Machine Learning for Positioning

Abstract

Various aspects of the present disclosure relate to methods, apparatuses, and systems that support machine learning for positioning. For instance, implementations provide for direct Artificial Intelligence (Al)-based positioning and Al-assisted positioning which can be leveraged to improve UE location accuracy performance. In example implementations, for direct AI / Machine Learning (ML) positioning, techniques such as fingerprinting can be leveraged by AI / ML models to obtain enhanced location accuracies, such as via measurement and environmental data. Accordingly, the present disclosure provides techniques to configure direct AI / ML positioning assistance data as well as define measurements to perform AI / ML direct positioning. Further, the present disclosure provides techniques to configure reporting criteria for nodes and / or other entities performing AI / ML direct positioning measurements.

Description

1 / 126 Machine Learning for Positioning RELATED ORDERS

[001] This application claims priority over U.S. Provisional Application Serial No. 63 / 484,102, filed February 9, 2023, entitled “MACHINE LEARNING FOR POSITIONING,” and U.S. Provisional Application Serial No. 63 / 444,469, filed February 9, 2023, entitled “MACHINE LEARNING FOR POSITIONING,” the disclosures of which are incorporated by reference herein in their entirety. TECHNICAL FIELD

[002] This disclosure relates to wireless communications and, more specifically, to position determination in wireless communications. BACKGROUND

[003] A wireless communications system may include one or multiple network communication devices, such as base stations, which may also be known as eNodeB (eNB), next-generation NodeB (gNB), or other suitable terminology. Each network communication device, such as a base station, may support wireless communications to one or multiple user communication devices, which may also be known as user equipment (UE) or other suitable terminology. The wireless communications system may support wireless communications with one or multiple user communication devices by utilizing wireless communication system features (e.g., timing features (e.g., symbols, slots, subframes, frames, or the like) or frequency features (e.g., subcarriers, carriers).In addition, the wireless communications system can support wireless communications in various radio access technologies, including third-generation (3G) radio access technology, fourth-generation (4G) radio access technology, fifth-generation (5G) radio access technology, among other access technologies. Petition 870260069601, dated 07 / 14 / 2026, page 7 / 135 2 / 126 radio suitable in addition to 5G (e.g., sixth generation (6G)).

[004] Some wireless communication systems offer ways to determine the position of a device (e.g., UE), such as the geographic position of a UE. However, current implementations for UE positioning can be inaccurate. SUMMARY

[005] This disclosure relates to methods, devices, and systems that support machine learning for positioning. For example, implementations provide direct positioning based on artificial intelligence / machine learning (AI / ML) and AI / ML-assisted positioning, which can be leveraged to improve UE location accuracy performance. In exemplary implementations, for direct AI / ML positioning, techniques such as fingerprinting can be leveraged by AI / ML models to achieve enhanced location accuracies, such as through measurements and environmental data. Consequently, this disclosure provides techniques for configuring direct AI / ML positioning assistance data, as well as defining measurements to perform direct AI / ML positioning. Furthermore, this disclosure provides techniques for configuring reporting criteria for us and / or other entities performing direct AI / ML positioning measurements.

[006] Thus, by using the techniques described, it is possible to obtain a more precise positioning of UEs and reduce the use of system resources to determine UE position.

[007] Some implementations of the methods and devices described in this document may also include receiving machine learning positioning configuration requests; and transmitting, based at least in part on the machine learning positioning configuration requests, machine learning positioning configuration responses that include signal configurations. Petition 870260069601, dated 07 / 14 / 2026, page 8 / 135 3 / 126 reference positioning to be measured at respective reference locations and with one or more associated validity criteria.

[008] Some implementations of the methods and devices described in this document may also include: receiving machine learning positioning configuration requests from one or more network nodes or user equipment (UE) and transmitting machine learning positioning configuration responses to one or more network nodes or to the UE; reference locations include one or more base truth reference locations; machine learning positioning configuration responses include artificial intelligence configuration for positioning reference signal settings; transmitting one or more machine learning positioning configuration responses independent of a machine learning positioning configuration request;Machine learning positioning configuration responses include one or more direct machine learning configurations or assisted machine learning configurations; direct machine learning configurations include configuration to perform radio frequency fingerprinting; machine learning positioning configuration requests include a request for one or more of a training type, a request for basic truth location, a machine learning method, a request for on-demand machine learning positioning reference signal, or pre-configured machine learning assistance data; positioning reference signal configurations include one or more positioning reference signal features, feature sets, transmit-receive points, or positioning frequency layer to be measured at each; Petition 870260069601, dated 07 / 14 / 2026, page 9 / 135 4 / 126 reference location.

[009] Some implementations of the methods and devices described in this document may also include: o one or more associated validity criteria include one or more temporal criteria or spatial criteria; the temporal criteria include a measurement time defined by one or more time bases; the one or more time bases include at least one system frame number, Coordinated Universal Time (UTC) or Global Navigation Satellite System (GNSS) time; o one or more associated validity criteria include a spatial criterion that refers to an indication of a geographic region, and the spatial criteria include one or more area identifiers, cell identifiers or zone identifiers;The positioning reference signal settings include one or more indications of one or more uplink radio access technology-dependent measurements, downlink radio access technology-dependent measurements, or sidelink radio access technology-dependent measurements to be performed; the positioning reference signal settings include one or more indications of one or more downlink radio access technology-independent measurements or sidelink radio access technology-independent measurements to be performed;Machine learning placement configuration responses also include one or more configurations belonging to at least one of: a k-nearest neighbor (k-NN) algorithm, a support vector machine, a decision tree, a random forest, a Gaussian mixture model, Bayesian networks, an artificial neural network, K-means, reinforcement learning, deep learning, or transfer learning.

[010] Some implementations of the methods and devices described in this document may also include: where the method is Petition 870260069601, dated 07 / 14 / 2026, page 10 / 135 5 / 126 performed by a device including at least one of a location server, a next-generation radio access network (NG-RAN), a positioning reference unit (PRU), a user equipment (UE), an anchor UE or a target UE; the positioning reference signal settings include a received signal strength indicator measurement for one or more downlink, uplink or sidelink; and the received signal strength indicator measurement for one or more downlink, uplink or sidelink is defined as including a linear average of a total received power observed in feature elements of a slot carrying the positioning reference signal configured for measurement; the positioning reference signal settings include a positioning reference signal arrival time measurement for one or more downlink, uplink or sidelink;and the measurement of arrival time of a positioning reference signal for one or more downlinks, uplinks, or sidelinks is defined as including the reception time of a positioning reference signal at a receiver reference point; the positioning reference signal settings include an indication that a location server is allowed to transmit an error cause related to an incorrect configuration of a positioning reference signal; the positioning reference signal settings include an indication that a target user equipment (UE) is allowed to transmit an error cause related to an error in one or more receptions of a machine learning positioning configuration response or a measurement error related to a machine learning positioning measurement.

[011] Some implementations of the methods and devices Petition 870260069601, dated 07 / 14 / 2026, page 11 / 135 6 / 126 described in this document may also include: transmitting a machine learning positioning configuration request; receiving a machine learning positioning configuration response that includes a positioning reference signal configuration to be measured at one or more respective reference locations and with one or more associated validity criteria; and performing, based at least in part on the positioning reference signal configuration, one or more machine learning position measurements.

[012] Some implementations of the methods and devices described in this document may further include: one or more of: receiving the machine learning positioning configuration response in response to the machine learning positioning configuration request; or receiving the machine learning positioning configuration response independent of the machine learning positioning configuration request; inserting one or more machine learning position measurements into a machine learning model and receiving an output from the machine learning model; generating an estimated device location based, at least in part, on the output of the machine learning model; the method is performed by a device including one or more of a target user equipment (UE), a positioning reference unit (PRU), or an anchor UE.

[013] Some implementations of the methods and devices described in this document may also include: transmitting a configuration request to set up reference signals for machine learning positioning measurements; receiving a configuration response including reference signal configuration; and transmitting, based at least in part on the reference signal configuration, a reference signal transmission activation command.

[014] Some implementations of the methods and devices Petition 870260069601, dated 07 / 14 / 2026, page 12 / 135 7 / 126 described in this document may also include: where the reference signals include one or more sounding reference signals or positioning reference signals; transmitting a reference signal transmission deactivation command; the method is performed by a device including a location server, and where the method further includes transmitting the reference signal transmission activation command to one or more other devices that are configured to transmit the reference signals. BRIEF DESCRIPTION OF THE DRAWINGS

[015] Figure 1 illustrates an example of a wireless communications system that supports machine learning for positioning in accordance with aspects of the present disclosure.

[016] Figure 2 illustrates a system in which positioning reference signals can be used to obtain positioning measurements.

[017] Figure 3 illustrates a scenario for multi-cell RTT placement.

[018] Figures 4a and 4b illustrate parts of an LPP RequestLocationInformation message.

[019] Figures 5a and 5b illustrate parts of an LPP ProvideLocationInformation message.

[020] Figure 6 illustrates a system for RAN intelligence based on machine learning.

[021] Figure 7 illustrates an example scenario that supports machine learning for positioning in accordance with aspects of this disclosure.

[022] Figure 8 illustrates an example scenario that supports machine learning for positioning according to aspects of the present disclosure.

[023] Figure 9 illustrates a message that supports machine learning for positioning in accordance with aspects of the present disclosure. Petition 870260069601, dated 07 / 14 / 2026, page 13 / 135 8 / 126

[024] Figures 10a and 10b illustrate different parts of a message that supports machine learning for positioning according to aspects of the present disclosure.

[025] Figure 11 illustrates a message that supports machine learning for positioning in accordance with aspects of the present disclosure.

[026] Figure 12 illustrates a message that supports machine learning for positioning in accordance with aspects of the present disclosure.

[027] Figure 13 illustrates scenarios that support machine learning for positioning according to aspects of this disclosure.

[028] Figures 14a and 14b illustrate scenarios that support machine learning for positioning according to aspects of this disclosure.

[029] Figure 15 illustrates a scenario that supports machine learning for positioning according to aspects of the present disclosure.

[030] Figure 16 illustrates a scenario that supports machine learning for positioning according to aspects of the present disclosure.

[031] Figure 17 illustrates a scenario that supports machine learning for positioning according to aspects of the present disclosure.

[032] Figures 18 and 19 illustrate example block diagrams of devices that support machine learning for positioning according to aspects of the present disclosure.

[033] Figures 20 to 25 illustrate flowcharts of methods that support machine learning for positioning according to aspects of this disclosure. DETAILED DESCRIPTION

[034] In wireless communication systems, techniques are used to estimate the position (e.g., location) of a Petition 870260069601, dated 07 / 14 / 2026, page 14 / 135 9 / 126 UE, such as the geographic position of UE and / or the relative network location of UE. For example, some systems use beam-based attempts to estimate UE's location, such as in commercial and regulatory scenarios (e.g., emergencies). Current position determination techniques, however, can be inaccurate and result in inaccurate indications of UE's location.

[035] Thus, this disclosure provides techniques that support machine learning for positioning. For example, implementations provide direct AI-based positioning and AI-assisted positioning, which can be leveraged to improve EU location accuracy performance, such as within a positioning framework defined by 3GPP. For example, for direct AI / ML positioning, techniques such as fingerprinting can be leveraged by AI / ML models to achieve enhanced location accuracies, such as through measurements and environmental data. Thus, this disclosure provides techniques for configuring direct AI / ML positioning assistance data, as well as defining measurements to perform direct AI / ML positioning. Furthermore, this disclosure provides techniques for configuring reporting criteria for nodes and / or other entities that perform direct AI / ML positioning measurements.

[036] Thus, by using the techniques described, a more precise positioning of UEs can be obtained by taking advantage of large amounts of radio data and other related data, and the use of system resources to determine the UE's position can be reduced.

[037] Aspects of this disclosure are described in the context of a wireless communications system. Aspects of this disclosure are illustrated and described in more detail with reference to device diagrams and flowcharts.

[038] Figure 1 illustrates an example of a system of Petition 870260069601, dated 07 / 14 / 2026, page 15 / 135 10 / 126 wireless communications 100 that supports machine learning for positioning, according to aspects of this disclosure. The wireless communications system 100 may include one or more network entities 102, one or more UEs 104, a core network 106, and a packet data network 108. The wireless communications system 100 may support various radio access technologies. In some implementations, the wireless communications system 100 may be a 4G network, such as an LTE network or an LTE Enhanced (LTE-A) network. In some other implementations, the wireless communications system 100 may be a 5G network, such as an NR network. In other implementations, the 100 wireless communications system may be a combination of a 4G network and a 5G network, or other suitable radio access technology, including the Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), and IEEE 802.20 standards.The 100 wireless communications system can support radio access technologies in addition to 5G. Furthermore, the 100 wireless communications system can support technologies such as Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), or Code Division Multiple Access (CDMA), etc.

[039] One or more network entities 102 may be dispersed over a geographical region to form the wireless communications system 100. One or more of the network entities 102 described in this document may be, include, or be referred to as a network node, a base station, a network element, a radio access network (RAN), a base transceiver station, an access point, a NodeB, an eNodeB (eNB), a next-generation NodeB (gNB), or other appropriate terminology. A network entity 102 and a UE 104 may communicate via a communication link 110, which may be a wireless or wired connection. For example, a network entity 102 and a UE 104 may perform wireless communication (e.g., receive signaling, transmit signaling) via a Uu interface. Petition 870260069601, dated 07 / 14 / 2026, p. 16 / 135 11 / 126

[040] A network entity 102 can provide a geographic coverage area 112 for which the network entity 102 can support services (e.g., voice, video, packet data, messaging, broadcast, etc.) for one or more UEs 104 within the geographic coverage area 112. For example, a network entity 102 and a UE 104 can support wireless communication of service-related signals (e.g., voice, video, packet data, messaging, broadcast, etc.) according to one or more radio access technologies. In some implementations, a network entity 102 can be mobile, for example, a satellite associated with a non-terrestrial network. In some implementations, different geographic coverage areas 112 associated with the same or different radio access technologies can overlap, but the different geographic coverage areas 112 can be associated with different network entities 102.The information and signals described in this document can be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the description can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[041] One or more UEs 104 may be dispersed across a geographic region of the wireless communications system 100. A UE 104 may include or be referred to as a mobile device, a wireless device, a remote device, a remote unit, a handheld device, or a subscriber device, or some other suitable terminology. In some implementations, the UE 104 may be referred to as a unit, a station, a terminal, or a client, among other examples. Additionally, or alternatively, the UE 104 may be referred to as an Internet of Things (IoT) device, an Internet of Everything device. Petition 870260069601, dated 07 / 14 / 2026, p. 17 / 135 12 / 126 (LoE) or a machine-type communication device (MTC), among other examples. In some implementations, a UE 104 may be stationary in the wireless communication system 100. In some other implementations, a UE 104 may be mobile in the wireless communication system 100.

[042] One or more UEs 104 can be devices in different forms or have different capabilities. Some examples of UEs 104 are illustrated in Figure 1. A UE 104 may be able to communicate with various types of devices, such as network entities 102, other UEs 104, or network equipment (e.g., the core network 106, the packet data network 108, a relay device, an integrated access and backhaul (IAB) node, or other network equipment), as shown in Figure 1. Additionally, or alternatively, a UE 104 may support communication with other network entities 102 or UEs 104, which may act as relays in the wireless communication system 100.

[043] A UE 104 may also be able to support wireless communication directly with other UE 104s via a communication link 114. For example, a UE 104 may support wireless communication directly with another UE 104 via a device-to-device (D2D) communication link. In some implementations, such as vehicle-to-vehicle (V2V) deployments, V2X deployments, or cellular-V2X deployments, the communication link 114 may be referred to as a side link. For example, a UE 104 may support wireless communication directly with another UE 104 via a PC5 interface.

[044] A network entity 102 can support communications with the core network 106, or with another network entity 102, or both. For example, a network entity 102 can interact with the core network 106 through one or more backhaul links 116 (for example, through an S1, N2, N2 interface or another network). Network entities 102 can communicate with each other by Petition 870260069601, dated 07 / 14 / 2026, page 18 / 135 13 / 126 via backhaul links 116 (e.g., via an X2, Xn, or other network interface). In some implementations, network entities 102 may communicate with each other directly (e.g., between network entities 102). In some other implementations, network entities 102 may communicate with each other indirectly (e.g., via the core network 106). In some implementations, one or more network entities 102 may include subcomponents, such as an access network entity, which may be an example of an access node controller (ANC). An ANC may communicate with one or more UEs 104 via one or more other access network transmission entities, which may be called radio heads, smart radio heads, or transmit-receive points (TRPs).

[045] In some implementations, a 102 network entity can be configured in a disaggregated architecture, which can be configured to utilize a protocol stack distributed physically or logically between two or more 102 network entities, such as an Integrated Access Backhaul (IAB) network, an Open RAN (O-RAN) (e.g., an O-RAN Alliance-sponsored network configuration), or a Virtualized RAN (vRAN) (e.g., a Cloud RAN (C-RAN)). For example, a 102 network entity might include one or more of a Central Unit (CU), a Distributed Unit (DU), a Radio Unit (RU), an Intelligent RAN Controller (RIC) (e.g., a Near-Real-Time (RT) RIC, a Non-RT RIC), a Service Management and Orchestration (SMO) system, or any combination thereof.

[046] A RU may also be called a radio head, intelligent radio head, remote radio head (RRH), remote radio unit (RRU), or transmit-receive point (TRP). One or more components of the 102 network entities in a disaggregated RAN architecture may be colocated, or one or more components of the 102 network entities may be located Petition 870260069601, dated 07 / 14 / 2026, page 19 / 135 14 / 126 in distributed locations (e.g., separate physical locations). In some implementations, one or more network entities 102 of a disaggregated RAN architecture may be implemented as virtual units (e.g., a virtual CU (VCU), a virtual DU (VDU), a virtual RU (VRU)).

[047] The division of functionality between a CU, a DU, and a RU can be flexible and can support different functionalities depending on which functions (e.g., network layer functions, protocol layer functions, baseband functions, radio frequency functions, and any combinations thereof) are performed in a CU, a DU, or an RU. For example, a functional division of a protocol stack can be employed between a CU and a DU so that the CU can support one or more layers of the protocol stack and the DU can support one or more different layers of the protocol stack. In some implementations, the CU can host higher protocol layer functionality and signaling (e.g., a layer 3 (L3), a layer 2 (L2)) (e.g., Radio Resource Control (RRC), Service Data Adaptation Protocol (SDAP), Packet Data Convergence Protocol (PDCP)).A CU can be connected to one or more DUs or RUs, and the one or more DUs or RUs can host lower protocol layers, such as a layer 1 (L1) (e.g., physical layer (PHY)) or an L2 (e.g., radio link control layer (RLC), medium access control layer (MAC)) functionality and signaling, and each can be at least partially controlled by the CU.

[048] Additionally, or alternatively, a functional split of the protocol stack can be employed between a DU and a RU so that the DU can support one or more layers of the protocol stack and the RU can support one or more different layers of the protocol stack. The DU can support one or multiple different cells (e.g., through one or Petition 870260069601, dated 07 / 14 / 2026, p. 20 / 135 15 / 126 plus RUs). In some implementations, a functional division between a CU and a DU, or between a DU and an RU, may be within a protocol layer (for example, some functions for a protocol layer may be performed by a CU, a DU, or an RU, while other protocol layer functions are performed by a different CU, DU, or RU).

[049] A CU can be further functionally divided into CU control plane (CU-CP) and CU user plane (CU-UP) functions. A CU can be connected to one or more DUs via a midhaul communication link (e.g., F1, F1-c, F1u), and a DU can be connected to one or more RUs via a fronthaul communication link (e.g., open fronthaul interface (FH)). In some implementations, a midhaul communication link or a fronthaul communication link can be implemented according to an interface (e.g., a channel) between layers of a protocol stack supported by respective network entities 102 that are communicating via such communication links.

[050] The 106 core network can support user authentication, access authorization, tracking, connectivity, and other access, routing, or mobility functions. The 106 core network can be an evolved packet core (EPC) or a 5G core (5GC), which may include a control plane entity that manages access and mobility (e.g., a mobility management entity (MME), mobility and access management functions (AMF)) and a user plane entity that routes packets or interconnects external networks (e.g., a service gateway (S-GW), a location management function (LMF), which is a control plane entity that manages location-related services, a packet data network (PDN) gateway (P-GW), or a user plane function (UPF)). In some implementations, the control plane entity may manage Petition 870260069601, dated 07 / 14 / 2026, page 21 / 135 16 / 126 non-access layer (NAS) functions, such as mobility, authentication, and carrier management (e.g., data carriers, signal carriers, etc.) for one or more UEs 104 served by one or more network entities 102 associated with the core network 106.

[051] The core network 106 can communicate with the packet data network 108 through one or more backhaul links 116 (for example, through an S1, N2, N2 interface or other network). The packet data network 108 may include an application server 118. In some implementations, one or more UEs 104 can communicate with the application server 118. A UE 104 can establish a session (for example, a PDU unit session or similar) with the core network 106 through a network entity 102. The core network 106 can route traffic (for example, control information, data and similar) between the UE 104 and the application server 118 using the established session (for example, the established PDU session). The PDU session can be an example of a logical connection between UE 104 and the core network 106 (for example, one or more network functions of the core network 106).

[052] In the wireless communication system 100, network entities 102 and UEs 104 can use resources of the wireless communication system 100 (e.g., timing resources (e.g., symbols, slots, subframes, frames, or the like) or frequency resources (e.g., subcarriers, carriers)) to perform various operations (e.g., wireless communications). In some implementations, network entities 102 and UEs 104 can support different resource structures. For example, network entities 102 and UEs 104 can support different frame structures. In some implementations, such as in 4G, network entities 102 and UEs 104 can support a single frame structure. In some other implementations, such as in 5G and among other radio access technologies Petition 870260069601, dated 07 / 14 / 2026, page 22 / 135 17 / 126 suitable, network entities 102 and UEs 104 can support various frame structures (e.g., multiple frame structures). Network entities 102 and UEs 104 can support various frame structures based on one or more numerologies.

[053] One or more numerologies may be supported in the 100 wireless communications system, and a numerology may include a subcarrier spacing and a cyclic prefix. A first numerology (e.g., μ=0) may be associated with a first subcarrier spacing (e.g., 15 kHz) and a normal cyclic prefix. The first numerology (e.g., μ=0) associated with the first subcarrier spacing (e.g., 15 kHz) may utilize one slot per subframe. A second numerology (e.g., μ=1) may be associated with a second subcarrier spacing (e.g., 30 kHz) and a normal cyclic prefix. A third numerology (e.g., μ=2) can be associated with a third subcarrier spacing (e.g., 60 kHz) and a normal cyclic prefix or an extended cyclic prefix. A fourth numerology (e.g., μ=3) can be associated with a fourth subcarrier spacing (e.g., 120 kHz) and a normal cyclic prefix.A fifth numerology (e.g., μ=4) may be associated with a fifth subcarrier spacing (e.g., 240 kHz) and a normal cyclic prefix.

