Positioning model performance monitoring

Abstract

Aspects of the present disclosure generally relate to wireless communications. In some aspects, an apparatus may obtain a set of training measurement information associated with a user equipment (UE). The apparatus may obtain a training location value associated with the UE. The apparatus may provide training location values and a training set of measurement information for training a model using a machine learning (ML) technique, the model being trained to output location information based at least in part on the measurement information. Numerous other aspects are described.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This patent application claims priority to U.S. Provisional Patent Application No. 63 / 377,974, filed on September 30, 2022, entitled “POSITIONING MODEL PERFORMANCE MONITORING,” and U.S. Non-Provisional Patent Application No. 18 / 466,088, filed on September 13, 2023, entitled “POSITIONING MODEL PERFORMANCE MONITORING,” which are expressly incorporated by reference into the patent application. Technical Field

[0003] Aspects of the present invention generally relate to wireless communications and to techniques and apparatus for positioning model performance monitoring. Background Art

[0004] Wireless communication systems are widely deployed to provide a variety of telecommunication services, such as telephony, video, data, messaging, and broadcasting. Typical wireless communication systems may employ multiple access technologies that are capable of supporting communication with multiple users by sharing available system resources (e.g., bandwidth, transmit power, etc.). Examples of such multiple access technologies include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency division multiple access (FDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, single carrier frequency division multiple access (SC-FDMA) systems, time division synchronous code division multiple access (TD-SCDMA) systems, and long term evolution (LTE). LTE / LTE-Advanced is a collection of enhancements to the Universal Mobile Telecommunications System (UMTS) mobile standard released by the Third Generation Partnership Project (3GPP).

[0005] A wireless network may include one or more network nodes that support communication for wireless communication devices (e.g., a user equipment (UE) or multiple UEs). The UE may communicate with the network node via downlink communication and uplink communication. A "downlink" (or "DL") refers to a communication link from a network node to a UE, and an "uplink" (or "UL") refers to a communication link from a UE to a network node. Some wireless networks may support device-to-device communication, such as via a local link (e.g., a sidelink (SL), a wireless local area network (WLAN) link, and / or a wireless personal area network (WPAN) link, among other examples).

[0006] The above-mentioned multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different UEs to communicate at a city, country, region, and / or global level. New Radio (NR), which may be referred to as 5G, is a collection of enhancements to the LTE mobile standard released by 3GPP. NR is designed to better support mobile broadband Internet access by improving spectrum efficiency, reducing costs, improving services, utilizing new spectrum, and using orthogonal frequency division multiplexing (OFDM) with cyclic prefix (CP) (CP-OFDM) on the downlink, using CP-OFDM and / or single carrier frequency division multiplexing (SC-FDM) (also known as discrete Fourier transform spread OFDM (DFT-s-OFDM)) on the uplink to better integrate with other open standards, as well as supporting beamforming, multiple input multiple output (MIMO) antenna technology, and carrier aggregation. As the demand for mobile broadband access continues to increase, further improvements in LTE, NR, and other radio access technologies remain useful. Summary of the invention

[0007] Some aspects described herein relate to a method of wireless communication performed by an apparatus. The method may include obtaining a set of training measurement information associated with a user equipment (UE). The method may include obtaining a training location value associated with the UE. The method may include providing the training location value and the set of training measurement information for use in training a model using a machine learning (ML) technique, the model being trained to output location information based at least in part on measurement information.

[0008] Some aspects described herein relate to a method of wireless communication performed by a network node. The method may include obtaining training measurement information of a UE, or information associated with the training measurement information, the training measurement information being associated with training a model using an ML technique. The method may include outputting a training position value for the UE based at least in part on the training measurement information or information associated with the training measurement information.

[0009] Some aspects described herein relate to an apparatus for wireless communication. The apparatus may include a memory and one or more processors coupled to the memory. The one or more processors may be configured to obtain a set of training measurement information associated with a UE. The one or more processors may be configured to obtain a training location value associated with the UE. The one or more processors may be configured to provide the training location value and the training measurement information set for training a model using a machine learning (ML) technique, the model being trained to output location information based at least in part on measurement information.

[0010] Some aspects described herein relate to a network node for wireless communication. The network node may include a memory and one or more processors coupled to the memory. The one or more processors may be configured to obtain training measurement information of a UE, or information associated with the training measurement information, the training measurement information being associated with training a model using an ML technique. The one or more processors may be configured to output a training position value for the UE based at least in part on the training measurement information or information associated with the training measurement information.

[0011] Some aspects described herein relate to a non-transitory computer-readable medium storing an instruction set for wireless communication. When executed by one or more processors of a device, the instruction set may cause the one or more processors to obtain a training measurement information set associated with a UE. When executed by one or more processors, the instruction set may cause the device to obtain a training location value associated with the UE. The instruction set, when executed by one or more processors, may cause the device to provide a training location value and a training measurement information set for training a model using a machine learning (ML) technique, the model being trained to output location information based at least in part on the measurement information.

[0012] Some aspects described herein relate to a non-transitory computer-readable medium storing an instruction set for wireless communication by a network node. The instruction set, when executed by one or more processors of the network node, may cause the network node to obtain training measurement information of a UE or information associated with the training measurement information, the training measurement information being associated with training a model using an ML technique. When executed by one or more processors of the network node, the instruction set may cause the network node to output a training position value for the UE based at least in part on the training measurement information or information associated with the training measurement information.

[0013] Some aspects described herein relate to an apparatus for wireless communication. The apparatus may include: a component for obtaining a training measurement information set associated with a UE. The apparatus may include: a component for obtaining a training location value associated with the UE. The apparatus may include: a component for providing the training location value and the training measurement information set for training a model using a machine learning (ML) technique, the model being trained to output location information based at least in part on measurement information.

[0014] Some aspects described herein relate to an apparatus for wireless communication. The apparatus may include: a component for obtaining training measurement information of a UE, or information associated with the training measurement information, the training measurement information being associated with training a model using ML techniques. The apparatus may include: a component for outputting a training position value for the UE based at least in part on the training measurement information or information associated with the training measurement information.

[0015] Aspects generally include methods, apparatus, systems, computer program products, non-transitory computer-readable media, user equipment, base stations, network entities, network nodes, wireless communication devices, and / or processing systems as substantially described herein with reference to and as illustrated by the accompanying figures.

[0016] The features and technical advantages of the examples according to the present disclosure have been outlined quite extensively above so that the following detailed description may be better understood. Additional features and advantages will be described below. The disclosed concepts and specific examples may be easily used as a basis for modifying or designing other structures for the same purpose of performing the present disclosure. Such equivalent constructions do not depart from the scope of the appended claims. When considered in conjunction with the accompanying drawings, the characteristics of the concepts disclosed herein (both their organization and methods of operation) and the associated advantages will be better understood according to the following description. Each of the drawings is provided for the purpose of illustration and description and not as a definition of limitations to the claims.

[0017] Although various aspects are described in the present disclosure by illustrating some examples, it will be understood by those skilled in the art that these aspects can be implemented in many different arrangements and scenarios. The technology described herein can be implemented using different platform types, devices, systems, shapes, sizes and / or packaging arrangements. For example, some aspects can be implemented via integrated chip embodiments or other non-module component-based devices (e.g., end-user devices, vehicles, communication devices, computing devices, industrial equipment, retail / purchase devices, medical devices, and / or artificial intelligence devices). Various aspects can be implemented in chip-level components, modular components, non-modular components, non-chip-level components, device-level components, and / or system-level components. The device in combination with the described aspects and features may include additional components and features for the implementation and practice of the claimed and described aspects. For example, the transmission and reception of wireless signals may include one or more components for analog and digital purposes (e.g., hardware components including antennas, radio frequency (RF) chains, power amplifiers, modulators, buffers, processors, interleavers, adders, and / or summers). The various aspects described herein are intended to be practiced in various devices, components, systems, distributed arrangements, and / or end-user devices of various sizes, shapes, and configurations. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order that the above-mentioned features of the present disclosure may be understood in detail, a more specific description briefly summarized above may be obtained by reference to various aspects, some of which are illustrated in the accompanying drawings. However, it should be noted that the accompanying drawings illustrate only certain typical aspects of the present disclosure and therefore should not be considered as limiting the scope thereof, as the description may allow for other equally effective aspects. The same reference numerals in different drawings may identify the same or similar elements.

[0019] Figure 1 is a schematic diagram illustrating an example of a wireless network according to the present disclosure.

[0020] Figure 2 is a schematic diagram illustrating an example of a network node in communication with a user equipment (UE) in a wireless network according to the present disclosure.

[0021] Figure 3 is a schematic diagram illustrating an example disaggregated base station architecture according to the present disclosure.

[0022] Figure 4 is a schematic diagram illustrating an example architecture of a functional framework for RAN intelligence implemented through data collection according to the present disclosure.

[0023] Figure 5 is a schematic diagram illustrating an example of artificial intelligence and / or machine learning (AI / ML) based position determination according to the present disclosure.

[0024] Figure 6 is a schematic diagram illustrating an example of signaling associated with obtaining training position values ​​for training a model according to the present disclosure.

[0025] Figure 7 is a schematic diagram illustrating an example process, for example, performed by an apparatus according to the present disclosure.

[0026] Figure 8 is a schematic diagram illustrating an example process, for example, performed by a network node, according to the present disclosure.

[0027] Fig. 9 is a schematic diagram of an example apparatus for wireless communications according to the present disclosure.

[0028] Fig.10 is a schematic diagram of an example apparatus for wireless communications according to the present disclosure. DETAILED DESCRIPTION

[0029] Various aspects of the present disclosure are described more fully below with reference to the accompanying drawings. However, the present disclosure can be embodied in many different forms and should not be construed as being limited to any specific structure or function given throughout the present disclosure. On the contrary, these aspects are provided so that the present disclosure will be thorough and complete, and the scope of the present disclosure will be fully conveyed to those skilled in the art. It should be understood by those skilled in the art that the scope of the present disclosure is intended to cover any aspect of the present disclosure disclosed herein, whether it is realized independently of any other aspect of the present disclosure or realized in combination with any other aspect of the present disclosure. For example, any number of aspects set forth herein can be used to implement a device or practice method. In addition, the scope of the present disclosure is intended to cover such a device or method that is practiced using other structures, functions, or structures and functions other than the various aspects of the present disclosure set forth herein or different from the various aspects of the present disclosure set forth herein. It should be understood that any aspect of the present disclosure disclosed herein can be embodied by one or more elements of the claim.

[0030] Several aspects of telecommunication systems will now be presented with reference to various devices and techniques. These devices and techniques will be described in the detailed description below and illustrated in the accompanying drawings by various blocks, modules, components, circuits, steps, processes, algorithms, etc. (collectively referred to as "elements"). These elements may be implemented using hardware, software, or a combination thereof. Whether these elements are implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system.

[0031] Although various aspects may be described herein using terminology generally associated with 5G or new radio (NR) radio access technologies (RATs), various aspects of the present disclosure may be applied to other RATs, such as 3G RATs, 4G RATs, and / or post-5G RATs (e.g., 6G).

