Artificial intelligence / machine learning round-trip positioning method

Abstract

Systems, methods, apparatuses, and computer program products for artificial intelligence / machine learning round-trip positioning. A method may include the UE transmitting at least one positioning capability of the UE to an LMF, the positioning capability indicating at least one artificial intelligence / machine learning positioning function, and receiving at least one configuration from the LMF to perform at least one positioning differential measurement based on the at least one positioning capability of the UE. The method may also include the UE performing the at least one positioning differential measurement with the at least one network entity according to the received configuration.

Description

TECHNICAL FIELD

[0001] Some example embodiments can generally relate to mobile or wireless telecommunication systems, such as Third Generation Partnership Project (3GPP) Long Term Evolution (LTE), 5thGeneration (5G) radio access technology (RAT), New Radio (NR) access technology, 6thGeneration (6G), and / or other communication systems. For example, certain example embodiments can relate to systems and / or methods for artificial intelligence (AI) / machine learning (ML) roundtrip positioning. BACKGROUND

[0002] Examples of mobile or wireless telecommunication systems can include radio frequency (RF) 5G RAT, Universal Mobile Telecommunication System (UMTS) Terrestrial Radio Access Network (UTRAN), LTE-Advanced (LTE-A), LTE-A Pro, NR access technology, and / or MulteFire Alliance. 5G wireless systems refer to the next generation (NG) wireless systems and network architecture. 5G systems are generally built on 5G NR, but 5G (or NG) networks can also be built on E-UTRA radio. NR is expected to provide enhanced mobile broadband (eMBB) and ultra-reliable low-latency SUMMARY

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

[0004] According to some example embodiments, an apparatus may include components for sending at least one positioning capability of the apparatus, indicating at least one AI / ML positioning function, to an LMF. The apparatus may also include components for receiving at least one configuration from the LMF to perform at least one PDM based on the at least one positioning capability of the apparatus. The apparatus may further include components for performing at least one PDM with network entities based on the received configuration.

[0005] According to various example embodiments, a non-transitory computer-readable medium may include program instructions that, when executed by a device, cause the device to perform at least one method. The method may include sending at least one positioning capability of the device, indicating at least one AI / ML positioning function, to an LMF. The method may also include receiving at least one configuration from the LMF to perform at least one PDM based on the at least one positioning capability of the device. The method may further include performing at least one PDM with a network entity based on the received configuration.

[0006] According to some example embodiments, a computer program product can perform a method. The method may include sending an instruction to an LMF indicating at least one positioning capability of a UE with at least one AI / ML positioning function. The method may also include receiving at least one configuration from the LMF to perform at least one PDM based on the UE's at least one positioning capability. The method may further include performing at least one PDM with a network entity based on the received configuration.

[0007] According to some 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 to send at least one location capability of the apparatus, indicating at least one AI / ML location function, to an LMF. When executed by the at least one processor, the instructions may also cause the apparatus to receive at least one configuration from the LMF to perform at least one PDM based on the at least one location capability of the apparatus. When executed by the at least one processor, the instructions may also cause the apparatus to perform at least one PDM with a network entity, at least according to the received configuration.

[0008] According to various example embodiments, an apparatus may include a transmitting circuit configured to send an indication to an LMF of at least one positioning capability of the apparatus, representing at least one AI / ML positioning function. The apparatus may also include a receiving circuit configured to receive at least one configuration from the LMF and perform at least one PDM based on the at least one positioning capability of the apparatus. The apparatus may further include an execution circuit configured to perform at least one PDM with a network entity based on the received configuration.

[0009] According to some example embodiments, a method may include a network entity sending to an LMF at least one positioning capability of the network entity indicating at least one AI / ML positioning function. The method may also include a network entity receiving at least one configuration from the LMF to perform at least one PDM based on the network entity's at least one positioning capability. The method may further include a network entity performing at least one PDM with the UE according to the received configuration.

[0010] According to some example embodiments, an apparatus may include components for sending at least one positioning capability of the apparatus indicating at least one AI / ML positioning function to an LMF. The apparatus may also include components for receiving at least one configuration from the LMF to perform at least one PDM based on the at least one positioning capability of the apparatus. The apparatus may further include components for performing at least one PDM with the UE according to the received configuration.

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

[0012] According to some example embodiments, a computer program product can perform a method. The method may include sending an instruction to an LMF indicating at least one positioning capability of a device with at least one AI / ML positioning function. The method may also include receiving at least one configuration from the LMF to perform at least one PDM based on the at least one positioning capability of the device. The method may further include performing at least one PDM with the UE based on the received configuration.

[0013] According to some 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 to send at least one positioning capability of the apparatus indicating at least one AI / ML positioning function to the LMF. When executed by the at least one processor, the instructions may also cause the apparatus to receive at least one configuration from the LMF to perform at least one PDM based on the at least one positioning capability of the apparatus. When executed by the at least one processor, the instructions may also cause the apparatus to perform at least one PDM with the UE at least according to the received configuration.

