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WO2024217405A1 - Procédé et appareil de positionnement de terminal, terminal, dispositif côté réseau et support - Google Patents

Procédé et appareil de positionnement de terminal, terminal, dispositif côté réseau et support Download PDF

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Publication number
WO2024217405A1
WO2024217405A1 PCT/CN2024/088012 CN2024088012W WO2024217405A1 WO 2024217405 A1 WO2024217405 A1 WO 2024217405A1 CN 2024088012 W CN2024088012 W CN 2024088012W WO 2024217405 A1 WO2024217405 A1 WO 2024217405A1
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Prior art keywords
trps
terminal
trp
target
positioning
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English (en)
Chinese (zh)
Inventor
贾承璐
孙鹏
杨昂
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Vivo Software Technology Co Ltd
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Vivo Software Technology Co Ltd
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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
    • 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
    • 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

Definitions

  • the present application belongs to the field of communication technology, and specifically relates to a terminal positioning method, device, terminal, network side equipment and medium.
  • TRPs transmission reception points
  • AI artificial intelligence
  • ML machine learning
  • the terminal positioning result can be obtained.
  • a trained AI model or ML model can only adapt to one or several TRP patterns.
  • Each TRP pattern associated with the model contains multiple TRPs at fixed positions.
  • These TRP patterns are TRP patterns included in the training set of the model.
  • the embodiments of the present application provide a terminal positioning method, apparatus, terminal, network-side equipment and medium, which can solve the problem in the related technology that the AI model or ML model fails and thus the terminal positioning cannot be achieved.
  • a terminal positioning method which is performed by a terminal, and the method includes:
  • the terminal obtains target indication information, where the target indication information is indication information related to the sending and receiving point TRP pattern;
  • the terminal determines a plurality of target TRPs according to the target indication information, and the plurality of target TRPs are used to locate the terminal based on a positioning model.
  • a terminal positioning method which is performed by a network side device, and the method includes:
  • the network side device sends target indication information, where the target indication information is indication information related to the TRP pattern, and the target indication information is used to determine multiple target TRPs, and the multiple target TRPs are used to locate the terminal based on the positioning model.
  • an end positioning device comprising:
  • the acquisition module is used for the terminal to acquire target indication information, wherein the target indication information is an indication related to the TRP pattern.
  • a determination module is used for the terminal to determine multiple target TRPs according to the target indication information, and the multiple target TRPs are used to locate the terminal based on a positioning model.
  • an end positioning device comprising:
  • a sending module is used for a network side device to send target indication information, wherein the target indication information is indication information related to a TRP pattern, and the target indication information is used to determine a plurality of target TRPs, and the plurality of target TRPs are used to locate the terminal based on a positioning model.
  • a terminal 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 terminal positioning method described in the first aspect are implemented.
  • a network side device which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the terminal positioning method described in 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 terminal positioning method as described in the first aspect are implemented, or the steps of the terminal positioning method as described in the second aspect are implemented.
  • a wireless communication system comprising: a terminal and a network side device, wherein the terminal can be used to execute the steps of the terminal positioning method as described in the first aspect, and the network side device can be used to execute the steps of the terminal positioning method as 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 terminal positioning method as described in the first aspect, and the network side device can be used to execute the steps of the terminal positioning method as described in the second aspect.
  • the network side device sends target indication information to the terminal, so that the terminal can determine multiple target TRPs that meet the positioning requirements according to the target indication information, and then the terminal positioning can be realized based on the AI model or the ML model and multiple target TRPs. Since the multiple target TRPs used for positioning are flexibly configured based on the target indication information, and are not fixed single TRP patterns, the situation that the positioning accuracy of the positioning model decreases or the positioning model fails due to the deterioration of the channel environment of some TRPs or the configuration changes is avoided, and the flexibility, reliability and accuracy of terminal positioning are improved.
  • the TRP with good channel conditions is dynamically selected for terminal positioning, and for the TRP that turns off the reference signal transmission for the purpose of energy saving, avoid selecting such TRP for terminal positioning, thereby realizing the flexible configuration of multiple target TRPs for terminal positioning in a dynamically changing environment.
  • the network side device can directly indicate the TRP associated with the model to the terminal according to the model-related information.
  • the terminal only needs to measure or report multiple target TRPs, which reduces the measurement overhead and reporting overhead.
  • FIG1 is a block diagram of a wireless communication system applicable to an embodiment of the present application.
  • FIG2 is a schematic diagram of a neural network in an embodiment of the present application.
  • FIG3 is a schematic diagram of a neuron of a neural network in an embodiment of the present application.
  • FIG4 is a flowchart of an implementation method of a terminal positioning method in an embodiment of the present application.
  • FIG5 is an example of a positioning result of a terminal positioning method in an embodiment of the present application.
