User equipment and network node in wireless communication system and method performed thereby

Abstract

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. The present disclosure relates to user equipment and a network node in a wireless communication system and methods performed by them. In one aspect, a method performed by user equipment (UE) in a communication system comprise: reporting first information related to AI model supported by the UE to a network node, wherein the first information includes information about at least one of: index of AI model, applicable condition of AI model, and feature associated with AI model; acquiring a first AI model, wherein the first AI model is determined from at least one AI model based on the first information; obtaining input data of the first AI model; based on the input data, obtaining output data related to positioning using the first AI model.

Description

USER EQUIPMENT AND NETWORK NODE IN WIRELESS COMMUNICATION SYSTEM AND METHOD PERFORMED THEREBY

[0001] The present invention relates to wireless communication, and more specifically, to a method and device for positioning in a wireless communication system.

[0002] In order to meet the increasing demand for wireless data communication services since the deployment of 4G communication systems, efforts have been made to develop improved 5G or pre-5G communication systems. Therefore, 5G or pre-5G communication systems are also called "Beyond 4G networks" or "Post-LTE systems".

[0003] In order to achieve a higher data rate, 5G communication systems are implemented in higher frequency (millimeter, mmWave) bands, e.g., 60 GHz bands. In order to reduce propagation loss of radio waves and increase a transmission distance, technologies such as beamforming, massive multiple-input multiple-output (MIMO), full-dimensional MIMO (FD-MIMO), array antenna, analog beamforming and large-scale antenna are discussed in 5G communication systems.

[0004] In addition, in 5G communication systems, developments of system network improvement are underway based on advanced small cell, cloud radio access network (RAN), ultra-dense network, device-to-device (D2D) communication, wireless backhaul, mobile network, cooperative communication, coordinated multi-points (CoMP), reception-end interference cancellation, etc.

[0005] In 5G systems, hybrid FSK and QAM modulation (FQAM) and sliding window superposition coding (SWSC) as advanced coding modulation (ACM), and filter bank multicarrier (FBMC), non-orthogonal multiple access (NOMA) and sparse code multiple access (SCMA) as advanced access technologies have been developed.

[0006] 5th generation (5G) or new radio (NR) mobile communications is recently gathering increased momentum with all the worldwide technical activities on the various candidate technologies from industry and academia. The candidate enablers for the 5G / NR mobile communications include massive antenna technologies, from legacy cellular frequency bands up to high frequencies, to provide beamforming gain and support increased capacity, new waveform (e.g., a new radio access technology (RAT)) to flexibly accommodate various services / applications with different requirements, new multiple access schemes to support massive connections, and so on.

[0007] 5G mobile communication technologies define broad frequency bands such that high transmission rates and new services are possible, and can be implemented not only in "Sub 6GHz" bands such as 3.5GHz, but also in "Above 6GHz" bands referred to as mmWave including 28GHz and 39GHz. In addition, it has been considered to implement 6G mobile communication technologies (referred to as Beyond 5G systems) in terahertz bands (for example, 95GHz to 3THz bands) in order to accomplish transmission rates fifty times faster than 5G mobile communication technologies and ultra-low latencies one-tenth of 5G mobile communication technologies.

[0008] At the beginning of the development of 5G mobile communication technologies, in order to support services and to satisfy performance requirements in connection with enhanced Mobile BroadBand (eMBB), Ultra Reliable Low Latency Communications (URLLC), and massive Machine-Type Communications (mMTC), there has been ongoing standardization regarding beamforming and massive MIMO for mitigating radio-wave path loss and increasing radio-wave transmission distances in mmWave, supporting numerologies (for example, operating multiple subcarrier spacings) for efficiently utilizing mmWave resources and dynamic operation of slot formats, initial access technologies for supporting multi-beam transmission and broadbands, definition and operation of BWP (BandWidth Part), new channel coding methods such as a LDPC (Low Density Parity Check) code for large amount of data transmission and a polar code for highly reliable transmission of control information, L2 pre-processing, and network slicing for providing a dedicated network specialized to a specific service.

[0009] Currently, there are ongoing discussions regarding improvement and performance enhancement of initial 5G mobile communication technologies in view of services to be supported by 5G mobile communication technologies, and there has been physical layer standardization regarding technologies such as V2X (Vehicle-to-everything) for aiding driving determination by autonomous vehicles based on information regarding positions and states of vehicles transmitted by the vehicles and for enhancing user convenience, NR-U (New Radio Unlicensed) aimed at system operations conforming to various regulation-related requirements in unlicensed bands, NR UE Power Saving, Non-Terrestrial Network (NTN) which is UE-satellite direct communication for providing coverage in an area in which communication with terrestrial networks is unavailable, and positioning.

[0010] Moreover, there has been ongoing standardization in air interface architecture / protocol regarding technologies such as Industrial Internet of Things (IIoT) for supporting new services through interworking and convergence with other industries, IAB (Integrated Access and Backhaul) for providing a node for network service area expansion by supporting a wireless backhaul link and an access link in an integrated manner, mobility enhancement including conditional handover and DAPS (Dual Active Protocol Stack) handover, and two-step random access for simplifying random access procedures (2-step RACH for NR). There also has been ongoing standardization in system architecture / service regarding a 5G baseline architecture (for example, service based architecture or service based interface) for combining Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) technologies, and Mobile Edge Computing (MEC) for receiving services based on UE positions.

[0011] As 5G mobile communication systems are commercialized, connected devices that have been exponentially increasing will be connected to communication networks, and it is accordingly expected that enhanced functions and performances of 5G mobile communication systems and integrated operations of connected devices will be necessary. To this end, new research is scheduled in connection with eXtended Reality (XR) for efficiently supporting AR (Augmented Reality), VR (Virtual Reality), MR (Mixed Reality) and the like, 5G performance improvement and complexity reduction by utilizing Artificial Intelligence (AI) and Machine Learning (ML), AI service support, metaverse service support, and drone communication.

[0012] A method performed by user equipment (UE) in a communication system, comprising: reporting first information related to AI model supported by the UE to a network node, wherein the first information includes information about at least one of: index of AI model, applicable condition of AI model, and feature associated with AI model; acquiring a first AI model, wherein the first AI model is determined from at least one AI model based on the first information; obtaining input data of the first AI model; based on the input data, obtaining output data related to positioning using the first AI model.

[0013] FIG. 1 illustrates an example wireless network according to various embodiments of the present disclosure;

[0014] FIGs. 2a and 2b illustrate example wireless transmission and reception paths according to the present disclosure;

[0015] FIG. 3a illustrates an example user equipment according to the present disclosure and FIG. 3b illustrates an example base station according to the present disclosure;

[0016] FIG. 4 illustrates an example diagram of the relationship between path interval time and sample interval time; and

[0017] FIG. 5 is a block diagram illustrating a communication device for performing a method of the present disclosure according to an embodiment of the present disclosure.

[0018] According to at least one embodiment of the present disclosure, there is provided a method performed by user equipment (UE) in a communication system, comprising:

[0019] reporting first information related to AI model supported by the UE to a network node, wherein the first information includes information about at least one of: index of AI model, applicable condition of AI model, and feature associated with AI model;

[0020] acquiring a first AI model, wherein the first AI model is determined from at least one AI model based on the first information;

[0021] obtaining input data of the first AI model;

[0022] based on the input data, obtaining output data related to positioning using the first AI model.

[0023] In an implementation, the first information further comprises at least one of:

[0024] corresponding relationship between index of AI model and associated feature;

[0025] input format related information of AI model supported by the UE;

[0026] output format related information of AI model supported by the UE.

[0027] In an implementation, the applicable condition includes at least one of:

[0028] cell index corresponding to AI model;

[0029] condition related to measurement result;

[0030] condition related to storage;

[0031] condition related to complexity;

[0032] condition related to accuracy of output data;

[0033] condition related to processing time.

[0034] In an implementation, wherein the condition related to measurement result includes:

[0035] a first measurement value of the measurement result is not lower than a first measurement threshold,

[0036] a first measurement value of the measurement result is within a first measurement threshold interval,

[0037] a number of paths not lower than a first power threshold in the measurement result is greater than a first number threshold,

[0038] a number of measurement results in which a number of paths not lower than a first power threshold is greater than a first number threshold is greater than a second number threshold,

[0039] a probability that a number of paths not lower than a first power threshold in the measurement result is greater than a first number threshold is greater than a first probability threshold.

[0040] In an implementation, wherein the input format information includes at least one of: information related to type of input data and / or information related to quantity of input data.

[0041] In an implementation, wherein the type of input data includes at least one of:

[0042] channel impulse response (CIR),

[0043] power delay profile (PDP),

[0044] delay profile (DP),

[0045] channel information feature obtained based on CIR, PDP or DP,

[0046] time value,

[0047] power value,

[0048] phase value,

[0049] time stamp of input data,

[0050] path index corresponding to input data.

[0051] In an implementation, the quantity of input data includes at least one of: information related to a total number applied to all types of input data, information related to each number applied to each type of input data, and information related to a third number applied to all types.

[0052] In an implementation, the output format information includes at least one of information related to type of output data, information related to quantity of output data, and information related to processing time of AI model.

[0053] In an implementation, the type of output data includes at least one of:

[0054] coordinate estimation of UE;

[0055] estimation of a second measurement value, wherein the second measurement value comprises at least one of a time measurement value, a power measurement value and a phase measurement value;

[0056] estimation of probability that the measurement result satisfies a third condition, the third condition includes that the measurement result corresponds to a line-of-sight path, or the number of multipaths in the measurement result exceeds a third number threshold.

[0057] In an implementation, wherein the quantity of output data includes the total number of output data, the number corresponding to each type of output data, the number corresponding to all types of output data.

[0058] In an implementation, the corresponding relationship between index of AI model and associated feature includes at least one of:

[0059] an AI model index corresponds to a feature,

[0060] an AI model index corresponds to multiple features,

[0061] multiple AI model indexes correspond to a feature,

[0062] the ratio between index of AI model and feature.

[0063] In an implementation, the at least one AI model includes AI models supported by the UE, or the at least one AI model is obtained based on AI models supported by the UE and AI models supported by the network node.

[0064] In an implementation, obtaining the first AI model comprises:

[0065] determining, by the UE, the first AI model from the at least one AI model based on applicable condition corresponding to AI model, or

[0066] acquiring the first AI model based on indication information received from a network node, the indication information is for indicating an AI model determined by the network node.

[0067] In an implementation, the method further comprising:

[0068] receiving first configuration information related to positioning measurement based on AI model, the first configuration information includes at least one of: configuration information related to resource for reference signal, configuration information related to a measurement window, and configuration information related to resource for reporting measurement result;

[0069] performing measurement based on the first configuration information to obtain a first measurement result;

[0070] wherein, the first measurement result is used for identifying the first AI model and / or obtaining input data of the first AI model.

[0071] In an implementation, the first measurement result is measurement result obtained based on measurement or is obtained by processing the measurement result obtained based on measurement, the processing includes at least one of:

[0072] selecting the first measurement result from the measurement result obtained based on measurement based on a first condition;

[0073] transforming the measurement result obtained based on measurement to obtain the first measurement result,

[0074] wherein, the first condition includes at least one of: power corresponding to the measurement result is not lower than a second power threshold, time corresponding to the measurement result is earlier than a first time threshold, phase corresponding to the measurement result is less than a first phase threshold.

[0075] In an implementation, the input data includes at least one of:

[0076] the first measurement result;

[0077] data obtained based on the first measurement result and the input format information of the first AI model.

[0078] In an implementation, the method further comprises:

[0079] reporting information related to output data to the network node,

[0080] wherein the information related to output data includes at least one of:

[0081] the output data, data processed based on the output data, information related to the time stamp of the output data, and quality indication information of the output data.

[0082] In an implementation, the method further comprising:

[0083] acquiring configuration information related to monitoring of AI model;

[0084] acquiring a second measurement result, wherein the second measurement result is obtained by measuring by the UE according to the configuration information, or obtained by measuring by the network node based on a reference signal transmitted by the UE according to the configuration information;

[0085] obtaining monitoring result of the first AI model based on the second measurement result;

[0086] determining an operation to be performed based on the monitoring result, or transmitting the monitoring result to the network node, and / or receiving an indicated operation that the UE needs to perform from the network node.

[0087] In an implementation, the method further comprising:

[0088] acquiring configuration information related to monitoring of AI model;

[0089] measuring according to the configuration information to obtain a second measurement result and transmitting the second measurement result to a network node, or transmitting a reference signal according to the configuration information;

[0090] receiving indication information for an operation from a network node, and performing the operation.

[0091] In an implementation, obtaining a monitoring result based on the second measurement result comprises: obtaining a monitoring metric based on the second measurement result, obtaining a monitoring result based on the monitoring metric,

[0092] wherein the monitoring metric includes at least one of: the second measurement result, a third measurement result obtained by processing the second measurement result, characteristic data obtained based on the second measurement result,

[0093] wherein the processing comprises: selecting a third measurement result from the second measurement result based on a second condition, or transforming the second measurement result to obtain a third measurement result,

[0094] wherein the characteristic data includes statistical characteristic data of the second measurement result, or a fourth measurement result selected from the second measurement result according to the second condition,

[0095] wherein, the second condition includes at least one of: power corresponding to measurement result is not lower than a third power threshold, time corresponding to measurement result is earlier than a second time threshold, and phase corresponding to measurement result is less than or equal to a second phase threshold.

[0096] In an implementation, if the monitoring metric is greater than a threshold value for consecutive N times, the monitoring result is determined to be passed, and N is a positive integer; otherwise, the monitoring result is determined to be not passed.