[0014] According to various example embodiments, an apparatus may include a transmitting circuit configured to perform transmitting to an LMF at least one positioning capability of the apparatus indicating at least one AI / ML positioning function. The apparatus may also include a receiving circuit configured to perform receiving at least one configuration from the LMF and performing at least one PDM based on at least one positioning capability of the apparatus. The apparatus may further include an execution circuit configured to perform performing at least one PDM with the UE based on the received configuration.

[0015] According to some example embodiments, a method may include receiving, by an LMF, at least one positioning capability of a UE or NE indicating at least one AI / ML positioning function of at least one UE or NE. The method may also include sending, by the LMF, at least one configuration to at least one UE or NE to perform at least one PDM based on the at least one positioning capability. The method may further include receiving, by the LMF, at least one PDM report containing at least one PDM from at least one UE or NE.

[0016] According to certain example embodiments, an apparatus may include components for receiving from at least one of a UE or network entity at least one of a UE or NE at least one indicating at least one AI / ML positioning function. The apparatus may also include components for sending at least one configuration to at least one of the UE or NE to perform at least one PDM based on at least one positioning capability. The apparatus may further include components for receiving from at least one of the UE or NE at least one PDM report containing at least one PDM.

[0017] According to various example embodiments, a non-transitory computer-readable medium may include program instructions that, when executed by a device, cause the device to perform at least one method. The method may include receiving from at least one of a UE or network entity at least one of the UE or NE at least one indicating at least one AI / ML positioning function. The method may also include sending at least one configuration to at least one of the UE or NE to perform at least one PDM based on the at least one positioning capability. The method may further include receiving from at least one of the UE or NE at least one PDM report containing at least one PDM.

[0018] According to some example embodiments, a computer program product can perform a method. The method may include receiving from at least one of the UE or NE at least one of the UE or NE at least one indicating at least one AI / ML positioning function. The method may also include sending at least one configuration to at least one of the UE or NE to perform at least one PDM based on the at least one positioning capability. The method may further include receiving from at least one of the UE or NE at least one PDM report containing at least one PDM.

[0019] According to some example embodiments, an apparatus may include at least one processor and at least one memory storing instructions that, when executed by at least one processor, cause the apparatus to receive, from at least one of the UEs or NEs, at least one positioning capability of the UE or NE indicating at least one AI / ML positioning function. When executed by at least one processor, the instructions may also cause the apparatus to send at least one configuration to at least one of the UEs or NEs to perform at least one PDM based on at least one positioning capability. When executed by at least one processor, the instructions may also cause the apparatus to receive, from at least one of the UEs or NEs, at least one PDM report containing at least one PDM.

[0020] According to various example embodiments, an apparatus may include a receiving circuit configured to perform receiving from at least one of the UEs or NEs at least one of the UEs or NEs, indicating at least one AI / ML positioning function of the UE or NE at least one location capability. The apparatus may also include a transmitting circuit configured to perform transmitting to at least one of the UEs or NEs at least one configuration to perform at least one PDM based on at least one location capability. The apparatus may further include a receiving circuit configured to perform receiving from at least one of the UEs or NEs at least one PDM report containing at least one PDM. Attached Figure Description

[0021] To correctly understand the exemplary embodiments, reference should be made to the accompanying drawings, in which: Figure 1 An example of the traditional round-trip time method is shown.

[0022] Figure 2 An example of a signaling diagram according to certain example embodiments is shown; Figure 3 An example of a flowchart illustrating a method according to some example embodiments is shown; Figure 4 An example flowchart illustrating another method according to various example embodiments is shown; Figure 5 An example flowchart illustrating another method according to certain example embodiments is shown; Figure 6 Examples of various network devices according to some example embodiments are shown; and Figure 7 Examples of 5G network and system architectures based on various example embodiments are shown.

[0023] Detailed description It is readily understood that components of certain example embodiments, as generally described and illustrated in the figures herein, can be arranged and designed in a variety of different configurations. Therefore, 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 any particular example embodiment, but rather represents selected example embodiments.

[0024] 3GPP 5G is considering enhancing positioning accuracy for various scenarios, including those with severe non-line-of-sight (NLOS) conditions. Multiple round-trip time (RTT) methods can help minimize synchronization errors between the UE and the transmit-receive point (TRP). Figure 1 A conventional RTT (Real-Time To-Time) method is described, in which the Location Management Function (LMF) sends Position Reference Signal (PRS) configuration information to the UE, indicating the downlink (DL) PRS configuration associated with different TRPs. The LMF can also indicate uplink (UL) PRS information to the UE, on which the UE transmits UL PRS for TRP measurement. The TRP can then transmit DL PRS, and the UE can measure the Time of Arrival (TOA), including synchronization offset and / or propagation delay. In response to the DL PRS, the UE can transmit a UL Sound Reference Signal (SRS), and the TRP can measure the TOA. The base station can then send a measurement report to the LMF, including the TRP receive (Rx) - transmit (Tx) time difference measurement. Finally, the LMF can perform subtraction to obtain the RTT for each TRP, which can be used to calculate the UE's location.

