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WO2025108390A1 - Procédé et appareil de positionnement basé sur un modèle d'ia, dispositif et support de stockage lisible - Google Patents

Procédé et appareil de positionnement basé sur un modèle d'ia, dispositif et support de stockage lisible Download PDF

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Publication number
WO2025108390A1
WO2025108390A1 PCT/CN2024/133612 CN2024133612W WO2025108390A1 WO 2025108390 A1 WO2025108390 A1 WO 2025108390A1 CN 2024133612 W CN2024133612 W CN 2024133612W WO 2025108390 A1 WO2025108390 A1 WO 2025108390A1
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WO
WIPO (PCT)
Prior art keywords
model
information
measurement result
positioning
terminal
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PCT/CN2024/133612
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English (en)
Chinese (zh)
Inventor
王园园
贾承璐
邬华明
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Vivo Mobile Communication Co Ltd
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Vivo Mobile Communication Co Ltd
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Publication of WO2025108390A1 publication Critical patent/WO2025108390A1/fr
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/025—Services making use of location information using location based information parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination

Definitions

  • the present application belongs to the field of communication technology, and specifically relates to a positioning method, device, equipment and readable storage medium based on an AI model.
  • AI artificial intelligence
  • the embodiments of the present application provide a positioning method, apparatus, device and readable storage medium based on an AI model, which can realize terminal positioning based on an AI model.
  • a positioning method based on an AI model comprising: a first device receives target information, the first device is a first terminal or a positioning reference device, an AI model is deployed on the first device, the AI model is used to obtain positioning-related information, and the target information includes AI model-related information.
  • a positioning method based on an AI model comprising: a network side device sends target information to a first device, the first device is a first terminal or a positioning reference device, an AI model is deployed on the first device, the AI model is used to obtain positioning-related information, and the target information includes AI model-related information.
  • a positioning device based on an AI model comprising:
  • a communication unit is used to receive target information.
  • An AI model is deployed on the positioning device.
  • the AI model is used to obtain positioning-related information.
  • the target information includes AI model-related information.
  • the positioning device is a first terminal or a positioning reference device.
  • a positioning device based on an AI model comprising:
  • a communication unit is used to send target information to a first device, where the first device is a first terminal or a positioning reference device.
  • An AI model is deployed on the first device, where the AI model is used to obtain positioning-related information, and the target information includes AI model-related information.
  • a communication device which includes a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the method described in the first aspect or the second aspect are implemented.
  • a readable storage medium on which a program or instruction is stored.
  • the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented, or the steps of the method described in the second aspect are implemented.
  • a wireless communication system comprising: a first device and a network side device, wherein the first device can be used to execute the steps of the method described in the first aspect, and the network side device can be used to execute the steps of the method described in the second aspect.
  • a chip comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the method described in the first aspect, or to implement the method described in the second aspect.
  • a computer program/program product is provided, wherein the computer program/program product is stored in a storage medium, and the program/program product is executed by at least one processor to implement the steps of the positioning method as described in the first aspect or the second aspect.
  • the network side device can send AI model related information to the first device (for example, the first terminal (i.e., the terminal to be located, or the target terminal), the positioning reference device), so that the first device can obtain positioning related information based on the AI model related information, thereby improving the positioning accuracy of the terminal positioning based on the AI model.
  • the first device for example, the first terminal (i.e., the terminal to be located, or the target terminal), the positioning reference device
  • FIG1 is a schematic diagram of a communication system provided in an embodiment of the present application.
  • FIG. 2 is a schematic diagram of a neuron structure.
  • FIG. 3 is a schematic diagram of a neural network.
  • FIG4 is a schematic diagram of a positioning method based on an AI model provided in an embodiment of the present application.
  • FIG5 is a schematic diagram of another positioning method based on an AI model provided in an embodiment of the present application.
  • FIG. 6 is a schematic diagram of a positioning device provided in an embodiment of the present application.
  • FIG. 7 is a schematic diagram of another positioning device provided in an embodiment of the present application.
  • FIG8 is a schematic diagram of a communication device provided in an embodiment of the present application.
  • FIG. 9 is a hardware structure diagram of a terminal provided in an embodiment of the present application.
  • FIG. 10 is a hardware structure diagram of a network-side device provided in an embodiment of the present application.
  • FIG. 11 is a hardware structure diagram of another network-side device provided in an embodiment of the present application.
  • first, second, etc. of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by “first” and “second” are generally of one type, and the number of objects is not limited, for example, the first object can be one or more.
  • “or” in the present application represents at least one of the connected objects.
  • “A or B” covers three schemes, namely, Scheme 1: including A but not including B; Scheme 2: including B but not including A; Scheme 3: including both A and B.
  • the character "/" generally indicates that the objects associated with each other are in an "or” relationship.
  • indication in this application can be a direct indication (or explicit indication) or an indirect indication (or implicit indication).
  • a direct indication can be understood as the sender explicitly informing the receiver of specific information, operations to be performed, or request results in the sent indication;
  • an indirect indication can be understood as the receiver determining the corresponding information according to the indication sent by the sender, or making a judgment and determining the operation to be performed or the request result according to the judgment result.
  • LTE Long Term Evolution
  • LTE-A Long Term Evolution-Advanced
  • CDMA Code Division Multiple Access
  • TDMA Time Division Multiple Access
  • FDMA Frequency Division Multiple Access
  • OFDMA Orthogonal Frequency Division Multiple Access
  • SC-FDMA Single-carrier Frequency-Division Multiple Access
  • NR New Radio
  • 6G 6th Generation
  • FIG1 shows a block diagram of a wireless communication system applicable to the embodiment of the present application.
  • the wireless communication system includes a terminal 11 and a network side device 12 .
  • the terminal 11 can be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), a notebook computer, a personal digital assistant (PDA), a handheld computer, a netbook, an ultra-mobile personal computer (Ultra-mobile Personal Computer, UMPC), a mobile Internet device (Mobile Internet Device, MID), an augmented reality (Augmented Reality, AR), a virtual reality (Virtual Reality, VR) device, a robot, a wearable device (Wearable Device), a flight vehicle (flight vehicle), a vehicle user equipment (VUE), a shipborne equipment, a pedestrian terminal (Pedestrian User Equipment, PUE), a smart home (home appliances with wireless communication functions, such as refrigerators, televisions, washing machines or furniture, etc.), a game console, a personal computer (Personal Computer, PC
  • Wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc.
  • the vehicle-mounted device can also be called a vehicle-mounted terminal, a vehicle-mounted controller, a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip or a vehicle-mounted unit, etc. It should be noted that the specific type of the terminal 11 is not limited in the embodiment of the present application.
  • the terminal can also be called user equipment (UE), terminal equipment, access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication equipment, user agent or user device, etc.
  • UE user equipment
  • terminal equipment access terminal
  • user unit user station
  • mobile station mobile station
  • remote station remote terminal
  • mobile device user terminal
  • terminal wireless communication equipment
  • user agent or user device etc.
  • the network side equipment 12 may include access network equipment or core network equipment, wherein the access network equipment may also be referred to as radio access network (RAN) equipment, radio access network function or radio access network unit.
  • the access network equipment may include a base station, a wireless local area network (WLAN) access point (AS) or a wireless fidelity (WiFi) node, etc.
  • WLAN wireless local area network
  • AS wireless local area network
  • WiFi wireless fidelity
  • the base station may be referred to as a node B (NB), an evolved node B (eNB), the next generation node B (gNB), a new radio node B (NR Node B), an access point, a relay station (RBS), a serving base station (SBS), a base transceiver station (BTS), a radio base station, a radio transceiver, a base
  • NB node B
  • eNB evolved node B
  • gNB next generation node B
  • NR Node B new radio node B
  • RBS serving base station
  • BTS base transceiver station
  • a radio base station a radio transceiver
  • a base station is not limited to specific technical terms as long as the same technical effect is achieved. It should be noted that in the embodiments of the present application, only the base station in the NR system is taken as an example for introduction, and the specific type of the base station is not limited.
  • the core network equipment may include but is not limited to at least one of the following: core network nodes, core network functions, mobility management entity (Mobility Management Entity, MME), access mobility management function (Access and Mobility Management Function, AMF), session management function (Session Management Function, SMF), user plane function (User Plane Function, UPF), policy control function (Policy Control Function, PCF), policy and charging rules function unit (Policy and Charging Rules Function, PCRF), edge application service discovery function (Edge Application Server Discovery Function, EASDF), unified data management (Unifie d Data Management, UDM), Unified Data Repository (UDR), Home Subscriber Server (HSS), Centralized network configuration (CNC), Network Repository Function (NRF), Network Exposure Function (NEF), Local NEF (or L-NEF), Binding Support Function (BSF), Application Function (AF), Location Management Function (LMF), Location Server and the positioning server.
  • MME mobility management entity
  • AMF Access and Mobility Management Function
  • SMF Session Management Function
  • the specific type of the core network device is not limited. But not limited to at least one of the following: core network node, core network function, Mobility Management Entity (MME), Access and Mobility Management Function (AMF), Session Management Function (SMF), User Plane Function (UPF), Policy Control Function (PCF), Policy and Charging Rules Function (PCRF), Edge Application Server Discovery Function (Edge Application Server Discovery Function, E The following are some of the following functions: ASDF), Unified Data Management (UDM), Unified Data Repository (UDR), Home Subscriber Server (HSS), Centralized Network Configuration (CNC), Network Repository Function (NRF), Network Exposure Function (NEF), Local NEF (or L-NEF), Binding Support Function (BSF), Application Function (AF), etc. It should be noted that in the embodiments of the present application, only the core network device in the NR system is taken as an example for introduction, and the specific
  • the positioning reference unit (PRU) related to the present application is explained.
