Enhanced beam management procedures

Abstract

This disclosure describes systems, methods, NG-RAN nodes, and devices related to enhanced positioning. An NG-RAN node may determine timing information for a link from a target device (UE) based on reception of SRS transmitted by the target device (UE) in a 5G network, utilizing one of: path-based or sample-based timing measurements. The NG-RAN node may utilize the path-based timing measurements to report time instances of detected paths in a multipath channel to a serving LMF for input to an Artificial Intelligence / Machine Learning (AI / ML) model, where the time instances are ordered by arrival time. The NG-RAN node may calculate power information for each path of a channel response, using a linear average of the channel response at specific path delays over the number of its receiver branches.

Description

[0001]Attorney Docket No. AG4358-PCT (31517-3584) ENHANCED BEAM MANAGEMENT PROCEDURES CROSS-REFERENCE TO RELATED PATENT APPLICATION(S) This application claims the benefit of U.S. Provisional Application No. 63 / 554,770, filed February 16, 2024, U.S. Provisional Application No.63 / 575,509, filed April 5, 2024, and U.S. Provisional Application No.63 / 641,362, filed May 1, 2024, the disclosures of which are incorporated herein by reference as if set forth in full. TECHNICAL FIELD This disclosure generally relates to systems and methods for wireless communications and, more particularly, to enhanced beam management procedures. BACKGROUND With the rapid expansion of wireless networks, there is a growing demand for precise positioning technologies. The integration of advanced positioning components in 5G networks is crucial for enhancing location accuracy. These components are designed to work seamlessly with 3GPP-defined standards, improving overall network performance and reliability. BRIEF DESCRIPTION OF THE DRAWINGS FIG.1 depicts user equipment (UE) based positioning, in accordance with one or more example embodiments of the present disclosure. FIG. 2 depicts UE-assisted positioning, in accordance with one or more example embodiments of the present disclosure. FIG.3 depicts location management function (LMF) based positioning, in accordance with one or more example embodiments of the present disclosure. FIG. 4 depicts Next Generation (NG)-radio access network (RAN) node assisted positioning, in accordance with one or more example embodiments of the present disclosure. FIG. 5 depicts node assisted positioning with LMF, in accordance with one or more example embodiments of the present disclosure. FIG.6 depicts timing information approaches, in accordance with one or more example embodiments of the present disclosure. FIG. 7 depicts a hybrid timing approach, in accordance with one or more example embodiments of the present disclosure. Attorney Docket No. AG4358-PCT (31517-3584) FIG. 8 illustrates a flow diagram of illustrative process for an illustrative enhanced positioning system, in accordance with one or more example embodiments of the present disclosure. FIG. 9 illustrates an example network architecture, in accordance with one or more example embodiments of the present disclosure. FIG. 10 schematically illustrates a wireless network, in accordance with one or more example embodiments of the present disclosure. FIG.11 illustrates components of a computing device, in accordance with one or more example embodiments of the present disclosure. FIG.12 illustrates a network in accordance with various embodiments. DETAILED DESCRIPTION The following description and the drawings sufficiently illustrate specific embodiments to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, algorithm, and other changes. Portions and features of some embodiments may be included in, or substituted for, those of other embodiments. Embodiments set forth in the claims encompass all available equivalents of those claims. Artificial Intelligence (AI) / Machine Learning (ML) for positioning accuracy enhancement has been studied in the third generation partnership project (3GPP) new radio (NR) release-18 (Rel-18) specifications. This exploration highlights the growing importance of AI / ML in improving the precision of location-based services, which are crucial for applications like autonomous vehicles and smart cities. For example, autonomous vehicles rely on precise positioning to navigate safely and efficiently. It has been agreed to support AI / ML for positioning accuracy enhancement in the 3GPP NR release-19 (Rel-19) specifications for the following sub-use-cases: This decision underscores the commitment to integrating advanced technologies into future communication standards, ensuring that networks can meet the demands of increasingly complex applications. Smart cities, for instance, use enhanced positioning to manage traffic flow and reduce congestion. Direct AI / ML positioning: This approach involves using AI / ML models directly to determine the position, offering potentially higher accuracy and efficiency. In industrial automation, direct positioning can optimize the movement of robots on the factory floor. Case 1: UE-based positioning with UE-side model, direct AI / ML positioning. This scenario uses the user equipment (UE) itself to process positioning data, which can reduce Attorney Docket No. AG4358-PCT (31517-3584) latency and improve real-time accuracy. Wearable devices, like fitness trackers, benefit from this by providing precise location data for health monitoring. Case 2b: UE-assisted / location management function (LMF)-based positioning with LMF-side model, direct AI / ML positioning. Here, the LMF processes data with assistance from the UE, balancing processing load and accuracy. This is useful in emergency services, where quick and accurate location data is critical. Case 3b: next generation (NG)-radio access network (RAN) node assisted positioning with LMF-side model, direct AI / ML positioning. This involves NG-RAN nodes aiding the LMF, which can enhance coverage and reliability. In rural areas, this can improve connectivity and service availability. AI / ML assisted positioning: This method uses AI / ML to support traditional positioning techniques, enhancing their performance. Retail environments can use this to track customer movements and optimize store layouts. Case 2a: user equipment (UE)-assisted / LMF-based positioning with UE-side model, AI / ML assisted positioning. The UE provides data to the LMF, which uses AI / ML to refine positioning, offering a collaborative approach. Smartphones use this to improve GPS accuracy in urban canyons. Case 3a: NG-RAN node assisted positioning with gNB-side model, AI / ML assisted positioning. In this case, the gNB (next-generation Node B) uses AI / ML to assist in positioning, enhancing network-side processing capabilities. This is beneficial in large venues like stadiums, where precise location data can enhance user experience. Example embodiments of the present disclosure relate to systems, methods, and devices for artificial intelligence (AI) / machine learning (ML) assisted beam management procedures. Embodiments herein may relate to or include model input representation, functionality / model monitoring, and / or data collection for AI / ML based positioning in 5G NR systems and beyond. These components are crucial for optimizing AI / ML models, ensuring they operate efficiently and accurately in real-world scenarios. For instance, model input representation can significantly impact the performance of AI systems in predicting user locations in dense urban environments. Functionality / model monitoring is essential to maintain the reliability and performance of AI / ML models over time, adapting to changes in network conditions. This is similar to how continuous monitoring in autonomous vehicles ensures safety and adaptability to different driving conditions. Data collection is foundational for training and refining AI / ML models, providing the necessary information to improve positioning Attorney Docket No. AG4358-PCT (31517-3584) accuracy. In smart agriculture, data collection helps in precise field mapping and resource allocation. The above descriptions are for purposes of illustration and are not meant to be limiting. Numerous other examples, configurations, processes, algorithms, etc., may exist, some of which are described in greater detail below. Example embodiments will now be described with reference to the accompanying figures. FIG. 1 depicts an illustrative schematic diagram for enhanced positioning, in accordance with one or more example embodiments of the present disclosure. Referring to FIG. 1, there is shown an example of Case 1 - UE-based positioning with UE-side model, direct AI / ML positioning. In Case 1, UE-based positioning uses an AI / ML model directly on the user equipment (UE) to estimate its position based on data like signal strength, time of arrival, or angle of arrival, along with optional environmental factors. The pre-trained model processes this data locally, ensuring low-latency operation, privacy, and offline functionality, as no external servers or networks are needed. While this approach offers fast and private positioning, it faces challenges such as optimizing for the UE's limited resources and requiring diverse data for effective model training. Referring to FIG. 2, there is shown an example of Case 2a - UE-assisted / LMF-based positioning with UE-side model, AI / ML assisted positioning. In this scenario, the user equipment (UE) plays a crucial role by collecting and processing data locally, which is then used to assist the location management function (LMF) in determining precise positioning. This approach leverages AI / ML to enhance the accuracy and efficiency of the positioning process. For example, in a crowded urban environment, the UE can use AI / ML algorithms to filter out noise and improve location accuracy, even when GPS signals are weak. The collaboration between the UE and LMF allows for a more dynamic and responsive positioning system, adapting to real-time changes in the environment. This is particularly useful in applications like augmented reality, where precise positioning is essential for overlaying digital information onto the physical world accurately. Referring to FIG. 3, there is shown an example of Case 2b - UE-assisted / LMF-based positioning with LMF-side model, direct AI / ML positioning. In this setup, the location management function (LMF) takes the lead in processing data, utilizing AI / ML models to directly enhance positioning accuracy. The user equipment (UE) assists by providing necessary data inputs, which the LMF uses to refine its calculations. This method is beneficial in scenarios like emergency response, where quick and Attorney Docket No. AG4358-PCT (31517-3584) precise location data is crucial for directing resources effectively. By centralizing the AI / ML processing at the LMF, the system can leverage more powerful computational resources, leading to improved efficiency and accuracy. For instance, in logistics, this approach can optimize delivery routes by accurately tracking vehicle locations in real-time. Referring to FIG. 4, there is shown an example of Case 3a - NG-RAN node assisted positioning with gNB-side model, AI / ML assisted positioning. Referring to FIG. 5, there is shown an example of Case 3b - NG-RAN node assisted positioning with LMF-side model, direct AI / ML positioning. FIGs. 1 through 5 present schematics for each of the above AI / ML-based positioning use-cases illustrating the information flow between the various involved network (transmit / receive point (TRP) / NG-RAN node, LMF), and UE entities. Note that possible neutral-site or UE-side Over The Top (OTT) servers are not shown in these figures. It should be noted that in the rest of this disclosure, the terms gNB, gNodeB, and NG- RAN node are used interchangeably. Model input characterization: the following have been widely considered for model input for AI / ML based positioning in Rel-18: - Channel impulse response (CIR): Timing, power, and phase information for multiple observed channel samples. - Power delay profile (PDP): Timing and power information for multiple observed channel samples. - Delay Profile (DP): Timing information for multiple observed channel samples - Other measurements in 5G NR, e.g., reference signal received power (RSRP) / RSRPP / reference signal time difference (RSTD) / relative time of arrival (RTOA). In some embodiments, the following definitions related to reporting of timing information may be used: Sample-based measurements, where the timing information is an integer multiple of sampling periods. Path-based measurements, where the timing information is according to the detected path timing and may not be an integer multiple of sampling periods. In an embodiment, timing information is represented based on a timing grid which is defined using a reference time and a timing granularity. In such embodiment, the timing information can be represented using any of the following example approaches, and / or some combination thereof: Attorney Docket No. AG4358-PCT (31517-3584) Sample-based approach: Representation of timing information by taking the first K consecutive time instances based on timing grid of the CIR, i.e., output of channel estimation. In specific, the timing information may include one or more of: reference time, sampling period, and value of K. The time instances represented by sample-based approach are intended to indicate the time instances of the first K samples (regardless of their power) of the CIR. The sampling period of the sample-based approach is referred to as nominal sampling period. In an example, the sampling rate may be specified or configured to a UE or gNB by the LMF. In another example, a base value of the sampling rate may be specified as 1 / (Nf*SCS) where Nf corresponds to the maximum Fast Fourier Transform (FFT) size, e.g., Nf = 4096 for 4K FFT size, and SCS is the active Subcarrier Spacing of the DL RS or UL RS used for the measurement that are mapped to the DL Positioning Frequency Layer (PFL) or UL carrier. Further, a factor for over- or under-sampling may be specified or configured by the LMF, possibly subject to UE capabilities for DL positioning. Path-based approach: Representation of timing information by taking K specific (not necessarily consecutive) time instances based on timing grid of output of path-detection. In specific, the timing information may include one or more of: reference time, sampling period, and value of K, and K timing instance values. The time instances represented by path-based approach are intended to indicate the time instances of the K detected paths based on the CIR. For reporting of the K time instances per the path-based approach, the time granularity may be typically smaller than the nominal sampling period for sample-based measurements. The value of K for the path-based approach can be typically smaller than that for the sample-based approach. It should be also noted that the sampling period and / or value of K for any of the above measurement approaches can be pre-specified in the standards specifications or configurable, in which case they will not be part of the measurement report. Alternatively, both parameters can be included as part of the measurement report. Also, the reference time for any of the reporting approaches can be specified in the specifications so that both source and destination entities of the measurement report have the same understanding of the measurement report. Path-based measurements can be interpreted as employing a feature extraction step applied to the CIR to determine the paths corresponding to which timing / power / phase information is reported. In light of this, to mitigate the signalling overhead downside of sample- based approach, variations of the sample-based measurements based on non-uniform sampling Attorney Docket No. AG4358-PCT (31517-3584) could be considered which utilizes information on detected paths to compress the representation. Hybrid approach: Representation of timing information by taking Kfine specific samples sampled at the nominal sampling rate and on the sampling grid of the estimated CIR defined by the nominal sampling rate around (i.e., on the left and right of, within the overall CIR window) each of M detected paths, while in between detected paths a coarser sampling rate with sampling period that is an integer multiple of the nominal sampling period could be applied. In an example, the coarse sampling rate could include the extreme case wherein coarse sampling rate is zero. The value of total number of samples K = Kfine*M for the hybrid approach can be typically larger than that for the path-based approach. In an example, K = Kfine*M may correspond to the maximum number of samples that may be reported for M detected paths. Power-per-sample-based approach: Representation of timing information by taking Kfine specific samples on the sampling grid of the estimated CIR around (i.e., on the left and right of, within the overall CIR window) N samples that correspond to the N samples with the highest power values within the estimated CIR. This approach could allow for a trade-off between the reliance on receiver multipath algorithms and the signalling overhead and can be seen as a generalization of reporting reduced samples. The value of K = Kfine*N for the power- per-sample-based approach can be typically smaller than that for the sample-based approach. FIG.6 illustrates the above four approaches. Referring to FIG. 6, there is shown an example of Different approaches for representation of timing information. Here, Kfine = 3 for the hybrid approach and Kfine=1 for the Power-per-sample approach. Some additional variations of the Hybrid approach discussed above are presented next. It will be noted that these variations are intended as non-limiting examples, and other embodiments may have additional / alternative variations, different variations may be combined, etc. Hybrid II approach: Representation of timing information by taking Kfinespecific samples sampled at the nominal sampling rate and on the sampling grid of the estimated CIR defined by the nominal sampling rate around (i.e., on the left and right of, within the overall CIR window) each of M detected paths, and including reporting of information (may include one or more of: timing, power, phase information) for the detected paths, while in between detected paths a coarser sampling rate with sampling period that is an integer multiple of the nominal sampling period could be applied. In an example, the coarse sampling rate could include the extreme case wherein coarse sampling rate is zero. The maximum value of total Attorney Docket No. AG4358-PCT (31517-3584) number of samples K = Kfine*M for the hybrid approach can be typically larger than that for the path-based approach. Options to determine Kfine are described next. Note that these options may be applied to Hybrid approach disclosed above with the exception that information on a detected path is reported only if it falls on the sampling grid. In an example, Kfinemay be limited to an even integer, e.g., Kfine= 2, 4, 8, etc. Accordingly, if a detected path falls in between two consecutive sampling points, Kfine / 2 samples on each side of the detected path may be reported for each detected path; else, floor((Kfine-1) / 2) samples to the left and floor((Kfine-1) / 2)+1 samples to the right (or vice versa) may be reported. Alternatively, Kfine may be limited to an odd integer, Kfine = 3, 5, 7, etc. Accordingly, if a detected path falls in between two consecutive sampling points, (Kfine-1) / 2 samples to the left and (Kfine-1) / 2+1 samples to the right of the detected path (or vice versa) may be reported for each detected path; else, (Kfine-1) / 2 samples on each side of the detected path may be reported along with one sample overlapping with (i.e., same as) the detected path. In another example, for ‘m’ a positive integer, Kfine may be defined as 2*m when a detected path does not fall on the nominal sampling grid and may be defined as 2*m+1 when a detected path falls on the nominal sampling grid with one of the samples overlapping with (i.e., same as) the detected path. In a further example, values of Kfine and / or ‘m’ may be specified or configured to a UE and / or NG-RAN node by the LMF, potentially subject to UE capability. Options for handling for some specific cases are described below: In an example, multiple samples with duplication may be reported, depending on relative location of the detected paths. Alternatively, when reporting multiple samples, a UE or NG-RAN node may be expected to remove any duplicated samples around the detected paths depending on relative location of the detected paths. In an example, the detected paths and the samples may be reported as part of a single list in increasing order of the time, i.e., earliest to latest. In another example, the detected paths and corresponding samples may be reported separately with or without explicit identification of the samples corresponding to one or more detected paths. In one example, if multiple paths are detected in between two consecutive sampling points on the grid defined by the nominal sampling rate, each of the detected paths in between two consecutive sampling points are reported along with the Kfinesamples corresponding to Attorney Docket No. AG4358-PCT (31517-3584) each of the multiple detected paths. Per this approach, there may be some samples that are reported with duplication corresponding to the multiple detected paths. In another example, if multiple paths are detected in between two consecutive sampling points on the grid defined by the nominal sampling rate, each of the detected paths in between two consecutive sampling points are reported along with the Kfine samples corresponding to each of the multiple detected paths with the removal of duplicated samples. In yet another example, if multiple paths are detected in between two consecutive sampling points on the grid defined by the nominal sampling rate, one of the detected paths in between two consecutive sampling points is reported along with the Kfine samples around the multiple detected paths. Furthermore, selection of one of the detected paths can be based on the earliest of the detected paths, or based on the “strongest path”, i.e., path with highest power amongst the multiple detected paths within two consecutive samples, or timing information corresponding to the earliest or strongest path and power information that is the sum of the powers for the detected paths. Further, the selection criterion / ia may be configured to a UE or be reported by a UE or NG-RAN node. Referring to FIG.7, there is shown an example of Hybrid II approach for representation of timing information. Here, Kfine = 3 for the Hybrid II approach with one sample on the left and two samples on the right of a detected path. Note that any duplicated samples are not highlighted separately in this illustration. From the point of view of model input content, timing, power, and phase information contribute to all or some of CIR, PDP, and DP for model input. These parameters are each discussed separately. Timing information as part of model input: Timing information is part of the model input for CIR, PDP, and DP, as well as for the currently specified measurements that include RSTD, RTOA, and UE / gNB Rx-Tx time difference. Different approaches can be used to represent a sequence of time stamps as the timing information of a CIR or PDP or DP. Consider a positioning framework consisting of ^^ TRPs and let ^^^ ^^ ^ ^^ଶ ^ ⋯ ^ ^^^^denote ^^ time stamps (with respect to a time reference ^^^^)corresponding to ^^ paths in channel from TRP ^^, 1 ^ ^^ ^ ^^, to a particular UE(or from the UE to TRP ^^ in case of UL based positioning). In the above, the ^^ time stamps may correspond to different paths comprising the multipath channel between a Tx and Rx node pair or the ^^ time stamps may correspond to different time domain samples of the CIR estimated at the receiver node. Further, the ^^ paths may correspond to the ^^ detected paths Attorney Docket No. AG4358-PCT (31517-3584) ordered by their arrival time or ordered by their relative power, i.e., the ^^ first paths or ^^ strongest paths, or, alternatively, be left up to implementation. The following considerations regarding determination of the reference time ^^^^corresponding to TRP ^^, are as follows. In an embodiment, for DL positioning, for a UE, one of the TRPs, namely TRP be configured as a reference TRP. In an example, the reference TRP may be configured to a UE by configuration of a reference cell via higher layer signalling, e.g., by the Location Management Function (LMF) using LTE Positioning Protocol (LPP) over the user plane (UP). The reference time ^^^^is defined as the time when the UE receives the start of one DL subframe from the reference TRP ^^ that is closest in time to the DL subframe containing the measurement RS received from TRP ^^. In another embodiment, for DL positioning, the reference time ^^^^may be defined as the start of the DL subframe containing the measurement RS from TRP ^^. In one option, the start of the DL subframe may be from UE i.e., containing any propagation delay relative to the start of the DL subframe at gNB. In another option, the start of the DL subframe may be from gNB perspective – in this case the UE is expected to estimate and compensate any propagation delay, either based on specified procedures (for example, Rel- 17 propagation delay compensation), non-AI / ML positioning methods like Multi-RTT, or based on implementation. In another embodiment, for DL positioning, the reference time ^^^^may be defined as an absolute time stamp which is the same for all TRPs. The absolute time stamp is estimated by the UE. In one embodiment, the absolute time stamp is associated with a reference TRP. In another embodiment, for DL positioning, the reference time ^^^^is the time instance when the first path is received by the UE from TRP ^^. In another embodiment, for DL positioning, one of the TRPs, namely TRP ^^, can be taken as the reference TRP. The reference time ^^^^is defined as the time when the UE receives first path from the reference TRP ^^ in a DL that is closest in time to the DL subframe containing the measurement RS received from TRP ^^. In another embodiment, for UL positioning, the reference time ^^^^can be defined as the start of the UL subframe containing the measurement RS received TRP ^^ from the UE. In an example, the measurement RS is a Sounding Reference Signal (SRS) or SRS for positioning. Attorney Docket No. AG4358-PCT (31517-3584) In another embodiment, for UL positioning, the reference time ^^^^may correspond to the UL Relative Time of Arrival (UL RTOA) reference time as defined in the 3GPP