Method and device used in node for positioning in wireless communication

Abstract

The present application discloses a method and device used in a node for positioning in wireless communication. A node receives first signaling, wherein the first signaling comprises configuration information of a fallback mode for positioning; and subsequently, the node determines whether an inference result of a first inference configuration is retained. The positioning of a first node depends on the first inference configuration, the first inference configuration being used for positioning; and whether the inference result of the first inference configuration is retained depends on whether the performance of the inference result of the first inference configuration satisfies a first condition, or on a reason why the first inference configuration is not applicable. In the present application, the inference result of the first inference configuration is retained when a certain condition is satisfied, such that the preparation time required for the inference configuration to be applicable is shortened when the inference configuration for positioning is reactivated, thereby improving positioning efficiency and overall system efficiency.

Description

A method and apparatus for positioning nodes in wireless communication Technical Field

[0001] This application relates to signal transmission methods and apparatus in wireless communication systems, and more particularly to positioning methods and apparatus that integrate artificial intelligence and communication. Background Technology

[0002] Leveraging AI / ML (Artificial Intelligence / Machine Learning) technologies to enhance 5G network performance is a crucial component of achieving deep integration of 5G and AI / ML and building intelligent dimensions for 5G-Advanced (5.5G) networks. The 3GPP (3rd Generation Partnership Project) standards organization initiated research on standards for RAN (Radio Access Networks) intelligence starting with Rel-16 (Release-16), primarily focusing on intelligent use cases, enhanced data collection, and the potential impact on RAN nodes and interfaces. Rel-18 formally established a project for AI / ML-based 5G air interface enhancement, initiating international standardization work on the integration of 5G air interface and AI / ML, mainly focusing on research into use cases, lifecycle management (LCM), simulation verification, and data collection.

[0003] Currently, AI / ML development has entered the large-scale model stage. Large-scale communication models can achieve autonomous networks and intelligent services, supporting network operation optimization and improving network efficiency. Deep integration of communication and AI is a crucial direction for future communication evolution. AI will empower the development and upgrade of 5G, 5.5G, and 6G, bringing new management models such as automated frequency band and traffic management, real-time analysis of user data and network load, and prediction of network status. In the Release-19 discussions, the application of AI / ML models in positioning scenarios was included in the discussion scope. For the application of AI / ML models, training the model before inference is usually an essential step. Summary of the Invention

[0004] Currently, traditional positioning methods mainly include Network-assisted GNSS (Global Navigation Satellite System), OTDOA (Observed Time Difference of Arrival), E-CID (Enhanced Cell-ID), DL-AoD (Downlink Angle-of-Departure), DL-TDOA (Downlink Time Difference of Arrival), UL-TDOA (Uplink Time Difference of Arrival), and UL-AoA (Uplink Angle of Arrival). In the future, with the introduction of AI / ML-based positioning methods, how to integrate traditional positioning methods with new AI / ML-based methods, and how to switch between them, will be a problem that needs to be considered.

[0005] To address the aforementioned scenarios, and considering the characteristics of the dataset and the reliability and efficiency of model training, this application discloses a solution. It should be noted that while this application is initially intended for location-based scenarios, it can also be applied to other non-location-based scenarios. Furthermore, adopting a unified design scheme for different scenarios (such as other non-location-based scenarios, including but not limited to sensor integration, Vehicle to Everything (V2X), capacity enhancement systems, short-range communication systems, NTN (Non-Terrestrial Network), IoT (Internet of Things), and URLLC (Ultra-Reliable Low-Latency Communication) networks) helps reduce hardware complexity and cost. Where there is no conflict, embodiments and features in any node of this application can be applied to any other node. Where there is no conflict, embodiments and features in any embodiment of this application can be arbitrarily combined with each other.

[0006] Specifically, the interpretation of terms, nouns, functions, and variables in this application (unless otherwise specified) can be found in the definitions of the TS38 and TS37 series of 3GPP (3rd Generation Partnership Project) Technical Specifications (TS). Where necessary, reference can be made to TS23.273, TS38.211, TS38.212, TS38.213, TS38.214, TS38.215, TS38.300, TS38.304, TS38.305, TS38.321, TS38.331, TS37.355, TS38.355, TS38.423, and TS38.455 in the 3GPP technical specifications to aid in understanding this application.

[0007] As an example, the interpretation of terms in this application is based on the definitions in the 3GPP specification protocol TS38 series.

[0008] As an example, the interpretation of terms in this application is based on the definitions in the 3GPP specification protocol TS37 series.

[0009] As an example, the interpretation of terms in this application is based on the definitions in the 3GPP specification protocol TS23 series.

[0010] As an example, the interpretation of the terms used in this application is based on the definitions in 3GPP specification protocol Rel-17.

[0011] As an example, the interpretation of the terms used in this application is based on the definitions in 3GPP specification protocol Rel-18.

[0012] This application discloses a method for positioning a first node in wireless communication, comprising:

[0013] Receive first signaling, the first signaling including configuration information of the location fallback mode; determine whether the inference result of the first inference configuration is retained;

[0014] The location of the first node depends on the first inference configuration, which is used for location; whether the inference result of the first inference configuration is retained depends on whether the performance of the inference result of the first inference configuration meets the first condition or the reason why the first inference configuration is not applicable.

[0015] As an example, the problem this application aims to solve includes: how to efficiently apply AI / ML models to localization.

[0016] As an example, the problem this application aims to solve includes: how to integrate and apply traditional positioning methods with AI / ML-based positioning methods.

[0017] As an example, the problem this application aims to solve includes: whether the output results of the previous AI / ML-based localization method need to be retained when switching from an AI / ML-based localization method to a traditional localization method.

[0018] As an example, the features of the above method include: when the AI / ML-based localization method is switched to a traditional localization method, if the inference results of the previously generated AI / ML-based localization method have good performance, the first node can retain the inference results so that when the AI / ML-based localization method is reactivated, the retained inference results can enable the reactivated AI / ML-based localization method to be quickly used for inference.

[0019] As an example, the features of the above method include: the retained inference results will only be used for the initialization or training of the subsequently activated AI / ML model for localization if the performance of the retained inference results is good, so as to ensure performance and avoid error propagation.

[0020] As an example, the features of the above method include: dynamically indicating the switching of the positioning method through the first signaling, which is more efficient and direct, so as to ensure that the positioning server and the terminal have a consistent understanding of the positioning method currently used.

[0021] As an example, the features of the above method include: it enables flexible switching between traditional positioning methods and AI / ML-based positioning methods, and has better compatibility with existing systems.

[0022] According to one aspect of this application, the above method is characterized by comprising:

[0023] Send a first information block indicating that the first inference configuration is not applicable.

[0024] As an example, the features of the above method include: explicitly indicating that the first inference configuration is not applicable through the first information block, so that the second node in this application can dynamically instruct the first node to enter the positioning fallback mode through the first signaling, so as to ensure performance.

[0025] According to one aspect of this application, the above method is characterized in that whether the inference result of the first inference configuration is retained depends on whether the performance of the inference result of the first inference configuration satisfies a first condition; when the performance of the inference result of the first inference configuration satisfies the first condition, the inference result of the first inference configuration is retained; when the performance of the inference result of the first inference configuration does not satisfy the first condition, the inference result of the first inference configuration is not retained.

[0026] As an example, the features of the above method include: retaining the inference result only when the inference result performs well, i.e., when the first condition is met, so as to ensure the performance of the inference result when used for subsequent model training or initialization.

[0027] According to one aspect of this application, the above method is characterized in that whether the inference result of the first inference configuration is retained depends on the reason why the first inference configuration is not applicable; when the first information block is used to determine that the reason why the first inference configuration is not applicable is based on performance reasons, the inference of the first inference configuration is not retained; when the first information block is used to determine that the reason why the first inference configuration is not applicable is based on reasons other than performance, the inference result of the first inference configuration is retained.

[0028] As an example, the features of the above method include: retaining the inference result only when the reason why the first inference configuration is not applicable is unrelated to performance, so as to ensure that the inference result is used for performance during subsequent model training or initialization.

[0029] According to one aspect of this application, the above method is characterized by comprising:

[0030] Start the first timer;

[0031] The inference result of the first inference configuration is retained, and the inference result of the first inference configuration is released when the first timer reaches the first time value.

[0032] As an example, the features of the above method include: when the inference result of the first inference configuration is determined to be retained, the inference result is only retained for the time corresponding to the first time value, so as to avoid occupying storage resources due to the retention of the inference result for too long and to avoid excessive resource consumption.

[0033] According to one aspect of this application, the above method is characterized by comprising:

[0034] Send the first data set;

[0035] The inference result of the first inference configuration is retained, the first data set includes the inference result of the first inference configuration, and the first data set is used for training inference configurations other than the first inference configuration.

[0036] As an example, the features of the above method include: by using the first data set for training inference configurations other than the first inference configuration, the training speed of the inference configuration is accelerated and the overall efficiency is improved.

[0037] As an example, the features of the above method include: it is assumed that the inference configuration other than the first inference configuration and the first inference configuration are used for the same scenario or for users associated with location information, thereby enabling the inference output of one model to be used for training another model, in order to improve overall performance.

[0038] According to one aspect of this application, the above method is characterized by comprising:

[0039] Receive a second signaling message, which indicates that the inference configuration for positioning of the first node is activated;

[0040] Wherein, the second signaling is later than the first signaling, the inference result of the first inference configuration is retained, and the inference result of the first inference configuration is used for the initialization of the inference configuration of the first node for positioning.

[0041] As an example, the features of the above method include: by using the first data set for the subsequent activation of the inference configuration for localization of the first node, the training speed of the inference configuration is accelerated and the overall efficiency is improved.

[0042] According to one aspect of this application, the above method is characterized in that the first data set includes at least one of the following:

[0043] - Input for generating the inference result of the first inference configuration;

[0044] - The first data set includes the positioning results obtained by the first node in the fallback mode.

[0045] As an example, the features of the above method include: the inference result of the first inference configuration and the positioning result obtained in the fallback mode are both used to form the first data set, thereby maximizing the use of the positioning result obtained by the first node to improve overall performance.

[0046] As an example, the features of the above method include: the inference result of the first inference configuration can be used to train or initialize the AI / ML model corresponding to other inference configurations, and the localization result obtained in the fallback mode can be used for performance monitoring and reinforcement learning of the AI / ML model corresponding to other inference configurations. All of the above data can be used to improve the performance of the localization method to ensure overall performance.

