Data collection for artificial intelligence / machine learning positioning mechanisms

Abstract

Methods and systems of data collection for artificial intelligence (AI) / machine learning (ML) positioning mechanisms are discussed herein. For example, a location management function (LMF) may send, to a UE and a base station, respective configurations related to a positioning signal to be transmitted between the UE and the base station. The LMF may further receive, from the UE or the base station, positioning feedback corresponding to the positioning signal, where the positioning feedback includes, e.g., a magnitude and / or a phase of a detected path or a real number component and / or a real imaginary number component of a power of a detected path of the positioning signal. The positioning feedback may then be applied by the LMF using an AI / ML model.

Description

DATA COLLECTION FOR ARTIFICIAL INTELLEGENCE / MACHINE LEARNINGPOSITIONING MECHANISMSTECHNICAL FIELD

[0001] This application relates generally to wireless communication systems, including wireless communication systems implementing artificial intelligence (AI) / machine learning (ML) models for positioning signaling.BACKGROUND

[0002] Wireless mobile communication technology uses various standards and protocols to transmit data between a base station and a wireless communication device. Wireless communication system standards and protocols can include, for example, 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) (e.g., 4G), 3GPP New Radio (NR) (e.g., 5G), and Institute of Electrical and Electronics Engineers (IEEE) 802. 11 standard for Wireless Local Area Networks (WLAN) (commonly known to industry groups as Wi-Fi®).

[0003] As contemplated by the 3GPP, different wireless communication systems' standards and protocols can use various radio access networks (RANs) for communicating between a base station of the RAN (which may also sometimes be referred to generally as a RAN node, a network node, or simply a node) and a wireless communication device known as a user equipment (UE). 3GPP RANs can include, for example. Global System for Mobile communications (GSM), Enhanced Data Rates for GSM Evolution (EDGE) RAN (GERAN). Universal Terrestrial Radio Access Network (UTRAN), Evolved Universal Terrestrial Radio Access Network (E-UTRAN), and / or Next-Generation Radio Access Network (NG-RAN).

[0004] Each RAN may use one or more radio access technologies (RATs) to perform communication between the base station and the UE. For example, the GERAN implements GSM and / or EDGE RAT, the UTRAN implements Universal Mobile Telecommunication System (UMTS) RAT or other 3 GPP RAT, the E-UTRAN implements LTE RAT (sometimes simply referred to as LTE). and NG-RAN implements NR RAT (sometimes referred to herein as 5G RAT, 5G NR RAT, or simply NR). In certain deployments, the E-UTRAN may also implement NR RAT. In certain deployments, NG-RAN may also implement LTE RAT.

[0005] A base station used by a RAN may correspond to that RAN. One example of an E-UTRAN base station is an Evolved Universal Terrestrial Radio Access Network (E- UTRAN) Node B (also commonly denoted as evolved Node B, enhanced Node B, eNodeB, or eNB). One example of an NG-RAN base station is a next generation Node B (also sometimes referred to as a g Node B or gNB).

[0006] A RAN provides its communication services with external entities through its connection to a core network (CN). For example, E-UTRAN may utilize an Evolved Packet Core (EPC) while NG-RAN may utilize a 5G Core Network (5GC).BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0007] To easily identify the discussion of any particular element or act. the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced.

[0008] FIG. 1 illustrates a flow diagram of a UE-based data collection procedure, according to embodiments herein.

[0009] FIG. 2 illustrates a flow diagram of a network-based data collection procedure, according to embodiments herein.

[0010] FIG. 3 illustrates an example of taps with corresponding examples of real / imaginary and magnitude / phase representations according to embodiments disclosed herein.

[0011] FIG. 4 illustrates examples of information elements (IES) and corresponding fields for downlink positioning reference signal (DL-PRS) feedback and uplink sounding reference signal (UL-SRS) feedback, according to embodiments disclosed herein.

[0012] FIG. 5 illustrates a method of a location management function (LMF) of a core network (CN), according to embodiments herein.

[0013] FIG. 6 illustrated a method of an LMF of a CN, according to embodiments herein.

[0014] FIG. 7 illustrates a method of a UE, according to embodiments herein.

[0015] FIG. 8 illustrates a method of a UE, according to embodiments herein.

[0016] FIG. 9 illustrates a method of a base station, according to embodiments herein.

[0017] FIG. 10 illustrates a method of a base station, according to embodiments herein.

[0018] FIG. 11 illustrates a method of an LMF of a CN, according to embodiments herein.

[0019] FIG. 12 illustrates a method of a UE, according to embodiments herein.

[0020] FIG. 13 illustrates a method of a base station, according to embodiments herein.

[0021] FIG. 14 illustrates an example architecture of a wireless communication system, according to embodiments disclosed herein.

[0022] FIG. 15 illustrates a system for performing signaling between a wireless device, a RAN device, and a CN device, according to embodiments disclosed herein.DETAILED DESCRIPTION

[0023] Various embodiments are described with regard to a UE. However, reference to a UE is merely provided for illustrative purposes. The example embodiments may be utilized with any electronic component that may establish a connection to a network and is configured with the hardware, software, and / or firmware to exchange information and data with the network. Therefore, the UE as described herein is used to represent any appropriate electronic component.

[0024] In some wireless communication systems, various objectives for artificial intelligence (Al)Zmachine learning (ML)-based air interface operation may be considered to provide support for various aspects of, for example, positioning accuracy enhancements. Positioning accuracy enhancements under consideration may include aspects related to cases of direct AI / ML positioning and / or to cases of AI / ML assisted positioning. Example cases of direct AI / ML positioning include a first case that includes UE-based positioning with a UE-side model and direct AI / ML positioning; a second case that includes UE-assisted / location management function (LMF)-based positioning with an LMF-side model and direct AI / ML positioning; and a third case that includes NG- RAN node / base station-assisted positioning with an LMF-side model and direct AI / ML positioning.

[0025] Example cases of AI / ML assisted positioning include, for example, a first case that includes UE-assisted / LMF-based positioning with a UE-side model and AI / ML assisted positioning; and a second case that includes NG-RAN node / base station-assisted positioning with a base station-side model and AI / ML assisted positioning.

[0026] Possible measurements and / or signaling / mechanism(s) may be provided to facilitate LCM operations specific to these positioning accuracy enhancements use cases.Signaling of related measurement enhancements may be also considered. Additionally, method(s) to ensure consistency between training and inference with respect to networkside additional conditions for inferencing at UE for relevant positioning sub-use cases may be identified and enabled.

[0027] Core parameters for the direct AI / ML positioning and AI / ML assisted positioning use cases for AI / ML LCM procedures (e.g., as described above) and corresponding UE features may include, for example, core parameters for the direct AI / ML positioning and AI / ML assisted positioning use cases and core parameters for LCM procedures including performance monitoring.

[0028] For various mechanisms with respect to data collection for AI / ML based positioning, data and the information corresponding to the data with potential specification impact may be identified. For example, ground truth labels that may be reported from a label data generation entity may be identified. Measurements (corresponding to a model input) that may be reported from the measurement data generation entity may be identified. Quality indicators that may be used for and / or are associated with ground truth label and / or measurement at least for model training may be identified. The quality’ indicators may be reported from the label and / or the measurement data generation entity and / or as requests from a different (e.g., data collection, etc.) entity. Reference signal (RS) configuration(s) that may be used for deriving measurements may be identified. The RS configuration(s) may be requested from a data generation entity (e.g., a UE or a positioning reference unit (PRU) or a transmi t / receive point (TRP)) to an LMF and / or as LMF assistance signaling to a UE, a PRU or a TRP. Additionally, time stamps that may be used at least for and / or that are associated with training data for model training may be identified. There may be separate time stamps for measurements and ground truth labels when measurements and ground truth labels are generated by different entities. The time stamps may be reported from the data generation entity together with training data and / or as LMF assistance signaling.

[0029] Additionally, it may be considered as to whether data and information discussed herein may be applied to other aspects of AI / ML model LCM (e.g., updating, monitoring, etc., of the AI / ML model). The transfer of data from the entity generating data to a different entity may not be precluded. Also, if any impact to a specification for a corresponding wireless communication system is identified, the impact may be different between differing positioning use cases (e.g., the direct AI / ML positioning andAI / ML assisted positioning use cases discussed herein). The possible importance of other information (e.g., scenario identifiers, line of sight (LOS) / non-line of sight (NLOS) conditions, timing errors, etc.) for data collection may be considered.

[0030] For direct AI / ML positioning with LMF -sided model(s) (e.g., for cases of UE- assisted / LMF-based positioning with an LMF-side model under a direct AI / ML positioning mechanism and / or for cases of NG-RAN node / base station-assisted positioning with LMF-side model under a direct AI / ML positioning mechanism), various ty pes of measurement reports are identified as potentially beneficial (e.g., the context of a tradeoff between a positioning accuracy requirement and signaling overhead). The measurement reports may take into account that measurements and / or measurement reports may contain timing, power and phase information of the channel response. For example, a measurement report may be identified, containing timing, power and phase information of the channel response (e.g., for the case of NG-RAN node / base station- assisted positioning with LMF-side model under a direct AI / ML positioning mechanism). In another example, a measurement report may be identified that contains timing and power information of the channel response. In yet another example, a measurement report may be identified that contains timing information of the channel response. It should be understood that combinations of multiple measurement reports and / or post processing of the measurement reports are not precluded.

[0031] For direct AI / ML positioning with an LMF-sided model (e.g., for UE- assisted / LMF-based positioning with an LMF-side model under a direct AI / ML mechanism and / or for NG-RAN node / base station-assisted positioning with an LMF-side model under a direct AI / ML positioning mechanism), various types of measurement reports may be considered for AI / ML based positioning accuracy enhancement. For example, a measurement report which contains timing, power and phase information of the channel response may be considered. If the measurement report is supported, there may be a new measurement report and / or an enhancement to an existing measurement report (e.g., truncation, feature extraction, alignment of sample / path determination) may be considered. In another example, a measurement report which contains timing and power information of the channel response may be considered. If the measurement report is supported, a new measurement report may be introduced and / or an enhancement to an existing measurement report (e.g., truncation, feature extraction, alignment of sample / path determination) may be considered. In yet another example, a measurementreport which contains timing information of the channel response may be considered. If the measurement report is supported, there may be an enhancement made to an existing measurement report (e.g., alignment of sample / path determination).

[0032] In some embodiments, it may be desirable to specify measurements, signaling, and procedures to facilitate training, inference, monitoring, and / or other LCM operations for either / both direct AI / ML positioning mechanisms and AI / ML assisted positioning mechanisms. For example, signaling for corresponding data collection may be specified, and the necessity of other information for supporting data collection may be considered. Further, the utility of and signaling details for measurement enhancements may be considered and / or specified.

[0033] It may be desirable to further consider various model input design aspects. For example, the model input type (e.g., channel impulse response (CIR), power delay profile (PDP). delay profile (DP)), the model dimension (e.g.. parameters N’TRP, Nt, N’t, Nport) and the related model format (e.g., for the timing information: absolute time or relative time), and the trade-off of positioning accuracy, signaling overhead, and AI / ML complexity may be considered.

[0034] It should be understood that CIR as discussed herein may include path timing, complex values (real and imaginary or magnitude and phase). PDP as discussed herein may include path timing, power magnitude and DP as discussed herein may include path timing only.

[0035] In some wireless communication systems, various measurements may be used for feedback in, for example, a location positioning protocol (LPP) and / or a new radio positioning protocol (NRPPa). In a first example of such measurements, a first path power field may specify an NR downlink positioning reference signal (DL-PRS) reference signal received path power (DL-PRS-RSRPP) of the first detected path in time of a DL-PRS. An example for a mapping of the measured quantity is provided in 3GPP Technical Specification (TS) 38.133 version 18.4.0 (December 2023) (hereinafter “3GPP TS 38.133”) where it is given as: nr-DL-PRS-FirstPathRSRP-Result-rl7 INTEGER (0..126)Note generally that a first detected path in time may be referred to more simply as a “first path” herein.