[0025] Some of the example embodiments described herein can have various technical effects to enhance positioning accuracy. For example, some example embodiments can provide LOS information, such as Rx-Tx time difference, at the UE and base station before acquiring at least one positioning differential measurement (PDM), including whether the LOS is detected independently by the UE and base station, or conversely, whether one entity dominates the detection, and whether the UE and / or base station will use AI / ML. Furthermore, various example embodiments can use AI / ML to acquire the most accurate PDM and determine whether cooperation between the UE and TRP is required. Additionally, some example embodiments can select the granularity of the PDM and support making LOS information available and acquiring the most accurate PDM. Therefore, some example embodiments discussed below relate to improvements in computer-related technologies.

[0026] Since round-trip (RT) positioning requires both the UE and the base station to measure positioning signals and report PDM, the use of AI / ML between the UE and the base station needs to be coordinated, which involves at least how to determine LOS, how to choose the granularity of measurement, and how to deploy AI / ML (e.g., independently on each side, or jointly on both sides).

[0027] To coordinate these determinations, AI / ML RT positioning methods can enhance existing RT positioning by introducing new elements. For example, a new Long Term Evolution Positioning Protocol (LPP) / New Radio Positioning Protocol A (NRPPa) Auxiliary Data Information Element (IE) sent from the LMF to the UE / base station can coordinate the deployment and use of AI / ML RT functions (e.g., RT LOS determination, RT PDM acquisition, etc.). Furthermore, new Radio Resource Control (RRC) IEs or Media Access Control (MAC) Control Elements (CEs) can be provided for coordination between the UE and base station, which can coordinate AI / ML RT procedures and / or synchronize the use of AI / ML RT functions, at least in terms of LOS determination and PDM granularity selection (e.g., whether a model switch / deactivation at one end requires an action at the other end).

[0028] Figure 2 An example signaling diagram 200 depicting certain example embodiments for implementing AI / ML RT procedures is shown. According to certain example embodiments, LMF 220 and NE 240 may be similar to NE 610, and UE 230 may be similar to UE 620, as... Figure 6 As shown.

[0029] In operation 201, UE 230 may send at least one positioning capability of UE 230 to LMF 220, which may indicate at least one AI / ML RT function of UE 230. For example, at least one AI / ML RT function of UE 230 may include LOS determination (e.g., AI / ML-based or non-AI / ML-based LOS detector) and / or PDM acquisition (e.g., AI / ML-based or non-AI / ML-based PDM, where PDM may include Rx-Tx time difference, carrier phase difference, etc.). However, any other AI / ML RT function of UE 230 may be indicated. In various example embodiments, PDM may refer to any type of PDM associated with, for example, the Rx-Tx time difference of the direct path, the Rx-Tx time difference of the reflected path (e.g., the strongest N multipaths), and / or the Rx-Tx carrier phase difference.

[0030] Similarly, in operation 202, NE 240 may send at least one positioning capability of NE 240 to LMF 220, which may indicate at least one AI / ML RT function of NE 240. For example, at least one AI / ML RT function of NE 240 may include LOS determination (e.g., AI / ML-based or non-AI / ML-based LOS detector) and / or PDM acquisition (e.g., AI / ML-based or non-AI / ML-based PDM). However, any other AI / ML RT function of NE 240 may be indicated.

[0031] Based on the AI / ML RT function report received in operations 201 and / or 202, LMF 220 can send configurations to UE 230 and NE 240 in operations 203 and 204 respectively to configure AI / ML RT. In an example embodiment, LMF 220 can send AI / ML RT configurations via LPP and / or NRPPa IE.

[0032] For example, the configuration may include UL SRS and DL PRS configurations, and may optionally indicate which entity (i.e., UE230 or NE240) should send 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 functions or not, or performed independently by UE230 or NE240.

[0033] In an alternative example embodiment, LMF 220 can select and configure UE 230 or NE 240 to lead the acquisition of LOS information. The selected entity (i.e., UE 230 or NE 240) can notify the other entity (i.e., UE 230 or NE 240) of the LOS information when transmitting its respective RS (i.e., UL SRS or DL ​​PRS), assuming that the UL and DL channels have the same LOS probability due to channel reciprocity. For example, the LOS information can be explicitly notified by attaching a LOS flag after the selected entity's RS transmission, or implicitly notified by selecting an RS identifier (ID) associated with the LOS flag. For example, if UE 230 performs the acquisition of LOS information, UE 230 can send the LOS flag along with the UL SRS and / or select an SRS whose ID is associated with the LOS flag.

[0034] In some example embodiments, the configuration may indicate at least one AI / ML-based PDM acquisition strategy. For example, LMF 220 may configure UE 230 and / or NE 240 regarding whether PDM acquisition is performed using AI / ML functions, and whether UE 230 or NE 240 uses their respective AI / ML functions (if any).

[0035] In some example embodiments, the configuration may indicate at least one PDM granularity selection strategy. For example, LMF 220 may configure UE 230 and / or NE 240 regarding how to select PDM acquisition granularity, such as which entity (i.e., UE 230 or NE 240) can 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 evaluated the RT configuration; whether the granularity is fixed and set by NE 240; or whether the granularity is selected independently by UE 230 and NE 240 respectively. The entity that selects or determines the granularity (i.e., UE 230 or NE 240) may notify the other entity (i.e., NE 240 or UE 230) of the decision.