  • some errors in the positioning process can be removed, such as TRP position error, TRP phase and group delay, positioning reference signal group delay and/or phase error, etc.
  • a network side device such as an LMF or a positioning server (Location Server, or service terminal) may send first time window information (i.e., measurement time window information) to a target terminal and a PRU, for instructing the target terminal and the PRU to measure signals used for positioning (e.g., positioning reference signals (PRS)) within the first time window.
  • first time window information i.e., measurement time window information
  • PRS positioning reference signals
  • the first time window information sent by the LMF or positioning server to the target terminal and the PRU is the same, and it is expected that the target terminal and the PRU can simultaneously measure the PRS sent by the same device (eg, TRP) within the first time window.
  • the same device eg, TRP
  • the first time window information includes at least one of the following:
  • the length of the first time window, the period of the first time window, and the starting position of the first time window are the length of the first time window, the period of the first time window, and the starting position of the first time window.
  • LMF can collect the measurement results and/or location information of PRU, and further forward the measurement results and/or location information of PRU to the target terminal.
  • the target terminal can determine its own location based on the measurement results and/or location information of PRU.
  • PRU can report measurement results and/or location information to LMF, and the target terminal also reports measurement results to LMF.
  • LMF calculates the location of the target terminal based on the collected measurement results and/or location information of PRU.
  • the measurement results of the above-mentioned PRU may optionally include carrier phase measurement results, reference signal time difference (RSTD) measurement results, reference signal receiving power (RSRP), etc. Further, the above-mentioned measurement results may be associated with at least one of the identification information of the measurement target and the measurement time.
  • the identification information of the measurement target is the identification information of the TRP and/or signal corresponding to the measurement results of the above-mentioned PRU, such as TRP ID, PRS identification information, etc.
  • a neural network is an operation model composed of multiple interconnected neuron nodes, where the connection between nodes represents the weighted value from the input signal to the output signal, called the weight; each node performs a weighted summation (summation, SUM) of different input signals and outputs them through a specific activation function (f).
  • Common activation functions include Sigmoid, tanh, ReLU (Rectified Linear Unit), etc.
  • a typical neural network is shown in Figure 3, which includes an input layer, a hidden layer, and an output layer. Through different connections, weights, and activation functions of multiple neurons, different outputs can be generated, thereby fitting the mapping relationship from input to output. Among them, each upper-level node is connected to all its lower-level nodes.
  • This neural network is a fully connected neural network, which can also be called a deep neural network (DNN).
  • DNN deep neural network
  • Deep learning uses a deep neural network with multiple hidden layers, which greatly improves the network's ability to learn features and fits complex nonlinear mappings from input to output. Therefore, it is widely used in speech and image processing.
  • deep learning also includes common basic structures such as convolutional neural networks (CNN) and recurrent neural networks (RNN) for different tasks.
  • CNN convolutional neural networks
  • RNN recurrent neural networks
  • the parameters of the neural network are optimized using a gradient optimization algorithm.
  • the gradient optimization algorithm is a type of algorithm that minimizes or maximizes an objective function (or loss function), and the objective function is often a mathematical combination of model parameters and data.
  • a neural network model f(.) can be constructed. With the model, the output f(x) can be predicted based on the input x, and the difference between the predicted value and the true value (f(x)-Y) can be calculated. This is the loss function.
  • Our goal is to find the right W and b to minimize the value of the above loss function. The smaller the loss value, the closer our model is to the actual situation.
  • BP error back propagation
  • the basic idea of the BP algorithm is that the learning process consists of two processes: the forward propagation of the signal and the back propagation of the error.
  • the input sample is transmitted from the input layer, processed by each hidden layer layer by layer, and then transmitted to the output layer. If the actual output of the output layer does not match the expected output, the error back propagation stage is entered.
  • Error back propagation is to propagate the output error layer by layer through the hidden layer to the input layer in some form, and distribute the error to all units in each layer, so as to obtain the error signal of each layer unit, and this error signal is used as the basis for correcting the weights of each unit.
  • This process of adjusting the weights of each layer of the signal forward propagation and error back propagation is repeated.
  • the process of continuous adjustment of weights is the learning and training process of the network. This process continues until the error of the network output is reduced to an acceptable level, or until the pre-set number of learning times is reached.
  • Common optimization algorithms may include but are not limited to: Gradient Descent, Stochastic Gradient Descent (SGD), mini-batch gradient descent, Momentum, Nesterov (the name of the inventor, specifically stochastic gradient descent with momentum), Adagrad (ADAptive GRADient descent, adaptive gradient descent), Adadelta, RMSprop (root mean square prop, root mean square error reduction), Adam (Adaptive Moment Estimation, adaptive momentum estimation), etc.
  • FIG4 is a schematic interactive diagram of a positioning method based on an AI model provided in an embodiment of the present application. As shown in FIG4 , the method 400 may include at least part of the following contents:
  • the network side device sends target information to the first device
  • the first device receives target information from the network side device
  • the first device is a first terminal or a positioning reference device
  • an AI model is deployed on the first device
  • the AI model is used to obtain positioning-related information
  • the target information includes AI model-related information.
  • the first device uses the AI model related information in the target information to obtain positioning related information.
  • the first device may be a first terminal or a positioning reference device, wherein the first terminal may be a terminal to be positioned, or a target UE.
  • the positioning reference device may refer to a device whose position-related information is known, and the position-related information may include but is not limited to direction information, distance information, at least one of absolute position and relative position.
  • the positioning reference device may be, for example, a PRU, an anchor UE, a reference UE, etc.
  • the first terminal and the positioning reference device are receiving devices of the reference signal.
  • a reference signal may refer to a signal used for positioning, for example, may include but is not limited to PRS, CSI-RS, etc.
  • the reference signal is taken as PRS as an example for explanation, but the present application is not limited to this.
  • the first terminal and the positioning reference device may measure the PRS based on the same or similar PRS measurement configuration (eg, a measurement time window, or PRS configuration information (eg, including sequence information), etc.).
  • PRS measurement configuration eg, a measurement time window, or PRS configuration information (eg, including sequence information), etc.
  • the time window used by the first terminal for PRS measurement and the time window used by the positioning reference device for PRS measurement may be the same, or partially overlap, or may be adjacent.
  • the network side device may schedule the first terminal and the positioning reference device to perform PRS measurement in the same time slot or adjacent time slots.
  • the first terminal may measure the PRS based on the first time window information
  • the positioning reference device may also measure the PRS based on the first time window information, wherein the first time window information is used to indicate that the PRS is measured within the first time window. That is, the first terminal and the positioning reference device may measure the PRS based on the same time window, thereby enabling terminal positioning based on simultaneous measurement.
  • the first time window information includes but is not limited to at least one of the following:
  • the length of the first time window, the period of the first time window, and the starting position of the first time window are the length of the first time window, the period of the first time window, and the starting position of the first time window.
  • S401 may specifically include:
  • the network side device sends target information to the first device, wherein the first device is the first terminal or the positioning reference device, an AI model is deployed on the first device, the AI model is used to obtain positioning-related information, and the target information includes AI model-related information.
  • the network side device can send model-related information to both parties participating in the simultaneous measurement, so as to assist the two parties participating in the simultaneous measurement to obtain positioning-related information through the AI model based on the model-related information, thereby improving the positioning accuracy of simultaneous measurement based on the AI model.
  • the first time window information may be indicated by the network side device to the first terminal and the positioning reference device.
  • the first time window information indicated by the network side device to the first terminal and the positioning reference device is the same, and it is expected that the positioning reference device and the first terminal can simultaneously measure the PRS sent by the same sending end device within the first time window.
  • the network side device may be an LMF, a positioning server (eg, a service terminal), a base station, or other functional entities capable of providing positioning services, etc., which is not limited in this application.
  • the present application does not limit the positioning method used for positioning the first terminal, for example, a positioning method based on signal arrival time, a positioning method based on signal arrival angle, a positioning method based on received signal strength, a positioning method based on signal arrival time difference (i.e., receive-transmit time difference (Rx-Tx time difference)), etc.
  • the measurement results of the PRS include but are not limited to at least one of the following:
  • PDP Power Delay Profile
  • RSTD Reference Signal Time Difference
  • Reference signal strength such as reference signal receiving power (RSRP), reference signal receiving quality (RSRQ), received signal strength indication (RSSI);
  • Line of Sight/Non-Line of Sight (LOS/NLOS identification) information.
  • Carrier phase measurement information such as received signal channel power (Received Signal Channel Power, RSCP) and reference signal carrier phase difference (reference signal carrier phase difference, RSCPD).
  • RSCP received Signal Channel Power
  • RSCPD reference signal carrier phase difference
  • an AI model is deployed on the first device, and the AI model can obtain positioning-related information, which can be the final positioning result (e.g., the location information of the first terminal), or can also be an intermediate processing result used to determine the final positioning result, such as measurement error information, wherein the measurement error information can be determined based on the measurement result of the positioning reference device on the PRS and the location information of the positioning reference device, or can also be a processed measurement result, etc.
