Model monitoring method, terminal equipment and network equipment

Abstract

The embodiment of the invention provides a model monitoring method, terminal equipment and network equipment, and the terminal equipment can monitor a neural network model used for terminal positioning, thereby guaranteeing the performance of the neural network model. The model monitoring method comprises the steps that the terminal equipment receives first information, the first information at least comprises configuration information used for monitoring a first neural network model, and the first neural network model is used for terminal positioning; and the terminal device monitors the first neural network model according to the first information.

Description

Model monitoring method, terminal device and network device Technical Field

[0001] Embodiments of the present application relate to the field of communications, and more specifically, to a model monitoring method, terminal device, and network device. Background Art

[0002] In New Radio (NR) systems, artificial intelligence (AI) and machine learning (ML) can be introduced to improve system performance. For example, AI / ML can be used for terminal positioning, where trained AI / ML models are used to predict terminal location information, improving terminal positioning accuracy. However, changes in the wireless propagation environment can limit the effectiveness of AI / ML models, making monitoring the effectiveness of AI / ML models a critical issue.

[0003] Summary of the Invention

[0004] The embodiments of the present application provide a model monitoring method, terminal device, and network device. The terminal device can monitor the neural network model (i.e., AI / ML model) used for terminal positioning, thereby ensuring the performance of the neural network model.

[0005] In a first aspect, a model monitoring method is provided, the method comprising:

[0006] The terminal device receives first information, wherein the first information includes at least configuration information for monitoring a first neural network model, and the first neural network model is used for terminal positioning;

[0007] The terminal device monitors the first neural network model according to the first information.

[0008] In a second aspect, a model monitoring method is provided, the method comprising:

[0009] The network device sends first information, wherein the first information at least includes configuration information for monitoring a first neural network model, the first neural network model is used for terminal positioning, and the first information is used by the terminal device to monitor the first neural network model.

[0010] In a third aspect, a terminal device is provided for executing the method in the first aspect.

[0011] Specifically, the terminal device includes a functional module for executing the method in the above-mentioned first aspect.

[0012] In a fourth aspect, a network device is provided for executing the method in the second aspect.

[0013] Specifically, the network device includes a functional module for executing the method in the above second aspect.

[0014] In a fifth aspect, a terminal device is provided, comprising a processor and a memory; the memory is used to store computer programs, and the processor is used to call and run the computer programs stored in the memory, so that the terminal device executes the method in the above-mentioned first aspect.

[0015] In a sixth aspect, a network device is provided, comprising a processor and a memory; the memory is used to store computer programs, and the processor is used to call and run the computer programs stored in the memory, so that the network device executes the method in the above-mentioned second aspect.

[0016] In a seventh aspect, a device is provided for implementing the method in any one of the first to second aspects above.

[0017] Specifically, the apparatus includes: a processor, configured to call and run a computer program from a memory, so that a device equipped with the apparatus executes the method in any one of the first to second aspects described above.

[0018] In an eighth aspect, a computer-readable storage medium is provided for storing a computer program, wherein the computer program enables a computer to execute the method in any one of the first to second aspects above.

[0019] In a ninth aspect, a computer program product is provided, comprising computer program instructions, wherein the computer program instructions enable a computer to execute the method in any one of the first to second aspects above.

[0020] In a tenth aspect, a computer program is provided, which, when executed on a computer, enables the computer to execute the method in any one of the first to second aspects above.

[0021] Through the above technical solution, the terminal device can monitor the first neural network model used for terminal positioning based on the configuration information used for monitoring the first neural network model, determine whether the first neural network model is valid based on the monitoring results, and request to update the network model when the first neural network model fails, thereby ensuring the performance of the neural network model used for terminal positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] FIG1 is a schematic diagram of a communication system architecture applied in an embodiment of the present application.

[0023] FIG2 is a schematic diagram of a neuron provided in the present application.

[0024] FIG3 is a schematic diagram of a neural network provided by the present application.

[0025] FIG4 is a schematic diagram of a convolutional neural network provided in this application.

[0026] FIG5 is a schematic diagram of an LSTM unit provided in this application.

[0027] FIG6 is a schematic diagram of the combination of an AI / ML model and a positioning method provided in this application.

[0028] FIG7 is a schematic flowchart of a model monitoring method provided according to an embodiment of the present application.

[0029] FIG8 is a schematic diagram of a first time window provided according to an embodiment of the present application.

[0030] FIG9 is a schematic flowchart of a model monitoring method according to an embodiment of the present application.

[0031] FIG10 is a schematic flowchart of another model monitoring method provided according to an embodiment of the present application.

[0032] FIG11 is a schematic block diagram of a terminal device provided according to an embodiment of the present application.

[0033] FIG12 is a schematic block diagram of a network device provided according to an embodiment of the present application.

[0034] FIG13 is a schematic block diagram of a communication device provided according to an embodiment of the present application.

[0035] FIG14 is a schematic block diagram of a device provided according to an embodiment of the present application.

[0036] FIG15 is a schematic block diagram of a communication system provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0037] The following will describe the technical solutions in the embodiments of this application in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of the embodiments. With respect to the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0038] The technical solutions of the embodiments of the present application can be applied to various communication systems, such as: Global System of Mobile communication (GSM) system, Code Division Multiple Access (CDMA) system, Wideband Code Division Multiple Access (WCDMA) system, General Packet Radio Service (GPRS), Long Term Evolution (LTE) system, Advanced Long Term Evolution (LTE-A) system, New Radio (NR) system, NR system evolution system, LTE-based access to unlicensed spectrum (LTE-U) system on unlicensed spectrum, NR-based access to unlicensed spectrum (NR-U) system on unlicensed spectrum, Non-Terrestrial Networks (NTN) system, Universal Mobile Telecommunication System (UMTS), Wireless Local Area Networks (WLAN), Internet of Things (IoT), Wireless Fidelity (WFI) system. Fidelity, WiFi), fifth-generation communication (5G) system, sixth-generation communication (6G) system or other communication systems.

[0039] Generally speaking, traditional communication systems support a limited number of connections and are easy to implement. However, with the development of communication technology, mobile communication systems will not only support traditional communications, but will also support, for example, device-to-device (D2D) communication, machine-to-machine (M2M) communication, machine type communication (MTC), vehicle-to-vehicle (V2V) communication, sidelink (SL) communication, vehicle-to-everything (V2X) communication, etc. The embodiments of the present application can also be applied to these communication systems.

[0040] In some embodiments, the communication system in the embodiments of the present application can be applied to a carrier aggregation (CA) scenario, a dual connectivity (DC) scenario, an independent (SA) networking scenario, or a non-standalone (NSA) networking scenario.

[0041] In some embodiments, the communication system in the embodiments of the present application can be applied to an unlicensed spectrum, where the unlicensed spectrum can also be considered as a shared spectrum; or, the communication system in the embodiments of the present application can also be applied to an authorized spectrum, where the authorized spectrum can also be considered as an unshared spectrum.

[0042] In some embodiments, the communication system in the embodiments of the present application can be applied to the FR1 frequency band (corresponding to the frequency band range of 410MHz to 7.125GHz), can also be applied to the FR2 frequency band (corresponding to the frequency band range of 24.25GHz to 52.6GHz), and can also be applied to new frequency bands such as high-frequency bands corresponding to the frequency band range of 52.6GHz to 71GHz or the frequency band range of 71GHz to 114.25GHz.

[0043] The embodiments of the present application describe various embodiments in conjunction with network devices and terminal devices, wherein the terminal device may also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent or user device, etc.

[0044] The terminal device can be a station (ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with wireless communication capabilities, a computing device or other processing device connected to a wireless modem, a vehicle-mounted device, a wearable device, a terminal device in a next-generation communication system such as an NR network, or a terminal device in a future evolved Public Land Mobile Network (PLMN) network, etc.

[0045] In an embodiment of the present application, the terminal device can be deployed on land, including indoors or outdoors, handheld, wearable or vehicle-mounted; it can also be deployed on the water surface (such as ships, etc.); it can also be deployed in the air (such as airplanes, balloons and satellites, etc.).

[0046] In an embodiment of the present application, the terminal device may be a mobile phone, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city or a wireless terminal device in a smart home, an in-vehicle communication device, a wireless communication chip / application specific integrated circuit (ASIC) / system on chip (SoC), etc.

[0047] As an example and not a limitation, in the embodiments of the present application, the terminal device may also be a wearable device. Wearable devices may also be referred to as wearable smart devices, which are a general term for wearable devices that are intelligently designed and developed using wearable technology for everyday wear, such as glasses, gloves, watches, clothing, and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not just hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are fully functional, large in size, and can achieve complete or partial functions without relying on smartphones, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0048] In an embodiment of the present application, the network device may be a device for communicating with a mobile device. The network device may be an access point (AP) in WLAN, a base station (BTS) in GSM or CDMA, a base station (NodeB, NB) in WCDMA, an evolved base station (eNB or eNodeB) in LTE, or a relay station or access point, or a network device or base station (gNB) or a transmission reception point (TRP) in a vehicle-mounted device, a wearable device, and an NR network, or a network device in a future evolved PLMN network or a network device in an NTN network, etc.

[0049] As an example and not a limitation, in an embodiment of the present application, the network device may have a mobile feature, for example, the network device may be a mobile device. In some embodiments, the network device may be a satellite or a balloon station. For example, the satellite may be a low earth orbit (LEO) satellite, a medium earth orbit (MEO) satellite, a geostationary earth orbit (GEO) satellite, a high elliptical orbit (HEO) satellite, etc. In some embodiments, the network device may also be a base station set up in a location such as land or water.

[0050] In an embodiment of the present application, the network device can provide services for a cell, and the terminal device communicates with the network device through the transmission resources used by the cell (for example, frequency domain resources, or spectrum resources). The cell can be a cell corresponding to the network device (for example, a base station). The cell can belong to a macro base station or a base station corresponding to a small cell. The small cells here may include: metro cells, micro cells, pico cells, femto cells, etc. These small cells have the characteristics of small coverage and low transmission power, and are suitable for providing high-speed data transmission services.

[0051] For example, a communication system 100 used in an embodiment of the present application is shown in FIG1 . The communication system 100 may include a network device 110, which may be a device that communicates with a terminal device 120 (or a communication terminal or terminal). The network device 110 may provide communication coverage for a specific geographic area and may communicate with terminal devices within the coverage area.

[0052] FIG1 exemplarily shows a network device and two terminal devices. In some embodiments, the communication system 100 may include multiple network devices and each network device may include another number of terminal devices within its coverage area, which is not limited in the embodiments of the present application.

[0053] In some embodiments, the communication system 100 may further include other network entities such as a network controller and a mobility management entity, which is not limited in the embodiments of the present application.