[0036] In some example embodiments, the configuration may indicate at least one policy for coordinating AI / ML functions between UE 230 and NE 240. For example, LMF 220 may configure UE 230 and / or NE 240 to coordinate AI / ML usage and its backup mechanisms in the event of AI / ML function failure. For example, if UE 230 decides to revert to a legacy method for LOS determination or PDM acquisition, LMF 220 may configure UE 230 to notify NE 240 about the reversion to the legacy method, and / or NE 240 to follow that decision, and vice versa. As another example, if UE 230 decides to update functions associated with AI / ML RT (e.g., switching between AI / ML models and / or deactivating models), LMF 220 may configure UE 230 to notify NE 240 about the switching and / or update, and / or NE 240 to mirror UE 230's decision, and vice versa.

[0037] During operation 205, UE 230 and / or NE 240 may apply the configuration received from LMF 220, including the coordination policy as described above.

[0038] In operation 206, NE 240 can send at least one DL PRS to UE 230.

[0039] In Operation 207, UE 230 can apply AI / ML RT functions and can acquire PDM.

[0040] During operation 208, UE 230 may send at least one UL SRS to NE 240.

[0041] In operation 209, if UE 230 is configured to dominate LOS acquisition, UE 230 may send at least one LOS flag (explicit or implicit) to NE 240.

[0042] In operation 210, if UE 230 is configured to select PDM granularity, UE 230 can send an indication of PDM granularity to NE 240.

[0043] In operation 211, NE 240 can apply AI / ML RT functions and can acquire PDM.

[0044] In operation 212, when the AI / ML RT function changes, UE 230 and / or NE 240 can notify the other party (i.e., NE240 or UE 230).

[0045] In various example embodiments, if UE 230 and / or NE 240 switch / deactivate a model between models for a given AI / ML RT-related function, UE 230 and / or NE 240 can notify another entity as configured in LMF 220. For example, if the AI / ML RT resides on both UE 230 and NE 240, the model switch must be agreed upon and synchronized (e.g., model parts can be activated simultaneously). As another example, if the AI / ML RT model resides only on either UE 230 or NE 240, the entity using the model can notify another entity that does not use the AI / ML model regarding the switch / deactivation.

[0046] In operation 213, UE 230 can send a PDM report including PDM to LMF 220. Similarly, in operation 214, NE 240 can send a PDM report including PDM to LMF 220.

[0047] In operation 214, LMF 220 can determine the location of UE 230 based on the PDM report received from UE 230 and / or NE 240. For example, using the PDM report, LMF 220 can obtain distance information between UE 230 and each TRP, and then triangulate the location of UE 230.

[0048] In some example implementations, such as the LMF 220 configuration, the NE 240 can lead the LOS acquisition, select the PDM acquisition granularity, and / or notify the UE 230 of such information.

[0049] Figure 3 This illustrates various example embodiments that can be provided by a UE, such as Figure 6 The flowchart of method 300 executed by UE 620 is shown in the figure.

[0050] In step 301, the method may include sending to the LMF (e.g. Figure 6The NE 610 shown in the figure transmits at least one positioning capability of the UE, the at least one positioning capability indicating at least one AI / ML positioning function.

[0051] In step 302, the method may further include receiving at least one configuration from the LMF to perform at least one PDM based on at least one positioning capability of the UE.

[0052] In step 303, the method may further include, based on the received configuration and network entity (e.g., Figure 6 The NE 610 shown in the figure performs at least one PDM.

[0053] In some example embodiments, the method may further include receiving at least one DL PRS from the NE. The at least one PDM includes a DL PDM with a determined granularity associated with the DL PDM based on the at least one DL PRS.

[0054] In some example embodiments, the method may further include sending at least one UL SRS to the NE. Furthermore, the at least one PDM may include a ULPDM with a defined granularity based on the at least one UL SRS and associated with the UL PDM acquisition.

[0055] In various example embodiments, the method may also include sending at least one PDM report, which includes at least one PDM, to the LMF.

[0056] In some example embodiments, the at least one AI / ML localization function may include at least one AI / ML-based LOS determination or at least one AI / ML-based PDM acquisition.

[0057] In some example embodiments, the at least one configuration may include 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 strategy; at least one AI / ML-based PDM acquisition strategy; at least one PDM granularity selection strategy; or at least one of at least one strategies for coordination of AI / ML functions between the UE and NE.

[0058] In various example embodiments, the at least one PDM may include at least one receive-transmit time difference measurement; or at least one receive-transmit carrier phase difference measurement.

[0059] In some example embodiments, the method may further include determining at least one PDM granularity; and sending the determined at least one PDM granularity to the network entity.

[0060] In some example embodiments, the method may further include receiving at least one PDM granularity determined by the NE from the NE.

[0061] In various example embodiments, the method may also include sending or receiving at least one indication of an AI / ML feature update to or from the NE.

[0062] Figure 4 It is shown that, according to various example embodiments, it can be made by NE (e.g. Figure 6 An example flowchart of method 400 performed by NE 610 is shown.

[0063] In step 401, the method may include sending at least one positioning capability of a network entity to the LMF, indicating at least one AI / ML positioning function.