  • FIG6 is an example of positioning results of another terminal positioning method in an embodiment of the present application.
  • FIG7 is a schematic structural diagram of a terminal positioning device corresponding to FIG4 ;
  • FIG8 is a flowchart of another terminal positioning method in an embodiment of the present application.
  • FIG9 is a schematic structural diagram of a terminal positioning device corresponding to FIG8 ;
  • FIG10 is a schematic diagram of the structure of a communication device in an embodiment of the present application.
  • FIG11 is a schematic diagram of the structure of a terminal in an embodiment of the present application.
  • FIG12 is a schematic diagram of the structure of a network-side device 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.
  • “Multiple” means including two or more.
  • 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
  • SC-FDMA Single-carrier Frequency-Division Multiple Access
  • 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), a teller machine or a self-service machine and other terminal side devices.
  • a mobile Internet device Mobile Internet Device, MID
  • an augmented reality Augmented Reality, AR
  • 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 network side equipment 12 may include access network equipment and core network equipment.
  • 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 base stations, wireless local area network (WLAN) access points (AS) or wireless fidelity (WiFi) nodes, etc.
  • WLAN wireless local area network
  • AS wireless local area network
  • WiFi wireless fidelity
  • the base station may be referred to as node B (Node B, NB), evolved node B (Evolved Node B, eNB), next generation node B (the next generation Node B, gNB), new radio node B (New Radio Node B, NR Node B), access point, relay station (Relay Base Station, RBS), serving base station (Serving Base Station, SBS), base transceiver station (Base Transceiver Station, BTS), radio base station, radio transceiver, base
  • the 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 device may include but is not limited to at least one of the following: a core network node, a core network function, a mobility management entity (Mobility Management Entity, MME), an access mobility management function (Access and Mobility Management Function, AMF), a session management function (Session Management Function, SMF), a user plane function (User Plane Function, UPF), a policy control function (Policy Control Function, PCF), Policy and Charging Rules Function (PCRF), Edge Application Server Discovery Function (EASDF), 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.
  • MME mobility management entity
  • AMF Access and Mobility Management Function
  • SMF Session Management Function
  • PCF Policy Control Function
  • PCF Policy and Charging Rules Function
  • EASDF Edge Application Server
  • the first type is direct AI/ML positioning, that is, the input of the model is the channel impulse response (CIR)/power delay profile (PDP), and the output of the model is location information;
  • the second type is AI/ML assisted positioning, the input of the model is CIR/PDP, and the output of the model is an intermediate feature quantity, such as the time of arrival (ToA), and the final position can be further calculated based on the intermediate feature quantity.
  • the terminal positioning method provided in the embodiment of the present application is applicable to both AI/ML positioning and AI/ML assisted positioning.
  • the positioning model (such as an AI model or an ML model) can be configured in the terminal, and then the terminal realizes terminal positioning based on the AI model or the ML model.
  • the AI model or the ML model can also be configured in the network side device, and then the network side device realizes terminal positioning based on the AI model or the ML model.
  • the AI model or the ML model used for terminal positioning can be an AI model or an ML model that has been pre-determined by the network side device according to the model related information, and then the network side device sends target indication information to the terminal according to the model related information, so that the terminal determines multiple target TRPs according to the target indication information, and then uses the target indication information and the pre-determined AI model or ML model to realize terminal positioning.
  • the terminal only needs to measure or report multiple target TRPs, which reduces the measurement overhead and reporting overhead; the network side device can also send target indication information to the terminal without pre-determining the AI model or the ML model in advance, and the terminal selects multiple TRPs (i.e., multiple target TRPs) for positioning according to the target indication information, and then determines the associated AI model or ML model according to the selected multiple TRPs, and then uses the selected multiple TRPs and the associated AI model or ML model to realize terminal positioning.
  • multiple TRPs i.e., multiple target TRPs
  • the multiple target TRPs used for positioning are flexibly configured based on the target indication information, rather than a fixed single TRP pattern, the situation where the positioning model's positioning accuracy decreases or the model fails due to the deterioration of the channel environment or configuration changes of some TRPs is avoided, thereby improving the flexibility, reliability and accuracy of terminal positioning.
  • a TRP with good channel conditions is dynamically selected for terminal positioning, and for TRPs that turn off reference signal transmission for the purpose of energy saving, such TRPs are avoided from being selected for terminal positioning, thereby achieving flexible configuration of multiple target TRPs used for terminal positioning in a dynamically changing environment.
  • the steps may include:
  • the terminal obtains target indication information, where the target indication information is indication information related to the sending and receiving point TRP pattern.
  • the terminal may be the terminal 11 in FIG. 1 , wherein examples of the terminal 11 may be found in the foregoing text and will not be described again here.