[0047] As an example, the features of the above method include: the first node is a user equipment.

[0048] As an example, the features of the above method include: the first node is a terminal.

[0049] As an example, the features of the above method include: the first node is configured with an entity for AI / ML.

[0050] As an example, the features of the above method include: the first node is configured with an AI / ML model.

[0051] As an example, the features of the above method include: the first node is configured with a Functionality for AI / ML.

[0052] As an example, the features of the above method include: the first node includes an entity for AI / ML.

[0053] As an example, the features of the above method include: the first node includes a core network device that provides AI services to the terminal.

[0054] As an example, the features of the above method include: the first node includes an application layer device that provides AI services to the terminal.

[0055] As an example, the features of the above method include: the first node includes an Agent that provides AI services to the terminal.

[0056] As an example, the features of the above method include: the first node includes a Handset.

[0057] As an example, the features of the above method include: the first node simultaneously supports both traditional positioning methods and AI / ML-based positioning methods.

[0058] As an example, the features of the above method include: the first node has GNSS capability.

[0059] As an example, the features of the above method include: the first node has GPS (Global Positioning System) capability.

[0060] This application discloses a method for positioning in a second node for wireless communication, comprising:

[0061] Send a first signaling message, the first signaling message including the configuration information of the location fallback mode;

[0062] Wherein, the receiver of the first signaling includes a first node, the location of the first node depends on the first inference configuration, the first inference configuration is for location; whether the inference result of the first inference configuration of the first node is retained depends on whether the performance of the inference result of the first inference configuration meets a first condition or the reason why the first inference configuration is not applicable.

[0063] As an example, the features of the above method include: the second node includes a core network.

[0064] As an example, the features of the above method include: the second node includes an entity for deploying AI / ML models.

[0065] As an example, the features of the above method include: the second node includes a node for deploying AI / ML models.

[0066] As an example, the features of the above method include: the second node includes a base station.

[0067] As an example, the features of the above method include: the second node includes an eNB.

[0068] As an example, the features of the above method include: the second node includes a gNB.

[0069] As an example, the features of the above method include: the second node includes an AMF (Access and Mobility Management Function).

[0070] As an example, the features of the above method include: the second node includes an LMF (Location Management Function).

[0071] As an example, the features of the above method include: the second node includes LPP (LTE Positioning Protocol).

[0072] As an example, the features of the above method include: the second node includes LCS (Location Service) Entities.

[0073] As an example, the features of the above method include: the second node includes E-SMLC (Enhanced Serving Mobile Location Centre).

[0074] As an example, the features of the above method include: the second node is a network device, which includes at least one of a core network device and an access network device.

[0075] As an example, the features of the above method include: the second node is a device that provides wireless communication function services, can communicate with terminal devices, and is usually located on the network side.

[0076] As an example, the features of the above method include: the base station in this application includes a core network.

[0077] As an example, the features of the above method include: the base station in this application includes core network equipment.

[0078] As an example, the features of the above method include: the base station in this application includes an entity for deploying AI / ML models.

[0079] As an example, the features of the above method include: the base station in this application includes nodes for deploying AI / ML models.

[0080] As an example, the features of the above method include: the second node includes an OTT (Over-The-Top) Server.

[0081] As an example, the second node in this application includes OAM (Operation Administration and Maintenance).

[0082] As an example, the features of the above method include: the second node includes a TRP (transmitter-receiver point).

[0083] According to one aspect of this application, the above method is characterized by comprising:

[0084] Receive a first information block, which indicates that the first inference configuration is not applicable.

[0085] According to one aspect of this application, the above method is characterized in that whether the inference result of the first inference configuration is retained depends on whether the performance of the inference result of the first inference configuration satisfies a first condition; when the performance of the inference result of the first inference configuration satisfies the first condition, the inference result of the first inference configuration is retained; when the performance of the inference result of the first inference configuration does not satisfy the first condition, the inference result of the first inference configuration is not retained.

[0086] According to one aspect of this application, the above method is characterized in that whether the inference result of the first inference configuration is retained depends on the reason why the first inference configuration is not applicable; when the first information block is used to determine that the reason why the first inference configuration is not applicable is based on performance reasons, the inference of the first inference configuration is not retained; when the first information block is used to determine that the reason why the first inference configuration is not applicable is based on reasons other than performance, the inference result of the first inference configuration is retained.

[0087] According to one aspect of this application, the above method is characterized in that when the first node determines that the inference result of the first inference configuration is retained, the first node starts a first timer; the inference result of the first inference configuration is released when the first timer counts to a first time value.

[0088] According to one aspect of this application, the above method is characterized by comprising:

[0089] Receive the first data set;

[0090] The inference result of the first inference configuration is retained, the first data set includes the inference result of the first inference configuration, and the first data set is used for training inference configurations other than the first inference configuration.

[0091] According to one aspect of this application, the above method is characterized by comprising:

[0092] Send a second signaling message, which indicates that the inference configuration for positioning of the first node is activated;

[0093] Wherein, the second signaling is later than the first signaling, the inference result of the first inference configuration is retained, and the inference result of the first inference configuration is used for the initialization of the inference configuration of the first node for positioning.

[0094] According to one aspect of this application, the above method is characterized in that the first data set includes at least one of the following:

[0095] - Input for generating the inference result of the first inference configuration;

[0096] - The first data set includes the positioning results obtained by the first node in the fallback mode.

[0097] This application discloses a first node for positioning in wireless communication, comprising:

[0098] The first transceiver receives the first signaling, which includes configuration information for the fallback mode of the location; and determines whether the inference result of the first inference configuration is retained.

[0099] The location of the first node depends on the first inference configuration, which is used for location; whether the inference result of the first inference configuration is retained depends on whether the performance of the inference result of the first inference configuration meets the first condition or the reason why the first inference configuration is not applicable.

[0100] This application discloses a second node for positioning in wireless communication, comprising:

[0101] The second transceiver sends a first signaling message, which includes configuration information for the location's fallback mode.

[0102] Wherein, the receiver of the first signaling includes a first node, the location of the first node depends on the first inference configuration, the first inference configuration is for location; whether the inference result of the first inference configuration of the first node is retained depends on whether the performance of the inference result of the first inference configuration meets a first condition or the reason why the first inference configuration is not applicable.

[0103] As an example, compared with conventional solutions, this application has the following advantages, but is not limited to:

[0104] This application supports the deep integration of AI and communication to improve the adaptability and intelligence of communication systems, thereby enhancing the performance, efficiency, and user experience of communication systems.

[0105] Enables flexible switching between AI / ML-based and traditional positioning algorithms, improving compatibility;

[0106] The first node determines whether to retain the inference results based on the performance of the inference results of the AI / ML-based localization method. When the AI / ML-based localization method is switched to a traditional localization method, if the performance of the previously generated inference results of the AI / ML-based localization method is good, the first node can retain the inference results so that when the AI / ML-based localization method is reactivated, the retained inference results can enable the reactivated AI / ML-based localization method to be quickly used for inference.

[0107] The inference results based on the inference configuration and the positioning results obtained based on the fallback mode are both used to form a dataset, thereby maximizing the use of the positioning results obtained by the first node and improving overall performance. Attached Figure Description

[0108] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0109] Figure 1 illustrates a flowchart of the first node transmission according to an embodiment of this application;

[0110] Figure 2 shows a schematic diagram of a network architecture according to an embodiment of this application;

[0111] Figure 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture for the user plane and control plane according to an embodiment of this application;

[0112] Figure 4 shows a schematic diagram of a first communication device and a second communication device according to an embodiment of this application;

[0113] Figure 5 shows a flowchart of transmission between a first node and a second node according to an embodiment of this application;

[0114] Figure 6 illustrates a flowchart of the transmission of a first information block according to an embodiment of this application;

[0115] Figure 7 illustrates a flowchart of a first data set transmission according to an embodiment of this application;

[0116] Figure 8 illustrates a flowchart of a second signaling transmission according to an embodiment of this application;

[0117] Figure 9 shows a flowchart of a first timer according to an embodiment of this application;

[0118] Figure 10 shows a schematic diagram of RAN domain AI / ML function deployment according to an embodiment of this application;

[0119] Figure 11 shows a schematic diagram of the AI / ML function deployment of a UE according to an embodiment of this application;

[0120] Figure 12 shows a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of this application;

[0121] Figure 13 illustrates a schematic diagram of artificial intelligence or machine learning according to an embodiment of this application;

[0122] Figure 14 shows a structural block diagram of a processing apparatus for a first node according to an embodiment of the present application;

[0123] Figure 15 shows a structural block diagram of a processing apparatus for a second node according to an embodiment of this application. Detailed Implementation

[0124] The technical solutions of this application will be further described in detail below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Considering performance, flexibility, complexity, overhead, and compatibility, those skilled in the art are motivated to flexibly combine the embodiments in different drawings without conflict, including but not limited to the embodiments in Figure 1 and the embodiments in Figures 5-15, the embodiments in Figure 5 and the embodiments in Figures 6-15, etc.

[0125] Example 1

[0126] Example 1 illustrates a flowchart of a first node transmission according to an embodiment of this application, as shown in Figure 1. In Figure 1, each block represents a step.

[0127] The first node receives a first signaling in step 101, the first signaling including configuration information of the location fallback mode; and determines in step 102 whether the inference result of the first inference configuration is retained.

[0128] In Example 1, the location of the first node depends on the first inference configuration, which is used for location; whether the inference result of the first inference configuration is retained depends on whether the performance of the inference result of the first inference configuration meets a first condition or the reason why the first inference configuration is not applicable.

[0129] As one example, the first node is a user equipment (UE).

[0130] As one example, the first node is a terminal.

[0131] As an example, the first node is the first node in this application.

[0132] As an example, step 102 further includes: determining that the inference result of the first inference configuration is retained, and retaining the inference result of the first inference configuration.

[0133] As an example, step 102 further includes: determining that the inference result of the first inference configuration is not retained, and discarding the inference result of the first inference configuration.

[0134] As an example, step 102 further includes: determining that the inference result of the first inference configuration is not retained, and discarding the inference result of the first inference configuration.

[0135] As one example, the first signaling includes DCI (Downlink Control Information).

[0136] As an example, the physical layer channel occupied by the first signaling includes the PDCCH (Physical Downlink Control Channel).

[0137] As an example, the first signaling includes RRC (Radio Resource Control) signaling.

[0138] As an example, the first signaling includes one or more RRC IEs (Information Elements).

[0139] As an example, the first signaling includes one or more fields in an RRC IE.