[0064] In step 402, the method may further include receiving at least one configuration from the LMF by the network entity to perform at least one PDM based on at least one positioning capability of the network entity.

[0065] In step 403, the method may further include the network entity performing the at least one PDM with the UE based on the received configuration.

[0066] In some example embodiments, the method may further include receiving at least one UL PRS from the UE by a network entity. The at least one PDM may include: a UL PDM based on the at least one UL PRS, according to a determined granularity associated with UL PDM acquisition.

[0067] In some example embodiments, the method may further include sending at least one DL SRS to the UE by a network entity, and wherein the at least one PDM includes: a DL PDM based on the at least one DL SRS, according to a determined granularity associated with DL PDM acquisition.

[0068] In various example embodiments, the method may also include sending at least one PDM report, which includes at least one PDM, to the LMF by a network entity.

[0069] In some example embodiments, the method may further include determining at least one PDM granularity by a network entity, and sending the determined at least one PDM granularity to the UE by the network entity.

[0070] In some example embodiments, the method may further include receiving at least one PDM granularity determined by the UE from the UE by a network entity.

[0071] In various example embodiments, the method may also include at least one indication sent by a network entity to the UE, or received by the NE from the UE, of an AI / ML function update.

[0072] Figure 5 It is shown that, according to various example embodiments, it can be made by LMF (e.g. Figure 6 An example flowchart of method 500 performed by NE 610 is shown.

[0073] In step 501, the method may include receiving, by the LMF, at least one positioning capability of at least one of the UE or NE that indicates at least one AI / ML positioning function.

[0074] In step 502, the method may further include sending at least one configuration from the LMF to at least one of the UE or NE to perform at least one PDM based on the at least one positioning capability.

[0075] In step 503, the method may further include receiving, by the LMF, at least one PDM report including at least one PDM from at least one of the UE or NE.

[0076] In some example embodiments, the at least one AI / ML localization function may include at least one AI / ML-based LOS determination, or at least one AI / ML-based PDM acquisition.

[0077] In some example embodiments, the at least one configuration may include 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 strategy; at least one AI / ML-based PDM acquisition strategy; at least one PDM granularity selection strategy; or at least one of at least one strategies for coordination of AI / ML functions between the UE and NE.

[0078] In various example embodiments, the at least one PDM includes at least one Rx-Tx time difference measurement, or at least one receive-transmit carrier phase difference measurement.

[0079] Figure 6 An example of a system according to certain example embodiments is shown. In one example embodiment, the system may include multiple devices, such as NE 610 and / or UE 620.

[0080] NE 610 can be one or more of a base station (e.g., a 3G UMTS NodeB, a 4G LTE evolved NodeB, or a 5G NR next-generation NodeB), an LMF, a serving gateway, a server, and / or any other access node or a combination thereof.

[0081] The NE 610 may also 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 communicate via at least one F1 interface, at least one Xn-C interface and / or via at least one NG interface of the fifth-generation core network (5GC).

[0082] UE 620 may include one or more mobile devices, such as mobile phones, smartphones, personal digital assistants (PDAs), tablets or portable media players, digital cameras, pocket cameras, video game consoles, navigation units (such as Global Positioning System (GPS) devices), desktop or laptop computers, single-location devices (such as sensors or smart meters), or any combination thereof. Furthermore, NE 610 and / or UE 620 may be one or more Citizen Broadband Wireless Service (CBSD) devices.

[0083] NE 610 and / or UE 620 may include at least one processor, denoted as 611 and 621 respectively. Processors 611 and 621 may be implemented by any computing or data processing device, such as a central processing unit (CPU), application-specific integrated circuit (ASIC), or similar device. The processor may be implemented as a single controller, or multiple controllers or processors.

[0084] As shown in 612 and 622, at least one memory can be provided in one or more devices. The memory can be fixed or removable. The memory can include computer program instructions or computer code contained therein. Memory 612 and 622 can be any suitable storage device independently, such as a non-transitory computer-readable medium. As used herein, the term "non-transitory" can correspond to a limitation of the medium itself (i.e., tangible, not tactile) rather than a limitation of the persistence of data storage (e.g., random access memory (RAM) versus read-only memory (ROM)). Hard disk drives (HDDs), random access memory (RAM), flash memory, or other suitable memory can be used. The memory can be combined with a processor on a single integrated circuit or can be separate from one or more processors. Furthermore, the computer program instructions stored in the memory and processed by the processor can be any suitable form of computer program code, such as a compiled or interpreted computer program written in any suitable programming language.

[0085] Processors 611 and 621, memories 612 and 622, and any subset thereof can be configured to provide corresponding Figures 2-5 The device comprises various blocks. Although not shown, the device may also include positioning hardware, such as GPS or microelectromechanical systems (MEMS) hardware, which can be used to determine the location of the device. Other sensors are also permitted and can be configured to determine position, altitude, speed, direction, etc., such as barometers, compasses, etc.

[0086] like Figure 6 As shown, transceivers 613 and 623 may be provided, and one or more devices may also include at least one antenna, shown as 614 and 624 respectively. The devices may have multiple antennas, such as an antenna array configured for multiple-input multiple-output (MIMO) communication, or multiple antennas for multiple RATs. Other configurations of these devices may be provided. Transceivers 613 and 623 may be transmitters, receivers, both transmitters and receivers, or units or devices configured for both transmitting and receiving.

