WO2026016094A1 - Dispositifs, procédés et support pour la communication - Google Patents

Dispositifs, procédés et support pour la communication

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Publication number
WO2026016094A1
WO2026016094A1 PCT/CN2024/105999 CN2024105999W WO2026016094A1 WO 2026016094 A1 WO2026016094 A1 WO 2026016094A1 CN 2024105999 W CN2024105999 W CN 2024105999W WO 2026016094 A1 WO2026016094 A1 WO 2026016094A1
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WO
WIPO (PCT)
Prior art keywords
terminal device
model
inference
data
location server
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/CN2024/105999
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English (en)
Inventor
Zhaobang MIAO
Wei Chen
Rao SHI
Peng Guan
Gang Wang
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
NEC Corp
Original Assignee
NEC Corp
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Filing date
Publication date
Application filed by NEC Corp filed Critical NEC Corp
Priority to PCT/CN2024/105999 priority Critical patent/WO2026016094A1/fr
Publication of WO2026016094A1 publication Critical patent/WO2026016094A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Definitions

  • Example embodiments of the present disclosure generally relate to the field of communication techniques and in particular, to devices, methods, and a computer readable medium for communication.
  • AI artificial intelligence
  • ML machine learning
  • An AI/ML positioning model may be trained based on training data, and accordingly the trained model can be used for model inference. It is proposed to ensure consistency between the model training and the model inference, details of which should be studied.
  • example embodiments of the present disclosure provide devices, methods, and a computer storage medium for communication.
  • a terminal device deployed with a trained positioning model comprises at least one processor configured to cause the terminal device at least to: transmit, to a location server, information about metadata associated with inference data of the positioning model, wherein the metadata associated with the inference data is determined based on further metadata associated with training data of the positioning model; receive, from at least one transmission and reception point (TRP) associated with the metadata associated with the inference data, at least one positioning reference signal (PRS) ; and perform, using the trained positioning model, a model inference process based on measurement of the at least one PRS.
  • TRP transmission and reception point
  • PRS positioning reference signal
  • a location server comprising at least one processor configured to cause the location server at least to: receive, from a terminal device deployed with a trained positioning model, information about metadata associated with inference data of the positioning model; determine at least one TRP based on the information about the metadata; and transmit, to the at least one TRP, an indication indicating to the at least one TRP to transmit at least one PRS for model inference at the terminal device.
  • a terminal device deployed with a trained positioning model comprises at least one processor configured to cause the terminal device at least to: receive, from a location server, a configuration for model inference; and in accordance with a determination that the configuration is consistent with a downlink (DL) PRS configuration for data collection of training data, perform a model inference process based on the configuration using the trained positioning model.
  • DL downlink
  • a location server comprises at least one processor configured to cause the location server at least to: transmit, to a terminal device deployed with a trained positioning model, a configuration for model inference; and receive, from the terminal device, an error indication indicating that the configuration is inconsistent with a DL-PRS configuration for data collection of training data.
  • a method of communication performed by a terminal device.
  • the method comprises: transmitting, at a terminal device deployed with a trained positioning model to a location server, information about metadata associated with inference data of the positioning model, wherein the metadata associated with the inference data is determined based on further metadata associated with training data of the positioning model; receiving, from at least one TRP associated with the metadata associated with the inference data, at least one PRS; and performing, using the trained positioning model, a model inference process based on measurement of the at least one PRS.
  • a method of communication performed by a location server.
  • the method comprises: receiving, at a location server from a terminal device deployed with a trained positioning model, information about metadata associated with inference data of the positioning model; determining at least one TRP based on the information about the metadata; and transmitting, to the at least one TRP, an indication indicating to the at least one TRP to transmit at least one PRS for model inference at the terminal device.
  • a method of communication performed by a terminal device.
  • the method comprises: receiving, at a terminal device deployed with a trained positioning model from a location server, a configuration for model inference; and in accordance with a determination that the configuration is consistent with a DL-PRS configuration for data collection of training data, performing a model inference process based on the configuration using the trained positioning model.
  • a method of communication performed by a location server.
  • the method comprises: transmitting, at a location server to a terminal device deployed with a trained positioning model, a configuration for model inference; and receiving, from the terminal device, an error indication indicating that the configuration is inconsistent with a DL-PRS configuration for data collection of training data.
  • a computer readable medium having instructions stored thereon, the instructions, when executed on at least one processor, cause the at least one processor to carry out the method according to any one of the fifth to eighth aspects above.
  • FIG. 1 an example communication network in which some embodiments of the present disclosure can be implemented
  • FIG. 2 illustrates a signalling chart illustrating a communication process in accordance with some example embodiments of the present disclosure
  • FIG. 3 illustrates a signalling chart illustrating a communication process in accordance with some example embodiments of the present disclosure
  • FIG. 4 illustrates a flowchart of an example method implemented at a terminal device for model training in accordance with some embodiments of the present disclosure
  • FIG. 5 illustrates a flowchart of an example method implemented at a location server in accordance with some embodiments of the present disclosure
  • FIG. 6 illustrates a flowchart of an example method implemented at a terminal device for model training in accordance with some embodiments of the present disclosure
  • FIG. 7 illustrates a flowchart of an example method implemented at a location server in accordance with some embodiments of the present disclosure.
  • FIG. 8 illustrates a simplified block diagram of a device that is suitable for implementing embodiments of the present disclosure.
  • references in the present disclosure to “one embodiment, ” “an embodiment, ” “an example embodiment, ” and the like indicate that the embodiment described may include a particular feature, structure, or characteristic, but it is not necessary that every embodiment includes the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.
  • first and second etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and similarly, a second element could be termed a first element, without departing from the scope of example embodiments.
  • the term “and/or” includes any and all combinations of one or more of the listed terms.
  • values, procedures, or apparatus are referred to as “best, ” “lowest, ” “highest, ” “minimum, ” “maximum, ” or the like. It will be appreciated that such descriptions are intended to indicate that a selection among many used functional alternatives can be made, and such selections need not be better, smaller, higher, or otherwise preferable to other selections.