[0140] As one example, the first signaling comes from the location server.

[0141] As an example, the first signaling comes from the LMF.

[0142] As an example, the first signaling comes from the AMF.

[0143] As an example, the first signaling comes from the LCS.

[0144] As one embodiment, the first signaling includes dynamic signaling.

[0145] As an example, the first signaling includes a "fallback" field.

[0146] As an example, the first signaling instructs the first node to enter fallback mode.

[0147] As an example, the first signaling indicates that the location of the first node no longer depends on the first inference configuration.

[0148] As an example, the first signaling indicates that the configuration information of the fallback mode is applied by the first node when the first inference configuration is not applicable.

[0149] As one embodiment, the first signaling includes the configuration of resources for reference signals used for positioning in the fallback mode.

[0150] As a sub-example of this embodiment, the reference signal used for positioning includes PRS (Positioning Reference Signal).

[0151] As a sub-example of this embodiment, the reference signal used for positioning includes SRS (Sounding Reference Signal).

[0152] As a sub-example of this embodiment, the reference signal used for positioning includes SSB.

[0153] As an example, SSB in this application refers to Synchronization Signal Block.

[0154] As an example, the SSB mentioned in this application refers to: SS (Synchronization Signal) / PBCH block, the synchronization signal / physical broadcast channel block.

[0155] Typically, the PBCH, PSS, and SSS are received in consecutive symbols and form an SS / PBCH block.

[0156] As one embodiment, the first signaling includes a set of triggering conditions for the first node to enter fallback mode.

[0157] As a sub-implementation of this embodiment, the set of triggering conditions includes: the inference performance of the first inference configuration is lower than a threshold.

[0158] As a sub-example of this embodiment, the set of triggering conditions includes: the remaining power value of the first node is lower than a threshold.

[0159] As an example, the positioning fallback mode includes: the first node is positioned using a traditional positioning method.

[0160] As a sub-example of this embodiment, the conventional positioning method is not based on reasoning.

[0161] As a sub-example of this embodiment, the conventional localization method is not based on training.

[0162] As a sub-example of this embodiment, the conventional localization method is not based on AI / ML.

[0163] As a sub-example of this embodiment, the conventional positioning method includes the positioning method in Release 18 and earlier.

[0164] As a sub-example of this embodiment, the conventional positioning method includes the positioning method described in Chapter 8 of TS 38.305V18.1.0.

[0165] As an example, the first node implements a related determination of whether the inference result of the first inference configuration is retained.

[0166] As an example, the first node determines whether the inference result of the first inference configuration is retained based on whether the performance of the inference result of the first inference configuration meets a first condition.

[0167] As an example, the first node determines whether the inference result of the first inference configuration should be retained based on the reason why the first inference configuration is not applicable.

[0168] As an example, the first node determines whether the inference result of the first inference configuration is to be retained based on the indication of the first signaling and whether the performance of the inference result of the first inference configuration meets a first condition.

[0169] As an example, the first node determines whether the inference result of the first inference configuration is to be retained based on the indication of the first signaling and the reason why the first inference configuration is not applicable.

[0170] As an example, the first node determines whether the inference result of the first inference configuration should be retained based on the indication of the first signaling and the fact that the first inference configuration is not applicable, and in conjunction with whether the performance of the inference result of the first inference configuration meets the first condition.

[0171] As an example, the first node determines whether the inference result of the first inference configuration is to be retained based on the indication of the first signaling and the fact that the first inference configuration is not applicable, and in conjunction with the reason why the first inference configuration is not applicable.

[0172] As an example, when the first node determines that the inference result of the first inference configuration is not to be retained, the first node discards the inference result generated by the first inference configuration.

[0173] As an example, when the first node determines that the inference result of the first inference configuration is not to be retained, the first node clears the storage space used to store the inference result generated by the first inference configuration.

[0174] As an example, when the first node determines that the inference result of the first inference configuration is to be retained, the first node implements the relevant storage of the inference result generated by the first inference configuration.

[0175] As an example, when the first node determines that the inference result of the first inference configuration is to be retained, the first node retains the inference result generated by the first inference configuration after a given time, wherein the given time is predefined or determined by the first node in a relevant way.

[0176] As an example, the first inference configuration is associated with an associated ID.

[0177] As an example, the first inference configuration is associated with a model ID, which corresponds to an AI / ML model.

[0178] As an example, the first inference configuration is associated with a Functionality ID, which corresponds to a function.

[0179] As an example, the first inference configuration is associated with an Entity ID, which corresponds to an entity.

[0180] As an example, the first inference configuration is used to configure at least one AI / ML model.

[0181] As an example, the first inference configuration is used to configure at least one function.

[0182] As an example, the first inference configuration is used to configure at least one entity.

[0183] As an example, the first inference configuration includes RRC signaling.

[0184] As one example, the first inference configuration includes one or more RRC IEs.

[0185] As an example, the first inference configuration includes one or more domains in an RRC IE.

[0186] As an example, the first inference configuration includes Positioning SIB.

[0187] As an example, the first inference configuration includes PosSystemInformation-r16-IEs.

[0188] As an example, the first inference configuration includes PosSI-SchedulingInfo.

[0189] As one embodiment, the first inference configuration includes SIBpos.

[0190] As an example, the first inference configuration is used for the broadcast signal for positioning.

[0191] As one example, the first inference configuration includes one or more domains in NR-DL-PRS-PDC-Info IE.

[0192] As an example, the first inference configuration includes NR-DL-PRS-PDC-Info IE.

[0193] As one example, the first inference configuration includes one or more PRS Configurations.

[0194] As an example, the first inference configuration is associated with one or more PRS Configurations.

[0195] As an example, the first inference configuration includes one or more DL-PRS Resource Coordinates.

[0196] As an example, the first inference configuration is associated with one or more DL-PRS Resource Coordinates.

[0197] As one example, the first inference configuration includes one or more DL-PRS Muting Patterns.

[0198] As an example, the first inference configuration is associated with one or more DL-PRS Muting Patterns.

[0199] As an example, the name of the RRC signaling used for the first inference configuration includes PRS.

[0200] As an example, the name of the RRC signaling used for the first inference configuration includes Configuration.

[0201] As an example, the name of the RRC signaling used for the first inference configuration includes Resource.

[0202] As an example, the first inference configuration is used to configure the reference signal for the first node to locate.

[0203] As an example, the first inference configuration is used to configure the reference signal resources for the first node to locate.

[0204] As an example, determining whether the inference result of the first inference configuration is retained is a response to the first signaling.

[0205] As an example, determining whether the inference result of the first inference configuration is retained is a response to the first inference configuration not being applicable.

[0206] As one embodiment, the first inference configuration includes inference-based configuration parameters for positioning.

[0207] As an example, the first inference configuration includes a PRS configuration based on inference for positioning.

[0208] As one embodiment, the first inference configuration includes a configuration of inference-based PRS resources for location.

[0209] As an example, the first inference configuration indicates the AI / ML model for localization.

[0210] As an example, the first inference configuration indicates the ID of the AI / ML model for localization.

[0211] As an example, the first inference configuration indicates the associated ID for location.

[0212] As an example, the first inference configuration indicates the Functionality for positioning.

[0213] As an example, the first inference configuration indicates the entity to be located.

[0214] As an example, the inference result of the first inference configuration includes the configuration parameters of the first inference configuration.

[0215] As an example, whether the inference result of the first inference configuration is retained depends on whether the performance of the inference result of the first inference configuration satisfies a first condition.

[0216] As an example, whether the inference result of the first inference configuration is retained depends on the reason why the first inference configuration is not applicable.

[0217] Example 2

[0218] Example 2 illustrates a schematic diagram of a network architecture according to an embodiment of this application, as shown in Figure 2.

[0219] Figure 2 illustrates network architecture 200. Network architecture 200 is the network architecture for LTE (Long-Term Evolution), LTE-A (Long-Term Evolution Advanced), 5G systems, 5G-Advanced, and future 6G systems. The network architectures for LTE, LTE-A, 5G systems, 5G-Advanced, and future 6G systems are referred to as EPS (Evolved Packet System). The 5G NR or LTE network architecture may be referred to as 5GS (5G System) / EPS or some other suitable terminology; the 6G network architecture may be referred to as 6GS (6G System) / EPS or some other suitable terminology.

[0220] The network architecture 200 may include one or more UEs 201, a RAN (Radio Access Network) 202, a core network 210, an HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and an Internet service 230. The network architecture 200 may interconnect with other access networks, but these entities / interfaces are not shown for simplicity.

[0221] As shown in Figure 2, the network architecture 200 provides packet switching services; however, those skilled in the art will readily understand that the various concepts presented throughout this application can be extended to networks providing circuit-switched services or other cellular networks. The RAN 202 includes Node B 203 and other nodes 204. Node B 203 provides user and control plane protocol termination toward the UE 201. Node B 203 may be connected to other nodes 204 via an Xn interface (e.g., backhaul). Node B 203 may also be referred to as eNB (evolved Node B), gNB, base station, base transceiver station, radio base station, radio transceiver, transceiver function, Basic Service Set (BSS), Extended Service Set (ESS), TRP (Transmitter Receiver Point), or some other suitable term. Node B 203 provides UE 201 with an access point to the core network 210; the core network 210 is a 5GC (5G Core network) / EPC (Evolved Packet Core), or the core network 210 is a 6GC (6G Core network). Examples of the UE 201 include cellular phones, smartphones, Session Initiation Protocol (SIP) phones, laptops, personal digital assistants (PDAs), satellite radios, GPS devices, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, drones, aircraft, narrowband physical network devices, machine-type communication devices, land vehicles, automobiles, wearable devices, or any other similar functional devices. Those skilled in the art may also refer to the UE 201 as a mobile station, subscriber station, mobile unit, subscriber unit, radio unit, remote unit, mobile device, radio device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, radio terminal, remote terminal, handheld device, user agent, mobile client, client, or any other suitable term. The Node B 203 is connected to the core network 210 via an S1 / NG interface.The core network 210 includes an MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MMEs / AMFs / SMFs 214, an S-GW (Service Gateway) / UPF (User Plane Function) 212, and a P-GW (Packet Data Network Gateway) / UPF 213. The MME / AMF / SMF 211 is the control node that handles signaling between the UE 201 and the core network 210. Generally, the MME / AMF / SMF 211 provides bearer and connection management. All user IP (Internet Protocol) packets are transmitted through the S-GW / UPF 212, which is itself connected to the P-GW / UPF 213. The P-GW provides UE IP address allocation and other functions. The P-GW / UPF 213 is connected to the Internet service 230. The Internet service 230 includes operator-compliant Internet protocol services, specifically including the Internet, intranet, IMS (IP Multimedia Subsystem), and packet-switched streaming services.

