WO2024065768A1 - Procédés et appareils de sélection de modèle d'ia/ml et d'identification de nlos pour l'amélioration d'estimation de positionnement nr - Google Patents

Procédés et appareils de sélection de modèle d'ia/ml et d'identification de nlos pour l'amélioration d'estimation de positionnement nr Download PDF

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Publication number
WO2024065768A1
WO2024065768A1 PCT/CN2022/123500 CN2022123500W WO2024065768A1 WO 2024065768 A1 WO2024065768 A1 WO 2024065768A1 CN 2022123500 W CN2022123500 W CN 2022123500W WO 2024065768 A1 WO2024065768 A1 WO 2024065768A1
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Prior art keywords
positioning
indication
nlos
los
mapping
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PCT/CN2022/123500
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English (en)
Inventor
Ahmed MOHAMMED MIKAEIL SALIH
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Shenzhen Tcl New Technology Co., Ltd.
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Priority to PCT/CN2022/123500 priority Critical patent/WO2024065768A1/fr
Publication of WO2024065768A1 publication Critical patent/WO2024065768A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems

Definitions

  • the present disclosure relates to the field of wireless communication systems, and more particularly, to methods and apparatuses for artificial intelligence (AI) /machine learning (ML) model selection and non-light of sight (NLOS) identification for new radio (NR) positioning estimation enhancement.
  • AI artificial intelligence
  • ML machine learning
  • NLOS non-light of sight
  • the present disclosure relates to enhancing the current NR radio access network (RAN) signaling and procedures to support AI/ML based positioning methods for improving the accuracy of UE location estimation.
  • RAN radio access network
  • AI positioning accuracy enhancements it was agreed in RAN1#109 meeting to study two options of AI-based positioning enhancements namely direct AI-based and AI-assisted positioning method.
  • direct AI positioning option an AI-based model is supposed to replace an existing positioning method and to be utilized directly for final UE location estimate.
  • AI-assisted positioning method an AI model is used to assist the existing positioning method on improving UE positioning estimation accuracy.
  • this option it is reported by some companies during RAN1#109 meeting that this option may provide a slightly less positioning accuracy than the AI-based positioning method but it has the advantage of backward compatibility with the existing non-AI based positioning methods, good model generalization capability and less overhead and/or system complexity.
  • NLOS non-line-of-sight
  • this method relies on leveraging AI/ML models on improving the accuracy of NLOS identification for positioning technologies which rely on the line-of-sight (LOS) measurements for UE positioning estimation.
  • LOS line-of-sight
  • 3gpp release 17 standard to introduce providing LOS/NLOS indication from UE or the positioning transmission and reception points (TRP) to node responsible for UE location estimation to assist in improving the positioning estimation; however, it has not been disclosed by the standard how NLOS/LOS indication can be obtained.
  • different node i.e., responsible for providing the NLOS/LOS indication may use different estimation approaches with different degree of estimation error to identify LOS/NOLS for a path, and such untrusted or unreliable NLOS/LOS indication may impact the final location estimation accuracy.
  • An object of the present disclosure is to propose methods and apparatuses for artificial intelligence (AI) /machine learning (ML) model selection and non-light of sight (NLOS) identification for new radio (NR) positioning estimation enhancement.
  • AI artificial intelligence
  • ML machine learning
  • NLOS non-light of sight
  • a method for artificial intelligence (AI) /machine learning (ML) model selection for non-light of sight (NLOS) identification for new radio (NR) positioning estimation enhancement performed by a communication network system includes performing a flexible selection among an AI/ML based positioning method, an AI/ML assisted positioning method, and a non AI/ML based positioning method based on characteristics of positioning methods and application level positioning quality of service (QoS) requirements; performing an activation and/or transfer for LOS/NLOS AI/ML identification model parameters to a node responsible for providing the LOS/NLOS indication based on an application layer to positioning method mapping indication; and/or performing a transfer of the same or identical LOS/NOLS classification AI/ML model parameters to the node responsible for providing the LOS/NLOS indication used to improve the degree of trust of the indication at the node responsible a final UE location estimate.