  • the first measurement result is PDP or CIR
  • the second measurement result can be the time information between the sending node and the first device obtained based on the first measurement result, such as TOA, RSTD, etc.
  • the second measurement result can be whether the transmission node is LOS or NLoS from the first device obtained based on the first measurement result.
  • the second location information is the location information of the first device obtained based on the first measurement result.
  • positioning can be performed directly based on the AI model (direct AI positioning or AI-Based positioning), or positioning can be performed with the assistance of the AI model (AI assisted positioning).
  • the first terminal may determine the final positioning result based on the intermediate processing result obtained, that is, obtain the second position result based on AI.
  • the intermediate processing result may be sent to other devices (such as LMF or positioning server), and the other devices may determine the final positioning result based on the intermediate processing result, that is, obtain the second measurement result based on AI, and report the second measurement result to other devices.
  • the present application does not limit the number of AI models deployed on the first device.
  • the number of AI models used to implement the positioning function can be one or more.
  • S401 may specifically include:
  • the network side device sends target information to the first device, wherein the first device is the first terminal or the positioning reference device, an AI model is deployed on the first device, the AI model is used to obtain positioning-related information, the target information includes AI model-related information, and at least one of the information included in the target information is associated with the first time window information.
  • the AI model may also be referred to as an AI unit, an ML (machine learning) model, an ML unit, an AI structure, an AI function, an AI characteristic, a machine learning model, a neural network, a neural network function, a neural network function, etc., or, the AI model may also refer to a processing unit that can implement specific algorithms, formulas, processing flows, capabilities, etc.
  • the AI model may be a processing method, algorithm, function, module or unit for a specific data set, or the AI model may be a processing method, algorithm, function, module or unit running on AI/ML related hardware such as a graphics processing unit (GPU), a neural network processing unit (NPU), a tensor processing unit (TPU), an application specific integrated circuit (ASIC), etc., and the present application does not make any specific limitation on this.
  • GPU graphics processing unit
  • NPU neural network processing unit
  • TPU tensor processing unit
  • ASIC application specific integrated circuit
  • AI model related information may include but is not limited to AI model usage related configuration, AI model monitoring related configuration, positioning related information obtained using the AI model, AI model monitoring information, etc.
  • the network side device can indicate the same AI model related information (such as AI model usage related configuration, AI model monitoring related configuration, etc.) to the first terminal and the positioning reference device, so that the first terminal and the positioning reference device can use and/or monitor the model based on the same configuration, thereby ensuring the performance gain brought by using the AI model to achieve positioning based on simultaneous measurements.
  • AI model related information such as AI model usage related configuration, AI model monitoring related configuration, etc.
  • the same configuration includes at least partial similarity, such as using the same AI function, such as using the same monitoring function, etc.
  • the present application does not limit the sending device of the AI model related information, as long as it can ensure that the AI model related information indicated to the first terminal is consistent with the AI model related information indicated to the positioning reference device.
  • the target information may include first information, and the first information includes model-related information.
  • the first information includes at least one of the following:
  • the first measurement result is a measurement result that has not been processed by the AI model
  • the second measurement result is a measurement result that has been processed by the AI model.
  • the first measurement result may include a known measurement result for positioning, or may also include a measurement result for positioning added as the standard evolves, and this application does not limit this.
  • the first measurement result includes at least one of the following:
  • the second measurement result is a measurement result obtained by applying an AI-related function, or a measurement result obtained by processing an AI model.
  • the second measurement result includes at least one of the following:
  • Time information such as TOA, RSTD or Rx-Tx time difference
  • the model configuration of the AI model can be used by the first terminal to determine the AI model to be used.
  • the network side device can enable the first terminal and the positioning reference device to use the same AI model or an AI model with the same function to obtain positioning related information by indicating the same model configuration to the first terminal and the positioning reference device.
  • model configuration of the AI model includes but is not limited to at least one of the following:
  • the identification information of the AI model is used by the first device to identify which model to use.
  • the network side device can enable the first terminal and the positioning reference device to use the same AI model to obtain positioning related information by indicating the same model identifier to the first terminal and the positioning reference device.
  • the identification information of the AI model may be, for example, an AI model identifier (model ID), an AI structure identifier, an AI algorithm identifier, or an identifier of a specific data set associated with the AI model, or an identifier of a specific scenario, environment, channel characteristic, device related to AI/ML, or an identifier of a function, feature, capability or module related to AI/ML, and the present invention does not specifically limit this.
  • model ID an AI model identifier
  • an AI structure identifier an AI algorithm identifier
  • an AI algorithm identifier or an identifier of a specific data set associated with the AI model
  • an identifier of a specific scenario, environment, channel characteristic, device related to AI/ML or an identifier of a function, feature, capability or module related to AI/ML
  • multiple AI models are stored on the first device, each AI model corresponds to corresponding identification information, and the first device can select the corresponding AI model for positioning according to the identification information of the AI model indicated by the network side device.
  • the function information of the AI model indicates, for example, the function of the AI model.
  • the network side device indicates the same model function to the first terminal and the positioning reference device, so that the first terminal and the positioning reference device can use the same function of the AI model to obtain positioning related information.
  • each AI model has a corresponding function
  • the first device can select an AI model that meets the functional information for positioning based on the functional information of the AI model indicated by the network side device.
  • the functional information of the AI model may, for example, indicate that the AI model supports positioning, or supports beam management, supports CSI feedback, etc.
  • the functional information of the AI model may also indicate a specific positioning method supported by the AI model, such as supporting AI-assisted positioning, or supporting direct AI-based positioning (direct AI positioning), etc.
  • the model configuration of the AI model includes data set information associated with the AI model.
  • the target AI model to be used can be determined based on the data set currently acquired by the first terminal, that is, the target AI model is an AI model associated with the acquired data set.
  • the parameter information of the AI model may include, but is not limited to, the structural type information of the AI model (such as CNN or RNN, etc.), the structural information of the AI model (such as the number of layers of the AI model, including specific layers, such as input layer, hidden layer, output layer, etc.), and the model parameters of the AI model (such as parameters of each layer of the AI model, gradient information of the model).
  • the structural type information of the AI model such as CNN or RNN, etc.
  • the structural information of the AI model such as the number of layers of the AI model, including specific layers, such as input layer, hidden layer, output layer, etc.
  • the model parameters of the AI model such as parameters of each layer of the AI model, gradient information of the model.
  • a plurality of AI models are stored on the first device, each AI model corresponds to a structural type, and the first device can select an AI model of a corresponding structural type for positioning based on the structural type information of the AI model indicated by the network side device.
  • a plurality of AI models are stored on the first device, each AI model corresponds to a set of structures, and the first device can select an AI model of a corresponding structure for positioning based on the structural information of the AI model indicated by the network side device.
  • the update information of the AI model may include at least one of the use conditions, application scenario information, update cycle, function information of the AI model applicable to the current scenario, and information associated with the function information.
  • the network side device can enable the first terminal and the positioning reference device to update the model based on the update information by indicating the same model update information to the first terminal and the positioning reference device, thereby ensuring the consistency of the models on the first terminal and the positioning reference device.
  • the model configuration of the AI model includes a usage scenario of the AI model.
  • the target AI model to be used can be determined based on the scenario of the current first terminal.
  • the use condition of the AI model is associated with some communication indicators, for example, the AI model can be used when the communication indicator meets certain conditions.
  • the communication indicator can be an indicator related to channel quality, such as RSRP, RSRQ, signal-to-noise ratio (SNR) or signal to interference plus noise ratio (SINR).
  • the AI model can be used when RSRP, RSRQ, SNR or SINR is within a certain range.
  • the input configuration of the AI model is used to indicate information related to the AI model input
  • the output configuration of the AI model is used to indicate information related to the AI model output.
  • the network-side device can ensure that the first terminal and the positioning reference device use the same input configuration and/or output configuration when using the AI model to obtain positioning-related information by indicating the same input configuration and/or output configuration to the first terminal and the positioning reference device, thereby ensuring the consistency of the AI model output information.
  • the input configuration may include which information can be used as input to the AI model, or a method of selecting the input to the AI model, for example, using the measurement result corresponding to the signal indicated in the target information as the input to the AI model, or giving priority to the measurement result corresponding to the PRS indicated in the target information as a candidate for the input to the AI model.
  • the input configuration may also include the type of input to the AI model, for example, using the type of measurement result indicated by the target information as the input to the AI model.
  • the input configuration of the AI model includes but is not limited to at least one of the following:
  • the preprocessing method of the input information of the AI model the format of the input information of the AI model, the type of the input information of the AI model, the size of the input information of the AI model, the identification information of the transmitting device associated with the input information of the AI model, and the identification information of the reference signal associated with the input information of the AI model.
  • the preprocessing method of the input information of the AI model may include, but is not limited to, whether to preprocess the input information, and the target preprocessing method adopted, such as truncation, extraction, quantization, etc.
  • the target preprocessing method adopted such as truncation, extraction, quantization, etc.
  • the input information of the AI model configured in the target information is 32
  • the input information of the AI model is a measurement result
  • the length of the measurement result corresponding to a reference signal is 32 or less.
  • the indicated preprocessing method is quantization of 4 bits
  • the information reported at each time point is quantized using 4 bits.
  • the quantization method can be fixed-length quantization or variable-length quantization.
  • the type of input information of the AI model such as TRP ID, measurement result, antenna ID, etc.