[0032] Figure 11 is a schematic diagram illustrating an example of a wireless network 100 according to the present disclosure. The wireless network 100 may be or may include elements of a 5G (e.g., NR) network and / or a 4G (e.g., Long Term Evolution (LTE)) network, among other examples. The wireless network 100 may include one or more network nodes 110 (shown as network node 110a, network node 110b, network node 110c, and network node 110d), a user equipment (UE) 120 or multiple UEs 120 (shown as UE 120a, UE 120b, UE 120c, UE 120d, and UE 120e), and / or other entities. The network node 110 is a network node that communicates with the UE 120. As shown, the network node 110 may include one or more network nodes. For example, the network node 110 may be an aggregated network node, which means that the aggregated network node is configured to utilize a radio protocol stack that is physically or logically integrated within a single radio access network (RAN) node (e.g., within a single device or unit). As another example, the network node 110 may be a disaggregated network node (sometimes referred to as a disaggregated base station), meaning that the network node 110 is configured to utilize a protocol stack that is physically or logically distributed between two or more nodes, such as one or more central units (CUs), one or more distributed units (DUs), or one or more radio units (RUs).

[0033] In some examples, the network node 110 is or includes a network node that communicates with the UE 120 via a radio access link, such as a RU. In some examples, the network node 110 is or includes a network node that communicates with other network nodes 110 via a fronthaul link or a midhaul link, such as a DU. In some examples, the network node 110 is or includes a network node that communicates with other network nodes 110 via a midhaul link or communicates with the core network via a backhaul link, such as a CU. In some examples, the network node 110 (such as an aggregation network node 110 or a disaggregation network node 110) may include multiple network nodes, such as one or more RUs, one or more CUs, and / or one or more DUs. The network node 110 may include, for example, an NR base station, an LTE base station, a Node B, an eNB (e.g., in 4G), a gNB (e.g., in 5G), an access point, a transmit receive point (TRP), a DU, a RU, a CU, a mobility element of a network, a core network node, a network element, a network device, a RAN node, or a combination thereof. In some examples, network nodes 110 may be interconnected to each other or to one or more other network nodes 110 in wireless network 100 using any suitable transport network through various types of fronthaul, midhaul, and / or backhaul interfaces, such as direct physical connections, air interfaces, or virtual networks.

[0034] In some examples, the network node 110 may provide communication coverage for a specific geographic area. In the Third Generation Partnership Project (3GPP), the term "cell" may refer to the coverage area of ​​the network node 110 and / or the network node subsystem serving the coverage area, depending on the context in which the term is used. The network node 110 may provide communication coverage for a macro cell, a micro cell, a femto cell, and / or another type of cell. A macro cell may cover a relatively large geographic area (e.g., an area with a radius of several kilometers) and may allow unrestricted access by a UE 120 with a service subscription. A micro cell may cover a relatively small geographic area and may allow unrestricted access by a UE 120 with a service subscription. A femto cell may cover a relatively small geographic area (e.g., a home) and may allow restricted access by a UE 120 associated with the femto cell (e.g., a UE 120 in a closed subscriber group (CSG)). A network node 110 for a macro cell may be referred to as a macro network node. A network node 110 for a micro cell may be referred to as a micro network node. The network node 110 for a femto cell may be referred to as a femto network node or an in-home network node. Figure 1 In the example shown in , network node 110a may be a macro network node for macro cell 102a, network node 110b may be a pico network node for pico cell 102b, and network node 110c may be a femto network node for femto cell 102c. The network node may support one or more (e.g., three) cells. In some examples, the cell may not necessarily be stationary, and the geographic area of ​​the cell may move according to the location of the mobile network node 110 (e.g., a mobile network node).

[0035] In some aspects, the term "base station" or "network node" may refer to an aggregated base station, a disaggregated base station, an integrated access and backhaul (IAB) node, a relay node, or one or more components thereof. For example, in some aspects, a "base station" or "network node" may refer to a CU, a DU, a RU, a near-real-time (Near-Real Time, near-RT) RAN Intelligent Controller (RANIntelligent Controller, RIC), or a non-real-time (non-RT) RIC, or a combination thereof. In some aspects, the term "base station" or "network node" may refer to a device configured to perform one or more functions (such as those described herein in conjunction with network node 110). In some aspects, the term "base station" or "network node" may refer to multiple devices configured to perform one or more functions. For example, in some distributed systems, each of a plurality of different devices (which may be located in the same geographic location or in different geographic locations) may be configured to perform at least a portion of a function, or to replicate the execution of at least a portion of a function, and the term "base station" or "network node" may refer to any one or more of those different devices. In some aspects, the term "base station" or "network node" may refer to one or more virtual base stations or one or more virtual base station functions. For example, in some aspects, two or more base station functions may be instantiated on a single device. In some aspects, the term "base station" or "network node" may refer to one of the base station functions but not another. In this manner, a single device may include more than one base station.

[0036] The wireless network 100 may include one or more relay stations. A relay station is a network node that can receive data transmissions from an upstream node (e.g., a network node 110 or a UE 120) and send data transmissions to a downstream node (e.g., a UE 120 or a network node 110). A relay station may be a UE 120 that is capable of relaying transmissions for other UEs 120. Figure 1 In the example shown, a network node 110d (e.g., a relay network node) may communicate with a network node 110a (e.g., a macro network node) and a UE 120d to facilitate communications between the network node 110a and the UE 120d. A network node 110 that relays communications may be referred to as a relay station, a relay base station, a relay network node, a relay node, a relay, etc.

[0037] The wireless network 100 may be a heterogeneous network that includes different types of network nodes 110, such as macro network nodes, pico network nodes, femto network nodes, relay network nodes, etc. These different types of network nodes 110 may have different transmit power levels, different coverage areas, and / or different impacts on interference in the wireless network 100. For example, a macro network node may have a high transmit power level (e.g., 5 to 40 Watts), while a pico network node, a femto network node, and a relay network node may have a lower transmit power level (e.g., 0.1 to 2 Watts).

[0038] The network controller 130 may be coupled to or communicate with the network node set 110 and may provide coordination and control for these network nodes 110. The network controller 130 may communicate with the network nodes 110 via a backhaul communication link or a midhaul communication link. The network nodes 110 may communicate with each other directly or indirectly via a wireless or wired backhaul communication link. In some aspects, the network controller 130 may be a CU or a core network device, or may include a CU or a core network device.

[0039] UE 120 can be dispersed throughout the wireless network 100, and each UE 120 can be stationary or mobile. UE 120 can include, for example, access terminals, terminals, mobile stations and / or subscriber units. UE 120 can be a cellular phone (e.g., a smart phone), a personal digital assistant (PDA), a wireless modem, a wireless communication device, a handheld device, a laptop computer, a cordless phone, a wireless local loop (WLL) station, a tablet computer, a camera, a gaming device, a netbook, a smartbook, an ultrabook, a medical device, a biometric device, a wearable device (e.g., a smart watch, smart clothing, smart glasses, a smart wristband, smart jewelry (e.g., a smart ring or a smart bracelet)), an entertainment device (e.g., a music device, a video device and / or a satellite radio), a vehicle component or sensor, a smart meter / sensor, an industrial manufacturing device, a global positioning system device, a UE function of a network node, and / or any other suitable device configured to communicate via a wireless or wired medium.

[0040] Some UEs 120 may be considered as machine type communication (MTC) or evolved or enhanced machine type communication (eMTC) UEs. MTC UEs and / or eMTC UEs may include, for example, robots, drones, remote devices, sensors, meters, monitors, and / or location tags, which may communicate with a network node, another device (e.g., a remote device), or some other entity. Some UEs 120 may be considered as Internet of Things (IoT) devices, and / or may be implemented as NB-IoT (Narrowband IoT) devices. Some UEs 120 may be considered as client equipment (Customer Premises Equipment). UE 120 may be included in a housing that houses components of UE 120 (such as processor components and / or memory components). In some examples, the processor component and the memory component may be coupled together. For example, a processor component (e.g., one or more processors) and a memory component (e.g., a memory) may be operably coupled, communicatively coupled, electronically coupled, and / or electrically coupled.

[0041] In general, any number of wireless networks 100 may be deployed in a given geographic area. Each wireless network 100 may support a specific RAT and may operate on one or more frequencies. RAT may be referred to as a radio technology, air interface, etc. Frequency may be referred to as a carrier, frequency channel, etc. Each frequency may support a single RAT in a given geographic area to avoid interference between wireless networks of different RATs. In some cases, NR or 5G RAT networks may be deployed.

[0042] In some examples, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) may communicate directly (e.g., without using network node 110 as an intermediary for communicating with each other) using one or more sidelink channels. For example, UE 120 may communicate using peer-to-peer (P2P) communication, device-to-device (D2D) communication, vehicle-to-everything (V2X) protocol (e.g., which may include vehicle-to-vehicle (V2V) protocol, vehicle-to-infrastructure (V2I) protocol, or vehicle-to-pedestrian (V2P) protocol), and / or mesh network. In such examples, UE 120 may perform scheduling operations, resource selection operations, and / or other operations described elsewhere herein as being performed by network node 110.

[0043] The devices of the wireless network 100 may communicate using an electromagnetic spectrum that may be subdivided into various categories, bands, channels, etc. by frequency or wavelength. For example, the devices of the wireless network 100 may communicate using one or more operating bands. In 5G NR, two initial operating bands have been identified with the frequency range names FR1 (410 MHz-7.125 GHz) and FR2 (24.25 GHz-52.6 GHz). It should be understood that, although a portion of FR1 is greater than 6 GHz, FR1 is often referred to (interchangeably) as a "below 6 GHz" band in various literature and articles. A similar naming issue sometimes occurs with respect to FR2, which is often referred to (interchangeably) as a "millimeter wave" band in literature and articles, although it is different from the extremely high frequency (EHF) band (30 GHz-300 GHz) identified as a "millimeter wave" band by the International Telecommunication Union (ITU).

[0044] Frequencies between FR1 and FR2 are often referred to as mid-band frequencies. Recent 5G NR research has identified operating bands for these mid-band frequencies as the frequency range designation FR3 (7.125 GHz-24.25 GHz). Frequency bands that fall within FR3 may inherit FR1 characteristics and / or FR2 characteristics, and thus may effectively extend the characteristics of FR1 and / or FR2 to mid-band frequencies. In addition, higher frequency bands are currently being explored to extend 5G NR operations beyond 52.6 GHz. For example, three higher operating bands have been identified as the frequency range designations FR4a or FR4-1 (52.6 GHz-71 GHz), FR4 (52.6 GHz-114.25GHz), and FR5 (114.25 GHz-300 GHz). Each of these higher frequency bands falls within the EHF band.

[0045] With the above examples in mind, unless otherwise specifically stated, it should be understood that the term "below 6 GHz," etc., if used herein, may broadly refer to frequencies that may be less than 6 GHz, may be within FR1, or may include mid-band frequencies. Additionally, unless otherwise specifically stated, it should be understood that the term "millimeter wave," etc., if used herein, may broadly refer to frequencies that may include mid-band frequencies, may be within FR2, FR4, FR4-a or FR4-1, and / or FR5, or may be within the EHF band. It is contemplated that the frequencies included in these operating bands (e.g., FR1, FR2, FR3, FR4, FR4-a, FR4-1, and / or FR5) may be modified, and the techniques described herein are applicable to those modified frequency ranges.

[0046] In some aspects, the UE may include a communications manager 140. As described in more detail elsewhere herein, the communications manager 140 may obtain a set of training measurement information associated with the UE; obtain a training location value associated with the UE; and provide the training location value and the set of training measurement information for use in training a model using machine learning (ML) techniques, the model being trained to output location information based at least in part on the measurement information. Additionally or alternatively, the communications manager 140 may perform one or more other operations described herein.

[0047] In some aspects, the network node 110 may include a communication manager 150. As described in more detail elsewhere herein, the communication manager 150 may obtain training measurement information of the UE or information associated with the training measurement information, the training measurement information being associated with training a model using ML techniques; and output a training position value for the UE based at least in part on the training measurement information or information associated with the training measurement information. Additionally or alternatively, the communication manager 150 may perform one or more other operations described herein.

