data collection and model inference.

[0053] For AI / ML based positioning, methods are provided to provide reference time in absolute time or clock time. Timing information in AI / ML model input and AI / ML model output (when applicable) is represented relative to the defined reference time, for both training data sample collection and model inference.

[0054] Certain embodiments may provide one or more of the following technical advantage(s). Embodiments of the present disclosure ensure that timing information in AI / ML model input and AI / ML model output (when applicable) can be correctly collected and interpreted at model training and model inference, regardless of the many variables in AI / ML operation. The variables include, for example: UEs and / or gNBs involved in model training and model inference, reference signal configuration (e.g., the subframes and symbols where Positioning Reference Symbol (PRS) or Sounding Reference Signal (SRS) is transmitted), the configuration of the legacypositioning methods (e.g., multi-Round-Trip Time (RTT), DL-Time Difference of Arrival (TDOA)), the designation of reference TRP, etc.

[0055] In some embodiments, the non-limiting terms “UE” and “wireless device” are used interchangeably. The UE herein can 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), Universal Serial Bus (USB) dongles, Customer Premises Equipment (CPE), an Internet of Things (loT) device, or a Narrowband loT (NB-IOT) device, etc.

[0056] In some embodiments, the generic term “anchor node” is used, where anchor nodes are used as reference points for determining the location of a target UE. In general, the anchor nodes for positioning can be a variety of nodes in the wireless network. For positioning using the radio link between the target UE and a radio network node, the anchor node can be any kind of a radio network node which may comprise any of base station, radio base station, base transceiver station, Node B, evolved Node B (eNB), Next-Generation Node B (gNodeB or gNB), NG-RAN node, Transmission Point (TP), Transmission-Reception Point (TRP), Multi-cell / multicast Coordination Entity (MCE), relay node, access point (AP), Antenna Reference Point (ARP), radio access point, Remote Radio Unit (RRU), Remote Radio Head (RRH). For positioning using sidelink between two UEs, the anchor node is a UE or a wireless device.

[0057] For ease of discussion, the methods are described using the radio links between a UE and an anchor node. TRP is used as a representative example of the anchor node, where the TRP is connected to a gNB. It is understood by those skilled in the art that TP can be used instead for positioning methods that rely on measurement of downlink reference signal only, e.g., DL-TDOA.

[0058] For timing information obtained based on DL positioning reference signal (PRS), it is proposed to define a reference time for DL relative time of arrival (RTOA), TDL-RTOA,ref. The DL RTOA reference time (TDL-RTOA,ref) is defined as an absolute time (= clock time), i.e., it does not change with configuration parameters or UE implementation. One example of such definition is as follows: The DL RTOA Reference Time (TDL-RTOA,ref) for the received PRS is defined as To+ tpRs, where• Tois the beginning time of SFN 0 provided by SFN Initialization Time associated with the TRP transmitting the PRS, and•fPRS=(10nf+ nsf) x IO’3, where nfand nsfare the SFN and the subframe number of the PRS, respectively.

[0059] For downlink signal based positioning (e.g., DL-TDOA), DL RTOA is not defined up to 3GPP Release (Rel)-18. For DL-TDOA, Reference Signal Time Difference (RSTD) has been used in measurement reporting by UE. This is in contrast to uplink and sidelink, where both have relative time of arrival defined, namely: UL Relative Time of Arrival (TUL-RTOA) and Sidelink Relative Time of Arrival (TSL-RTOA).

[0060] For AI / ML assisted positioning, RSTD is not appropriate to use as model output since RSTD calculation involves two TRP, namely, TRP j and the reference TRP z, where RSTD is calculated and reported for TRP j. The reference TRP is not fixed among different UEs, nor fixed over time for a given UE. The reference TRP is recommended by the Location Management Function (LMF) to the UE, and the UE can make a final decision on which TRP to use as its reference TRP. This creates a problem that the RSTD value changes when the reference TRP changes. In consequence, the AI / ML model cannot be trained to learn the fingerprint of a location if RSTD measurement is used as the model output for an AI / ML, unless the reference TRP is fixed. In other words, if RSTD is used as model output, then a mechanism is needed to ensure that the same reference TRP is used between model training stage and model inference stage. Furthermore, for training data collection, all data samples collected from all UEs need to use the same TRP as reference TRP. These demands are unreasonable and impractical.

[0061] Thus, it is desirable to find a pre-defined reference time (i.e., absolute time or clock time) for the model output, instead of the changeable reference TRP used by RSTD. The predefined reference time can be used for training data collection, model training, model inference, and it does not vary for different UEs.

[0062] Therefore, for AI / ML assisted positioning at UE side, the timing information at model output is proposed to be DL RTOA.

[0063] In one embodiment, the downlink relative time of arrival (DL-RTOA) is defined as the beginning time of downlink subframe i containing PRS received at the UE, which is relative to the DL RTOA Reference Time (TDL-RTOA,ref).

[0064] In determining DL RTOA as model output, the assisted AI / ML model(s) gives the first detected path in time, which is either a physical line-of-sight (LOS) path, or a virtual line-of-sight path if there is no physical LOS path between the TRP and the UE.

[0065] When the UE receives PRS from different gNBs, the value of DL RTOA Reference Time can be different for different PRS, since different gNBs may use different SFN Initialization Time, and have different SFN and subframe number for the PRS. However, the relative timing (DL RTOA) can be calculated and be valid for each PRS. This is illustrated in Figure 6. Inparticular, Figure 6 illustrates DL RTOA Reference Time (TDL-RTOA,ref) for three PRS resources received at the UE, and the DL RTOA for each PRS.

[0066] After model output is generated, post processing can be applied to the model output to generate the timing information for a measurement report, where the reported timing information can follow any desired format (e.g., RSTD or UE RxTxTimeDiff) according to the associated legacy positioning method. This is illustrated in Figure 7. In particular, Figure 7 illustrates postprocessing of the model output of assisted AI / ML models to generate the timing information required by legacy positioning method.

[0067] With the post-processor converting the model output to the desired format, this ensures that the AI / ML model can be constructed and trained to provide the same timing info (i.e., DL RTOA) regardless of the measurement report expected by the legacy positioning method (e.g., multi-RTT, DL-TDOA) or its configuration (e.g., reference TRP).

[0068] In regard to model deployment and postprocessing of the model output for measurement reporting, when the model output contains timing information, it is in the format of DL RTOA. As illustrated in Figure 7, the model output of AI / ML model(s) in assisted positioning is postprocessed to provide the measurement report expected by legacy positioning methods. The postprocessor allows the measurement report from UE to be compliant with the existing reporting format.

[0069] Some part of the measurement report from the UE can be simplified. For example, since the multi-path effect is absorbed by the AI / ML model, there is no need to report multipath information. This includes: NR-AdditionalPathList, NR-AdditionalPathListExt.

