WO2025156501A1

WO2025156501A1 - Artificial intelligence/machine learning positioning control in wireless communications

Info

Publication number: WO2025156501A1
Application number: PCT/CN2024/092187
Authority: WO
Inventors: Yu Pan; Jing Liu; Chuangxin JIANG; Cong Wang; Fei DONG
Original assignee: ZTE Corp
Current assignee: ZTE Corp
Priority date: 2024-05-10
Filing date: 2024-05-10
Publication date: 2025-07-31
Anticipated expiration: 2026-11-10

Abstract

This document generally relates to wireless communication that includes a communication node that sends an on-demand artificial intelligence (AI) data set request to a location management function (LMF), and the LMF transmits oneor more on-demand AI data sets to the communication node in response to the request. In addition or alternatively, the LMF determine whether to activate or deactivate an AI functionality or an AI model, or whether the communication node should activate the AI functionality or the AI model. The LMF transmits a control indication to the communication node indicating whether the AI functionality or the AI model is to be activated or deactivated, and/or that the communication node is allowed to switch from reporting a preferred measurement to reporting a different measurement.

Description

ARTIFICIAL INTELLIGENCE/MACHINE LEARNING POSITIONING CONTROL IN WIRELESS COMMUNICATIONS

TECHNICAL FIELD

This document is directed generally to artificial intelligence and machine learning positioning control in wireless communication.

BACKGROUND

Artificial Intelligence (AI) may be implemented in wireless communication systems. In some implementations, an AI function using an AI model is performed at the user equipment (UE) side, and an AI feature or other output of the AI model is provided to the network side. In other implementations, the network side performs an AI function using an AI model, and the UE side reports measurements dedicated for input to the AI model. Ways to optimize use of AI for positioning of wireless communication nodes may be desirable.

SUMMARY

This document relates to methods, systems, apparatuses and devices for wireless communication. In some implementations, a method for wireless communication includes: sending, by a communication node, an on-demand artificial intelligence (AI) data set request to a location management function (LMF) ; and receiving, by the communication node, one or more on-demand AI data sets from the LMF in response to the on-demand AI data set request.

In some other implementations, a method for wireless communication includes: receiving, by a location management function (LMF) , an on-demand artificial intelligence (AI) data set request from a communication node; and transmitting, by the LMF, one or more on-demand AI data sets to the communication node in response to the on-demand AI data set request.

In some other implementations, a method for wireless communication includes: receiving, by a communication node, a control indication of an AI functionality or an AI model from a location management function (LMF) ; and activating or deactivating, by the communication node, the AI functionality or the AI model according to the control indication.

In some other implementations, a method for wireless communication includes: determining, by a location management function (LMF) , whether the LMF should activate or deactivate an artificial intelligence (AI) functionality or an AI model, or whether a communication node should activate or deactivate the AI functionality or the AI model; and activating or deactivating, by the LMF, the AI functionality or the AI model, and/or transmitting, by the LMF, a control indication to the communication node, the control indication indicating at least one of: whether the AI functionality or the AI model is to be activated or deactivated; or that the communication node is allowed to switch from reporting a preferred measurement to reporting a different measurement.

In some other implementations, a device, such as a network device, is disclosed. The device may include one or more processors and one or more memories, wherein the one or more processors are configured to read computer code from the one or more memories to implement any of the methods above.

In yet some other implementations, a computer program product is disclosed. The computer program product may include a non-transitory computer-readable program medium with computer code stored thereupon, the computer code, when executed by one or more processors, causing the one or more processors to implement any of the methods above.

The above and other aspects and their implementations are described in greater detail in the drawings, the descriptions, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a block diagram of an example of a wireless communication system.

FIG. 2 shows a block diagram of an example configuration of a wireless access node of the wireless communication system of Fig. 1.

FIG. 3 shows a flow chart of an example method for wireless communication.

FIG. 4 shows a flow chart of another example method for wireless communication.

FIG. 5 shows a flow chart of another example method for wireless communication.

FIG. 6 shows a flow chart of another example method for wireless communication

FIG. 7 shows a schematic diagram of a plurality of use cases for artificial intelligence (AI) positioning.

FIG. 8 a schematic diagram illustrating example signaling between one or more positioning reference units (PRUs) , a location management function (LMF) , and a target user device, including signaling associated with an on-demand AI PRU data set information request.

DETAILED DESCRIPTION

The present description describes various embodiments of systems, apparatuses, devices, and methods for wireless communications that relate to indications for artificial intelligence and/or machine learning positioning control.

Fig. 1 shows a diagram of an example wireless communication system 100 including a plurality of communication nodes (or just nodes) that are configured to wirelessly communicate with each other. In general, the communication nodes include at least one user device 102 and at least one wireless access node 104. The example wireless communication system 100 in Fig. 1 is shown as including two user devices 102, including a first user device 102 (1) and a second user device 102 (2) , and one wireless access node 104. However, various other examples of the wireless communication system 100 that include any of various combinations of one or more user devices 102 and/or one or more wireless access nodes 104 may be possible.

In general, a user device as described herein, such as the user device 102, may include a single electronic device or apparatus, or multiple (e.g., a network of) electronic devices or apparatuses, capable of communicating wirelessly over a network. A user device may comprise or otherwise be referred to as a user terminal, a user terminal device, or a user equipment (UE) . Additionally, a user device may be or include, but not limited to, a mobile device (such as a mobile phone, a smart phone, a smart watch, a tablet, a laptop computer, vehicle or other vessel (human, motor, or engine-powered, such as an automobile, a plane, a train, a ship, a bicycle, a drone, an unmanned aerial vehicle (UAV) , as non-limiting examples) or a fixed or stationary device, (such as a desktop computer or other computing device that is not ordinarily moved for long periods of time, such as appliances, other relatively heavy devices including Internet of things (IoT) , or computing devices used in commercial or industrial environments, as non-limiting examples) . In addition or alternatively, in any of various embodiments, a user device 102 may include an ambient IoT device, a normal user device, a reduced capacity (RedCap) user device, a low power high-accuracy positioning (LPHAP) user device, a sidelink user device, or a V2X user device.

In various embodiments, a user device 102 may include transceiver circuitry 106 coupled to an antenna 108 to effect wireless communication with the wireless access node 104. The transceiver circuitry 106 may also be coupled to a processor 110, which may also be coupled to a memory 112 or other storage device. The memory 112 may store therein instructions or code that, when read and executed by the processor 110, cause the processor 110 to implement various ones of the methods described herein.

Additionally, in general, a wireless access node as described herein, such as the wireless access node 104, may include at least one device, electronic and/or network device or apparatus, and may comprise one or more base stations or other wireless network access points capable of communicating wirelessly over a network with one or more user devices and/or with one or more other wireless access nodes 104. For example, the wireless access node 104 may comprise at least one of: a 4G LTE base station, a 5G NR base station, a 5G central-unit base station, a 5G distributed-unit base station, a next generation Node B (gNB) , an enhanced Node B (eNB) , or other similar or next-generation (e.g., 6G) base stations, or a location management function (LMF) , in various embodiments. A wireless access node 104 may include transceiver circuitry 114 coupled to an antenna 116, which may include an antenna tower 118 in various approaches, to effect wireless communication with the user device 102 or another wireless access node 104. The transceiver circuitry 114 may also be coupled to one or more processors 120, which may also be coupled to a memory 122 or other storage device. The memory 122 may store therein instructions or code that, when read and executed by the processor 120, cause the processor 120 to implement one or more of the methods described herein.

Fig. 2 shows a block diagram of an example configuration of a wireless access node 104. In the example configuration, the wireless access node (or network) 104 may include a core network element 202 and one or more radio access network (RAN) nodes 204. Some embodiments may include only one RAN node 204. Other embodiments, such as shown in Fig. 2, may include a plurality, or an n-number, of RAN nodes 204 (1) to 204 (n) , where n is two or more. In any of various embodiments, a RAN node 204 may be or include a Next Generation (NG) -RAN node, a gNB, a ng-eNB, a transmission reception point (TRP) , a transmission point (TP) , a reception point (RP) , a base station, and/or an integrated access and backhaul (IAB) node, an example of which is shown in FIG. 2. Also, in any of various embodiments, the core network element 202 may include at least one of: a location management function (LMF) 210, an access and mobility management function (AMF) 212, a user plane function (UPF) 214, and/or a sensing function (SF) 216. The core newtork element 202 may include alternative, other, or additional components in any of various other embodiments, such as a network data analytics function (NWDAF) for example. Additionally, each component of the wireless access node 104, such as the core network element 202 and each RAN node 204, may include at least one network device, and/or may be configured in hardware or a combination of hardware and software, such as by having a processor 120, a memory 122, transceiver circuitry 114, an antenna 116, and/or an antenna tower 118, such as shown in Fig. 1 for the wireless access node 104.

Additionally, as shown in Fig. 2, the core network element 202 and each of the RAN nodes 204 may be configured to communicate (transmit and receive) with each other, such as signals or messages, and may be configured to communicate (transmit and receive) with one or more user device 102, either directly or indirectly via another component of the wireless access node (network) 104. For example, the core network element 202 (e.g., the LMF 210) may directly communicate with a user device 10, such as according to a Long-Term Evolution (LTE) positioning protocol (LPP) (i.e., via LPP signaling) , sidelink positioning protocol (SLPP) (i.e., via SLPP signaling) , and/or non-access-stratus (NAS) messaging, as non-limiting examples. In addition or alternatively, the signaling may be UE-associated signaling or non-UE-associated signaling. Also, a RAN node 204 may directly communicate with a user device 102. In particular embodiments, a RAN node 204 may directly communicate with a user device 102 at least via radio resource control (RRC) signaling. In addition, the core network element 202 may directly communicate with each RAN node 204, such as according to dedicated messaging for sensing, New Radio Positioning Protocol A (NRPPa) (i.e., via NRPPa signaling) , and/or Next Generation Application Protocol (NGAP) (i.e., via NGAP messaging) . In addition, RAN nodes 204 may directly communicate with each other, such as according to Xn application protocol (XnAP) (i.e., via XnAP messaging) . Additionally, although not shown in Fig. 2, two user devices 102 may directly communicate with each other, such as via dedicating messaging for sensing, SLPP signaling, PC5-RRC messaging, SL medium access control (MAC) control element (CE) , and/or sidelink control information (SCI) .

Also, for at least some embodiments, such as shown in Fig. 2, each RAN node 204 may include one or more sub-components. For example, a RAN node 204 may include a gNB and/or at least one transmission/reception point (TRP) 208. Additionally, as used herein unless specified otherwise, the terms “network” or “network device” may include at least one gNB 206, at least one ng-eNB, at least one TRP 208, at least one base station, at least one RAN node 204 (e.g., at least one NG-RAN node) and/or at least one core network element 202. Further functionality of the core network element 202 and the RAN nodes 204 is described in further detail below.

In addition, referring back to Fig. 1, in various embodiments, two communication nodes in the wireless system 100-such as a user device 102 and a wireless access node 104, two user devices 102 without a wireless access node 104, or two wireless access nodes 104 without a user device 102-may be configured to wirelessly communicate with each other in or over a mobile network and/or a wireless access network according to one or more standards and/or specifications. In general, the standards and/or specifications may define the rules or procedures under which the communication nodes can wirelessly communicate, which, in various embodiments, may include those for communicating in millimeter (mm) -Wave bands, and/or with multi-antenna schemes and beamforming functions. In addition or alternatively, the standards and/or specifications are those that define a radio access technology and/or a cellular technology, such as Fourth Generation (4G) Long Term Evolution (LTE) , Fifth Generation (5G) New Radio (NR) , or New Radio Unlicensed (NR-U) , as non-limiting examples.

Additionally, in the wireless system 100, the communication nodes are configured to wirelessly communicate signals between each other. In general, a communication in the wireless system 100 between two communication nodes can be or include a transmission or a reception, and is generally both simultaneously, depending on the perspective of a particular node in the communication. For example, for a given communication between a first node and a second node where the first node is transmitting a signal to the second node and the second node is receiving the signal from the first node, the first node may be referred to as a source or transmitting node or device, the second node may be referred to as a destination or receiving node or device, and the communication may be considered a transmission for the first node and a reception for the second node. Of course, since communication nodes in a wireless system 100 can both send and receive signals, a single communication node may be both a transmitting/source node and a receiving/destination node simultaneously or switch between being a source/transmitting node and a destination/receiving node.