[0036] In another example, an additional path absolute power field may specify7the DL- PRS reference signal received path power (DL-PRS-RSRPP) of the NR-AdditionalPathreported (e.g., an additional path from the first detected path in time). An example for a mapping of this quantity is provided in 3GPP TS 38. 133. where it is given as: nr-DL-PRS-RSRPP-rl7 INTEGER (0 .126) OPTIONAL

[0037] In yet another example, an additional path differential and / or relative power field may provide the additional DL-PRS RSRP measurement result relative to the nr- DL-PRS-RSRP-Result field. The DL-PRS RSRP value of this measurement is obtained by adding the value of this field to the value of an nr-DL-PRS-RSRP-Resiilt field. An example of the mapping of the field is provided in 3GPP TS 38.133, where it is given as: nr-DL-PRS-FirstPathRSRP-ResultDiff-rl 7 INTEGER(0..61) OPTIONAL

[0038] In yet another example, an nr-RelativeTimeDifference field may specify an additional detected path timing relative to the detected path timing of the reference resource. An example of the mapping of reported values and measured quantity value is provided in, for example, 3GPP TS 38.133 clause 10.1.23.3.3 and 10.1.25.3.3.

[0039] Embodiments disclosed herein discuss the structure and details of a measurement report for both / either of UE-based data collection and network-based data collection in AI / ML based positioning. Further, details of data collection procedures, configurations, signaling for different input types (e.g., CIR, PDP, DP), feedback (e.g., DL-PRS feedback and uplink (UL) sounding reference signal (SRS) (UL-SRS) feedback), and feedback structures are discussed herein.Embodiments with Respect to Data Collection Procedures

[0040] FIG. 1 illustrates a flow diagram 100 of a UE-based data collection procedure, according to embodiments herein.

[0041] In some embodiments for UE based data collection in a UE-assisted / LMF-based positioning with LMF-side model with direct AI / ML positioning, a UE may measure a DL-PRS and, in some examples, the ground truth (GT) associated with the DL-PRS. Then, the UE may feed back the data collected to an LMF using an LPP.

[0042] For example, as illustrated in FIG. 1. for a UE based data collection procedure, a base station 104 may receive a reporting configuration 108 from the LMF 106 and a UE 102 may receive a reporting configuration 110 from the LMF 106. The base station 104 then transmits the DL-PRS 112 (a positioning signal) to the UE 102 according to the configuration 108. The UE 102 measures the DL-PRS 112 according to the configuration110 and performs corresponding processing 114 to generate positioning feedback 116 and transmits the positioning feedback 116 back to the LMF 106 using an LPP. In some cases, the LMF 106 may apply the received positioning feedback 116 using an AI / ML model.

[0043] FIG. 2 illustrates a flow diagram 200 of a network-based data collection procedure, according to embodiments herein.

[0044] In some embodiments for network-based data collection in a base station (e.g., NG-RAN node)-assisted positioning with LMF-side model with direct AI / ML positioning, the NG-RAN node / base station may measure an UL SRS (a positioning signal) and, in some examples, estimates the GT. Then, the NG-RAN node / base station feeds back the data collected to the LMF using an NRPPa.

[0045] For example, as illustrated in FIG. 2, a base station data collection procedure begins by a base station 204 receiving a reporting configuration 208 from an LMF 206. Similarly, the UE 202 receives a reporting configuration 208 from an LMF 206. The UE then transmits a UL-SRS 212 according to the configuration 210. The base station 104 measures the UL-SRS 212 according to the configuration 208 and performs corresponding processing 214 to generate positioning feedback 216 and transmits the resulting positioning feedback 216 to the LMF 106 using an NRPPa. In some instances, the LMF 106 may apply the received positioning feedback 216 using an AI / ML model.Configuration Details

[0046] In some embodiments, the configuration for both UE-based data collection and base station-based data collection in AI / ML based positioning mechanisms may include one or a combination of various parameters. For example, a configuration parameter may include the number of taps (samples) to be reported (e.g., 8, 9, 16, 32, 64,

[0128] ,

[0256] ). In some cases, consecutive taps may be reported, which implies that tap timing is specified as a fixed number of Tcs. In alternative cases, tap timing may be explicitly configured. According to a first option, it may be that consecutive taps are provided based on a sampling rate of the channel. According to a second option, it may be that the best N't taps may be reported (which case may imply that a tap timing is signaled with the tap).

[0047] Note that, in general, channels may be sampled over time (generating taps) as certain elements of a signal may arrive over time and not all at once. For example, for areference signal received power (RSRP) measurement, the use of taps may provide an understanding of the measurement as a whole due to performing taps on the channel over a relevant time period (a procedure which may be particularly relevant in cases where, for example, a reference signal experiences multi-path effects).

[0048] In some embodiments, a configuration parameter may include a model input type. The model input type may include a DP (e.g., with timing only), a PDP (e.g., with timing and power), and / or a CIR (e.g., with a timing, value that is real and / or imaginary or power and / or phase).

[0049] In some embodiments, a configuration parameter may include a signaling type. In some instances, the signaling type may include an absolute measurement, where the measurements are the absolute values of the value, power, and / or timing of a positioning signal.

[0050] In some other instances, the signaling type may include a relative measurement, where the measurements of additional taps of a positioning signal are understood relative to the first path in time for the positioning signal (and where a measurement for the first path is not included). It should be noted for such cases that this type of relative measurement may be used for non-timing based positioning methods (e.g., angle of arrival (AoA) based methods).

[0051] In yet some other instances, the signaling type may include a mixed measurement, where the measurement of a first tap in time relates the absolute value of the magnitude, power, and / or timing of the first tap. Then, in a first option, measurements of additional tap timings may be related relative to the first tap, while value and / or power are related as absolute values in a second option, the measurements of the additional tap value, tap power, and tap timing are all related relative to corresponding measurements for the first tap.

[0052] FIG. 3 illustrates a diagram 300 with an example of taps with corresponding examples of real / imaginary and magnitude / phase representations according to embodiments herein.

[0053] In some embodiments discussed herein, taps 302 are used. For example, over an effective channel 304, taps 302 may be performed according to the sampling rate for the effective channel 304. In the case illustrated in FIG. 3, there are six taps 302 according to the sampling of the effective channel 304, where the first tap corresponds to a first detected path 306 (that is non-zero), the third tap corresponds to a second detected path312 (that is non-zero), and the taps 302 correspond to a third detected path 318 (that is non-zero).

[0054] In some embodiments discussed herein, real / imaginary representations and / or magnitude and phase representations are utilized corresponding to taps.

[0055] As an example, first real / imaginary representation 308 of 5 + 4j may be used corresponding to the first detected path 306 at the first tap, where the 5 corresponds to the real component of the first detected path 306 and 4j corresponds to the imaginary' component of the first detected path 306. Note that when the real component and the imaginary component of first real / imaginary' representation 308 (5 and 4j respectively) are squared and then added together, the resulting value (41) would be the power domain value corresponding to the first detected path 306.

[0056] The first magnitude and phase representation 310 for the first detected path 306 is now discussed. The magnitude component of the magnitude and phase representation of the first magnitude and phase representation 310 corresponds to the square root of the power domain value (41) for the first detected path 306, as illustrated. The corresponding phase is then calculated, as shown. Thus, the resulting magnitude and phase representation for the first detected path 306 is (sqrt(41), phase) as shown.

[0057] Corresponding procedures to those just discussed apply accordingly to each of the second detected path 312 at the third tap 302 (corresponding to the second real / imaginary7representation 314 and the second magnitude and phase representation 316, as illustrated) and the third detected path 318 at the sixth tap 302 (corresponding to the third real / imaginary representation 320 and the third magnitude and phase representation 322, as illustrated).Embodiments for Signaling for Different Input Data Types (CIR, PDP / DP)

[0058] In certain embodiments, different input data types may include different information as discussed herein. For example, CIR-related signaling may include a first path real value information, a first path imaginary' value information, additional / relative path real information and / or additional / relative path imaginary information as provided in Table 1. Note CIR-related signaling could additionally or alternatively include first path magnitude information, first path phase information, additional / relative path magnitude information, and / or additional / relative path phase information. It should be noted that differential / relative values of magnitude, phase, real, and imaginary valuesmay use the first path as a reference (e.g., corresponding absolute values for the first path may be used to anchor the relative / differential indication).Table 1: Signaling Options for CIR Cases

[0059] In a second example, PDP-related signaling may include a first path magnitude and an additional / relative magnitude as provided in Table 2.Table 2: Signaling Options for PDP CasesEmbodiments for DL-PRS based Feedback

[0060] In some embodiments. DL-PRS based feedback may include a magnitude component and a phase component for representation. For example, for a magnitude representation, a first path magnitude field may specify the NR DL-PRS reference signal received path magnitude (DL-PRS-RSRPM) of a first detected path in time. A DL-PRS reference signal received path magnitude (DL-PRS-RSRPM) may be defined as the magnitude of the linear average of the channel response at the z-th path delay of the resource elements that carry DL-PRS signal configured for the measurement, where DL- PRS-RSRPM for the first path delay is the magnitude corresponding to the first detected path in time. It may be obtained as the square root of the DL-PRS reference signal received path power (DL-PRS-RSRPP). An example magnitude representation / field for a first path may be given as: nr-DL-PRS-FirstPathRSRM-Result-rXX INTEGER (0..126)

[0061] In another example, for an additional path magnitude representation, an additional path magnitude field may specify the DL-PRS reference signal received path magnitude (DL-PRS-RSRPM) of an NR-AdditionalPath-ML that is reported. An example additional path magnitude representation / field is given as: nr-DL-PRS-RSRPM-rXX INTEGER (0 . 126) OPTIONAL

[0062] In yet another example, for an additional path differential / relative magnitude representation, an additional path differential / relative magnitude field may provide the additional DL-PRS RSRP measurement result relative to an nr-DL-PRS-RSRM-Result field. The DL-PRS RSRPM value of this measurement is obtained by adding the value of this field to the value of the nr-DL-PRS-RSRPM-Result field. An example additional path differential / relative magnitude representation / field is given as: nr-DL-PRS-RSRPM-ResultDiff-rXX INTEGER(0..61) OPTIONAL

[0063] It should be understood that one or a combination of the foregoing examples may be used for magnitude representation in DL-PRS feedback and that field and information element (IE) names similar to the examples given may provide the same details and utility as the examples discussed herein.

[0064] For phase representation, for example, a first path phase field may specify the NR DL-PRS reference signal received path phase (DL-PRS-RSRPPh) of the first detected path in time. The mapping of the measured quantify' is provided in degrees or radians. In some cases, the mapping may be provided as a fixed and / or configured number of degrees and / or radians and in other cases the mapping to degrees or radians may be configured. An example first path phase representation / field may be given as: nr-DL-PRS-FirstPathRSRP-Phase-Result-rXX INTEGER (0, ... , x) where x is degrees or radians, sign (0,1), where 0 is + and 1 is -

[0065] In another example, for an additional path phase representation, an additional path phase field may specify the NR DL-PRS reference signal received path phase (DL- PRS-RSRPPh) of the additional detected paths in time. An example additional path phase representation / field may be given as: nr-DL-PRS-Phase-rXX INTEGER (0.... , x) where x is degrees or radians, sign (0,1), where 0 is + and 1 is -

[0066] In yet another example, for an additional path phase representation, an additional path differential / relative phase field may provide the additional DL-PRS RSRPPh measurement result relative to the nr-DL-PRS-FirstPathRSRP-Phase-Result- rl8 field. The DL-PRS RSRPPh value of this measurement is obtained by adding the value of this field to the value of the nr-DL-PRS-FirstPathRSRP-Phcise-Result-r 18 field. An example additional path phase representation / field may be given as: nr-DL-PRS-Phase-ResultDiff-rXX INTEGER(0,...,y) OPTIONAL, sign (0,1), where 0 is + and 1 is -

[0067] It should be understood that one or a combination of the foregoing examples can be used for phase representation in DL-PRS feedback and that field and IE names similar to the examples given may provide the same details and utility' as the examples discussed herein.