  • the terminal determines a plurality of target TRPs according to the target indication information, and the plurality of target TRPs are used to locate the terminal based on a positioning model.
  • the TRP pattern is a combination of multiple TRPs with fixed positions or fixed IDs.
  • Terminal positioning is achieved based on the measurement information between the terminal and the TRP contained in the TRP pattern, wherein the measurement information includes: time domain channel impulse response, multipath delay spectrum, frequency domain channel impulse response, channel energy response, delay power spectrum and other information.
  • the measurement information between the terminal and the TRP contained in the TRP pattern is input into the AI model or ML model to obtain the model output information, and the terminal positioning result is determined based on the model output information.
  • the model output information includes at least one of the following: location coordinates, ToA, angle of arrival (AoA), angle of departure (AoD), line of sight (LoS) information or non-line of sight (NLoS) information. If it is direct AI/ML positioning, the AI model or ML model directly outputs the location coordinates of the terminal. If it is AI/ML assisted positioning, the AI model or ML model outputs ToA, AoA, AoD, LoS information or NLoS information, and then the location coordinates of the terminal can be estimated based on ToA, AoA, AoD, LoS information or NLoS information and TRP related information.
  • the network side device sends target indication information to the terminal to instruct the terminal to determine multiple target TRPs for terminal positioning according to the target indication information, and then measure the reference signals associated with the multiple target TRPs.
  • the terminal will report the measurement information of the multiple target TRPs to the network side device, so that the network side device can realize terminal positioning based on the measurement information of the multiple target TRPs and the AI model or ML model associated with the multiple target TRPs.
  • the terminal realizes terminal positioning based on the measurement information of the multiple target TRPs and the AI model or ML model associated with the multiple target TRPs.
  • the association of multiple target TRPs with the AI model or ML model means that the TRP patterns corresponding to the multiple target TRPs are included in the model training set of the AI model or ML model.
  • the AI model or ML model for realizing terminal positioning is a pre-trained AI model or ML model, and the AI model or ML model can select any one of the AI models or ML models of fully connected neural network, convolutional neural network, decision tree, support vector machine, Bayesian classifier.
  • the neural network model as an example, its schematic diagram can be shown in Figure 2.
  • the neural network consists of an input layer, a hidden layer and an output layer.
  • the input layer is responsible for directly accepting the input data;
  • the hidden layer is the most important part of the entire neural network, which is used to process the data;
  • the output layer is used to output the value processed by the entire network.
  • the neural network is composed of neurons, and the schematic diagram of the neuron is shown in Figure 3.
  • a 1 , a 2 , ... a K represents input
  • w represents weight (i.e., multiplicative coefficient)
  • b represents bias (i.e., additive coefficient)
  • z is the intermediate processing result
  • a is the output result of the neuron
  • ⁇ (.) represents the activation function.
  • the activation function includes Sigmoid (mapping variables to to between 0 and 1), tanh (translation and contraction of Sigmoid), linear rectification function/rectified linear unit (Rectified Linear Unit, ReLU), etc.
  • the model training process can be: optimizing the parameters of the neural network through the gradient optimization algorithm.
  • the gradient optimization algorithm is a type of algorithm that minimizes or maximizes the objective function (sometimes also called the loss function), and the objective function is often a mathematical combination of model parameters and data. For example, given the data X and its corresponding label Y, a neural network model f(.) can be constructed, then the predicted output f(x) can be obtained based on the input x, and the difference between the predicted value and the true value (f(x)-Y) can be calculated, which is the loss function.
  • the optimization goal of the gradient optimization algorithm is to find the appropriate w (i.e. weight) and b (i.e. bias) to minimize the value of the above loss function, and the smaller the loss value, the closer the model is to the actual situation.
  • the optimization algorithm can be based on the error back propagation (BP) algorithm.
  • 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, it will enter the back propagation stage of the error.
  • 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 of 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.
  • optimization algorithms may also include gradient descent, stochastic gradient descent (SGD), mini-batch gradient descent, momentum method (Momentum), Nesterov (a type of stochastic gradient descent algorithm with momentum), adaptive gradient descent (Adagrad), Adagrad's extended algorithm Adadelta, root mean square prop (RMSprop), adaptive momentum estimation (Adam), etc.
  • these optimization algorithms calculate the derivative/partial derivative of the current neuron based on the error/loss obtained by the loss function, add the influence of the learning rate, the previous gradient/derivative/partial derivative, etc., get the gradient, and pass the gradient to the previous layer.
  • a trained AI model or ML model can usually only adapt to one or several TRP patterns, and each TRP pattern associated with the model contains multiple TRPs at fixed positions, which are TRP patterns included in the training set of the model.