technical specification (TS) 38.215 as ^^^^ ൌ ^^^ ^ ^^ௌ^ோௌwhere: ^^^is the nominal beginning time of system frame number (SFN) 0 provided by SFN Initialization Time. ^^^ ൌ ^10^^ ^ ^ ିଷௌோௌ ^ ^^^^ ൈ 10 , where ^^^ and ^^^^ are the system frame number and thesubframe number of the measurement RS, respectively. of the above embodiments for determination of reference time ^^^^(with exception of when the reference time corresponds to the start of the DL subframe from the gNB’s perspective, i.e., factoring the propagation delay) do not include the propagation delay as part of time domain information, and thus, the resulting time stamps do not include propagation delay. Thus, in an embodiment, the propagation delay is not accounted for as part of the CIR or PDP or DP that may be used as AI / ML model input. In an example of the embodiment, this approach may apply when the timing information for link from each reported TRP / cell / gNB is with respect to the timing of the FAP for a reference TRP / cell / gNB. In another embodiment, the propagation delay information is estimated separately and considered as another input to the AI / ML model in addition to CIR / PDP / DP. In an example of the embodiment, the propagation delay estimation is reported as Time of Flight (ToF) information. In the following embodiments, the propagation delay may be measured separately using other measurements. In an embodiment, the propagation delay from a transmit (TX) node at a receive (RX) node is obtained based on UE Rx-Tx time difference estimation using an RTT-based positioning method for a UE and based on gNB Rx-Tx time difference estimation using an RTT-based positioning method for a gNB. Accordingly, an RS is transmitted by a TX node and received by an RX node. The RX node then transmits another RS back to the TX node.The RX node additionally transmits the RX-TX time difference ^^ோ^ െ ^^்^ to the TX node. TheTX node then estimates the propagation delay by subtracting ^^ோ^ െ ^^்^ from the total round-trip time (RTT). In one example of the embodiment, the TX node is a reference TRP, TRP ^^, and the RX node is the UE. In another example of the embodiment, the TX node is the UE and the RX node is a reference TRP, TRP ^^. In yet another embodiment, the above RTT measurement process is performed from each TRP ^^. In this case, the TX node for each Attorney Docket No. AG4358-PCT (31517-3584) measurement is the corresponding TRP ^^ and the RX node is the UE, or alternatively, the TX node for each measurement is the UE and the RX node is the corresponding TRP ^^. In another embodiment, a TRP estimates the propagation delay using the timing advance (TA) calculation. In this embodiment, the TA calculation is done using RACH procedure. The TA values may be subsequently adjusted using other RS receptions. Regarding representation of the time stamps in the timing information, example embodiments may include one or more of the following: In a first embodiment, the measured time stamps ^^^^, 1 ^ ^^ ^ ^^, 1 ^ ^^ ^ ^^, aredirectly used as the timing information. This option has the largest data size as each time stamp is represented with its full dynamic range. In a variant of the first embodiment, the measured time stamps ^^^^, 1 ^ ^^ ^ ^^, 1 ^ ^^ ^^^, are directly used as the timing information and are represented in terms of absolute time and not relative to ^^^^, e.g., using UTC format. In another variant of the first embodiment, the measured time stamps ^^^^, 1 ^ ^^ ^ ^^,1 ^ ^^ ^ ^^, are directly used as the timing information and are represented to ^^^^while the value of ^^^^is indicated in terms of absolute time, e.g., using UTC format. In a second embodiment, the sequence of quantities ^^^^ ^ ^ ^ ^ ^^ , ^^ଶ െ ^^^ , ^^ଷ െ ^^^ ,⋯ , ^^^ െ ^^^^^ are used as the timing information. This option has lower data compared with Option 1. Moreover, the existing mechanism of RSTD determination and additional detected paths for DL positioning as described in TS38.214 can be re-used in principle for this option, in which case the maximum number of additional paths may need to be increased from the existing value of 8, e.g., to 16, 32, or 64 paths. Further, when a UE or gNB reports a CIR comprising of fewer than the maximum number of additional paths, it is understood that the remaining paths were not detected. In a third embodiment, the sequence of quantities ^^^^ ^ ^ ^ ^ ^^, ^^ଶ െ ^^^ , ^^ଷ െ ^^ଶ,⋯ , ^^^ െ ^^^^ି^^ are used as the timing information. This option has the In a fourth embodiment, the timing information is represented similar to the representation for DL RSTD or UE / gNB Rx-Tx time difference or UL RTOA with the smallest value of ReportingGranularityfactor (‘k’) that is less than zero, e.g., including k = {-6, -5, -4, - 3, -2, -1}. In another embodiment, the combination of the timing information from different receiver branches can use the following principle. The delay (referred to as time stamp above) is only included in the list of delays if the receive power in any of the receiver branches exceeds Attorney Docket No. AG4358-PCT (31517-3584) a threshold or exceeds a portion of the combined overall receive power. The delay is only included in the list of delays if a function of the receive power on all receiver branches exceeds a threshold or a percentage of the overall receive power after processing all delays with this function. This function could be an average or another deterministic, potentially learned function of the receive power with this delay at different receiver branches. Power information as part of model input: Power information is part of the model input for CIR and PDP, as well as for the currently specified measurements that include Reference Signal Received Power (RSRP) and Reference Signal Received Power per Path (RSRPP). In one embodiment, the power ^^^^of the ^^’th path of the channel response from / to TRP ^^, is defined as the linear average of channel response at the ^^’th path delay of the resource elements that carry the RS for the measurement, which is associated with each measured time stamps ^^^^. In such embodiment, if samples are used to represent the channel response, the power^the ^^’th sample of the channel response from / to TRP ^^, is defined as the linear average channel response at the ^^’th sample delay of the resource elements that carry the RS configured for the measurement. In one embodiment, only one power value per path of the channel response is measured and included in model input. In such embodiment, if UE or gNB / TRP uses more than one receiver branch, the power value used as model input is no less than the power measured on either of the receiver branches. In one example, the power value used as model input is the maximum of the measured powers all individual receiver branches. In such embodiment, if samples are used to represent the channel response, only one power value per sample of the channel response is measured and included in model input. In such embodiment, if UE or gNB / TRP uses more than one receiver branch, the power value used as model input is no less than the power measured on either of the receiver branches. In another example, it could be sum of the powers measured on the respective receiver branches. In one embodiment, the relative power of the k’th path to the first path of the channel response from / to TRP n can be included in the power information as part of model input. In such embodiment, if samples are used to represent the channel response, the relative power of the k’th sample to the first sample of the channel response from / to TRP n can be included in the power information as part of model input. In another embodiment, for each measured time stamps ^^^^, a pair {^^^^,^^^^^ can be jointly defined as part of model input. Attorney Docket No. AG4358-PCT (31517-3584) In another embodiment, one power value per path of the channel response is measured for each receiver branch and included in model inputs. Another option is that for each delay only the strongest power across all receive branches is included in the model. Note it is also possible that the average, or any other deterministic, potentially learned function is combining the power from different receive branches before using it as a model input. In such embodiment, if samples are used to represent the channel response, one power value per sample of the channel response is measured for each receiver branch and included in model inputs. Another option is that for each delay only the strongest power across all receive branches is included in the model. In another embodiment, in addition to the power of the channel response the noise power level, the SNR or the CQI can be used as an additional input to the AI / ML model. Note that in the case of a UE based model SNR is directly available. For the case of a LMF or gNB based model the network does know about the UEs channel quality either via CSI feedback or channel reciprocity. In another embodiment, which paths are included in the definition for the CIR, PDP, DP is defined by the path exceeding a power threshold. In such embodiment, if samples are used to represent the channel response, which samples are included in the definition for the CIR, PDP, DP is defined by the sample exceeding a power threshold. This power threshold can be configured using the LMF, RRC or MAC CE signalling, or in a implementation specific definition by the UE. Note that this threshold can be defined relative to the noise experience by the UEs receiver. In one embodiment, the power, potentially along with the timing and / or phaseinformation, may be reported for more than one receive branch ^^ோ^ ^ 1. In one option, eachof the multiple diversity branches may report antenna phase difference set during the reception. Phase information as part of model input: Phase information is part of the model input for CIR only, as well as for the currently specified measurements that include DL Reference Signal Carrier Phase (DL RSCP), DL Reference Signal Carrier Phase Difference (DL RSCPD), and UL Reference Signal Carrier Phase (UL RSCP). In one embodiment, DL reference signal carrier phase of the ^^′th path, ^^^^, may be defined as the phase of the channel response at the ^^’th path delay derived from the resource elements carrying DL RS configured for the measurement, which is associated with each measured time stamps ^^^^. In one example of the embodiment, the DL RS is DL PRS. In such Attorney Docket No. AG4358-PCT (31517-3584) embodiment, if samples are used to represent the channel response, DL reference signal carrier phase of the ^^′th sample, ^^^^, may be defined as the phase of the channel response at the ^^’th sample delay derived from the resource elements carrying DL RS configured for the measurement. In one example of the embodiment, DL reference signal carrier phases of the detected paths, that include the First Arrival Path (FAP) and a number of additional paths, are represented by normalizing the carrier phases for each detected path relative to the phase observed for the FAP. In such embodiment, if samples are used to represent the channel response, DL reference signal carrier phases of the channel response samples, that include the first sample and a number of additional samples, are represented by normalizing the carrier phases for each sample relative to the phase observed for the first sample. In one embodiment, UL reference signal carrier phase of the ^^′th path, ^^^^, may be defined as the phase of the channel response at the ^^’th path delay derived from the resource elements carrying UL RS configured for the measurement. In one example of the embodiment, the UL RS is UL SRS. In such embodiment, if samples are used to represent the channel response, UL reference signal carrier phase of the ^^′th sample, ^^^^, may be defined as the phase of the channel response at the ^^’th sample delay derived from the resource elements carrying UL RS configured for the measurement. In one example of the embodiment, the UL RS is UL SRS. In one example of the embodiment, UL reference signal carrier phases of the detected paths, that include the First Arrival Path (FAP) and a number of additional paths, are represented by normalizing the carrier phases for each detected path relative to the phase observed for the FAP. In such embodiment, if samples are used to represent the channel response, UL reference signal carrier phases of the channel response samples, that include the first sample and a number of additional samples, are represented by normalizing the carrier phases for each sample relative to the phase observed for the first sample. In another embodiment, uniform quantization of phase information may be defined as part of model input. In one example, the phase value can be defined as k, where k = {0, 1, …, Np-1}, where Np can be predefined in the specification or configured by higher layers via RRC signalling. In one example, Np = 16. In this case, the corresponding phase value can be determined as exp^^^2^^ / ^^^^. In another embodiment, for each measured time stamps ^^^^, {^^^^,^^^^,^^^^^ can be jointly defined as part of model input. Attorney Docket No. AG4358-PCT (31517-3584) Assistance information as part of model input: In addition to the information on observed radio channel conditions, some assistance information may also be provided as part of model input. For UE-sided models, such assistance information may be provided to a UE by LMF. In one embodiment, for UE-sided models, a UE may be provided with mapping information of DL PRS resources to different TRPs, including one or more of: TRP location, TRP antenna orientation in terms of TRP antenna boresight direction, TRP Antenna Reference Point (ARP) information, TRP timing error and / or Timing Error Group (TEG) information. In an example, such information may be provided for UE-based positioning with UE-side model with direct AI / ML positioning. In another example, the TRP-related information may be conveyed to a UE using an Information Element (IE) similar to NR- PositionCalculationAssistance defined in TS 37.335 or one or more of: nr-DL-PRS-BeamInfo, NR-TRP-BeamAntennaInfo, NR-RTD-Info, NR-TRP-LocationInfo defined in TS 37.355. Functionality / Model monitoring metrics: Depending on the source of data used for functionality / model monitoring, different approaches of functionality / model monitoring are identified. Functionality / model monitoring based on model input: In one embodiment, the model monitoring entity (UE, NG-RAN node, or LMF) monitors statistics of the model input and makes appropriate decisions if “significant change” is detected in the measured model input compared with training data. A significant change in statistics of the model input can be tested using different methods of hypothesis testing, e.g., t- tests, z-tests, Chi-Square test, analysis of variance (ANOVA), KL Divergence. The critical value (or confidence level) for such testing methods is adjusted to control the sensitivity of the testing. In one embodiment, for cases where the AI / ML model resides in UE, subject to reported UE capabilities, one or more of the following can be configured and signaled to the UE by the network: - Model input information to be used for functionality / model monitoring. - Type of monitoring / testing method and its corresponding critical value. In another embodiment, for cases where the AI / ML model resides in the UE, subject to reported UE capabilities, one or more of the following may be reported by the UE as part of UE assistance information (UAI) along with model input monitoring results: - Model input information used for functionality / model monitoring. - Type of monitoring / testing method and its corresponding critical value. Attorney Docket No. AG4358-PCT (31517-3584) In yet another embodiment, for model monitoring based on model input for cases where the AI / ML model resides in UE, the type of monitoring / testing method or the type of monitoring / testing method and one or more corresponding critical value(s)may be specified. In an example of either of the above embodiments, the network entity may be an LMF. In one embodiment, for cases where the AI / ML model resides at a gNB, one or more of the following can be requested to the gNB by another network entity, e.g., LMF: - Model input information to be used for functionality / model monitoring. - Assistance information. - Type of monitoring / testing method and its corresponding critical value. In another embodiment, for cases where the AI / ML model resides at a gNB, one or more of the following may be reported by the gNB along with model input monitoring results: - Model input information used for functionality / model monitoring. - Assistance information. - Type of monitoring / testing method and its corresponding critical value. Functionality / model monitoring based on model output: In one embodiment, when the model is not provided with ground-truth label or its approximation, the monitoring entity (UE, NG-RAN node, or LMF) monitors statistics of the model output and makes appropriate decisions if “significant change” is detected over time in the measured model output compared. A significant change in statistics of the model output is tested using different methods of hypothesis testing, e.g., t-tests, z-tests, Chi-Square test, analysis of variance (ANOVA), and KL divergence. In an embodiment, for cases where the AI / ML model resides in UE, subject to reported UE capabilities, one or more of the following can be configured and signaled to the UE by the network: - Model output information to be used for functionality / model monitoring. - Assistance information used for data generation. - Type of monitoring / testing method and its corresponding critical value. In another embodiment, for cases where the AI / ML model resides in UE, subject to reported UE capabilities, one or more of the following may be reported by the UE as part of UE assistance information (UAI) along with model output monitoring results: - Model output information to be used for functionality / model monitoring. - Assistance information used for data generation. - Type of monitoring / testing method and its corresponding critical value. Attorney Docket No. AG4358-PCT (31517-3584) In yet another embodiment, for model monitoring based on model output for cases where the AI / ML model resides in UE, the type of monitoring / testing method or the type of monitoring / testing method and one or more corresponding critical value(s) may be specified. In an example of either of the above embodiments, the network entity may be an LMF. In one embodiment, for cases where the AI / ML model resides at a gNB, one or more of the following can be requested to the gNB by another network entity, e.g., LMF: - Model output information to be used for functionality / model monitoring. - Assistance information used for data generation. - Type of monitoring / testing method and its corresponding critical value. In another embodiment, for cases where the AI / ML model resides at a gNB, one or more of the following may be reported by the gNB along with model output monitoring results: - Model output information used for functionality / model monitoring. - Assistance information used for data generation. - Type of monitoring / testing method and its corresponding critical value. In another embodiment, where the model monitoring unit is provided with ground-truth label or its approximation, depending on the type of model output, a distance measure such as Euclidean distance (on the 2-D plane or 3-D space) between the model outputs and the ground- truth labels for continuous value outputs and Hamming distance between the model outputs and the ground-truth labels for binary outputs (e.g. LOS / NLOS) can be calculated. The calculated distance is compared with a threshold to decide whether there is a significant difference between the output and ground-truth label or its approximation. In an embodiment, for cases where the AI / ML model resides in the UE, subject to reported UE capabilities, one or more of the following can be configured and signaled to the UE by the network: - Model output information to be used for functionality / model monitoring. - Type of distance calculation method and its corresponding threshold value. In another embodiment, for cases where the AI / ML model resides in UE, subject to reported UE capabilities, one or more of the following may be reported by the UE as part of UE assistance information (UAI) along with model output monitoring results: - Model output information to be used for functionality / model monitoring. - Type of distance calculation method and its corresponding threshold value. In yet another embodiment, for cases where the AI / ML model resides in UE, the type of distance calculation method or the type of distance calculation method and one or more corresponding threshold value(s) may be specified. Attorney Docket No. AG4358-PCT (31517-3584) In an example of either of the above embodiments, the network entity may be an LMF. Further, similar to the case of model monitoring based on model inputs, the above embodiments and examples could be extended to the case wherein the AI / ML model resides at the gNB. It should be noted that a combination of the above embodiments can be also used for functionality / model monitoring. In an embodiment, for model performance monitoring of AI / ML positioning Case 2a, the model performance monitoring metric may be calculated by the LMF. In this case, the performance monitoring metric could be determined based on the reported model output (e.g., timing information, LOS / NLOS probabilities / indications) from the target UE and one or more of: other measurements (e.g., measurements for non-RAT positioning methods or non-AI / ML- based positioning methods) reported by the target UE and measurements with or without location information from Positioning Reference Units (PRUs). Alternatively, for model performance monitoring of AI / ML positioning Case 2a, the model performance monitoring metric may be calculated by the target UE. This could be achieved based on comparison of model output and one or more of: other measurements (e.g., measurements for non-RAT positioning methods or non-AI / ML-based positioning methods) at the target UE, assistance information received from LMF regarding timing information and / or LOS / NLOS probabilities / indications, assistance information received from LMF including measurements with or without location information of a PRU. Note that, for all the above measurements and ground-truth information, quality and timestamp information may be included as well. Data collection is a critical aspect for AI / ML based positioning with different manifestations, e.g., data collection for model training, data collection for model inference, data collection for model / functionality monitoring, etc. For cases wherein a UE serves as the data collection entity, the network can provide guidance in the form of assistance information for data collection for model training or other model / functionality Life Cycle Management (LCM) purposes. In an embodiment, for UE-based AI / ML positioning or UE-assisted AI / ML positioning, a UE may be provided by a network entity with one or more time windows during which the UE may perform measurements towards data collection and / or report ground-truth labels (e.g., for UEs serving as Positioning Reference Units (PRUs)) for data collection for model training. Such time windows may be defined as one-shot or periodically occurring time windows with a configuration of the start of the window, duration / length of the window, and periodicity of Attorney Docket No. AG4358-PCT (31517-3584) the window (if applicable). The duration / length and periodicity of the window may be defined in terms of absolute time units or in numbers of frames, subframes, slots, or a combination thereof. In an example, the time reference for indicating location of the time windows may be absolute time (e.g., UTC) or start of SFN 0. In an embodiment, for UE-based or UE-assisted positioning, a gNB may be provided by another network entity with one or more time windows to configure and transmit DL PRS with certain requested configuration(s) for data collection for model training. In an embodiment, for UE-based or UE-assisted positioning, a UE may be provided by a network entity with configuration of a measurement validity area within which the UE may perform the measurements and / or report ground-truth labels for data collection for model training. In an example of the embodiment, the measurement validity area may be provided to a UE using an IE similar to AreaID-CellList, as defined in TS 37.355, that provides the NR Cell-IDs of the TRPs belonging to a particular network area within which the UE is to perform measurements and / or report ground-truth labels. In an embodiment, for UE-sided models, a UE may be configured with inference validity area as part of network-side conditions to apply one or more AI / ML models or AI / ML functionalities to ensure consistency between model training and inference. In an example, the inference validity area configuration provided for model training may be assumed to apply also for model inference. In another example, the validity areas provided for model training and model inference may be different. Similarly, for gNB (or NG-RAN node)-assisted positioning, a gNB may be provided by a network entity with one or more time windows during which the gNB may be expected to schedule SRS transmissions from its served UEs and perform associated measurements on the SRS resources for data collection for model training. – Such time windows may be defined as one-shot or periodically occurring time windows with a configuration of the start of the window, duration / length of the window, and periodicity of the window (if applicable). The duration / length and periodicity of the window may be defined in terms of absolute time units or in numbers of frames, subframes, slots, or a combination thereof. In an example, the time reference for indicating location of the time windows may be absolute time (e.g., UTC) or start of SFN 0 as provided by SFN Initialization Time as defined in TS 38.455. Similar methods as the above embodiments and examples and their combinations may also be applied to provide guidance for data collection for model or functionality monitoring. Further, in an embodiment, for UE-based or UE-assisted positioning with model residing at the UE-side, a UE may be provided with multiple time window configurations Attorney Docket No. AG4358-PCT (31517-3584) and / or multiple validity area configurations such that each configuration of either or both of measurement time window and validity area correspond to an AI / ML model or AI / ML functionality. Accordingly, a particular choice of one or both of