[0068] In some embodiments, DL-PRS based feedback may include a real component and an imaginary component for representation. For example, a first path real field may specify the NR DL-PRS reference signal received path real magnitude (DL-PRS-RSRPM-real) of the first detected path in time. DL-PRS reference signal received path real magnitude (DL-PRS-RSRPM-real) is defined as the real component of the linear average of the channel response at the z-th path delay of the resource elements that carry DL-PRS signal configured for the measurement, where DL-PRS-RSRPM-real for the first path delay is the real value of the channel response corresponding to the first detected path in time. It may be a signed value. An example first path real representation / field may be given as: nr-DL-PRS-FirstPathRSRPM-real-Result-rXX INTEGER (0, ... , 126) , sign (0,1) corresponding to + or - OPTIONAL

[0069] In another example, a first path imaginary field may specify the NR DL-PRS reference signal received path imaginary magnitude (DL-PRS-RSRPM-imag) of the first detected path in time. The DL-PRS reference signal received path imaginary magnitude (DL-PRS-RSRPM-imag) is provided as the imaginary' value of the linear average of the channel response at the z-th path delay of the resource elements that carry DL-PRS signal configured for the measurement, where DL-PRS-RSRPM-imag for the first path delay is the imaginary value of the channel response corresponding to the first detected path in time. It may be a signed value. An example first path imaginary representation / field may be given as: nr-DL-PRS-FirstPathRSRPM-imag-Result-rXX INTEGER (0,... .126). sign (0,1) corresponding to + or - OPTIONAL

[0070] In yet another example, an additional path real field may specify the NR DL- PRS reference signal received path real magnitude (DL-PRS-RSRPM-real) of the NR- AdditionalPath-ML reported. An example additional path real representation / field may be given as: nr-DL-PRS-RSRPM-real-rXX INTEGER (0,... , 126) , sign (0.1) corresponding to + or -

[0071] In yet another example, an additional path imaginary' field may specify the NR DL-PRS reference signal received path imaginary magnitude (DL-PRS-RSRPM-imag) of the NR-AdditionalPath-ML reported. An example additional path imaginary representation / field may be given as: nr-DL-PRS-RSRPM-imag-rXX INTEGER (0,... , 126) , sign (0,1) corresponding to + or - OPTIONAL

[0072] In yet another example, an additional path differential / relative real field may provide the additional NR DL-PRS reference signal received path real magnitude measurement result relative to nr-DL-PRS-FirstPathRSRPM-real-Result-rXX. The DL- PRS-RSRPM-real value of this measurement is obtained by adding the value of this field to the value of the nr-DL-PRS-FirstPathRSRPM-real-Result-rXX field. An example additional path differential / relative real representation / field may be given as: nr-DL-PRS-RSRPM-real-ResultDiff-rXX INTEGER (0.... , 126) . sign (0,1) corresponding to + or - OPTIONAL

[0073] In yet another example, an additional path differential / relative imaginary’ field may provide the additional NR DL-PRS reference signal received path imaginary magnitude measurement result relative to nr-DL-PRS-FirstPathRSRPM-imag-Result- rXX. The DL-PRS-RSRPM-imag value of this measurement is obtained by adding the value of this field to the value of the nr-DL-PRS-FirstPathRSRPM-imag-Result-rXX field. An example additional path differential / relative imaginary representation / field may be given as: nr-DL-PRS-RSRPM-imag-ResultDiff-rXX INTEGER (0,... ,126) , sign (0,1) corresponding to + or - OPTIONAL

[0074] It should be understood that one or a combination of the foregoing examples can be used for real and imaginary value representation in DL-PRS feedback and that field and IE names similar to the examples given may provide the same details and utility as the examples discussed herein.

[0075] It should be further understood that, in some embodiments, a combination of magnitude, phase, real, and / or imaginary' representation may be used for DL-PRS based feedback.UL-SRS based Feedback

[0076] In some embodiments. UL-SRS based feedback may include a magnitude component and phase component representation. For example, for a magnitude representation, a first path magnitude field may specify the NR UL-SRS reference signal received path magnitude (UL SRS-RSRPM) of the first detected path in time. The UL SRS reference signal received path magnitude (UL SRS-RSRPM) is provided as the magnitude of the linear average of the channel response at the z-th path delay of the resource elements that carry UL SRS signal configured for the measurement, where ULSRS-RSRPM for the first path delay is the magnitude corresponding to the first detected path in time. It may be obtained as the square root of the UL SRS reference signal received path Power (UL SRS-RSRPP). An example first path magnitude representation / field may be given as: nr-UL-SRS-FirstPathRSRM-Result-rXX INTEGER (0..126)

[0077] In another example, for a magnitude representation, an additional path magnitude field may specify the UL SRS reference signal received path magnitude (UL SRS-RSRPM) of the NR- Additional? ath-ML reported. An example additional path magnitude representation / field may be given as: nr-UL-SRS-RSRPM-rXX INTEGER (0 .126) OPTIONAL

[0078] In yet another example, an additional path differential / relative magnitude field may provide the additional UL-SRS RSRP measurement result relative to nr-UL-SRS- RSRM-Result. The UL-SRS RSRPM value of this measurement is obtained by adding the value of this field to the value of the nr-UL-SRS-RSRPM-Result field. An example additional path differential / relative magnitude representation / field may be given as: nr-UL-SRS-RSRPM-ResultDiff-rXX INTEGER(0..61) OPTIONAL.

[0079] It should be understood that one or a combination of the foregoing examples can be used for magnitude representation in UL-SRS feedback and that field and IE names similar to the examples given may provide the same details and utility’ as the examples discussed herein.

[0080] For phase representation, for example, a first path phase field may specify the NR UL-SRS reference signal received path phase (UL-SRS-RSRPPh) of the first detected path in time. The mapping of the measured quantity is provided in degrees or radians. In some cases, the mapping may be provided as a fixed number of degrees and / or radians and in other cases, the mapping to degrees or radians may be configured. An example first path phase representation / field may be given as: nr-UL-SRS-FirstPathRSRP-Phase-Result-rXX INTEGER (0,... , x) where x is degrees or radians, sign (0,1), where 0 is + and 1 is -

[0081] In another example, for an additional phase representation, an additional path phase field may specify the NR UL-SRS reference signal received path phase (UL-SRS- RSRPPh) of the additional detected paths in time. An example additional path phase representation / field may be given as:nr-UL-SRS-Phase-rXX INTEGER (0,... , x) where x is degrees or radians, sign (0,1), where 0 is + and 1 is -

[0082] In yet another example, for phase representation, an additional path differential / relative phase field may provide the additional UL-SRS RSRPPh measurement result relative to nr-UL-SRS-FirstPathRSRP-Phase-Result-rl8. The UL- SRS RSRPPh value of this measurement is obtained by adding the value of this field to the value of the nr-UL-SRS-FirstPathRSRP-Phase-Result-rl8 field. An example additional path differential / relative phase representation / field may be given as: nr-UL-SRS-Phase-ResultDiff-rXX INTEGER(0,... ,y) OPTIONAL, sign (0,1). where 0 is + and 1 is -

[0083] It should be understood that one or a combination of the foregoing examples can be used for magnitude representation in UL-SRS feedback and that field and IE names similar to the examples given may provide the same details and uti 1 i ty as the examples discussed herein.

[0084] In some embodiments, the UL-SRS based feedback may include a real component and an imaginary component for representation. For example, a first path real field may specify the NR UL-SRS reference signal received path real magnitude (UL- SRS-RSRPM-real) of the first detected path in time. UL-SRS reference signal received path real magnitude (UL-SRS-RSRPM-real) is defined as the real value of the linear average of the channel response at the z-th path delay of the resource elements that carry UL-SRS signal configured for the measurement, where UL-SRS-RSRPM-real for the first path delay is the real value of the channel response corresponding to the first detected path in time. It may be a signed value. An example is first path real representation / field may be given as: nr-UL-SRS-FirstPathRSRPM-real-Result-rXX INTEGER (0,... , 126) , sign (0,1) corresponding to + or - OPTIONAL

[0085] In another example, an additional first path imaginary field may specify the NR UL-SRS reference signal received path imaginary magnitude (UL-SRS-RSRPM-imag) of the first detected path in time. The UL-SRS reference signal received path imaginary magnitude (UL-SRS-RSRPM-imag) is defined as the imaginary value of the linear average of the channel response at the z-th path delay of the resource elements that carry UL-SRS signal configured for the measurement, where UL-SRS-RSRPM-imag for the first path delay is the imaginary value of the channel response corresponding to the firstdetected path in time. It may be a signed value. An example first path imaginary representation / field may be given as: nr-UL-SRS-FirstPathRSRPM-imag-Result-rXX INTEGER (0,... ,126), sign (0,1) corresponding to + or - OPTIONAL

[0086] In yet another example, an additional path real field may specify the NR UL- SRS reference signal received path real magnitude (UL-SRS-RSRPM-real) of the NR- AdditionalPath-ML reported. An example additional path real representation / field may be given as: nr-UL-SRS-RSRPM-real-rXX INTEGER (0,... , 126) , sign (0.1) corresponding to + or -

[0087] In yet another example, an additional path imaginary field may specify the NR UL-SRS reference signal received path imaginary’ magnitude (UL-SRS-RSRPM-imag) of the NR-AdditionalPath-ML reported. An example additional path imaginary representation / field may be given as: nr-UL-SRS-RSRPM-imag-rXX INTEGER (0,... , 126) , sign (0,1) corresponding to + or - OPTIONAL

[0088] In yet another example, an additional path differential / relative real field may provide the additional NR UL-SRS reference signal received path real magnitude measurement result relative to nr-UL-SRS-FirstPathRSRPM-real-Result-rXX. The UL- SRS-RSRPM-real value of this measurement is obtained by adding the value of this field to the value of the nr-UL-SRS-FirstPathRSRPM-real-Result-rXX field. An example additional path differential / relative real representation / field may be given as: nr-UL-SRS-RSRPM-real-ResultDiff-rXX INTEGER (0,... , 126) , sign (0,1) corresponding to + or - OPTIONAL

[0089] In yet another example, an additional path differential / relative imaginary field may provide the additional NR UL-SRS reference signal received path imaginary' magnitude measurement result relative to nr-UL-SRS-FirstPathRSRPM-imag-Result- rXX. The UL-SRS-RSRPM-imag value of this measurement is obtained by adding the value of this field to the value of the nr-UL-SRS-FirstPathRSRPM-imag-Result-rXX field. An example additional path differential / relative imaginary' representation / field may be given as: nr-UL-SRS-RSRPM-imag-ResultDiff-rXX INTEGER (0.... ,126) , sign (0,1) corresponding to + or - OPTIONAL

[0090] It should be understood that one or a combination of the foregoing examples can be used for real and imaginary value representation in UL-SRS feedback and that field and IE names similar to the examples given may provide the same details and utility as the examples discussed herein. It should be further understood that, in some embodiments, a combination of magnitude, phase, real, and / or imaginary representation may be used for UL-SRS based feedback.Feedback Structure

[0091] In certain embodiments, an NR-FirstPath-ExtML IE may be used by a data collection device to provide information on the first path as associated to the data collection measurement data for the data collection input type used by the active AI / ML- based positioning scheme. For example, such measurement data for a DP case may include an absolute time and / or a quality value. As another example, such measurement data for a PDP case may include an absolute time, an RSRPP, and / or a quality value. As another example, such measurement data for a CIR case may include an absolute time, RSRPM / phase or real / imaginary, and / or a quality component. The quality' value may be either for the specific path or for the entire measurement.

[0092] In some embodiments, an NR-AdditionalPathList-ExtML IE may be used by a data collection device to provide information about additional paths associated to the data collection measurement data for the data collection input type used by the active AI / ML-based positioning scheme. For example, such measurement data for a DP case may include an absolute time and a quality value. As another example, such measurement data for a PDP case may include an absolute time, an RSRPP and a quality' value. As another example, such measurement data for a CIR case may include an absolute time, RSRPP and / or phase or real and / or imaginary and a quality component. Corresponding to such cases, an additional path nr -RelativeTime Difference IE represents the corresponding value(s) for the detected path timing relative to the detected path timing of the first path, and each additional path may be associated with a quality value (e.g., nr-PathQuality).

[0093] It should be understood that various field and IE names / identifiers similar to the examples given may provide the same effect and utility as the examples discussed herein.Embodiments for Feedback Structures Corresponding to DL-PRS Use

[0094] FIG. 4 illustrates examples of TEs and corresponding fields for DL-PRS feedback and UL-SRS feedback, according to embodiments herein.