[0048] As mentioned above, Figure 1 are provided as examples. Other examples can be found in the Figure 1 The examples described are different.

[0049] Figure 2 2 is a schematic diagram showing an example 200 in which a network node 110 and a UE 120 are in communication in a wireless network 100 according to the present disclosure. The network node 110 may be equipped with a set of antennas 234a to 234t, such as T antennas (T≥1). The UE 120 may be equipped with a set of antennas 252a to 252r, such as R antennas (R≥1). The network node 110 of example 200 includes one or more radio frequency components, such as an antenna 234 and a modem 254. In some examples, the network node 110 may include an interface, a communication component, or another component that facilitates communication with the UE 120 or another network node. Some network nodes 110 may not include radio frequency components that facilitate direct communication with the UE 120, such as one or more CUs or one or more DUs.

[0050] At the network node 110, a transmit processor 220 may receive data intended for a UE 120 (or a set of UEs 120) from a data source 212. The transmit processor 220 may select one or more modulation and coding schemes (MCS) for the UE 120 based at least in part on one or more channel quality indicators (CQIs) received from the UE 120. The network node 110 may process (e.g., encode and modulate) the data for the UE 120 based at least in part on the MCS selected for the UE 120, and may provide data symbols for the UE 120. The transmit processor 220 may process system information (e.g., for semi-static resource partitioning information (SRPI)) and control information (e.g., CQI requests, grants, and / or upper layer signaling), and provide overhead symbols and control symbols. The transmit processor 220 may generate reference symbols for reference signals (e.g., a reference signal (CRS) or a demodulation reference signal (DMRS) for a specific cell) and synchronization signals (e.g., a primary synchronization signal (PSS) or a secondary synchronization signal (SSS)). The transmit (TX) multiple-input multiple-output (MIMO) processor 230 may perform spatial processing (e.g., precoding) on ​​data symbols, control symbols, overhead symbols, and / or reference symbols, where applicable, and may obtain a set of output symbol streams (e.g., T output symbol streams) to a corresponding set of modems 232 (e.g., T modems) (shown as modems 232a to 232t). For example, each output symbol stream may be provided to a modulator component (shown as MOD) of the modem 232. Each modem 232 may process a corresponding output symbol stream (e.g., for OFDM) using a corresponding modulator component to obtain an output sample stream. Each modem 232 may also process (e.g., convert to analog, amplify, filter, and / or upconvert) the output sample stream using a corresponding modulator component to obtain a downlink signal. The modems 232a through 232t may transmit a set of downlink signals (eg, T downlink signals) via a corresponding set of antennas 234 (eg, T antennas) (shown as antennas 234a through 234t).

[0051] At the UE 120, a set of antennas 252 (shown as antennas 252a to 252r) may receive downlink signals from the network node 110 and / or other network nodes 110, and may obtain a set of received signals (e.g., R received signals) to a set of modems 254 (e.g., R modems) (shown as modems 254a to 254r). For example, each received signal may be provided to a demodulator component (shown as DEMOD) of the modem 254. Each modem 254 may use a corresponding demodulator component to condition (e.g., filter, amplify, downconvert, and / or digitize) the received signal to obtain input samples. Each modem 254 may use a demodulator component to further process the input samples (e.g., for OFDM) to obtain received symbols. A MIMO detector 256 may obtain received symbols from the modem 254, may perform MIMO detection on the received symbols where applicable, and may provide detected symbols. The receive processor 258 may process (e.g., demodulate and decode) detected symbols, may provide decoded data of the UE 120 to a data sink 260, and may provide decoded control information and system information to a controller / processor 280. The term "controller / processor" may refer to one or more controllers, one or more processors, or a combination thereof. The channel processor may determine a reference signal received power (RSRP) parameter, a received signal strength indicator (RSSI) parameter, a reference signal received quality (RSRQ) parameter, and / or a CQI parameter, among other examples. In some examples, one or more components of the UE 120 may be included in a housing 284.

[0052] The network controller 130 may include a communication unit 294, a controller / processor 290, and a memory 292. The network controller 130 may include, for example, one or more devices in a core network. The network controller 130 may communicate with the network node 110 via the communication unit 294.

[0053] One or more antennas (e.g., antennas 234a through 234t and / or antennas 252a through 252r) may include, or may be included in, one or more antenna panels, one or more antenna groups, one or more antenna element sets, and / or one or more antenna arrays, among other examples. Antenna panels, antenna groups, antenna element sets, and / or antenna arrays may include one or more antenna elements (within a single housing or multiple housings), coplanar antenna element sets, non-coplanar antenna element sets, and / or antenna elements coupled to one or more transmit and / or receive components (e.g., Figure 2 One or more antenna elements of one or more components).

[0054] On the uplink, at the UE 120, a transmit processor 264 may receive and process data from a data source 262 and control information from a controller / processor 280 (e.g., for reports including RSRP, RSSI, RSRQ, and / or CQI). The transmit processor 264 may generate reference symbols for one or more reference signals. The symbols from the transmit processor 264 may be precoded by the TX MIMO processor 266, where applicable, further processed by the modem 254 (e.g., for DFT-s-OFDM or CP-OFDM), and sent to the network node 110. In some examples, the modem 254 of the UE 120 may include a modulator and a demodulator. In some examples, the UE 120 includes a transceiver. The transceiver may include any combination of an antenna 252, a modem 254, a MIMO detector 256, a receive processor 258, a transmit processor 264, and / or a TX MIMO processor 266. The transceiver may be used by a processor (eg, controller / processor 280) and memory 282 to perform the operations described herein (eg, with reference to Figure 4-Figure 10 )

[0055] At the network node 110, uplink signals from the UE 120 and / or other UEs may be received by an antenna 234, processed by a modem 232 (e.g., a demodulator component of the modem 232, shown as DEMOD), detected by a MIMO detector 236 where applicable, and further processed by a receive processor 238 to obtain decoded data and control information sent by the UE 120. The receive processor 238 may provide the decoded data to a data sink 239 and the decoded control information to a controller / processor 240. The network node 110 may include a communication unit 244 and may communicate with the network controller 130 via the communication unit 244. The network node 110 may include a scheduler 246 to schedule one or more UEs 120 for downlink and / or uplink communications. In some examples, the modem 232 of the network node 110 may include a modulator and a demodulator. In some examples, the network node 110 includes a transceiver. The transceiver may include any combination of antenna 234, modem 232, MIMO detector 236, receive processor 238, transmit processor 220, and / or TX MIMO processor 230. The transceiver may be used by a processor (e.g., controller / processor 240) and memory 242 to perform operations described herein (e.g., with reference to Figure 4-Figure 10 )

[0056] The controller / processor 240 of the network node 110, the controller / processor 280 of the UE 120, and / or Figure 2Any other components of the network node 110 may perform one or more techniques associated with positioning model performance monitoring, as described in more detail elsewhere herein. For example, the controller / processor 240 of the network node 110, the controller / processor 280 of the UE 120, and / or Figure 2 Any other component of the can perform or direct e.g. Figure 7 The process of 700 Figure 8 800 and / or other processes as described herein. Memory 242 and memory 282 may store data and program codes for network node 110 and UE 120, respectively. In some examples, memory 242 and / or memory 282 may include a non-transitory computer-readable medium storing one or more instructions (e.g., code and / or program code) for wireless communication. For example, the one or more instructions, when executed by one or more processors of network node 110 and / or UE 120 (e.g., directly or after compilation, conversion, and / or interpretation), may cause the one or more processors, UE 120, and / or network node 110 to perform or direct, for example, Figure 7 The process of 700 Figure 8 The process 800 and / or operations of other processes as described herein. In some examples, executing instructions may include running instructions, converting instructions, compiling instructions, and / or interpreting instructions, among other examples.

[0057] In some aspects, an apparatus (e.g., a UE) includes: a component for obtaining a set of training measurement information associated with the UE; a component for obtaining a training location value associated with the UE; and / or a component for providing the training location value and the set of training measurement information for training a model using ML techniques, the model being trained to output location information based at least in part on the measurement information. In some aspects, the components of the apparatus performing the operations described herein may include, for example, one or more of the following: a communication manager 140, an antenna 252, a modem 254, a MIMO detector 256, a receive processor 258, a transmit processor 264, a TX MIMO processor 266, a controller / processor 280, or a memory 282.

[0058] In some aspects, the network node 110 includes: a component for obtaining training measurement information of the UE or information associated with the training measurement information, the training measurement information being associated with training the model using the ML technique; and / or a component for outputting a training position value for the UE based at least in part on the training measurement information or information associated with the training measurement information. The components of the network node 110 that perform the operations described herein may include, for example, one or more of the following: a communication manager 150, a transmit processor 220, a TX MIMO processor 230, a modem 232, an antenna 234, a MIMO detector 236, a receive processor 238, a controller / processor 240, a memory 242, or a scheduler 246.

[0059] Although Figure 2 The blocks in the 200 and 210 are shown as distinct components, but the functionality described above with respect to the blocks may be implemented in a single hardware, software, or combined component or in various combinations of components. For example, the functionality described with respect to the transmit processor 264, the receive processor 258, and / or the TX MIMO processor 266 may be performed by or under the control of the controller / processor 280.

[0060] As mentioned above, Figure 2 are provided as examples. Other examples can be found in the Figure 2 The examples described are different.

[0061] The deployment of a communication system (such as a 5G NR system) can be arranged in a variety of ways with various components or constituent parts. In a 5G NR system or network, a network node, a network entity, a mobility element of a network, a RAN node, a core network node, a network element, a base station, or a network device can be implemented in an aggregated or disaggregated architecture. For example, a base station (such as a Node B (NB), an evolved NB (eNB), an NR BS, a 5G NB, an access point (AP), a TRP, or a cell, among other examples) or one or more units (or one or more components) performing base station functions can be implemented as an aggregated base station (also referred to as an independent base station or a monolithic base station) or a disaggregated base station. A "network entity" or "network node" may refer to a disaggregated base station, or one or more units of a disaggregated base station (e.g., one or more CUs, one or more DUs, one or more RUs, or a combination thereof).

[0062] An aggregated base station (e.g., an aggregated network node) may be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node (e.g., within a single device or unit). A disaggregated base station (e.g., a disaggregated network node) may be configured to utilize a protocol stack that is physically or logically distributed between two or more units (e.g., one or more CUs, one or more DUs, or one or more RUs). In some examples, the CU may be implemented within a network node, and one or more DUs may be co-located with the CU, or alternatively, may be geographically or virtually distributed across one or more other network nodes. The DU may be implemented to communicate with one or more RUs. Each of the CU, DU, and RU may also be implemented as a virtual unit, such as a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU), as well as other examples.

[0063] Base station type operation or network design may take into account the aggregated nature of base station functions. For example, a disaggregated base station may be utilized in an IAB network, an open radio access network (O-RAN (such as a network configuration facilitated by the O-RAN Alliance (sponsor)) or a virtualized radio access network (vRAN, also known as a cloud radio access network (C-RAN)) to facilitate expansion of the communication system by dividing the base station functions into one or more units that can be deployed separately. A disaggregated base station may include functions implemented by two or more units at various physical locations, as well as functions implemented virtually for at least one unit, which may enable flexibility in network design. The various units of the disaggregated base station may be configured for wired or wireless communication with at least one other unit of the disaggregated base station.