  • the TRP may turn off the reference signal transmission for energy saving purposes, and the transmission parameter adjustment between the TRP and the terminal may cause changes in the channel estimation results (for example, adjusting the transmit power, reference signal (RS) pattern), etc., which may cause the AI model or ML model to fail.
  • the reporting overhead of the time domain channel information is relatively large, and there is redundancy in the time domain channel information reported by the terminal for all configured TRPs used for positioning.
  • the terminal needs to measure the time domain channel information between all configured TRPs used for positioning, which results in a large measurement overhead.
  • the multiple target TRPs used for positioning are flexibly configured based on the target indication information, and are not fixed to a single TRP pattern. Therefore, the situation in which the positioning accuracy of the AI model or ML model decreases or the model fails due to the deterioration of the channel environment of some TRPs or changes in the configuration is avoided, and the flexibility, reliability and accuracy of terminal positioning are improved.
  • the multiple target TRPs are a part of the TRPs (model training sets of AI models or ML models) used for positioning in all configurations used to train the AI model or ML model.
  • TRPs multiple target TRPs
  • the terminal since a small number of TRPs are used to achieve terminal positioning, the terminal does not need to measure the time domain channel information of all configured TRPs used for positioning and report the time domain channel information of all configured TRPs used for positioning, thereby reducing the measurement overhead and reporting overhead.
  • the target indication information includes at least one of the following:
  • TRP pattern ID TRP pattern ID
  • Item A-2 Number of TRPs
  • Item A-3 Number of cells used for terminal positioning
  • Item A-4 Number of TRPs per cell used for terminal positioning
  • Item A-5 IDs of N TRPs, where N is an integer greater than 1;
  • Item A-6 IDs of P cells, where P is an integer greater than 1;
  • Item A-7 Minimum number of TRPs
  • Item A-8 Minimum number of cells used for terminal positioning
  • Item A-9 TRP selection criteria.
  • each TRP pattern is a combination of multiple TRPs with fixed positions or fixed IDs.
  • the network-side device indicates the ID of the TRP pattern in the target indication information so that the terminal determines the TRP used for positioning according to the ID of the TRP pattern.
  • the TRPs corresponding to TRP pattern A are TRP1, TRP2, TRP3, and TRP4.
  • the terminal determines that the TRPs used for positioning are RP1, TRP2, TRP3, and TRP4.
  • the network side device When the network side device predetermines the AI model or ML model based on model-related information, it can directly indicate the TRP pattern ID used for positioning (i.e., the ID of the TRP pattern adapted to the AI model or ML model) to the terminal side through the target indication information.
  • the terminal determines the multiple TRPs contained in the TRP pattern with the TRP pattern ID as multiple target TRPs, and then measures the associated reference signals of the multiple target TRPs, and reports the measurement information of the multiple target TRPs to the network side device, so that the network side device can realize terminal positioning according to the determined AI model or ML model and the measurement information of the multiple target TRPs.
  • the target indication information may directly indicate a TRP pattern and multiple TRPs corresponding to the TRP pattern, or may indicate multiple TRP patterns and multiple TRPs corresponding to each TRP pattern.
  • the terminal preferentially uses the TRP pattern with the highest priority according to the priority information of the TRP pattern (i.e., determines the multiple TRPs included in the TRP pattern with the highest priority as multiple target TRPs). If one or more TRPs included in the TRP pattern with the highest priority changes (e.g., the channel between the TRP and the terminal changes), the terminal may use the TRP pattern with the highest priority as the target TRP.
  • the TRP pattern with the highest priority is not used for terminal positioning, and the TRP corresponding to the TRP pattern with the second highest priority is used for terminal positioning.
  • the first refers to the information containing TRP pattern 1, TRP pattern 2, and TRP pattern 3, and the priority of TRP pattern 1> the priority of TRP pattern 2> the priority of TRP pattern 3.
  • the terminal preferentially uses the multiple TRPs corresponding to TRP pattern 1 for terminal positioning. If the channel conditions between some TRPs in TRP pattern 1 and the terminal deteriorate, or the TRP turns off reference signal transmission, TRP pattern 1 is not used, and the multiple TRPs contained in TRP pattern 2 are used for terminal positioning.
  • the number of TRPs refers to the number of target TRPs determined by the terminal from all configured TRPs for positioning. For example, if the number of TRPs is 4 and there are 6 configured TRPs for positioning, 4 TRPs are selected from the 6 configured TRPs for positioning as 4 target TRPs.
  • a cell is composed of one or more TRPs, and the multiple target TRPs used for positioning can be determined according to the specified number of cells. For example, if the number of cells is 2 and each cell is composed of 3 TRPs, the number of target TRPs used for positioning is 6.