these configurations may implicitly indicate a model or functionality selection or switching between models or functionalities. Similar methods may also be applied for NG-RAN node-assisted positioning with model residing at the NG-RAN node. In an embodiment, the alignment between configured measurement validity area and inference validity area as a NW-side additional condition to ensure consistency between training and inference is ensured. In one embodiment, to ensure the consistency between training and inference, NW may use either same RS configuration or “almost-same” RS configurations for generation of model input for training and inference. Examples of aspects which can be considered to ensure RS configurations are “almost same” can be listed as follows: RS resource consistency: - Training and inference RSs having the same SCS; - Training and inference RSs occupying either the same time / frequency resource, i.e., one or more of: the same OFDM symbol indices within a slot , same number of OFDM symbols for RS resource, the same REs over each occupied OFDM symbol, the same number of REs over each occupied OFDM symbol (i.e., same comb size) or nested time / frequency resources, i.e., one or more of: OFDM symbol and REs of the inference RS being subset of the OFDM symbol and REs of the training RS, or vice versa. RS antenna port consistency: Training and inference RSs being transmitted over one or more of: the same antenna port(s) or nested sets of antenna ports with one set being a subset of the other, same Transceiver Unit (TXRU) mapping, same Quasi Colocation (QCL) relationships for the transmitted RSs. In one embodiment, to ensure the consistency between training and inference, NW may ensure consistency of channel / link conditions / characteristics between training and inference. Example of aspects which can be considered for consistency of channel / link conditions / characteristics are as follows: SINR consistency: The SINRs of channel measurements for training and inference are within a certain dB offset from each other. Doppler characteristics consistency: The channel measurements for training and inference are conducted under “similar” Doppler characteristics. Similar UE mobilities can be Attorney Docket No. AG4358-PCT (31517-3584) considered as one condition for this purpose, e.g. UEs with speeds differences below a certain threshold can be used for training and inference. Antenna configurations consistency: It is preferred that both TX node and RX node have consistent antenna configurations between training and inference. However, feasibility of ensuring such consistency at UE side could be challenging due to implementation-specific details involved in the antenna configuration. On the network side, however, such consistency can be ensured more readily by ensuring gNB with similar TX antenna configurations for DL- based measurements and similar RX antenna configurations for UL-based are used for training and inference. Examples of aspects of the similarity of antenna configurations include antenna array dimensions, horizonal and vertical antenna element spacings, sub-array configurations, analog or digital or hybrid beamforming configurations (if applicable), and the antenna radiation patterns. UL RS processing consistency: NW uses gNBs with the same UL splits for UL-based measurements for model training and inference. Furthermore, consistency of other implementation-specific aspects of UL processing can also be considered to ensure the consistency between training and inference. Examples include consistency of UL RS channel estimation schemes or configurations used by gNBs in training and inference, consistency of path detection schemes used by gNBs in training and inference. In another embodiment, to ensure the consistency between training and inference, NW may ensure model input format consistency. In such embodiment, the same configuration of model input format is used for training and inference. This may include using the same type of model input as well as the same values for parameter(s) associated with the model input format, e.g., one or more of: the number of paths in the path-based measurement, the number of samples in the sample-based measurement, rules / conditions for selection in case of a subset of detected paths in case of path-based measurement are reported / used, rules / conditions for selection in a sub-sampled version of the CIR samples are reported / used. In another embodiment, a network entity might configure a UE to report data for the purpose of data collection only for the case when the reported ground truth data is above a minimum quality. In addition to the minimum quality metric, the network entity can also configure are window for the reporting or a maximum or minimum number of reports per time unit. In one set of examples of the above embodiments in this sub-section, the network entity may be an LMF. Attorney Docket No. AG4358-PCT (31517-3584) In an embodiment, model input used for model training and model inference / monitoring are defined using the same representation format. In an example of the embodiment, alignment between training and inference / monitoring entities is enabled via exchange of assistance information associated with the data generation or collection steps. For Cases 2b or 3b involving AI / ML model residing at the LMF, data for training, inference, and monitoring would need to be provided by UEs and PRUs (for Case 2b) or by NG-RAN nodes (for Case 3b). This can result in a significant amount of signalling overhead. Thus, various techniques to compress the data may be considered. In an embodiment, methods to compress the reported data may include truncation and / or sub-sampling of the measurement data based on certain rules that may be provided to a data generation / collection entity (e.g., UE or NG-RAN node) by LMF. Examples of the embodiment include: - Configurations of a measurement reporting granularity in time that may include uniform sampling in time, i.e., uniformly skipping a number of measurements or non-uniform skipping of a number of measurements based on some pre-defined or configured rules. - Skipping reporting based on evaluation of certain conditions. Per one example, for data involving ground-truth labels, a data generation / collection entity (e.g., UE or NG-RAN node) may skip inclusion of certain data-points if the location ground-truth does not change beyond a configured or predetermined threshold. In an example, such a threshold may be defined in terms of one or more of: Euclidean distance based on coordinates in the horizontal plane only, Euclidean distance based on coordinates in the 3D space, Rx antenna orientation, or relative angle of rotation (e.g., in case of a UE), etc., jointly or separately for some or all of the characteristics. Per another example, when reporting intermediate metrics like RSTD, RTOA, ToF, UE / gNB Rx-Tx time difference, RSRP, RSRPP, etc., a data generation / collection entity (e.g., UE or NG-RAN node) may skip inclusion of certain data points or inclusion of one or more metrics for a data point unless the metrics have changed beyond configured or predetermined threshold(s). In an example, the thresholds may be set separately for each metric and shared between network and / or UE entities as part of assistance information. Combinations of the above approaches. The above data compression methods may also be applied to other AI / ML-based positioning use-cases other than Cases 2b and 3b. In some embodiments, the electronic device(s), network(s), system(s), chip(s) or component(s), or portions or implementations thereof, of FIGs. 9-12, or some other figure Attorney Docket No. AG4358-PCT (31517-3584) herein, may be configured to perform one or more processes, techniques, or methods as described herein, or portions thereof. One such process 800 is depicted in FIG.8. For example, the process may include, at 802, determining one or both of: timing information and associated power information for a wireless link from a target UE to the NG- RAN node from reception of Sounding Reference Signal (SRS) transmitted by the target UE in a 5G network. The process further includes, at 804, calculating power information for each path of a channel response, using a linear average of the power of the channel response at specific path delays. The process further includes, at 806, reporting one or both of: the timing information and the power information to a serving Location Management Function (LMF) for use as input to an Artificial Intelligence / Machine Learning (AI / ML) model deployed at the LMF. For one or more embodiments, at least one of the components set forth in one or more of the preceding figures may be configured to perform one or more operations, techniques, processes, and / or methods as set forth in the example section below. For example, the baseband circuitry as described above in connection with one or more of the preceding figures may be configured to operate in accordance with one or more of the examples set forth below. For another example, circuitry associated with a UE, base station, network element, etc. as described above in connection with one or more of the preceding figures may be configured to operate in accordance with one or more of the examples set forth below in the example section. It is understood that the above descriptions are for purposes of illustration and are not meant to be limiting. FIGs. 9-12 illustrate various systems, devices, and components that may implement aspects of disclosed embodiments. FIG. 9 illustrates an example network architecture 900 according to various embodiments. The network 900 may operate in a manner consistent with 3GPP technical specifications for LTE or 5G / NR systems. However, the example embodiments are not limited in this regard and the described embodiments may apply to other networks that benefit from the principles described herein, such as future 3GPP systems, or the like. The network 900 includes a UE 902, which is any mobile or non-mobile computing device designed to communicate with a RAN 904 via an over-the-air connection. The UE 902 is communicatively coupled with the RAN 904 by a Uu interface, which may be applicable to both LTE and NR systems. Examples of the UE 902 include, but are not limited to, a smartphone, tablet computer, wearable computer, desktop computer, laptop computer, in- Attorney Docket No. AG4358-PCT (31517-3584) vehicle infotainment system, in-car entertainment system, instrument cluster, head-up display (HUD) device, onboard diagnostic device, dashtop mobile equipment, mobile data terminal, electronic engine management system, electronic / engine control unit, electronic / engine control module, embedded system, sensor, microcontroller, control module, engine management system, networked appliance, machine-type communication device, machine-to-machine (M2M), device-to-device (D2D), machine-type communication (MTC) device, Internet of Things (IoT) device, and / or the like. The network 900 may include a plurality of UEs 902 coupled directly with one another via a D2D, ProSe, PC5, and / or sidelink (SL) interface. These UEs 902 may be M2M / D2D / MTC / IoT devices and / or vehicular systems that communicate using physical sidelink channels such as, but not limited to, PSBCH, PSDCH, PSSCH, PSCCH, PSFCH, etc. The UE 902 may perform blind decoding attempts of SL channels / links according to the various embodiments herein. In some embodiments, the UE 902 may additionally communicate with an AP 906 via an over-the-air (OTA) connection. The AP 906 manages a WLAN connection, which may serve to offload some / all network traffic from the RAN 904. The connection between the UE 902 and the AP 906 may be consistent with any IEEE 802.11 protocol. Additionally, the UE 902, RAN 904, and AP 906 may utilize cellular-WLAN aggregation / integration (e.g., LWA / LWIP). Cellular-WLAN aggregation may involve the UE 902 being configured by the RAN 904 to utilize both cellular radio resources and WLAN resources. The RAN 904 includes one or more access network nodes (ANs) 908. The ANs 908 terminate air-interface(s) for the UE 902 by providing access stratum protocols including RRC, PDCP, RLC, MAC, and PHY / L1 protocols. In this manner, the AN 908 enables data / voice connectivity between CN 920 and the UE 902. The ANs 908 may be a macrocell base station or a low power base station for providing femtocells, picocells or other like cells having smaller coverage areas, smaller user capacity, or higher bandwidth compared to macrocells; or some combination thereof. In these implementations, an AN 908 be referred to as a BS, gNB, RAN node, eNB, ng-eNB, NodeB, RSU, TRxP, etc. One example implementation is a “CU / DU split” architecture where the ANs 908 are embodied as a gNB-Central Unit (CU) that is communicatively coupled with one or more gNB- Distributed Units (DUs), where each DU may be communicatively coupled with one or more Radio Units (RUs) (also referred to as RRHs, RRUs, or the like) (see e.g., 3GPP TS 38.401 v16.1.0 (2020-03)). In some implementations, the one or more RUs may be individual RSUs. In some implementations, the CU / DU split may include an ng-eNB-CU and one or more ng- eNB-DUs instead of, or in addition to, the gNB-CU and gNB-DUs, respectively. The ANs 908 Attorney Docket No. AG4358-PCT (31517-3584) employed as the CU may be implemented in a discrete device or as one or more software entities running on server computers as part of, for example, a virtual network including a virtual Base Band Unit (BBU) or BBU pool, cloud RAN (CRAN), Radio Equipment Controller (REC), Radio Cloud Center (RCC), centralized RAN (C-RAN), virtualized RAN (vRAN), and / or the like (although these terms may refer to different implementation concepts). Any other type of architectures, arrangements, and / or configurations can be used. The plurality of ANs may be coupled with one another via an X2 interface (if the RAN 904 is an LTE RAN or Evolved Universal Terrestrial Radio Access Network (E-UTRAN) 910) or an Xn interface (if the RAN 904 is a NG-RAN 914). The X2 / Xn interfaces, which may be separated into control / user plane interfaces in some embodiments, may allow the ANs to communicate information related to handovers, data / context transfers, mobility, load management, interference coordination, etc. The ANs of the RAN 904 may each manage one or more cells, cell groups, component carriers, etc. to provide the UE 902 with an air interface for network access. The UE 902 may be simultaneously connected with a plurality of cells provided by the same or different ANs 908 of the RAN 904. For example, the UE 902 and RAN 904 may use carrier aggregation to allow the UE 902 to connect with a plurality of component carriers, each corresponding to a Pcell or Scell. In dual connectivity scenarios, a first AN 908 may be a master node that provides an MCG and a second AN 908 may be secondary node that provides an SCG. The first / second ANs 908 may be any combination of eNB, gNB, ng-eNB, etc. The RAN 904 may provide the air interface over a licensed spectrum or an unlicensed spectrum. To operate in the unlicensed spectrum, the nodes may use LAA, eLAA, and / or feLAA mechanisms based on CA technology with PCells / Scells. Prior to accessing the unlicensed spectrum, the nodes may perform medium / carrier-sensing operations based on, for example, a listen-before-talk (LBT) protocol. In V2X scenarios the UE 902 or AN 908 may be or act as a roadside unit (RSU), which may refer to any transportation infrastructure entity used for V2X communications. An RSU may be implemented in or by a suitable AN or a stationary (or relatively stationary) UE. An RSU implemented in or by: a UE may be referred to as a “UE-type RSU”; an eNB may be referred to as an “eNB-type RSU”; a gNB may be referred to as a “gNB-type RSU”; and the like. In one example, an RSU is a computing device coupled with radio frequency circuitry located on a roadside that provides connectivity support to passing vehicle UEs. The RSU may also include internal data storage circuitry to store intersection map geometry, traffic statistics, media, as well as applications / software to sense and control ongoing vehicular and pedestrian Attorney Docket No. AG4358-PCT (31517-3584) traffic. The RSU may provide very low latency communications required for high speed events, such as crash avoidance, traffic warnings, and the like. Additionally or alternatively, the RSU may provide other cellular / WLAN communications services. The components of the RSU may be packaged in a weatherproof enclosure suitable for outdoor installation, and may include a network interface controller to provide a wired connection (e.g., Ethernet) to a traffic signal controller or a backhaul network. In some embodiments, the RAN 904 may be an E-UTRAN 910 with one or more eNBs 912. The an E-UTRAN 910 provides an LTE air interface (Uu) with the following characteristics: SCS of 15 kHz; CP-OFDM waveform for DL and SC-FDMA waveform for UL; turbo codes for data and TBCC for control; etc. The LTE air interface may rely on CSI- RS for CSI acquisition and beam management; PDSCH / PDCCH DMRS for PDSCH / PDCCH demodulation; and CRS for cell search and initial acquisition, channel quality measurements, and channel estimation for coherent demodulation / detection at the UE. The LTE air interface may operating on sub-6 GHz bands. In some embodiments, the RAN 904 may be an next generation (NG)-RAN 914 with one or more gNB 916 and / or on or more ng-eNB 918. The gNB 916 connects with 5G-enabled UEs 902 using a 5G NR interface. The gNB 916 connects with a 5GC 940 through an NG interface, which includes an N2 interface or an N3 interface. The ng-eNB 918 also connects with the 5GC 940 through an NG interface, but may connect with a UE 902 via the Uu interface. The gNB 916 and the ng-eNB 918 may connect with each other over an Xn interface. In some embodiments, the NG interface may be split into two parts, an NG user plane (NG-U) interface, which carries traffic data between the nodes of the NG-RAN 914 and a UPF 948 (e.g., N3 interface), and an NG control plane (NG-C) interface, which is a signaling interface between the nodes of the NG-RAN 914 and an AMF 944 (e.g., N2 interface). The NG-RAN 914 may provide a 5G-NR air interface (which may also be referred to as a Uu interface) with the following characteristics: variable SCS; CP-OFDM for DL, CP- OFDM and DFT-s-OFDM for UL; polar, repetition, simplex, and Reed-Muller codes for control and LDPC for data. The 5G-NR air interface may rely on CSI-RS, PDSCH / PDCCH DMRS similar to the LTE air interface. The 5G-NR air interface may not use a CRS, but may use PBCH DMRS for PBCH demodulation; PTRS for phase tracking for PDSCH; and tracking reference signal for time tracking. The 5G-NR air interface may operating on FR1 bands that include sub-6 GHz bands or FR2 bands that include bands from 24.25 GHz to 52.6 GHz. The 5G-NR air interface may include an SSB that is an area of a downlink resource grid that includes PSS / SSS / PBCH. Attorney Docket No. AG4358-PCT (31517-3584) The 5G-NR air interface may utilize BWPs for various purposes. For example, BWP can be used for dynamic adaptation of the SCS. For example, the UE 902 can be configured with multiple BWPs where each BWP configuration has a different SCS. When a BWP change is indicated to the UE 902, the SCS of the transmission is changed as well. Another use case example of BWP is related to power saving. In particular, multiple BWPs can be configured for the UE 902 with different amount of frequency resources (e.g., PRBs) to support data transmission under different traffic loading scenarios. A BWP containing a smaller number of PRBs can be used for data transmission with small traffic load while allowing power saving at the UE 902 and in some cases at the gNB 916. A BWP containing a larger number of PRBs can be used for scenarios with higher traffic load. The RAN 904 is communicatively coupled to CN 920 that includes network elements and / or network functions (NFs) to provide various functions to support data and telecommunications services to customers / subscribers (e.g., UE 902). The components of the CN 920 may be implemented in one physical node or separate physical nodes. In some embodiments, NFV may be utilized to virtualize any or all of the functions provided by the network elements of the CN 920 onto physical compute / storage resources in servers, switches, etc. A logical instantiation of the CN 920 may be referred to as a network slice, and a logical instantiation of a portion of the CN 920 may be referred to as a network sub-slice. The CN 920 may be an LTE CN 922 (also referred to as an Evolved Packet Core (EPC) 922). The EPC 922 may include MME 924, SGW 926, SGSN 928, HSS 930, PGW 932, and PCRF 934 coupled with one another over interfaces (or “reference points”) as shown. The NFs in the EPC 922 are briefly introduced as follows. The MME 924 implements mobility management functions to track a current location of the UE 902 to facilitate paging, bearer activation / deactivation, handovers, gateway selection, authentication, etc. The SGW 926 terminates an S1 interface toward the RAN 910 and routes data packets between the RAN 910 and the EPC 922. The SGW 926 may be a local mobility anchor point for inter-RAN node handovers and also may provide an anchor for inter-3GPP mobility. Other responsibilities may include lawful intercept, charging, and some policy enforcement. The SGSN 928 tracks a location of the UE 902 and performs security functions and access control. The SGSN 928 also performs inter-EPC node signaling for mobility between different RAT networks; PDN and S-GW selection as specified by MME 924; MME 924 selection for handovers; etc. The S3 reference point between the MME 924 and the SGSN 928 Attorney Docket No. AG4358-PCT (31517-3584) enable user and bearer information exchange for inter-3GPP access network mobility in idle / active states. The HSS 930 includes a database for network users, including subscription-related information to support the network entities’ handling of communication sessions. The HSS 930 can provide support for routing / roaming, authentication, authorization, naming / addressing resolution, location dependencies, etc. An S6a reference point between the HSS 930 and the MME 924 may enable transfer of subscription and authentication data for authenticating / authorizing user access to the EPC 920. The PGW 932 may terminate an SGi interface toward a data network (DN) 936 that may include an application (app) / content server 938. The PGW 932 routes data packets between the EPC 922 and the data network 936. The PGW 932 is communicatively coupled with the SGW 926 by an S5 reference point to facilitate user plane tunneling and tunnel management. The PGW 932 may further include a node for policy enforcement and charging data collection (e.g., PCEF). Additionally, the SGi reference point may communicatively couple the PGW 932 with the same or different data network 936. The PGW 932 may be communicatively coupled with a PCRF 934 via a Gx reference point. The PCRF 934 is the policy and charging control element of the EPC 922. The PCRF 934 is communicatively coupled to the app / content server 938 to determine appropriate QoS and charging parameters for service flows. The PCRF 932 also provisions associated rules into a PCEF (via Gx reference point) with appropriate TFT and QCI. The CN 920 may be a 5GC 940 including an AUSF 942, AMF 944, SMF 946, UPF 948, NSSF 950, NEF 952, NRF 954, PCF 956, UDM 958, and AF 960 coupled with one another over various interfaces as shown. The NFs in the 5GC 940 are briefly introduced as follows. The AUSF 942 stores data for authentication of UE 902 and handle authentication- related functionality. The AUSF 942 may facilitate a common authentication framework for various access types.. The AMF 944 allows other functions of the 5GC 940 to communicate with the UE 902 and the RAN 904 and to subscribe to notifications about mobility events with respect to the UE 902. The AMF 944 is also responsible for registration management (e.g., for registering UE 902), connection management, reachability management, mobility management, lawful interception of AMF-related events, and access authentication and authorization. The AMF 944 provides transport for SM messages between the UE 902 and the SMF 946, and acts as a transparent proxy for routing SM messages. AMF 944 also provides transport for SMS Attorney Docket No. AG4358-PCT (31517-3584) messages between UE 902 and an SMSF. AMF 944 interacts with the AUSF 942 and the UE 902 to perform various security anchor and context management functions. Furthermore, AMF 944 is a termination point of a RAN-CP interface, which includes the N2 reference point between the RAN 904 and the AMF 944. The AMF 944 is also a termination point of NAS (N1) signaling, and performs NAS ciphering and integrity protection. AMF 944 also supports NAS signaling with the UE 902 over an N3IWF interface. The N3IWF provides access to untrusted entities. N3IWF may be a termination point for the N2 interface between the (R)AN 904 and the AMF 944 for the control plane, and may be a termination point for the N3 reference point between the (R)AN 914 and the 948 for the user plane. As such, the AMF 944 handles N2 signalling from the SMF 946 and the AMF 944 for PDU sessions and QoS, encapsulate / de-encapsulate packets for IPSec and N3 tunnelling, marks N3 user-plane packets in the uplink, and enforces QoS corresponding to N3 packet marking taking into account QoS requirements associated with such marking received over N2. N3IWF may also relay UL and DL control-plane NAS signalling between the UE 902 and AMF 944 via an N1 reference point between the UE 902and the AMF 944, and relay uplink and downlink user-plane packets between the UE 902 and UPF 948. The N3IWF also provides mechanisms for IPsec tunnel establishment with the UE 902. The AMF 944 may exhibit an Namf service- based interface, and may be a termination point for an N14 reference point between two AMFs 944 and an N17 reference point between the AMF 944 and a 5G-EIR (not shown by FIG.9). The SMF 946 is responsible for SM (e.g., session establishment, tunnel management between UPF 948 and AN 908); UE IP address allocation and management (including optional authorization); selection and control of UP function; configuring traffic steering at UPF 948 to route traffic to proper destination; termination of interfaces toward policy control functions; controlling part of policy enforcement, charging, and QoS; lawful intercept (for SM events and interface to LI system); termination of SM parts of NAS messages; downlink data notification; initiating AN specific SM information, sent via AMF 944 over N2 to AN 908; and determining SSC mode of a session. SM refers