[0064] Figure 3 3 is a diagram illustrating an example disaggregated base station architecture 300 according to the present disclosure. The disaggregated base station architecture 300 may include a CU 310, which may communicate directly with a core network 320 via a backhaul link, or indirectly with the core network 320 through one or more disaggregated control units (such as, a near RT RIC 325 via an E2 link, or a non-RT RIC 315 associated with a service management and orchestration (SMO) framework 305, or both). The CU 310 may communicate with one or more DUs 330 via respective midhaul links (such as, via an F1 interface). Each of the DUs 330 may communicate with one or more RUs 340 via respective fronthaul links. Each of the RUs 340 may communicate with one or more UEs 120 via respective radio frequency (RF) access links. In some embodiments, a UE 120 may be served simultaneously by multiple RUs 340.

[0065] Each unit (including CU 310, DU 330, RU 340, near-RT RIC 325, non-RT RIC 315, and SMO framework 305) may include one or more interfaces, or be coupled to one or more interfaces configured to receive or send signals, data, or information (collectively referred to as signals) via a wired or wireless transmission medium. Each unit or an associated processor or controller that provides instructions to one or more communication interfaces of the corresponding unit may be configured to communicate with one or more other units via a transmission medium. In some examples, each unit may include a wired interface and a wireless interface, the wired interface being configured to receive or send signals to one or more other units via a wired transmission medium, the wireless interface may include a receiver, a transmitter, or a transceiver (such as an RF transceiver), and the wireless interface being configured to receive or send signals or both to one or more other units via a wireless transmission medium.

[0066] In some aspects, CU 310 may host one or more high-level control functions. Such control functions may include, among other examples, radio resource control (RRC) functions, packet data convergence protocol (PDCP) functions, or service data adaptation protocol (SDAP) functions, as well as other examples. Each control function may be implemented using an interface configured to transmit signals with other control functions hosted by CU310. CU 310 may be configured to process user plane functions (e.g., central unit-user plane (CU-UP) functions), control plane functions (e.g., central unit-control plane (CU-CP) functions), or a combination thereof. In some embodiments, CU 310 may be logically split into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, the CU-UP unit may communicate bidirectionally with the CU-CP unit via an interface (e.g., an E1 interface). CU 310 may be implemented to communicate with DU 330 as needed for network control and signaling.

[0067] Each DU 330 may correspond to a logical unit including one or more base station functions for controlling the operation of one or more RUs 340. In some aspects, the DU 330 may host one or more of a radio link control (RLC) layer, a MAC layer, and one or more high physical (PHY) layers, at least in part, depending on a functional split (such as a functional split defined by 3GPP). In some aspects, one or more high PHY layers may be implemented by one or more modules for forward error correction (FEC) encoding and decoding, scrambling and modulation and demodulation, and other examples. In some aspects, the DU 330 may further host one or more low PHY layers, such as by one or more modules for fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming, or physical random access channel (PRACH) extraction and filtering, and other examples. Each layer (which may also be referred to as a module) may be implemented using an interface configured to transmit signals with other layers (and modules) hosted by the DU 330 or with a control function hosted by the CU 310.

[0068] Each RU 340 may implement low-layer functions. In some deployments, a RU 340 controlled by a DU 330 may correspond to a logical node that hosts RF processing functions or low PHY layer functions, such as performing FFT, performing iFFT, digital beamforming, or PRACH extraction and filtering based on functional partitioning (e.g., functional partitioning defined by 3GPP) (e.g., low-layer functional partitioning), and other examples. In such an architecture, each RU 340 may be operated to handle over-the-air (OTA) communications with one or more UEs 120. In some embodiments, real-time and non-real-time aspects of control and user plane communications with the RU 340 may be controlled by the corresponding DU 330. In some scenarios, this configuration may enable each DU 330 and CU 310 to be implemented in a cloud-based RAN architecture (such as a vRAN architecture).

[0069] The SMO framework 305 may be configured to support RAN deployment and provisioning of non-virtualized and virtualized network elements. For non-virtualized network elements, the SMO framework 305 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which may be managed via an operation and maintenance interface, such as an O1 interface. For virtualized network elements, the SMO framework 305 may be configured to interact with a cloud computing platform, such as an open cloud (O-cloud) platform 390, to perform network element lifecycle management (such as instantiating virtualized network elements) via a cloud computing platform interface, such as an O2 interface. Such virtualized network elements may include, but are not limited to, CU 310, DU 330, RU 340, non-RT RIC 315, and near-RT RIC 325. In some embodiments, the SMO framework 305 may communicate with hardware aspects of the 4G RAN, such as an open eNB (O-eNB) 311, via an O1 interface. In addition, in some embodiments, the SMO framework 305 may communicate directly with each of the one or more RUs 340 via a corresponding O1 interface. The SMO framework 305 may also include a non-RT RIC 315 configured to support the functionality of the SMO framework 305 .

[0070] The non-RT RIC 315 may be configured to include logic functions that implement non-real-time control and optimization of RAN elements and resources, artificial intelligence / machine learning (AI / ML) workflows including model training and updating, or policy-based guidance of applications / features in the near-RT RIC 325. The non-RT RIC 315 may be coupled to or in communication with the near-RT RIC 325 (e.g., via an A1 interface). The near-RT RIC 325 may be configured to include logic functions that implement near-real-time control and optimization of RAN elements and resources via data collection and actions via an interface that connects one or more CUs 310, one or more DUs 330, or both, and an O-eNB to the near-RT RIC 325, such as via an E2 interface.

[0071] In some embodiments, in order to generate an AI / ML model to be deployed in the near-RT RIC 325, the non-RT RIC 315 may receive parameters or external enrichment information from an external server. Such information may be utilized by the near-RT RIC 325 and may be received from a non-network data source or from a network function at the SMO framework 305 or the non-RT RIC 315. In some examples, the non-RT RIC 315 or the near-RT RIC 325 may be configured to adjust RAN behavior or performance. For example, the non-RT RIC 315 may monitor long-term trends and patterns of performance and employ AI / ML models to perform corrective actions through the SMO framework 305 (such as via reconfiguration of the O1 interface) or via the creation of RAN management policies (such as A1 interface policies).

[0072] As mentioned above, Figure 3 are provided as examples. Other examples can be found in the Figure 3 The examples described are different.

[0073] Figure 4 is a diagram of an example architecture 400 for a functional framework for RAN intelligence implemented through data collection according to the present disclosure. In some scenarios, data collection can be further enhanced by use cases and / or examples to enable the functional framework for RAN intelligence. For example, principles or algorithms for RAN intelligence implemented by AI / ML and associated functional frameworks (e.g., inputs / outputs of AI functions and / or components for AI-implemented optimization) have been utilized or studied to identify the benefits of AI-implemented RAN through possible use cases (e.g., beam management, energy saving, load balancing, mobility management, and / or coverage optimization, among other examples). In one example, as shown in the architecture 400, the functional framework for RAN intelligence may include multiple logical entities, such as a model training host 402, a model reasoning host 404, a data source 406, and an actor 408.

[0074] The model inference host 404 may be configured to run an AI / ML model based on the inference data provided by the data source 406, and the model inference host 404 may generate output (e.g., prediction) using the inference data input to the actor 408. The actor 408 may be an element or entity of the core network or RAN. For example, the actor 408 may be a UE, a network node, a base station (e.g., a gNB), a CU, a DU, and / or a RU, among other examples. In addition, the actor 408 may also depend on the type of task performed by the model inference host 404, the type of inference data provided to the model inference host 404, and / or the type of output generated by the model inference host 404. For example, if the output from the model inference host 404 is associated with location determination, the actor 408 may be a UE, a DU, or a RU. In some examples, the model inference host 404 may be hosted on the actor 408. For example, a UE may be an actor 408 and may host the model inference host 404. In some aspects, the UE (e.g., the actor 408) may be the data source 406. For example, the UE may perform measurements (e.g., NR measurements), may input the measurements into an AI / ML model at the model inference host 404 (or may provide the measurements to the model inference host 404), and may act based on the output of the AI / ML model (e.g., location information).

[0075] After actor 408 receives the output from model inference host 404, actor 408 may determine whether to act based on the output. For example, if actor 408 is a UE and the output from model inference host 404 is associated with location information, actor 408 may determine whether to report the location information, reconfigure a beam, etc. If actor 408 determines an action based on the output, in some examples, actor 408 may instruct at least one action subject 410 on the action.

[0076] The data source 406 may also be configured to collect data for use as training data for training the ML model or as inference data for feeding the ML model inference operation. For example, the data source 406 may collect data from one or more core network and / or RAN entities (which may include actors 408 or action subjects 410) and provide the collected data to the model training host 402 for use in ML model training. In some aspects, the model training host 402 may be co-located with the model inference host 404 and / or the actors 408. For example, the actors 408 or action subjects 410 may provide performance feedback associated with the beam configuration to the data source 406, wherein the performance feedback may be used by the model training host 402 to monitor or evaluate ML model performance, such as whether the output (e.g., prediction) provided to the actor 408 is accurate. In some examples, the model training host 402 may monitor or evaluate ML model performance using training position values, which may be provided by a node (e.g., UE 120 or network node 110), as described elsewhere herein. In some examples, if the output provided by actor 408 is inaccurate (or the accuracy is below an accuracy threshold), model training host 402 can determine to modify or retrain the ML model used by the model inference host, such as via ML model deployment / update.

[0077] Monitoring or evaluating AI / ML model performance may be valuable for lifecycle management (LCM). In this context, LCM may include management of AI / ML functions of a wireless network. For example, LCM may include activation of an AI / ML model, deactivation of an AI / ML model, switching from one AI / ML model to another AI / ML model (such as in response to conditions related to performance, suitability, or resource usage associated with an AI / ML model), training of an AI / ML model, etc.

[0078] As mentioned above, Figure 4 are provided as examples. Other examples can be found in the Figure 4 The examples described are different.

[0079] Figure 5 is a diagram illustrating an example 500 of AI / ML-based location determination according to the present disclosure. Figure 5As shown, the AI / ML model 510 can be deployed at or on the UE 120. For example, a model inference host (such as the model inference host 404) can be deployed at or on the UE 120. The AI / ML model 510 can enable the UE 120 to determine one or more inferences or predictions based on data input to the AI / ML model 510. In some examples, the AI / ML model 510 can be deployed at an AI / ML server associated with the UE 120 (not shown). For example, the AI / ML server can run the AI / ML model 510 and / or can train the AI / ML model 510.

[0080] For example, as indicated by reference numeral 515, the input of the AI / ML model 510 may include measurement information. For example, the UE 120 may perform measurements (e.g., channel frequency response (CFR) measurements, channel input response (CIR) measurements, RSRP measurements, RSRQ measurements, signal to interference plus noise ratio (SINR) measurements, etc.) to obtain measurement information. The UE 120 may provide the measurement information as input to the AI / ML model 510 (e.g., at the UE 120, or at the AI / ML server). In some aspects, the UE 120 may provide intermediate values ​​for the AI / ML model 510, such as line of sight (LOS) information (e.g., a binary indication of a LOS state, a probability of LOS, an indication of a non-LOS (NLOS) state, a probability of NLOS, or a combination thereof), a reference signal time difference (RSTD), an angle of departure (AoD), an angle of arrival (AoA), a receive-transmit turnaround time, a channel multipath value (e.g., a channel path delay, a channel path phase, a channel path gain, or a combination thereof). And so on. Intermediate values ​​may include information derived from the measurement information.