[0097] In an implementation, if a third condition is satisfied, the operation comprises at least one of:

[0098] stopping using an AI-based positioning method,

[0099] falling back to a non-AI-based method for positioning,

[0100] redetermining an AI model to use,

[0101] performing updating, finetuning or retraining of the first AI model,

[0102] selecting a second AI model different from the first AI model and informing it to the network node,

[0103] receiving a second AI model indicated by the network node,

[0104] receiving an indication on fallback to a non-AI-based method for positioning from the network node and fallback based on the indication,

[0105] reporting an error in an AI-model-based method;

[0106] wherein, the third condition includes at least one of: the first AI model is the same as an AI model determined to use the last time, the monitoring result is determined to be not passed for consecutive M times, and M is an integer greater than or equal to 1.

[0107] In an implementation, the configuration information includes at least one of: resource configuration information for reference signal, measurement configuration information, and resource configuration information for reporting measurement result.

[0108] According to at least one embodiment of the present disclosure, there is provided a method performed by a network node in a communication system, comprising:

[0109] receiving first information related to AI model supported by user equipment (UE) from the UE, the first information includes information about at least one of: index of AI model, applicable condition of AI model, and feature associated with AI model;

[0110] receiving information related to a first AI model from the UE, or determining the first AI model and transmitting the information related to the first AI model to the UE, wherein the first AI model is determined from at least one AI model based on the first information.

[0111] In an implementation, the first information further comprises at least one of:

[0112] corresponding relationship between index of AI model and associated feature;

[0113] input format related information of AI model supported by the UE;

[0114] output format related information of AI model supported by the UE.

[0115] In an implementation, the applicable condition includes at least one of:

[0116] cell index corresponding to AI model;

[0117] condition related to measurement result;

[0118] condition related to storage;

[0119] condition related to complexity;

[0120] condition related to accuracy of output data;

[0121] condition related to processing time.

[0122] In an implementation, wherein the condition related to measurement result includes:

[0123] a first measurement value of the measurement result is not lower than a first measurement threshold,

[0124] a first measurement value of the measurement result is within a first measurement threshold interval,

[0125] a number of paths not lower than a first power threshold in the measurement result is greater than a first number threshold,

[0126] a number of measurement results in which a number of paths not lower than a first power threshold is greater than a first number threshold is greater than a second number threshold,

[0127] a probability that a number of paths not lower than a first power threshold in the measurement result is greater than a first number threshold is greater than a first probability threshold.

[0128] In an implementation, wherein the input format information includes at least one of: information related to type of input data and / or information related to quantity of input data.

[0129] In an implementation, wherein the type of input data includes at least one of:

[0130] channel impulse response (CIR),

[0131] power delay profile (PDP),

[0132] delay profile (DP),

[0133] channel information feature obtained based on CIR, PDP or DP,

[0134] time value,

[0135] power value,

[0136] phase value,

[0137] time stamp of input data,

[0138] path index corresponding to input data.

[0139] In an implementation, the quantity of input data includes at least one of: information related to a total number applied to all types of input data, information related to each number applied to each type of input data, and information related to a third number applied to all types.

[0140] In an implementation, the output format information includes at least one of information related to type of output data, information related to quantity of output data, and information related to processing time of AI model.

[0141] In an implementation, the type of output data includes at least one of:

[0142] coordinate estimation of UE;

[0143] estimation of a second measurement value, wherein the second measurement value comprises at least one of a time measurement value, a power measurement value and a phase measurement value;

[0144] estimation of probability that the measurement result satisfies a third condition, the third condition includes that the measurement result corresponds to a line-of-sight path, or the number of multipaths in the measurement result exceeds a third number threshold;

[0145] wherein the quantity of output data includes the total number of output data, the number corresponding to each type of output data, the number corresponding to all types of output data.

[0146] In an implementation, the corresponding relationship between index of AI model and associated feature includes at least one of:

[0147] an AI model index corresponds to a feature,

[0148] an AI model index corresponds to multiple features,

[0149] multiple AI model indexes correspond to a feature,

[0150] the ratio between index of AI model and feature.

[0151] In an implementation, the at least one AI model includes AI models supported by the UE, or the at least one AI model is obtained based on AI models supported by the UE and AI models supported by the network node.

[0152] In an implementation, the AI model is determined by the UE from the at least one AI model based on applicable condition corresponding to AI model.

[0153] In an implementation, the method further comprises:

[0154] transmitting to the UE first configuration information related to positioning measurement based on AI model, the first configuration information includes at least one of: configuration information related to resource for reference signal, configuration information related to a measurement window, and configuration information related to resource for reporting measurement result;

[0155] obtaining a first measurement result, wherein the first measurement result is obtained by the network node measuring a first reference signal transmitted by the UE or obtained by the UE measuring a second reference signal;

[0156] wherein, the first measurement result is used for determining of the first AI model and / or obtaining input data of the first AI model.

[0157] In an implementation, the first measurement result is measurement result obtained based on measurement or is obtained by processing the measurement result obtained based on measurement, the processing includes at least one of:

[0158] selecting the first measurement result from the measurement result obtained based on measurement based on a first condition;

[0159] transforming the measurement result obtained based on measurement to obtain the first measurement result,

[0160] wherein, the first condition includes at least one of: power corresponding to the measurement result is not lower than a second power threshold, time corresponding to the measurement result is earlier than a first time threshold, phase corresponding to the measurement result is less than a first phase threshold.

[0161] In an implementation, the input data includes at least one of:

[0162] the first measurement result;

[0163] data obtained based on the first measurement result and the input format information of the first AI model.

[0164] In an implementation, the method further comprises:

[0165] receiving from the UE, information related to output data of the first AI model,

[0166] wherein the information related to output data of the first AI model includes at least one of:

[0167] output data related to positioning obtained by processing the input data using a first AI model, data obtained by processing based on the output data, information related to the time stamp of the output data, and quality indication information of the output data.

[0168] In an implementation, the method further comprising:

[0169] transmitting to the UE, configuration information related to monitoring of AI model;

[0170] obtaining a monitoring result of the first AI model, the monitoring result is based on a second measurement result obtained by a network node by measuring a reference signal transmitted by the UE according to the configuration information, or the monitoring result is received from the UE;

[0171] determining an operation to be performed based on the monitoring result;

[0172] transmitting indication for the operation to the UE.

[0173] In an implementation, the monitoring result is obtained based on a monitoring metric, the monitoring metric is obtained based on a second measurement result,

[0174] wherein the monitoring metric includes at least one of: the second measurement result, a third measurement result obtained by processing the second measurement result, characteristic data obtained based on the second measurement result,

[0175] wherein the processing comprises: selecting a third measurement result from the second measurement result based on a second condition, or transforming the second measurement result to obtain a third measurement result,

[0176] wherein the characteristic data includes statistical characteristic data of the second measurement result, or a fourth measurement result selected from the second measurement result according to the second condition,

[0177] wherein, the second condition includes at least one of: power corresponding to measurement result is not lower than a third power threshold, time corresponding to measurement result is earlier than a second time threshold, and phase corresponding to measurement result is less than or equal to a second phase threshold.

[0178] In an implementation, if the monitoring metric is greater than a threshold value for consecutive N times, the monitoring result is determined to be passed, and N is a positive integer; otherwise, the monitoring result is determined to be not passed.

[0179] In an implementation, if a third condition is satisfied, the operation comprises at least one of:

[0180] stopping using an AI-based positioning method,

[0181] falling back to a non-AI-based method for positioning,

[0182] redetermining an AI model to use,

[0183] performing updating, finetuning or retraining of the first AI model,

[0184] selecting a second AI model different from the first AI model and informing it to the network node,

[0185] receiving a second AI model indicated by the network node,

[0186] receiving an indication on fallback to a non-AI-based method for positioning from the network node and fallback based on the indication,

[0187] reporting an error in an AI-model-based method;

[0188] wherein, the third condition includes at least one of: the first AI model is the same as an AI model determined to use the last time, the monitoring result is determined to be not passed for consecutive M times, and M is an integer greater than or equal to 1.

[0189] In an implementation, the configuration information includes at least one of: resource configuration information for reference signal, measurement configuration information, and resource configuration information for reporting measurement result.

[0190] According to at least one embodiment of the present disclosure, there is provided a user equipment (UE) in a communication system, comprising a transceiver configured to transmit and / or receive signals; and a controller configured to control the UE to perform the method according to the embodiment of the present disclosure.

[0191] According to at least one embodiment of the present disclosure, there is provided a network node in a communication system, comprising a transceiver configured to transmit and / or receive signals; and a controller configured to control the network node to perform the method according to the embodiment of the present disclosure.

[0192] The following description with reference to the accompanying drawings is provided to facilitate a comprehensive understanding of various embodiments of the present disclosure defined by the claims and their equivalents. This description includes various specific details to facilitate understanding but should only be considered as exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the various embodiments described herein without departing from the scope and spirit of the present disclosure. In addition, for the sake of clarity and conciseness, descriptions of well-known functions and structures may be omitted.

[0193] The terms and expressions used in the following specification and claims are not limited to their dictionary meanings, but are only used by the inventors to enable a clear and consistent understanding of the present disclosure. Therefore, it should be obvious to those skilled in the art that the following descriptions of various embodiments of the present disclosure are provided for illustration purposes only and are not intended to limit the purposes of the present disclosure as defined in the appended claims and their equivalents.

[0194] It should be understood that singular forms of "a", "an" and "the" include plural referents unless the context clearly indicates otherwise. Thus, for example, a reference to a "component surface" includes a reference to one or more such surfaces.

[0195] The terms "include" or "may include" refer to the existence of a corresponding disclosed function, operation or component that can be used in various embodiments of the present disclosure, and do not limit the existence of one or more additional functions, operations or features. In addition, the terms "including" or "having" can be interpreted as indicating certain characteristics, numbers, steps, operations, constituent elements, components or combinations thereof, but should not be interpreted as excluding the possibility of the existence of one or more other characteristics, numbers, steps, operations, constituent elements, components or combinations thereof.

[0196] The term "or" used in various embodiments of the present disclosure includes any of the listed terms and all combinations thereof. For example, "a or b" may include a, may include b, or may include both a and b.

[0197] Unless defined differently, all terms (including technical terms or scientific terms) used in this disclosure have the same meaning as those understood by those skilled in the art in this disclosure. Common terms, as defined in dictionaries, are interpreted as having meanings consistent with the context in the relevant technical fields, and should not be interpreted in an idealized or overly formal way unless explicitly defined in this disclosure.

[0198] The technical solution of the embodiment of the application can be applied to various communication systems, such as the Global System for Mobile Communications (GSM) system, code division multiple access (CDMA) system, wideband code division multiple access (WCDMA) system, general packet radio service (GPRS), long term evolution (LTE) system, LTE frequency division duplex (FDD) system, LTE time division duplex (TDD), universal mobile telecommunications system (UMTS), worldwide interoperability for microwave access (WiMAX) communication system, 5th generation (5G) system or new radio (NR), etc. In addition, the technical solution of the embodiment of the application can be applied to future-oriented communication technologies.

[0199] FIG. 1 shows an example wireless network 100 according to various embodiments of the present disclosure. The embodiment of the wireless network 100 shown in fig. 1 is for illustration only. Other embodiments of the wireless network 100 can be used without departing from the scope of this disclosure.

[0200] Wireless network 100 includes GNB 101, gNB 102 and gNB 103. GNB 101 communicates with gNB 102 and gNB 103. GNB 101 also communicates with at least one Internet Protocol (IP) network 130, such as the Internet, a proprietary IP network or other data networks.

[0201] Depending on a type of the network, other well-known terms such as "base station" or "access point" can be used instead of "gNodeB" or "gNB". For convenience, the terms "gNodeB" and "gNB" are used in this patent document to refer to network infrastructure components that provide wireless access for remote terminals. And, depending on the type of the network, other well-known terms such as "mobile station", "user station", "remote terminal", "wireless terminal" or "user apparatus" can be used instead of "user equipment" or "UE". For convenience, the terms "user equipment" and "UE" are used in this patent document to refer to remote wireless devices that wirelessly access the gNB, no matter whether the UE is a mobile device (such as a mobile phone or a smart phone) or a fixed device (such as a desktop computer or a vending machine).

[0202] gNB 102 provides wireless broadband access to the network 130 for a first plurality of User Equipments (UEs) within a coverage area 120 of gNB 102. The first plurality of UEs include a UE 111, which may be located in a Small Business (SB); a UE 112, which may be located in an enterprise (E); a UE 113, which may be located in a WiFi Hotspot (HS); a UE 114, which may be located in a first residence (R); a UE 115, which may be located in a second residence (R); a UE 116, which may be a mobile device (M), such as a cellular phone, a wireless laptop computer, a wireless PDA, etc. GNB 103 provides wireless broadband access to network 130 for a second plurality of UEs within a coverage area 125 of gNB 103. The second plurality of UEs include a UE 115 and a UE 116. In some embodiments, one or more of gNBs 101-103 can communicate with each other and with UEs 111-116 using 5G, Long Term Evolution (LTE), LTE-A, WiMAX or other advanced wireless communication technologies.

[0203] The dashed lines show approximate ranges of the coverage areas 120 and 125, and the ranges are shown as approximate circles merely for illustration and explanation purposes. It should be clearly understood that the coverage areas associated with the gNBs, such as the coverage areas 120 and 125, may have other shapes, including irregular shapes, depending on configurations of the gNBs and changes in the radio environment associated with natural obstacles and man-made obstacles.

[0204] As will be described in more detail below, one or more of gNB 101, gNB 102, and gNB 103 include a 2D antenna array as described in embodiments of the present disclosure. In some embodiments, one or more of gNB 101, gNB 102, and gNB 103 support codebook designs and structures for systems with 2D antenna arrays.