to management of a PDU session, and a PDU session or “session” refers to a PDU connectivity service that provides or enables the exchange of PDUs between the UE 902 and the DN 936. The UPF 948 acts as an anchor point for intra-RAT and inter-RAT mobility, an external PDU session point of interconnect to data network 936, and a branching point to support multi- homed PDU session. The UPF 948 also performs packet routing and forwarding, packet inspection, enforces user plane part of policy rules, lawfully intercept packets (UP collection), performs traffic usage reporting, perform QoS handling for a user plane (e.g., packet filtering, Attorney Docket No. AG4358-PCT (31517-3584) gating, UL / DL rate enforcement), performs uplink traffic verification (e.g., SDF-to-QoS flow mapping), transport level packet marking in the uplink and downlink, and performs downlink packet buffering and downlink data notification triggering. UPF 948 may include an uplink classifier to support routing traffic flows to a data network. The NSSF 950 selects a set of network slice instances serving the UE 902. The NSSF 950 also determines allowed NSSAI and the mapping to the subscribed S-NSSAIs, if needed. The NSSF 950 also determines an AMF set to be used to serve the UE 902, or a list of candidate AMFs 944 based on a suitable configuration and possibly by querying the NRF 954. The selection of a set of network slice instances for the UE 902 may be triggered by the AMF 944 with which the UE 902 is registered by interacting with the NSSF 950; this may lead to a change of AMF 944. The NSSF 950 interacts with the AMF 944 via an N22 reference point; and may communicate with another NSSF in a visited network via an N31 reference point (not shown). The NEF 952 securely exposes services and capabilities provided by 3GPP NFs for third party, internal exposure / re-exposure, AFs 960, edge computing or fog computing systems (e.g., edge compute node, etc. In such embodiments, the NEF 952 may authenticate, authorize, or throttle the AFs. NEF 952 may also translate information exchanged with the AF 960 and information exchanged with internal network functions. For example, the NEF 952 may translate between an AF-Service-Identifier and an internal 5GC information. NEF 952 may also receive information from other NFs based on exposed capabilities of other NFs. This information may be stored at the NEF 952 as structured data, or at a data storage NF using standardized interfaces. The stored information can then be re-exposed by the NEF 952 to other NFs and AFs, or used for other purposes such as analytics. The NRF 954 supports service discovery functions, receives NF discovery requests from NF instances, and provides information of the discovered NF instances to the requesting NF instances. NRF 954 also maintains information of available NF instances and their supported services. The NRF 954 also supports service discovery functions, wherein the NRF 954 receives NF Discovery Request from NF instance or an SCP (not shown), and provides information of the discovered NF instances to the NF instance or SCP. The PCF 956 provides policy rules to control plane functions to enforce them, and may also support unified policy framework to govern network behavior. The PCF 956 may also implement a front end to access subscription information relevant for policy decisions in a UDR of the UDM 958. In addition to communicating with functions over reference points as shown, the PCF 956 exhibit an Npcf service-based interface. Attorney Docket No. AG4358-PCT (31517-3584) The UDM 958 handles subscription-related information to support the network entities’ handling of communication sessions, and stores subscription data of UE 902. For example, subscription data may be communicated via an N8 reference point between the UDM 958 and the AMF 944. The UDM 958 may include two parts, an application front end and a UDR. The UDR may store subscription data and policy data for the UDM 958 and the PCF 956, and / or structured data for exposure and application data (including PFDs for application detection, application request information for multiple UEs 902) for the NEF 952. The Nudr service- based interface may be exhibited by the UDR 221 to allow the UDM 958, PCF 956, and NEF 952 to access a particular set of the stored data, as well as to read, update (e.g., add, modify), delete, and subscribe to notification of relevant data changes in the UDR. The UDM may include a UDM-FE, which is in charge of processing credentials, location management, subscription management and so on. Several different front ends may serve the same user in different transactions. The UDM-FE accesses subscription information stored in the UDR and performs authentication credential processing, user identification handling, access authorization, registration / mobility management, and subscription management. In addition to communicating with other NFs over reference points as shown, the UDM 958 may exhibit the Nudm service-based interface. AF 960 provides application influence on traffic routing, provide access to NEF 952, and interact with the policy framework for policy control. The AF 960 may influence UPF 948 (re)selection and traffic routing. Based on operator deployment, when AF 960 is considered to be a trusted entity, the network operator may permit AF 960 to interact directly with relevant NFs. Additionally, the AF 960 may be used for edge computing implementations, The 5GC 940 may enable edge computing by selecting operator / 3rd party services to be geographically close to a point that the UE 902 is attached to the network. This may reduce latency and load on the network. In edge computing implementations, the 5GC 940 may select a UPF 948 close to the UE 902 and execute traffic steering from the UPF 948 to DN 936 via the N6 interface. This may be based on the UE subscription data, UE location, and information provided by the AF 960, which allows the AF 960 to influence UPF (re)selection and traffic routing. The data network (DN) 936 may represent various network operator services, Internet access, or third party services that may be provided by one or more servers including, for example, application (app) / content server 938. The DN 936 may be an operator external public, a private PDN, or an intra-operator packet data network, for example, for provision of IMS services. In this embodiment, the app server 938 can be coupled to an IMS via an S-CSCF or Attorney Docket No. AG4358-PCT (31517-3584) the I-CSCF. In some implementations, the DN 936 may represent one or more local area DNs (LADNs), which are DNs 936 (or DN names (DNNs)) that is / are accessible by a UE 902 in one or more specific areas. Outside of these specific areas, the UE 902 is not able to access the LADN / DN 936. Additionally or alternatively, the DN 936 may be an Edge DN 936, which is a (local) Data Network that supports the architecture for enabling edge applications. In these embodiments, the app server 938 may represent the physical hardware systems / devices providing app server functionality and / or the application software resident in the cloud or at an edge compute node that performs server function(s). In some embodiments, the app / content server 938 provides an edge hosting environment that provides support required for Edge Application Server's execution. In some embodiments, the 5GS can use one or more edge compute nodes to provide an interface and offload processing of wireless communication traffic. In these embodiments, the edge compute nodes may be included in, or co-located with one or more RAN910, 914. For example, the edge compute nodes can provide a connection between the RAN 914 and UPF 948 in the 5GC 940. The edge compute nodes can use one or more NFV instances instantiated on virtualization infrastructure within the edge compute nodes to process wireless connections to and from the RAN 914 and UPF 948. The interfaces of the 5GC 940 include reference points and service-based itnterfaces. The reference points include: N1 (between the UE 902 and the AMF 944), N2 (between RAN 914 and AMF 944), N3 (between RAN 914 and UPF 948), N4 (between the SMF 946 and UPF 948), N5 (between PCF 956 and AF 960), N6 (between UPF 948 and DN 936), N7 (between SMF 946 and PCF 956), N8 (between UDM 958 and AMF 944), N9 (between two UPFs 948), N10 (between the UDM 958 and the SMF 946), N11 (between the AMF 944 and the SMF 946), N12 (between AUSF 942 and AMF 944), N13 (between AUSF 942 and UDM 958), N14 (between two AMFs 944; not shown), N15 (between PCF 956 and AMF 944 in case of a non- roaming scenario, or between the PCF 956 in a visited network and AMF 944 in case of a roaming scenario), N16 (between two SMFs 946; not shown), and N22 (between AMF 944 and NSSF 950). Other reference point representations not shown in FIG. 9 can also be used. The service-based representation of FIG.9 represents NFs within the control plane that enable other authorized NFs to access their services. The service-based interfaces (SBIs) include: Namf (SBI exhibited by AMF 944), Nsmf (SBI exhibited by SMF 946), Nnef (SBI exhibited by NEF 952), Npcf (SBI exhibited by PCF 956), Nudm (SBI exhibited by the UDM 958), Naf (SBI exhibited by AF 960), Nnrf (SBI exhibited by NRF 954), Nnssf (SBI exhibited by NSSF Attorney Docket No. AG4358-PCT (31517-3584) 950), Nausf (SBI exhibited by AUSF 942). Other service-based interfaces (e.g., Nudr, N5g-eir, and Nudsf) not shown in FIG. 9 can also be used. In some embodiments, the NEF 952 can provide an interface to edge compute nodes 936x, which can be used to process wireless connections with the RAN 914.In some implementations, the system 900 may include an SMSF, which is responsible for SMS subscription checking and verification, and relaying SM messages to / from the UE 902 to / from other entities, such as an SMS-GMSC / IWMSC / SMS- router. The SMS may also interact with AMF 944 and UDM 958 for a notification procedure that the UE 902 is available for SMS transfer (e.g., set a UE not reachable flag, and notifying UDM 958 when UE 902 is available for SMS). The 5GS may also include an SCP (or individual instances of the SCP) that supports indirect communication (see e.g., 3GPP TS 23.501 section 7.1.1); delegated discovery (see e.g., 3GPP TS 23.501 section 7.1.1); message forwarding and routing to destination NF / NF service(s), communication security (e.g., authorization of the NF Service Consumer to access the NF Service Producer API) (see e.g., 3GPP TS 33.501), load balancing, monitoring, overload control, etc.; and discovery and selection functionality for UDM(s), AUSF(s), UDR(s), PCF(s) with access to subscription data stored in the UDR based on UE's SUPI, SUCI or GPSI (see e.g., 3GPP TS 23.501 section 6.3). Load balancing, monitoring, overload control functionality provided by the SCP may be implementation specific. The SCP may be deployed in a distributed manner. More than one SCP can be present in the communication path between various NF Services. The SCP, although not an NF instance, can also be deployed distributed, redundant, and scalable. FIG. 10 schematically illustrates a wireless network 1000 in accordance with various embodiments. The wireless network 1000 may include a UE 1002 in wireless communication with an AN 1004. The UE 1002 and AN 1004 may be similar to, and substantially interchangeable with, like-named components described with respect to FIG.9. The UE 1002 may be communicatively coupled with the AN 1004 via connection 1006. The connection 1006 is illustrated as an air interface to enable communicative coupling, and can be consistent with cellular communications protocols such as an LTE protocol or a 5G NR protocol operating at mmWave or sub-6GHz frequencies. The UE 1002 may include a host platform 1008 coupled with a modem platform 1010. The host platform 1008 may include application processing circuitry 1012, which may be coupled with protocol processing circuitry 1014 of the modem platform 1010. The application processing circuitry 1012 may run various applications for the UE 1002 that source / sink application data. The application processing circuitry 1012 may further implement one or more Attorney Docket No. AG4358-PCT (31517-3584) layer operations to transmit / receive application data to / from a data network. These layer operations may include transport (for example UDP) and Internet (for example, IP) operations The protocol processing circuitry 1014 may implement one or more of layer operations to facilitate transmission or reception of data over the connection 1006. The layer operations implemented by the protocol processing circuitry 1014 may include, for example, MAC, RLC, PDCP, RRC and NAS operations. The modem platform 1010 may further include digital baseband circuitry 1016 that may implement one or more layer operations that are “below” layer operations performed by the protocol processing circuitry 1014 in a network protocol stack. These operations may include, for example, PHY operations including one or more of HARQ acknowledgement (ACK) functions, scrambling / descrambling, encoding / decoding, layer mapping / de-mapping, modulation symbol mapping, received symbol / bit metric determination, multi-antenna port precoding / decoding, which may include one or more of space-time, space-frequency or spatial coding, reference signal generation / detection, preamble sequence generation and / or decoding, synchronization sequence generation / detection, control channel signal blind decoding, and other related functions. The modem platform 1010 may further include transmit circuitry 1018, receive circuitry 1020, RF circuitry 1022, and RF front end (RFFE) 1024, which may include or connect to one or more antenna panels 1026. Briefly, the transmit circuitry 1018 may include a digital-to-analog converter, mixer, intermediate frequency (IF) components, etc.; the receive circuitry 1020 may include an analog-to-digital converter, mixer, IF components, etc.; the RF circuitry 1022 may include a low-noise amplifier, a power amplifier, power tracking components, etc.; RFFE 1024 may include filters (for example, surface / bulk acoustic wave filters), switches, antenna tuners, beamforming components (for example, phase-array antenna components), etc. The selection and arrangement of the components of the transmit circuitry 1018, receive circuitry 1020, RF circuitry 1022, RFFE 1024, and antenna panels 1026 (referred generically as “transmit / receive components”) may be specific to details of a specific implementation such as, for example, whether communication is TDM or FDM, in mmWave or sub-6 gHz frequencies, etc. In some embodiments, the transmit / receive components may be arranged in multiple parallel transmit / receive chains, may be disposed in the same or different chips / modules, etc. In some embodiments, the protocol processing circuitry 1014 may include one or more instances of control circuitry (not shown) to provide control functions for the transmit / receive components. Attorney Docket No. AG4358-PCT (31517-3584) A UE 1002 reception may be established by and via the antenna panels 1026, RFFE 1024, RF circuitry 1022, receive circuitry 1020, digital baseband circuitry 1016, and protocol processing circuitry 1014. In some embodiments, the antenna panels 1026 may receive a transmission from the AN 1004 by receive-beamforming signals received by a plurality of antennas / antenna elements of the one or more antenna panels 1026. A UE 1002 transmission may be established by and via the protocol processing circuitry 1014, digital baseband circuitry 1016, transmit circuitry 1018, RF circuitry 1022, RFFE 1024, and antenna panels 1026. In some embodiments, the transmit components of the UE 1004 may apply a spatial filter to the data to be transmitted to form a transmit beam emitted by the antenna elements of the antenna panels 1026. Similar to the UE 1002, the AN 1004 may include a host platform 1028 coupled with a modem platform 1030. The host platform 1028 may include application processing circuitry 1032 coupled with protocol processing circuitry 1034 of the modem platform 1030. The modem platform may further include digital baseband circuitry 1036, transmit circuitry 1038, receive circuitry 1040, RF circuitry 1042, RFFE circuitry 1044, and antenna panels 1046. The components of the AN 1004 may be similar to and substantially interchangeable with like- named components of the UE 1002. In addition to performing data transmission / reception as described above, the components of the AN 1008 may perform various logical functions that include, for example, RNC functions such as radio bearer management, uplink and downlink dynamic radio resource management, and data packet scheduling. FIG.11 illustrates components of a computing device 1100 according to some example embodiments, able to read instructions from a machine-readable or computer-readable medium (e.g., a non-transitory machine-readable storage medium) and perform any one or more of the methodologies discussed herein. Specifically, FIG.11 shows a diagrammatic representation of hardware resources 1101 including one or more processors (or processor cores) 1110, one or more memory / storage devices 1120, and one or more communication resources 1130, each of which may be communicatively coupled via a bus 1140 or other interface circuitry. For embodiments where node virtualization (e.g., NFV) is utilized, a hypervisor 1102 may be executed to provide an execution environment for one or more network slices / sub-slices to utilize the hardware resources 1101. The processors 1110 include, for example, processor 1112 and processor 1114. The processors 1110 include circuitry such as, but not limited to one or more processor cores and one or more of cache memory, low drop-out voltage regulators (LDOs), interrupt controllers, serial interfaces such as SPI, I2C or universal programmable serial interface circuit, real time Attorney Docket No. AG4358-PCT (31517-3584) clock (RTC), timer-counters including interval and watchdog timers, general purpose I / O, memory card controllers such as secure digital / multi-media card (SD / MMC) or similar, interfaces, mobile industry processor interface (MIPI) interfaces and Joint Test Access Group (JTAG) test access ports. The processors 1110 may be, for example, a central processing unit (CPU), reduced instruction set computing (RISC) processors, Acorn RISC Machine (ARM) processors, complex instruction set computing (CISC) processors, graphics processing units (GPUs), one or more Digital Signal Processors (DSPs) such as a baseband processor, Application-Specific Integrated Circuits (ASICs), an Field-Programmable Gate Array (FPGA), a radio-frequency integrated circuit (RFIC), one or more microprocessors or controllers, another processor (including those discussed herein), or any suitable combination thereof. In some implementations, the processor circuitry 1110 may include one or more hardware accelerators, which may be microprocessors, programmable processing devices (e.g., FPGA, complex programmable logic devices (CPLDs), etc.), or the like. The memory / storage devices 1120 may include main memory, disk storage, or any suitable combination thereof. The memory / storage devices 1120 may include, but are not limited to, any type of volatile, non-volatile, or semi-volatile memory such as random access memory (RAM), dynamic RAM (DRAM), static RAM (SRAM), synchronous DRAM (SDRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), Flash memory, solid-state storage, phase change RAM (PRAM), resistive memory such as magnetoresistive random access memory (MRAM), etc., and may incorporate three-dimensional (3D) cross-point (XPOINT) memories from Intel® and Micron®. The memory / storage devices 1120 may also comprise persistent storage devices, which may be temporal and / or persistent storage of any type, including, but not limited to, non-volatile memory, optical, magnetic, and / or solid state mass storage, and so forth. The communication resources 1130 may include interconnection or network interface controllers, components, or other suitable devices to communicate with one or more peripheral devices 1104 or one or more databases 1106 or other network elements via a network 1108. For example, the communication resources 1130 may include wired communication components (e.g., for coupling via USB, Ethernet, Ethernet, Ethernet over GRE Tunnels, Ethernet over Multiprotocol Label Switching (MPLS), Ethernet over USB, Controller Area Network (CAN), Local Interconnect Network (LIN), DeviceNet, ControlNet, Data Highway+, PROFIBUS, or PROFINET, among many others), cellular communication components, NFC components, Bluetooth® (or Bluetooth® Low Energy) components, WiFi® components, and Attorney Docket No. AG4358-PCT (31517-3584) other communication components. Network connectivity may be provided to / from the computing device 1100 via the communication resources 1130 using a physical connection, which may be electrical (e.g., a “copper interconnect”) or optical. The physical connection also includes suitable input connectors (e.g., ports, receptacles, sockets, etc.) and output connectors (e.g., plugs, pins, etc.). The communication resources 1130 may include one or more dedicated processors and / or FPGAs to communicate using one or more of the aforementioned network interface protocols. Instructions 1150 may comprise software, a program, an application, an applet, an app, or other executable code for causing at least any of the processors 1110 to perform any one or more of the methodologies discussed herein. The instructions 1150 may reside, completely or partially, within at least one of the processors 1110 (e.g., within the processor’s cache memory), the memory / storage devices 1120, or any suitable combination thereof. Furthermore, any portion of the instructions 1150 may be transferred to the hardware resources 1101 from any combination of the peripheral devices 1104 or the databases 1106. Accordingly, the memory of processors 1110, the memory / storage devices 1120, the peripheral devices 1104, and the databases 1106 are examples of computer-readable and machine-readable media. FIG. 12 illustrates a network 1200 in accordance with various embodiments. The network 1200 may operate in a matter consistent with 3GPP technical specifications or technical reports for 6G systems. In some embodiments, the network 1200 may operate concurrently with network 900. For example, in some embodiments, the network 1200 may share one or more frequency or bandwidth resources with network 900. As one specific example, a UE (e.g., UE 1202) may be configured to operate in both network 1200 and network 900. Such configuration may be based on a UE including circuitry configured for communication with frequency and bandwidth resources of both networks 900 and 1200. In general, several elements of network 1200 may share one or more characteristics with elements of network 900. For the sake of brevity and clarity, such elements may not be repeated in the description of network 1200. The network 1200 may include a UE 1202, which may include any mobile or non- mobile computing device designed to communicate with a RAN 1208 via an over-the-air connection. The UE 1202 may be similar to, for example, UE 902. The UE 1202 may be, but is not limited to, a smartphone, tablet computer, wearable computer device, desktop computer, laptop computer, in-vehicle infotainment, in-car entertainment device, instrument cluster, head-up display device, onboard diagnostic device, dashtop mobile equipment, mobile data terminal, electronic engine management system, electronic / engine control unit, Attorney Docket No. AG4358-PCT (31517-3584) electronic / engine control module, embedded system, sensor, microcontroller, control module, engine management system, networked appliance, machine-type communication device, M2M or D2D device, IoT device, etc. Although not specifically shown in FIG. 12, in some embodiments the network 1200 may include a plurality of UEs coupled directly with one another via a sidelink interface. The UEs may be M2M / D2D devices that communicate using physical sidelink channels such as, but not limited to, PSBCH, PSDCH, PSSCH, PSCCH, PSFCH, etc. Similarly, although not specifically shown in FIG.12, the UE 1202 may be communicatively coupled with an AP such as AP 906 as described with respect to FIG. 9. Additionally, although not specifically shown in FIG. 12, in some embodiments the RAN 1208 may include one or more ANss such as AN 908 as described with respect to FIG.9. The RAN 1208 and / or the AN of the RAN 1208 may be referred to as a base station (BS), a RAN node, or using some other term or name. The UE 1202 and the RAN 1208 may be configured to communicate via an air interface that may be referred to as a sixth generation (6G) air interface. The 6G air interface may include one or more features such as communication in a terahertz (THz) or sub-THz bandwidth, or joint communication and sensing. As used herein, the term “joint communication and sensing” may refer to a system that allows for wireless communication as well as radar-based sensing via various types of multiplexing. As used herein, THz or sub-THz bandwidths may refer to communication in the 80 GHz and above frequency ranges. Such frequency ranges may additionally or alternatively be referred to as “millimeter wave” or “mmWave” frequency ranges. The RAN 1208 may allow for communication between the UE 1202 and a 6G core network (CN) 1210. Specifically, the RAN 1208 may facilitate the transmission and reception of data between the UE 1202 and the 6G CN 1210. The 6G CN 1210 may include various functions such as NSSF 950, NEF 952, NRF 954, PCF 956, UDM 958, AF 960, SMF 946, and AUSF 942. The 6G CN 1210 may additional include UPF 948 and DN 936 as shown in FIG. 12. Additionally, the RAN 1208 may include various additional functions that are in addition to, or alternative to, functions of a legacy cellular network such as a 4G or 5G network. Two such functions may include a Compute Control Function (Comp CF) 1224 and a Compute Service Function (Comp SF) 1236. The Comp CF 1224 and the Comp SF 1236 may be parts or functions of the Computing Service Plane. Comp CF 1224 may be a control plane function that provides functionalities such as management of the Comp SF 1236, computing task context generation and management (e.g., create, read, modify, delete), interaction with the Attorney Docket No. AG4358-PCT (31517-3584) underlaying computing infrastructure for computing resource management, etc.. Comp SF 1236 may be a user plane function that serves as the gateway to interface computing service users (such as UE 1202) and computing nodes behind a Comp SF instance. Some functionalities of the Comp SF 1236 may include: parse computing service data received from users to compute tasks executable by computing