[0087] Memory and computer program instructions can be configured together with the processor of a specific device to cause the hardware device (e.g., UE) to perform any of the above processes (i.e., Figures 2-5 Therefore, in some example embodiments, the non-transitory computer-readable medium may be encoded with computer instructions that, when executed in hardware, perform one of the procedures described herein. Alternatively, some example embodiments may be executed entirely in hardware.

[0088] In some example embodiments, the apparatus may include being configured to perform Figures 2-5The term "circuit" as used herein may refer to one or more of the following: (a) a hardware circuit implementation only (e.g., an implementation only in analog and / or digital circuitry), (b) a combination of hardware circuitry and software, such as (if applicable): (i) a combination of analog and / or digital hardware circuitry with software / firmware, and (ii) a hardware processor with software (including digital signal processors), software, and any portion of memory that works together to enable a device (e.g., a mobile phone or server) to perform various functions; and (c) a hardware circuitry and / or processor, such as a microprocessor or a portion thereof, which requires software (e.g., firmware) to operate, but may be absent when operation is not required. This definition of "circuit" applies to all uses of the term in this application, including in any claim. As a further example, as used herein, the term "circuit" also covers a hardware circuitry or processor (or multiple processors) or a portion thereof and its accompanying software and / or firmware implementation. The term "circuit" also covers, for example and if applicable to a particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device, or a similar integrated circuit in a server, cellular network device, or other computing or networking device.

[0089] Figure 7 Examples of 5G network and system architectures are illustrated according to certain example embodiments. Several network functions are shown, which can be implemented in software as part of a network device or dedicated hardware, as the network device itself or dedicated hardware, or as virtual functions operating as a network device or dedicated hardware. Figure 7 The NE and UE shown can be similar to NE 610 and UE 620, respectively. User plane functions (UPF) can provide services such as intra- and inter-RAT mobility, packet routing and forwarding, packet inspection, user plane quality of service (QoS) processing, DL packet buffering, and / or triggering of DL data notifications. Application functions (AF) can primarily interface with the core network to facilitate application use of traffic routing and interact with the policy framework.

[0090] According to some example embodiments, processors 611 and 621, and memories 612 and 622 may be included in or may form part of processing or control circuitry. Furthermore, in some example embodiments, transceivers 613 and 623 may be included in or may form part of transceiver circuitry.

[0091] In some example embodiments, the apparatus (e.g., NE 610 and / or UE 620) may include means for performing methods, processes, or any variations discussed herein. Examples of the apparatus may include one or more processors, memory, controllers, transmitters, receivers, and / or computer program code for causing operations to be performed.

[0092] In various example embodiments, device 620 may be controlled by memory 622 and processor 621 to send at least one positioning capability of the device indicating at least one AI / ML positioning function to LMF; receive at least one configuration from LMF to perform at least one PDM based on at least one positioning capability of the device; and perform at least one PDM with NE according to the received configuration.

[0093] Some example embodiments may be directed to an apparatus that includes components for performing any of the methods described herein, such as: components for sending an instruction to the LMF for at least one AI / ML component; components for receiving at least one configuration from the LMF to perform at least one PDM based on at least one positioning capability of the apparatus; and components for performing at least one PDM with the NE according to the received configuration.

[0094] In various example embodiments, device 610 may be controlled by memory 612 and processor 611 to send at least one positioning capability of the device indicating at least one AI / ML positioning function to LMF; receive at least one configuration from LMF to perform at least one PDM based on at least one positioning capability of the device; and perform at least one PDM with UE according to the received configuration.

[0095] Some example embodiments may be directed to an apparatus that includes components for performing any of the methods described herein, such as: components for sending at least one positioning capability of the apparatus indicating at least one AI / ML positioning function to the LMF; components for receiving at least one configuration from the LMF and performing at least one PDM based on at least one positioning capability of the apparatus; and components for performing at least one PDM with the UE according to the received configuration.

[0096] In various example embodiments, the device 610 may be controlled by the memory 612 and the processor 611 to receive from at least one of the UEs or NEs ...

[0097] Some example embodiments may be directed to an apparatus that includes components for performing any of the methods described herein, such as: components for receiving from at least one of the UEs or NEs at least one location capability of the UE or NE indicating at least one AI / ML location function; components for sending at least one configuration to at least one of the UEs or NEs to perform at least one PDM based on at least one location capability; and components for receiving from at least one of the UEs or NEs at least one PDM report containing at least one PDM.

[0098] The features, structures, or characteristics of the exemplary embodiments described in this specification can be combined in one or more exemplary embodiments in any suitable manner. For example, the phrases "various embodiments," "some embodiments," "some embodiments," or other similar language used throughout this specification refer to the fact that a particular feature, structure, or characteristic described in connection with the exemplary embodiments may be included in at least one exemplary embodiment. Therefore, the phrases "in various embodiments," "in some embodiments," "in some embodiments," or other similar language appearing throughout this specification do not necessarily all refer to the same set of exemplary embodiments, and the described features, structures, or characteristics can be combined in one or more exemplary embodiments in any suitable manner.