[0081] As indicated by reference numeral 520, the AI / ML model 510 may output one or more predictions. The one or more predictions may include location information about the UE 120. This may enable the UE 120 to determine a predicted (e.g., estimated, inferred) location of the UE 120, thereby saving power of the UE 120 and / or resources that would otherwise be used to determine the location of the UE 120 using more resource-intensive methods (such as light detection and ranging (Lidar), global navigation satellite system (GNSS) data, inertial sensing, etc.).

[0082] The AI / ML model 510 may be trained using a training measurement information set. The training measurement information set may include, for example, measurement information obtained by the UE 120 and corresponding location information. The AI / ML model 510 may be trained to output location information based on the input measurement information. For example, the AI / ML model 510 may output information indicating the location of the UE (referred to as a direct AI / ML method), and / or may output information indicating intermediate values ​​associated with the location (such as LOS values, RSTD, AoD, AoA, Rx-Tx turnaround, channel multipath values, uplink measurement values, etc.) (referred to as an AI / ML assisted method). An entity (e.g., UE 120, network node 110, or AI / ML server) that trains the AI / ML model may monitor the performance of the AI / ML model by comparing the output of the AI / ML model with groundtruth (sometimes referred to as a groundtruth label). Groundtruth or groundtruth labels as used herein may include information that is known to be true or deemed to be true for the purpose of training or monitoring a model. For example, for the purpose of determining (using the model) whether the UE's inferred position is accurate and / or for the purpose of training the model, real data indicative of the UE's position may be considered as the UE's actual observed position.

[0083] If the performance of the model is unsatisfactory (e.g., if the output of the model deviates from the corresponding true data labels by at least a certain threshold amount), the entity may train the AI / ML model (such as by using the corresponding true data labels and the measurement information used to generate the model output). However, some UEs may not be able to generate true data labels with sufficient accuracy to enable useful performance monitoring of the AI / ML model. In addition, different sources of true data labels may provide true data labels of different accuracy and quality. In addition, derived parameters (i.e., parameters such as non-positioning-based performance metrics derived from the operation of the UE, such as throughput values ​​or beam failure parameters) may not be useful for performance monitoring of the positioning AI / ML model. For example, the values ​​of the derived parameters may vary due to reasons other than the positioning of the UE 120, which means that these parameters may provide limited information about the performance of the positioning AI / ML model. The unavailability of true data labels at the UE 120 and the inapplicability of the derived parameters for true data labeling may result in inaccurate model outputs, thereby reducing the accuracy of the UE positioning and reducing the accuracy of the AI / ML model.

[0084] Some techniques described herein provide signaling of training location values ​​associated with training or monitoring of models such as AI / ML models. For example, a UE may obtain or receive a training location value associated with the UE from a node (e.g., another UE or network node 110). The UE may provide the training location value, and (optionally) a set of training measurement information (which may include measurement information as described above) for training or monitoring the model. As used herein, "training" may include training an AI / ML model (e.g., using ML techniques) and / or monitoring the performance of an AI / ML model (which may trigger further training of the AI / ML model). In this way, the accuracy of UE positioning and the output of the AI / ML model are improved by enabling the UE 120 or an AI / ML server associated with the UE 120 to obtain appropriate real data labels from another node.

[0085] As mentioned above, Figure 5 are provided as examples. Other examples can be found in the Figure 5 The examples described are different.

[0086] Figure 6 600 is a schematic diagram illustrating an example of signaling associated with obtaining a training location value for a training model (e.g., an AI / ML model) according to the present disclosure. As shown, example 600 includes a UE (e.g., UE 120) and a node. The node may include, for example, another UE (e.g., UE 120), a network node (e.g., network node 110), etc. As further shown, in some aspects, the UE may be associated with a server (such as an AI / ML server). In some aspects, the server may train the model. In some aspects, the server may run the model. For example, the server may receive measurement information from at least the UE, may input the measurement information into the model, and may provide location information (e.g., a location value or an intermediate value) output by the model to at least the UE.

[0087] As indicated by reference numeral 610, in some aspects, the UE may optionally obtain a training measurement information set. The training measurement information set may include measurement information, such as a CIR value, a CFR value, RSRP, a reference signal received path power (RSRPP), RSTD, a receive-transmit time difference, AoD, and the like. In some aspects, the training measurement information set may include location information, such as location information associated with the measurement information included in the training measurement information (e.g., information indicating a location or an intermediate value). The location information may be associated with the measurement information because the measurement information is related to measurements performed at a location indicated by the location information. In some other aspects, the training measurement information set may omit the location information. In some aspects, the UE may perform one or more measurements to obtain the training measurement information set, such as one or more downlink measurements (e.g., one or more NR measurements).

[0088] As indicated by reference numeral 620, the UE may obtain training position values. For example, the UE may receive training position values ​​from a node. As another example, the UE may determine training position values. Examples of training position values ​​and nodes from which each training position value may be received are provided below. The training position values ​​may include a position (e.g., information indicating a position, an estimate of a position), one or more intermediate values, or a combination thereof. In some aspects, the training position values ​​may include or be provided with measurement information associated with the training position values ​​(such as for training of a model).

[0089] In some aspects, the UE may receive a training position value from another UE (e.g., a second UE). For example, the node may include a second UE. In some aspects, the second UE may provide the training position value directly to the UE (e.g., via a sidelink). In some aspects, the second UE may provide the training position value to the UE via a network node. In some aspects, the training position value may include information related to the UE. In some aspects, the training position value may include or indicate a round trip time (RTT) value between the UE and the second UE. In some aspects, the training position value may include information indicating an angle between the UE and the second UE.

[0090] In some aspects, the UE may receive a training position value from a network node (e.g., network node 110). For example, the node may include a network node. In some aspects, the network node may determine the training position value. For example, the network node may determine the training position value based at least in part on an uplink reference signal (e.g., a sounding reference signal, etc.) sent by the UE or a measurement report sent by the UE, as shown by reference numeral 630. The measurement report may indicate a downlink measurement value determined by the UE, such as one or more measurement values ​​of measurement information determined by the UE. The network node may determine the training position value (e.g., a position or an intermediate value) using an uplink reference signal or a measurement report.

[0091] As another example, the network node may provide an uplink intermediate value to the UE. For example, the uplink intermediate value may include parameters based at least in part on the UE's uplink transmission (e.g., determined by the network node). In some aspects, the uplink intermediate value may include uplink RSRP, RSTD, channel multipath value, LOS value, uplink CFR, uplink CIR, etc. In some aspects, the uplink intermediate value may include a quasi-co-location (QCL) relationship. For example, the uplink intermediate value may indicate a QCL relationship between a downlink positioning reference signal (PRS) (e.g., a channel state information reference signal (CSI-RS) configured for positioning measurement) resource and an uplink sounding reference signal (SRS) resource, so that the UE can derive spatial information for uplink SRS transmission from spatial information of the downlink PRS, and vice versa. Therefore, the network node can utilize observations about the uplink (which are generally not available to the UE) to enhance the information available to the UE, thereby improving the accuracy of the training (e.g., monitoring) of the model.

[0092] In some aspects, the network node may provide a correction value, such as a correction value of a training measurement information set or a corrected measurement value, to the UE. For example, the UE may send (e.g., report) a measurement value (e.g., RSTD, RSRP, RSRPP, receive / transmit time difference, or AoD, as well as other examples) to the network node, as described with respect to reference numeral 630. For example, the measurement value may be included in a measurement report, or may be provided in a training measurement information set. As another example, the measurement report may include a training measurement information set. The network node may use the measurement value and / or the training measurement information set to determine a correction value, such as a correction value of a training measurement information set or a corrected measurement value. For example, the network node may determine the correction value based at least in part on determining an estimate of the location of the UE. The network node may use the training measurement information set or the reported measurement value to determine an estimate of the location. The network node may provide the correction value and / or the estimate of the location to the UE. In the case where the UE cannot obtain information indicating the location of the transmit / receive point or beam information of the network node, providing the correction value and / or the estimate of the location to the UE may be beneficial.

[0093] In some aspects, the correction value may be related to one or more measurement values ​​reported by the UE. For example, the measurement value may indicate a measurement of a set of cells, and the correction value may be related to the measurement of the set of cells (e.g., and not measurements performed by the UE on cells other than the set of cells). In some aspects, the correction value may be related to one or more cells other than the set of cells for which the UE reported the measurement. For example, the network node may provide a correction value (e.g., a true data label) for one or more cells for which the UE performed measurements but did not report the measurements. In some aspects, the correction value may be related to one or more cells in the set of cells for which the UE reported the measurements, and to one or more cells other than the set of cells for which the UE reported the measurements. For example, the network node may provide a correction value for one or more cells for which the UE has reported the measurements and for one or more cells for which the UE has not yet reported the measurements.

[0094] In some aspects, the network node may output an indication to measure one or more cells by the UE. The UE may perform measurements on reference signals associated with (e.g., transmitted by) the one or more cells in accordance with the indication. For example, the one or more cells may include one or more cells that the UE is not configured to report measurements on and / or one or more cells that the UE is not configured to perform measurements on. Thus, the network node may instruct the UE to measure additional cells, which may improve the accuracy of determining the UE's location using the model.

[0095] In some aspects, the UE may obtain the training position value by determining the training position value. For example, the UE may obtain the training position value using a technique other than radio frequency (e.g., reference signal) measurement (referred to as a non-NR technique). The non-NR technique may include, for example, Lidar measurement, GNSS measurement, non-terrestrial network (NTN) determination, inertial measurement unit (IMU) sensor measurement, WiFi positioning technology, ultra-wideband (UWB) positioning technology, radio frequency identifier (RFID) technology, passive IoT technology, etc.

[0096] In some aspects, the UE may receive multiple training position values. For example, the UE may receive real data labels from the above multiple sources (e.g., from another UE, from a network node, etc.). As another example, the UE may receive the correction value as a training position value and another form of training position value (e.g., an uplink intermediate value, a training position value from a second UE, etc.). Thus, variations in the accuracy of real data labels from different sources are mitigated, and the accuracy of training (e.g., monitoring) of the model is improved.

[0097] In some aspects, the training position value is for each TRP (per TRP). A TRP may be configured with multiple PRS resource sets (e.g., in some examples, two PRS resource sets). PRS is a type of reference signal used to determine the position of the UE and may include a CSI-RS configured as a PRS. Each PRS resource set may include one or more PRS resources. A PRS resource may indicate or include one or more resources on which the UE may perform measurements (e.g., PRS measurements). The UE may report one or more measurements for each TRP (e.g., multiple measurements of one or more types such as RSTD type, RSRP type, RSRPP type, receive-transmit time difference type, or AoD type). For example, the network node may output a first set of training position values ​​based at least in part on (e.g., indicated as associated with) measurements of PRS sent by a first TRP, output a second set of training position values ​​based at least in part on (e.g., indicated as associated with) measurements of PRS sent by a second TRP, and so on. In some aspects, the training position value is for each PRS resource set. For example, the network node may output a first set of training position values ​​based at least in part on (e.g., indicated as being associated with) measurements of PRSs of a first PRS resource set, output a second set of training position values ​​based at least in part on (e.g., indicated as being associated with) measurements of PRSs of a second PRS resource set, and so on. For example, if the network node reports a correction value (e.g., RSTD error) for RSTD of one PRS resource in a PRS resource set, the network node may indicate that the correction value applies to all PRS resources within the PRS resource set (because the LOS delay may be ideally the same for all resources). In some aspects, the training position values ​​are for each PRS resource. For example, the network node may output a first set of training position values ​​based at least in part on (e.g., indicated as being associated with) measurements of PRSs of a first PRS resource, output a second set of training position values ​​based at least in part on (e.g., indicated as being associated with) measurements of PRSs of a second PRS resource, and so on. For example, for angle measurement or joint timing-angle measurement, the network node may determine and output a training position value at the PRS resource level.

