Performance monitoring for artificial intelligence positioning functionality associated with a user equipment

Abstract

An apparatus, e.g. a network entity, determines candidate AI positioning functionalities associated with a UE based on UE capability information, determines a subset of the candidate models based on a

Description

[0002] Fourth generation (4G) wireless mobile telecommunications technology, also known as Long Term Evolution (LTE) technology, was designed to provide high-capacity mobile multimedia with high data rates and machine type communications. Next generation or fifth generation (5G) technology is intended to achieve even higher data rates via enhanced Mobile Broadband (eMBB), massive Machine-Type Communication (mMTC), Ultra-Reliable and Low-Latency Communications (URLLC), etc. Third generation partnership project (3GPP) 5G technology is a next generation of radio systems and network architecture that can deliver extreme broadband and ultra-robust, low latency connectivity. BRIEF SUMMARY

[0003] Apparatuses, methods and computer program products are provided in accordance with an example embodiment to provide performance monitoring for artificial intelligence (AI) positioning functionality associated with a user equipment.

[0004] In an 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 determine candidate artificial intelligence (AI) positioning functionalities associated with a user equipment based on capability information associated with 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 a subset of the candidate AI positioning functionalities based on an indication provided by 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 select an AI positioning functionality from the subset of the candidate AI positioning functionalities based on at least one performance metric associated with the subset of the candidate AI positioning functionalities. 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 cause activation of the AI positioning functionality via the user equipment.

[0005] 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 cause transmission of first positioning functionality information that indicates the candidate AI positioning functionalities to 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 receive second positioning functionality information that indicates the subset of the candidate AI positioning functionalities selected by 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 select the AI positioning functionality from the subset of the candidate AI positioning functionalities indicated in the second positioning functionality information.

[0006] 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 receive a request from the user equipment to determine functionality criteria information for selection of the subset of the candidate AI positioning functionalities. 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 the functionality criteria information based on information associated with a network cell. 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 the functionality criteria information based on information associated with a positioning reference signal. 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 cause transmission of the functionality criteria information to the user equipment.

[0007] 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 receive, from the user equipment, functionality prioritization information associated with a priority order for the subset of the candidate AI positioning functionalities. 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 the subset of the candidate AI positioning functionalities based on the functionality prioritization information.

[0008] 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 execute a performance monitoring technique with respect to the subset of the candidate AI positioning functionalities to determine the at least one performance metric.

[0009] 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 configure an inference operation for the user equipment based on the AI positioning functionality.

[0010] 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 cause transmission of positioning functionality information associated with the AI positioning functionality to 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 receive positioning reporting information from the user equipment in response to activation of the AI positioning functionality via the user equipment.

[0011] 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 the capability information based on a capability report provided by the user equipment. In one or more embodiments, the capability report includes at least radio access capabilities of the user equipment.

[0012] In another example embodiment, a method is provided. In one or more embodiments, the method includes determining candidate AI positioning functionalities associated with a user equipment based on capability information associated with the user equipment. In one or more embodiments, the method additionally or alternatively includes determining a subset of the candidate AI positioning functionalities based on an indication provided by the user equipment. In one or more embodiments, the method additionally or alternatively includes selecting an AI positioning functionality from the subset of the candidate AI positioning functionalities based on at least one performance metric associated with the subset of the candidate AI positioning functionalities. In one or more embodiments, the method additionally or alternatively includes causing activation of the AI positioning functionality via the user equipment.

[0013] In one or more embodiments, the method additionally or alternatively includes causing transmission of first positioning functionality information that indicates the candidate AI positioning functionalities to the user equipment. In one or more embodiments, the method additionally or alternatively includes receiving second positioning functionality information that indicates the subset of the candidate AI positioning functionalities selected by the user equipment. In one or more embodiments, the method additionally or alternatively includes selecting the AI positioning functionality from the subset of the candidate AI positioning functionalities indicated in the second positioning functionality information.

[0014] In one or more embodiments, the method additionally or alternatively includes receiving a request from the user equipment to determine functionality criteria information for selection of the subset of the candidate AI positioning functionalities. In one or more embodiments, the method additionally or alternatively includes determining the functionality criteria information based on information associated with a network cell. In one or more embodiments, the method additionally or alternatively includes determining the functionality criteria information based on information associated with a positioning reference signal. In one or more embodiments, the method additionally or alternatively includes causing transmission of the functionality criteria information to the user equipment.

[0015] In one or more embodiments, the method additionally or alternatively includes receiving, from the user equipment, functionality prioritization information associated with a priority order for the subset of the candidate AI positioning functionalities. In one or more embodiments, the method additionally or alternatively includes determining the subset of the candidate AI positioning functionalities based on the functionality prioritization information.

[0016] In one or more embodiments, the method additionally or alternatively includes executing a performance monitoring technique with respect to the subset of the candidate AI positioning functionalities to determine the at least one performance metric.

[0017] In one or more embodiments, the method additionally or alternatively includes configuring an inference operation for the user equipment based on the AI positioning functionality.

[0018] In one or more embodiments, the method additionally or alternatively includes causing transmission of positioning functionality information associated with the AI positioning functionality to the user equipment. In one or more embodiments, the method additionally or alternatively includes receiving positioning reporting information from the user equipment in response to activation of the AI positioning functionality via the user equipment.

[0019] In one or more embodiments, the method additionally or alternatively includes determining the capability information based on a capability report provided by the user equipment. In one or more embodiments, the capability report includes at least radio access capabilities of the user equipment.

[0020] In another example embodiment, an apparatus is provided. In one or more embodiments, the apparatus provides means for determining candidate AI positioning functionalities associated with a user equipment based on capability information associated with the user equipment. In one or more embodiments, the apparatus additionally or alternatively provides means for determining a subset of the candidate AI positioning functionalities based on an indication provided by the user equipment. In one or more embodiments, the apparatus additionally or alternatively provides means for selecting an AI positioning functionality from the subset of the candidate AI positioning functionalities based on at least one performance metric associated with the subset of the candidate AI positioning functionalities. In one or more embodiments, the apparatus additionally or alternatively provides means for causing activation of the AI positioning functionality via the user equipment.

[0021] In one or more embodiments, the apparatus additionally or alternatively provides means for causing transmission of first positioning functionality information that indicates the candidate AI positioning functionalities to the user equipment. In one or more embodiments, the apparatus additionally or alternatively provides means for receiving second positioning functionality information that indicates the subset of the candidate AI positioning functionalities selected by the user equipment. In one or more embodiments, the apparatus additionally or alternatively provides means for selecting the AI positioning functionality from the subset of the candidate AI positioning functionalities indicated in the second positioning functionality information.

[0022] In one or more embodiments, the apparatus additionally or alternatively provides means for receiving a request from the user equipment to determine functionality criteria information for selection of the subset of the candidate AI positioning functionalities. In one or more embodiments, the apparatus additionally or alternatively provides means for determining the functionality criteria information based on information associated with a network cell. In one or more embodiments, the apparatus additionally or alternatively provides means for determining the functionality criteria information based on information associated with a positioning reference signal. In one or more embodiments, the apparatus additionally or alternatively provides means for causing transmission of the functionality criteria information to the user equipment.

[0023] In one or more embodiments, the apparatus additionally or alternatively provides means for receiving, from the user equipment, functionality prioritization information associated with a priority order for the subset of the candidate AI positioning functionalities. In one or more embodiments, the apparatus additionally or alternatively provides means for determining the subset of the candidate AI positioning functionalities based on the functionality prioritization information.

[0024] In one or more embodiments, the apparatus additionally or alternatively provides means for executing a performance monitoring technique with respect to the subset of the candidate AI positioning functionalities to determine the at least one performance metric.

[0025] In one or more embodiments, the apparatus additionally or alternatively provides means for configuring an inference operation for the user equipment based on the AI positioning functionality.

[0026] In one or more embodiments, the apparatus additionally or alternatively provides means for causing transmission of positioning functionality information associated with the AI positioning functionality to the user equipment. In one or more embodiments, the apparatus additionally or alternatively provides means for receiving positioning reporting information from the user equipment in response to activation of the AI positioning functionality via the user equipment.

[0027] In one or more embodiments, the apparatus additionally or alternatively provides means for determining the capability information based on a capability report provided by the user equipment. In one or more embodiments, the capability report includes at least radio access capabilities of the user equipment.

[0028] 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 that are configured to determine candidate AI positioning functionalities associated with a user equipment based on capability information associated with the user equipment. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to determine a subset of the candidate AI positioning functionalities based on an indication provided by the user equipment. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to select an AI positioning functionality from the subset of the candidate AI positioning functionalities based on at least one performance metric associated with the subset of the candidate AI positioning functionalities. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to cause activation of the AI positioning functionality via the user equipment.

