Artificial intelligence / machine learning round trip positioning method

Abstract

A method comprising: transmitting, by a user equipment (UE), to a location management function (LMF), at least one positioning capability of the UE indicating at least one artificial intelligence / machine learning (AIML) positioning functionality; transmitting, by a network entity to a LMF, at least one positioning capability of the network entity indicating at least one AIML positioning functionality; receiving, by the UE, from the LMF, at least one configuration to perform at least one positioning differential measurement based upon the at least one positioning capability of the UE; receiving, by the network entity, from the LMF, at least one configuration to perform at least one positioning differential measurement based upon the at least one positioning capability of the network entity; performing, by the UE and the network entity, the at least one positioning differential measurement according to the received configurations. The AIML positioning functionality may comprise an AIML-based line of sight (LOS) determination or AIML-based positioning differential measurement acquisition. The configuration may relate to uplink sounding reference signals (UL-SRS) and downlink positioning reference signals (DL-PRS). Also provided is a corresponding method at the LMF and corresponding apparatuses.

Description

[0002] Examples of mobile or wireless telecommunication systems may include radio frequency (RF) 5G RAT, the Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access Network (UTRAN), LTE Evolved UTRAN (E-UTRAN), LTE-Advanced (LTE-A), LTE-A Pro, NR access technology, and / or MulteFire Alliance. 5G wireless systems refer to the next generation (NG) of radio systems and network architecture. A 5G system is typically built on a 5G NR, but a 5G (or NG) network may also be built on E-UTRA radio. It is expected that NR can support service categories such as enhanced mobile broadband (eMBB), ultra-reliable low-latency-communication (URLLC), and massive machine-type communication (mMTC). NR is expected to deliver extreme broadband, ultra-robust, low-latency connectivity, and massive networking to support the Internet of Things (loT). The next generation radio access network (NG-RAN) represents the radio access network (RAN) for 5G, which may provide radio access for NR, LTE, and LTE-A. It is noted that the nodes in 5G providing radio access functionality to a user equipment (UE) (e.g., similar to the Node B in UTRAN or the Evolved Node B (eNB) in LTE) may be referred to as nextgeneration Node B (gNB) when built on NR radio, and may be referred to as nextgeneration eNB (NG-eNB) when built on E-UTRA radio. SUMMARY

[0003] In accordance with some example embodiments, a method may include transmitting, by a UE, to an LMF, at least one positioning capability of the UE indicating at least one AI / ML positioning functionality. The method may further include receiving, by the UE, from the LMF, at least one configuration to perform at least one PDM based upon the at least one positioning capability of the UE. The method may further include performing, by the UE, the at least one PDM with a network entity according to the received configuration.

[0004] In accordance with certain example embodiments, an apparatus may include means for transmitting, to an LMF, at least one positioning capability of the apparatus indicating at least one AI / ML positioning functionality. The apparatus may further include means for receiving, from the LMF, at least one configuration to perform at least one PDM based upon the at least one positioning capability of the apparatus. The apparatus may further include means for perfoiming the at least one PDM with a network entity according to the received configuration.

[0005] In accordance with various example embodiments, a non-transitory computer readable medium may include program instructions that, when executed by an apparatus, cause the apparatus to perform at least a method. The method may include transmitting, to an LMF, at least one positioning capability of the apparatus indicating at least one AI / ML positioning functionality. The method may further include receiving, from the LMF, at least one configuration to perfoim at least one PDM based upon the at least one positioning capability of the apparatus. The method may further include performing the at least one PDM with a network entity according to the received configuration.

[0006] In accordance with some example embodiments, a computer program product may perform a method. The method may include transmitting, to an LMF, at least one positioning capability of a UE indicating at least one AI / ML positioning functionality. The method may further include receiving, from the LMF, at least one configuration to perform at least one PDM based upon the at least one positioning capability of the UE. The method may further include performing the at least one PDM with a network entity according to the received configuration.

[0007] In accordance with certain example embodiments, an apparatus may include at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to transmit, to an LMF, at least one positioning capability of the apparatus indicating at least one AI / ML positioning functionality. The instructions, when executed by the at least one processor, may further cause the apparatus at least to receive, from the LMF, at least one configuration to perform at least one PDM based upon the at least one positioning capability of the apparatus. The instructions, when executed by the at least one processor, may further cause the apparatus at least to perform the at least one PDM with a network entity according to the received configuration.

[0008] In accordance with various example embodiments, an apparatus may include transmitting circuitry configured to perform transmit, to an LMF, at least one positioning capability of the apparatus indicating at least one AI / ML positioning functionality. The apparatus may further include receiving circuitry configured to perform receiving, from the LMF, at least one configuration to perform at least one PDM based upon the at least one positioning capability of the apparatus. The apparatus may further include performing circuitry configured to perform the at least one PDM with a network entity according to the received configuration.

[0009] In accordance with some example embodiments, a method may include transmitting, by a network entity, to a LMF, at least one positioning capability of the network entity indicating at least one AI / ML positioning functionality. The method may further include receiving, by the network entity, from the LMF, at least one configuration to perform at least one PDM based upon the at least one positioning capability of the network entity. The method may further include performing, by the network entity, the at least one PDM with a UE according to the received configuration.

[0010] In accordance with certain example embodiments, an apparatus may include means for transmitting, to an LMF, at least one positioning capability of the apparatus indicating at least one AI / ML positioning functionality. The apparatus may further include means for receiving, from the LMF, at least one configuration to perform at least one PDM based upon the at least one positioning capability of the apparatus. The apparatus may further include means for performing the at least one PDM with a UE according to the received configuration.

[0011] In accordance with various example embodiments, a non-transitory computer readable medium may include program instructions that, when executed by an apparatus, cause the apparatus to perform at least a method. The method may include transmitting, to an LMF, at least one positioning capability of the apparatus indicating at least one AI / ML positioning functionality. The method may further include receiving, from the LMF, at least one configuration to perform at least one PDM based upon the at least one positioning capability of the apparatus. The method may further include performing the at least one PDM with a UE according to the received configuration.