nodes; hold service mesh ingress gateway or service API gateway; service and charging policies enforcement; performance monitoring and telemetry collection, etc. In some embodiments, a Comp SF 1236 instance may serve as the user plane gateway for a cluster of computing nodes. A Comp CF 1224 instance may control one or more Comp SF 1236 instances. Two other such functions may include a Communication Control Function (Comm CF) 1228 and a Communication Service Function (Comm SF) 1238, which may be parts of the Communication Service Plane. The Comm CF 1228 may be the control plane function for managing the Comm SF 1238, communication sessions creation / configuration / releasing, and managing communication session context. The Comm SF 1238 may be a user plane function for data transport. Comm CF 1228 and Comm SF 1238 may be considered as upgrades of SMF 946 and UPF 948, which were described with respect to a 5G system in FIG.9. The upgrades provided by the Comm CF 1228 and the Comm SF 1238 may enable service-aware transport. For legacy (e.g., 4G or 5G) data transport, SMF 946 and UPF 948 may still be used. Two other such functions may include a Data Control Function (Data CF) 1222 and Data Service Function (Data SF) 1232 may be parts of the Data Service Plane. Data CF 1222 may be a control plane function and provides functionalities such as Data SF 1232 management, Data service creation / configuration / releasing, Data service context management, etc. Data SF 1232 may be a user plane function and serve as the gateway between data service users (such as UE 1202 and the various functions of the 6G CN 1210) and data service endpoints behind the gateway. Specific functionalities may include include: parse data service user data and forward to corresponding data service endpoints, generate charging data, report data service status. Another such function may be the Service Orchestration and Chaining Function (SOCF) 1220, which may discover, orchestrate and chain up communication / computing / data services provided by functions in the network. Upon receiving service requests from users, SOCF 1220 may interact with one or more of Comp CF 1224, Comm CF 1228, and Data CF 1222 to identify Comp SF 1236, Comm SF 1238, and Data SF 1232 instances, configure service resources, and generate the service chain, which could contain multiple Comp SF 1236, Comm SF 1238, and Data SF 1232 instances and their associated computing endpoints. Workload Attorney Docket No. AG4358-PCT (31517-3584) processing and data movement may then be conducted within the generated service chain. The SOCF 1220 may also responsible for maintaining, updating, and releasing a created service chain. Another such function may be the service registration function (SRF) 1214, which may act as a registry for system services provided in the user plane such as services provided by service endpoints behind Comp SF 1236 and Data SF 1232 gateways and services provided by the UE 1202. The SRF 1214 may be considered a counterpart of NRF 954, which may act as the registry for network functions. Other such functions may include an evolved service communication proxy (eSCP) and service infrastructure control function (SICF) 1226, which may provide service communication infrastructure for control plane services and user plane services. The eSCP may be related to the service communication proxy (SCP) of 5G with user plane service communication proxy capabilities being added. The eSCP is therefore expressed in two parts: eCSP-C 1212 and eSCP-U 1234, for control plane service communication proxy and user plane service communication proxy, respectively. The SICF 1226 may control and configure eCSP instances in terms of service traffic routing policies, access rules, load balancing configurations, performance monitoring, etc. Another such function is the AMF 1244. The AMF 1244 may be similar to 944, but with additional functionality. Specifically, the AMF 1244 may include potential functional repartition, such as move the message forwarding functionality from the AMF 1244 to the RAN 1208. Another such function is the service orchestration exposure function (SOEF) 1218. The SOEF may be configured to expose service orchestration and chaining services to external users such as applications. The UE 1202 may include an additional function that is referred to as a computing client service function (comp CSF) 1204. The comp CSF 1204 may have both the control plane functionalities and user plane functionalities, and may interact with corresponding network side functions such as SOCF 1220, Comp CF 1224, Comp SF 1236, Data CF 1222, and / or Data SF 1232 for service discovery, request / response, compute task workload exchange, etc. The Comp CSF 1204 may also work with network side functions to decide on whether a computing task should be run on the UE 1202, the RAN 1208, and / or an element of the 6G CN 1210. The UE 1202 and / or the Comp CSF 1204 may include a service mesh proxy 1206. The service mesh proxy 1206 may act as a proxy for service-to-service communication in the user Attorney Docket No. AG4358-PCT (31517-3584) plane. Capabilities of the service mesh proxy 1206 may include one or more of addressing, security, load balancing, etc. For one or more embodiments, at least one of the components set forth in one or more of the preceding figures may be configured to perform one or more operations, techniques, processes, and / or methods as set forth in the example section below. For example, the baseband circuitry as described above in connection with one or more of the preceding figures may be configured to operate in accordance with one or more of the examples set forth below. For another example, circuitry associated with a UE, base station, network element, etc. as described above in connection with one or more of the preceding figures may be configured to operate in accordance with one or more of the examples set forth below in the example section. Additional examples of the presently described embodiments include the following, non-limiting implementations. Each of the following non-limiting examples may stand on its own or may be combined in any permutation or combination with any one or more of the other examples provided below or throughout the present disclosure. For one or more embodiments, at least one of the components set forth in one or more of the preceding figures may be configured to perform one or more operations, techniques, processes, and / or methods as set forth in the example section below. For example, the baseband circuitry as described above in connection with one or more of the preceding figures may be configured to operate in accordance with one or more of the examples set forth below. For another example, circuitry associated with a UE, base station, network element, etc. as described above in connection with one or more of the preceding figures may be configured to operate in accordance with one or more of the examples set forth below. The following examples pertain to further embodiments. Example 1 may include the system and method of wireless communication for a fifth generation (5G) or beyond or new radio (NR) system: wherein for AI / ML-based positioning with AI / ML models residing at NW side (e.g., NG-RAN node that is responsible for one or more Transmission-Reception Points (TRPs) or Location Management Function (LMF) or at UE side, input data for model training or model inference or model monitoring purposes that are collected or generated at a NG-RAN node or UE includes one or more of timing, power or amplitude, and phase information to represent observed channels determined based on reception of DL or UL reference signals at a UE or NG-RAN node respectively. Example 2 may include the system and method of example 1 and / or some other example herein, wherein, timing information as model input is represented by a set of ^^ time stamps Attorney Docket No. AG4358-PCT (31517-3584) ^^^^ ^ ^^^ଶ ^ ⋯ ^ ^^^^that correspond to different paths comprising the multipath channel between a Tx and Rx node pair or correspond to different time domain samples of the CIR node. Example 3 may include the system and method of example 1 and / or some other example herein, wherein the ^^ time stamps ^^^^ ^ ^^^ଶ ^ ⋯ ^ ^^^^are defined relative to a reference time ^^^^. Example 4 may include the system and of example 1 and / or some other example herein, wherein the first of the ^^ time stamps ^^^ ^^ ^ ^^ଶ ^ ⋯ ^ ^^^^(i.e., ^^^^) is defined using absolute time (e.g., UTC format) and the subsequent time are defined relative to a reference time ^^^^. Example 5 may include the system and method of example 1 and / or some other example herein, wherein, for DL positioning, for a UE, one of the TRPs is configured as a reference TRP. Example 6 may include the system and method of examples 3 and 5 and / or some other example herein, wherein, for DL positioning, the reference time ^^^^is defined as the time when the UE receives the start of one DL subframe from the reference TRP ^^ that is closest in time to the DL subframe containing the measurement RS received from TRP ^^. Example 7 may include the system and method of examples 3 and 5 and / or some other example herein, wherein, for DL positioning, the reference time ^^^^is the start of the DL subframe containing the measurement RS received from TRP ^^. Example 8 may include the system and method of example 7 and / or some other example herein, wherein, for DL positioning, the start of the DL subframe is defined from UE’s perspective, i.e., containing any propagation delay relative to the start of the DL subframe at gNB. Example 9 may include the system and method of example 7 and / or some other example herein, wherein, for DL positioning, the start of the DL subframe is defined from gNB’s perspective, i.e., the UE is expected to estimate and compensate any propagation delay, either based on specified procedures (for example, Rel-17 propagation delay compensation), non- AI / ML positioning methods like Multi-RTT, or based on implementation. Example 10 may include the system and method of example 3 and / or some other example herein, wherein, for UL positioning, the reference time ^^^^isdefined as the start of the UL subframe containing the UL RS received by TRP ^^ from UE. Attorney Docket No. AG4358-PCT (31517-3584) Example 11 may include the system and method of example 3 and / or some other example herein, wherein the measured time stamps ^^^^, 1 ^ ^^ ^ ^^, 1 ^ ^^ ^ ^^, are directlyused as the timing information. Example 12 may include the system and of example 11 and / or some other example herein, wherein the measured time stamps ^^^^, 1 ^ ^^ ^ ^^, 1 ^ ^^ ^ ^^, are directlyused as the timing information and are represented in terms of absolute time and not relative to ^^^, e.g. ^ , using UTC format. Example 13 may include the system and method of example 11 and / or some other example herein, wherein the measured time stamps ^^^^, 1 ^ ^^ ^ ^^, 1 ^ ^^ ^ ^^, are directlyused as the timing information and are represented relative to ^^^^and the value of ^^^^is indicated in terms of absolute time, e.g., using UTC format. Example 14 may include the system and method of example 3 and / or some other example herein, wherein the sequence of quantities ^^^^ ^ ^ ^ ^ ^^ , ^^ଶ െ ^^^ , ^^ଷ െ ^^^,⋯ , ^^^ െ ^^^^^ are used as the timing information. Example 15 may include the system and method of example some other example herein, wherein the sequence of quantities ^^^^ ^ ^ ^ ^ ^^ , ^^ଶ െ ^^^ , ^^ଷ െ ^^ଶ,⋯ , ^^^ െ ^^^^ି^^ are used as the timing information. Example 16 may include the system and method of example 3 and / or some other example herein, wherein the timing information is represented similar to the representation for DL RSTD or UE / gNB Rx-Tx time difference or UL RTOA with the smallest value of ReportingGranularityfactor (‘k’) that is less than zero, e.g., including k = {-6, -5, -4, -3, -2, - 1}. Example 17 may include the system and method of example 2 and / or some other example herein, wherein the power ^^^^of the ^^’th path of the channel response from / to TRP ^^, is defined as the linear average of the channel response at the ^^’th path delay of the resource elements that carry the RS configured for the measurement, which is associated with each measured time stamps ^^^^. Example 18 include the system and method of example 2 and / or some other example herein, wherein the relative power of the k’th path to the first path of the channel response from / to TRP n can be included in the power information as part of model input. Example 19 may include the system and method of example 2 and / or some other example herein, wherein DL reference signal carrier phase of the ^^′th path, ^^^^, is defined as the phase of the channel response at the ^^’th path delay derived from the resource elements Attorney Docket No. AG4358-PCT (31517-3584) carrying DL RS configured for the measurement, which is associated with each measured time stamps ^^^^. Example 20 may include the system and method of example 2 and / or some other example herein, wherein DL reference signal carrier phases of the detected paths, that include the First Arrival Path (FAP) and a number of additional paths, are represented by normalizing the carrier phases for each detected path relative to the phase observed for the FAP. Example 21 may include the system and method of example 2 and / or some other example herein, wherein UL reference signal carrier phase of the ^^′th path, ^^^^, is defined as the phase of the channel response at the ^^’th path delay derived from the resource elements carrying UL RS configured for the measurement. Example 22 may include the system and method of example 2 and / or some other example herein, wherein UL reference signal carrier phases of the detected paths, that include the First Arrival Path (FAP) and a number of additional paths, are represented by normalizing the carrier phases for each detected path relative to the phase observed for the FAP. Example 23 may include the system and method of example 2 and / or some other example herein, wherein uniform quantization of phase information is defined as part of model input. Example 24 may include the system and method of example 1 and / or some other example herein, wherein, for UE-sided models, the UE is provided with mapping information of DL PRS resources to different TRPs, including one or more of: TRP location, TRP antenna orientation in terms of TRP antenna boresight direction, TRP Antenna Reference Point (ARP) information, TRP timing error and / or Timing Error Group (TEG) information. Example 25 may include the system and method of example 24 and / or some other example herein, wherein the TRP-related information may be conveyed to a UE using an Information Element (IE) similar to NR-PositionCalculationAssistance defined in 3GPP TS 37.335. Example 26 may include the system and method of example 1 and / or some other example herein, wherein a model monitoring entity (UE, NG-RAN node, or LMF) monitors statistics of the model input and makes appropriate decisions if “significant change” is detected in the measured model input compared with training data, where “significant change” is defined by using one or more methods of hypothesis testing: t-tests, z-tests, Chi-Square test, analysis of variance (ANOVA), KL Divergence. Attorney Docket No. AG4358-PCT (31517-3584) Example 27 may include the system and method of example 26 and / or some other example herein, wherein, for when the AI / ML model resides in UE, subject to reported UE capabilities, one or more of the following is / are configured and signaled to the UE by the network: (I) Model input information to be used for functionality / model monitoring, (II) Type of monitoring / testing method and its corresponding critical value. Example 28 may include the system and method of example 26 and / or some other example herein, wherein for cases where the AI / ML model resides in the UE, subject to reported UE capabilities, one or more of the following is / are reported by the UE as part of UE assistance information (UAI) along with model input monitoring results: (I) Model input information used for functionality / model monitoring, (II) Type of monitoring / testing method and its corresponding critical value. Example 29 may include the system and method of example 26 and / or some other example herein, wherein, for cases where the AI / ML model resides at a gNB, one or more of the following is / are requested to the gNB by another network entity, e.g., LMF: (I) Model input information to be used for functionality / model monitoring, (II) Assistance information, (III) Type of monitoring / testing method and its corresponding critical value. Example 30 may include the system and method of example 1 and / or some other example herein, wherein, when the model is not provided with ground-truth label or its approximation, a model monitoring entity (UE, NG-RAN node, or LMF) monitors statistics of the model output and makes appropriate decisions if “significant change” is detected in the measured model input compared with training data, where “significant change” is defined by using one or more methods of hypothesis testing: t-tests, z-tests, Chi-Square test, analysis of variance (ANOVA), KL Divergence. Example 31 may include the system and method of example 30 and / or some other example herein, wherein, for cases where the AI / ML model resides in UE, subject to reported UE capabilities, one or more of the following is / are configured and signaled to the UE by the network: (I) Model output information to be used for functionality / model monitoring; (II) Assistance information used for data generation; (III) Type of monitoring / testing method and its corresponding critical value. Example 32 may include the system and method of example 30 and / or some other example herein, wherein, for cases where the AI / ML model resides at a gNB, one or more of the following is / are requested to the gNB by another network entity, e.g., LMF: (I) Model output information to be used for functionality / model monitoring; (II) Assistance information Attorney Docket No. AG4358-PCT (31517-3584) used for data generation; (III) Type of monitoring / testing method and its corresponding critical value. Example 33 may include the system and method of example 1 and / or some other example herein, wherein, when the model monitoring unit is provided with ground-truth label or its approximation, depending on the type of model output, a distance measure such as Euclidean distance (on the 2-D plane or 3-D space) between the model outputs and the ground- truth labels for continuous value outputs and Hamming distance between the model outputs and the ground-truth labels for binary outputs (e.g. LOS / NLOS) is calculated and calculated distance is compared with a threshold to decide whether there is a significant difference between the output and ground-truth label or its approximation. Example 34 may include the system and method of example 33 and / or some other example herein, wherein, for cases where the AI / ML model resides in the UE, subject to reported UE capabilities, one or more of the following can be configured and signaled to the UE by the network: (I) Model output information to be used for functionality / model monitoring; (II) Type of distance calculation method and its corresponding threshold value. Example 35 may include the system and method of example 1 and / or some other example herein, wherein, for UE-based AI / ML positioning or UE-assisted AI / ML positioning, the UE is provided by a network entity with one or more time windows during which the UE may perform measurements towards data collection and / or report ground-truth labels (e.g., for UEs serving as Positioning Reference Units (PRUs)) for data collection for model training. Example 36 may include the system and method of example 1 and / or some other example herein, wherein, for UE-based or UE-assisted positioning, the gNB is provided by another network entity with one or more time windows to configure and transmit DL PRS with certain requested configuration(s) for data collection for model training. Example 37 may include the system and method of example 1 and / or some other example herein, wherein, for UE-based or UE-assisted positioning, the UE is provided by a network entity with configuration of a measurement validity area within which the UE may perform the measurements and / or report ground-truth labels for data collection for model training. Example 38 may include the system and method of example 1 and / or some other example herein, wherein, for UE-sided models, the UE is configured with inference validity area as part of network-side conditions to apply one or more AI / ML models or AI / ML functionalities to ensure consistency between model training and inference. Attorney Docket No. AG4358-PCT (31517-3584) Example 39 may include the system and method of example 1 and / or some other example herein, wherein, for gNB (or NG-RAN node)-assisted positioning, the gNB is provided by a network entity with one or more time windows during which the gNB may be expected to schedule SRS transmissions from its served UEs and perform associated measurements on the SRS resources for data collection for model training. Example 40 may include the system and method of examples 35, 36, and 37 and / or some other example herein, wherein, for UE-based or UE-assisted positioning with model residing at the UE-side, the UE is provided with multiple time window configurations and / or multiple validity area configurations such that each configuration of either or both of measurement time window and validity area correspond to an AI / ML model or AI / ML functionality. Accordingly, a particular choice of one or both of these configurations implicitly indicates a model or functionality selection or switching between models or functionalities. Example 41 may include the system and method of example 1 and / or some other example herein, wherein a network entity configures the UE to report data for the purpose of data collection only for the case when the reported ground truth data is above a minimum quality. Example 42 may include the system and method of examples 35 through 41 and / or some other example herein, wherein the network entity is the LMF. Example 43 may include the system and method of example 1 and / or some other example herein, wherein methods to compress the reported data include truncation and / or sub- sampling of the measurement data based on certain rules that are provided to the data generation / collection entity (e.g., UE or NG-RAN node) by the LMF. Example 44 may include the system and method of example 43 and / or some other example herein, wherein the data generation / collection entity (e.g., UE or NG-RAN node) is provided with one or more configurations of a measurement reporting granularity in time that may include uniform sampling in time, i.e., uniformly skipping a number of measurements or non-uniform skipping of a number of measurements based on some pre-defined or configured rules. Example 45 may include the system and method of example 44 and / or some other example herein, wherein configured rules to skip reporting of certain datapoints include one or more of: for data involving ground-truth labels, a data generation / collection entity (e.g., UE or NG-RAN node) skips inclusion of certain data-points if the location ground-truth does not change beyond a configured or predetermined threshold, Attorney Docket No. AG4358-PCT (31517-3584) when reporting intermediate metrics like RSTD, RTOA, ToF, UE / gNB Rx-Tx time difference, RSRP, RSRPP, etc., the data generation / collection entity (e.g., UE or NG-RAN node) skips inclusion of certain data points or inclusion of one or more metrics for a data point unless the metrics have changed beyond configured or predetermined threshold(s). Example 46 may include the system and method of example 1 and / or some other example herein, wherein timing information is conveyed by a sample-based approach where the timing information is an integer multiple of sampling periods by taking the first K consecutive time instances based on timing grid of the Channel Impulse Response (CIR) and timing information includes one or more of: reference time, sampling period, and value of K. Example 47 may include the system and method of example 46 and / or some other example herein, wherein the sampling rate is specified or configured to a UE or gNB by the LMF the sampling rate may be specified or configured to a UE or gNB by the LMF. Example 48 may include the system and method of example 46 and / or some other example herein, wherein a base value of the sampling rate is specified as 1 / (Nf*SCS) where Nf corresponds to the maximum Fast Fourier Transform (FFT) size, e.g., Nf = 4096 for 4K FFT size, and SCS is the active Subcarrier Spacing of the DL RS or UL RS used for the measurement that are mapped to the DL Positioning Frequency Layer (PFL) or UL carrier, and, optionally, a factor for over- or under-sampling is specified or configured by the LMF, possibly subject to UE capabilities for DL positioning. Example 49 may include the system and method of example 1 and / or some other example herein, wherein timing information is represented by taking K specific (not necessarily consecutive) time instances based on timing grid of output of path-detection and includes one or more of: reference time, sampling period, and value of K, and K timing instance values, and the time instances indicate the time instances of the K detected paths based on the CIR. Example 50 may include the system and method of example 1 and / or some other example herein, wherein timing information is represented by taking Kfine specific samples sampled at the nominal sampling rate and on the sampling grid of the estimated CIR defined by the nominal sampling rate around (i.e., on the left and right of, within the overall CIR window) each of M detected paths, while in between detected paths a coarser sampling rate with sampling period that is an integer multiple of the nominal sampling period is applied. Example 51 may include the system and method of example 1 and / or some other example herein, wherein timing information is represented by taking Kfine specific samples on the sampling grid of the estimated CIR around (i.e., on the left and right of, within the overall Attorney Docket No. AG4358-PCT (31517-3584) CIR window) N samples that correspond to the N samples with the highest power values within the estimated CIR. Example 52 may include the system and method of example 1 and / or some other example herein, wherein to ensure the consistency between training and inference, the network entity uses either same Reference Signal (RS) configuration or “almost-same” RS configurations for generation