Positioning technology selection for ground truth generation associated with artificial intelligence positioning estimation via user equipment

Abstract

Methods, systems, apparatuses, and computer program products provide positioning technology selection for ground truth generation associated with artificial intelligence (AI) positioning estimation. In one implementation, positioning technology information associated with an AI positioning function of a device is determined, a positioning-related request for the device is generated based on the positioning technology information, and the positioning-related request is caused to be transmitted to a network entity. In one implementation, the positioning-related request includes an indicator that the positioning-related request is requesting ground truth positioning-related information for the device for use by the AI positioning function.

Description

Technical Field

[0001] The exemplary embodiments of this disclosure generally relate to communication systems, and more specifically, to network functions and artificial intelligence functions provided by communication networks. Background Technology

[0002] Fourth-generation (4G) wireless mobile telecommunications technology, also known as Long Term Evolution (LTE) technology, is designed to provide high-capacity mobile multimedia with high data rates and machine-type communications. Next-generation or fifth-generation (5G) technology aims to achieve even higher data rates through enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). The 3rd Generation Partnership Project (3GPP) 5G technology is a next-generation radio system and network architecture capable of providing extremely wide bandwidth and ultra-robust low-latency connectivity. Summary of the Invention

[0003] According to example embodiments, apparatus, methods, and computer program products are provided to provide location technology selection via user equipment for truth generation associated with artificial intelligence (AI) location estimation.

[0004] In one or more exemplary embodiments, an apparatus is provided. In one or more embodiments, the apparatus includes at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to determine location technology information associated with the apparatus's AI location function. In one or more embodiments, the apparatus additionally or alternatively includes instructions that, when executed by the at least one processor, cause the apparatus to generate a location-related request for the apparatus based on the location technology information. In one or more embodiments, the location-related request includes an indicator that the location-related request is requesting truth-based location-related information for the apparatus to be used by the AI ​​location function. In one or more embodiments, the apparatus additionally or alternatively includes instructions that, when executed by the at least one processor, cause the apparatus to transmit the location-related request to a network entity.

[0005] In one or more embodiments, the device additionally or alternatively includes instructions that, when executed by at least one processor, cause the device to determine positioning technology information based on positioning technology used by the AI ​​positioning function to generate location information for the device.

[0006] In one or more embodiments, the apparatus is further configured to: determine one or more candidate positioning technologies based on positioning technology information, the one or more candidate positioning technologies being used by the network entity to derive truth positioning-related information. Additionally, in one or more embodiments, the positioning-related request includes information related to the one or more candidate positioning technologies.

[0007] In one or more embodiments, the apparatus is further configured to: determine one or more non-downlink-based positioning technologies based on positioning technology information, wherein the one or more non-downlink-based positioning technologies are used by a network entity to derive truth positioning-related information. Additionally, in one or more embodiments, the positioning-related request includes information related to the one or more non-downlink-based positioning technologies.

[0008] In one or more embodiments, the apparatus is further configured to: determine, based on location technology information, one or more undesirable location technologies to be avoided when the network entity derives truth location-related information. Additionally, in one or more embodiments, the location-related request includes information related to one or more undesirable location technologies.

[0009] In one or more embodiments, the apparatus is further configured to: determine one or more candidate localization reference units (PRUs) based on localization technology information, wherein the one or more PRUs are used by network entities to derive truth localization-related information. Additionally, in one or more embodiments, the localization-related request includes information related to the one or more candidate PRUs.

[0010] In one or more embodiments, the location-related request includes one or more location-related estimation preferences associated with truth location-related information.

[0011] In one or more embodiments, the apparatus additionally or alternatively includes instructions that, when executed by at least one processor, cause the apparatus to: receive truth-based location-related information from a network entity in response to transmitting a location-related request to the network entity. In one or more embodiments, the truth-based location-related information received from the network entity includes a location-related estimate for the apparatus. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes a location-related estimate for a PRU associated with the truth-based location-related information. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes information related to one or more features or positioning techniques to be used for the location-related estimate for the apparatus. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes information related to one or more features or positioning techniques to be used for the location-related estimate associated with the truth-based location-related information. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes information regarding whether and / or which PRU is used for the location-related position estimate associated with the truth-based location-related information.

[0012] In one or more embodiments, the apparatus additionally or alternatively includes instructions that, when executed by at least one processor, cause the apparatus to transmit a location-related request to the network entity based on a location protocol for the network associated with the network entity.

[0013] In one or more embodiments, the apparatus additionally or alternatively includes instructions that, when executed by at least one processor, cause the apparatus to receive truth-based location-related information in response to transmitting a location-related request to a network entity. In one or more embodiments, the apparatus additionally or alternatively includes instructions that, when executed by at least one processor, cause the apparatus to train or retrain an AI location function based on truth-based location-related information received from a network entity.

[0014] In one or more embodiments, the apparatus additionally or alternatively includes instructions that, when executed by at least one processor, cause the apparatus to receive truth-based location-related information in response to transmitting a location-related request to a network entity. In one or more embodiments, the apparatus additionally or alternatively includes instructions that, when executed by at least one processor, cause the apparatus to determine the performance of the AI ​​location function based on the truth-based location-related information received from the network entity.

[0015] In one or more embodiments, network entities are associated with location management functions.

[0016] In another example embodiment, a method is provided. In one or more embodiments, the method includes determining location technology information associated with an AI location function of a device. In one or more embodiments, the method additionally or alternatively includes generating a location-related request for the device based on the location technology information. In one or more embodiments, the location-related request includes an indicator that the location-related request is requesting truth-based location-related information for the device to be used by the AI ​​location function. In one or more embodiments, the method additionally or alternatively includes causing the location-related request to be transmitted to a network entity.

[0017] In one or more embodiments, the method additionally or alternatively includes determining positioning technology information based on positioning technology used by AI positioning functionality to generate location information for the device.

[0018] In one or more embodiments, the method additionally or alternatively includes: determining one or more candidate positioning technologies based on positioning technology information, the one or more candidate positioning technologies being used by a network entity to derive truth positioning-related information. Additionally, in one or more embodiments, the positioning-related request includes information related to the one or more candidate positioning technologies.

[0019] In one or more embodiments, the method additionally or alternatively includes: determining one or more non-downlink-based positioning technologies based on positioning technology information, wherein the one or more non-downlink-based positioning technologies are used by a network entity to derive truth positioning-related information. Additionally, in one or more embodiments, the positioning-related request includes information related to the one or more non-downlink-based positioning technologies.

[0020] In one or more embodiments, the method additionally or alternatively includes: determining one or more undesirable location technologies to be avoided when deriving truth-based location-related information from network entities, based on location technology information. Additionally, in one or more embodiments, the location-related request includes information related to one or more undesirable location technologies.

[0021] In one or more embodiments, the method additionally or alternatively includes: determining one or more candidate PRUs based on location technology information, the one or more candidate PRUs being used by network entities to derive truth location-related information. Additionally, in one or more embodiments, the location-related request includes information associated with the one or more candidate PRUs.

[0022] In one or more embodiments, the location-related request includes one or more location-related estimation preferences associated with truth location-related information.

[0023] In one or more embodiments, the method additionally or alternatively includes: receiving truth-based location-related information from the network entity in response to transmitting a location-related request to the network entity. In one or more embodiments, the truth-based location-related information received from the network entity includes a location-related estimate for a device. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes a location-related estimate for a PRU associated with the truth-based location-related information. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes information related to one or more features or positioning techniques to be used for the location-related estimate for the device. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes information related to one or more features or positioning techniques to be used for the location-related estimate associated with the truth-based location-related information. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes information regarding whether and / or which PRU is used for the location-related position estimate associated with the truth-based location-related information.

[0024] In one or more embodiments, the method additionally or alternatively includes, based on a location protocol for a network associated with a network entity, causing a location-related request to be transmitted to the network entity.

[0025] In one or more embodiments, the method additionally or alternatively includes receiving ground truth location-related information in response to transmitting a location-related request to a network entity. In one or more embodiments, the method additionally or alternatively includes training or retraining an AI location function based on the ground truth location-related information received from the network entity.

[0026] In one or more embodiments, the method additionally or alternatively includes receiving truth-based location-related information in response to transmitting a location-related request to a network entity. In one or more embodiments, the method additionally or alternatively includes determining the performance of the AI ​​location function based on the truth-based location-related information received from the network entity.

[0027] In one or more embodiments, network entities are associated with location management functions.

[0028] In another example embodiment, an apparatus is provided. In one or more embodiments, the apparatus provides components for determining location technology information associated with the apparatus's AI location function. In one or more embodiments, the apparatus additionally or alternatively provides components for generating a location-related request for the device based on the location technology information. In one or more embodiments, the location-related request includes an indicator that the location-related request is requesting truth-based location-related information for the device to be used by the AI ​​location function. In one or more embodiments, the apparatus additionally or alternatively provides components for causing the location-related request to be transmitted to a network entity.

[0029] In one or more embodiments, the device additionally or alternatively provides components for determining positioning technology information based on positioning technology utilized by the AI ​​positioning function to generate location information for the device.

[0030] In one or more embodiments, the apparatus additionally or alternatively provides components for determining one or more candidate positioning techniques based on positioning technique information, the one or more candidate positioning techniques being used by a network entity to derive truth positioning-related information. Additionally, in one or more embodiments, the positioning-related request includes information related to the one or more candidate positioning techniques.

[0031] In one or more embodiments, the apparatus additionally or alternatively provides components for determining one or more non-downlink-based positioning technologies based on positioning technology information, wherein the one or more non-downlink-based positioning technologies are used by a network entity to derive truth positioning-related information. Additionally, in one or more embodiments, the positioning-related request includes information related to the one or more non-downlink-based positioning technologies.

[0032] In one or more embodiments, the apparatus additionally or alternatively provides components for determining one or more undesirable positioning techniques based on positioning technique information, the one or more undesirable positioning techniques being used to avoid when truth-based positioning information is derived by network entities. Additionally, in one or more embodiments, the positioning-related request includes information related to one or more undesirable positioning techniques.

[0033] In one or more embodiments, the apparatus additionally or alternatively provides components for determining one or more candidate PRUs based on location technology information, the one or more candidate PRUs being used by network entities to derive truth location-related information. Additionally, in one or more embodiments, the location-related request includes information associated with the one or more candidate PRUs.

[0034] In one or more embodiments, the location-related request includes one or more location-related estimation preferences associated with truth location-related information.

[0035] In one or more embodiments, in response to transmitting a location-related request to a network entity, the device additionally or alternatively provides components for receiving truth-based location-related information from the network entity. In one or more embodiments, the truth-based location-related information received from the network entity includes a location-related estimate for the device. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes a location-related estimate for a PRU associated with the truth-based location-related information. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes information related to one or more features or positioning techniques to be used for the location-related estimate for the device. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes information related to one or more features or positioning techniques to be used for the location-related estimate associated with the truth-based location-related information. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes information regarding whether and / or which PRU is used for the location-related position estimate associated with the truth-based location-related information.

[0036] In one or more embodiments, the apparatus additionally or alternatively provides components for transmitting location-related requests to network entities based on a location protocol associated with a network entity.

[0037] In one or more embodiments, the apparatus additionally or alternatively provides components for receiving truth-based location information in response to transmitting a location-related request to a network entity. In one or more embodiments, the apparatus additionally or alternatively provides components for training or retraining an AI localization function based on the truth-based location information received from the network entity.