[054] A time interval of a resource (for example, a communication resource) can be organized according to frames (also called radio frames). Each frame can have a duration, for example, a duration of 10 milliseconds (ms). In some implementations, each frame can include several subframes. For example, each frame can include 10 subframes, and each subframe can have a duration, for example, a duration of 1 ms. In some implementations, each frame can have the same duration. In some implementations, each subframe of a frame can have the same duration. Petition 870260069601, dated 07 / 14 / 2026, p. 23 / 135 18 / 126

[055] Additionally or alternatively, a time interval of a resource (e.g., a communication resource) can be organized according to slots. For example, a subframe might include a number (e.g., quantity) of slots. Each slot might include a number (e.g., quantity) of symbols (e.g., orthogonal frequency division multiplexing (OFDM) symbols). In some implementations, the number (e.g., quantity) of slots for a subframe might depend on a numerology. For a normal cyclic prefix, a slot might include 14 symbols. For an extended cyclic prefix (e.g., applicable for 60 kHz subcarrier spacing), a slot might include 12 symbols. The relationship between the number of symbols per slot, the number of slots per subframe, and the number of slots per frame for a normal cyclic prefix and an extended cyclic prefix might depend on a numerology.It should be understood that the reference to a first numerology (e.g., μ = 0) associated with a first subcarrier spacing (e.g., 15 kHz) can be used interchangeably between subframes and slots.

[056] In the 100 wireless communications system, an electromagnetic (EM) spectrum can be divided, based on frequency or wavelength, into various classes, frequency bands, frequency channels, etc. By way of example, the 100 wireless communications system can support one or multiple operating frequency bands, such as frequency band designations FR1 (410 MHz - 7.125 GHz), FR2 (24.25 GHz - 52.6 GHz), FR3 (7.125 GHz - 24.25 GHz), FR4 (52.6 GHz - 114.25 GHz), FR4a or FR4-1 (52.6 GHz - 71 GHz) and FR5 (114.25 GHz - 300 GHz). In some implementations, network entities 102 and UEs 104 can perform wireless communications in one or more of the operating frequency bands. In some implementations, FR1 can be used by network entities 102 and UEs 104, among other equipment or devices, for communication traffic. Petition 870260069601, dated 07 / 14 / 2026, page 24 / 135 19 / 126 cellular (e.g., control information, data). In some implementations, FR2 can be used by network entities 102 and UEs 104, among other equipment or devices for short-range, high-data-rate capabilities.

[057] FR1 can be associated with one or multiple numerologies (e.g., at least three numerologies). For example, FR1 can be associated with a first numerology (e.g., μ=0), which includes a subcarrier spacing of 15 kHz; a second numerology (e.g., μ=1), which includes a subcarrier spacing of 30 kHz; and a third numerology (e.g., μ=2), which includes a subcarrier spacing of 60 kHz. FR2 can be associated with one or multiple numerologies (e.g., at least 2 numerologies). For example, FR2 can be associated with a third numerology (e.g., μ=2), which includes a subcarrier spacing of 60 kHz; and a fourth numerology (e.g., μ=3), which includes a subcarrier spacing of 120 kHz.

[058] According to implementations for machine learning for positioning, a network entity 102 transmits an ML positioning configuration to a UE 104. The ML positioning configuration 120, for example, includes various information and / or parameters for the UE 104 to measure various positioning information, generate positioning measurements, and / or process positioning measurements. Thus, based at least in part on the ML positioning configuration 120, the UE 104 performs ML positioning measurements 122, such as to measure and process various attributes of the wireless signal detected in the UE 104. The UE 104 generates an ML positioning response 124 based at least in part on the ML positioning measurements 122 and / or the ML positioning configuration 120 and transmits the ML positioning response to the network entity 102.In implementations, the ML 124 positioning response may include an estimated position (e.g., location) of UE 104 and / or the network entity 102 may utilize ML techniques. Petition 870260069601, dated 07 / 14 / 2026, page 25 / 135 20 / 126 to process the ML positioning response to estimate a position for EU 104.

[059] In some wireless communication systems, NR positioning is specified based on Uu NR signals and autonomous architecture (SA) (e.g., beam-based transmissions). Target use cases include commercial and regulatory scenarios (emergency services). Performance parameters include the following [Technical Report (TR) 38.855]: Table 1 Positioning Error Indoor Outdoor Horizontal positioning < 3m for 80% of UEs < 10m for 80% of UEs Vertical positioning < 3m for 80% of UEs < 3m for 80% of UEs

[060] In addition, some systems specify positioning performance parameters for commercial and IIoT use cases as follows [TR 38.857]: Table 2 Commercial IIoT Positioning Error: Horizontal positioning (< 1 m) for 90% of UEs; Vertical positioning (< 3 m) for 90% of UEs; Physical layer latency for UE position estimation (< 10 ms); End-to-end latency for UE position estimation (< 100 ms); (< 100 ms, on the order of 10 ms is desired)

[061] At least some of the supported positioning techniques are listed in Table 3 [TS38.305]: Table 3 EU-Based Method EU-Assisted, LMF-Based Node NG-RAN Assisted SUPL A-GNSS Yes Yes No Yes (EU-based and EU-assisted) OTDOA Note 1, Note 2 No Yes No Yes (EU-assisted) E-CID Note 4 No Yes Yes Yes for E-UTRA (EU-assisted) Sensor Yes Yes No No WLAN Yes Yes No Yes Bluetooth No Yes No No TBS Note 5 Yes Yes No Yes (MBS) DL-TDOA Yes Yes No No DL-AoD Yes Yes No No Multi-RTT No Yes Yes No NR E-CID No Yes FFS No UL-TDOA No No Yes No UL-AoA No No Yes No Petition 870260069601, dated 07 / 14 / 2026, page 26 / 135 21 / 126 NOTE 1: This includes Terrestrial Signaling System (TBS) positioning based on PRS signals. NOTE 2: In this version of the specification, only LTE-based OTDOA is supported. NOTE 3: NOTE 4: Null. This includes Cell Identifier (Cell-ID) for the NR method. NOTE 5: In this version of the specification, only for TBS positioning based on Metropolitan Beacon System (MBS) signals. NOTE 6: Null

[062] Separate positioning techniques, as indicated in Table 3, can currently be configured and implemented based on LMF requirements and UE capabilities. The transmission of Positioning Reference Signals (PRS) allows the UE to perform UE positioning-related measurements to enable the calculation of the UE location estimate, and are configured by Transmission and Reception Point (TRP), where a TRP can transmit one or more beams.

[063] Figure 2 illustrates a 200 system in which positioning reference signals can be used to obtain positioning measurements. For example, the PRS can be transmitted by different base stations (server and neighbors) using narrow beams over FR1 and FR2, which is relatively different when compared to LTE, where the PRS was transmitted throughout the cell. The PRS can be locally associated with a PRS resource identifier (ID) and a resource set ID (TRP) for a base station. Similarly, UE positioning measurements, such as Reference Signal Time Difference (RSTD) and PRS RSRP measurements, are made between beams (e.g., between a different pair of downlink (DL) PRS resources or DL ​​PRS resource sets), as opposed to different cells, as was the case in LTE. Furthermore, there are additional UL positioning methods that the network can exploit to calculate the target UE location.

[064] Tables 4 and 5 show the reference signal mapping for measurements required for each of the RAT-dependent positioning techniques supported in UE and gNB, respectively. The RAT-dependent positioning techniques Petition 870260069601, dated 07 / 14 / 2026, page 27 / 135 22 / 126 RATs involve the 3GPP RAT and core network entities to perform UE position estimation, which differs from RAT-independent positioning techniques that rely on GNSS, inertial measurement unit (IMU) sensor, WLAN, and Bluetooth technologies to perform target device (UE) positioning. Table 4: UE measurements to enable RAT-dependent positioning techniques DL / UL Reference Signals UE Measurements To facilitate support for the following positioning techniques DL Version 16 PRS DL RSTD DL-TDOA DL Version 16 PRS DL PRS RSRP DL-TDOA, DL-AoD, Multi-RTT DL Version 16 PRS / Version 16 SRS for positioning UE Time Difference Rx-Tx Multi-RTT Version 15 SSB / CSIRS for Radio Resource Management (RRM) SS-RSRP (RSRP for RRM), SS-RSRQ (for RRM), CSI-RSRP (for RRM), CSIRSRQ (for RRM), SSRSRP (for RRM) E-CID Table 5: gNB measurements to enable RAT-dependent positioning techniques Reference Signals for DL / UL gNB Measurements To facilitate support for the following positioning techniques Version 16 SRS for UL RTOA positioning UL-TDOA Version 16 SRS for UL SRS-RSRP positioning UL-TDOA, UL-AoA, Multi-RTT Version 16 SRS for positioning, Version 16 DL PRS gNB Time Difference Rx-Tx Multi-RTT Version 16 SRS for AoA and ZoA positioning UL-AoA, Multi-RTT

[065] The following positioning techniques dependent on RAT can be supported [TS38.305]:

[066] Downlink arrival time difference (DL-TDOA) positioning methods use the DL reference signal time difference (RSTD) (and optionally the DL PRS received signal power (RSRP)) of downlink signals received from multiple TPs in the UE. The UE measures the DL RSTD (and optionally the DL PRS RSRP) of the received signals using assistance data received from the Petition 870260069601, dated 07 / 14 / 2026, page 28 / 135 23 / 126 positioning server, and the resulting measurements are used, along with other configuration information, to locate the UE in relation to neighboring TPs.

[067] DL AoD positioning methods use the DL PRS RSRP measured from downlink signals received from multiple TPs in the UE. The UE measures the DL PRS RSRP of the received signals using assistance data received from the positioning server, and the resulting measurements are used along with other configuration information to locate the UE relative to neighboring TPs.

[068] Figure 3 illustrates a 300 scenario for a multi-cell round-trip time (RTT) positioning. Round-trip time (RTT) positioning methods use UE Rx-Tx and DL PRS RSRP measurements of downlink signals received from multiple TRPs, measured by the UE, and gNB Rx-Tx and Uplink (UL) Probing Reference Signal (SRS) measurements on multiple TRPs of uplink signals transmitted from the UE. The UE measures the UE Rx-Tx measurements (and optionally the DL PRS RSRP of the received signals) using assistance data received from the positioning server, and the TRPs measure the gNB Rx-Tx measurements (and optionally the UL SRS-RSRP of the received signals) using assistance data received from the positioning server. The measurements are used to determine the RTT on the positioning server, which is used to estimate the EU's location.

[069] In an Enhanced Cell Identifier (E-CID) positioning method, the position of a UE is estimated based on knowledge of its ng-eNB, gNB, and service cell and is based on LTE signals. Information about the ng-eNB, gNB, and service cell can be obtained by paging, logging, or other methods. E-CID NR positioning refers to techniques that use additional measurements of the UE and / or NR radio capabilities and other measurements to enhance the location estimate of Petition 870260069601, dated 07 / 14 / 2026, page 29 / 135 24 / 126 UE using NR signals. Although E-CID NR positioning may use some of the same measurements as the measurement control system in the RRC protocol, the UE is generally not expected to make additional measurements for the sole purpose of positioning; for example, positioning procedures do not provide a measurement setting or measurement control message, and the UE reports the measurements it has available rather than being required to take additional measurements.

[070] UL TDOA positioning methods use UL TDOA (and optionally UL SRS-RSRP) at multiple receiving points (RPs) of uplink signals transmitted by the UE. The RPs measure the UL TDOA (and optionally UL SRS-RSRP) of the received signals using assistance data received from the positioning server, and the resulting measurements are used, along with other configuration information, to estimate the UE location.

[071] UL AoA positioning methods use measured azimuth and arrival zenith on multiple RPs of uplink signals transmitted by the UE. The RPs measure A-AoA and Z-AoA of the received signals using assist data received from the positioning server, and the resulting measurements are used along with other configuration information to estimate the UE location.

[072] RAT-independent positioning techniques can also be implemented, including [TS38.305]:

[073] Network-assisted Global Navigation Satellite System (GNSS) methods: These methods make use of UEs that are equipped with radio receivers capable of receiving GNSS signals. In the 3GPP specifications, the term GNSS encompasses global and regional / augmentation satellite navigation systems. Examples of global navigation satellite systems include GPS, Modernized GPS, Galileo, GLONASS, and BeiDou. Regional navigation systems Petition 870260069601, dated 07 / 14 / 2026, page 30 / 135 25 / 126 satellite-based systems include Near-Zenith Satellite Systems (QZSS), while some augmentation systems are classified under the generic term Space-Based Augmentation Systems (SBAS) and provide regional augmentation services. In this concept, different GNSSs (e.g., GPS, Galileo, etc.) can be used separately or in combination to determine the location of a UE.

[074] Barometric pressure sensor positioning: The barometric pressure sensor method uses barometric sensors to determine the vertical component of the UE's position. The UE measures barometric pressure, optionally aided by assist data, to calculate the vertical component of its location or to send measurements to the positioning server for position calculation. This method can be combined with other positioning methods to determine the UE's 3D position.

[075] Wireless Local Area Network (WLAN) Positioning: The WLAN positioning method uses WLAN measurements (access point (AP) identifiers and optionally other measurements) and databases to determine the UE location. UE measurements received signals from WLAN access points [1], optionally assisted by assistance data, to send measurements to the positioning server for position calculation. Using the measurement results and a reference database, the UE location is calculated. Alternatively, the UE uses WLAN measurements and optionally WLAN AP assistance data provided by the positioning server to determine its location.

[076] Bluetooth Positioning: The Bluetooth positioning method uses Bluetooth measurements (indicator identifiers and optionally other measurements) to determine the UE's location. The UE measures signals received from Bluetooth indicators [2]. Using the measurement results and a Petition 870260069601, dated 07 / 14 / 2026, page 31 / 135 Using a 26 / 126 reference database, the UE's location is calculated. Bluetooth methods can be combined with other positioning methods (e.g., WLAN) to improve the accuracy of UE positioning.

[077] TBS Positioning: A TBS consists of a network of ground-based transmitters, transmitting signals solely for positioning purposes. The current type of TBS positioning signals are MBS (Multi-Based Signaling System) signals. Metropolitan Signaling) [3] and Reference Signs Positioning (PRS) (TS 36.211 [4]). The UE measures received TBS signals, optionally assisted by assist data, to calculate its location or to send measurements to the positioning server for position calculation.

[078] Motion sensor positioning: The motion sensor method uses different sensors, such as accelerometers, gyroscopes, magnetometers, to calculate the UE's displacement. The UE estimates a relative displacement based on a reference position and / or reference time. The UE sends a report comprising the determined relative displacement, which can be used to determine the absolute position. This method should be used with other positioning methods for hybrid positioning.

[079] Figures 4a and 4b illustrate parts of an LPP RequestLocationInformation 400 message. The body of the RequestLocationInformation 400 message in an LPP message can be used by the location server to request positioning measurements or a position estimate of the target device.

[080] Figures 5a and 5b illustrate parts of an LPP ProvideLocationInformation 500 message. The body of the ProvideLocationInformation 500 message in an LPP message can be used by the target device to provide positioning measurements or position estimates to the location server. Petition 870260069601, dated 07 / 14 / 2026, page 32 / 135 27 / 126

[081] For RAT-dependent positioning measurements, different DL measurements, including DL PRS-RSRP, DL RSTD, and UE Rx-Tx Time Difference, used for the supported RAT-dependent positioning techniques, are shown in Table 6 below. For example, the following measurement settings are specified [TS38.215]: • 4 pairs of DL RSTD measurements can be performed per cell pair. Each measurement is performed between a different pair of DL PRS Features / Feature Sets with a single reference time. • 8 DL PRS RSRP measurements can be performed on different DL PRS features of the same cell. Table 6: DL measurements required for DL-based positioning methods [TS38.215] Received DL PRS Reference Signal Power (DL PRS-RSRP) Definition The received DL PRS reference signal power (DL PRS-RSRP) is defined as the linear average over the power contributions (in [W]) of the feature elements carrying DL PRS reference signals configured for RSRP measurements within the considered measurement frequency bandwidth. For frequency band 1, the reference point for DL ​​PRS-RSRP should be the UE antenna connector. For frequency band 2, DL PRS-RSRP should be measured based on the combined signal of antenna elements corresponding to a given receiver branch. For frequency bands 1 and 2, if receiver diversity is in use by the UE, the reported DL PRS-RSRP value should not be less than the corresponding DL PRS-RSRP of any of the individual receiver branches.Applicable to intra-frequency RRC_CONNECTED, inter-frequency RRC_CONNECTED DL Reference Signal Time Difference (DL RSTD) Definition The DL Reference Signal Time Difference (DL RSTD) is the relative DL timing difference between positioning node j and reference positioning node i, defined as TgubframeRxj — TgubframeRxi, where: TSubframeRxj is the time at which the UE receives the start of a subframe from positioning node j. TSubframeRxi is the time at which the UE receives the corresponding start of a subframe from the positioning node i that is closest in time to the subframe received from positioning node j. Multiple DL PRS features can be used to determine the start of a subframe from a positioning node. For frequency band 1, the reference point for the DL RSTD should be the UE antenna connector. For frequency band 2, the reference point for the DL RSTD should be... Petition 870260069601, dated 07 / 14 / 2026, p. 33 / 135 28 / 126 UE antenna. Applicable for intrafrequency RRC_CONNECTED and interfrequency RRC_CONNECTED. UE Rx - Tx Time Difference Definition: The UE Rx - Tx time difference is defined as Tue-rx — Tue-TX. Where: Tue-rx is the timing received by the UE of the downlink subframe #ia from a positioning node, defined by the first path detected in time. Tue-tx is the UE transmission timing of the uplink subframe #j that is closest in time to the subframe #i received from the positioning node. Multiple DL PRS features can be used to determine the start of a subframe from the first arrival path of the positioning node. For frequency band 1, the reference point for measuring Tue-rx should be the UE Rx antenna connector and the reference point for measuring Tue-tx should be the UE Tx antenna connector.For frequency band 2, the reference point for Tue-rx measurement should be the UE Rx antenna and the reference point for Tue-tx measurement should be the UE Tx antenna. Applicable for intra-frequency RRC_CONNECTED and inter-frequency RRC_CONNECTED. DL PRS RSRPP (received path power of reference signal) Definition: The received path power of the DL PRS reference signal (DL PRS-RSRPP) is defined as the linear average power of the channel response at the i-th path delay of the feature elements carrying the DL PRS signal configured for measurement, where DL PRS-RSRPP for the 1st path delay is the power contribution corresponding to the first path detected in time. For frequency band 1, the reference point for DL ​​PRS-RSRPP should be the UE antenna connector. For frequency band 2, the DL PRS-RSRPP should be measured based on the combined signal of antenna elements corresponding to a given receiver branch.Applicable to RRC_CONNECTED, RRC_INACTIVE.

[082] Figure 6 illustrates a 600 system for intelligence of RAN based on machine learning. In the 600 system, Data Collection is a function that provides input data for the Training and Model Inference functions. Specific AI / ML algorithm data preparation (e.g., data preprocessing and cleaning, formatting, and transformation) may not be performed in the Data Collection function. Examples of input data might include measurements from UEs or different network entities, Actor feedback, and output from an AI / ML Model. Training data: data required as input for Petition 870260069601, dated 07 / 14 / 2026, page 34 / 135 29 / 126 the AI / ML model training function. Inference data: data required as input for the AI / ML model inference function.

[083] Model Training is a function that performs the training, validation, and testing of ML models, and can generate model performance metrics as part of the testing procedure. The Model Training function is also responsible for preparing the data (e.g., preprocessing and cleaning, data formatting and transformation) based on the Training Data provided by a Data Collection function.

[084] Model deployment / update: used to initially deploy a trained, validated, and tested AI / ML model to the model inference function or to deliver an updated model to the model inference function.

[085] Model Inference is a function that provides inference results from AI / ML models (e.g., predictions or decisions). The Model Inference function is also responsible for data preparation (e.g., preprocessing and cleaning, data formatting and transformation) based on the Inference Data provided by a Data Collection function, if necessary.

[086] Output: The AI / ML model inference output produced by a model inference function.

[087] Model Performance Feedback: Applied if certain information derived from the Model Inference function is suitable for improving the AI / ML model trained in the Model Training function. Actor feedback or feedback from other network entities (via the Data Collection function) may be required in the Model Inference function to create Model Performance Feedback.

[088] An actor is a function that receives the output of the Model inference function and triggers or performs corresponding actions. The actor can trigger actions directed to Petition 870260069601, dated 07 / 14 / 2026, page 35 / 135 30 / 126 other entities or oneself.

[089] Feedback: information that can be used to derive training data or inference or performance feedback.

[090] Thus, this disclosure presents solutions that support machine learning for positioning. For example, AI-based direct positioning methods and AI-assisted positioning can be used to improve the location accuracy performance of a UE. In direct AI / ML positioning scenarios, techniques such as fingerprinting can be used by AI / ML models to achieve improved location accuracies using measurement and environmental data. This disclosure describes techniques for configuring direct AI / ML positioning assistance data, as well as for defining measurements to perform direct AI / ML positioning. Furthermore, this disclosure describes techniques for configuring reporting criteria for devices, nodes, and / or entities that perform direct AI / ML positioning measurements.

[091] Regarding the aspects of this disclosure, implementations are described for: configuring a target UE, positioning reference unit (PRU) UE, side link (SL) UE, or NG-RAN node to perform direct AI / ML positioning measurements based on the transmission of the positioning reference signal to generate a training dataset; enabling a target UE, PRU UE, SL UE, or NG-RAN node to perform requested measurements for different scenarios, which may form part of the fingerprint based on the environment topology and AI / ML measurement parameters; enabling a target UE, PRU UE, NG-RAN node, configuration entity, SL UE, and / or location server to indicate AI / ML positioning assistance data or causes of measurement error; enabling a UE reporting configuration framework Petition 870260069601, dated 07 / 14 / 2026, page 36 / 135 31 / 126 target, PRU UE, SL UE and / or NG-RAN node to receive the desired direct AI / ML positioning measurements, e.g., fingerprint information; to enable a plurality of common reporting criteria for AI / ML positioning measurements; to enable configuration and reporting of assistance information to accurately report AI / ML positioning measurements.