  • the network side device indicates the number of TRPs for each cell, and then the terminal determines the number of target TRPs for positioning based on the number of TRPs for each cell and the number of cells. For example, if the number of selected cells is 2, the number of TRPs for cell A is 2, and the number of TRPs for cell B is 3, then the number of target TRPs for positioning is 5.
  • N TRPs are multiple target TRPs indicated by the network side device for terminal positioning, and then the terminal measures the reference signals associated with the N TRPs respectively, and performs terminal positioning based on the measurement information of the N TRPs and the associated AI model or ML model, or reports the measurement information of the N TRPs to the network side device, so that the network side device performs terminal positioning according to the measurement information of the N TRPs and the associated AI model or ML model.
  • P cells are cells indicated by the network side device for the terminal to locate the terminal. Since each cell is composed of one or more TRPs, multiple target TRPs for terminal positioning can be determined based on the P cells indicated by the network side. For example, the cell with cell ID A contains: TRP1, TRP2, TRP3. When the target indication information contains the cell ID A, the terminal can determine the target TRPs for positioning as: TRP1, TRP2, TRP3.
  • the network-side device Before the network-side device sends the target indication information, the network-side device indicates all configured TRPs for positioning (i.e., all TRPs included in the model training set of the AI model or ML model) to the terminal, and then the terminal selects the target TRP for positioning based on the minimum number of TRPs in the target indication information, where the minimum number of TRPs refers to the minimum number of target TRPs selected by the terminal from all configured TRPs for positioning. For example, if the minimum number of TRPs is 3, if there are 6 configured TRPs for positioning, then no less than 3 TRPs are selected from the 6 configured TRPs for positioning as the target TRPs.
  • the minimum number of TRPs is 3, if there are 6 configured TRPs for positioning, then no less than 3 TRPs are selected from the 6 configured TRPs for positioning as the target TRPs.
  • the network side device Before the network side device sends the target indication information, the network side device indicates to the terminal all the cells configured for positioning, as well as the number of TRPs in each cell and the ID of each TRP, and then the terminal determines the cells used for positioning according to the minimum number of cells in the target indication information, and then determines the target TRP used for positioning according to the cells used for positioning, wherein the minimum number of cells refers to the terminal selecting the cells used for positioning from all the cells configured for positioning. For example, if the minimum number of cells is 3, if there are 6 cells configured for positioning, then no less than 3 cells are selected from the 6 cells configured for positioning, and then multiple target TRPs are determined based on the 3 cells.
  • the TRP selection criteria are used to instruct the terminal to select M target TRPs from all TRPs included in the model training set of the positioning model, where M is not less than the number of TRPs in the target indication information or the minimum number of TRPs.
  • the terminal can determine multiple target TRPs for positioning according to the target indication information.
  • the terminal only needs to measure and report the multiple target TRPs, thereby reducing the measurement overhead and reporting overhead.
  • the terminal determines a plurality of target TRPs according to the target indication information, including:
  • the TRP selection index includes at least one of the following: reference signal received power RSRP, reference signal received quality RSRQ, interference signal-to-noise ratio SINR, signal-to-noise ratio SNR, synchronization error, Doppler spread, delay spread, channel estimation error, arrival time ToA, line-of-sight LoS information or non-line-of-sight NLoS information.
  • TRP with good channel quality is preferentially selected for positioning.
  • the TRP index is an index related to channel conditions, which can measure the quality of the channel conditions between the terminal and the TRP.
  • parameters such as reference signal received power (RSRP), reference signal received quality (RSRQ), signal to interference noise ratio (SINR), signal to noise ratio (SNR), synchronization error, Doppler spread, delay spread, channel estimation error, etc. can reflect the channel status between the terminal and the TRP.
  • ToA can reflect the distance between the terminal and the TRP. If the distance is far, the TRP will not be selected as the target TRP.
  • LoS information and NLoS information can reflect the transmission mode of the signal between the terminal and the TRP.
  • LoS means there is no obstacle between the terminal and the TRP, while NLoS means there is an obstacle between the terminal and the TRP. There is no obstacle between the terminal and the TRP, and the attenuation of the reference signal is smaller.
  • the TRP of LoS can be preferentially selected as the target TRP.
  • the first threshold is related to the TRP selection index, and a TRP with a TRP selection index greater than the first threshold can be selected as the target TRP.
  • a TRP with a TRP selection index greater than the first threshold can be selected as the target TRP.
  • multiple target TRPs are determined according to the TRP selection index of the AI model or ML model and the corresponding first threshold, and then the terminal is positioned according to the determined multiple target TRPs and the AI model or ML model. For example, if the AI model or ML model is adapted to the situation where RSRP conditions are good, the TRP with RSRP greater than the first threshold is selected as the target TRP.