[0029] In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to cause transmission of first positioning functionality information that indicates the candidate AI positioning functionalities to the user equipment. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to receive second positioning functionality information that indicates the subset of the candidate AI positioning functionalities selected by the user equipment. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to select the AI positioning functionality from the subset of the candidate AI positioning functionalities indicated in the second positioning functionality information.

[0030] In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to receive a request from the user equipment to determine functionality criteria information for selection of the subset of the candidate AI positioning functionalities. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to determine the functionality criteria information based on information associated with a network cell. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to determine the functionality criteria information based on information associated with a positioning reference signal. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to cause transmission of the functionality criteria information to the user equipment.

[0031] In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to receive, from the user equipment, functionality prioritization information associated with a priority order for the subset of the candidate AI positioning functionalities. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to determine the subset of the candidate AI positioning functionalities based on the functionality prioritization information.

[0032] In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to execute a performance monitoring technique with respect to the subset of the candidate AI positioning functionalities to determine the at least one performance metric.

[0033] In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to configure an inference operation for the user equipment based on the AI positioning functionality.

[0034] In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to cause transmission of positioning functionality information associated with the AI positioning functionality to the user equipment. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to receive positioning reporting information from the user equipment in response to activation of the AI positioning functionality via the user equipment.

[0035] In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to determine the capability information based on a capability report provided by the user equipment. In one or more embodiments, the capability report includes at least radio access capabilities of the user equipment.

[0036] In an 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, from a network entity, first positioning functionality information that indicates candidate AI positioning functionalities for the apparatus. 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 a subset of the candidate AI positioning functionalities based on a set of selection rules. 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 cause transmission of second positioning functionality information that indicates the subset of the candidate AI positioning functionalities to the network entity. 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 receive positioning functionality selection information associated with an AI positioning functionality selection from the network entity. 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 select an AI positioning functionality of the subset of the candidate AI positioning functionalities based on the positioning functionality selection information.

[0037] 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 execute the AI positioning functionality of the subset of the candidate AI positioning functionalities based on the positioning functionality selection information provided by the network entity.

[0038] 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 cause transmission of a request to the network entity to determine functionality criteria information for selection of the subset of the candidate AI positioning functionalities. 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 receive the functionality criteria information from the network entity. 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 the subset of the candidate AI positioning functionalities based on the functionality criteria information.

[0039] In one or more embodiments, the functionality criteria information includes information associated with a network cell. In one or more embodiments, the functionality criteria information additionally or alternatively includes information associated with a positioning reference signal.

[0040] 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 functionality prioritization information associated with a priority order for the subset of the candidate AI positioning functionalities. 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 cause transmission of the functionality prioritization information to the network entity.

[0041] 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 execute an inference operation based on the AI positioning functionality.

[0042] 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 positioning reporting information in response to activation of the AI positioning functionality. 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 cause transmission of the positioning reporting information to the network entity.

[0043] 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 a capability report associated with the apparatus. In one or more embodiments, the capability report includes at least radio access capabilities of a 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 cause transmission of capability information associated with the capability report to the network entity.

[0044] In another example embodiment, a method is provided. In one or more embodiments, the method includes receiving, from a network entity, first positioning functionality information that indicates candidate AI positioning functionalities for the apparatus. In one or more embodiments, the method additionally or alternatively includes determining a subset of the candidate AI positioning functionalities based on a set of selection rules. In one or more embodiments, the method additionally or alternatively includes causing transmission of second positioning functionality information that indicates the subset of the candidate AI positioning functionalities to the network entity. In one or more embodiments, the method additionally or alternatively includes receiving positioning functionality selection information associated with an AI positioning functionality selection from the network entity. In one or more embodiments, the method additionally or alternatively includes selecting an AI positioning functionality of the subset of the candidate AI positioning functionalities based on the positioning functionality selection information.

[0045] In one or more embodiments, the method additionally or alternatively includes executing the AI positioning functionality of the subset of the candidate AI positioning functionalities based on the positioning functionality selection information provided by the network entity.

[0046] In one or more embodiments, the method additionally or alternatively includes causing transmission of a request to the network entity to determine functionality criteria information for selection of the subset of the candidate AI positioning functionalities. In one or more embodiments, the method additionally or alternatively includes receiving the functionality criteria information from the network entity. In one or more embodiments, the method additionally or alternatively includes determining the subset of the candidate AI positioning functionalities based on the functionality criteria information.

[0047] In one or more embodiments, the functionality criteria information includes information associated with a network cell. In one or more embodiments, the functionality criteria information additionally or alternatively includes information associated with a positioning reference signal.

[0048] In one or more embodiments, the method additionally or alternatively includes determining functionality prioritization information associated with a priority order for the subset of the candidate AI positioning functionalities. In one or more embodiments, the method additionally or alternatively includes causing transmission of the functionality prioritization information to the network entity.

[0049] In one or more embodiments, the method additionally or alternatively includes executing an inference operation based on the AI positioning functionality.

[0050] In one or more embodiments, the method additionally or alternatively includes generating positioning reporting information in response to activation of the AI positioning functionality. In one or more embodiments, the method additionally or alternatively includes causing transmission of the positioning reporting information to the network entity.

[0051] In one or more embodiments, the method additionally or alternatively includes determining a capability report associated with the apparatus. In one or more embodiments, the capability report includes at least radio access capabilities of a user equipment. In one or more embodiments, the method additionally or alternatively includes causing transmission of capability information associated with the capability report to the network entity.

[0052] In another example embodiment, an apparatus is provided. In one or more embodiments, the apparatus provides means for receiving, from a network entity, first positioning functionality information that indicates candidate AI positioning functionalities for the apparatus.

[0053] In one or more embodiments, the apparatus additionally or alternatively provides means for determining a subset of the candidate AI positioning functionalities based on a set of selection rules. In one or more embodiments, the apparatus additionally or alternatively provides means for causing transmission of second positioning functionality information that indicates the subset of the candidate AI positioning functionalities to the network entity. In one or more embodiments, the apparatus additionally or alternatively provides means for receiving positioning functionality selection information associated with an AI positioning functionality selection from the network entity. In one or more embodiments, the apparatus additionally or alternatively provides means for selecting an AI positioning functionality of the subset of the candidate AI positioning functionalities based on the positioning functionality selection information.

[0054] In one or more embodiments, the apparatus additionally or alternatively provides means for executing the AI positioning functionality of the subset of the candidate AI positioning functionalities based on the positioning functionality selection information provided by the network entity.

[0055] In one or more embodiments, the apparatus additionally or alternatively provides means for causing transmission of a request to the network entity to determine functionality criteria information for selection of the subset of the candidate AI positioning functionalities. In one or more embodiments, the apparatus additionally or alternatively provides means for receiving the functionality criteria information from the network entity. In one or more embodiments, the apparatus additionally or alternatively provides means for determining the subset of the candidate AI positioning functionalities based on the functionality criteria information.

[0056] In one or more embodiments, the functionality criteria information includes information associated with a network cell. In one or more embodiments, the functionality criteria information additionally or alternatively includes information associated with a positioning reference signal.

[0057] In one or more embodiments, the apparatus additionally or alternatively provides means for determining functionality prioritization information associated with a priority order for the subset of the candidate AI positioning functionalities. In one or more embodiments, the apparatus additionally or alternatively provides means for causing transmission of the functionality prioritization information to the network entity.

[0058] In one or more embodiments, the apparatus additionally or alternatively provides means for executing an inference operation based on the AI positioning functionality.

[0059] In one or more embodiments, the apparatus additionally or alternatively provides means for generating positioning reporting information in response to activation of the AI positioning functionality. In one or more embodiments, the apparatus additionally or alternatively provides means for causing transmission of the positioning reporting information to the network entity.

[0060] In one or more embodiments, the apparatus additionally or alternatively provides means for determining a capability report associated with the apparatus. In one or more embodiments, the capability report includes at least radio access capabilities of a user equipment. In one or more embodiments, the apparatus additionally or alternatively provides means for causing transmission of capability information associated with the capability report to the network entity.

[0061] 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 that are configured to receive, from a network entity, first positioning functionality information that indicates candidate AI positioning functionalities for the apparatus. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to determine a subset of the candidate AI positioning functionalities based on a set of selection rules. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to cause transmission of second positioning functionality information that indicates the subset of the candidate AI positioning functionalities to the network entity. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to receive positioning functionality selection information associated with an AI positioning functionality selection from the network entity. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to select an AI positioning functionality of the subset of the candidate AI positioning functionalities based on the positioning functionality selection information.

[0062] In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to execute the AI positioning functionality of the subset of the candidate AI positioning functionalities based on the positioning functionality selection information provided by the network entity.