[092] Some notes on the implementations described in this disclosure: The different implementations are combinable with each other in various ways; a positioning-related reference signal may be called a reference signal used for positioning procedures and / or purposes to estimate the location of a target UE, for example, PRS, signal based on existing reference signals, such as channel state information (CSI) reference signal (CSI-RS) or SRS, etc.A target UE can be called the device and / or entity to be located and / or positioned; the term 'PRS' can refer to any signal, such as a reference signal, which may or may not be used primarily for positioning; a target UE can be called the UE of interest whose position (e.g., absolute and / or relative) must be obtained by the network and / or the UE itself; the terms AI and ML can be used interchangeably to refer to an intelligent software component or system, and AI can represent a subset and / or implementation of ML; a reference made to the device's position and / or location information can refer to an absolute 2D / 3D position, relative position with respect to another node and / or entity, varying in terms of distance, varying in terms of direction, and combinations thereof.

[093] The implementations disclosed in this document support the configuration of direct AI / ML positioning measurements and processing. For example, fingerprinting is described, as when an AI / ML inference model can be deployed. Petition 870260069601, dated 07 / 14 / 2026, page 37 / 135 32 / 126 in different entities. Examples of these implementations include UE-based positioning with a UE-side ML mode, UE-assisted and / or LMF-based positioning with an LMF-side ML model, NG-RAN node-assisted positioning with an LMF-side ML model, etc.

[094] In implementations that include UE-based positioning, a target UE can request training datasets based on DL and SL reference signals (e.g., DLPRS, SL-PRS, etc.) from various data sources, including measurements taken and collected internally on the target UE, other UEs, a location server, an NG-RAN node, PRU, network operation, administration and maintenance (OAM), tracking collection entities (TCE), and / or combinations thereof. Training datasets may include reference points and / or reference locations, and fingerprint information and / or other positioning measurements may be sampled, measured, and / or associated. Multiple entities and / or network nodes can be enabled using the following procedures to enable the configuration: • A target UE, anchor UE and / or PRU UE can transmit a request and receive a response including configuration for direct downlink (DL) AI / ML positioning assistance data (e.g., configuration), for example, fingerprinting with respect to a location server; • A target UE can transmit a request and receive training data (e.g., instead of configuration to perform measurement) or instructions to obtain training data from a second node, for example, a location server, an NG-RAN node, positioning reference units, network OAM, TCE, or combinations thereof; • Another node (e.g., the location server, the NG-RAN node, and / or another node) can transmit a request to the target UE to receive training data that the target UE has collected and / or Petition 870260069601, dated 07 / 14 / 2026, page 38 / 135 33 / 126 measured; • A location server can receive AI / ML positioning assistance data directly from DL (e.g., configuration), for example, for a radio frequency (RF) fingerprint request from a plurality of UEs, including the target UE and the PRU UE, and the location server can provide a configuration response; • One or more UEs, including an anchor UE and / or PRU UE, may receive a request for AI / ML positioning assistance data directly from SL (e.g., configuration and / or RF fingerprint) and may provide an appropriate configuration response.

[095] In implementations that include UE-assisted positioning, a location server can request a plurality of training datasets based on DL and SL reference signals (e.g., DL-PRS, SL-PRS) from various data sources, including measurements collected internally on the location server, multiple UEs, other neighboring location servers, NG-RAN nodes, positioning reference units, and combinations thereof. These datasets may contain reference points and / or reference locations where fingerprint information can be sampled, measured, and / or associated. Several entities and / or network nodes can be enabled with the following functionalities to enable such implementations: • The location server can transmit AI / ML positioning assistance (configuration) directly via downlink (DL) or sidelink (SL), for example, fingerprint data. • At least one or more UEs, including the target UE or the PRU UE, may receive a response / configuration for a direct DL and / or SL AI / ML placement configuration, for example, fingerprinting against a server. Petition 870260069601, dated 07 / 14 / 2026, page 39 / 135 34 / 126 location.

[096] Figure 7 illustrates an example scenario 700 that supports machine learning for positioning according to aspects of the present disclosure. Scenario 700, for example, includes representations of the direct AI / ML configuration mechanisms of DL and SL, as discussed above, within a rectangular environment 702 (e.g., an internal factory environment) with length L and width W, including reference points and 18 gNB / TRPs separated by an inter-gNB / TRP distance D.

[097] In scenario 700, LTE Positioning Protocol (LPP) DL signaling 704 from a location server 706 to a target UE 708 and / or an anchor UE / PRU 710 can be used to transmit the various direct AI / ML positioning settings, for example, using the LPP message ProvideAssistanceData or a new LPP message ProvideMLAssistanceData, while the Side Link Positioning Protocol (SLPP) or the new positioning protocol related to SL positioning message exchange can be used for signaling 712 in order to provide the various direct AI / ML positioning settings from the anchor UE / PRU 710, for example, using the SLPP message ProvideAssistanceData. Alternatively or additionally, an SL Positioning Server UE can provide the direct AI / ML positioning settings via SLPP or similar.Furthermore, the target UE 708 can configure surrounding UEs, PRU UEs, and / or SL positioning server UEs to perform direct AI / ML positioning measurements, e.g., fingerprinting based on locations / landmarks. For illustrative purposes, direct signaling is shown from location server 706. However, this signaling can be transparently routed via a service gNB to a UE.

[098] In implementations, a PRU UE / anchor 710 can configure Petition 870260069601, dated 07 / 14 / 2026, page 40 / 135 35 / 126 other anchor UEs / PRU 710s to perform AI / ML positioning measurements. Additionally, an LMF can configure a first set of anchor UEs / PRU 710s to transmit SL PRS to a second set of anchor UEs / PRU 710s.

[099] In implementations such as that illustrated in scenario 700, target UE 708, anchor UEs / PRU 710 and / or location server 706 can request a plurality of AI / ML positioning assistance data directly from DL or SL, for example, using the LPP RequestAssistanceData message. Alternatively or additionally, target UE 708 can request a plurality of AI / ML positioning assistance data directly from SL from an anchor UE / PRU 710 and / or an SL positioning server UE, for example, using SLPP and / or the SL positioning protocol RequestAssistanceData message.

[100] In implementations that include NG-RAN assisted positioning, a location server can request a plurality of training datasets based on UL reference signals (e.g., SRS for positioning) from various data sources, including gNBs, TRPs, NG-RAN nodes, PRUs, TRPs, CUs, neighboring DUs, and / or combinations thereof. The training datasets may include reference points and / or reference locations corresponding to an NG-RAN node, CU locations, and / or DU locations, and fingerprint information may be sampled, measured, and / or associated. In implementations, multiple entities and / or network nodes may follow the following procedures: • An NG-RAN node including gNB and / or TRP can transmit a direct UL AI / ML positioning configuration (e.g., fingerprint) to one or more UEs upon request from a location server. • UEs, including a target UE, may receive a response and / or configuration for direct UL AI / ML positioning configuration (e.g., fingerprint) with respect to a server. Petition 870260069601, dated 07 / 14 / 2026, page 41 / 135 Location 36 / 126. • A location server can transmit direct UL AI / ML positioning assistance, for example, a fingerprint data request to one or more NG-RAN nodes, including gNB, TRP, CUs, DUs, PRU and / or combinations thereof, and then receive a configuration response.

[101] Figure 8 illustrates an example scenario 800 that supports machine learning for positioning according to aspects of the present disclosure. In scenario 800, in step 802, a location server 804 triggers a configuration request to an NG-RAN node 806, for example, a service gNB and / or neighboring gNBs. Step 802, for example, involves NRPPa with AI / ML positioning configuration directly from UL, for example, fingerprinting of N reference locations.

[102] In step 808, the NG-RAN node 806 forwards a UL-RS configuration (e.g., SRS for positioning configuration) to a target UE 810 and / or PRU UE / anchor 812, in order to enable a UL transmission and subsequent direct AI / ML measurement by UL on a gNB / TRP. Step 808, for example, may involve RRC with direct AI / ML positioning configuration from UL, e.g., fingerprinting of N reference locations.

[103] In implementations, location server 804 can directly forward a UL-RS configuration via DL LPP signaling to perform a UL transmission and subsequent direct AI / ML measurement of UL at NG-RAN node 806, for example, a gNB / TRP. In an alternative implementation, NG-RAN node 806 can be implemented as a CU and a DU, where location server 804 can signal the CU and the DU can perform UL-RS measurements at different locations and / or distributed reference points, for example, in an internal factory scenario as described above. For example, in step 814, location server 804 can transmit the configuration of Petition 870260069601, dated 07 / 14 / 2026, page 42 / 135 37 / 126 direct positioning of AI / ML from UL, for example, the fingerprint of N reference locations.

[104] In implementations, the UE / anchor PRU location 812 can be used together with a gNB / TRP basic truth reference location to construct a fingerprint including the basic truth reference location. In such implementations, the UE / anchor PRU 812 can signal a 2D and / or 3D location where the SRS is being transmitted to the configuration entity, for example, the NG-RAN node 806 and / or the location server 804. In scenarios involving signaling location information to the location server 804, LPP signaling (e.g., the ProvideLocationInformation message) can be used and / or in scenarios involving signaling location information to the NG-RAN node 806, RRC signaling can be used, for example, the LocationMeasurementIndication signaling.

[105] Alternatively or additionally, the configuration entity may request that the UE / 812 anchor PRU transmit UL SRS or SL PRS at certain predefined locations. These predefined locations may be included in the direct AI / ML configurations, along with the positioning reference signal configurations.

[106] In implementations, a type of NG-RAN node (e.g., PRU gNB / TRP) can also transmit SRS to other NG-RAN nodes, UEs, and / or devices. NG-RAN nodes that receive the SRS can perform direct AI / ML positioning measurements for direct AI / ML position estimation purposes. Thus, NG-RAN nodes capable of transmitting and receiving SRS, such as a PRU TRP, can support this type of measurement. In implementations, a new reference signal can also be supported between NG-RAN nodes for direct AI / ML position estimation purposes and can be transmitted, for example, via the Xn interface and / or via another wireless transmission medium. In implementations, the methods of Petition 870260069601, dated 07 / 14 / 2026, page 43 / 135 The 38 / 126 configurations discussed above can be combined to allow the use of direct AI / ML positioning measurements from DL, SL, and / or UL individually and in combination.

[107] In implementations, configurations such as those flagged, as described above, can be used to enable direct AI / ML position estimation, for example, using fingerprinting methods. The configuration content, for example, is considered according to the implementations described above and as discussed below.

[108] In UE-based positioning scenarios, such as in a UE-side model, a target UE can create a plurality of fingerprint training datasets based on measurements performed, data, measurements received, and / or data from other network entities, e.g., location servers, other UEs, PRUs, and so on. The target UE can initiate a request to the location server for settings related to DL or SL measurements of direct AI / ML measurements (e.g., fingerprint measurements) and / or request UL-RS transmission settings related to direct AI / ML positioning. An example configuration is discussed below.

[109] Figure 9 illustrates a 900 message that supports machine learning for positioning, according to aspects of the present disclosure. The 900 message, for example, represents an NR-Direct-AI-MLAssistanceData information element that can be used by a target device to request AI / ML positioning assistance data directly from a location server and / or a configuration entity, e.g., UE SL positioning server, UE anchor, etc.

[110] Table 7 below provides examples of field descriptions for message 900. Table 7 Petition 870260069601, dated 07 / 14 / 2026, page 44 / 135 39 / 126 Field descriptions NR-Direct-AI-ML-RequestAssistanceData nr-PhysCellID This field specifies the physical NR cell identity of the target device's current primary cell. nr-AdType This field indicates the requested assistance data or DL ​​positioning configuration. dl-prs refers to the requested DL-PRS configuration for direct Al / ML measurement, posCalc means the requested assistance data is nr-PosiciónCalculaciónAssistance for UE-based positioning, nr-Traíníng-Type This field indicates whether the requested direct AI / ML configuration is used for online / offline training. nr-on-demand-DL-PRS-Request This field indicates the on-demand DL-PRS requested for direct AI / ML positioning. In one implementation, this may apply to UE-initiated on-demand PRS. This field can be included when the dlprs or sl-prs bit in nr-AdType is set to '1' or '2'. nr-Learning-Method This field indicates the type of learning method employed for the requested assistance data. It is represented by a sequence of bits, with a value of 1 in the bit position indicating that specific assistance data was requested; a value of 0 means that it was not requested. - Bit 0 indicates whether supervised learning model(s) are employed on the target device. - Bit 1 indicates whether semi-supervised learning model(s) are employed on the target device. - Bit 2 indicates whether unsupervised learning model(s) are employed on the target device nr-PosCalcAssistanceRequest This field indicates the Position Calculation Assistance Data requested to perform AI / ML direct positioning. Represented by a bit sequence, where a value of one in the bit position means that the specific assistance data is requested; a value of zero means that it is not requested. bit 0 indicates whether the nr-TRP-Locatíonlnfo field in the information element (TE) NR-PosítíonCalculatíonAssístance is requested or not; bit 1 indicates whether the nr-DL-PRS-Beamlnfo field in IE NRPositionCalculationAssistance is requested or not; bit 2 indicates whether the nr-RTD-Info field in IE NRPositionCalculationAssistance is requested or not; Bit 3 indicates whether the nr-TRP-BeamAntennalnfo field in IE NRPosítíonCalculatíonAssístance is requested or not; Bit 4 indicates whether the nr-DL-PRS-Expected-LOS-NLOS-Assistance field in the TE NR-PosiciónCalculaciónAssistance is requested or not. Bit 5 indicates whether or not the basic truth reference points / locations of the fingerprint are requested. This field can only be present if the 'posCalc' bit in nr-AdType is set to the value '1'. pre-configured-AT-ML-AssístanceDataRequest This field, if present, indicates that the target device requests pre-configured assistance data for area-valid AI / ML direct positioning.

[111] In implementations, message 900 may include AI / ML direct positioning assistance data, AI / ML assisted positioning, or a combination thereof. Measurements of Petition 870260069601, dated 07 / 14 / 2026, page 45 / 135 40 / 126 AI / ML assisted positioning can be defined as measurements that have been enhanced and / or optimized using AI / ML models, for example, RAT-dependent measurements such as RSTD, relative time of arrival (RTOA), RSRP, RSRPP, Rx-Tx, AoAs, AoDs, time difference, and so on.

[112] In implementations related to UE-based positioning, as described above, a target UE can provide an index or list of basic truth reference locations, for example, absolute and / or relative locations. Examples of basic truth reference locations are defined below.

[113] In implementations such as those involving UE-based positioning (e.g., with a UE side model and / or UE-assisted positioning with an LMF side model), a location server can transmit direct AI / ML positioning assistance data. Alternatively or additionally, the location server can transmit assisted AI / ML positioning assistance data to one or more target devices, PRU UEs, anchor UEs, SL positioning server UEs, etc., to perform direct and / or assisted AI / ML measurements to be used as training data input. A target UE, for example, can receive from the location server a plurality of settings related to DL or SL measurements (direct AI / ML measurements), e.g., fingerprint measurements and / or UL-RS transmission settings related to direct AI / ML positioning.

[114] Figures 10a and 10b illustrate different parts of a 1000 message that supports machine learning for positioning according to aspects of the present disclosure. The 1000 message, for example, represents an NR-DirectAI-ML-ProvideAssistanceData message.

[115] Table 8 below provides descriptions of fields of Petition 870260069601, dated 07 / 14 / 2026, page 46 / 135 41 / 126 example for message 1000. Table 8 Field descriptions NR-Direct-AI-ML-ProvideAssistanceData dl-PRS-ID or sl-PRS-ID This field is used in conjunction with a DL-PRS Resource Set ID and a DL-PRS Resource ID or an SL-PRS Resource Set ID and an SL-PRS Resource ID to uniquely identify a DL-PRS Resource or an SL-PRS Resource. This ID can be associated with multiple DL-PRS Resource Sets associated with a single TRP or multiple SL-PRS Resource Sets associated with a single SL transmission point. Each TRP or SL transmission point must be associated with only one ID. nr-PhysCellID This field specifies the physical cell identity of the TRP associated with nr-CellGlobalID. This field specifies the NR Global Cell Identifier (NCGI), the globally unique identity of an NR cell, from the associated TRP. The server should include this field if deemed necessary to resolve ambiguities in the TRP indicated by nr-PhysCellID.nr-ARFCN This field specifies the Absolute Radio Frequency Channel Number (ARFCN) of the CD-SSB (Cell Defining SSB) of the TRP corresponding to nrPhysCellID. associated-DL-PRS-ID This field specifies the dl-PRS-ID of the associated TRP from which beam information is obtained. GND-referencePoint This field specifies a configured base truth reference point used to define the base truth location in the GND-ReferenceLocationInfoList. GND-Reference-LocalizationInfoList This field provides an index or list of basic truth reference point locations for performing Al / ML DL or SL positioning measurements, based on the reception and measurement of DL-PRS or SL-PRS features._______________________________________________________________________________ GND-Reference-Location This field provides basic truth reference locations for performing AI / ML DL or SL positioning measurements, e.g., fingerprinting, which are based on receiving and measuring DLPRS or SL-PRS features. These can be represented by an absolute location or a location relative to the GND-referencePoint or other UE / device. The absolute / relative location can be determined by offline position determination or a location estimate defined using one of the geographic forms defined in TS23.032. This can be based on dependent RAT, e.g., DL-TDOA, Multi-RTT, DL-AoD, etc., or independent RAT, e.g., GNSS coordinates, Bluetooth, WiFi, etc. location determination. This can comprise 2D or 3D location estimates. GND-Reference-Location-Source It provides the origin positioning technology used to determine the location estimate or the value of the location or basic truth reference point. Petition 870260069601, dated 07 / 14 / 2026, page 47 / 135 42 / 126 Field descriptions: NR-Direct-AI-ML-ProvideAssistanceData GND-Reference-measurementTime This field provides the time for which the location or baseline truth point is valid for performing direct AI / ML measurements. Time formats can be represented in terms of System Frame Number (SEN), UTC time, GNSS time, and so on. In other implementations, this time may be associated with validity for performing assisted AI / ML measurements. In an alternative implementation, this may also be represented as a time window. AI-ML-assístanceDataValídítyArea This field provides the geospatial criteria for which the AI / ML positioning configuration must be valid. It may include a cell ID, TRP ID, beam ID, a list of areas, tracking area, RAN notification area, zone ID, or any combination thereof.

[116] In implementations, the configuration information of DL-PRS described above can be used to allow a device (e.g., target UE, PRU UE, SL UE, etc.) to perform direct AI / ML measurements at each of the configured base truth reference locations, as signaled above. This process can be performed during an offline phase, and the measurements and their respective locations can be signaled to the location server or stored in a single UE or multiple UEs.

[117] In implementations involving NG-RAN assisted positioning (e.g., with LMF model), a location server can transmit one or more requests for a plurality of direct AI / ML positioning assistance data in terms of SRS configurations and / or other UL-PRS configurations available to multiple gNBs and / or TRPs. One or more gNBs and / or TRPs can determine the SRS configuration per target UE to perform AI / ML positioning-related SRS transmission, which can be configured per carrier. In implementations, the SRS configuration can be transmitted to multiple UEs for use in multiple cells, within a predefined positioning system information area, within an area with associated validity in terms of time and / or area, and combinations thereof.

[118] In implementations, a gNB and / or TRP can configure a UE to perform SRS for positioning transmissions via RRC signaling using, for example, message Petition 870260069601, dated 07 / 14 / 2026, page 48 / 135 43 / 126 RRCReconfiguration. The gNB and / or TRP can configure the UE to perform SRS for positioning transmissions in order to perform direct AI / ML positioning or assisted AI / ML positioning.

[119] In implementations, a target UE can confirm receipt of the SRS for positioning configuration to perform direct AI / ML positioning and / or AI / ML-assisted positioning measurements, along with other time or angle measurements unrelated to AI / ML. A location server (e.g., LMF) can request that one or more gNBs and / or TRPs activate the SRS for positioning configuration for transmission by the target UE. The gNB and / or TRP can activate SRS transmission to a UE by transmitting a DL MAC control element (CE) activation command to the target UE. The location server can further disable SRS transmission via the gNB, and the gNB can transmit a disable command using, for example, a DL MAC CE.

[120] In implementations, a location server can receive a plurality of available SRS or UL-PRS configurations to perform UL Direct AI / ML positioning. In an example scenario, a gNB can derive SRS and / or UL-PRS fingerprints for location estimates for multiple UEs.

[121] In implementations, lower-layer signaling relative to LPP (e.g., RRC signaling, MAC CE, downlink control information (DCI) signaling, and combinations thereof) can be used to transmit direct or assisted IA / ML configurations. In at least one implementation, LPP and / or RRC signaling can be used to add, modify, remove, update, enable, and / or disable one or more direct UE IA / ML positioning configurations.

[122] Implementations enable measurement procedures and Petition 870260069601, dated 07 / 14 / 2026, page 49 / 135 44 / 126 Direct AI / ML processing. For example, methods for performing direct AI / ML measurements are presented based on a type of AI / ML approach. In at least one implementation, fingerprint measurements may be based on Received Signal Strength (RSS) measurements, including RSRP, Received Reference Signal Quality (RSRQ), RSSI, or combinations thereof. In implementations, fingerprint measurements may include a combination of RSS, time-based measurements, and angle-based measurements to obtain the location estimate of a UE.

[123] In implementations, a target UE, PRU UE, SL UE, etc., can be configured to measure the following measurements to build an RF fingerprint applicable to: • RAT-dependent measures: DL / SL RSTD (DL or SL based measurements) DL / SL PRS RSRP (DL or SL based measurements) DL / SL PRS RSRPP (DL or SL based measurements) UE Rx-Tx time difference (DL or SL based measurements) Synchronization Signal (SS)-RSRP (RSRP for RRM), SSRSRQ (for RRM), CSI-RSRP (for RRM), CSI-RSRQ (for RRM), SSRSRP (for RRM) (DL based measurements) DL / SL carrier phase measurements (DL or SL based measurements) DL / SL carrier phase difference measurements (DL or SL based measurements) DL E-CID LTE E-CID SL Physical Side Link Shared Channel (PSSCH) RSRP, DL PRS RSSI (Received Signal Strength Indicator) or Observed Time Difference of Arrival (OTDOA) measurements Petition 870260069601, dated 07 / 14 / 2026, p. 50 / 135 45 / 126 RTT LTE • Independent RAT measurements or A-GNSS measurements, including common assistance data, which may be applicable to any GNSS constellation, e.g., Galileo, GPS, GLONASS, etc., generic assistance data for specific GNSS constellations, or periodic GNSS assistance data that are used to provide GNSS control information periodically to the UE / device. Bluetooth RSS measurements including RSSI WLAN (WiFi) measurements, including RSSI information and IMU sensor measurements, including gyroscope, accelerometer, and so on. Barometric sensor measurements

[124] In implementations, an NG-RAN, gNB / TRP, CU-DU, etc. node can be configured to measure the following measurements applicable to: • RAT-dependent measurements o UL-RTOA (UL-based measurement) o UL SRS RSRP (UL-based measurement) o UL SRS RSRPP (UL-based measurement) o gNB Rx-Tx time difference measurements (UL-based measurement) o UL-AoA (UL-based measurement) o UL carrier phase measurements (UL-based measurements) o UL carrier phase difference measurements (UL-based measurements) o UL NR E-CID o LTE E-CID

[125] Instances of the above measurements may constitute a plurality of fingerprint measurements, including part of a direct IA / ML DL or UL positioning measurement on a Petition 870260069601, dated 07 / 14 / 2026, p. 51 / 135 46 / 126 or more basic truth locations. The use of RAT-dependent and RAT-independent methods can aid in obtaining hybrid fingerprints to increase the accuracy of AI / ML direct positioning methods.