  • AI artificial intelligence
  • ML machine learning
  • NLOS non-light of sight
  • NR new radio
  • a communication network system comprises a memory, a transceiver, and a processor coupled to the memory and the transceiver.
  • the processor is configured to perform the above method.
  • a non-transitory machine readable storage medium has stored thereon instructions that, when executed by a computer, cause the computer to perform the above method.
  • a chip includes a processor, configured to call and run a computer program stored in a memory, to cause a device in which the chip is installed to execute the above method.
  • a computer readable storage medium in which a computer program is stored, causes a computer to execute the above method.
  • a computer program product includes a computer program, and the computer program causes a computer to execute the above method.
  • a computer program causes a computer to execute the above method.
  • FIG. 1 is a block diagram of one or more user equipments (UEs) and first and second RAN nodes of communication in a communication network system according to an embodiment of the present disclosure.
  • UEs user equipments
  • FIG. 2 is a flowchart illustrating a method for artificial intelligence (AI) /machine learning (ML) model selection for non-light of sight (NLOS) identification for new radio (NR) positioning estimation enhancement performed by a communication network system according to an embodiment of the present disclosure.
  • AI artificial intelligence
  • ML machine learning
  • NLOS non-light of sight
  • NR new radio
  • FIG. 3 is an example of NR positioning system architectures with different AI positioning model location and collaboration options according to an embodiment of the present disclosure.
  • FIG. 4 is an example of Direct AI/ML based positioning and AI/ML assisted positioning methods according to an embodiment of the present disclosure.
  • FIG. 5 is an example of AI/ML assisted positioning methods for LOS/NLOS identification according to an embodiment of the present disclosure.
  • FIG. 6 is an example of Possible Signaling for exchanging the mapping or indication for positioning methods selection according to an embodiment of the present disclosure.
  • FIG. 7A is an example of Signaling for exchanging the mapping or indication for AI-assisted measurement configuration and LOS/NLOS identification according to an embodiment of the present disclosure.
  • FIG. 7B is an example of Signaling for exchanging the mapping or indication for AI-assisted/AI-based measurement configuration, LOS/NLOS identification and/or model parameters transfer according to an embodiment of the present disclosure.
  • FIG. 8 is an example of signaling for exchanging the mapping or indication for AI-assisted/AI-based measurement configuration, LOS/NLOS identification and/or model parameters transfer according to an embodiment of the present disclosure.
  • FIG. 9 is an example of signaling and procedure related to requesting or transferring of AI/ML model parameters assuming model at location server (AMF/LMF/5GC) according to an embodiment of the present disclosure.
  • FIG. 10 is a block diagram of a system for wireless communication according to an embodiment of the present disclosure.
  • FIG. 1 illustrates that, in some embodiments, a target device 10, an RAN node (e.g., gNB) 20, and a network node (e.g., LMF or AMF, location server (LCS) ) 30 for communication in a communication network system 40 according to an embodiment of the present disclosure are provided.
  • the communication network system 40 includes the target device 10, the RAN node (e.g., gNB) 20, and the network node (e.g., LMF or AMF, location server (LCS) ) 30.
  • the target device 10 may include a memory 12, a transceiver 13, and a processor 11 coupled to the memory 12 and the transceiver 13.
  • the RAN node (e.g., gNB) 20 may include a memory 22, a transceiver 23, and a processor 21 coupled to the memory 22 and the transceiver 23.
  • the network node (e.g., LMF or AMF, location server (LCS) ) 30 may include a memory 32, a transceiver 33, and a processor 31 coupled to the memory 32 and the transceiver 33.
  • the processor 11, 21, or 31 may be configured to implement proposed functions, procedures and/or methods described in this description. Layers of radio interface protocol may be implemented in the processor 11, 21, or 31.
  • the memory 12, 22, or 32 is operatively coupled with the processor 11, 21, or 31 and stores a variety of information to operate the processor 11, 21, or 31.
  • the transceiver 13, 23, or 33 is operatively coupled with the processor 11, 21, or 31, and the transceiver 13, 23, or 33 transmits and/or receives a radio signal.