  • the input information of the AI model includes identification information of TRP ID or PRS, it means that the input information of the AI model is expected to include the measurement result associated with the identification information of the TRP ID or PRS as input.
  • the type of input information of the AI model indicates a specific measurement result (such as PDP or RSTD), it means that the input information of the AI model is expected to be the measurement result of the above specified type.
  • the size of the input information of the AI model may refer to the size of the input information, such as the number of TRPs, the number of antennas, the number of measurement results, etc.
  • the number of TRPs is 8, the number of antennas is 1, the type of the input information of the AI model is the measurement result, the type of the measurement result is PDP, and each TRP includes a maximum of 4 PDPs.
  • the input information of the AI model is 8*4 PDPs.
  • the preprocessing method of the input information of the AI model is to stipulate that the truncation length of the PDP is 32, then the dimension of the input information of the AI model is 8*4*32. (Wherein, the size and order of the above information are only examples, but the present application is not limited to this.
  • the format of the input information of the AI model is used to indicate the composition and sorting of each piece of input information.
  • the format of the input information can be three-dimensional data: TRP ID*measurement result*antenna ID, or TRP ID*PRS ID*measurement result, etc.
  • the identification information of the transmitting device associated with the input information of the AI model is used to identify the transmitting device of the PRS corresponding to the measurement result associated with the input information.
  • the identification information may include one TRP ID, or may include multiple TRP IDs.
  • the identification information of the PRS associated with the input information of the AI model is used to identify the PRS corresponding to the measurement result associated with the input information.
  • the identification information may include a PRS ID, or may also include multiple PRS IDs.
  • the identification information of the PRS specifically includes: a PRX resource set ID (PRS resource set ID) and/or a PRS resource ID (PRS resource ID). That is, the PRS ID includes a PRS resource set ID and/or a PRS resource ID.
  • the output configuration of the AI model includes but is not limited to at least one of the following:
  • the post-processing method of the output information of the AI model the format of the output information of the AI model, the type of the output information of the AI model, the size of the output information of the AI model, the identification information of the sending end device associated with the output information of the AI model, and the identification information of the PRS associated with the input information of the AI model.
  • the post-processing method of the output information may include but is not limited to whether to post-process the output information and the target post-processing method to be adopted, for example, the output information may be decompressed, extracted, etc.
  • the type of output information of the AI model is, for example, positioning result (e.g., position information), measurement error information, and measurement result.
  • the type of the output information may indicate position information or measurement result, and the measurement result may further be one or more of RSTD, ToA, Rx-Tx time difference, and carrer phase measurement.
  • the size of the output information of the AI model may refer to the size of the output information.
  • the number of RSTDs may be indicated, such as the same as the number of input information of the AI model, for example, if the input is the number of TRPs*the number of PRS signals*PDP, then the output is the number of TRPs*the number of PRS signals*RSTD. For example, if the output is 1/N of the input, and the input is the number of TRPs*the number of PRS signals*PDP, then the output is the number of TRPs*the number of PRS signals/N*PDP.
  • the format of the output information of the AI model is used to indicate the composition and sorting method of the output information.
  • the format of the output information can be three-dimensional data: TRP ID*positioning result*antenna ID, or TRP ID*PRS ID*positioning result, etc.
  • the identification information of the transmitting device associated with the output information of the AI model is used to identify the transmitting device of the PRS corresponding to the measurement result associated with the output information.
  • the identification information may include one TRP ID, or may include multiple TRP IDs.
  • the identification information of the PRS associated with the output information of the AI model is used to identify the PRS corresponding to the measurement result associated with the output information.
  • the identification information may include a PRS ID, or may also include multiple PRS IDs.
  • the identification information of the PRS specifically includes: a PRX resource set ID (PRS resource set ID) and/or a PRS resource ID (PRS resource ID). That is, the PRS ID includes a PRS resource set ID and/or a PRS resource ID.
  • the network side device may indicate the same configuration related to the first measurement result to the first terminal and the positioning reference device, which is conducive to ensuring that the first terminal and the positioning reference device measure the PRS based on the same configuration to obtain the first measurement result.
  • the configuration related to the first measurement result includes but is not limited to at least one of the following:
  • the identification information of the PRS transmitter device corresponding to the measurement result is used to identify the PRS transmitter device.
  • the identification information may include a TRP ID, or may include multiple TRP IDs.
  • the first device may measure the PRS sent by one or more TRPs associated with the identification information to obtain a first measurement result.
  • the identification information of the PRS corresponding to the measurement result may be used to identify one or more PRSs.
  • the first device may measure one or more PRSs associated with the identification information to obtain a first measurement result.
  • the identification information of the cell identifies the cell, for example, the identification information may be a cell identifier (Cell ID), indicating that the first measurement result is obtained by measuring the PRS sent by the TRP in the cell.
  • Cell ID cell identifier
  • the reporting configuration of the measurement result may include but is not limited to at least one of the following:
  • the reporting time of the measurement results such as the reporting cycle
  • the number of signal samples corresponding to the measurement result that is, the number of reference signals measured to obtain the measurement result
  • the time window information corresponding to the measurement result that is, the reference signal in which time window the measurement result is obtained by measuring
  • the reporting type of the measurement results can be the first measurement result or the second measurement result, and can be further divided into RSTD, AoA, RSRP, RSRQ, SINR, Rx-Tx time difference, carrier phase and other types.
  • the network side device can indicate the same configuration related to the second measurement result to the first terminal and the positioning reference device, which is conducive to ensuring that the first terminal and the positioning reference device measure the PRS based on the same configuration, and process the measurement results based on the AI model to obtain the second measurement result.
  • the configuration related to the second measurement result includes at least one of the following:
  • AI model enabling information
  • the second measurement result includes at least one of the following:
  • Rx-Tx time difference RSTD, AoA, ToA, LOS/NLOS identification, carrier phase.
  • the second measurement result is associated with an indication information for indicating that the second measurement result is a measurement result processed by AI.
  • the specific implementation of the identification information of the reference signal transmitting device corresponding to the measurement result, the identification information of the reference signal identification information cell and the reporting configuration of the measurement result refers to the implementation method in the configuration related to the first measurement result. For the sake of brevity, it will not be repeated here.
  • the identification information of a reference signal is associated with the first measurement result and the second measurement result.
  • the first device may report the first measurement result and the second measurement result, and the first measurement result and the second measurement result are associated with the identification information of the same reference signal.
  • the first device may report only one of the first measurement result and the second measurement result.
  • the indication information may be used to indicate whether the reported measurement result is the first measurement result or the second measurement result.
  • the enabling information of the AI model can be used to indicate whether the AI model is enabled, that is, whether the AI model can be used to obtain positioning related information.
  • the preprocessing configuration of the measurement result may include a configuration adopted for preprocessing the measurement result based on the AI model, for example, may include but is not limited to at least one of the following:
  • the preprocessing method of the measurement results may include but is not limited to whether to preprocess the measurement results, and the target preprocessing method adopted, such as truncation, extraction, quantization, etc.;
  • Pre-processing of measurement results such as converting measurement results into input information in a specific format.
  • the output configuration of the measurement results may include the configuration of output information obtained by processing the measurement results based on the AI model, for example, including but not limited to the format of the output information, the post-processing method of the output information, and the reporting method of the output information, such as the reporting time, reporting format, reporting type, etc.
  • the monitoring configuration of the AI model is used by the first device to monitor the AI model.
  • the network side device can indicate the same monitoring configuration to the first terminal and the positioning reference device, so that the first terminal and the positioning reference device can monitor the AI model based on the same monitoring configuration, which is conducive to ensuring that the first terminal and the positioning reference device obtain the performance of the AI model in a timely manner.
  • the monitoring configuration of the AI model includes but is not limited to at least one of the following:
  • Time window information used for model monitoring (referred to as second time window information for ease of distinction and explanation);
  • the monitoring result type of the AI model is the monitoring result type of the AI model
  • the monitoring result update cycle of the AI model is the monitoring result update cycle of the AI model.
  • the monitoring method used to monitor the AI model may include, for example, monitoring based on network indications, that is, determining the validity of the AI model based on network information; or, monitoring based on terminals, that is, determining the validity of the AI model based on terminal information.
  • the validity of the AI model is determined by the relationship between the first measurement result and/or location information of the terminal and the output of the AI model.
  • monitoring based on data distribution such as comparing the relationship between one or more input data of the AI model and the data distribution associated with the AI model to determine the validity of the model, if the deviation between the data exceeds a certain condition, the AI model is considered invalid.
  • the validity of the AI model is determined based on the relationship between the input and output.
  • the monitoring configuration of the AI model may be indicated together with other configurations of the AI model, or may be configured separately.
  • the model configuration, input configuration, output configuration, monitoring configuration, etc. of the AI model may be configured by the network side device to the first device when the first device obtains the AI model from the network side device.
  • the model configuration, input configuration, and output configuration of the AI model may be configured when the first device obtains the AI model from the network side device (such as a base station), and the monitoring configuration of the AI model may be obtained from other devices (such as LMF or positioning terminal).
  • the monitoring configuration of the AI model may include: the above data distribution or the relationship between the input and output of the AI model.
  • the terminal and the PRU use the same data distribution to evaluate the effectiveness of the AI model.