[0222] As an example, the first node in this application includes the UE 201.

[0223] As an example, the second node in this application includes node B 203.

[0224] As an example, node B 203 is a macrocell base station.

[0225] As an example, node B 203 is a microcell base station.

[0226] As an example, node B 203 is a pico cell base station.

[0227] As an example, node B 203 is a femtocell.

[0228] As an example, node B 203 is a base station device that supports large latency differences.

[0229] As an example, node B 203 is a flight platform device.

[0230] As an example, node B 203 is a satellite device.

[0231] As one embodiment, the node B 203 is a test device (e.g., a transceiver device simulating part of the base station's functions, a signaling tester).

[0232] As an example, node B 203 is an LMF.

[0233] As an example, node B 203 includes an E-SMLC.

[0234] As an example, node B 203 is an entity used for positioning.

[0235] As an example, the UE 201 includes a mobile phone.

[0236] As an example, the UE 201 is a vehicle including a car.

[0237] As an example, the wireless link from the UE 201 to the node B 203 is an uplink, which is used to perform uplink transmissions.

[0238] As an example, the radio link from the node B 203 to the UE 201 is a downlink, which is used to perform downlink transmissions.

[0239] As an example, the wireless link between the node B 203 and the UE 201 includes a cellular link.

[0240] As an example, the node B 203 and the UE 201 are connected via the Uu air interface.

[0241] As an example, the node B 203 supports the deployment of network-side (NW-side) AI / ML models.

[0242] As an example, the UE 201 supports the deployment of UE-side AI / ML models.

[0243] As an example, the UE 201 supports a 5G system.

[0244] As an example, the node B 203 supports a 5G system.

[0245] As an example, the UE 201 supports at least a 6G system.

[0246] As an example, the node B 203 supports at least a 6G system.

[0247] As an example, the sender of the first signaling in this application includes the node B 203.

[0248] As an example, the recipient of the first signaling in this application includes the UE 201.

[0249] As an example, the sender of the first information block in this application includes the UE 201.

[0250] As an example, the recipient of the first information block in this application includes the node B 203.

[0251] As an example, the sender of the first data set in this application includes the UE 201.

[0252] As an example, the recipient of the first data set in this application includes the node B 203.

[0253] As an example, the sender of the second signaling in this application includes the node B 203.

[0254] As an example, the recipient of the second signaling in this application includes the UE 201.

[0255] Example 3

[0256] Example 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture for the user plane and control plane according to an embodiment of this application, as shown in Figure 3.

[0257] Figure 3 is a schematic diagram illustrating an embodiment of the wireless protocol architecture for the user plane 350 and the control plane 300. Figure 3 shows the wireless protocol architecture for the control plane 300 between a first communication node device (UE or RSU in V2X, on-board equipment or on-board communication module) and a second node device (gNB, RSU in UE or V2X, on-board equipment or on-board communication module), or between two UEs, using three layers: Layer 1 (L1), Layer 2 (L2), and Layer 3 (L3). L1 is the lowest layer and implements various PHY (Physical layer) signal processing functions. L1 will be referred to herein as PHY 301. L2305 is above PHY 301 and is responsible for the link between the first node device and the second node device, or between two UEs, through PHY 301. L2305 includes a MAC (Medium Access Control) sublayer 302, an RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304, which terminate at the second node device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. It also provides security through encrypted data packets and supports cross-cell mobility between the second communication node devices and the first communication node device. The RLC sublayer 303 provides upper-layer packet segmentation and reassembly, retransmission of lost packets, and packet reordering to compensate for out-of-order reception due to HARQ (Hybrid Automatic Repeat reQuest). The MAC sublayer 302 provides multiplexing between logical and transport channels. It is also responsible for allocating various radio resources (e.g., resource blocks) within a cell between the first communication node devices. Furthermore, the MAC sublayer 302 handles HARQ operations. The RRC (Radio Resource Control) sublayer 306 in L3 of the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and using RRC signaling between the second communication node device and the first communication node device to configure the lower layer.The wireless protocol architecture of user plane 350 includes Layer 1 (L1) and Layer 2 (L2). The wireless protocol architecture for the first and second communication node devices in user plane 350 is largely the same as the corresponding layers and sublayers in control plane 300 for Physical Layer 351, PDCP sublayer 354 in L2355, RLC sublayer 353 in L2355, and MAC sublayer 352 in L2355. However, PDCP sublayer 354 also provides header compression for upper-layer packets to reduce wireless transmission overhead. L2355 in user plane 350 also includes SDAP (Service Data Adaptation Protocol) sublayer 356. SDAP sublayer 356 is responsible for mapping between QoS (Quality of Service) streams and Data Radio Bearer (DRB) to support service diversity. Although not illustrated, the first communication node device may have several upper layers above L2355, including a network layer (e.g., IP (Internet Protocol) layer) terminating at the P-GW on the network side and an application layer terminating at the other end of the connection (e.g., remote UE, server, etc.).

[0258] As an example, the wireless protocol architecture in Figure 3 is applicable to the first node in this application.

[0259] As an example, the wireless protocol architecture in Figure 3 is applicable to the second node in this application.

[0260] As an example, the higher layer mentioned in this application refers to the layer above the physical layer.

[0261] As an example, in this application, the first signaling is generated in the PHY301 or PHY351.

[0262] As an example, in this application, the first signaling is generated in MAC302 or MAC352.

[0263] As an example, in this application, the first signaling is generated in the RRC306.

[0264] As an example, in this application, the first signaling is generated in the core network above the RRC306.

[0265] As an example, in this application, the first information block is generated in the PHY301 or PHY351.

[0266] As an example, in this application, the first information block is generated in MAC302 or MAC352.

[0267] As an example, in this application, the first information block is generated in the RRC306.

[0268] As an example, the first data set in this application is generated in the RRC306.

[0269] As an example, in this application, the first data set is generated in the core network above the RRC306.

[0270] As an example, the second signaling in this application is generated in the PHY301 or PHY351.

[0271] As an example, in this application, the second signaling is generated in MAC302 or MAC352.

[0272] Example 4

[0273] Example 4 illustrates a schematic diagram of a first communication device and a second communication device according to an embodiment of this application, as shown in Figure 4. Figure 4 is a block diagram of a first communication device 410 and a second communication device 450 communicating with each other in an access network.

[0274] The first communication device 410 includes a controller / processor 475, a memory 476, a receiver processor 470, a transmitter processor 416, a multi-antenna receiver processor 472, a multi-antenna transmitter processor 471, a transmitter / receiver 418, and an antenna 420.

[0275] The second communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmitting processor 468, a receiving processor 456, a multi-antenna transmitting processor 457, a multi-antenna receiving processor 458, a transmitter / receiver 454, and an antenna 452.

[0276] In the transmission from the first communication device 410 to the second communication device 450, at the first communication device 410, upper-layer data packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements L2 functionality. In the DL, the controller / processor 475 provides header compression, encryption, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocation to the second communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for HARQ operation, retransmission of lost packets, and signaling to the second communication device 450. The transmit processor 416 and the multi-antenna transmit processor 471 implement various signal processing functions for L1 (i.e., the physical layer). Transmit processor 416 performs encoding and interleaving to facilitate forward error correction (FEC) at the second communication device 450, and mapping of signal clusters based on various modulation schemes (e.g., Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK), M-PSK, and M-Quadrature Amplitude Modulation (M-QAM)). Multi-antenna transmit processor 471 performs digital spatial precoding on the encoded and modulated symbols, including codebook-based precoding and non-codebook-based precoding, and beamforming processing, generating one or more parallel streams. The transmit processor 416 then maps each parallel stream to a subcarrier, multiplexes the modulated symbols with a reference signal (e.g., a pilot) in the time and / or frequency domains, and then uses an inverse fast fourier transform (IFFT) to generate a physical channel carrying the time-domain multicarrier symbol stream. The multi-antenna transmit processor 471 then performs transmit analog precoding / beamforming operations on the time-domain multicarrier symbol stream. Each transmitter 418 converts the baseband multicarrier symbol stream provided by the multi-antenna transmit processor 471 into an RF stream, which is then provided to a different antenna 420.

[0277] In the transmission from the first communication device 410 to the second communication device 450, at the second communication device 450, each receiver 454 receives a signal through its corresponding antenna 452. Each receiver 454 recovers the information modulated onto the radio frequency carrier and converts the radio frequency stream into a baseband multicarrier symbol stream, which is then provided to the receiver processor 456. The receiver processor 456 and the multi-antenna receiver processor 458 implement various L1 signal processing functions. The multi-antenna receiver processor 458 performs receive analog precoding / beamforming operations on the baseband multicarrier symbol stream from the receiver 454. The receiver processor 456 uses a Fast Fourier Transform (FFT) to convert the baseband multicarrier symbol stream after the receive analog precoding / beamforming operations from the time domain to the frequency domain. In the frequency domain, the physical layer data signal and the reference signal are demultiplexed by the receiver processor 456, where the reference signal is used for channel estimation, and the data signal is recovered in the multi-antenna receiver processor 458 after multi-antenna detection to recover any parallel stream destined for the second communication device 450. Symbols on each parallel stream are demodulated and recovered in the receive processor 456, generating soft decisions. The receive processor 456 then decodes and deinterleaves the soft decisions to recover the upper-layer data and control signals transmitted by the first communication device 410 over the physical channel. The upper-layer data and control signals are then provided to the controller / processor 459. The controller / processor 459 implements L2 functionality. The controller / processor 459 may be associated with a memory 460 storing program code and data. The memory 460 may be referred to as computer-readable media. In the DL, the controller / processor 459 provides multiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transmission and logical channels to recover upper-layer packets from the core network. The upper-layer packets are then provided to all protocol layers above L2. Various control signals may also be provided to L3 for L3 processing. The controller / processor 459 is also responsible for error detection using Acknowledgement (ACK) and / or Negative Acknowledgement (NACK) protocols to support HARQ operation.