[0095] In some embodiments, a DL-PRS feedback structure may include an: nr-AdditionalPathListExt-ML :: sequence (size(l .., N)) in an NR-AdditioncdPath-ML field which may provide up to N= [8, 9, 16, 32, 64, 128, 256, signaled] additional detected path timing values for the TRP or resource, relative to the path timing of the first path. If this field was requested but is not included, the UE did not detect any additional path timing values. If this field is present, an nr- AdditioncdPathList field may be absent.

[0096] Additionally, in some embodiments, a DL-PRS feedback structure may include an NR-AdditionalPath-ML IE. The IE may include an nr-RelativeTimeDifference field that specifies an additional detected path timing relative to a detected path timing of a reference resource. It may be that a positive value indicates that the particular path is later in time than the detected path of the reference and a negative value indicates that the particular path is earlier in time than the detected path of the reference. A timing reporting granularity factor may indicate path timing factor to use.

[0097] The NR-AdditionalPath-ML IE may include a TimeStamp field.

[0098] The NR-AdditionalPath-ML IE may include an nr-PathQuality field that specifies the measurement device’s best estimate of a quality of the detected timing of an additional path.

[0099] The NR-AdditionalPath-ML IE may further include one or more of the following: an nr-DL-PRS-RSRPP field , an nr-DL-PRS-RSRPM-ResultDiff-rXX field, an nr-DL-PRS-RSRPh field, an nr-DL-PRS-Phase-ResultDiff-rXX field, an nr-DL-PRS- RPRPM-real field, an nr-DL-PRS-RSRPM-real-ResultDiff-rXX field, an nr-DL-PRS- RSRPM-imag field, and an nr-DL-PRS-RSRPM-ResultDiff-rXX field.

[0100] In some other embodiments, the DL-PRS feedback structure may include an NR- FirstPath-ExtML IE that includes an nr-FirstPath-timing field that provides a path timing value of a first path.

[0101] The NR-FirstPath-ExtML IE may include a timing reporting granularity factor that may indicate a path timing factor to use. The IE may include an nr-pathQuality field and / or an nr-measurement-Quality field that may specify the measurement device’s bestestimate of the quality of the detected timing of the first path (or the entire measurement). Additionally, the IE may include one or more of the following: an nr-DL- PRS-FirstPathRSRM-Result-rXX field, an nr-DL-PRS-FirstPathRSRP-Phase-Result-rXX field, an nr-DL-PRS-FirstPathRSRPM-real-Resiilt-rXX field, an nr-DL-PRS- FirstPathRSRPM-imag-Result-rXX field, a TimeStamp field, and a GT-label field.

[0102] Further included IES and corresponding fields for the DL-PRS feedback structure are illustrated in FIG. 4. For example, such fields may be represented by / included in an nr-RelativeTimeDifference 402 IE and / or an nr-flrstpath-timing 404 IE, as illustrated.

[0103] It should be understood that one or a combination of the foregoing examples may be used for DL-PRS feedback.

[0104] Embodiments Feedback Structures Corresponding to UL-SRS Use

[0105] In some embodiments, a UL-SRS feedback structure may include an: nr-AdditionalPathListExt-ML :: sequence (size(l .., N)) in an NR-AdditionalPath-ML field which provides up to N= [8, 9, 16, 32 64, 128, 256, signaled] additional detected path timing values for the TRP or resource, relative to the path timing of the first path. If this field was requested but is not included, it means the UE did not detect any additional path timing values. If this field is present, the nr- AdditionalPathList field may be absent.

[0106] The UL-SRS feedback structure may also include an NR-AdditionalPath-ML IE that may include an nr-RelativeTimeDifference field that specifies the additional detected path timing relative to the detected path timing of the reference resource. A positive value indicates that the particular path is later in time than the detected path of the reference, and a negative value indicates that the particular path is earlier in time than the detected path of the reference.

[0107] The NR-AdditionalPath-ML IE may include a TimeStamp field.

[0108] The NR-AdditionalPath-ML IE may further include an nr-PathQualityThis field that may specify the measurement device’s best estimate of the quality of the detected timing of the additional path.

[0109] The NR-AdditionalPath-ML IE may further include one or more of the following fields: an nr-UL-SRS-RSRPP field, an nr-UL-SRS-RSRPM-ResultDiff-rXX field, an nr- UL-SRS-RSRPh field, an nr- UL-SRS-Phase-ResultDiff-rXX field, an nr-UL-SRS-RPRPM-real field, an nr-UL-SRS-RSRPM-real-ResultDiff-rXX field, an nr-UL-SRS-RSRPM-imag field, and an nr-UL-SRS-RSRPM-ResultDiff-rXX field.

[0110] In some embodiments, the UL-SRS feedback structure may include an NR- FirstPath-ExtML IE that includes an nr-FirstPath-timing field that provides a path timing value of a first path.

[0111] The NR-FirstPath-ExtML IE may include a timing reporting granularity factor which indicates a path timing factor to use. The IE may include an nr-pathQuality field and / or an nr-measurement-Quality field that specifies the measurement device’s best estimate of the quality of the detected timing of the first path (or the entire measurement). Additionally, the IE may include one or more of the following: an nr-UL- SRS-FirstPathRSRM-Result-rXX field, an nr-UL-SRS-FirstPathRSRP-Phase-Result-rXX field, an nr-UL-SRS-FirstPathRSRPM-real-Result-rXX field, an nr-UL-SRS- FirstPathRSRPM-imag-Result-rXX field, a Timestamp field, and a GT-label field.

[0112] Additional included IES and corresponding fields for the UL SRS feedback structure are illustrated in FIG. 4. For example, such fields may be represented by / included in an nr-RelativeTimeDifference 402 IE and / or an nr-firstpath-timing 404 IE, as illustrated.

[0113] It should be understood that one or a combination of the foregoing examples may be used for UL-SRS feedback.Additional Embodiments

[0114] In some embodiments, for time domain feedback, a bitmap may be used of various sizes (e.g., size X = 8, 9, 16, 32, 64, 2, 256). A bit of the bitmap may be set to one if a tap is present.

[0115] In some instances, assistance data indicating an applicable scenario may be added. An example of an assistance data IE may be given as: assistanceDataV alidity ArealD { nr-CellGlobalID-rl7 NCGI-rl5 OPTIONAL, - Need ON nr-PhysCellID-r!7 NR-PhysCellID-rl6 OPTIONAL, - Need ON nr-ARFCN-r!7 nr-ML-sub-scenario INTEGER}

[0116] The nr-ML-sub-scenario INTEGER field discussed herein may specify a subscenario signaling for assistance data (e.g., a cell may have multiple sub-scenarios that may need to be tracked and / or modeled). In the case that feedback is used for training, a ground truth may be added.

[0117] FIG. 5 illustrates a method 500 of an LMF of a CN, according to embodiments herein. The illustrated method 500 includes sending 502, to one of a UE and a base station, a reporting configuration for a positioning signal transmitted between the UE and the base station. The method 500 further includes receiving 504, from the one of the UE and the base station, positioning feedback corresponding to the positioning signal, wherein the positioning feedback comprises, according to the reporting configuration, a first magnitude of a first detected path of the positioning signal. The method 500 further includes applying 506 the positioning feedback with an AI / ML model of the LMF.

[0118] In some embodiments of the method 500, the positioning feedback further comprises, according to the reporting configuration, a second magnitude of a second detected path of the positioning signal.

[0119] In some embodiments of the method 500, the positioning feedback further comprises, according to the reporting configuration, a magnitude differential for a second detected path of the positioning signal, wherein the magnitude differential indicates a difference between a second magnitude of the second detected path and the first magnitude of the first detected path.

[0120] In some embodiments of the method 500, the positioning feedback further comprises, according to the reporting configuration, a first phase of the first detected path of the positioning signal. In some such embodiments, the positioning feedback further comprises, according to the reporting configuration, a second phase of a second detected path of the positioning signal. In some other such embodiments, the positioning feedback further comprises, according to the reporting configuration, a phase differential for a second detected path of the positioning signal, wherein the phase differential indicates a difference between a second phase of the second detected path and the first phase of the first detected path.

[0121] In some embodiments of the method 500, the applying the positioning feedback with the AI / ML model comprises using the positioning feedback to train the AI / ML model.

[0122] In some embodiments of the method 500, the applying the positioning feedback with the AI / ML model comprises using the positioning feedback at the AI / ML model to generate an inference.

[0123] In some embodiments of the method 500, the applying the positioning feedback with the AI / ML model comprises using the positioning feedback at the AI / ML model to monitor the AI / ML model.

[0124] In some embodiments of the method 500, the applying the positioning feedback with the AI / ML model comprises using the positioning feedback at the AI / ML model to train the AI / ML model.

[0125] In some embodiments of the method 500, the one of the UE and the base station comprises the UE. and the positioning signal comprises a DL-PRS transmitted by the base station.

[0126] In some embodiments of the method 500, the one of the UE and the base station comprises the base station, and the positioning signal comprises a UL-SRS transmitted by the UE. In some such embodiments, the UL-SRS comprises an UL-SRS for positioning.

[0127] In some embodiments of the method 500, the positioning feedback further comprises a path timing value for the first detected path.

[0128] In some embodiments of the method 500, the positioning feedback further comprises a quality of the first detected path.

[0129] In some embodiments of the method 500, the positioning feedback further comprises a quality of a full measurement of the positioning signal.

[0130] In some embodiments of the method 500, the positioning feedback further comprises a quality of a ground truth value corresponding to the positioning signal.

[0131] In some embodiments of the method 500, the positioning feedback further comprises a timing granularity factor.

[0132] FIG. 6 illustrated a method 600 of an LMF of a CN. according to embodiments herein. The illustrated method 600 includes sending 602. to one of a UE and a base station, a reporting configuration for a positioning signal transmitted between the UE and the base station. The method 600 further includes receiving 604, from the one of the UE and the base station, positioning feedback corresponding to the positioning signal, wherein the positioning feedback comprises, according to the reporting configuration: afirst real number component of a first power of a first detected path of the positioning signal and a first imaginary number component of the first power of a first detected path of the positioning signal. The method 600 further includes applying 606 the positioning feedback with an AI / ML model of the LMF.

[0133] In some embodiments of the method 600, the positioning feedback further comprises, according to the reporting configuration, a second real number component of a second power of a second detected path of the positioning signal.

[0134] In some embodiments of the method 600, the positioning feedback further comprises, according to the reporting configuration, a second imaginary number component of a second power of a second detected path of the positioning signal.

[0135] In some embodiments of the method 600, the positioning feedback further comprises, according to the reporting configuration, a real differential of a second power of a second detected path of the positioning signal, wherein the real differential indicates a difference between a second real number component of the second power of the second detected path and the first real number component of the first power of the first detected path.

[0136] In some embodiments of the method 600, the positioning feedback further comprises, according to the reporting configuration, an imaginary differential of a second power of a second detected path of the positioning signal, wherein the imaginary differential indicates a difference between a second imaginary number component of the second power of the second detected path and the first imaginary number component of the first power of the first detected path.

[0137] In some embodiments of the method 600, the applying the positioning feedback with the AI / ML model comprises using the positioning feedback to train the AI / ML model.

[0138] In some embodiments of the method 600, the applying the positioning feedback with the AI / ML model comprises using the positioning feedback at the AI / ML model to generate an inference.

[0139] In some embodiments of the method 600, the applying the positioning feedback with the AI / ML model comprises using the positioning feedback at the AI / ML model to monitor the AI / ML model.

[0140] In some embodiments of the method 600, the applying the positioning feedback with the AI / ML model comprises using the positioning feedback at the AI / ML model to train the AI / ML model.

[0141] In some embodiments of the method 600, the one of the UE and the base station comprises the UE. and the positioning signal comprises a DL-PRS transmitted by the base station.

[0142] In some embodiments of the method 600, the one of the UE and the base station comprises the base station, and the positioning signal comprises a UL-SRS transmitted by the UE. In some such embodiments, the UL-SRS comprises an UL-SRS for positioning.

[0143] In some embodiments of the method 600, the positioning feedback further comprises a path timing value for the first detected path.

[0144] In some embodiments of the method 600, the positioning feedback further comprises a quality of the first detected path.

[0145] In some embodiments of the method 600, the positioning feedback further comprises a quality of a full measurement of the positioning signal.