[0038] In one or more embodiments, the apparatus additionally or alternatively provides components for receiving truth-based location-related information in response to transmitting a location-related request to a network entity. In one or more embodiments, the apparatus additionally or alternatively provides components for determining the performance of the AI ​​location function based on the truth-based location-related information received from the network entity.

[0039] In one or more embodiments, network entities are associated with location management functions.

[0040] In another example embodiment, a non-transitory computer-readable storage medium is provided. In one or more embodiments, the non-transitory computer-readable storage medium includes program instructions stored thereon, the program instructions being configured to determine location technology information associated with an AI location function of a device. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to generate a location-related request for the device based on the location technology information. In one or more embodiments, the location-related request includes an indicator that the location-related request is requesting truth-based location-related information for the device for use by the AI ​​location function. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to cause the location-related request to be transmitted to a network entity.

[0041] In one or more embodiments, a non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to determine positioning technology information based on positioning technology utilized by the AI ​​positioning function to generate location information for the device.

[0042] In one or more embodiments, a non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to determine one or more candidate positioning techniques based on positioning technique information, the one or more candidate positioning techniques being used by a network entity to derive truth positioning-related information. Additionally, in one or more embodiments, a positioning-related request includes information related to the one or more candidate positioning techniques.

[0043] In one or more embodiments, a non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to determine one or more non-downlink-based positioning technologies based on positioning technology information, the one or more non-downlink-based positioning technologies being used by a network entity to derive truth positioning-related information. Additionally, in one or more embodiments, a positioning-related request includes information related to one or more non-downlink-based positioning technologies.

[0044] In one or more embodiments, a non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to determine one or more undesired location techniques based on location technique information, the one or more undesired location techniques being used to avoid when truth-based location-related information is derived by a network entity. Additionally, in one or more embodiments, a location-related request includes information related to one or more undesired location techniques.

[0045] In one or more embodiments, a non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to determine one or more candidate PRUs based on location technology information, the one or more candidate PRUs being used by a network entity to derive truth location-related information. Additionally, in one or more embodiments, the location-related request includes information associated with the one or more candidate PRUs.

[0046] In one or more embodiments, the location-related request includes one or more location-related estimation preferences associated with truth location-related information.

[0047] In one or more embodiments, a non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to: receive truth-based location-related information from a network entity in response to transmitting a location-related request to the network entity. In one or more embodiments, the truth-based location-related information received from the network entity includes a location-related estimate for a device. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes a location-related estimate for a PRU associated with the truth-based location-related information. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes information related to one or more features or positioning techniques to be used for the location-related estimate of the device. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes information related to one or more features or positioning techniques to be used for the location-related estimate associated with the truth-based location-related information. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes information regarding whether and / or which PRU is used for the location-related position estimate associated with the truth-based location-related information.

[0048] In one or more embodiments, a non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to cause a location-related request to be transmitted to the network entity based on a location protocol for a network associated with the network entity.

[0049] In one or more embodiments, a non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to receive truth-based location-related information in response to transmitting a location-related request to a network entity. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to train or retrain an AI location function based on truth-based location-related information received from a network entity.

[0050] In one or more embodiments, a non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to receive truth-based location-related information in response to transmitting a location-related request to a network entity. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to determine the performance of the AI ​​location function based on truth-based location-related information received from the network entity.

[0051] In one or more embodiments, network entities are associated with location management functions.

[0052] In yet another example embodiment, an apparatus is provided. In one or more embodiments, the apparatus includes at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to receive a location-related request from a user equipment. The location-related request includes an indicator that the location-related request is requesting truth-valued location-related information for use by an AI location function of the user equipment. In one or more embodiments, the apparatus additionally or alternatively includes instructions that, when executed by the at least one processor, cause the apparatus to determine truth-valued location-related information based on the indicator. In one or more embodiments, the apparatus additionally or alternatively includes instructions that, when executed by the at least one processor, cause the apparatus to transmit truth-valued location-related information to the user equipment.

[0053] In one or more embodiments, the apparatus additionally or alternatively includes instructions that, when executed by at least one processor, cause the apparatus to transmit truth location-related information via a location protocol for a network.

[0054] In one or more embodiments, the apparatus is further configured to determine a location-related estimate for the user equipment. Additionally, in one or more embodiments, the true location-related information includes the location-related estimate for the user equipment.

[0055] In one or more embodiments, the apparatus is further configured to determine a location-related estimate for the PRU. Additionally, in one or more embodiments, the true location-related information includes the location-related estimate for the PRU.

[0056] In one or more embodiments, the apparatus is further configured to determine one or more features or positioning techniques to be used by the user equipment for positioning-related estimations for the user equipment. Additionally, in one or more embodiments, the true-value positioning-related information includes information associated with the one or more features or positioning techniques.

[0057] In one or more embodiments, the apparatus is further configured to determine one or more features or positioning techniques, which are utilized in a positioning-related estimate associated with truth-based positioning information. Additionally, in one or more embodiments, the truth-based positioning information includes information associated with one or more features or positioning techniques.

[0058] In one or more embodiments, the apparatus is further configured to determine whether and / or which PRU is used for location-related estimation associated with truth-based location-related information. Additionally, in one or more embodiments, the truth-based location-related information includes information relating to whether and / or which PRU is used for location-related estimation associated with truth-based location-related information.

[0059] In one or more embodiments, the location-related request includes information related to one or more candidate location techniques, which are used to determine true location-related information. Additionally, in one or more embodiments, the apparatus additionally or alternatively includes instructions that, when executed by at least one processor, cause the apparatus to determine true location-related information based on the information related to the one or more candidate location techniques.

[0060] In one or more embodiments, the location-related request includes information related to one or more non-downlink-based location technologies used to determine truth location-related information. Additionally, in one or more embodiments, the apparatus additionally or alternatively includes instructions that, when executed by at least one processor, cause the apparatus to determine truth location-related information based on information related to one or more non-downlink-based location technologies.

[0061] In one or more embodiments, the location-related request includes information related to one or more undesirable location techniques to be avoided when determining truth-value location-related information. Additionally, in one or more embodiments, the apparatus additionally or alternatively includes instructions that, when executed by at least one processor, cause the apparatus to determine truth-value location-related information based on information related to one or more undesirable location techniques.

[0062] In one or more embodiments, the location-related request includes information associated with one or more candidate PRUs, which are used to determine truth location-related information. Additionally, in one or more embodiments, the apparatus additionally or alternatively includes instructions that, when executed by at least one processor, cause the apparatus to determine truth location-related information based on the information associated with the one or more candidate PRUs.

[0063] In yet another example embodiment, a method is provided. In one or more embodiments, the method includes receiving a location-related request from a user equipment, the location-related request including an indicator that the location-related request is requesting truth-valued location-related information for use by an AI location function of the user equipment. In one or more embodiments, the method additionally or alternatively includes determining truth-valued location-related information based on the indicator. In one or more embodiments, the method additionally or alternatively includes causing the truth-valued location-related information to be transmitted to the user equipment.

[0064] In one or more embodiments, the method additionally or alternatively includes transmitting truth location-related information via a location protocol for the network.

[0065] In one or more embodiments, the method additionally or alternatively includes determining a location-related estimate for a user equipment. Additionally, in one or more embodiments, the true location-related information includes the location-related estimate for the user equipment.

[0066] In one or more embodiments, the method additionally or alternatively includes determining a location-related estimate for the PRU. Additionally, in one or more embodiments, the true location-related information includes the location-related estimate for the PRU.

[0067] In one or more embodiments, the method additionally or alternatively includes determining one or more features or positioning techniques to be used by a user equipment for positioning-related estimations for the user equipment. Additionally, in one or more embodiments, true-value positioning-related information includes information associated with one or more features or positioning techniques.

[0068] In one or more embodiments, the method additionally or alternatively includes determining one or more features or positioning techniques, which are used to perform positioning-related estimations associated with truth-based positioning information. Additionally, in one or more embodiments, the truth-based positioning information includes information related to one or more features or positioning techniques.

[0069] In one or more embodiments, the method additionally or alternatively includes determining whether and / or which PRU is used for location-related estimation associated with truth-based location-related information. Additionally, in one or more embodiments, truth-based location-related information includes information relating to whether and / or which PRU is used for location-related estimation associated with truth-based location-related information.

[0070] In one or more embodiments, the location-related request includes information related to one or more candidate location techniques, which are used to determine true location-related information. Additionally, in one or more embodiments, the method additionally or alternatively includes determining true location-related information based on information related to one or more candidate location techniques.

[0071] In one or more embodiments, the location-related request includes information related to one or more non-downlink-based location technologies used to determine truth location-related information. Additionally, in one or more embodiments, the method additionally or alternatively includes determining truth location-related information based on information related to one or more non-downlink-based location technologies.

[0072] In one or more embodiments, the location-related request includes information related to one or more undesirable location techniques to be avoided when determining truth-value location-related information. Additionally, in one or more embodiments, the method additionally or alternatively includes determining truth-value location-related information based on information related to one or more undesirable location techniques.

[0073] In one or more embodiments, the location-related request includes information associated with one or more candidate PRUs, which are used to determine truth location-related information. Additionally, in one or more embodiments, the method additionally or alternatively includes determining truth location-related information based on information associated with one or more candidate PRUs.

[0074] In yet another example embodiment, an apparatus is provided. In one or more embodiments, the apparatus provides components for receiving a location-related request from a user equipment, the location-related request including an indicator that the location-related request is requesting truth-valued location-related information for use by the user equipment's AI location function. In one or more embodiments, the apparatus additionally or alternatively provides components for determining truth-valued location-related information based on the indicator. In one or more embodiments, the apparatus additionally or alternatively provides components for causing truth-valued location-related information to be transmitted to the user equipment.

[0075] In one or more embodiments, the apparatus additionally or alternatively provides components for enabling the transmission of truth location-related information via a location protocol for the network.

[0076] In one or more embodiments, the apparatus additionally or alternatively provides components for determining a location-related estimate for a user equipment. Additionally, in one or more embodiments, the true location-related information includes the location-related estimate for the user equipment.

[0077] In one or more embodiments, the apparatus additionally or alternatively provides components for determining a location-related estimate for the PRU. Additionally, in one or more embodiments, the true location-related information includes the location-related estimate for the PRU.

[0078] In one or more embodiments, the apparatus additionally or alternatively provides components for determining one or more features or positioning techniques to be used by a user equipment for positioning-related estimations for the user equipment. Additionally, in one or more embodiments, true-value positioning-related information includes information associated with one or more features or positioning techniques.

[0079] In one or more embodiments, the apparatus additionally or alternatively provides components for determining one or more features or positioning techniques, which are utilized in a positioning-related estimate associated with truth-based positioning information. Additionally, in one or more embodiments, the truth-based positioning information includes information related to one or more features or positioning techniques.

[0080] In one or more embodiments, the apparatus additionally or alternatively provides components for determining whether and / or which PRU is used for location-related estimations associated with truth-based location-related information. Additionally, in one or more embodiments, the truth-based location-related information includes information relating to whether and / or which PRU is used for location-related estimations associated with truth-based location-related information.

[0081] In one or more embodiments, the location-related request includes information related to one or more candidate location techniques, which are used to determine true location-related information. Additionally, in one or more embodiments, the apparatus additionally or alternatively provides components for determining true location-related information based on information related to one or more candidate location techniques.

[0082] In one or more embodiments, the location-related request includes information related to one or more non-downlink-based location technologies used to determine truth location-related information. Additionally, in one or more embodiments, the apparatus additionally or alternatively provides components for determining truth location-related information based on information related to one or more non-downlink-based location technologies.