[0054] It should be understood that in the embodiments of the present application, a device having a communication function in a network / system may be referred to as a communication device. Taking the communication system 100 shown in FIG1 as an example, the communication device may include a network device 110 and a terminal device 120 having a communication function. The network device 110 and the terminal device 120 may be the specific devices described above and will not be described in detail here. The communication device may also include other devices in the communication system 100, such as a network controller, a mobility management entity, and other network entities, which are not limited in the embodiments of the present application.

[0055] It should be understood that the terms "system" and "network" are often used interchangeably herein. The term "and / or" is simply a description of an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " generally indicates that the related objects are in an "or" relationship.

[0056] It should be understood that this article involves terminal devices and network devices. Terminal devices include mobile phones, machine facilities, customer premises equipment (CPE), industrial equipment, vehicles, etc.; network devices can be the opposite communication devices of terminal devices, such as base stations (gNBs), AMF entities, LMF entities, etc.

[0057] The terms used in the embodiments of this application are intended only to explain the specific embodiments of this application and are not intended to limit this application. The terms "first," "second," "third," and "fourth," etc. in the specification and claims of this application and the accompanying drawings are used to distinguish different objects, not to describe a specific order. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions.

[0058] It should be understood that the "indication" mentioned in the embodiments of this application can be a direct indication, an indirect indication, or an indication of an association. For example, "A indicates B" can mean that A directly indicates B, for example, B can be obtained through A; it can also mean that A indirectly indicates B, for example, A indicates C, and B can be obtained through C; it can also mean that there is an association between A and B.

[0059] In the description of the embodiments of the present application, the term "corresponding" may indicate a direct or indirect correspondence between the two, or an association relationship between the two, or a relationship between indication and being indicated, configuration and being configured, etc.

[0060] In the embodiments of the present application, "pre-definition" or "pre-configuration" may be implemented by pre-storing corresponding codes, tables, or other methods that can be used to indicate relevant information in a device (e.g., a terminal device and a network device). The present application does not limit the specific implementation method. For example, pre-definition may refer to information defined in a protocol.

[0061] In the embodiments of the present application, the “protocol” may refer to a standard protocol in the communications field, for example, it may be an evolution of an existing LTE protocol, NR protocol, Wi-Fi protocol, or a protocol related to other communication systems. The present application does not limit the protocol type.

[0062] To facilitate understanding of the technical solutions of the embodiments of the present application, the technical solutions of the present application are described in detail below through specific embodiments. The following related technologies can be combined with the technical solutions of the embodiments of the present application as optional solutions, and they all fall within the scope of protection of the embodiments of the present application. The embodiments of the present application include at least part of the following contents.

[0063] To facilitate a better understanding of the embodiments of the present application, the neural network and machine learning (ML) related to the present application are explained.

[0064] A neural network is a computational model composed of multiple interconnected neuron nodes. The connections between nodes represent weighted values ​​from input signals to output signals, called weights. Each node performs a weighted summation (SUM) of different input signals and outputs them using a specific activation function (f). An example of a neuron structure is shown in Figure 2. A simple neural network, shown in Figure 3, consists of an input layer, a hidden layer, and an output layer. By using different connections between multiple neurons, weights, and activation functions, different outputs can be generated, thereby fitting the mapping relationship from input to output.

[0065] Deep learning utilizes deep neural networks with multiple hidden layers, significantly improving the network's ability to learn features and fitting complex, nonlinear mappings from input to output. Consequently, it has found widespread application in speech and image processing. In addition to deep neural networks, deep learning also includes other commonly used basic structures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for different tasks.

[0066] The basic structure of a convolutional neural network includes an input layer, multiple convolutional layers, multiple pooling layers, a fully connected layer, and an output layer, as shown in Figure 4. Each neuron in the convolution kernel of the convolutional layer is locally connected to its input, and the introduction of the pooling layer extracts the local maximum or average features of a certain layer, effectively reducing the network parameters and mining local features, enabling the convolutional neural network to converge quickly and achieve excellent performance.

[0067] RNNs are neural networks that model sequential data and have achieved remarkable success in natural language processing applications such as machine translation and speech recognition. Specifically, the network memorizes information from past moments and uses it in the calculation of current outputs. This means that nodes in the hidden layers are no longer disconnected but connected, and the input to a hidden layer includes not only the input layer but also the output of the previous hidden layer. Common RNN structures include long short-term memory (LSTM) and gated recurrent unit (GRU). Figure 5 shows a basic LSTM cell structure, which can include a tanh activation function. Unlike RNNs, which only consider the most recent state, the LSTM cell state determines which states should be retained and which should be forgotten, addressing the shortcomings of traditional RNNs in long-term memory.

[0068] To facilitate a better understanding of the embodiments of the present application, the positioning technology related to the present application is described.

[0069] In traditional positioning methods, for different methods, a terminal device (UE) or a location management function (LMF) entity applies traditional algorithms, such as the Chan algorithm, Taylor expansion, etc., to estimate the location of the terminal device.

[0070] UE-based positioning method: The terminal directly estimates the location of the target UE. The terminal device uses traditional algorithms to estimate the location of the target UE.

[0071] UE-assisted positioning method / LMF-based positioning method: The terminal reports measurement results to the LMF entity, which estimates the target UE's location based on the collected measurement results. The LMF side uses traditional algorithms to estimate the target UE's location.

[0072] In the 5G radio access network node-assisted (NG-RAN) positioning method, the base station reports the measurement results of the transmission reception point (TRP) to the LMF entity, which estimates the target UE's location based on the collected measurement results. The LMF side uses traditional algorithms to estimate the target UE's location.

[0073] Artificial Intelligence (AI) / Machine Learning (ML) models can be combined with any positioning method to replace traditional algorithms and estimate the terminal device's location. AI / ML models can be deployed on the UE side, the LMF side, or both. The combination of AI / ML models and positioning methods can be categorized as AI / ML-based direct positioning and AI / ML-based assisted positioning, as shown in Figure 6.

[0074] In order to facilitate a better understanding of the embodiments of the present application, the problems solved by the present application are explained.

[0075] Currently, AI / ML models can be combined with positioning methods for terminal positioning. For example, with direct positioning using AI / ML models, the terminal device's location can be directly determined using a trained AI / ML model. However, positioning accuracy is affected by the AI / ML model. For example, AI / ML model 1 trained with data from communication scenario 1 may not be suitable for communication scenario 2. This can significantly increase positioning errors when using AI / ML model 1 for positioning in communication scenario 2.

[0076] During the AI / ML model monitoring process, the terminal needs to evaluate the performance of the currently running AI / ML model and determine whether the AI / ML model needs to be updated based on the evaluation results. However, how to monitor the AI / ML model specifically is a problem that needs to be solved.

[0077] Based on the above problems, this application proposes a model monitoring solution, whereby the terminal device can monitor the neural network model (i.e., AI / ML model) used for terminal positioning, thereby ensuring the performance of the neural network model.

[0078] The technical solution of this application is described in detail below through specific embodiments.

[0079] FIG7 is a schematic flow chart of a method 200 for model monitoring according to an embodiment of the present application. As shown in FIG7 , the method 200 for model monitoring may include at least part of the following contents:

[0080] S210: The network device sends first information, wherein the first information includes at least configuration information for monitoring a first neural network model, where the first neural network model is used for terminal positioning.

[0081] S220, the terminal device receives the first information;

[0082] S230, the terminal device monitors the first neural network model according to the first information.

[0083] In an embodiment of the present application, the terminal device can monitor the first neural network model used for terminal positioning based on the configuration information for monitoring the first neural network model, can determine whether the first neural network model is valid based on the monitoring results, and request to update the network model if the first neural network model fails, thereby ensuring the performance of the neural network model used for terminal positioning.

[0084] In some embodiments, the first neural network model can be deployed on the terminal side and / or the network side, and the first neural network model is the above-mentioned AI / ML model.

[0085] For example, the first neural network model is deployed on the terminal side, which can be understood as a combination of the AI / ML model and the UE-based positioning method.

[0086] For another example, the first neural network model is deployed on the LMF side, which can be understood as: the AI / ML model is combined with the UE-assisted / LMF-based positioning method, or the AI / ML model is combined with the NG-RAN node assisted positioning method.

[0087] The embodiment of the present application does not limit the model structure and model parameters of the first neural network model.

[0088] In some embodiments, the monitoring behavior of the terminal device for the first neural network model is triggered by one of the following:

[0089] The terminal device, the network device.

[0090] In some embodiments, the network device includes but is not limited to at least one of the following: an LMF entity, an access network device, and an access and mobility management function (AMF) entity.

[0091] In some embodiments, the configuration information for monitoring the first neural network model includes at least one of the following: a monitoring period, a monitoring start time, a monitoring end time, a monitoring time window, a monitored reference signal type, a monitored reference signal period and / or time slot offset, a monitoring count, and a monitoring timer. The monitoring timer monitors the first neural network model within the effective time of the timer, stops monitoring the first neural network model after the timer times out, or starts monitoring the first neural network model after the timer times out.

[0092] In some embodiments, the configuration information for monitoring the first neural network model includes configuration information of a reference signal for monitoring the first neural network model. Specifically, the terminal device may measure the reference signal for monitoring the first neural network model based on the configuration information of the reference signal for monitoring the first neural network model, and evaluate the performance of the first neural network model based on the measurement result to determine whether the first neural network model is effective.

[0093] In some embodiments, the reference signal used for monitoring the first neural network model is a periodic reference signal or a semi-persistent scheduling (SPS) reference signal. That is, the terminal device can measure and monitor the first neural network model periodically, or the terminal device can measure and monitor the first neural network model semi-statically.

[0094] In some embodiments, the reference signal for monitoring the first neural network model is one of the following:

[0095] Downlink positioning reference signal (PRS), sounding reference signal (SRS), channel state information reference signal (CSI-RS), synchronization signal block (SSB), demodulation reference signal (DMRS).

[0096] Of course, the reference signal used for monitoring the first neural network model may also be other reference signals, and this application is not limited to this.

[0097] In some embodiments, the first information is carried by a Long Term Evolution Positioning Protocol (LPP) message sent by an LMF entity, or the first information is carried by Radio Resource Control (RRC) signaling.

[0098] In some embodiments, when the reference signal used for monitoring the first neural network model is a downlink PRS, the first information is carried by an LPP message sent by the LMF entity. For example, the LMF entity configures a periodic or semi-continuous downlink PRS for monitoring the first neural network model through the LPP protocol.

[0099] For example, for the positioning method that combines the UE-based positioning method with the AI ​​network model, that is, for the positioning scheme in which the terminal device directly estimates the position of the target UE through the first neural network model, the LMF entity configures the periodic or semi-continuous downlink PRS for monitoring by the first neural network model through the LPP protocol.

[0100] For example, for the positioning method that combines the UE-assisted positioning method with the AI ​​network model, that is, for the positioning scheme in which the terminal device reports the measurement results to the LMF entity, and the LMF entity estimates the position of the target UE based on the collected measurement results and the first neural network model, the LMF entity configures the periodic or semi-continuous downlink PRS for monitoring by the first neural network model through the LPP protocol.