[0146] In some embodiments of the method 600, the positioning feedback further comprises a quality of a ground truth value corresponding to the positioning signal.

[0147] In some embodiments of the method 600, the positioning feedback further comprises a timing granularity factor.

[0148] FIG. 7 illustrates a method 700 of a UE, according to embodiments herein. The illustrated method 700 includes receiving 702, from an LMF of a CN. a reporting configuration for a positioning signal transmitted by a base station. The method 700 further includes receiving 704, from the base station, the positioning signal. The method 700 further includes generating 706 positioning feedback for the LMF based on the positioning signal, the positioning feedback comprising, according to the reporting configuration, a first magnitude of a first detected path of the positioning signal. The method 700 further includes sending 708 the positioning feedback to the LMF.

[0149] In some embodiments of the method 700, the positioning feedback further comprises, according to the reporting configuration, a second magnitude of a second detected path of the positioning signal.

[0150] In some embodiments of the method 700, the positioning feedback further comprises, according to the reporting configuration, a magnitude differential for a second detected path of the positioning signal, wherein the magnitude differential indicates a difference between a second magnitude of the second detected path and the first magnitude of the first detected path.

[0151] In some embodiments of the method 700, the positioning feedback further comprises, according to the reporting configuration, a first phase of the first detected path of the positioning signal. In some such embodiments, the positioning feedback further comprises, according to the reporting configuration, a second phase of a second detected path of the positioning signal. In some other such embodiments, the positioning feedback further comprises, according to the reporting configuration, a phase differential for a second detected path of the positioning signal, wherein the phase differential indicates a difference between a second phase of the second detected path and the first phase of the first detected path.

[0152] In some embodiments of the method 700, the positioning signal comprises a DL- PRS.

[0153] In some embodiments of the method 700, the positioning feedback further comprises a path timing value for the first detected path.

[0154] In some embodiments of the method 700, the positioning feedback further comprises a quality of the first detected path.

[0155] In some embodiments of the method 700, the positioning feedback further comprises a quality of a full measurement of the positioning signal.

[0156] In some embodiments of the method 700, the positioning feedback further comprises a quality of a ground truth value corresponding to the positioning signal.

[0157] In some embodiments of the method 700, the positioning feedback further comprises a timing granularity factor.

[0158] FIG. 8 illustrates a method 800 of a UE, according to embodiments herein. The illustrated method 800 includes receiving 802, from an LMF of a CN, a reporting configuration for a positioning signal transmitted by a base station. The method 800 further includes receiving 804, from the base station, the positioning signal. The method 800 further includes generating 806 positioning feedback for the LMF based on the positioning signal, the positioning feedback comprising, according to the reportingconfiguration: a first real number component of a first power of a first detected path of the positioning signal and a first imaginary number component of the first power of a first detected path of the positioning signal. The method 800 further includes sending 808 the positioning feedback to the LMF.

[0159] In some embodiments of the method 800, the positioning feedback further comprises, according to the reporting configuration, a second real number component of a second power of a second detected path of the positioning signal.

[0160] In some embodiments of the method 800, the positioning feedback further comprises, according to the reporting configuration, a second imaginary number component of a second power of a second detected path of the positioning signal.

[0161] In some embodiments of the method 800, the positioning feedback further comprises, according to the reporting configuration, a real differential of a second power of a second detected path of the positioning signal, wherein the real differential indicates a difference between a second real number component of the second power of the second detected path and the first real number component of the first power of the first detected path.

[0162] In some embodiments of the method 800, the positioning feedback further comprises, according to the reporting configuration, an imaginary differential of a second power of a second detected path of the positioning signal, wherein the imaginary differential indicates a difference between a second imaginary number component of the second power of the second detected path and the first imaginary number component of the first power of the first detected path.

[0163] In some embodiments of the method 800, the positioning signal comprises a DL- PRS.

[0164] In some embodiments of the method 800, the positioning feedback further comprises a path timing value for the first detected path.

[0165] In some embodiments of the method 800, the positioning feedback further comprises a quality of the first detected path.

[0166] In some embodiments of the method 800, the positioning feedback further comprises a quality of a full measurement of the positioning signal.

[0167] In some embodiments of the method 800, the positioning feedback further comprises a quality of a ground truth value corresponding to the positioning signal.

[0168] In some embodiments of the method 800, the positioning feedback further comprises a timing granularity factor.

[0169] FIG. 9 illustrates a method 900 of a base station, according to embodiments herein. The illustrated method 900 includes receiving 902, from an LMF of a CN, a reporting configuration for a positioning signal transmitted by a UE. The method 900 further includes receiving 904, from the UE, the positioning signal. The method 900 further includes generating 906 positioning feedback for the LMF based on the positioning signal, the positioning feedback comprising, according to the reporting configuration, a first magnitude of a first detected path of the positioning signal. The method 900 further includes sending 908 the positioning feedback to the LMF.

[0170] In some embodiments of the method 900, the positioning feedback further comprises, according to the reporting configuration, a second magnitude of a second detected path of the positioning signal.

[0171] In some embodiments of the method 900, the positioning feedback further comprises, according to the reporting configuration, a magnitude differential for a second detected path of the positioning signal, wherein the magnitude differential indicates a difference between a second magnitude of the second detected path and the first magnitude of the first detected path.

[0172] In some embodiments of the method 900, the positioning feedback further comprises a first phase of the first detected path of the positioning signal. In some such embodiments, the positioning feedback further comprises, according to the reporting configuration, a second phase of a second detected path of the positioning signal. In some other such embodiments, the positioning feedback further comprises, according to the reporting configuration, a phase differential for a second detected path of the positioning signal, wherein the phase differential indicates a difference between a second phase of the second detected path and the first phase of the first detected path.

[0173] In some embodiments of the method 900, the positioning signal comprises a UL- SRS. In some such embodiments, the UL-SRS comprises a UL-SRS for positioning.

[0174] In some embodiments of the method 900, the positioning feedback further comprises a path timing value for the first detected path.

[0175] In some embodiments of the method 900, the positioning feedback further comprises a quality of the first detected path.

[0176] In some embodiments of the method 900, the positioning feedback further comprises a quality of a full measurement of the positioning signal.

[0177] In some embodiments of the method 900, the positioning feedback further comprises a quality of a ground truth value corresponding to the positioning signal.

[0178] In some embodiments of the method 900, the positioning feedback further comprises a timing granularity factor.

[0179] FIG. 10 illustrates a method 1000 of a base station, according to embodiments herein. The illustrated method 1000 includes receiving 1002, from an LMF of a CN, a reporting configuration for a positioning signal transmitted by a UE. The method 1000 further includes receiving 1004, from the UE, the positioning signal. The method 1000 further includes generating 1006 positioning feedback for the LMF based on the positioning signal, the positioning feedback comprising, according to the reporting configuration: a first real number component of a first power of a first detected path of the positioning signal, and a first imaginary number component of the first power of a first detected path of the positioning signal. The method 1000 further includes sending 1008 the positioning feedback to the LMF.

[0180] In some embodiments of the method 1000, the positioning feedback further comprises, according to the reporting configuration, a second real number component of a second power of a second detected path of the positioning signal.

[0181] In some embodiments of the method 1000, the positioning feedback further comprises, according to the reporting configuration, a second imaginary number component of a second power of a second detected path of the positioning signal.

[0182] In some embodiments of the method 1000, the positioning feedback further comprises, according to the reporting configuration, a real differential of a second power of a second detected path of the positioning signal, wherein the real differential indicates a difference between a second real number component of the second power of the second detected path and the first real number component of the first power of the first detected path.

[0183] In some embodiments of the method 1000, the positioning feedback further comprises, according to the reporting configuration, an imaginary differential of a second power of a second detected path of the positioning signal, wherein the imaginary differential indicates a difference between a second imaginary number component of thesecond power of the second detected path and the first imaginary number component of the first power of the first detected path.

[0184] In some embodiments of the method 1000, the positioning signal comprises a UL-SRS. In some such embodiments, the UL-SRS comprises a UL-SRS for positioning.

[0185] In some embodiments of the method 1000, the positioning feedback further comprises a path timing value for the first detected path.

[0186] In some embodiments of the method 1000. the positioning feedback further comprises a quality of the first detected path.

[0187] In some embodiments of the method 1000, the positioning feedback further comprises a quality of a full measurement of the positioning signal.

[0188] In some embodiments of the method 1000, the positioning feedback further comprises a timing granularity factor.

[0189] FIG. 11 illustrates a method 1100 of an LMF of a CN, according to embodiments herein. The illustrated method 1100 includes sending 1102, to one of a UE and a base station, a reporting configuration for a positioning signal transmitted between the UE and the base station. The method 1100 further includes receiving 1104. from the one of the UE and the base station, positioning feedback corresponding to the positioning signal, wherein the positioning feedback comprises, according to the reporting configuration, a path timing value of a first detected path of the positioning signal and a quality of the first detected path of the positioning signal. The method 1100 further includes applying 1106 the positioning feedback with an AI / ML model of the LMF.

[0190] In some embodiments of the method 1100, the applying the positioning feedback with the AI / ML model comprises using the positioning feedback to train the AI / ML model.

[0191] In some embodiments of the method 1100, the applying the positioning feedback with the AI / ML model comprises using the positioning feedback at the AI / ML model to generate an inference.

[0192] In some embodiments of the method 1100, the applying the positioning feedback with the AI / ML model comprises using the positioning feedback at the AI / ML model to monitor the AI / ML model.

[0193] In some embodiments of the method 1100, the applying the positioning feedback with the AI / ML model comprises using the positioning feedback at the AI / ML model to train the AI / ML model.

[0194] In some embodiments of the method 1100, the one of the UE and the base station comprises the UE, and the positioning signal comprises a DL-PRS transmitted by the base station.

[0195] In some embodiments of the method 1100, the one of the UE and the base station comprises the base station, and the positioning signal comprises a UL-SRS transmitted by the UE. In some such embodiments, the UL-SRS comprises a UL-SRS for positioning.

[0196] FIG. 12 illustrates a method 1200 of a UE. according to embodiments herein. The illustrated method 1200 includes receiving 1202, from an LMF of a CN, a reporting configuration for a positioning signal transmitted by a base station. The method 1200 further includes receiving 1204, from the base station, the positioning signal. The method 1200 further includes generating 1206 positioning feedback for the LMF based on the positioning signal, the positioning feedback comprising, according to the reporting configuration, a path timing value of a first detected path of the positioning signal and a quality of the first detected path of the positioning signal. The method 1200 further includes sending 1208 the positioning feedback to the LMF.

[0197] In some embodiments of the method 1200, the positioning signal comprises a DL-PRS.

[0198] FIG. 13 illustrates a method 1300 of a base station, according to embodiments herein. The illustrated method 1300 includes receiving 1302 from an LMF of a CN, a reporting configuration for a positioning signal transmitted by a UE. The method 1300 further includes receiving 1304, from the UE, the positioning signal. The method 1300 further includes generating 1306 positioning feedback for the LMF based on the positioning signal, the positioning feedback comprising, according to the reporting configuration, a path timing value of a first detected path of the positioning signal and a quality of the first detected path of the positioning signal. The method 1300 further includes sending 1308 the positioning feedback to the LMF.

[0199] In some embodiments of the method 1300, the positioning signal comprises a UL-SRS. In some such embodiments, the UL-SRS comprises a UL-SRS for positioning.

[0200] FIG. 14 illustrates an example architecture of a wireless communication system 1400, according to embodiments disclosed herein. The following description is provided for an example wireless communication system 1400 that operates in conjunction with the LTE system standards and / or 5G or NR system standards as provided by 3 GPP technical specifications.

[0201] As shown by FIG. 14, the wireless communication system 1400 includes UE 1402 and UE 1404 (although any number of UEs may be used). In this example, the UE 1402 and the UE 1404 are illustrated as smartphones (e.g., handheld touchscreen mobile computing devices connectable to one or more cellular networks), but may also comprise any mobile or non-mobile computing device configured for wireless communication.

[0202] The UE 1402 and UE 1404 may be configured to communicatively couple with a RAN 1406. In embodiments, the RAN 1406 may be NG-RAN. E-UTRAN, etc. The UE 1402 and UE 1404 utilize connections (or channels) (shown as connection 1408 and connection 1410, respectively) with the RAN 1406, each of which comprises a physical communications interface. The RAN 1406 can include one or more base stations (such as base station 1412 and base station 1414) that enable the connection 1408 and connection 1410.