[0278] In the transmission from the second communication device 450 to the first communication device 410, at the second communication device 450, a data source 467 is used to provide upper-layer data packets to the controller / processor 459. The data source 467 represents all protocol layers above L2. Similar to the transmission functions at the first communication device 410 described in the DL, the controller / processor 459 implements header compression, encryption, packet segmentation and reordering, and multiplexing between logical and transport channels based on the radio resource allocation of the first communication device 410, implementing L2 functions for the user plane and control plane. The controller / processor 459 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the first communication device 410. Transmit processor 468 performs modulation mapping and channel coding processing, while multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based and non-codebook-based precoding, and beamforming processing. Subsequently, transmit processor 468 modulates the generated parallel stream into a multi-carrier / single-carrier symbol stream. After analog precoding / beamforming operations in multi-antenna transmit processor 457, the stream is provided to different antennas 452 via transmitter 454. Each transmitter 454 first converts the baseband symbol stream provided by multi-antenna transmit processor 457 into a radio frequency symbol stream before providing it to antenna 452.

[0279] In the transmission from the second communication device 450 to the first communication device 410, the function at the first communication device 410 is similar to the receiving function at the second communication device 450 described in the transmission from the first communication device 410 to the second communication device 450. Each receiver 418 receives radio frequency signals through its corresponding antenna 420, converts the received radio frequency signals into baseband signals, and provides the baseband signals to the multi-antenna receiving processor 472 and the receiving processor 470. The receiving processor 470 and the multi-antenna receiving processor 472 jointly implement the L1 function. The controller / processor 475 implements the L2 function. The controller / processor 475 may be associated with a memory 476 storing program code and data. The memory 476 may be referred to as computer-readable media. The controller / processor 475 provides multiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transmission and logical channels to recover upper-layer data packets from the second communication device 450. The upper-layer data packets from the controller / processor 475 may be provided to the core network. The controller / processor 475 is also responsible for error detection using ACK and / or NACK protocols to support HARQ operation.

[0280] As one embodiment, the second communication device 450 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor. The second communication device 450 apparatus at least first receives first signaling, the first signaling including configuration information for a location fallback mode; then determines whether the inference result of a first inference configuration is retained; the location of the first node depends on the first inference configuration, the first inference configuration being for location; whether the inference result of the first inference configuration is retained depends on whether the performance of the inference result of the first inference configuration meets a first condition or a reason why the first inference configuration is not applicable.

[0281] As one embodiment, the second communication device 450 includes: a memory storing a computer-readable instruction program that produces actions when executed by at least one processor, the actions including: receiving first signaling including configuration information of a location fallback mode; and subsequently determining whether a reasoning result of a first reasoning configuration is to be retained.

[0282] As one embodiment, the first communication device 410 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor. The first communication device 410 transmits at least a first signaling, the first signaling including configuration information for a location fallback mode; the receiver of the first signaling includes a first node, the location of the first node depending on a first inference configuration, the first inference configuration being configured for location; whether the inference result of the first node's first inference configuration is retained depends on whether the performance of the inference result of the first inference configuration meets a first condition or the reason why the first inference configuration is not applicable.

[0283] As one embodiment, the first communication device 410 includes: a memory storing a computer-readable instruction program that produces an action when executed by at least one processor, the action including: sending a first signaling that includes configuration information for a location fallback mode.

[0284] As an example, the first node in this application includes the second communication device 450.

[0285] As an example, the second node in this application includes the first communication device 410.

[0286] As an example, at least one of {the antenna 420, the transmitter 418, the transmission processor 416, the multi-antenna transmission processor 471, the controller / processor 475, and the memory 476} is used to transmit the first signaling; at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, and the data source 467} is used to receive the first signaling.

[0287] As an example, at least one of {the antenna 452, the transmitter 454, the transmitter processor 468, the multi-antenna transmitter processor 457, the controller / processor 459, the memory 460, and the data source 467} is used to transmit the first information block; at least one of {the antenna 420, the receiver 418, the receiver processor 470, the multi-antenna receiver processor 472, the controller / processor 475, and the memory 476} is used to receive the first information block.

[0288] As an example, at least one of {the antenna 452, the transmitter 454, the transmitter processor 468, the multi-antenna transmitter processor 457, the controller / processor 459, the memory 460, and the data source 467} is used to transmit a first data set; at least one of {the antenna 420, the receiver 418, the receiver processor 470, the multi-antenna receiver processor 472, the controller / processor 475, and the memory 476} is used to receive the first data set.

[0289] As an example, at least one of {the antenna 420, the transmitter 418, the transmission processor 416, the multi-antenna transmission processor 471, the controller / processor 475, and the memory 476} is used to transmit the second signaling; and at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, and the data source 467} is used to receive the second signaling.

[0290] Example 5

[0291] Example 5 illustrates a flowchart of a transmission between a first node and a second node according to an embodiment of this application, as shown in Figure 5. In Figure 5, the first node U1 and the second node N2 communicate via a wireless link.

[0292] For the first node U1, the first signaling is received in step S510; in step S511, it is determined whether the inference result of the first inference configuration is retained.

[0293] For the second node N2, the first signaling is sent in step S520.

[0294] In Embodiment 5, the first signaling includes configuration information for the fallback mode of the location; the location of the first node depends on the first inference configuration, which is configured for location; whether the inference result of the first inference configuration is retained depends on whether the performance of the inference result of the first inference configuration meets a first condition or the reason why the first inference configuration is not applicable.

[0295] As an example, the first node U1 is the first node in this application.

[0296] As an example, the second node N2 is the second node in this application.

[0297] As one embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between the base station equipment and the user equipment.

[0298] As one embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between the relay node device and the user equipment.

[0299] As one embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between user equipment and user equipment.

[0300] As one example, the second node N2 and the first node U1 communicate via the Uu interface.

[0301] As one example, the second node N2 is the maintenance base station of the serving cell of the first node U1.

[0302] As an example, step 511 includes: determining that the inference result of the first inference configuration is retained, and retaining the inference result of the first inference configuration.

[0303] As an example, step 511 includes: determining that the inference result of the first inference configuration is not retained, and discarding the inference result of the first inference configuration.

[0304] As an example, step 511 includes: determining that the inference result of the first inference configuration is not retained, and discarding the inference result of the first inference configuration.

[0305] Typically, whether the inference result of the first inference configuration is retained depends on whether the performance of the inference result of the first inference configuration meets a first condition; when the performance of the inference result of the first inference configuration meets the first condition, the inference result of the first inference configuration is retained; when the performance of the inference result of the first inference configuration does not meet the first condition, the inference result of the first inference configuration is not retained.

[0306] As one example, the first condition includes a first threshold.

[0307] As a sub-implementation of this embodiment, the first threshold is predefined or configurable.

[0308] As a sub-implementation of this embodiment, the first condition includes GCS (Generalized Cosine Similarity), and the first threshold corresponds to a GCS value.

[0309] As a sub-implementation of this embodiment, the first condition includes SGCS (Squared Generalized Cosine Similarity), and the first threshold corresponds to an SGCS value.

[0310] As a sub-implementation of this embodiment, the first condition includes NMSE (Normalized Mean Squared Error), and the first threshold corresponds to an NMSE value.

[0311] As a sub-implementation of this embodiment, the first condition includes equivalent MSE (equivalent mean squared error), and the first threshold corresponds to an equivalent MSE value.

[0312] As a sub-implementation of this embodiment, the first condition includes the numerical spectral efficiency gap, and the first threshold corresponds to a numerical spectral efficiency gap value.

[0313] As a sub-implementation of this embodiment, the first condition includes throughput, and the first threshold corresponds to a throughput value.

[0314] As a sub-implementation of this embodiment, the first condition includes BLER (Block Error Rate), and the first threshold corresponds to a BLER value.

[0315] As a sub-implementation of this embodiment, the first condition includes a hypothetical BLER, and the first threshold corresponds to a hypothetical BLER value.

[0316] As a sub-implementation of this embodiment, the first condition includes truth ground CSI, and the first threshold corresponds to a truth ground CSI value.

[0317] As a sub-implementation of this embodiment, the first condition includes MSE (Mean Squared Error), and the first threshold corresponds to an MSE value.

[0318] As a sub-implementation of this embodiment, the first condition includes MAE (Mean Absolute Error), and the first threshold corresponds to an MAE value.

[0319] As a sub-implementation of this embodiment, the first condition includes RMSE (Root Mean Squared Error), and the first threshold corresponds to an RMSE value.

[0320] As a sub-implementation of this embodiment, the first condition includes cosine similarity, and the first threshold corresponds to a cosine similarity value.

[0321] As a sub-implementation of this embodiment, the first condition includes convergence speed, and the first threshold corresponds to a convergence speed value.

[0322] As an example, satisfying the first condition means that the performance of the inference result of the first inference configuration is higher than the first threshold included in the first condition.

[0323] As a sub-example of this embodiment, the performance is higher than the corresponding threshold.

[0324] As a sub-example of this embodiment, the performance is higher than the corresponding value less than the first threshold.

[0325] As an example, satisfying the first condition means that the performance of the inference result of the first inference configuration is better than the first threshold included in the first condition.

[0326] As a sub-example of this embodiment, the performance is better than the first threshold.

[0327] As a sub-example of this embodiment, the performance is better than the corresponding value less than the first threshold.

[0328] As an example, satisfying the first condition means that the performance of the inference result of the first inference configuration is better than the first threshold included in the first condition.

[0329] As a sub-example of this embodiment, the performance is better than the corresponding value greater than the first threshold.

[0330] As a sub-implementation of this embodiment, the performance is better than the corresponding value less than the first threshold.

[0331] As an example, the first condition includes a threshold for accuracy, which includes horizontal accuracy and vertical accuracy.

[0332] As a sub-example of this embodiment, the horizontal accuracy and the vertical accuracy are referenced to TS 22.071 18.0.1.

[0333] As an example, the first condition includes a threshold for response time.

[0334] As a sub-example of this embodiment, the response time is referenced to TS 22.071 18.0.1 version.

[0335] Typically, whether the inference result of the first inference configuration is retained depends on the reason why the first inference configuration is not applicable; when the first information block is used to determine that the reason why the first inference configuration is not applicable is based on performance reasons, the inference of the first inference configuration is not retained; when the first information block is used to determine that the reason why the first inference configuration is not applicable is based on reasons other than performance, the inference result of the first inference configuration is retained.

[0336] As an example, the reasons other than performance include at least one of the following:

[0337] - The first node has limited energy consumption;

[0338] - The first node enters power-saving mode;

[0339] - The first node is overheating;

[0340] - The inference resources of the first node have been exhausted.