[0063] In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to cause transmission of a request to the network entity to determine functionality criteria information for selection of the subset of the candidate AI positioning functionalities. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to receive the functionality criteria information from the network entity. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to determine the subset of the candidate AI positioning functionalities based on the functionality criteria information.

[0064] In one or more embodiments, the functionality criteria information includes information associated with a network cell. In one or more embodiments, the functionality criteria information additionally or alternatively includes information associated with a positioning reference signal.

[0065] In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to determine functionality prioritization information associated with a priority order for the subset of the candidate AI positioning functionalities. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to cause transmission of the functionality prioritization information to the network entity.

[0066] In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to execute an inference operation based on the AI positioning functionality.

[0067] In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to generate positioning reporting information in response to activation of the AI positioning functionality. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to cause transmission of the positioning reporting information to the network entity.

[0068] In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to determine a capability report associated with the apparatus. In one or more embodiments, the capability report includes at least radio access capabilities of a user equipment. In one or more embodiments, the non-transitory computer-readable storage medium additionally or alternatively includes program instructions stored thereon that are configured to cause transmission of capability information associated with the capability report to the network entity. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Having thus described certain example embodiments of the present disclosure in general terms, reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:

[0070] Figure 1 depicts an example communication network in which implementations in accordance with one or more example embodiments of the present disclosure;

[0071] Figure 2 another example communication network in which implementations in accordance with one or more example embodiments of the present disclosure;

[0072] Figure 3 is a block diagram of an apparatus configured in accordance with one or more example embodiments of the present disclosure;

[0073] Figure 4 illustrates example transmissions between user equipment and a network entity in accordance with one or more example embodiments of the present disclosure;

[0074] Figure 5 illustrates a flowchart illustrating operations performed, such as by the apparatus of Figure 3, in order to provide performance monitoring for artificial intelligence (AI) positioning functionality associated with a user equipment, in accordance with one or more example embodiments of the present disclosure; and

[0075] Figure 6 illustrates another flowchart illustrating operations performed, such as by the apparatus of Figure 3, in order to provide performance monitoring for AI positioning functionality associated with a user equipment, in accordance with one or more other example embodiments of the present disclosure; DETAILED DESCRIPTION

[0076] Some embodiments of the present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all, embodiments of the present disclosure are shown. Indeed, various embodiments of the present 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. Like reference numerals refer to like elements throughout. As used herein, the terms “data,” “content,” “information,” and similar terms may be used interchangeably to refer to data capable of being transmitted, received and / or stored in accordance with an embodiment of the present disclosure. Thus, use of any such terms should not be taken to limit the spirit and scope of one or more embodiments of the present disclosure.

[0077] Additionally, as used herein, the term ‘circuitry’ refers to (a) hardware-only circuit implementations (e.g., implementations in analog circuitry and / or digital circuitry); (b) combinations of circuits and computer program product(s) comprising software and / or firmware instructions stored on one or more computer readable memories that work together to cause an apparatus to perform one or more functions described herein; and (c) circuits, such as, for example, a microprocessor s) or a portion of a microprocessor s), that require software or firmware for operation even if the software or firmware is not physically present. This definition of ‘circuitry’ applies to all uses of this term herein, including in any claims. As a further example, as used herein, the term ‘circuitry’ also includes an implementation comprising one or more processors and / or portion(s) thereof and accompanying software and / or firmware. As defined herein, a “computer-readable storage medium,” which refers to a physical storage medium (e.g., volatile or non-volatile memory device), may be differentiated from a “computer-readable transmission medium,” which refers to an electromagnetic signal.

[0078] Communication networks (e.g., wireless communication networks) employ a variety of technologies and use various standards throughout the world. There are generally agreed upon standards that promote some degree of unity between networks, with some of those standards being defined by 3GPP (3rd Generation Partnership Project), such as the Third Generation (3G), a Fourth Generation (4G), and / or a next generation (e.g., Fifth Generation, or 5G) network. 5G is the fifth generation of the technology standard for broadband cellular networks. These standards provide an architecture through which user equipment (UE) can communicate with networks and other UE devices

[0079] Certain example embodiments will be illustrated herein in conjunction with example communication systems and associated techniques to provide performance monitoring for artificial intelligence (AI) positioning functionality associated with a user equipment. It should be understood, however, that the scope of the claims is not limited to particular types of communication networks, communication systems, and / or processes disclosed. An example embodiment can be implemented in a terminal device (e.g., a user equipment) or a network (e.g., a communication network) of a communication system, using alternative processes and operations. For example, although illustrated in the context of wireless cellular systems utilizing 3GPP system elements such as a 3GPP next generation core network, the disclosed embodiment can be adapted in a straightforward manner to a variety of other types of communication systems. Additionally, while the present disclosure may describe certain embodiments in conjunction with a 5G communication system, other embodiments also apply to and comprise other networks and network technologies, such as 3G, 4G, Long Term Evolution (LTE), 6G, etc. without limitation.

[0080] In accordance with an illustrative embodiment implemented in a 5G communication system environment, one or more 3GPP technical specifications (TS) and technical reports (TR) provide further explanation of user equipment and core network elements / entities / functions and / or operations performed by the user equipment and the core network elements / entities / functions, e g., 3GPP TR 38.843, 3GPP TR 37.355, 3GPP TS 38.305, etc. Other 3GPP TS / TR documents provide other conventional details that one of ordinary skill in the art will realize. Additionally or alternatively, an AI and / or machine learning (ML) embodiment implemented in a 5G communication system environment in accordance with one or more embodiments disclosed herein may be related to one or more core network elements / entities / functions and / or operations described in 3GPP Release 19 and / or 3GPP technical specification groups (TSG) radio access network (RAN). However, while an illustrative embodiment is well-suited for implementation associated with the above-mentioned 3GPP standards for 5G, an alternative embodiment is not necessarily intended to be limited to any particular standards.

[0081] In certain communication systems, a next generation-RAN (NG-RAN) node and / or a UE can provide information such as, for example, measurement and / or other location assistance information to a location management function (LMF). For example, an NG-RAN and / or a UE can send the information to the LMF via an access and mobility management function (AMF). Based on the information the LMF can configure the UE using a positioning protocol via the AMF. Additionally or alternatively, the NG-RAN can configure the UE using a radio resource control (RRC) protocol.

[0082] Moreover, in certain communication systems, AL / ML positioning can be utilized to provide a new measurement and / or to enhance a measurement related to location estimation. For example, an AI / ML model can be implemented by a network entity and / or a UE to provide a new measurement and / or to enhance a measurement related to location estimation (e g., positioning estimation). However, for certain network conditions, an inconsistency may occur between training and location estimation inferences (e.g., positioning inferences) related to an AI / ML model. For example, an inconsistency may occur between training and inference regarding network-side conditions for positioning inferences via an AI / ML model at a user equipment. In a non-limiting example, training an AI / ML model with a particular training dataset may result in output associated with a 60% clutter density. However, utilizing the AI / ML model for a location estimation inference using an input dataset may result in output associated with a 40% clutter density such that the performance of the AI / ML model is degraded for the location estimation inference as opposed to the training. As such, typical communication systems lack functionality to enable consistency between training and location estimation inferences for an AI / ML model associated with user equipment, which in turn can impact the performance of the AI / ML model and / or accuracy of positioning estimates provided by the AI / ML model.

[0083] Accordingly, described herein are apparatuses, methods, and computer program products for providing performance monitoring for AI positioning functionality and / or ML positioning functionality associated with a user equipment to resolve some or all of the described limitations of current communication networks and / or current network protocols. For example, performance monitoring can be provided by one or more network entities to enable consistency between training and inferences associated with AI positioning functionality and / or ML positioning functionality (e.g., an AI / ML model) at a user equipment.

[0084] In various embodiments, based on capabilities of the user equipment, a network entity can identify a set of AI / ML positioning functionalities (e.g., Set-Fl) based on a permutation of settings and capabilities of the user equipment. The set of AI / ML positioning functionalities (e.g., Set-Fl) can be indicated to the user equipment to enable the user equipment to select a subset of the AI / ML positioning functionalities (e.g., Set-F2) based on available AI / ML models that are mapped to each functionality in the subset of the AI / ML positioning functionalities (e.g., Set-F2).

[0085] In some embodiments, the user equipment can utilize additional assistance from the network entity to select the subset of the AI / ML positioning functionalities (e.g., Set-F2). For example, the user equipment can utilize functionality criteria information provided by the network entity to select the subset of the AI / ML positioning functionalities (e.g., Set-F2). The functionality criteria information can include information associated with a network cell (e.g., a global cell identifier assigned to a cell tower of a communication network, a validity area, etc.), information associated with a positioning reference signal (PRS) (e.g., a PRS identifier, etc.), and / or other functionality criteria information. In some embodiments, the user equipment can utilize the functionality criteria information to enhance monitoring assessment and / or identification of applicable functionalities on specific areas of a communication network.