[0083] In one or more embodiments, the location-related request includes information related to one or more undesirable location techniques to be avoided when determining truth-value location-related information. Additionally, in one or more embodiments, the apparatus additionally or alternatively provides components for determining truth-value location-related information based on information related to one or more undesirable location techniques.

[0084] In one or more embodiments, the location-related request includes information associated with one or more candidate PRUs, which are used to determine true location-related information. Additionally, in one or more embodiments, the apparatus additionally or alternatively provides components for determining true location-related information based on information associated with one or more candidate PRUs.

[0085] In yet another example embodiment, a non-transitory computer-readable storage medium is provided. In one or more embodiments, the non-transitory computer-readable storage medium includes program instructions stored thereon, the program instructions being configured to receive a location-related request from a user equipment, the location-related request including an indicator that the location-related request is requesting truth-valued location-related information for use by the user equipment's AI location function. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to determine truth-valued location-related information based on the indicator. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to cause truth-valued location-related information to be transmitted to the user equipment.

[0086] In one or more embodiments, a non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to transmit truth location-related information via a location protocol for a network.

[0087] In one or more embodiments, a non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to determine a location-related estimate for a user equipment. Additionally, in one or more embodiments, true-value location-related information includes the location-related estimate for the user equipment.

[0088] In one or more embodiments, a non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to determine a location-related estimate for the PRU. Additionally, in one or more embodiments, true-value location-related information includes the location-related estimate for the PRU.

[0089] In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to determine one or more features or positioning techniques to be used by a user equipment for positioning-related estimations for the user equipment. Additionally, in one or more embodiments, true-value positioning-related information includes information related to one or more features or positioning techniques.

[0090] In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to determine one or more features or positioning techniques, the one or more features or positioning techniques being utilized in a positioning-related estimate associated with truth-value positioning-related information. Additionally, in one or more embodiments, the truth-value positioning-related information includes information associated with one or more features or positioning techniques.

[0091] In one or more embodiments, a non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon, the program instructions being configured to determine whether and / or which PRU is used for location-related estimation associated with truth-value location-related information. Additionally, in one or more embodiments, the truth-value location-related information includes information relating to whether and / or which PRU is used for location-related estimation associated with truth-value location-related information.

[0092] In one or more embodiments, the location-related request includes information related to one or more candidate location techniques, which are used to determine true location-related information. Additionally, in one or more embodiments, a non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon configured to determine true location-related information based on information related to one or more candidate location techniques.

[0093] In one or more embodiments, the location-related request includes information related to one or more non-downlink-based location techniques used to determine truth location-related information. Additionally, in one or more embodiments, a non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon configured to determine truth location-related information based on information related to one or more non-downlink-based location techniques.

[0094] In one or more embodiments, the location-related request includes information related to one or more undesirable location techniques to be avoided when determining truth-value location-related information. Additionally, in one or more embodiments, a non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon configured to determine truth-value location-related information based on information related to one or more undesirable location techniques.

[0095] In one or more embodiments, the location-related request includes information associated with one or more candidate PRUs, which are used to determine truth location-related information. Additionally, in one or more embodiments, a non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon configured to determine truth location-related information based on information associated with one or more candidate PRUs. Attached Figure Description

[0096] Therefore, some exemplary embodiments of this disclosure have been described in general terms. Reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and in which: Figure 1An example communication network is described, illustrating implementations of one or more exemplary embodiments of this disclosure; Figure 2 This is another example communication network that implements one or more example embodiments of this disclosure; Figure 3 This is a block diagram of an apparatus configured according to one or more exemplary embodiments of the present disclosure; Figure 4 Example transmissions between user equipment, positioning reference unit, and / or location management functions according to one or more example embodiments of the present disclosure are illustrated; Figure 5 Example transmissions between network nodes, positioning reference units, location management functions, and / or user equipment according to one or more example embodiments of the present disclosure are illustrated; Figure 6 Examples of one or more exemplary embodiments of the present disclosure, such as those provided by [the present disclosure], are shown. Figure 3 A flowchart of the operations performed by the device to provide localization technique selection for truth generation associated with AI localization estimation; Figure 7 Examples of one or more other example embodiments according to this disclosure, such as those by... Figure 3 A flowchart of the operations performed by the device to provide localization technique selection for truth generation associated with AI localization estimation; Figure 8 Examples of one or more other example embodiments according to this disclosure, such as those by... Figure 3 A flowchart of the operations performed by the device to provide a localization technique selection for truth generation associated with AI localization estimation; and Figure 9 Examples of one or more other example embodiments according to this disclosure, such as those by... Figure 3 The flowchart describes the operations performed by the device to provide a localization technique selection for truth generation associated with AI localization estimation. Detailed Implementation

[0097] Some embodiments of this disclosure will now be described more fully below with reference to the accompanying drawings, which illustrate some, but not all, of the embodiments of this disclosure. In fact, various embodiments of this disclosure may be embodied in many different forms and should not be construed as limited to the certain embodiments set forth herein; rather, these certain embodiments are provided so that this disclosure will satisfy applicable legal requirements. The same reference numerals throughout refer to the same elements. As used herein, the terms “data,” “content,” “information,” and similar terms are used interchangeably to refer to data capable of being transmitted, received, and / or stored according to embodiments of this disclosure. Therefore, the use of any such terms should not be construed as limiting the spirit and scope of one or more embodiments of this disclosure.

[0098] Additionally, as used herein, the term "circuit" means (a) a hardware circuit implementation (e.g., an implementation in analog and / or digital circuitry); (b) a combination of a circuit and a computer program product comprising software and / or firmware instructions stored on one or more computer-readable storage media, which work together to enable a device to perform one or more functions described herein; and (c) a circuit, such as a microprocessor or a portion thereof, which requires software or firmware to operate even if the software or firmware is not physically present. This definition of "circuit" applies to all uses of the term herein, including in any claim. As another example, as used herein, the term "circuit" also includes an implementation comprising one or more processors and / or portions thereof, along with accompanying software and / or firmware. As defined herein, "computer-readable storage medium" referring to a physical storage medium (e.g., a volatile or non-volatile memory device) can be distinguished from "computer-readable transmission medium" referring to an electromagnetic signal.

[0099] Communication networks (e.g., wireless communication networks) employ various technologies and use a wide range of standards worldwide. There are often agreed-upon standards that promote a certain degree of uniformity between networks, some of which are defined by 3GPP (3rd Generation Partnership Project), such as third-generation (3G), fourth-generation (4G), and / or next-generation (e.g., fifth-generation or 5G) networks. 5G is the fifth generation of broadband cellular network technology standards. These standards provide the architecture through which user equipment (UE) can communicate with the network and other UE devices. This document illustrates certain example embodiments in conjunction with example communication systems and associated technologies to provide location technology selection for truth generation associated with artificial intelligence (AI) location estimation. However, it should be understood that the scope of the claims is not limited to the specific types of communication networks, communication systems, and / or processes disclosed. Example embodiments may be implemented in the terminal equipment (e.g., user equipment) or network (e.g., communication network) of a communication system using alternative processes and operations. For example, although illustrated in the context of a wireless cellular system utilizing 3GPP system elements such as the 3GPP Next Generation Core Network, the disclosed embodiments are directly applicable to a variety of other types of communication systems. Furthermore, while this disclosure may describe certain embodiments in conjunction with 5G communication systems, other embodiments are also applicable to and include, but are not limited to, other networks and network technologies such as 3G, 4G, LTE, 6G, etc.

[0100] According to illustrative embodiments implemented in a 5G communication system environment, one or more 3GPP Technical Specifications (TS) and Technical Reports (TRs) provide further descriptions of user equipment and core network elements / entities / functions, as well as the operations performed by user equipment and core network elements / entities / functions, such as 3GPP TR 38.843, 3GPP TR 38.817, etc. Other 3GPP TS / TR documents provide additional general details that will be understood by those skilled in the art. Additionally or alternatively, AI and / or machine learning (ML) embodiments implemented in a 5G communication system environment, according to one or more embodiments disclosed herein, may be associated with one or more core network elements / entities / functions and / or operations described in 3GPP Release 19. However, while the illustrative embodiments are applicable to implementations associated with the 3GPP standards for 5G described above, alternative embodiments are not necessarily intended to be limited to any particular standard.

[0101] In some communication systems, Next-Generation Radio Access Network (NG-RAN) nodes and / or UEs can provide information such as measurement and / or other location-aided information to the Location Management Function (LMF). For example, the NG-RAN and / or UE can send information to the LMF via the Access and Mobility Management Function (AMF). Based on this information, the LMF can configure the UE using a positioning protocol via the AMF. Additionally or alternatively, the NG-RAN can use the Radio Resource Control (RRC) protocol to configure the UE.

[0102] Furthermore, in some communication systems, AI / ML positioning can be leveraged to provide new measurements and / or enhance measurements related to location estimation. For example, AI / ML models can be implemented by network entities and / or UEs to provide new measurements and / or enhance measurements related to location estimation. For AI / ML-based positioning, ground truth labels (or ground truth approximations) associated with the model inference output (e.g., estimated UE location in UE-based direct AI / ML positioning) can be used to train and / or monitor the performance of the AI / ML model. Typically, non-ML-based positioning techniques can be used to generate ground truth labels, such as uplink / downlink positioning techniques or non-Radio Access Technology (RAT) sensor-based techniques. However, if the positioning technique used to generate ground truth labels uses the same or similar radio channels as the ML-based positioning technique, any defects that reduce the accuracy of location estimation are also likely to degrade the quality of the ground truth. As an illustration, for direct AI / ML positioning that uses downlink positioning reference signal (PRS) measurements as input, if another downlink-based positioning technique (e.g., downlink time difference of arrival (TDOA)) is used, the existence of a non-line-of-sight (NLOS) path between the transmit and receive point (TRP) and the target UE will degrade the quality of the truth label. Additionally, inaccurate UE measurements, timing errors, and / or estimation errors can also degrade the quality of the truth label. Therefore, the positioning technique used to generate the truth label can affect its quality, which in turn can affect the performance of the AI / ML model and / or the accuracy of the positioning estimates provided by the AI / ML model.

[0103] Therefore, this document describes apparatus, methods, and computer program products for providing location technology selection for truth generation (or truth approximation) associated with AI and / or ML location estimation, addressing some or all of the limitations described in current communication networks and / or current network protocols. For example, a specific location technology selection may be provided by a user equipment, network entity, and / or network function (e.g., LMF) to generate truth values ​​for AI and / or ML location estimations associated with the communication network. In one example, the UE may provide the LMF with a location technology selection to be used for truth generation. Thus, the LMF can assist the UE in generating truth values. The location technology selection may be notified via an indicator (e.g., a flag) indicating a location request for truth generation. Additionally or alternatively, the location technology selection may include preferences for using a specific location technology (e.g., a non-downlink-based method) and / or a specific location reference unit (PRU) during truth generation.

[0104] In various embodiments, the selection of a positioning technology can be used to generate truth labels associated with the output of a model configured for positioning estimation (e.g., an AI model or an ML model). The model's output may correspond to inference output (e.g., UE location estimation). In various embodiments, truth information (e.g., truth labels) associated with truth generation can be used to train and / or monitor the performance of the model (e.g., an AI model or an ML model). In some embodiments, one or more positioning-related estimation preferences can also be selected to further improve truth generation associated with AI and / or ML positioning estimation. One or more positioning-related estimation preferences may include quality criteria for positioning estimation, time windows / time periods for positioning estimation (e.g., corresponding to desired timestamps), reference signal configuration preferences (e.g., bandwidth, TRP identifier, etc.), and / or one or more other types of positioning-related estimation preferences.