[0341] As an example, the performance-based reasons include at least one of the following:

[0342] - The serving cell of the first node changes;

[0343] - The performance of the downlink reference signal received by the first node is lower than a threshold, and the downlink reference signal includes at least one of CSI-RS (Channel State Information Reference Signal) or SSB;

[0344] - The first node's movement speed is higher than a threshold.

[0345] As an example, the associated ID in this application is a non-negative integer.

[0346] As an example, the association ID in this application is associated with at least one RS (Reference Signal) resource set.

[0347] As an example, the associated ID in this application is associated with at least one CSI (Channel State Information) report configuration.

[0348] As an example, the ID mentioned in this application refers to IDentify, proof.

[0349] As an example, the ID mentioned in this application refers to: IDentification, identity verification.

[0350] As an example, the ID mentioned in this application refers to: IDentity, identity, or identifier.

[0351] As an example, the ID mentioned in this application refers to: Identifier, identifier.

[0352] As an example, the ID mentioned in this application refers to: InDex, index.

[0353] As an example, the ID mentioned in this application refers to: InDicator, indicator.

[0354] Example 6

[0355] Example 6 illustrates a flowchart of the transmission of a first information block according to an embodiment of this application, as shown in Figure 6. In Figure 6, the first node U3 and the second node N4 communicate via a wireless link.

[0356] For the first node U3, the first information block is sent in step S610.

[0357] For the second node N4, the first information block is received in step S620.

[0358] In Example 6, the first information block indicates that the first inference configuration is not applicable.

[0359] As an example, the first information block includes the reason why the first inference configuration is not applicable.

[0360] As an example, the reason why the first inference configuration is not applicable includes at least one of the following:

[0361] - The first node has limited energy consumption;

[0362] - The first node enters power-saving mode;

[0363] - The first node is overheating;

[0364] - The inference resources of the first node have been exhausted.

[0365] As a sub-implementation of this embodiment, the first information block includes an index that is used to indicate that the first node has limited energy consumption.

[0366] As a sub-implementation of this embodiment, the first information block includes an index that is used to indicate that the first node enters a power-saving mode.

[0367] As a sub-implementation of this embodiment, the first information block includes an index used to indicate the overheating of the first node.

[0368] As a sub-implementation of this embodiment, the first information block includes an index used to indicate that the inference resources of the first node have been exhausted.

[0369] As an example, the reason why the first inference configuration is not applicable includes at least one of the following:

[0370] - The serving cell of the first node changes;

[0371] - The performance of the downlink reference signal received by the first node is below a threshold, and the downlink reference signal includes at least one of CSI-RS or SSB;

[0372] - The first node's movement speed is higher than a threshold.

[0373] As a sub-implementation of this embodiment, the first information block includes an index used to indicate changes in the serving cell of the first node.

[0374] As a sub-implementation of this embodiment, the first information block includes an index used to indicate that the performance of the downlink reference signal received by the first node is below a threshold, the downlink reference signal including at least one of CSI-RS or SSB.

[0375] As a sub-implementation of this embodiment, the first information block includes an index used to indicate that the movement speed of the first node is higher than a threshold.

[0376] As an example, step S610 is located before step S510 in Example 5.

[0377] As an example, step S620 is located before step S520 in Example 5.

[0378] As one embodiment, the transmission of the first information block is used to trigger the reception of the first signaling.

[0379] As one embodiment, the reception of the first information block is used to trigger the transmission of the first signaling.

[0380] Example 7

[0381] Example 7 illustrates a flowchart of a first data set transmission according to an embodiment of this application, as shown in Figure 7. In Figure 7, the first node U5 and the second node N6 communicate via a wireless link.

[0382] For the first node U5, the first data set is sent in step S710.

[0383] For the second node N6, the first data set is received in step S720.

[0384] In Example 7, the inference result of the first inference configuration is retained, the first data set includes the inference result of the first inference configuration, and the first data set is used for training inference configurations other than the first inference configuration.

[0385] As an example, step S610 is performed before the first timer of the first node in this application reaches the first time value.

[0386] As an example, the physical layer channels occupied by the first data set include PUSCH (Physical Uplink Shared Channel).

[0387] As an example, the data channels occupied by the first data set include UL-SCH (Uplink Shared Channel).

[0388] As an example, the first data set is carried by UE variables.

[0389] As an example, the name of the IE used to carry the first data set includes Var.

[0390] As an example, the name of the IE used to carry the first data set includes Positioning.

[0391] As an example, the name of the IE used to carry the first data set includes Interfere.

[0392] As an example, the name of the IE used to carry the first data set includes AI.

[0393] As an example, the name of the IE used to carry the first data set includes LCM.

[0394] As an example, the inference configuration other than the first inference configuration corresponds to an AI / ML model other than the AI / ML model corresponding to the first inference configuration.

[0395] As an example, the inference configuration other than the first inference configuration corresponds to a functionality other than the functionality corresponding to the first inference configuration.

[0396] As an example, the inference configuration other than the first inference configuration corresponds to an entity other than the entity corresponding to the first inference configuration.

[0397] As an example, the inference configuration other than the first inference configuration corresponds to an association ID other than the association ID corresponding to the first inference configuration.

[0398] As an example, the inference configuration other than the first inference configuration corresponds to a PRS resource configuration for inference other than the PRS resource configuration included in the first inference configuration.

[0399] As an example, the inference configuration other than the first inference configuration corresponds to the inference configuration of the node other than the first node.

[0400] Typically, the first data set includes at least one of the following:

[0401] - Input for generating the inference result of the first inference configuration;

[0402] - The first data set includes the positioning results obtained by the first node in the fallback mode.

[0403] As one embodiment, the first data set includes inputs that generate the inference result of the first inference configuration.

[0404] As one embodiment, the first data set includes the positioning results obtained by the first node in the fallback mode.

[0405] As an example, the first data set includes the input for generating the inference result of the first inference configuration, and the positioning result obtained by the first node in the fallback mode.

[0406] As an example, the fact that the location of the first node depends on the first inference configuration means that the first inference configuration is used to determine the location information of the first node.

[0407] As an example, the positioning result includes: the location information of the first node.

[0408] As an example, the fact that the location of the first node depends on the first inference configuration means that the first inference configuration is used to generate the data results required for the location method of the first node.

[0409] As a sub-example of this embodiment, the positioning method includes one or more of Network-assisted GNSS, OTDOA, E-CID, DL-AoD, DL-TDOA, UL-TDOA, and UL-AoA.

[0410] As a sub-example of this embodiment, the data results include one or more of the following: AoA (Angle of Arrival), CID (Cell-ID), AoD (Angle-of-Departure), TDOA (Time Difference of Arrival), RSRP (Reference Signal Received Power), RSRPP (Reference Signal Received Path Power), RSRQ (Reference Signal Received Quality), RSSI (Received Signal Strength Indicator), and RSTD (Reference Signal Time Difference).

[0411] As one embodiment, the first data set includes channel measurement results.

[0412] As one example, the first data set includes the Time Stamp corresponding to the channel measurement results.

[0413] As an example, step S710 is located after step S511 in Example 5.

[0414] As an example, step S720 is located after step S520 in Example 5.

[0415] Example 8

[0416] Example 8 illustrates a flowchart of a second signaling transmission according to an embodiment of this application, as shown in Figure 8. In Figure 8, the first node U7 and the second node N8 communicate via a wireless link.

[0417] For the first node U7, the second signaling is received in step S810.

[0418] For the second node N8, a second signaling is sent in step S820.

[0419] In embodiment 8, the second signaling indicates that the inference configuration for positioning of the first node is activated; the second signaling is later than the first signaling, the inference result of the first inference configuration is retained, and the inference result of the first inference configuration is used to initialize the inference configuration for positioning of the first node.

[0420] As one example, the second signaling includes DCI.

[0421] As one example, the second signaling includes MAC (Medium Access Control) and CE (Control Elements).

[0422] As one embodiment, the physical layer channel occupied by the second signaling includes PDCCH.

[0423] As an example, the inference configuration for positioning is the first inference configuration.

[0424] As an example, the inference configuration for positioning is an inference configuration other than the first inference configuration.

[0425] As an example, the initialization of the inference configuration includes: training the inference configuration.

[0426] As an example, the initialization of the inference configuration includes setting the parameter set of the inference configuration.

[0427] As an example, step S810 is located after step S511 in Example 5.

[0428] As an example, step S820 is located after step S520 in Example 5.

[0429] As an example, step S810 is located after step S710 in Example 7.

[0430] As an example, step S820 is located after step S720 in Example 7.

[0431] As an example, step S810 does not depend on step S710 in Example 7.

[0432] As an example, step S820 does not depend on step S720 in Example 7.

[0433] Example 9

[0434] Example 9 illustrates a flowchart of a first timer according to an embodiment of this application, as shown in Figure 9. In Figure 9, the first node U9 performs a first operation in step S910, starts the first timer in step S911, stops the first timer and releases the retained inference result of the first inference configuration in step S912.

[0435] As one embodiment, the first operation includes: receiving a first signaling.

[0436] As an example, the first operation includes: determining that the inference result of the first inference configuration is retained.

[0437] As an example, the first operation is used to trigger the start of the first timer.

[0438] As an example, step S911 includes determining that the inference result of the first inference configuration is retained, and retaining the inference result of the first inference configuration.

[0439] As an example, the start time of the first timer corresponds to the time when the inference result of the first inference configuration is retained.

[0440] As an example, the start time of the first timer corresponds to the time when the first node receives the first signaling.

[0441] As an example, the first timer counting to the first time value means that the initial value of the first timer is 0, and the first timer increments to the first time value.

[0442] As an example, the first timer counting down to the first time value means that the initial value of the first timer is the first time value, and the first timer counts down to 0.

[0443] Example 10

[0444] Example 10 illustrates a schematic diagram of RAN domain AI / ML function deployment according to one embodiment of this application, as shown in Figure 10. In Figure 10, the gNB can be replaced with, for example, an eNB, or a network device such as a 6G base station.

[0445] In Example 10, the management of ML inference functions of multiple base stations is completed by the RAN domain management function 1002, that is, data interaction with the RAN domain MnS (Management Service) consumer / cross-domain management 1001 (as shown by the dashed arrow in Figure 10). The RAN domain ML training function 1003 is located in the RAN domain management function 1002; while the ML inference functions are located in the base stations, that is, the AI / ML inference function 1004 is located in gNB 1005, the AI / ML inference function 1006 is located in gNB 1007, and so on.