[0203] In this example, the connection 1408 and connection 1410 are air interfaces to enable such communicative coupling, and may be consistent with RAT(s) used by the RAN 1406, such as. for example, an UTE and / or NR.

[0204] In some embodiments, the UE 1402 and UE 1404 may also directly exchange communication data via a sidelink interface 1416. The UE 1404 is shown to be configured to access an access point (shown as AP 1418) via connection 1420. By way of example, the connection 1420 can comprise a local wireless connection, such as a connection consistent with any IEEE 802.11 protocol, wherein the AP 1418 may comprise a Wi-Fi® router. In this example, the AP 1418 may be connected to another network (for example, the Internet) without going through a CN 1424.

[0205] In embodiments, the UE 1402 and UE 1404 can be configured to communicate using orthogonal frequency division multiplexing (OFDM) communication signals with each other or with the base station 1412 and / or the base station 1414 over a multicarrier communication channel in accordance with various communication techniques, such as, but not limited to, an orthogonal frequency division multiple access (OFDMA) communication technique (e.g., for downlink communications) or a single carrierfrequency division multiple access (SC-FDMA) communication technique (e.g., for uplink and ProSe or sidelink communications), although the scope of the embodiments is not limited in this respect. The OFDM signals can comprise a plurality of orthogonal subcarriers.

[0206] In some embodiments, all or parts of the base station 1412 or base station 1414 may be implemented as one or more software entities running on server computers as part of a virtual network. In addition, or in other embodiments, the base station 1412 or base station 1414 may be configured to communicate with one another via interface 1422. In embodiments where the wireless communication system 1400 is an LTE system (e.g.. when the CN 1424 is an EPC), the interface 1422 may be an X2 interface. The X2 interface may be defined between two or more base stations (e.g., two or more eNBs and the like) that connect to an EPC, and / or between two eNBs connecting to the EPC. In embodiments where the wireless communication system 1400 is an NR system (e.g., when CN 1424 is a 5GC), the interface 1422 may be an Xn interface. The Xn interface is defined betw een two or more base stations (e.g., two or more gNBs and the like) that connect to 5GC, between a base station 1412 (e.g., a gNB) connecting to 5GC and an eNB, and / or between two eNBs connecting to 5GC (e.g., CN 1424).

[0207] The RAN 1406 is shown to be communicatively coupled to the CN 1424. The CN 1424 may comprise one or more netw ork elements 1426, which are configured to offer various data and telecommunications services to customers / subscribers (e.g., users of UE 1402 and UE 1404) who are connected to the CN 1424 via the RAN 1406. The components of the CN 1424 may be implemented in one physical device or separate physical devices including components to read and execute instructions from a machine- readable or computer-readable medium (e.g., a non-transitory machine-readable storage medium).

[0208] In embodiments, the CN 1424 may be an EPC, and the RAN 1406 may be connected with the CN 1424 via an SI interface 1428. In embodiments, the SI interface 1428 may be split into two parts, an SI user plane (Sl-U) interface, which carries traffic data between the base station 1412 or base station 1414 and a serving gateway (S-GW), and the SI -MME interface, which is a signaling interface between the base station 1412 or base station 1414 and mobility management entities (MMEs).

[0209] In embodiments, the CN 1424 may be a 5GC, and the RAN 1406 may be connected wdth the CN 1424 via an NG interface 1428. In embodiments, the NGinterface 1428 may be split into two parts, an NG user plane (NG-U) interface, which carries traffic data between the base station 1412 or base station 1414 and a user plane function (UPF), and the SI control plane (NG-C) interface, which is a signaling interface between the base station 1412 or base station 1414 and access and mobility management functions (AMFs).

[0210] Generally, an application server 1430 may be an element offering applications that use internet protocol (IP) bearer resources with the CN 1424 (e.g., packet switched data services). The application server 1430 can also be configured to support one or more communication services (e g., VoIP sessions, group communication sessions, etc.) for the UE 1402 and UE 1404 via the CN 1424. The application server 1430 may communicate with the CN 1424 through an IP communications interface 1432.

[0211] FIG. 15 illustrates a system 1500 for performing signaling 1534 between a wireless device 1502. a RAN device 1518, and a CN device 1536. according to embodiments disclosed herein. The system 1500 may be a portion of a wireless communications system as herein described. The wireless device 1502 may be, for example, a UE of a wireless communication system. The RAN device 1518 may be, for example, a base station (e.g.. an eNB or a gNB) of a wireless communication system. The CN device 1536 may be, for example, an LMF of the CN.

[0212] The wireless device 1502 may include one or more processor(s) 1504. The processor(s) 1504 may execute instructions such that various operations of the wireless device 1502 are performed, as described herein. The processor(s) 1504 may include one or more baseband processors implemented using, for example, a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a controller, a field programmable gate array (FPGA) device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein.

[0213] The wireless device 1502 may include a memory 1506. The memory 1506 may be a non-transitory computer-readable storage medium that stores instructions 1508 (which may include, for example, the instructions being executed by the processor(s) 1504). The instructions 1508 may also be referred to as program code or a computer program. The memory 1506 may also store data used by. and results computed by, the processor(s) 1504.

[0214] The wireless device 1502 may include one or more transceiver(s) 1510 that may include radio frequency (RF) transmitter circuitry and / or receiver circuitry that use the antenna(s) 1512 of the wireless device 1502 to facilitate signaling (e.g., the signaling 1534) to and / or from the wireless device 1502 with other devices (e.g., the RAN device 1518) according to corresponding RATs.

[0215] The wireless device 1502 may include one or more antenna(s) 1512 (e.g., one, two, four, or more). For embodiments with multiple antenna(s) 1512, the wireless device 1502 may leverage the spatial diversity of such multiple antenna(s) 1512 to send and / or receive multiple different data streams on the same time and frequency resources. This behavior may be referred to as. for example, multiple-input multiple-output (MIMO) behavior (referring to the multiple antennas used at each of a transmitting device and a receiving device that enable this aspect). MIMO transmissions by the wireless device 1502 may be accomplished according to precoding (or digital beamforming) that is applied at the wireless device 1502 that multiplexes the data streams across the antenna(s) 1512 according to known or assumed channel characteristics such that each data stream is received with an appropriate signal strength relative to other streams and at a desired location in the spatial domain (e.g., the location of a receiver associated with that data stream). Certain embodiments may use single user MIMO (SU-MIMO) methods (where the data streams are all directed to a single receiver) and / or multi user MIMO (MU-MIMO) methods (where individual data streams may be directed to individual (different) receivers in different locations in the spatial domain).

[0216] In certain embodiments having multiple antennas, the wireless device 1502 may implement analog beamforming techniques, whereby phases of the signals sent by the antenna(s) 1512 are relatively adjusted such that the (joint) transmission of the antenna(s) 1512 can be directed (this is sometimes referred to as beam steering).

[0217] The wireless device 1502 may include one or more interface(s) 1514. The interface(s) 1514 may be used to provide input to or output from the wireless device 1502. For example, a wireless device 1502 that is a UE may include interface(s) 1514 such as microphones, speakers, a touchscreen, buttons, and the like in order to allow for input and / or output to the UE by a user of the UE. Other interfaces of such a UE may be made up of transmitters, receivers, and other circuitry (e.g., other than the transceiver(s) 1510 / antenna(s) 1512 already described) that allow for communication between the UEand other devices and may operate according to known protocols (e.g., Wi-Fi®, Bluetooth®, and the like).

[0218] The wireless device 1502 may include a positioning module 1516. The positioning module 1516 may be implemented via hardware, software, or combinations thereof. For example, the positioning module 1516 may be implemented as a processor, circuit, and / or instructions 1508 stored in the memory 1506 and executed by the processor(s) 1504. In some examples, the positioning module 1516 may be integrated within the processor(s) 1504 and / or the transceiver(s) 1510. For example, the positioning module 1516 may be implemented by a combination of software components (e.g.. executed by a DSP or a general processor) and hardware components (e.g.. logic gates and circuitry) within the processor(s) 1504 or the transceiver(s) 1510.

[0219] The positioning module 1516 may be used for various aspects of the present disclosure, for example, aspects of any of FIG. 1 through FIG. 13. The positioning module 1516 may be configured to receive, from an LMF, a reporting configuration for a positioning signal and receive, from a base station, the positioning signal. The positioning module 1516 may also be configured to generate, according to the received reporting configuration, positioning feedback that may include a magnitude and / or a phase of a detected path of the positioning signal and / or a real and / or an imaginary’ component of the power of the detected path of the positioning signal. The positioning module 1516 may be further configured to send the generated positioning feedback to the LMF.

[0220] The RAN device 1518 may include one or more processor(s) 1520. The processor(s) 1520 may execute instructions such that various operations of the RAN device 1518 are performed, as described herein. The processor(s) 1520 may include one or more baseband processors implemented using, for example, a CPU, a DSP, an ASIC, a controller, an FPGA device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein.

[0221] The RAN device 1518 may include a memory 1522. The memory 1522 may be a non-transitory computer-readable storage medium that stores instructions 1524 (which may include, for example, the instructions being executed by the processor(s) 1520). The instructions 1524 may also be referred to as program code or a computer program. The memory 1522 may also store data used by, and results computed by, the processor(s) 1520.

[0222] The RAN device 1518 may include one or more trans ceiver(s) 1526 that may include RF transmitter circuitry and / or receiver circuitry that use the antenna(s) 1528 of the RAN device 1518 to facilitate signaling (e.g., the signaling 1534) to and / or from the RAN device 1518 with other devices (e.g., the wireless device 1502) according to corresponding RATs.

[0223] The RAN device 1518 may include one or more antenna(s) 1528 (e.g., one, two, four, or more). In embodiments having multiple antenna(s) 1528, the RAN device 1518 may perform MIMO, digital beamforming, analog beamforming, beam steering, etc., as has been described.

[0224] The RAN device 1518 may include one or more interface(s) 1530. The interface(s) 1530 may be used to provide input to or output from the RAN device 1518. For example, a RAN device 1518 that is a base station may include interface(s) 1530 made up of transmitters, receivers, and other circuitry (e.g., other than the transceiver(s) 1526 / antenna(s) 1528 already described) that enables the base station to communicate with other RAN equipment and / or equipment in a core network (e.g., FIG. 15 illustrates the interface between the RAN device 1518 and the CN device 1536). and / or that enables the base station to communicate with external networks, computers, databases, and the like for purposes of operations, administration, and maintenance of the base station or other equipment operably connected thereto.

[0225] The RAN device 1518 may include a positioning module 1532. The positioning module 1532 may be implemented via hardware, software, or combinations thereof. For example, the positioning module 1532 may be implemented as a processor, circuit, and / or instructions 1524 stored in the memory 1522 and executed by the processor(s) 1520. In some examples, the positioning module 1532 may be integrated within the processor(s) 1520 and / or the transceiver(s) 1526. For example, the positioning module 1532 may be implemented by a combination of softw are components (e.g., executed by a DSP or a general processor) and hardware components (e.g., logic gates and circuitry) within the processor(s) 1520 or the transceiver(s) 1526.

[0226] The positioning module 1532 may be used for various aspects of the present disclosure, for example, aspects of any of FIG. 1 through FIG. 13. The positioning module 1532 may be configured to receive a reporting configuration, from an LMF, for a positioning signal that is transmitted by a UE and receive, from the UE. the positioning signal. The positioning module 1532 may also be configured to generate positioningfeedback, according to the received reporting configuration, which may include a magnitude and / or a phase of a detected path of the positioning signal and / or a real and / or an imaginary component of the power of the detected path of the positioning signal. The positioning module 1532 may then send the generated positioning feedback to the LMF.

[0227] The CN device 1536 may include one or more processor(s) 1538. The processor(s) 1538 may execute instructions such that various operations of the CN device 1536 are performed, as described herein. The processor(s) 1538 may include one or more baseband processors implemented using, for example, a CPU, a DSP, an ASIC, a controller, an FPGA device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein.