Also, particular signals can be characterized or defined as either an uplink (UL) signal, a downlink (DL) signal, or a sidelink (SL) signal. An uplink signal is a signal transmitted from a user device 102 to a wireless access node 104. A downlink signal is a signal transmitted from a wireless access node 104 to a user device 102. A sidelink signal is a signal transmitted from a one user device 102 to another user device 102, or a signal transmitted from one wireless access node 104 to a another wireless access node 104. Also, for sidelink transmissions, a first/source user device 102 directly transmits a sidelink signal to a second/destination user device 102 without any forwarding of the sidelink signal to a wireless access node 104.

Additionally, signals communicated between communication nodes in the system 100 may be characterized or defined as a data signal or a control signal. In general, a data signal is a signal that includes or carries data, such multimedia data (e.g., voice and/or image data) , and a control signal is a signal that carries control information that configures the communication nodes in certain ways in order to communicate with each other, or otherwise controls how the communication nodes communicate data signals with each other. Also, certain signals may be defined or characterized by combinations of data/control and uplink/downlink/sidelink, including uplink control signals, uplink data signals, downlink control signals, downlink data signals, sidelink control signals, and sidelink data signals.

For at least some specifications, such as 5G NR, data and control signals are transmitted and/or carried on physical channels. Generally, a physical channel corresponds to a set of time-frequency resources used for transmission of a signal. Different types of physical channels may be used to transmit different types of signals. For example, physical data channels (or just data channels) are used to transmit data signals, and physical control channels (or just control channels) are used to transmit control signals. Example types of physical data channels include, but are not limited to, a physical downlink shared channel (PDSCH) used to communicate downlink data signals, a physical uplink shared channel (PUSCH) used to communicate uplink data signals, and a physical sidelink shared channel (PSSCH) used to communicate sidelink data signals. In addition, example types of physical control channels include, but are not limited to, a physical downlink control channel (PDCCH) used to communicate downlink control signals, a physical uplink control channel (PUCCH) used to communicate uplink control signals, and a physical sidelink control channel (PSCCH) used to communicate sidelink control signals. As used herein for simplicity, unless specified otherwise, a particular type of physical channel is also used to refer to a signal that is transmitted on that particular type of physical channel, and/or a transmission on that particular type of transmission. As an example illustration, a PDSCH refers to the physical downlink shared channel itself, a downlink data signal transmitted on the PDSCH, or a downlink data transmission. Accordingly, a communication node transmitting or receiving a PDSCH means that the communication node is transmitting or receiving a signal on a PDSCH.

Additionally, for at least some specifications, such as 5G NR, and/or for at least some types of control signals, a control signal that a communication node transmits may include control information comprising the information necessary to enable transmission of one or more data signals between communication nodes, and/or to schedule one or more data channels (or one or more transmissions on data channels) . For example, such control information may include the information necessary for proper reception, decoding, and demodulation of a data signals received on physical data channels during a data transmission, and/or for uplink scheduling grants that inform the user device about the resources and transport format to use for uplink data transmissions. In some embodiments, the control information includes downlink control information (DCI) that is transmitted in the downlink direction from a wireless access node 104 to a user device 102. In other embodiments, the control information includes uplink control information (UCI) that is transmitted in the uplink direction from a user device 102 to a wireless access node 104, or sidelink control information (SCI) that is transmitted in the sidelink direction from one user device 102 (1) to another user device 102 (2) .

In addition, in some embodiments, a transmitting node may transmit a reference signal for positioning, such via an interface. For example, a gNB 206 may transmit a downlink positioning reference signal (DL-PRS) to a user device 102 via a Uu interface. As another example, a user device 102 may transmit a sounding reference signal (SRS) to a gNB 206, such as via a Uu interface. As another example, a user device 102 may transmit a sidelink positioning reference signal (SL-PRS) to another user device 102.

Fig. 3 is a flow chart of an example method 300 for wireless communication related to AI data sets. At block 302, a communication node sends an on-demand artificial intelligence (AI) data set request to a location management function (LMF) 206. At block 304, the communication node receives one or more on-demand AI data sets from the LMF 206 in response to the on-demand AI data set request.

Fig. 4 is a flow chart of another example method 400 for wireless communication related to AI data set. At block 402, a location management function (LMF) 206 receives an on-demand artificial intelligence (AI) data set request from a communication node. At block 404, the LMF 206 transmits one or more on-demand AI data sets to the communication node in response to the on-demand AI data set request.

In some implementations of the method 300 and/or the method 400, the communication node includes a user device 102 or a radio access network (RAN) node 204.

In addition or alternatively, in some implementations of the method 300 and/or the method 400, the communication node includes a user device 102, the one or more on-demand AI data sets includes one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request includes a cell or a cell list indicating that the user device 102 wants the LMF 206 to provide an AI PRU data set derived within the cell or one or more cells in the cell list.

In addition or alternatively, in some implementations of the method 300 and/or the method 400, the communication node includes a user device 102, the one or more on-demand AI data sets includes one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request includes a value range of a number of measured transmission reception points (TRPs) , the value range indicating that the user device 102 wants the LMF 206 to provide a channel measurement of a user device or PRU or an AI intermediate feature that satisfies the value range of the number of measured TRPs.

In addition or alternatively, in some implementations of the method 300 and/or the method 400, the communication node includes a user device 102, the one or more on-demand AI data sets includes one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request includes a value range of a TRP synchronization error, the value range indicating that the user device 102 wants the LMF 206 to provide a user device or PRU channel measurement or an AI intermediate feature that is derived from one or more TRPs 208 that satisfy the value range of the TRP synchronization error.

In addition or alternatively, in some implementations of the method 300 and/or the method 400, the communication node includes a user device 102, the one or more on-demand AI data sets includes one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request includes a value range of a measurement quality, the value range indicating that the user device 102 wants the LMF 206 to provide a user device or PRU channel measurement, an AI intermediate feature, or a user device or PRU location that is associated with a measurement quality satisfying the value range.

In addition or alternatively, in some implementations of the method 300 and/or the method 400, the communication node includes a user device 102, the one or more on-demand AI data sets includes one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request includes a value range of velocity, indicating that the user device 102 wants the LMF 206 to provide an AI PRU data set that is derived by a PRU or a user device under the value range of velocity.

In addition or alternatively, in some implementations of the method 300 and/or the method 400, the communication node includes a user device 102, the one or more on-demand AI data sets includes one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request includes a preferred user device direction, indicating that the user device 102 wants the LMF 206 to provide an AI PRU data set that is derived by a PRU or a user device under the preferred user device direction.

In addition or alternatively, in some implementations of the method 300 and/or the method 400, the communication node includes a user device 102, the one or more on-demand AI data sets includes one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request comprises a positioning reference signal (PRS) configuration, indicating that the user device 102 wants the LMF 206 to provide a channel measurement of a user device or a PRU or an AI intermediate feature that is derived based on the PRS configuration.

In addition or alternatively, in some implementations of the method 300 and/or the method 400, the communication node includes a user device 102, and the one or more on-demand AI data sets includes one or more positioning reference signal (PRS) configurations, and the on-demand AI data set request includes a cell or a cell list, indicating that the user device 102 wants the LMF 206 to provide an AI assistance data set including at least one transmission reception point (TRP) configuration and/or at least one PRS configuration, wherein the at least one TRP configuration and/or the at least one PRS configuration is located within the cell or one or more cells of the cell list.

In addition or alternatively, in some implementations of the method 300 and/or the method 400, the communication node includes the user device, the one or more on-demand AI data sets includes one or more positioning reference signal (PRS) configurations, and the on-demand AI data set request includes a value range of a first number of TRPs, indicating that the user device 102 wants the LMF 206 to provide an AI assistance data set that includes PRS configurations derived from a second number of TRPs, where the second number of TRPs is within the value range of the first number of TRPs.

In addition or alternatively, in some implementations of the method 300 and/or the method 400, the communication node includes the user device 102, and the one or more on-demand AI data sets includes one or more positioning reference signal (PRS) configurations, and the on-demand AI data set request includes a value range of a TRP synchronization error, indicating that the user device 102 wants the LMF 206 to provide an AI assistance data set that includes at least one TRP that satisfies the value range of the TRP synchronization error.

In addition or alternatively, in some implementations of the method 300 and/or the method 400, the communication node includes a RAN node 204, the one or more on-demand AI data sets includes one or more AI measurements of one or more other RAN nodes 204, the on-demand AI data set request comprises an area, indicating that the RAN node 204 wants the LMF 206 to provide an AI other-node data set indicating one or more other RAN nodes 204 or one or more transmission reception points (TRPs) 208 that are within the area.

In addition or alternatively, in some implementations of the method 300 and/or the method 400, the communication node includes a RAN node 204, the one or more on-demand AI data sets includes one or more AI measurements of one or more other RAN nodes 204, the on-demand AI data set request includes a value range of a first number of measured TRPs 208, indicating that the RAN node 204 wants the LMF 206 to provide a RAN node channel measurement or a RAN node AI intermediate feature derived from a second number of measured TRPs, where the second number of measured TRPs is within the value range of the number of measured TRPs.

In addition or alternatively, in some implementations of the method 300 and/or the method 400, the communication node includes a RAN node 204, the one or more on-demand AI data sets includes one or more AI measurements of one or more other RAN nodes 204, the on-demand AI data set request includes a value range of a TRP synchronization error, indicating that the RAN node 204 wants the LMF 206 to provide a RAN node channel measurement or a RAN node AI intermediate feature that is derived from at least one TRP 208 that satisfies the value range of the TRP synchronization error.

In addition or alternatively, in some implementations of the method 300 and/or the method 400, the communication node includes a RAN node 204, the one or more on-demand AI data sets ncludes one or more AI measurements of one or more other RAN nodes 204, the on-demand AI data set request comprises a value range of a measurement quality, indicating that the RAN node 204 wants the LMF 206 to provide a RAN node channel measurement or a RAN node AI intermediate feature associated with a measurement quality satisfying the value range.

In addition or alternatively, in some implementations of the method 300 and/or the method 400, the communication node includes a RAN node 204, the one or more on-demand AI data sets includes one or more AI measurements of one or more other RAN nodes 204, the on-demand AI data set request includes a sounding reference signal (SRS) configuration, indicating that the RAN node wants the LMF 206 to provide a RAN node channel measurement or a RAN node AI intermediate feature that is derived based on the SRS configuration.

In addition or alternatively, in some implementations of the method 300 and/or the method 400, the communication node includes a RAN node 204, each of the one or more on-demand AI data sets includes at least one of: a sounding reference signal (SRS) configuration, one or more associated user device or positioning reference unit (PRU) identifications (IDs) , or one or more associated user device or PRU locations.

In addition or alternatively, in some implementations of the method 300 and/or the method 400, the on-demand AI data set request includes at least one of: a cell or a cell list, indicating that the RAN node 204 wants the LMF 206 to provide an AI assistance data set, and a user device or a PRU that sends a SRS according to the SRS configuration in the AI assistance data set is within the cell or within one or more cells of the cell list; or the SRS configuration, which the RAN node 204 wants to measure to train or monitor an AI model.

Fig. 5 is a flow chart of an example method 500 for wireless communication related to functionality activation and deactivation. At block 502, a communication node receives a control indication of an artificial intelligence (AI) functionality or an AI model from a location management function (LMF) 206. At block 504, the communication node activates or deactivates the AI functionality or the AI model according to the control indication.

Fig. 6 is a flow chart of another example method 600 for wireless communication related to functionality activation and deactivation. At block 602, a location management function (LMF) 206 determines whether the LMF 206 should activate or deactivate an artificial intelligence (AI) functionality or an AI model, whether or whether a communication node should activate or deactivate the AI functionality or the AI model. At block 604, the LMF 206 activates or deactivates the AI functionality or the AI model, and/or transmits a control indication to the communication node. The control indication indicates at least one of: whether the AI functionality or the AI model is to be activated or deactivated, or that the communication node is allowed to switch from reporting a preferred measurement to reporting a different measurement.