  • the processor 11, 21, or 31 may include application specific integrated circuit (ASIC) , other chipset, logic circuit and/or data processing device.
  • the memory 12, 22, or 32 may include read only memory (ROM) , random access memory (RAM) , flash memory, memory card, storage medium and/or other storage device.
  • the transceiver 13, 23, or 33 may include baseband circuitry to process radio frequency signals.
  • the memory 12, 22, or 32 can be implemented within the processor 11, 21, or 31 or external to the processor 11, 21, or 31 in which case those can be communicatively coupled to the processor 11, 21, or 31 via various means as is known in the art.
  • the communication network system 40 is configured to perform a flexible selection among an AI/ML based positioning method, an AI/ML assisted positioning method, and a non AI/ML based positioning method based on characteristics of positioning methods and application level positioning quality of service (QoS) requirements; perform an activation and/or transfer for LOS/NLOS AI/ML identification model parameters to a node responsible for providing the LOS/NLOS indication based on an application layer to positioning method mapping indication; and/or perform a transfer of the same or identical LOS/NOLS classification AI/ML model parameters to the node responsible for providing the LOS/NLOS indication used to improve the degree of trust of the indication at the node responsible a final UE location estimate.
  • QoS application level positioning quality of service
  • FIG. 2 is a flowchart illustrating a method for artificial intelligence (AI) /machine learning (ML) model selection for non-light of sight (NLOS) identification for new radio (NR) positioning estimation enhancement performed by a communication network system according to an embodiment of the present disclosure.
  • AI artificial intelligence
  • ML machine learning
  • NLOS non-light of sight
  • NR new radio
  • a method for artificial intelligence (AI) /machine learning (ML) model selection for non-light of sight (NLOS) identification for new radio (NR) positioning estimation enhancement performed by a communication network system includes performing a flexible selection among an AI/ML based positioning method, an AI/ML assisted positioning method, and a non AI/ML based positioning method based on characteristics of positioning methods and application level positioning quality of service (QoS) requirements; performing an activation and/or transfer for LOS/NLOS AI/ML identification model parameters to a node responsible for providing the LOS/NLOS indication based on an application layer to positioning method mapping indication; and/or performing a transfer of the same or identical LOS/NOLS classification AI/ML model parameters to the node responsible for providing the LOS/NLOS indication used to improve the degree of trust of the indication at the node responsible a final UE location estimate.
  • AI artificial intelligence
  • ML machine learning
  • NLOS non-light of sight
  • NR new radio
  • AI based positioning model e.g., AI model one side or one entity (UE, gNB or LMF) ; where, both training and inference are conducted at either, or partial AI model at one side; where, the training and inference are conducted at one side of network or UE, but requires additional signaling or procedure enhancements between two sides, potentially with existing signaling framework.
  • AI based positioning model e.g., AI model one side or one entity (UE, gNB or LMF) ; where, both training and inference are conducted at either, or partial AI model at one side; where, the training and inference are conducted at one side of network or UE, but requires additional signaling or procedure enhancements between two sides, potentially with existing signaling framework.
  • network-UE collaboration e.g., no collaboration, signaling-based collaboration without model transfer and signaling-based collaboration with model transfer for further considerations (as illustrated in FIG. 3)
  • AI/ML model generalization aspect for NR positioning e.g., AI model one side or one entity (UE, g
  • Direct AI/ML based positioning where an AI/ML based method is can replace the existing positioning methods and utilized to estimate UE location with reference to new type of measurements and/or measurement reports such as the amplitude, time of arrival (TOA) , Angle of Arrival (AoA) , channel impulse response (CIR) , Beam index, TRP index, power and/or reflection order of signal received from multiple transmission and reception points (TRPs) (as illustrated in FIG. 4) .
  • TOA time of arrival
  • AoA Angle of Arrival
  • CIR channel impulse response
  • Beam index Beam index
  • TRP index power and/or reflection order of signal received from multiple transmission and reception points (TRPs) (as illustrated in FIG. 4) .