  • the term “communication network” refers to a network following any suitable communication standards or technologies, such as New Radio (NR) , Long Term Evolution (LTE) , LTE-Advanced (LTE-A) , Code Divided Multiple Address (CDMA) , Frequency Divided Multiple Address (FDMA) , Time Divided Multiple Address (TDMA) , Frequency Divided Duplexer (FDD) , Time Divided Duplexer (TDD) , Multiple-Input Multiple-Output (MIMO) , Orthogonal Frequency Divided Multiple Access (OFDMA) , cdma2000, Wideband Code Division Multiple Access (WCDMA) , High-Speed Packet Access (HSPA) , Global System for Mobile Communications (GSM) , Narrow Band Internet of Things (NB-IoT) and so on.
  • NR New Radio
  • LTE Long Term Evolution
  • LTE-A LTE-Advanced
  • CDMA Code Divided Multiple Address
  • FDMA Frequency Divided Multiple Address
  • the communications between a terminal device and a network device in the communication network may be performed according to any suitable generation communication protocols, including, but not limited to, the first generation (1G) , the second generation (2G) , 2.5G, 2.75G, the third generation (3G) , the fourth generation (4G) , 4.5G, the fifth generation (5G) , 5.5G, 5G-Advanced networks, beyond 5G (B5G) , the sixth generation (6G) communication protocols, wireless local network communication protocols such as Institute for Electrical and Electronics Engineers (IEEE) 802.11 and the like, and/or any other protocols either currently known or to be developed in the future.
  • the techniques described herein may be used for the wireless networks and radio technologies mentioned above as well as other wireless networks and radio technologies.
  • Embodiments of the present disclosure may be applied in various communication systems. Given the rapid development in communications, there will of course also be future type communication technologies and systems with which the present disclosure may be embodied. It should not be seen as limiting the scope of the present disclosure to only the aforementioned system.
  • terminal device refers to any device having wireless or wired communication capabilities.
  • Examples of terminal device include, but not limited to, user equipment (UE) , personal computers, desktops, mobile phones, cellular phones, smart phones, personal digital assistants (PDAs) , portable computers, tablets, wearable devices, internet of things (IoT) devices, Ultra-reliable and Low Latency Communications (URLLC) devices, Internet of Everything (IoE) devices, machine type communication (MTC) devices, device on vehicle for V2X communication where X means pedestrian, vehicle, or infrastructure/network, devices for Integrated Access and Backhaul (IAB) , Space borne vehicles or Air borne vehicles in Non-terrestrial networks (NTN) including Satellites and High Altitude Platforms (HAPs) encompassing Unmanned Aircraft Systems (UAS) , eXtended Reality (XR) devices including different types of realities such as Augmented Reality (AR) , Mixed Reality (MR) and Virtual Reality (VR) , the unmanned aerial vehicle (UAV) commonly
  • UE user equipment
  • the ‘terminal device’ can further has ‘multicast/broadcast’ feature, to support public safety and mission critical, V2X applications, transparent IPv4/IPv6 multicast delivery, IPTV, smart TV, radio services, software delivery over wireless, group communications and IoT applications. It may also be incorporated one or multiple Subscriber Identity Module (SIM) as known as Multi-SIM.
  • SIM Subscriber Identity Module
  • the term “terminal device” can be used interchangeably with a UE, a mobile station, a subscriber station, a mobile terminal, a user terminal or a wireless device.
  • the term “network device” refers to a device which is capable of providing or hosting a cell or coverage where terminal devices can communicate.
  • a network device include, but not limited to, a satellite, an unmanned aerial systems (UAS) platform, a Node B (NodeB or NB) , an evolved NodeB (eNodeB or eNB) , a next generation NodeB (gNB) , a transmission reception point (TRP) , a remote radio unit (RRU) , a radio head (RH) , a remote radio head (RRH) , an IAB node, a low power node such as a femto node, a pico node, a reconfigurable intelligent surface (RIS) , and the like.
  • UAS unmanned aerial systems
  • NodeB Node B
  • eNodeB or eNB evolved NodeB
  • gNB next generation NodeB
  • TRP transmission reception point
  • RRU remote radio unit
  • RH radio
  • TRP may refer to an antenna port or an antenna array (with one or more antenna elements) available to the network device located at a specific geographical location, or a set of geographically co-located antennas (e.g. antenna array (with one or more antenna elements) ) supporting transmission point (TP) and/or reception point (RP) functionality.
  • TP transmission point
  • RP reception point
  • a network device may be coupled with multiple TRPs in different geographical locations to achieve better coverage.
  • multiple TRPs may be incorporated into a network device, or in other words, the network device may comprise the multiple TRPs.
  • TRP may be also referred to as a cell, such as a macro-cell, a micro-cell, a small cell, a pico-cell, a femto-cell, a remote radio head, a relay node, etc. It is to be understood that the term “TRP” may refer to a logical concept which may be physically implemented by various manners. There may be an explicit TRP ID for a TRP.
  • the terminal device may be connected with a first network device and a second network device.
  • One of the first network device and the second network device may be a master node (MN) and the other one may be a secondary node (SN) .
  • the first network device and the second network device may use different radio access technologies (RATs) .
  • the first network device may be a first RAT device and the second network device may be a second RAT device.
  • the first RAT device is eNB and the second RAT device is gNB.
  • Information related with different RATs may be transmitted to the terminal device from at least one of the first network device and the second network device.
  • first information may be transmitted to the terminal device from the first network device and second information may be transmitted to the terminal device from the second network device directly or via the first network device.
  • information related with configuration for the terminal device configured by the second network device may be transmitted from the second network device via the first network device.
  • Information related with reconfiguration for the terminal device configured by the second network device may be transmitted to the terminal device from the second network device directly or via the first network device.
  • the terminal device or the network device may have Artificial intelligence (AI) or machine learning capability. It generally includes a model which has been trained from numerous collected data for a specific function, and can be used to predict some information.
  • AI Artificial intelligence
  • machine learning capability it generally includes a model which has been trained from numerous collected data for a specific function, and can be used to predict some information.
  • the terminal device or the network device may work on several frequency ranges, e.g. frequency range 1 (FR1) (410 MHz –7125 MHz) , frequency range 2 (FR2) (24.25GHz to 71GHz) , frequency band larger than 100GHz as well as Tera Hertz (THz) . It can further work on licensed/unlicensed/shared spectrum.
  • the terminal device may have more than one connection with the network device under Multi-Radio Dual Connectivity (MR-DC) application scenario.