[0105] Therefore, apparatus, methods, and computer program products are described that provide improved performance and accuracy for AI and / or ML functions of network entities and / or user equipment associated with a network (e.g., a communication network, a location network, or another type of network). For example, by utilizing the location technology selections disclosed herein, AI and / or ML features, feature groups, models, configurations, and / or functions can be improved compared to conventional techniques. In various embodiments, improved truth labels for AI and / or ML functions can be provided by utilizing the location technology selections disclosed herein. Improved location estimations for user equipment can also be provided by utilizing the location technology selections disclosed herein. Furthermore, the number of computational resources and / or processing tasks required for user equipment to access a communication network and / or employ network functions (e.g., LMF, etc.) for the communication network can be reduced. The efficiency and / or connectivity of the communication network can also be improved. For example, overall network signaling can be reduced to provide improved resilience, security, network latency, and / or network speed provided by the communication network. Furthermore, by utilizing the location technology selection disclosed herein, signaling load and / or bandwidth dedicated to signaling, machine learning model adoption, machine learning application adoption, and / or other such activities can be minimized.

[0106] Now for reference Figure 1 This illustration shows an example communication network 100 according to one or more embodiments of the present disclosure. The communication network 100 (also referred to as a wireless communication network, cellular network, or mobile network) is a type of network in which at least one link is wireless and provides voice and / or data services to multiple devices. The communication network 100 may be a 5G network. Additionally or alternatively, at least a portion of the communication network may be a 3G network, a 4G network, an LTE network, a 6G network, and / or another type of network.

[0107] Figure 1 A communication network 100 in which implementations according to exemplary embodiments of the present disclosure can be executed is depicted. Figure 1 The depiction of the communication network 100 herein is not intended to limit or otherwise restrict the exemplary embodiments described and contemplated herein to any particular configuration of elements or systems, nor is it intended to exclude any alternative configurations or systems that may be used in conjunction with the exemplary embodiments of this disclosure. Rather, Figure 1 The communication network 100 disclosed herein is presented only to provide an illustrative basis and context for some features, aspects, and uses of the methods, apparatus, and computer program products contemplated herein. It should be understood that, although... Figure 1 Many aspects and components presented herein are shown as discrete individual elements, but other configurations can be used in conjunction with the methods, apparatus and computer programs described herein, including configurations that combine, omit and / or add aspects and / or components.

[0108] Communication network 100 is shown as providing communication services to UE 110. UE 110 may be enabled for voice services, data services, machine-to-machine (M2M) or machine-type communication (MTC) services, Internet of Things (IoT) services, and / or other services. Although UE 110 may be configured in various ways, UE 110 may be embodied as a mobile terminal, such as a mobile phone, smartphone, pager, mobile TV, gaming device, laptop computer, computer with mobile broadband adapter, camera, tablet computer, portable digital assistant (PDA), communicator, tablet computer, wearable device, headset, touch surface, video recorder, audio / video player, radio, e-book, positioning device (e.g., Global Positioning System (GPS) device), virtual reality device, augmented reality device, or any combination thereof, as well as other types of voice, text, and multimodal communication systems.

[0109] In the context of a 5G network, communication network 100 may include a series of connected network devices and dedicated hardware distributed throughout a service area, state, province, city, or country, as well as one or more network entities that may be stored at and / or hosted by one or more connected network devices or dedicated hardware. In some embodiments, UE 110 may connect to Radio Access Network (RAN) 120, which may then relay communication between UE 102 and core network 130. In some embodiments, UE 110 may communicate with RAN 120, which may act as a relay between UE 110 and other components or services of core network 130. For example, in some embodiments, UE 110 may communicate with RAN 120, which in turn may communicate with an AMF associated with core network 130.

[0110] In one or more embodiments, RAN 120 can communicate with UE 110 via a radio interface. RAN 120 may support Next Generation Radio Access Network (NG-RAN) access, Evolved UMTS Terrestrial Radio Access Network (E-UTRAN) access, Wireless Local Area Network (WLAN) access, fixed access, satellite radio access, New Radio Access Technology (RAT), etc. To provide communication between UE 110 and core network 130, RAN 120 includes one or more network nodes 124 and one or more gateway network nodes 126. In various embodiments, the one or more network nodes 124 may be distributed across a geographical area. Each network node 124 may include an entity that communicates with one or more UEs 110 via one or more communication channels using radio communication technology. For example, a corresponding network node 124 may be configured as a base station. In various embodiments, the one or more communication channels may be associated with licensed spectrum. The corresponding network node 124 may also interface one or more UEs 110 with core network 130. In one or more embodiments, a corresponding network node 124 may interface one or more UEs 110 with the core network 130 via a corresponding gateway network node 126 or a gateway network node 126 used among two or more network nodes 124.

[0111] In one or more embodiments, one or more network nodes 124 may be configured as one or more RAN nodes, such as one or more NG-RAN nodes or one or more home NG-RAN nodes. In another embodiment, one or more network nodes 124 may be configured as one or more femto base stations, such as one or more Femto 5G base stations or one or more home gNBs.

[0112] One or more gateway network nodes 126 may be one or more RAN node gateways, such as one or more NG-RAN node gateways or one or more home NG-RAN node gateways. In another embodiment, one or more gateway network nodes 126 may be configured as one or more femtogates, such as one or more femto 5G gateways or one or more home gNB gateways.

[0113] In some embodiments, network node 124 in NG-RAN may be referred to as gNodeB (NR base station) and / or ng-eNodeB (LTE base station supporting 5G core network). In some embodiments, network node 124 is one or more wireless access points (WAPs) that enable UE 110 to connect to a local area network (LAN) via a wireless (radio) connection. For example, in some embodiments, network node 124 may employ radio communication technologies to communicate with UE 110 via unlicensed spectrum and / or provide UE 110 with access to core network 130. An example of a WAP is a Wi-Fi access point operating on a 2.4 GHz or 5 GHz radio band. Therefore, in some embodiments, the term "network node" may refer to eNodeB, gNodeB, ng-eNodeB, WAP, etc.

[0114] In various embodiments, UE 110 may be attached to a cell of RAN 120 to access core network 130. Therefore, RAN 120 may represent the radio interface between UE 110 and core network 130. Core network 130 may be part of communication network 100, which provides various services to UE 110 connected by RAN 120. One example of core network 130 is a 5G core (5GC) network according to 3GPP. Another example of core network 130 is an evolved packet core (EPC) network according to 3GPP.

[0115] The core network 130 includes network element 132. Network element 132 may include servers, devices, apparatuses, or equipment (including hardware) that provide services to UE 110. Network element 132 may include one or more network functions. For example, network element 132 in a 5G network may include application function (AF), AMF, LMF, PRU, session management function (SMF), user plane function (UPF), policy control function (PCF), unified data management (UDM), authentication server function (AUSF), data network (DN) (e.g., operator services, internet access, or third-party services), unstructured data storage function (UDSF), network exposure function (NEF), network repository function (NRF), network slice selection function (NSSF), session management function (SMF), unified data repository (UDR), user plane function (UPF), UE radio capability management function (UCMF), network data analysis function (NWDAF), charging function (CHF), etc. Alternatively or concurrently, network element 132 in the EPC network may include a Mobility Management Entity (MME), a Serving Gateway (S-GW), a Packet Data Network Gateway (P-GW), etc.

[0116] In some embodiments, UE 110 may include a single-mode or dual-mode device, enabling UE 110 to connect to RAN 120. In some embodiments, RAN 120 may be configured to implement one or more radio access technologies (RATs), such as Bluetooth, Wi-Fi, and Global System for Mobile Communications (GSM), Universal Mobile Telecommunications Service (UMTS), LTE, or 5G NR, which can be used to connect UE 110 to core network 130. In some embodiments, RAN 120 may include or utilize chips (e.g., silicon chips) in the corresponding UE 110, which may be paired with or otherwise identified with similar chips in core network 130, enabling RAN 120 to establish a connection or communication line between the corresponding UE 110 and core network 130 by identifying and pairing the chips in the corresponding UE 110 with the chips in core network 130.

[0117] In some embodiments, the communication network 100 or its components may be configured to communicate with communication devices (e.g., UE 110) on multiple different frequency bands (e.g., FR1 (below 6 GHz), FR2 (millimeter wave), other suitable frequency bands, their subbands, etc.). In some embodiments, the communication network 100 may include or employ a massive MIMO antenna. In some embodiments, the communication network 100 may include a multi-user MIMO (MU-MIMO) antenna. In some embodiments, the communication network 100 may employ edge computing, whereby the computing server is communicatively, physically, computationally, and / or temporally closer to the communication device (e.g., UE 110) to reduce latency and data traffic congestion. In some embodiments, the communication network 100 may employ other technologies, devices, or solutions, such as small cells, low-power RAN, radio beamforming, Wi-Fi-cellular convergence, non-orthogonal multiple access (NOMA), channel coding, etc.

[0118] Figure 2 An example communication network 200 according to one or more embodiments of the present disclosure is illustrated. The communication network 200 may illustrate an exemplary architecture for providing location estimation according to one or more embodiments of the present disclosure. Figure 2 As shown, the communication network 200 includes a UE 110, a RAN 120, and a core network 130. The UE 110 can be communicatively coupled to the RAN 120. In some embodiments, the RAN 120 may include the UE 110. The RAN 120 can also be communicatively coupled to and / or interface with the core network 130. Figure 2 As shown, the core network 130 includes at least AMF 202 and LMF 204.

[0119] In some embodiments, UE 110 may connect to RAN 120, which may then relay communication between UE 110 and core network 130. In some embodiments, UE 110 may communicate with RAN 120, which may act as a relay between UE 110 and other components or services of core network 130. For example, in some embodiments, UE 110 may communicate with RAN 120, which in turn may communicate with AMF 202 of core network 130. In other embodiments, UE 110 may communicate directly with AMF 202. AMF 202 may communicate with at least LMF 204. LMF 204 may be a network entity that provides positioning functionality related to determining the geographic location (e.g., position) of UE 110. In some embodiments, LMF 204 may determine the geographic location of UE 110 based on measurements, such as, but not limited to, downlink and / or uplink position measurement signals. In some embodiments, AMF 202 may also communicate with one or more other network functions (NFs) of core network 130.

[0120] In one or more embodiments, it can be achieved by employing Figure 3 The illustrated device 300 provides, within communication network 100 and / or communication network 200, a selection of positioning techniques for truth generation (or truth approximation) associated with AI and / or ML positioning estimation. Device 300 may be implemented by and / or incorporated into one or more network nodes (e.g., network node 124), one or more gateway network nodes (e.g., gateway network node 126), one or more UEs (e.g., UE 110), or by means of... Figure 1 or Figure 2 Any other device discussed may be implemented and / or incorporated into any other device, such as another device incorporated into RAN 120 and / or core network 130 or otherwise associated with RAN 120 and / or core network 130. Alternatively, device 300 may be implemented by another device external to these devices. For example, device may be implemented by a computing device, such as a personal computer, computer workstation, server, etc., or by any of a variety of mobile computing devices, such as mobile terminals, including but not limited to smartphones, tablet computers, etc.