[0446] AI / ML related functions include ML training (also known as AI training or AI / ML training), ML testing, and ML inference (also known as AI inference or AI / ML inference), etc. ML training, ML testing, and ML inference functions can be deployed independently or co-located. Deployment of AI / ML related functions can be implemented through software, such as downloading and / or running executable files; or it can be implemented through a combination of software and hardware, such as accelerating specific computing units through hardware to improve computing speed or save power.

[0447] ML training functions can be deployed in a cross-domain management system or a domain-specific management system; the domain-specific management system is used to manage the RAN domain or the CN (Core Network) domain. For example, ML training functions for MDA (Management Data Analytics) can be deployed in MDAF (Management Data Analytic Function); ML training for network data analytics can be deployed in NWDAF (Network Data Analytics Function), meaning the ML training function is an MTLF (Model Training Logical Function).

[0448] The ML inference function can also be deployed in a cross-domain management system or a domain-specific management system; for example, the ML inference function is MDAF, or the ML inference function is AnLF (Analytics Logical Function) located in NWDAF.

[0449] Similarly, ML testing capabilities can also be deployed in cross-domain management systems or domain-specific management systems.

[0450] Optionally, the management of ML inference function can also be completed by the base station itself, that is, each base station can independently interact with the RAN domain MnS consumer / cross-domain management 1001.

[0451] It should be noted that Embodiment 10 is merely a non-limiting implementation method; optionally, the ML training function of the RAN domain may also be deployed in the base station; or optionally, some base stations may deploy both the ML inference function and the ML training function of the RAN domain, while some base stations may only deploy the ML inference function.

[0452] As an example, one of the gNBs (or base stations) in Example 10 is the second node of this application.

[0453] Example 11

[0454] Example 11 illustrates a schematic diagram of the deployment of AI / ML functions in a UE according to one embodiment of this application, as shown in Figure 11. In Figure 11, the RAN domain ML training function 1104 is optional.

[0455] UE function 1103 is deployed in the first node of this application, and the UE function 1103 includes AI / ML inference function 1105; the AI / ML inference function 1105 uses an ML model (also called an AI model) for inference; an ML model is typically trained before being used for AI / ML inference.

[0456] As an example, the UE function 1103 includes a RAN domain ML training function 1104, which runs training data through an ML model to obtain a relevant loss and adjusts the parameters of the ML model based on the calculated loss; the ML training includes at least one of ML initial training, ML re-training, and reinforcement learning.

[0457] The above embodiments can reduce the complexity of the base station, or save air interface resources caused by reporting training data; however, the above embodiments place high demands on the processing capabilities of the UE side.

[0458] Optionally, the UE function 1103 also includes a CN domain ML training function (not shown in Figure 11).

[0459] Optionally, the UE function 1103 also includes an AI / ML deployment function—not shown in Figure 11—for loading ML models and data.

[0460] As an example, the first node indicates whether it supports ML training function (RAN domain or CN domain) through capability reporting. The capability reporting is RRC signaling or NAS (Non-Access Stratum) signaling.

[0461] As an example, the ML model and the associated metadata are loaded by the first node from a network device or a remote server.

[0462] Optionally, the UE function 1103 is an MnS producer that provides data to the CN domain MnF (Management Function) and / or the RAN domain MnF and / or the cross-domain management system 1101 for management or analysis (as shown by the double arrow 1102).

[0463] Optionally, the UE function 1103 is an MnS consumer that loads data from the CN domain MnF and / or RAN domain MnF and / or cross-domain management system 1101 for AI / ML-related management, such as managing data requests, ML model activation, and / or ML training (as shown by double arrow 1102).

[0464] As an example, the ML model is based on NN (Neural Networks).

[0465] As an example, the ML model is based on ANN (Artificial Neural Networks).

[0466] As an example, the ML model is based on CNN.

[0467] As an example, the ML model is based on the LLM (Large Language Model) architecture.

[0468] As an example, the ML model is based on the Transformer architecture.

[0469] As an example, the ML model is based on the GPT (Generative Pre-Trained) architecture.

[0470] As an example, the ML model is based on LSTM (Long Short-Term Memory network).

[0471] As an example, the ML model is based on MLP (MultiLayer Perceptron).

[0472] As an example, the ML model is based on GAN (Generative Adversarial Nets).

[0473] As an example, the ML model is based on a lightweight neural network.

[0474] As a sub-example of this embodiment, the lightweight neural network includes one or more of MobileNet, ShuffleNet, and SqueezeNet.

[0475] Example 12

[0476] Example 12 illustrates a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of this application, as shown in Figure 12. In Figure 12, the processing system based on artificial intelligence or machine learning includes a first processor, a second processor, a third processor, and a fourth processor.

[0477] In Example 12, the first processor sends a first dataset to the second processor and a second dataset to the third processor; the second processor generates a target first-class parameter set based on the first dataset, and sends the generated target first-class parameter set to the third processor; the third processor processes the second dataset using the target first-class parameter set to obtain a first-class output, optionally sending the first-class output to the fourth processor. In Figure 12, the first-class feedback and the second-class feedback are optional; the second processor includes ML training functionality; the third processor includes ML inference functionality.

[0478] As one embodiment, the fourth processor includes ML testing functionality.

[0479] As one embodiment, the fourth processor includes performance monitoring / evaluation of the ML model.

[0480] As an example, the third processor sends a first type of feedback to the second processor; the first type of feedback is used to trigger the recalculation or update of the target first type of parameter set, that is, to trigger ML initial training or ML retraining.

[0481] As one embodiment, the fourth processor sends a second type of feedback to the first processor; the second type of feedback is used to generate the first dataset or the second dataset, or the second type of feedback is used to trigger the sending of the first dataset or the sending of the second dataset.

[0482] As one embodiment, the first processor generates the first dataset and the second dataset based on the measurement of the reference signal.

[0483] As one embodiment, the third processor belongs to the first node, and the fourth processor belongs to the second node.

[0484] As one example, the third processor belongs to the first node.

[0485] As an example, the first dataset includes training data.

[0486] As one embodiment, the second processor is used to train an ML model, and the trained model is described by the target first class of parameter sets.

[0487] As an example, the second processor belongs to the first node; the above method avoids passing the first dataset to the second node.

[0488] As an example, the second processor belongs to the second node in this application; the above method supports joint training and optimizes system performance.

[0489] As an example, the second processor belongs to the core network; the above method supports network-wide joint training, further optimizing system performance.

[0490] As an example, the second dataset includes inference data.

[0491] As an example, the third processor constructs a model based on the target first type of parameter group, and then inputs the second dataset into the constructed model to obtain the first type of output.

[0492] As an example, the output of the third processor includes the performance parameters described in this application.

[0493] As an example, the third processor generates a recovery dataset based on the first type of output, and the error between the recovery dataset and the second dataset is used to generate the first type of feedback.

[0494] As an example, the first type of feedback is used to reflect the performance of the trained model; when the performance of the trained model fails to meet the requirements, the second processing opportunity will recalculate the target first type of parameter set.

[0495] As an example, when the error is too large or the update has not been performed for too long, the performance of the trained model is considered to be unsatisfactory.

[0496] As an example, the target first type of parameter group includes one or more of the following: convolution kernel, pooling kernel, pooling function, activation function, parameters of the pooling function, or parameters of the activation function.

[0497] As an example, the target first type of parameter group includes one or more of the following: convolution kernel size, number of convolution layers, convolution stride, pooling kernel size, pooling kernel stride, pooling function, activation function, or number of feature maps.

[0498] As an example, the target first type of parameter group corresponds to the first parameter set in this application.

[0499] As an example, the target first type of parameter group corresponds to the second parameter set in this application.

[0500] Example 13

[0501] Example 13 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of this application, as shown in Figure 13. In Figure 13, the first and second operations belong to a first stage, the third operation belongs to a second stage, the fourth operation belongs to a third stage, and the fifth operation belongs to a fourth stage; the arrowed lines indicate the sequence of the process.

[0502] As an example, the first operation includes AI / ML training, the second operation includes AI / ML testing, the third operation includes AI / ML emulation, the fourth operation includes AI / ML entity loading, and the fifth operation includes AI / ML inference.

[0503] As one embodiment, the first stage includes a training phase, the second stage includes an emulation phase, the third stage includes a deployment phase, and the fourth stage includes an inference phase.

[0504] As an example, the first stage includes AI / ML model training.

[0505] As an example, the first stage includes AI / ML model training and AI / ML testing.

[0506] As an example, the AI / ML model training includes initial training and re-training of one or a group of AI / ML entities.

[0507] As an example, the training of the AI / ML model depends on training data.

[0508] As an example, the AI / ML model training includes AI / ML entity validation.

[0509] As an example, the AI / ML entity verification is used to evaluate the performance of the AI / ML entity.

[0510] As an example, the AI / ML entity verification relies on verification data.

[0511] As an example, if the AI / ML entity verification results do not meet expectations, the AI / ML model will be retrained.

[0512] As an example, the AI / ML testing includes testing the validated AI / ML entities to estimate the performance of the trained AI / ML model.

[0513] As an example, if the AI / ML test results meet expectations, the AI / ML entity proceeds to the next stage; otherwise, the AI / ML model will be retrained.

[0514] As an example, the AI / ML test relies on test data.

[0515] As one embodiment, the second stage includes AI / ML simulation, which performs AI / ML entity reasoning in a simulation environment.

[0516] As an example, the AI / ML simulation estimates the performance of AI / ML entity reasoning in a simulation environment before using AI / ML entities.

[0517] As one embodiment, the second stage is optional.

[0518] As an example, the third stage includes AI / ML entity loading, which is to obtain trained AI / ML entities to obtain the desired AI / ML inference function.

[0519] As an example, the third stage is optional.

[0520] As an example, the third stage is no longer needed when the training and inference functions are co-located.

[0521] As an example, the fourth stage includes AI / ML inference.

[0522] Example 14

[0523] Example 14 illustrates a structural block diagram of a processing device in a first node according to an embodiment of the present application, as shown in Figure 14. In Figure 14, the processing device 1400 in the first node includes a first transceiver 1401.

[0524] The first transceiver 1401 receives a first signaling message, the first signaling message including configuration information of the location fallback mode; and determines whether the inference result of the first inference configuration is retained.

[0525] In Example 14, the location of the first node depends on the first inference configuration, which is used for location; whether the inference result of the first inference configuration is retained depends on whether the performance of the inference result of the first inference configuration meets the first condition or the reason why the first inference configuration is not applicable.