[0101] In some embodiments, when the reference signal used for monitoring the first neural network model is one of SRS, CSI-RS, SSB, and DMRS, the first information is carried via RRC signaling. For example, the gNB or TRP configures, via RRC signaling, a periodic or semi-persistent SRS, CSI-RS, SSB, or DM-RS reference signal for monitoring the first neural network model.

[0102] For example, for the positioning method that combines the NG-RAN node assisted positioning method with the AI ​​network model, that is, for the positioning scheme in which the base station reports the measurement results of the TRP to the LMF entity, and the LMF entity estimates the position of the target UE based on the collected measurement results and the first neural network model, the gNB or TRP configures the periodic or semi-continuous SRS or CSI-RS or SSB or DM-RS reference signal for monitoring by the first neural network model through RRC signaling.

[0103] In some embodiments, the terminal device sends second information, wherein the second information is used to request monitoring of the first neural network model. Specifically, the second information may be sent before the terminal device receives the first information. That is, after receiving the second information, the network device sends the first information to the terminal device based on the second information.

[0104] In some embodiments, the second information includes at least one of the following: a monitoring period, a monitoring start time, a monitoring end time, a monitoring time window, a monitored reference signal type, a monitored reference signal period and / or time slot offset, a monitoring count, and a monitoring timer. That is, the terminal device may report some parameter configurations for monitoring the first neural network model, wherein the parameter configurations may be recommended values ​​for the terminal device, so that the network device may refer to the relevant parameters when configuring configuration information for monitoring the first neural network model.

[0105] In some embodiments, when the reference signal used for monitoring the first neural network model is a downlink PRS, the second information sample is sent using an on-demand PRS mechanism.

[0106] Specifically, the terminal device triggers monitoring of the first neural network model. For example, the terminal device uses the on-demand PRS mechanism to request a downlink PRS from the LMF entity for monitoring the first neural network model. The LMF entity sends the on-demand PRS to the terminal device. The terminal device performs model monitoring and reports the model monitoring results.

[0107] In some embodiments, the second information includes identification information of a downlink PRS configuration monitored by the first neural network model.

[0108] Optionally, the second information is an on-demand PRS request. Specifically, the LMF entity preconfigures a downlink PRS configuration for monitoring the first neural network model, and the terminal device carries an identifier corresponding to the downlink PRS configuration for monitoring the first neural network model in the on-demand PRS request.

[0109] In some embodiments, the second information includes downlink PRS parameter configuration information for monitoring by the first neural network model. That is, the terminal device may report the downlink PRS parameter configuration information for monitoring by the first neural network model to inform the network device, or so that the network device can refer to the relevant parameters when configuring the downlink PRS configuration information for monitoring by the first neural network model.

[0110] In some embodiments, the downlink PRS parameter configuration information for monitoring by the first neural network model includes at least one of the following:

[0111] The period of the PRS signal, the subcarrier spacing of the PRS signal, the cyclic prefix length of the PRS signal, the frequency domain resource bandwidth of the PRS, the frequency domain starting frequency position of the PRS resource, the frequency domain reference point A of the PRS signal, and the comb tooth size of the PRS signal.

[0112] Specifically, if the LMF entity does not provide the terminal device with a downlink PRS configuration for monitoring the first neural network model, the terminal device may explicitly notify the LMF entity of the parameter configuration for monitoring the first neural network model. Specifically, for example, the parameter configuration includes PRS parameters and corresponding recommended values. For example, one or more parameters of the period of the PRS signal, the subcarrier spacing of the PRS signal, the cyclic prefix length of the PRS signal, the frequency domain resource bandwidth of the PRS, the frequency domain starting frequency position of the PRS resource, the frequency domain reference point pointA of the PRS signal, and the comb tooth size of the PRS signal.

[0113] In some embodiments, the LMF entity triggers monitoring of the first neural network model. For example, the LMF entity can configure a PRS reference signal for the terminal device for monitoring the first neural network model based on the measurement results reported by the terminal device.

[0114] In some embodiments, the terminal device sends third information, wherein the third information is used to request a reference signal configuration and / or a reference signal measurement interval for monitoring the first neural network model.

[0115] For example, the terminal device requests the PRS configuration and / or PRS measurement interval for monitoring the first neural network model from the network device through a Media Access Control Control Element (MAC CE) signaling. The network device may configure the PRS configuration information for monitoring the first neural network model for the terminal device through the MAC CE, or the network device may configure the SRS configuration information for monitoring the first neural network model through the DCI.

[0116] In some embodiments, the monitoring behavior of the terminal device for the first neural network model is triggered when a first condition is met;

[0117] Among them, the first condition includes at least one of the following: the terminal device performs cell switching, detects that the wireless link quality has deteriorated, beam failure recovery (Beam Failure Recovery, BFR) has occurred, and uplink desynchronization has occurred.

[0118] In some embodiments, the configuration information for monitoring the first neural network model includes the first condition.

[0119] In some embodiments, the above S230 may specifically include:

[0120] The terminal device monitors the first neural network model within a first time window based on the first information.

[0121] In some embodiments, the first time window is predefined, or the first time window is preconfigured, or the first time window is configured by the network device.

[0122] In some embodiments, the first time window is configured periodically, or the first time window is configured aperiodically.

[0123] In some embodiments, the configuration granularity of the first time window can be milliseconds, seconds, time slots, mini-time slots, symbols, etc.

[0124] In some embodiments, the configuration information for monitoring the first neural network model includes configuration information of the first time window.

[0125] For example, as shown in FIG8 , the terminal device monitors the first neural network model during periodic or semi-continuous monitoring opportunities within the first time window, and does not monitor during periodic or semi-continuous monitoring opportunities outside the first time window.

[0126] Therefore, in the embodiments of the present application, the first neural network model is monitored based on different methods to ensure the positioning performance of the first neural network model. Periodic monitoring / semi-static monitoring, triggered monitoring, and monitoring based on the first time window can be configured in different scenarios, or configured simultaneously, to ensure the performance of the first neural network model.

[0127] In some embodiments, different AI positioning methods may use different metrics for model monitoring.

[0128] In some embodiments, the above S230 may specifically include:

[0129] In the case where the difference between the input parameter and the verification parameter of the first neural network model is greater than or equal to the first threshold, the terminal device determines that the first neural network model is invalid; and / or,

[0130] When a difference between an input parameter and a verification parameter of the first neural network model is less than a first threshold, the terminal device determines that the first neural network model is valid;

[0131] The type of the input parameter of the first neural network model is the same as the type of the verification parameter.

[0132] It should be noted that the failure of the first neural network model can be understood as the first neural network model being unsuitable for the current scenario.

[0133] In some embodiments, the verification parameter is obtained by reverse deduction based on the prediction result of the first neural network model.

[0134] For example, as shown in FIG9 , the first neural network model is recorded as AI / ML model 1, the input parameter of AI model 1 is X, the output result (i.e., prediction result) of AI / ML model 1 is Y, and the verification parameter is X*, where X* is obtained by inverse deduction from Y. Specifically, as shown in FIG9 , the terminal device determines whether the difference between X and X* exceeds a first threshold. If so, AI / ML model 1 is invalid; otherwise, AI / ML model 1 is valid.

[0135] In some embodiments, the first threshold may be preconfigured, or the first threshold may be agreed upon by a protocol, or the first threshold may be configured by a network device.

[0136] In some embodiments, the above S230 may specifically include:

[0137] During the monitoring of the first neural network model, if the number of times that the difference between the input parameter and the verification parameter of the first neural network model is greater than or equal to the first threshold is greater than or equal to the second threshold, the terminal device determines that the first neural network model has failed; and / or,

[0138] During the monitoring period of the first neural network model, if the number of times that the difference between the input parameter and the verification parameter of the first neural network model is greater than or equal to the first threshold is less than the second threshold, the terminal device determines that the first neural network model is valid;

[0139] The type of the input parameter of the first neural network model is the same as the type of the verification parameter.

[0140] For example, the input parameter of the first neural network model is a numerical value, the verification parameter is also a numerical value, the first threshold is also a numerical value, and the output result of the first neural network model is the position of the target terminal.

[0141] For another example, the input parameter of the first neural network model is a vector, the verification parameter is also a vector, the first threshold is also a vector, and the output result of the first neural network model is the position of the target terminal.

[0142] For another example, the input parameter of the first neural network model is an angle, the verification parameter is also an angle, the first threshold is also an angle, and the output result of the first neural network model is the position of the target terminal.

[0143] For another example, the input parameter of the first neural network model is a distribution function, the verification parameter is also a distribution function, the first threshold is also a distribution function, and the output result of the first neural network model is the position of the target terminal.

[0144] For example, as shown in FIG10 , the first neural network model is recorded as AI / ML model 1, the input parameter of AI model 1 is X, the output result (i.e., prediction result) of AI / ML model 1 is Y, and the verification parameter is X*, where X* is obtained by inverse deduction from Y. Specifically, as shown in FIG10 , the terminal device determines whether the difference between X and X* exceeds a first threshold value. If so, the cumulative count value is increased by 1; and the terminal device determines whether the cumulative count value during the model monitoring period exceeds a second threshold value. If so, AI / ML model 1 is invalid; if not, AI / ML model 1 is valid.

[0145] In some embodiments, the second threshold may be preconfigured, or the second threshold may be agreed upon by a protocol, or the second threshold may be configured by a network device.

[0146] In some embodiments, the input parameters of the first neural network model are parameters of the terminal device relative to a single TRP, and the verification parameters are verification parameters of the terminal device relative to a single TRP.

[0147] In some embodiments, the input parameters of the first neural network model are parameters of the terminal device relative to multiple TRPs, and the verification parameters are verification parameters of the terminal device relative to multiple TRPs.

[0148] In some embodiments, when the input parameter of the first neural network model is a parameter of the terminal device relative to a plurality of TRPs, the difference between the input parameter of the first neural network model and the verification parameter is greater than or equal to a first threshold, including:

[0149] The difference between the parameters of the terminal device relative to some or all of the TRPs in the multiple TRPs and the verification parameters of the terminal device relative to the corresponding TRPs is greater than or equal to the first threshold.

[0150] In some embodiments, when the input parameter of the first neural network model is a parameter of the terminal device relative to a plurality of TRPs, the difference between the input parameter of the first neural network model and the verification parameter is less than a first threshold, including:

[0151] The difference between the parameters of the terminal device relative to some or all of the TRPs in the multiple TRPs and the verification parameters of the terminal device relative to the corresponding TRPs is less than the first threshold.

[0152] In some embodiments, the input parameters of the first neural network model include at least one of the following: downlink time difference of arrival (DL-TDOA), reference signal received power (RSRP), downlink reference signal time difference (DL RSTD), time of arrival (TOA), downlink angle of departure (DL AoD), uplink time difference of arrival (UL-TDOA), uplink relative time of arrival (UL RTOA), uplink angle of arrival (UL-AoA).