  • AI is used to assist an existing positioning method to enhance the UE location estimation e.g., by extracting an intermediate feature from measurements and/or measurement reports such as downlink-reference signal timing difference (DL-RSTD) , DL-angle of departure (DL-AoD) , uplink-relative time of arrival (UL-RTOA) , uplink sounding reference signal-reference signal received power (SRS-RSRP) , DL positioning reference signal RSRP (DL PRS-RSRP) , UE Rx-Tx time difference and gNB Rx-Tx time difference, UL-AOA or AoD or zenith angle of arrival (ZOA) values per path; then, estimating the final UE position according to the extracted intermediate feature (as illustrated in FIG. 4 and FIG. 5) .
  • DL-RSTD downlink-reference signal timing difference
  • DL-AoD DL-angle of departure
  • UL-RTOA uplink sounding reference signal-reference signal received power
  • Table 1 NR positioning application categories
  • a QoS based or QoS-assisted method for positioning methods selection is proposed which allows flexible selection between AI-based, AI assisted and non-AI based positioning methods based on the characteristics of these positioning methods (e.g., drawback and advantages) as well as the application level positioning QoS requirements as recommended per TR 38.857.
  • the method also proposed to utilizes the application level indication and/or the mapping information as a trigger for providing or requesting or transferring of a proper AI/ML classification model parameters to assist the entity responsible for providing the LOS/NLOS indication for identifying or classifying the NLOS/LOS conditions process at UE or TRPs.
  • This disclosure provides a method for A Method for AI/ML model selection for Non-Light of Sight Identification for NR Positioning Estimation Enhancement.
  • the major advantages of these methods include:
  • the new method introduces the concept of flexible selection between AI/ML based, AI/ML assisted, and non-AI/ML based positioning method based on the characteristics of these positioning methods and the application level positioning QoS requirements as recommended per TR 38.857, which could help in providing an agile integration of AI/ML with the existing traditional positioning methods.
  • the new methods introduce the concept of activation and/or transfer for LOS/NOLS AI/ML identification model to node responsible for providing the LOS/NLOS indication (UE or TRP) based on the application layer to positioning method mapping indication which could help addressing an issue in the prior art related to how the node responsible for providing the LOS/NLOS indication determines or estimate the NLOS/LOS condition.
  • the new method proposes the concept of transfer of the same or identical LOS/NOLS classification AI/ML model parameters to the node responsible for providing the LOS/NLOS indication (UE or TRP) which could help unifying the degree of trust of the indication at the node which calculate the final location and help in addressing the reliability issue of NLOS/LOS indication which could improve the final location estimations.
  • UE or TRP LOS/NLOS indication
  • Embodiment 1 Detailed Description
  • a QoS based or QoS-assisted method for positioning methods selection is proposed which allows flexible selection between AI-based, AI assisted and non-AI based positioning methods based on the characteristics of these positioning methods (e.g., drawback and advantages) as well as the application level positioning QoS requirements as recommended per TR 38.857.
  • an application QoS level to a positioning methods mapping or indication is proposed to be exchanged between the entity or the node or device requesting a UE location estimate (e.g., a target device such as a UE or a location server ) and the entity or the node or responsible for final location estimation (a target device or a location server or location management entity LMF or a signaling reference source) for the purpose of assisting the entity/node on selecting the appropriate positioning methods among one of AI/ML-based positioning methods, AI/ML assisted positioning methods or a non-AI positioning methods based on the exchanged indication or the positioning QoS mapping information provided by upper layer (FIG. 6) .
  • a UE location estimate e.g., a target device such as a UE or a location server
  • the entity or the node or responsible for final location estimation a target device or a location server or location management entity LMF or a signaling reference source
  • the method also proposed to utilizes the application level indication and/or the mapping information as a trigger for providing or requesting or transferring of an identical AI/ML classification model parameters to the all entity/node responsible of providing the LOS/NLOS indications (e.g., a target device UE or reference source TRPs) to assist the entity on identifying or classifying the NLOS/LOS conditions or in LOS/NLOS identification process (FIG. 7A and FIG. 7B) .
  • the all entity/node responsible of providing the LOS/NLOS indications e.g., a target device UE or reference source TRPs
  • FIG. 7A and FIG. 7B The detail of the method is given below (FIG. 8) .