[126] In implementations, a location server (e.g., LMF) can provision a direct and / or assisted AI / ML configuration for measurement, such as based on a type of AI and / or machine learning model. These models may include the following, and are not limited to one or more of the following combinations: • Supervised learning approaches: the k-NN cluster (nearest neighbor) This model classifies fingerprints according to the Euclidean distance between neighboring training data points and determines the K-neighbors that have the greatest proximity to the input fingerprints. Support Vector Machines (SVMs) This model is based on margin calculation, where the input fingerprint data is plotted in n-dimensional space with the n-1 hyperplane drawn to divide the training data into n classes, so that the distance between each class and the hyperplane is maximized. Decision tree The classification or regression problem related to fingerprint matching can be solved using a decision tree-like structure. Rules are used to divide the training data into multiple labels, and these labels are predicted for any new fingerprint data points using this decision tree. Random Forest This model is a collection of several decision trees where the result of each tree provides a fingerprint classification or, in another implementation, the average prediction. Petition 870260069601, dated 07 / 14 / 2026, page 52 / 135 47 / 126 of all decision trees are in the output. This helps overcome the overfitting problem faced by independent decision trees. Artificial Neural Networks (ANNs) Based on backpropagation learning algorithms, an input dataset of fingerprints is transformed, using a non-linear transfer function within intermediate units / nodes that make up a hidden layer, into an output of final location estimates. These models can be robust against fingerprint data with noise or limited by interference. • Unsupervised approaches: K-means This model divides fingerprints into K unique, non-overlapping groups or clusters to characterize specific location points. Gaussian Mixture Models (GMM) The GMM is a probabilistic model that can be used to estimate the distribution of RF fingerprints in different locations, including base-truth reference locations as well as unknown locations. The GMM can be trained using collected RF fingerprints and then used to determine the most likely location for a given set of RF fingerprints. • Supervised and unsupervised approaches: Bayesian Networks (BN) Broadband networks can be used to model the probabilistic relationships between RF fingerprints and environmental factors, such as radio channel parameters (e.g., channel state information, path loss, and fading parameters), for a given location. Broadband networks can be trained using a training set configured for broadband networks. Petition 870260069601, dated 07 / 14 / 2026, page 53 / 135 48 / 126 with RF fingerprints and environmental data to perform RF fingerprint location. • Learning or Reinforcement Learning A learning algorithm based on trial-and-error methods, where decisions are based on so-called rewards or punishments, in which correct decisions are rewarded while incorrect decisions are penalized to improve the model's performance. Deep Learning Based on RNAs, which employ iterative weight-adjustment techniques between pairs of neurons / nodes, trained with large datasets of fingerprints collected from the environment. Transfer learning It leverages the model's ability to learn new features and subjects based on its own system knowledge, allowing for minimal changes to an already trained model. This can be leveraged through direct AI / ML positioning, such as fingerprinting, to provide a scalable solution to avoid the significant overhead of collecting fingerprints on-site during initial location surveying, for example, during the offline phase.

[127] In implementations, a location server can explicitly indicate ML models for a target UE and / or PRU UE using UE-specific LPP / SLPP signaling, for example, SLPP / LPP ProvideAssistanceData messages. In implementations, models can be indicated using positioning system information broadcast messages, for example, new and / or existing posSIBs for multiple UEs.

[128] In implementations, a location server and / or configuration entity may receive a request from a target UE, PRU UE and / or SL UE for direct AI / ML positioning or Petition 870260069601, dated 07 / 14 / 2026, page 54 / 135 49 / 126 AI / ML assisted positioning assistance data, along with an indication of the models described above for which the measurements should be used as input data.

[129] In implementations, the ML models used to initiate a direct or assisted AI / ML placement session can be indicated by means of applicable UE capability signaling (e.g., LPP ProvideCapabilities message), which can be based on a request made from the location server and / or configuration entity, e.g., an LPP RequestCapabilities message.

[130] In implementations, a PRS RSSI measurement can be defined for AI / ML positioning purposes, including direct and assisted techniques. In implementations, PRS RSSI can be applied to non-AI / ML positioning techniques. Furthermore, these measurements can be performed in the RRC_CONNECTED, RRC_INACTIVE, and RRC_IDLE states. PRS RSSI for downlink, sidelink, and uplink can be defined as per Table 9. Table 9: RSSI PRS DL, SL and UL measurement definitions DL PRS RSSI (Received Signal Strength Indicator) Definition The received path power of the DL PRS reference signal (DL PRS-RSRPP) is defined as the linear average of the total received power (in [W]) observed at feature elements of a slot carrying the DL PRS signal configured for measurement. In other deployments, the power unit may include dBm or dB. For frequency band 1, the reference point for the DL PRS-RSSI will be the UE antenna connector. For frequency band 2, the DL PRS-RSSI will be measured based on the combined signal from the antenna elements corresponding to a given receiver branch. Applicable to RRC_CONNECTED, RRC_INACTIVE, RRC_IDLE. The above includes various operational states, which refer to the state of a User Equipment (UE) in terms of radio connection to the LTE / 5G network.The RRC state of a UE is determined by the network based on the UE / device's activity, including any power / energy requirements and the network's requirements for radio resources. The UE transitions between different RRC states in response to network commands or as a result of changes in network conditions. Therefore, this measurement can be... Petition 870260069601, dated 07 / 14 / 2026, page 55 / 135 50 / 126 Supported in the operational states mentioned above. SL PRS RSSI (Received Signal Strength Indicator) Definition The Side Link PRS Received Signal Strength Indicator (SL PRS RSSI) is defined as the linear average of the total received power (in [W]) observed in the subchannel configured in OFDM symbols of a slot configured for a physical side link control channel (PSCCH) and PSSCH containing SL PRS symbols, starting from the second OFDM symbol. In other implementations, the power unit may include dBm or dB. For frequency band 1, the reference point for the PRS SL RSSI should be the UE antenna connector. For frequency band 2, the PRS SL RSSI should be measured based on the combined signal of the antenna elements corresponding to a given receiver branch.For frequency bands 1 and 2, if the UE is using receiver diversity, the reported PRS SL RSSI value must not be less than the corresponding PRS SL RSSI of any of the individual receiver branches. Applicable to RRC_CONNECTED, RRC_INACTIVE, RRC_IDLE. The above includes several operational states, which refers to the state of a User Equipment (UE) in terms of its radio connection to the LTE / 5G network. The RRC status of a UE is determined by the network based on UE / device activity, including any power / energy requirements and network requirements for radio resources. The UE transitions between different RRC states in response to network commands or as a result of changes in network conditions. Therefore, this measurement can be supported in the operational states mentioned above. NOTE: Currently, no state is defined for SL communication and positioning.However, this applicability would extend to any future support of the above operational states for SL communication and positioning. UL SRS RSSI (Received Signal Strength Indicator) Definition The received power of the UL SRS reference signal (UL SRS-RSRP) is defined as the linear average of the total received power (in [W]) observed on feature elements of a slot carrying sounding reference signals (SRS). In other implementations, the power unit may include dBm or dB. The UL SRS RSSI must be measured on the configured feature elements within a slot of the considered measurement frequency bandwidth, at the configured measurement times. In other implementations, the power unit may include dBm or dB. Applicable to RRC_CONNECTED, RRC_INACTIVE, RRC_IDLE The above includes several operational states, which refer to the state of a User Equipment (UE) in terms of radio connection to the LTE / 5G network.The RRC state of a UE is determined by the network based on the UE / device's activity, including any power / energy requirements and the network's requirements for radio resources. The UE transitions between different RRC states in response to network commands or as a result of changes in network conditions. Therefore, this measurement can be... Petition 870260069601, dated 07 / 14 / 2026, page 56 / 135 51 / 126 I Supported in the operational states mentioned above.

[131] In implementations and according to the measurement definitions presented in Table 9, DL, SL, and UL PRS RSSI may include a feature or signature of a fingerprint that can be used to enable direct AI / ML positioning. Additionally, a PRS or SRS TOA measurement may be defined to act as an additional feature or signature for AI / ML positioning purposes, including direct and assisted techniques. In implementations, the PRS TOA may be applicable to non-AI / ML positioning techniques. Furthermore, these measurements may be performed in the RRC_CONNECTED, RRC_INACTIVE, and RRC_IDLE states. The PRS TOA for downlink, sidelink, and uplink (SRS) is defined as follows in Table 10. Table 10: Definitions of DL, SL and UL PRS TOA measurements DL PRS TOA (Time of Arrival) Definition The DL PRS Time of Arrival is the measured arrival time from the start of a subframe containing the received SL PRS at the Receiving Point (RP) j. It can also be defined as the reception time of the DL PRS at the receiver reference point. In one implementation, the reference point will be the UE / device antenna connector. Multiple DL PRS features can be used to determine the start of a subframe containing received DL PRS at an RP. Applicable to RRC_CONNECTED, RRC_INACTIVE, RRC_IDLE The above includes several operational states, which refer to the state of a User Equipment (UE) in terms of radio connection to the LTE / 5G network. The RRC status of a UE is determined by the network based on the UE / device activity, including any power / energy requirements and the network requirements for radio resources.The UE transitions between different RRC states in response to network commands or as a result of changes in network conditions. Therefore, this measurement can be supported in the operational states mentioned above. SL PRS TOA (Time to Arrival). Petition 870260069601, dated 07 / 14 / 2026, page 57 / 135 52 / 126 Definition: SL PRS Time of Arrival is the measured arrival time from the start of the subframe containing the received SL PRS at the Receiving Point (RP) j. It can also be defined as the reception time of the SL PRS at the receiver reference point. In one implementation, the reference point will be the UE / device antenna connector. Several SL PRS features can be used to determine the start of a subframe containing received SL PRS at an RP. Applicable to RRC_CONNECTED, RRC_INACTIVE, RRC_IDLE. The above includes several operational states, which refer to the state of a User Equipment (UE) in terms of its radio connection to the LTE / 5G network. The RRC status of a UE is determined by the network based on the UE / device's activity, including any power / energy requirements and the network's requirements for radio resources. The UE transitions between different RRC states in response to network commands or as a result of changes in network conditions.Therefore, this measurement can be supported in the operational states mentioned above. NOTE: Currently, no state is defined for SL communication and positioning. However, this applicability would extend to any future support of the above operational states for SL communication and positioning. UL SRS TOA (Time to Arrival) Definition The SRS Time to Arrival UL is the measured arrival time from the start of the subframe containing the received SRS at the Reception Point (RP) j. It can be further defined as the reception time of the SRS UL at the receiver reference point. In one implementation, the reference point should be the receiver antenna connector of the base station, while in another implementation the reference point may be the center location of the radiating region of the receiver antenna of the base station. In another implementation, the reference point may include the transceiver boundary set connector of a base station receiver.Several SRS features can be used to determine the start of a subframe containing received SRS in a RP. Petition 870260069601, dated 07 / 14 / 2026, p. 58 / 135 53 / 126 Applicable to RRC_CONNECTED, RRC_INACTIVE, RRC_IDLE. The above includes various operational states, which refer to the state of a User Equipment (UE) in terms of its radio connection to the LTE / 5G network. The RRC status of a UE is determined by the network based on the UE / device's activity, including any power / energy requirements and the network's requirements for radio resources. The UE transitions between different RRC states in response to network commands or as a result of changes in network conditions. Therefore, this measurement can be supported in the operational states mentioned above.

[132] In deployments, a target UE and / or PRU UE can be configured to measure DL PRS and / or SL PRS for AI / ML positioning, as in one or more of the following scenarios: • Configured with a Measurement Gap (MG) associated with the Gap Pattern ID, Measurement Gap Length (MGL), and Measurement Gap Repetition Period (MGRP), or a combination thereof. This MG can be pre-configured with an activation or deactivation command. • Configured without or outside of a measurement gap, in the case of the DL PRS being within a bandwidth portion (BWP) of an active DL with the same numerology as the active DL BWP. • A training measurement gap (T-MG) gap has been configured, with a predefined gap pattern ID, measurement gap length (MGL), and measurement gap repetition period (TMGRP), T-MG start time, T-MG duration, T-MG end time, and an indicator showing whether measurements are based on offline or online training, or a combination of both. In other implementations, a time duration provided by the measurement window or timer expiration can be used to indicate the start and end of a duration for performing measurements for online or offline training of an AI / ML positioning model. Petition 870260069601, dated 07 / 14 / 2026, page 59 / 135 54 / 126

[133] In implementations, for each DL / SL PRS received, a UE can be configured with a priority setting of the PRS resources used to perform AI / ML and non-AI / ML positioning measurements. These resources can form a subset of resources, which can be part of the same PRS resource set or a different set. This priority signaled to the UE / devices can indicate the priority of performing measurements, which can build an additional training dataset or alternatively perform measurements for non-AI / ML positioning.

[134] The implementations also allow the provision of assistance data and / or causes of measurement errors. For example, a UE can indicate to a network and / or configuration entity that one or more measurements associated with a location and / or basic truth fingerprint have an associated error cause. The error cause may be, for example, the non-receipt of the PRS configuration or the absence of configuration parameters in the PRS configuration. For example, using the implementations described above, LPP and / or SLPP signaling can be used to indicate the causes of errors to the network. In addition, the causes of errors can be initiated by the UE, for example, originating on the UE side. In implementations, the causes of errors can be an indication initiated by the location server to the UE.

[135] Figure 11 illustrates a 1100 message that supports machine learning for positioning, according to aspects of the present disclosure. The 1100 message, for example, represents an IE that shows the error causes supported by the location server and / or configuration entity, such as those that can be transmitted via LPP. For example, the 1100 message represents an NR-AI-ML-LocationServerErrorCauses IE that can be used by a location server to provide AI / ML assistance data error reasons to a target device. A Petition 870260069601, dated 07 / 14 / 2026, page 60 / 135 Message 55 / 126 1100 can also be used by SL configuration entities that provide such error causes to a UE and / or target device.

[136] Figure 12 illustrates a 1200 message that supports machine learning for positioning, according to aspects of the present disclosure. The 1200 message, for example, presents error causes supported by a target UE, which can be transmitted via LPP. The 1200 message, for example, represents an IE NR-AI-ML-TargetDeviceErrorCauses that can be used by the target UE to provide NR measurement error reasons by direct AI / ML or by AI / ML assisted to a location server. Such implementations may be applicable to the target UE and / or SL device that provides the above error causes to the SL configuration entities.

[137] In implementations, an NG-RAN node can signal an IE NR-AI-ML-NG-RANnodeErrorCauses, with one or more parameter combinations included in the IE NR-AI-ML-TargetDeviceErrorCauses described above. Error causes originating in the NG-RAN node (e.g., gNB and / or TRP) can be signaled to a location server via an NRPPa interface, for example, using the Error Indication message. Additionally, the following error causes can be signaled from the NG-RAN node side: • The target UE or PRU UE has been moved to another cell. • The requested AI / ML positioning measurement could not be provided in time. • The AI / ML training model is invalid and needs to be retrained. • The AI / ML inference model is invalid and needs to be reacquired.

[138] In the implementations, the positioning measurements, including the definitions contained in Table 9 and Table 10, may have an associated quality indicator showing the following: Petition 870260069601, dated 07 / 14 / 2026, page 61 / 135 56 / 126 • Quality of the measurement performed based on time and RSS parameters. • Quality of the measurement performed in relation to similar measurements performed at surrounding baseline truth reference locations.

[139] The implementations described in this document also provide procedures for configuring reports. For example, implementations that allow the configuration of measurement reports and direct AI / ML positioning reports (e.g., fingerprints) are described, as in the following scenarios, where an AI / ML inference model can be deployed on the following entities to perform positioning: • UE-based positioning with UE-side model • UE-assisted / LMF-based positioning with LMF-side model • NG-RAN node-assisted positioning with LMF-side model

[140] The above scenarios are presented only as examples and the corresponding details can be extended beyond these example scenarios.

[141] In UE-based positioning scenarios, a target UE can request training datasets based on DL and SL reference signals (e.g., DL-PRS, SL-PRS, etc.) from various data sources, including measurements taken and collected internally on the target UE, on other UEs, on a location server, on an NG-RAN node, on a PRU, on the OAM, on the TCE, and / or combinations thereof. Additionally, training can be performed on the UE side, and a computed location estimate can be computed by the target UE device. In such implementations, a reporting configuration, including reporting criteria and / or assistance information and associated measurement reports, can be initiated from Petition 870260069601, dated 07 / 14 / 2026, page 62 / 135 57 / 126 of the EU target.

[142] Multiple network entities, UEs and / or nodes can be enabled with the following procedures to enable a reporting configuration, such as in scenarios where AI / ML model training is performed on the UE side.

[143] Figure 13 illustrates 1300 scenarios that support machine learning for positioning according to aspects of the present disclosure. The 1300 scenarios, for example, include a 1300a scenario in which UE side training can be performed with inference of the target UE with other UEs, and a 1300b scenario in which UE side training can be performed with inference of the target UE with network entities.

[144] In scenarios 1300, in 1302, a target UE 1304 can request a plurality of direct AI / ML positioning measurements based on defined reporting criteria. For example, in scenario 1300a for signaling transport towards a UE, in 1304a, the SL Positioning Protocol (SLPP) and / or other defined positioning protocol can be employed by a target UE 104a to request direct AI / ML positioning reports from UEs 104b, including measurement reporting criteria and / or assistance information. In 1300b, and for network entities 102, such as the location server, in 1304b, LPP signaling can be employed to request direct AI / ML positioning reports, including measurement reporting criteria and / or assistance information. In scenarios involving an NG-RAN node, RRC and / or related UL signaling, such as UL MAC CE, can be used.

[145] In addition to scenarios 1300, the respective network entities 102 and / or UEs 104 may provide responses 1306 to requests 1304 to provide measurements correlated to the reporting criteria and / or assistance information indicated in requests 1304. For example, UEs 104b may respond Petition 870260069601, dated 07 / 14 / 2026, page 63 / 135 58 / 126 on 1306a via SLPP, and network entities can respond on 1306b via LPP and / or RRC. In scenarios for network entities 102 reporting measurements, a location server can receive measurement reports from other UEs based on solicited or unsolicited requests (e.g., PRU UEs and / or other UEs) and report such measurements to the target UE 104a.

[146] In 1308, the target UE 104a can build a training dataset based on measurement reports from different sources and perform AI / ML model training. In implementations, the target UE 104a can perform inference using a trained ML model and based on new measurement data, repeating steps 1304a, 1304b, and 1306a and 1306b to obtain new measurement data for processing through a trained ML model. In implementations, training and inference datasets can be requested at once, avoiding the repetition of steps 1304 and 1306 to train an ML model and subsequently perform inference.

[147] In implementations, ML model training can be performed on the network side (e.g., on the location server or on the NG-RAN node (e.g., gNB)) and / or on another UE / device, such as, for example, anchor UE, PRU UE, UE SL, and so on. In such implementations, the network entity and / or UE / device can request a plurality of reporting criteria so that the desired measurements and assistance information are reported in a timely and accurate manner. These datasets may contain reference points or reference locations where fingerprint information or other positioning measurements are sampled, measured, or associated.

[148] Figures 14a and 14b illustrate the 1400 scenarios that support machine learning for positioning, according to aspects of the present disclosure. The 1400 scenarios, for example, include a 1400a scenario and a 1400c scenario, in which Petition 870260069601, dated 07 / 14 / 2026, page 64 / 135 59 / 126 the UE-side training is performed with inference from the target UE, and a scenario 1400b, in which the network-side training is performed with inference from the target UE.

[149] In scenarios 1400 to 1402, the respective network entities 102 and / or UEs 104 may request from the target UE 104a a plurality of direct AI / ML positioning measurements based on certain defined reporting criteria. In terms of signaling transport to UEs 104, SLPP and / or a newly defined positioning protocol may be employed, while in the case of network entities 102, such as the location server, LPP signaling may be employed, and in scenarios for an NG-RAN node, RRC or any related DL signaling, such as DL MAC CE, may be employed.

[150] In 1404, the target UE 104a can respond to received requests and provide the requested measurements according to the reporting criteria and / or assistance information. For network entities 102, the location server can, for example, receive measurement reports from other UEs based on solicited or unsolicited requests (e.g., PRU UEs, other UEs, etc.) and then report such measurements to the target UE 104a.

[151] In 1406, the other UEs 104b and / or network entities 102 can generate a training dataset based on measurement reports from different sources and perform the training of an AI / ML model.

[152] In 1408 and according to the implementations, the other UEs 104b and / or network entities 102 can perform inferences based on new measurement data by repeating the procedures in 1402, 1404. In implementations, the training and inference datasets can be requested in a single attempt, as an alternative to repeating 1402, 1404.

[153] In implementations, the various entities or network nodes can be enabled with the following procedures for Petition 870260069601, dated 07 / 14 / 2026, page 65 / 135 60 / 126 Enable a reporting configuration in scenarios where AI / ML model training is not performed on the EU side: • The target UE performing the training can transmit a direct AI / ML positioning report configuration to the network entity and / or other UEs / devices and receive a corresponding report for direct AI / ML positioning measurements on the downlink (DL) or sidelink (SL), for example, fingerprint measurements. In other implementations, UL positioning measurements may also be provided, such as in cases where the UE can train a model based on these UL measurements, for example, fingerprints. • The network entity (e.g., location server and / or NG-RAN node) performing the training can transmit a direct AI / ML positioning report configuration towards the target UE and receive a corresponding report for direct downlink (DL) or sidelink (SL) AI / ML positioning measurements, e.g., fingerprint measurements. • A UE / node (e.g., anchor UE, PRU UE, UE SL, etc.) performing the training can transmit a direct AI / ML positioning report configuration to the target UE and receive a corresponding report for direct AI / ML positioning measurements via downlink (DL) or sidelink (SL), e.g., fingerprint measurements.