[0012] In accordance with some example embodiments, a computer program product may perform a method. The method may include transmitting, to an LMF, at least one positioning capability of the apparatus indicating at least one AI / ML positioning functionality. The method may further include receiving, from the LMF, at least one configuration to perform at least one PDM based upon the at least one positioning capability of the apparatus. The method may further include performing the at least one PDM with a UE according to the received configuration.

[0013] In accordance with certain example embodiments, an apparatus may include at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to transmit, to an LMF, at least one positioning capability of the apparatus indicating at least one AI / ML positioning functionality. The instructions, when executed by the at least one processor, may further cause the apparatus at least to receive, from the LMF, at least one configuration to perform at least one PDM based upon the at least one positioning capability of the apparatus. The instructions, when executed by the at least one processor, may further cause the apparatus at least to perform the at least one PDM with a UE according to the received configuration.

[0014] In accordance with various example embodiments, an apparatus may include transmitting circuitry configured to perform transmitting, to an LMF, at least one positioning capability of the apparatus indicating at least one AI / ML positioning functionality. The apparatus may further include receiving circuitry configured to perform receiving, from the LMF, at least one configuration to perform at least one PDM based upon the at least one positioning capability of the apparatus. The apparatus may further include performing circuitry configured to perform performing the at least one PDM with a UE according to the received configuration.

[0015] In accordance with some example embodiments, a method may include receiving, by a LMF, from at least one of a UE or a NE, at least one positioning capability of the at least one of the UE or the NE indicating at least one AI / ML positioning functionality. The method may further include transmitting, by the LMF, to the at least one of the UE or the NE, at least one configuration to perform at least one PDM based upon the at least one positioning capability. The method may further include receiving, by the LMF, from the at least one of the UE or the NE, at least one PDM report comprising at least one PDM.

[0016] In accordance with certain example embodiments, an apparatus may include means for receiving, from at least one of a UE or a network entity, at least one positioning capability of the at least one of the UE or the NE indicating at least one AI / ML positioning functionality. The apparatus may further include means for transmitting, to the at least one of the UE or the NE, at least one configmation to perform at least one PDM based upon the at least one positioning capability. The apparatus may further include means for receiving, from the at least one of the UE or the NE, at least one PDM report comprising at least one PDM.

[0017] In accordance with various example embodiments, a non-transitory computer readable medium may include program instructions that, when executed by an apparatus, cause the apparatus to perform at least a method. The method may include receiving, from at least one of a UE or a network entity, at least one positioning capability of the at least one of the UE or the NE indicating at least one AI / ML positioning functionality. The method may further include transmitting, to the at least one of the UE or the NE, at least one configuration to perform at least one PDM based upon the at least one positioning capability. The method may further include receiving, from the at least one of the UE or the NE, at least one PDM report comprising at least one PDM.

[0018] In accordance with some example embodiments, a computer program product may perform a method. The method may include receiving, from at least one of a UE or a NE, at least one positioning capability of the at least one of the UE or the NE indicating at least one AI / ML positioning functionality. The method may further include transmitting, to the at least one of the UE or the NE, at least one configuration to perform at least one PDM based upon the at least one positioning capability. The method may further include receiving, from the at least one of the UE or the NE, at least one PDM report comprising at least one PDM.

[0019] In accordance with certain example embodiments, an apparatus may include at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to receive, from at least one of a UE or a NE, at least one positioning capability of the at least one of the UE or the NE indicating at least one AI / ML positioning functionality. The instructions, when executed by the at least one processor, may further cause the apparatus at least to transmit, to the at least one of the UE or the NE, at least one configuration to perform at least one PDM based upon the at least one positioning capability. The instructions, when executed by the at least one processor, may further cause the apparatus at least to receive, from the at least one of the UE or the NE, at least one PDM report comprising at least one PDM.

[0020] In accordance with various example embodiments, an apparatus may include receiving circuitry configured to perform receiving, from at least one of a UE or a NE, at least one positioning capability of the at least one of the UE or the NE indicating at least one AI / ML positioning functionality. The apparatus may further include transmitting circuitry configured to perform transmitting, to the at least one of the UE or the NE, at least one configuration to perform at least one PDM based upon the at least one positioning capability. The apparatus may further include receiving circuitry configured to perform receiving, from the at least one of the UE or the NE, at least one PDM report comprising at least one PDM. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] For a proper understanding of example embodiments, reference should be made to the accompanying drawings, wherein:

[0022] FIG. 1 illustrates an example of a legacy round trip time method.

[0023] FIG. 2 illustrates an example of a signaling diagram according to certain example embodiments;

[0024] FIG. 3 illustrates an example of a flow diagram of a method according to some example embodiments;

[0025] FIG. 4 illustrates an example of a flow diagram of another method according to various example embodiments;

[0026] FIG. 5 illustrates an example of a flow diagram of another method according to certain example embodiments;

[0027] FIG. 6 illustrates an example of various network devices according to some example embodiments; and

[0028] FIG. 7 illustrates an example of a 5G network and system architecture according to various example embodiments. DETAILED DESCRIPTION

[0029] It will be readily understood that the components of certain example embodiments, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of some example embodiments of systems, methods, apparatuses, and computer program products for AI / ML round trip positioning is not intended to limit the scope of certain example embodiments, but is instead representative of selected example embodiments.

[0030] 3GPP 5G is considering positioning accuracy enhancements for various scenarios, including scenarios with heavy no line-of-sight (NLOS) conditions. Multiround trip time (RTT) methods may help to minimize synchronization errors between the UE and the transmission reception point (TRP). FIG. 1 depicts a legacy RTT method, wherein a location management function (LMF) transmits positioning reference signal (PRS) configuration information to a UE that indicates to the UE a downlink (DL) PRS configuration associated with different TRPs. The LMF may also indicate to the UE uplink (UL) PRS information, on which it transmits the UL PRS for the TRPs to measure. TRPs may then transmit DL PRS, and the UEs may measure time of arrival (TOA), including synchronization offset and / or propagation delay. The UE may transmit a UL sounding reference signal (SRS) in response to the DL PRS, and the TRPs may measure TOA. The base station may then transmit a measurement report to the LMF, including the TRP receive (Rx)-transmit (Tx) time difference measurement. Finally, the LMF may perform the subtraction, thus obtaining the RTT per each TRP, which may be used to compute the location of the UE.