[0070] Some part of the measurement report can be improved by leveraging the AI / ML model output. For example, if the AI / ML model provides improved LOS / NLOS indicator, the UE can more reliably select a good TRP (e.g., a TRP with LOS link to UE) as the reference TRP when performing the post-processing.

[0071] In the following, the conversion by the post-processor is described using the examples of multi-RTT and DL-TDOA methods.

[0072] In regard to postprocessing of DL RTOA to generate UERxTxTimeDiff for multi-RTT positioning method, if legacy positioning method selected is multi-RTT, the LMF expects that the UE sends the measurement report which carries timing info in the format of UE RxTxTimeDiff. Thus, the post-processor converts the DL RTOA values generated by the AI / ML model(s) to UE RxTxTimeDiff. Given the definition of UE RxTxTimeDiff (=TUE-RX - TUE-TX), TUE-RX is obtained from DL RTOA.

[0073] In regard to postprocessing of DL TDOA to generate RSTD for DL-TDOA positioning method, if legacy positioning method selected is DL-DTOA, the LMF expects that the UE sends the measurement report which carries timing info in the format of RSTD. Thus, the post-processor converts the multiple DL RTOA values (one for each TRP) generated by the AI / ML model(s) to RSTD, which is defined as TsubframeRxj—TsubframeRxi-• First, the reference TRP i is selected by the UE, which may or may not be the same as recommended by LMF. In one example the reference TRP is selected by the UE to be the TRP with the highest quality TDL-RTOA at model output. In another example, the reference TRP is selected by the UE to be a TRP that has a LoS link to the UE.• Then, the RSTD for TRP j is obtained by finding the difference between TDL-RTOAJ and TDL-RTOA, i, while also taking into account their associated DL RTOA reference time.

[0074] Figure 8 is an illustration of DL RTOA, which is produced at the AI / ML model output for assisted positioning. With the knowledge of reference time (TDL-RTOA, ref) for each PRS resource, the RSTD between a given TRP and a reference TRP can be calculated and reported. In other words, in Figure 8, an example is shown to illustrate the DL RTOA values by the AI / ML model(s), where each DL RTOA value is associated with a different TRP, and also associated with a different DL RTOA reference time TDL-RTOA, ref. Since each of the reference time is pre-defined and known to the UE, the UE can find the RSTD between a given TRP and the reference TRP. For example, in Figure 8, TRP of PRS#0 is designated as the reference TRP. Then reference time for PRS #0 can be used as a common reference timing to find the time-of-arrival of each PRS. After that, their relative timing difference (i.e., RSTD) can be found.

[0075] As discussed above, the UE needs to know the DL RTOA reference time (TDL-RTOA, ref) associated with each PRS. Providing SFN0 offset to UE as in the existing specification is inadequate. Thus, in order to obtain TDL-RT0A ref= To+ tPRS, there is a need to send the SFN Initialization Time To to the UE as a part of assistance data. It is noted that system frame number (SFN) and the subframe number of the PRS are known to the UE (i.e., when UE prepares to receive PRS), thus there is no need to send again. One example of signaling the SFN Initialization Time To as part of a per- TRP PRS assistance data is shown below, where the assistance data is provided by the LMF to the target UE. Since the SFN Initialization Time To is provided for each TRP separately, different TRPs may have different To values.NR-DL-PRS-AssistanceDataPerTRP-rl6 ::= SEQUENCE { dl-PRS-ID-rl6 INTEGER (0 .255),nr-Phy sCelllD-r 16 NR-PhysCellID-rl6 OPTIONAL,- Need ON nr-CellGloballD-r 16 NCGI-rl5 OPTIONAL,- Need ON nr-ARFCN-rl6 ARFCN-ValueNR-rl5 OPTIONAL,- Need ON nr-DL-PRS-SFNO-Offset-r 16 NR-DL-PRS-SFNO-Offset-r 16, nr-SFN-Initialisation Time BIT STRING (SIZE(64)) nr-DL-PRS-ExpectedRSTD-r 16 INTEGER (-3841..3841), nr-DL-PRS-ExpectedRSTD-Uncertainty-rl6INTEGER (0..246), nr-DL-PRS-Info-r!6 NR-DL-PRS-Info-rl6,[[ prs-OnlyTP-rl6 ENUMERATED { true } OPTIONAL- Need ON]],[[ nr-DL-PRS-ExpectedAoD-or-AoA-rl7NR-DL-PRS-ExpectedAoD-or-AoA-r 17OPTIONAL - Need ON]]

[0076] Now, a description of a reference time for timing information in downlink channel measurement for model input will be provided. For downlink measurement for model input (e.g.,CIR, PDP, DP), timing information is needed to describe the path timing of the multiple paths. Such timing information needs to be provided relative to a pre-defined reference time. This applies to all variants of AI / ML based positioning and all life-cycle-management (LCM) stages. For example, it applies to model input of both direct AI / ML positioning and AI / ML assisted positioning; it applies to training data collection, model training, and model inference.

[0077] For downlink measurement by UE, the multipath timing information is represented relative to a downlink relative time of arrival (DL RTOA) reference time (TDL-RTOA,ref).

[0078] For the first detected path, the path timing is defined as follows: UE received timing of the beginning time of downlink subframe #i from a Transmission Point (TP), defined by the first detected path in time, where the received timing is relative to the DL RTOA reference time (TDL-RTOA,ref).

[0079] The timing information of additional paths are also represented as the UE received timing relative to the downlink time of arrival (DL RTOA) reference time (TDL-RTOA,ref).

[0080] Figure 9 is an illustration of timing information of three multi-path channel measurements. The channel measurements are used as model input for AI / ML based positioning. With the knowledge of DL RTOA reference time for each PRS resource, the channel measurements can be represented with proper relative timing values for model input.

[0081] While the discussion above is for downlink, the same principle applies to uplink, where the model input is channel measurements based on gNB observation of reference signal SRS. For uplink measurement by gNB, the multipath timing information is represented relative to uplink relative time of arrival (RTOA) reference time (TuL-RTOA,ref). For uplink measurement for model input (e.g., CIR, PDP, DP), the timing information for the first path and additional paths is represented relative to the pre-defined reference time TuL-RTOA,ref.

[0082] For training data collection, for each data sample, the (DL, UL) RTOA reference time is recorded. This is applicable to both uplink and downlink reference signal based measurement; for both direct and assisted AI / ML positioning; for both channel measurements corresponding to model input and label data corresponding to model output. Individual samples need their RTOA reference time attached since different reference signal (PRS for DL; SRS for UL) may arrive at different SFN and subframe, which give different (DL; UL) RTOA reference time value.