[0121] Regardless of how device 300 is implemented, the device 300 of the example embodiments is configured to include processing circuitry 302 and memory 304, or otherwise communicate with processing circuitry 302 and memory 304. In some embodiments, device 300 is configured to also include communication interface 306, or otherwise communicate with communication interface 306. In some embodiments, processing circuitry 302 may communicate with memory 304 via a bus to transfer information between components of device 300. Memory 304 may be non-transitory and may include, for example, one or more volatile and / or non-volatile memories. In other words, for example, memory 304 may be an electronic storage device (e.g., a computer-readable storage medium) including gates configured to store data (e.g., bits) that can be retrieved by a machine (e.g., a computing device such as processing circuitry 302). Memory 304 may be configured to store information, data, content, applications, instructions, or the like to enable device 300 to perform various functions according to the example embodiments of this disclosure. For example, memory 304 may be configured to buffer input data processed by processing circuitry system 302. Additionally or alternatively, memory 304 may be configured to store instructions for execution by processing circuitry system 302.

[0122] As described above, device 300 can be embodied by a computing device. However, in some embodiments, device 300 can be embodied as a chip or chipset. In other words, device 300 can include one or more physical packages (e.g., chips) that include materials, components, and / or wires on structural components (e.g., substrates). Structural components can provide physical strength, size savings, and / or electrical interaction constraints for the component circuitry included thereon. Thus, in some cases, device 300 can be configured to implement embodiments of this disclosure on a single chip or as a single "system-on-a-chip." Thus, in some cases, a chip or chipset can constitute components for performing one or more operations to provide the functions described herein.

[0123] The processing circuitry system 302 can be embodied in a variety of different ways. For example, the processing circuitry system 302 can be embodied as one or more of various hardware processing components, including processors such as coprocessors, microprocessors, controllers, digital signal processors (DSPs), processing elements with or without an accompanying DSP, or various other processing circuits, including integrated circuits such as, for example, ASICs (Application-Specific Integrated Circuits), FPGAs (Field-Programmable Gate Arrays), microcontroller units (MCUs), hardware accelerators, application-specific computer chips, etc. Therefore, in some embodiments, the processing circuitry system 302 may include one or more processing cores configured to execute independently. Multi-core processors can implement multiprocessing within a single physical package. Additionally or alternatively, the processing circuitry system 302 may include one or more processors configured in series via a bus to enable independent execution of instructions, pipelines, and / or multiple threads.

[0124] In an example embodiment, the processing circuitry 302 may be configured to execute instructions stored in memory 304 or otherwise accessible by the processing circuitry 302. Alternatively or additionally, the processing circuitry 302 may be configured to perform hard-coded functions. Thus, whether configured by hardware or software methods or a combination thereof, the processing circuitry 302 may represent an entity (e.g., physically embodied in circuitry) capable of performing operations according to embodiments of this disclosure when appropriately configured. Thus, for example, when the processing circuitry 302 is embodied as an ASIC, FPGA, etc., the processing circuitry 302 may be hardware specifically configured to perform the operations described herein. Alternatively, as another example, when the processing circuitry 302 is embodied as an executor of software instructions, the instructions may specifically configure the processing circuitry 302 to perform the algorithms and / or operations described herein when the instructions are executed. However, in some cases, the processing circuitry 302 may be a processor of a specific device (e.g., a pass-through display or a mobile terminal) configured to further configure the processing circuitry 302 to employ embodiments of the present disclosure via instructions for executing the algorithms and / or operations described herein. The processing circuitry 302 may include clocks, arithmetic logic units (ALUs), and logic gates configured to support the operation of the processing circuitry 302.

[0125] Device 300 may optionally include a communication interface 306. The communication interface 306 can be any component, such as a device or circuit embodied in hardware or a combination of hardware and software, configured to receive and / or transmit data from / to a network and / or any other device or module communicating with the device. In this regard, the communication interface 306 may include, for example, an antenna (or multiple antennas) and supporting hardware and / or software for enabling communication with a wireless communication network. Additionally or alternatively, the communication interface 306 may include circuitry for interacting with the antenna to enable the transmission of signals via the antenna or the processing of signals received via the antenna. In some environments, the communication interface 306 may alternatively or also support wired communication. Thus, for example, the communication interface 306 may include a communication modem and / or other hardware / software for supporting communication via cable, Digital Subscriber Line (DSL), Universal Serial Bus (USB), or other mechanisms.

[0126] Figure 4 Example transmissions between UE 110 and one or more network entities (e.g., PRU 402 and LMF 204) are illustrated according to one or more embodiments. The following example transmissions provide the expected desired steps or messages. In one or more embodiments, the example transmissions illustrate UE-based positioning and / or UE-assisted / LMF-based positioning with AI positioning capabilities (e.g., AI models) located at the UE. The AI ​​positioning capabilities may be associated with direct AI positioning for UE-based positioning. Alternatively, the AI ​​positioning capabilities may be associated with AI-assisted positioning for UE-assisted / LMF-based positioning.

[0127] pass Figure 4In the example transmission shown, UE 110 may request LMF 204 to generate truth values ​​(e.g., one or more truth labels) for UE 110's AI positioning function 410. In some embodiments, AI positioning function 410 may include an AI model (e.g., an ML model). For example, the AI ​​model may be a positioning model that determines the location of UE 110. In another example, the AI ​​model may be a positioning model that determines one or more parameters (e.g., time of arrival (ToA), received power, etc.) that can be used to assist in location determination associated with UE 110. Therefore, UE 110 may request LMF 204 to generate truth values ​​(e.g., one or more truth labels) associated with the output of the AI ​​model. In some embodiments, UE 110 may provide preferences regarding one or more positioning technologies to select (or not select) for truth value generation. In some embodiments, the request may be integrated with a positioning request. Furthermore, the request may at least instruct a positioning request to be sent for truth value generation purposes (so that LMF 204 can optimally select appropriate positioning technologies and / or configurations for truth value generation). In some embodiments, the request may instruct one or more positioning-related estimation preferences associated with truth value generation. One or more location-related estimation preferences may include quality criteria for location estimation, time windows / time periods for location estimation (e.g., corresponding to desired timestamps), reference signal configuration preferences (e.g., bandwidth, TRP identifier, etc.), and / or one or more other types of location-related estimation preferences associated with truth generation. As an example, UE 110 may request the use of uplink-based NR RAT techniques (e.g., uplink TDOA) for truth generation because downlink-based location techniques (e.g., downlink TDOA) would be affected by any deficiencies in the truth. Similarly, if LMF 204 is provided with knowledge associated with this request and truth generation, LMF 204 can avoid using downlink-based location techniques even if UE 110 does not indicate a specific location technique preference.

[0128] In some embodiments, UE 110 and / or PRU 402 perform sidelink procedures (e.g., sidelink discovery or sidelink location) at point 1. The sidelink procedure may include one or more sidelink communications with one or more other UEs and / or one or more other devices in the communication network. Furthermore, the sidelink procedure may enable UE 110 to become aware of one or more candidate PRUs (e.g., PRU 402), allowing UE 110 to determine which candidate PRUs (e.g., PRU 402) to use for at least a portion of truth generation. Additionally, LMF 204 may send a list of one or more candidate PRUs to UE 110 for truth generation. In some embodiments, the sidelink procedure may identify the PRU previously used to generate truth for UE 110. Additionally or alternatively, the sidelink procedure may determine conditions such as the PRU being in the same serving cell as UE 110, or the PRU being within a specific range (e.g., distance and / or range) around UE 110.

[0129] In one embodiment, UE 110 determines location technology information for truth generation at location 2. The location technology information may be associated with AI positioning function 410. For example, UE 110 may determine the location technology information based on positioning technology used by AI positioning function 410 to generate location information for UE 110. In some embodiments, the positioning technology used by AI positioning function 410 may be a positioning technology used to train AI positioning function 410 (e.g., an AI model used to train AI positioning function 410). In some embodiments, UE 110 may determine the location technology information based on sidelink procedures and / or information determined via sidelink procedures.

[0130] In some embodiments, UE 110 may determine one or more candidate positioning technologies for use in deriving truth-based positioning information from LMF 204 and / or PRU 402. For example, UE 110 may determine one or more non-downlink-based positioning technologies, one or more uplink-based positioning technologies, and / or one or more sidelink-based positioning technologies for deriving truth-based positioning information from LMF 204 and / or PRU 402. In some examples, one or more candidate positioning technologies include uplink TDOA (UL TDOA), SL RTT, or one or more other candidate positioning technologies. Additionally or alternatively, UE 110 may determine one or more undesired positioning technologies (e.g., one or more downlink-based positioning technologies) to avoid deriving truth-based positioning information from LMF 204 and / or PRU 402. Additionally or alternatively, UE 110 may determine one or more candidate PRUs for deriving truth-based positioning information from LMF 204 and / or PRU 402. Alternatively or concurrently, UE 110 may determine one or more location-related estimation preferences for use in deriving true location-related information from LMF 204 and / or PRU 402.

[0131] Additionally, in one embodiment, UE 110 generates and / or transmits a location-related request (e.g., POSITIONING-RELATED REQUEST) with an indicator for requesting truth value generation, at point 3. For example, UE 110 may generate a location-related request for UE 110 based on location technology information. The location-related request may include an indicator that the location-related request is requesting truth-based location-related information for UE 110 for use by AI location function 410. Furthermore, UE 110 may cause the location-related request to be transmitted to LMF 204. In some embodiments, the location-related request includes information related to one or more candidate location technologies (e.g., one or more non-downlink-based location technologies, one or more uplink-based location technologies and / or one or more sidelink-based location technologies), one or more undesired location technologies (e.g., one or more downlink-based location technologies), and / or one or more candidate PRUs. In some embodiments, the location-related request may also include one or more location-related estimation preferences for deriving truth-based location-related information by LMF 204 and / or PRU 402. In some embodiments, the indicator includes a list of one or more candidate positioning technologies, one or more unwanted positioning technologies, and / or one or more candidate PRUs, wherein the list is ordered based on the preferences of UE 110. In some embodiments, UE 110 may cause a location-related request to be transmitted to LMF 204 based on a positioning protocol for the communication network associated with LMF 204. For example, the positioning protocol may be the LTE Positioning Protocol (LPP) or another type of positioning protocol. In some embodiments, the location-related request is transmitted as part of a location request associated with the positioning protocol.

[0132] Additionally, in one embodiment, LMF 204 and / or PRU 402 derive truth location-related information based on a location-related request, at point 4. For example, LMF 204 and / or PRU 402 may derive truth location-related information based on an indicator included in the location-related request. In some embodiments, LMF 204 and / or PRU 402 may determine truth location-related information based on information associated with one or more candidate location technologies. In some embodiments, LMF 204 and / or PRU 402 may determine truth location-related information based on information associated with one or more non-downlink-based location technologies. In some embodiments, LMF 204 and / or PRU 402 may determine truth location-related information based on information associated with one or more undesired location technologies. In some embodiments, LMF 204 and / or PRU 402 may determine truth location-related information based on information associated with one or more candidate PRUs.

[0133] In some embodiments, truth-based positioning information may include positioning-related estimates for UE 110. Alternatively, truth-based positioning information may include positioning-related estimates for PRU 402 associated with the truth-based positioning information. Alternatively, truth-based positioning information may include information related to one or more features or positioning techniques to be used in positioning-related estimates for UE 110. One or more features may include one or more intermediate positioning-related features, such as ToA, LOS / NLOS indication, etc. Alternatively, truth-based positioning information may include information regarding whether and / or which PRU (e.g., PRU 402) is used for positioning-related location estimation for UE 110. In some embodiments, the configuration of reference signals associated with the truth-based positioning information may depend on the capabilities of UE 110 and / or PRU 402 (e.g., power class, one or more antenna configurations, supported bandwidth, etc.). Alternatively, the configuration of reference signals associated with the truth-based positioning information may depend on one or more real-time conditions associated with UE 110 (e.g., one or more mobility conditions).