[0099] 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 is connected by "and" or "or", means at least any one element, or at least any two or more elements, or at least all elements.

[0100] Furthermore, if necessary, the different functions or processes discussed above can be executed in different orders and / or concurrently with each other. Additionally, if necessary, one or more of the described functions or processes can be optional or can be combined. Therefore, the above description should be considered as an illustration of the principles and teachings of certain example embodiments, and not as a limitation thereof.

[0101] Those skilled in the art will readily understand that the exemplary embodiments discussed above can be practiced with processes of a different order and / or with hardware elements in a configuration different from the disclosed configuration. Therefore, although some embodiments have been described based on these exemplary embodiments, certain modifications, variations, and alternative constructions will be apparent to those skilled in the art, while remaining within the spirit and scope of the exemplary embodiments.

[0102] Partial Glossary 3GPP Third Generation Partnership Project 5G (Fifth Generation) 5GC Fifth Generation Core Network 6G sixth generation AF Application Functions AI / ML Artificial Intelligence / Machine Learning ASIC (Application-Specific Integrated Circuit) CBSD Citizen Broadband Radio Service Equipment CE control unit CPU (Central Processing Unit) CU (Centralized Unit) DL downlink DU distribution unit eMBB Enhanced Mobile Broadband eNB Evolutionary Node B gNB Next Generation Node B GPS Global Positioning System HDD (Hard Disk Drive) IE Information Elements IoT (Internet of Things) LMF location management function LOS (Location of View) LPP (Long Term Evolution Positioning Protocol) LTE Long Term Evolution LTE-A Long Term Evolution Enhancement MAC Media Access Control MEMS (Micro-Electro-Mechanical Systems) MIMO (Multiple Input Multiple Output) mMTC (Mass Machine Type Communication) NE network entity NG Next Generation NG-eNB Next Generation Evolution Node B NG-RAN (Next Generation Radio Access Network) NR New Radio NRPPa New Radio Positioning Protocol A PDA (Personal Digital Assistant) PDM Positioning Differential Measurement PRS Positioning Reference Signal PUCCH (Physical Uplink Control Channel) PUSCH Physical Uplink Shared Channel QoS (Quality of Service) RAM (Random Access Memory) RAN (Radio Access Network) RAT wireless access technology RF (Radio Frequency) ROM (Read-Only Memory) RRC (Radio Resource Control) RS reference signal RT round trip RTT round trip time Rx Receive SRS Detection Reference Signal TOA Arrival Time TRP Send / Receive Point Tx Send UE User Equipment UL uplink UMTS Universal Mobile Telecommunication System UPF User Plane Functions URLLC Ultra-Reliable Low-Latency Communication UTRAN (Universal Mobile Radio Access Network)

Claims

1. A method comprising: The user equipment sends at least one positioning capability of the user equipment to the location management function, the at least one positioning capability indicating at least one artificial intelligence / machine learning positioning function; The user equipment receives at least one configuration from the location management function to perform at least one location differential measurement based on the at least one positioning capability of the user equipment; as well as The user equipment performs the at least one location differential measurement with at least one network entity according to the received configuration.

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

3. The method according to claim 1 or 2, further comprising: The user equipment sends at least one uplink probe reference signal to the at least one network entity, and the at least one location differential measurement includes: uplink location differential measurement based on the at least one uplink probe reference signal, at a determined granularity associated with the uplink location differential measurement.

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

5. The method according to any one of claims 1 to 4, further comprising: The user equipment determines at least one positioning differential measurement granularity; as well as The user equipment sends at least one determined location differential measurement granularity to the at least one network entity.

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

7. The method according to any one of claims 1 to 6, further comprising: At least one instruction for updating the artificial intelligence machine learning function is sent by the user equipment to the at least one network entity or received by the user equipment from the at least one network entity.

8. A method comprising: The network entity sends at least one location capability of the network entity to the location management function, the at least one location capability indicating at least one artificial intelligence / machine learning location function; The network entity receives at least one configuration from the location management function to perform at least one location differential measurement based on the at least one positioning capability of the network entity; as well as The network entity performs the at least one positioning differential measurement with the user equipment according to the received configuration.

9. The method according to claim 8, further comprising: The network entity receives at least one uplink positioning reference signal from the user equipment. The at least one positioning differential measurement includes: an uplink positioning differential measurement based on the at least one uplink positioning reference signal, at a determined granularity associated with the uplink positioning differential measurement.

10. The method according to claim 8 or 9, further comprising: The network entity sends at least one downlink probe reference signal to the user equipment. The at least one positioning differential measurement includes a downlink positioning differential measurement based on the at least one downlink probe reference signal, at a determined granularity associated with the downlink positioning differential measurement.

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

12. The method according to any one of claims 8 to 11, further comprising: At least one positioning differential measurement granularity is determined by the network entity; as well as The network entity sends at least one determined positioning differential measurement granularity to the user equipment.

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

14. The method according to any one of claims 8 to 13, further comprising: At least one instruction is sent by the network entity to the user equipment or received by the network entity from the user equipment regarding an update to the artificial intelligence machine learning function.