[0086] In various embodiments, the user equipment can report the subset of the AI / ML positioning functionalities (e.g., Set-F2) to network entity to indicate a preferred order to assist performance monitoring via the network entity or another network entity. In various embodiments, the network entity or the other network entity can execute, manage, and / or monitor the performance monitoring with respect to the subset of the AI / ML positioning functionalities (e.g., Set-F2).

[0087] Such methods, apparatuses, and computer program products are thus described that provide improved performance and improved accuracy for AI and / or ML functionality for a network entity and / or user equipment associated with a network such as a communication network, a positioning network, or another type of network. For example, by utilizing performance monitoring for AI positioning functionality associated with a user equipment as disclosed herein, AI and / or ML features, feature groups, models, configurations, functions, training, and / or inferences can be improved as compared to traditional techniques. In various embodiments, consistency can be provided between training and location estimation inferences (e.g., positioning inferences) related to an AI / ML model by utilizing performance monitoring for AI positioning functionality associated with a user equipment as disclosed herein. Improved positioning estimates for a user equipment can also be provided by utilizing performance monitoring for AI positioning functionality associated with a user equipment as disclosed herein. Furthermore, a number of computing resources and / or processing tasks for user equipment to access a communication network and / or employ network functions (e g., LMF, etc.) for the communication network can be reduced. Efficiency and / or network connectivity for a communication network can also be improved. For example, overall network signaling can be reduced to provide improved resiliency, security, network latency, and / or network speeds provided by a communication network. Additionally, by utilizing performance monitoring for AI positioning functionality associated with a user equipment as disclosed herein, it is possible to minimize signaling load and / or reduce bandwidth being dedicated to signaling, machine learning model employment, machine learning application employment, machine learning training, machine learning inferences, and / or other such activities.

[0088] Referring now to Figure 1, an example communication network 100 is illustrated according to one or more embodiments of the present disclosure. Communication network 100 (also referred to as a wireless communication network, a cellular network, or a mobile network) is a type of network where at least at least one link is wireless, and provides voice and / or data services to a plurality of devices. Communication network 100 can be a 5G network. Additionally or alternatively, at least a portion of the communication network can be a 3G network, a 4G network, an LTE network, a 6G network, and / or another type of network.

[0089] Figure 1 depicts the communication network 100 in which implementations in accordance with an example embodiment of the present disclosure may be performed. The depiction of the communication network 100 in Figure 1 is not intended to limit or otherwise confine the example embodiment described and contemplated herein to any particular configuration of elements or systems, nor is it intended to exclude any alternative configurations or systems for the set of configurations and systems that can be used in connection with an example embodiment of the present disclosure. Rather, Figure 1, and the communication network 100 disclosed therein is merely presented to provide an example basis and context for the facilitation of some of the features, aspects, and uses of the methods, apparatuses, and computer program products disclosed and contemplated herein. It will be understood that while many of the aspects and components presented in Figure 1 are shown as discrete, separate elements, other configurations may be used in connection with the methods, apparatuses, and computer programs described herein, including configurations that combine, omit, and / or add aspects and / or components.

[0090] Communication network 100 is illustrated as providing communication services to UEs 110. UEs 110 can be enabled for voice services, data services, Machine-to-Machine (M2M) or Machine Type Communications (MTC) services, Internet of Things (loT) services, and / or other services. Although the UEs 110 may be configured in a variety of different manners, the UEs 110 may respectively be embodied as a mobile terminal, such as a mobile phone, a smartphone, a pager, a mobile television, a gaming device, a laptop computer, a computer with a mobile broadband adapter, a camera, a tablet computer, a portable digital assistant (PDA), a communicator, pad, a wearable device, a headset, a touch surface, a video recorder, an audio / video player, radio, an electronic book, a positioning device (e.g., global positioning system (GPS) device), a virtual reality device, an augmented reality device, or any combination of the aforementioned, and other types of voice and text and multi-modal communication systems.

[0091] In the context of a 5G network, the communication network 100 can comprise a series of connected network devices and specialized hardware that is distributed throughout a service region, state, province, city, or country, and one or more network entities, which can be stored at and / or hosted by one or more of the connected network devices or specialized hardware. In some embodiments, a UE 110 can connect to a radio access network (RAN) 120, which can then relay the communications between the UE 102 and the core network 130. In some embodiments, the UE 110 can be in communication with the RAN 120, which can act as a relay between the UE 110 and other components or services of the core network 130. For instance, in some embodiments, the UE 110 can communicate with the RAN 120, which can in turn communicate with an AMF associated with the core network 130.

[0092] In one or more embodiments, the RAN 120 can communicate with UEs 110 over a radio interface. The RAN 120 can support Next Generation RAN (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 Technologies (RAT), and / or the like. To provide communication between the UE 110 and the core network 130, the 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 can be dispersed over a geographic area. A respective network node 124 can include an entity that uses radio communication technology to communicate with one or more UEs 110 via one or more communication channels. For example, a respective network node 124 can be configured as a base station. In various embodiments, the one or more communication channels can be associated with a licensed spectrum. A respective network node 124 can also interface one or more UEs 110 with a core network 130. In one or more embodiments, a respective network node 124 can interface one or more UEs 110 with the core network 130 via a respective gateway network node 126 or a gateway network node 126 employed among two or more network nodes 124.

[0093] In one or more embodiments, the one or more network nodes 124 can be configured as one or more RAN nodes such as, for example, one or more NG-RAN nodes or one or more home NG-RAN nodes. In another embodiment, the one or more network nodes 124 can be configured as one or more Femto base stations such as, for example, one or more Femto 5G base stations or one or more Home gNBs.

[0094] The one or more gateway network nodes 126 can be one or more RAN node gateways such as, for example, one or more NG-RAN node gateways or one or more home NG-RAN node gateways. In another embodiments, the one or more gateway network nodes 126 can be configured as one or more Femto gateways such as, for example, one or more Femto 5G gateways or one or more Home gNB gateways.

[0095] In certain embodiments, the network nodes 124 in a NG-RAN can be referred to as gNodeBs (NR base stations) and / or ng-eNodeBs (LTE base stations supporting a 5G Core Network). In certain embodiments, the network nodes 124 are one or more Wireless Access Points (WAP) to enable a UE 110 to connect to a Local Area Network (LAN) through a wireless (radio) connection. For example, in certain embodiments, the network nodes 124 can employ radio communication technology to communicate with a UE 110 over an unlicensed spectrum and / or provides the UE 110 access to the core network 130. One example of a WAP is a Wi-Fi access point that operates on the 2.4 GHz or 5 GHz radio bands. Accordingly, the term “network node”, in certain embodiments, can refer to an eNodeB, a gNodeB, an ng-eNodeB, a WAP, and / or the like.

[0096] In various embodiments, the UEs 110 can attach to a cell of the RAN 120 to access the core network 130. The RAN 120 can therefore represent a radio interface between UEs 110 and the core network 130. The core network 130 can be a portion of the communication network 100 that provides various services to UEs 110 connected by the RAN 120. One example of the core network 130 is a 5G Core (5GC) network according to the 3GPP. Another example of the core network 130 is an Evolved Packet Core (EPC) network according to the 3GPP.

[0097] The core network 130 includes network elements 132. The network elements 132 can include servers, devices, apparatuses, or equipment (including hardware) that provide services for the UEs 110. The network elements 132 can include one or more network functions. For example, the network elements 132, in a 5G network, can include Application Function (AF), AMF, LMF, PRU, a Session Management Function (SMF), a User Plane Function (UPF), a Policy Control Function (PCF), a Unified Data Management (UDM), Authentication Server Function (AUSF), Data Network (DN), e.g. operator services, Internet access or 3rd 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 Analytics Function (NWDAF), Charging Function (CHF), and / or the like. Additionally or alternatively, the network elements 132, in an EPC network, can include a Mobility Management Entity (MME), a Service Gateway (S-GW), a Packet Data Network Gateway (P-GW), and / or the like.

[0098] In some embodiments, the UEs 110 can include a single-mode or a dual-mode device such that the UEs 110 can be connected to the RAN 120. In some embodiments, the RAN 104 can 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 5GNR, among others, that can be used to connect the UE 102 to the core network 130. In some embodiments, the RAN 120 can comprise or be implemented using a chip, such as a silicon chip, in a respective UE 110 that can be paired with or otherwise recognized by a similar chip in the core network 130, such that the RAN 120 can establish a connection or line of communication between the respective UE 110 and the core network 130 by identifying and pairing the chip within the respective UE 110 with the chip within the core network 130.