[0134] Additionally, in one embodiment, LMF 204 transmits truth-based positioning information (e.g., ground truth positioning-related information) to UE 110 at point 5. In some embodiments, LMF 204 may enable the transmission of truth-based positioning information to UE 110 via a positioning protocol for the communication network associated with LMF 204. For example, the positioning protocol may be a new NR Positioning Protocol A (NRPPa), LPP, or another type of positioning protocol. In some embodiments, UE 110 may train or retrain AI positioning function 410 based on the truth-based positioning information received from LMF 204. For example, UE 110 may train or retrain AI positioning function 410 based on the truth-based positioning information received from LMF 204 to improve tag calculation / generation and / or improve tag validity / quality conditions. Alternatively, in some embodiments, another entity (e.g., a server outside the 3GPP network associated with UE 110, such as the LMF of LMF 204, or a network entity of a core network entity or network function, etc.) may train or retrain the AI ​​positioning function 410 based on truth-based positioning information received from LMF 204. In some embodiments, UE 110 may determine the performance of the AI ​​positioning function 410 based on the truth-based positioning information received from LMF 204. Alternatively, in some embodiments, another entity (e.g., a server outside the 3GPP network associated with UE 110, such as the LMF of LMF 204, or a network entity of a core network entity or network function, etc.) may determine the performance of the AI ​​positioning function 410 based on the truth-based positioning information received from LMF 204.

[0135] In some embodiments, LMF 204 and / or PRU 402 may determine location-related estimates for UE 110. Additionally, truth-based location-related information may include location-related estimates for UE 110. In some embodiments, LMF 204 and / or PRU 402 may determine location-related estimates for PRU 402. Additionally, truth-based location-related information may include location-related estimates for PRU 402. In some embodiments, LMF 204 and / or PRU 402 may determine one or more features or positioning techniques to be used by UE 110 in location-related estimates for UE 110. Additionally, truth-based location-related information may include information related to one or more features or positioning techniques. In some embodiments, LMF 204 and / or PRU 402 may determine one or more features or positioning techniques that are used in location-related estimates associated with truth-based location-related information. Additionally, truth-based location-related information may include information related to one or more features or positioning techniques. In some embodiments, LMF 204 and / or PRU 402 may determine whether and / or which PRU is used for location-related estimation for UE 110. Additionally, the truth-based location-related information may include information related to one or more features or positioning techniques. In some embodiments, the truth-based location-related information additionally or alternatively includes information related to one or more features or positioning techniques that are used in the location-related estimation associated with the truth-based location-related information. In some embodiments, the truth-based location-related information additionally or alternatively includes information regarding whether and / or which PRU is used for the location-related estimation associated with the truth-based location-related information.

[0136] Figure 5 Example transmissions between one or more network entities (e.g., network node 124, PRU 502, and LMF 204) are illustrated according to one or more embodiments. In some embodiments, the example transmissions between network node 124, PRU 502, and LMF 204 may also include one or more transmissions concerning UE 110. In some embodiments, network node 124 may be a gNB. The following example transmissions provide the expected desired steps or messages. In one or more embodiments, the example transmissions illustrate NG-RAN node-assisted localization with AI localization capabilities (e.g., AI models) at the network node. The AI ​​localization capabilities may be associated with direct AI localization used for NG-RAN node-assisted localization.

[0137] pass Figure 5In the example transmission shown, network node 124 may request LMF 204 to generate truth values ​​(e.g., one or more truth labels) for network node 124's AI positioning function 510. In some embodiments, AI positioning function 510 may include an AI model (e.g., an ML model). For example, the AI ​​model may be a positioning model that determines the location of UE 110. In another example, the AI ​​model may be a positioning model that determines one or more parameters (e.g., time of arrival (ToA), received power, etc.), which may be used to assist network entities (e.g., LMF 204) in location determination. Therefore, network node 124 may request LMF 204 to generate truth values ​​(e.g., one or more truth labels) associated with the output of the AI ​​model. In some embodiments, network node 124 may provide preferences regarding one or more positioning technologies to be selected (or not selected) for truth value generation. In some embodiments, the request may be integrated with a positioning request. Furthermore, the request may at least instruct a positioning request to be sent for truth value generation purposes (so that LMF 204 can optimally select appropriate positioning technologies and / or configurations for truth value generation). In some embodiments, the request may indicate one or more location-related estimation preferences associated with truth generation. One or more location-related estimation preferences may include quality criteria for location estimation, time windows / time periods for location estimation (e.g., corresponding to desired timestamps), reference signal configuration preferences (e.g., bandwidth, TRP identifier, etc.), and / or one or more other types of location-related estimation preferences associated with truth generation. As an example, network node 124 may request the use of uplink-based NR RAT techniques (e.g., uplink TDOA) for truth generation because downlink-based location techniques (e.g., downlink TDOA) would be affected by any deficiencies in the truth. Similarly, if LMF 204 is provided with knowledge associated with the request and truth generation, LMF 204 can avoid using downlink-based location techniques even if network node 124 does not indicate a specific location technique preference.

[0138] In some embodiments, network node 124 and / or PRU 502 perform a localization process at point 1. The localization process enables network node 124 to become aware of one or more candidate PRUs (e.g., PRU 502), allowing network node 124 to determine whether to utilize one or more candidate PRUs (e.g., PRU 502) for at least a portion of truth generation. In some embodiments, the localization process may be related to PRU 502.

[0139] In one embodiment, network node 124 determines location technology information for truth generation at location 2. The location technology information may be associated with AI positioning function 510. For example, network node 124 may determine the location technology information based on positioning technology used by AI positioning function 510 to generate location information for UE 110. In some embodiments, the positioning technology used by AI positioning function 510 may be a positioning technology used to train AI positioning function 510 (e.g., an AI model used to train AI positioning function 510). In some embodiments, network node 124 may determine the location technology information based on the positioning process and / or information determined via the positioning process.

[0140] In some embodiments, network node 124 may determine one or more candidate positioning techniques for deriving truth-based positioning information from LMF 204, PRU 502, and / or UE 110. For example, network node 124 may determine one or more non-uplink-based positioning techniques, one or more downlink-based positioning techniques, and / or one or more sidelink-based positioning techniques for deriving truth-based positioning information from LMF 204, PRU 502, and / or UE 110. In some examples, one or more candidate positioning techniques include downlink TDOA (DL TDOA), SL RTT, or one or more other candidate positioning techniques. Additionally or alternatively, network node 124 may determine one or more undesired positioning techniques (e.g., one or more uplink-based positioning techniques) to avoid deriving truth-based positioning information from LMF 204, PRU 502, and / or UE 110. Alternatively or concurrently, network node 124 may determine one or more candidate PRUs for deriving truth location-related information from LMF 204, PRU 502, and / or UE 110. Alternatively or concurrently, network node 124 may determine one or more location-related estimation preferences for deriving truth location-related information from LMF 204, PRU 502, and / or UE 110.

[0141] Additionally, in one embodiment, network node 124 generates and / or transmits a location-related request (e.g., a POSITIONING-RELATED REQUEST) with an indicator for requesting truth value generation, at point 3. For example, network node 124 may generate a location-related request for network node 124 based on location technology information. The location-related request may include an indicator that the location-related request is requesting truth-valued location-related information for network node 124 for use by AI location function 510. Furthermore, network node 124 may cause the location-related request to be transmitted to LMF 204. In some embodiments, the location-related request includes information related to one or more candidate location technologies (e.g., one or more non-uplink-based location technologies, one or more downlink-based location technologies and / or one or more sidelink-based location technologies), one or more undesired location technologies (e.g., one or more uplink-based location technologies), and / or one or more candidate PRUs. In some embodiments, the location-related request may also include one or more location-related estimation preferences for deriving truth-valued location-related information from LMF 204, PRU 502, and / or UE 110. In some embodiments, the indicator includes a list of one or more candidate positioning technologies, one or more unwanted positioning technologies, and / or one or more candidate PRUs, wherein the list is sorted based on the preferences of network node 124. In some embodiments, network node 124 may cause a location-related request to be transmitted to LMF 204 based on a positioning protocol for the communication network associated with LMF 204. In some embodiments, the location-related request is transmitted as part of a location request associated with the positioning protocol.

[0142] Additionally, in one embodiment, LMF 204, PRU 502, and / or UE 110 derive truth location-related information based on a location-related request, at point 4. For example, LMF 204, PRU 502, and / or UE 110 may derive truth location-related information based on an indicator included in the location-related request. In some embodiments, LMF 204, PRU 502, and / or UE 110 may determine truth location-related information based on information associated with one or more candidate location technologies. In some embodiments, LMF 204, PRU 502, and / or UE 110 may determine truth location-related information based on information associated with one or more non-downlink-based location technologies. In some embodiments, LMF 204, PRU 502, and / or UE 110 may determine truth location-related information based on information associated with one or more undesired location technologies. In some embodiments, LMF 204, PRU 502, and / or UE 110 may determine truth location-related information based on information associated with one or more candidate PRUs.

[0143] In some embodiments, truth-based positioning information may include positioning-related estimates for UE 110. Alternatively, truth-based positioning information may include positioning-related estimates for PRU 502 associated with the truth-based positioning information. Alternatively, truth-based positioning information may include information related to one or more features or positioning techniques to be used in positioning-related estimates for UE 110. One or more features may include one or more intermediate positioning-related features, such as ToA, LOS / NLOS indication, etc. Alternatively, truth-based positioning information may include information regarding whether and / or which PRU (e.g., PRU 502) is used for positioning-related location estimation for UE 110. In some embodiments, the configuration of reference signals associated with truth-based positioning information may depend on the capabilities of UE 110 and / or PRU 502 (e.g., power class, one or more antenna configurations, supported bandwidth, etc.). Alternatively, the configuration of reference signals associated with truth-based positioning information may depend on one or more real-time conditions associated with UE 110 (e.g., one or more mobility conditions).

[0144] Additionally, in one embodiment, LMF 204 transmits truth-based positioning information (e.g., ground truth positioning-related information) to network node 124 at point 5. In some embodiments, LMF 204 may enable the transmission of truth-based positioning information to network node 124 via a positioning protocol for the communication network associated with LMF 204. In some embodiments, network node 124 may train or retrain AI positioning function 510 based on the truth-based positioning information received from LMF 204. For example, network node 124 may train or retrain AI positioning function 410 based on the truth-based positioning information received from LMF 204 to improve tag calculation / generation and / or improve tag validity / quality conditions. In some embodiments, network node 124 may determine the performance of AI positioning function 510 based on the truth-based positioning information received from LMF 204.

[0145] In some embodiments, LMF 204, PRU 502, and / or UE 110 may determine a location-related estimate for UE 110. Additionally, truth-based location-related information may include a location-related estimate for UE 110. In some embodiments, LMF 204, PRU 502, and / or UE 110 may determine a location-related estimate for PRU 502. Additionally, truth-based location-related information may include a location-related estimate for PRU 502. In some embodiments, LMF 204, PRU 502, and / or UE 110 may determine one or more features or location techniques to be used by network node 124 in the location-related estimate for UE 110. Additionally, truth-based location-related information may include information associated with one or more features or location techniques. In some embodiments, LMF 204, PRU 502, and / or UE 110 may determine one or more features or location techniques that are used in the location-related estimate associated with truth-based location-related information. Additionally, the truth-based location-related information may include information related to one or more features or location technologies. In some embodiments, LMF 204, PRU 502, and / or UE 110 may determine whether and / or which PRU is used for location-related estimation for UE 110. Additionally, the truth-based location-related information may include information related to one or more features or location technologies. In some embodiments, the truth-based location-related information additionally or alternatively includes information related to one or more features or location technologies that are used in location-related estimations associated with the truth-based location-related information. In some embodiments, the truth-based location-related information additionally or alternatively includes information regarding whether and / or which PRU is used for location-related estimations associated with the truth-based location-related information.