[0205] Although FIG. 1 illustrates an example of the wireless network 100, various changes can be made to FIG. 1. The wireless network 100 can include any number of gNBs and any number of UEs in any suitable arrangement, for example. Furthermore, gNB 101 can directly communicate with any number of UEs and provide wireless broadband access to the network 130 for those UEs. Similarly, each gNB 102-103 can directly communicate with the network 130 and provide direct wireless broadband access to the network 130 for the UEs. In addition, gNB 101, 102 and / or 103 can provide access to other or additional external networks, such as external telephone networks or other types of data networks.

[0206] FIGs. 2a and 2b illustrate example wireless transmission and reception paths according to the present disclosure. In the following description, the transmission path 200 can be described as being implemented in a gNB, such as gNB 102, and the reception path 250 can be described as being implemented in a UE, such as UE 116. However, it should be understood that the reception path 250 can be implemented in a gNB and the transmission path 200 can be implemented in a UE. In some embodiments, the reception path 250 is configured to support codebook designs and structures for systems with 2D antenna arrays as described in embodiments of the present disclosure.

[0207] The transmission path 200 includes a channel coding and modulation block 205, a Serial-to-Parallel (S-to-P) block 210, a size N Inverse Fast Fourier Transform (IFFT) block 215, a Parallel-to-Serial (P-to-S) block 220, a cyclic prefix addition block 225, and an up-converter (UC) 230. The reception path 250 includes a down-converter (DC) 255, a cyclic prefix removal block 260, a Serial-to-Parallel (S-to-P) block 265, a size N Fast Fourier Transform (FFT) block 270, a Parallel-to-Serial (P-to-S) block 275, and a channel decoding and demodulation block 280.

[0208] In the transmission path 200, the channel coding and modulation block 205 receives a set of information bits, applies coding (such as Low Density Parity Check (LDPC) coding), and modulates the input bits (such as using Quadrature Phase Shift Keying (QPSK) or Quadrature Amplitude Modulation (QAM)) to generate a sequence of frequency-domain modulated symbols. The Serial-to-Parallel (S-to-P) block 210 converts (such as demultiplexes) serial modulated symbols into parallel data to generate N parallel symbol streams, where N is a size of the IFFT / FFT used in gNB 102 and UE 116. The size N IFFT block 215 performs IFFT operations on the N parallel symbol streams to generate a time-domain output signal. The Parallel-to-Serial block 220 converts (such as multiplexes) parallel time-domain output symbols from the Size N IFFT block 215 to generate a serial time-domain signal. The cyclic prefix addition block 225 inserts a cyclic prefix into the time-domain signal. The up-converter 230 modulates (such as up-converts) the output of the cyclic prefix addition block 225 to an RF frequency for transmission via a wireless channel. The signal can also be filtered at a baseband before switching to the RF frequency.

[0209] The RF signal transmitted from gNB 102 arrives at UE 116 after passing through the wireless channel, and operations in reverse to those at gNB 102 are performed at UE 116. The down-converter 255 down-converts the received signal to a baseband frequency, and the cyclic prefix removal block 260 removes the cyclic prefix to generate a serial time-domain baseband signal. The Serial-to-Parallel block 265 converts the time-domain baseband signal into a parallel time-domain signal. The Size N FFT block 270 performs an FFT algorithm to generate N parallel frequency-domain signals. The Parallel-to-Serial block 275 converts the parallel frequency-domain signal into a sequence of modulated data symbols. The channel decoding and demodulation block 280 demodulates and decodes the modulated symbols to recover the original input data stream.

[0210] Each of gNBs 101-103 may implement a transmission path 200 similar to that for transmitting to UEs 111-116 in the downlink, and may implement a reception path 250 similar to that for receiving from UEs 111-116 in the uplink. Similarly, each of UEs 111-116 may implement a transmission path 200 for transmitting to gNBs 101-103 in the uplink, and may implement a reception path 250 for receiving from gNBs 101-103 in the downlink.

[0211] Each of the components in FIGs. 2a and 2b can be implemented using only hardware, or using a combination of hardware and software / firmware. As a specific example, at least some of the components in FIGs. 2a and 2b may be implemented in software, while other components may be implemented in configurable hardware or a combination of software and configurable hardware. For example, the FFT block 270 and IFFT block 215 may be implemented as configurable software algorithms, in which the value of the size N may be modified according to the implementation.

[0212] Furthermore, although described as using FFT and IFFT, this is only illustrative and should not be interpreted as limiting the scope of the present disclosure. Other types of transforms can be used, such as Discrete Fourier transform (DFT) and Inverse Discrete Fourier Transform (IDFT) functions. It should be understood that for DFT and IDFT functions, the value of variable N may be any integer (such as 1, 2, 3, 4, etc.), while for FFT and IFFT functions, the value of variable N may be any integer which is a power of 2 (such as 1, 2, 4, 8, 16, etc.).

[0213] Although FIGs. 2a and 2b illustrate examples of wireless transmission and reception paths, various changes may be made to FIGs. 2a and 2b. For example, various components in FIGs. 2a and 2b can be combined, further subdivided or omitted, and additional components can be added according to specific requirements. Furthermore, FIGs. 2a and 2b are intended to illustrate examples of types of transmission and reception paths that can be used in a wireless network. Any other suitable architecture can be used to support wireless communication in a wireless network.

[0214] FIG. 3a illustrates an example UE 116 according to the present disclosure. The embodiment of UE 116 shown in FIG. 3a is for illustration only, and UEs 111-115 of FIG. 1 can have the same or similar configuration. However, a UE has various configurations, and FIG. 3a does not limit the scope of the present disclosure to any specific implementation of the UE.

[0215] UE 116 includes an antenna 305, a radio frequency (RF) transceiver 310, a transmission (TX) processing circuit 315, a microphone 320, and a reception (RX) processing circuit 325. UE 116 also includes a speaker 330, a processor / controller 340, an input / output (I / O) interface 345, an input device(s) 350, a display 355, and a memory 360. The memory 360 includes an operating system (OS) 361 and one or more applications 362.

[0216] The RF transceiver 310 receives an incoming RF signal transmitted by a gNB of the wireless network 100 from the antenna 305. The RF transceiver 310 down-converts the incoming RF signal to generate an intermediate frequency (IF) or baseband signal. The IF or baseband signal is transmitted to the RX processing circuit 325, where the RX processing circuit 325 generates a processed baseband signal by filtering, decoding and / or digitizing the baseband or IF signal. The RX processing circuit 325 transmits the processed baseband signal to speaker 330 (such as for voice data) or to processor / controller 340 for further processing (such as for web browsing data).

[0217] The TX processing circuit 315 receives analog or digital voice data from microphone 320 or other outgoing baseband data (such as network data, email or interactive video game data) from processor / controller 340. The TX processing circuit 315 encodes, multiplexes, and / or digitizes the outgoing baseband data to generate a processed baseband or IF signal. The RF transceiver 310 receives the outgoing processed baseband or IF signal from the TX processing circuit 315 and up-converts the baseband or IF signal into an RF signal transmitted via the antenna 305.

[0218] The processor / controller 340 can include one or more processors or other processing devices and execute an OS 361 stored in the memory 360 in order to control the overall operation of UE 116. For example, the processor / controller 340 can control the reception of forward channel signals and the transmission of backward channel signals through the RF transceiver 310, the RX processing circuit 325 and the TX processing circuit 315 according to well-known principles. In some embodiments, the processor / controller 340 includes at least one microprocessor or microcontroller.

[0219] The processor / controller 340 is also capable of performing other processes and programs residing in the memory 360, such as operations for channel quality measurement and reporting for systems with 2D antenna arrays as described in embodiments of the present disclosure. The processor / controller 340 can move data into or out of the memory 360 as required by an execution process. In some embodiments, the processor / controller 340 is configured to execute the application 362 based on the OS 361 or in response to signals received from the gNB or the operator. The processor / controller 340 is also coupled to an I / O interface 345, where the I / O interface 345 provides UE 116 with the ability to connect to other devices such as laptop computers and handheld computers. I / O interface 345 is a communication path between these accessories and the processor / controller 340.

[0220] The processor / controller 340 is also coupled to the input device(s) 350 and the display 355. An operator of UE 116 can input data into UE 116 using the input device(s) 350. The display 355 may be a liquid crystal display or other display capable of presenting text and / or at least limited graphics (such as from a website). The memory 360 is coupled to the processor / controller 340. A part of the memory 360 can include a random access memory (RAM), while another part of the memory 360 can include a flash memory or other read-only memory (ROM).

[0221] Although FIG. 3a illustrates an example of UE 116, various changes can be made to FIG. 3a. For example, various components in FIG. 3a can be combined, further subdivided or omitted, and additional components can be added according to specific requirements. As a specific example, the processor / controller 340 can be divided into a plurality of processors, such as one or more central processing units (CPUs) and one or more graphics processing units (GPUs). Furthermore, although FIG. 3a illustrates that the UE 116 is configured as a mobile phone or a smart phone, UEs can be configured to operate as other types of mobile or fixed devices.

[0222] FIG. 3b illustrates an example gNB 102 according to the present disclosure. The embodiment of gNB 102 shown in FIG. 3b is for illustration only, and other gNBs of FIG. 1 can have the same or similar configuration. However, a gNB has various configurations, and FIG. 3b does not limit the scope of the present disclosure to any specific implementation of a gNB. It should be noted that gNB 101 and gNB 103 can include the same or similar structures as gNB 102.

[0223] As shown in FIG. 3b, gNB 102 includes a plurality of antennas 370a-370n, a plurality of RF transceivers 372a-372n, a transmission (TX) processing circuit 374, and a reception (RX) processing circuit 376. In certain embodiments, one or more of the plurality of antennas 370a-370n include a 2D antenna array. gNB 102 also includes a controller / processor 378, a memory 380, and a backhaul or network interface 382.

[0224] RF transceivers 372a-372n receive an incoming RF signal from antennas 370a-370n, such as a signal transmitted by UEs or other gNBs. RF transceivers 372a-372n down-convert the incoming RF signal to generate an IF or baseband signal. The IF or baseband signal is transmitted to the RX processing circuit 376, where the RX processing circuit 376 generates a processed baseband signal by filtering, decoding and / or digitizing the baseband or IF signal. RX processing circuit 376 transmits the processed baseband signal to controller / processor 378 for further processing.

[0225] The TX processing circuit 374 receives analog or digital data (such as voice data, network data, email or interactive video game data) from the controller / processor 378. TX processing circuit 374 encodes, multiplexes and / or digitizes outgoing baseband data to generate a processed baseband or IF signal. RF transceivers 372a-372n receive the outgoing processed baseband or IF signal from TX processing circuit 374 and up-convert the baseband or IF signal into an RF signal transmitted via antennas 370a-370n.

[0226] The controller / processor 378 can include one or more processors or other processing devices that control the overall operation of gNB 102. For example, the controller / processor 378 can control the reception of forward channel signals and the transmission of backward channel signals through the RF transceivers 372a-372n, the RX processing circuit 376 and the TX processing circuit 374 according to well-known principles. The controller / processor 378 can also support additional functions, such as higher-level wireless communication functions. For example, the controller / processor 378 can perform a Blind Interference Sensing (BIS) process such as that performed through a BIS algorithm, and decode a received signal from which an interference signal is subtracted. A controller / processor 378 may support any of a variety of other functions in gNB 102. In some embodiments, the controller / processor 378 includes at least one microprocessor or microcontroller.

[0227] The controller / processor 378 is also capable of performing programs and other processes residing in the memory 380, such as a basic OS. The controller / processor 378 can also support channel quality measurement and reporting for systems with 2D antenna arrays as described in embodiments of the present disclosure. In some embodiments, the controller / processor 378 supports communication between entities such as web RTCs. The controller / processor 378 can move data into or out of the memory 380 as required by an execution process.

[0228] The controller / processor 378 is also coupled to the backhaul or network interface 382. The backhaul or network interface 382 allows gNB 102 to communicate with other devices or systems through a backhaul connection or through a network. The backhaul or network interface 382 can support communication over any suitable wired or wireless connection(s). For example, when gNB 102 is implemented as a part of a cellular communication system, such as a cellular communication system supporting 5G or new radio access technology or NR, LTE or LTE-A, the backhaul or network interface 382 can allow gNB 102 to communicate with other gNBs through wired or wireless backhaul connections. When gNB 102 is implemented as an access point, the backhaul or network interface 382 can allow gNB 102 to communicate with a larger network, such as the Internet, through a wired or wireless local area network or through a wired or wireless connection. The backhaul or network interface 382 includes any suitable structure that supports communication through a wired or wireless connection, such as an Ethernet or an RF transceiver.

[0229] The memory 380 is coupled to the controller / processor 378. A part of the memory 380 can include an RAM, while another part of the memory 380 can include a flash memory or other ROMs. In certain embodiments, a plurality of instructions, such as the BIS algorithm, are stored in the memory. The plurality of instructions are configured to cause the controller / processor 378 to execute the BIS process and decode the received signal after subtracting at least one interference signal determined by the BIS algorithm.

[0230] As will be described in more detail below, the transmission and reception paths of gNB 102 (implemented using RF transceivers 372a-372n, TX processing circuit 374 and / or RX processing circuit 376) support aggregated communication with FDD cells and TDD cells.

[0231] Although FIG. 3b illustrates an example of gNB 102, various changes may be made to FIG. 3b. For example, gNB 102 can include any number of each component shown in FIG. 3a. As a specific example, the access point can include many backhaul or network interfaces 382, and the controller / processor 378 can support routing functions to route data between different network addresses. As another specific example, although shown as including a single instance of the TX processing circuit 374 and a single instance of the RX processing circuit 376, gNB 102 can include multiple instances of each (such as one for each RF transceiver).