[0031] Certain example embodiments described herein may have various technical effects to enhance the positioning accuracy. For example, certain example embodiments may provide LOS information at both the UE and the base station prior to acquiring at least one positioning differential measurement (PDM), such as Rx-Tx time difference, including whether LOS is detected independently by the UE and base station, or conversely, whether one entity leads the detection, as well as whether the UE and / or base station would use AI / ML. Furthermore, various example embodiments may acquire the most accurate PDM with the usage of AI / ML, and determine whether cooperation between the UE and TRP is required. In addition, certain example embodiments may select the granularity of the PDM, and support making LOS information available and acquiring the most accurate PDM. Thus, certain example embodiments discussed below are directed to improvements in computer-related technology.

[0032] Since round hip (RT) positioning methods require both the UE and base station to measure positioning signals and report PDMs, the usage of AI / ML across the UE and base station needs to be coordinated regarding at least how LOS is determined, how the granularity of the measurements are selected, and how AI / ML is deployed (e.g., independently at each side, or jointly across the two sides).

[0033] In order to coordinate these determinations, an AI / ML RT positioning method may enhance existing RT positioning by introducing new elements. For example, a new Long Term Evolution Positioning Protocol (LPP) / New Radio Positioning Protocol A (NRPPa) assistance data information element (IE) transmitted from the LMF to the UE / base station may coordinate the deployment and usage of AI / ML RT functionalities (e.g., RT LOS determination, RT PDM acquisition, etc.). In addition, new radio resource control (RRC) IE or medium access control (MAC) control element (CE) coordination between the UE and the base station may be provided that may coordinate AI / ML RT procedures in terms of at least LOS determination and PDM granularity selection and / or synchronize the usage of AI / ML RT functionalities (e.g, whether model switching / deactivation at one end requires action at the other end).

[0034] FIG. 2 illustrates an example of a signaling diagram 200 depicting certain example embodiments for implementing an AI / ML RT procedure. LMF 220 and NE 240 may be similar to NE 610, and UE 230 may be similar to UE 620, as illustrated in FIG. 6, according to certain example embodiments.

[0035] At operation 201, UE 230 may transmit to LMF 220 at least one positioning capability of UE 230, which may indicate at least one AI / MF RT functionality of UE 230. For example, the at least one AI / MF RT functionality of UE 230 may include LOS determination (e.g, AI / ML-based LOS detector or not) and / or PDM acquisition (e.g, AI / ML-based PDM or not, whereby PDM may include Rx-Tx time difference, carrier phase difference, etc.). However, any other AI / MF RT functionality of UE 230 may be indicated. In various example embodiments, PDM may refer to any type of PDM associated with, for example, a Rx-Tx time difference of the direct path, a Rx-Tx time difference for reflections (e.g, the strongest N multipaths), and / or an Rx-Tx carrier phase difference.

[0036] Similarly, at operation 202, NE 240 may transmit to LMF 220 at least one positioning capability of NE 240, which may indicate at least one AI / MF RT functionality of NE 240. For example, the at least one AI / MF RT functionality of NE 240 may include LOS determination (e.g, AI / ML-based LOS detector or not) and / or PDM acquisition (e.g, AI / ML-based PDM or not). However, any other AI / MF RT functionality of NE 240 may be indicated.

[0037] Based upon the AI / ML RT functionality reports received at operations 201 and / or 202, LMF 220 may, at operations 203 and 204, transmit a configuration to UE 230 and NE 240, respectively, to configure AI / ML RT. In an example embodiment, LMF 220 may transmit the AI / ML RT configurations via an LPP and / or NRPPa IE.

[0038] For example, the configuration may include an UL SRS and DL PRS configuration, and may optionally indicate which entity (i.e., UE 230 or NE 240) should transmit first. The configuration may further indicate at least one LOS determination policy. For example, the configuration may indicate whether LOS detection is performed using AI / ML functionality or not, or is performed independently by UE 230 or NE 240.

[0039] In alternative example embodiments, LMF 220 may select and configure UE 230 or NE 240 to lead the acquisition of the LOS information. The selected entity ( / . e., UE 230 or NE 240) may inform the other entity ( / . e., UE 230 or NE 240) of the LOS information when it transmits its respective RS (i.e., UL SRS or DL PRS), assuming that both UL and DL channels have the same LOS probability due to charnel reciprocity. For example, the LOS information may be infoimed explicitly by following the RS transmission of the selected entity with a LOS flag, or implicitly by choosing an RS identifier (ID) associated with the LOS flag. For example, if UE 230 performs the acquisition of the LOS information, UE 230 may transmit a LOS flag with the UL SRS and / or choose an SRS whose ID is associated with the LOS flag.

[0040] In some example embodiments, the configuration may indicate at least one AI / ML-based PDM acquisition policy. For example, LMF 220 may configure UE 230 and / or NE 240 on whether PDM acquisition is performed using AI / ML functionality, and whether UE 230 or NE 240 uses respective AI / ML functionality (if any).

[0041] In certain example embodiments, the configuration may indicate at least one PDM granularity selection policy. For example, LMF 220 may configure UE 230 and / or NE 240 on how to select the PDM acquisition granularity, for example, which entity (i.e., UE 230 or NE 240) may select the granularity, whether the granularity is selected based on a handshake between UE 230 and NE 240, after UE 230 and NE 240 have assessed the RT configuration; whether the granularity is fixed and set by NE 240; or whether the granularity is independently selected by UE 230 and NE 240, respectively. The entity (i.e., UE 230 or NE 240) that selects or determines the granularity may inform the other entity (i.e., NE 240 or UE 230) of the decision.