[0526] As an example, the first transceiver 1401 sends a first information block indicating that the first inference configuration is not applicable.

[0527] As an example, whether the inference result of the first inference configuration is retained depends on whether the performance of the inference result of the first inference configuration meets a first condition; when the performance of the inference result of the first inference configuration meets the first condition, the inference result of the first inference configuration is retained; when the performance of the inference result of the first inference configuration does not meet the first condition, the inference result of the first inference configuration is not retained.

[0528] As an example, whether the inference result of the first inference configuration is retained depends on the reason why the first inference configuration is not applicable; when the first information block is used to determine that the reason why the first inference configuration is not applicable is based on performance reasons, the inference of the first inference configuration is not retained; when the first information block is used to determine that the reason why the first inference configuration is not applicable is based on reasons other than performance, the inference result of the first inference configuration is retained.

[0529] As an example, the first transceiver 1401 starts a first timer; the inference result of the first inference configuration is retained, and the inference result of the first inference configuration is released when the first timer reaches a first time value.

[0530] As an example, the first transceiver 1401 transmits a first data set; the inference result of the first inference configuration is retained, the first data set includes the inference result of the first inference configuration, and the first data set is used for training inference configurations other than the first inference configuration.

[0531] As an example, the first transceiver 1401 receives a second signaling, the second signaling indicating that the location-for-inference configuration of the first node is activated; the second signaling is later than the first signaling, the inference result of the first inference configuration is retained, and the inference result of the first inference configuration is used for the initialization of the location-for-inference configuration of the first node.

[0532] As one embodiment, the first data set includes at least one of the following:

[0533] - Input for generating the inference result of the first inference configuration;

[0534] - The first data set includes the positioning results obtained by the first node in the fallback mode.

[0535] As an example, the first node 1400 is a user equipment.

[0536] As an example, the first node 1400 is a Handset.

[0537] As an example, the first node 1400 is a terminal.

[0538] As an example, the first transceiver 1401 includes at least one of the following in Example 4: {the antenna 452, the receiver 454, the receiver processor 456, the multi-antenna receiver processor 458, the transmitter 454, the transmitter processor 468, the multi-antenna transmitter processor 457, the controller / processor 459, the memory 460, and the data source 467}.

[0539] Example 15

[0540] Example 15 illustrates a structural block diagram of a processing apparatus for a second node according to an embodiment of the present application, as shown in Figure 15. In Figure 15, the processing apparatus 1500 in the second node includes a second transceiver 1501.

[0541] The second transceiver 1501 sends a first signaling message, which includes configuration information for the location fallback mode;

[0542] In Example 15, the receiver of the first signaling includes a first node, the location of the first node depends on the first inference configuration, the first inference configuration is for location; whether the inference result of the first inference configuration of the first node is retained depends on whether the performance of the inference result of the first inference configuration meets a first condition or the reason why the first inference configuration is not applicable.

[0543] As an example, the second transceiver 1501 receives a first information block indicating that the first inference configuration is not applicable.

[0544] As an example, whether the inference result of the first inference configuration is retained depends on whether the performance of the inference result of the first inference configuration meets a first condition; when the performance of the inference result of the first inference configuration meets the first condition, the inference result of the first inference configuration is retained; when the performance of the inference result of the first inference configuration does not meet the first condition, the inference result of the first inference configuration is not retained.

[0545] As an example, whether the inference result of the first inference configuration is retained depends on the reason why the first inference configuration is not applicable; when the first information block is used to determine that the reason why the first inference configuration is not applicable is based on performance reasons, the inference of the first inference configuration is not retained; when the first information block is used to determine that the reason why the first inference configuration is not applicable is based on reasons other than performance, the inference result of the first inference configuration is retained.

[0546] As an example, when the first node determines that the inference result of the first inference configuration is to be retained, the first node starts a first timer; the inference result of the first inference configuration is released when the first timer counts to a first time value.

[0547] As an example, the second transceiver 1501 receives a first data set; the inference result of the first inference configuration is retained, the first data set includes the inference result of the first inference configuration, and the first data set is used for training inference configurations other than the first inference configuration.

[0548] As an example, the second transceiver 1501 sends a second signaling message, the second signaling message indicating that the location-for-inference configuration of the first node is activated; the second signaling message is later than the first signaling message, the inference result of the first inference configuration is retained, and the inference result of the first inference configuration is used for the initialization of the location-for-inference configuration of the first node.

[0549] As one embodiment, the first data set includes at least one of the following:

[0550] - Input for generating the inference result of the first inference configuration;

[0551] - The first data set includes the positioning results obtained by the first node in the fallback mode.

[0552] As an example, the second node 1500 is a base station device.

[0553] As one embodiment, the second node 1500 is a user equipment.

[0554] As an example, the second node 1500 is a TRP.

[0555] As an example, the second node 1500 includes an LMF.

[0556] As one embodiment, the second node 1500 includes an AMF.

[0557] As one embodiment, the second node 1500 includes an E-SMLC.

[0558] As one embodiment, the second node 1500 includes an LCS.

[0559] As an example, the second transceiver 1501 includes at least one of the following in embodiment 4: the antenna 420, the transmitter 418, the transmitter processor 416, the multi-antenna transmitter processor 471, the receiver 418, the receiver processor 470, the multi-antenna receiver processor 472, the controller / processor 475, and the memory 476.

[0560] Those skilled in the art will understand that all or part of the steps in the above methods can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium, such as a read-only memory, hard disk, or optical disk. Optionally, all or part of the steps in the above embodiments can also be implemented using one or more integrated circuits. Correspondingly, each module unit in the above embodiments can be implemented in hardware or in the form of software functional modules. This application is not limited to any specific combination of software and hardware. The user equipment, terminal, and UE in this application include, but are not limited to, drones, communication modules on drones, remote-controlled aircraft, aircraft, small aircraft, mobile phones, tablets, laptops, vehicle-mounted communication equipment, vehicles, RSUs, wireless sensors, internet cards, IoT terminals, RFID (Radio Frequency Identification) terminals, NB-IoT (Narrow Band Internet of Things) terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, internet cards, vehicle-mounted communication equipment, low-cost mobile phones, low-cost tablets, and other wireless communication devices. The base station or system equipment in this application includes, but is not limited to, macrocell base stations, microcell base stations, small cell base stations, home base stations, relay base stations, eNB (evolved Node B), gNB, TRP, GNSS (Global Navigation Satellite System), relay satellites, satellite base stations, airborne base stations, RSUs, unmanned aerial vehicles, and test equipment, such as transceivers or signaling testers that simulate some functions of a base station, and other wireless communication equipment.

[0561] Those skilled in the art will understand that the present invention can be practiced in other specified forms without departing from its core or essential characteristics. Therefore, the embodiments disclosed herein should be considered descriptive rather than restrictive in any way. The scope of the invention is defined by the appended claims rather than the foregoing description, and all modifications within their equivalent meaning and scope are considered to be included therein.

Claims

1. A first node for positioning in wireless communication, characterized in that, include: The first transceiver receives the first signaling, which includes configuration information for the location's fallback mode. Determine whether the reasoning result of the first reasoning configuration is retained; The location of the first node depends on the first inference configuration, which is used for location; whether the inference result of the first inference configuration is retained depends on whether the performance of the inference result of the first inference configuration meets the first condition or the reason why the first inference configuration is not applicable.

2. The first node according to claim 1, characterized in that, include: The first transceiver sends a first information block, which indicates that the first inference configuration is not applicable.

3. The first node according to claim 1 or 2, characterized in that, Whether the inference result of the first inference configuration is retained depends on whether the performance of the inference result of the first inference configuration satisfies the first condition; When the performance of the inference result of the first inference configuration meets the first condition, the inference result of the first inference configuration is retained; When the performance of the inference result of the first inference configuration does not meet the first condition, the inference result of the first inference configuration is not retained.

4. The first node according to any one of claims 1 to 3, characterized in that, Whether the inference result of the first inference configuration is retained depends on the reason why the first inference configuration is not applicable; when the first information block is used to determine that the reason why the first inference configuration is not applicable is based on performance reasons, the inference of the first inference configuration is not retained. When the first information block is used to determine that the reason why the first inference configuration is not applicable is based on reasons other than performance, the inference result of the first inference configuration is retained.

5. The first node according to any one of claims 1 to 4, characterized in that, include: The first transceiver starts the first timer; The inference result of the first inference configuration is retained, and the inference result of the first inference configuration is released when the first timer reaches the first time value.

6. The first node according to any one of claims 1 to 5, characterized in that, include: The first transceiver sends the first data set; The inference result of the first inference configuration is retained, the first data set includes the inference result of the first inference configuration, and the first data set is used for training inference configurations other than the first inference configuration.

7. The first node according to any one of claims 1 to 6, characterized in that, include: The first transceiver receives a second signaling message, which indicates that the inference configuration for positioning of the first node is activated; Wherein, the second signaling is later than the first signaling, the inference result of the first inference configuration is retained, and the inference result of the first inference configuration is used for the initialization of the inference configuration of the first node for positioning.

8. The first node according to claim 6, characterized in that, The first data set includes at least one of the following: - Input for generating the inference result of the first inference configuration; - The first data set includes the positioning results obtained by the first node in the fallback mode.

9. A second node for positioning in wireless communication, characterized in that, include: The second transceiver sends a first signaling message, which includes configuration information for the location's fallback mode. Wherein, the receiver of the first signaling includes a first node, the location of the first node depends on the first inference configuration, the first inference configuration is for location; whether the inference result of the first inference configuration of the first node is retained depends on whether the performance of the inference result of the first inference configuration meets a first condition or the reason why the first inference configuration is not applicable.

10. A method for positioning in a first node for wireless communication, characterized in that, include: Receive the first signaling, which includes configuration information for the location fallback mode; Determine whether the reasoning result of the first reasoning configuration is retained; The location of the first node depends on the first inference configuration, which is used for location; whether the inference result of the first inference configuration is retained depends on whether the performance of the inference result of the first inference configuration meets the first condition or the reason why the first inference configuration is not applicable.

11. A method for positioning in a second node for wireless communication, characterized in that, include: Send a first signaling message, the first signaling message including the configuration information of the location fallback mode; Wherein, the receiver of the first signaling includes a first node, the location of the first node depends on the first inference configuration, the first inference configuration is for location; whether the inference result of the first inference configuration of the first node is retained depends on whether the performance of the inference result of the first inference configuration meets a first condition or the reason why the first inference configuration is not applicable.

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