[0146] According to one or more example embodiments of this disclosure, Figure 6 A flowchart of the drawing method 600 is shown. Figure 7 A flowchart of the drawing method 700 is shown. Figure 8 A flowchart of the depiction method 800 is shown, and Figure 9A flowchart depicting method 900 is shown. It should be understood that each block of the flowchart and combinations of blocks in the flowchart can be implemented by various components, such as hardware, firmware, processors, circuitry, and / or other communication devices associated with the execution of software including one or more computer program instructions. For example, one or more of the processes described above can be embodied by computer program instructions. In this regard, computer program instructions embodying the processes described above can be stored, for example, by memory 304 of apparatus 300 employing embodiments of the present disclosure and executed by processing circuitry system 302. As will be understood, any such computer program instructions can be loaded onto a computer or other programmable device (e.g., hardware) to produce a machine that causes the resulting computer or other programmable device to perform the functions specified in the flowchart blocks. These computer program instructions can also be stored in a computer-readable storage medium that can instruct a computer or other programmable device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of art whose execution performs the functions specified in the flowchart blocks. Computer program instructions may also be loaded onto a computer or other programmable device to cause a series of operations to be performed on the computer or other programmable device to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable device, provide operations for implementing the function specified in the flowchart box.

[0147] Therefore, the boxes in a flowchart support combinations of components used to perform a specified function, as well as combinations of operations used to perform the specified function. It will also be understood that one or more boxes in a flowchart, and combinations of boxes in a flowchart, can be implemented by a dedicated hardware-based computer system or a combination of dedicated hardware and computer instructions that performs the specified function.

[0148] Now for reference Figure 6 The description depicts one or more embodiments of the invention, such as those described by [the party]. Figure 3 The device 300 performs operations to provide a location technology selection for truth generation associated with AI location estimation. In some embodiments, method 600 is associated with functionality of UE 110. Figure 6 As shown in block 602, device 300 includes components, such as processing circuitry 302, memory 304, or similar components, configured to determine location technology information associated with the artificial intelligence (AI) location capabilities of the device (e.g., device 300). In some embodiments, the device corresponds to UE 110. Figure 6As shown in box 604, device 300 includes components, such as processing circuitry 302, memory 304, or similar components, configured to generate a location-related request for the device based on location technology information, wherein the location-related request includes an indicator that the location-related request is requesting truth-based location-related information for the device for use by an AI location function. Figure 6 As shown in block 606, device 300 includes components, such as processing circuitry 302, memory 304, or similar components, configured to transmit location-related requests to network entities.

[0149] In one or more embodiments, device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine positioning technology information based on positioning technology used by AI positioning functionality to generate location information for the device.

[0150] In one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine one or more candidate positioning techniques based on positioning technique information, the one or more candidate positioning techniques being used by network entities to derive truth positioning-related information. Additionally, in one or more embodiments, the positioning-related request includes information related to the one or more candidate positioning techniques.

[0151] In one or more embodiments, device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine one or more non-downlink-based positioning technologies based on positioning technology information, wherein the one or more non-downlink-based positioning technologies are used by network entities to derive truth positioning-related information. Additionally, in one or more embodiments, the positioning-related request includes information related to one or more non-downlink-based positioning technologies.

[0152] In one or more embodiments, device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to identify one or more undesirable location techniques based on location technique information, which should be avoided when the network entity derives truth location-related information. Additionally, in one or more embodiments, the location-related request includes information related to one or more undesirable location techniques.

[0153] In one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine one or more candidate PRUs based on location technology information, the one or more candidate PRUs being used by network entities to derive truth location-related information. Additionally, in one or more embodiments, the location-related request includes information associated with the one or more candidate PRUs.

[0154] In one or more embodiments, the location-related request includes one or more location-related estimation preferences associated with truth location-related information.

[0155] In one or more embodiments, device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to receive truth-based location-related information from a network entity in response to transmitting a location-related request to the network entity. In one or more embodiments, the truth-based location-related information received from the network entity includes a location-related estimate for the device. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes a location-related estimate for a PRU associated with the truth-based location-related information. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes information related to one or more features or positioning techniques to be used for the location-related estimate for the device. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes information related to one or more features or positioning techniques to be used for the location-related estimate associated with the truth-based location-related information. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes information regarding whether and / or which PRU is used for location-related location estimation associated with the truth-based location-related information.

[0156] In one or more embodiments, the device 300 additionally or alternatively includes components (such as processing circuitry 302, memory 304, or similar components) configured to transmit location-related requests to network entities based on a location protocol for a network associated with a network entity.

[0157] In one or more embodiments, device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to receive truth-based location-related information in response to transmitting a location-related request to a network entity. In one or more embodiments, device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to train or retrain an AI location function based on truth-based location-related information received from a network entity.

[0158] In one or more embodiments, device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to receive truth-based location-related information in response to transmitting a location-related request to a network entity. In one or more embodiments, device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine the performance of the AI ​​location function based on the truth-based location-related information received from the network entity.

[0159] In one or more embodiments, network entities are associated with location management functions.

[0160] Now refer to Figure 7 According to one or more embodiments of this disclosure, operations performed to provide a localization technique selection for truth generation associated with AI localization estimation are described, such as by... Figure 3 The operation performed by device 300. In some embodiments, method 700 is associated with the function of LMF204. Figure 7 As shown in block 702, device 300 includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to receive a location-related request from a user equipment. The location-related request includes an indicator that the location-related request is requesting truth-based location-related information for use by the user equipment's artificial intelligence (AI) location function. Figure 7 As shown in block 704, device 300 includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine truth location-related information based on an indicator. Figure 7 As shown in block 706, device 300 includes components (such as processing circuitry 302, memory 304, or similar components) configured to transmit truth location-related information to user equipment.

[0161] In one or more embodiments, the device 300 additionally or alternatively includes components (such as processing circuitry 302, memory 304, or similar components) configured to transmit truth location-related information via a location protocol for the network.

[0162] In one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine location-related estimates for a user equipment. Additionally, in one or more embodiments, the true location-related information includes the location-related estimates for the user equipment.

[0163] In one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine a location-related estimate for the PRU. Additionally, in one or more embodiments, the true location-related information includes the location-related estimate for the PRU.

[0164] In one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine one or more features or positioning techniques to be used by a user equipment for positioning-related estimations for the user equipment. Additionally, in one or more embodiments, true-value positioning-related information includes information associated with one or more features or positioning techniques.

[0165] In one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine one or more features or positioning techniques, which are utilized in a positioning-related estimate associated with truth-based positioning information. Additionally, in one or more embodiments, the truth-based positioning information includes information related to one or more features or positioning techniques.

[0166] In one or more embodiments, the apparatus 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine whether and / or which PRU is used for location-related estimation associated with truth-based location-related information. Additionally, in one or more embodiments, the truth-based location-related information includes information relating to whether and / or which PRU is used for location-related estimation associated with truth-based location-related information.

[0167] In one or more embodiments, the location-related request includes information related to one or more candidate location techniques, which are used to determine true location-related information. Additionally, in one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine true location-related information based on information related to one or more candidate location techniques.

[0168] In one or more embodiments, the location-related request includes information related to one or more non-downlink-based location techniques used to determine truth-valued location-related information. Additionally, in one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine truth-valued location-related information based on information related to one or more non-downlink-based location techniques.

[0169] In one or more embodiments, the location-related request includes information related to one or more undesirable location techniques to be avoided when determining truth-value location-related information. Additionally, in one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine truth-value location-related information based on information related to one or more undesirable location techniques.

[0170] In one or more embodiments, the location-related request includes information associated with one or more candidate PRUs, which are used to determine truth location-related information. Additionally, in one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine truth location-related information based on information associated with one or more candidate PRUs.

[0171] Now refer to Figure 8 According to one or more embodiments of this disclosure, operations performed to provide a localization technique selection for truth generation associated with AI localization estimation are described, such as by... Figure 3 The operation performed by device 300. In some embodiments, method 800 is associated with the functionality of network node 124. Figure 8 As shown in block 802, device 300 includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine location technology information associated with the artificial intelligence (AI) location capabilities of the device (e.g., device 300). In some embodiments, the device corresponds to network node 124. Figure 8As shown in block 804, device 300 includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to generate a location-related request for a user device based on location technology information, wherein the location-related request includes an indicator that the location-related request is requesting truth-based location-related information for the user device to be used by an AI location function. Figure 8 As shown in block 806, device 300 includes components (such as processing circuitry 302, memory 304, or similar components) configured to transmit location-related requests to network entities.

[0172] In one or more embodiments, the device 300 additionally or alternatively includes components (such as processing circuitry 302, memory 304, or similar components) configured to determine location technology information based on a location technology used by the AI ​​location function to generate location information for a user device.

[0173] In one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine one or more candidate positioning techniques based on positioning technique information, the one or more candidate positioning techniques being used by network entities to derive truth positioning-related information. Additionally, in one or more embodiments, the positioning-related request includes information related to the one or more candidate positioning techniques.

[0174] In one or more embodiments, device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to identify one or more undesirable location techniques based on location technique information, which should be avoided when the network entity derives truth location-related information. Additionally, in one or more embodiments, the location-related request includes information related to one or more undesirable location techniques.

[0175] In one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine one or more candidate PRUs based on location technology information, the one or more candidate PRUs being used by network entities to derive truth location-related information. Additionally, in one or more embodiments, the location-related request includes information associated with the one or more candidate PRUs.

[0176] In one or more embodiments, the location-related request includes one or more location-related estimation preferences associated with truth location-related information.

[0177] In one or more embodiments, the apparatus 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to receive truth-based location-related information from a network entity in response to transmitting a location-related request to the network entity. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes a location-related estimate for a user equipment. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes a location-related estimate for a PRU associated with the truth-based location-related information. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes information related to one or more features or positioning techniques to be used in the location-related estimate for the user equipment. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes information related to one or more features or positioning techniques to be used in the location-related estimate associated with the truth-based location-related information. In one or more embodiments, the truth-based location-related information received from the network entity additionally or alternatively includes information regarding whether and / or which PRU is used for location-related location estimation associated with the truth-based location-related information.

[0178] In one or more embodiments, the device 300 additionally or alternatively includes components (such as processing circuitry 302, memory 304, or similar components) configured to transmit location-related requests to network entities based on a location protocol for a network associated with a network entity.

[0179] In one or more embodiments, device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to receive truth-based location-related information in response to transmitting a location-related request to a network entity. In one or more embodiments, device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to train or retrain an AI location function based on truth-based location-related information received from a network entity.

[0180] In one or more embodiments, device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to receive truth-based location-related information in response to transmitting a location-related request to a network entity. In one or more embodiments, device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine the performance of the AI ​​location function based on the truth-based location-related information received from the network entity.

[0181] In one or more embodiments, network entities are associated with location management functions.