  • the target device (UE) or the location server receives an indication from application layer level containing a mapping between the positioning application QoS requirement and preferred positioning method as given in Table 2 or an indication of a preferred positioning method derived from the mapping as given in Table 3.
  • the target device (UE) and the location server may exchange a request and/or response carrying information related to the exchanging of the indication between each other’s.
  • the target device (UE) and the location server may exchange request and/or response carrying information related to the exchanging of the indication between each other directly via LTE location protocols (LLP) signaling procedures.
  • LLP LTE location protocols
  • the target device (UE) and the location server may exchange request and/or response carrying information related to the exchanging of the indication between each other directly via LTE location protocols (LLP) signaling procedures indirectly through the reference source (gNB) via NR positioning protocol a (NRPPa) signaling and NR Radio interface Signaling.
  • LLP LTE location protocols
  • gNB reference source
  • NRPPa NR positioning protocol a
  • the target device (UE) and the location server may exchange request and/or response carrying information related to the exchanging of the indication to the reference source (gNB) via NR positioning protocol a (NRPPa) signaling and NR Radio interface Signaling.
  • NRPPa NR positioning protocol a
  • the reference source (gNB) may exchange, the indication about the mapping between the positioning application QoS requirement and the preferred positioning method or the indication of the preferred positioning method according to the mapping to other neighboring reference source (gNB) involved in location estimation over Xn or X2 interface.
  • the target device (UE) and/or the location server and/or the reference source (gNB) may utilized the indication about the mapping between the positioning application QoS requirement and the preferred positioning method or the indication of the preferred positioning method according to the mapping, to select among one of AI-based, AI assisted and non-AI based positioning methods locally and/or to requesting or providing an activation/deactivation of a positioning method and/or requesting or transferring of a trained AI/ML model parameters for an AI-based or AI-assisted position method or transferring measurements configurations related to a AI-based or AI-assisted or an existing positioning method between each other’s.
  • the target device (UE) and an entity of core network (CN) may perform a mothed selection according to Table 4, which indicate.
  • the trained AI/ML for AI-based or AI-assisted model parameters could be as follows:
  • NN neural network
  • the input type could be a Channel Impulse Response (CIR)
  • the Power Delay Profile PDP
  • L1-RSRP Layer 1 Reference Signal Received Power
  • the output type could be a final UE location or (X, Y and Z) UE coordinate.
  • AI-assisted method could be a simple AI/ML classifier model parameters such as [the classifier input and output types, initial weights, learning rate parameter, binary variables, (0, 1) for binary classification, or an enumerated variables for multi-level classification] .
  • the input type can be a Channel Impulse Response (CIR) , the Power Delay Profile (PDP) a Layer 1 Reference Signal Received Power (L1-RSRP) , and/or channel frequency response in frequency domain (CFR) .
  • CIR Channel Impulse Response
  • PDP Power Delay Profile
  • L1-RSRP Layer 1 Reference Signal Received Power
  • CFR channel frequency response in frequency domain
  • the output type could be an LOS/NLOS probability of the different channel paths and/or a DL-RSTD, or UE Rx-Tx time difference, PRS RSRPP and/or DL-AoD/ZoD or DL-AoA/ZoA for the path.
  • the trained AI/ML positioning model is located at reference source (gNB) and in some other embodiments assuming the trained AI/ML positioning model is located at Location Server (LMF, AMF or 5GC) .
  • LMF Location Server
  • Table 2 Direct Mapping between positioning level application QoS and the preferred positioning method
  • Table 3 indication of a preferred positioning method based on application level QoS
  • Table 4 Target device/location server/reference source entity actions upon the reception of the indication/mapping
  • Embodiment 2 Signaling and procedure related to requesting or transferring of AI/ML model parameters assuming trained AI model is located at reference source or gNB
  • the target device (UE) or the location server receives an indication from application layer level containing a mapping between the positioning application QoS requirement and preferred positioning method as given in or an indication of a preferred positioning method derived from the mapping.
  • the target device (UE) exchanges the indication or mapping to the location server over NAS signaling or LPP signaling exchange procedures.