[154] In UE-assisted positioning scenarios, a location server can request measurement training datasets based on DL and SL reference signals (e.g., DL-PRS, SL-PRS, etc.) from various data sources, including measurements collected internally on the location server, multiple UEs, other neighboring location servers, NG-RAN nodes, positioning reference units, and combinations thereof.

[155] Figure 15 illustrates a 1500 scenario that supports Petition 870260069601, dated 07 / 14 / 2026, page 66 / 135 61 / 126 Machine learning for positioning, according to aspects of this disclosure. Scenario 1500, for example, represents implementations for report exchange when network-side training is performed and network-side inference is performed. In scenario 1500, training and inference are performed on a network entity 102 (e.g., location server), and network entity 102 can employ the illustrated signaling mechanisms to signal the AI / ML direct positioning report configuration, as well as receive measurements from a UE 104, e.g., target UE, PRU UE, and so on.

[156] In 1502, the respective network entities 102 can request a plurality of direct AI / ML positioning measurements based on certain defined reporting criteria from a UE 104. In terms of signaling transport towards UEs 104 for 1502 requests, LPP signaling can be employed, while in scenarios for an NG-RAN node, RRC and / or related DL signaling, such as DL MAC CE, can be employed.

[157] In 1504, UE 104 may respond in kind to requests 1502 received from network entities 102 and provides, as part of the responses 1504, measurements in accordance with the reporting criteria and / or assistance information specified by the requests 1502.

[158] In 1506, network entity 102 constructs a training dataset based on measurement reports from different UEs 104 and performs AI / ML model training. In 1508, network entity 102 performs inference based on new measurement data, repeating steps 1502 and 1504. Alternatively, or additionally, the training and inference datasets can be requested at once.

[159] Figure 16 illustrates a 1600 scenario that supports machine learning for positioning, according to Petition 870260069601, dated 07 / 14 / 2026, page 67 / 135 62 / 126 aspects of this disclosure. Scenario 1600, for example, represents implementations for request and response procedures for stored AI / ML training datasets based on reported AI / ML measurements. In scenario 1500, where training is performed on a UE 104 (e.g., PRU UE), the UE can employ the illustrated signaling, as illustrated in scenario 1600 and described below, to receive the AI / ML training dataset to perform training on the UE 104.

[160] In 1602, network entity 102 can request a plurality of direct AI / ML positioning measurements based on defined reporting criteria from a UE 104. In terms of signaling transport from network entity 102 to UE 104, LPP signaling can be employed and, in scenarios for an NG-RAN node, RRC and / or related DL signaling, such as DL MAC CE, can be employed.

[161] In 1604, UE 104 can respond to request 1602 from network entities and provide measurements according to the reporting criteria and / or assistance information specified in request 1602. In 1606, network entity 102 constructs the training dataset based on measurement reports from different UEs / source nodes and stores the AI / ML training dataset. The storage of the training dataset can be associated with additional validity criteria, such as temporal criteria (e.g., time window, validity time duration, timer expiration) and / or spatial criteria, e.g., geographic region ID, area ID, cell ID, tracking area ID, system information area ID, zone ID, AI / ML Dataset Validity Area, or combinations thereof.

[162] In 1608, in scenarios where training is performed on the UE 104 side, a UE 104 can request the dataset from Petition 870260069601, dated 07 / 14 / 2026, page 68 / 135 63 / 126 AI / ML training, which is based in part on measurements provided by 1604. In 1610, network entity 102 can respond to request 1608 with the AI / ML training dataset, which can be based on certain criteria, including the applicability of the training dataset to UE 104 location, radio link quality, radio channel parameters, mobility pattern, orientation, and so on.

[163] In 1612, UE 104 performs training based on the training dataset received from network entity 102 and in 1614, network entity 102 and / or UE 104 can perform inference to derive an estimated location of a target UE 104 based on the trained AI / ML model.

[164] In implementations, scenario 1600 may be applicable to scenarios where, if training is performed on an NGRAN node, the NG-RAN node may request and receive a constructed training dataset stored on a location server (e.g., LMF) based in part on measurements provided by the NG-RAN node to the location server, as described below with reference to Figure 17.

[165] In implementations, model training can be performed on the UE side, where the signaling mechanisms of scenario 1300b can be used to signal the configuration of the direct AI / ML positioning report, as well as receive measurements from a network entity. One or more UEs 104 (e.g., a target UE) can provide a plurality of direct AI / ML measurements for inference on the network side.

[166] In implementations, various entities and / or network nodes can be enabled with the following procedures to enable a reporting configuration in scenarios where AI / ML model training and inference are performed on the network side. For example, the network entity (e.g., location server) can transmit a configuration of Petition 870260069601, dated 07 / 14 / 2026, page 69 / 135 64 / 126 direct AI / ML positioning report for UEs and receive a corresponding report for direct AI / ML positioning measurements from DL and / or SL, for example, fingerprint measurements.

[167] In NG-RAN assisted positioning scenarios, a location server can request a plurality of UL Direct AI / ML positioning measurements based on UL reference signals (e.g., SRS) for positioning from multiple data sources, including neighboring gNBs / TRPs or NG-RAN nodes, PRU TRPs, CUs, DUs, or combinations thereof.

[168] Figure 17 illustrates a 1700 scenario that supports machine learning for positioning, according to aspects of the present disclosure. The 1700 scenario, for example, represents report exchange when LMF side training is performed with LMF side inference. In the 1700 scenario, where training and inference are performed on a 1702 location server, the 1702 location server can employ the illustrated signaling mechanisms to signal the AI / ML positioning report directly from UL.

[169] In 1704, the location server 1702 can request a plurality of AI / ML positioning measurements directly from UL based on defined reporting criteria from an NGRAN node 1706, for example, a service gNB / TRP, neighboring gNB / TRP, PRU gNB, TRP, etc. In terms of signaling transport towards the NG-RAN node 1706, NRPPa signaling can be employed, for example, positioning measurement request messages and positioning measurement response messages.

[170] In 1708, NG-RAN node 1706 can respond to request(s) 1704 from location server 1702 and provide measurements according to the reporting criteria and / or assistance information as specified in request 1704.

[171] In 1710, location server 1702 builds a Petition 870260069601, dated 07 / 14 / 2026, page 70 / 135 65 / 126 training dataset based on measurement reports from different nodes of the NG-RAN source 1706 and performs training of an AI / ML model.

[172] In 1712, the location server 1702 performs inference based on new measurement data by repeating steps 1704 and 1706. In one implementation, the training and inference datasets can be requested at once.

[173] In implementations where training is performed on NG-RAN node 1706, NG-RAN node 1706 can request and receive AI / ML positioning measurement reports directly from UL from location server 1702, for example, via NRPPa and / or other NG-RAN nodes, for example, via the Xn interface.

[174] In implementations, several network entities and / or nodes can be enabled with the following procedures: The location server 1702 can transmit a direct AI / ML positioning report configuration to a plurality of NG-RAN nodes 1706 and receive a corresponding report for direct AI / ML positioning measurements from UL, for example, fingerprint measurements.

[175] In additional or alternative implementations, the construction of the training dataset and the training of the dataset may not necessarily occur on the same entity and may occur on other separate entities or network nodes. For example, according to scenario 1400Error! Reference source not found., a PRU UE, an anchor UE, and / or another UE may perform the construction or training of the dataset. Furthermore, and similarly to scenario 1600, a gNB / TRP server, a neighboring gNB / TRP, or a gNB / TRP PRU may perform the construction or training of the training dataset. In addition, the construction and inference of the AI / ML model training dataset may also follow the same behavior, not necessarily being performed on the same entity. Alternatively or additionally, the methods of Petition 870260069601, dated 07 / 14 / 2026, page 71 / 135 The 66 / 126 report configuration discussed above can be combined in various ways, such as using a plurality of direct AI / ML positioning measurements from DL, SL, and UL in one or more combinations.

[176] The implementations described in this document also provide several ML-related reporting criteria. For example, the settings for the reporting criteria are detailed to support direct AI / ML position estimation, for example, using fingerprinting methods.

[177] In UE-based positioning scenarios (e.g., with UE side model), the target UE may request a plurality of direct AI / ML positioning measurements from DL or SL or other data needed for training or inference. A set of common reporting criteria can be defined for network entities or UE / devices that provide measurement reports for building datasets. Table 11: Common reporting criteria Parameter Description Fingerprint Type This Information Element (IE) describes the type of fingerprint to be reported. This implies that the fingerprint is generated at the measurement entity / node and then reported to the target UE. The fingerprint may comprise one or more combinations of the following RAT-dependent DL / SL positioning measurements: RSTD, UE Rx-Tx time difference, ToA, RSS metrics such as PRS / SRS RSRP, RSRPP, RSSI, RSRQ or AoD, AoA- or RAT-independent measurements, e.g., AGNSS, Bluetooth, Wi-Fi, IMU sensor, etc. In an implementation, the reporting configuration entity may configure different measurements depending on the type of AI / ML model used. For example, for AI / ML model A, RSS-based and time-based measurements may characterize a fingerprint, while for AI / ML model B, only RAT-independent measurements are used.In a different implementation, these fingerprints can be organized into a hierarchical request for different fingerprints, depending on the AI / ML model type. Baseline Truth / Reference Location Type This IE describes the baseline truth reference location type, where each of the AI / ML direct positioning fingerprints or measurements were obtained as referenced in TS 23052, including ellipsoid point, ellipsoid point with uncertainty ellipse, ellipsoid point with uncertainty circle, polygon, ellipsoid point with altitude, ellipsoid point. Petition 870260069601, dated 07 / 14 / 2026, page 72 / 135 67 / 126 with altitude and uncertainty ellipsoid, arc ellipsoid, high-precision ellipsoid point with uncertainty ellipse, high-precision ellipsoid point with altitude and uncertainty ellipse, and so on. In different implementations, the basic truth reference locations may correspond to 2D or 3D location points (including height / altitude). Immediate Reports This time-domain reporting IE indicates that immediate direct AI / ML reports, including fingerprint measurements, are requested based on available processed measurements. Periodic Reports This time-domain reporting indicates that periodic direct AI / ML reports, including fingerprint measurements, are requested based on available processed measurements.This may include additional subfields, such as Number of Reports, which indicate the number of direct AI / ML measurement reports, and Report Interval, which indicate the periodicity or interval between direct AI / ML measurement reports, or a combination thereof. In other implementations, periodic reports may be enabled and disabled if a number of reports is not configured. Triggered Reports This time domain (TD) report indicates that triggered direct AI / ML reports, including fingerprint measurements, are requested based on available processed measurements. This may include other subfields such as geographic area change, such as cell ID, zone ID, new location or base truth reference point, or a combination of changes. Another subfield may include a validity time associated with the time the triggered report remains active or inactive.Pre-processed Measurements: This IE indicates whether the measurement should be pre-processed, for example, applying normalization to the measurement, and so on. This IE can be in the form of an indicator that shows whether pre-processing should be applied or not. In one implementation, an additional subfield might include data cleaning, where the measurement entity needs to clean and prune measurements before reporting, for example, removing incorrect labels and incorrectly classified data. This could be implemented, for example, in the form of an indicator. In one implementation, the measurement entity might be required to remove measurement biases or imbalances in the reported measurement dataset through a subfield, for example, through a Bias or Sampling subfield. In one implementation, another subfield might include whether the measurement data requires normalization before reporting the plurality of direct AI / ML measurements.Fingerprint Environment This IE indicates the type of environment in which direct AI / ML or fingerprint measurements should be reported, for example, indoor / outdoor / semi-indoor / semi-outdoor, office, factory. Additional subfields may include floor plan information, including the number of rooms, room area, and height. Petition 870260069601, dated 07 / 14 / 2026, page 73 / 135 68 / 126 Measurement Validity This IE indicates the validity period of direct AI / ML / fingerprint measurements to be reported. This may also apply to assisted AI / ML positioning measurements. Assisted AI / ML positioning measurements can be defined as measurements that have been enhanced and / or optimized using AI / ML models, for example, RAT-dependent measurements such as RSTD, RTOA, RSRP, RSRPP, Rx-Tx, AoAs, AoDs, time difference, and so on. UE Type This IE indicates the UE type configuration, including information about the antenna, form factor, dimensions, portable UE, CPE, and so on. In another implementation, this may indicate whether the UE can report only DL positioning measurements, only SL positioning measurements, or a combination thereof.In another implementation, the UE type may also indicate the UE's function, for example, PRU UE, normal UE, anchor UE, roadside unit, SL positioning server UE, and so on. Number of fingerprint measurements reported per base truth reference location This IE indicates the number of direct AI / ML or fingerprint measurements to be reported by the measurement entity at a given base truth reference location. This can be signaled as a single value or minimum-maximum range within which measurements should be reported. In another implementation, an index of base truth reference locations may be mapped to an index of the number of measurements required at each location. Mobility This IE is used to indicate whether measurements are reported depending on the type of mobility pattern of the measurement entity, including static, low, medium, or high mobility patterns.An additional subfield may include horizontal / vertical velocity or acceleration parameters. Orientation This IE is used to report the orientation of the measurement entity in terms of the Local Coordinate System (LCS) or the Global Coordinate System (GCS). This may extend to the overall orientation or the orientation of the antenna configurations relative to the measurement entity or relative to a global reference. Fingerprint / Measurement Quality This IE is used to report the confidence in the measurement or the actual quality of the measurement, depending on whether the measurement is time-based measurement, angle-based measurement, or RSS metric-based measurement. In another implementation, this field may be used to indicate the overall quality of a composite fingerprint of one or more measurements. Label Quality This IE is used to report the label quality of a direct AI / ML or assisted AI / ML measurement.Quality can be presented in the form of a confidence indicator, for example, a binary indicator where 0 refers to low-quality labels, while 1 refers to high-quality labels, or in the form of a smooth indicator that shows the percentage of a label's quality, for example, 0%, 10%, 20%...100%. In another implementation, where a label comprises a basic truth reference location, the label quality corresponds to the location estimate quality depending on the origin of. Petition 870260069601, dated 07 / 14 / 2026, p. 74 / 135 69 / 126 Location and the method used to derive the location, for example, offline location input, RAT-dependent methods, RAT-independent methods, or a combination thereof. Fingerprint Quality Validity This IE is used to report the validity associated with the quality of a fingerprint and may include temporal validity criteria, for example, time window, after the expiration of a timer, for example, UTC time, GNSS time, etc. In another implementation, the validity associated with the quality of a fingerprint may be associated with some spatial validity criteria, associated with the geographical region in which the fingerprint was measured, for example, cell ID, area ID, zone ID, and so on.Label Quality Validity This IE is used to report the validity associated with the quality of a label and may include temporal validity criteria, e.g., time window, after expiration of a timer, specified time base, e.g., UTC time, GNSS time, etc. In another implementation, the validity associated with the quality of a label may be associated with some spatial validity criteria, associated with the geographic region in which the fingerprint was measured, e.g., cell ID, area ID, zone ID, and so on.

[178] For EU-assisted positioning scenarios, one or more common reporting criteria detailed in Table 11 may be signaled by a network entity, for example, location server, for reporting data types, for example, measurement data.

[179] For NG-RAN assisted positioning scenarios, one or more common reporting criteria detailed in Table 11 can be signaled by a network entity, for example, location server for an NG-RAN node, for example, gNB for reporting data types, for example, measurement data.

[180] In implementations, lower-layer signaling relative to LPP (e.g., RRC signaling, MAC CE, DCI signaling, or a combination thereof) can be used to transmit AI / ML direct positioning reporting criteria settings. In implementations, LPP or RRC signaling can be used to add, modify, remove, update, enable, and / or disable one or more UE AI / ML direct positioning reporting settings.

[181] In the implementations, the indicated reporting criteria Petition 870260069601, dated 07 / 14 / 2026, page 75 / 135 70 / 126 in Table 11 and the associated implementation details can be extended to assisted AI / ML positioning measurements for Cases A, B, and C, where positioning measurements are enhanced using one or more AI / ML models.

[182] In implementations, common reporting criteria can be transmitted via system information blocks (SIBs) or positioning system information block (posSIBs) messages to multiple UEs within a given geographic region, for example, based on the same cell ID, based on a system information area, based on the zone ID, or a combination thereof.

[183] The implementations also allow the reporting of various assistance information related to direct AI / ML positioning measurements to assist in obtaining a target UE location estimate. For example, this can be extended to scenarios where AI / ML assisted positioning measurements are being reported, when applicable.

[184] In implementations, a measurement entity (e.g., UE or NG-RAN node performing AI / ML positioning measurements) can be configured to report the positioning measurement correlation between different sets of measurements performed on the same measurement entity. This correlation metric, for example, may be subject to UE capability. In at least one implementation, the measurement entity can report, via a top-layer parameter (e.g., LPP signaling), the RSS-Correlation associated with a set of AI / ML positioning RSRP / RSSI measurements with each DL PRS or SL feature, e.g., DL feature ID. In scenarios for an NG-RAN node measurement, the RSS-Correlation can be associated with different sets of UL RSS measurements with each UL feature, e.g., SRS feature ID. In extended implementations, the correlation metric can be applied to time-based measurements (e.g., RSTD, ToA, etc.).or based on. Petition 870260069601, dated 07 / 14 / 2026, page 76 / 135 71 / 126 angle (e.g., AoA, AoD, etc.). The correlation measurement metric can be obtained by basic truth reference location to accurately and fairly calculate the measurement correlation of multiple measurements made at the same basic truth reference location.

[185] In implementations, positioning measurement correlation can be obtained from different UEs / devices at the same base truth reference location. Measurements may be based, at least in part, on vendor-specific variations of the NGRAN UE / node and therefore there may be variations of the same positioning measurement at the same base truth reference location. In such implementations, the network entity or UE / device collecting the positioning measurements can correlate the different measurements received from different network nodes / UEs / devices. Correlation can also be performed in the time domain (e.g., using a (sliding) time window) in addition to the spatial domain, e.g., based on location. In implementations, measurement correlations between adjacent reference location points can also be configured, determined, and reported.

[186] According to the implementations, a measurement entity (e.g., UE and / or NG-RAN node) that performs AI / ML positioning measurements can be configured to report channel characteristics associated with a direct AI / ML positioning measurement, including whether the measurement is line-of-sight (LOS) or non-line-of-sight (NLOS), based on a binary (e.g., hard decision) or flexible indicator, link path loss, channel coefficients, or a combination thereof. Reporting additional channel characteristics associated with a measurement can increase the stability and reliability of a reported AI / ML positioning measurement, for example, Petition 870260069601, dated 07 / 14 / 2026, p. 77 / 135 72 / 126 is a measurement of RSS, such as RSRP.

[187] In implementations, the difference in trajectory loss between a direct IA / ML measurement from a baseline reference location point (e.g., fingerprint measurement) and a measurement from the target UE can be used to derive a PRS / SRS RSS measurement as a function of the Tx and Rx antenna gains, trajectory loss reference, trajectory loss exponents, standard deviation of fading parameters, e.g., shadow fading at the baseline reference location point(s) and at the unknown location of the target UE. One or more of the parameters mentioned above can be configured to report and report to the requesting entity, e.g., UE / device or NG-RAN node or location server. In implementations, the measuring entity and / or the target UE can calculate the trajectory losses and report them to the requesting entity along with the direct IA / ML positioning measurement.

[188] In implementations, the measurement entity (e.g., UE or NG-RAN node) that performs AI / ML positioning measurements can be configured to report the average measurement at each base truth reference location point in (N x Mi) j sample points, where N is the configured sample of each measurement instance, while M is the total number of measurements of each ith gNB / TRP in the case of DL positioning measurements at each jth base truth reference location, while in the case of UL measurements it is the total number of measurements collected from each ith UE. In implementations, additional statistical measures can be obtained at N x Mi measurement points, including variance, standard deviation, probability distribution functions, cumulative distribution functions, and so on at each location / reference point. N x Mi can also be configurable using upper-layer signaling, such as LPP, RRC, SLPP, or a combination thereof. Petition 870260069601, dated 07 / 14 / 2026, p. 78 / 135 73 / 126

[189] In implementations where the configured AI / ML direct positioning model includes k-NN in the supervised case or K-Means in the case of an unsupervised model, a network entity that uses inference to determine the location of a target UE based on a fingerprint dataset can use the following generalized distance formula according to Minkowski distance to derive the target UE location by processing the newly received measurements using: 1 dMinkowSki(.x,y) = (ΣΓ=ι|χί -ΎίΓ)“ (1) where n is the total number of measurements received with a parameter pair fx,y), while a can be configurable depending on the distance algorithm used, for example, if a = 1, the Manhattan distance approach is used, while if a = 2, the Euclidean distance approach is used.In other implementations, Hamming distance or cosine distance and cosine similarity can be used to determine the similarity between multidimensional positioning data from direct AI / ML.

[190] In implementations where online measurements must be combined with fingerprint measurements at each location / baseline reference point, a similarity score based on the cumulative Manhattan distance in Eq. (1), where a=1, can be used to determine the target UE location, where the measuring entity is configured to report the minimum and maximum PRS / SRS RSS measurement of a total of Mi,j measurements where i refers to measurements originating from each ith gNB / TRP or UE at each jth basic truth reference location. The basic truth reference similarity score (PRef-Location) follows the following mathematical relationship, where: in each location can be given by yn (\pAI / ML AI / ML \_,\pAI / ML AI / MLΣί=1#|%ί,'ίηrT-UE,min\2\ri,maxrT-UE,max PRef-Location (j) = l· where P9 / ML is the minimum PRS positioning measure or V,8 bbn Petition 870260069601, dated 07 / 14 / 2026, p. 79 / 135 74 / 126 SRS RSS / fingerprint of the ii gNB / TRP or UE, while 79 / ^8^ is the minimum sample positioning measurement from the set of measurements provided by the target UE, 7-9, / ^ is the maximum PRS or SRS RSS / fingerprint measurement of the im gNB / TRP or UE, while 79 / ,^-^^ is the maximum sample positioning measurement from the set of measurements provided by the target UE. The lowest value of PRef-Location(j) corresponds to the most probable location where the target UE may be located.

[191] In implementations, PRef-Location(T) can be derived according to a predefined time window associated with a start time, window duration, end time and periodicity to capture the variation of positioning measurements and therefore the similarity score over time.

[192] In implementations, a first arrival path is considered for the RSS measurements above, to be used as part of the fingerprint training dataset. In implementations, the first arrival path and up to T additional configurable paths can be associated with a fingerprint RSS measurement and can be reported to the requesting network entity / node / UE.