  • MR-DC Multi-Radio Dual Connectivity
  • the terminal device or the network device can work on full duplex, flexible duplex and cross division duplex modes.
  • test equipment e.g., signal generator, signal analyzer, spectrum analyzer, network analyzer, test terminal device, test network device, or channel emulator.
  • the embodiments of the present disclosure may be performed according to any generation communication protocols either currently known or to be developed in the future.
  • Examples of the communication protocols include, but not limited to, the 1G, 2G, 2.5G, 2.75G, 3G, 4G, 4.5G, 5G, 5.5G, 5G-Advanced networks, or 6G networks.
  • circuitry used herein may refer to hardware circuits and/or combinations of hardware circuits and software.
  • the circuitry may be a combination of analog and/or digital hardware circuits with software/firmware.
  • the circuitry may be any portions of hardware processors with software including digital signal processor (s) , software, and memory (ies) that work together to cause an apparatus, such as a terminal device or a network device, to perform various functions.
  • the circuitry may be hardware circuits and or processors, such as a microprocessor or a portion of a microprocessor, that requires software/firmware for operation, but the software may not be present when it is not needed for operation.
  • the term circuitry also covers an implementation of merely a hardware circuit or processor (s) or a portion of a hardware circuit or processor (s) and its (or their) accompanying software and/or firmware.
  • values, procedures, or apparatus are referred to as “best, ” “lowest, ” “highest, ” “minimum, ” “maximum, ” or the like. It will be appreciated that such descriptions are intended to indicate that a selection among many used functional alternatives can be made, and such selections need not be better, smaller, higher, or otherwise preferable to other selections.
  • the terminal device or the network device may have AI or ML capability. It generally includes a model which has been trained from numerous collected data for a specific function, and can be used to predict some information.
  • a model may be equivalent to at least one of the following: an AI/ML model, an ML model, an AI model, a data-driven, a data processing model, an algorithm, a functionality, a procedure, a process, an entity, a function, a feature, a feature group, a model identifier (ID) , an ID, a functionality ID, a configuration ID, a scenario ID, a site ID, or a dataset ID.
  • ID may refer to an identifier, an identity, an identification, etc.
  • the model may be represented by or associated with a channel, a resource, a resource set, a reference signal (RS) resource, an RS resource set, an RS port, a set of RS ports, an RS port ID, or a set of RS port IDs.
  • RS reference signal
  • the model may comprise a set of weight values that may be learned during training, e.g., for a specific architecture or configuration, where a set of weight values may also be called a parameter set.
  • the model may be used to predict a target cell, or measurements of a set of beams of a set of candidate cells in future based on at least historical measurements (e.g., layer 1 (L1) -reference signal received power (RSRP) , L1-signal to interference plus noise ratio (SINR) ) of a set of beams of a set of candidate cells.
  • L1 layer 1
  • RSRP reference signal received power
  • SINR L1-signal to interference plus noise ratio
  • an input of the AI/ML model may refer to the input of a model and indicate data inputted into the model, which may be equivalent to data.
  • an output of AI/ML model may refers to the output of a model and indicate result (s) outputted by the model, which is equivalent to label/data.
  • ground truth , “ground truth label” , “ground truth label of data” , “input label” , “input data” and “data” can be used interchangeably.
  • a ground truth label of data (or ground-truth label) for monitoring or training the ML model may refers to the authoritative, accepted data, or true answer or outcome for AI/ML model.
  • the ground truth can be interpreted as actual/factual (i.e. actual/factual measured) data/values/results/collections/parameters, which can be used as reference, compared to prediction or inference.
  • AI/ML techniques play a significant role in enhancing the accuracy and reliability of positioning, which is particularly useful in indoor environments where global position system (GPS) signals might be weak or unavailable.
  • GPS global position system
  • An AI/ML model may be deployed at a terminal device (such as a UE) , a network device (such as one or more gNBs or TRPs) , or a core network entity (such as an LMF) .
  • the AI/ML model may be used for positioning, e.g. determining a positon (or location) of a UE.
  • Some cases (case 1, case 2b, and case 3b below) are discussed as direct AI/ML positioning, and some other cases (case 2a, and case 3a below) are discussed as AI/ML assisted positioning:
  • Case 1 UE-based positioning with UE-side model, direct AI/ML positioning.
  • ⁇ Case 2b UE-assisted/LMF-based positioning with LMF-side model, direct AI/ML positioning.
  • Case 2a UE-assisted/LMF-based positioning with UE-side model, AI/ML assisted positioning.
  • An enhancement for the accuracy of the AI/ML based positioning is a work item (WI) in release 19.
  • An AI/ML model can be deployed at UE side, gNB side, or LMF side.
  • a model input may be integrated information of timing, power and phase, such as channel impulse response (CIR) , power delay profile (PDP) , or delay of path (DP) .
  • a model output may be a UE location (i.e., direct AI/ML positioning) or an intermediate measurement (i.e., AI/ML assisted positioning) .
  • the candidate output may include timing information of light of sight (LOS) or non-line of sight (NLOS) indicator, timing information like reference signal time difference (RSTD) , downlink reference signal time of arrival (DL-RTOA) , or UE Rx-Tx time difference for DL positioning, or uplink reference signal time of arrival (UL-RTOA) or gNB Rx-Tx time difference for UL positioning.
  • LOS light of sight
  • NLOS non-line of sight
  • RSTD reference signal time difference
  • DL-RTOA downlink reference signal time of arrival
  • UL-RTOA uplink reference signal time of arrival
  • gNB Rx-Tx time difference for UL positioning gNB Rx-Tx time difference for UL positioning.
  • Embodiments of the present disclosure provide a solution of communication.
  • a terminal device deployed with a trained positioning model transmits information about metadata associated with inference data to a location server, and accordingly the location server may determine one or more TRPs for transmitting PRSs, which will be used for model inference. Since the metadata associated with the inference data is determined based on metadata associated with training data, the consistency between training and inference can be ensured. Therefore, the model can be used for efficiency and the result of model inference will be more accurate. Principles and implementations of the present disclosure will be described in detail below with reference to the figures.
  • FIG. 1 illustrates an example communication network 100 in which some embodiments of the present disclosure can be implemented.