[0099] In some embodiments, the communication network 100 or components thereof can be configured to communicate with a communication device (e.g., the UEs 110) or the like over multiple different frequency bands, e.g., FR1 (below 6 GHz), FR2 (mm Wave), other suitable frequency bands, sub-bands thereof, and / or the like. In some embodiments, the communication network 100 can comprise or employ massive Multiple Input and Multiple Output (massive MIMO) antennas. In some embodiments, the communication network 100 can comprise multi-user MIMO (MU-MIMO) antennas. In some embodiments, the communication network 100 can employ edge computing whereby the computing servers are communicatively, physically, computationally, and / or temporally closer to the communications device (e.g., UE 110) in order to reduce latency and data traffic congestion. In some embodiments, the communication network 100 can employ other technologies, devices, or techniques, such as small cell, low-powered RAN, beamforming of radio waves, WIFI-cellular convergence, Non-Orthogonal Multiple Access (NOMA), channel coding, and the like.

[00100] Figure 2 illustrates an example communication network 200 according to one or more embodiments of the present disclosure. The communication network 200 can illustrate an exemplary architecture for providing positioning estimations according to one or more embodiments of the present disclosure. As illustrated in Figure 2, the communication network 200 includes UE 110, the RAN 120, and the core network 130. The UE 110 can be communicatively coupled to the RAN 120. In certain embodiments, the RAN 120 can include the UE 110. The RAN 120 can also be communicatively coupled to and / or interfaced with the core network 130. As illustrated in Figure 2, the core network 130 includes at least an AMF 202 and a LMF 204.

[00101] In some embodiments, the UE 110 can connect to the RAN 120, which can then relay the communications between the UE 110 and the core network 130. In some embodiments, the UE 110 can be in communication with the RAN 120, which can act as a relay between the UE 110 and other components or services of the core network 130. For instance, in some embodiments, the UE 110 can communicate with the RAN 120, which can in turn communicate with the AMF 202 of the core network 130. In other embodiments, the UE 110 can communicate directly with the AMF 202. The AMF 202 can be in communication with at least the LMF 204. The LMF 204 can be a network entity that provides positioning functionality related to determining a geographic position (e.g., a location) of the UE 110. In some embodiments, the LMF 204 can determine the geographic position of the UE 110 based on measurements such as, but not limited to, downlink and / or uplink location measurement signals. In some embodiments, the LMF 204 can provide performance monitoring for AI positioning functionality and / or ML positioning functionality associated with a user equipment. In some embodiments, the LMF 204 can determine one or more candidate AI positioning functionalities for the UE 110. In some embodiments, the LMF 204 can additionally or alternatively select a particular AI positioning functionality for the UE 110. In some embodiments, the AMF 202 can additionally be in communication with one or more other network functions (NFs) of the core network 130.

[00102] In one or more embodiments, performance monitoring for AI positioning functionality and / or ML positioning functionality associated with the UE 110 can be provided within the communication network 100 and / or the communication network 200 by employing an apparatus 300 as depicted in Figure 3. The apparatus 300 may be embodied 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 any of the other devices discussed with respect to Figure 1 or Figure 2, such as another device incorporated or otherwise associated with the RAN 120 and / or the core network 130. Alternatively, the apparatus 300 may be embodied by another device, external to such devices. For example, the apparatus may be embodied by a computing device, such as a personal computer, a computer workstation, a server or the like, or by any of various mobile computing devices, such as a mobile terminal, including but not limited to a smartphone, a tablet computer, or the like, for example.

[00103] Regardless of the manner in which the apparatus 300 is embodied, the apparatus 300 of an example embodiment is configured to include or otherwise be in communication with a processing circuitry 302 and a memory 304. In some embodiments, the apparatus 300 is configured to additionally include or otherwise be in communication with a communication interface 306. In some embodiments, the processing circuitry 302 may be in communication with the memory 304 via a bus for passing information among components of the apparatus 300. The 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, the memory 304 may be an electronic storage device (e.g., a computer readable storage medium) comprising gates configured to store data (e.g., bits) that may be retrievable by a machine (e.g., a computing device like the processing circuitry 302). The memory 304 may be configured to store information, data, content, applications, instructions, or the like for enabling the apparatus 300 to carry out various functions in accordance with an example embodiment of the present disclosure. For example, the memory 304 can be configured to buffer input data for processing by the processing circuitry 302. Additionally or alternatively, the memory 304 can be configured to store instructions for execution by the processing circuitry 302.

[00104] As described above, the apparatus 300 may be embodied by a computing device. However, in some embodiments, the apparatus 300 may be embodied as a chip or chip set. In other words, the apparatus 300 may comprise one or more physical packages (e.g., chips) including materials, components and / or wires on a structural assembly (e.g., a baseboard). The structural assembly may provide physical strength, conservation of size, and / or limitation of electrical interaction for component circuitry included thereon. The apparatus 300 may therefore, in some cases, be configured to implement an embodiment of the present disclosure on a single chip or as a single “system on a chip.” As such, in some cases, a chip or chipset may constitute means for performing one or more operations for providing the functionalities described herein.

[00105] The processing circuitry 302 may be embodied in a number of different ways. For example, the processing circuitry 302 may be embodied as one or more of various hardware processing means including a processor, such as a coprocessor, a microprocessor, a controller, a digital signal processor (DSP), a processing element with or without an accompanying DSP, or various other processing circuitry including integrated circuits such as, for example, an ASIC (application specific integrated circuit), an FPGA (field programmable gate array), a microcontroller unit (MCU), a hardware accelerator, a special-purpose computer chip, or the like. As such, in some embodiments, the processing circuitry 302 may include one or more processing cores configured to perform independently. A multi-core processor may enable multiprocessing within a single physical package. Additionally or alternatively, the processing circuitry 302 may include one or more processors configured in tandem via the bus to enable independent execution of instructions, pipelining and / or multithreading.

[00106] In an example embodiment, the processing circuitry 302 may be configured to execute instructions stored in the memory 304 or otherwise accessible to the processing circuitry 302. Alternatively or additionally, the processing circuitry 302 may be configured to execute hard coded functionality. As such, whether configured by hardware or software methods, or by a combination thereof, the processing circuitry 302 may represent an entity (e.g., physically embodied in circuitry) capable of performing operations according to an embodiment of the present disclosure while configured accordingly. Thus, for example, when the processing circuitry 302 is embodied as an ASIC, FPGA or the like, the processing circuitry 302 may be specifically configured hardware for conducting 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 employ an embodiment of the present disclosure by further configuration of the processing circuitry 302 by instructions for performing the algorithms and / or operations described herein. The processing circuitry 302 may include, among other things, a clock, an arithmetic logic unit (ALU) and logic gates configured to support operation of the processing circuitry 302.

[00107] The apparatus 300 may optionally include the communication interface 306. The communication interface 306 may be any means such as a device or circuitry embodied in either hardware or a combination of hardware and software that is configured to receive and / or transmit data from / to a network and / or any other device or module in communication with the apparatus. In this regard, the communication interface 306 may include, for example, an antenna (or multiple antennas) and supporting hardware and / or software for enabling communications with a wireless communication network. Additionally or alternatively, the communication interface 306 may include the circuitry for interacting with the antenna(s) to cause transmission of signals via the antenna(s) or to handle receipt of signals received via the antenna(s). In some environments, the communication interface 306 may alternatively or also support wired communication. As such, 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.

[00108] Figure 4 illustrates example transmissions between the UE 110 and a network entity 404, according to one or more embodiments. In various embodiments, the network entity 404 can correspond to an LMF (e.g., the LMF 204), a network element (e.g., the network element 132), a core network entity (e.g., one or more portions of the core network 130), a network function, a RAN entity (e.g., one or more portions of the RAN 120), and / or one or more other portions of a communication network. The below example transmissions provide the foreseen steps or messaging needed. In one or more embodiments, the example transmissions illustrate signalization to provide training and inference consistency for AI positioning functionality of the UE 110 based on LMF monitoring for the AI positioning functionality related to the UE 110. In various embodiments, the example transmissions provide improved UE-based positioning and / or UE-assisted / LMF-based positioning with an AI positioning functionality (e.g., an AI model) at the UE 110. The AI positioning functionality can be related to direct AI positioning for UE-based positioning. Alternatively, the AI positioning functionality can be related to AI assisted positioning for UE-assisted / LMF-based positioning.