In some implementations of the method 500 and/or the method 600, the communication node includes a user device 102 or a radio access network (RAN) node 204.

In addition or alternatively, in some implementations of the method 500 and/or the method 600, the communication node includes the user device 102, the control indication indicates a first positioning method, where in the first positioning method, the LMF 206 schedules the user device 102 to report an AI user device location as user device-based positioning, and schedules an AI intermediate feature or a channel measurement of a received positioning reference signal (PRS) as user device-assisted positioning.

In addition or alternatively, in some implementations of the method 500 and/or the method 600, the communication node includes the user device 102, the control indication indicates a second positioning method, where in the second positioning method, the LMF 206 schedules the user device 102 to report an AI user device location and/or an AI intermediate feature, where the user device 102 reports the AI user device location as user device-based positioning, and/or the user device 102 reports the AI intermediate feature as user device-assisted positioning.

In addition or alternatively, in some implementations of the method 500 and/or the method 600, the communication node includes the user device 102, the control indication indicates a third positioning method, where in the third positioning method, the LMF 206 schedules the user device 102 to report a channel measurement of a received positioning reference signal (PRS) as user device-assisted positioning.

In addition or alternatively, in some implementations of the method 500 and/or the method 600, the communication node includes the user device 102, the control indication indicates a fourth positioning method, where in the fourth positioning method, the LMF 206 schedules the user device 102 to report an AI user device location and/or a channel measurement of a received positioning reference signal (PRS) , and the user device 102 reports the AI user device location as user device-based positioning, and/or the user device 102 reports the channel measurement of a received positioning reference signal (PRS) as user device-assisted positioning.

In addition or alternatively, in some implementations of the method 500 and/or the method 600, the communication node includes the user device 102, the control indication indicates a fifth positioning method, where in the fifth positioning method, the LMF 206 schedules the user device 102 to report an AI user device location as user device-based positioning.

In addition or alternatively, in some implementations of the method 500 and/or the method 600, the communication node includes the user device 102, the control indication indicates a sixth positioning method, where in the sixth positioning method, the LMF 206 schedules the user device 102 to report an AI intermediate feature as user device-assisted positioning.

In addition or alternatively, in some implementations of the method 500 and/or the method 600, the communication node includes the user device 102, the control indication requests the user device 102 to report an AI user device location together with at least one of a downlink (DL) -time difference of arrival (TDOA) method, a DL-angle or departure (AoD) method, or a multi-round trip time (RTT) method.

In addition or alternatively, in some implementations of the method 500 and/or the method 600, the communication node includes the user device 102, the control indication requests the user device 102 to report an AI intermediate feature together with at least one of: a downlink (DL) -time difference of arrival (TDOA) method, a DL-angle of departure (AoD) method, or a multi-round trip time (RTT) method.

In addition or alternatively, in some implementations of the method 500 and/or the method 600, the control indication indicates at least one specific type of AI intermediate feature to be reported.

In addition or alternatively, in some implementations of the method 500 and/or the method 600, the communication node includes the user device 102, the control indication requests the user device 102 to report a channel measurement of a received positioning reference signal (PRS) together with at least one of downlink (DL) -time difference of arrival (TDOA) method, a DL-angle of departure (AoD) method, or a multi-round trip time (RTT) method.

In addition or alternatively, in some implementations of the method 500 and/or the method 600, the control indication indicates at least one specific type of channel measurement to be reported.

In addition or alternatively, in some implementations of the method 500 and/or the method 600, the communication node includes the user device 102, the control indication indicates that the LMF 206 prefers the user device 102 to report an AI user device location over other kinds of measurements, but allows the user device 102 to provide a user device location estimate based on non-AI positioning.

In addition or alternatively, in some implementations of the method 500 and/or the method 600, the communication node includes the user device 102, the control indication indicates that the LMF 206 prefers the user device 102 to report an AI user device location over other kinds of measurements, but allows the user device 102 to provide an AI intermediate feature, and further indicates at least one preferred type of AI intermediate feature.

In addition or alternatively, in some implementations of the method 500 and/or the method 600, the communication node includes the user device 102, the control indication indicates that the LMF 206 prefers the user device 102 to report an AI user device location over other kinds of measurements, but allows the user device 102 to provide a channel measurement, and further indicates at least one preferred type of channel measurement.

In addition or alternatively, in some implementations of the method 500 and/or the method 600, the control indication indicates that the LMF 206 prefers the communication node to report an AI intermediate feature over other kinds of measurements, but allows the communication node to provide the measurement based on a non-AI positioning method.

In addition or alternatively, in some implementations of the method 500 and/or the method 600, the LMF 206 indicates to the communication node at least one type of non-AI-based measurement that the LMF 206 prefers, or wherein a non-AI-based measurement includes a reference signal time difference (RSTD) when an AI intermediate feature is to be reported as the RSTD, or a non-AI-based measurement includes a reference time of arrival (RTOA) when an AI intermediate feature is to be reported as the RTOA, or that a non-AI-based measurement includes a receiver (Rx) -transmitter (Tx) time difference measurement when the AI intermediate feature is to be reported as the Rx-Tx time difference measurement.

In addition or alternatively, in some implementations of the method 500 and/or the method 600, the control indication indicates that the LMF 206 prefers the communication node to report an AI intermediate feature over other kinds of measurements, but allows the communication node to provide a channel measurement. In some of these implementations, the LMF 206 further indicates at least one preferred type of channel measurement.

In addition or alternatively, in some implementations of the method 500 and/or the method 600, the control indication indicates that the LMF 206 prefers the communication node to report a channel measurement over other kinds of measurements, but allows the communication node to provide a measurement based on a non-AI positioning method. In some of these implementations, the LMF further indicates at least one preferred type of measurement based on the non-AI positioning method.

In addition or alternatively, in some implementations of the method 300, 400, 500, and/or the 600, a user device 102 reports an error indication to the LMF 206, where the error indication indicates that the user device 102 at least one of: does not receive a sufficient amount of model training data to train a model; cannot generate an AI user device location using an AI model; cannot generate an AI intermediate feature using an AI model; cannot generate a channel measurement; or cannot download or acquire an AI model.

In addition or alternatively, in some implementation of the method 300, 400, 500, and/or 600, a RAN node 204 reports an error indication to the LMF 206, the error indication indicating that the RAN node 204 at least one of: does not receive a sufficient amount of model training data to train a model; cannot generate an AI intermediate feature using an AI model; cannot generate a channel measurement; or cannot download or acquire an AI model.

In addition or alternatively, in some implementations of the method 300, 400, 500, and/or 600, the LMF 206 indicates an error indication to the communication node, the error indication indicating that the LMF 206 cannot process the channel measurement of the communication node. In some of these implementations, the communication node includes a user device 102 or a RAN node 204.

Further details of actions performed by one or more communication nodes in the wireless communication system 100, any of which may be incorporated into any of various implementations of the method 300, the method 400 or other methods, are now described.

In some implementations positioning utilizing AI (or AI positioning) may include and/or be performed in at least one of three phase, including: an AI training phase, an AI inference phase, and an AI performance monitoring phase. In addition or alternatively, AI positioning may be performed according to one or more use cases. Fig. 7 shows a schematic diagram of a plurality of use cases, further details of which are described as follows.

In a first use case, when a user device 102 performs inference using an AI model, the model input may be a channel measurement of a received positioning reference signal (PRS) of the user device 102. Also, the model output generated by the user device 102 may be a location estimate of the user device 102. As used herein, a user device’s location estimate may also be called an AI user device or UE location.

In a second use case, when a user device 102 performs inference using an AI model, the model input may be a channel measurement of a received PRS of the user device 102, and the model output may be an AI intermediate feature.

In a third use case, when a LMF 206 performs inference using an AI model, the model input may be a channel measurement of a received PRS of a user device 102 that is reported by the user device 102 to the LMF 206, and the model output generated by the LMF 206 may be a location estimate of a user device 102 (or AI UE location) .

Additionally, for at least some implementations, each AI functionality may be recognized as, or in accordance with, each or at least one of the use cases mentioned above. To support different use cases or AI functionality, a user device 102 may have different user device (or UE) capabilities. A UE capability may be or include fixed UE capability and/or a real-time applicable capability. Also, under each AI functionality, there can be one or more AI models to be trained or used, and different AI models may correspond to different UE-side additional conditions and/or network-side additional conditions.

Additionally, in some implementations, a channel measurement made by a user device 102 may include at least one of the following: a channel impulse response (CIR) for a PRS measurement, a power delay profile (PDP) for a PRS measurement, or a delay profile (DP) for a PRS measurement.

Additionally, in some implementations, a channel measurement made by a RAN node 204 may include at least one of the following: a CIR for a sounding reference signal (SRS) measurement, a PDP for a SRS measurement, or a DP for a SRS measurement.

Additionally, in some implementations, an AI intermediate feature generated by a user device 102 may include at least one of the following: a PRS reference signal received power (RSRP) , a PRS reference signal received power per path (RSRPP) , a PRS reference signal carrier phase (RSCP) , a PRS reference signal carrier phase difference (RSCPD) , a PRS reference signal time difference (RSTD) , a user device (or UE) receiver (Rx) -transmitter (Tx) time difference, a PRS angle of arrival (AoA) , a SRS angle of departure (AoD) , PRS Rx beam index, a PRS timing error group (TEG) , a PRS Tx TEG, a UE Rx-Tx TEG, a line of sight (LOS) or a non-line of sight (NLOS) indicator.

Additionally, in some implementations, an AI intermediate feature generated by a RAN node 204 may include at least one of the following: a SRS AoA, a PRS AoD, a SRS Zenith Angle of Arrival (Z-AoA) , a SRS Rx beam index or a SRS Rx beam information, an ARP (Antenna reference point) identification (ID) , a SRS RSRP, a SRS RSRPP, a SRS RSCP, a SRS RSCPD, a SRS reference time of arrival (RTOA) , a network or gNB Rx-Tx time difference, a LOS/NLOS indicator, a SRS Rx TEG, a SRS Tx TEG, or a gNB Rx-Tx TEG.

Additionally, in some implementations, an AI intermediate feature, such as one or more of the ones described above, may be included in a measurement report of a positioning method.

Additionally, in implementations, a capability of a user device 102 (or UE capability) , a UE-side additional condition, or a network-side additional condition may include at least one of the following.

An area or cell information, where the area may be represented as a cell or a cell list, or using geographical coordinates. For example, one AI functionality or AI model may be applicable in one cell list, and another AI functionality or AI model is applicable in another cell list.

Velocity information of a user device 102. For example, one AI functionality or AI model is applicable when a user device’s 102 current velocity is within a range, and the other AI functionality or AI model is applicable when the user device’s 102 current velocity is within another range.

Current remaining power capacity of a user device 102. For example, one AI functionality or AI model is applicable when a user device’s 102 current remaining power capacity is within a range, and the other AI functionality or AI model is applicable when the user device’s 102 current remaining power capacity is within another range.

Current remaining memory or storage of a user device 102. For example, one AI functionality or AI model is applicable when a user device’s 102 current remaining memory/storage is within a range, and another AI functionality or AI model is applicable when the user device’s 102 current remaining memory/storage is within another range.

A TRP location. For example, one AI functionality or AI model is applicable when the AI functionality or AI model is trained using training data from one TRP or one TRP group, and another AI functionality or AI model is applicable when the other AI functionality or AI model is trained using training data from another TRP or another TRP group. In some implementations, a TRP group includes TRPs that have a similar TRP location.

A TRP synchronization error. For example, one AI functionality or AI model is applicable when the AI functionality or AI model is trained using training data from one TRP or one TRP group, the another AI functionality or AI model is applicable when another AI functionality or AI model is trained using training data from another TRP or another TRP group. In some implementations, a TRP group includes TRPs that have a similar TRP synchronization error value.

A TRP or port number. For example, one AI functionality or AI model is applicable when the AI functionality or AI model is trained using training data from a smaller number of TRPs or ports, and another AI functionality or AI model is applicable when another AI functionality or AI model is trained using training data from a larger number of TRPs or ports.

A PRS configuration. For example, one AI functionality or AI model is applicable when the AI functionality or AI model is trained using training data derived from a specific PRS configuration, and another AI functionality or AI model is applicable when another AI functionality or AI model is trained using training data derived from another specific PRS configuration. The PRS configuration may include at least one of: PRS periodicity, PRS bandwidth, PRS number of symbols, PRS comb size, PRS frequency layer, PRS beam or quasi co-location (QCL) information, PRS repetition factor.