[0098] In some aspects, a node may output an explicit value of a training position value. For example, a node may output an explicit measurement value, a correction value, a position, etc. In some aspects, a node may output a probability function describing a training position value. For example, a node may output a cumulative density function (CDF) indicating the cumulative density of training position values ​​(e.g., correction values) determined by a network node. In some aspects, a node may output a range or range separator of training position values, such as a minimum value (e.g., a training position value cannot be less than X), a maximum value (e.g., a training position value cannot be greater than Y), or a combination thereof. In some aspects, a node may output an uncertainty value associated with a training position value, such as a value indicating the standard deviation or variance of the training position value. In some aspects, a node may output a combination of two or more of the above values.

[0099] In some aspects, the UE may receive a training position value within a time window after sending the measurement report, and the node may output the training position value within the time window after sending the measurement report (e.g., as shown in reference numeral 630). For example, the UE may receive a training position value (e.g., a correction value of the training position value) within a time window (e.g., a T ms time window) after sending the measurement report. In some aspects, the UE may receive a training position value in a batch of training position values, and the node may output the training position value in a batch of training position values. For example, the UE may report multiple measurements (e.g., a batch of measurements) in a single measurement report (e.g., in a single message). Each measurement reported in the measurement report may include a timestamp indicating the time when each measurement was performed. In some aspects, the node may output a batch of training position values ​​for multiple measurements of the measurement report. For example, the node may output a batch of training position values ​​according to a maximum time window (e.g., a time window within which a batch of training position values ​​should be output after receiving the measurement report), a maximum batch size (e.g., a maximum number of training position values ​​that may be included in a batch of training position values), or a combination thereof. In some aspects, the maximum time window and / or the maximum batch size may be specified, for example, in a wireless communication specification. In some aspects, a node may discard one or more training position values ​​(e.g., one or more individual training position values, one or more batches of training position values, or a combination thereof), for example, based at least in part on processing capabilities of the node, based on preemption of higher priority transmissions, and the like.

[0100] As shown in reference numeral 640, the UE may provide training position values ​​and / or training measurement information sets for training the model. For example, the UE may provide training position values ​​and / or training measurement information sets to a server (e.g., an AI / ML server) for training (e.g., monitoring) the model using an ML algorithm. As another example, the UE may provide training position values ​​and / or training measurement information sets to the ML algorithm of the UE for training the model using the ML algorithm. As described above, "training" may include monitoring the model. For example, the UE or server may compare the output of the model with the training position values ​​(e.g., real data labels). If the output of the model deviates from the training position values ​​by a threshold amount, the UE or server may use the training position values ​​to train the model. In this way, the accuracy of the training and operation of the model is improved. In some aspects, the UE or server may use the model. For example, the UE or server may determine the location information using the model and measurement information determined by the UE.

[0101] As mentioned above, Figure 6 are provided as examples. Other examples can be found in the Figure 6 The examples described are different.

[0102] Figure 7 7 is a diagram illustrating an example process 700, for example, performed by an apparatus according to the present disclosure. Example process 700 is an example of an apparatus (eg, an apparatus of UE 120, a server such as an AI / ML server, etc.) performing operations associated with positioning model performance monitoring.

[0103] like Figure 7 As shown in , in some aspects, process 700 may include: obtaining a set of training measurement information associated with a UE (block 710). For example, an apparatus (e.g., using Fig. 9 The communication manager 140 and / or the transmitting component 904 or the training component 908 depicted in FIG. 1 may obtain a set of training measurement information associated with a UE (eg, a UE including an apparatus or another UE), as described above.

[0104] like Figure 7 As further shown in FIG. 7 , in some aspects, process 700 may include obtaining a training location value associated with the UE (block 720). For example, an apparatus (eg, using Fig. 9 The communication manager 140 and / or receiving component 902 depicted in FIG. 1 may obtain a training position value associated with the UE, as described above. In some aspects, the apparatus may obtain the training position value by determining the training position value. In some other aspects, the apparatus may receive the training position value from a node.

[0105] like Figure 7As further shown in FIG. 7 , in some aspects, process 700 may include providing a training location value and a set of training measurement information for training a model using an ML technique, the model being trained to output location information based at least in part on the measurement information (block 730). For example, a device (e.g., using Fig. 9 The communication manager 140 and / or the sending component 904 or the training component 908 depicted in FIG. 1 can provide a set of training location values ​​and training measurement information for training or performance monitoring a model using ML techniques, the model being trained to output location information based at least in part on the measurement information, as described above.

[0106] Process 700 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in combination with one or more other processes described elsewhere herein.

[0107] In a first aspect, the node comprises a second UE.

[0108] In a second aspect alone or in combination with the first aspect, receiving the trained position value from the node comprises receiving the trained position value from the node via a network node or another UE.

[0109] In a third aspect alone or in combination with one or more of the first and second aspects, the training position values ​​are based at least in part on at least one of an uplink reference signal or a measurement report of the UE.

[0110] In a fourth aspect alone or in combination with one or more of the first to third aspects, the trained position value comprises an estimate of the position of the UE or an estimate of an intermediate value derived from an uplink reference signal or a measurement report.

[0111] In the fifth aspect, either alone or in combination with one or more of the first to fourth aspects, the training position value includes an intermediate value, which includes at least one of the following: a reference signal received power value, a reference signal time difference value, a channel multipath value, line of sight information, an uplink channel frequency response value, an uplink channel pulse response value, quasi-co-location information, or a combination thereof.

[0112] In a sixth aspect alone or in combination with one or more of the first to fifth aspects, process 700 includes sending information indicating a measurement value to the node, the training position value being based at least in part on the measurement value.

[0113] In the seventh aspect alone or in combination with one or more of the first to sixth aspects, the training position value indicates at least one of: a correction value of a training measurement information set based at least in part on the measurement value, or a measurement value based at least in part on a correction of the measurement value.

[0114] In the eighth aspect alone or in combination with one or more of the first to seventh aspects, the corrected measurement value is based at least in part on uplink measurements at the node.

[0115] In a ninth aspect alone or in combination with one or more of the first to eighth aspects, the corrected measurement value is based at least in part on training measurement information.

[0116] In the tenth aspect alone or in combination with one or more of the first to ninth aspects, the measurement value indicates a measurement of a set of cells, and the training position value relates to the set of cells.

[0117] In an eleventh aspect alone or in combination with one or more of the first to tenth aspects, the training position value is further related to one or more cells measured by the UE in addition to the cell set.

[0118] In a twelfth aspect alone or in combination with one or more of the first to eleventh aspects, the process 700 includes performing measurements on reference signals associated with one or more cells.

[0119] In the thirteenth aspect alone or in combination with one or more of the first to twelfth aspects, the training position value is for one of: each transmission and reception point, each PRS resource set, or each PRS resource.

[0120] In the fourteenth aspect, alone or in combination with one or more of the first to thirteenth aspects, the training position value comprises at least one of: an explicit value of the training position value, a probability function, a range or range delimiter of the training position value, an uncertainty value associated with the training position value, or a combination thereof.

[0121] In the fifteenth aspect alone or in combination with one or more of the first to fourteenth aspects, receiving the training position values ​​further comprises receiving the training position values ​​within a time window after sending the measurement report.

[0122] In the sixteenth aspect alone or in combination with one or more of the first to fifteenth aspects, receiving the training position value further comprises receiving the training position value in a batch of training position values.

[0123] In the seventeenth aspect alone or in combination with one or more of the first to sixteenth aspects, the UE comprises an apparatus.

[0124] In the eighteenth aspect, alone or in combination with one or more of the first to seventeenth aspects, providing training measurement information and training position values ​​for training the model further comprises training the model using the training measurement information and training position values.

[0125] In the nineteenth aspect, alone or in combination with one or more of the first to eighteenth aspects, providing training measurement information and training position values ​​for training the model further comprises providing the training measurement information and training position values ​​to a server associated with the model.

[0126] In the twentieth aspect alone or in combination with one or more of the first to nineteenth aspects, process 700 includes determining position information using a model and measurement information.

[0127] although Figure 7 Example blocks of process 700 are shown, but in some aspects, process 700 may include Figure 7 The blocks in the process 700 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in the process 700. Additionally or alternatively, two or more blocks in the blocks of the process 700 may be executed in parallel.

[0128] Figure 8 8 is a diagram illustrating an example process 800 performed, for example, by a network node according to the present disclosure. The example process 800 is a diagram illustrating an example process 800 performed by a network node (for example, network node 110, Figure 6 An example of performing operations associated with positioning model performance monitoring.

[0129] like Figure 8 As shown in FIG. 8 , in some aspects, process 800 may include obtaining training measurement information for a UE or information associated with training measurement information, the training measurement information being associated with training a model using ML techniques (block 810). For example, a network node (e.g., using Fig.10 The communication manager 150 and / or receiving component 1002 depicted in FIG. 1002 may obtain training measurement information for the UE or information associated with the training measurement information, the training measurement information being associated with training the model using ML techniques, as described above. The information associated with the training measurement information may include, for example, uplink reference signal transmissions, uplink measurements, measurement values ​​reported by the UE, or measurements described as being received by the UE. Figure 6 Another value received by the node.

[0130] like Figure 8 As further shown in FIG. 8 , in some aspects, process 800 may include: outputting a training position value for the UE based at least in part on the training measurement information or information associated with the training measurement information (block 820). For example, a network node (e.g., using Fig.10 The communication manager 150 and / or the sending component 1004 depicted in FIG. 1004 may output a trained position value for the UE based at least in part on the training measurement information or information associated with the training measurement information, as described above.

[0131] Process 800 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in combination with one or more other processes described elsewhere herein.

[0132] In a first aspect, the training position values ​​are based at least in part on at least one of an uplink reference signal or a measurement report of the UE.

[0133] In a second aspect alone or in combination with the first aspect, the trained position value comprises an estimate of the position of the UE or an estimate of an intermediate value derived from an uplink reference signal or a measurement report.

[0134] In a third aspect, either alone or in combination with one or more of the first and second aspects, the training position value comprises an intermediate value comprising at least one of: a reference signal received power value, a reference signal time difference value, a channel multipath value, line of sight information, an uplink channel frequency response value, an uplink channel impulse response value, quasi co-location information, or a combination thereof.

[0135] In a fourth aspect alone or in combination with one or more of the first to third aspects, the information associated with the training measurement information comprises information indicating downlink measurement values, wherein outputting the training position values ​​further comprises outputting the training position values ​​based at least in part on the downlink measurement values.

[0136] In the fifth aspect, either alone or in combination with one or more of the first to fourth aspects, the training position value indicates at least one of: a correction value of training measurement information based at least in part on a downlink measurement value, or a correction measurement value based at least in part on a downlink measurement value.

[0137] In a sixth aspect alone or in combination with one or more of the first to fifth aspects, process 800 includes performing uplink measurements, wherein the corrected measurement values ​​are based at least in part on the uplink measurements.

[0138] In a seventh aspect alone or in combination with one or more of the first to sixth aspects, the corrected measurements are based at least in part on downlink measurements.

[0139] In the eighth aspect alone or in combination with one or more of the first to seventh aspects, the downlink measurement value relates to a set of cells and the training position value relates to the set of cells.

[0140] In the ninth aspect alone or in combination with one or more of the first to eighth aspects, the training position value is also related to one or more cells measured by the UE in addition to the cell set.

[0141] In the tenth aspect alone or in combination with one or more of the first to ninth aspects, the process 800 includes outputting an indication of measurement of one or more cells by the UE.