  • the communication network 100 may also be called as a network environment, a network system, a communication environment, a communication system, or the like, the present disclosure does not limit for this aspect.
  • the communication network 100 includes a terminal device 110, multiple network devices 120-1 to 120-N, and a location server 130.
  • the location server 130 may be implemented as a location management function (LMF) which is located in the access network or in a core network.
  • LMF location management function
  • the multiple network devices 120-1 to 120-N may be separately or collectively be referred to as a network device 120, which may be a gNB or a TRP.
  • N may equal to 18 or another integer.
  • one of the network devices 120-1 to 120-N may be a serving gNB, which can control and manage other network devices (e.g. TRPs) .
  • TRPs network devices
  • the network device 120 can communicate/transmit data and control information to the terminal device 110, and the terminal device 110 can also communicate/transmit data and control information to the network device 120.
  • a link from the network device 120 to the terminal device 110 is referred to as a DL, while a link from the terminal device 110 to the network device 120 is referred to as a UL.
  • DL may comprise one or more logical channels, including but not limited to a Physical Downlink Control Channel (PDCCH) and a Physical Downlink Shared Channel (PDSCH) .
  • UL may comprise one or more logical channels, including but not limited to a Physical Uplink Control Channel (PUCCH) and a Physical Uplink Shared Channel (PUSCH) .
  • the term “channel” may refer to a carrier or a part of a carrier consisting of a contiguous set of resource blocks (RBs) on which a channel access procedure is performed in shared spectrum.
  • RBs resource blocks
  • the terminal device 110 can communicate with the location server 130 according to any proper communication protocol, such as an LTE positioning protocol (LPP) .
  • LTP LTE positioning protocol
  • the network device 120 can communicate with the location server 130 according to any proper communication protocol, such as an NR positioning protocol A (NRPPa) . It is to be understood that other protocol may also be applied and will not be listed herein.
  • NRPPa NR positioning protocol A
  • Embodiments of the present disclosure can be applied to any suitable scenarios.
  • embodiments of the present disclosure can be implemented at reduced capability NR devices.
  • embodiments of the present disclosure can be implemented in one of the followings: NR multiple-input and multiple-output (MIMO) , NR sidelink enhancements, NR systems with frequency above 52.6GHz, an extending NR operation up to 71GHz, narrow band-Internet of Thing (NB-IOT) /enhanced Machine Type Communication (eMTC) over non-terrestrial networks (NTN) , NTN, UE power saving enhancements, NR coverage enhancement, NB-IoT and LTE-MTC, Integrated Access and Backhaul (IAB) , NR Multicast and Broadcast Services, or enhancements on Multi-Radio Dual-Connectivity.
  • MIMO multiple-input and multiple-output
  • NR sidelink enhancements NR systems with frequency above 52.6GHz, an extending NR operation up to 71GHz
  • NB-IOT narrow band-Internet of
  • the communication network 100 may include any suitable numbers of devices adapted for implementing embodiments of the present disclosure.
  • the communication network 100 may include one or more entities which are not shown in FIG. 1, for example, there may be a positioning reference unit (PRU) which can communicate with at least one of the network devices 120 and the server location 130.
  • PRU positioning reference unit
  • AI/ML model is a data driven algorithm that applies AI/ML techniques to generate a set of outputs based on a set of inputs
  • data collection is a process of collecting data by the network nodes, management entity, or UE for the purpose of AI/ML model training, data analytics and inference
  • model training is a process to train an AI/ML model by learning the input/output relationship in a data driven manner and obtain the trained AI/ML model for inference
  • model inference is a process of using a trained AI/ML model to produce a set of outputs based on a set of inputs
  • UE side model means an AI/ML model whose inference is performed entirely at the UE
  • model performance monitoring is a procedure that monitors the inference performance of the AI/ML model.
  • FIG. 2 illustrates a signalling chart illustrating communication process 200 in accordance with some example embodiments of the present disclosure.
  • the process 200 may involve a terminal device 110, a location server 130, and TRPs 120 with reference to FIG. 1. It would be appreciated that the process 200 may be applied to other communication scenarios, which will not be described in detail.
  • the positioning model deployed at the terminal device 110, for example, the positioning model has been trained using training data.
  • the positioning model is a UE side model, which may be applied for above-mentioned Case 1 or Case 2a.
  • the terminal device 110 may collect the training data and train the positioning model based on the training data.
  • metadata associated with the training data may be determined and used.
  • the metadata associated with the training data may include a validity area for training data collection and/or a downlink PRS (DL-PRS) configuration for training data collection.
  • the validity area refers to an area in which the training data is collected, which may be defined by an information element (IE) “AreaID-CellList” .
  • IE AreaID-CellList provides NR Cell-IDs of TRPs belonging to a particular network area where the associated assistance data are valid, where each cell is included in only one area.
  • the DL-PRS configuration refers to DL-PRS configuration of NR candidate TRPs for data collection, which may be included in one or more of the following IEs: NR-DL-PRS-PositioningFrequencyLayer, NR-DL-PRS-ResourceSet, and NR-DL-PRS-Resource.
  • the metadata may further include some other information, such as part or all of the following: information describing the model (e.g., a model version, a model size, a provider of the model) , measurement data quality range (e.g. SNR/SINR range) , label data quality range (e.g. mean label positioning error) , time range when data generated, network synchronization error, Rx-Tx timing error, phase offset error, antenna/beam pattern information, TRP set, or the like.
  • information describing the model e.g., a model version, a model size, a provider of the model
  • measurement data quality range e.g. SNR/SINR range
  • label data quality range e.g. mean label positioning error
  • each positioning model is trained based on corresponding training data (or metadata associated with the training data) . It is to be noted metadata for different positioning models may be the same or may be different, the present disclosure does not limit for this aspect.
  • the term “metadata associated with training data” can be interchangeably used with metadata of training data, validity area for training data collection, DL-PRS configuration for training data collection, or the like, which may include information that describes the training data.
  • training data may be used interchangeably with training dataset, a set of training data, a set of data for model training, or the like, the present disclosure does not limit for this aspect.
  • the terminal device 110 transmits information about metadata associated with inference data to the location server 130 at 210.
  • the positioning model is triggered or activated for model inference, e.g., for determining a position of the terminal device 110, the information at 210 may be transmitted.