  • one or more TRP selection indicators can be arbitrarily determined, and then multiple target TRPs are determined according to the selected TRP selection index and the corresponding first threshold, so as to locate the terminal based on multiple target TRPs and the AI model or ML model associated with multiple target TRPs.
  • the first threshold can also be used in conjunction with the TRP pattern selection criterion, that is, the first threshold and the second threshold mentioned in the description of the TRP pattern selection criterion below can be the same.
  • the multiple target TRPs used for positioning are selected by the terminal from all configured TRPs for positioning according to the TRP selection criteria, that is, for the TRP with poor channel conditions between the terminal and the TRP, the TRP with good channel conditions is dynamically selected for terminal positioning, and for the TRP with reference signal transmission turned off for the purpose of energy saving, such TRP is avoided from being selected for terminal positioning, thereby achieving the terminal positioning in a dynamically changing environment.
  • the multiple target TRPs for terminal positioning can be flexibly configured. When performing terminal positioning, the terminal only needs to measure the reference signals of the selected multiple target TRPs and report the measurement information of the multiple target TRPs. Compared with the related art that uses all configured TRPs for positioning to perform terminal positioning, the embodiment of the present application greatly reduces the measurement overhead and reporting overhead.
  • the TRP selection index includes: L1 measurement quantity (or layer 1 measurement quantity) and L3 measurement quantity or (layer 3 measurement quantity).
  • L1 measurement quantity refers to the instantaneous measurement quantity at a certain moment
  • L3 measurement quantity refers to the measurement quantity obtained by smoothing or filtering the L1 measurement quantities at multiple moments.
  • the RSRP measured by the terminal at the nth moment and a certain TRP is L1-RSRP
  • the terminal takes the average or weighted average of the L1-RSRP of the previous n moments to obtain L3-RSRP.
  • L3 measurement quantity can eliminate fast fading and reduce the impact of changes in short-term periodic measurement results.
  • L3 measurement quantity can be preferentially used as the TRP selection index to select multiple target TRPs.
  • the method before the terminal obtains the target indication information, the method further includes:
  • the terminal obtains TRP pattern configuration information, where the TRP pattern configuration information is used to indicate multiple TRP pattern IDs and TRP patterns corresponding to each of the multiple TRP pattern IDs.
  • the TRP pattern is a combination of multiple TRPs with fixed positions or fixed IDs.
  • the network side device sends the TRP pattern configuration information to the terminal, and then the terminal knows the correspondence between each TRP pattern ID and the TRP pattern, as well as the IDs of the multiple TRPs contained in each TRP pattern according to the TRP pattern configuration information.
  • the TRP pattern configuration information can be expressed as:
  • the target indication information further includes: IDs of multiple TRP patterns and at least one of the following parameters of each of the multiple TRP patterns:
  • the number of cells used for terminal positioning is the number of cells used for terminal positioning
  • the number of TRPs in each cell used for terminal positioning is the number of TRPs in each cell used for terminal positioning
  • T is an integer greater than 1;
  • the minimum number of cells used for terminal positioning is the minimum number of cells used for terminal positioning.
  • the terminal since the terminal already knows the correspondence between each TRP pattern ID and the TRP pattern, as well as the IDs of multiple TRPs contained in each TRP pattern, when the network side device sends the first indication information to the terminal, it only needs to send the TRP pattern ID, without sending the number of TRPs contained in each TRP pattern and the ID of each TRP. Thus, the transmission overhead of the target indication information is reduced.
  • the target indication information further includes: TRP pattern priority information; when the target indication information further includes TRP pattern priority information, the terminal determines multiple target TRPs according to the target indication information, including:
  • the terminal determines a plurality of candidate TRP patterns according to the target indication information
  • the terminal determines a target TRP pattern from the multiple candidate TRP patterns according to the TRP pattern priority information, and each TRP included in the multiple target TRP patterns is the multiple target TRPs.
  • the network side device can send TRP pattern priority information to the terminal, and then the terminal determines the TRP pattern to be used first according to the TRP pattern priority.
  • TRP pattern priority there are 4 TRP patterns configured in the TRP pattern configuration information: TRP pattern 1, TRP pattern 2, TRP pattern 3, and TRP pattern 4.
  • TRP pattern priority information in the target indication information is: TRP pattern 4 priority> TRP pattern 2 priority> TRP pattern 1 priority> TRP pattern 3 priority
  • the terminal uses TRP pattern 4 for terminal positioning according to the priority of the TRP pattern. If the channel conditions between some TRPs in TRP pattern 4 and the terminal deteriorate or the TRP turns off the reference signal transmission, TRP pattern 4 is not used, and multiple TRPs corresponding to TRP pattern 2 are used for terminal positioning.
  • the target indication information further includes a TRP pattern selection criterion.