[00109] In various embodiments, the UE 110 includes and / or utilizes AI positioning functionality 410. In some embodiments, the AI positioning functionality 410 can include an AI model (e.g., a ML model). For example, the AI model can be a positioning model that determines the position of the UE 110. In another example, the AI model can be a positioning model that determines one or more parameters (e.g., ToA, received power, etc.) that may be utilized to assist a location determination associated with the UE 110. In various embodiments, the example transmissions enable allocation of network parameter settings that influence measurements associated with the UE 110 for training an AI model (e.g., a ML model) associated with the AI positioning functionality 410 for model identification in an inference step performed via the AI positioning functionality 410.

[00110] In some embodiments, the UE 110 transmits capability information (e.g., CAPABILITY INFORMATION) to the network entity 404, at 1. The capability information can include one or more capabilities associated with the user equipment. For example, the capability information can include one or more capabilities associated with the AI positioning functionality 410. In some embodiments, the capability information can include and / or be based on a capability report for the UE 110. The capability report can include one or more conditions associated with the UE 110, AI / ML capabilities of the UE 110, radio access capabilities of the UE 110, LTE positioning protocol (LPP) capabilities of the UE 110, and / or one or more other capabilities associated with the UE 110. In some embodiments, the capability information (e.g., the capability report) can include information related to sampling parameters, positioning features, positioning parameters, transmission and reception points (TRPs), network antenna ports, supported conditions for measure PRS, supported performance monitoring conditions, supported AI / ML functionalities, TOA information, channel type indications, supported channel classes, supported channel features, channel impulse response (CIR) quantization information, network drop information, clutter parameters, network synchronization information, receiver timing performance information, transmitter timing performance information, signa-to-noise ratio performance information, time varying changes, channel estimation error information, and / or other capability information associated with the UE 110 to achieve a particular AI positioning functionality.

[00111] In a non-limiting example, an AI positioning functionality can correspond to a particular AI positioning functionality where a fingerprint position is estimated in the UE-side when the input is CIR, a particular AI positioning functionality where the intermediate feature is estimated in the UE-side when the input is CIR for AI / ML assisted, a particular AI positioning functionality where the fingerprint position is calculated in the LMF-side when the input is CIR for the AI / ML assisted positioning, and / or another particular AI positioning functionality for an AI model.

[00112] In various embodiments, the UE 110 can transmit the capability information asynchronously with respect to one or more other transmissions associated with FIG. 4. In some embodiments, the UE 110 can transmit the capability information during an initial registration process between the UE 110 and the network entity 404. In some embodiments, the UE 110 can transmit the capability information in response to a request associated with the network entity 404 to facilitate AI positioning functionality. In some embodiments, the capability information can be a positioning protocol message associated with the one or more capabilities. For example, the capability information can be an LTE positioning protocol (LPP) message associated with the one or more capabilities. In some embodiments, the capability information can be an RRC message associated with the one or more capabilities. However, it is to be appreciated that, in some embodiments, the capability information can be another type of network message associated with the one or more capabilities.

[00113] In some embodiments, the network entity 404 determines candidate AI positioning functionalities for the UE 110, at 2. For example, the network entity 404 can determine candidate AI positioning functionalities associated with the UE 110 based on the capability information associated with the UE 110. In various embodiments, the candidate AI positioning functionalities determined by the network entity 404 can be enabled by one or more portions of the capability information.

[00114] In some embodiments, the network entity 404 transmits first positioning functionality information (e.g, FIRST POSITIONING FUNCTIONALITY INFORMATION) to the UE 110, at 3. For example, the network entity 404 can cause transmission of the first positioning functionality information that indicates the candidate AI positioning functionalities to the UE 110.

[00115] In some embodiments, the UE 110 transmits a functionality criteria information request (e.g, FUNCTIONALITY CRITERIA INFORMATION REQUEST) to the network entity 404, at 4a. For example, the UE 110 can cause transmission of a request to the network entity 404 to determine functionality criteria information for selection of the subset of the candidate AI positioning functionalities.

[00116] In some embodiments, the network entity 404 transmits functionality criteria information (e.g, FUNCTIONALITY CRITERIA INFORMATION) to the UE 110, at 4b. The functionality criteria information can include information associated with a network cell (e.g, a global cell identifier assigned to a cell tower of a communication network, a validity area, etc.), information associated with a PRS (e.g, a PRS identifier, etc.), and / or other functionality criteria information. In some embodiments, the UE 110 can utilize the functionality criteria information to enhance monitoring assessment and / or identification of applicable AI positioning functionalities for one or more particular areas of a communication network.

[00117] In some embodiments, the UE 110 determines a subset of the candidate AI positioning functionalities, at 5. For example, the UE 110 can determine a subset of the candidate AI positioning functionalities based on a set of selection rules. The set of selection rules can be based on a particular AI positioning implementation and / or a particular UE. In some embodiments, the UE 110 can apply a down selection rule of available AI positioning functionalities for the UE 110 that are also included in the candidate AI positioning functionalities. In some embodiments, the UE 110 can determine the subset of the candidate AI positioning functionalities based on a time duration for down selection of the candidate AI positioning functionalities. For example, the UE 110 can utilize the time duration to detect a PRS transmission and / or to evaluate an initial performance level of one or more AI models associated with the candidate AI positioning functionalities.

[00118] In some embodiments, the UE 110 can prioritize and / or rank candidate AI positioning functionalities in the subset of the candidate AI positioning functionalities. In some embodiments, the UE 110 can prioritize and / or rank candidate AI positioning functionalities in the subset of the candidate AI positioning functionalities based on information related to one or more features or positioning techniques to be utilized for a positioning-related estimation for the UE 110. The one or more features can include one or more intermediate positioning related features such as ToA, a LOS / NLOS indication, etc. Additionally or alternatively, the information can be related to capabilities of the UE 110 and / or the network entity 404 (e.g., a power category, one or more antenna configurations, supported bandwidth, etc.). Additionally or alternatively, the information can be related to one or more real-time conditions (e.g., one or more mobility conditions) related to the UE 110. In some embodiments, criteria for determining the subset of the candidate AI positioning functionalities may not be specified or defined. As such, in some embodiments, the UE 110 can additionally or alternatively utilize one or more other implementation considerations associated with the UE 110, the network entity 404, and / or one or more other portions of a communication network to determine the subset of the candidate AI positioning functionalities.

[00119] In some embodiments, the UE 110 transmits second positioning functionality information (e.g, SECOND POSITIONING FUNCTIONALITY INFORMATION) to the network entity 404, at 6. For example, the UE 110 can cause transmission of second positioning functionality information that indicates the subset of the candidate AI positioning functionalities to the network entity 404. In some embodiments, the subset of the candidate AI positioning functionalities included in the second positioning functionality information can be formatted based on the priority determined by the UE 110. In some embodiments, the second positioning functionality information can include both the subset of the candidate AI positioning functionalities and functionality prioritization information associated with a priority order for the subset of the candidate AI positioning functionalities. In some embodiments, the UE 110 can transmit the functionality prioritization information separate from the indication of the subset of the candidate AI positioning functionalities. In some embodiments, the functionality prioritization information can be implicitly reported in the second positioning functionality information. For example, a metric (e.g., an initial performance level) can be reported in the second positioning functionality information for respective candidate AI positioning functionalities of the subset of the candidate AI positioning functionalities.

[00120] In some embodiments, the network entity 404 assesses the subset of the candidate AI positioning functionalities, at 7. For example, the network entity 404 and / or another network entity can analyze the respective candidate AI positioning functionalities in the subset of the candidate AI positioning functionalities. In various embodiments, the network entity 404 can assess the subset of the candidate AI positioning functionalities based on the prioritization among the candidate AI positioning functionalities. The assessment can be based on performance monitoring of each specific candidate AI functionality. In various embodiments, for respective candidate AI positioning functionalities, a regular performance monitoring signalization can be expected between the network entity 404 and the UE 110. For example, the network entity 404 can assess the subset of the candidate AI positioning functionalities via network-sided performance monitoring (e.g., LMF-sided performance monitoring) performed via the network entity 404 and / or UE-assisted performance monitoring performed via the UE 110. When the network-sided performance monitoring is applied, the priority of applicable candidate AI positioning functionalities reported by the UE 110 can be utilized to assist the network-sided performance monitoring, where the higher priority applicable candidate AI positioning functionality may be monitored with a higher priority than a lower priority applicable candidate AI positioning functionality. When the UE-assisted performance monitoring is applied, the priority of applicable candidate AI positioning functionalities reported by the UE 110 can be utilized to assist the UE-sided performance monitoring, where the higher priority applicable candidate AI positioning functionality may be monitored with a higher priority than a lower priority applicable candidate AI positioning functionality.