  • one or more positioning models may be determined or triggered or activated for positioning.
  • the information about metadata associated with inference data may be determined, by the terminal device 110, based on metadata associated with training data of the positioning model, e.g., by considering a consistency. For example, the information about metadata associated with inference data may be called as consistency information.
  • the metadata associated with the inference data may include a validity area for data collection of the inference data.
  • the information about the metadata may include a list of cell IDs of at least one TRP, e.g., a list of NR Cell-IDs of TPS belonging to a particular network area where the inference data is expected to be generated/collected by the terminal device 110.
  • the list of cell IDs of TRPs may be part or all of those used for training data collection.
  • the information about the metadata may include a list of cell IDs of a serving cell of the terminal device, e.g., a list of NR Cell-IDs of the serving cell belonging to a particular network area where the inference data is expected to be generated/collected by the terminal device 110.
  • a plurality of validity areas for inference data corresponding to the plurality of positioning models respectively may be determined.
  • information about the metadata may include joint information for the plurality of validity areas or separate information for respective validity area in the plurality of validity areas.
  • the joint information may indicate a joint list of the plurality of validity areas, e.g., a joint list of cell IDs of TRPs.
  • a unit validity area may be determined by combining the plurality of validity areas, and the joint list of cell IDs of TRPs may be determined based on the unit validity area.
  • a plurality of lists of cell IDs may be determined for the plurality of validity areas respectively, and the joint list of cell IDs of TRPs may be determined based on a combination of the plurality of lists.
  • a validity area A is for a first positioning model and a validity area B is for a second positioning model
  • the joint information may indicate a whole area (A+B) without indicating corresponding models.
  • separate information may indicate a plurality of validity areas for the plurality of positioning models respectively, e.g., a plurality of lists of cell IDs of TRPs for the plurality of positioning models respectively.
  • the separate information may further include a model ID associated with a list of cell IDs of TRPs, for indicating which model the list of cell IDs is used for.
  • a plurality of lists of cell IDs may be determined for the plurality of validity areas respectively.
  • the separate information may include a first list of cell IDs of TRPs corresponding to the validity area A for the first positioning model and a second list of cell IDs of TRPs corresponding to validity area B for the second positioning model.
  • the metadata associated with the inference data may include a DL-PRS configuration for data collection of the inference data.
  • the information about the metadata may include a plurality of identifiers of candidate TRPs associated with the DL PRS configuration or information about one or more validity areas that are mapped with the DL PRS configuration. For example, there may be a one-to-multiple mapping relationship between the DL-PRS configuration and validity area (s) .
  • a DL-PRS configuration has transferred from the location server 130 to the terminal device 110 for training data collection, and the transferred DL-PRS configuration may be correspond to (or have, indicate, be associated with) a validity area for training data collection.
  • a validity area for training data collection may have been transferred from the server location 130 to the terminal device 110, in this case, a corresponding DL-PRS configuration may be implicitly indicated.
  • the metadata associated with the inference data may include a validity area and/or a DL-PRS configuration for data collection of the inference data.
  • the information about the metadata associated with the inference data may include a specific ID which is associated with the metadata.
  • the specific ID may be a model ID, a metadata ID, a UE ID, a serving cell ID, or a dedicated associated ID configured by the location server 130.
  • the location server 130 may maintain an association between the specific ID and metadata for training data collection. In some instances, for training data collection, the location server 130 may transmit the association between the specific ID and metadata for training data collection to the terminal device 110. In some instances, for inference data collection, the terminal device 110 may transmit the specific ID to the location server 130 to request consistency configuration.
  • the information about metadata associated with inference data at 210 may be transmitted via an LPP message, which may be an existing LPP message or a newly-defined LPP message.
  • the information about metadata associated with inference data at 210 may be transferred in a procedure related to capability transfer procedure with or without a request.
  • the information about metadata associated with inference data may be included in a capability message.
  • the location server 130 may transmit, and the terminal device 110 may receive, a request for the capability message, and accordingly, the terminal device 110 may transmit, and the location server 130 may receive, the capability message which includes the information about metadata.
  • a capability of the terminal device 110 may include (or refers to) positioning and protocol capabilities related to LPP and/or positioning methods supported by LPP.
  • the information about metadata associated with inference data at 210 may be transferred in a request assistance data message.
  • the terminal device 110 may transmit an LPP request assistance data message to the location server 130, where the LPP request assistance data message may include the information about metadata associated with inference data.
  • the terminal device 110 may request measurement report resources by transmitting an LPP request assistance data message which includes the information about metadata associated with inference data.
  • the information about metadata associated with inference data at 210 may be transferred in a dedicated procedure related to AI/ML positioning data transfer, e.g., with or without a request from the location server 130.
  • a newly-defined procedure may be used for the transfer of the information about metadata associated with inference data at 210.
  • the information about metadata associated with inference data at 210 may be transferred in a procedure regarding functionality report from the terminal device 110 to the location server 130.
  • a functionality report may be related to supported/available/applicable/configured/activated functionalities.
  • the location server 130 determines at least one TRP at 220 based on the information about metadata associated with inference data received from the terminal device 110.
  • NR cell IDs of the at least one TRP may belong to the validity area or may be related to the DL-PRS configuration, which is indicated by the information about metadata associated with inference data.
  • the location server 130 may transmit an indication to the at least one TRP 120 at 230, e.g., indicating to the at least one TRP to transmit at least one PRS to the terminal device 110 for model inference. Accordingly, at 240, the at least one TRP 120 transmits, and the terminal device 110 receives, the at least one PRS.
  • the terminal device 110 further measures, at 245, the at least one PRS to obtain a measurement result, and thus determine a model input.
  • the terminal device 110 performs a model inference at 250 based on the model input.
  • a model output may be obtained after performing the model inference, and the model output may be related to a location of the terminal device 110.
  • a terminal device transmits information about metadata associated with inference data to a location server, and accordingly the location server may determine one or more TRPs for transmitting PRSs, which will be used for model inference at the terminal device. Since the metadata associated with the inference data is determined based on metadata associated with training data, the consistency between training and inference can be ensured. Therefore, at least for Case 1 and Case 2a with UE side model mentioned above, the terminal device can get a measurement (as inference input) within a same or similar validity area and/or based on a same or similar DL-PRS configuration, so as to keep a consistency.