  • the monitoring results of the AI model include but are not limited to at least one of the following:
  • the validity indication of the AI model may be indicated by 1 bit, for example, a value of 0 for the 1 bit indicates that the AI model is invalid, and a value of 1 for the 1 bit indicates that the AI model is valid.
  • the effectiveness degree indication of the AI model can be indicated by N bits, and different values of the N bits are used to indicate different effectiveness degrees of the AI model.
  • N can be determined according to the number of effectiveness degrees.
  • the effectiveness degrees include 100%, 80%, 50%, and 20%, and 2 bits can be used to indicate the effectiveness degrees of four bits. Among them, the higher the effectiveness degree, the higher the positioning accuracy that can be achieved using the AI model for positioning.
  • the score indication of the AI model can be used to indicate the performance score of positioning using the AI model, such as a positioning accuracy score.
  • the higher the score the higher the positioning accuracy that can be achieved using the AI model for positioning.
  • the score indication of the AI model can be indicated by M bits, and different values of the M bits are used to indicate different scores of the AI model. M can be determined according to the number of scores. For example, if the scores include 100, 80, 50, and 20, 2 bits can be used to indicate four bits of scores.
  • the ranking identifier of the AI model can be used to indicate the effectiveness order, effectiveness degree order, scoring order, etc. of the AI model.
  • the effective AI model can be determined based on the monitoring results of multiple AI models.
  • the monitoring result of the AI model included in the first information may be the monitoring result of the AI model deployed on the positioning reference device, so that the first terminal can obtain the monitoring result of the AI model deployed on the positioning reference device. Further, the first terminal can determine whether to use the positioning-related information obtained by the positioning reference device using the AI model to assist in the positioning of the first terminal based on the monitoring result.
  • the monitoring result of the AI model deployed on the positioning reference device is that the AI model is effective, or the degree of effectiveness is higher than a certain threshold (for example, greater than or equal to 80%), or the score is higher than a certain threshold (for example, higher than 80 points), or the AI model is ranked relatively high and the AI model is an effective AI model, it is determined to use the positioning reference device to obtain the positioning-related information obtained by the AI model to assist in the positioning of the first terminal. Otherwise, the positioning reference device is not used to obtain the positioning-related information obtained by the AI model to assist in the positioning of the first terminal.
  • a certain threshold for example, greater than or equal to 80%
  • the score is higher than a certain threshold (for example, higher than 80 points)
  • the AI model is ranked relatively high and the AI model is an effective AI model
  • both devices performing simultaneous measurements can obtain information related to the AI model used by the other party, and further determine whether the positioning-related information obtained by the other party is valid based on the information related to the AI model used by the other party, or whether it can be used to assist in offsetting measurement errors.
  • the AI models used by both parties are quite different, positioning based on positioning-related information obtained from models with large differences will introduce additional errors and reduce the performance gain brought by the AI model. Therefore, when using the positioning-related information provided by the other party for positioning, obtaining information related to the AI model used by the other party, and then assisting in judging the validity of the AI model based on the relevant information of the AI model is beneficial to ensuring the accuracy of positioning based on the AI model.
  • Embodiment 2 is a diagrammatic representation of Embodiment 1:
  • the first terminal and/or the positioning reference device may obtain the AI model related information used by the other party to obtain the positioning related information.
  • the positioning reference device may also send the measured positioning related information or the positioning related information obtained based on the AI model to the first terminal, so that the first terminal can assist in positioning the first terminal based on the positioning related information.
  • Example 2-1 the specific implementation of the AI model related information obtained by the first terminal and the positioning reference device is described in detail.
  • Embodiment 2-1 The first device is a first terminal, and the target information includes second information.
  • the second information includes information related to the AI model used by the positioning reference device.
  • the first terminal can assist in determining the validity of the AI model based on information related to the AI model used by the positioning reference device, such as the similarity between the AI model used by the positioning reference device and the AI model used by the first terminal (for example, whether the functions are the same), and the performance of the AI model used by the positioning reference device, which is conducive to ensuring the accuracy of positioning based on the AI model.
  • the second information includes at least one of the following:
  • the second position information obtained by the positioning reference device is the position information processed by the AI model
  • the third information is information related to the AI model used by the positioning reference device to obtain positioning related information.
  • the third information includes information related to the AI model used by the positioning reference device to obtain at least one of the second measurement result and the second location information.
  • the first measurement result may be a measurement result obtained by measuring the positioning reference device
  • the second measurement result may be a measurement result obtained by processing the first measurement result by an AI model.
  • the third information includes at least one of the following:
  • the input configuration of the AI model used by the positioning reference device is the input configuration of the AI model used by the positioning reference device
  • the second information may be received by the first terminal from a network side device, or may be received from a positioning reference device.
  • Embodiment 2-2 The first device is a positioning reference device, the target information includes fourth information, and the fourth information includes information related to the AI model used by the first terminal.
  • the positioning reference device can configure the AI model deployed on the positioning reference device based on information related to the AI model used by the first terminal, and further assist in positioning the first terminal based on the AI model, which is conducive to ensuring the accuracy of positioning based on the AI model.
  • the fourth information includes information related to the AI model used by the first terminal to obtain at least one of the second measurement result and the second location information, wherein the second measurement result is a measurement result processed by the AI model, and the second location information is location information processed by the AI model.
  • the fourth information includes at least one of the following:
  • a model configuration of an AI model used by the first terminal A model configuration of an AI model used by the first terminal
  • the monitoring configuration of the AI model used by the first terminal is the monitoring configuration of the AI model used by the first terminal.
  • each configuration in the fourth information refers to the relevant description in the aforementioned embodiment, and for the sake of brevity, it will not be repeated here.
  • the fourth information may be received by the positioning reference device from the network side device, or may be received from the first terminal side.
  • first time window information used by the first device to measure the PRS is associated with the target information.
  • the first time window information is associated with the first information in Example 1.
  • the measurement result obtained by the first device based on the first time window information can be processed based on the AI model related information indicated by the first information.
  • the first time window information is associated with the first measurement result in Embodiment 2-1.
  • the first measurement result is a measurement result obtained by measuring based on the first time window information.
  • the first time window information is associated with the third information or the fourth information in Example 2.
  • the measurement result obtained by the positioning reference device based on the first time window information can be processed based on the AI model related information indicated by the third information.
  • the first terminal may process the measurement result obtained by performing measurement based on the first time window information based on the AI model related information indicated by the fourth information.
  • the method 400 further includes:
  • the network side device sends second time window information to the first device, where the second time window information is used to instruct the first device to monitor the AI model within a second time window.
  • other information of the second time window may also be specified by the protocol.
  • the length or period of the second time window may be specified by the protocol.
  • the starting position of the second time window may be an offset relative to a starting position of a cycle.
  • the starting position of the second time window may be time information relative to a reference point, and the reference point may be the last symbol of the scheduling information of the scheduling PRS, or the last symbol of the time slot.
  • the starting position may be an offset relative to the reference point.
  • the second time window information is associated with the target information.
  • the second time window information is associated with the first information in Example 1.
  • the first device may monitor the AI model determined based on the first information based on the second time window information.
  • the second time window information is associated with the third information or the fourth information in Example 2.
  • the positioning reference device can monitor the AI model determined based on the third information based on the second time window information.
  • the first terminal may monitor the AI model determined based on the fourth information based on the second time window information.
  • the method 400 when the target information includes identification information of a PRS transmitting end device and/or identification information of the PRS, the method 400 further includes:
  • the first device measures the identification information of the PRS transmitting device and/or the reference signal associated with the identification information of the PRS based on the first time window information, or preferentially measures the identification information of the PRS transmitting device and/or the reference signal associated with the identification information of the PRS.
  • both the third information and the fourth information include identification information of the transmitting device of the PRS and/or identification information of the PRS, and when the identification information included in the third information and the fourth information is the same, it indicates that the first terminal and the positioning reference device measure the same transmitting device and/or PRS within the first time window.
  • the third information or the fourth information may include identification information of multiple transmitting end devices, such as a TRP ID list, and the format of the input information of the AI model may be sorted in the order of the TRP IDs in the TRP ID list.
  • the input information includes the measurement results corresponding to the PRS sent by each TRP in the TRP ID list.
  • the third information or the fourth information may include identification information of multiple PRSs, such as a PRS ID list, and the format of the input information of the AI model may be sorted according to the order of the PRS IDs in the PRS ID list.
  • the input information includes the measurement results corresponding to each PRS in the PRS ID list.
  • the input information of the AI model is related to the identification information of the transmitting device and/or the identification information of the PRS.
  • the input information of the AI model may include a measurement result obtained by measuring the PRS associated with the identification information of the transmitting device and/or the identification information of the PRS.
  • the method when the target information includes a reporting configuration of the measurement result, and the reporting configuration includes a reporting type, the method further includes:
  • the first device measures the PRS according to the first time window information to obtain a measurement result of the reporting type.
  • the first device may measure the PRS to obtain RSTD, or if the indicated reporting type is RSRP, the first device may measure the PRS to obtain RSRP.
  • the method 400 further includes:
  • the first device When the first device does not obtain a measurement result, or the measurement result does not meet the first condition, measurement is performed on the identification information of the transmitting device of the PRS or other reference signals other than the PRS associated with the identification information of the PRS, or the corresponding measurement result information is set as predefined information.
  • the first device not obtaining the measurement result may include the first device not receiving the PRS, or not receiving the PRS associated with the indicated identification information (e.g., TRP ID or PRS ID).