  • the terminal determines a plurality of target TRPs according to the target indication information, including:
  • the TRP pattern selection criterion includes at least one of the following selection parameters:
  • TRP pattern selection index includes at least one of the following: reference signal received power RSRP, reference signal received quality RSRQ, interference signal-to-noise ratio SINR, signal-to-noise ratio SNR, synchronization error, Doppler spread, delay spread, channel estimation error, arrival time ToA, line-of-sight LoS information or non-line-of-sight NLoS information.
  • the second threshold is related to the TRP pattern selection index, and multiple target TRPs are determined according to the TRP pattern selection index and the corresponding second threshold. For example, a TRP pattern with an average RSRP greater than the second threshold is selected.
  • multiple TRPs corresponding to the TRP pattern belong to the same service cell, and the terminal can select the TRP pattern corresponding to its service cell for terminal positioning.
  • multiple TRPs in TRP pattern 1 belong to service cell A
  • multiple TRPs in TRP pattern 2 belong to service cell B. If the service cell where the terminal is located is B, the terminal selects TRP pattern 2 for positioning.
  • the area is a network area pre-divided by the network side device or terminal (for example: pre-divided according to the reference signal associated with TRP), each area corresponds to its own ID, and the terminal can select the TRP pattern corresponding to the area for terminal positioning.
  • TRP patterns with good channel conditions are preferred for terminal positioning.
  • the TRP pattern selection index can It can reflect the quality of the channel conditions between the terminal and each TRP in the TRP pattern. Specifically, 1) when selecting a TRP pattern based on RSRP, it can be selected in the following manner: select the TRP pattern with the largest average RSRP; select the TRP pattern with the largest minimum RSRP; select the TRP pattern with the largest RSRP of the Kth largest, where K is a positive integer. For example, the four TRP patterns are in descending order according to the largest RSRP: TRP pattern 1, TRP pattern 2, TRP pattern 3, and TRP pattern 4. If K is equal to 2, then TRP pattern 2 is selected for positioning; select the TRP pattern with the largest average value of the first K largest RSRPs; select the TRP pattern with the largest average value of all RSRPs exceeding the first RSRP threshold.
  • the selection can be made in the following manner: select the TRP pattern with the smallest average ToA; select the TRP pattern with the smallest maximum ToA; select the TRP pattern with the smallest K-th smallest ToA; select the TRP pattern with the smallest average value of the first K smallest ToAs; select the TRP pattern with the smallest average value of all ToAs below the first ToA threshold.
  • the selection can be made in the following manner: select the TRP pattern with the largest average SINR; select the TRP pattern with the largest minimum SINR; select the TRP pattern with the largest K-th largest SINR; select the TRP pattern with the largest average value of the first K largest SINRs; select the TRP pattern with the largest average value of all SINRs exceeding the first SINR threshold.
  • the selection can be made in the following manner: select the TRP pattern with the largest average SNR; select the TRP pattern with the largest minimum SNR; select the TRP pattern with the largest K-th largest SNR; select the TRP pattern with the largest average of the first K largest SNRs; select the TRP pattern with the largest average of all SNRs exceeding the first SNR threshold.
  • the selection can be made in the following manner: select the TRP pattern with the largest average synchronization error; select the TRP pattern with the largest minimum synchronization error; select the TRP pattern with the largest K-th largest synchronization error; select the TRP pattern with the largest average value of the first K largest synchronization errors; select the TRP pattern with the largest average value of all synchronization errors that exceed the first synchronization error threshold.
  • the selection can be made in the following manner: select the TRP pattern with the largest average channel estimation error; select the TRP pattern with the largest minimum channel estimation error; select the TRP pattern with the largest K-th largest channel estimation error; select the TRP pattern with the largest average value of the first K largest channel estimation errors; select the TRP pattern with the largest average value of all channel estimation errors that exceed the first channel estimation error threshold.
  • the selection may be made in the following manner: select the TRP pattern with the largest average Doppler spread; select the TRP pattern with the largest minimum Doppler spread; select the TRP pattern with the largest K-th largest Doppler spread; select the TRP pattern with the largest average value of the first K largest Doppler spreads; select the TRP pattern with the largest average value of all Doppler spreads that exceed the first Doppler spread threshold.
  • the selection can be made in the following manner: select the TRP pattern with the largest average delay spread; select the TRP pattern with the largest minimum delay spread; select the TRP pattern with the largest K-th largest delay spread; select the TRP pattern with the largest average value of the first K largest delay spreads; select the TRP pattern with the largest average value of all delay spreads that exceed the first delay spread threshold.
  • the network side device can indicate multiple TRP patterns to the terminal, and then the terminal can The sample selection criteria selects a TRP pattern for positioning from multiple TRP patterns indicated by the network side (the TRPs contained in the TRP pattern are multiple target TRPs). For TRP patterns with poor TRP channel conditions and TRP patterns with TRP reference signal transmission turned off, the selection of such TRP patterns for terminal positioning is avoided, thereby achieving flexible configuration of multiple target TRPs used for positioning in a dynamically changing environment.