[0142] In the eleventh aspect alone or in combination with one or more of the first to tenth aspects, the training position value is for one of: each transmission reception point, each PRS resource set, or each PRS resource.

[0143] although Figure 8 Example blocks of process 800 are shown, but in some aspects, process 800 may include Figure 8 The blocks in the process 800 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in the process 800. Additionally or alternatively, two or more blocks in the blocks of process 800 may be executed in parallel.

[0144] Fig. 9 is a schematic diagram of an exemplary apparatus 900 for wireless communication according to the present disclosure. In some aspects, the apparatus 900 may be a UE. In some aspects, the UE may include the apparatus 900. In some aspects, the apparatus 900 may be a server, such as an AI / ML server. In some aspects, the server may include the apparatus 900. In some aspects, the apparatus 900 includes a receiving component 902 and a sending component 904, which may communicate with each other (e.g., via one or more buses and / or one or more other components). As shown, the apparatus 900 may communicate with another apparatus 906 (e.g., a UE, a base station, or another wireless communication device) using the receiving component 902 and the sending component 904. As further shown, the apparatus 900 may include a communication manager 140. The communication manager 140 may include a training component 908 and other examples.

[0145] In some aspects, the apparatus 900 may be configured to perform the Figure 4-Figure 6 Additionally or alternatively, the apparatus 900 may be configured to perform one or more of the processes described herein, such as Figure 7 In some aspects, Fig. 9 The apparatus 900 and / or one or more components shown in FIG. 1 may include a combination of Figure 2 Additionally or alternatively, Fig. 9 One or more of the components shown in the figure may be combined with Figure 2In one or more components described herein, the components may be implemented in a computer program product. Additionally or alternatively, one or more components in the component set may be at least partially implemented as software stored in a memory. For example, a component (or a portion of a component) may be implemented as instructions or codes stored in a non-transitory computer-readable medium and executable by a controller or processor to perform the function or operation of the component.

[0146] The receiving component 902 may receive information from the device 906, such as a reference signal, control information, data communication, or a combination thereof. The receiving component 902 may provide the received information to one or more other components of the device 900. In some aspects, the receiving component 902 may perform signal processing (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, demapping, equalization, interference cancellation, or decoding, among other examples) on the received information and may provide the processed signal to one or more other components of the device 900. In some aspects, the receiving component 902 may include combining Figure 2 One or more antennas, modems, demodulators, MIMO detectors, receive processors, controllers / processors, memories, or combinations thereof of a UE are described.

[0147] Transmitting component 904 may transmit information, such as a reference signal, control information, data communication, or a combination thereof, to device 906. In some aspects, one or more other components of device 900 may generate information and may provide the generated information to transmitting component 904 for transmission to device 906. In some aspects, transmitting component 904 may perform signal processing (e.g., filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, among other examples) on the generated information and may transmit the processed signal to device 906. In some aspects, transmitting component 904 may include combining Figure 2 One or more antennas, modems, modulators, transmit MIMO processors, transmit processors, controllers / processors, memories, or combinations thereof of the described UE. In some aspects, the transmit component 904 can be co-located with the receive component 902 in a transceiver.

[0148] In some aspects, the sending component 904 can obtain a training measurement information set associated with the UE. In some aspects, the training component 908 can obtain a training measurement information set. The receiving component 902 or the training component 908 can obtain a training location value associated with the UE. The sending component 904 can provide the training location value and the training measurement information set for training a model using ML techniques, the model being trained to output location information based at least in part on the measurement information. In some aspects, the training component 908 can obtain the training location value and the training measurement information set for training a model using ML techniques, the model being trained to output location information based at least in part on the measurement information.

[0149] Transmitting component 904 can transmit information indicating the measurement values ​​to the node, the training position value being based at least in part on the measurement values.

[0150] Receiving component 902 can perform measurements on reference signals (eg, PRS) associated with one or more cells.

[0151] Fig. 9 The number and arrangement of components shown in the figure are provided as examples. In practice, there may be Fig. 9 The components shown in FIG. 1 may include additional components, fewer components, different components, or differently arranged components. In addition, Fig. 9 Two or more of the components shown in may be implemented in a single component, or Fig. 9 The single component shown in can be implemented as multiple distributed components. Additionally or alternatively, Fig. 9 The assembly (or assemblies) of components shown in the figure may perform the operations described by Fig. 9 One or more functions performed by another component collection shown in .

[0152] Fig.10 1 is a schematic diagram of an exemplary apparatus 1000 for wireless communication according to the present disclosure. Apparatus 1000 may be a network node, or a network node may include apparatus 1000. In some aspects, apparatus 1000 includes a receiving component 1002 and a sending component 1004, which may communicate with each other (e.g., via one or more buses and / or one or more other components). As shown, apparatus 1000 may communicate with another apparatus 1006 (e.g., a UE, a base station, or another wireless communication device) using receiving component 1002 and sending component 1004. As further shown, apparatus 1000 may include a communication manager 150. Communication manager 150 may include a positioning component 1008, as well as other examples.

[0153] In some aspects, the apparatus 1000 may be configured to perform the Figure 4-Figure 6Additionally or alternatively, the apparatus 1000 may be configured to perform one or more of the processes described herein, such as Figure 8 In some aspects, Fig.10 The apparatus 1000 and / or one or more components shown in the drawings may include a combination of Figure 2 Additionally or alternatively, Fig.10 One or more of the components shown in the figure may be combined with Figure 2 In some embodiments, the present invention may be implemented in one or more components described in the present invention. Additionally or alternatively, one or more components in the component set may be at least partially implemented as software stored in a memory. For example, a component (or a portion of a component) may be implemented as an instruction or code stored in a non-transitory computer-readable medium and executable by a controller or processor to perform the function or operation of the component.

[0154] The receiving component 1002 may receive information from the apparatus 1006, such as a reference signal, control information, data communication, or a combination thereof. The receiving component 1002 may provide the received information to one or more other components of the apparatus 1000. In some aspects, the receiving component 1002 may perform signal processing (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, demapping, equalization, interference cancellation, or decoding, among other examples) on the received information and may provide the processed signal to one or more other components of the apparatus 1000. In some aspects, the receiving component 1002 may include combining Figure 2 One or more antennas, modems, demodulators, MIMO detectors, receive processors, controllers / processors, memories, or combinations thereof of the described network nodes.

[0155] The transmitting component 1004 may transmit information, such as a reference signal, control information, data communication, or a combination thereof, to the apparatus 1006. In some aspects, one or more other components of the apparatus 1000 may generate information and may provide the generated information to the transmitting component 1004 for transmission to the apparatus 1006. In some aspects, the transmitting component 1004 may perform signal processing (e.g., filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, among other examples) on the generated information and may transmit the processed signal to the apparatus 1006. In some aspects, the transmitting component 1004 may include combining Figure 2 One or more antennas, modems, modulators, transmit MIMO processors, send processors, controllers / processors, memories, or combinations thereof of the described network nodes. In some aspects, the transmit component 1004 can be co-located with the receive component 1002 in a transceiver.

[0156] Receiving component 1002 can obtain (e.g., receive from the UE, receive from another network node) training measurement information of the UE or information associated with the training measurement information, the training measurement information being associated with training the model using the ML technique. Sending component 1004 or positioning component 1008 can output (e.g., send to the UE or provide to another network node for transmission) a training position value for the UE based at least in part on the training measurement information or information associated with the training measurement information.

[0157] Fig.10 The number and arrangement of components shown in the figure are provided as examples. In practice, there may be Fig.10 The components shown in FIG. 1 may include additional components, fewer components, different components, or differently arranged components. In addition, Fig.10 Two or more of the components shown in may be implemented in a single component, or Fig.10 The single component shown in can be implemented as multiple distributed components. Additionally or alternatively, Fig.10 The assembly (or assemblies) of components shown in the figure may perform the operations described by Fig.10 One or more functions performed by another component collection shown in .

[0158] The following provides an overview of some aspects of the disclosure:

[0159] Aspect 1: A method of wireless communication performed by an apparatus, comprising: obtaining a training measurement information set associated with a user equipment (UE); obtaining a training location value associated with the UE; and providing the training location value and the training measurement information set for training or performance monitoring a model using machine learning (ML) techniques, wherein the model is trained to output location information based at least in part on measurement information.

[0160] Aspect 2: The method according to aspect 1, wherein the training position value is based at least in part on at least one of an uplink reference signal or a measurement report of the UE.

[0161] Aspect 3: The method according to aspect 2, wherein the trained position value comprises an estimate of the position of the UE or an estimate of an intermediate value derived from an uplink reference signal or a measurement report.

[0162] Aspect 4: A method according to any one of Aspects 1-3, wherein the training position value includes an intermediate value, which includes at least one of the following items: a reference signal received power value, a reference signal time difference value, a channel multipath value, line of sight information, an uplink channel frequency response value, an uplink channel pulse response value, quasi-co-location information or a combination thereof.

[0163] Aspect 5: The method according to any one of aspects 1-4, wherein obtaining the training position value further comprises receiving the training position value from a node.

[0164] Aspect 6: The method according to aspect 5, wherein the node includes a second UE.

[0165] Aspect 7: The method according to aspect 5, wherein receiving the training position value from the node includes receiving the training position value from the node via a network node or another UE.

[0166] Aspect 8: The method according to aspect 5 further comprises sending information indicating a measurement value to the node, wherein the training position value is based at least in part on the measurement value.

[0167] Aspect 9: The method according to aspect 8, wherein the training position value indicates at least one of the following: a correction value of a training measurement information set based at least in part on the measurement value, or a measurement value based at least in part on a correction of the measurement value.

[0168] Aspect 10: The method according to aspect 9, wherein the corrected measurement value is based at least in part on uplink measurements at the node.

[0169] Aspect 11: The method according to aspect 9, wherein the corrected measurement value is based at least in part on the training measurement information set.

[0170] Aspect 12: The method according to aspect 8, wherein the measurement value indicates a measurement of a set of cells, and wherein the training position value is related to the set of cells.

[0171] Aspect 13: The method according to aspect 12, wherein the training position value is also related to one or more cells measured by the UE in addition to the cell set.

[0172] Aspect 14: The method according to aspect 13 further comprises receiving an indication to measure one or more cells, the method further comprising performing measurements on reference signals associated with the one or more cells.

[0173] Aspect 15: The method according to aspect 8, wherein the training position value is for one of the following: each transmission and reception point, each positioning reference signal (PRS) resource set, or each PRS resource.

[0174] Aspect 16: A method according to any one of Aspects 1-15, wherein the training position value includes at least one of the following: an explicit value of the training position value, a probability function, a range or range separator of the training position value, an uncertainty value associated with the training position value, or a combination thereof.

[0175] Aspect 17: The method according to any one of aspects 1-16, wherein obtaining the training position value further comprises receiving the training position value within a time window after sending the measurement report.

[0176] Aspect 18: The method according to any one of Aspects 1-17, wherein obtaining the training position value further comprises receiving the training position value from a batch of training position values.

[0177] Aspect 19: The method according to any one of aspects 1-18, wherein the UE comprises an apparatus.

[0178] Aspect 20: The method according to any one of aspects 1-19, wherein providing a training measurement information set and a training position value for training a model further comprises training the model using the training measurement information set and the training position value.

[0179] Aspect 21: The method according to any one of aspects 1-20, wherein providing a training measurement information set and a training position value for training the model further comprises providing the training measurement information set and the training position value to a server associated with the model.

[0180] Aspect 22: The method according to any one of aspects 1-21, further comprising determining the position information using the model and the measurement information.