[0083] As explained above, the timing info at both model input and model output is represented as values relative to a pre-defined reference time. This ensures that the timing information uses a common interpretation regardless of the many variables in the life-cycle of an AI / ML model. Such variables include: which UE and TRP are used in training data collection, which UE and TRP are used in model inference, direct vs assisted AI / ML positioning, the legacypositioning method associated with the AI / ML assisted positioning, the reference TRP selection, the time training data is collected, the time model inference is performed, the PRS or SRS configuration, etc.

[0084] For AI / ML model training, the training data samples are used to train the AI / ML model(s), where a training data sample is composed of channel measurements (corresponding to model input) and ground truth label (corresponding to model output). For the assisted AI / ML model illustrated in Figure 7, the model output is TDL-RTOA.

[0085] The channel measurements typically include timing information and possibly additional information such as power (e.g., Reference Signal Received Power (RSRP), Reference Signal Received Path Power (RSRPP)), for example, channel impulse response (CIR), power delay profile (PDP), delay profile (DP). The timing information can be per-path timing (e.g., first path and numerous additional paths). The timing information should be represented in values that are relative to a RTOA reference time, when collecting the training data of CIR, PDP, or DP for the AI / ML model. The reference time is the downlink relative time of arrival (DL RTOA) reference time (TDL-RTOA, ref) for the measurements performed by UE using DL PRS, and is the Relative Time of Arrival (UL RTOA) reference time (TUL-RTOA) for the measurements performed by gNB using UL SRS.

[0086] For training data collection, measurements (e.g., Positioning Reference Unit (PRU) location) are collected to provide the ground truth label of TDL-RTOA, which corresponds to the model output of AI / ML assisted positioning. For example, if PRU location is collected, then the propagation delay between TRP and UE can be calculated based on triangulation and knowledge of TRP location. With propagation delay, ground truth label of TDL-RTOA values can be obtained using the DL RTOA reference time (i.e., SFN 0, system frame number and the subframe number of the PRS).

[0087] Figure 10 illustrates the operation of a UE 1000 and a network node 1002 (e.g., an LMF), in accordance with one example embodiment of the present disclosure. Optional steps are represented by dashed lines / boxes. As illustrated, the steps of the process are as follows.

[0088] Step 1004: Optionally, the network node 1002 sends, and the UE 1000 receives, assistance information that enables the UE 1000 to determine a downlink, DL, relative time of arrival, RTOA, reference time (step 1004). In some embodiments, the DL RTOA reference time is associated with a DL reference signal (e.g., DL PRS). As discussed above, in some embodiments, the DL RTOA reference time is defined as TDL-RT0A ref= To+ tPRS, and the assistance information is per-TRP PRS assistance data that indicates the SFN Initialization Time To. In another embodiment, the network node 1002 is an LMF, and the LMF provides informationthat indicates the DL RTOA reference time (e.g., the value of the DL RTOA reference time) in assistance information.

[0089] Step 1006: The UE 1000 determines the DL RTOA reference time, e.g., based on the assistance information received in step 1004. For example, as discussed above, the DL RTOA reference time is, in some embodiments, associated to PRS, and the UE 1000 computes the DL RTOA reference time as TDL-RT0A ref= To+ tPRS, where the SFN Initialization Time To is received in the assistance information of step 1004 and tPRS= (10nf+ nsf) x 10-3, where nfand nsfare the system frame number (SFN) and the subframe number of the PRS, respectively.

[0090] Step 1008: The UE 1000 uses the DL RTOA reference time in association with training or inference of an AI / ML model(s) related to UE positioning. More specifically, the UE 1000 may use the DL RTOA reference time for any one or more of the following:• Representing timing information of one or more model inputs (e.g., CIR, PDP, DP) (or representing timing information of DL measurement s) used for one or more model inputs) to the AI / ML model(s) in the case of either direct or assisted AI / ML positioning in the UE (Case 1, 2a, 2b);• Generating one or more model outputs (e.g., DL RTOA) such that timing information in the model outputs is relative to a pre-defined clock time (e.g., DL RTOA reference time).

[0091] In other words, in Step 1006, a DL RTOA reference time for a particular DL measurement for a model input (e.g., CIR, PDP, or DP) to the AI / ML model(s) related to UE positioning is determined. More specifically, as described herein, the particular DL measurement is on a certain DL reference signal (e.g., a certain PRS), and the DL RTOA reference time for this DL measurement is determined as, e.g., TDL-RT0A ref= To+ tPRS. Step 1006 is repeated for each DL measurement for the model input(s) of the AI / ML model(s) related to UE positioning. In Step 1008, the DL RTOA reference times are used in association with the respective DL measurements for the input(s) to the AI / ML model(s) related to UE positioning. For instance, as described above, timing information for the DL measurements may be represented as time instances relative to the respective DL RTOA reference times of the DL measurements in association with training or inference of the AI / ML model(s). During training, the DL measurements and their timing information, or model input(s) derived therefrom, are used to train the AI / ML model used for UE positioning. During inference, model output(s) of the AI / ML model(s) are output by the AI / ML model(s) responsive to the model input(s), which are or are derived from the DL measurements and their respective timing information.

[0092] Step 1010 (Optional): For assisted AI / ML positioning, during inference, the model outputs of the AI / ML model(s) including the represented timing information may be postprocessed as described above.

[0093] Step 1012 (Optional): For assisted AI / ML positioning, during inference, the (optionally post-processed) model outputs of the AI / ML model(s) including the represented timing information are reported to the LMF.

[0094] Step 1014 (Optional): For direct AI / ML positioning, the output of the AI / ML model(s) includes one or more position estimates for the UE. In this case, the UE 1000 may perform one or more actions using the position estimate(s) such as, e.g., using the position estimate(s) to perform one or more operational tasks and / or reporting the position estimate(s) to another node (e.g., a network node, another UE, or the like).

[0095] Figure 11 illustrates the operation of a network node (e.g., a RAN node such as, e.g., a gNB), in accordance with one example embodiment of the present disclosure. Optional steps are represented by dashed lines / boxes. As illustrated, the steps of the process are as follows.

[0096] Step 1100: The network node determines a UL RTOA reference time.

[0097] Step 1102: The network node uses the UL RTOA reference time in association with training or inference of an AI / ML model(s) related to UE positioning. More specifically, the network node may use the UL RTOA reference time for any one or more of the following:• Representing timing information of UL measurements (e.g., UL RTOA) for one or more model inputs (e.g., CIR, PDP, DP) to the AI / ML model(s) in the case of either direct or assisted AI / ML positioning in the network node.Notably, as understood from the description above, steps 1100 and 1102 may be repeated to determine the UL RTOA reference time and to use the determined UL RTOA reference time to represent timing information for multiple UL measurements for one or more model inputs, which may be used during training and / or inference of the AI / ML model(s) for UE positioning.