[193] In implementations, the measurement entity (e.g., a UE, PRU UE, or SL UE) can be configured to indicate whether an RSS / fingerprint measurement (e.g., DL PRS RSRP) of a set of PRS features configured within the same feature set was measured with the same DL reception beam or even spatial filter for reception.

[194] In implementations, the measurement, training, and / or inference entity can be configured to autocalibrate direct or assisted AI / ML positioning measurements based on the provision of certain parameters for inclusion in the training or inference dataset for a Petition 870260069601, dated 07 / 14 / 2026, page 80 / 135 75 / 126 reference device, for example, a UE PRU. In at least one instance, a linear calibration can be employed to direct the UE measurement to that of a reference device, such as a UE PRU, which can be represented as follows: 71^=^ where υ denotes the average target UE PRS / SRS positioning measurement from the ith gNB / TRP or UE at each j-baseline reference location, δTυ denotes the average UE PRS / SRS positioning measurement from the i-th gNB / TRP or UE at each j-baseline reference location, while δTυ and μTυ are the linear calibration parameters to map the target UE RSS measurements to the UE PRU. This is especially useful if different network entities or UEs / devices are performing measurements from different vendors. The linear parameters, δTυ and μTυ, can be configured for the measurement entity via upper-layer signaling, e.g., LPP, NRPPa, SLPP, etc., to calibrate the measurements before reporting. The measurement, training, and / or inference entity may also receive a request to perform self-calibration of direct or assisted AI / ML positioning measurements.In another implementation, a nonlinear function can also be used to autocalibrate direct or assisted AI / ML positioning measurements with similar procedures described for linear autocalibration in terms of providing and reporting the nonlinear autocalibration parameters.

[195] The RSS measurements mentioned in the implementations described in this document may include RSRP, RSRPP, RSSI, RSRQ values, which are associated with DL PRS, SL PRS or UL SRS.

[196] Figure 18 illustrates an example of an 1800 block diagram of an 1802 device (e.g., an apparatus) that supports machine learning for positioning, according to Petition 870260069601, dated 07 / 14 / 2026, p. 81 / 135 76 / 126 with aspects of the present disclosure. Device 1802 may be an example of UE 104, as described in this document. Device 1802 may support wireless communication with one or more network entities 102, UEs 104, or any combination thereof. Device 1802 may include components for bidirectional communications, including components for transmitting and receiving communications, such as a processor 1804, a memory 1806, a transceiver 1808, and an I / O controller 1810. These components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more interfaces (e.g., buses).

[197] The 1804 processor, the 1806 memory, the 1808 transceiver, or various combinations thereof, or various components thereof, may be examples of means for carrying out various aspects of the present disclosure, as described in this document. For example, the 1804 processor, the 1806 memory, the 1808 transceiver, or various combinations or components thereof may support a method for carrying out one or more of the operations described in this document.

[198] In some implementations, the 1804 processor, the 1806 memory, the 1808 transceiver, or various combinations or components thereof may be implemented in hardware (for example, in a communications management circuitry). The hardware may include a processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic, discrete hardware components, or any combination thereof configured as or otherwise supporting a means to perform the functions described in this disclosure. In some implementations, the processor Petition 870260069601, dated 07 / 14 / 2026, page 82 / 135 77 / 126 Processor 1804 and the memory 1806 coupled to processor 1804 can be configured to perform one or more of the functions described in this document (for example, executing instructions stored in memory 1806 by processor 1804). In the context of UE 104, for example, transceiver 1808 and the coupled processor 1804 coupled to transceiver 1808 are configured to make UE 104 perform the various operations described and / or combinations thereof.

[199] For example, the 1804 processor and / or the 1808 transceiver may support wireless communication on the 1802 device, as per the examples disclosed in this document. For example, the 1804 processor and / or the 1808 transceiver may be configured as and / or otherwise support a means of receiving machine learning positioning configuration requests; and transmitting, based at least in part on the machine learning positioning configuration requests, machine learning positioning configuration responses that include positioning reference signal configurations to be measured at respective reference locations and with one or more associated validity criteria.

[200] In addition, in some implementations, the processor is configured to have the device receive machine learning positioning configuration requests from one or more network nodes or user equipment (UE) and transmit machine learning positioning configuration responses to one or more network nodes or to the UE; the reference locations include one or more base truth reference locations; the machine learning positioning configuration responses include artificial intelligence configuration for the positioning reference signal settings; the processor is configured to have the device transmit one or more Petition 870260069601, dated 07 / 14 / 2026, page 83 / 135 78 / 126 machine learning positioning configuration responses independent of a machine learning positioning configuration request; machine learning positioning configuration responses include one or more direct machine learning configurations or assisted machine learning configurations; direct machine learning configuration includes configuration to perform radio frequency fingerprinting.

[201] In addition, in some implementations, machine learning positioning configuration requests include a request for one or more training types, a request for basic truth location, a machine learning method, a request for on-demand machine learning positioning reference signal, or pre-configured machine learning assistance data; positioning reference signal configurations include one or more positioning reference signal features, feature sets, transmit-receive points, or positioning frequency layer to be measured at each reference location; the one or more associated validity criteria include one or more temporal criteria or spatial criteria; the temporal criteria include a measurement time defined by one or more time bases;One or more time bases include at least one system frame number, Coordinated Universal Time (UTC), or Global Navigation Satellite System (GNSS) time; one or more associated validity criteria include a spatial criterion that refers to an indication of a geographic region, and the spatial criteria include one or more of an area identifier, a cell identifier, or a zone identifier.

[202] In addition, in some implementations, the positioning reference signal settings include one or more indications of one or more technology-dependent measurements. Petition 870260069601, dated 07 / 14 / 2026, page 84 / 135 79 / 126 uplink radio access, downlink radio access technology-dependent measurements, or sidelink radio access technology-dependent measurements to be performed; the positioning reference signal configurations include one or more indications of one or more downlink radio access technology-independent measurements or sidelink radio access technology-independent measurements to be performed; the machine learning positioning configuration responses further include one or more configurations belonging to at least one of: a k-nearest neighbors (k-NN) algorithm, a support vector machine, a decision tree, a random forest, a Gaussian mixture model, Bayesian networks, an artificial neural network, K-means, reinforcement learning, deep learning, or transfer learning;The device includes at least one of the following: a location server, a next-generation radio access network (NG-RAN), a positioning reference unit (PRU), a user equipment (UE), an anchor UE, or a target UE.

[203] In addition, in some implementations, the positioning reference signal settings include a received signal strength indicator measurement for one or more downlinks, uplinks, or sidelinks; and the received signal strength indicator measurement for one or more downlinks, uplinks, or sidelinks is defined as including a linear average of a total received power observed in feature elements of a slot carrying the positioning reference signal configured for measurement; the positioning reference signal settings include a positioning reference signal arrival time measurement for one or more downlinks, uplinks, or sidelinks; and the positioning reference signal arrival time measurement for one or more downlinks Petition 870260069601, dated 07 / 14 / 2026, page 85 / 135 80 / 126 downlink, uplink, or sidelink is defined as including a reception time of a positioning reference signal at a receiver reference point; positioning reference signal settings include an indication that a location server is allowed to transmit an error cause related to an incorrect positioning reference signal setting; positioning reference signal settings include an indication that a target user equipment (UE) is allowed to transmit an error cause related to an error in one or more receptions of a machine learning positioning configuration response or a measurement error related to a machine learning positioning measurement.

[204] In a further example, the 1804 processor and / or the 1808 transceiver may support wireless communication on the 1802 device, as per the examples disclosed in this document. The 1804 processor and / or the 1808 transceiver, for example, may be configured as, or otherwise support, a means of transmitting a machine learning positioning configuration request; receiving a machine learning positioning configuration response that includes a positioning reference signal configuration to be measured at one or more respective reference locations and with one or more associated validity criteria; and performing, based at least in part on the positioning reference signal configuration, one or more machine learning position measurements.

[205] In addition, in some implementations, the processor and transceiver are configured to cause the device to do one or more of the following: receive the machine learning positioning configuration response in response to the machine learning positioning configuration request. Petition 870260069601, dated 07 / 14 / 2026, page 86 / 135 81 / 126 machine; or receive the machine learning positioning configuration response independent of the machine learning positioning configuration request; the processor is configured to cause the device to input one or more machine learning position measurements into a machine learning model and receive an output from the machine learning model; the processor is configured to cause the device to generate an estimated device location based, at least in part, on the output of the machine learning model; the device includes one or more of the following: a target user equipment (UE), a positioning reference unit (PRU), or an anchor UE.

[206] In another example, the 1804 processor and / or the 1808 transceiver may support wireless communication on the 1802 device, according to the examples disclosed in this document. The 1804 processor and / or the 1808 transceiver, for example, may be configured as, or otherwise support, a means of transmitting a configuration request to set up reference signals for machine learning positioning measurements; receiving a configuration response including the reference signal configuration; and transmitting, based at least in part on the reference signal configuration, a reference signal transmission enablement command.

[207] In addition, in some implementations, the reference signals include one or more probing reference signals or positioning reference signals; the processor is configured to cause the device to transmit a reference signal transmission disable command; the device includes a location server, and in which the processor is configured to cause the device to transmit the reference signal transmission enable command to one or more other devices that are configured to transmit the reference signals. Petition 870260069601, dated 07 / 14 / 2026, page 87 / 135 82 / 126

[208] In another example, the 1804 processor and / or the 1808 transceiver may support wireless communication on the 1802 device, according to the examples disclosed in this document. The 1804 processor and / or the 1808 transceiver, for example, may be configured as, or otherwise support, a means of transmitting one or more machine learning positioning report requests, including a machine learning report configuration and one or more common reporting criteria; receiving one or more machine learning positioning reports; processing one or more machine learning positioning reports through a machine learning model; and generating, based at least in part on the output of the machine learning model, an estimated location of a user device (UD).

[209] In addition, in some implementations, the machine learning reporting configuration includes one or more direct machine learning reporting configurations or assisted machine learning reporting configurations; the device includes a configuration entity, and wherein the configuration entity includes at least one location server, a next-generation radio access network (NGRAN), a positioning reference unit (PRU), a UE, an anchor UE or a target UE;The processor is configured to cause the device to transmit one or more machine learning positioning report requests to one or more second devices, and to receive one or more machine learning positioning reports from the one or more second devices, wherein the one or more second devices include at least one location server, a next-generation radio access network (NG-RAN), a positioning reference unit (PRU), a UE, an anchor UE, or a target UE.

[210] In addition, in some implementations, one or more common reporting criteria include one or more types of Petition 870260069601, dated 07 / 14 / 2026, page 88 / 135 83 / 126 fingerprint, a type of basic truth reference location, a type of time domain report, a measurement preprocessing, a measurement validity, a UE type, a mobility indication, an orientation indication, a fingerprint environment indication, a fingerprint quality indication, or a label quality indication; for at least one machine learning positioning report request, one or more common reporting criteria are configured to be one or more of the following: augmented, removed, updated, enabled, or disabled; the processor is configured to cause the device to transmit one or more common reporting criteria via the positioning system information transmission signaling;The machine learning reporting configuration includes an option to report the correlation of machine learning positioning measurements between different sets of measurements.

[211] In addition, in some implementations, the machine learning positioning measurement correlation includes one or more spatial domain correlations or temporal domain correlations; one or more machine learning positioning reports include one or more machine learning positioning measurements or machine learning positioning location information; the machine learning report configuration includes an indication to report path loss at different locations, including a base truth reference location; the machine learning report configuration includes an indication to average machine learning positioning measurements across a configured number of measurements and report the average as part of one or more machine learning positioning reports; the machine learning report configuration includes a Petition 870260069601, dated 07 / 14 / 2026, page 89 / 135 84 / 126 indication to determine a similarity score at a configured location to determine an ideal mapping between a fingerprint measurement and an estimated location of a target UE.

[212] In another example, the 1804 processor and / or the 1808 transceiver may support wireless communication on the 1802 device, according to the examples disclosed in this document. The 1804 processor and / or the 1808 transceiver, for example, may be configured as, or otherwise support, a means of receiving one or more machine learning positioning report requests, including a machine learning report configuration and one or more common reporting criteria; generating one or more machine learning positioning reports based at least in part on the machine learning report configuration and one or more common reporting criteria; and transmitting the one or more machine learning positioning reports.

[213] In addition, in some implementations, the device includes one or more user equipment (UE), an anchor UE or a target UE; the device includes a configuration entity, and wherein the configuration entity includes at least one location server, a next-generation radio access network (NG-RAN) or a positioning reference unit (PRU).

[214] In another example, the 1804 processor and / or the 1808 transceiver may support wireless communication on the 1802 device, according to the examples disclosed in this document. The 1804 processor and / or the 1808 transceiver, for example, may be configured as, or otherwise support, a means of transmitting one or more machine learning positioning report requests, including a machine learning report configuration and one or more common reporting criteria; receiving one or more machine learning positioning reports; generating, based at least in part on Petition 870260069601, dated 07 / 14 / 2026, pp. 90 / 135 85 / 126 one or more machine learning positioning reports, a machine learning positioning training dataset; and train a positioning machine learning model using the machine learning positioning training dataset to generate a trained positioning machine learning model.

[215] In addition, in some implementations, the processor and transceiver are configured to cause the device to receive one or more additional machine learning positioning reports; insert at least part of one or more additional machine learning positioning reports into the trained positioning machine learning model; and estimate a position of a target user equipment (UE) based at least in part on the output of the trained positioning machine learning model; the processor is configured to cause the device to: receive a request for machine learning positioning training data for positioning; and transmit, based at least in part on the request, the machine learning positioning training dataset; the processor is configured to cause the device to: receive a request for a trained positioning machine learning model;and transmit, based at least in part on the request, at least a portion of the trained positioning machine learning model.

[216] In another example, the 1804 processor and / or the 1808 transceiver may support wireless communication on the 1802 device, according to the examples disclosed in this document. The 1804 processor and / or the 1808 transceiver, for example, may be configured as, or otherwise support, a means of receiving machine learning positioning configuration requests; and transmitting, based at least in part, on the Petition 870260069601, dated 07 / 14 / 2026, pp. 91 / 135 86 / 126 requests for machine learning positioning configuration, machine learning positioning configuration responses that include positioning reference signal configurations to be measured at respective reference locations and with one or more associated validity criteria.

[217] In addition, in some implementations, the 1804 processor and / or the 1808 transceiver, for example, may be configured as or otherwise support a means of receiving machine learning positioning configuration requests from one or more network nodes or user equipment (UE) and transmitting machine learning positioning configuration responses to one or more network nodes or UE; reference locations include one or more base truth reference locations; machine learning positioning configuration responses include artificial intelligence configuration for the positioning reference signal settings; transmitting one or more machine learning positioning configuration responses independent of a machine learning positioning configuration request;Machine learning positioning configuration responses include one or more direct machine learning configurations or assisted machine learning configurations; the direct machine learning configuration includes configuration to perform radio frequency fingerprinting.

[218] In addition, in some implementations, machine learning positioning configuration requests include a request for one or more training types, a request for basic truth location, a machine learning method, a request for on-demand machine learning positioning reference signal, or pre-configured machine learning assistance data; as Petition 870260069601, dated 07 / 14 / 2026, page 92 / 135 87 / 126 Positioning reference signal configurations include one or more positioning reference signal features, feature sets, transmit-receive points, or positioning frequency layer to be measured at each reference location; the one or more associated validity criteria include one or more temporal criteria or spatial criteria; the temporal criteria include a measurement time defined by one or more time bases; the one or more time bases include at least one system frame number, Coordinated Universal Time (UTC), or Global Navigation Satellite System (GNSS) time; the one or more associated validity criteria include a spatial criterion that refers to an indication of a geographic region, and the spatial criteria include one or more of an area identifier, a cell identifier, or a zone identifier.

[219] In addition, in some implementations, the positioning reference signal settings include one or more indications of one or more uplink radio access technology dependent measurements, downlink radio access technology dependent measurements, or sidelink radio access technology dependent measurements to be performed; the positioning reference signal settings include one or more indications of one or more downlink radio access technology independent measurements or sidelink radio access technology independent measurements to be performed;The machine learning placement configuration responses also include one or more configurations belonging to at least one of: a k-nearest neighbor (k-NN) algorithm, a support vector machine, a decision tree, a random forest, a Gaussian mixture model, Bayesian networks, an artificial neural network, K-means, reinforcement learning, deep learning, or transfer learning; the method is performed; Petition 870260069601, dated 07 / 14 / 2026, pp. 93 / 135 88 / 126 by a device including at least one of the following: a location server, a next-generation radio access network (NG-RAN), a positioning reference unit (PRU), a user equipment (UE), an anchor UE or a target UE.

[220] In addition, in some implementations, the positioning reference signal settings include a received signal strength indicator measurement for one or more downlink, uplink, or sidelink; and the received signal strength indicator measurement for one or more downlink, uplink, or sidelink is defined as including a linear average of a total received power observed in feature elements of a slot that carry the positioning reference signal configured for measurement; the positioning reference signal settings include a positioning reference signal arrival time measurement for one or more downlink, uplink, or sidelink;and the measurement of arrival time of a positioning reference signal for one or more downlinks, uplinks, or sidelinks is defined as including the reception time of a positioning reference signal at a receiver reference point; the positioning reference signal settings include an indication that a location server is allowed to transmit an error cause related to an incorrect configuration of a positioning reference signal; the positioning reference signal settings include an indication that a target user equipment (UE) is allowed to transmit an error cause related to an error in one or more receptions of a machine learning positioning configuration response or a measurement error related to a machine learning positioning measurement. Petition 870260069601, dated 07 / 14 / 2026, pp. 94 / 135 89 / 126

[221] In another example, the 1804 processor and / or the 1808 transceiver may support wireless communication on the 1802 device, as per the examples disclosed in this document. The 1804 processor and / or the 1808 transceiver, for example, may be configured as, or otherwise support, a means of transmitting a machine learning positioning configuration request; receiving a machine learning positioning configuration response that includes a positioning reference signal configuration to be measured at one or more respective reference locations and with one or more associated validity criteria; and performing, based at least in part on the positioning reference signal configuration, one or more machine learning position measurements.

[222] In addition, in some implementations, the 1804 processor and / or the 1808 transceiver, for example, may be configured as or otherwise support a means for one or more of the following: receiving the machine learning positioning configuration response in response to the machine learning positioning configuration request; or receiving the machine learning positioning configuration response independent of the machine learning positioning configuration request; inputting one or more machine learning position measurements into a machine learning model and receiving an output from the machine learning model; generating an estimated device location based, at least in part, on the output of the machine learning model; the method is performed by a device including one or more of a target user equipment (UE), a positioning reference unit (PRU), or an anchor UE.

[223] In another example, the 1804 processor and / or the 1808 transceiver can support wireless communication in the 1802 device, according to the examples disclosed in this document. The processor Petition 870260069601, dated 07 / 14 / 2026, pp. 95 / 135 90 / 126 Transceivers 1804 and / or 1808, for example, can be configured as, or otherwise support, a means of transmitting a configuration request to set up reference signals for machine learning positioning measurements; receiving a configuration response including the reference signal configuration; and transmitting, based at least in part on the reference signal configuration, a reference signal transmission activation command.

[224] In addition, in some implementations, the reference signals include one or more of the following: probing reference signals or positioning reference signals; transmission of a reference signal transmission deactivation command; the method is performed by a device including a location server, and wherein the method further includes transmitting the reference signal transmission activation command to one or more other devices that are configured to transmit the reference signals.

[225] In another example, the 1804 processor and / or the 1808 transceiver may support wireless communication on the 1802 device, according to the examples disclosed in this document. The 1804 processor and / or the 1808 transceiver, for example, may be configured as, or otherwise support, a means of transmitting one or more machine learning positioning report requests, including a machine learning report configuration and one or more common reporting criteria; receiving one or more machine learning positioning reports; processing one or more machine learning positioning reports through a machine learning model; and generating, based at least in part on the output of the machine learning model, an estimated location of a user device (UD).

[226] In addition, in some implementations, the machine learning reporting configuration includes one or more of Petition 870260069601, dated 07 / 14 / 2026, pp. 96 / 135 91 / 126 direct machine learning reporting configuration or assisted machine learning reporting configuration; wherein the method is performed by an appliance including a configuration entity, and wherein the configuration entity includes at least one of a location server, a next-generation radio access network (NG-RAN), a positioning reference unit (PRU), a UE, an anchor UE or a target UE; including further transmitting one or more machine learning positioning report requests from a first appliance to one or more second appliances, and receiving one or more machine learning positioning reports from the one or more second appliances, and wherein the one or more second appliances include at least one of a location server, a next-generation radio access network (NG-RAN), a positioning reference unit (PRU), a UE, an anchor UE or a target UE.

[227] In addition, in some implementations, one or more common reporting criteria include one or more of a fingerprint type, a basic truth reference location type, a time domain reporting type, a measurement preprocessing, a measurement validity, a UE type, a mobility indication, an orientation indication, a fingerprint environment indication, a fingerprint quality indication, or a label quality indication; wherein for at least one machine learning positioning reporting request, one or more of the common reporting criteria are configured to be one or more of augmented, removed, updated, enabled, or disabled; including further transmitting one or more of the common reporting criteria via positioning system information transmission signaling;where the machine learning reporting configuration includes an indication to report the measurement correlation of machine learning positioning; Petition 870260069601, dated 07 / 14 / 2026, page 97 / 135 92 / 126 machine between different sets of measurements.

[228] In addition, in some implementations, the machine learning positioning measurement correlation includes one or more spatial domain correlations or temporal domain correlations; wherein one or more machine learning positioning reports include one or more machine learning positioning measurements or machine learning positioning location information; wherein the machine learning report configuration includes an indication to report path loss at different locations, including a base truth reference location; wherein the machine learning report configuration includes an indication to average the machine learning positioning measurements across a configured number of measurements and report the average as part of one or more machine learning positioning reports;where the machine learning report configuration includes an indication to determine a similarity score at a configured location to determine an ideal mapping between a fingerprint measurement and an estimated location of a target UE.

[229] In another example, the 1804 processor and / or the 1808 transceiver may support wireless communication on the 1802 device, as per the examples disclosed in this document. The 1804 processor and / or the 1808 transceiver, for example, may be configured to, or otherwise support, a means of receiving one or more machine learning positioning report requests, including a machine learning report configuration and one or more common reporting criteria; generating one or more machine learning positioning reports based, at least in part, on the machine learning report configuration and one or more common reporting criteria; and transmitting one or more reports. Petition 870260069601, dated 07 / 14 / 2026, pp. 98 / 135 93 / 126 machine learning positioning.

[230] In addition, in some implementations, the method is performed by a device including one or more user equipment (UE), an anchor UE or a target UE; wherein the method is performed by a device including a configuration entity and wherein the configuration entity includes at least one location server, a next-generation radio access network (NG-RAN) or a positioning reference unit (PRU).