  • FIG. 3 illustrates a signalling chart illustrating communication process 300 in accordance with some example embodiments of the present disclosure.
  • the process 300 may involve a terminal device 110 and a location server 130 with reference to FIG. 1. It would be appreciated that the process 300 may be applied to other communication scenarios, which will not be described in detail.
  • the positioning model deployed at the terminal device 110, for example, the positioning model has been trained using training data.
  • the positioning model is a UE side model, which may be applied for above-mentioned Case 1 or Case 2a.
  • the terminal device 110 may collect the training data and train the positioning model based on the training data.
  • metadata associated with the training data may be determined and used.
  • the metadata associated with the training data may include a validity area for training data collection and/or a DL-PRS configuration for training data collection.
  • the terminal device 110 maintains an association between the positioning model and the metadata for training data collection at 305.
  • each positioning model is trained based on corresponding training data (or metadata associated with the training data) .
  • an association between each positioning model and corresponding metadata may be maintained at the terminal device 110.
  • the location server 130 transmits, and the terminal device 110 receives, a configuration for model inference at 310.
  • the configuration for model inference may indicate to the terminal device 110 to perform inference data collection.
  • the terminal device 110 may determine whether the configuration for model inference that received at 310 is consistent with a DL-PRS configuration for data collection of training data. Depending on the determination result, the terminal device 110 may perform the operation 320 or 330.
  • the terminal device 110 may further determine a corresponding positioning model based on the maintained association.
  • the terminal device 110 performs a model inference process. For example, the model inference process is performed based on the configuration received at 310 using the corresponding positioning model.
  • the terminal device 110 may, at 330, transmit an error indication to the location server 130.
  • the error indication may indicate that the configuration is inconsistent with a DL-PRS configuration for data collection of training data.
  • the error indication may be regarded as a metadata request.
  • the error indication may include information about metadata associated with inference data, e.g., that discussed with reference to FIG. 2.
  • the server location 130 may provide, based on the error indication, a new inference configuration which is consistent with a DL-PRS configuration for data collection of training data.
  • the terminal device 110 may fall back to a legacy positioning method to determine the location. In some examples, after transmitting the error indication, the terminal device 110 may perform a model training procedure, e.g., triggering training data collection to retrain the model.
  • a model training procedure e.g., triggering training data collection to retrain the model.
  • the model inference is performed when a consistency between the configuration for model inference and a DL-PRS configuration for data collection of training data is determined. Therefore, the model can be used for efficiency and the result of model inference will be more accurate.
  • FIG. 4 illustrates a flowchart of an example method 400 implemented at a terminal device in accordance with some embodiments of the present disclosure.
  • the terminal device may be the terminal device 110 with reference to FIG. 1 which has a deployed AI/ML positioning model.
  • the terminal device 110 transmits, to a location server, information about metadata associated with inference data of the positioning model, wherein the metadata associated with the inference data is determined based on further metadata associated with training data of the positioning model.
  • the terminal device 110 receives, from at least one TRP associated with the metadata associated with the inference data, at least one PRS.
  • the terminal device performs, using the trained positioning model, a model inference process based on measurement of the at least one PRS.
  • the method 400 may include various other operations which may be performed by the terminal device 110 as described above with reference to FIG. 2.
  • FIG. 5 illustrates a flowchart of an example method 500 implemented at a location server in accordance with some embodiments of the present disclosure.
  • the location server may be the location server 130 (such as an LMF) with reference to FIG. 1.
  • the location server 130 receive, from a terminal device deployed with a trained positioning model, information about metadata associated with inference data of the positioning model.
  • the location server 130 determines at least one TRP based on the information about the metadata.
  • the location server 130 transmits, to the at least one TRP, an indication indicating to the at least one TRP to transmit at least one PRS for model inference at the terminal device.
  • the method 500 may include various other operations which may be performed by the location server 130 as described above with reference to FIG. 2.
  • FIG. 6 illustrates a flowchart of an example method 600 implemented at a terminal device in accordance with some embodiments of the present disclosure.
  • the terminal device may be the terminal device 110 with reference to FIG. 1 which has a deployed AI/ML positioning model.
  • the terminal device 110 receives, from a location server, a configuration for model inference.
  • the terminal device 110 performs a model inference process based on the configuration using the trained positioning model.
  • the method 600 may include various other operations which may be performed by the terminal device 110 as described above with reference to FIG. 3.
  • FIG. 7 illustrates a flowchart of an example method 700 implemented at a location server in accordance with some embodiments of the present disclosure.
  • the location server may be the location server 130 (such as an LMF) with reference to FIG. 1.
  • the location server 130 transmits, to a terminal device deployed with a trained positioning model, a configuration for model inference.
  • the location server 130 receives, from the terminal device, an error indication indicating that the configuration is inconsistent with a DL-PRS configuration for data collection of training data.
  • the method 700 may include various other operations which may be performed by the location server 130 as described above with reference to FIG. 3.
  • FIGS. 1-7 Details of some embodiments according to the present disclosure have been described with reference to FIGS. 1-7. Now an example implementation of the device deployed with at least one positioning model will be discussed below.
  • a terminal device comprises circuitry configured to: transmit, to a location server, information about metadata associated with inference data of the positioning model, wherein the metadata associated with the inference data is determined based on further metadata associated with training data of the positioning model; receive, from at least one TRP associated with the metadata associated with the inference data, at least one PRS; and perform, using the trained positioning model, a model inference process based on measurement of the at least one PRS.
  • terminal device comprises circuitry configured to perform various other operations as described above with reference to FIG. 2.
  • a location server comprises circuitry configured to: receive, from a terminal device deployed with a trained positioning model, information about metadata associated with inference data of the positioning model; determine at least one TRP based on the information about the metadata; and transmit, to the at least one TRP, an indication indicating to the at least one TRP to transmit at least one PRS for model inference at the terminal device.
  • location server comprises circuitry configured to perform various other operations as described above with reference to FIG. 2.
  • a terminal device comprises circuitry configured to: receive, from a location server, a configuration for model inference; and in accordance with a determination that the configuration is consistent with a DL-PRS configuration for data collection of training data, perform a model inference process based on the configuration using the trained positioning model.