[0098] Step 1004 (Optional): For assisted AI / ML positioning, during inference, the model outputs of the AI / ML model(s) including the represented timing information may be postprocessed as described above.

[0099] Step 1006 (Optional): For assisted AI / ML positioning, during inference, the (optionally post-processed) model outputs of the AI / ML model(s) including the represented timing information are reported to the LMF.

[0100] Step 1008 (Optional): For direct AI / ML positioning, the output of the AI / ML model(s) includes one or more position estimates for a UE. In this case, network node may perform one or more actions using the position estimate(s) of the UE such as, e.g., using the position estimate(s)to perform one or more operational tasks and / or reporting the position estimate(s) to another node (e.g., another network node, the UE, or the like).

[0101] Figure 12 shows an example of a communication system 1200 in which embodiments of the present disclosure may be implemented.

[0102] In the example, the communication system 1200 includes a telecommunication network 1202 that includes an access network 1204, such as a Radio Access Network (RAN), and a core network 1206, which includes one or more core network nodes 1208. The access network 1204 includes one or more access network nodes, such as network nodes 1210A and 1210B (one or more of which may be generally referred to as network nodes 1210), or any other similar Third Generation Partnership Project (3GPP) access nodes or non-3GPP Access Points (APs). Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network 1202 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 1202 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network 1202, including one or more network nodes 1210 and / or core network nodes 1208.

[0103] Examples of an ORAN network node include an Open Radio Unit (O-RU), an Open Distributed Unit (O-DU), an Open Central Unit (O-CU), including an O-CU Control Plane (O- CU-CP) or an O-CU User Plane (O-CU-UP), a RAN intelligent controller (near-real time or non- real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an Al, Fl, Wl, El, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an O-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes 1210 facilitate direct orindirect connection of User Equipment (UE), such as by connecting UEs 1212A, 1212B, 1212C, and 1212D (one or more of which may be generally referred to as UEs 1212) to the core network 1206 over one or more wireless connections.

[0104] 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 1200 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 1200 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0105] The UEs 1212 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 1210 and other communication devices. Similarly, the network nodes 1210 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 1212 and / or with other network nodes or equipment in the telecommunication network 1202 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 1202.

[0106] In the depicted example, the core network 1206 connects the network nodes 1210 to one or more hosts, such as host 1216. 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 1206 includes one more core network nodes (e.g., core network node 1208) 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 1208. 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).

[0107] The host 1216 may be under the ownership or control of a service provider other than an operator or provider of the access network 1204 and / or the telecommunication network 1202,and may be operated by the service provider or on behalf of the service provider. The host 1216 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.

[0108] As a whole, the communication system 1200 of Figure 12 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 1200 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 Second, Third, Fourth, or Fifth Generation (2G, 3G, 4G, or 5G) standards, or any applicable future generation standard (e.g., Sixth Generation (6G)); Wireless Local Area Network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any Low Power Wide Area Network (LPWAN) standards such as LoRa and Sigfox.

[0109] In some examples, the telecommunication network 1202 is a cellular network that implements 3 GPP standardized features. Accordingly, the telecommunication network 1202 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 1202. For example, the telecommunication network 1202 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing enhanced Mobile Broadband (eMBB) services to other UEs, and / or massive Machine Type Communication (mMTC) / massive Internet of Things (loT) services to yet further UEs.

[0110] In some examples, the UEs 1212 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 1204 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 1204. Additionally, a UE may be configured for operating in single- or multi-Radio Access Technology (RAT) or multi -standard mode. For example, a UE may operate with any one or combination of WiFi, New Radio (NR), and LTE, i.e. being configured for Multi-Radio Dual Connectivity (MR-DC), such as Evolved UMTS Terrestrial RAN (E-UTRAN) NR - Dual Connectivity (EN-DC).

[0111] In the example, a hub 1214 communicates with the access network 1204 to facilitate indirect communication between one or more UEs (e.g., UE 1212C and / or 1212D) and network nodes (e.g., network node 1210B). In some examples, the hub 1214 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 1214 may be a broadband router enabling access to the core network 1206 for the UEs. As another example, the hub 1214 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 1210, or by executable code, script, process, or other instructions in the hub 1214. As another example, the hub 1214 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 1214 may be a content source. For example, for a UE that is a Virtual Reality (VR) headset, display, loudspeaker or other media delivery device, the hub 1214 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 1214 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 1214 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0112] The hub 1214 may have a constant / persistent or intermittent connection to the network node 1210B. The hub 1214 may also allow for a different communication scheme and / or schedule between the hub 1214 and UEs (e.g., UE 1212C and / or 1212D), and between the hub 1214 and the core network 1206. In other examples, the hub 1214 is connected to the core network 1206 and / or one or more UEs via a wired connection. Moreover, the hub 1214 may be configured to connect to a Machine-to-Machine (M2M) service provider over the access network 1204 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 1210 while still connected via the hub 1214 via a wired or wireless connection. In some embodiments, the hub 1214 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 1210B. In other embodiments, the hub 1214 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and the network node 1210B, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0113] Figure 13 shows a UE 1300 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 smartphone, mobile phone, cell phone, Voice over Internet Protocol (VoIP) phone, wireless local loop phone, desktop computer, Personal Digital Assistant (PDA), wireless camera, 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, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3GPP, including a Narrowband Internet of Things (NB-IoT) UE, a Machine Type Communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0114] A UE may support Device-to-Device (D2D) communication, for example by implementing a 3 GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), Vehi cl e-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).

[0115] The UE 1300 includes processing circuitry 1302 that is operatively coupled via a bus 1304 to an input / output interface 1306, a power source 1308, memory 1310, a communication interface 1312, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 13. 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.

[0116] The processing circuitry 1302 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 1310. The processing circuitry 1302 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 1302 may include multiple Central Processing Units (CPUs).

[0117] In the example, the input / output interface 1306 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 1300. 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.

[0118] In some embodiments, the power source 1308 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 1308 may further include power circuitry for delivering power from the power source 1308 itself, and / or an external power source, to the various parts of the UE 1300 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 1308. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 1308 to make the power suitable for the respective components of the UE 1300 to which power is supplied.

[0119] The memory 1310 may be or be configured to include memory such as Random Access Memory (RAM), Read Only Memory (ROM), Programmable ROM (PROM), Erasable PROM (EPROM), Electrically EPROM (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 1310 includes one or more application programs 1314, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1316. The memory 1310 may store, for use by the UE 1300, any of a variety of various operating systems or combinations of operating systems.