[00121] In some embodiments, the network entity 404 determine performance metric(s) associated with the assessment(s) with respect to the subset of the candidate AI positioning functionalities, at 8. For example, the performance monitoring technique(s) with respect to the subset of the candidate AI positioning functionalities can be executed by the network entity 404 and / or the UE 110 to determine the performance metric(s). In some embodiments, the network entity 404 can determine a key performance indicator associated with the assessment(s) with respect to the subset of the candidate AI positioning functionalities. For example, the network entity 404 can determine a key performance indicator for the respective candidate AI positioning functionalities included in the subset of the candidate AI positioning functionalities.

[00122] In some embodiments, the network entity 404 select an AI positioning functionality from the subset of the candidate AI positioning functionalities based on the performance metric(s), at 9. For example, the network entity 404 can select an AI positioning functionality from the subset of the candidate AI positioning functionalities based on at least one performance metric (e.g., at least one KPI) associated with the subset of the candidate AI positioning functionalities. In some embodiments, the network entity 404 can select the AI positioning functionality from the subset of the candidate AI positioning functionalities based on top KPI monitoring performance. For example, when the assessment of all functionalities in the subset of the candidate AI positioning functionalities is concluded, the subset of the candidate AI positioning functionalities can be re-ordered and / or filtered based on the performance metric(s) to determine the AI positioning functionality for the UE 110. In some embodiments, the network entity 404 can select two or more AI positioning functionalities from the subset of the candidate AI positioning functionalities. In some embodiments, a selection of the AI positioning functionality for the UE 110 and / or a prioritization order associated with the re-ordered subset of the candidate AI positioning functionalities can be based on an optimal performance as determined based on the performance metric(s).

[00123] In some embodiments, the network entity 404 transmits positioning functionality selection information (e.g, POSITIONING FUNCTIONALITY SELECTION INFORMATION) to the UE 110, at 10. For example, the positioning functionality selection information can be associated with the AI positioning functionality selection from the network entity 404. In some embodiments, the network entity 404 can cause activation of the AI positioning functionality via the UE 110. For example, the positioning functionality selection information can cause activation of the AI positioning functionality via the UE 110. In some embodiments, the network entity 404 can configure the AI positioning functionality 410 for the UE 110 based on the AI positioning functionality selection from the network entity 404. In some embodiments, the network entity 404 can configure an inference operation for the UE 110 based on the AI positioning functionality selection from the network entity 404. As such, by utilizing the positioning functionality selection information, the network entity 404 can activate the AI positioning functionality with the optimal performance for the UE 110 and / or the AI positioning functionality 410 to ensure consistency between training and inferences for a specific time interval and / or a specific geographical area associated with the UE 110.

[00124] In some embodiments, the UE 110 executes the AI positioning functionality, at 11. For example, the UE 110 can receive the positioning functionality selection information associated with an AI positioning functionality selection from the network entity 404. Additionally, the UE 110 can select an AI positioning functionality of the subset of the candidate AI positioning functionalities based on the positioning functionality selection information. The UE 110 can also execute the AI positioning functionality of the subset of the candidate AI positioning functionalities based on the positioning functionality selection information provided by the network entity 404. In some embodiments, the UE 110 can determine a positioning-related estimation by executing the AI positioning functionality. In some embodiments, the UE 110 can execute an inference operation associated with the AI positioning functionality 410 based on the positioning functionality selection information provided by the network entity 404.

[00125] In some embodiments, the UE 110 transmits positioning reporting information (e.g., POSITIONING REPORTING INFORMATION) to the network entity 404, at 12. For example, the UE 110 can cause transmission of the positioning reporting information to the network entity 404. In some embodiments, the positioning reporting information can include positioning inference reporting associated with the inference operation executed by the UE 110.

[00126] Figure 5 illustrates a flowchart depicting a method 500 and Figure 6 illustrates a flowchart depicting a method 600, according to one or more example embodiments of the present disclosure. It will be understood that each block of the flowcharts and combination of blocks in the flowcharts can be implemented by various means, such as hardware, firmware, processor, circuitry, and / or other communication devices associated with execution of software including one or more computer program instructions. For example, one or more of the procedures described above can be embodied by computer program instructions. In this regard, the computer program instructions which embody the procedures described above can be stored, for example, by the memory 304 of the apparatus 300 employing an embodiment of the present disclosure and executed by the processing circuitry 302. As will be appreciated, any such computer program instructions can be loaded onto a computer or other programmable apparatus (for example, hardware) to produce a machine, such that the resulting computer or other programmable apparatus implements the functions specified in the flowchart blocks. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture the execution of which implements the function specified in the flowchart blocks. The computer program instructions can also be loaded onto a computer or other programmable apparatus to cause a series of operations to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide operations for implementing the functions specified in the flowchart blocks.

[00127] Accordingly, blocks of the flowcharts support combinations of means for performing the specified functions and combinations of operations for performing the specified functions for performing the specified functions. It will also be understood that one or more blocks of the flowcharts, and combinations of blocks in the flowcharts, can be implemented by special purpose hardware-based computer systems which perform the specified functions, or combinations of special purpose hardware and computer instructions.

[00128] Referring now to Figure 5, the operations performed, such as by the apparatus 300 of Figure 3, in order to provide performance monitoring for AI positioning functionality associated with a user equipment, in accordance with one or more embodiments of the present disclosure. In some embodiments, the method 500 is associated with functionality of the network entity 404. As shown in block 502 of Figure 5, the apparatus 300 includes means, such as the processing circuitry 302, the memory 304, or the like, configured to determine candidate artificial intelligence (AI) positioning functionalities associated with a user equipment based on capability information associated with the user equipment. As shown in block 504 of Figure 5, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to determine a subset of the candidate AI positioning functionalities based on an indication provided by the user equipment. As shown in block 506 of Figure 5, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to select an AI positioning functionality from the subset of the candidate AI positioning functionalities based on at least one performance metric associated with the subset of the candidate AI positioning functionalities. As shown in block 508 of Figure 5, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to cause activation of the AI positioning functionality via the user equipment.

[00129] In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to cause transmission of first positioning functionality information that indicates the candidate AI positioning functionalities to the user equipment. In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to receive second positioning functionality information that indicates the subset of the candidate AI positioning functionalities selected by the user equipment. In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to select the AI positioning functionality from the subset of the candidate AI positioning functionalities indicated in the second positioning functionality information.

[00130] In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to receive a request from the user equipment to determine functionality criteria information for selection of the subset of the candidate AI positioning functionalities. In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to determine the functionality criteria information based on information associated with a network cell. In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to additionally or alternatively determine the functionality criteria information based on information associated with a positioning reference signal. In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to cause transmission of the functionality criteria information to the user equipment.

[00131] In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to receive, from the user equipment, functionality prioritization information associated with a priority order for the subset of the candidate AI positioning functionalities. In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to determine the subset of the candidate AI positioning functionalities based on the functionality prioritization information.

[00132] In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to execute a performance monitoring technique with respect to the subset of the candidate AI positioning functionalities to determine the at least one performance metric.

[00133] In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to configure an inference operation for the user equipment based on the AI positioning functionality.

[00134] In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to cause transmission of positioning functionality information associated with the AI positioning functionality to the user equipment. In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to receive positioning reporting information from the user equipment in response to activation of the AI positioning functionality via the user equipment.

[00135] In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to determine the capability information based on a capability report provided by the user equipment. In some embodiments, the capability report includes at least radio access capabilities of the user equipment.

[00136] Referring now to Figure 6, the operations performed, such as by the apparatus 300 of Figure 3, in order to provide performance monitoring for AI positioning functionality associated with a user equipment, in accordance with one or more embodiments of the present disclosure. In some embodiments, the method 600 is associated with functionality of the UE 110. As shown in block 602 of Figure 6, the apparatus 300 includes means, such as the processing circuitry 302, the memory 304, or the like, configured to receive, from a network entity, first positioning functionality information that indicates candidate AI positioning functionalities for an apparatus. In some embodiments, the apparatus corresponds to the UE 110. As shown in block 604 of Figure 6, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to determine a subset of the candidate AI positioning functionalities based on a set of selection rules. As shown in block 606 of Figure 6, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to cause transmission of second positioning functionality information that indicates the subset of the candidate AI positioning functionalities to the network entity. As shown in block 608 of Figure 6, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to receive positioning functionality selection information associated with an AI positioning functionality selection from the network entity. As shown in block 610 of Figure 6, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to select an AI positioning functionality of the subset of the candidate AI positioning functionalities based on the positioning functionality selection information.

[00137] In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to execute the AI positioning functionality of the subset of the candidate AI positioning functionalities based on the positioning functionality selection information provided by the network entity.