[0182] Now refer to Figure 9 According to one or more embodiments of this disclosure, operations performed to provide a localization technique selection for truth generation associated with AI localization estimation are described, such as by... Figure 3 The operation performed by device 300. In some embodiments, method 900 is associated with the function of LMF204. Figure 9 As shown in block 902, device 300 includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to receive location-related requests from network entities. These requests include indicators requesting truth-based location-related information for use by the network entity's artificial intelligence (AI) location capabilities. Figure 9 As shown in block 904, device 300 includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine truth location-related information based on an indicator. Figure 9 As shown in block 906, device 300 includes components (such as processing circuitry 302, memory 304, or similar components) configured to transmit truth location-related information to network entities.

[0183] In one or more embodiments, the device 300 additionally or alternatively includes components (such as processing circuitry 302, memory 304, or similar components) configured to transmit truth location-related information via a location protocol for the network.

[0184] In one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine location-related estimates for a user equipment. Additionally, in one or more embodiments, the true location-related information includes the location-related estimates for the user equipment.

[0185] In one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine a location-related estimate for the PRU. Additionally, in one or more embodiments, the true location-related information includes the location-related estimate for the PRU.

[0186] In one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine one or more features or positioning techniques to be used by network entities for location-related estimations for user equipment. Additionally, in one or more embodiments, truth-based positioning-related information includes information associated with one or more features or positioning techniques.

[0187] In one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine one or more features or positioning techniques, which are utilized in a positioning-related estimate associated with truth-based positioning information. Additionally, in one or more embodiments, the truth-based positioning information includes information related to one or more features or positioning techniques.

[0188] In one or more embodiments, the apparatus 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine whether and / or which PRU is used for location-related estimation associated with truth-based location-related information. Additionally, in one or more embodiments, the truth-based location-related information includes information relating to whether and / or which PRU is used for location-related estimation associated with truth-based location-related information.

[0189] In one or more embodiments, the location-related request includes information related to one or more candidate location techniques, which are used to determine true location-related information. Additionally, in one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine true location-related information based on information related to one or more candidate location techniques.

[0190] In one or more embodiments, the location-related request includes information related to one or more non-downlink-based location techniques used to determine truth-valued location-related information. Additionally, in one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine truth-valued location-related information based on information related to one or more non-downlink-based location techniques.

[0191] In one or more embodiments, the location-related request includes information related to one or more undesirable location techniques to be avoided when determining truth-value location-related information. Additionally, in one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine truth-value location-related information based on information related to one or more undesirable location techniques.

[0192] In one or more embodiments, the location-related request includes information associated with one or more candidate PRUs, which are used to determine truth location-related information. Additionally, in one or more embodiments, the device 300 additionally or alternatively includes components (e.g., processing circuitry 302, memory 304, or similar components) configured to determine truth location-related information based on information associated with one or more candidate PRUs.

[0193] As mentioned above, Figures 6 to 9 This is a flowchart illustrating various methods that can be executed by, for example, apparatus 300 and / or according to a computer program product, based on exemplary embodiments of this disclosure. Thus, a computer program product is defined in those instances where computer program instructions (such as computer-readable program code portions) are stored by at least one non-transitory computer-readable storage medium, wherein the computer program instructions (such as computer-readable program code portions) are configured to perform the aforementioned functions upon execution, such as, for example, in combination with… Figure 4 Communication flowchart Figure 5 Communication flowchart and / or as Figure 1 and / or Figure 2 It is part of the system. In other embodiments, computer program instructions (such as computer-readable program code portions) do not need to be stored or otherwise embodied by a non-transitory computer-readable storage medium, but may be embodied by a transient medium, wherein the computer program instructions (such as computer-readable program code portions) are still configured to perform the functions described above when executed.

[0194] Therefore, the boxes in a flowchart support combinations of components used to perform a specified function, as well as combinations of operations used to perform the specified function. It will also be understood that one or more boxes in a flowchart, and combinations of boxes in a flowchart, can be implemented by a dedicated hardware-based computer system or a combination of dedicated hardware and computer instructions that performs the specified function.

[0195] In some embodiments, certain operations described above may be modified or further enhanced. Additionally, in some embodiments, additional optional operations may be included. Modifications, additions, or enhancements to the operations described above may be performed in any order and in any combination.

[0196] Many modifications and other embodiments of the present disclosure will occur to those skilled in the art upon which this disclosure pertains, thanks to the teachings presented in the foregoing description and the accompanying drawings. Therefore, it should be understood that this disclosure is not limited to the specific embodiments presented herein, and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terminology is used herein, it is used only in a general and descriptive sense and not for limiting purposes.

Claims

1. 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: Determine the positioning technology information associated with the artificial intelligence (AI) positioning function of the device; Based on the positioning technology information, a positioning-related request is generated for the device, wherein the positioning-related request includes an indicator that the positioning-related request is requesting truth-value positioning-related information for the device to be used by the AI ​​positioning function; and This enables the transmission of the location-related request to the network entity.

2. The apparatus of claim 1, further comprising instructions that, when executed by the at least one processor, cause the apparatus to: The positioning technology information is determined based on the positioning technology used by the AI ​​positioning function to generate location information for the device.

3. The apparatus of claim 1, wherein the apparatus is further configured to: determine one or more candidate positioning technologies based on the positioning technology information, the one or more candidate positioning technologies being used by the network entity to derive the truth positioning related information, and wherein the positioning related request includes information related to the one or more candidate positioning technologies.

4. The apparatus of claim 1, wherein the apparatus is further configured to: determine one or more non-downlink-based positioning technologies based on the positioning technology information, the one or more non-downlink-based positioning technologies being used by the network entity to derive the truth positioning-related information, and wherein the positioning-related request includes information related to the one or more non-downlink-based positioning technologies.

5. The apparatus of claim 1, wherein the apparatus is further configured to: determine, based on the positioning technology information, one or more undesirable positioning technologies to be avoided when the network entity derives the truth positioning-related information, and wherein the positioning-related request includes information related to the one or more undesirable positioning technologies.

6. The apparatus of claim 1, wherein the apparatus is further configured to: determine one or more candidate location reference units (PRUs) based on the location technology information, the one or more PRUs being used by the network entity to derive the truth location-related information, and wherein the location-related request includes information related to the one or more candidate PRUs.

7. The apparatus of claim 1, wherein the location-related request includes one or more location-related estimation preferences associated with the truth location-related information.

8. The apparatus of claim 1, further comprising instructions that, when executed by the at least one processor, cause the apparatus to: In response to transmitting the location-related request to the network entity, the truth location-related information is received from the network entity.

9. The apparatus of claim 8, wherein the truth-based location-related information received from the network entity includes a location-related estimate for the apparatus.

10. The apparatus of claim 8, wherein the truth-based positioning information received from the network entity includes a positioning-related estimate for a positioning reference unit (PRU) associated with the truth-based positioning information.

11. The apparatus of claim 8, wherein the truth-based location-related information received from the network entity includes information related to one or more features or location techniques to be used for location-related estimation for the apparatus.

12. The apparatus of claim 8, wherein the truth-based location information received from the network entity includes information related to one or more features or location techniques, said one or more features or location techniques being used for location-related estimations associated with the truth-based location information.

13. The apparatus of claim 8, wherein the truth-based location information received from the network entity includes: Information relating to whether and / or which positioning reference unit (PRU) is used for positioning-related location estimation associated with the true positioning information.

14. The apparatus of claim 1, further comprising instructions that, when executed by the at least one processor, cause the apparatus to: This enables the transmission of the location-related request to the network entity based on the location protocol of the network associated with the network entity.

15. The apparatus of claim 1, further comprising instructions that, when executed by the at least one processor, cause the apparatus to: In response to transmitting the location-related request to the network entity, the truth location-related information is received; and The AI ​​positioning function is trained or retrained based on the truth-based positioning information received from the network entity.

16. The apparatus of claim 1, further comprising instructions that, when executed by the at least one processor, cause the apparatus to: In response to transmitting the location-related request to the network entity, the truth location-related information is received; and The performance of the AI ​​positioning function is determined based on the truth-based positioning information received from the network entity.

17. The apparatus of claim 1, wherein the network entity is associated with a location management function.

18. A method comprising: Determine the location technology information associated with the device's artificial intelligence (AI) location function; Based on the positioning technology information, a positioning-related request is generated for the device, wherein the positioning-related request includes an indicator that the positioning-related request is requesting truth-value positioning-related information for the device to be used by the AI ​​positioning function; and This enables the transmission of the location-related request to the network entity.

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: A location-related request is received from a user equipment, the location-related request including an indicator that the location-related request is requesting truth location-related information for use by the user equipment's artificial intelligence (AI) location function; Based on the indicator, determine the truth value location-related information; as well as This enables the transmission of the truth value positioning information to the user equipment.

20. The apparatus of claim 19, further comprising instructions that, when executed by the at least one processor, cause the apparatus to: This enables the transmission of the truth location-related information via a network-specific location protocol.

21. The apparatus of claim 19, wherein the apparatus is further configured to: determine a location-related estimate for the user equipment, and wherein the true location-related information includes the location-related estimate for the user equipment.

22. The apparatus of claim 19, wherein the apparatus is further configured to: determine a positioning-related estimate for a positioning reference unit (PRU), and wherein the true positioning-related information includes the positioning-related estimate for the PRU.

23. The apparatus of claim 19, wherein the apparatus is further configured to: determine one or more features or positioning techniques to be used by the user equipment for positioning-related estimation of the user equipment, and wherein the true positioning-related information includes information related to the one or more features or positioning techniques.

24. The apparatus of claim 19, wherein the apparatus is further configured to: determine one or more features or positioning techniques, the one or more features or positioning techniques being used for positioning-related estimations associated with the truth-value positioning-related information, and wherein the truth-value positioning-related information includes information associated with the one or more features or positioning techniques.

25. The apparatus of claim 19, wherein the apparatus is further configured to: determine whether and / or which positioning reference unit (PRU) is used for positioning-related estimation associated with the truth-value positioning-related information, and wherein the truth-value positioning-related information includes: Information related to whether and / or which PRU is used for the location-related estimation associated with the truth location-related information.

26. The apparatus of claim 19, wherein the location-related request includes information related to one or more candidate location techniques used to determine the true location-related information, and the apparatus further includes instructions that, when executed by the at least one processor, cause the apparatus to: Based on the information associated with the one or more candidate localization techniques, the true localization information is determined.

27. The apparatus of claim 19, wherein the location-related request includes information related to one or more non-downlink-based location technologies used to determine the truth location-related information, and the apparatus further includes instructions that, when executed by the at least one processor, cause the apparatus to: The truth location information is determined based on the information related to the one or more non-downlink-based positioning technologies.

28. The apparatus of claim 19, wherein the location-related request includes information relating to one or more undesirable location techniques to be avoided when determining the truth location-related information, and the apparatus further includes instructions that, when executed by the at least one processor, cause the apparatus to: The truth location information is determined based on the information associated with the one or more undesirable positioning techniques.

29. The apparatus of claim 19, wherein the positioning-related request includes information associated with one or more candidate positioning reference units (PRUs), the one or more PRUs being used to determine the true positioning-related information, and the apparatus further includes instructions that, when executed by the at least one processor, cause the apparatus to: Based on the information associated with the one or more candidate PRUs, the truth location information is determined.

30. A method comprising: A location-related request is received from a user equipment, the location-related request including an indicator that the location-related request is requesting truth location-related information for use by the user equipment's artificial intelligence (AI) location function; Based on the indicator, determine the truth value location-related information; as well as This enables the transmission of the truth value positioning information to the user equipment.

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