[0232] The time domain unit (also called time unit) in this application can be: an OFDM symbol, an OFDM symbol group (composed of multiple OFDM symbols), a slot, a slot group (composed of multiple slots), a subframe, a subframe group (composed of multiple subframes), a system frame and a system frame group (composed of multiple system frames); it can also be an absolute time unit, such as 1 millisecond, 1 second, etc. A time unit can also be a combination of various granularities, such as N1 slots plus N2 OFDM symbols.

[0233] The frequency domain unit (also called frequency unit) in this application can be: a subcarrier, a subcarrier group (composed of multiple subcarriers), a resource block (RB), which can also be called a physical resource block (PRB), a resource block group (composed of multiple RBs), a bandwidth part (BWP). It can also be an absolute frequency domain unit, such as 1 Hz, 1 kHz, etc. The frequency domain unit can also be a combination of multiple granularities, such as M1 PRBs plus M2 subcarriers.

[0234] Exemplary embodiments of the present disclosure are further described below with reference to the accompanying drawings.

[0235] Text and drawings are provided as examples only to help readers understand the present disclosure. They are not intended and should not be construed to limit the scope of the present disclosure in any way. Although certain embodiments and examples have been provided, based on the disclosure herein, it is obvious to those skilled in the art that changes can be made to the illustrated embodiments and examples without departing from the scope of this disclosure.

[0236] It can be understood by those skilled in the art that the singular forms "a", "an", "the" and "the" used herein can also include plural forms unless specifically stated. It should be further understood that the word "comprising" used in the specification of this application refers to the presence of said features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we say that an element is "connected" or "coupled" to another element, it can be directly connected or coupled to the other element, or intervening elements may also exist. Furthermore, "connected" or "coupled" as used herein may include wireless connection or wireless coupling. As used herein, the phrase "and / or" includes all or any unit and all combinations of one or more associated listed items.

[0237] It can be understood by those skilled in the art that unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. It should also be understood that terms, such as those defined in general dictionaries, should be understood to have meanings consistent with those in the context of the prior art, and will not be interpreted in an idealized or overly formal sense unless they are specifically defined as here.

[0238] It can be understood by those skilled in the technical field that the "terminal" and "terminal equipment" used here include both the equipment of wireless signal receiver, which only has the equipment of wireless signal receiver without transmission capability, and the equipment of receiving and transmitting hardware, which has the equipment of receiving and transmitting hardware capable of bidirectional communication on the bidirectional communication link. Such devices may include a cellular or other communication device having a single-line display or a multi-line display or a cellular or other communication device without a multi-line display; pCs (Personal Communications Service), which can combine voice, data processing, fax and / or data communication capabilities; pDA(Personal Digital Assistant), which may include RF receiver, pager, Internet / Intranet access, web browser, notepad, calendar and / or GPS(Global Positioning System) receiver; a conventional laptop and / or palmtop computer or other device having and / or including a radio frequency receiver. As used herein, "terminal" and "terminal equipment" can be portable, transportable, installed in vehicles (aviation, marine and / or land), or suitable and / or configured to operate locally, and / or operate in any other location on the earth and / or space in a distributed form. The "terminal" and "terminal device" used here can also be communication terminals, internet terminals and music / video playing terminals, such as PDA, mobile internet device (Mobile Internet Device) and / or mobile phone with music / video playing function, as well as smart TV, set-top box and other devices.

[0239] Without departing from the scope of the present invention, the term "send" in the present invention can be used interchangeably with "transmit", "report" and "notify".

[0240] Text and drawings are provided as examples only to help readers understand the present disclosure. They are not intended and should not be construed to limit the scope of the present disclosure in any way. Although certain embodiments and examples have been provided, based on the disclosure herein, it is obvious to those skilled in the art that changes can be made to the illustrated embodiments and examples without departing from the scope of this disclosure.

[0241] The transmission link of wireless communication system mainly includes: downlink communication link from 5G gNB to User Equipment (UE) and uplink communication link from UE to network.

[0242] Nodes used for positioning measurement in wireless communication systems, such as current wireless communication systems, include: UE that initiates positioning request message, Location Management Function (LMF) that is used for UE positioning and sending positioning assistance data, gNB or transmission-reception point (TRP) that broadcasts positioning assistance data and performs uplink positioning measurement, and UE that is used for downlink positioning measurement. In addition, the method of the present invention can also be extended to apply to other communication systems, such as automobile communication (V2X), such as sidelink communication, where the transmitting and receiving point or UE may be any device in V2X.

[0243] In recent years, artificial intelligence (AI) technology, with deep learning algorithm as a representative, has risen again, which has solved the problems that have existed in all walks of life for many years and achieved great technical and commercial success. With the continuous evolution of wireless communication system, these problems in air interface have been studied and tried to introduce new methods to solve them. In order to solve some problems encountered in communication, machine learning method can be enabled. Among them, the method of machine learning (ML) usually includes the algorithm design of machine learning and the design of machine learning model on which the algorithm is based. The solution based on AI deep learning (DL) technology usually refers to the algorithm taking an artificial neural network as a model in machine learning technology. The deep learning network model is usually composed of artificial neural networks with multiple layers stacked, adjusting weight parameters in the neural network by training the existing data, and then it is used to achieve the task goal for unprecedented situations in the inference stage. At the same time, generally speaking, compared with the general solution or algorithm based on fixed rules, the solution based on DL needs better computing power than the original classical algorithm, which usually requires a dedicated computing chip in the equipment running DL algorithm to support the more efficient operation of DL algorithm.

[0244] Problems in communication to be solved using AI algorithm based on machine learning usually needs to meet the conditions of problems for machine learning. For example, obtaining the positioning of a device satisfies the above conditions to some extent, so it can be solved by using machine learning algorithm and achieve better effect than legacy solutions in the process of communication transmission, for example, in the environment of non-line-of-sight path.

[0245] Despite in the wireless communication system, the legacy positioning algorithm can provide normal services in some scenarios; however, for the machine learning algorithm, because it is completely different from the architecture and characteristics of the legacy algorithm, the method of application is completely different from that of the legacy algorithm. Because the current wireless communication systems (the fourth generation, the fifth generation, and the possible sixth generation wireless communication systems in the future) have strict and unified standards to restrict the configuration method and behavior process of the air interface in the communication process, considering using new technology of machine learning in the new generation wireless communication system, the design of air interface must be combined with the characteristics of new communication system and machine learning algorithm. Among them, for the algorithm based on machine learning to be implemented in the air interface of wireless communication system, it is necessary to specify its specific implementation process, for example, how to transmit and / or interact signals between user equipment and network nodes (such as base stations), for another example, the process of activating and / or shutting down / turning off machine learning algorithms and models, and for yet another example, the updating of machine learning algorithms and models in use, which are the key points to be considered.

[0246] Therefore, it is necessary to improve the process or method related to device positioning, so that the solution based on machine learning can be used in device positioning.

[0247] In the present disclosure, the expression of AI method is used to include algorithm and / or model based on machine learning, technology based on AI (artificial intelligence) / ML (machine learning), AI / ML for NR air interface, AI / ML technology, AI / ML architecture, AI / ML model, AI / ML model for air interface, AI / ML method and AI / ML related algorithm, AI / ML-based algorithm and AI / ML scheme / solution, etc., and similar expressions. Among them, the used model involved in AI method is called AI model, including other description methods, such as AI / ML model, etc.

[0248] The present invention provides a method for applying and / or configuring an algorithm and / or model based on machine learning in a wireless communication system to complete or realize positioning operation and / or acquisition of positioning information of the wireless communication system. The purpose of the present invention is to solve the problems that need to be solved in the air interface of wireless communication by using a solution based on machine learning in the wireless communication system, propose the architecture, procedure and / or method of how to use the machine learning solution in the wireless communication system, and realize the application of the machine learning algorithm in the wireless communication system by designing these architectures, procedures and / or methods, to achieve a technical effect that the machine learning method with better effect compared with the legacy existing methods can be successfully used and implemented in the communication system, thereby further improving the positioning performance of the wireless communication system.

[0249] In the following, some details of the positioning-related operations using the AI method proposed by the present invention will be introduced in detail, especially for the AI model deployed at the UE end, the specific operations involved in the embodiment of the present disclosure may include, for example, one or more of:

[0250] ● the UE reports the supported AI model, for example, the UE reports the AI model supported by the UE to a network node (such as TRP or LMF), for example, including one or more of:

[0251] o UE determines the signaling mode used for the report, including one or more of:

[0252] ■ uplink control channel PUCCH

[0253] ■ MAC CE

[0254] ■ RRC high-layer signaling

[0255] ■ LPPa (LTE positioning protocol A) message (for example, when sending to a core network node such as LMF)

[0256] o UE determines the centent to report, including one or more of:

[0257] ■ indexes (or a set or a list of indexes) of supported AI models, such as a list of model ID;

[0258] ■ feature(s) (or a set or a list of features) associated with supported AI models, optionally, the corresponding feature can also be replaced by a corresponding feature index; similarly, a set of features can be replaced by a set of feature indexes, or a list of features can be replaced by a list of feature indexes; for example, features may include some functions, such as functions related to positioning, tracking, beam management, channel information feedback, etc.

[0259] ■ corresponding relationship between the indexes of supported AI models and the features associated with AI model; specifically, including one or more of:

[0260] ▷ an index of an AI model may correspond to a feature associated with AI model one by one; for example, the feature associated with AI model may correspond to an unique index of an AI model; besides,

[0261] ▷ an index of an AI model may correspond to multiple features associated with AI model; for example, the index of an AI model may correspond to multiple features associated with AI model (such as a set of features or a list of features), and it may be replaced by configuration for an index of an AI model including multiple features associated with AI model (such as a set of features or a list of features); this is applicable to the case that an AI model may be applicable to multiple features; and / or

[0262] ▷ indexes of multiple AI models may correspond to one feature associated with AI model; for example, multiple indexes (a set of indexes or a list of indexes) of AI models may correspond to the same feature associated with AI model (for example, to the same one feature, which is associated with AI model), which may be replaced by configuration for one feature associated with AI model includes multiple indexes (a set of indexes or a list of indexes) of AI models; this is applicable to a situation where there may be multiple available AI models for one feature associated with AI model; and / or

[0263] ▷ □UE receives configuration of ratio of index of AI model to feature associated with AI model by network device, and determines the number of features corresponding to an index of an AI model or the number of model indexes corresponding to a feature associated with AI model according to the configuration;

[0264] ■ applicable conditions of the supported AI model, including one or more of:

[0265] ▷ one or more cell indexes (or a set of indexes or a list of indexes), for example, an AI model may be used in the cell(s) corresponding to the one or more cell indexes; the cell index may be a logical index or a physical cell index; the cell index may be replaced by a TRP index, a sector index or a zone index, etc.;

[0266] ▷ RSRP threshold related conditions, including at least one of:

[0267] √ single RSRP threshold, when the measured RSRP value in downlink is higher (or not lower) than the RSRP threshold, the supported AI model can be used; for example, when the measured RSRP value in downlink is not higher (or lower) than the RSRP threshold value, the supported AI model cannot be used; vice versa; when the measured RSRP value in downlink is not higher (or lower) than the RSRP threshold value, the supported AI model can be used; for example, when the measured RSRP value in downlink is higher (or not lower) than the RSRP threshold, the supported AI model cannot be used; and / or

[0268] √ an interval of RSRP thresholds (such as an interval consisting of an upper limit value of RSRP and a lower limit value of RSRP), when the measured RSRP value in downlink is within the interval (such as not greater than the upper limit value and not less than the lower limit value), the supported AI model can be used; for example, when the measured RSRP value in downlink is not within the interval (such as greater than the upper limit or less than the lower limit), the supported AI model cannot be used;

[0269] √ The measured RSRP value in downlink may be replaced by the measured RSRP value in uplink, for example, it is suitable when TRP performs uplink measurement;

[0270] √ The measured RSRP value may be replaced by a measured SNR or SINR value;

[0271] ▷ multipath threshold related conditions, including

[0272] √ a threshold T1(T1 is a positive integer) for the number of paths higher (or not lower) than a power threshold P1 in the measurement result. For example, in the measurement result of downlink measurement, there are X (X is a positive integer) paths higher (or not lower) than a power threshold. When X is greater (or not less) than T1, the supported AI model may be used, for example, when X is not greater than (or less than) T1, the supported AI model cannot be used; and / or

[0273] √ a threshold T2 (T2 is a positive integer) number of measurements in each there is larger or no less than a threshold T1 (T1 is a positive integer) for the number of paths with power larger than power threshold P1. For example, in the measurement results of Y (Y is a positive integer) downlink measurements, in each measurement result, there are X (X is a positive integer) paths higher (or not lower) than a power threshold and X is greater (or not less) than T1, when Y is greater than (or not less than) T2, the supported AI model can be used, for example, when Y is not greater than (or less than) T2, the supported AI model cannot be used; and / or

[0274] √ a threshold G1 for the probability where the number of paths higher (or not lower) than a power threshold P1 is greater than a threshold T1 (T1 is a positive integer) among the measurement results. For example, in the measurement results of downlink measurement, the probability value where there are X paths higher (or not lower) than a power threshold P1 and X is greater (or not less) than T1 is GX, where G1 and GX are decimals ranging from 0 to 1 with a certain stepsize, the stepsize for example may be 0.1; or percentage values, when GX is greater than (or not less than) G1, the supported AI model may be used, for example, when GX is not greater than (or less than) G1, the supported AI model cannot be used;

[0275] √ the RSRP value of downlink measurement may be replaced by the RSRP value of uplink measurement, for example, it is suitable when TRP performs uplink measurement;

[0276] ▷ storage threshold related conditions, for example, the storage threshold related conditions required for using an AI model. When the storage space provided by a node or UE to use an AI model can meet (for example, greater than or not less than) the storage threshold required for using an AI model, the node or UE can use the AI model, otherwise it cannot use the AI model; wherein, the storage space or storage capacity may include one or more of the input data, output data, operation intermediate data and hyperparameters used by an AI model, which are expressed by bit and byte related values;

[0277] ▷ complexity threshold related conditions, for example, the complexity threshold related conditions required for using an AI model, when the computing space provided by a node or UE to use an AI model can meet (for example, greater than or not less than) the complexity threshold required for using an AI model, the node or UE can use the AI model, otherwise it cannot use the AI model; wherein, the computing space may include the number of calculations required for one or more of the input data, output data, operation intermediate data and hyperparameters used by an AI model, for example, expressed by FLOPS;

[0278] ▷ output data accuracy (e.g. error) requirement related conditions, for example, the error of output data supported by an AI model can meet (e.g. not greater than or less than) the required threshold value of data error, then the node or UE can use the AI model; otherwise, if it is not satisfied, the AI model cannot be used; for example, the output data is position coordinates, and the accuracy (error) requirement is within 1 meter, if the accuracy of position coordinate estimation of AI model can only be guaranteed within 5 meters, the AI model cannot be used; if the accuracy of the position coordinate estimation of an AI model may be guaranteed in the range of 0.8 meters, such as meeting the error requirement, then the AI model is applicable; similarly, it may be extended to other output data types, such as estimated values of measurements;

[0279] ▷ processing time requirement related conditions, such as processing time related conditions required for using an AI model, when the processing time required for running an AI model can meet (for example, not more than or less than) a processing time threshold, the node or UE can use the AI model; otherwise, if it is not satisfied, the AI model cannot be used; wherein, the processing time may include the processing time required for one or more of the input data, output data, operation intermediate data, and hyperparameters used by an AI model, and may be expressed by the number and value of time units, such as X symbols, X slots, etc. or X milliseconds, X seconds, etc.