of model input for training and inference. Example 53 may include the system and method of example 52 and / or some other example herein, wherein “almost-same” RS configurations may be characterized by one or more of: RS resource consistency, RS antenna port consistency. Example 54 may include the system and method of example 1 and / or some other example herein, wherein to ensure the consistency between training and inference, the network entity ensures consistency of channel / link conditions / characteristics between training and inference. Example 55 may include the system and method of example 54 and / or some other example herein, wherein consistency of channel / link conditions / characteristics is characterized by one or more of: SINR consistency, Doppler characteristics consistency, antenna configuration consistency, UL RS processing consistency. Example 56 may include the system and method of example 1 and / or some other example herein, wherein to ensure the consistency between training and inference, the network entity ensures consistency in terms of model input format for training and inference. Example 57 may include the system and method of example 1 and / or some other example herein, wherein timing information is represented by taking Kfine specific samples sampled at the nominal sampling rate and on the sampling grid of the estimated CIR defined by the nominal sampling rate around (i.e., on the left and right of, within the overall CIR window) each of M detected paths, and including reporting of information (may include one or more of: timing, power, phase information) for the detected paths, while in between detected paths a coarser sampling rate with sampling period that is an integer multiple of the nominal sampling period is applied. Example 58 may include the system and method of example 57 and / or some other example herein, wherein Kfine is an even integer (2*m) or an odd integer (2*m+1). Example 59 may include the system and method of example 58 and / or some other example herein, wherein one or more values of Kfine and m are specified or configured to a UE and / or NG-RAN node by the LMF, potentially subject to UE capability. Attorney Docket No. AG4358-PCT (31517-3584) Example 60 may include the system and method of example 57 and / or some other example herein, wherein multiple samples with duplication may be reported, depending on relative location of the detected paths. Example 61 may include the system and method of example 57 and / or some other example herein, wherein when reporting multiple samples, a UE or NG-RAN node is expected to remove any duplicated samples around the detected paths depending on relative location of the detected paths. Example 62 may include the system and method of example 57 and / or some other example herein, wherein if multiple paths are detected in between two consecutive sampling points on the grid defined by the nominal sampling rate, each of the detected paths in between two consecutive sampling points are reported along with the Kfine samples corresponding to each of the multiple detected paths. Example 63 may include the system and method of example 57 and / or some other example herein, wherein if multiple paths are detected in between two consecutive sampling points on the grid defined by the nominal sampling rate, each of the detected paths in between two consecutive sampling points are reported along with the Kfine samples corresponding to each of the multiple detected paths with the removal of duplicated samples. Example 64 may include the system and method of example 57 and / or some other example herein, wherein if multiple paths are detected in between two consecutive sampling points on the grid defined by the nominal sampling rate, one of the detected paths in between two consecutive sampling points is reported along with the Kfine samples around the multiple detected paths, with selection of the detected path is based on earliest path or strongest path, etc. Example 65 may include an apparatus comprising means for performing any of the methods of examples 1-64. Example 66 may include a network node comprising a communication interface and processing circuitry connected thereto and configured to perform the methods of examples 1- 64. Example 67 may include an apparatus comprising means to perform one or more elements of a method described in or related to any of examples 1-64, or any other method or process described herein. Example 68 may include one or more non-transitory computer-readable media comprising instructions to cause an electronic device, upon execution of the instructions by one or more processors of the electronic device, to perform one or more elements of a method Attorney Docket No. AG4358-PCT (31517-3584) described in or related to any of examples 1-64, or any other method or process described herein. Example 69 may include an apparatus comprising logic, modules, or circuitry to perform one or more elements of a method described in or related to any of examples 1-64, or any other method or process described herein. Example 70 may include a method, technique, or process as described in or related to any of examples 1-64, or portions or parts thereof. Example 71 may include an apparatus comprising: one or more processors and one or more computer-readable media comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform the method, techniques, or process as described in or related to any of examples 1-64, or portions thereof. Example 72 may include a signal as described in or related to any of examples 1-64, or portions or parts thereof. Example 73 may include a datagram, packet, frame, segment, protocol data unit (PDU), or message as described in or related to any of examples 1-64, or portions or parts thereof, or otherwise described in the present disclosure. Example 74 may include a signal encoded with data as described in or related to any of examples 1-64, or portions or parts thereof, or otherwise described in the present disclosure. Example 75 may include a signal encoded with a datagram, packet, frame, segment, protocol data unit (PDU), or message as described in or related to any of examples 1-64, or portions or parts thereof, or otherwise described in the present disclosure. Example 76 may include an electromagnetic signal carrying computer-readable instructions, wherein execution of the computer-readable instructions by one or more processors is to cause the one or more processors to perform the method, techniques, or process as described in or related to any of examples 1-64, or portions thereof. Example 77 may include a computer program comprising instructions, wherein execution of the program by a processing element is to cause the processing element to carry out the method, techniques, or process as described in or related to any of examples 1-64, or portions thereof. Example 78 may include a signal in a wireless network as shown and described herein. Example 79 may include a method of communicating in a wireless network as shown and described herein. Example 80 may include a system for providing wireless communication as shown and described herein. Attorney Docket No. AG4358-PCT (31517-3584) Example 81 may include a device for providing wireless communication as shown and described herein. An example implementation is an edge computing system, including respective edge processing devices and nodes to invoke or perform the operations of the examples above, or other subject matter described herein. Another example implementation is a client endpoint node, operable to invoke or perform the operations of the examples above, or other subject matter described herein. Another example implementation is an aggregation node, network hub node, gateway node, or core data processing node, within or coupled to an edge computing system, operable to invoke or perform the operations of the examples above, or other subject matter described herein. Another example implementation is an access point, base station, road-side unit, street-side unit, or on-premise unit, within or coupled to an edge computing system, operable to invoke or perform the operations of the examples above, or other subject matter described herein. Another example implementation is an edge provisioning node, service orchestration node, application orchestration node, or multi-tenant management node, within or coupled to an edge computing system, operable to invoke or perform the operations of the examples above, or other subject matter described herein. Another example implementation is an edge node operating an edge provisioning service, application or service orchestration service, virtual machine deployment, container deployment, function deployment, and compute management, within or coupled to an edge computing system, operable to invoke or perform the operations of the examples above, or other subject matter described herein. Another example implementation is an edge computing system operable as an edge mesh, as an edge mesh with side car loading, or with mesh-to-mesh communications, operable to invoke or perform the operations of the examples above, or other subject matter described herein. Another example implementation is an edge computing system including aspects of network functions, acceleration functions, acceleration hardware, storage hardware, or computation hardware resources, operable to invoke or perform the use cases discussed herein, with use of the examples above, or other subject matter described herein. Another example implementation is an edge computing system adapted for supporting client mobility, vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), or vehicle-to-infrastructure (V2I) scenarios, and optionally operating according to ETSI MEC specifications, operable to invoke or perform the use cases discussed herein, with use of the examples above, or other subject matter described herein. Another example implementation is an edge computing system adapted for mobile wireless communications, including configurations according to an 3GPP 4G / LTE or 5G network capabilities, operable to invoke or perform the use cases discussed Attorney Docket No. AG4358-PCT (31517-3584) herein, with use of the examples above, or other subject matter described herein. Another example implementation is a computing system adapted for network communications, including configurations according to an O-RAN capabilities, operable to invoke or perform the use cases discussed herein, with use of the examples above, or other subject matter described herein. Any of the above-described examples may be combined with any other example (or combination of examples), unless explicitly stated otherwise. The foregoing description of one or more implementations provides illustration and description, but is not intended to be exhaustive or to limit the scope of embodiments to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of various embodiments. TERMINOLOGY The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a,” “an” and “the” are intended to include plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specific the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operation, elements, components, and / or groups thereof. For the purposes of the present disclosure, the phrase “A and / or B” means (A), (B), or (A and B). For the purposes of the present disclosure, the phrase “A, B, and / or C” means (A), (B), (C), (A and B), (A and C), (B and C), or (A, B and C). The description may use the phrases “in an embodiment,” or “In some embodiments,” which may each refer to one or more of the same or different embodiments. Furthermore, the terms “comprising,” “including,” “having,” and the like, as used with respect to embodiments of the present disclosure, are synonymous. The terms “coupled,” “communicatively coupled,” along with derivatives thereof are used herein. The term “coupled” may mean two or more elements are in direct physical or electrical contact with one another, may mean that two or more elements indirectly contact each other but still cooperate or interact with each other, and / or may mean that one or more other elements are coupled or connected between the elements that are said to be coupled with each other. The term “directly coupled” may mean that two or more elements are in direct Attorney Docket No. AG4358-PCT (31517-3584) contact with one another. The term “communicatively coupled” may mean that two or more elements may be in contact with one another by a means of communication including through a wire or other interconnect connection, through a wireless communication channel or ink, and / or the like. The term “circuitry” as used herein refers to, is part of, or includes hardware components such as an electronic circuit, a logic circuit, a processor (shared, dedicated, or group) and / or memory (shared, dedicated, or group), an Application Specific Integrated Circuit (ASIC), a field-programmable device (FPD) (e.g., a field-programmable gate array (FPGA), a programmable logic device (PLD), a complex PLD (CPLD), a high-capacity PLD (HCPLD), a structured ASIC, or a programmable SoC), digital signal processors (DSPs), etc., that are configured to provide the described functionality. In some embodiments, the circuitry may execute one or more software or firmware programs to provide at least some of the described functionality. The term “circuitry” may also refer to a combination of one or more hardware elements (or a combination of circuits used in an electrical or electronic system) with the program code used to carry out the functionality of that program code. In these embodiments, the combination of hardware elements and program code may be referred to as a particular type of circuitry. The term “processor circuitry” as used herein refers to, is part of, or includes circuitry capable of sequentially and automatically carrying out a sequence of arithmetic or logical operations, or recording, storing, and / or transferring digital data. Processing circuitry may include one or more processing cores to execute instructions and one or more memory structures to store program and data information. The term “processor circuitry” may refer to one or more application processors, one or more baseband processors, a physical central processing unit (CPU), a single-core processor, a dual-core processor, a triple-core processor, a quad-core processor, and / or any other device capable of executing or otherwise operating computer-executable instructions, such as program code, software modules, and / or functional processes. Processing circuitry may include more hardware accelerators, which may be microprocessors, programmable processing devices, or the like. The one or more hardware accelerators may include, for example, computer vision (CV) and / or deep learning (DL) accelerators. The terms “application circuitry” and / or “baseband circuitry” may be considered synonymous to, and may be referred to as, “processor circuitry.” The term “memory” and / or “memory circuitry” as used herein refers to one or more hardware devices for storing data, including RAM, MRAM, PRAM, DRAM, and / or SDRAM, core memory, ROM, magnetic disk storage mediums, optical storage mediums, flash memory Attorney Docket No. AG4358-PCT (31517-3584) devices or other machine readable mediums for storing data. The term “computer-readable medium” may include, but is not limited to, memory, portable or fixed storage devices, optical storage devices, and various other mediums capable of storing, containing or carrying instructions or data. The term “interface circuitry” as used herein refers to, is part of, or includes circuitry that enables the exchange of information between two or more components or devices. The term “interface circuitry” may refer to one or more hardware interfaces, for example, buses, I / O interfaces, peripheral component interfaces, network interface cards, and / or the like. The term “user equipment” or “UE” as used herein refers to a device with radio communication capabilities and may describe a remote user of network resources in a communications network. The term “user equipment” or “UE” may be considered synonymous to, and may be referred to as, client, mobile, mobile device, mobile terminal, user terminal, mobile unit, mobile station, mobile user, subscriber, user, remote station, access agent, user agent, receiver, radio equipment, reconfigurable radio equipment, reconfigurable mobile device, etc. Furthermore, the term “user equipment” or “UE” may include any type of wireless / wired device or any computing device including a wireless communications interface. The term “network element” as used herein refers to physical or virtualized equipment and / or infrastructure used to provide wired or wireless communication network services. The term “network element” may be considered synonymous to and / or referred to as a networked computer, networking hardware, network equipment, network node, router, switch, hub, bridge, radio network controller, RAN device, RAN node, gateway, server, virtualized VNF, NFVI, and / or the like. The term “computer system” as used herein refers to any type interconnected electronic devices, computer devices, or components thereof. Additionally, the term “computer system” and / or “system” may refer to various components of a computer that are communicatively coupled with one another. Furthermore, the term “computer system” and / or “system” may refer to multiple computer devices and / or multiple computing systems that are communicatively coupled with one another and configured to share computing and / or networking resources. The term “appliance,” “computer appliance,” or the like, as used herein refers to a computer device or computer system with program code (e.g., software or firmware) that is specifically designed to provide a specific computing resource. A “virtual appliance” is a virtual machine image to be implemented by a hypervisor-equipped device that virtualizes or emulates a computer appliance or otherwise is dedicated to provide a specific computing Attorney Docket No. AG4358-PCT (31517-3584) resource. The term “element” refers to a unit that is indivisible at a given level of abstraction and has a clearly defined boundary, wherein an element may be any type of entity including, for example, one or more devices, systems, controllers, network elements, modules, etc., or combinations thereof. The term “device” refers to a physical entity embedded inside, or attached to, another physical entity in its vicinity, with capabilities to convey digital information from or to that physical entity. The term “entity” refers to a distinct component of an architecture or device, or information transferred as a payload. The term “controller” refers to an element or entity that has the capability to affect a physical entity, such as by changing its state or causing the physical entity to move. The term “cloud computing” or “cloud” refers to a paradigm for enabling network access to a scalable and elastic pool of shareable computing resources with self-service provisioning and administration on-demand and without active management by users. Cloud computing provides cloud computing services (or cloud services), which are one or more capabilities offered via cloud computing that are invoked using a defined interface (e.g., an API or the like). The term “computing resource” or simply “resource” refers to any physical or virtual component, or usage of such components, of limited availability within a computer system or network. Examples of computing resources include usage / access to, for a period of time, servers, processor(s), storage equipment, memory devices, memory areas, networks, electrical power, input / output (peripheral) devices, mechanical devices, network connections (e.g., channels / links, ports, network sockets, etc.), operating systems, virtual machines (VMs), software / applications, computer files, and / or the like. A “hardware resource” may refer to compute, storage, and / or network resources provided by physical hardware element(s). A “virtualized resource” may refer to compute, storage, and / or network resources provided by virtualization infrastructure to an application, device, system, etc. The term “network resource” or “communication resource” may refer to resources that are accessible by computer devices / systems via a communications network. The term “system resources” may refer to any kind of shared entities to provide services, and may include computing and / or network resources. System resources may be considered as a set of coherent functions, network data objects or services, accessible through a server where such system resources reside on a single host or multiple hosts and are clearly identifiable. As used herein, the term “cloud service provider” (or CSP) indicates an organization which operates typically large-scale “cloud” resources comprised of centralized, regional, and edge data centers (e.g., as used in the context of the public cloud). In other examples, a CSP may also be referred to as a Cloud Service Operator (CSO). References to “cloud computing” generally refer to computing resources and Attorney Docket No. AG4358-PCT (31517-3584) services offered by a CSP or a CSO, at remote locations with at least some increased latency, distance, or constraints relative to edge computing. As used herein, the term “data center” refers to a purpose-designed structure that is intended to house multiple high-performance compute and data storage nodes such that a large amount of compute, data storage and network resources are present at a single location. This often entails specialized rack and enclosure systems, suitable heating, cooling, ventilation, security, fire suppression, and power delivery systems. The term may also refer to a compute and data storage node in some contexts. A data center may vary in scale between a centralized or cloud data center (e.g., largest), regional data center, and edge data center (e.g., smallest). As used herein, the term “edge computing” refers to the implementation, coordination, and use of computing and resources at locations closer to the “edge” or collection of “edges” of a network. Deploying computing resources at the network’s edge may reduce application and network latency, reduce network backhaul traffic and associated energy consumption, improve service capabilities, improve compliance with security or data privacy requirements (especially as compared to conventional cloud computing), and improve total cost of ownership). As used herein, the term “edge compute node” refers to a real-world, logical, or virtualized implementation of a compute-capable element in the form of a device, gateway, bridge, system or subsystem, component, whether operating in a server, client, endpoint, or peer mode, and whether located at an “edge” of an network or at a connected location further within the network. References to a “node” used herein are generally interchangeable with a “device”, “component”, and “sub-system”; however, references to an “edge computing system” or “edge computing network” generally refer to a distributed architecture, organization, or collection of multiple nodes and devices, and which is organized to accomplish or offer some aspect of services or resources in an edge computing setting. Additionally or alternatively, the term “Edge Computing” refers to a concept that enables operator and 3rd party services to be hosted close to the UE's access point of attachment, to achieve an efficient service delivery through the reduced end-to-end latency and load on the transport network. As used herein, the term “Edge Computing Service Provider” refers to a mobile network operator or a 3rd party service provider offering Edge Computing service. As used herein, the term “Edge Data Network” refers to a local Data Network (DN) that supports the architecture for enabling edge applications. As used herein, the term “Edge Hosting Environment” refers to an environment providing support required for Edge Application Server's execution. As used herein, the term “Application Server” refers to application software resident in the cloud performing the server function. Attorney Docket No. AG4358-PCT (31517-3584) The term “Internet of Things” or “IoT” refers to a system of interrelated computing devices, mechanical and digital machines capable of transferring data with little or no human interaction, and may involve technologies such as real-time analytics, machine learning and / or AI, embedded systems, wireless sensor networks, control systems, automation (e.g., smarthome, smart building and / or smart city technologies), and the like. IoT devices are usually low-power devices without heavy compute or storage capabilities. “Edge IoT devices” may be any kind of IoT devices deployed at a network’s edge. As used herein, the term “cluster” refers to a set or grouping of entities as part of an edge computing system (or systems), in the form of physical entities (e.g., different computing systems, networks or network groups), logical entities (e.g., applications, functions, security constructs, containers), and the like. In some locations, a “cluster” is also referred to as a “group” or a “domain”. The membership of cluster may be modified or affected based on conditions or functions, including from dynamic or property-based membership, from network or system management scenarios, or from various example techniques discussed below which may add, modify, or remove an entity in a cluster. Clusters may also include or be associated with multiple layers, levels, or properties, including variations in security features and results based on such layers, levels, or properties. The term “application” may refer to a complete and deployable package, environment to achieve a certain function in an operational environment. The term “AI / ML application” or the like may be an application that contains some AI / ML models and application-level descriptions. The term “machine learning” or “ML” refers to the use of computer systems implementing algorithms and / or statistical models to perform specific task(s) without using explicit instructions, but instead relying on patterns and inferences. ML algorithms build or estimate mathematical model(s) (referred to as “ML models” or the like) based on sample data (referred to as “training data,” “model training information,” or the like) in order to make predictions or decisions without being explicitly programmed to perform such tasks. Generally, an ML algorithm is a computer program that learns from experience with respect to some task and some performance measure, and an ML model may be any object or data structure created after an ML algorithm is trained with one or more training datasets. After training, an ML model may be used to make predictions on