[0153] In some embodiments, when the terminal positioning method executed by the first neural network model is DL TDOA positioning, the input parameter X of the first neural network model includes at least one of the following: DL TDOA, RSRP, DL RSTD, TOA. For example, the input parameter X of the first neural network model can be DL TDOA, RSRP, DL RSTD, TOA, etc., and the output result Y of the first neural network model is the location of the terminal device. The verification parameter X* is the corresponding result obtained by inversely deducing the output result Y. X* corresponds to X and can be DL TDOA, RSRP, DL RSTD, TOA, etc.

[0154] Optionally, the input parameter X of the first neural network model may also be a combination of DL TDOA and RSRP, or a combination of DL RSTD and RSRP, or a combination of TOA and RSRP. Correspondingly, the verification parameter X* is the combination of DL TDOA and RSRP, or the combination of DL RSTD and RSRP, or the combination of TOA and RSRP obtained by inversely deducing the output result Y.

[0155] Optionally, the input parameter X of the first neural network model may be for a single TRP or for multiple TRPs.

[0156] Optionally, when the input parameter X of the first neural network model is for multiple TRPs, the output result Y is still the position of the terminal device, and the verification parameter X* is for multiple TRPs. For example, the number of TRPs is n (n is greater than 1), then the input parameters are the DL TDOA of the terminal device relative to n TRPs, the RSRP of the terminal device relative to n TRPs, the DL RSTD of the terminal device relative to n TRPs, and the TOA of the terminal device relative to n TRPs; the DL TDOA, RSRP, DL RSTD, and TOA corresponding to each TRP may be greater than 1. The verification parameter X* is the DL TDOA of the terminal device relative to n TRPs, the RSRP of the terminal device relative to n TRPs, the DL RSTD of the terminal device relative to n TRPs, and the TOA of the terminal device relative to n TRPs obtained by inverting the output result Y. In this case, "whether the difference between X and X* exceeds the first threshold" in Figures 9 and 10 is for the same TRP, and can also be replaced by "whether the difference between X and X* corresponding to m TRPs out of n TRPs exceeds the threshold", where m is less than or equal to n.

[0157] In some embodiments, when the terminal positioning method executed by the first neural network model is DL AOD positioning, the input parameter of the first neural network model includes DL AOD. For example, the input parameter X of the first neural network model can be the DL AoD of the terminal device to the network device (such as TRP). The output result Y is the location of the terminal device. The verification parameter X* is the corresponding result obtained by inverting the output result Y. X* corresponds to X and can be DL AoD.

[0158] Specifically, the input parameter X can be for a single TRP or for multiple TRPs. When the input parameter X is for multiple TRPs, the output result Y is still the position of the terminal device, and the verification parameter X* is for multiple TRPs. For example, the number of TRPs is n (n is greater than 1), then the input parameter X is the DL AoD of the terminal device relative to n TRPs. In this case, "whether the difference between X and X* exceeds the first threshold" in Figures 9 and 10 is for the same TRP, and can also be replaced by "whether the difference between X and X* corresponding to m of the n TRPs exceeds the threshold", m is less than or equal to n. It should be understood that the DL AoD corresponding to each TRP can be greater than 1.

[0159] In some embodiments, when the terminal positioning method executed by the first neural network model is UL TDOA positioning, the input parameters of the first neural network model include at least one of the following: UL TDOA, RSRP, and UL RTOA. For example, the NG-RAN node assisted positioning method is combined with the AI / ML method, which can also be understood as the AI / ML model being deployed on the LMF side.

[0160] When the UE-based positioning method is UL TDOA, the input parameter X is the UL TDOA, RSRP, UL RTOA, etc. of the terminal device relative to the network device (such as TRP). The output result Y is the terminal device's location. The verification parameter X* is the corresponding result obtained by inferring the output result Y. X* corresponds to X and can be UL TDOA, RSRP, UL RTOA, etc.

[0161] Optionally, the input parameter X may be a combination of UL TDOA and RSRP, or a combination of UL RTOA and RSRP. Accordingly, the verification parameter X* is the combination of UL TDOA and RSRP, or the combination of UL RTOA and RSRP, obtained by inferring the output result Y.

[0162] Optionally, the input parameter X can be for a single TRP or for multiple TRPs. When the input parameter X is for multiple TRPs, the output result Y is still the position of the terminal device, and the verification parameter X* is for multiple TRPs. For example, if the number of TRPs is n (n is greater than 1), the input parameter X is the UL TDOA of the terminal device relative to n TRPs, the RSRP of the terminal device relative to n TRPs, and the UL RTOA of the terminal device relative to n TRPs; it should be understood that the number of UL TDOA, RSRP, and UL RTOA corresponding to each TRP can be greater than 1. The verification parameter X* is the UL TDOA of the terminal device relative to n TRPs, the RSRP of the terminal device relative to n TRPs, and the UL RTOA of the terminal device relative to n TRPs obtained by reverse deduction from the output result Y. In this case, "whether the difference between X and X* exceeds the first threshold" in Figures 9 and 10 is for the same TRP, and can also be replaced by "whether the difference between X and X* corresponding to m of the n TRPs exceeds the threshold", where m is less than or equal to n.

[0163] In some embodiments, when the terminal positioning method executed by the first neural network model is UL AOA positioning, the input parameters of the first neural network model include UL AOA.

[0164] For example, when the UE-based positioning method is the UL AoA positioning method, the input parameter X is the uplink arrival angle of the terminal device relative to the network device (such as TRP), such as the azimuth angle and / or the zenith angle. The output result Y is the position of the terminal device. The verification parameter X* is the corresponding result obtained by inverting the output result Y. X* corresponds to X and can be the uplink arrival angle of the terminal device relative to the network device, such as the azimuth angle and / or the zenith angle.

[0165] Specifically, the input parameter X can be for a single TRP or for multiple TRPs. When the input parameter X is for multiple TRPs, the output result Y is still the position of the terminal device, and the verification parameter X* is for multiple TRPs. For example, the number of TRPs is n (n is greater than 1), then the input parameter X is the AoA of the terminal device relative to n TRPs. In this case, "whether the difference between X and X* exceeds the first threshold" in Figures 9 and 10 is for the same TRP, and can also be replaced by "whether the difference between X and X* corresponding to m of the n TRPs exceeds the threshold", m is less than or equal to n. It should be understood that the AoA corresponding to each TRP can be greater than 1.

[0166] Therefore, in the embodiment of the present application, when the first neural network model is no longer applicable to the current communication scenario, the problem is promptly discovered through the performance monitoring of the first neural network model. Because the accuracy of the positioning error cannot be directly obtained in actual deployment scenarios, this embodiment provides performance monitoring metrics for different positioning methods.

[0167] In some embodiments, when the terminal device determines that the first neural network model has failed, the terminal device sends fourth information, wherein the fourth information is used to request an update of the network model, or the fourth information is used to indicate that the first neural network model has failed, or the fourth information is used to request terminal positioning through other means.

[0168] For example, the other method for implementing terminal positioning is to fall back to the traditional positioning method to implement terminal positioning.

[0169] In some embodiments, the fourth information includes information of at least one AI / ML model supported by the terminal device that has the same function as that implemented by the first neural network model.

[0170] In some embodiments, the terminal device sends first capability information, where the first capability information includes type information of the AI / ML model supported by the terminal device.

[0171] In some embodiments, the terminal device receives fifth information, wherein the fifth information includes at least one of the following: identification information of a second neural network model, configuration information of the second neural network model, and configuration information required for online training of the second neural network model; the second neural network model is an AI / ML model that implements the same function as the first neural network model. The identification information of the second neural network model includes an index or identifier (ID) of the second neural network model.

[0172] The terminal device switches from the first neural network model to the second neural network model.

[0173] In some embodiments, the terminal device implements the function implemented by the first neural network model through other methods within a first duration; wherein the start time of the first duration is the time when the terminal device determines that the first neural network model has failed, and the end time of the first duration is the time when the terminal device successfully switches to the second neural network model. For example, the other method can be a traditional positioning method.

[0174] In some embodiments, assuming that the first neural network model is AI / ML model 1, and AI / ML model 1 is a trained AI / ML model. If the result of AI / ML model monitoring is that AI / ML model 1 needs to be updated to AI / ML model 2, AI / ML model 2 is an AI / ML model in a set of already trained (offline trained) AI / ML models (referred to as type 1), or AI / ML model 2 is online training based on the training set of AI / ML model 1 (fine-tuning, a new model obtained by updating part of the data in AI / ML model 1, referred to as type 2), or AI / ML model 2 is a new AI / ML model trained online (retraining a new data set, referred to as type 3), or AI / ML model 2 is a new AI / ML model trained online (the AI / ML model structure remains unchanged, only the weights are updated, referred to as type 4).

[0175] In some embodiments, the first capability information includes one or more of type 1, type 2, type 3, and type 4.

[0176] Specifically, the steps for updating the AI ​​model include some or all of the following steps:

[0177] Step 1: UE sends a model update request to the network device;

[0178] Step 2: The UE sends the type of supported AI / ML model 2 (type 1, 2, 3, 4) to the network device (which can be one of the first capability information);

[0179] Step 3-1: If AI / ML model 2 is type 1, the UE receives the configuration of AI / ML model 2 or the index of AI / ML model 2 in the AI / ML model set sent by the network device;

[0180] Step 3-2: The UE receives auxiliary information related to AI / ML model update from the network device. The auxiliary information includes configuration information required for online training if the AI / ML model 2 is type 2, type 3, or type 4.

[0181] Step 4: Based on step 3-2, the UE performs online training.

[0182] Step 5: The AI / ML model is updated to AI / ML model 2.

[0183] It should be noted that after the UE sends the AI / ML model update request, it falls back to the traditional positioning method until the AI / ML model is updated to AI / ML model 2. The fallback mechanism can avoid positioning errors caused by inaccurate AI / ML models.

[0184] Therefore, in an embodiment of the present application, the terminal device can monitor the first neural network model used for terminal positioning based on the configuration information for monitoring the first neural network model, can determine whether the first neural network model is valid based on the monitoring results, and request to update the network model if the first neural network model fails, thereby ensuring the performance of the neural network model used for terminal positioning.

[0185] The above text, in combination with Figures 7 to 10, describes in detail the method embodiment of the present application. The following text, in combination with Figures 11 to 15, describes in detail the device embodiment of the present application. It should be understood that the device embodiment and the method embodiment correspond to each other, and similar descriptions can refer to the method embodiment.

[0186] FIG11 shows a schematic block diagram of a terminal device 300 according to an embodiment of the present application. As shown in FIG11 , the terminal device 300 includes:

[0187] The communication unit 310 is configured to receive first information, wherein the first information includes at least configuration information for monitoring a first neural network model, the first neural network model being used for terminal positioning;

[0188] The processing unit 320 is configured to monitor the first neural network model according to the first information.

[0189] In some embodiments, the configuration information for monitoring the first neural network model includes configuration information of a reference signal for monitoring the first neural network model.