[0120] The memory 1310 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 DataStorage (HDDS) optical disc drive, external mini Dual In-line Memory Module (DIMM), Synchronous Dynamic RAM (SDRAM), external micro-DIMM SDRAM, smartcard memory such as a tamper resistant module in the form of a Universal Integrated Circuit Card (UICC) including one or more Subscriber Identity Modules (SIMs), such as a Universal SIM (USIM) and / or Internet Protocol Multimedia Services Identity Module (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 a ‘SIM card.’ The memory 1310 may allow the UE 1300 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 1310, which may be or comprise a device-readable storage medium.

[0121] The processing circuitry 1302 may be configured to communicate with an access network or other network using the communication interface 1312. The communication interface 1312 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 1322. The communication interface 1312 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 1318 and / or a receiver 1320 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 1318 and receiver 1320 may be coupled to one or more antennas (e.g., the antenna 1322) and may share circuit components, software, or firmware, or alternatively be implemented separately.

[0122] In the illustrated embodiment, communication functions of the communication interface 1312 may include cellular communication, WiFi communication, LPWAN communication, data communication, voice communication, multimedia communication, short- range communications such as Bluetooth, NFC, 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 according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband CDMA (WCDMA), GSM, LTE, NR, UMTS, WiMax, Ethernet, Transmission Control Protocol / Internet Protocol (TCP / IP), Synchronous Optical Networking (SONET), Asynchronous Transfer Mode (ATM), Quick User Datagram Protocol Internet Connection (QUIC), Hypertext Transfer Protocol (HTTP), and so forth.

[0123] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 1312, 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).

[0124] 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.

[0125] A UE, when in the form of an 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 television, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or VR, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an 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 1300 shown in Figure 13.

[0126] 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 UEmay represent a vehicle, such as a car, a bus, a truck, a ship, an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0127] 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.

[0128] Figure 14 shows a network node 1400 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, APs (e.g., radio APs), Base Stations (BSs) (e.g., radio BSs, Node Bs, evolved Node Bs (eNBs), NR Node Bs (gNBs)), and 0-RAN nodes or components of an 0-RAN node (e.g., 0-RU, 0-DU, O-CU).

[0129] 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, distributed units (e.g., in an 0-RAN access node), and / or Remote Radio Units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such RRUs 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).

[0130] 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 BS 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).

[0131] The network node 1400 includes processing circuitry 1402, memory 1404, a communication interface 1406, and a power source 1408. The network node 1400 may be composed of multiple physically separate components (e.g., a NodeB component and an 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 1400 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 1400 may be configured to support multiple RATs. In such embodiments, some components may be duplicated (e.g., separate memory 1404 for different RATs) and some components may be reused (e.g., a same antenna 1410 may be shared by different RATs). The network node 1400 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1400, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, Long Range Wide Area Network (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 the network node 1400.

[0132] The processing circuitry 1402 may comprise a combination of one or more of a microprocessor, controller, microcontroller, CPU, DSP, ASIC, FPGA, or any other suitable computing device, resource, or combination of hardware, software, and / or encoded logic operable to provide, either alone or in conjunction with other network node 1400 components, such as the memory 1404, to provide network node 1400 functionality.

[0133] In some embodiments, the processing circuitry 1402 includes a System on a Chip (SOC). In some embodiments, the processing circuitry 1402 includes one or more of Radio Frequency (RF) transceiver circuitry 1412 and baseband processing circuitry 1414. In some embodiments, the RF transceiver circuitry 1412 and the baseband processing circuitry 1414 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 the RF transceiver circuitry 1412 and the baseband processing circuitry 1414 may be on the same chip or set of chips, boards, or units.

[0134] The memory 1404 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, RAM, 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 1402. The memory 1404 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 1402 and utilized by the network node 1400. The memory 1404 may be used to store any calculations made by the processing circuitry 1402 and / or any data received via the communication interface 1406. In some embodiments, the processing circuitry 1402 and the memory 1404 are integrated.

[0135] The communication interface 1406 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 1406 comprises port(s) / terminal(s) 1416 to send and receive data, for example to and from a network over a wired connection. The communication interface 1406 also includes radio front-end circuitry 1418 that may be coupled to, or in certain embodiments a part of, the antenna 1410. The radio front-end circuitry 1418 comprises filters 1420 and amplifiers 1422. The radio front-end circuitry 1418 may be connected to the antenna 1410 and the processing circuitry 1402. The radio front-end circuitry 1418 may be configured to condition signals communicated between the antenna 1410 and the processing circuitry 1402. The radio front-end circuitry 1418 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 1418 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of the filters 1420 and / or the amplifiers 1422. The radio signal may then be transmitted via the antenna 1410. Similarly, when receiving data, the antenna 1410 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1418. The digital data may be passed to the processing circuitry 1402. In other embodiments, the communication interface 1406 may comprise different components and / or different combinations of components.

[0136] In certain alternative embodiments, the network node 1400 does not include separate radio front-end circuitry 1418; instead, the processing circuitry 1402 includes radio front-end circuitry and is connected to the antenna 1410. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1412 is part of the communication interface 1406. In still other embodiments, the communication interface 1406 includes the one or more ports or terminals 1416, the radio front-end circuitry 1418, and the RF transceiver circuitry 1412 as part of a radio unit (not shown), and the communication interface 1406 communicates with the baseband processing circuitry 1414, which is part of a digital unit (not shown).

[0137] The antenna 1410 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 1410 may be coupled to the radio front-end circuitry 1418 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 1410 is separate from the network node 1400 and connectable to the network node 1400 through an interface or port.

[0138] The antenna 1410, the communication interface 1406, and / or the processing circuitry 1402 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node 1400. Any information, data, and / or signals may be received from a UE, another network node, and / or any other network equipment. Similarly, the antenna 1410, the communication interface 1406, and / or the processing circuitry 1402 may be configured to perform any transmitting operations described herein as being performed by the network node 1400. Any information, data, and / or signals may be transmitted to a UE, another network node, and / or any other network equipment.

[0139] The power source 1408 provides power to the various components of the network node 1400 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 1408 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1400 with power for performing the functionality described herein. For example, the network node 1400 may be connectable to an external power source (e.g., the power grid or an electricity outlet) via input circuitry or an interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 1408. As a further example, the power source 1408 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0140] Embodiments of the network node 1400 may include additional components beyond those shown in Figure 14 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 1400 may include user interface equipment to allow input of information into the network node 1400 and to allow output of information from the network node 1400. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1400.

[0141] Figure 15 is a block diagram of a host 1500, which may be an embodiment of the host 1216 of Figure 12, in accordance with various aspects described herein. As used herein, the host 1500 may be or comprise various combinations of hardware and / or software including a standaloneserver, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host 1500 may provide one or more services to one or more UEs.