new datasets. Although the term “ML algorithm” refers to different concepts than the term “ML model,” these terms as discussed herein may be used interchangeably for the purposes of the present disclosure. The term “machine learning model,” “ML model,” or the like may also refer to ML methods and concepts used by an ML-assisted solution. An “ML-assisted solution” is a Attorney Docket No. AG4358-PCT (31517-3584) solution that addresses a specific use case using ML algorithms during operation. ML models include supervised learning (e.g., linear regression, k-nearest neighbor (KNN), decision tree algorithms, support machine vectors, Bayesian algorithm, ensemble algorithms, etc.) unsupervised learning (e.g., K-means clustering, principle component analysis (PCA), etc.), reinforcement learning (e.g., Q-learning, multi-armed bandit learning, deep RL, etc.), neural networks, and the like. Depending on the implementation a specific ML model could have many sub-models as components and the ML model may train all sub-models together. Separately trained ML models can also be chained together in an ML pipeline during inference. An “ML pipeline” is a set of functionalities, functions, or functional entities specific for an ML-assisted solution; an ML pipeline may include one or several data sources in a data pipeline, a model training pipeline, a model evaluation pipeline, and an actor. The “actor” is an entity that hosts an ML assisted solution using the output of the ML model inference). The term “ML training host” refers to an entity, such as a network function, that hosts the training of the model. The term “ML inference host” refers to an entity, such as a network function, that hosts model during inference mode (which includes both the model execution as well as any online learning if applicable). The ML-host informs the actor about the output of the ML algorithm, and the actor takes a decision for an action (an “action” is performed by an actor as a result of the output of an ML assisted solution). The term “model inference information” refers to information used as an input to the ML model for determining inference(s); the data used to train an ML model and the data used to determine inferences may overlap, however, “training data” and “inference data” refer to different concepts. The terms “instantiate,” “instantiation,” and the like as used herein refers to the creation of an instance. An “instance” also refers to a concrete occurrence of an object, which may occur, for example, during execution of program code. The term “information element” refers to a structural element containing one or more fields. The term “field” refers to individual contents of an information element, or a data element that contains content. As used herein, a “database object”, “data structure”, or the like may refer to any representation of information that is in the form of an object, attribute-value pair (AVP), key-value pair (KVP), tuple, etc., and may include variables, data structures, functions, methods, classes, database records, database fields, database entities, associations between data and / or database entities (also referred to as a “relation”), blocks and links between blocks in block chain implementations, and / or the like. An “information object,” as used herein, refers to a collection of structured data and / or any representation of information, and may include, for example electronic documents (or Attorney Docket No. AG4358-PCT (31517-3584) “documents”), database objects, data structures, files, audio data, video data, raw data, archive files, application packages, and / or any other like representation of information. The terms “electronic document” or “document,” may refer to a data structure, computer file, or resource used to record data, and includes various file types and / or data formats such as word processing documents, spreadsheets, slide presentations, multimedia items, webpage and / or source code documents, and / or the like. As examples, the information objects may include markup and / or source code documents such as HTML, XML, JSON, Apex®, CSS, JSP, MessagePack™, Apache® Thrift™, ASN.1, Google® Protocol Buffers (protobuf), or some other document(s) / format(s) such as those discussed herein. An information object may have both a logical and a physical structure. Physically, an information object comprises one or more units called entities. An entity is a unit of storage that contains content and is identified by a name. An entity may refer to other entities to cause their inclusion in the information object. An information object begins in a document entity, which is also referred to as a root element (or "root"). Logically, an information object comprises one or more declarations, elements, comments, character references, and processing instructions, all of which are indicated in the information object (e.g., using markup). The term “data item” as used herein refers to an atomic state of a particular object with at least one specific property at a certain point in time. Such an object is usually identified by an object name or object identifier, and properties of such an object are usually defined as database objects (e.g., fields, records, etc.), object instances, or data elements (e.g., mark-up language elements / tags, etc.). Additionally or alternatively, the term “data item” as used herein may refer to data elements and / or content items, although these terms may refer to difference concepts. The term “data element” or “element” as used herein refers to a unit that is indivisible at a given level of abstraction and has a clearly defined boundary. A data element is a logical component of an information object (e.g., electronic document) that may begin with a start tag (e.g., “<element>”) and end with a matching end tag (e.g., “< / element>”), or only has an empty element tag (e.g., “<element / >”). Any characters between the start tag and end tag, if any, are the element’s content (referred to herein as “content items” or the like). The content of an entity may include one or more content items, each of which has an associated datatype representation. A content item may include, for example, attribute values, character values, URIs, qualified names (qnames), parameters, and the like. A qname is a fully qualified name of an element, attribute, or identifier in an information object. A qname associates a URI of a namespace with a local name of an element, attribute, or identifier in that namespace. To make this association, the qname assigns a prefix to the local name that Attorney Docket No. AG4358-PCT (31517-3584) corresponds to its namespace. The qname comprises a URI of the namespace, the prefix, and the local name. Namespaces are used to provide uniquely named elements and attributes in information objects. Content items may include text content (e.g., “<element>content item< / element>”), attributes (e.g., “<element attribute="attributeValue">”), and other elements referred to as “child elements” (e.g., “<element1><element2>content item< / element2>< / element1>”). An “attribute” may refer to a markup construct including a name–value pair that exists within a start tag or empty element tag. Attributes contain data related to its element and / or control the element’s behavior. The term “resource” as used herein refers to a physical or virtual device, a physical or virtual component within a computing environment, and / or a physical or virtual component within a particular device, such as computer devices, mechanical devices, memory space, processor / CPU time, processor / CPU usage, processor and accelerator loads, hardware time or usage, electrical power, input / output operations, ports or network sockets, channel / link allocation, throughput, memory usage, storage, network, database and applications, workload units, and / or the like. A “hardware resource” may refer to compute, storage, and / or network resources provided by physical hardware element(s). A “virtualized resource” may refer to compute, storage, and / or network resources provided by virtualization infrastructure to an application, device, system, etc. The term “network resource” or “communication resource” may refer to resources that are accessible by computer devices / systems via a communications network. The term “system resources” may refer to any kind of shared entities to provide services, and may include computing and / or network resources. System resources may be considered as a set of coherent functions, network data objects or services, accessible through a server where such system resources reside on a single host or multiple hosts and are clearly identifiable.The term “channel” as used herein refers to any transmission medium, either tangible or intangible, which is used to communicate data or a data stream. The term “channel” may be synonymous with and / or equivalent to “communications channel,” “data communications channel,” “transmission channel,” “data transmission channel,” “access channel,” “data access channel,” “link,” “data link,” “carrier,” “radiofrequency carrier,” and / or any other like term denoting a pathway or medium through which data is communicated. Additionally, the term “link” as used herein refers to a connection between two devices through a RAT for the purpose of transmitting and receiving information. As used herein, the term “radio technology” refers to technology for wireless transmission and / or reception of electromagnetic radiation for information transfer. The term “radio access technology” or “RAT” refers to the technology used for the underlying physical connection to Attorney Docket No. AG4358-PCT (31517-3584) a radio based communication network. As used herein, the term “communication protocol” (either wired or wireless) refers to a set of standardized rules or instructions implemented by a communication device and / or system to communicate with other devices and / or systems, including instructions for packetizing / depacketizing data, modulating / demodulating signals, implementation of protocols stacks, and / or the like. As used herein, the term “radio technology” refers to technology for wireless transmission and / or reception of electromagnetic radiation for information transfer. The term “radio access technology” or “RAT” refers to the technology used for the underlying physical connection to a radio based communication network. As used herein, the term “communication protocol” (either wired or wireless) refers to a set of standardized rules or instructions implemented by a communication device and / or system to communicate with other devices and / or systems, including instructions for packetizing / depacketizing data, modulating / demodulating signals, implementation of protocols stacks, and / or the like. Examples of wireless communications protocols may be used in various embodiments include a Global System for Mobile Communications (GSM) radio communication technology, a General Packet Radio Service (GPRS) radio communication technology, an Enhanced Data Rates for GSM Evolution (EDGE) radio communication technology, and / or a Third Generation Partnership Project (3GPP) radio communication technology including, for example, 3GPP Fifth Generation (5G) or New Radio (NR), Universal Mobile Telecommunications System (UMTS), Freedom of Multimedia Access (FOMA), Long Term Evolution (LTE), LTE- Advanced (LTE Advanced), LTE Extra, LTE-A Pro, cdmaOne (2G), Code Division Multiple Access 2000 (CDMA 2000), Cellular Digital Packet Data (CDPD), Mobitex, Circuit Switched Data (CSD), High-Speed CSD (HSCSD), Universal Mobile Telecommunications System (UMTS), Wideband Code Division Multiple Access (W-CDM), High Speed Packet Access (HSPA), HSPA Plus (HSPA+), Time Division-Code Division Multiple Access (TD-CDMA), Time Division-Synchronous Code Division Multiple Access (TD-SCDMA), LTE LAA, MuLTEfire, UMTS Terrestrial Radio Access (UTRA), Evolved UTRA (E-UTRA), Evolution- Data Optimized or Evolution-Data Only (EV-DO), Advanced Mobile Phone System (AMPS), Digital AMPS (D-AMPS), Total Access Communication System / Extended Total Access Communication System (TACS / ETACS), Push-to-talk (PTT), Mobile Telephone System (MTS), Improved Mobile Telephone System (IMTS), Advanced Mobile Telephone System (AMTS), Cellular Digital Packet Data (CDPD), DataTAC, Integrated Digital Enhanced Network (iDEN), Personal Digital Cellular (PDC), Personal Handy-phone System (PHS), Wideband Integrated Digital Enhanced Network (WiDEN), iBurst, Unlicensed Mobile Access Attorney Docket No. AG4358-PCT (31517-3584) (UMA), also referred to as also referred to as 3GPP Generic Access Network, or GAN standard), Bluetooth®, Bluetooth Low Energy (BLE), IEEE 802.15.4 based protocols (e.g., IPv6 over Low power Wireless Personal Area Networks (6LoWPAN), WirelessHART, MiWi, Thread, 802.11a, etc.) WiFi-direct, ANT / ANT+, ZigBee, Z-Wave, 3GPP device-to-device (D2D) or Proximity Services (ProSe), Universal Plug and Play (UPnP), Low-Power Wide- Area-Network (LPWAN), Long Range Wide Area Network (LoRA) or LoRaWAN™ developed by Semtech and the LoRa Alliance, Sigfox, Wireless Gigabit Alliance (WiGig) standard, Worldwide Interoperability for Microwave Access (WiMAX), mmWave standards in general (e.g., wireless systems operating at 10-300 GHz and above such as WiGig, IEEE 802.11ad, IEEE 802.11ay, etc.), V2X communication technologies (including 3GPP C-V2X), Dedicated Short Range Communications (DSRC) communication systems such as Intelligent- Transport-Systems (ITS) including the European ITS-G5, ITS-G5B, ITS-G5C, etc. In addition to the standards listed above, any number of satellite uplink technologies may be used for purposes of the present disclosure including, for example, radios compliant with standards issued by the International Telecommunication Union (ITU), or the European Telecommunications Standards Institute (ETSI), among others. The examples provided herein are thus understood as being applicable to various other communication technologies, both existing and not yet formulated. The term “access network” refers to any network, using any combination of radio technologies, RATs, and / or communication protocols, used to connect user devices and service providers. In the context of WLANs, an “access network” is an IEEE 802 local area network (LAN) or metropolitan area network (MAN) between terminals and access routers connecting to provider services. The term “access router” refers to router that terminates a medium access control (MAC) service from terminals and forwards user traffic to information servers according to Internet Protocol (IP) addresses. The term “SMTC” refers to an SSB-based measurement timing configuration configured by SSB-MeasurementTimingConfiguration. The term “SSB” refers to a synchronization signal / Physical Broadcast Channel (SS / PBCH) block, which includes a Primary Syncrhonization Signal (PSS), a Secondary Syncrhonization Signal (SSS), and a PBCH. The term “a “Primary Cell” refers to the MCG cell, operating on the primary frequency, in which the UE either performs the initial connection establishment procedure or initiates the connection re-establishment procedure. The term “Primary SCG Cell” refers to the SCG cell in which the UE performs random access when performing the Reconfiguration with Sync procedure for DC operation. The term “Secondary Cell” refers to a cell providing additional Attorney Docket No. AG4358-PCT (31517-3584) radio resources on top of a Special Cell for a UE configured with CA. The term “Secondary Cell Group” refers to the subset of serving cells comprising the PSCell and zero or more secondary cells for a UE configured with DC. The term “Serving Cell” refers to the primary cell for a UE in RRC_CONNECTED not configured with CA / DC there is only one serving cell comprising of the primary cell. The term “serving cell” or “serving cells” refers to the set of cells comprising the Special Cell(s) and all secondary cells for a UE in RRC_CONNECTED configured with CA. The term “Special Cell” refers to the PCell of the MCG or the PSCell of the SCG for DC operation; otherwise, the term “Special Cell” refers to the Pcell. The term “A1 policy” refers to a type of declarative policies expressed using formal statements that enable the non-RT RIC function in the SMO to guide the near-RT RIC function, and hence the RAN, towards better fulfilment of the RAN intent. The term “A1 Enrichment information” refers to information utilized by near-RT RIC that is collected or derived at SMO / non-RT RIC either from non-network data sources or from network functions themselves. The term “A1-Policy Based Traffic Steering Process Mode” refers to an operational mode in which the Near-RT RIC is configured through A1 Policy to use Traffic Steering Actions to ensure a more specific notion of network performance (for example, applying to smaller groups of E2 Nodes and UEs in the RAN) than that which it ensures in the Background Traffic Steering. The term “Background Traffic Steering Processing Mode” refers to an operational mode in which the Near-RT RIC is configured through O1 to use Traffic Steering Actions to ensure a general background network performance which applies broadly across E2 Nodes and UEs in the RAN. The term “Baseline RAN Behavior” refers to the default RAN behavior as configured at the E2 Nodes by SMO The term “E2” refers to an interface connecting the Near-RT RIC and one or more O- CU-CPs, one or more O-CU-UPs, one or more O-DUs, and one or more O-eNBs. The term “E2 Node” refers to a logical node terminating E2 interface. In this version of the specification, ORAN nodes terminating E2 interface are: for NR access: O-CU-CP, O- CU-UP, O-DU or any combination; and for E-UTRA access: O-eNB. The term “Intents”, in the context of O-RAN systems / implementations, refers to declarative policy to steer or guide the behavior of RAN functions, allowing the RAN function to calculate the optimal result to achieve stated objective. Attorney Docket No. AG4358-PCT (31517-3584) The term “O-RAN non-real-time RAN Intelligent Controller” or “non-RT RIC” refers to a logical function that enables non-real-time control and optimization of RAN elements and resources, AI / ML workflow including model training and updates, and policy-based guidance of applications / features in Near-RT RIC. The term “Near-RT RIC” or “O-RAN near-real-time RAN Intelligent Controller” refers to a logical function that enables near-real-time control and optimization of RAN elements and resources via fine-grained (e.g., UE basis, Cell basis) data collection and actions over E2 interface. The term “O-RAN Central Unit” or “O-CU” refers to a logical node hosting RRC, SDAP and PDCP protocols. The term “O-RAN Central Unit – Control Plane” or “O-CU-CP” refers to a logical node hosting the RRC and the control plane part of the PDCP protocol. The term “O-RAN Central Unit – User Plane” or “O-CU-UP” refers to a logical node hosting the user plane part of the PDCP protocol and the SDAP protocol The term “O-RAN Distributed Unit” or “O-DU” refers to a logical node hosting RLC / MAC / High-PHY layers based on a lower layer functional split. The term “O-RAN eNB” or “O-eNB” refers to an eNB or ng-eNB that supports E2 interface. The term “O-RAN Radio Unit” or “O-RU” refers to a logical node hosting Low-PHY layer and RF processing based on a lower layer functional split. This is similar to 3GPP’s “TRP” or “RRH” but more specific in including the Low-PHY layer (FFT / iFFT, PRACH extraction). The term “O1” refers to an interface between orchestration & management entities (Orchestration / NMS) and O-RAN managed elements, for operation and management, by which FCAPS management, Software management, File management and other similar functions shall be achieved. The term “RAN UE Group” refers to an aggregations of UEs whose grouping is set in the E2 nodes through E2 procedures also based on the scope of A1 policies. These groups can then be the target of E2 CONTROL or POLICY messages. The term “Traffic Steering Action” refers to the use of a mechanism to alter RAN behavior. Such actions include E2 procedures such as CONTROL and POLICY. The term “Traffic Steering Inner Loop” refers to the part of the Traffic Steering processing, triggered by the arrival of periodic TS related KPM (Key Performance Measurement) from E2 Node, which includes UE grouping, setting additional data collection Attorney Docket No. AG4358-PCT (31517-3584) from the RAN, as well as selection and execution of one or more optimization actions to enforce Traffic Steering policies. The term “Traffic Steering Outer Loop” refers to the part of the Traffic Steering processing, triggered by the near-RT RIC setting up or updating Traffic Steering aware resource optimization procedure based on information from A1 Policy setup or update, A1 Enrichment Information (EI) and / or outcome of Near-RT RIC evaluation, which includes the initial configuration (preconditions) and injection of related A1 policies, Triggering conditions for TS changes. The term “Traffic Steering Processing Mode” refers to an operational mode in which either the RAN or the Near-RT RIC is configured to ensure a particular network performance. This performance includes such aspects as cell load and throughput, and can apply differently to different E2 nodes and UEs. Throughout this process, Traffic Steering Actions are used to fulfill the requirements of this configuration. The term “Traffic Steering Target” refers to the intended performance result that is desired from the network, which is configured to Near-RT RIC over O1. Furthermore, any of the disclosed embodiments and example implementations can be embodied in the form of various types of hardware, software, firmware, middleware, or combinations thereof, including in the form of control logic, and using such hardware or software in a modular or integrated manner. Additionally, any of the software components or functions described herein can be implemented as software, program code, script, instructions, etc., operable to be executed by processor circuitry. These components, functions, programs, etc., can be developed using any suitable computer language such as, for example, Python, PyTorch, NumPy, Ruby, Ruby on Rails, Scala, Smalltalk, Java™, C++, C#, “C”, Kotlin, Swift, Rust, Go (or “Golang”), EMCAScript, JavaScript, TypeScript, Jscript, ActionScript, Server- Side JavaScript (SSJS), PHP, Pearl, Lua, Torch / Lua with Just-In Time compiler (LuaJIT), Accelerated Mobile Pages Script (AMPscript), VBScript, JavaServer Pages (JSP), Active Server Pages (ASP), Node.js, ASP.NET, JAMscript, Hypertext Markup Language (HTML), extensible HTML (XHTML), Extensible Markup Language (XML), XML User Interface Language (XUL), Scalable Vector Graphics (SVG), RESTful API Modeling Language (RAML), wiki markup or Wikitext, Wireless Markup Language (WML), Java Script Object Notion (JSON), Apache® MessagePack™, Cascading Stylesheets (CSS), extensible stylesheet language (XSL), Mustache template language, Handlebars template language, Guide Template Language (GTL), Apache® Thrift, Abstract Syntax Notation One (ASN.1), Google® Protocol Buffers (protobuf), Bitcoin Script, EVM® bytecode, Solidity™, Vyper (Python derived), Attorney Docket No. AG4358-PCT (31517-3584) Bamboo, Lisp Like Language (LLL), Simplicity provided by Blockstream™, Rholang, Michelson, Counterfactual, Plasma, Plutus, Sophia, Salesforce® Apex®, and / or any other programming language or development tools including proprietary programming languages and / or development tools. The software code can be stored as a computer- or processor- executable instructions or commands on a physical non-transitory computer-readable medium. Examples of suitable media include RAM, ROM, magnetic media such as a hard-drive or a floppy disk, or an optical medium such as a compact disk (CD) or DVD (digital versatile disk), flash memory, and the like, or any combination of such storage or transmission devices. ABBREVIATIONS Unless used differently herein, terms, definitions, and abbreviations may be consistent with terms, definitions, and abbreviations defined in 3GPP TR 21.905 v16.0.0 (2019-06). For the purposes of the present document, the following abbreviations may apply to the examples and embodiments discussed herein. Table 1 Abbreviations: 3GPP Third Generation IBE In-Band Emission PUSCH Physical Uplink Shared Partnership Project Channel de er e TI , st on rk Attorney Docket No. AG4358-PCT (31517-3584) API Application Programming IoT Internet of Things RAR Random Access Response Interface APN Access Point Name IP Internet Protocol RAT Radio Access io p ed ng el r Attorney Docket No. AG4358-PCT (31517-3584) CCA Clear Channel Assessment L1 Layer 1 (physical RRC Radio Resource layer) Control, Radio Resource Control layer me g, ne k nt C Attorney Docket No. AG4358-PCT (31517-3584) CPE Customer Premise MAC-I MAC used for data SCEF Service Capability Equipment integrity of signalling Exposure Function messages (TSG T p ol n me nce er ort Attorney Docket No. AG4358-PCT (31517-3584) CWS Contention Window Size MPBCH MTC Physical SIM Subscriber Identity Broadcast CHannel Module D2D Device-to-Device MPDCCH MTC Physical SIP Session Initiated t t ce g nal nal Attorney Docket No. AG4358-PCT (31517-3584) EAS Edge Application Server NE-DC NR-E-UTRA Dual SSBRI SS / PBCH Block Connectivity Resource Indicator, Synchronization Signal nal nal nal nal nal e o nal k up ty e tor Attorney Docket No. AG4358-PCT (31517-3584) EPRE Energy per resource NR New Radio, Neighbour TDD Time Division Duplex element Relation EPS Evolved Packet System NRF NF Repository TDM Time Division ple te ng Attorney Docket No. AG4358-PCT (31517-3584) FDMA Frequency Division PAPR Peak-to-Average UDSF Unstructured Data Multiple Access Power Ratio Storage Network Function ow rk ot n g Attorney Docket No. AG4358-PCT (31517-3584) GTP GPRS Tunneling Protocol PLMN Public Land Mobile VIM Virtualized Network Infrastructure Manager GTP-U GPRS Tunnelling Protocol PIN Personal Identification VL Virtual Link, e- l ck n ea Attorney Docket No. AG4358-PCT (31517-3584) The foregoing description provides illustration and description of various example embodiments, but is not intended to be exhaustive or to limit the scope of embodiments to the precise forms disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of various embodiments. Where specific details are set forth in order to describe example embodiments of the disclosure, it should be apparent to one skilled in the art that the disclosure can be practiced without, or with variation of, these specific details. It should be understood, however, that there is no intent to limit the concepts of the present disclosure to the particular forms disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives consistent with the present disclosure and the appended claims.