  • terminal device comprises circuitry configured to perform various other operations as described above with reference to FIG. 3.
  • a location server comprises circuitry configured to: transmit, to a terminal device deployed with a trained positioning model, a configuration for model inference; and receive, from the terminal device, an error indication indicating that the configuration is inconsistent with a DL-PRS configuration for data collection of training data.
  • location server comprises circuitry configured to perform various other operations as described above with reference to FIG. 3.
  • FIG. 8 illustrates a simplified block diagram of a device 800 that is suitable for implementing embodiments of the present disclosure.
  • the device 800 can be considered as a further example implementation of the terminal device 110 or the location server 130 as described above. Accordingly, the device 800 can be implemented at or as at least a part of the terminal device 110 or the location server 130 as shown in FIG. 1.
  • the device 800 includes a processor 810, a memory 820 coupled to the processor 810, a suitable transceiver 840 coupled to the processor 810, and a communication interface coupled to the transceiver 840.
  • the memory 820 stores at least a part of a program 830.
  • the transceiver 840 may be for bidirectional communications or a unidirectional communication based on requirements.
  • the transceiver 840 may include at least one of a transmitter and a receiver.
  • the transmitter and the receiver may be functional modules or physical entities.
  • the transceiver 840 has at least one antenna to facilitate communication, though in practice an Access Node mentioned in this application may have several ones.
  • the communication interface may represent any interface that is necessary for communication with other network elements, such as X2/Xn interface for bidirectional communications between eNBs/gNBs, S1/NG interface for communication between a Mobility Management Entity (MME) /Access and Mobility Management Function (AMF) /serving gateway (SGW) /user plane function (UPF) and the eNB/gNB, Un interface for communication between the eNB/gNB and a relay node (RN) , or Uu interface for communication between the eNB/gNB and a terminal device.
  • MME Mobility Management Entity
  • AMF Access and Mobility Management Function
  • SGW serving gateway
  • UPF user plane function
  • Un interface for communication between the eNB/gNB and a relay node (RN)
  • RN relay node
  • Uu interface for communication between the eNB/gNB and a terminal device.
  • the program 830 is assumed to include program instructions that, when executed by the associated processor 810, enable the device 800 to operate in accordance with the embodiments of the present disclosure, as discussed herein with reference to FIGS. 1-7.
  • the embodiments herein may be implemented by computer software executable by the processor 810 of the device 800, or by hardware, or by a combination of software and hardware.
  • the processor 810 may be configured to implement various embodiments of the present disclosure.
  • a combination of the processor 810 and memory 820 may form processing means 850 adapted to implement various embodiments of the present disclosure.
  • the memory 820 may be of any type suitable to the local technical network and may be implemented using any suitable data storage technology, such as a non-transitory computer readable storage medium, semiconductor-based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory and removable memory, as non-limiting examples. While only one memory 820 is shown in the device 800, there may be several physically distinct memory modules in the device 800.
  • the processor 810 may be of any type suitable to the local technical network, and may include one or more of general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs) and processors based on multicore processor architecture, as non-limiting examples.
  • the device 800 may have multiple processors, such as an application specific integrated circuit chip that is slaved in time to a clock which synchronizes the main processor.
  • embodiments of the present disclosure may provide the following solutions.
  • the present disclosure provides a terminal device deployed with a trained positioning model, comprising at least one processor configured to cause the terminal device at least to: transmit, to a location server, information about metadata associated with inference data of the positioning model, wherein the metadata associated with the inference data is determined based on further metadata associated with training data of the positioning model; receive, from at least one TRP associated with the metadata associated with the inference data, at least one PRS; and perform, using the trained positioning model, a model inference process based on measuring the at least one PRS.
  • the terminal device as above, the metadata associated with the inference data comprises a validity area for data collection of the inference data, and wherein the information about the metadata associated with the inference data comprises at least one of: a list of cell IDs of the at least one TRP, or a list of cell IDs of a serving cell of the terminal device.
  • the terminal device as above, there are a plurality of trained positioning models deployed at the terminal device, and wherein the information about the metadata associated with the inference data comprises information about a combination of a plurality of validity areas for the plurality of trained positioning models.
  • the terminal device as above, the information about the metadata associated with the inference data is associated with a model identifier of the positioning model.
  • the terminal device as above, the metadata associated with the inference data comprises a DL-PRS configuration for data collection of the inference data, and wherein the information about the metadata associated with the inference data comprises at least one of: a plurality of identifiers of candidate TRPs associated with the DL PRS configuration, or information about one or more validity areas that are mapped with the DL PRS configuration.
  • the terminal device as above, the information about the metadata associated with the inference data is comprised in at least one of: a capability message, a request assistance data message, or a dedicated data transfer message.
  • the terminal device as above, the at least one processor is further configured to cause the terminal device to: receive, from the location server, a request for the capability message.
  • the terminal device as above, the information about the metadata associated with the inference data comprises a specific ID, and wherein an association between the specific ID and a DL PRS configuration for data collection of the training data is maintained at the location server.
  • the terminal device as above, the specific ID comprises at least one of: a model ID, a metadata ID, a UE ID, or a serving cell ID.
  • the present disclosure provides a location server, comprising at least one processor configured to cause the location server at least to: receive, from a terminal device deployed with a trained positioning model, information about metadata associated with inference data of the positioning model; determine at least one TRP based on the information about the metadata; and transmit, to the at least one TRP, an indication indicating to the at least one TRP to transmit at least one PRS for model inference at the terminal device.
  • the location server as above, the metadata comprises a validity area for data collection of the inference data, and wherein the information about the metadata comprises at least one of: a list of cell IDs of the at least one TRP, or a list of cell IDs of a serving cell of the terminal device.
  • the location server as above, there are a plurality of trained positioning models deployed at the terminal device, and wherein the information about the metadata comprises information about a combination of a plurality of validity areas for the plurality of trained positioning models.
  • the location server as above, the information about the metadata is associated with a model identifier of the positioning model.
  • the location server as above, the metadata comprises a DL-PRS configuration for data collection of the inference data, and wherein the information about the metadata comprises at least one of: a plurality of identifiers of candidate TRPs associated with the DL PRS configuration, or information about one or more validity areas that are mapped with the DL PRS configuration.