[0142] The host 1500 includes processing circuitry 1502 that is operatively coupled via a bus 1504 to an input / output interface 1506, a network interface 1508, a power source 1510, and memory 1512. 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 13 and 14, such that the descriptions thereof are generally applicable to the corresponding components of the host 1500.

[0143] The memory 1512 may include one or more computer programs including one or more host application programs 1514 and data 1516, which may include user data, e.g. data generated by a UE for the host 1500 or data generated by the host 1500 for a UE. Embodiments of the host 1500 may utilize only a subset or all of the components shown. The host application programs 1514 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), Moving Picture Experts Group (MPEG), VP9) and audio codecs (e.g., Free Lossless Audio Codec (FLAC), Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of LEs (e.g., handsets, desktop computers, wearable display systems, and heads-up display systems). The host application programs 1514 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 1500 may select and / or indicate a different host for Over-The-Top (OTT) services for a UE. The host application programs 1514 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 (DASH or MPEG-DASH), etc.

[0144] Figure 16 is a block diagram illustrating a virtualization environment 1600 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 virtualenvironments 1600 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. In some embodiments, the virtualization environment 1600 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface.

[0145] Applications 1602 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1600 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0146] Hardware 1604 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 1606 (also referred to as hypervisors or VM Monitors (VMMs)), provide VMs 1608 A and 1608B (one or more of which may be generally referred to as VMs 1608), and / or perform any of the functions, features, and / or benefits described in relation with some embodiments described herein. The virtualization layer 1606 may present a virtual operating platform that appears like networking hardware to the VMs 1608.

[0147] The VMs 1608 comprise virtual processing, virtual memory, virtual networking, or interface and virtual storage, and may be run by a corresponding virtualization layer 1606. Different embodiments of the instance of a virtual appliance 1602 may be implemented on one or more of the VMs 1608, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as Network Function Virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers and customer premise equipment.

[0148] In the context of NFV, a VM 1608 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 1608, and that part of the hardware 1604 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs 1608, 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 1608 on top of the hardware 1604 and corresponds to the application 1602.

[0149] The hardware 1604 may be implemented in a standalone network node with generic or specific components. The hardware 1604 may implement some functions via virtualization. Alternatively, the hardware 1604 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 1610, which, among others, oversees lifecycle management of the applications 1602. In some embodiments, the hardware 1604 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 RAN or a base station. In some embodiments, some signaling can be provided with the use of a control system 1612 which may alternatively be used for communication between hardware nodes and radio units.

[0150] Figure 17 shows a communication diagram of a host 1702 communicating via a network node 1704 with a UE 1706 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as the UE 1212A of Figure 12 and / or the UE 1300 of Figure 13), the network node (such as the network node 1210A of Figure 12 and / or the network node 1400 of Figure 14), and the host (such as the host 1216 of Figure 12 and / or the host 1500 of Figure 15) discussed in the preceding paragraphs will now be described with reference to Figure 17.

[0151] Like the host 1500, embodiments of the host 1702 include hardware, such as a communication interface, processing circuitry, and memory. The host 1702 also includes software, which is stored in or is accessible by the host 1702 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 1706 connecting via an OTT connection 1750 extending between the UE 1706 and the host 1702. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 1750.

[0152] The network node 1704 includes hardware enabling it to communicate with the host 1702 and the UE 1706. The connection 1760 may be direct or pass through a core network (like the core network 1206 of Figure 12) 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.

[0153] The UE 1706 includes hardware and software, which is stored in or accessible by the UE 1706 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 aservice to a human or non-human user via the UE 1706 with the support of the host 1702. In the host 1702, an executing host application may communicate with the executing client application via the OTT connection 1750 terminating at the UE 1706 and the host 1702. 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 1750 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 1750.

[0154] The OTT connection 1750 may extend via the connection 1760 between the host 1702 and the network node 1704 and via a wireless connection 1770 between the network node 1704 and the UE 1706 to provide the connection between the host 1702 and the UE 1706. The connection 1760 and the wireless connection 1770, over which the OTT connection 1750 may be provided, have been drawn abstractly to illustrate the communication between the host 1702 and the UE 1706 via the network node 1704, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0155] As an example of transmitting data via the OTT connection 1750, in step 1708, the host 1702 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 1706. In other embodiments, the user data is associated with a UE 1706 that shares data with the host 1702 without explicit human interaction. In step 1710, the host 1702 initiates a transmission carrying the user data towards the UE 1706. The host 1702 may initiate the transmission responsive to a request transmitted by the UE 1706. The request may be caused by human interaction with the UE 1706 or by operation of the client application executing on the UE 1706. The transmission may pass via the network node 1704 in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 1712, the network node 1704 transmits to the UE 1706 the user data that was carried in the transmission that the host 1702 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 1714, the UE 1706 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 1706 associated with the host application executed by the host 1702.

[0156] In some examples, the UE 1706 executes a client application which provides user data to the host 1702. The user data may be provided in reaction or response to the data received from the host 1702. Accordingly, in step 1716, the UE 1706 may provide user data, which may be performed by executing the client application. In providing the user data, the client applicationmay further consider user input received from the user via an input / output interface of the UE 1706. Regardless of the specific manner in which the user data was provided, the UE 1706 initiates, in step 1718, transmission of the user data towards the host 1702 via the network node 1704. In step 1720, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 1704 receives user data from the UE 1706 and initiates transmission of the received user data towards the host 1702. In step 1722, the host 1702 receives the user data carried in the transmission initiated by the UE 1706.

[0157] One or more of the various embodiments improve the performance of OTT services provided to the UE 1706 using the OTT connection 1750, in which the wireless connection 1770 forms the last segment.

[0158] In an example scenario, factory status information may be collected and analyzed by the host 1702. As another example, the host 1702 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 1702 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 1702 may store surveillance video uploaded by a UE. As another example, the host 1702 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 1702 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.

[0159] 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 1750 between the host 1702 and the UE 1706 in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection 1750 may be implemented in software and hardware of the host 1702 and / or the UE 1706. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 1750 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or by supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 1750 may include message format, retransmission settings, preferred routing, etc.; the reconfiguring need not directly alter the operation of the network node 1704. Such procedures and functionalities may be known and practiced in the art. In certainembodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency, and the like by the host 1702. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 1750 while monitoring propagation times, errors, etc.

[0160] 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.

[0161] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored 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 hardwired 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 processingcircuitry 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.

[0162] Those skilled in the art will recognize improvements and modifications to the embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein.

[0163] Some exemplary embodiments of the present disclosure are as follows.Group A Embodiments

[0164] Embodiment 1 : A method performed by a User Equipment, UE, the method comprising: obtaining (1006) a downlink, DL, relative time of arrival, RTOA, reference time, the DL RTOA reference time being an absolute time; and using (1008) the DL RTOA reference time in association with training or inference of one or more Artificial Intelligence, Al, / Machine Learning, ML, models related to UE positioning based on DL RTOA measurements.