  • the indicated identification information e.g., TRP ID or PRS ID.
  • the first device may measure other PRSs to obtain the measurement result, or may set the corresponding measurement result as predefined information, which may be indicated by the network side device or specified by the protocol.
  • the fact that the measurement result does not satisfy the first condition can be understood as poor signal quality between the first device and the PRS transmitting device. Therefore, the reliability of the measurement result is low, and positioning based on the measurement result may affect the accuracy of positioning. In this case, the measurement result can be ignored.
  • the first condition includes but is not limited to at least one of the following:
  • the measurement result is less than the first threshold
  • the channel condition corresponding to the measurement result is non-line-of-sight NLOS;
  • the line-of-sight LOS probability corresponding to the measurement result is less than the second threshold.
  • the measurement result being less than the first threshold may include, but is not limited to:
  • the measured RSRP is less than a first RSRP threshold
  • the measured RSRQ is less than the first RSRQ threshold
  • the measured SINR is less than the first SINR threshold
  • the measured RSSI is less than the first RSSI threshold.
  • the channel condition corresponding to the measurement result is NLOS, or the LOS probability of the measurement result is less than the second threshold, indicating that the first device and the PRS transmitting end device may be blocked, and therefore, the measurement result may be ignored.
  • the method 400 further includes:
  • the network side device obtains fifth information of at least one positioning reference device, the fifth information including at least one of a target measurement result obtained by the positioning reference device and target position information of the positioning reference device, the target measurement result information including at least one second measurement result, and the target position information including at least one second position information.
  • the positioning reference device can use N AI models to obtain the second measurement result and/or second location information, such as obtaining N second measurement results and/or N second location information, and further report N1 second measurement results and/or N1 second location information to the network side device, where N is a positive integer and N1 is less than or equal to N.
  • the fifth information is associated with at least one of the following:
  • the identification information of the AI model in the target information and the functional information of the AI model in the target information are associated.
  • the fifth information may be obtained using the AI model indicated in the target information, or obtained using the AI model with the function indicated in the target information.
  • N1 second measurement results and/or N1 second location information reported by the positioning reference device are associated with N1 AI models, and each group of second measurement results and/or second location information is obtained using one AI model.
  • the network side device can send the fifth information of the at least one positioning reference device to the first terminal, and the first terminal determines the monitoring results of at least one associated AI model based on the fifth information of the at least one positioning device, and then determines the target AI model based on the monitoring results of the at least one AI model.
  • the at least one positioning reference device may also send the fifth information directly to the first terminal, and then the first terminal determines the monitoring results of at least one associated AI model based on the fifth information of the at least one positioning device, and then determines the target AI model based on the monitoring results of the at least one AI model.
  • the method 400 further includes:
  • the network side device determines the monitoring result of at least one AI model associated with the fifth information based on the fifth information of the at least one positioning reference device.
  • the network side device can determine the monitoring results of the associated N1 AI models based on the N1 second measurement results and/or N1 second location information reported by the positioning reference device.
  • the network side device can send the monitoring results of at least one AI model to the first terminal, and the first terminal selects the target AI model based on the monitoring results of the at least one model.
  • the network side device may also select a target AI model from the at least one AI model based on the monitoring results of the at least one AI model, and then send a first indication message to the first terminal, where the first indication message is used to indicate the target AI model used by the first terminal for positioning.
  • the first terminal may also report fifth information to the network side device. Further, the network side device may determine the target AI model adopted by the first terminal based on the fifth information reported by the first terminal and the fifth information reported by at least one positioning reference device.
  • the network side device may also update the target information, such as updating the first information, or updating the second information sent to the first terminal, based on the monitoring results of at least one AI model and/or the capabilities of the first terminal.
  • the capabilities of the first terminal may include a list of identification information of AI models supported by the first terminal.
  • the network side device can select an AI model with better monitoring results and supported by the first terminal as the AI model indicated in the first information or the second information based on the AI model supported by the first terminal and the monitoring results of at least one AI model.
  • the network side device obtains the monitoring results of N1 AI models, and the N1 AI models include N2 AI models supported by the first terminal. If there are N3 AI models among the N2 AI models with better monitoring results (for example, effective, higher scores, or higher rankings), the first information or second information sent by the network side device may include identification information of the N3 AI models.
  • the method 200 further includes:
  • the first device sends at least one of the following to the network side device:
  • Obtain relevant information of the target AI model used by the second measurement result or the second position information such as model configuration (such as gradient information), input data, etc.
  • the first device can update the AI model and/or update the parameters of the AI model based on the first measurement result obtained and the location information of the first device, and further send the updated information of the AI model to the network side device, so that the subsequent network side device can indicate appropriate target information to the first device based on the updated information of the AI model.
  • target information such as the model configuration of the AI model
  • Embodiment 1 is a diagrammatic representation of Embodiment 1:
  • the positioning method may include the following steps:
  • the S501, LMF or service terminal sends first information to the first terminal and the positioning reference device.
  • the first information includes AI model related information.
  • the first terminal and the positioning reference device can obtain positioning related information based on the same AI model related information, which is conducive to ensuring the accuracy of positioning based on the AI model.
  • the first information includes at least one of the following:
  • the first terminal or positioning reference device may utilize the AI model to process measurement results obtained by measuring the PRS sent by at least one TRP in the TRP list to obtain positioning-related information, such as a second measurement result or second location information.
  • S501 before S501, it also includes S500: LMF or the service terminal receives fifth information from at least one positioning reference device, and the fifth information may include positioning-related information obtained by the positioning reference device using at least one AI model, such as a second measurement result or second location information.
  • the fifth information may include positioning-related information obtained by the positioning reference device using at least one AI model, such as a second measurement result or second location information.
  • the LMF or the service terminal may determine the content of the first information sent to the first terminal and the positioning reference device based on the fifth information obtained from the at least one positioning reference device.
  • the LMF or service terminal may also determine the monitoring result of at least one AI model based on the fifth information obtained from at least one positioning reference device, further select a target AI model based on the monitoring result, and indicate the target AI model to the first terminal.
  • the monitoring result of the at least one AI model may be sent to the first terminal for the first terminal to select the target AI model.
  • Embodiment 2 is a diagrammatic representation of Embodiment 1:
  • the positioning method may include the following steps:
  • the second information includes at least one of the following:
  • the first position information obtained by the positioning reference device is the first position information obtained by the positioning reference device.
  • the third information is information related to the AI model used by the positioning reference device to obtain positioning related information.
  • the third information includes information related to the AI model used by the positioning reference device to obtain at least one of the second measurement result and the second location information.
  • the third information includes at least one of the following:
  • the input configuration of the AI model used by the positioning reference device is the input configuration of the AI model used by the positioning reference device
  • the fourth information includes at least one of the following:
  • a model configuration of an AI model used by the first terminal A model configuration of an AI model used by the first terminal
  • the monitoring configuration of the AI model used by the first terminal is the monitoring configuration of the AI model used by the first terminal.
  • S501 before S501, it also includes S500: LMF or the service terminal receives fifth information from at least one positioning reference device, and the fifth information may include positioning-related information obtained by the positioning reference device using at least one AI model, such as a second measurement result or second location information.
  • the fifth information may include positioning-related information obtained by the positioning reference device using at least one AI model, such as a second measurement result or second location information.
  • the LMF or the service terminal may determine the content of the second information sent to the first terminal based on the fifth information obtained from at least one positioning reference device.
  • the LMF or service terminal may also determine the monitoring result of at least one AI model based on the fifth information obtained from at least one positioning reference device, further select a target AI model based on the monitoring result, and indicate the target AI model to the first terminal.
  • the monitoring result of the at least one AI model may be sent to the first terminal for the first terminal to select the target AI model.
  • the network side device can send AI model-related information to the first device (for example, the first terminal (i.e., the terminal to be located, or the target terminal), the positioning reference device), so that the first device can obtain positioning-related information based on the AI model-related information, and can realize terminal positioning based on the AI model.
  • the first device for example, the first terminal (i.e., the terminal to be located, or the target terminal), the positioning reference device
  • the first device can obtain positioning-related information based on the AI model-related information, and can realize terminal positioning based on the AI model.
  • the positioning method based on the AI model provided in the embodiment of the present application can be executed by a positioning device based on the AI model.
  • the positioning device provided in the embodiment of the present application is illustrated by taking the positioning method based on the AI model performed by the positioning device based on the AI model as an example.
  • FIG. 6 shows a schematic block diagram of a positioning device 600 based on the AI model according to an embodiment of the present application. As shown in FIG. 6, the positioning device 600 includes:
  • the communication unit 610 is used to receive target information.
  • the positioning device 600 is a first terminal or a positioning reference device.
  • An AI model is deployed on the positioning device 600.
  • the AI model is used to obtain positioning-related information.
  • the target information includes AI model-related information.
  • the target information includes first information, and the first information includes at least one of the following:
  • the positioning device 600 is a first terminal, the target information includes second information, and the second information includes at least one of the following:
  • the third information includes information related to an AI model used by the positioning reference device to obtain at least one of the second measurement result and the second position information.
  • the third information includes at least one of the following:
  • the input configuration of the AI model used by the positioning reference device is the input configuration of the AI model used by the positioning reference device
  • the positioning device 600 is a positioning reference device
  • the target information includes fourth information
  • the fourth information includes AI model related information used by the first terminal to obtain at least one of the second measurement result and the second location information
  • the second measurement result is a measurement result processed by the AI model
  • the second location information is location information processed by the AI model.