[0042] In some example embodiments, the configuration may indicate at least one policy for coordination between UE 230 and NE 240 for AI / ML functionality. For example, LMF 220 may configure UE 230 and / or NE 240 to coordinate AI / ML usage and their fallback mechanisms in case of AI / ML functionality failure. For example, if UE 230 decides to revert to a legacy method for either LOS determination or PDM acquisition, LMF 220 may configure UE 230 on whether UE 230 should inform NE 240 about UE 230 reverting to a legacy method, and / or whether NE 240 should follow the decision or not, and vice versa. As another example, if UE 230 decides to update a functionality (e.g., switch among AI / ML models, and / or deactivate a model) associated with AI / ML RT, LMF 220 may configure UE 230 on whether UE 230 should inform NE 240 about the switching and / or updating, and / or whether NE 240 should mirror the decision by UE 230, and vice versa.

[0043] At operation 205, UE 230 and / or NE 240 may apply the configuration received from LMF 220, including a coordination strategy, as discussed above.

[0044] At operation 206, NE 240 may transmit to UE 230 at least one DL PRS.

[0045] At operation 207, UE 230 may apply AI / ML RT functionalities, and may acquire PDM.

[0046] At operation 208, UE 230 may transmit to NE 240 at least one UL SRS.

[0047] At operation 209, UE 230 may transmit to NE 240 at least one LOS flag (explicit or implicit), if UE 230 is configured to lead the LOS acquisition.

[0048] At operation 210, UE 230 may transmit to NE 240 an indication of PDM granularity, if UE 230 is configured to select the PDM granularity.

[0049] At operation 211, NE 240 may apply AI / ML RT functionalities, and may acquire PDM.

[0050] At operation 212, UE 230 and / or NE 240 may inform the other (i.e., NE 240 or UE 230) upon AI / ML RT functionality changing.

[0051] In various example embodiments, if UE 230 and / or NE 240 switch among models / deactivate a model for a given AI / ML RT-related functionality, UE 230 and / or NE 240 may inform the other entity about it as configured by LMF 220. For example, if the AI / ML RT resides at UE 230 and NE 240, then the model switching must be agreed and synchronized (e.g., model parts may be activated at the same time). As another example, if the AI / ML RT model only resides at either UE 230 or NE 240, then the entity that uses the model may notify the other entity that does not use an AI / ML model about the switch / deactivation.

[0052] At operation 213, UE 230 may transmit to LMF 220 a PDM report including PDM. Similarly, at operation 214, NE 204 may transmit to LMF 220 a PDM report including PDM.

[0053] At operation 214, LMF 220 may determine the location of UE 220 based on the PDM report received from the UE 230 and / or NE 240. For example, using the PDM report, LMF 220 may obtain range information between UE 230 and each TRP, and then triangulate the location of UE 230.

[0054] In some example embodiments, NE 240 may lead the LOS acquisition, select the PDM acquisition granularity, and / or inform UE 230 of such information, as configured by LMF 220.

[0055] FIG. 3 illustrates an example of a flow diagram of a method 300 that may be performed by a UE, such as UE 620 illustrated in FIG. 6, according to various example embodiments.

[0056] At step 301, the method may include transmitting, to an LMF, such as NE 610 illustrated in FIG. 6, at least one positioning capability of the UE indicating at least one AI / ML positioning functionality.

[0057] At step 302, the method may further include receiving, from the LMF, at least one configuration to perform at least one PDM based upon the at least one positioning capability of the UE.

[0058] At step 303, the method may further include performing the at least one PDM with a network entity, such as NE 610 illustrated in FIG. 6, according to the received configuration.

[0059] In certain example embodiments, the method may further include receiving at least one DL PRS from the NE. The at least one PDM comprises a DL PDM based on the at least one DL PRS according to a determined granularity associated with DL PDM acquisition.

[0060] In some example embodiments, the method may further include transmitting, to the NE, at least one UL SRS. Furthermore, the at least one PDM may include an UL PDM based on the at least one UL SRS according to a determined granularity associated with UL PDM acquisition.

[0061] In various example embodiments, the method may further include transmitting, to the LMF, at least one PDM report comprising at least one PDM.

[0062] In certain example embodiments, the at least one AI / ML positioning functionality may include at least one of at least one AI / ML-based LOS determination or at least one AI / ML-based PDM acquisition.

[0063] In some example embodiments, the at least one configuration may at least one of at least one UL SRS configuration; at least one DL PRS configuration; at least one transmission order of the at least one UL SRS and the at least one DL PRS; at least one LOS determination policy; at least one AI / ML-based PDM acquisition policy; at least one PDM granularity selection policy; or at least one policy for coordination between the UE and the NE for AI / ML functionality.

[0064] In various example embodiments, the at least one PDM may include at least one of at least one receive-transmit time difference measurement; or at least one receivetransmit carrier phase difference measurement.

[0065] In certain example embodiments, the method may further include determining, at least one PDM granularity; and transmitting, to the network entity, the determined at least one PDM granularity.

[0066] In some example embodiments, the method may further include receiving, from the NE, at least one PDM granularity determined by the NE.

[0067] In various example embodiments, the method may further include transmitting, to the NE, or receiving, from the NE, at least one indication of an AI / ML functionality update.

[0068] FIG. 4 illustrates an example of a flow diagram of a method 400 that may be performed by a NE, such as NE 610 illustrated in FIG. 6, according to various example embodiments.

[0069] At step 401, the method may include transmitting, by a network entity, to a LMF, at least one positioning capability of the network entity indicating at least one AI / ML positioning functionality.

[0070] At step 402, the method may further include receiving, by the network entity, from the LMF, at least one configuration to perform at least one PDM based upon the at least one positioning capability of the network entity.

[0071] At step 403, the method may further include performing, by the network entity, the at least one PDM with a UE according to the received configuration.

[0072] In certain example embodiments, the method may further include receiving, by the network entity, at least one UL PRS from the UE. The at least one PDM may include an UL PDM based on the at least one UL PRS according to a determined granularity associated with UL PDM acquisition.

[0073] In some example embodiments, the method may further include transmitting, by the network entity, to the UE, at least one DL SRS, and wherein the at least one PDM comprises a DL PDM based on the at least one DL SRS according to a determined granularity associated with DL PDM acquisition.

[0074] In various example embodiments, the method may further include transmitting, by the network entity, to the LMF, at least one PDM report comprising at least one PDM.