  • the lactation server (LMF/AMF) entity forwards a positioning measurement and/or an AI-based/AI-assisted model parameters transfer request toward the reference source or the gNB via NR PPa signaling exchange procedures.
  • the reference source or the gNB transfers an AI-based/AI-assisted model parameters as indicated by the location server and/or initiates a measurement request or provides a gap configuration to support the specific AI-based/AI-assisted model to the target device (UE) via NR/LTE radio signaling.
  • the target device utilizes the transferred model parameters to perform the AI-based/AI-assisted measurement as requested and transmit the measurement and/or the LOS/NLOS indication to the lactation server (LMF/AMF) entity either directly via NAS or LLP signaling or indirectly via NR/LTE radio signaling and NR-PPa signaling for final UE locations estimation.
  • LMF/AMF lactation server
  • Embodiment 3 Signaling and procedure related to requesting or transferring of AI/ML model parameters assuming that the trained AI model is located at location server (AMF/LMF/5GC)
  • the target device (UE) or the location server receives an indication from application layer level containing a mapping between the positioning application QoS requirement and preferred positioning method as given in or an indication of a preferred positioning method derived from the mapping.
  • the target device exchanges the indication or mapping to the location server over NAS signaling or LPP signaling exchange procedures.
  • the lactation server (LMF/AMF) initiates an AI-based/AI-assisted model parameters process and forwards a positioning measurement request to support the transferred AI-based/AI-assisted toward the target device (UE) either directly via directly via NAS or LLP signaling or indirectly via NR-PPa signaling and NR/LTE radio signaling.
  • the target device utilizes the transferred model parameters to perform the AI-based/AI-assisted measurement as requested and transmit the measurement and/or the LOS/NLOS indication to the lactation server (LMF/AMF) entity either directly via NAS or LLP signaling or indirectly via NR/LTE radio signaling and NR-PPa signaling for final UE locations estimation.
  • LMF/AMF lactation server
  • FIG. 10 is a block diagram of an example system 700 for wireless communication according to an embodiment of the present disclosure. Embodiments described herein may be implemented into the system using any suitably configured hardware and/or software.
  • FIG. 10 illustrates the system 700 including a radio frequency (RF) circuitry 710, a baseband circuitry 720, an application circuitry 730, a memory/storage 740, a display 750, a camera 760, a sensor 770, and an input/output (I/O) interface 780, coupled with each other at least as illustrated.
  • the application circuitry 730 may include a circuitry such as, but not limited to, one or more single core or multi core processors.
  • the processors may include any combination of general purpose processors and dedicated processors, such as graphics processors, application processors.
  • the processors may be coupled with the memory/storage and configured to execute instructions stored in the memory/storage to enable various applications and/or operating systems running on the system.

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Abstract

Un procédé d'amélioration de sélection de modèle d'intelligence artificielle (IA)/apprentissage automatique (ML) et d'identification d'absence de ligne de vision (NLOS) pour l'estimation de positionnement NR (New Radio) effectué par un système de réseau de communication comprend la réalisation d'une sélection flexible parmi un procédé de positionnement basé sur l'IA/ML, un procédé de positionnement assisté par IA/ML, et un procédé de positionnement non IA/ML sur la base de caractéristiques de procédés de positionnement et de critères de qualité de service (QoS) de positionnement de niveau d'application ; la réalisation d'une activation et/ou d'un transfert pour des paramètres de modèle d'identification IA/ML de LOS/NLOS à un nœud responsable de la fourniture de l'indication de LOS/NLOS sur la base d'une couche d'application pour l'indication de mappage de procédé de positionnement ; et/ou la réalisation d'un transfert de ceux-ci ou de paramètres de modèle IA/ML de classification de LOS/NOLS identiques au nœud responsable de la fourniture de l'indication de LOS/NLOS utilisé pour améliorer le niveau de confiance de l'indication au niveau du nœud responsable d'une estimation d'emplacement d'UE finale.
PCT/CN2022/123500 2022-09-30 2022-09-30 Procédés et appareils de sélection de modèle d'ia/ml et d'identification de nlos pour l'amélioration d'estimation de positionnement nr WO2024065768A1 (fr)

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