[0190] In some embodiments, the reference signal used for monitoring the first neural network model is a periodic reference signal or a semi-persistent scheduling (SPS) reference signal.

[0191] In some embodiments, the reference signal used for monitoring the first neural network model is one of the following:

[0192] Downlink positioning reference signal PRS, sounding reference signal SRS, channel state information reference signal CSI-RS, synchronization signal block SSB, demodulation reference signal DMRS.

[0193] In some embodiments, the first information is carried by a Long Term Evolution Positioning Protocol (LPP) message sent by a Location Management Function (LMF) entity, or the first information is carried by Radio Resource Control (RRC) signaling.

[0194] In some embodiments, when the reference signal used for monitoring the first neural network model is a downlink PRS, the first information is carried by an LPP message sent by an LMF entity; or,

[0195] In the case where the reference signal used for monitoring the first neural network model is one of SRS, CSI-RS, SSB and DMRS, the first information is carried through RRC signaling.

[0196] In some embodiments, the configuration information for monitoring the first neural network model includes at least one of the following:

[0197] Monitoring period, monitoring start time, monitoring end time, monitoring time window, monitored reference signal type, monitored reference signal period and / or time slot offset, monitoring times, monitoring timer.

[0198] In some embodiments, before the terminal device receives the first information, the communication unit 310 is further used to send second information, wherein the second information is used to request monitoring of the first neural network model.

[0199] In some embodiments, when the configuration information for monitoring the first neural network model is a downlink PRS for monitoring the first neural network model, the second information sampling is sent using an on-demand PRS mechanism.

[0200] In some embodiments, the second information includes identification information of a downlink PRS configuration monitored by the first neural network model.

[0201] In some embodiments, the second information includes downlink PRS parameter configuration information for monitoring by the first neural network model.

[0202] In some embodiments, the downlink PRS parameter configuration information for monitoring by the first neural network model includes at least one of the following:

[0203] The period of the PRS signal, the subcarrier spacing of the PRS signal, the cyclic prefix length of the PRS signal, the frequency domain resource bandwidth of the PRS, the frequency domain starting frequency position of the PRS resource, the frequency domain reference point A of the PRS signal, and the comb tooth size of the PRS signal.

[0204] In some embodiments, the second information includes at least one of the following:

[0205] Monitoring period, monitoring start time, monitoring end time, monitoring time window, monitored reference signal type, monitored reference signal period and / or time slot offset, monitoring times, monitoring timer.

[0206] In some embodiments, before the terminal device receives the first information, the communication unit 310 is further used to send third information, wherein the third information is used to request a reference signal configuration and / or a reference signal measurement interval for monitoring the first neural network model.

[0207] In some embodiments, the monitoring behavior of the terminal device for the first neural network model is triggered by one of the following:

[0208] The terminal device, network device.

[0209] In some embodiments, the network device includes at least one of the following:

[0210] LMF entity, access network equipment, access and mobility management function AMF entity.

[0211] In some embodiments, the monitoring behavior of the terminal device for the first neural network model is triggered when a first condition is met;

[0212] Among them, the first condition includes at least one of the following: the terminal device performs cell switching, detects that the wireless link quality has deteriorated, beam failure recovery BFR occurs, and uplink desynchronization occurs.

[0213] In some embodiments, the configuration information for monitoring the first neural network model includes the first condition.

[0214] In some embodiments, the processing unit 320 is specifically configured to:

[0215] The first neural network model is monitored within a first time window according to the first information.

[0216] In some embodiments, the first time window is predefined, or the first time window is preconfigured, or the first time window is configured by the network device.

[0217] In some embodiments, the first time window is configured periodically, or the first time window is configured aperiodically.

[0218] In some embodiments, the configuration information for monitoring the first neural network model includes configuration information of the first time window.

[0219] In some embodiments, the processing unit 320 is specifically configured to:

[0220] If the difference between the input parameter and the verification parameter of the first neural network model is greater than or equal to a first threshold, determining that the first neural network model is invalid; and / or,

[0221] When the difference between the input parameter and the verification parameter of the first neural network model is less than a first threshold, determining that the first neural network model is valid;

[0222] The type of the input parameter of the first neural network model is the same as the type of the verification parameter.

[0223] In some embodiments, the processing unit 320 is specifically configured to:

[0224] During the monitoring of the first neural network model, if the number of times that the difference between the input parameter and the verification parameter of the first neural network model is greater than or equal to the first threshold is greater than or equal to the second threshold, the first neural network model is determined to be invalid; and / or,

[0225] During the monitoring period of the first neural network model, if the number of times that the difference between the input parameter and the verification parameter of the first neural network model is greater than or equal to the first threshold is less than the second threshold, determining that the first neural network model is valid;

[0226] The type of the input parameter of the first neural network model is the same as the type of the verification parameter.

[0227] In some embodiments, the verification parameter is obtained by reverse deduction based on the prediction result of the first neural network model.

[0228] In some embodiments, the input parameters of the first neural network model include at least one of the following: downlink arrival time difference DL TDOA, reference signal received power RSRP, downlink reference signal time difference DL RSTD, arrival time TOA, downlink angle of departure DL AoD, uplink arrival time difference UL TDOA, uplink relative time of arrival UL RTOA, uplink angle of arrival UL AoA.

[0229] In some embodiments, when the terminal positioning method executed by the first neural network model is DL TDOA positioning, the input parameters of the first neural network model include at least one of the following: DL TDOA, RSRP, DL RSTD, TOA.

[0230] In some embodiments, when the terminal positioning method executed by the first neural network model is DL AOD positioning, the input parameters of the first neural network model include DL AOD.

[0231] In some embodiments, when the terminal positioning method executed by the first neural network model is UL TDOA positioning, the input parameters of the first neural network model include at least one of the following: UL TDOA, RSRP, UL RTOA.

[0232] In some embodiments, when the terminal positioning method executed by the first neural network model is UL AOA positioning, the input parameters of the first neural network model include UL AOA.

[0233] In some embodiments, the input parameter of the first neural network model is a parameter of the terminal device relative to a single transmission and reception point TRP, and the verification parameter is a verification parameter of the terminal device relative to a single TRP; or,

[0234] The input parameters of the first neural network model are parameters of the terminal device relative to multiple TRPs, and the verification parameters are verification parameters of the terminal device relative to multiple TRPs.

[0235] In some embodiments, when the input parameters of the first neural network model are parameters of the terminal device relative to multiple TRPs, the difference between the input parameters of the first neural network model and the verification parameters is greater than or equal to a first threshold, including: the difference between the parameters of the terminal device relative to some or all of the multiple TRPs and the verification parameters of the terminal device relative to the corresponding TRPs is greater than or equal to the first threshold; and / or,

[0236] In the case where the input parameters of the first neural network model are the parameters of the terminal device relative to multiple TRPs, the difference between the input parameters of the first neural network model and the verification parameters is less than a first threshold, including: the difference between the parameters of the terminal device relative to some or all of the multiple TRPs and the verification parameters of the terminal device relative to the corresponding TRPs is less than the first threshold.

[0237] In some embodiments, when the terminal device determines that the first neural network model has failed, the communication unit 310 is further used to send fourth information, wherein the fourth information is used to request an update of the network model, or the fourth information is used to indicate that the first neural network model has failed, or the fourth information is used to request terminal positioning by other means.

[0238] In some embodiments, the fourth information includes information about at least one artificial intelligence AI / machine learning ML model supported by the terminal device that has the same function as that implemented by the first neural network model.

[0239] In some embodiments, the communication unit 310 is further used to send first capability information, where the first capability information includes type information of the AI / ML model supported by the terminal device.

[0240] In some embodiments, the communication unit 310 is further configured to receive fifth information, wherein the fifth information includes at least one of the following: identification information of the second neural network model, configuration information of the second neural network model, and configuration information required for online training of the second neural network model; the second neural network model is a network model that implements the same function as the first neural network model;

[0241] The processing unit 320 is further configured to switch from the first neural network model to the second neural network model.

[0242] In some embodiments, the processing unit 320 is further configured to implement the function implemented by the first neural network model in other ways within the first time period;

[0243] The start time of the first duration is the time when the terminal device determines that the first neural network model is invalid, and the end time of the first duration is the time when the terminal device successfully switches to the second neural network model.

[0244] In some embodiments, the communication unit may be a communication interface or a transceiver, or an input / output interface of a communication chip or a system on chip. The processing unit may be one or more processors.

[0245] It should be understood that the terminal device 300 according to the embodiment of the present application may correspond to the terminal device in the embodiment of the method of the present application, and the above-mentioned and other operations and / or functions of each unit in the terminal device 300 are respectively for realizing the corresponding processes of the terminal device in the method 200 shown in Figure 7. For the sake of brevity, they will not be repeated here.

[0246] FIG12 shows a schematic block diagram of a network device 400 according to an embodiment of the present application. As shown in FIG12 , the network device 400 includes:

[0247] The communication unit 410 is used to send first information, wherein the first information at least includes configuration information for monitoring a first neural network model, the first neural network model is used for terminal positioning, and the first information is used by the terminal device to monitor the first neural network model.

[0248] In some embodiments, the configuration information for monitoring the first neural network model includes configuration information of a reference signal for monitoring the first neural network model.

[0249] In some embodiments, the reference signal used for monitoring the first neural network model is a periodic reference signal or a semi-persistent scheduling (SPS) reference signal.

[0250] In some embodiments, the reference signal used for monitoring the first neural network model is one of the following:

[0251] Downlink positioning reference signal PRS, sounding reference signal SRS, channel state information reference signal CSI-RS, synchronization signal block SSB, demodulation reference signal DMRS.

[0252] In some embodiments, the first information is carried by a Long Term Evolution Positioning Protocol (LPP) message sent by a Location Management Function (LMF) entity, or the first information is carried by Radio Resource Control (RRC) signaling.

[0253] In some embodiments, when the reference signal used for monitoring the first neural network model is a downlink PRS, the first information is carried by an LPP message sent by an LMF entity; or,

[0254] In the case where the reference signal used for monitoring the first neural network model is one of SRS, CSI-RS, SSB and DMRS, the first information is carried through RRC signaling.

[0255] In some embodiments, the configuration information for monitoring the first neural network model includes at least one of the following:

[0256] Monitoring period, monitoring start time, monitoring end time, monitoring time window, monitored reference signal type, monitored reference signal period and / or time slot offset, monitoring times, monitoring timer.

[0257] In some embodiments, before the network device sends the first information, the communication unit 410 is further used to receive second information, wherein the second information is used to request monitoring of the first neural network model, and the first information is determined based on the second information.

[0258] In some embodiments, when the configuration information for monitoring the first neural network model is a downlink PRS for monitoring the first neural network model, the second information sampling is sent using an on-demand PRS mechanism.

[0259] In some embodiments, the second information includes identification information of a downlink PRS configuration monitored by the first neural network model.

[0260] In some embodiments, the second information includes downlink PRS parameter configuration information for monitoring by the first neural network model.