[0165] Embodiment 2: The method of embodiment 1, wherein the DL RTOA reference time is associated to a downlink reference signal (e.g., PRS).

[0166] Embodiment 3: The method of embodiment 1, wherein the DL RTOA reference time is associated to a downlink reference signal (e.g., PRS) and transmission and reception point, TRP.

[0167] Embodiment 4: The method of any of embodiments 1 to 3, wherein the downlink reference signal is a positioning reference signal, PRS, and the DL RTOA reference time is defined as:^DL-RTOA,ref = ?0 +fPRS wherein:• To is the beginning time of System Frame Number, SFN, 0 provided by a SFN Initialization Time associated with a TRP transmitting the PRS; and• fpRS=(10nf+ nsf) x 10-3, where nfand nsfare a system frame number (SFN) and the subframe number of the PRS, respectively.

[0168] Embodiment 5: The method of any of embodiments 1 to 4, wherein using (1008) the DL RTOA reference time in association with training or inference of one or more AI / ML models related to UE positioning based on DL RTOA measurements comprises representing timing information of one or more model inputs (e.g., CIR, PDP, DP) to the one or more AI / ML model, based on the DL RTOA reference time.

[0169] Embodiment 6: The method of any of embodiments 1 to 4, wherein using (1008) the DL RTOA reference time in association with training or inference of one or more AI / ML models related to UE positioning based on DL RTOA measurements comprises representing timinginformation of DL RTOA measurements for one or more model inputs (e.g., CIR, PDP, DP) to the one or more AI / ML model, based on the DL RTOA reference time.

[0170] Embodiment 7: The method of any of embodiments 1 to 6, wherein using (1008) the DL RTOA reference time in association with training or inference of one or more AI / ML models related to UE positioning based on DL RTOA measurements comprises generating one or more model outputs of the one or more AI / ML models such that timing information in the one or more model outputs is relative to the DL RTOA reference time.

[0171] Embodiment 8: The method of embodiment 7, further comprising post-processing (1010) the one or more model outputs including the timing information in accordance with a desired measurement report format.

[0172] Embodiment 9: The method of any of the previous embodiments, further comprising: providing user data; and forwarding the user data to a host via a transmission to a network node.Group B Embodiments

[0173] Embodiment 10: A method performed by a network node, the method comprising: determining (1100) an uplink, UL, relative time of arrival, RTOA, reference time, the UL RTOA reference time being an absolute time; and using (1102) the UL RTOA reference time in association with training or inference of one or more Artificial Intelligence, Al, / Machine Learning, ML, models related to UE positioning based on UL RTOA measurements.

[0174] Embodiment 11 : The method of embodiment 10, wherein using (1102) the UL RTOA reference time in association with training or inference of one or more AI / ML models related to UE positioning based on UL RTOA measurements comprises representing timing information of one or more model inputs (e.g., CIR, PDP, DP) to the one or more AI / ML model, based on the UL RTOA reference time.

[0175] Embodiment 12: The method of embodiment 10, wherein using (1102) the UL RTOA reference time in association with training or inference of one or more AI / ML models related to UE positioning based on UL RTOA measurements comprises representing timing information of UL RTOA measurements for one or more model inputs (e.g., CIR, PDP, DP) to the one or more AI / ML model, based on the UL RTOA reference time.

[0176] Embodiment 13: The method of any of the previous embodiments, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment.Group C Embodiments

[0177] Embodiment 14: A user equipment comprising: processing circuitry configured to perform any of the steps of any of the Group A embodiments; and power supply circuitry configured to supply power to the processing circuitry.

[0178] Embodiment 15: A network node comprising: processing circuitry configured to perform any of the steps of any of the Group B embodiments; and power supply circuitry configured to supply power to the processing circuitry.

[0179] Embodiment 16: 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.

[0180] Embodiment 17: A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a network node in a cellular network for transmission to a user equipment (UE), the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE.

[0181] Embodiment 18: The host of the previous embodiment, wherein: the processing circuitry of the host is configured to execute a host application that provides the user data; and the UE comprises processing circuitry configured to execute a client application associated with the host application to receive the transmission of user data from the host.

[0182] Embodiment 19: A method implemented in a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; and initiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the network node performs any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE.

[0183] Embodiment 20: The method of the previous embodiment, further comprising, at the network node, transmitting the user data provided by the host for the UE.

[0184] Embodiment 21 : The method of any of the previous 2 embodiments, wherein the user data is provided at the host by executing a host application that interacts with a client application executing on the UE, the client application being associated with the host application.

[0185] Embodiment 22: A communication system configured to provide an over-the-top (OTT) service, the communication system comprising a host comprising: processing circuitry configured to provide user data for a user equipment (UE), the user data being associated with the over-the-top service; and a network interface configured to initiate transmission of the user data toward a cellular network node for transmission to the UE, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE.

[0186] Embodiment 23: The communication system of the previous embodiment, further comprising: the network node; and / or the UE.

[0187] Embodiment 24: A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to initiate receipt of user data; and a network interface configured to receive the user data from a network node in a cellular network, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to receive the user data from a user equipment (UE) for the host.

[0188] Embodiment 25: The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application that receives the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

[0189] Embodiment 26: The host of the any of the previous 2 embodiments, wherein the initiating receipt of the user data comprises requesting the user data.

[0190] Embodiment 27: A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: at the host, initiating receipt of user data from the UE, the user data originating from a transmission which the network node has received from the UE, wherein the network node performs any of the steps of any of the Group B embodiments to receive the user data from the UE for the host.

[0191] Embodiment 28: The method of the previous embodiment, further comprising at the network node, transmitting the received user data to the host.

[0192] Embodiment 29: A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a cellular network for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UE being configured to perform any of the operations of any of the Group A embodiments to receive the user data from the host.

[0193] Embodiment 30: The host of the previous embodiment, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data to the UE from the host.

[0194] Embodiment 31 : The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

[0195] Embodiment 32: A method implemented by a host operating in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; and initiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the UE performs any of the operations of any of the Group A embodiments to receive the user data from the host.

[0196] Embodiment 33: The method of the previous embodiment, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the host application.

[0197] Embodiment 34: The method of the previous embodiment, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application.

[0198] Embodiment 35: A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a cellular network for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UEbeing configured to perform any of the steps of any of the Group A embodiments to transmit the user data to the host.

[0199] Embodiment 36: The host of the previous embodiment, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data from the UE to the host.

[0200] Embodiment 37: The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

[0201] Embodiment 38: A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: at the host, receiving user data transmitted to the host via the network node by the UE, wherein the UE performs any of the steps of any of the Group A embodiments to transmit the user data to the host.