Additionally, as used herein the term “legacy positioning method” includes at least one of the following positioning methods: network-assisted global navigation satellite system (GNSS) methods; observed time difference of arrival (OTDOA) positioning based on LTE signals; enhanced cell ID methods based on LTE signals; wireless local area network (WLAN) positioning; Bluetooth positioning; terrestrial beacon system (TBS) positioning; sensor based methods: (including those involving a barometric pressure sensor or a motion sensor) ; NR enhanced cell ID methods (NR E-CID) based on NR signals; Multi-Round Trip Time Positioning (Multi-RTT based on NR signals) ; Downlink Angle-of-Departure (DL-AoD) based on NR signals; Downlink Time Difference of Arrival (DL-TDOA) based on NR signals; Uplink Time Difference of Arrival (UL-TDOA) based on NR signals; Uplink Angle-of-Arrival (UL-AoA) , including A-AoA and Z-AoA based on NR signals; and/or SL positioning and ranging based on sidelink signals, including: Sidelink Round Trip Time Positioning (SL-RTT) ; Sidelink Angle-of-Arrival (SL-AoA) ; Sidelink Time Difference of Arrival (SL-TDOA) ; and/or Sidelink Time of Arrival (SL-TOA) Additionally, in some implementations, when a target user device 102 is to train an AI model, the target user device 102 may need to receive or obtain training data sets. The LMF 206 may provide one or more AI positioning reference unit (PRU) data sets to a user device 102 for AI training and/or AI performance monitoring. Each of the AI PRU data sets may include AI measurements of a user device 102 or a PRU, and the AI PRU data set or the AI measurements may include at least one of the following: a user device (or UE) , or PRU ID; one or more UE or PRU locations; one or more UE or PRU channel measurements; one or more UE or PRU intermediate features; a timestamp associated with each UE or PRU location, each UE or PRU channel measurement, or each UE or PRU intermediate feature; a quality associated with each UE or PRU location, each UE or PRU channel measurement, or each PRU intermediate feature; and/or an ID to identify each AI PRU data set.

Additionally, in some implementations, a model may be trained differently under different UE-side and/or network-side additional conditions. In this way, different training data sets may have an impact on a model’s performance, such as generalization or complexity of the model. Correspondingly, if a user device 102 wants to get the best performance of an AI model under some certain UE-side and/or network-side additional conditions, the training data set should satisfy these additional conditions. Therefore, before the LMF 206 provides AI PRU data sets, the user device 102 may send an on-demand AI PRU data set information request to the LMF 206 to notify or inform the LMF 206 of a wanted or desired attribute of an AI PRU data set. The user device 102 may include, indicate, or report at least one of the following parameters in the AI PRU data set information request.

A cell or a cell list, which indicates that the user device 102 wants the LMF 206 to provide an AI PRU data set derived within the requested cell or one or more cells in the cell list. In some implementations, at least one measured TRP or at least one measured PRS by a PRU is to be within the cell or the cell list, or at least one PRU’s location when making the PRU measurement is to be in the cell or the cell list.

A value range of a number of measured TRPs, which indicates that the user device 102 wants the LMF 206 to provide a UE or PRU channel measurement or an AI intermediate feature that satisfies the requested value range of the number of measured TRPs. In some implementations, the value range may be a threshold value. The number of measured TRPs may also be a number of measured ports. In addition or alternatively, the number of measured TRPs may be the number of measured samples of a PRS. In addition or alternatively, the number of measured TRPs may be the number of measured paths of a PRS. In addition or alternatively, the number of measured TRPs may be the number of measured PRS resources.

A value range of a TRP synchronization error, which indicates that the user device 102 wants the LMF 206 to provide a UE or PRU channel measurement or an AI intermediate feature derived from one or more TRPs 208 that satisfy the requested value range of the TRP synchronization error. In some implementations, the value range is a threshold value.

A value range of measurement quality, which indicates that the user device 102 wants the LMF 206 to provide a UE or PRU channel measurement, an AI intermediate feature, or a UE or PRU location that is associated with a measurement quality satisfying the requested value range. The unit of measurement quality may be meter, degree, or milliseconds, in some implementations. In addition or alternatively, the value range is a threshold value. In addition or alternatively, the measurement quality is a measurement uncertainty or a measurement confidence.

A value range of velocity, which indicates that the user device 102 wants the LMF 206 to provide an AI PRU data set that is derived by the user device 102 or PRU under the value range of velocity. In some implementations, the value range is a threshold value.

A preferred UE direction, which indicates that the user device 102 wants the LMF 206 to provide an AI PRU data set that is derived by the user device 102 or PRU under the preferred UE direction.

A specific PRS configuration, which indicates that the user device 102 wants the LMF 206 to provide a UE or PRU channel measurement or an AI intermediate feature that is derived based on a specific PRS configuration. In some implementations, the specific PRS configuration may include at least one of: a PRS periodicity, a PRS bandwidth, a PRS number of symbols, a PRS comb size, a PRS frequency layer, a PRS beam or QCL information, and/or a PRS repetition factor.

Fig. 8 is a schematic diagram illustrating example signaling between one or more PRUs, the LMF 206, and a target user device (UE) 102, including an on-demand AI PRU data set information request from the target user device 102 to the LMF 206, and AI PRU data sets from the LMF 206 to the target user device 102. In some implementations, an on-demand AI PRU data set information request is or includes a UE side additional condition reporting. In addition or alternatively, in some implementations, an on-demand AI PRU data set information request is transmitted in a ProvideCapabilities or a RequestAssistanceData message.

Additionally, in some implementations, a RAN node 204 may use a RAN node-side AI model to generate a RAN node AI intermediate feature. In turn, the RAN node 204 may report the -RAN node AI intermediate feature to the LMF 206. When a RAN node 204 is to train an AI model, the RAN node 204 may need to obtain or receive one or more training data sets. In such situations, the LMF 206 may provide one or more AI other-node data sets to the RAN node 204 for AI training and/or AI performance monitoring. Each of the AI other-node data sets may include one or more AI measurements of one or more RAN nodes 204. In addition or alternatively, each of the AI other-node data sets and/or the AI measurement of the RAN nodes 204 may include at least one of the following: AI other-node data set ID; another RAN node’s ID; another RAN node’s location; one or more other RAN node’s channel measurement on a received SRS; one or more associated SRS configurations; one or more other NG-RAN node’s AI intermediate feature on the received SRS; and/or a location of a user device 102 or a PRU that is sending the SRS.

In some implementations, the RAN node 204 can also or instead be or include a TRP 208, since one RAN node 204 may control one or more TRPs 208.

In addition or alternatively, in some implementations, a model may be trained differently under different UE-side additional conditions and/or network-side additional conditions. In this way, different training data sets may have different impacts on a model’s performance, such as generalization or complexity of model. In event that a RAN node 204 wants to achieve the best performance of an AI model under a certain UE-side additional condition and/or a certain network-side additional condition, the training data set may satisfy these additional conditions. Therefore, before the LMF 206 provides AI other-node data sets, the RAN node 204 may send an on-demand AI other-node data set information request to the LMF 206 to inform the LMF 206 of the wanted or desired attribute of the AI other-node data set. In some of these implementations, the RAN node 204 may report at least one of the following parameters in the request.

An area, indicating that the RAN node 204 wants the LMF 206 to provide an AI other-node data set including RAN nodes 204 or TRPs 208 that are within the area. In some of these implementations, the area can be represented as at least one of the following: geographical coordinates, a NR Cell Global Identifier (NCGI) list, a Physical Cell Identity (PCI) list, a RAN node ID list, or a TRP ID list.

A value range of a number of measured TRPs, indicating that the RAN node 204 wants the LMF 206 to provide a RAN node channel measurement or a RAN node AI intermediate feature that satisfies the requested value range of the number of measured TRPs. In some implementations, the value range is a threshold value. In addition or alternatively, in some implementations, the number of measured TRPs is a number of measured ports. In addition or alternatively, in some implementations, the number of measured TRPs is a number of measured samples of a SRS. In addition or alternatively, in some implementations, the number of measured TRPs is a number of measured paths of a SRS. In addition or alternatively, in some implementations, the number of measured TRPs is a number of measured SRS resources.

A value range of a TRP synchronization error, indicating that the RAN node 204 wants the LMF 206 to provide a RAN node channel measurement or a RAN node AI intermediate feature that is derived from TRPs 208 that satisfy the requested value range of a TRP synchronization error. In some implementations, the value range is a threshold value.

A value range of measurement quality, indicating that the RAN node 204 wants the LMF 206 to provide a RAN node channel measurement or a RAN node AI intermediate feature that is associated with a measurement quality satisfying the requested value range. In at least some implementations, the unit of measurement quality is meters, degrees or milliseconds, as non-limiting examples. In addition or alternatively, in some implementations, the value range is a threshold value. In addition or alternatively, in some implementations, the measurement quality is a measurement uncertainty or measurement confidence.

A specific SRS configuration, indicating that the RAN node 204 wants the LMF 206 to provide a RAN node channel measurement or a RAN node AI intermediate feature that is derived based on the specific SRS configuration. In some implementations, the specific SRS configuration includes at least one of: a SRS periodicity, a SRS bandwidth, a SRS number of symbols, a SRS comb size, a SRS beam or spatial relation information, and/or a SRS repetition factor.

Additionally, in some implementations, a RAN node 204 may obtain or receive PRU information from the LMF 206 or from a user device 102. The PRU information may include at least one of: a PRU ID, a PRU location, area information where the PRU is located, velocity of the PRU, a timestamp when the velocity or the area information is valid, a quality of the velocity, or a quality of the PRU location. In addition or alternatively, in some implementations, the RAN node 204 may obtain or receive PRU information including information of one or more PRUs from the LMF 206 using a NRPPa message, and/or the RAN node 204 may obtain or receive PRU information from a PRU using an UL RRC message or an UL medium access control (MAC) control element (CE) message.

In addition or alternatively, in some implementations, the LMF 206 may provide one or more AI assistance data sets to a user device 102 for AI training and/or AI performance monitoring. In some of these implementations, each of the AI assistance data sets may include contain at least one of the following: an AI assistance data set ID; a set of one or more PRS configurations; a set of TRP information, which may include at least one of: a list of one or more TRP locations; a list of one or more TRP synchronization errors; or a list of TRP beam antenna information.

In addition or alternatively, in some implementations, the AI assistance data set ID may be allocated per user device 102. In other implementations, the AI assistance data set ID may be allocated but different LMFs 206 may assign different AI assistance data set IDs. In addition or alternatively, in some implementations, the AI assistance data set ID may be associated with a validity time. When the validity time expires, the AI assistance data set ID and the associated AI assistance data set is invalid. In some of these implementations, the validity time is configured or indicated by the network device 104 to the user device 102, or the validity time is a fixed time, such as defined in a specification or protocol according to which the communication nodes in the wireless communication system 100 communicate.

In addition or alternatively, in some implementations, the one or more AI assistance data sets may be associated with a validity area. When a user device 102 is within the validity area, the user device 102 may use the one or more AI assistance data sets of the validity area to train or monitor an AI model. In some of these implementations, the validity area may be configured or indicated by the network device 104 to the user device 102. In addition or alternatively, before using one or more AI assistance data sets of the validity area, the user device 102 may send an on-demand AI assistance data set information request to the LMF 206 to inform or notify the LMF 206 of a wanted or desired AI assistance data set attribute. In some of these implementations, the user device 102 may include or indicate at least one of the following parameters in the request.

A cell or a cell list, which indicates that the user device 102 wants the LMF 206 to provide an AI assistance data set that includes at least one TRP configuration and/or at least one PRS configuration. In some of these implementations, the TRP and PRS configurations are within the requested cell or one or more cells in the cell list.

A value range of a number of TRPs, which indicates that the user device 102 wants the LMF 206 to provide an AI assistance data set that includes contains at least one TRP, where the number of the included TRPs satisfies the requested value range. In some of these implementations, the value range is a threshold value. In addition or alternatively, in some of these implementations, the number of TRPs is a number of PRS resources.