Claims

Attorney Docket No. AG4358-PCT (31517-3584) CLAIMS What is claimed is:

1. An apparatus for a next generation (NG)-radio access network (RAN) node comprising: processing circuitry configured to: determine one or both of: timing information and associated power information for a wireless link from a target UE to the NG-RAN node from reception of Sounding Reference Signal (SRS) transmitted by the target UE in a 5G network; calculate power information for each path of a channel response, using a linear average of the power of the channel response at specific path delays; and report one or both of: the timing information and the power information to a serving Location Management Function (LMF) for use as input to an Artificial Intelligence / Machine Learning (AI / ML) model deployed at the LMF; and a memory to store the power information.

2. The apparatus of claim 1, wherein the timing information is conveyed by utilizing path-based timing measurements that determine: time instances of detected paths in a multipath channel, where the time instances are ordered by arrival time of the detected paths; and power information for each path of a channel response, corresponding to the power of the channel response at specific path delays.

3. The apparatus of claim 1, wherein the timing information is conveyed by utilizing sample-based measurements that determine a number of time instances corresponding to integer multiples of a sampling period with the highest power values based on an estimated channel impulse response (CIR) and a first time instance corresponds to a first detected path.Attorney Docket No. AG4358-PCT (31517-3584) 4. The apparatus of claim 1, wherein the processing circuitry is further configured to determine a reference time that corresponds to a UL Relative Time of Arrival (UL RTOA) reference time: ^^^is the nominal beginning time of SFN 0 provided by SFN Initialization Time [15, TS 38.455], and ^^^ௌோௌ ൌ ^10^^^ ^ ^^^^^ ൈ 10ିଷ, where ^^^ and ^^^^ are a system frame numberand a subframe number of a measurement RS, respectively.

5. The apparatus of claim 1, wherein the power information includes the linear average of the channel response at specific path delays or samples, respectively, calculated for each receiver branch.

6. The apparatus of claim 1, wherein the timing information is represented in absolute time using a universal time format.

7. The apparatus of claim 2, wherein the processing circuitry is further configured to report the power information for the detected paths or samples with highest powers, respectively, where the power information includes: the power value of the first detected path and the power values for additional detected paths or samples that are represented relative to the power value of the first detected path.

8. The apparatus of claim 7, wherein the maximum number of detected path timings, other than the first detected path, is larger than 8.

9. The apparatus of claim 3, wherein the number of time instances corresponding to integer multiples of a sampling period with the highest power values based on an estimated channel impulse response (CIR) is specified or can be requested by the LMF.

10. The apparatus of any one of claims 1-9, wherein the processing circuitry is further configured to report measurements or ground-truth information to the LMF along with quality and time stamp information associated with the measurements.

11. A computer-readable medium storing computer-executable instructions which when executed by one or more processors result in performing operations comprising:Attorney Docket No. AG4358-PCT (31517-3584) determining one or both of: timing information and associated power information for a wireless link from a target UE to the NG-RAN node from reception of Sounding Reference Signal (SRS) transmitted by the target UE in a 5G network; calculating power information for each path of a channel response, using a linear average of the power of the channel response at specific path delays; and reporting one or both of: the timing information and the power information to a serving Location Management Function (LMF) for use as input to an Artificial Intelligence / Machine Learning (AI / ML) model deployed at the LMF.

12. The computer-readable medium of claim 11, wherein the timing information is conveyed by utilizing path-based timing measurements that determine: time instances of detected paths in a multipath channel, where the time instances are ordered by arrival time of the detected paths; and power information for each path of a channel response, corresponding to the power of the channel response at specific path delays.

13. The computer-readable medium of claim 11, wherein the timing information is conveyed by utilizing sample-based measurements that determine a number of time instances corresponding to integer multiples of a sampling period with the highest power values based on an estimated channel impulse response (CIR) and a first time instance corresponds to a first detected path.

14. The computer-readable medium of claim 11, wherein the operations further comprise determining a reference time that corresponds to a UL Relative Time of Arrival (UL RTOA) reference time: ^^^is the nominal beginning time of SFN 0 provided by SFN Initialization Time [15, TS 38.455], and ^^^ ൌ ^10^^ ^ ^^ ^ ൈ 10ିଷௌோௌ ^ ^^ , where ^^^ and ^^^^ are a system frame numberand a subframe number of a measurement RS, respectively.

15. The computer-readable medium of claim 11, wherein the power information includes the linear average of the channel response at specific path delays or samples, respectively, calculated for each receiver branch.Attorney Docket No. AG4358-PCT (31517-3584) 16. The computer-readable medium of claim 11, wherein the timing information is represented in absolute time using a universal time format.

17. The computer-readable medium of claim 12, wherein the operations further comprise reporting the power information for the detected paths or samples with highest powers, respectively, where the power information includes: the power value of the first detected path and the power values for additional detected paths or samples that are represented relative to the power value of the first detected path.

18. The computer-readable medium of claim 17, wherein the maximum number of detected path timings, other than the first detected path, is larger than 8.

19. The computer-readable medium of any one of claims 11-18, wherein the number of time instances corresponding to integer multiples of a sampling period with the highest power values based on an estimated channel impulse response (CIR) is specified or can be requested by the LMF.

20. A method comprising: determining one or both of: timing information and associated power information for a wireless link from a target UE to the NG-RAN node from reception of Sounding Reference Signal (SRS) transmitted by the target UE in a 5G network; calculating power information for each path of a channel response, using a linear average of the power of the channel response at specific path delays; and reporting one or both of: the timing information and the power information to a serving Location Management Function (LMF) for use as input to an Artificial Intelligence / Machine Learning (AI / ML) model deployed at the LMF.

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