[231] In another example, the 1804 processor and / or the 1808 transceiver may support wireless communication on the 1802 device, as per the examples disclosed in this document. The 1804 processor and / or the 1808 transceiver, for example, may be configured as, or otherwise support, a means to transmit one or more machine learning positioning report requests, including a machine learning report configuration and one or more common reporting criteria; receive one or more machine learning positioning reports; generate, based at least in part on one or more machine learning positioning reports, a machine learning positioning training dataset; and train a positioning machine learning model using the machine learning positioning training dataset to generate a trained positioning machine learning model.

[232] In addition, in some implementations, the 1804 processor and / or the 1808 transceiver, for example, may be configured as or otherwise support a means of receiving one or more additional machine learning positioning reports; inserting at least a portion of one or more additional machine learning positioning reports into the trained positioning machine learning model; and estimating a position of a target user equipment (UE) based on, Petition 870260069601, dated 07 / 14 / 2026, pp. 99 / 135 94 / 126 at least in part, in the output of the trained positioning machine learning model; including further: receiving a request for positioning machine learning training data for positioning; and transmitting, based at least in part on the request, the positioning machine learning training dataset; including further: receiving a request for a trained positioning machine learning model; and transmitting, based at least in part on the request, at least a portion of the trained positioning machine learning model.

[233] The 1804 processor of the 1802 device, as a UE 104, can support wireless communication according to the examples disclosed in this document. The 1804 processor includes at least one controller coupled to at least one memory, and at least one controller is configured and / or operable to cause the processor to transmit a machine learning positioning configuration request; receive a machine learning positioning configuration response comprising a positioning reference signal configuration to be measured at one or more respective reference locations and with one or more associated validity criteria; and perform, based at least in part on the positioning reference signal configuration, one or more machine learning position measurements.Furthermore, the 1804 processor may be operable to perform any of the various operations described in this document, such as with reference to a UE 104 and / or the 1802 device.

[234] The 1804 processor may include an intelligent hardware device (for example, a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component or Petition 870260069601, dated 07 / 14 / 2026, pages 100 / 135 95 / 126 any combination thereof). In some implementations, the 1804 processor may be configured to operate a memory array using a memory controller. In some other implementations, a memory controller may be integrated into the 1804 processor. The 1804 processor may be configured to execute computer-readable instructions stored in a memory (for example, memory 1806) to cause the 1802 device to perform various functions of the present description.

[235] 1806 memory may include random access memory (RAM) and read-only memory (ROM). 1806 memory may store computer-readable and executable code, including instructions that, when executed by the 1804 processor, cause the 1802 device to perform various functions described in this document. The code may be stored in a non-transient, computer-readable medium, such as system memory or another type of memory. In some implementations, the code may not be directly executable by the 1804 processor, but may cause a computer (e.g., when compiled and executed) to perform the functions described in this document. In some implementations, 1806 memory may include, among other things, a basic I / O system (BIOS) that may control basic hardware or software operation, such as interaction with peripheral components or devices.

[236] The 1810 I / O controller can manage input and output signals for the 1802 device. The 1810 I / O controller can also manage peripherals not integrated into the M02 device. In some implementations, the 1810 I / O controller may represent a physical connection or port for an external peripheral. In some implementations, the 1810 I / O controller may utilize an operating system such as iOS®, ANDROID®, MS-DOS®, MS-WINDOWS®, OS / 2®, UNIX®, LINUX®, or another known operating system. In some implementations, the 1810 I / O controller may be implemented as part of a processor, Petition 870260069601, dated 07 / 14 / 2026, pp. 101 / 135 96 / 126 like the M08 processor. In some implementations, a user can interact with the 1802 device through the 1810 I / O controller or through hardware components controlled by the 1810 I / O controller.

[237] In some implementations, the 1802 device may include a single 1812 antenna. However, in some other implementations, the 1802 device may have more than one 1812 antenna (e.g., multiple antennas), including multiple antenna panels or antenna arrays, which may be capable of simultaneously transmitting or receiving multiple wireless transmissions. The 1808 transceiver may communicate bidirectionally, via one or more 1812 antennas, wired or wireless links, as described in this document. For example, the 1808 transceiver may represent a wireless transceiver and may communicate bidirectionally with another wireless transceiver. The 1808 transceiver may also include a modem to modulate the packets, provide the modulated packets to one or more 1812 antennas for transmission, and demodulate the packets received from one or more 1812 antennas.

[238] Figure 19 illustrates an example of a 1900 block diagram of a 1902 device (e.g., an apparatus) that supports machine learning for positioning, according to aspects of the present disclosure. The 1902 device may be an example of a 102 network entity, as described in this document. The 1902 device may support wireless communication with one or more 102 network entities, 104 UEs, or any combination thereof. The 1902 device may include components for bidirectional communications, including components for transmitting and receiving communications, such as a 1904 processor, a 1906 memory, a 1908 transceiver, and a 1910 I / O controller. These components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, Petition 870260069601, dated 07 / 14 / 2026, pp. 102 / 135 97 / 126 electronically, electrically) through one or more interfaces (e.g., buses).

[239] The 1904 processor, the 1906 memory, the 1908 transceiver, or various combinations thereof, or various components thereof, may be examples of means for carrying out various aspects of the present disclosure, as described in this document. For example, the 1904 processor, the 1906 memory, the 1908 transceiver, or various combinations or components thereof may support a method for carrying out one or more of the operations described in this document.

[240] In some implementations, the 1904 processor, the 1906 memory, the 1908 transceiver, or various combinations or components thereof may be implemented in hardware (for example, in a communications management circuitry). The hardware may include a processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic, discrete hardware components, or any combination thereof configured as or otherwise supporting a means for performing the functions described in this disclosure. In some implementations, the 1904 processor and the 1906 memory coupled to the 1904 processor may be configured to perform one or more of the functions described in this document (for example, executing, by the 1904 processor, instructions stored in the 1906 memory).In the context of network entity 102, for example, transceiver 1908 and processor 1904 coupled to transceiver 1908 are configured to cause network entity 102 to perform the various operations described and / or combinations thereof.

[241] For example, the 1904 processor and / or the 1908 transceiver can support wireless communication in the 1902 device, according to the examples disclosed in this document. For example, the Petition 870260069601, dated 07 / 14 / 2026, pp. 103 / 135 Processor 1904 and / or transceiver 1908 may be configured as, and / or otherwise support, a means of receiving machine learning positioning configuration requests; and transmitting, based at least in part on machine learning positioning configuration requests, machine learning positioning configuration responses that include positioning reference signal configurations to be measured at respective reference locations and with one or more associated validity criteria.

[242] In addition, in some implementations, the processor is configured to have the device receive machine learning positioning configuration requests from one or more network nodes or user equipment (UE) and transmit machine learning positioning configuration responses to the one or more network nodes or to the UE; the reference locations include one or more base truth reference locations; the machine learning positioning configuration responses include artificial intelligence configuration for the positioning reference signal settings; the processor is configured to have the device transmit one or more machine learning positioning configuration responses independent of a machine learning positioning configuration request;Machine learning positioning configuration responses include one or more direct machine learning configurations or assisted machine learning configurations; the direct machine learning configuration includes configuration to perform radio frequency fingerprinting.

[243] In addition, in some implementations, machine learning placement configuration requests include a request for one or more training types, Petition 870260069601, dated 07 / 14 / 2026, pp. 104 / 135 99 / 126 a basic truth location request, a machine learning method, a machine learning on-demand positioning reference signal request, or pre-configured machine learning assistance data; the positioning reference signal configurations include one or more positioning reference signal features, feature sets, transmit-receive points, or positioning frequency layer to be measured at each reference location; the one or more associated validity criteria include one or more temporal criteria or spatial criteria; the temporal criteria include a measurement time defined by one or more time bases; the one or more time bases include at least one system frame number, Coordinated Universal Time (UTC), or Global Navigation Satellite System (GNSS) time;One or more associated validity criteria include a spatial criterion that refers to an indication of a geographic region, and the spatial criteria include one or more of an area identifier, a cell identifier, or a zone identifier.

[244] In addition, in some implementations, the positioning reference signal configurations include one or more indications of one or more uplink radio access technology-dependent measurements, downlink radio access technology-dependent measurements, or sidelink radio access technology-dependent measurements to be performed; the positioning reference signal configurations include one or more indications of one or more downlink radio access technology-independent measurements or sidelink radio access technology-independent measurements to be performed; the machine learning positioning configuration responses further include one or more configurations belonging to at least one of: a k-nearest neighbors (k-NN) algorithm, a machine Petition 870260069601, dated 07 / 14 / 2026, pages 105 / 135 100 / 126 support vector, a decision tree, a random forest, a Gaussian mixture model, Bayesian networks, an artificial neural network, K-means, reinforcement learning, deep learning, or transfer learning; the apparatus includes at least one of the following: a location server, a next-generation radio access network (NG-RAN), a positioning reference unit (PRU), a user equipment (UE), an anchor UE, or a target UE.

[245] In addition, in some implementations, the positioning reference signal settings include a received signal strength indicator measurement for one or more downlink, uplink, or sidelink; and the received signal strength indicator measurement for one or more downlink, uplink, or sidelink is defined as including a linear average of a total received power observed in feature elements of a slot that carry the positioning reference signal configured for measurement; the positioning reference signal settings include a positioning reference signal arrival time measurement for one or more downlink, uplink, or sidelink;and the measurement of arrival time of a positioning reference signal for one or more downlinks, uplinks, or sidelinks is defined as including the reception time of a positioning reference signal at a receiver reference point; the positioning reference signal settings include an indication that a location server is permitted to transmit an error cause related to an incorrect positioning reference signal setting; the positioning reference signal settings include an indication that a target user equipment (UE) is permitted to transmit an error cause related to an error in one or more receptions of a; Petition 870260069601, dated 07 / 14 / 2026, pages 106 / 135 101 / 126 response from machine learning positioning configuration or a measurement error related to a machine learning positioning measurement.

[246] In another example, processor 1904 and / or transceiver 1908 may support wireless communication on device 1902, as per the examples disclosed in this document. Processor 1904 and / or transceiver 1908, for example, may be configured as, or otherwise support, a means of transmitting a machine learning positioning configuration request; receiving a machine learning positioning configuration response that includes a positioning reference signal configuration to be measured at one or more respective reference locations and with one or more associated validity criteria; and performing, based at least in part on the positioning reference signal configuration, one or more machine learning position measurements.

[247] In addition, in some implementations, the processor and transceiver are configured to cause the device to do one or more of the following: receive the machine learning positioning configuration response in response to the machine learning positioning configuration request; or receive the machine learning positioning configuration response independent of the machine learning positioning configuration request; the processor is configured to cause the device to input one or more machine learning position measurements into a machine learning model and receive output from the machine learning model; the processor is configured to cause the device to generate an estimated location of the device based, at least in part, on the output of the machine learning model;The device includes one or more of the following: a target user equipment (UE), a positioning reference unit (PRU), or an anchor UE. Petition 870260069601, dated 07 / 14 / 2026, pp. 107 / 135 102 / 126

[248] In another example, the 1904 processor and / or the 1908 transceiver may support wireless communication on the 1902 device, according to the examples disclosed in this document. The 1904 processor and / or the 1908 transceiver, for example, may be configured as, or otherwise support, a means of transmitting a configuration request to set up reference signals for machine learning positioning measurements; receiving a configuration response including the reference signal configuration; and transmitting, based at least in part on the reference signal configuration, a reference signal transmission enablement command.

[249] In addition, in some implementations, the reference signals include one or more probing reference signals or positioning reference signals; the processor is configured to cause the device to transmit a reference signal transmission disable command; the device includes a location server, and in which the processor is configured to cause the device to transmit the reference signal transmission enable command to one or more other devices that are configured to transmit the reference signals.

[250] In another example, the 1904 processor and / or the 1908 transceiver may support wireless communication on the 1902 device, according to the examples disclosed in this document. The 1904 processor and / or the 1908 transceiver, for example, may be configured as, or otherwise support, a means of transmitting one or more machine learning positioning report requests, including a machine learning report configuration and one or more common reporting criteria; receiving one or more machine learning positioning reports; processing one or more machine learning positioning reports through a machine learning model; and generating, based on at least Petition 870260069601, dated 07 / 14 / 2026, pages 108 / 135 Part 103 / 126, in the output of the machine learning model, provides an estimated location of a user device (UD).

[251] In addition, in some implementations, the machine learning reporting configuration includes one or more direct machine learning reporting configurations or assisted machine learning reporting configurations; the device includes a configuration entity, and wherein the configuration entity includes at least one location server, a next-generation radio access network (NGRAN), a positioning reference unit (PRU), a UE, an anchor UE or a target UE;The processor is configured to cause the device to transmit one or more machine learning positioning report requests to one or more second devices, and to receive one or more machine learning positioning reports from the one or more second devices, wherein the one or more second devices include at least one location server, a next-generation radio access network (NG-RAN), a positioning reference unit (PRU), a UE, an anchor UE, or a target UE.

[252] In addition, in some implementations, one or more common reporting criteria include one or more of a fingerprint type, a basic truth reference location type, a time domain reporting type, a measurement preprocessing, a measurement validity, a UE type, a mobility indication, an orientation indication, a fingerprint environment indication, a fingerprint quality indication, or a label quality indication; for at least one machine learning positioning reporting request, one or more of the common reporting criteria are configured to be one or more of the following: augmented, removed, updated, enabled, or disabled; the processor is configured to cause the device to transmit one or more of the common reporting criteria via signaling. Petition 870260069601, dated 07 / 14 / 2026, pp. 109 / 135 104 / 126 positioning system information transmission; the machine learning reporting configuration includes an indication to report the correlation of machine learning positioning measurements between different sets of measurements.

[253] In addition, in some implementations, the machine learning positioning measurement correlation includes one or more spatial domain correlations or temporal domain correlations; one or more machine learning positioning reports include one or more machine learning positioning measurements or machine learning positioning location information; the machine learning report configuration includes an indication to report path loss at different locations, including a base truth reference location; the machine learning report configuration includes an indication to average the machine learning positioning measurements across a configured number of measurements and report the average as part of one or more machine learning positioning reports;The machine learning report configuration includes an option to determine a similarity score at a configured location to determine an ideal mapping between a fingerprint measurement and an estimated location of a target UE.

[254] In another example, processor 1904 and / or transceiver 1908 may support wireless communication on device 1902, according to the examples disclosed in this document. Processor 1904 and / or transceiver 1908, for example, may be configured as, or otherwise support, a means of receiving one or more machine learning positioning report requests, including a machine learning report configuration and one or more common reporting criteria; Petition 870260069601, dated 07 / 14 / 2026, pp. 110 / 135 105 / 126 generate one or more machine learning positioning reports based, at least in part, on the machine learning reporting configuration and one or more common reporting criteria; and transmit the one or more machine learning positioning reports.

[255] In addition, in some implementations, the device includes one or more user equipment (UE), an anchor UE or a target UE; the device includes a configuration entity, and wherein the configuration entity includes at least one location server, a next-generation radio access network (NG-RAN) or a positioning reference unit (PRU).

[256] In a further example, processor 1904 and / or transceiver 1908 may support wireless communication on device 1902, as per the examples disclosed in this document. Processor 1904 and / or transceiver 1908, for example, may be configured as, or otherwise support, a means of transmitting one or more machine learning positioning report requests, including a machine learning report configuration and one or more common reporting criteria; receiving one or more machine learning positioning reports; generating, based at least in part on one or more machine learning positioning reports, a machine learning positioning training dataset; and training a positioning machine learning model using the machine learning positioning training dataset to generate a trained positioning machine learning model.

[257] In addition, in some implementations, the processor and transceiver are configured to cause the device to receive one or more additional machine learning positioning reports; insert at least part of one or more machine learning positioning reports. Petition 870260069601, dated 07 / 14 / 2026, pp. 111 / 135 106 / 126 additional data in the trained positioning machine learning model; and estimate a position of a target user equipment (UE) based, at least in part, on the output of the trained positioning machine learning model; the processor is configured to cause the device to: receive a request for positioning machine learning training data; and transmit, based at least in part on the request, the positioning machine learning training dataset; the processor is configured to cause the device to: receive a request for a trained positioning machine learning model; and transmit, based at least in part on the request, at least a portion of the trained positioning machine learning model.

[258] In another example, processor 1904 and / or transceiver 1908 may support wireless communication on device 1902, as per the examples disclosed in this document. Processor 1904 and / or transceiver 1908, for example, may be configured as, or otherwise support, a means of receiving machine learning positioning configuration requests; and transmitting, based at least in part on the machine learning positioning configuration requests, machine learning positioning configuration responses that include positioning reference signal configurations to be measured at respective reference locations and with one or more associated validity criteria.

[259] In addition, in some implementations, the 1904 processor and / or the 1908 transceiver, for example, may be configured as or otherwise support a means of receiving machine learning positioning configuration requests from one or more network nodes or user equipment (UE) and transmitting the configuration responses. Petition 870260069601, dated 07 / 14 / 2026, pp. 112 / 135 107 / 126 machine learning positioning for one or more network or UE nodes; reference locations include one or more base truth reference locations; machine learning positioning configuration responses include artificial intelligence configuration for positioning reference signal settings; transmit one or more machine learning positioning configuration responses independent of a machine learning positioning configuration request; machine learning positioning configuration responses include one or more direct machine learning configurations or assisted machine learning configurations; direct machine learning configuration includes configuration to perform radio frequency fingerprinting.

[260] In addition, in some implementations, machine learning positioning configuration requests include a request for one or more training types, a request for basic truth location, a machine learning method, a request for on-demand machine learning positioning reference signal, or pre-configured machine learning assistance data; positioning reference signal configurations include one or more positioning reference signal features, feature sets, transmit-receive points, or positioning frequency layer to be measured at each reference location; the one or more associated validity criteria include one or more temporal criteria or spatial criteria; the temporal criteria include a measurement time defined by one or more time bases;One or more time bases include at least one system frame number, Coordinated Universal Time (UTC), or Global Navigation Satellite System (GNSS) time; one or more associated validity criteria include a spatial criterion that refers to a; Petition 870260069601, dated 07 / 14 / 2026, pages 113 / 135 108 / 126 indication of a geographic region, and the spatial criteria include one or more of an area identifier, a cell identifier, or a zone identifier.

[261] In addition, in some implementations, the positioning reference signal settings include one or more indications of one or more uplink radio access technology dependent measurements, downlink radio access technology dependent measurements, or sidelink radio access technology dependent measurements to be performed; the positioning reference signal settings include one or more indications of one or more downlink radio access technology independent measurements or sidelink radio access technology independent measurements to be performed;The machine learning positioning configuration responses further include one or more configurations belonging to at least one of: a k-nearest neighbors (k-NN) algorithm, a support vector machine, a decision tree, a random forest, a Gaussian mixture model, Bayesian networks, an artificial neural network, K-means, reinforcement learning, deep learning, or transfer learning; the method is performed by an apparatus including at least one of the following: a location server, a next-generation radio access network (NG-RAN), a positioning reference unit (PRU), a user equipment (UE), an anchor UE, or a target UE.

[262] In addition, in some implementations, the positioning reference signal settings include a received signal strength indicator measurement for one or more downlinks, uplinks, or sidelinks; and the received signal strength indicator measurement for one or more downlinks, uplinks, or sidelinks is defined as including a linear average of a total power. Petition 870260069601, dated 07 / 14 / 2026, pp. 114 / 135 109 / 126 received observed in resource elements of a slot that carry the positioning reference signal configured for measurement; the positioning reference signal settings include a measurement of the time of arrival of the positioning reference signal for one or more downlinks, uplinks, or sidelinks; and the measurement of the time of arrival of the positioning reference signal for one or more downlinks, uplinks, or sidelinks is defined as including a reception time of a positioning reference signal at a receiver reference point; the positioning reference signal settings include an indication that a location server is permitted to transmit an error cause related to an incorrect configuration of a positioning reference signal setting;The positioning reference signal settings include an indication that a target user equipment (UE) is permitted to transmit an error cause related to an error in one or more receptions of a machine learning positioning configuration response or a measurement error related to a machine learning positioning measurement.

[263] In another example, the 1904 processor and / or the 1908 transceiver may support wireless communication in the 1902 device, according to the examples disclosed in this document. The 1904 processor and / or the 1908 transceiver, for example, may be configured as, or otherwise support, a means of transmitting a machine learning positioning configuration request; receiving a machine learning positioning configuration response that includes a positioning reference signal configuration to be measured at one or more respective reference locations and with one or more associated validity criteria; and performing, based at least in part on the positioning reference signal configuration, one or Petition 870260069601, dated 07 / 14 / 2026, pages 115 / 135 110 / 126 plus machine learning position measurements.

[264] In addition, in some implementations, the 1904 processor and / or the 1908 transceiver, for example, may be configured as or otherwise support a means for one or more of the following: receiving the machine learning positioning configuration response in response to the machine learning positioning configuration request; or receiving the machine learning positioning configuration response independent of the machine learning positioning configuration request; inputting one or more machine learning position measurements into a machine learning model and receiving an output from the machine learning model; generating an estimated device location based, at least in part, on the output of the machine learning model; the method is performed by a device including one or more of a target user equipment (UE), a positioning reference unit (PRU), or an anchor UE.

[265] In another example, the 1904 processor and / or the 1908 transceiver may support wireless communication on the 1902 device, according to the examples disclosed in this document. The 1904 processor and / or the 1908 transceiver, for example, may be configured as, or otherwise support, a means of transmitting a configuration request to set up reference signals for machine learning positioning measurements; receiving a configuration response including the reference signal configuration; and transmitting, based at least in part on the reference signal configuration, a reference signal transmission enablement command.

[266] In addition, in some implementations, the reference signals include one or more of the following: probing reference signals or positioning reference signals; transmission of a signal transmission disable command Petition 870260069601, dated 07 / 14 / 2026, pages 116 / 135 Reference 111 / 126; the method is performed by a device including a location server, and wherein the method further includes transmitting the activation command for reference signal transmission to one or more other devices that are configured to transmit reference signals.

[267] In another example, the 1904 processor and / or the 1908 transceiver may support wireless communication on the 1902 device, according to the examples disclosed in this document. The 1904 processor and / or the 1908 transceiver, for example, may be configured as, or otherwise support, a means of transmitting one or more machine learning positioning report requests, including a machine learning report configuration and one or more common reporting criteria; receiving one or more machine learning positioning reports; processing one or more machine learning positioning reports through a machine learning model; and generating, based at least in part on the output of the machine learning model, an estimated location of a user device (UD).