15. A method comprising: The location management function receives at least one positioning capability of the user equipment or the network entity from at least one of the user equipment or the network entity, the at least one positioning capability indicating at least one artificial intelligence / machine learning positioning function; The location management function sends at least one configuration to at least one of the user equipment or the network entity to perform at least one location differential measurement based on the at least one positioning capability; as well as The location management function receives at least one location differential measurement report, including at least one location differential measurement, from at least one of the user equipment or the network entity.

16. The method according to any one of claims 1 to 15, wherein the at least one artificial intelligence / machine learning localization function comprises at least one of the following: At least one gaze determination method based on artificial intelligence / machine learning; or At least one location differential measurement based on artificial intelligence / machine learning is required.

17. The method according to any one of claims 1 to 16, wherein the at least one configuration comprises at least one of the following: At least one uplink probe reference signal configuration; At least one downlink positioning reference signal is configured; At least one transmission order of the at least one uplink detection reference signal and the at least one downlink positioning reference signal; At least one line-of-sight determination strategy; At least one positioning differential measurement acquisition strategy based on artificial intelligence / machine learning; At least one positioning differential measurement granularity selection strategy; or At least one strategy for coordinating artificial intelligence / machine learning functions between the user equipment and the network entity.

18. The method according to any one 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; or At least one receive-transmit carrier phase difference measurement.

19. An apparatus comprising: At least one processor; as well as At least one memory stores instructions that, when executed by the at least one processor, cause the device to at least: Send at least one positioning capability of the device to the location management function, the at least one positioning capability indicating at least one artificial intelligence / machine learning positioning function; Receive at least one configuration from the location management function to perform at least one positioning differential measurement based on the at least one positioning capability of the device; as well as Based on the received configuration, perform the at least one location differential measurement with at least one network entity.

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

21. The apparatus of claim 19 or 20, wherein the instructions, when executed by the at least one processor, further cause the apparatus to at least: Sending at least one uplink probe reference signal to the at least one network entity, wherein the at least one positioning differential measurement includes: Based on the at least one uplink probe reference signal, uplink positioning differential measurements are performed at a determined granularity associated with the uplink positioning differential measurements.

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

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

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

25. The apparatus according to any one of claims 19 to 24, wherein the instructions, when executed by the at least one processor, further cause the apparatus to at least: Sending or receiving at least one instruction to the at least one network entity for an AI / machine learning function update.

26. An apparatus comprising: At least one processor; as well as At least one memory stores instructions that, when executed by the at least one processor, cause the device to at least: The network entity sends at least one positioning capability of the device to the location management function, the at least one positioning capability indicating at least one artificial intelligence / machine learning positioning function; The network entity receives at least one configuration from the location management function to perform at least one location differential measurement based on the at least one positioning capability of the device; as well as The network entity performs the at least one positioning differential measurement with the 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 means to at least: Receive at least one uplink positioning reference signal from the user equipment. The at least one positioning differential measurement includes: Based on the at least one uplink positioning reference signal, uplink positioning differential measurements are acquired at a determined granularity associated with the uplink positioning differential measurements.

28. The apparatus of claim 26 or 27, wherein the instructions, when executed by the at least one processor, further cause the apparatus to at least: Send at least one downlink probe reference signal to the user equipment. The at least one positioning differential measurement includes: Based on the at least one downlink probe reference signal, downlink positioning differential measurements are performed at a determined granularity associated with downlink positioning differential measurements.

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

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

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

32. The apparatus according to any one of claims 26 to 31, wherein the instructions, when executed by the at least one processor, further cause the apparatus to at least: Sending or receiving at least one instruction to the user equipment for an update of the artificial intelligence machine learning function.

33. An apparatus comprising: At least one processor; as well as At least one memory stores instructions that, when executed by at least one processor, cause the device to at least: Receive at least one positioning capability of the user equipment or the network entity from at least one of the user equipment or the network entity, wherein the at least one positioning capability indicates at least one artificial intelligence / machine learning positioning function; Send at least one configuration to at least one of the user equipment or the network entity to perform at least one positioning differential measurement based on at least one positioning capability; as well as Receive at least one location differential measurement report, including at least one location differential measurement, from at least one user equipment or network entity.

34. The apparatus of any one of claims 19 to 33, wherein the at least one artificial intelligence / machine learning localization function comprises at least one of the following: At least one gaze determination method based on artificial intelligence / machine learning; or At least one location differential measurement based on artificial intelligence / machine learning is required.

35. The apparatus according to any one of claims 19 to 34, wherein said at least one configuration comprises at least one of the following: At least one uplink probe reference signal configuration; At least one downlink positioning reference signal is configured; The transmission order of the at least one uplink detection reference signal and the at least one downlink positioning reference signal; At least one line-of-sight determination strategy; At least one positioning differential measurement acquisition strategy based on artificial intelligence / machine learning; At least one positioning differential measurement granularity selection strategy; or At least one strategy for coordinating artificial intelligence / machine learning functions between the user equipment and the network entity.

36. The apparatus according to any one 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; or At least one receive-transmit carrier phase difference measurement.

37. An apparatus comprising components for performing the method according to any one of claims 1 to 18.

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

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