[0261] In some embodiments, the downlink PRS parameter configuration information for monitoring by the first neural network model includes at least one of the following:

[0262] The period of the PRS signal, the subcarrier spacing of the PRS signal, the cyclic prefix length of the PRS signal, the frequency domain resource bandwidth of the PRS, the frequency domain starting frequency position of the PRS resource, the frequency domain reference point A of the PRS signal, and the comb tooth size of the PRS signal.

[0263] In some embodiments, the second information includes at least one of the following:

[0264] Monitoring period, monitoring start time, monitoring end time, monitoring time window, monitored reference signal type, monitored reference signal period and / or time slot offset, monitoring times, monitoring timer.

[0265] In some embodiments, before the network device sends the first information, the communication unit 410 is also used to receive third information, wherein the third information is used to request a reference signal configuration and / or a reference signal measurement interval for monitoring the first neural network model, and the first information is determined based on the third information.

[0266] In some embodiments, the monitoring behavior of the terminal device for the first neural network model is triggered by one of the following:

[0267] The terminal device, the network device.

[0268] In some embodiments, the network device includes at least one of the following:

[0269] LMF entity, access network equipment, access and mobility management function AMF entity.

[0270] In some embodiments, the monitoring behavior of the terminal device for the first neural network model is triggered when a first condition is met;

[0271] The first condition includes at least one of the following: the terminal device performs cell switching, detects that the radio link quality has degraded, beam failure recovery BFR has occurred, or uplink desynchronization has occurred.

[0272] In some embodiments, the configuration information for monitoring the first neural network model includes the first condition.

[0273] In some embodiments, the first information is used by the terminal device to monitor the first neural network model, including:

[0274] The first information is used by the terminal device to monitor the first neural network model within a first time window.

[0275] In some embodiments, the first time window is predefined, or the first time window is preconfigured, or the first time window is configured by the network device.

[0276] In some embodiments, the first time window is configured periodically, or the first time window is configured aperiodically.

[0277] In some embodiments, the configuration information for monitoring the first neural network model includes configuration information of the first time window.

[0278] In some embodiments, the first information is used by the terminal device to monitor the first neural network model, including:

[0279] If the difference between the input parameter and the verification parameter of the first neural network model is greater than or equal to a first threshold, the first neural network model fails; and / or,

[0280] When the difference between the input parameter and the verification parameter of the first neural network model is less than a first threshold, the first neural network model is valid;

[0281] The type of the input parameter of the first neural network model is the same as the type of the verification parameter.

[0282] In some embodiments, the first information is used by the terminal device to monitor the first neural network model, including:

[0283] During the monitoring of the first neural network model, if the number of times that the difference between the input parameter and the verification parameter of the first neural network model is greater than or equal to the first threshold is greater than or equal to the second threshold, the terminal device determines that the first neural network model has failed; and / or,

[0284] During the monitoring period of the first neural network model, if the number of times that the difference between the input parameter and the verification parameter of the first neural network model is greater than or equal to the first threshold is less than the second threshold, the terminal device determines that the first neural network model is valid;

[0285] The type of the input parameter of the first neural network model is the same as the type of the verification parameter.

[0286] In some embodiments, the verification parameter is obtained by reverse deduction based on the prediction result of the first neural network model.

[0287] In some embodiments, the input parameters of the first neural network model include at least one of the following: downlink arrival time difference DL TDOA, reference signal received power RSRP, downlink reference signal time difference DL RSTD, arrival time TOA, downlink angle of departure DL AoD, uplink arrival time difference UL TDOA, uplink relative time of arrival UL RTOA, uplink angle of arrival UL AoA.

[0288] In some embodiments, when the terminal positioning method executed by the first neural network model is DL TDOA positioning, the input parameters of the first neural network model include at least one of the following: DL TDOA, RSRP, DL RSTD, TOA.

[0289] In some embodiments, when the terminal positioning method executed by the first neural network model is DL AOD positioning, the input parameters of the first neural network model include DL AOD.

[0290] In some embodiments, when the terminal positioning method executed by the first neural network model is UL TDOA positioning, the input parameters of the first neural network model include at least one of the following: UL TDOA, RSRP, UL RTOA.

[0291] In some embodiments, when the terminal positioning method executed by the first neural network model is UL AOA positioning, the input parameters of the first neural network model include UL AOA.

[0292] In some embodiments, the input parameter of the first neural network model is a parameter of the terminal device relative to a single transmission and reception point TRP, and the verification parameter is a verification parameter of the terminal device relative to a single TRP; or,

[0293] The input parameters of the first neural network model are parameters of the terminal device relative to multiple TRPs, and the verification parameters are verification parameters of the terminal device relative to multiple TRPs.

[0294] In some embodiments, when the input parameters of the first neural network model are parameters of the terminal device relative to multiple TRPs, the difference between the input parameters of the first neural network model and the verification parameters is greater than or equal to a first threshold, including: the difference between the parameters of the terminal device relative to some or all of the multiple TRPs and the verification parameters of the terminal device relative to the corresponding TRPs is greater than or equal to the first threshold; and / or,

[0295] In the case where the input parameters of the first neural network model are the parameters of the terminal device relative to multiple TRPs, the difference between the input parameters of the first neural network model and the verification parameters is less than a first threshold, including: the difference between the parameters of the terminal device relative to some or all of the multiple TRPs and the verification parameters of the terminal device relative to the corresponding TRPs is less than the first threshold.

[0296] In some embodiments, the communication unit 410 is also used to receive fourth information, wherein the fourth information is used to request an update of the network model, or the fourth information is used to indicate that the first neural network model has failed, or the fourth information is used to request terminal positioning by other means.

[0297] In some embodiments, the fourth information includes information about at least one artificial intelligence AI / machine learning ML model supported by the terminal device that has the same function as that implemented by the first neural network model.

[0298] In some embodiments, the communication unit 410 is further used to receive first capability information, where the first capability information includes type information of the AI / ML model supported by the terminal device.

[0299] In some embodiments, the communication unit 410 is also used to send fifth information, wherein the fifth information includes at least one of the following: identification information of the second neural network model, configuration information of the second neural network model, and configuration information required for the second neural network model to perform online training; the second neural network model is a network model that implements the same function as the first neural network model; the fifth information is used for the terminal device to switch from the first neural network model to the second neural network model.

[0300] In some embodiments, during the first time period, the terminal device implements the function implemented by the first neural network model through other means;

[0301] The start time of the first duration is the time when the terminal device determines that the first neural network model is invalid, and the end time of the first duration is the time when the terminal device successfully switches to the second neural network model.

[0302] In some embodiments, the communication unit may be a communication interface or a transceiver, or an input / output interface of a communication chip or a system on chip.

[0303] It should be understood that the network device 400 according to the embodiment of the present application may correspond to the network device in the embodiment of the method of the present application, and the above-mentioned and other operations and / or functions of each unit in the network device 400 are respectively for implementing the corresponding processes of the network device in the method 200 shown in Figure 7. For the sake of brevity, they will not be repeated here.

[0304] Figure 13 is a schematic structural diagram of a communication device 500 provided in an embodiment of the present application. The communication device 500 shown in Figure 13 includes a processor 510, which can call and run a computer program from a memory to implement the method in the embodiment of the present application.

[0305] In some embodiments, as shown in FIG13 , the communication device 500 may further include a memory 520. The processor 510 may call and execute a computer program from the memory 520 to implement the method in the embodiment of the present application.

[0306] The memory 520 may be a separate device independent of the processor 510 , or may be integrated into the processor 510 .

[0307] In some embodiments, as shown in FIG13 , the communication device 500 may further include a transceiver 530 , and the processor 510 may control the transceiver 530 to communicate with other devices. Specifically, the transceiver 530 may send information or data to other devices, or receive information or data sent by other devices.

[0308] The transceiver 530 may include a transmitter and a receiver. The transceiver 530 may further include an antenna, and the number of antennas may be one or more.

[0309] In some embodiments, the processor 510 may implement the functions of a processing unit in a terminal device, or the processor 510 may implement the functions of a processing unit in a network device, which will not be described in detail here for the sake of brevity.

[0310] In some embodiments, the transceiver 530 may implement the functions of a communication unit in a terminal device, which will not be described in detail here for the sake of brevity.

[0311] In some embodiments, the transceiver 530 may implement the function of a communication unit in a network device, which will not be described in detail here for the sake of brevity.

[0312] In some embodiments, the communication device 500 may specifically be a network device of an embodiment of the present application, and the communication device 500 may implement the corresponding processes implemented by the network device in each method of the embodiment of the present application. For the sake of brevity, they will not be repeated here.

[0313] In some embodiments, the communication device 500 may specifically be a terminal device of an embodiment of the present application, and the communication device 500 may implement the corresponding processes implemented by the terminal device in each method of the embodiment of the present application. For the sake of brevity, they will not be repeated here.

[0314] Figure 14 is a schematic structural diagram of an apparatus according to an embodiment of the present application. The apparatus 600 shown in Figure 14 includes a processor 610, which can call and execute a computer program from a memory to implement the method according to the embodiment of the present application.

[0315] In some embodiments, as shown in FIG14 , the apparatus 600 may further include a memory 620. The processor 610 may call and execute a computer program from the memory 620 to implement the method in the embodiment of the present application.

[0316] The memory 620 may be a separate device independent of the processor 610 , or may be integrated into the processor 610 .

[0317] In some embodiments, the apparatus 600 may further include an input interface 630. The processor 610 may control the input interface 630 to communicate with other devices or chips, and specifically, may obtain information or data sent by other devices or chips. Optionally, the processor 610 may be located inside or outside the chip.

[0318] In some embodiments, the processor 610 may implement the functions of a processing unit in a terminal device, or the processor 610 may implement the functions of a processing unit in a network device, which will not be described in detail here for the sake of brevity.

[0319] In some embodiments, the input interface 630 may implement the function of a communication unit in a terminal device, or the input interface 630 may implement the function of a communication unit in a network device.

[0320] In some embodiments, the apparatus 600 may further include an output interface 640. The processor 610 may control the output interface 640 to communicate with other devices or chips, and specifically, may output information or data to other devices or chips. Optionally, the processor 610 may be located inside or outside the chip.

[0321] In some embodiments, the output interface 640 may implement the function of a communication unit in a terminal device, or the output interface 640 may implement the function of a communication unit in a network device.

[0322] In some embodiments, the device can be applied to the network equipment in the embodiments of the present application, and the device can implement the corresponding processes implemented by the network equipment in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0323] In some embodiments, the apparatus can be applied to the terminal device in the embodiments of the present application, and the apparatus can implement the corresponding processes implemented by the terminal device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0324] In some embodiments, the device mentioned in the embodiments of the present application may also be a chip, such as a system-on-chip, a system-on-chip, a system-on-chip, or a system-on-chip.

[0325] FIG15 is a schematic block diagram of a communication system 700 provided in an embodiment of the present application. As shown in FIG15 , the communication system 700 includes a terminal device 710 and a network device 720 .