[0075] In certain example embodiments, the method may further include determining, by the network entity, at least one PDM granularity, and transmitting, by the network entity to the UE, the determined at least one PDM granularity.

[0076] In some example embodiments, the method may further include receiving, by the network entity, from the UE, at least one PDM granularity determined by the UE.

[0077] In various example embodiments, the method may further include transmitting, by the network entity, to the UE, or receiving, by NE, from the UE, at least one indication of an AI / ML functionality update.

[0078] FIG. 5 illustrates an example of a flow diagram of a method 500 that may be performed by an LMF, such as NE 610 illustrated in FIG. 6, according to various example embodiments.

[0079] At step 501, the method may include receiving, by the LMF, from at least one of a UE or a NE, at least one positioning capability of the at least one of the UE or the NE indicating at least one AI / ML positioning functionality.

[0080] At step 502, the method may further include transmitting, by the LMF, to the at least one of the UE or the NE, at least one configuration to perform at least one PDM based upon the at least one positioning capability.

[0081] At step 503, the method may further include receiving, by the LMF, from the at least one of the UE or the NE, at least one PDM report comprising at least one PDM.

[0082] In certain example embodiments, the at least one AI / ML positioning functionality may include at least one of at least one AI / ML-based LOS determination, or at least one AI / ML-based PDM acquisition.

[0083] In some example embodiments, the at least one configuration may include at least one of at least one UL SRS configuration; at least one DL PRS configuration; at least one transmission order of the at least one UL SRS and the at least one DL PRS; at least one LOS deteimination policy; at least one AI / ML-based PDM acquisition policy; at least one PDM granularity selection policy; or at least one policy for coordination between the UE and the NE for AI / ML functionality.

[0084] In various example embodiments, the at least one PDM comprises at least one of at least one Rx-Tx time difference measurement, or at least one receive-transmit carrier phase difference measurement.

[0085] FIG. 6 illustrates an example of a system according to certain example embodiments. In one example embodiment, a system may include multiple devices, such as, for example, NE 610 and / or UE 620.

[0086] NE 610 may be one or more of a base station (e.g., 3G UMTS NodeB, 4G LTE Evolved NodeB, or 5G NR Next Generation NodeB), a LMF, a serving gateway, a server, and / or any other access node or combination thereof.

[0087] NE 610 may further include at least one gNB-centralized unit (CU), which may be associated with at least one gNB-distributed unit (DU). The at least one gNB-CU and the at least one gNB-DU may be in communication via at least one FI interface, at least one Xn-C interface, and / or at least one NG interface via a 5th generation core (5GC).

[0088] UE 620 may include one or more of a mobile device, such as a mobile phone, smart phone, personal digital assistant (PDA), tablet, or portable media player, digital camera, pocket video camera, video game console, navigation unit, such as a global positioning system (GPS) device, desktop or laptop computer, single-location device, such as a sensor or smart meter, or any combination thereof. Furthermore, NE 610 and / or UE 620 may be one or more of a citizens broadband radio service device (CBSD).

[0089] NE 610 and / or UE 620 may include at least one processor, respectively indicated as 611 and 621. Processors 611 and 621 may be embodied by any computational or data processing device, such as a central processing unit (CPU), application specific integrated circuit (ASIC), or comparable device. The processors may be implemented as a single controller, or a plurality of controllers or processors.

[0090] At least one memory may be provided in one or more of the devices, as indicated at 612 and 622. The memory may be fixed or removable. The memory may include computer program instructions or computer code contained therein. Memories 612 and 622 may independently be any suitable storage device, such as a non-transitory computer-readable medium. The term “non-transitory,” as used herein, may correspond to a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., random access memory (RAM) vs. read-only memory (ROM)). A hard disk drive (HDD), random access memory (RAM), flash memory, or other suitable memory may be used. The memories may be combined on a single integrated circuit as the processor, or may be separate from the one or more processors. Furthermore, the computer program instructions stored in the memory, and which may be processed by the processors, may be any suitable form of computer program code, for example, a compiled or interpreted computer program written in any suitable programming language.

[0091] Processors 611 and 621, memories 612 and 622, and any subset thereof, may be configured to provide means corresponding to the various blocks of FIGs. 2-5. Although not shown, the devices may also include positioning hardware, such as GPS or micro electrical mechanical system (MEMS) hardware, which may be used to determine a location of the device. Other sensors are also permitted, and may be configured to determine location, elevation, velocity, orientation, and so forth, such as barometers, compasses, and the like.

[0092] As shown in FIG. 6, transceivers 613 and 623 may be provided, and one or more devices may also include at least one antenna, respectively illustrated as 614 and 624. The device may have many antennas, such as an array of antennas configured for multiple input multiple output (MIMO) communications, or multiple antennas for multiple RATs. Other configurations of these devices, for example, may be provided. Transceivers 613 and 623 may be a transmitter, a receiver, both a transmitter and a receiver, or a unit or device that may be configured both for transmission and reception.

[0093] The memory and the computer program instructions may be configured, with the processor for the particular device, to cause a hardware apparatus, such as UE, to perform any of the processes described above (i.e., FIGs. 2-5). Therefore, in certain example embodiments, a non-transitory computer-readable medium may be encoded with computer instructions that, when executed in hardware, perform a process such as one of the processes described herein. Alternatively, certain example embodiments may be performed entirely in hardware.

[0094] In certain example embodiments, an apparatus may include circuitry configured to perform any of the processes or functions illustrated in FIGs. 2-5. As used in this application, the term “circuitry” may refer to one or more or all of the following: (a) hardware-only circuit implementations (such as implementations in only analog and / or digital circuitry), (b) combinations of hardware circuits and software, such as (as applicable): (i) a combination of analog and / or digital hardware circuit(s) with software / firmware and (ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions), and (c) hardware circuit(s) and or processor(s), such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation. This definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and / or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

[0095] FIG. 7 illustrates an example of a 5G network and system architecture according to certain example embodiments. Shown are multiple network functions that may be implemented as software operating as pail of a network device or dedicated hardware, as a network device itself or dedicated hardware, or as a virtual function operating as a network device or dedicated hardware. The NE and UE illustrated in FIG. 7 may be similar to NE 610 and UE 620, respectively. The user plane function (UPF) may provide services such as intra-RAT and inter-RAT mobility, routing and forwarding of data packets, inspection of packets, user plane quality of service (QoS) processing, buffering of DL packets, and / or triggering of DL data notifications. The application function (AF) may primarily interface with the core network to facilitate application usage of traffic routing and interact with the policy framework.