[0228] The CN device 1536 may include a memory 1540. The memory' 1540 may be a non-transitory computer-readable storage medium that stores instructions 1542 (which may include, for example, the instructions being executed by the processor(s) 1538). The instructions 1542 may also be referred to as program code or a computer program. The memory 1540 may also store data used by, and results computed by, the processor(s) 1538.

[0229] The CN device 1536 may include one or more interface(s) 1544. The interface(s) 1544 may be used to provide input to or output from the CN device 1536. For example, a CN device 1536 that comprises an LMF may include interface(s) 1544 made up of transmitters, receivers, and other circuitry that enables the base station to communicate with other equipment in a core network and / or with a base station (e.g., FIG. 15 illustrates an interface between the RAN device 1518 and the CN device 1536), and / or that enables the base station to communicate with external networks, computers, databases, and the like for purposes of operations, administration, and maintenance of the base station or other equipment operably connected thereto.

[0230] The CN device 1536 may include a positioning module 1546. The positioning module 1546 may be implemented via hardware, software, or combinations thereof. For example, the positioning module 1546 may be implemented as a processor, circuit, and / or instructions 1542 stored in the memory 1540 and executed by the processor(s) 1538. In some examples, the positioning module 1546 may be integrated within the processor(s) 1538. For example, the positioning module 15164 may be implemented by a combination of software components (e.g., executed by a DSP or a general processor) and hardware components (e.g., logic gates and circuitry) within the processor(s) 1538.

[0231] The positioning module 1546 may be used for various aspects of the present disclosure, for example, aspects of any of FIG. 1 through FIG. 13. The positioning module 1546 may be configured to sending, to one of a UE and a base station, a reporting configuration for a positioning signal transmitted between the UE and the base station; receive, from the one of the UE and the base station, positioning feedback corresponding to the positioning signal; and apply the positioning feedback with an AI / ML model of the LMF.

[0232] Embodiments contemplated herein include an apparatus comprising means to perform one or more elements of any one of the method 700, the method 800, and the method 1200. This apparatus may be, for example, an apparatus of a UE (such as a wireless device 1502 that is a UE, as described herein).

[0233] Embodiments contemplated herein 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 any one of the method 700, the method 800, and the method 1200. This non-transitory' computer-readable media may be, for example, a memory of a UE (such as a memory 1506 of a wireless device 1502 that is a UE. as described herein).

[0234] Embodiments contemplated herein include an apparatus comprising logic, modules, or circuitry to perform one or more elements of any one of the method 700, the method 800, and the method 1200. This apparatus may be, for example, an apparatus of a UE (such as a wireless device 1502 that is a UE, as described herein).

[0235] Embodiments contemplated herein 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 one or more elements of any one of the method 700, the method 800, and the method 1200. This apparatus may be, for example, an apparatus of a UE (such as a wireless device 1502 that is a UE, as described herein).

[0236] Embodiments contemplated herein include a signal as described in or related to one or more elements of any one of the method 700, the method 800, and the method 1200.

[0237] Embodiments contemplated herein include a computer program or computer program product comprising instructions, wherein execution of the program by aprocessor is to cause the processor to carry out one or more elements of any one of the method 700. the method 800. and the method 1200. The processor may be a processor of a UE (such as a processor(s) 1504 of a wireless device 1502 that is a UE, as described herein). These instructions may be, for example, located in the processor and / or on a memory7of the UE (such as a memory 1506 of a wireless device 1502 that is a UE, as described herein).

[0238] Embodiments contemplated herein include an apparatus comprising means to perform one or more elements of any one of the method 900, the method 1000, and the method 1300. This apparatus may be, for example, an apparatus of a base station (such as a RAN device 1518 that is a base station, as described herein).

[0239] Embodiments contemplated herein 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 any one of the method 900, the method 1000, and the method 1300. This non-transitory' computer-readable media may be, for example, a memory of a base station (such as a memory' 1522 of a RAN device 1518 that is a base station, as described herein).

[0240] Embodiments contemplated herein include an apparatus comprising logic, modules, or circuitry to perform one or more elements of any one of the method 900, the method 1000, and the method 1300. This apparatus may be, for example, an apparatus of a base station (such as a RAN device 1518 that is a base station, as described herein).

[0241] Embodiments contemplated herein 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 one or more elements of any one of the method 900, the method 1000, and the method 1300. This apparatus may be, for example, an apparatus of a base station (such as a RAN device 1518 that is a base station, as described herein).

[0242] Embodiments contemplated herein include a signal as described in or related to one or more elements of any one of the method 900, the method 1000, and the method 1300.

[0243] Embodiments contemplated herein include a computer program or computer program product comprising instructions, wherein execution of the program by a processing element is to cause the processing element to carry out one or more elementsof any one of the method 900, the method 1000, and the method 1300. The processor may be a processor of a base station (such as a processor(s) 1520 of a RAN device 1518 that is a base station, as described herein). These instructions may be, for example, located in the processor and / or on a memory of the base station (such as a memory 1522 of a RAN device 1518 that is a base station, as described herein).

[0244] Embodiments contemplated herein include an apparatus comprising means to perform one or more elements of any one of the method 500, the method 600, and the method 1100. This apparatus may be, for example, an apparatus of a CN (such as a CN device 1536 that comprises an LMF. as described herein).

[0245] Embodiments contemplated herein 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 any one of the method 500, the method 600, and the method 1100. This non-transitory computer-readable media may be, for example, a memory of a CN device (such as a memory 1540 of a CN device 1536 that comprises and LMF, as described herein).

[0246] Embodiments contemplated herein include an apparatus comprising logic, modules, or circuitry to perform one or more elements of any one of the method 500, the method 600, and the method 1100. This apparatus may be, for example, an apparatus of a CN device (such as a CN device 1536 that comprises an LMF. as described herein).

[0247] Embodiments contemplated herein 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 one or more elements of any one of the method 500, the method 600, and the method 1100. This apparatus may be, for example, an apparatus of a CN device (such as a CN device 1536 that comprises an LMF, as described herein).

[0248] Embodiments contemplated herein include a signal as described in or related to one or more elements of any one of the method 500, the method 600, and the method 1100.

[0249] Embodiments contemplated herein include a computer program or computer program product comprising instructions, wherein execution of the program by a processor is to cause the processor to carry' out one or more elements of any one of the method 500, the method 600. and the method 1100. The processor may be a processor ofa CN device (such as a processor(s) 1538 of a CN device 1536 that comprises an LMF, as described herein). These instructions may be, for example, located in the processor and / or on a memory of the CN device (such as a memory 1540 of a CN device 1536 that comprises an LMF, as described herein).

[0250] 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 herein. For example, a baseband processor as described herein 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 herein. 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 herein.

[0251] Any of the above described embodiments may be combined with any other embodiment (or combination of embodiments), 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.

[0252] Embodiments and implementations of the systems and methods described herein may include various operations, which may be embodied in machine-executable instructions to be executed by a computer system. A computer system may include one or more general-purpose or special-purpose computers (or other electronic devices). The computer system may include hardware components that include specific logic for performing the operations or may include a combination of hardware, software, and / or firmware.

[0253] It should be recognized that the systems described herein include descriptions of specific embodiments. These embodiments can be combined into single systems, partially combined into other systems, split into multiple systems or divided or combined in other ways. In addition, it is contemplated that parameters, attributes, aspects, etc. of one embodiment can be used in another embodiment. The parameters, attributes, aspects, etc. are merely described in one or more embodiments for clarity, and it is recognized that the parameters, attributes, aspects, etc. can be combined with or substituted forparameters, attributes, aspects, etc. of another embodiment, unless specifically disclaimed herein.

[0254] It is well understood that the use of personally identifiable information should follow privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining the privacy of users. In particular, personally identifiable information data should be managed and handled so as to minimize risks of unintentional or unauthorized access or use, and the nature of authorized use should be clearly indicated to users.

[0255] Although the foregoing has been described in some detail for purposes of clarity, it will be apparent that certain changes and modifications may be made without departing from the principles thereof. It should be noted that there are many alternative ways of implementing both the processes and apparatuses described herein. Accordingly, the present embodiments are to be considered illustrative and not restrictive, and the description is not to be limited to the details given herein, but may be modified within the scope and equivalents of the appended claims.

Claims

CLAIMS1. A method of a location management function (LMF) of a core network (CN), comprising: sending, to one of a user equipment (UE) and a base station, a reporting configuration for a positioning signal transmitted between the UE and the base station; receiving, from the one of the UE and the base station, positioning feedback corresponding to the positioning signal, wherein the positioning feedback comprises, according to the reporting configuration, a first magnitude of a first detected path of the positioning signal; and applying the positioning feedback with an artificial intelligence (AI) / machine learning (ML) model of the LMF.

2. The method of claim 1, wherein the positioning feedback further comprises, according to the reporting configuration, a second magnitude of a second detected path of the positioning signal.

3. The method of claim 1, wherein the positioning feedback further comprises, according to the reporting configuration, a magnitude differential for a second detected path of the positioning signal, wherein the magnitude differential indicates a difference between a second magnitude of the second detected path and the first magnitude of the first detected path.

4. The method of claim 1, wherein the positioning feedback further comprises, according to the reporting configuration, a first phase of the first detected path of the positioning signal.

5. The method of claim 4, wherein the positioning feedback further comprises, according to the reporting configuration, a second phase of a second detected path of the positioning signal.

6. The method of claim 4, wherein the positioning feedback further comprises, according to the reporting configuration, a phase differential for a second detected path of the positioning signal, wherein the phase differential indicates a difference between a second phase of the second detected path and the first phase of the first detected path.

7. The method of claim 1, wherein the applying the positioning feedback with the AI / ML model comprises using the positioning feedback to train the AI / ML model.

8. The method of claim 1, wherein the applying the positioning feedback with the AI / ML model comprises using the positioning feedback at the AI / ML model to generate an inference.

9. The method of claim 1, wherein the applying the positioning feedback with the AI / ML model comprises using the positioning feedback at the AI / ML model to monitor the AI / ML model.

10. The method of claim 1, wherein the applying the positioning feedback with the AI / ML model comprises using the positioning feedback at the AI / ML model to train the AI / ML model.

11. The method of claim 1, wherein: the one of the UE and the base station comprises the UE; and the positioning signal comprises a downlink positioning reference signal (DL- PRS) transmitted by the base station.

12. The method of claim 1, wherein: the one of the UE and the base station comprises the base station; and the positioning signal comprises an uplink sounding reference signal (UL-SRS) transmitted by the UE.

13. The method of claim 12, wherein the UL-SRS comprises an UL-SRS for positioning.

14. The method of claim 1, wherein the positioning feedback further comprises a path timing value for the first detected path.

15. The method of claim 1. herein the positioning feedback further comprises a quality’ of the first detected path.

16. The method of claim 1, wherein the positioning feedback further comprises a quality' of a full measurement of the positioning signal.

17. The method of claim 1, wherein the positioning feedback further comprises a quality' of a ground truth value corresponding to the positioning signal.

18. The method of claim 1, wherein the positioning feedback further comprises a timing granularity factor.

19. A method of a location management function (LMF) of a core network (CN), comprising: sending, to one of a user equipment (UE) and a base station, a reporting configuration for a positioning signal transmitted between the UE and the base station; receiving, from the one of the UE and the base station, positioning feedback corresponding to the positioning signal, wherein the positioning feedback comprises, according to the reporting configuration: a first real number component of a first power of a first detected path of the positioning signal; and a first imaginary number component of the first power of a first detected path of the positioning signal; and applying the positioning feedback with an artificial intelligence (AI) / machine learning (ML) model of the LMF.

20. The method of claim 19, wherein the positioning feedback further comprises, according to the reporting configuration, a second real number component of a second power of a second detected path of the positioning signal.

21. The method of claim 19, wherein the positioning feedback further comprises, according to the reporting configuration, a second imaginary number component of a second power of a second detected path of the positioning signal.

22. The method of claim 19, wherein the positioning feedback further comprises, according to the reporting configuration, a real differential of a second power of a second detected path of the positioning signal, wherein the real differential indicates a difference between a second real number component of the second power of the second detected path and the first real number component of the first power of the first detected path.

23. The method of claim 19, wherein the positioning feedback further comprises, according to the reporting configuration, an imaginary differential of a second power of a second detected path of the positioning signal, wherein the imaginary differential indicates a difference between a second imaginary number component of the secondpower of the second detected path and the first imaginary number component of the first power of the first detected path.