[0280] ■ a type and / or number of input data required by the supported AI model, for example, the input format information of the AI model, wherein

[0281] ▷ the input data type may include channel impulse response (CIR), power delay profile (PDP), delay profile (DP), and channel information feature (for example, feature values of channel information extracted by calculation in CIR or PDP or DP); and / or

[0282] ▷ the input data type may also include one or more of:

[0283] √ time value,

[0284] √ power value

[0285] √ phase value

[0286] √ feature value of channel information

[0287] √ time stamp of input data, such as the time unit index corresponding to the input data;

[0288] √ one or more or all of the above three values may be the value per path, so it may also include a path index;

[0289] ▷ the number of input data, for example, the size of quantity corresponding to any of the above data types; such as the number of required path indexes, and / or the number of required time values, and / or the number of required power values, and / or the number of required phase values; among them, a data type may have a size of quantity, and / or multiple or all data types share the same quantity;

[0290] ■ type and / or number of output data of support AI model, for example, the output format information of the AI model, wherein

[0291] ▷ the output data type includes one or more of:

[0292] √ coordinate estimation of the UE, which may be the coordinate estimation in the local coordinate system or the coordinate estimation in the global coordinate system;

[0293] √ estimation of a specific measurement value, the specific measurement value including at least one or more of a time measurement value (e.g., arrival time or arrival time difference), a power measurement value, a phase measurement value (e.g., a phase value or a phase difference value), etc.;

[0294] √ estimation of a specific probability value, the specific probability value includes at least one or more of the probabilities of being light-of sight path (for example, the probability of light-of sight path), the probability that the number of multipaths exceeds the threshold, etc.;

[0295] ▷ the number of output data, for example, the size of quantity value corresponding to any of the above output data types; such as the number of output coordinate estimates, and / or the number of output estimations of a specific measurement value and / or the number of output estimations of a specific probability value; among them, an output data type may have a size of quantity value, and / or multiple or all output data types may share the same quantity value;

[0296] ▷ time unit value required by running the AI model from the input data to obtain the output data, such as how long it takes to obtain the output data from the input data through the AI model;

[0297] o optionally, the network node feeds back the AI models supported by the network node as well from among the supported AI models reported by the UE; for example, the UE receives AI models supported by the network node indicated by the network node (such as an index, a set of indexes or a list of indexes of AI models; or a feature index, a set of indexes or a list of indexes associated with AI models); for example, the AI models supported by the network node indicated by the network node may be a subset of the supported AI models reported by the UE; for example, UE reports supporting AI model indexes 0, 1, 2, 3 and 4; the UE receives feedback from the network node that the network node supports AI model indexes 0, 1 and 4; for example, the UE may determine that the AI model indexes supported by both the UE and the network node are 0, 1 and 4;

[0298] ● the UE or the network node determines the currently AI model to use, for example, the UE or the network node determines the currently AI model to use according to a certain method; specifically, it includes one or more of:

[0299] o optionally, before the UE or the network node determines the currently available AI model, the UE or the network node triggers the positioning operation using the AI method, for example, the UE or the network node determines not to use other non-AI methods (such as the legacy RAT dependent method, DL-TDOA, etc.) for positioning operation, and the trigger includes one or more of:

[0300] ■ determining whether a certain trigger condition is met; wherein, if the certain trigger condition is met, the AI method is determined to be used; if the certain trigger condition is not met, the AI method is not used; the advantage of this is that it is beneficial to use AI method with better pertinence, and it may be used when legacy methods may not give good results; optionally, the certain trigger condition includes one or more of:

[0301] ▷ the measured first signal is a multipath signal, specifically, including the measured first signal has more than one path; wherein different paths have different arrival times and / or received power values;

[0302] ▷ the measured first signal is a non-line-of-sight (NLoS) signal, which specifically includes: when the line-of-sight / non-line-of-sight indicator (LoS / NLoS indicator) is false, for example, the measured first signal is a non-line-of-sight (NLoS) signal (for example, when the indicator is hard indication, it is indicated as NLoS); and / or when the value of the line-of-sight / non-line-of-sight indication (for example, when the indication is a soft indication) is less than (or not greater than) a probability threshold value, for example, when the measured first signal has a high probability of being non-line-of-sight; wherein the threshold value is obtained by receiving an instruction and / or preset;

[0303] ▷ the measured reference signal received power (RSRP) value of the first signal is greater than (or not less than) a threshold value; wherein the threshold value is obtained by receiving an instruction and / or preset;

[0304] ▷ when the transmitting time error (Tx Timing Error, Tx TE) or transmitting time error group (TE group, TEG) of the first signal is greater than (or not less than) a threshold; wherein the threshold value is obtained by receiving an instruction and / or preset; and / or

[0305] ▷ the receiving timing error (Rx Timing Error, Rx TE) or the receiving TEG of the first signal is greater than or (or not less than) a threshold value; wherein the threshold value is obtained by receiving an instruction and / or preset;

[0306] ▷ the transmitting and receiving TE or TEG of the first signal is greater than (or not less than) a threshold value; wherein the threshold value is obtained by receiving an instruction and / or preset;

[0307] ▷ the transmitting and receiving TE or TEG of the first signal belongs to a specific range, wherein the specific range is obtained by receiving an instruction and / or preset;

[0308] ▷ an uncertainty range in the positioning assistance information is greater than (or not less than) a threshold value; wherein the threshold value is obtained by receiving an instruction and / or preset;

[0309] ▷ optionally, when an instruction to use the AI method is received; for example, the instruction of the AI method may be sent to the UE by a network device such as a base station device or LMF;

[0310] ▷ optionally, when at least one of the above-mentioned trigger condition occurs not less than (or more than) N times, N is a positive integer not less than 1, and the N is obtained by receiving an instruction and / or preset, for example, when the trigger condition counter reaches N+1 times;

[0311] ▷ when the AI method is a valid AI method, such as an AI method that passes a test, the test includes all or part of the operations in the following test section;

[0312] ▷ the first signal includes reference signals for positioning (for example, downlink PRS (positioning reference signal) and uplink SRS (sounding reference signal) for positioning in a cellular wireless communication system, etc.), and / or other reference signals in the wireless system, such as SSB (synchronization signal block) and / or CSI-RS (channel state information-reference signal), etc.

[0313] ■ performing a trigger procedure; optionally, performing the trigger procedure includes one or more of the following operations:

[0314] ▷ when the network side device (such as LMF (Location Management Function) and / or base station device) triggers using the AI method according to the above triggering conditions; indicating and / or activating to use the AI method by an LPP (LTE Positioning Protocol) message and / or a RRC (radio resource control) configuration message and / or MAC CE (media access control control element) and / or DCI (downlink control information);

[0315] ▷ when the UE triggers using the AI method according to the trigger condition; the UE receives an instruction to use or activate the AI method through an LPP (LTE Positioning Protocol) message and / or a RRC (Radio Resource Control) configuration message and / or MAC CE (Media Access Control Control Element) and / or DCI (Downlink Control Information), or the UE requests using the AI method from the network side device through PUCCH (physical uplink control channel) and / or MAC CE and / or PRACH (physical random access channel) channel and / or an LPP message; the UE receives the feedback for the request from the network side device to determine whether to use the AI method; the feedback includes the network side device indicating and / or activating the method of using the AI method;

[0316] ▷ when the UE triggers using the AI method according to the trigger conditions; the UE directly starts to use the AI method; this method is more suitable when the AI method is deployed on the UE side;

[0317] o when determined by the UE, for example, it is UE that finally determines the AI model to use; specifically, it includes one or more of the following

[0318] ■ the UE determines one or more AI models that meet the applicable conditions of the supported AI model, wherein

[0319] ▷ the supported AI model may be a supported AI model reported by the UE or an AI model supported by both the UE and the network node;

[0320] ▷ for the applicable conditions of the supported AI model, please refer to the description of the applicable conditions of the supported AI model in the supported AI models reported by the UE; details are omitted here;

[0321] ▷ the meeting the applicable conditions of the supported AI model includes meeting all the applicable conditions before being determined as meeting the conditions; or one or X of the conditions are met, it may be determined that the conditions are met; UE receives an indication from the network node to determine whether to operate as the conditions been determined to be met according to one or X or all (whole) conditions been met;

[0322] ▷ the one or more AI models may include indexes (e.g., a set of indexes or a list of indexes) of one or more AI models or indexes (e.g., a set of indexes or a list of indexes) of features associated with one or more AI models;

[0323] ■ when the UE determines that the applicable conditions of a supported AI model are met, it determines that the AI model is the AI model to use;

[0324] ■ when the UE determines that the applicable conditions of X supported AI models (for example, X is a positive integer) are met; the UE determines the AI model to use according to certain rules, which include one or more of:

[0325] ▷ the UE selects one of the X AI models with optimal characteristic as the AI model to use, and the optimal characteristic includes at least one of:

[0326] √ AI model requiring the least storage

[0327] √ AI model requiring the least computational complexity

[0328] √ AI model requiring the least input data type and / or quantity.

[0329] √ AI model with the highest accuracy of output data.

[0330] √ AI model corresponding to the largest number of features associated with AI models

[0331] √ AI model with the largest number of applicable conditions been met;

[0332] √ not the AI model selected in the previous positioning operation using AI method.

[0333] ▷ UE randomly selects an AI model from the X AI models (for example, with equal probability) and determines it to be the AI model to use; particularly, when there are Y AI models satisfying the above optimal characteristic, the UE randomly selects one AI model from the Y AI models (for example, Y is a positive integer) (for example, with equal probability) and determines it to be the AI model to use;

[0334] ■ UE determines independently,

[0335] ■ determining with the assistance of the network node

[0336] ■ optionally, the UE reports the determined AI model to the network node; for example, the UE reports the determined AI model index and / or the feature index associated with the AI model to the network node;

[0337] o when determined by the network node, for example, it is the network node that determines the AI model to use; and notifies the UE of the determined AI model to use (for example, including the AI model index and / or the feature index associated with the AI model determined to use), the specific operation for determining by the network node includes one or more of:

[0338] ■ the network node determines one or more AI models that meet the applicable conditions of the supported AI model, wherein

[0339] ▷ the supported AI model may be a supported AI model reported by the UE or an AI model supported by both the UE and the network node;

[0340] ▷ for the applicable conditions of the supported AI model, please refer to the description of the applicable conditions of the supported AI model in the supported AI models reported by the UE; details are omitted here;

[0341] ▷ the meeting the applicable conditions of the supported AI model includes meeting all the applicable conditions before being determined as meeting the conditions; or one or X of the conditions are met, it may be determined that the conditions are met; UE receives an indication from the network node to determine whether to operate as the conditions been determined to be met according to one or X or all (whole) conditions been met;

[0342] ▷ the one or more AI models may include indexes (e.g., a set of indexes or a list of indexes) of one or more AI models or indexes (e.g., a set of indexes or a list of indexes) of features associated with one or more AI models;

[0343] ■ when the network node determines that applicable conditions of one supported AI model are met, it determines that the AI model is the AI model to use;

[0344] ■ when the network node determines that the applicable conditions of X supported AI models (for example, X is a positive integer) are met; the UE determines the AI model to use according to certain rules, which include one or more of:

[0345] ▷ The network node selects one of the X AI models with optimal characteristic as the AI model to use, and the optimal characteristic includes at least one of:

[0346] √ AI model requiring the least storage

[0347] √ AI model requiring the least computational complexity

[0348] √ AI model requiring the least input data type and / or quantity

[0349] √ AI model with the highest accuracy of output data

[0350] √ AI model corresponding to the largest number of features associated with AI models

[0351] √ AI model with the largest number of applicable conditions been met;

[0352] √ not the AI model selected in the previous positioning operation using AI method;

[0353] ▷ the network node selects an AI model randomly (for example, with equal probability) from the X AI models and determines it as the AI model to use; particularly, when there are Y AI models satisfying the above optimal characteristic, the UE selects one AI model from the Y (for example, Y is a positive integer) AI models randomly (for example, with equal probability) and determines it as the AI model to use;

[0354] ■ determining with the assistance of UE

[0355] ■ determining by the network node independently

[0356] o optionally, when the AI model determined to use by the UE or the network node is different from the AI model determined the last time, the UE or the network node determines to use the AI model; otherwise, for example, when the AI model determined to use by the UE or the network node is the same as the AI model determined the last time, the subsequent operations may include at least one of (for example, the following operations may also be performed when the monitoring result of the model by the UE is not passed):

[0357] ■ UE terminates the use of AI method;

[0358] ■ UE selects another AI model and notifies the network node; for example, the UE enters the operations involved in the aforementioned procedure of determining the AI model to use currently by the UE or the network node;

[0359] ■ the network node selects another AI model and notifies the UE; the UE receives the AI model to use indicated by the notification;

[0360] ■ UE determines to fall back to other non-AI methods for positioning

[0361] ■ the network node determines to fall back to other non-AI methods for positioning, and notifies the UE; the UE receives the notification and determines to fall back to other non-AI methods for positioning;

[0362] ■ UE reports error in AI method;

[0363] ■ TRP reports error in AI method;

[0364] ● UE obtains measurement results, for example, the UE obtains measurement results by measuring related downlink signals, and the measurement results are used in AI-method-based positioning, such as the determination of AI model, the generation of input data, training, etc.; specifically, operations related to the UE obtaining measurement results include one or more of:

[0365] o UE obtains resource configuration of reference signal for measurement, the resource configuration of reference signal is provided by a network node; the resource configuration of reference signal includes: the number and locations of reference signals within a certain time unit (such as the symbol indexes of reference signals in a slot, etc.), the locations of frequency domain units where reference signal is located (such as the start location of reference signal in frequency domain is determined by a frequency domain unit interval value from a frequency domain reference point), the number of time units and / or frequency domain units occupied by a reference signal (such as the number of symbols and the number of PRBs of a reference signal); the number of repetitions of a reference signal in a certain time unit, etc.