A value range of a TRP synchronization error, which indicates that the user device 102 wants the LMF 206 to provide an AI assistance data set that includes one or more TRPs that satisfy the requested value range of the TRP synchronization error. In some implementations, the value range is a threshold value.

Additionally, in some implementations, the LMF 206 may provide one or more AI assistance data sets to a RAN node 204 for AI training and/or AI performance monitoring. In some of these implementations, each of the AI assistance data sets may include at least one of the following: an AI assistance data set ID; a set of one or more SRS configurations; one or more associated UE or PRU IDs; or one or more associated UE or PRU locations.

Additionally, in some implementations, the AI assistance data set ID may be allocated per RAN node 206. In other implementations, the AI assistance data set ID may be allocated per LMF 206. In addition or alternatively, in some implementations, the AI assistance data set ID may be associated with a validity time. When the validity time expires, the AI assistance data set ID and the associated SRS configuration may not be workable. In some implementations, the validity time may be configured or indicated by the LMF 206 to the RAN node 204, or the validity time is a fixed time in a specification or protocol according to which the communication nodes in the wireless communication system 100 are configuration to communicate. In other implementations, before receiving an AI assistance data set, the RAN node 204 may send an on-demand AI assistance data set information request to the LMF 206 to inform or notify the LMF 206 of the wanted or desired AI assistance data set attribute. In addition or alternatively, in some implementations, the RAN node 204 may report at least one of the following parameters in the request:

A cell or a cell list, which indicates that the RAN node 204 wants the LMF 206 to provide an AI assistance data set, and the user device 102 or PRU that sends the SRS according to the SRS configuration in the AI assistance data set is to be within the cell or one or more cells in the cell list.

A specific SRS configuration that the RAN node 204 wants to measure to train a model. In some of these implementations, the specific SRS configuration may include at least one of: a SRS periodicity, a SRS bandwidth, a SRS number of symbols, a SRS comb size, a SRS beam or spatial relation information, and/or a SRS repetition factor.

Additionally, in some implementations, the LMF 206 may control a user device’s 102 positioning process. The LMF 206 may be able to do so according to one or more of the following ways.

In one way, the LMF 206 may activate or deactivate an AI functionality of the user device 102, and the LMF 206 may activate or deactivate an AI model of the user device 102.

In a second way, the LMF 206 may activate or deactivate an AI functionality of the user device 102, and the user device 102 may activate or deactivate an AI model based on the user device’s 102 own decision or determination. In some of these implementations, an indication on whether to allow the user deice 102 to activate or deactivate the AI model based on the user device’s 102 own decision or determination may be sent from the LMF 206 to the user device 102. In other of these implementations, the user device 102 may report the model ID, the AI PRU data set ID, or the AI assistance data set ID that the user device 102 uses when reporting the AI UE location, the AI intermediate feature, or the channel measurement to the LMF 206.

In a third way, the user device 102 may activate or deactivate the AI functionality based on the user device’s 102 own decision or determination, and the user device 102 may activate or deactivate the AI model based on the user device’s 102 own decision or determination. In some implementations, an indication of whether to allow the user device 102 to activate or deactivate the AI functionality based on the user device’s 102 own decision or determination may be sent from the LMF 206 to the user device 102. In addition or alternatively, an indication of whether to allow the user device 102 to activate or deactivate the AI model based on the user device’s 102 own decision may be sent from the LMF 206 to the user device 102. In addition or alternatively, the user device 102 may report the model ID, the AI PRU data set ID, or the AI assistance data set ID that the user device 102 uses when reporting the AI UE location, the AI intermediate feature, or the channel measurement to the LMF 206.

In addition or alternatively, in some implementations, the LMF 206 may control a RAN node’s 204 positioning process. The LMF 206 may do so according to one or more of the following ways.

In a first way, the LMF 206 may activate or deactivate the AI functionality of the RAN node 204, and the LMF 206 may activate or deactivate the AI model of the RAN node 204.

In a second way, the LMF 206 may activate or deactivate the AI functionality of the RAN node 204, and the RAN node 204 may activate or deactivate the AI model based on the RAN node’s 204 own decision or determination. In some implementations, an indication of whether to allow the RAN node 204 to activate or deactivate the AI model based on the RAN node’s 204 own decision or determination may be sent from the LMF 206 to the RAN node 204. In addition or alternatively, the RAN node 204 may report the model ID, the AI other-node data set ID, or the AI assistance data set ID that the RAN node 204 uses when reporting an AI intermediate feature or a SRS channel measurement to the LMF 206

In a third way, the RAN node 204 may activate or deactivate the AI functionality based on the RAN node’s 204 own decision or determination, and the RAN node 204 may activate or deactivate the AI model based on RAN node’s 204 own decision or determination. Additionally, in some implementations, an indication of whether to allow the RAN node 204 to activate or deactivate the AI functionality based on the RAN node’s 204 own decision or determination may be sent from the LMF 206 to the RAN node 204. In addition or alternatively, an indication of whether to allow the RAN node 204 to activate or deactivate the AI model based on the RAN node’s 204 own decision may be sent from the LMF 206 to the RAN node 204. In addition or alternatively, the RAN node 204 may report the model ID, the AI other-node data set ID, or the AI assistance data set ID that the RAN node 204 uses when reporting the AI intermediate feature or the SRS channel measurement to the LMF 206.

Additionally, in some implementations, the activation or deactivation of an AI functionality may be achieved via activation or deactivation of a positioning method. In some of these implementations, different AI functionalities may have different reporting configurations. Correspondingly, the LMF 206 may schedule a user device 102 to report AI-related information in a separate positioning method independent of a positioning method, or may schedule a user device 102 to report AI-related information together with the positioning method.

Additionally, in some implementations, when the LMF 206 schedules a user device 102 to report AI-related information in conjunction with one or more of a plurality of separate or different positioning methods, at least one of the following schemes may be implemented.

In a first scheme, a user device 102 may be scheduled to report according to a first positioning method. In the first positioning method, the LMF 206 may schedule the user device 102 to report an AI UE location as UE based positioning, and may schedule an AI intermediate feature or a channel measurement of a received PRS as UE assisted positioning.

In a second scheme, a user device 102 may be scheduled to report according to a second positioning method. In the second positioning method, the LMF 206 may schedule a user device 102 to report an AI UE location and/or an AI intermediate feature. In some of these implementations, the user device 102 may report an AI UE location as UE based positioning, and may report an AI intermediate feature as UE assisted positioning.

In a third scheme, a user device 102 may be scheduled to report according to a third positioning method. In the third positioning method, the LMF 206 may schedule a user device 102 to report a channel measurement of a received PRS, which is UE assisted positioning.

In a fourth scheme, a user device 102 may be scheduled to report according to a fourth positioning method. In the fourth positioning method, the LMF 206 may schedule a user device 102 to report an AI UE location and/or a channel measurement of a received PRS.

In a fifth scheme, a user device 102 may be scheduled to report according to a fifth positioning method. In the fifth positioning method, the LMF 206 may schedule a user device 102 to report an AI UE location, which is UE based positioning.

In a sixth scheme, a user device 102 may be scheduled to report according to a sixth positioning method. In the sixth positioning method, the LMF 206 may schedule a user device 102 to report an AI intermediate feature, which is a UE assisted positioning.

In any of various implementations, any two or more of the above positioning methods may be scheduled independently or together.

Additionally, in some implementations, the LMF 206 may schedule a user device 102 to report AI related information together with, or according to, one or more other positioning methods. In doing so, one or more of the following schemes may be implemented.

In a first scheme, the LMF 206 may schedule a user device 102 to report an AI UE location together with, or according to, one or more other positioning methods. For example, the LMF 206 may schedule a downlink (DL) -time difference of arrival (TDOA) positioning method and the type is UE-based positioning. In some implementations of the first scheme, the LMF 206 may send CommonIEsRequestLocationInformation and nr-DL-TDOA-RequestLocationInformation to the user device 102, such in accordance with NR or other wireless communication specifications or protocols. In the nr-DL-TDOA-RequestLocationInformation, the LMF 206 may further indicate to the user device 102 to report the AI UE location using an AI model. In response, the user device 102 may respond with commonIEsProvideLocationInformation and NR-DL-TDOA-LocationInformation to the LMF 206, such as in accordance with NR or other wireless communication specifications or protocols. In commonIEsProvideLocationInformation or in NR-DL-TDOA-LocationInformation, the user device 102 may report whether an estimated location is derived using an AI model or not, e.g., 1 bit. Alternatively, the user device 102 may report two estimated locations, one being derived using a DL-TDOA method, and the other being derived using an AI model.

In a second scheme, the LMF 206 may schedule a user device 102 to report an AI intermediate feature together with, or according to, one or more other positioning methods. For example, the LMF 206 may schedule a multi-round trip time (RTT) method and the type is UE-assisted positioning. In some of these implementations, the LMF 206 may send CommonIEsRequestLocationInformation and nr-Multi-RTT-RequestLocationInformation to the user device 102, such as according to NR or other wireless communication specifications or protocols. In nr-Multi-RTT-RequestLocationInformation, the LMF 206 may further schedule the user deice 102 to report a UE Rx-Tx time difference measurement, a LOS/NLOS indicator, a RSRP, a RSRPP, and/or a RSCP measurement based on an AI model. In response, the user device 102 may respond with a NR-Multi-RTT-ProvideLocationInformation to the LMF 206, such as according to NR or other wireless communication specifications or protocols. In NR-Multi-RTT-ProvideLocationInformation or NR-Multi-RTT-SignalMeasurementInformation, the user device 102 may report whether or not the reported Rx-Tx time difference measurement, the LOS/NLOS indicator, the RSRP, the RSRPP, and/or the RSCP measurement is to be derived using AI model, such as by utilizing a one bit flag for each kind of measurement. Alternatively, the user device 102 may report an additional Rx-Tx time difference measurement, LOS/NLOS indicator, RSRP, RSRPP, and/or RSCP measurement based on an AI model, which may include a separate or additional information element (IE) . Alternatively, the user device 102 may also report a TRP ID, a PRS resource ID, a PRS resource set ID, a timestamp, or a timing quality associated with the reported AI intermediate feature.

In addition or alternatively, the LMF 206 may schedule a user device 102 to report a channel measurement of the received PRS together with, or according to, other positioning methods. For example, the LMF 206 may schedule a multi-RTT method and the type is UE-assisted positioning. In some of these implementations, the LMF 206 may send CommonIEsRequestLocationInformation and nr-Multi-RTT-RequestLocationInformation to the user device 102, such as according to NR or other wireless communication standards or protocols. In nr-Multi-RTT-RequestLocationInformation, the LMF 206 may further schedule the user device 102 to report CIR, PDP and/or DP of a received PRS. In response, the user device 102 may respond with NR-Multi-RTT-ProvideLocationInformation to the LMF 206, such according to NR or other wireless communication standards or protocols. In NR-Multi-RTT-ProvideLocationInformation or NR-Multi-RTT-SignalMeasurementInformation, the user device 102 may report the CIR, PDP, and/or DP of a received PRS. In addition or alternatively, the user device 102 may also report the TRP ID, PRS resource ID, PRS resource set ID, timestamp, and/or timing quality associated with the reported channel measurement.

In addition or alternatively, in some embodiments, when the LMF 206 requests the user device 102 or the RAN node 204 to report an AI intermediate feature, the LMF 206 may indicate which specific kind (s) of AI intermediate feature is requested. In addition or alternatively, when the LMF 206 requests the user device 102 or the RAN node 204 to report a channel measurement, the LMF 206 may indicate which specific kind (s) of channel measurements is requested.

Additionally, in some implementations, different AI models may have different impacts on a user device 102. For example, using different AI models may cause different power consumption, different memory or storage consumption, or different latency to a user device 102, as non-limiting examples. Consequently, it is possible that a user device 102 can only get some AI models at some certain UE condition, and the network device 104 may not be able to get such UE conditions in real time. Furthermore, a user device 102 may use different AI models in or for different AI functionalities to output an AI UE location and/or AI intermediate features. Therefore, it may be beneficial that the LMF 206 can allow a user device 102 to adjust an AI functionality or an AI model based on the user device’s 102 own condition. Also, in event that the LMF 206 deactivates an AI functionality and then activates another AI functionality, the LMF may incur a relatively large amount of latency, and as a result may not be able to satisfy positioning latency requirements.