[0096] According to certain example embodiments, processors 611 and 621, and memories 612 and 622, may be included in or may form a part of processing circuitry or control circuitry. In addition, in some example embodiments, transceivers 613 and 623 may be included in or may form a part of transceiving circuitry.

[0097] In some example embodiments, an apparatus (e.g., NE 610 and / or UE 620) may include means for performing a method, a process, or any of the variants discussed herein. Examples of the means may include one or more processors, memory, controllers, transmitters, receivers, and / or computer program code for causing the performance of the operations.

[0098] In various example embodiments, apparatus 620 may be controlled by memory 622 and processor 621 to transmit, to an LMF, at least one positioning capability of the apparatus indicating at least one AI / ML positioning functionality; receive, from the LMF, at least one configuration to perform at least one PDM based upon the at least one positioning capability of the apparatus; and perform the at least one PDM with a NE according to the received configuration.

[0099] Certain example embodiments may be directed to an apparatus that includes means for performing any of the methods described herein including, for example, means for transmitting, to a LMF, at least one positioning capability of the apparatus indicating at least one AI / ML positioning functionality; means for receiving, from the LMF, at least one configuration to perform at least one PDM based upon the at least one positioning capability of the apparatus; and means for performing the at least one PDM with a NE according to the received configuration.

[0100] In various example embodiments, apparatus 610 may be controlled by memory 612 and processor 611 to transmit, to a LMF, at least one positioning capability of the apparatus indicating at least one AI / ML positioning functionality; receive, from the LMF, at least one configuration to perform at least one PDM based upon the at least one positioning capability of the apparatus; and perform the at least one PDM with a UE according to the received configmation.

[0101] Certain example embodiments may be directed to an apparatus that includes means for performing any of the methods described herein including, for example, means for transmitting, to a LMF, at least one positioning capability of the apparatus indicating at least one AI / ML positioning functionality; means for receiving, from the LMF, at least one configuration to perform at least one PDM based upon the at least one positioning capability of the apparatus; and means for performing the at least one PDM with a UE according to the received configuration.

[0102] In various example embodiments, apparatus 610 may be controlled by memory 612 and processor 611 to receive, from at least one of a UE or a NE, at least one positioning capability of the at least one of the UE or the NE indicating at least one AI / ML positioning functionality; transmit, to the at least one of the UE or the NE, at least one configmation to perform at least one PDM based upon the at least one positioning capability; and receive, from the at least one of the UE or the NE, at least one PDM report comprising at least one PDM.

[0103] Certain example embodiments may be directed to an apparatus that includes means for performing any of the methods described herein including, for example, means for receiving, from at least one of a UE or a NE, at least one positioning capability of the at least one of the UE or the NE indicating at least one AI / ML positioning functionality; means for transmitting, to the at least one of the UE or the NE, at least one configuration to perform at least one PDM based upon the at least one positioning capability; and means for receiving, from the at least one of the UE or the NE, at least one PDM report comprising at least one PDM.

[0104] The features, structures, or characteristics of example embodiments described throughout this specification may be combined in any suitable manner in one or more example embodiments. For example, the usage of the phrases “various embodiments,” “certain embodiments,” “some embodiments,” or other similar language throughout this specification refers to the fact that a particular feature, structure, or characteristic described in connection with an example embodiment may be included in at least one example embodiment. Thus, appearances of the phrases “in various embodiments,” “in certain embodiments,” “in some embodiments,” or other similar language throughout this specification does not necessarily all refer to the same group of example embodiments, and the described features, structures, or characteristics may be combined in any suitable manner in one or more example embodiments.

[0105] As used herein, “at least one of the following: ” and “at least one of ” and similar wording, where the list of two or more elements are joined by “and” or “or,” mean at least any one of the elements, or at least any two or more of the elements, or at least all the elements.

[0106] Additionally, if desired, the different functions or procedures discussed above may be performed in a different order and / or concurrently with each other. Furthermore, if desired, one or more of the described functions or procedures may be optional or may be combined. As such, the description above should be considered as illustrative of the principles and teachings of certain example embodiments, and not in limitation thereof.

[0107] One having ordinary skill in the art will readily understand that the example embodiments discussed above may be practiced with procedures in a different order, and / or with hardware elements in configurations which are different than those which are disclosed. Therefore, although some embodiments have been described based upon these example embodiments, it would be apparent to those of skill in the art that certain modifications, variations, and alternative constructions would be apparent, while remaining within the spirit and scope of the example embodiments.