[0366] o UE obtains the configuration for measurement, and according to the configuration for measurement, UE may determine the time window for measurement, such as the configuration of measurement gap (MG) and / or the configuration of positioning reference signal processing window (PRS process window, PPW) for AI method; or a measurement period obtained according to the period of the reference signal and the configuration period of MG (or PPW), for example, the time window may be MG,PPW or the measurement period;

[0367] ■ Optionally, the UE obtains the priority of the reference signal in the time window, for example, when the UE receives indication of the priority or makes judgment on priority, and according to the indication of the priority or judgment on priority, it is obtained that when the reference signal collides with other signals (such as downlink signal reception and / or uplink signal transmission) (for example, the time domain units of the two signals overlap or the time domain unit interval is less than or not greater than a certain threshold value), UE gives priority to measuring the signal for measurement (for example, the priority of the signal for measurement is higher than or not lower than other signals) or UE gives priority to receiving or sending other signals (for example, the priority of the signal for measurement is lower than or not higher than other signals), and when the two have the same priority, UE independently determines to measure the signal for measurement and / or receive or send other signals;

[0368] o UE obtains the resource configuration for reporting the measurement result, and according to the resource configuration, UE may determine the resource configuration that may be used to report the related information of the measurement result. For example, the resource configuration of PUSCH includes the number of symbols and locations (e.g., symbol index) of the PUSCH in a time unit.

[0369] o UE measures the reference signal according to the obtained reference signal resource configuration for measurement and / or the configuration for measurement and / or the resource configuration for measurement result reporting

[0370] o UE determines the measurement result, for example, the UE obtains the measurement result by measuring the reference signal, which also includes processing the measurement result, specifically including one or more of:

[0371] ■ obtaining a final measurement result according to certain rules according to the obtained initial measurement result, wherein the measurement result may include one or more of the aforementioned AI input data types; the type and quantity of the initial measurement results may be the same as or different from that of the final measurement results; the certain rules include one or more of:

[0372] ▷ selecting measurement results meeting a certain threshold value from the initial measurement results as the final measurement results; the meeting a certain threshold value includes at least one of:

[0373] √ a power value contained in the initial measurement result is higher (or not lower) than a power threshold value;

[0374] √ a time value contained in the initial measurement result is earlier (or not later) or less (or not greater) than a time threshold value; in particular, the time threshold value may be the threshold value of time unit index, or a set or a list of time unit indexes for the final measurement results; for example, the measurement results of the first X (X is a positive integer) time unit indexes of paths are counted as the final measurement results;

[0375] √ a phase value contained in the initial measurement result is less than (or not greater than) a phase threshold value;

[0376] ▷ calculating from the initial measurement result according to a certain calculation method to obtain the final measurement result; for example, the initial measurement result is CIR or PDP or DP, and the final measurement result is the channel information feature values (or a set or a list of channel information feature values) calculated by the existing calculation formula;

[0377] o optionally, the UE reports or feeds back the measurement result, for example, the UE reports the feedback content related to the obtained measurement result (for example, it may be the initial measurement result or the final measurement result) to the network node; specifically, including:

[0378] ■ UE determines feedback content, which includes an initial measurement result and / or a final measurement result;

[0379] ■ UE determines the feedback method, for example, the UE feeds back trough uplink control channel PUCCH (such as in the form of UCI) or MAC CE or RRC high-layer signaling; if reporting to a core network entity such as LMF, then through LPPa signaling or other dedicated signaling;

[0380] ● UE obtains the input data of the AI model, for example, the UE obtains the input data of the AI model determined to use according to the above measurement results, specifically including at least one of:

[0381] o UE determines the measurement result as the input data of AI model;

[0382] o when the measurement result is the final measurement result, the UE determines the measurement result as the input data of the AI model;

[0383] o when the measurement result is the initial measurement result, the UE processes the measurement result according to the same method as that for obtaining the final measurement result from the initial measurement result according to certain rules to obtain the input data of model;

[0384] o UE obtains the input data to use according to the measurement result and the type and size of the input data required in the AI model determined to use;

[0385] o in addition, in an implementation, the UE may obtain the characteristic data of the measurement result or input data, the characteristic data may include statistical characteristic data of the measurement result or input data (such as mean, variance, X-order norm, etc., where X is a positive integer) and / or data selected from the measurement result or input data according to a certain rule. For example, the characteristic data may be used by the UE or network node to monitor the performance of the AI model. In an implementation, optionally, the UE may store or feed back the characteristic data to the network node; the certain rule may be at least one of:

[0386] ■ the power value contained in the input data is higher (or not lower) than a power threshold value;

[0387] ■ the time value contained in the input data is earlier (or not later) or less (or not greater) than a time threshold value; in particular, the time threshold value may be the threshold value of time unit index, or a set or a list of time unit indexes used to get the selected input data; for example, the input data of the first X (X is a positive integer) time unit indexes of paths are counted as the selected input data;

[0388] ■ the phase value contained in the input data is less than (or not greater than) a phase threshold value;

[0389] ● UE obtains output data of the AI model, for example, the UE obtains the output data of the AI model according to the AI model determined to use and the obtained input data; wherein

[0390] o the type and / or quantity of the output data of the AI model is determined according to the type and / or quantity of the output data reported for the AI model determined to use;

[0391] o optionally, the UE reports the obtained output data related information to the network node, specifically including one or more of:

[0392] ■ the obtained output data related information may include at least one of:

[0393] ▷ directly the output data of the AI model;

[0394] ▷ data processed according to the output data of AI model, the processing method may be obtaining calculated data value calculated according to the existing technical formula;

[0395] ▷ time stamp of the obtained output data of the AI model, optionally, the time stamp of the output data may be a separate time stamp, or obtained according to the time stamp of the input data used by the AI model, for example, the time stamp of the output data is the same as that of the input data used by the AI model, or the time stamp of the output data is equal to that of the input data used by the AI model plus a time unit interval value; optionally, the time unit interval value may be a preset value, or obtained according to the time unit value required to obtain the output data from the input data by running the AI model;

[0396] ▷ quality indication of the obtained output data of AI model, wherein the quality indication may be:

[0397] √ hard indication, such as 1-bit indication, "0" means that the quality of output data is not good or does not meet the requirements; "1" means that the output data is of good quality or meets the requirements; and / or

[0398] √ soft indication, such as indicating the quality level of output data and the probability value of good quality by a certain interval step size (such as 0.1) between 0 and 1, for example, 0 represents the worst output data, 1 represents the best output data, and increasing by a stepsize of 0.1 step by step from 0, means that the quality of data gradually increases from the worst to the best;

[0399] ■ UE needs to determine the resource for reporting the obtained output data related information; for example, the resource (including time domain resource, frequency domain resource, etc.) for reporting the obtained output data related information may be configured in the indication signaling for indicating by the network node the AI model to use.

[0400] ● UE and / or the network node monitor the AI model used currently, for example, the UE and / or the network node need to detect whether the AI model used works normally or meets the current service requirements in a certain way; specifically, the certain way includes one or more of:

[0401] o when the network node performs monitoring, for example, the monitoring result is determined by the network node; specific operations include one or more of:

[0402] ■ UE acquires resource configuration for required reference signal for monitoring, the resource configuration for reference signal is provided by a network node; the resource configuration for reference signal includes: the number and locations of reference signals within a certain time unit (such as the symbol indexes of reference signals in a slot, etc.), the locations of frequency domain units where reference signals are located (for example, the frequency domain start location of reference signal is determined by the frequency domain unit interval value from a frequency domain reference point), the number of time units and / or frequency domain units occupied by a reference signal (such as the number of symbols and the number of PRBs of a reference signal); the number of repetitions of a reference signal in a certain time unit, etc.;

[0403] ■ optionally, the UE obtains a configuration for measuring required reference signal for monitoring, and according to the configuration for measuring, the UE may determine a time window for measurement, such as a measurement gap (MG) configuration and / or a positioning reference signal processing window (PRS process window, PPW) configuration for the AI method; or a measurement period obtained according to the period of reference signal and the configuration period of MG (or PPW), for example, the time window may be MG,PPW or the measurement period;

[0404] ▷ optionally, the UE obtains the priority of the reference signal in the time window, for example, the UE receives indication of the priority or makes judgment on priority, and according to the indication of the priority or judgment on priority, it is obtained that when the reference signal collides with other signals (such as downlink signal reception and / or uplink signal transmission) (for example, the time domain units of the two signals overlap or the time domain unit interval is less than or not greater than a certain threshold value), UE gives priority to measuring the signal for measurement (for example, the priority of the signal for measurement is higher than or not lower than other signals) or UE gives priority to receiving or sending other signals (for example, the priority of the signal for measurement is lower than or not higher than other signals), and when the two have the same priority, UE independently determines to measure the signal for measurement and / or receive or send other signals;

[0405] ■ optionally, the UE obtains the resource configuration for reporting the measurement result of the required reference signal for monitoring, and according to the resource configuration, the UE may determine the resource configuration available for reporting the related information of the measurement result. For example, the resource configuration of PUSCH includes the number and locations of symbols (e.g., symbol indexes) of the PUSCH in a time unit;

[0406] ■ according to the obtained resource configuration for required reference signal for monitoring and / or the configuration for measuring required reference signal for monitoring and / or the resource configuration for reporting the measurement result of required reference signal for monitoring, the UE measures the reference signal, and according to the measurement, the UE obtains the measurement result, and the UE feeds back the obtained measurement result to the network node; the measurement result may be similar to the aforementioned initial measurement result or final measurement result; the details will not be repeated;

[0407] ■ according to the resource configuration for required reference signal, the UE sends the required reference signal; for example, when the resource for required reference signal is configured as uplink reference signal resource, the network node obtains the measurement result by measuring the reference signal sent by the UE;

[0408] ■ according to the obtained measurement result, the network node obtains the monitoring metric according to certain operations; specifically, include:

[0409] ▷ the network node determines the measurement result as a monitoring metric;

[0410] ▷ when the measurement result is the final measurement result, the network node determines the measurement result as a monitoring metric;

[0411] ▷ when the measurement result is the initial measurement result, the network node processes the measurement result according to the same method as that for obtaining the final measurement result from the initial measurement result according to a certain rule, to obtain the monitoring metric;

[0412] ▷ the network node obtains characteristic data of the measurement result, the characteristic data includes the statistical characteristic data of the measurement result (such as mean, variance, X-order norm, etc.) and / or the measurement result selected from the measurement result according to a certain rule as the characteristic data, and the network node takes such characteristic data as the obtained monitoring metric; the certain rule may be at least one of:

[0413] √ a power value contained in the measurement result is higher (or not lower) than a power threshold value;

[0414] √ a time value contained in the measurement result is earlier (or not later) or less (or not greater) than a time threshold value; in particular, the time threshold value may be the threshold value of time unit index, or a set or a list of time unit indexes for obtaining the characteristic data; for example, the measurement results of the first X (X is a positive integer) time unit indexes of paths are counted as characteristic data;

[0415] √ a phase value contained in the measurement result is less than (or not greater than) a phase threshold value;

[0416] ■ according to the obtained monitoring metric, the network node obtains the monitoring result (including passed or not passed) according to certain operations, which particularly include: when the obtained monitoring metric is better than a certain threshold, (for example, when such situation occurs once, or such situation occurs more than (or not less than) N times (or equal to N+1 times), or such situation occurs continuously for more than (or not less than) N times (or equal to N+1 times)), the network judges the monitoring result as passed; otherwise, for example, when the obtained monitoring metric is worse than a certain threshold (for example, when such situation occurs once, or such situation occurs more than (or not less than) N times (or equal to N+1 times), or such situation occurs continuously for more than (or not less than) N times (or equal to N+1 times)), the network node judges the monitoring result as not passed;

[0417] ■ according to the obtained monitoring metric and / or the obtained monitoring result, the network node determines the subsequent operation and instructs the UE to perform the subsequent operation; the UE receives the indication of the subsequent operation from the network node and performs the subsequent operation according to the indication; wherein,