In the present description, in some embodiments, the LMF 206 may allow a user device 102 to adjust an AI functionality or an AI model according to one or more of the following ways.

In a first way, the LMF 206 may request the user device 102 to report an AI intermediate feature or an AI UE location, depending on user device’s own UE condition.

In a second way, the LMF 206 may request the user device 102 to report an AI intermediate feature or a channel measurement, depending on the user device’s own UE condition.

In a third way, the LMF 206 may request the user device 102 to report an AI location or a channel measurement, depending on the user device’s own UE condition.

In a fourth way, the LMF 206 may indicate to a user device 102 that the LMF prefers the user device 102 to report an AI UE location, but allows the user device 102 to fallback or switch to provide a UE location estimate based on a non-AI positioning method.

In a fifth way, the LMF 206 may indicate to a user device 102 that the LMF 206 prefers the user device 102 to report an AI UE location, but allows the user device 102 to fallback or switch to provide an AI intermediate feature, and further, the LMF 206 may indicate which kind (s) of AI intermediate feature is preferred.

In a sixth way, the LMF 206 may indicate to a user device 102 that the LMF 206 prefers the user device 102 to report an AI UE location, but allows the user device 102 to fallback or switch to provide channel measurement, and further, the LMF 206 may indicate which kind (s) of channel measurement is preferred.

In a seventh way, the LMF 206 may indicate to a user device 102 that the LMF 206 prefers the user device 102 to report an AI intermediate feature, but allows the user device 102 to fallback or switch to provide a measurement based on a non-AI based positioning method. In some implementations of the seventh way, the LMF 206 may indicate which kind (s) of non-AI based measurements is/are preferred. In some other implementations, if an AI intermediate feature is requested as a reference signal time difference (RSTD) , the fallback measurement is RSTD. In other implementations, if the AI intermediate feature is requested as a Rx-Tx time difference measurement, the fallback measurement is a Rx-Tx time difference measurement.

In an eighth way, the LMF 206 may indicate to a user device 102 that the LMF 206 prefers the user device 102 to report an AI intermediate feature, but allows the user device 102 to fallback or switch to provide channel measurement, and further, the LMF 206 may indicate which kind (s) of channel measurement is/are preferred.

In a ninth way, the LMF 206 may indicate to a user device 102 that the LMF 206 prefers a user device 102 to report a channel measurement, but allows the user device 102 to fallback or switch to provide measurement based on a non-AI-based positioning method. In some implementations, the LMF 206 may indicate which kind (s) of legacy measurement is/are preferred.

In addition or alternatively, in some implementations, the LMF 206 may allow a RAN node 204 to adjust an AI functionality or an AI model in one or more of the following ways.

In a first way, the LMF 206 may request a RAN node 204 to report an AI intermediate feature or a SRS channel measurement, depending on the RAN node’s 204 own condition.

In a second way, the LMF 206 may indicate to a RAN node 204 that the LMF 206 prefers the RAN node 204 to report an AI intermediate feature, but allows the RAN node 204 to fallback or switch to provide a measurement based on a non-AI-based positioning method. In some implementations, the LMF 206 may indicate which kind (s) of non-AI-based measurement is preferred. In some other implementations, if the AI intermediate feature is requested as relative time of arrival (RTOA) , then the fallback (or non-AI-based) measurement is RTOA. In some other implementations, if the AI intermediate feature is requested as a gNB Rx-Tx time difference measurement, then the fallback (or non-AI-based) measurement is a gNB Rx-Tx time difference measurement.

In a third way, the LMF 206 may indicate to a RAN node 204 that the LMF 206 prefers the RAN node 204 to report an AI intermediate feature, but allows the RAN node 204 to fallback or switch to provide a SRS channel measurement, and further, the LMF 206 may indicate which kind (s) of SRS channel measurement is/are preferred.

In a fourth way, the LMF 206 may indicate to a RAN node 204 that the LMF 206 prefers the RAN node 204 to report a SRS channel measurement, but allows the RAN node 204 to fallback or switch to provide a measurement based on a non-AI-based positioning method. In some implementations, the LMF 206 may indicate which kind (s) of legacy measurement is/are preferred.

Additionally, in some implementations, the LMF 206 may indicate an error indication to a user device 102 with respect to AI/ML positioning. For example, the LMF 206 may indicate that the LMF 206 cannot process a channel measurement of a PRS reported by the user device 102. Additionally, in some implementations, the LMF 206 may indicate an error indication to a RAN node 204 with respect to AI/ML positioning. For example, LMF 206 may indicate that the LMF 206 cannot process a channel measurement of a SRS reported by the RAN node 204.

Additionally, in some implementations, the user device 102 may report an error indication to the LMF 206 with respect to AI/ML positioning. For example, the user device 102 may indicate to the LMF 206 that: the user device 102 does not receive or obtain a sufficient amount of model training data to train a model; the user device 102 cannot generate an AI UE location using an AI model; the user device 102 cannot generate an AI intermediate feature using an AI model; the user device 102 cannot generate a channel measurement; and/or the user device 102 cannot download or acquire a AI model.

In addition or alternatively, in some implementations, a RAN node 204 may report an error indication to the LMF 206 with respect to AI/ML positioning. For example, the RAN node 204 may indicate to the LMF 206 that: the RAN node 204 cannot receive or obtain a sufficient amount of model training data to train a model; the RAN node 204 cannot generate an AI intermediate feature using an AI model; the RAN node 204 cannot generate a channel measurement; or the RAN node 204 cannot download or acquire an AI model.

The description and accompanying drawings above provide specific example embodiments and implementations. The described subject matter may, however, be embodied in a variety of different forms and, therefore, covered or claimed subject matter is intended to be construed as not being limited to any example embodiments set forth herein. A reasonably broad scope for claimed or covered subject matter is intended. Among other things, for example, subject matter may be embodied as methods, devices, components, systems, or non-transitory computer-readable media for storing computer codes. Accordingly, embodiments may, for example, take the form of hardware, software, firmware, storage media or any combination thereof. For example, the method embodiments described above may be implemented by components, devices, or systems including memory and processors by executing computer codes stored in the memory.

Throughout the specification and claims, terms may have nuanced meanings suggested or implied in context beyond an explicitly stated meaning. Likewise, the phrase “in one embodiment/implementation” as used herein does not necessarily refer to the same embodiment and the phrase “in another embodiment/implementation” as used herein does not necessarily refer to a different embodiment. It is intended, for example, that claimed subject matter includes combinations of example embodiments in whole or in part.

In general, terminology may be understood at least in part from usage in context. For example, terms, such as “and” , “or” , or “and/or, ” as used herein may include a variety of meanings that may depend at least in part on the context in which such terms are used. Typically, “or” if used to associate a list, such as A, B or C, is intended to mean A, B, and C, here used in the inclusive sense, as well as A, B or C, here used in the exclusive sense. In addition, the term “one or more” as used herein, depending at least in part upon context, may be used to describe any feature, structure, or characteristic in a singular sense or may be used to describe combinations of features, structures or characteristics in a plural sense. Similarly, terms, such as “a, ” “an, ” or “the, ” may be understood to convey a singular usage or to convey a plural usage, depending at least in part upon context. In addition, the term “based on” may be understood as not necessarily intended to convey an exclusive set of factors and may, instead, allow for existence of additional factors not necessarily expressly described, again, depending at least in part on context.

Reference throughout this specification to features, advantages, or similar language does not imply that all of the features and advantages that may be realized with the present solution should be or are included in any single implementation thereof. Rather, language referring to the features and advantages is understood to mean that a specific feature, advantage, or characteristic described in connection with an embodiment is included in at least one embodiment of the present solution. Thus, discussions of the features and advantages, and similar language, throughout the specification may, but do not necessarily, refer to the same embodiment.

Furthermore, the described features, advantages and characteristics of the present solution may be combined in any suitable manner in one or more embodiments. One of ordinary skill in the relevant art will recognize, in light of the description herein, that the present solution can be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments of the present solution.

The subject matter of the disclosure may also relate to or include, among others, the following aspects:

A first aspect includes a method for wireless communication that includes: sending, by a communication node, an on-demand artificial intelligence (AI) data set request to a location management function (LMF) ; and receiving, by the communication node, one or more on-demand AI data sets from the LMF in response to the on-demand AI data set request.

A second aspect includes a method for wireless communication that includes: receiving, by a location management function (LMF) , an on-demand artificial intelligence (AI) data set request from a communication node; and transmitting, by the LMF, one or more on-demand AI data sets to the communication node in response to the on-demand AI data set request.

A third aspect includes any of the first or second aspects, and further includes wherein the communication node comprises a user device or a radio access network (RAN) node.

A fourth aspect includes the third aspect, and further includes wherein the communication node comprises the user device, the one or more on-demand AI data sets comprises one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request comprises a cell or a cell list indicating that the user device wants the LMF to provide an AI PRU data set derived within the cell or one or more cells in the cell list.

A fifth aspect includes any of the third or fourth aspects, and further includes wherein the communication node comprises the user device, the one or more on-demand AI data sets comprises one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request comprises a value range of a number of measured transmission reception points (TRPs) , the value range indicating that the user device wants the LMF to provide a channel measurement of a user device or a PRU or an AI intermediate feature that satisfies the value range of the number of measured TRPs.

A sixth aspect includes any of the third through fifth aspects, and further includes wherein the communication node comprises the user device, the one or more on-demand AI data sets comprises one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request comprises a value range of a TRP synchronization error, the value range indicating that the user device wants the LMF to provide a user device or PRU channel measurement or an AI intermediate feature that is derived from one or more TRPs that satisfy the value range of the TRP synchronization error.

A seventh aspect includes any of the third through sixth aspects, and further includes wherein the communication node comprises the user device, the one or more on-demand AI data sets comprises one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request comprises a value range of a measurement quality, the value range indicating that the user device wants the LMF to provide a user device or PRU channel measurement, an AI intermediate feature, or a user device or PRU location that is associated with a measurement quality satisfying the value range.

An eighth aspect includes any of the third through seventh aspects, and further includes wherein the communication node comprises the user device, the one or more on-demand AI data sets comprises one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request comprises a value range of velocity, indicating that the user device wants the LMF to provide an AI PRU data set that is derived by a PRU or a user device under the value range of velocity.

A ninth aspect includes any of the third through eighth aspects, and further includes wherein the communication node comprises the user device, the one or more on-demand AI data sets comprises one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request comprises a preferred user device direction, indicating that the user device wants the LMF to provide an AI PRU data set that is derived by a PRU or a user device under the preferred user device direction.

A tenth aspect includes any of the third through ninth aspects, and further includes wherein the communication node comprises the user device, the one or more on-demand AI data sets comprises one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request comprises a positioning reference signal (PRS) configuration, indicating that the user device wants the LMF to provide a channel measurement of a user device or a PRU or an AI intermediate feature that is derived based on the PRS configuration.

An eleventh aspect includes any of the third through tenth aspects, and further includes wherein the communication node comprises the user device, and the one or more on-demand AI data sets comprises one or more positioning reference signal (PRS) configurations, and the on-demand AI data set request comprises a cell or a cell list, indicating that the user device wants the LMF to provide an AI assistance data set comprising at least one transmission reception point (TRP) configuration and/or at least one PRS configuration, wherein the at least one TRP configuration and/or the at least one PRS configuration is located within the cell or one or more cells of the cell list.

A twelfth aspect includes any of the third through eleventh aspects, and further includes wherein the communication node comprises the user device, the one or more on-demand AI data sets comprises one or more positioning reference signal (PRS) configurations, and the on-demand AI data set request comprises a value range of a first number of TRPs, indicating that the user device wants the LMF to provide an AI assistance data set that comprises PRS configurations derived from a second number of TRPs, where the second number of TRPs is within the value range of the first number of TRPs.

A thirteenth aspect includes any of the third through twelfth aspects, and further includes wherein the communication node comprises the user device, and the one or more on-demand AI data sets comprises one or more positioning reference signal (PRS) configurations, and the on-demand AI data set request comprises a value range of a TRP synchronization error, indicating that the user device wants the LMF to provide an AI assistance data set that comprises at least one TRP that satisfies the value range of the TRP synchronization error.