[0108] Partial Glossary

[0109] 3GPP 3rd Generation Partnership Project

[0110] 5G 5th Generation

[0111] 5GC 5th Generation Core

[0112] 6G 6th Generation

[0113] AF Application Function

[0114] AI / ML Artificial Intelligence / Machine Learning

[0115] ASIC Application Specific Integrated Circuit

[0116] CBSD Citizens Broadband Radio Service Device

[0117] CE Control Element

[0118] CPU Central Processing Unit

[0119] CU Centralized Unit

[0120] DL Downlink

[0121] DU Distributed Unit

[0122] eMBB Enhanced Mobile Broadband

[0123] eNB Evolved Node B

[0124] gNB Next Generation Node B

[0125] GPS Global Positioning System

[0126] HDD Hard Disk Drive

[0127] IE Information Element

[0128] loT Internet of Things

[0129] LMF Location Management Function

[0130] LOS Line of Sight

[0131] LPP Long Term Evolution Positioning Protocol

[0132] LTE Long-Term Evolution

[0133] LTE-A Long-Term Evolution Advanced

[0134] MAC Medium Access Control

[0135] MEMS Micro Electrical Mechanical System

[0136] MIMO Multiple Input Multiple Output

[0137] mMTC Massive Machine Type Communication

[0138] NE Network Entity

[0139] NG Next Generation

[0140] NG-eNB Next Generation Evolved Node B 5

[0141] NG-RAN Next Generation Radio Access Network

[0142] NR New Radio

[0143] NRPPa New Radio Positioning Protocol A

[0144] PDA Personal Digital Assistance

[0145] PDM Positioning Differential Measurement 10

[0146] PRS Positioning Reference Signal

[0147] PUCCH Physical Uplink Control Channel

[0148] PUSCH Physical Uplink Shared Channel

[0149] QoS Quality of Service

[0150] RAM Random Access Memory 15

[0151] RAN Radio Access Network

[0152] RAT Radio Access Technology

[0153] RF Radio Frequency

[0154] ROM Read-Only Memory

[0155] RRC Radio Resource Control 20

[0156] RS Reference Signal

[0157] RT Round Trip

[0158] RTT Round Trip Time

[0159] Rx Receive

[0160] SRS Sounding Reference Signal 25

[0161] TOA Time of Arrival

[0162] TRP Transmission Reception Point

[0163] Tx Transmit

[0164] UE User Equipment

[0165] UL Uplink 30

[0166] UMTS Universal Mobile Telecommunications System

[0167] UPF User Plane Function

[0168] URLLC Ultra-Reliable and Low-Latency Communication

[0169] UTRAN Universal Mobile Telecommunications System Terrestrial Radio Access Network

Claims

1. A method comprising:transmitting, by a user equipment, to a location management function, at least one positioning capability of the user equipment indicating at least one artificial intelligence / machine learning positioning functionality;receiving, by the user equipment, from the location management function, at least one configuration to perform at least one positioning differential measurement based upon the at least one positioning capability of the user equipment; andperforming, by the user equipment, the at least one positioning differential measurement with at least one network entity according to the received configuration.

2. The method of claim 1, further comprising:receiving, by the user equipment, at least one downlink positioning reference signal from the at least one network entity,wherein the at least one positioning differential measurement comprises a downlink positioning differential measurement based on the at least one downlink positioning reference signal according to a determined granularity associated with downlink positioning differential measurement acquisition.

3. The method of claim 1 or 2, further comprising:transmitting, by the user equipment, to the at least one network entity, at least one uplink sounding reference signal, and wherein the at least one positioning differential measurement comprises an uplink positioning differential measurement based on the at least one uplink sounding reference signal according to a determined granularity associated with uplink positioning differential measurement acquisition.

4. The method of any of claims 1 to 3, further comprising:transmitting, by the user equipment, to the location management function, at least one positioning differential measurement report comprising at least one positioning differential measurement.

5. The method of any of claims 1 to 4, further comprising:determining, by the user equipment, at least one positioning differential measurement granularity; andtransmitting, by the user equipment to the at least one network entity, the determined at least one positioning differential measurement granularity.

6. The method of any of claims 1 to 5, further comprising:receiving, by the user equipment, from the at least one network entity, at least one positioning differential measurement granularity determined by the network entity.

7. The method of any of claims 1 to 6, further comprising:transmitting, by the user equipment, to the at least one network entity, or receiving, by the user equipment, from the at least one network entity, at least one indication of an artificial intelligence machine learning functionality update.

8. A method comprising:transmitting, by a network entity, to a location management function, at least one positioning capability of the network entity indicating at least one artificial intelligence / machine learning positioning functionality;receiving, by the network entity, from the location management function, at least one configuration to perform at least one positioning differential measurement based upon the at least one positioning capability of the network entity; andperforming, by the network entity, the at least one positioning differential measurement with a user equipment according to the received configuration.

9. The method of claim 8, further comprising:receiving, by the network entity, at least one uplink positioning reference signal from the user equipment,wherein the at least one positioning differential measurement comprises an uplink positioning differential measurement based on the at least one uplink positioningreference signal according to a determined granularity associated with uplink positioning differential measurement acquisition.

10. The method of claim 8 or 9, further comprising:transmitting, by the network entity, to the user equipment, at least one downlink sounding reference signal,wherein the at least one positioning differential measurement comprises a downlink positioning differential measurement based on the at least one downlink sounding reference signal according to a determined granularity associated with downlink positioning differential measurement acquisition.

11. The method of any of claims 8 to 10, further comprising:transmitting, by the network entity, to the location management function, at least one positioning differential measurement report comprising at least one positioning differential measurement.

12. The method of any of claims 8 to 11, further comprising:determining, by the network entity, at least one positioning differential measurement granularity; andtransmitting, by the network entity to the user equipment, the determined at least one positioning differential measurement granularity.

13. The method of any of claims 8 to 12, further comprising:receiving, by the network entity, from the user equipment, at least one positioning differential measurement granularity determined by the user equipment.

14. The method of any of claims 8 to 13, further comprising:transmitting, by the network entity, to the user equipment, or receiving, by network entity, from the user equipment, at least one indication of an artificial intelligence machine learning functionality update.

15. A method comprising:receiving, by a location management function, from at least one of a user equipment or a network entity, at least one positioning capability of the at least one of the user equipment or the network entity indicating at least one artificial intelligence / machine learning positioning functionality;transmitting, by the location management function, to the at least one of the user equipment or the network entity, at least one configuration to perform at least one positioning differential measurement based upon the at least one positioning capability; andreceiving, by the location management function, from the at least one of the user equipment or the network entity, at least one positioning differential measurement report comprising at least one positioning differential measurement.

16. The method of any of claims 1 to 15, wherein the at least one artificial intelligence / machine learning positioning functionality comprises at least one of the following:at least one artificial intelligence / machine learning-based line of sight determination; orat least one artificial intelligence / machine learning-based positioning differential measurement acquisition.

17. The method of any of claims 1 to 16, wherein the at least one configuration comprises at least one of the following:at least one uplink sounding reference signal configuration;at least one downlink positioning reference signal configuration;at least one transmission order of the at least one uplink sounding reference signal and the at least one downlink positioning reference signal;at least one line of sight determination policy;at least one artificial intelligence / machine learning-based positioning differential measurement acquisition policy;at least one positioning differential measurement granularity selection policy; orat least one policy for coordination between the user equipment and the network entity for artificial intelligence / machine learning functionality.