[0418] ▷ when the obtained monitoring metric is better than a certain threshold (for example, when such situation occurs once, or such situation occurs more than (or not less than) N times (or equal to N+1 times), or such situation occurs continuously for more than (or not less than) N times (or equal to N+1 times)), the subsequent operation is to continue using the AI model determined to use currently and / or AI method for positioning; otherwise, for example, when the obtained monitoring metric is worse than a certain threshold, (for example, when such situation occurs once, or such situation occurs more than (or not less than) N times (or equal to N+1 times), or such situation occurs continuously for more than (or not less than) N times (or equal to N+1 times)), the subsequent operation is to stop using the AI method for positioning and / or to fall back to non-AI method (RAT-dependent positioning method and other RAT-independent positioning methods) and / or to trigger the update (or finetuning or retraining) of the current AI model and / or enter the aforementioned operation of determining the AI model to use currently by the UE or the network node; or

[0419] ▷ when the obtained monitoring result is passed (for example, when such situation occurs once, or such situation occurs more than (or not less than) N times (or equal to N+1 times), or such situation occurs continuously for more than (or not less than) N times (or equal to N+1 times)), the subsequent operation is to continue using the AI model determined to use currently and / or AI method for positioning; otherwise, for example, when the obtained monitoring result is not passed, (for example, when such situation occurs once, or such situation occurs more than (or not less than) N times (or equal to N+1 times), or such situation occurs continuously for more than (or not less than) N times (or equal to N+1 times)), the subsequent operation is to stop using the AI method for positioning and / or to fall back to non-AI method (RAT-dependent positioning method and other RAT-independent positioning methods) and / or to trigger the update (or finetuning or retraining) of the current AI model and / or enter the aforementioned operation of determining the AI model to use currently by the UE or network node;

[0420] ■ the network node monitors independently,

[0421] ■ monitoring with the assistance of UE,

[0422] o when the UE performs monitoring, for example, the monitoring result is determined by the UE; specific operations include one or more of:

[0423] ■ UE acquires resource configuration for reference signal required for monitoring, and the resource configuration for reference signal is provided by a network node; the resource configuration for reference signal includes: the number and locations of reference signals in a certain time unit (such as the symbol indexes of reference signals in a slot, etc.), the locations of frequency domain units where reference signals are located (for example, the start location of reference signal in frequency domain is determined by the frequency domain unit interval value from a frequency domain reference point), the number of time units and / or frequency domain units occupied by a reference signal (such as the number of symbols and the number of PRBs of a reference signal); the number of repetitions of a reference signal in a certain time unit, etc.;

[0424] ■ optionally, the UE obtains a configuration for measuring the required reference signal for monitoring, and according to the configuration for measuring, the UE may determine a time window for measurement, such as a measurement gap (MG) configuration and / or a positioning reference signal processing window (PRS process window, PPW) configuration for the AI method; or a measurement period obtained according to the period of the reference signal and the configuration period of MG (or PPW), for example, the time window may be MG,PPW or the measurement period;

[0425] ▷ optionally, the UE obtains the priority of the reference signal in the time window, for example, the UE receives indication of the priority or makes judgment on priority, and according to the indication of the priority or judgment on priority, it is obtained that when the reference signal collides with other signals (such as downlink signal reception and / or uplink signal transmission) (for example, the time domain units of the two signals overlap or the time domain unit interval is less than or not greater than a certain threshold value), UE gives priority to measuring the signal for measurement (for example, the priority of the signal for measurement is higher than or not lower than other signals) or UE gives priority to receiving or sending other signals (for example, the priority of the signal for measurement is lower than or not higher than other signals), and when the two have the same priority, UE independently determines to measure the signal for measurement and / or receive or send other signals;

[0426] ■ optionally, the UE obtains the resource configuration for reporting the measurement result of the required reference signal for monitoring, and according to the resource configuration, the UE may determine the resource configuration available for reporting the related information of the measurement result. For example, the resource configuration of PUSCH includes the number and locations of symbols (e.g., symbol indexes) of the PUSCH in a time unit;

[0427] ■ according to the obtained resource configuration for required reference signal for monitoring and / or the configuration for measuring required reference signal for monitoring and / or the resource configuration for reporting the measurement result of required reference signal for monitoring, the UE measures the reference signal, and according to the measurement, the UE obtains the measurement result; the measurement result may be similar to the aforementioned initial measurement result or final measurement result; the details will not be repeated;

[0428] ■ according to the resource configuration for required reference signal, the UE sends the required reference signal; for example, when the resource for required reference signal is configured as uplink reference signal resource, the network node obtains the measurement result by measuring the reference signal sent by the UE; the network node feeds back the obtained measurement result, and the UE receives the obtained measurement result fed back by the network node;

[0429] ■ according to the obtained measurement result, UE obtains the monitoring metric according to certain operations; specifically, include:

[0430] ▷ UE determines the measurement result as a monitoring metric;

[0431] ▷ when the measurement result is the final measurement result, UE determines the measurement result as a monitoring metric;

[0432] ▷ when the measurement result is the initial measurement result, UE processes the measurement result according to the same method as that for obtaining the final measurement result from the initial measurement result according to a certain rule, to obtain the monitoring metric;

[0433] ▷ UE obtains characteristic data of the measurement result, the characteristic data includes the statistical characteristic data of the measurement result (such as mean, variance, X-order norm, etc.) and / or the measurement result selected from the measurement result according to a certain rule as the characteristic data, and UE takes such characteristic data as the obtained monitoring metric; the certain rule may be at least one of:

[0434] √ a power value contained in the measurement result is higher (or not lower) than a power threshold value;

[0435] √ a time value contained in the measurement result is earlier (or not later) or less (or not greater) than a time threshold value; in particular, the time threshold value may be the threshold value of time unit index, or a set or a list of time unit indexes for obtaining the characteristic data; for example, the measurement results of the first X (X is a positive integer) time unit indexes of paths are counted as characteristic data;

[0436] √ a phase value contained in the measurement result is less than (or not greater than) a phase threshold value;

[0437] ■ according to the obtained monitoring metric, UE obtains the monitoring result (including passed or not passed) according to certain operations, which particularly include: when the obtained monitoring metric is better than a certain threshold, (for example, when such situation occurs once, or such situation occurs more than (or not less than) N times (or equal to N+1 times), or such situation occurs continuously for more than (or not less than) N times (or equal to N+1 times)), UE judges the monitoring result as passed; otherwise, for example, when the obtained monitoring metric is worse than a certain threshold (for example, when such situation occurs once, or such situation occurs more than (or not less than) N times (or equal to N+1 times), or such situation occurs continuously for more than (or not less than) N times (or equal to N+1 times)), UE judges the monitoring result as not passed;

[0438] ■ UE feeds back and reports the monitoring metric and / or the monitoring result; the network node determines the subsequent operation according to the obtained monitoring metric and / or the obtained monitoring result, and instructs the UE to perform the subsequent operation; the UE receives the indication of the subsequent operation from the network node and performs the subsequent operation according to the indication; wherein,

[0439] ▷ when the obtained monitoring metric is better than a certain threshold (for example, when such situation occurs once, or such situation occurs more than (or not less than) N times (or equal to N+1 times), or such situation occurs continuously for more than (or not less than) N times (or equal to N+1 times)), the subsequent operation is to continue using the AI model determined to use currently and / or AI method for positioning; otherwise, for example, when the obtained monitoring metric is worse than a certain threshold, (for example, when such situation occurs once, or such situation occurs more than (or not less than) N times (or equal to N+1 times), or such situation occurs continuously for more than (or not less than) N times (or equal to N+1 times)), the subsequent operation is to stop using the AI method for positioning and / or to fall back to non-AI method (RAT-dependent positioning method and other RAT-independent positioning methods) and / or to trigger the update (or finetuning or retraining) of the current AI model and / or enter the aforementioned operation of determining the AI model to use currently by the UE or the network node; or

[0440] ▷ when the obtained monitoring result is passed (for example, when such situation occurs once, or such situation occurs more than (or not less than) N times (or equal to N+1 times), or such situation occurs continuously for more than (or not less than) N times (or equal to N+1 times)), the subsequent operation is to continue using the AI model determined to use currently and / or AI method for positioning; otherwise, for example, when the obtained monitoring result is not passed, (for example, when such situation occurs once, or such situation occurs more than (or not less than) N times (or equal to N+1 times), or such situation occurs continuously for more than (or not less than) N times (or equal to N+1 times)), the subsequent operation is to stop using the AI method for positioning and / or to fall back to non-AI method (RAT-dependent positioning method and other RAT-independent positioning methods) and / or to trigger the update (or finetuning or retraining) of the current AI model and / or enter the aforementioned operation of determining the AI model to use currently by the UE or network node;

[0441] ■ according to the obtained monitoring index and / or the obtained monitoring result, the UE determines the subsequent operation and feeds back the determined subsequent operation to the network node; wherein,

[0442] ▷ when the obtained monitoring metric is better than a certain threshold (for example, when such situation occurs once, or such situation occurs more than (or not less than) N times (or equal to N+1 times), or such situation occurs continuously for more than (or not less than) N times (or equal to N+1 times)), the subsequent operation is to continue using the AI model determined to use currently and / or AI method for positioning; otherwise, for example, when the obtained monitoring metric is worse than a certain threshold, (for example, when such situation occurs once, or such situation occurs more than (or not less than) N times (or equal to N+1 times), or such situation occurs continuously for more than (or not less than) N times (or equal to N+1 times)), the subsequent operation is to stop using the AI method for positioning and / or to fall back to non-AI method (RAT-dependent positioning method and other RAT-independent positioning methods) and / or to trigger the update (or finetuning or retraining) of the current AI model and / or enter the aforementioned operation of determining the AI model to use currently by the UE or the network node; or

[0443] ▷ when the obtained monitoring result is passed (for example, when such situation occurs once, or such situation occurs more than (or not less than) N times (or equal to N+1 times), or such situation occurs continuously for more than (or not less than) N times (or equal to N+1 times)), the subsequent operation is to continue using the AI model determined to use currently and / or AI method for positioning; otherwise, for example, when the obtained monitoring result is not passed, (for example, when such situation occurs once, or such situation occurs more than (or not less than) N times (or equal to N+1 times), or such situation occurs continuously for more than (or not less than) N times (or equal to N+1 times)), the subsequent operation is to stop using the AI method for positioning and / or to fall back to non-AI method (RAT-dependent positioning method and other RAT-independent positioning methods) and / or to trigger the update (or finetuning or retraining) of the current AI model and / or enter the aforementioned operation of determining the AI model to use currently by the UE or network node;

[0444] In another embodiment of the present invention, for the time value related to the AI model, there may be the following operations, including at least one of:

[0445] ● the UE receives indication from a network device (base station or LMF) which notifies to use time report of sample based measurement and / or time report of path based measurement, for example, 1bit indication, where "1" represents time report of sample based measurement and "0" represents time report of path based measurement; or vice versa; the UE determines whether to use time report of sample based measurement and / or time report of path based measurement according to the received indication;

[0446] ● the UE receives the indication of ratio relation R between the time report of sample based measurement and time report of path based measurement from the network device. For example, the time report of sample based measurement is in accordance with an integer multiple of the sample interval Tsample, that is N1*Tsample(i.e. the UE reports the value of N1), then the time report of path based measurement may be an integer multiple of the path interval Tpath=R*Tsample, that is, N2*Tpath(which represents UE reports the value of N2), where R may be indicated from a certain data set, such as {1,1 / 2,1 / 4,1 / 8}, and may also be extended to other values, such as values of 1 / (2N) or other specified values; as shown in the example of FIG. 4, an example of the ratio relation when R=1 / 4 is illustrated. Other of the data set may also be obtained through base station configuration, for example, the base station configures a set of values{R1,R2,R3,R4}, and then indicates by 2 bits, "00" indicates R1, "01" indicates R2, "10" indicates R3, and "11" indicates R4; when the numerical values in the set becomes more, the indication bit may also be increased accordingly, and the method is the same, so the details are not repeated;

[0447] ■ optionally, it is also possible to combine the time report of the sample interval Tsampleand the path interval Tpath, that is, N21*Tsample+N22*Tpath, which has the advantage of reducing the number of bits required to indicate a given time value, that is, the UE reports N21and N22values at this time, especially, the value range of N22 at this time may be limited to 1, 2, ... Max=Tsample / Tpath, optionally, Max=[Tsample / Tpath], where [x] means rounding operation on x, including rounding up or down;

[0448] ■ where N, N1, N2, N21and N22are all positive integers.

[0449] In some embodiments, the time value related to the AI model includes at least one of:

[0450] ● a time value of input data

[0451] ● a time value in measurement result

[0452] ● time measurement value in the second measurement value

[0453] where, the time value related to the AI model may be an absolute time value or a relative time value from a time reference point.