A fourteenth aspect includes the third aspect, and further includes wherein the communication node comprises the RAN node, the one or more on-demand AI data sets comprises one or more AI measurements of one or more other RAN nodes, the on-demand AI data set request comprises an area, indicating that the RAN node wants the LMF to provide an AI other-node data set indicating one or more other RAN nodes or one or more transmission reception points (TRPs) that are within the area.

A fifteenth aspect includes any of the third or fourteenth aspects, and further includes wherein the communication node comprises the RAN node, the one or more on-demand AI data sets comprises one or more AI measurements of one or more other RAN nodes, the on-demand AI data set request comprises a value range of a first number of measured TRPs, indicating that the RAN node wants the LMF to provide a RAN node channel measurement or a RAN node AI intermediate feature derived from a second number of measured TRPs, where the second number of measured TRPs is within the value range of the first number of measured TRPs.

A sixteenth aspect includes any of the third, fourteenth, or fifteenth aspects, and further includes wherein the communication node comprises the RAN node, the one or more on-demand AI data sets comprises one or more AI measurements of one or more other RAN nodes, the on-demand AI data set request comprises a value range of a TRP synchronization error, indicating that the RAN node wants the LMF to provide a RAN node channel measurement or a RAN node AI intermediate feature that is derived from at least one TRP that satisfies the value range of the TRP synchronization error.

A seventeenth aspect includes any of the third or fourteenth through sixteenth aspects, and further includes wherein the communication node comprises the RAN node, the one or more on-demand AI data sets comprises one or more AI measurements of one or more other RAN nodes, the on-demand AI data set request comprises a value range of a measurement quality, indicating that the RAN node wants the LMF to provide a RAN node channel measurement or a RAN node AI intermediate feature associated with a measurement quality satisfying the value range.

An eighteenth aspect includes any of the third or fourteenth through seventeenth aspects, and further includes wherein the communication node comprises the RAN node, the one or more on-demand AI data sets comprises one or more AI measurements of one or more other RAN nodes, the on-demand AI data set request comprises a sounding reference signal (SRS) configuration, indicating that the RAN node wants the LMF to provide a RAN node channel measurement or a RAN node AI intermediate feature that is derived based on the SRS configuration.

A nineteenth aspect includes any of the third or fourteenth through eighteenth aspects, and further includes wherein the communication node comprises the RAN node, wherein each of the one or more on-demand AI data sets comprises at least one of: a sounding reference signal (SRS) configuration, one or more associated user device or positioning reference unit (PRU) identifications (IDs) , or one or more associated user device or PRU locations.

A twentieth aspect includes the nineteenth aspect, and further includes wherein the on-demand AI data set request comprises at least one of: a cell or a cell list, indicating that the RAN node wants the LMF to provide an AI assistance data set, and a user device or a PRU that sends a SRS according to the SRS configuration in the AI assistance data set is within the cell or within one or more cells of the cell list; or the SRS configuration, which the RAN node wants to measure to train or monitor an AI model.

A twenty-first aspect includes a method for wireless communication that includes: receiving, by a communication node, a control indication of an AI functionality or an AI model from a location management function (LMF) ; and activating or deactivating, by the communication node, the AI functionality or the AI model according to the control indication.

A twenty-second aspect includes a method for wireless communication that includes: determining, by a location management function (LMF) , whether the LMF should activate or deactivate an artificial intelligence (AI) functionality or an AI model, or whether a communication node should activate or deactivate the AI functionality or the AI model; and activating or deactivating, by the LMF, the AI functionality or the AI model, and/or transmitting, by the LMF, a control indication to the communication node, the control indication indicating at least one of: whether the AI functionality or the AI model is to be activated or deactivated; or that the communication node is allowed to switch from reporting a preferred measurement to reporting a different measurement.

A twenty-third aspect includes any of the twenty-first or twenty-second aspects, and further includes wherein the communication node comprises a user device or a radio access network (RAN) node.

A twenty-fourth aspect includes the twenty-third aspect, and further includes wherein the communication node comprises the user device, wherein the control indication indicates a first positioning method, wherein in the first positioning method, the LMF schedules the user device to report an AI user device location as user device-based positioning, and schedules an AI intermediate feature or a channel measurement of a received positioning reference signal (PRS) as user device-assisted positioning.

A twenty-fifth aspect includes any of the twenty-third or twenty-fourth aspects, and further includes wherein the communication node comprises the user device, wherein the control indication indicates a second positioning method, wherein in the second positioning method, the LMF schedules the user device to report an AI user device location and/or an AI intermediate feature, wherein the user device reports the AI user device location as user device-based positioning, and/or the user device reports the AI intermediate feature as user device-assisted positioning.

A twenty-sixth aspect includes any of the twenty-third through twenty-fifth aspects, and further includes wherein the communication node comprises the user device, wherein the control indication indicates a third positioning method, wherein in the third positioning method, the LMF schedules the user device to report a channel measurement of a received positioning reference signal (PRS) as user device-assisted positioning.

A twenty-seventh aspect includes any of the twenty-third through twenty-sixth aspects, and further includes wherein the communication node comprises the user device, wherein the control indication indicates a fourth positioning method, wherein in the fourth positioning method, the LMF schedules the user device to report an AI user device location and/or a channel measurement of a received positioning reference signal (PRS) , wherein the user device reports the AI user device location as user device-based positioning, and/or the user device reports the channel measurement of a received positioning reference signal (PRS) as user device-assisted positioning.

A twenty-eighth aspect includes any of the twenty-third through twenty-seventh aspects, and further includes wherein the communication node comprises the user device, wherein the control indication indicates a fifth positioning method, wherein in the fifth positioning method, the LMF schedules the user device to report an AI user device location as user device-based positioning.

A twenty-ninth aspect includes any of the twenty-third through twenty-eighth aspects, and further includes wherein the communication node comprises the user device, wherein the control indication indicates a sixth positioning method, wherein in the sixth positioning method, the LMF schedules the user device to report an AI intermediate feature as user device-assisted positioning.

A thirtieth aspect includes any of the twenty-third through twenty-ninth aspects, and further includes wherein the communication node comprises the user device, wherein the control indication requests the user device to report an AI user device location together with at least one of a downlink (DL) -time difference of arrival (TDOA) method, a DL-angle or departure (AoD) method, or a multi-round trip time (RTT) method.

A thirty-first aspect includes any of the twenty-third through thirtieth aspects, and further includes wherein the communication node comprises the user device, wherein the control indication requests the user device to report an AI intermediate feature together with at least one of: a downlink (DL) -time difference of arrival (TDOA) method, a DL-angle of departure (AoD) method, or a multi-round trip time (RTT) method.

A thirty-second aspect includes the twenty-third aspect, and further includes wherein the control indication indicates at least one specific type of AI intermediate feature to be reported.

A thirty-third aspect includes any of the twenty-third through thirty-second aspects, and further includes wherein the communication node comprises the user device, wherein the control indication requests the user device to report a channel measurement of a received positioning reference signal (PRS) together with at least one of downlink (DL) -time difference of arrival (TDOA) method, a DL-angle of departure (AoD) method, or a multi-round trip time (RTT) method.

A thirty-fourth aspect includes the twenty-third aspect, and further includes wherein control indication indicates at least one specific type of channel measurement to be reported.

A thirty-fifth aspect includes any of the twenty-third through thirty-fourth aspects, and further includes wherein the communication node comprises the user device, wherein the control indication indicates that the LMF prefers the user device to report an AI user device location over other kinds of measurements, but allows the user device to provide a user device location estimate based on the non-AI positioning.

A thirty-sixth aspect includes any of the twenty-third through thirty-fifth aspects, and further includes wherein the communication node comprises the user device, wherein the control indication indicates that the LMF prefers the user device to report an AI user device location over other kinds of measurements, but allows the user device to provide an AI intermediate feature, and further indicates at least one preferred type of AI intermediate feature.

A thirty-seventh aspect includes any of the twenty-third through thirty-sixth aspects, and further includes wherein the communication node comprises the user device, wherein the control indication indicates that the LMF prefers the user device to report an AI user device location over other kinds of measurements, but allows the user device to provide a channel measurement, and further indicates at least one preferred type of channel measurement.

A thirty-eighth aspect includes any of the twenty-third through thirty-seventh aspects, and further includes wherein the control indication indicates that the LMF prefers the communication node to report an AI intermediate feature over other kinds of measurements, but allows the communication node to provide a measurement based on a non-AI positioning method.

A thirty-ninth aspect includes the thirty-eighth aspect, and further includes wherein the LMF indicates to the communication node at least one type of non-AI-based measurement that the LMF prefers, or wherein a non-AI-based measurement comprises a reference signal time difference (RSTD) when an AI intermediate feature is to be reported as the RSTD, or a non-AI-based measurement comprises a reference time of arrival (RTOA) when an AI intermediate feature is to be reported as the RTOA, or that a non-AI-based measurement comprises a receiver (Rx) -transmitter (Tx) time difference measurement when the AI intermediate feature is to be reported as the Rx-Tx time difference measurement.

A fortieth aspect includes any of the twenty-third through thirty-ninth aspects, and further includes wherein the control indication indicates that the LMF prefers the communication node to report an AI intermediate feature over other kinds of measurements, but allows the communication node to provide a channel measurement.

A forty-first aspect includes the fortieth aspect, and further includes wherein the LMF further indicates at least one preferred type of channel measurement.

A forty-second aspect includes any of the twenty-third through forty-first aspects, and further includes wherein the control indication indicates that the LMF prefers the communication node to report a channel measurement over other kinds of measurements, but allows the communication node to provide a measurement based on a non-AI positioning method.

A forty-third aspect includes the forty-second aspect, and further includes wherein the LMF further indicates at least one preferred type of measurement based on the non-AI positioning method.

A forty-fourth aspect includes any of the first through forty-third aspects, and further includes wherein the communication node comprises a user device, the method further comprising: reporting, by the user device, an error indication to the LMF, the error indication indicating that the user device at least one of: does not receive a sufficient amount of model training data to train a model; cannot generate an AI user device location using an AI model; cannot generate an AI intermediate feature using an AI model; cannot generate a channel measurement; or cannot download or acquire an AI model.

A forty-fifth aspect includes any of the first through forth-third aspects, and further includes wherein the communication node comprises a RAN node, the method further comprising: reporting, by the RAN node, an error indication to the LMF, the error indication indicating that the RAN node at least one of: does not receive a sufficient amount of model training data to train a model; cannot generate an AI intermediate feature using an AI model; cannot generate a channel measurement; or cannot download or acquire an AI model.

A forty-sixth aspect includes any of the first through forty-fifth aspects, and further includes: indicating, by the LMF, an error indication to the communication node, the error indication indicating that the LMF cannot process the channel measurement of the communication node.

A forty-seventh aspect includes a wireless communications apparatus comprising a processor and a memory, wherein the processor is configured to read code from the memory to implement any of the first through forty-sixth aspects.

A forty-eighth aspect includes a computer program product including a computer-readable program medium comprising code stored thereupon, the code, when executed by a processor, causing the processor to implement any of the first through forty-sixth aspects.

In addition to the features mentioned in each of the independent aspects enumerated above, some examples may show, alone or in combination, the optional features mentioned in the dependent aspects and/or as disclosed in the description above and shown in the figures.