[0202] Embodiment 39: The method of the previous embodiment, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the UE.

[0203] Embodiment 40: The method of the previous 2 embodiments, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application.

Claims

CLAIMS1. A method performed by a User Equipment, UE, the method comprising: obtaining (1006) a downlink, DL, relative time of arrival, RTOA, reference time for a downlink measurement for an Artificial Intelligence, Al, / Machine Learning, ML, model input for an AI / ML model related to UE positioning based on DL RTOA measurements, the DL RTOA reference time being an absolute time; and using (1008) the DL RTOA reference time for the downlink measurement for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on DL RTOA measurements.

2. The method of claim 1, wherein the DL RTOA reference time is associated to a downlink reference signal.

3. The method of claim 1, wherein the DL RTOA reference time is associated to a downlink reference signal and transmission and reception point, TRP.

4. The method of any of claims 1 to 3, wherein the downlink reference signal is a positioning reference signal, PRS, and the DL RTOA reference time is defined as:^DL-RTOA,ref=T0+ tPRSwherein:• To is the beginning time of System Frame Number, SFN, 0 provided by a SFN Initialization Time associated with a TRP transmitting the PRS; and•fPRS=(10nf+ nsf) x 10-3, where nfand nsfare an SFN and the subframe number of the PRS, respectively.

5. The method of any of claims 1 to 4, wherein using (1008) the DL RTOA reference time for the downlink measurement for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on DL RTOA measurements comprises representing timing information of the AI / ML model input to the AI / ML model based on the DL RTOA reference time.

6. The method of any of claims 1 to 4, wherein using (1008) the DL RTOA reference time for the downlink measurement for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on DL RTOA measurementscomprises representing timing information of the downlink measurement for the AI / ML model input to the AI / ML model based on the DL RTOA reference time.

7. The method of any of claims 1 to 6, wherein using (1008) the DL RTOA reference time for the downlink measurement for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on DL RTOA measurements comprises generating one or more model outputs of the AI / ML model such that timing information of the one or more model outputs is relative to DL RTOA reference time.

8. The method of claim 7, further comprising post-processing (1010) the one or more model outputs including the timing information in accordance with a desired measurement report format.

9. The method of claim 8, further comprising reporting (1012) the one or more postprocessed model outputs to a Location Management Function, LMF.

10. A User Equipment, UE, adapted to: obtain (1006) a downlink, DL, relative time of arrival, RTOA, reference time for a downlink measurement for an Artificial Intelligence, Al, / Machine Learning, ML, model input for an AI / ML model related to UE positioning based on DL RTOA measurements, the DL RTOA reference time being an absolute time; and use (1008) the DL RTOA reference time for the downlink measurement for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on DL RTOA measurements.

11. The UE of claim 10, further adapted to perform the method of any of claims 2 to 9.

12. A User Equipment, UE, (1300) compri sing : a communication interface (1312) comprising a transmitter (1318) and a receiver (1320); and processing circuitry (1302) associated with the communication interface (1312), the processing circuitry (1302) configured to cause the UE (1300) to: obtain (1006) a downlink, DL, relative time of arrival, RTOA, reference time for a downlink measurement for an Artificial Intelligence, Al, / Machine Learning, ML,model input for an AI / ML model related to UE positioning based on DL RTOA measurements, the DL RTOA reference time being an absolute time; and use (1008) the DL RTOA reference time for the downlink measurement for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on DL RTOA measurements.

13. The UE of claim 12, wherein the processing circuitry is further configured to cause the UE to perform the method of any of claims 2 to 9.

14. A method performed by a network node, the method comprising: determining (1100) an uplink, UL, relative time of arrival, RTOA, reference time for an uplink measurement for an Artificial Intelligence, Al, / Machine Learning, ML, model input for an AI / ML model related to User Equipment, UE, positioning based on UL RTOA measurements, the UL RTOA reference time being an absolute time; and using (1102) the UL RTOA reference time for the uplink measurement for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on UL RTOA measurements.

15. The method of claim 14, wherein the UL RTOA reference time is defined as To + tsRS, where To is a nominal beginning time of System Frame Number, SFN, 0 provided by SFN Initialization Time and tsRS is equal to (10nf + nSf)xl0'3where nf and nSf are an SFN and subframe number of a corresponding SRS used for the uplink measurement, respectively.

16. The method of claim 14 or 15, further comprising repeating the steps of determining (1100) and using (1102) for a plurality of uplink measurements for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on UL RTOA measurements.

17. The method of claim 14 or 15, wherein using (1102) the UL RTOA reference time for the uplink measurement for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on UL RTOA measurements comprises representing timing information of the uplink measurement for the AI / ML model input based on the UL RTOA reference time.

18. The method of claim 17, wherein the AI / ML model input comprises a Channel Impulse Response, CIR, or a Power Delay Profile, PDP, or a Delay Profile, DP.

19. The method of any of claims 14 to 18, wherein the AI / ML model is for direct AI / ML positioning.

20. The method of any of claims 14 to 18, wherein the AI / ML model is for assisted AI / ML positioning.

21. The method of claim 20, further comprising, during inference, reporting (1006) one or more outputs of the AI / ML model including associated timing information represented based on UL RTOA reference time to a Location Management Function, LMF.

22. The method of claim 20, further comprising, during inference, post-processing one or more outputs of the AI / ML model including associated timing information represented based on UL RTOA reference time such that the timing information of the post-processed outputs is in a desired reporting format.

23. The method of claim 22, further comprising, during inference, reporting (1006) the postprocessed outputs to a Location Management Function, LMF.

24. A network node for a cellular communications system, the network node adapted to: determine (1100) an uplink, UL, relative time of arrival, RTOA, reference time for an uplink measurement for an Artificial Intelligence, Al, / Machine Learning, ML, model input for an AI / ML model related to User Equipment, UE, positioning based on UL RTOA measurements, the UL RTOA reference time being an absolute time; and use (1102) the UL RTOA reference time for the uplink measurement for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on UL RTOA measurements.

25. The network node of claim 24, wherein the network node is further adapted to perform the method of any of claims 15 to 23.

26. A network node for a cellular communications system, the network node comprisingprocessing circuitry configured to cause the network node to: determine (1100) an uplink, UL, relative time of arrival, RTOA, reference time for an uplink measurement for an Artificial Intelligence, Al, / Machine Learning, ML, model input for an AI / ML model related to User Equipment, UE, positioning based on UL RTOA measurements, the UL RTOA reference time being an absolute time; and use (1102) the UL RTOA reference time for the uplink measurement for the AI / ML model input in association with training or inference of the AI / ML model related to UE positioning based on UL RTOA measurements.

27. The network node of claim 26, wherein the processing circuitry is further configured to cause the network node is to perform the method of any of claims 15 to 23.

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