24. The method of claim 19, wherein the applying the positioning feedback with the AI / ML model comprises using the positioning feedback to train the AI / ML model.

25. The method of claim 19, wherein the applying the positioning feedback with the AI / ML model comprises using the positioning feedback at the AI / ML model to generate an inference.

26. The method of claim 19, wherein the applying the positioning feedback with the AI / ML model comprises using the positioning feedback at the AI / ML model to monitor the AI / ML model.

27. The method of claim 19, wherein the applying the positioning feedback with the AI / ML model comprises using the positioning feedback at the AI / ML model to train the AI / ML model.

28. The method of claim 19, wherein: the one of the UE and the base station comprises the UE; and the positioning signal comprises a downlink positioning reference signal (DL- PRS) transmitted by the base station.

29. The method of claim 19, wherein: the one of the UE and the base station comprises the base station; and the positioning signal comprises an uplink sounding reference signal (UL-SRS) transmitted by the UE.

30. The method of claim 29, wherein the UL-SRS comprises an UL-SRS for positioning.

31. The method of claim 19, wherein the positioning feedback further comprises a path timing value for the first detected path.

32. The method of claim 19, wherein the positioning feedback further comprises a quality of the first detected path.

33. The method of claim 19, wherein the positioning feedback further comprises a quality of a full measurement of the positioning signal.

34. The method of claim 19, wherein the positioning feedback further comprises a quality of a ground truth value corresponding to the positioning signal.

35. The method of claim 19, wherein the positioning feedback further comprises a timing granularity factor.

36. A method of a user equipment (UE), comprising: receiving, from a location management function (LMF) of a core network (CN), a reporting configuration for a positioning signal transmitted by a base station; receiving, from the base station, the positioning signal; generating positioning feedback for the LMF based on the positioning signal, the positioning feedback comprising, according to the reporting configuration, a first magnitude of a first detected path of the positioning signal; and sending the positioning feedback to the LMF.

37. The method of claim 36, wherein the positioning feedback further comprises, according to the reporting configuration, a second magnitude of a second detected path of the positioning signal.

38. The method of claim 36, wherein the positioning feedback further comprises, according to the reporting configuration, a magnitude differential for a second detected path of the positioning signal, wherein the magnitude differential indicates a difference between a second magnitude of the second detected path and the first magnitude of the first detected path.

39. The method of claim 36, wherein the positioning feedback further comprises, according to the reporting configuration, a first phase of the first detected path of the positioning signal.

40. The method of claim 39, wherein the positioning feedback further comprises, according to the reporting configuration, a second phase of a second detected path of the positioning signal.

41. The method of claim 39, wherein the positioning feedback further comprises, according to the reporting configuration, a phase differential for a second detected pathof the positioning signal, wherein the phase differential indicates a difference between a second phase of the second detected path and the first phase of the first detected path.

42. The method of claim 36, wherein the positioning signal comprises a downlink positioning reference signal (DL-PRS).

43. The method of claim 36, wherein the positioning feedback further comprises a path timing value for the first detected path.

44. The method of claim 36, wherein the positioning feedback further comprises a quality of the first detected path.

45. The method of claim 36, wherein the positioning feedback further comprises a quality of a full measurement of the positioning signal.

46. The method of claim 36, wherein the positioning feedback further comprises a quality of a ground truth value corresponding to the positioning signal.

47. The method of claim 36, wherein the positioning feedback further comprises a timing granularity factor.

48. A method of a user equipment (UE). comprising: receiving, from a location management function (LMF) of a core network (CN), a reporting configuration for a positioning signal transmitted by a base station; receiving, from the base station, the positioning signal; generating positioning feedback for the LMF based on the positioning signal, the positioning feedback comprising, according to the reporting configuration: a first real number component of a first power of a first detected path of the positioning signal; and a first imaginary number component of the first power of a first detected path of the positioning signal; and sending the positioning feedback to the LMF.

49. The method of claim 48, wherein the positioning feedback further comprises, according to the reporting configuration, a second real number component of a second power of a second detected path of the positioning signal.

50. The method of claim 48, wherein the positioning feedback further comprises, according to the reporting configuration, a second imaginary number component of a second power of a second detected path of the positioning signal.

51. The method of claim 48, wherein the positioning feedback further comprises, according to the reporting configuration, a real differential of a second power of a second detected path of the positioning signal, wherein the real differential indicates a difference between a second real number component of the second power of the second detected path and the first real number component of the first power of the first detected path.

52. The method of claim 48, wherein the positioning feedback further comprises, according to the reporting configuration, an imaginary differential of a second power of a second detected path of the positioning signal, wherein the imaginary differential indicates a difference between a second imaginary number component of the second power of the second detected path and the first imaginary number component of the first power of the first detected path.

53. The method of claim 48, wherein the positioning signal comprises a downlink positioning reference signal (DL-PRS).

54. The method of claim 48, wherein the positioning feedback further comprises a path timing value for the first detected path.

55. The method of claim 48, wherein the positioning feedback further comprises a quality of the first detected path.

56. The method of claim 48, wherein the positioning feedback further comprises a quality7of a full measurement of the positioning signal.

57. The method of claim 48, wherein the positioning feedback further comprises a quality of a ground truth value corresponding to the positioning signal.

58. The method of claim 48, wherein the positioning feedback further comprises a timing granularity factor.

59. A method of a base station, comprising:receiving, from a location management function (LMF) of a core network (CN), a reporting configuration for a positioning signal transmitted by a user equipment (UE); receiving, from the UE, the positioning signal; generating positioning feedback for the LMF based on the positioning signal, the positioning feedback comprising, according to the reporting configuration, a first magnitude of a first detected path of the positioning signal; and sending the positioning feedback to the LMF.

60. The method of claim 59, wherein the positioning feedback further comprises, according to the reporting configuration, a second magnitude of a second detected path of the positioning signal.

61. The method of claim 59, wherein the positioning feedback further comprises, according to the reporting configuration, a magnitude differential for a second detected path of the positioning signal, wherein the magnitude differential indicates a difference between a second magnitude of the second detected path and the first magnitude of the first detected path.

62. The method of claim 59, wherein the positioning feedback further comprises a first phase of the first detected path of the positioning signal.

63. The method of claim 62, wherein the positioning feedback further comprises, according to the reporting configuration, a second phase of a second detected path of the positioning signal.

64. The method of claim 62, wherein the positioning feedback further comprises, according to the reporting configuration, a phase differential for a second detected path of the positioning signal, wherein the phase differential indicates a difference between a second phase of the second detected path and the first phase of the first detected path.

65. The method of claim 59, wherein the positioning signal comprises an uplink sounding reference signal (UL-SRS).

66. The method of claim 65, wherein the UL-SRS comprises an UL-SRS for positioning.

67. The method of claim 59, wherein the positioning feedback further comprises a path timing value for the first detected path.

68. The method of claim 59, wherein the positioning feedback further comprises a quality of the first detected path.

69. The method of claim 59, wherein the positioning feedback further comprises a quality of a full measurement of the positioning signal.

70. The method of claim 59, wherein the positioning feedback further comprises a quality of a ground truth value corresponding to the positioning signal.

71. The method of claim 59, wherein the positioning feedback further comprises a timing granularity factor.

72. A method of a base station, comprising: receiving, from a location management function (LMF) of a core network (CN), a reporting configuration for a positioning signal transmitted by a user equipment (UE); receiving, from the UE, the positioning signal; generating positioning feedback for the LMF based on the positioning signal, the positioning feedback comprising, according to the reporting configuration: a first real number component of a first power of a first detected path of the positioning signal; and a first imaginary number component of the first power of a first detected path of the positioning signal; and sending the positioning feedback to the LMF.

73. The method of claim 72, wherein the positioning feedback further comprises, according to the reporting configuration, a second real number component of a second power of a second detected path of the positioning signal.

74. The method of claim 72, wherein the positioning feedback further comprises, according to the reporting configuration, a second imaginary number component of a second power of a second detected path of the positioning signal.

75. The method of claim 72, wherein the positioning feedback further comprises, according to the reporting configuration, a real differential of a second power of a second detected path of the positioning signal, wherein the real differential indicates a difference between a second real number component of the second power of the seconddetected path and the first real number component of the first power of the first detected path.

76. The method of claim 72, wherein the positioning feedback further comprises, according to the reporting configuration, an imaginary differential of a second power of a second detected path of the positioning signal, wherein the imaginary differential indicates a difference between a second imaginary number component of the second power of the second detected path and the first imaginary number component of the first power of the first detected path.

77. The method of claim 72, wherein the positioning signal comprises an uplink sounding reference signal (UL-SRS).

78. The method of claim 77, wherein the UL-SRS comprises an UL-SRS for positioning.

79. The method of claim 72, wherein the positioning feedback further comprises a path timing value for the first detected path.

80. The method of claim 72, wherein the positioning feedback further comprises a qualify of the first detected path.

81. The method of claim 72, wherein the positioning feedback further comprises a qualify of a full measurement of the positioning signal.

82. The method of claim 72, wherein the positioning feedback further comprises a timing granularity factor.

83. A method of a location management function (LMF) of a core network (CN), comprising: sending, to one of a user equipment (UE) and a base station, a reporting configuration for a positioning signal transmitted between the UE and the base station; receiving, from the one of the UE and the base station, positioning feedback corresponding to the positioning signal, wherein the positioning feedback comprises, according to the reporting configuration, a path timing value of a first detected path of the positioning signal and a qualify of the first detected path of the positioning signal; andapplying the positioning feedback with an artificial intelligence (AI) / machine learning (ML) model of the LMF.

84. The method of claim 83, wherein the applying the positioning feedback with the AI / ML model comprises using the positioning feedback to train the AI / ML model.

85. The method of claim 83, wherein the applying the positioning feedback with the AI / ML model comprises using the positioning feedback at the AI / ML model to generate an inference.

86. The method of claim 83, wherein the applying the positioning feedback with the AI / ML model comprises using the positioning feedback at the AI / ML model to monitor the AI / ML model.

87. The method of claim 83, wherein the applying the positioning feedback with the AI / ML model comprises using the positioning feedback at the AI / ML model to train the AI / ML model.

88. The method of claim 83, wherein: the one of the UE and the base station comprises the UE; and the positioning signal comprises a downlink positioning reference signal (DL- PRS) transmitted by the base station.

89. The method of claim 83, wherein: the one of the UE and the base station comprises the base station; and the positioning signal comprises an uplink sounding reference signal (UL-SRS) transmitted by the UE.

90. The method of claim 89, wherein the UL-SRS comprises an UL-SRS for positioning.

91. A method of a user equipment (UE). comprising: receiving, from a location management function (LMF) of a core network (CN), a reporting configuration for a positioning signal transmitted by a base station; receiving, from the base station, the positioning signal; generating positioning feedback for the LMF based on the positioning signal, the positioning feedback comprising, according to the reporting configuration, a path timingvalue of a first detected path of the positioning signal and a quality of the first detected path of the positioning signal; and sending the positioning feedback to the LMF.

92. The method of claim 91, wherein the positioning signal comprises a downlink positioning reference signal (DL-PRS).

93. A method of a base station, comprising: receiving, from a location management function (LMF) of a core network (CN), a reporting configuration for a positioning signal transmitted by a user equipment (UE); receiving, from the UE. the positioning signal; generating positioning feedback for the LMF based on the positioning signal, the positioning feedback comprising, according to the reporting configuration, a path timing value of a first detected path of the positioning signal and a quality of the first detected path of the positioning signal; and sending the positioning feedback to the LMF.

94. The method of claim 93, wherein the positioning signal comprises an uplink sounding reference signal (UL-SRS).

95. The method of claim 94, wherein the UL-SRS comprises an UL-SRS for positioning.

96. An apparatus comprising means to perform the method of any of claim 1 to claim 95.

97. A 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 the method of any of claim 1 to claim 95.

98. An apparatus comprising logic, modules, or circuitry to perform the method of any of claim 1 to claim 95.

99. A baseband processor for a user equipment (UE) that is configured to perform one or more elements of any one of claim 36 to claim 58, claim 91, and claim 92.

100. A baseband processor for a base station that is configured to perform one or more elements of any one of claim 59 to claim 82 and claim 93 to claim 95.

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