  • the fourth information includes at least one of the following:
  • a model configuration of an AI model used by the first terminal A model configuration of an AI model used by the first terminal
  • the monitoring configuration of the AI model used by the first terminal is the monitoring configuration of the AI model used by the first terminal.
  • the model configuration of the AI model includes at least one of the following:
  • the input configuration of the AI model includes at least one of the following:
  • the preprocessing method of the input information of the AI model the format of the input information of the AI model, the type of the input information of the AI model, the size of the input information of the AI model, the identification information of the transmitting device associated with the input information of the AI model, and the identification information of the reference signal associated with the input information of the AI model.
  • the output configuration of the AI model includes at least one of the following:
  • the post-processing method of the output information of the AI model the format of the output information of the AI model, the input information type of the AI model, the size of the output information of the AI model, the identification information of the transmitting device associated with the output information of the AI model, and the identification information of the reference signal associated with the input information of the AI model.
  • the configuration related to the first measurement result includes at least one of the following:
  • the configuration related to the second measurement result includes at least one of the following:
  • AI model enabling information
  • the monitoring configuration of the AI model includes at least one of the following:
  • the monitoring result type of the AI model is the monitoring result type of the AI model
  • the monitoring result update cycle of the AI model is the monitoring result update cycle of the AI model.
  • the monitoring result of the AI model includes at least one of the following:
  • the communication unit 610 is further configured to:
  • first time window information sent by a network side device, where the first time window information is used to instruct the positioning device 600 to measure a reference signal within the first time window;
  • the first time window information includes at least one of the following:
  • the period of the first time window, the starting position of the first time window, and the length of the first time window is the period of the first time window, the starting position of the first time window, and the length of the first time window.
  • the first time window information is associated with the target information.
  • the positioning device 600 when the target information includes identification information of a transmitter device of a reference signal or identification information of the reference signal, the positioning device 600 further includes:
  • a processing unit is used to measure the identification information of the transmitting end device of the reference signal or the reference signal associated with the identification information of the reference signal according to the first time window information, or to preferentially measure the identification information of the transmitting end device of the reference signal or the reference signal associated with the identification information of the reference signal.
  • the positioning device 600 when the target information includes a reporting configuration of a measurement result, and the reporting configuration includes a reporting type, the positioning device 600 further includes:
  • the processing unit is used to measure the reference signal according to the first time window information to obtain the measurement result of the target reporting type.
  • the positioning device 600 further includes:
  • a processing unit is used to measure other reference signals other than the identification information of the transmitting end device of the reference signal or the reference signal associated with the identification information of the reference signal, or to set the corresponding measurement result information as predefined information when the positioning device 600 fails to obtain a measurement result or the measurement result obtains a measurement result that does not satisfy the first condition.
  • the first condition includes at least one of the following:
  • the measurement result is less than the first threshold
  • the channel condition corresponding to the measurement result is non-line-of-sight NLOS;
  • the line-of-sight LOS probability corresponding to the measurement result is less than the second threshold.
  • the communication unit 610 is further configured to:
  • the second time window information includes at least one of the following:
  • the period of the second time window, the starting position of the second time window, and the length of the second time window is the period of the second time window, the starting position of the second time window, and the length of the second time window.
  • the second time window information is associated with the target information.
  • the positioning device 600 is the first terminal, and the method further includes:
  • the first terminal receives fifth information of at least one positioning reference device, the fifth information including at least one of a target measurement result obtained by the positioning reference device and target position information of the positioning reference device, the target measurement result information including at least one second measurement result, and the target position information including at least one second position information.
  • the fifth information of the at least one positioning reference device is received from a network side device, or is received from the at least one positioning reference device.
  • the fifth information is associated with at least one of the following:
  • the identification information of the AI model in the target information and the functional information of the AI model in the target information are associated.
  • the positioning device 600 further includes:
  • a processing unit is used to select a target AI model from at least one AI model associated with the fifth information according to the fifth information of the at least one positioning reference device.
  • the communication unit 610 is further used to: receive monitoring results of at least one AI model
  • a target AI model is selected from the at least one AI model.
  • the monitoring result of the at least one AI model is received from a network side device, or from at least one positioning reference device.
  • the communication unit 610 is also used to: receive first indication information sent by a network side device, where the first indication information is used to indicate a target AI model used by the first terminal for positioning.
  • the input information of the AI model is related to the identification information of the transmitting device or the identification information of the reference signal.
  • 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.
  • the positioning device 600 may correspond to the first device, the first terminal or the positioning reference device in the method embodiment of the present application, and the above-mentioned and other operations and/or functions of each unit in the positioning device 600 are respectively for realizing the corresponding processes of the positioning device 600 in the method embodiment shown in Figures 4 to 5, and achieving the same technical effect. To avoid repetition, they will not be repeated here.
  • FIG9 shows a schematic block diagram of a positioning device 700 based on an AI model according to an embodiment of the present application.
  • the positioning device 700 includes:
  • the communication unit 710 is used to send target information to a first device, where the first device is a first terminal or a positioning reference device.
  • An AI model is deployed on the first device, and the AI model is used to obtain positioning-related information.
  • the target information includes AI model-related information.
  • the target information includes first information, and the first information includes at least one of the following:
  • the first device is a first terminal
  • the target information includes second information
  • the second information includes at least one of the following:
  • the third information includes information related to an AI model used by the positioning reference device to obtain at least one of the second measurement result and the second position information.
  • the third information includes at least one of the following:
  • the input configuration of the AI model used by the positioning reference device is the input configuration of the AI model used by the positioning reference device
  • the first device is a positioning reference device
  • the target information includes fourth information
  • the fourth information includes information related to an AI model used by the first terminal to obtain at least one of a second measurement result and a second location information
  • the second measurement result is a measurement result processed by the AI model
  • the second location information is location information processed by the AI model.
  • the fourth information includes at least one of the following:
  • a model configuration of an AI model used by the first terminal A model configuration of an AI model used by the first terminal
  • the monitoring configuration of the AI model used by the first terminal is the monitoring configuration of the AI model used by the first terminal.
  • the model configuration of the AI model includes at least one of the following:
  • the input configuration of the AI model includes at least one of the following:
  • the preprocessing method of the input information of the AI model the format of the input information of the AI model, the type of the input information of the AI model, the size of the input information of the AI model, the identification information of the transmitting device associated with the input information of the AI model, and the identification information of the reference signal associated with the input information of the AI model.
  • the output configuration of the AI model includes at least one of the following:
  • the post-processing method of the output information of the AI model the format of the output information of the AI model, the input information type of the AI model, the size of the output information of the AI model, the identification information of the transmitting device associated with the output information of the AI model, and the identification information of the reference signal associated with the input information of the AI model.
  • the configuration related to the first measurement result includes at least one of the following:
  • the configuration related to the second measurement result includes at least one of the following:
  • AI model enabling information
  • the monitoring configuration of the AI model includes at least one of the following:
  • the monitoring result type of the AI model is the monitoring result type of the AI model
  • the monitoring result update cycle of the AI model is the monitoring result update cycle of the AI model.
  • the monitoring result of the AI model includes at least one of the following:
  • the communication unit 710 is further configured to:
  • First time window information is sent to the first device, where the first time window information is used to instruct the first device to measure a reference signal within a first time window.
  • the first time window information includes at least one of the following:
  • the period of the first time window, the starting position of the first time window, and the length of the first time window is the period of the first time window, the starting position of the first time window, and the length of the first time window.
  • the first time window information is associated with the target information.
  • the communication unit 710 is further configured to:
  • the second time window information includes at least one of the following:
  • the period of the second time window, the starting position of the second time window, and the length of the second time window is the period of the second time window, the starting position of the second time window, and the length of the second time window.
  • the communication unit 710 is further configured to:
  • Acquire fifth information of at least one positioning reference device including at least one of a target measurement result obtained by the positioning reference device and target position information of the positioning reference device, the target measurement result information including at least one second measurement result, and the target position information including at least one second position information.
  • the fifth information is associated with at least one of the following:
  • the identification information of the AI model in the target information and the functional information of the AI model in the target information are associated.
  • the positioning device 700 further includes:
  • a processing unit is used to determine, based on the fifth information of the at least one positioning reference device, a monitoring result of at least one AI model associated with the fifth information.
  • the communication unit 710 is further configured to:
  • the positioning device 700 further includes:
  • a processing unit is used to select a target AI model from the at least one model based on the monitoring result of the at least one AI model.
  • the communication unit 710 is further configured to:
  • 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 positioning device 700 may correspond to the network side device or LMF or service terminal in the method embodiment of the present application, and the above-mentioned and other operations and/or functions of each unit in the positioning device 700 are respectively for realizing the corresponding processes of the network side device or LMF or service terminal in the method embodiment shown in Figures 4 to 5, and achieving the same technical effect. To avoid repetition, they will not be repeated here.
  • the apparatus 600 and the apparatus 700 in the embodiments of the present application may be electronic devices, such as electronic devices with an operating system, or components in electronic devices, such as integrated circuits or chips.
  • the electronic device may be a terminal, or may be other devices other than a terminal.
  • the terminal may include but is not limited to the types of the terminal 11 listed above, and other devices may be servers, network attached storage (NAS), etc., which are not specifically limited in the embodiments of the present application.