[0326] Among them, the terminal device 710 can be used to implement the corresponding functions implemented by the terminal device in the above method, and the network device 720 can be used to implement the corresponding functions implemented by the network device in the above method. For the sake of brevity, they will not be repeated here.

[0327] It should be understood that the processor of the embodiments of the present application may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiment can be completed by hardware integrated logic circuits in the processor or software instructions. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The various methods, steps, and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0328] It is understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0329] It should be understood that the above-mentioned memories are exemplary but not restrictive. For example, the memories in the embodiments of the present application may also be static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM RAM (DR RAM), etc. In other words, the memories in the embodiments of the present application are intended to include, but are not limited to, these and any other suitable types of memories.

[0330] An embodiment of the present application also provides a computer-readable storage medium for storing a computer program.

[0331] In some embodiments, the computer-readable storage medium can be applied to the network device in the embodiments of the present application, and the computer program enables the computer to execute the corresponding processes implemented by the network device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0332] In some embodiments, the computer-readable storage medium can be applied to the terminal device in the embodiments of the present application, and the computer program enables the computer to execute the corresponding processes implemented by the terminal device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0333] An embodiment of the present application also provides a computer program product, including computer program instructions.

[0334] In some embodiments, the computer program product can be applied to the network device in the embodiments of the present application, and the computer program instructions enable the computer to execute the corresponding processes implemented by the network device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0335] In some embodiments, the computer program product can be applied to the terminal device in the embodiments of the present application, and the computer program instructions enable the computer to execute the corresponding processes implemented by the terminal device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0336] The embodiment of the present application also provides a computer program.

[0337] In some embodiments, the computer program can be applied to the network device in the embodiments of the present application. When the computer program runs on a computer, the computer executes the corresponding processes implemented by the network device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0338] In some embodiments, the computer program can be applied to the terminal device in the embodiments of the present application. When the computer program runs on the computer, the computer executes the corresponding processes implemented by the terminal device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0339] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0340] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0341] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0342] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0343] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0344] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. In view of this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0345] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A model monitoring method, characterized in that: include: The terminal device receives first information, wherein the first information includes at least configuration information for monitoring a first neural network model, and the first neural network model is used for terminal positioning; The terminal device monitors the first neural network model according to the first information.

2. The method according to claim 1, wherein The configuration information for monitoring the first neural network model includes configuration information of a reference signal for monitoring the first neural network model.

3. The method according to claim 2, wherein The reference signal used for monitoring the first neural network model is a periodic reference signal or a reference signal of semi-persistent scheduling (SPS).

4. The method according to claim 3, wherein The reference signal used for monitoring the first neural network model is one of the following: Downlink positioning reference signal PRS, sounding reference signal SRS, channel state information reference signal CSI-RS, synchronization signal block SSB, demodulation reference signal DMRS.

5. The method according to claim 4, wherein The first information is carried by a Long Term Evolution Positioning Protocol LPP message sent by a Location Management Function LMF entity, or the first information is carried by Radio Resource Control RRC signaling.

6. The method according to claim 4 or 5, characterized in that In the case where the reference signal used for monitoring the first neural network model is a downlink PRS, the first information is carried by an LPP message sent by an LMF entity; or, In the case where the reference signal used for monitoring the first neural network model is one of SRS, CSI-RS, SSB and DMRS, the first information is carried through RRC signaling.

7. The method according to claim 1, wherein The configuration information for monitoring the first neural network model includes at least one of the following: Monitoring period, monitoring start time, monitoring end time, monitoring time window, monitored reference signal type, monitored reference signal period and / or time slot offset, monitoring times, monitoring timer.

8. The method according to any one of claims 1 to 7, characterized in that Before the terminal device receives the first information, the method further includes: The terminal device sends second information, wherein the second information is used to request monitoring of the first neural network model.

9. The method according to claim 8, wherein In a case where the configuration information for monitoring the first neural network model is a downlink PRS for monitoring the first neural network model, the second information sampling is sent using an on-demand PRS mechanism.

10. The method according to claim 9, wherein The second information includes identification information of the downlink PRS configuration monitored by the first neural network model.

11. The method according to claim 9, wherein The second information includes downlink PRS parameter configuration information used for monitoring by the first neural network model.

12. The method according to claim 11, wherein The downlink PRS parameter configuration information used for monitoring by the first neural network model includes at least one of the following: The period of the PRS signal, the subcarrier spacing of the PRS signal, the cyclic prefix length of the PRS signal, the frequency domain resource bandwidth of the PRS, the frequency domain starting frequency position of the PRS resource, the frequency domain reference point A of the PRS signal, and the comb tooth size of the PRS signal.

13. The method according to claim 8, wherein The second information includes at least one of the following: Monitoring period, monitoring start time, monitoring end time, monitoring time window, monitored reference signal type, monitored reference signal period and / or time slot offset, monitoring times, monitoring timer.

14. The method according to any one of claims 1 to 7, characterized in that Before the terminal device receives the first information, the method further includes: The terminal device sends third information, wherein the third information is used to request a reference signal configuration and / or a reference signal measurement interval for monitoring the first neural network model.

15. The method according to any one of claims 1 to 14, characterized in that The monitoring behavior of the terminal device for the first neural network model is triggered by one of the following: The terminal device and the network device.

16. The method according to claim 15, wherein The network device includes at least one of the following: LMF entity, access network equipment, access and mobility management function AMF entity.

17. The method according to any one of claims 1 to 16, characterized in that The monitoring behavior of the terminal device for the first neural network model is triggered when a first condition is met; Among them, the first condition includes at least one of the following: the terminal device performs cell switching, detects that the wireless link quality has deteriorated, beam failure recovery BFR occurs, and uplink desynchronization occurs.

18. The method according to claim 17, wherein The configuration information for monitoring the first neural network model includes the first condition.

19. The method according to any one of claims 1 to 18, characterized in that The terminal device monitors the first neural network model according to the first information, including: The terminal device monitors the first neural network model within a first time window based on the first information.

20. The method according to claim 19, wherein The first time window is predefined, or the first time window is preconfigured, or the first time window is configured by the network device.

21. The method according to claim 19, wherein The first time window is configured periodically, or the first time window is configured aperiodically.

22. The method of claim 19, wherein: The configuration information for monitoring the first neural network model includes configuration information of the first time window.

23. The method according to any one of claims 1 to 22, characterized in that The terminal device monitors the first neural network model according to the first information, including: In a case where a difference between an input parameter and a verification parameter of the first neural network model is greater than or equal to a first threshold, the terminal device determines that the first neural network model is invalid; and / or, When a difference between an input parameter and a verification parameter of the first neural network model is less than a first threshold, the terminal device determines that the first neural network model is valid; The type of the input parameter of the first neural network model is the same as the type of the verification parameter.

24. The method according to any one of claims 1 to 22, characterized in that The terminal device monitors the first neural network model according to the first information, including: During the monitoring of the first neural network model, if the number of times that the difference between the input parameter and the verification parameter of the first neural network model is greater than or equal to the first threshold is greater than or equal to the second threshold, the terminal device determines that the first neural network model has failed; and / or, During the monitoring period of the first neural network model, if the number of times that the difference between the input parameter and the verification parameter of the first neural network model is greater than or equal to the first threshold is less than the second threshold, the terminal device determines that the first neural network model is valid; The type of the input parameter of the first neural network model is the same as the type of the verification parameter.

25. The method according to claim 23 or 24, wherein: The verification parameters are obtained by reverse deduction based on the prediction results of the first neural network model.

26. The method according to any one of claims 23 to 25, characterized in that The input parameters of the first neural network model include at least one of the following: downlink arrival time difference DL TDOA, reference signal received power RSRP, downlink reference signal time difference DL RSTD, arrival time TOA, downlink angle of departure DL AoD, uplink arrival time difference UL TDOA, uplink relative time of arrival UL RTOA, uplink angle of arrival UL AoA.

27. The method according to claim 26, wherein In the case where the terminal positioning method executed by the first neural network model is DL TDOA positioning, the input parameters of the first neural network model include at least one of the following: DL TDOA, RSRP, DL RSTD, TOA.

28. The method of claim 26, wherein: In a case where the terminal positioning method executed by the first neural network model is DL AOD positioning, the input parameters of the first neural network model include DL AOD.

29. The method of claim 26, wherein: In the case where the terminal positioning method executed by the first neural network model is UL TDOA positioning, the input parameters of the first neural network model include at least one of the following: UL TDOA, RSRP, UL RTOA.

30. The method of claim 26, wherein: In a case where the terminal positioning method executed by the first neural network model is UL AOA positioning, the input parameters of the first neural network model include UL AOA.

31. The method according to any one of claims 26 to 30, wherein The input parameters of the first neural network model are parameters of the terminal device relative to a single transmission and reception point TRP, and the verification parameters are verification parameters of the terminal device relative to a single TRP; or, The input parameters of the first neural network model are parameters of the terminal device relative to multiple TRPs, and the verification parameters are verification parameters of the terminal device relative to multiple TRPs.

32. The method of claim 31, wherein In a case where the input parameters of the first neural network model are parameters of the terminal device relative to a plurality of TRPs, the difference between the input parameters of the first neural network model and the verification parameters is greater than or equal to a first threshold, including: the difference between the parameters of the terminal device relative to some or all of the plurality of TRPs and the verification parameters of the terminal device relative to the corresponding TRPs is greater than or equal to the first threshold; and / or, In the case where the input parameters of the first neural network model are the parameters of the terminal device relative to multiple TRPs, the difference between the input parameters of the first neural network model and the verification parameters is less than a first threshold, including: the difference between the parameters of the terminal device relative to some or all of the multiple TRPs and the verification parameters of the terminal device relative to the corresponding TRPs is less than the first threshold.

33. The method according to any one of claims 23 to 32, wherein When the terminal device determines that the first neural network model fails, the method further includes: The terminal device sends fourth information, wherein the fourth information is used to request updating the network model, or the fourth information is used to indicate that the first neural network model has failed, or the fourth information is used to request terminal positioning through other means.

34. The method of claim 33, wherein: The fourth information includes information about at least one artificial intelligence AI / machine learning ML model supported by the terminal device that has the same function as that implemented by the first neural network model.

35. The method of claim 33, wherein: The method further comprises: The terminal device sends first capability information, where the first capability information includes type information of the AI / ML model supported by the terminal device.

36. The method according to any one of claims 33 to 35, wherein The method further comprises: The terminal device receives fifth information, wherein the fifth information includes at least one of the following: identification information of the second neural network model, configuration information of the second neural network model, and configuration information required for online training of the second neural network model; the second neural network model is an AI / ML model that implements the same function as the first neural network model; The terminal device switches from the first neural network model to the second neural network model.

37. The method of claim 36, wherein: The method further comprises: The terminal device implements the function implemented by the first neural network model by other means within the first time period; The start time of the first duration is the time when the terminal device determines that the first neural network model is invalid, and the end time of the first duration is the time when the terminal device successfully switches to the second neural network model.