[0181] Aspect 23: A method of wireless communication performed by a network node, comprising: obtaining training measurement information of a user equipment (UE) or information associated with the training measurement information, wherein the training measurement information is associated with training or performance monitoring of a model using machine learning (ML) technology; and outputting a training position value for the UE based at least in part on the training measurement information or information associated with the training measurement information.

[0182] Aspect 24: The method according to aspect 23, wherein the training position value is based at least in part on at least one of an uplink reference signal or a measurement report of the UE.

[0183] Aspect 25: The method according to aspect 24, wherein the trained position value comprises an estimate of the position of the UE or an estimate of an intermediate value derived from an uplink reference signal or a measurement report.

[0184] Aspect 26: A method according to any one of Aspects 23-25, wherein the training position value includes an intermediate value, which includes at least one of the following items: a reference signal received power value, a reference signal time difference value, a channel multipath value, line of sight information, an uplink channel frequency response value, an uplink channel pulse response value, quasi-co-location information or a combination thereof.

[0185] Aspect 27: A method according to any one of Aspects 23-26, wherein the information associated with the training measurement information includes information indicating a downlink measurement value, and wherein outputting the training position value further includes outputting the training position value based at least in part on the downlink measurement value.

[0186] Aspect 28: A method according to aspect 27, wherein the training position value indicates at least one of the following: a correction value of training measurement information based at least in part on a downlink measurement value, or a corrected measurement value based at least in part on a downlink measurement value.

[0187] Aspect 29: The method according to aspect 28 further comprises performing uplink measurements, wherein the corrected measurement value is based at least in part on the uplink measurements.

[0188] Aspect 30: A method according to aspect 28, wherein the corrected measurement value is based at least in part on the downlink measurement value.

[0189] Aspect 31: The method according to aspect 27, wherein the downlink measurement value is related to a set of cells, and wherein the training position value is related to the set of cells.

[0190] Aspect 32: The method according to aspect 31, wherein the training position value is also related to one or more cells measured by the UE in addition to the set of cells.

[0191] Aspect 33: The method according to aspect 32 further includes: outputting an indication that the one or more cells are measured by the UE.

[0192] Aspect 34: The method according to aspect 27, wherein the training position value is for one of: each transmission reception point, each positioning reference signal (PRS) resource set, or each PRS resource.

[0193] Aspect 35: An apparatus for wireless communication at a device, comprising: a processor; a processor coupled to the memory; and instructions stored in the memory and executable by the processor to cause the apparatus to perform one or more methods of aspects 1-34.

[0194] Aspect 36: An apparatus for wireless communication, comprising a memory and one or more processors coupled to the memory, the one or more processors configured to perform one or more of the methods described in aspects 1-34.

[0195] Aspect 37: An apparatus for wireless communication, comprising at least one means for performing the method according to one or more of aspects 1-34.

[0196] Aspect 38: A non-transitory computer-readable medium storing code for wireless communication, the code comprising instructions executable by a processor to perform the method according to one or more of aspects 1-34.

[0197] Aspect 39: A non-transitory computer-readable medium storing an instruction set for wireless communication, the instruction set comprising one or more instructions that, when executed by one or more processors of a device, cause the device to perform one or more of the methods described in aspects 1-39.

[0198] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the various aspects to the precise forms disclosed. Modifications and variations may be made in light of the foregoing disclosure, or may be acquired from practice of various aspects.

[0199] As used herein, the term "component" is intended to be broadly interpreted as a combination of hardware and / or hardware and software. Whether referred to as software, firmware, middleware, microcode, hardware description language or other terms, "software" should be broadly interpreted as meaning instructions, instruction sets, codes, code segments, program codes, programs, subroutines, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, processes and / or functions, and other examples. As used herein, a "processor" is implemented with a combination of hardware and / or hardware and software. It is obvious that the system and / or method described herein can be implemented with a combination of hardware and / or hardware and software in different forms. The actual dedicated control hardware or software code used to implement these systems and / or methods does not limit these aspects. Therefore, the operation and behavior of the system and / or method are described herein without reference to specific software codes, because those skilled in the art will understand that software and hardware can be designed to implement the system and / or method based at least in part on the description herein.

[0200] As used herein, "satisfying a threshold" may refer to a value greater than a threshold, greater than or equal to a threshold, less than a threshold, less than or equal to a threshold, equal to a threshold, not equal to a threshold, etc., depending on the context.

[0201] Even if a specific combination of features is described in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of each aspect. Many of these features can be combined in a manner not specifically described in the claims and / or not disclosed in the specification. The disclosure of each aspect includes the combination of each dependent claim with each other claim in the claim set. As used herein, the phrase "at least one" mentioned in the list of items refers to any combination of these items, including a single member. As an example, "at least one of a, b or c" is intended to cover a, b, c, a+b, a+c, b+c and a+b+c, and any combination with multiple identical elements (e.g., a+a, a+a+a, a+a+b, a+a+c, a+b+b, a+c+c, b+b, b+b+b, b+b+c, c+c and c+c+c. Or any other ordering of a, b and c).

[0202] Unless explicitly described as such, the elements, actions or instructions used herein should not be interpreted as key or necessary. In addition, as used herein, the articles "a" and "an" are intended to include one or more items, and can be used interchangeably with "one or more". In addition, as used herein, the article "the" is intended to include one or more items quoted in conjunction with the article "the", and can be used interchangeably with "one or more". In addition, as used herein, the terms "set" and "group" are intended to include one or more items, and can be used interchangeably with "one or more". In the case of only one item, the phrase "only one" or similar language is used. In addition, as used herein, the terms "has", "have", "having" etc. are intended to be open terms, which do not limit the elements they modify (for example, "elements with" A can also have B). In addition, unless otherwise expressly stated, the phrase "based on" is intended to mean "based at least in part on". Furthermore, as used herein, the term "or" when used in tandem is intended to be inclusive and used interchangeably with "and / or" unless expressly stated otherwise (eg, if used in combination with "either" or "only one of").

Claims

1. A device for wireless communication, comprising: Memory; as well as one or more processors coupled to the memory, the one or more processors configured to: Obtaining a set of training measurement information associated with a user equipment (UE); obtaining a training location value associated with the UE; and The training position values ​​and the training measurement information set are provided for use in training or performance monitoring of a model using machine learning (ML) techniques, the model being trained to output position information based at least in part on the measurement information.

2. The device according to claim 1, wherein: The trained position value is based at least in part on at least one of an uplink reference signal or a measurement report of the UE.

3. The device according to claim 2, wherein: The trained position value comprises an estimate of the position of the UE or an estimate of an intermediate value derived from the uplink reference signal or the measurement report.

4. The device according to claim 1, wherein: The training position values ​​include intermediate values, the intermediate values ​​including at least one of: Reference signal received power value, Reference signal time difference, Channel multipath value, Line of sight information, Uplink channel frequency response value, Uplink channel impulse response value, Quasi-co-location information, or Its combination.

5. The device according to claim 1, wherein: To obtain the trained position values, the one or more processors are configured to receive the trained position values ​​from a node.

6. The device according to claim 5, wherein: The node comprises a second UE.

7. The device according to claim 5, wherein: To receive the trained position values ​​from the node, the one or more processors are configured to receive the trained position values ​​from the node via a network node or another UE.

8. The device according to claim 5, wherein: The one or more processors are further configured to send information indicative of measurements to the node, the trained position values ​​being based at least in part on the measurements.

9. The device according to claim 8, wherein: The trained position value indicates at least one of: a correction value based at least in part on a set of training measurement information for said measurement value, or A corrected measurement based at least in part on the measurement.

10. The device according to claim 8, wherein: The measurement values ​​indicate measurements of a set of cells, and wherein the training position values ​​relate to the set of cells.

11. The device according to claim 8, wherein: The training position value includes at least one of the following: the explicit value of the training position value, Probability function, the range or range separator of the training position values, an uncertainty value associated with the training position value, or Its combination.

12. The device according to claim 8, wherein: To receive the trained position value, the one or more processors are configured to receive the trained position value within a time window after sending a measurement report.

13. The device according to claim 1, wherein: To receive the training position value, the one or more processors are configured to receive the training position value in a batch of training position values.

14. The device according to claim 1, wherein: The UE includes the device.

15. The apparatus according to claim 1, wherein in order to provide the training measurement information set and the training position values ​​for training or performance monitoring of the model, the one or more processors are configured to train the model using the training measurement information set and the training position values.

16. An apparatus according to claim 1, wherein in order to provide the training measurement information set and the training position value for training or performance monitoring of the model, the one or more processors are configured to provide the training measurement information set and the training position value to a server associated with the model.

17. A network node for wireless communication, comprising: Memory; as well as one or more processors coupled to the memory, the one or more processors configured to: Obtaining training measurement information of a user equipment (UE) or information associated with the training measurement information, wherein the training measurement information is associated with training or performance monitoring of a model using a machine learning (ML) technique; as well as A trained position value is output for the UE based at least in part on the trained measurement information or information associated with the trained measurement information.

18. The network node according to claim 17, wherein: The trained position values ​​are based at least in part on at least one of an uplink reference signal or a measurement report of the UE.

19. The network node according to claim 17, wherein: The training position values ​​include intermediate values, the intermediate values ​​including at least one of: Reference signal received power value, Reference signal time difference, Channel multipath value, Line of sight information, Uplink channel frequency response value, Uplink channel impulse response value, Quasi-co-location information, or Its combination.

20. The network node according to claim 17, wherein: The information associated with the training measurement information includes information indicating downlink measurement values, wherein, to output the training position values, the one or more processors are configured to output the training position values ​​based at least in part on the downlink measurement values.

21. The network node according to claim 20, wherein: The downlink measurement value is related to a set of cells, and wherein the training position value is related to the set of cells.

22. The network node according to claim 20, wherein: The training position value is for at least one of: Each sending and receiving point, Each positioning reference signal (PRS) resource set, or Each PRS resource.

23. A method of wireless communication performed by an apparatus, comprising: Obtaining a set of training measurement information associated with a user equipment (UE); Obtaining a training location value associated with the UE; as well as The training position values ​​and the training set of measurement information are provided for use in training or performance monitoring of a model using machine learning (ML) techniques, the model being trained to output position information based at least in part on the measurement information.

24. The method according to claim 23, wherein: Obtaining the training position value includes receiving the training position value from a node via a network node or another UE.

25. The method according to claim 23, wherein: The trained position value is based at least in part on at least one of an uplink reference signal or a measurement report of the UE.

26. A method of wireless communication performed by a network node, comprising: Obtaining training measurement information of a user equipment (UE) or information associated with the training measurement information, wherein the training measurement information is associated with training or performance monitoring of a model using a machine learning (ML) technique; as well as A trained position value is output for the UE based at least in part on the trained measurement information or information associated with the trained measurement information.

27. The method according to claim 26, wherein: The trained position value is based at least in part on at least one of an uplink reference signal or a measurement report of the UE.

28. The method according to claim 27, wherein: The trained position value comprises an estimate of the position of the UE or an estimate of an intermediate value derived from the uplink reference signal or the measurement report.

29. The method according to claim 26, wherein: The training position values ​​include intermediate values, the intermediate values ​​including at least one of: Reference signal received power value, Reference signal time difference, Channel multipath value, Line of sight information, Uplink channel frequency response value, Uplink channel impulse response value, Quasi-co-location information, or Its combination.

30. The method of claim 26, wherein: The training position value is for one of: Each sending and receiving point, Each positioning reference signal (PRS) resource set, or Each PRS resource.

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