[0454] In another embodiment of the present invention, for measurement results (i.e., measurement data) obtained from signal measurement performed by UE to perform AI-related operations (including, for example, model training and / or inference and / or monitoring and / or updating and / or retraining and / or finetuning, etc.), the specific behaviors also include at least one of the following:

[0455] ● The UE reports capability related to whether it can provide measurement data for AI-related operations, which may be one or more of the following:

[0456] ■ UE reports whether it is a positioning reference unit (PRU), for example, represented by 1 bit, "1" indicates that it supports PRU-related functions or can be a PRU, "0" indicates that it does not support PRU-related functions or cannot be a PRU; This method is more suitable when PRU is of the capability to provide measurement data related to AI operations by default;

[0457] ■ The UE reports a capability of whether it supports to provide measurement data for AI-related operations, for example, represented by 1 bit, "1" indicates the capability to provide measurement data for AI-related operations. When the UE reports such information, it indicates that it supports this capability, then the UE may expect that the base station could instruct the UE to receive and / or measure reference signals and / or calculate for AI-related operations and / or to report related results; and, "0" indicates the capability of not providing measurement data for AI-related operations, and when the UE reports the information, it indicates that this capability is not supported, then the UE may expect not to receive instruction from the base station to receive and / or measure reference signals and / or calculate for AI-related operations and / or to report related results; This solution is beneficial to a type of UE devices that are unable or unwilling to make additional reception of signal, measurement, calculation, reports, and can save energy overhead and / or time overhead; in particular, the above-mentioned capability to provide measurement data for AI-related operations may also be replaced by the capability to provide measurement data, that is, there is no need to indicate whether the measurement data is associated with AI-related operations; such particular method is suitable for the UE to inform the network that the measurement is not supported when the UE cannot know whether the instruction from the base station to receive and / or measure reference signals and / or calculate and / or to report related results are related to AI-related operations, thus avoiding additional energy overhead and / or time overhead;

[0458] ■ The UE reports the capability through higher layer signaling (RRC and / or LPPa) and / or MAC CE and / or UCI;

[0459] ● When the UE receives instruction from the base station to receive and / or measure reference signals and / or calculate for AI-related operations and / or to report related results, when certain conditions are met, the UE feeds back to the network that it is inapplicable to provide reception and / or measurement of reference signals and / or calculation for AI-related operationsand / or report of related results, and / or suspension indication on reception and / or measurement of reference signals and / or calculation for AI-related operations and / or report of related results. wherein, at least one of the following is supported:

[0460] ■ The UE feeds back or informs the indication through high layer signaling (RRC and / or LPPa) and / or MAC CE and / or UCI;

[0461] ■ The feedback or notification may be after the UE reports the aforementioned capability to provide measurement data for AI-related operations;

[0462] ■ The meeting of certain conditions specifically includes at least one of the following:

[0463] ◆ The downlink reference signal reception power (DL RSRP) of the UE does not meet certain conditions, such as not being within the range of an RSRP, that is, the RSRP of the UE is not greater than (or less than) a threshold of RSRP_low_threshold, and / or the RSRP of the UE is not less than (or greater than) a threshold of RSRP_high_threshold, the threshold of RSRP_low_threshold, and the threshold of RSRP_high_threshold may be obtained by receiving configuration from the base station by the UE;

[0464] · The downlink reference signal receiving power (DL RSRP) of the UE may be the latest or most recent or the newest RSRP value;

[0465] · The RSRP can be replaced by a path RSRP (namely RSRPP) or SNR (signal-to-noise ratio); the corresponding threshold of RSRP_low_threshold, threshold of RSRP_high_threshold can be replaced by threshold of RSRP_low_threshold, threshold of RSRP_high_threshold, threshold of SNR_low_threshold, threshold of SNR_high_threshold value.

[0466] ◆ The index of the downlink reference signal selected by the UE (such as SSB index and / or CSI-RS index and / or PRS index) is different from the reference downlink reference signal index (and / or not within multiple reference downlink reference signal indexes), wherein the reference downlink reference signal index is obtained by receiving configuration from the base station by the UE; wherein,

[0467] · The index of the downlink reference signal selected by the UE can be replaced by the index(es) of the downlink reference signal(s) with the largest RSRP measured by the UE;

[0468] · The downlink reference signal receiving power (DL RSRP) of the UE may be the latest or the most recent or the newest RSRP value;

[0469] · The RSRP can be replaced by a path RSRP (namely RSRPP) or SNR (signal-to-noise ratio);

[0470] ◆ The area where the UE is located is different from the reference area, wherein

[0471] · The area may be represented as an area index, such as a cell index, a sector index, or a defined geographic area index (area id)

[0472] · The reference area index can be obtained by the UE through receiving the configuration of the base station;

[0473] ◆ The multipath-related information and / or NLOS-related information of the downlink reference signal measured by UE is different from the reference multipath-related information and / or NLOS-related information, for example, the reference multipath is N paths or more than 1 path, while the multipath-related information of the downlink reference signal measured by UE is non-N paths or equal to or less than 1 path; similarly, if the reference NLOS-related information is NLOS being true (that is, the line of sight being false), while NLOS-related information of the downlink reference signal measured by UE is NLOS being false (that is, the line of sight being true), or if the reference NLOS information is NLOS being false (that is, the line of sight being true), while NLOS-related information of the downlink reference signal measured by UE is NLOS being true (that is, the line of sight being false); the reference multipath related information and / or NLOS related information may be obtained by UE through receiving network configuration;

[0474] ◆ The Rx Timing Error (Rx TE) or the receiving TEG of the downlink reference signal measured by the UE is greater than or (or not less than) a threshold value; wherein the threshold value is obtained by receiving an instruction and / or preset;

[0475] ◆ The uncertainty range in the configuration information obtained by UE is greater than (or not less than) a threshold value; wherein the threshold value is obtained by receiving an instruction and / or preset;

[0476] ◆ Optionally, when the above-mentioned at least one trigger condition occurs not less than (or more than) N times, N is a positive integer not less than 1, and the N is obtained by receiving an instruction and / or preset, for example, when the trigger condition counter reaches N+1;

[0477] ◆ When the supported model or function index or the (association index) reported by the UE is different from the reference model or function index or (association index is different); wherein the reference model or function index or (association index) is obtained by UE through base station configuration;

[0478] FIG. 5 shows a schematic structural diagram of a communication device 400 according to at least one embodiment of the present disclosure. Referring to fig. 5, the communication device 400 includes a transceiver 401 and a controller 402. The transceiver 401 is configured to transmit data or signals and receive data or signals. The controller 402 is coupled with the transceiver 401 and configured to perform control so that the communication device 400 performs the method according to the embodiment of the present disclosure. In an implementation, the communication device 400 may further include a memory (not shown) on which computer-executable instructions are stored. When the instructions are performed by the controller 402, the communication device 400 may perform at least one method corresponding to the above-mentioned embodiments of the present disclosure. For example, the communication device may be a user device or a network device (such as a base station) or a network node.

[0479] The above is only the preferred embodiment of the invention, and it is not used to limit the invention. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the invention should be included in the scope of protection of the invention.

[0480] Those skill in that art will understand that the present invention includes apparatus for perform one or more of the operations described in this application. These devices may be specially designed and manufactured for required purposes, or they may also include known devices in general-purpose computers. These devices have computer programs stored therein, which are selectively activated or reconfigured. Such a computer program may be stored in a device (e.g., a computer) readable medium including but not limited to any type of disk (including floppy disk, hard disk, optical disk, CD-ROM, and magneto-optical disk), ROM(Read-Only Memory, Read-only memory), RAM(Random Access Memory), EPROM (erasable programmable read-only memory), EEPROM (electrically erasable programmable read-only memory), flash memory, magnetic card or optical card. That is, a readable medium includes any medium in which information is stored or transmitted by a device (e.g., a computer) in a readable form.

[0481] It will be understood by those skilled in the art that each block in these structural diagrams and / or block diagrams and / or flow diagrams and combinations of blocks in these structural diagrams and / or block diagrams and / or flow diagrams may be implemented by computer program instructions. It may be understood by those skilled in the art that these computer program instructions may be provided to a general-purpose computer, a professional computer or a processor of other programmable data processing methods for implementation, so that the scheme specified in the block or blocks of the structure diagram and / or block diagram and / or flow diagram disclosed in the present invention may be performed by the processor of the computer or other programmable data processing methods.

[0482] Those skilled in the art may understand that the steps, measures and schemes in various operations, methods and processes discussed in the present invention may be alternated, modified, combined or deleted. Further, other steps, measures and schemes in the various operations, methods and processes already discussed in the present invention may also be alternated, changed, rearranged, decomposed, combined or deleted. Further, steps, measures and schemes in various operations, methods and flows disclosed in the present invention in the prior art may also be alternated, changed, rearranged, decomposed, combined or deleted.

[0483] The text and drawings are provided as examples only to help readers understand the present disclosure. They are not intended and should not be interpreted as limiting the scope of the present disclosure in any way. Although certain embodiments and examples have been provided, based on the content disclosed herein, it is obvious to those skilled in the art that modifications to the illustrated embodiments and examples may be made without departing from the scope of the present disclosure.

Claims

1.A method performed by user equipment (UE) in a communication system, comprising:reporting first information related to AI model supported by the UE to a network node, wherein the first information includes information about at least one of: index of AI model, applicable condition of AI model, and feature associated with AI model;acquiring a first AI model, wherein the first AI model is determined from at least one AI model based on the first information;obtaining input data of the first AI model;based on the input data, obtaining output data related to positioning using the first AI model.2.The method according to claim 1, wherein the first information further comprises at least one of:corresponding relationship between index of AI model and associated feature;input format related information of AI model supported by the UE;output format related information of AI model supported by the UE.3.The method according to claim 1, wherein the applicable condition includes at least one of:cell index corresponding to AI model;condition related to measurement result;condition related to storage;condition related to complexity;condition related to accuracy of output data;condition related to processing time.4.The method according to claim 3, wherein the condition related to measurement result includes:a first measurement value of the measurement result is not lower than a first measurement threshold,a first measurement value of the measurement result is within a first measurement threshold interval,a number of paths not lower than a first power threshold in the measurement result is greater than a first number threshold,a number of measurement results in which a number of paths not lower than a first power threshold is greater than a first number threshold is greater than a second number threshold,a probability that a number of paths not lower than a first power threshold in the measurement result is greater than a first number threshold is greater than a first probability threshold.5.The method according to claim 2, wherein the input format information includes at least one of: information related to type of input data and / or information related to quantity of input data.6.The method according to claim 5, wherein the type of input data includes at least one of:channel impulse response (CIR),power delay profile (PDP),delay profile (DP),channel information feature obtained based on CIR, PDP or DP,time value,power value,phase value,time stamp of input data,path index corresponding to input data,wherein, the quantity of input data includes at least one of: information related to a total number applied to all types of input data, information related to each number applied to each type of input data, and information related to a third number applied to all types.7.The method according to claim 1, wherein obtaining the first AI model comprises:determining, by the UE, the first AI model from the at least one AI model based on applicable condition corresponding to AI model, oracquiring the first AI model based on indication information received from a network node, the indication information is for indicating an AI model determined by the network node.8.The method of claim 1, further comprising:receiving first configuration information related to positioning measurement based on AI model, the first configuration information includes at least one of: configuration information related to resource for reference signal, configuration information related to a measurement window, and configuration information related to resource for reporting measurement result;performing measurement based on the first configuration information to obtain a first measurement result;wherein, the first measurement result is used for identifying the first AI model and / or obtaining input data of the first AI model.9.The method according to claim 8, wherein the first measurement result is measurement result obtained based on measurement or is obtained by processing the measurement result obtained based on measurement, the processing includes at least one of:selecting the first measurement result from the measurement result obtained based on measurement based on a first condition;transforming the measurement result obtained based on measurement to obtain the first measurement result,wherein, the first condition includes at least one of: power corresponding to the measurement result is not lower than a second power threshold, time corresponding to the measurement result is earlier than a first time threshold, phase corresponding to the measurement result is less than a first phase threshold.10.The method according to claim 1, further comprising:acquiring configuration information related to monitoring of AI model;acquiring a second measurement result, wherein the second measurement result is obtained by measuring by the UE according to the configuration information, or obtained by measuring by the network node based on a reference signal transmitted by the UE according to the configuration information;obtaining monitoring result of the first AI model based on the second measurement result;determining an operation to be performed based on the monitoring result, or transmitting the monitoring result to the network node, and / or receiving an indicated operation that the UE needs to perform from the network node.11.The method according to claim 1, further comprising:acquiring configuration information related to monitoring of AI model;measuring according to the configuration information to obtain a second measurement result and transmitting the second measurement result to a network node, or transmitting a reference signal according to the configuration information;receiving indication information for an operation from a network node, and performing the operation.12.The method according to claim 10 or 11, wherein obtaining a monitoring result based on the second measurement result comprises: obtaining a monitoring metric based on the second measurement result, obtaining a monitoring result based on the monitoring metric,wherein the monitoring metric includes at least one of: the second measurement result, a third measurement result obtained by processing the second measurement result, characteristic data obtained based on the second measurement result,wherein the processing comprises: selecting a third measurement result from the second measurement result based on a second condition, or transforming the second measurement result to obtain a third measurement result,wherein the characteristic data includes statistical characteristic data of the second measurement result, or a fourth measurement result selected from the second measurement result according to the second condition,wherein, the second condition includes at least one of: power corresponding to measurement result is not lower than a third power threshold, time corresponding to measurement result is earlier than a second time threshold, and phase corresponding to measurement result is less than or equal to a second phase threshold.13.The method according to claim 12, wherein if the monitoring metric is greater than a threshold value for consecutive N times, the monitoring result is determined to be passed, and N is a positive integer; otherwise, the monitoring result is determined to be not passed.14.The method according to any one of claims 10-13, wherein if a third condition is satisfied, the operation comprises at least one of:stopping using an AI-based positioning method,falling back to a non-AI-based method for positioning,redetermining an AI model to use,performing updating, finetuning or retraining of the first AI model,selecting a second AI model different from the first AI model and informing it to the network node,receiving a second AI model indicated by the network node,receiving an indication on fallback to a non-AI-based method for positioning from the network node and fallback based on the indication,reporting an error in an AI-model-based method;wherein, the third condition includes at least one of: the first AI model is the same as an AI model determined to use the last time, the monitoring result is determined to be not passed for consecutive M times, and M is an integer greater than or equal to 1.15.A user equipment (UE) in a communication system, comprising:a transceiver configured to transmit and / or receive signals,a controller configured to control the UE to perform the method according to any one of claims 1-14.

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