[268] Furthermore, in some implementations, the machine learning reporting configuration includes one or more direct machine learning reporting configurations or assisted machine learning reporting configurations; wherein the method is performed by an appliance including a configuration entity, and wherein the configuration entity includes at least one of a location server, a next-generation radio access network (NG-RAN), a positioning reference unit (PRU), a UE, an anchor UE or a target UE; including further transmitting one or more machine learning positioning report requests from a first appliance to one or more second appliances, and receiving one or more machine learning positioning reports from the one or more second appliances, and wherein the one Petition 870260069601, dated 07 / 14 / 2026, pp. 117 / 135 112 / 126 or more second devices include at least one of the following: a location server, a next-generation radio access network (NG-RAN), a positioning reference unit (PRU), a UE, an anchor UE, or a target UE.

[269] Furthermore, in some implementations, one or more common reporting criteria include one or more of a fingerprint type, a basic truth reference location type, a time domain reporting type, a measurement preprocessing, a measurement validity, a UE type, a mobility indication, an orientation indication, a fingerprint environment indication, a fingerprint quality indication, or a label quality indication; wherein for at least one machine learning positioning reporting request, one or more of the common reporting criteria are configured to be one or more of augmented, removed, updated, enabled, or disabled; including further transmitting one or more of the common reporting criteria via positioning system information transmission signaling;where the machine learning reporting configuration includes an indication to report the correlation of machine learning positioning measurements between different sets of measurements.

[270] In addition, in some implementations, the machine learning positioning measurement correlation includes one or more spatial domain correlations or temporal domain correlations; wherein one or more machine learning positioning reports include one or more machine learning positioning measurements or machine learning positioning location information; wherein the machine learning reporting configuration includes an indication to report path loss at different locations, including a base truth reference location; wherein the reporting configuration of Petition 870260069601, dated 07 / 14 / 2026, pages 118 / 135 113 / 126 machine learning includes a setting to average machine learning positioning measurements across a configured number of measurements and report the average as part of one or more machine learning positioning reports; wherein the machine learning report setting includes a setting to determine a similarity score at a configured location to determine an optimal mapping between a fingerprint measurement and an estimated location of a target UE.

[271] In another example, processor 1904 and / or transceiver 1908 may support wireless communication on device 1902, according to the examples disclosed in this document. Processor 1904 and / or transceiver 1908, for example, may be configured as, or otherwise support, a means of receiving one or more machine learning positioning report requests, including a machine learning report configuration and one or more common reporting criteria; generating one or more machine learning positioning reports based at least in part on the machine learning report configuration and one or more common reporting criteria; and transmitting one or more machine learning positioning reports.

[272] In addition, in some implementations, the method is performed by a device including one or more user equipment (UE), an anchor UE or a target UE; wherein the method is performed by a device including a configuration entity and wherein the configuration entity includes at least one location server, a next-generation radio access network (NG-RAN) or a positioning reference unit (PRU).

[273] In another example, the 1904 processor and / or the 1908 transceiver can support wireless communication on the 1902 device, according to the examples disclosed in this document. The 1904 processor and / or the 1908 transceiver, for example, can be configured Petition 870260069601, dated 07 / 14 / 2026, pp. 119 / 135 114 / 126 as, or otherwise support, a means to transmit one or more machine learning positioning report requests, including a machine learning report configuration and one or more common reporting criteria; receive one or more machine learning positioning reports; generate, based at least in part on one or more machine learning positioning reports, a machine learning positioning training dataset; and train a positioning machine learning model using the machine learning positioning training dataset to generate a trained positioning machine learning model.

[274] Furthermore, in some implementations, the 1904 processor and / or the 1908 transceiver, for example, may be configured as or otherwise support a means of receiving one or more additional machine learning positioning reports; inserting at least a portion of one or more additional machine learning positioning reports into the trained positioning machine learning model; and estimating a position of a target user equipment (UE) based at least in part on the output of the trained positioning machine learning model; including further: receiving a request for machine learning positioning training data for positioning; and transmitting, based at least in part on the request, the machine learning positioning training dataset; including further: receiving a request for a trained positioning machine learning model;and transmit, based at least in part on the request, at least a portion of the trained positioning machine learning model.

[275] The 1904 processor may include a device for Petition 870260069601, dated 07 / 14 / 2026, pages 120 / 135 115 / 126 intelligent hardware (for example, a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof). In some implementations, the 1904 processor may be configured to operate a memory array using a memory controller. In some other implementations, a memory controller may be integrated into the 1904 processor. The 1904 processor may be configured to execute computer-readable instructions stored in a memory (for example, the 1906 memory) to cause the 1902 device to perform various functions of the present disclosure.

[276] The 1906 memory may include random access memory (RAM) and read-only memory (ROM). The 1906 memory may store computer-readable and executable code, including instructions that, when executed by the 1904 processor, cause the 1902 device to perform various functions described in this document. The code may be stored in a non-transient, computer-readable medium, such as system memory or another type of memory. In some implementations, the code may not be directly executable by the 1904 processor, but may cause a computer (e.g., when compiled and executed) to perform the functions described in this document. In some implementations, the 1906 memory may include, among other things, a basic I / O system (BIOS) that may control basic hardware or software operation, such as interaction with peripheral components or devices.

[277] The 1910 I / O controller can manage input and output signals for the 1902 device. The 1910 I / O controller can also manage peripherals not integrated into the M02 device. In some implementations, the 1910 I / O controller may represent a physical connection or port for an external peripheral. In some implementations, the controller of Petition 870260069601, dated 07 / 14 / 2026, pages 121 / 135 116 / 126 The 1910 I / O controller can utilize an operating system such as iOS®, ANDROID®, MS-DOS®, MS-WINDOWS®, OS / 2®, UNIX®, LINUX®, or another known operating system. In some implementations, the 1910 I / O controller may be implemented as part of a processor, such as the M06 processor. In some implementations, a user may interact with the 1902 device through the 1910 I / O controller or through hardware components controlled by the 1910 I / O controller.

[278] In some implementations, the 1902 device may include a single 1912 antenna. However, in some other implementations, the 1902 device may have more than one 1912 antenna (e.g., multiple antennas), including multiple antenna panels or antenna arrays, which may be capable of simultaneously transmitting or receiving multiple wireless transmissions. The 1908 transceiver may communicate bidirectionally, via one or more 1912 antennas, wired or wireless links, as described in this document. For example, the 1908 transceiver may represent a wireless transceiver and may communicate bidirectionally with another wireless transceiver. The 1908 transceiver may also include a modem to modulate the packets, provide the modulated packets to one or more 1912 antennas for transmission, and demodulate the packets received from one or more 1912 antennas.

[279] Figure 20 illustrates a flowchart of a 2000 method that supports machine learning for positioning, according to aspects of the present disclosure. The operations of the 2000 method can be implemented by a device or its components, as described in this document. For example, the operations of the 2000 method can be performed by a network entity 102 and / or a UE 104, as described with reference to Figures 1 to 19. In some implementations, the device may execute a set of instructions to control its functional elements to perform the described functions. Additionally, or Petition 870260069601, dated 07 / 14 / 2026, pages 122 / 135 117 / 126 Alternatively, the device may perform aspects of the described functions using purpose-built hardware.

[280] In 2002, the method may include receiving machine learning positioning configuration requests. The 2002 operations may be performed according to the examples described in this document. In some implementations, aspects of the 2002 operations may be performed by a device as described with reference to Figure 1.

[281] In 2004, the method may include transmitting, based at least in part on machine learning positioning configuration requests, machine learning positioning configuration responses comprising positioning reference signal configurations to be measured at respective reference locations and with one or more associated validity criteria. The 2004 operations may be performed according to the examples described in this document. In some implementations, aspects of the 2004 operations may be performed by a device as described with reference to Figure 1.

[282] Figure 21 illustrates a flowchart of a 2100 method that supports machine learning for positioning, according to aspects of the present disclosure. The operations of the 2100 method can be implemented by a device or its components, as described in this document. For example, the operations of the 2100 method can be performed by a network entity 102 and / or a UE 104, as described with reference to Figures 1 to 19. In some implementations, the device may execute a set of instructions to control its functional elements to perform the described functions. Additionally, or alternatively, the device may perform aspects of the described functions using purpose-built hardware.

[283] In 2102, the method may include transmitting a learning positioning configuration request. Petition 870260069601, dated 07 / 14 / 2026, pages 123 / 135 118 / 126 machine. The 2102 operations can be performed according to the examples described in this document. In some implementations, aspects of the 2102 operations can be performed by a device as described with reference to Figure 1.

[284] In 2104, the method may include receiving a machine learning positioning configuration response comprising a positioning reference signal configuration to be measured at one or more respective reference locations and with one or more associated validity criteria. The 2104 operations may be performed according to the examples described in this document. In some implementations, aspects of the 2104 operations may be performed by a device as described with reference to Figure 1.

[285] In 2106, the method may include performing, based at least in part on the positioning reference signal configuration, one or more machine learning position measurements. The 2106 operations may be performed according to the examples described in this document. In some implementations, aspects of the 2106 operations may be performed by a device as described with reference to Figure 1.

[286] Figure 22 illustrates a flowchart of a 2200 method that supports machine learning for positioning, according to aspects of the present disclosure. The operations of the 2200 method can be implemented by a device or its components, as described in this document. For example, the operations of the 2200 method can be performed by a network entity 102 and / or a UE 104, as described with reference to Figures 1 to 19. In some implementations, the device may execute a set of instructions to control its functional elements to perform the described functions. Additionally, or alternatively, the device may perform aspects of Petition 870260069601, dated 07 / 14 / 2026, pages 124 / 135 119 / 126 functions described using purpose-built hardware.

[287] In 2202, the method may include transmitting a configuration request to set up reference signals for machine learning positioning measurements. The 2202 operations can be performed according to the examples described in this document. In some implementations, aspects of the 2202 operations may be performed by a device as described with reference to Figure 1.

[288] In 2204, the method may include receiving a configuration response comprising the reference signal configuration. 2204 operations may be performed according to the examples described in this document. In some implementations, aspects of 2204 operations may be performed by a device as described with reference to Figure 1.

[289] In 2206, the method may include transmitting, based at least in part on the reference signal configuration, a reference signal transmission activation command. 2206 operations may be performed according to the examples described in this document. In some implementations, aspects of 2206 operations may be performed by a device as described with reference to Figure 1.

[290] Figure 23 illustrates a flowchart of a 2300 method that supports machine learning for positioning, according to aspects of the present disclosure. The operations of the 2300 method can be implemented by a device or its components, as described in this document. For example, the operations of the 2300 method can be performed by a network entity 102 and / or a UE 104, as described with reference to Figures 1 to 19. In some implementations, the device may execute a set of instructions to control its functional elements to perform the described functions. Additionally, or alternatively, the device may perform aspects of Petition 870260069601, dated 07 / 14 / 2026, pages 125 / 135 120 / 126 functions described using purpose-built hardware.

[291] In 2302, the method may include transmitting one or more machine learning positioning report requests, comprising a machine learning report configuration and one or more common reporting criteria. The 2302 operations may be performed according to the examples described in this document. In some implementations, aspects of the 2302 operations may be performed by a device as described with reference to Figure 1.

[292] In 2304, the method may include receiving one or more machine learning positioning reports. The 2304 operations may be performed according to the examples described in this document. In some implementations, aspects of the 2304 operations may be performed by a device as described with reference to Figure 1.

[293] In 2306, the method may include processing one or more machine learning positioning reports by means of a machine learning model. The 2306 operations may be performed according to the examples described in this document. In some implementations, aspects of the 2306 operations may be performed by a device as described with reference to Figure 1.

[294] In 2308, the method may include generating, based at least in part on the output of the machine learning model, an estimated location of a user device (UD). The 2308 operations may be performed according to the examples described in this document. In some implementations, aspects of the 2308 operations may be performed by a device as described with reference to Figure 1.

[295] Figure 24 illustrates a flowchart of a 2400 method that supports machine learning for positioning, according to aspects of the present disclosure. The operations of the 2400 method can be implemented by a device or its components, Petition 870260069601, dated 07 / 14 / 2026, pages 126 / 135 121 / 126 as described in this document. For example, the operations of method 2400 can be performed by a network entity 102 and / or a UE 104, as described with reference to Figures 1 to 19. In some implementations, the device may execute a set of instructions to control its functional elements to perform the described functions. Additionally, or alternatively, the device may perform aspects of the described functions using purpose-built hardware.

[296] In 2402, the method may include receiving one or more machine learning positioning report requests, comprising a machine learning report configuration and one or more common reporting criteria. 2402 operations may be performed according to the examples described in this document. In some implementations, aspects of 2402 operations may be performed by a device as described with reference to Figure 1.

[297] In 2404, the method may include generating one or more machine learning positioning reports based, at least in part, on the machine learning reporting configuration and one or more common reporting criteria. The 2404 operations may be performed according to the examples described in this document. In some implementations, aspects of the 2404 operations may be performed by a device as described with reference to Figure 1.

[298] In 2406, the method may include transmitting one or more machine learning positioning reports. 2406 operations may be performed according to the examples described in this document. In some implementations, aspects of 2406 operations may be performed by a device as described with reference to Figure 1.

[299] Figure 25 illustrates a flowchart of a 2500 method that supports machine learning for positioning, according to aspects of the present disclosure. The operations of the 2500 method Petition 870260069601, dated 07 / 14 / 2026, pages 127 / 135 122 / 126 can be implemented by a device or its components, as described in this document. For example, the operations of method 2500 can be performed by a network entity 102 and / or a UE 104, as described with reference to Figures 1 to 19. In some implementations, the device may execute a set of instructions to control its functional elements to perform the described functions. Additionally, or alternatively, the device may perform aspects of the described functions using purpose-built hardware.

[300] In 2502, the method may include transmitting one or more machine learning positioning report requests, comprising a machine learning report configuration and one or more common reporting criteria. The 2502 operations may be performed according to the examples described in this document. In some implementations, aspects of the 2502 operations may be performed by a device as described with reference to Figure 1.

[301] In 2504, the method may include receiving one or more machine learning positioning reports. The 2504 operations may be performed according to the examples described in this document. In some implementations, aspects of the 2504 operations may be performed by a device as described with reference to Figure 1.

[302] In 2506, the method may include generating, based at least in part on one or more machine learning positioning reports, a machine learning positioning training dataset. The 2506 operations may be performed according to the examples described in this document. In some implementations, aspects of the 2506 operations may be performed by a device as described with reference to Figure 1.

[303] In 2508, the method may include training a positioning machine learning model using the set of Petition 870260069601, dated 07 / 14 / 2026, pages 128 / 135 123 / 126 machine learning positioning training data to generate a trained positioning machine learning model. The 2508 operations can be performed according to the examples described in this document. In some implementations, aspects of the 2508 operations can be performed by a device as described with reference to Figure 1.

[304] It should be noted that the methods described in this document describe possible implementations, and that operations and steps may be rearranged or otherwise modified and that other implementations are possible. Furthermore, aspects of two or more methods may be combined.

[305] The various illustrative blocks and components described in connection with the disclosure in this document may be implemented or realized with a general-purpose processor, a DSP, an ASIC, a CPU, an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described in this document. A general-purpose processor may be a microprocessor, but alternatively, the processor may be any processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration).

[306] The functions described in this document can be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions can be stored or transmitted as one or more instructions or code in a computer-readable medium. Other examples and Petition 870260069601, dated 07 / 14 / 2026, pages 129 / 135 124 / 126 implementations are within the scope of the disclosure and accompanying claims. For example, due to the nature of the software, the functions described in this document may be implemented using software running on a processor, hardware, firmware, wiring, or combinations thereof. Resources implementing functions may also be physically located in multiple locations, including being distributed so that parts of the functions are implemented in different physical locations.

[307] Computer-readable media includes nontransient computer storage media and communication media, including any media that facilitates the transfer of a computer program from one place to another. Nontransient storage media may be any available media that can be accessed by a general-purpose or special-purpose computer. By way of example, and not limitation, nontransient computer-readable media may include RAM, ROM, electrically erasable programmable ROM (EEPROM), flash memory, compact disc (CD) ROM or other optical disc storage, magnetic disc storage or other magnetic storage devices, or any other nontransient media that can be used to carry or store desired program code media in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor.

[308] Any connection can be appropriately termed a computer-readable medium. For example, if software is transmitted from a remote site, server, or other source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition. Petition 870260069601, dated 07 / 14 / 2026, pages 130 / 135 125 / 126 of computer-readable media. Disk and disc, as used in this document, include CD, laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray disc, wherein disks generally reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above are also included within the scope of computer-readable media.

[309] As used in this document, including in the claims, or as used in a list of items (for example, a list of items preceded by a phrase such as at least one of or one or more of or one or both of) indicates an inclusive list such that, for example, a list of at least one of A, B, or C means A or B or C or AB or AC or BC or ABC (i.e., A and B and C). Furthermore, as used in this document, the phrase "based on" should not be interpreted as referring to a closed set of conditions. For example, an example step that is described as being based on condition A may be based on condition A and condition B without departing from the scope of this disclosure. In other words, as used in this document, the phrase "based on" should be interpreted in the same way as the phrase "based at least in part on". Furthermore, as used in this document, including in the claims, a set may include one or more elements.

[310] The terms transmit, receive or communicate, when referring to a network entity, may refer to any part of a network entity (for example, a base station, a CU, a DU, a RU) of a RAN communicating with another device (for example, directly or through one or more other network entities).

[311] The description presented in this document, in connection with the attached drawings, describes example configurations and does not represent all examples that can be implemented or that Petition 870260069601, dated 07 / 14 / 2026, pp. 131 / 135 126 / 126 are within the scope of the claims. The term "example" used in this document means serving as an example, instance, or illustration, and not preferred or advantageous over other examples. The detailed description includes specific details with the use to provide an understanding of the techniques described. These techniques, however, can be practiced without these specific details. In some cases, known structures and devices are shown in block diagram form to avoid obscuring the concepts of the example described.

[312] The description in this document is provided to enable a person of ordinary skill in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to a person of ordinary skill in the art, and the generic principles set forth in this document may be applied to other variations without departing from the scope of the disclosure. Thus, the disclosure is not limited to the examples and designs described in this document, but should be given the broader scope consistent with the principles and new features disclosed in this document. Petition 870260069601, dated 07 / 14 / 2026, pp. 132 / 135

Claims

1 / 5 CLAIMS 1. Apparatus characterized in that it comprises: a processor; and a memory coupled to the processor, the processor configured to make the apparatus: receive one or more machine learning positioning configuration requests; and transmit, based at least in part on one or more machine learning positioning configuration requests, one or more machine learning positioning configuration responses that include one or more positioning reference signal configurations associated with the measurement at respective reference locations and with one or more associated validity criteria.

2. Device according to claim 1, characterized in that the processor is configured to make the device: receive one or more machine learning positioning configuration requests from one or more network nodes or user equipment (UE); and transmit one or more machine learning positioning configuration responses to one or more of the network nodes or to the UE.

3. Device, according to claim 1, characterized in that one or more machine learning positioning configuration responses comprise artificial intelligence configuration for one or more positioning reference signal configurations.

4. Device, according to claim 1, characterized in that the processor is configured to make the device transmit one or more machine learning positioning configuration responses independently of one or more machine learning positioning configuration requests.

5. Device according to claim 1, characterized in that one or more machine learning positioning configuration requests comprise a request for one or more training types, a basic truth location request, a machine learning method, a request for on-demand machine learning positioning reference signal, or pre-configured machine learning assistance data.

6. Device according to claim 1, characterized in that the processor is configured to make the device transmit or receive machine learning positioning assistance data or an error cause, wherein the error cause comprises one or more of: an error cause related to an incorrect configuration of a positioning reference signal, a missing parameter in the positioning reference signal configuration, an error in receiving a machine learning positioning configuration response, a measurement error related to a machine learning positioning measurement, an invalid machine learning training model, or an invalid machine learning inference model.

7. Apparatus, according to claim 1, characterized in that the apparatus comprises at least one of a location server, a next-generation radio access network (NG-RAN), a positioning reference unit (PRU), a user equipment (UE), an anchor UE or a target UE.

8. Apparatus, according to claim 1, Petition 870250069400, dated 06 / 08 / 2025, pp. 199 / 210 3 / 5 characterized in that: the measurement comprises a measurement of received signal strength indicator for one or more downlink, uplink or sidelink; and the measurement of received signal strength indicator for one or more downlink, uplink or sidelink is defined as comprising a linear average of a total received power observed in slot resource elements carrying positioning reference signals.

9. User equipment (UE) for wireless communication, characterized in that it comprises: at least one memory; and at least one processor coupled to at least one memory and configured to make the UE: transmit a machine learning positioning configuration request; receive a machine learning positioning configuration response comprising a positioning reference signal configuration associated with the measurement at one or more respective reference locations and with one or more associated validity criteria; and perform, based at least in part on the positioning reference signal configuration, one or more machine learning position measurements.

10. EU, according to claim 9, characterized in that the machine learning positioning configuration response comprises an artificial intelligence configuration for the positioning reference signal configuration.

11. EU, according to claim 9, characterized in that the machine learning positioning configuration request comprises a request for one or more of a training type, a basic truth location request, a machine learning method, a machine learning on-demand positioning reference signal request or pre-configured machine learning assistance data.

12. UE, according to claim 9, characterized in that the processor is configured to make the UE transmit or receive machine learning positioning assistance data or an error cause, wherein the error cause comprises one or more of: an error cause related to an incorrect configuration of a positioning reference signal, a missing parameter in the positioning reference signal configuration, an error in receiving a machine learning positioning configuration response, a measurement error related to a machine learning positioning measurement, an invalid machine learning training model, or an invalid machine learning inference model.

13. EU, according to claim 9, characterized in that: the measurement comprises a measurement of received signal strength indicator for one or more downlink, uplink or sidelink; and the measurement of received signal strength indicator for one or more downlink, uplink or sidelink is defined as comprising a linear average of a total received power observed in feature elements of a slot carrying a positioning reference signal.

14. Method for wireless communication, characterized in that it comprises: receiving one or more machine learning positioning configuration requests; and transmitting, based at least in part on one or more machine learning positioning configuration requests, one or more machine learning positioning configuration responses that include one or more positioning reference signal configurations associated with the measurement at respective reference locations and with one or more associated validity criteria.

15. Method for wireless communication, characterized in that it comprises: transmitting a machine learning positioning configuration request; receiving a machine learning positioning configuration response comprising a positioning reference signal configuration associated with the measurement at one or more respective reference locations and with one or more associated validity criteria; and performing, based at least in part on the positioning reference signal configuration, one or more machine learning position measurements. Petition 870250069400, dated 06 / 08 / 2025, pp. 202 / 210

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