  • the location server as above, the information about the metadata is comprised in at least one of: a capability message, a request assistance data message, or a dedicated data transfer message.
  • the location server as above, the at least one processor is further configured to cause the location server to: transmit, to the terminal device, a request for the capability message.
  • the location server as above, the information about the metadata comprises a specific ID, and wherein an association between the specific ID and a DL PRS configuration for data collection of the training data is maintained at the location server.
  • the location server as above, the specific ID comprises at least one of: a model ID, a metadata ID, a UE ID, or a serving cell ID.
  • the present disclosure provides a terminal device deployed with a trained positioning model, comprising at least one processor configured to cause the terminal device at least to: receive, from a location server, a configuration for model inference; and in accordance with a determination that the configuration is consistent with a DL-PRS configuration for data collection of training data, perform a model inference process based on the configuration using the trained positioning model.
  • the terminal device as above, the at least one processor is further configured to cause the terminal device to: in accordance with a determination that the configuration is inconsistent with the DL PRS configuration of training data, transmit, to the location server, an error indication.
  • the present disclosure provides a location server, comprising at least one processor configured to cause the location server at least to: transmit, to a terminal device deployed with a trained positioning model, a configuration for model inference; and receive, from the terminal device, an error indication indicating that the configuration is inconsistent with a DL-PRS configuration for data collection of training data.
  • the present disclosure provides a method of communication, comprising the operations implemented at the terminal device or the server location discussed above.
  • the present disclosure provides a device, comprising: a processor; and a memory storing computer program codes; the memory and the computer program codes configured to, with the processor, cause the device to perform the method implemented at the terminal device or the server location discussed above.
  • the present disclosure provides a non-transitory computer readable storage medium having instructions stored thereon, the instructions, when executed by a processor of an apparatus, cause the apparatus to perform the method implemented at the terminal device or the server location discussed above.
  • the present disclosure provides a computer program product having instructions stored thereon, the instructions, when executed by a processor of an apparatus, cause the apparatus to perform the method implemented at the terminal device or the server location discussed above.
  • various embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device. While various aspects of embodiments of the present disclosure are illustrated and described as block diagrams, flowcharts, or using some other pictorial representation, it will be appreciated that the blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.
  • the present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer readable storage medium.
  • the computer program product includes computer-executable instructions, such as those included in program modules, being executed in a device on a target real or virtual processor, to carry out the process or method as described above.
  • program modules include routines, programs, libraries, objects, classes, components, data structures, or the like that perform particular tasks or implement particular abstract data types.
  • the functionality of the program modules may be combined or split between program modules as desired in various embodiments.
  • Machine-executable instructions for program modules may be executed within a local or distributed device. In a distributed device, program modules may be located in both local and remote storage media.
  • Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowcharts and/or block diagrams to be implemented.
  • the program code may execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
  • the above program code may be embodied on a machine readable medium, which may be any tangible medium that may contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
  • the machine readable medium may be a machine readable signal medium or a machine readable storage medium.
  • a machine readable medium may include but not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.
  • machine readable storage medium More specific examples of the machine readable storage medium would include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM) , a read-only memory (ROM) , an erasable programmable read-only memory (EPROM or Flash memory) , an optical fiber, a portable compact disc read-only memory (CD-ROM) , an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
  • RAM random access memory
  • ROM read-only memory
  • EPROM or Flash memory erasable programmable read-only memory
  • CD-ROM portable compact disc read-only memory
  • magnetic storage device or any suitable combination of the foregoing.

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Abstract

Des exemples de modes de réalisation de la présente divulgation portent sur des dispositifs, des procédés, et un support de stockage informatique pour la communication. Un équipement terminal déployé avec un modèle de positionnement entraîné transmet des informations concernant des métadonnées associées à des données d'inférence à un serveur de localisation, et le serveur de localisation peut par conséquent déterminer un ou plusieurs TRP pour transmettre des PRS, qui seront utilisés pour une inférence de modèle. Étant donné que les métadonnées associées aux données d'inférence sont déterminées sur la base d'autres métadonnées associées à des données d'entraînement, la cohérence entre l'entraînement et l'inférence peut être assurée. Par conséquent, le modèle peut être utilisé pour l'efficacité et le résultat d'une inférence de modèle sera plus précis.
PCT/CN2024/105999 2024-07-17 2024-07-17 Dispositifs, procédés et support pour la communication Pending WO2026016094A1 (fr)

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US20150262197A1 (en) * 2014-03-13 2015-09-17 Qualcomm Incorporated Trajectory based context inference
WO2023154710A1 (fr) * 2022-02-09 2023-08-17 Interdigital Patent Holdings, Inc. Positionnement adaptatif à la demande
WO2023212224A2 (fr) * 2022-04-27 2023-11-02 Interdigital Patent Holdings, Inc. Détermination de position assistée par apprentissage automatique
US20230354254A1 (en) * 2022-04-29 2023-11-02 Qualcomm Incorporated Measurement error feedback to enable machine learning-based positioning
WO2024020086A1 (fr) * 2022-07-19 2024-01-25 Interdigital Patent Holdings, Inc. Localisation à partir de mesures estimées
WO2024120445A1 (fr) * 2022-12-09 2024-06-13 维沃移动通信有限公司 Procédé, appareil, dispositif et système de détermination d'informations d'entrée de modèle, support de stockage

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Publication number Priority date Publication date Assignee Title
US20150262197A1 (en) * 2014-03-13 2015-09-17 Qualcomm Incorporated Trajectory based context inference
WO2023154710A1 (fr) * 2022-02-09 2023-08-17 Interdigital Patent Holdings, Inc. Positionnement adaptatif à la demande
WO2023212224A2 (fr) * 2022-04-27 2023-11-02 Interdigital Patent Holdings, Inc. Détermination de position assistée par apprentissage automatique
US20230354254A1 (en) * 2022-04-29 2023-11-02 Qualcomm Incorporated Measurement error feedback to enable machine learning-based positioning
WO2024020086A1 (fr) * 2022-07-19 2024-01-25 Interdigital Patent Holdings, Inc. Localisation à partir de mesures estimées
WO2024120445A1 (fr) * 2022-12-09 2024-06-13 维沃移动通信有限公司 Procédé, appareil, dispositif et système de détermination d'informations d'entrée de modèle, support de stockage

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