38. A model monitoring method, characterized in that: include: The network device sends first information, wherein the first information includes at least configuration information for monitoring a first neural network model, the first neural network model is used for terminal positioning, and the first information is used by the terminal device to monitor the first neural network model.

39. The method of claim 38, wherein The configuration information for monitoring the first neural network model includes configuration information of a reference signal for monitoring the first neural network model.

40. The method of claim 39, wherein The reference signal used for monitoring the first neural network model is a periodic reference signal or a reference signal of semi-persistent scheduling (SPS).

41. The method of claim 40, wherein: The reference signal used for monitoring the first neural network model is one of the following: Downlink positioning reference signal PRS, sounding reference signal SRS, channel state information reference signal CSI-RS, synchronization signal block SSB, demodulation reference signal DMRS.

42. The method of claim 41, wherein The first information is carried by a Long Term Evolution Positioning Protocol LPP message sent by a Location Management Function LMF entity, or the first information is carried by Radio Resource Control RRC signaling.

43. The method according to claim 41 or 42, wherein In the case where the reference signal used for monitoring the first neural network model is a downlink PRS, the first information is carried by an LPP message sent by an LMF entity; or, In the case where the reference signal used for monitoring the first neural network model is one of SRS, CSI-RS, SSB and DMRS, the first information is carried through RRC signaling.

44. The method of claim 38, wherein The configuration information for monitoring the first neural network model includes at least one of the following: Monitoring period, monitoring start time, monitoring end time, monitoring time window, monitored reference signal type, monitored reference signal period and / or time slot offset, monitoring times, monitoring timer.

45. The method according to any one of claims 38 to 44, wherein Before the network device sends the first information, the method further includes: The network device receives second information, wherein the second information is used to request monitoring of the first neural network model, and the first information is determined based on the second information.

46. ​​The method of claim 45, wherein In a case where the configuration information for monitoring the first neural network model is a downlink PRS for monitoring the first neural network model, the second information sampling is sent using an on-demand PRS mechanism.

47. The method of claim 46, wherein The second information includes identification information of the downlink PRS configuration monitored by the first neural network model.

48. The method of claim 46, wherein The second information includes downlink PRS parameter configuration information used for monitoring by the first neural network model.

49. The method of claim 48, wherein The downlink PRS parameter configuration information used for monitoring by the first neural network model includes at least one of the following: The period of the PRS signal, the subcarrier spacing of the PRS signal, the cyclic prefix length of the PRS signal, the frequency domain resource bandwidth of the PRS, the frequency domain starting frequency position of the PRS resource, the frequency domain reference point A of the PRS signal, and the comb tooth size of the PRS signal.

50. The method of claim 45, wherein The second information includes at least one of the following: Monitoring period, monitoring start time, monitoring end time, monitoring time window, monitored reference signal type, monitored reference signal period and / or time slot offset, monitoring times, monitoring timer.

51. The method according to any one of claims 38 to 44, wherein Before the network device sends the first information, the method further includes: The network device receives third information, wherein the third information is used to request a reference signal configuration and / or a reference signal measurement interval for monitoring the first neural network model, and the first information is determined based on the third information.

52. The method according to any one of claims 38 to 51, wherein The monitoring behavior of the terminal device for the first neural network model is triggered by one of the following: The terminal device, the network device.

53. The method according to any one of claims 38 to 52, wherein The network device includes at least one of the following: LMF entity, access network equipment, access and mobility management function AMF entity.

54. The method according to any one of claims 38 to 53, wherein The monitoring behavior of the terminal device for the first neural network model is triggered when a first condition is met; Among them, the first condition includes at least one of the following: the terminal device performs cell switching, detects that the wireless link quality has deteriorated, beam failure recovery BFR occurs, and uplink desynchronization occurs.

55. The method of claim 54, wherein The configuration information for monitoring the first neural network model includes the first condition.

56. The method according to any one of claims 38 to 55, wherein The first information is used by the terminal device to monitor the first neural network model, including: The first information is used by the terminal device to monitor the first neural network model within a first time window.

57. The method of claim 56, wherein: The first time window is predefined, or the first time window is preconfigured, or the first time window is configured by the network device.

58. The method of claim 56, wherein: The first time window is configured periodically, or the first time window is configured aperiodically.

59. The method of claim 56, wherein The configuration information for monitoring the first neural network model includes configuration information of the first time window.

60. The method according to any one of claims 38 to 59, wherein The first information is used by the terminal device to monitor the first neural network model, including: If the difference between the input parameter and the verification parameter of the first neural network model is greater than or equal to a first threshold, the first neural network model fails; and / or, When the difference between the input parameter and the verification parameter of the first neural network model is less than a first threshold, the first neural network model is valid; The type of the input parameter of the first neural network model is the same as the type of the verification parameter.

61. The method according to any one of claims 38 to 59, wherein The first information is used by the terminal device to monitor the first neural network model, including: During the monitoring of the first neural network model, if the number of times that the difference between the input parameter and the verification parameter of the first neural network model is greater than or equal to the first threshold is greater than or equal to the second threshold, the terminal device determines that the first neural network model has failed; and / or, During the monitoring period of the first neural network model, if the number of times that the difference between the input parameter and the verification parameter of the first neural network model is greater than or equal to the first threshold is less than the second threshold, the terminal device determines that the first neural network model is valid; The type of the input parameter of the first neural network model is the same as the type of the verification parameter.

62. The method according to claim 60 or 61, wherein The verification parameters are obtained by reverse deduction based on the prediction results of the first neural network model.

63. The method according to any one of claims 60 to 62, wherein The input parameters of the first neural network model include at least one of the following: downlink arrival time difference DL TDOA, reference signal received power RSRP, downlink reference signal time difference DL RSTD, arrival time TOA, downlink angle of departure DL AoD, uplink arrival time difference UL TDOA, uplink relative time of arrival UL RTOA, uplink angle of arrival UL AoA.

64. The method of claim 63, wherein: In the case where the terminal positioning method executed by the first neural network model is DL TDOA positioning, the input parameters of the first neural network model include at least one of the following: DL TDOA, RSRP, DL RSTD, TOA.

65. The method of claim 63, wherein In a case where the terminal positioning method executed by the first neural network model is DL AOD positioning, the input parameters of the first neural network model include DL AOD.

66. The method of claim 63, wherein In the case where the terminal positioning method executed by the first neural network model is UL TDOA positioning, the input parameters of the first neural network model include at least one of the following: UL TDOA, RSRP, UL RTOA.

67. The method of claim 63, wherein In a case where the terminal positioning method executed by the first neural network model is UL AOA positioning, the input parameters of the first neural network model include UL AOA.

68. The method according to any one of claims 63 to 67, wherein The input parameters of the first neural network model are parameters of the terminal device relative to a single transmission and reception point TRP, and the verification parameters are verification parameters of the terminal device relative to a single TRP; or, The input parameters of the first neural network model are parameters of the terminal device relative to multiple TRPs, and the verification parameters are verification parameters of the terminal device relative to multiple TRPs.

69. The method of claim 68, wherein In a case where the input parameters of the first neural network model are parameters of the terminal device relative to a plurality of TRPs, the difference between the input parameters of the first neural network model and the verification parameters is greater than or equal to a first threshold, including: the difference between the parameters of the terminal device relative to some or all of the plurality of TRPs and the verification parameters of the terminal device relative to the corresponding TRPs is greater than or equal to the first threshold; and / or, In the case where the input parameters of the first neural network model are the parameters of the terminal device relative to multiple TRPs, the difference between the input parameters of the first neural network model and the verification parameters is less than a first threshold, including: the difference between the parameters of the terminal device relative to some or all of the multiple TRPs and the verification parameters of the terminal device relative to the corresponding TRPs is less than the first threshold.

70. The method according to any one of claims 60 to 69, wherein The method further comprises: The network device receives fourth information, wherein the fourth information is used to request an update of the network model, or the fourth information is used to indicate that the first neural network model has failed, or the fourth information is used to request terminal positioning through other means.

71. The method of claim 70, wherein The fourth information includes information about at least one artificial intelligence AI / machine learning ML model supported by the terminal device that has the same function as that implemented by the first neural network model.

72. The method of claim 70, wherein The method further comprises: The network device receives first capability information, where the first capability information includes type information of an AI / ML model supported by the terminal device.

73. The method according to any one of claims 70 to 72, wherein The method further comprises: The network device sends fifth information, wherein the fifth information includes at least one of the following: identification information of the second neural network model, configuration information of the second neural network model, and configuration information required for the second neural network model to perform online training; the second neural network model is an AI / ML model with the same function as the first neural network model; the fifth information is used by the terminal device to switch from the first neural network model to the second neural network model.

74. The method of claim 73, wherein During the first time period, the terminal device implements the function implemented by the first neural network model by other means; The start time of the first duration is the time when the terminal device determines that the first neural network model is invalid, and the end time of the first duration is the time when the terminal device successfully switches to the second neural network model.

75. A terminal device, characterized in that: include: A communication unit, configured to receive first information, wherein the first information includes at least configuration information for monitoring a first neural network model, the first neural network model being used for terminal positioning; A processing unit is configured to monitor the first neural network model based on the first information.

76. A network device, characterized in that include: A communication unit is used to send first information, wherein the first information at least includes configuration information for monitoring a first neural network model, the first neural network model is used for terminal positioning, and the first information is used by the terminal device to monitor the first neural network model.

77. A terminal device, characterized in that: include: A processor and a memory, the memory being used to store a computer program, the processor being used to call and run the computer program stored in the memory, so that the terminal device executes the method according to any one of claims 1 to 37.

78. A network device, characterized in that include: A processor and a memory, the memory being used to store a computer program, the processor being used to call and run the computer program stored in the memory, so that the network device executes the method according to any one of claims 38 to 74.

79. A chip, characterized in that include: A processor, configured to call and execute a computer program from a memory, so that a device equipped with the chip executes a method as claimed in any one of claims 1 to 37.

80. A chip, characterized in that: include: A processor, configured to call and execute a computer program from a memory, so that a device equipped with the chip executes a method as claimed in any one of claims 38 to 74.

81. A computer-readable storage medium, characterized in that For storing a computer program, when the computer program is executed, the method according to any one of claims 1 to 37 is implemented.

82. A computer-readable storage medium, characterized in that For storing a computer program, when said computer program is executed, the method according to any one of claims 38 to 74 is implemented.

83. A computer program product, characterized in that The method comprises computer program instructions which, when executed, implement the method according to any one of claims 1 to 37.

84. A computer program product, characterized in that comprising computer program instructions which, when executed, implement the method of any one of claims 38 to 74.

85. A computer program, characterized in that When the computer program is executed, the method according to any one of claims 1 to 37 is implemented.

86. A computer program, characterized in that When the computer program is executed, the method according to any one of claims 38 to 74 is implemented.

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