[00138] In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to cause transmission of a request to the network entity to determine functionality criteria information for selection of the subset of the candidate AI positioning functionalities. In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to receive the functionality criteria information from the network entity. In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to determine the subset of the candidate AI positioning functionalities based on the functionality criteria information.

[00139] In some embodiments, the functionality criteria information includes information associated with a network cell. In some embodiments, the functionality criteria information additionally or alternatively includes information associated with a positioning reference signal.

[00140] In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to determine functionality prioritization information associated with a priority order for the subset of the candidate AI positioning functionalities. In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to cause transmission of the functionality prioritization information to the network entity.

[00141] In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to execute an inference operation based on the AI positioning functionality.

[00142] In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to generate positioning reporting information in response to activation of the AI positioning functionality. In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to cause transmission of the positioning reporting information to the network entity.

[00143] In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to determine a capability report associated with the apparatus. In some embodiments, the capability report includes at least radio access capabilities of a user equipment. In some embodiments, the apparatus 300 additionally or alternatively includes means, such as the processing circuitry 302, the memory 304, or the like, configured to cause transmission of capability information associated with the capability report to the network entity.

[00144] As described above, Figures 5-6 are flowcharts of various methods that can be carried out by, e.g., the apparatus 300, and / or according to a computer program product, according to an example embodiment of the disclosure. A computer program product is therefore defined in those instances in which the computer program instructions, such as computer-readable program code portions, are stored by at least one non-transitory computer-readable storage medium with the computer program instructions, such as the computer-readable program code portions, being configured, upon execution, to perform the functions described above, such as, e.g., in conjunction with the communications flowchart of Figure 4, and / or as part of the system of Figure 1 and / or Figure 2. In other embodiments, the computer program instructions, such as the computer-readable program code portions, need not be stored or otherwise embodied by a non-transitory computer-readable storage medium, but may, instead, be embodied by a transitory medium with the computer program instructions, such as the computer-readable program code portions, still being configured, upon execution, to perform the functions described above.

[00145] Accordingly, blocks of the flowcharts support combinations of means for performing the specified functions and combinations of operations for performing the specified functions for performing the specified functions. It will also be understood that one or more blocks of the flowcharts, and combinations of blocks in the flowcharts, may be implemented by special purpose hardware-based computer systems which perform the specified functions, or combinations of special purpose hardware and computer instructions.

[00146] In some embodiments, certain ones of the operations above may be modified or further amplified. Furthermore, in some embodiments, additional optional operations may be included. Modifications, additions, or amplifications to the operations above may be performed in any order and in any combination.

[00147] Many modifications and other embodiments of the disclosure set forth herein will come to mind to one skilled in the art to which this disclosure pertains having the benefit of the 5 teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the disclosure is not to be 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 terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation. 10

Claims

What is claimed is:

1. An apparatus comprising:at least one processor; andat least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to:determine candidate artificial intelligence (AI) positioning functionalities associated with a user equipment based on capability information associated with the user equipment;determine a subset of the candidate AI positioning functionalities based on an indication provided by the user equipment;select an AI positioning functionality from the subset of the candidate AI positioning functionalities based on at least one performance metric associated with the subset of the candidate AI positioning functionalities; andcause activation of the AI positioning functionality via the user equipment.

2. The apparatus of claim 1, further comprising instructions that, when executed by the at least one processor, cause the apparatus to:cause transmission of first positioning functionality information that indicates the candidate AI positioning functionalities to the user equipment;receive second positioning functionality information that indicates the subset of the candidate AI positioning functionalities selected by the user equipment; andselect the AI positioning functionality from the subset of the candidate AI positioning functionalities indicated in the second positioning functionality information.

3. The apparatus of any claims 1 to 2, further comprising instructions that, when executed by the at least one processor, cause the apparatus to:receive a request from the user equipment to determine functionality criteria information for selection of the subset of the candidate AI positioning functionalities;determine the functionality criteria information based on information associated with a network cell; andcause transmission of the functionality criteria information to the user equipment.

4. The apparatus of any claims 1 to 3, further comprising instructions that, when executed by the at least one processor, cause the apparatus to:receive a request from the user equipment to determine functionality criteria information for the subset of the candidate AI positioning functionalities;determine the functionality criteria information based on information associated with a positioning reference signal; andcause transmission of the functionality criteria information to the user equipment.

5. The apparatus of any claims 1 to 4, further comprising instructions that, when executed by the at least one processor, cause the apparatus to:receive, from the user equipment, functionality prioritization information associated with a priority order for the subset of the candidate AI positioning functionalities; anddetermine the subset of the candidate AI positioning functionalities based on the functionality prioritization information.

6. The apparatus of any claims 1 to 5, further comprising instructions that, when executed by the at least one processor, cause the apparatus to:execute a performance monitoring technique with respect to the subset of the candidate AI positioning functionalities to determine the at least one performance metric.

7. The apparatus of any claims 1 to 6, further comprising instructions that, when executed by the at least one processor, cause the apparatus to:configure an inference operation for the user equipment based on the AI positioning functionality.

8. The apparatus of any claims 1 to 7, further comprising instructions that, when executed by the at least one processor, cause the apparatus to:cause transmission of positioning functionality information associated with the AI positioning functionality to the user equipment; andreceive positioning reporting information from the user equipment in response to activation of the AI positioning functionality via the user equipment.

9. The apparatus of any claims 1 to 8, further comprising instructions that, when executed by the at least one processor, cause the apparatus to:determine the capability information based on a capability report provided by the user equipment, wherein the capability report comprises at least radio access capabilities of the user equipment.

10. A method, comprising:determining candidate artificial intelligence (AI) positioning functionalities associated with a user equipment based on capability information associated with the user equipment;determining a subset of the candidate AI positioning functionalities based on an indication provided by the user equipment;selecting an AI positioning functionality from the subset of the candidate AI positioning functionalities based on at least one performance metric associated with the subset of the candidate AI positioning functionalities; andcausing activation of the AI positioning functionality via the user equipment.

11. An apparatus comprising:at least one processor; andat least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to:receive, from a network entity, first positioning functionality information that indicates candidate artificial intelligence (AI) positioning functionalities for the apparatus;determine a subset of the candidate AI positioning functionalities based on a set of selection rules;cause transmission of second positioning functionality information that indicates the subset of the candidate AI positioning functionalities to the network entity;receive positioning functionality selection information associated with an AI positioning functionality selection from the network entity; andselect an AI positioning functionality of the subset of the candidate AI positioning functionalities based on the positioning functionality selection information.

12. The apparatus of claim 11, further comprising instructions that, when executed by theat least one processor, cause the apparatus to:execute the AI positioning functionality of the subset of the candidate AI positioning functionalities based on the positioning functionality selection information provided by the network entity.

13. The apparatus of any claims 11 to 12, further comprising instructions that, when executed by the at least one processor, cause the apparatus to:cause transmission of a request to the network entity to determine functionality criteria information for selection of the subset of the candidate AI positioning functionalities;receive the functionality criteria information from the network entity; anddetermine the subset of the candidate AI positioning functionalities based on the functionality criteria information.

14. The apparatus of any claims 11 to 13, wherein the functionality criteria information comprises information associated with a network cell.

15. The apparatus of any claims 11 to 14, wherein the functionality criteria information comprises information associated with a positioning reference signal.

16. The apparatus of any claims 11 to 15, further comprising instructions that, when executed by the at least one processor, cause the apparatus to:determine functionality prioritization information associated with a priority order for the subset of the candidate AI positioning functionalities; andcause transmission of the functionality prioritization information to the network entity.

17. The apparatus of any claims 11 to 16, further comprising instructions that, when executed by the at least one processor, cause the apparatus to:execute an inference operation based on the AI positioning functionality.

18. The apparatus of any claims 11 to 17, further comprising instructions that, when executed by the at least one processor, cause the apparatus to:generate positioning reporting information in response to activation of the AI positioning functionality; andcause transmission of the positioning reporting information to the network entity.

19. The apparatus of any claims 11 to 18, further comprising instructions that, when executed by the at least one processor, cause the apparatus to:determine a capability report associated with the apparatus, wherein the capabilityreport comprises at least radio access capabilities of a user equipment; andcause transmission of capability information associated with the capability report to the network entity.

20. A method, comprising:receiving, from a network entity, first positioning functionality information thatindicates candidate artificial intelligence (AI) positioning functionalities for an apparatus;determining a subset of the candidate AI positioning functionalities based on a set of selection rules;causing transmission of second positioning functionality information that indicates the subset of the candidate AI positioning functionalities to the network entity;receiving positioning functionality selection information associated with an AIpositioning functionality selection from the network entity; andselecting an AI positioning functionality of the subset of the candidate AI positioning functionalities based on the positioning functionality selection information.

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