18. The method of any of claims 1 to 17, wherein the at least one positioning differential measurement comprises at least one of the following:at least one receive-transmit time difference measurement; orat least one receive-transmit carrier phase difference measurement.

19. An apparatus comprising:at least one processor; andat least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to:transmit, to a location management function, at least one positioning capability of the apparatus indicating at least one artificial intelligence / machine learning positioning functionality;receive, from the location management function, at least one configuration to perform at least one positioning differential measurement based upon the at least one positioning capability of the apparatus; andperform the at least one positioning differential measurement with at least one network entity according to the received configuration.

20. The apparatus of claim 19, wherein the instructions, when executed by the at least one processor, further cause the apparatus at least to:receive at least one downlink positioning reference signal from the at least one network entity, andwherein the at least one positioning differential measurement comprises a downlink positioning differential measurement based on the at least one downlink positioning reference signal according to a determined granularity associated with downlink positioning differential measurement acquisition.

21. The apparatus of claim 19 or 20, wherein the instructions, when executedby the at least one processor, further cause the apparatus at least to:transmit, to the at least one network entity, at least one uplink sounding reference signal, and wherein the at least one positioning differential measurement comprises an uplink positioning differential measurement based on the at least one uplink sounding reference signal according to a determined granularity associated with uplink positioning differential measurement acquisition.

22. The apparatus of any of claims 19 to 21, wherein the instructions, when executed by the at least one processor, further cause the apparatus at least to:transmit, to the location management function, at least one positioning differential measurement report comprising at least one positioning differential measurement.

23. The apparatus of any of claims 19 to 22, wherein the instructions, when executed by the at least one processor, further cause the apparatus at least to:determine at least one positioning differential measurement granularity; andtransmit, to the at least one network entity, the determined at least one positioning differential measurement granularity.

24. The apparatus of any of claims 19 to 23, wherein the instructions, when executed by the at least one processor, further cause the apparatus at least to:receive, from the at least one network entity, at least one positioning differential measurement granularity determined by the network entity.

25. The apparatus of any of claims 19 to 24, wherein the instructions, when executed by the at least one processor, further cause the apparatus at least to:transmit, to the at least one network entity, or receive, from the at least one network entity, at least one indication of an artificial intelligence machine learning functionality update.

26. An apparatus comprising:at least one processor; andat least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to:transmitting, by a network entity, to a location management function, at least one positioning capability of the apparatus indicating at least one artificial intelligence / machine learning positioning functionality;receiving, by the network entity, from the location management function, at least one configuration to perform at least one positioning differential measurement based upon the at least one positioning capability of the apparatus; andperforming, by the network entity, the at least one positioning differential measurement with a user equipment according to the received configuration.

27. The method of claim 26, wherein the instructions, when executed by the at least one processor, further cause the apparatus at least to:receive at least one uplink positioning reference signal from the user equipment, wherein the at least one positioning differential measurement comprises an uplink positioning differential measurement based on the at least one uplink positioning reference signal according to a determined granularity associated with uplink positioning differential measurement acquisition.

28. The apparatus of claim 26 or 27, wherein the instructions, when executed by the at least one processor, further cause the apparatus at least to:transmit, to the user equipment, at least one downlink sounding reference signal, wherein the at least one positioning differential measurement comprises a downlink positioning differential measurement based on the at least one downlink sounding reference signal according to a determined granularity associated with downlink positioning differential measurement acquisition.

29. The apparatus of any of claims 26 to 28, wherein the instructions, when executed by the at least one processor, further cause the apparatus at least to:transmit, to the location management function, at least one positioning differential measurement report comprising at least one positioning differential measurement.

30. The apparatus of any of claims 26 to 29, wherein the instructions, when executed by the at least one processor, further cause the apparatus at least to:determine at least one positioning differential measurement granularity; andtransmit, to the user equipment, the determined at least one positioning differential measurement granularity.

31. The apparatus of any of claims 26 to 30, wherein the instructions, when executed by the at least one processor, further cause the apparatus at least to:receive, from the user equipment, at least one positioning differential measurement granularity determined by the user equipment.

32. The apparatus of any of claims 26 to 31, wherein the instructions, when executed by the at least one processor, further cause the apparatus at least to:transmit, to the user equipment, or receive, from the user equipment, at least one indication of an artificial intelligence machine learning functionality update.3 3. An apparatus comprising:at least one processor; andat least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to:receive, from at least one of a user equipment or a network entity, at least one positioning capability of the at least one of the user equipment or the network entity indicating at least one artificial intelligence / machine learning positioning functionality;transmit, to the at least one of the user equipment or the network entity, at least one configuration to perform at least one positioning differential measurement based upon the at least one positioning capability; andreceive, from the at least one of the user equipment or the network entity, at least one positioning differential measurement report comprising at least one positioning differential measurement.

34. The apparatus of any of claims 19 to 33, wherein the at least one artificial intelligence / machine learning positioning functionality comprises at least one of the following:at least one artificial intelligence / machine learning-based line of sight determination; orat least one artificial intelligence / machine learning-based positioning differential measurement acquisition.

35. The apparatus of any of claims 19 to 34, wherein the at least one configuration comprises at least one of the following:at least one uplink sounding reference signal configuration;at least one downlink positioning reference signal configuration;at least one transmission order of the at least one uplink sounding reference signal and the at least one downlink positioning reference signal;at least one line of sight determination policy;at least one artificial intelligence / machine learning-based positioning differential measurement acquisition policy;at least one positioning differential measurement granularity selection policy; or at least one policy for coordination between the user equipment and the network entity for artificial intelligence / machine learning functionality.

36. The apparatus of any of claims 19 to 35, wherein the at least one positioning differential measurement comprises at least one of the following:at least one receive-transmit time difference measurement; orat least one receive-transmit earner phase difference measurement.

37. An apparatus, comprising means for performing a method according toany of claims 1 to 18.

38. A computer readable medium may include program instructions that, when executed by an apparatus, cause the apparatus to perform a method according to 5 any of claims 1 to 18.

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