  • NAS network attached storage
  • the embodiment of the present application further provides a communication device 800, including a processor 801 and a memory 802, the memory 802 storing programs or instructions that can be run on the processor 801, for example, when the communication device 800 is a first device, a first terminal or a positioning reference device, the program or instruction is executed by the processor 801 to implement the steps performed by the first device, the first terminal or the positioning reference device in the above positioning method embodiment, and can achieve the same technical effect.
  • the communication device 800 is an LMF or a service terminal or a network side device
  • the program or instruction is executed by the processor 801 to implement the various steps performed by the LMF or the service terminal or the network side device in the above positioning method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the embodiment of the present application also provides a terminal, including a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the steps in the method embodiment shown in Figures 4 to 5.
  • This terminal embodiment corresponds to the above-mentioned first device or first terminal or positioning reference device or service terminal side method embodiment, and each implementation process and implementation method of the above-mentioned method embodiment can be applied to the terminal embodiment and can achieve the same technical effect.
  • Figure 9 is a schematic diagram of the hardware structure of a terminal implementing an embodiment of the present application.
  • the terminal 900 includes but is not limited to: a radio frequency unit 901, a network module 902, an audio output unit 903, an input unit 904, a sensor 905, a display unit 906, a user input unit 907, an interface unit 908, a memory 909 and at least some of the components of a processor 910.
  • the terminal 900 may also include a power source (such as a battery) for supplying power to each component, and the power source may be logically connected to the processor 910 through a power management system, so as to implement functions such as managing charging, discharging, and power consumption management through the power management system.
  • a power source such as a battery
  • the terminal structure shown in FIG9 does not constitute a limitation on the terminal, and the terminal may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently, which will not be described in detail here.
  • the input unit 904 may include a graphics processing unit (GPU) 9041 and a microphone 9042, and the graphics processor 9041 processes the image data of the static picture or video obtained by the image capture device (such as a camera) in the video capture mode or the image capture mode.
  • the display unit 906 may include a display panel 9061, and the display panel 9061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc.
  • the user input unit 907 includes a touch panel 9071 and at least one of other input devices 9072.
  • the touch panel 9071 is also called a touch screen.
  • the touch panel 9071 may include two parts: a touch detection device and a touch controller.
  • Other input devices 9072 may include, but are not limited to, a physical keyboard, function keys (such as a volume control key, a switch key, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.
  • the RF unit 901 can transmit the data to the processor 910 for processing; in addition, the RF unit 901 can send uplink data to the network side device.
  • the RF unit 901 includes but is not limited to an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc.
  • the memory 909 can be used to store software programs or instructions and various data.
  • the memory 909 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instruction required for at least one function (such as a sound playback function, an image playback function, etc.), etc.
  • the memory 909 may include a volatile memory or a non-volatile memory.
  • 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), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM).
  • RAM random access memory
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • SDRAM synchronous dynamic random access memory
  • DDRSDRAM double data rate synchronous dynamic random access memory
  • ESDRAM enhanced synchronous dynamic random access memory
  • SLDRAM synchronous link dynamic random access memory
  • DRRAM direct memory bus random access memory
  • the processor 910 may include one or more processing units; optionally, the processor 910 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 910.
  • the processor 910 is used to receive target information
  • the first device is a first terminal or a positioning reference device
  • an AI model is deployed on the first device
  • the AI model is used to obtain positioning-related information
  • the target information includes AI model-related information, thereby enabling terminal positioning based on the AI model.
  • the embodiment of the present application also provides a network side device, including a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the steps of the method embodiment shown in Figures 4 to 5.
  • the network side device embodiment corresponds to the above-mentioned LMF or network side device side method embodiment, and each implementation process and implementation method of the above-mentioned method embodiment can be applied to the network side device embodiment, and can achieve the same technical effect.
  • the embodiment of the present application also provides a network side device.
  • the network side device 1000 includes: an antenna 1001, a radio frequency device 1002, a baseband device 1003, a processor 1004 and a memory 1005.
  • the antenna 1001 is connected to the radio frequency device 1002.
  • the radio frequency device 1002 receives information through the antenna 1001 and sends the received information to the baseband device 1003 for processing.
  • the baseband device 1003 processes the information to be sent and sends it to the radio frequency device 1002.
  • the radio frequency device 1002 processes the received information and sends it out through the antenna 1001.
  • the method executed by the network-side device in the above embodiment may be implemented in the baseband device 1003, which includes a baseband processor.
  • the baseband device 1003 may include, for example, at least one baseband board, on which a plurality of chips are arranged, as shown in FIG10 , wherein one of the chips is, for example, a baseband processor, which is connected to the memory 1005 through a bus interface to call a program in the memory 1005 and execute the network device operations shown in the above method embodiment.
  • the network side device may also include a network interface 1006, which is, for example, a Common Public Radio Interface (CPRI).
  • CPRI Common Public Radio Interface
  • the network side device 1000 of the embodiment of the present application also includes: instructions or programs stored in the memory 1005 and executable on the processor 1004.
  • the processor 1004 calls the instructions or programs in the memory 1005 to execute the steps performed by the LMF or the network side device in the method embodiments shown in Figures 4 to 5, and achieves the same technical effect. To avoid repetition, they are not described here.
  • the embodiment of the present application also provides a network side device.
  • the network side device 1100 includes: a processor 1101, a network interface 1102 and a memory 1103.
  • the network interface 1102 is, for example, a common public radio interface (CPRI).
  • CPRI common public radio interface
  • the network side device 1100 of the embodiment of the present application also includes: instructions or programs stored in the memory 1103 and executable on the processor 1101.
  • the processor 1101 calls the instructions or programs in the memory 1103 to execute the steps performed by the LMF or the network side device in the method embodiments shown in Figures 4 to 5, and can achieve the same technical effect. To avoid repetition, they will not be repeated here.
  • An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored.
  • a program or instruction is stored.
  • the various processes of the method embodiments of Figures 4 to 5 above are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.
  • the processor is a processor in the first device or the network side device described in the above embodiment.
  • the readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.
  • the readable storage medium may be a non-transient readable storage medium.
  • the processors mentioned in the embodiments of the present application may include general-purpose processors, special-purpose processors, etc., for example, central processing units (CPU), microprocessors, digital signal processors (DSP), artificial intelligence (AI) processors, graphics processors (GPU), application specific integrated circuits (ASIC), network processors (NP), field programmable gate arrays (FPGA) or other programmable logic devices, gate circuits, transistors, discrete hardware components, etc.
  • CPU central processing units
  • DSP digital signal processors
  • AI artificial intelligence
  • GPU graphics processors
  • ASIC application specific integrated circuits
  • NP network processors
  • FPGA field programmable gate arrays
  • An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the method embodiments of Figures 4 to 5 above, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.
  • the embodiments of the present application further provide a computer program/program product, which is stored in a storage medium and is executed by at least one processor to implement the various processes of the method embodiments of Figures 3 to 7 above, and can achieve the same technical effect. To avoid repetition, it will not be described here.
  • An embodiment of the present application also provides a communication system, including: a first device and a second device, wherein the first device can be used to execute the steps performed by the first device, the first terminal or the positioning reference device in the positioning method as described above, and the second device can be used to execute the steps performed by the second device, LMF, service terminal or network side device in the positioning method as described above.

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Abstract

La présente demande se rapporte au domaine des communications et divulgue un procédé et un appareil de positionnement basé sur un modèle d'IA, ainsi qu'un dispositif et un support de stockage lisible. Le procédé décrit dans des modes de réalisation de la présente demande comprend les étapes suivantes : un premier dispositif reçoit des informations cibles, le premier dispositif étant un premier terminal ou un dispositif de référence de positionnement, un modèle d'IA étant déployé sur le premier dispositif, le modèle d'IA étant utilisé pour acquérir des informations relatives au positionnement, et les informations cibles comprenant des informations relatives au modèle d'IA.
PCT/CN2024/133612 2023-11-23 2024-11-21 Procédé et appareil de positionnement basé sur un modèle d'ia, dispositif et support de stockage lisible Pending WO2025108390A1 (fr)

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CN202311581766.9A CN120034820A (zh) 2023-11-23 2023-11-23 基于ai模型的定位方法、装置、设备及可读存储介质
CN202311581766.9 2023-11-23

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WO2025108390A1 true WO2025108390A1 (fr) 2025-05-30

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115988520A (zh) * 2021-10-15 2023-04-18 维沃软件技术有限公司 定位方法、终端及网络侧设备
CN116567806A (zh) * 2022-01-29 2023-08-08 维沃移动通信有限公司 基于人工智能ai模型的定位方法及通信设备
CN116634553A (zh) * 2022-02-10 2023-08-22 维沃移动通信有限公司 信息处理方法及通信设备
CN116684296A (zh) * 2022-02-23 2023-09-01 维沃移动通信有限公司 数据采集方法及设备

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115988520A (zh) * 2021-10-15 2023-04-18 维沃软件技术有限公司 定位方法、终端及网络侧设备
CN116567806A (zh) * 2022-01-29 2023-08-08 维沃移动通信有限公司 基于人工智能ai模型的定位方法及通信设备
CN116634553A (zh) * 2022-02-10 2023-08-22 维沃移动通信有限公司 信息处理方法及通信设备
CN116684296A (zh) * 2022-02-23 2023-09-01 维沃移动通信有限公司 数据采集方法及设备

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