Claims

A method for wireless communication, the method comprising:

sending, by a communication node, an on-demand artificial intelligence (AI) data set request to a location management function (LMF) ; and

receiving, by the communication node, one or more on-demand AI data sets from the LMF in response to the on-demand AI data set request.
A method for wireless communication, the method comprising:

receiving, by a location management function (LMF) , an on-demand artificial intelligence (AI) data set request from a communication node; and

transmitting, by the LMF, one or more on-demand AI data sets to the communication node in response to the on-demand AI data set request.
The method of any of claims 1 or 2, wherein the communication node comprises a user device or a radio access network (RAN) node.
The method of claim 3, wherein the communication node comprises the user device, the one or more on-demand AI data sets comprises one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request comprises a cell or a cell list indicating that the user device wants the LMF to provide an AI PRU data set derived within the cell or one or more cells in the cell list.
The method of claim 3, wherein the communication node comprises the user device, the one or more on-demand AI data sets comprises one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request comprises a value range of a number of measured transmission reception points (TRPs) , the value range indicating that the user device wants the LMF to provide a channel measurement of a user device or a PRU or an AI intermediate feature that satisfies the value range of the number of measured TRPs.
The method of claim 3, wherein the communication node comprises the user device, the one or more on-demand AI data sets comprises one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request comprises a value range of a TRP synchronization error, the value range indicating that the user device wants the LMF to provide a user device or PRU channel measurement or an AI intermediate feature that is derived from one or more TRPs that satisfy the value range of the TRP synchronization error.
The method of claim 3, wherein the communication node comprises the user device, the one or more on-demand AI data sets comprises one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request comprises a value range of a measurement quality, the value range indicating that the user device wants the LMF to provide a user device or PRU channel measurement, an AI intermediate feature, or a user device or PRU location that is associated with a measurement quality satisfying the value range.
The method of claim 3, wherein the communication node comprises the user device, the one or more on-demand AI data sets comprises one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request comprises a value range of velocity, indicating that the user device wants the LMF to provide an AI PRU data set that is derived by a PRU or a user device under the value range of velocity.
The method of claim 3, wherein the communication node comprises the user device, the one or more on-demand AI data sets comprises one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request comprises a preferred user device direction, indicating that the user device wants the LMF to provide an AI PRU data set that is derived by a PRU or a user device under the preferred user device direction.
The method of claim 3, wherein the communication node comprises the user device, the one or more on-demand AI data sets comprises one or more AI measurements of one or more positioning reference units (PRUs) or one or more user devices, and the on-demand AI data set request comprises a positioning reference signal (PRS) configuration, indicating that the user device wants the LMF to provide a channel measurement of a user device or a PRU or an AI intermediate feature that is derived based on the PRS configuration.
The method of claim 3, wherein the communication node comprises the user device, and the one or more on-demand AI data sets comprises one or more positioning reference signal (PRS) configurations, and the on-demand AI data set request comprises a cell or a cell list, indicating that the user device wants the LMF to provide an AI assistance data set comprising at least one transmission reception point (TRP) configuration and/or at least one PRS configuration, wherein the at least one TRP configuration and/or the at least one PRS configuration is located within the cell or one or more cells of the cell list.
The method of claim 3, wherein the communication node comprises the user device, the one or more on-demand AI data sets comprises one or more positioning reference signal (PRS) configurations, and the on-demand AI data set request comprises a value range of a first number of TRPs, indicating that the user device wants the LMF to provide an AI assistance data set that comprises PRS configurations derived from a second number of TRPs, where the second number of TRPs is within the value range of the first number of TRPs.
The method of claim 3, wherein the communication node comprises the user device, and the one or more on-demand AI data sets comprises one or more positioning reference signal (PRS) configurations, and the on-demand AI data set request comprises a value range of a TRP synchronization error, indicating that the user device wants the LMF to provide an AI assistance data set that comprises at least one TRP that satisfies the value range of the TRP synchronization error.
The method of claim 3, wherein the communication node comprises the RAN node, the one or more on-demand AI data sets comprises one or more AI measurements of one or more other RAN nodes, the on-demand AI data set request comprises an area, indicating that the RAN node wants the LMF to provide an AI other-node data set indicating one or more other RAN nodes or one or more transmission reception points (TRPs) that are within the area.
The method of claim 3, wherein the communication node comprises the RAN node, the one or more on-demand AI data sets comprises one or more AI measurements of one or more other RAN nodes, the on-demand AI data set request comprises a value range of a first number of measured TRPs, indicating that the RAN node wants the LMF to provide a RAN node channel measurement or a RAN node AI intermediate feature derived from a second number of measured TRPs, where the second number of measured TRPs is within the value range of the first number of measured TRPs.
The method of claim 3, wherein the communication node comprises the RAN node, the one or more on-demand AI data sets comprises one or more AI measurements of one or more other RAN nodes, the on-demand AI data set request comprises a value range of a TRP synchronization error, indicating that the RAN node wants the LMF to provide a RAN node channel measurement or a RAN node AI intermediate feature that is derived from at least one TRP that satisfies the value range of the TRP synchronization error.
The method of claim 3, wherein the communication node comprises the RAN node, the one or more on-demand AI data sets comprises one or more AI measurements of one or more other RAN nodes, the on-demand AI data set request comprises a value range of a measurement quality, indicating that the RAN node wants the LMF to provide a RAN node channel measurement or a RAN node AI intermediate feature associated with a measurement quality satisfying the value range.
The method of claim 3, wherein the communication node comprises the RAN node, the one or more on-demand AI data sets comprises one or more AI measurements of one or more other RAN nodes, the on-demand AI data set request comprises a sounding reference signal (SRS) configuration, indicating that the RAN node wants the LMF to provide a RAN node channel measurement or a RAN node AI intermediate feature that is derived based on the SRS configuration.
The method of claim 3, wherein the communication node comprises the RAN node, wherein each of the one or more on-demand AI data sets comprises at least one of: a sounding reference signal (SRS) configuration, one or more associated user device or positioning reference unit (PRU) identifications (IDs) , or one or more associated user device or PRU locations.
The method of claim 19, wherein the on-demand AI data set request comprises at least one of:

a cell or a cell list, indicating that the RAN node wants the LMF to provide an AI assistance data set, and a user device or a PRU that sends a SRS according to the SRS configuration in the AI assistance data set is within the cell or within one or more cells of the cell list; or

the SRS configuration, which the RAN node wants to measure to train or monitor an AI model.
A method for wireless communication, the method comprising:

receiving, by a communication node, a control indication of an AI functionality or an AI model from a location management function (LMF) ; and

activating or deactivating, by the communication node, the AI functionality or the AI model according to the control indication.
A method for wireless communication, the method comprising:

determining, by a location management function (LMF) , whether the LMF should activate or deactivate an artificial intelligence (AI) functionality or an AI model, or whether a communication node should activate or deactivate the AI functionality or the AI model; and

activating or deactivating, by the LMF, the AI functionality or the AI model, and/or transmitting, by the LMF, a control indication to the communication node, the control indication indicating at least one of:

whether the AI functionality or the AI model is to be activated or deactivated; or

that the communication node is allowed to switch from reporting a preferred measurement to reporting a different measurement.
The method of any of claims 21 or 22, wherein the communication node comprises a user device or a radio access network (RAN) node.
The method of claim 23, wherein the communication node comprises the user device, wherein the control indication indicates a first positioning method, wherein in the first positioning method, the LMF schedules the user device to report an AI user device location as user device-based positioning, and schedules an AI intermediate feature or a channel measurement of a received positioning reference signal (PRS) as user device-assisted positioning.
The method of claim 23, wherein the communication node comprises the user device, wherein the control indication indicates a second positioning method, wherein in the second positioning method, the LMF schedules the user device to report an AI user device location and/or an AI intermediate feature, wherein the user device reports the AI user device location as user device-based positioning, and/or the user device reports the AI intermediate feature as user device-assisted positioning.
The method of claim 23, wherein the communication node comprises the user device, wherein the control indication indicates a third positioning method, wherein in the third positioning method, the LMF schedules the user device to report a channel measurement of a received positioning reference signal (PRS) as user device-assisted positioning.
The method of claim 23, wherein the communication node comprises the user device, wherein the control indication indicates a fourth positioning method, wherein in the fourth positioning method, the LMF schedules the user device to report an AI user device location and/or a channel measurement of a received positioning reference signal (PRS) , wherein the user device reports the AI user device location as user device-based positioning, and/or the user device reports the channel measurement of a received positioning reference signal (PRS) as user device-assisted positioning.
The method of claim 23, wherein the communication node comprises the user device, wherein the control indication indicates a fifth positioning method, wherein in the fifth positioning method, the LMF schedules the user device to report an AI user device location as user device-based positioning.
The method of claim 23, wherein the communication node comprises the user device, wherein the control indication indicates a sixth positioning method, wherein in the sixth positioning method, the LMF schedules the user device to report an AI intermediate feature as user device-assisted positioning.
The method of claim 23, wherein the communication node comprises the user device, wherein the control indication requests the user device to report an AI user device location together with at least one of a downlink (DL) -time difference of arrival (TDOA) method, a DL-angle or departure (AoD) method, or a multi-round trip time (RTT) method.
The method of claim 23, wherein the communication node comprises the user device, wherein the control indication requests the user device to report an AI intermediate feature together with at least one of: a downlink (DL) -time difference of arrival (TDOA) method, a DL-angle of departure (AoD) method, or a multi-round trip time (RTT) method.
The method of claim 23, wherein the control indication indicates at least one specific type of AI intermediate feature to be reported.
The method of claim 23, wherein the communication node comprises the user device, wherein the control indication requests the user device to report a channel measurement of a received positioning reference signal (PRS) together with at least one of downlink (DL) -time difference of arrival (TDOA) method, a DL-angle of departure (AoD) method, or a multi-round trip time (RTT) method.
The method of claim 23, wherein control indication indicates at least one specific type of channel measurement to be reported.
The method of claim 23, wherein the communication node comprises the user device, wherein the control indication indicates that the LMF prefers the user device to report an AI user device location over other kinds of measurements, but allows the user device to provide a user device location estimate based on the non-AI positioning.
The method of claim 23, wherein the communication node comprises the user device, wherein the control indication indicates that the LMF prefers the user device to report an AI user device location over other kinds of measurements, but allows the user device to provide an AI intermediate feature, and further indicates at least one preferred type of AI intermediate feature.
The method of claim 23, wherein the communication node comprises the user device, wherein the control indication indicates that the LMF prefers the user device to report an AI user device location over other kinds of measurements, but allows the user device to provide a channel measurement, and further indicates at least one preferred type of channel measurement.
The method of claim 23, wherein the control indication indicates that the LMF prefers the communication node to report an AI intermediate feature over other kinds of measurements, but allows the communication node to provide a measurement based on a non-AI positioning method.
The method of claim 38, wherein the LMF indicates to the communication node at least one type of non-AI-based measurement that the LMF prefers, or wherein a non-AI-based measurement comprises a reference signal time difference (RSTD) when an AI intermediate feature is to be reported as the RSTD, or a non-AI-based measurement comprises a reference time of arrival (RTOA) when an AI intermediate feature is to be reported as the RTOA, or that a non-AI-based measurement comprises a receiver (Rx) -transmitter (Tx) time difference measurement when the AI intermediate feature is to be reported as the Rx-Tx time difference measurement.
The method of claim 23, wherein the control indication indicates that the LMF prefers the communication node to report an AI intermediate feature over other kinds of measurements, but allows the communication node to provide a channel measurement.
The method of claim 40, wherein the LMF further indicates at least one preferred type of channel measurement.
The method of claim 23, wherein the control indication indicates that the LMF prefers the communication node to report a channel measurement over other kinds of measurements, but allows the communication node to provide a measurement based on a non-AI positioning method.
The method of claim 42, wherein the LMF further indicates at least one preferred type of measurement based on the non-AI positioning method.
The method of any of claims 1 to 43, wherein the communication node comprises a user device, the method further comprising:

reporting, by the user device, an error indication to the LMF, the error indication indicating that the user device at least one of:

does not receive a sufficient amount of model training data to train a model;

cannot generate an AI user device location using an AI model;

cannot generate an AI intermediate feature using an AI model;

cannot generate a channel measurement; or

cannot download or acquire an AI model.
The method of any of claims 1 to 43, wherein the communication node comprises a RAN node, the method further comprising:

reporting, by the RAN node, an error indication to the LMF, the error indication indicating that the RAN node at least one of:

does not receive a sufficient amount of model training data to train a model;

cannot generate an AI intermediate feature using an AI model;

cannot generate a channel measurement; or

cannot download or acquire an AI model.
The method of any of claims 1 to 43, further comprising:

indicating, by the LMF, an error indication to the communication node, the error indication indicating that the LMF cannot process the channel measurement of the communication node.
A wireless communications apparatus comprising a processor and a memory, wherein the processor is configured to cause the apparatus to perform a method of any of claims 1 to 46.
A computer program product comprising a computer-readable program medium comprising code stored thereupon, the code, when executed by a processor, causing the processor to perform a method of any of claims 1 to 46.