  • the method further comprises at least one of the following steps:
  • the terminal obtains and reports measurement information of the multiple TRPs by measuring reference signals associated with the multiple TRPs, where the multiple TRPs at least include the multiple target TRPs;
  • the terminal obtains a positioning result of the terminal according to the measurement information of the multiple TRPs and the ML model.
  • the network side device predetermines an AI model or ML model based on model-related information, and then directly indicates multiple target TRPs to the terminal based on the model-related information, or the multiple target TRPs are selected by the terminal from all configured TRPs for positioning.
  • the measurement information includes: time domain channel impulse response, multipath delay spectrum, frequency domain channel impulse response, channel energy response, delay power spectrum and other information, which can reflect the channel quality of TRP.
  • the terminal measures the reference signals associated with multiple TRPs (at least including multiple target TRPs for positioning), and reports the measurement information of multiple TRPs to the network side device, so that the network side device can realize terminal positioning based on the measurement information of multiple TRPs and the associated AI model or ML model.
  • the following two cases are included:
  • the network side device When the network side device has already determined an AI model or ML model in advance according to the model-related information, multiple target TRPs are directly indicated to the terminal by the network side device according to the model-related information. Then the terminal measures multiple TRPs (at least including multiple target TRPs for positioning) and reports the measurement information to the network side device, so that the network side device can obtain the positioning result of the terminal according to the measurement information of multiple TRPs and the determined AI model or ML model.
  • the network side device indicates 5 TRPs for terminal positioning according to the relevant information of model A, then the terminal measures the reference signals associated with the 5 TRPs, and reports the measurement information of the 5 TRPs to the network side device, so that the network side device inputs the measurement information of the 5 TRPs into model A to obtain the positioning result of the terminal. Since the multiple target TRPs for terminal positioning are directly indicated by the network side device, the terminal can only measure and report the multiple target TRPs indicated by the network side device, reducing the TRP measurement overhead and reporting overhead.
  • multiple target TRPs are selected by the terminal from all configured TRPs for positioning.
  • the terminal measures the reference signals associated with all configured TRPs for positioning, and selects multiple target TRPs that meet the positioning requirements (such as TRPs with good channel conditions) based on the measurement information, and reports the measurement information of the multiple target TRPs to the network side device, so that the network side device determines the AI model or ML model associated with the multiple target TRPs, and uses the measurement information of the multiple target TRPs and the associated AI model or ML model to obtain the positioning result of the terminal.
  • the terminal measures the reference signals associated with the 10 configured TRPs for positioning, and selects 3 TRPs (TRP1, TRP2, TRP3) that meet the positioning requirements based on the measurement information, and reports the measurement information of the 3 TRPs (TRP1, TRP2, TRP3) to the network side device, and then the network side device determines the reference signals associated with the 3 TRPs (TRP1, TRP2, TRP3).
  • the associated AI model or ML model is model B, and the measurement information of the three TRPs (TRP1, TRP2, TRP3) is input into model B to obtain the positioning result of the terminal.
  • the multiple target TRPs used for positioning are flexibly configured based on the target indication information, rather than a fixed single TRP pattern, the situation in which the positioning accuracy of the AI model or ML model decreases or the model fails due to the deterioration of the channel environment or configuration changes of some TRPs is avoided, thereby improving the flexibility, reliability and accuracy of terminal positioning.
  • the AI model or ML model used for terminal positioning is deployed in the terminal, S440 is executed.
  • the terminal inputs the measurement information of multiple TRPs into the associated AI or ML model to obtain the positioning result of the terminal.
  • the associated AI model or ML model is determined and notified to the terminal by the network side device according to the IDs of multiple target TRPs reported by the terminal or the TRP pattern information containing multiple target TRPs, or the associated AI model or ML model is an AI model or ML model associated with multiple target TRPs determined by the terminal from multiple AI models or ML models deployed locally.
  • the positioning model is the target indication information deployed on the network side device and is used to indicate to the terminal the multiple target TRPs associated with the positioning model, and the positioning model and the multiple target TRPs are used to locate the terminal.
  • the network side device predetermines the AI model or ML model for positioning according to the model-related information, and generates the associated target indication information according to the AI model or ML model, and then the terminal measures the reference signals associated with the multiple target TRPs indicated by the target indication information for terminal positioning, obtains the measurement information of the multiple target TRPs, and reports the measurement information of the multiple target TRPs to the network side device, so that the network side device can realize terminal positioning according to the measurement information of the multiple target TRPs and the predetermined AI model or ML model. Since the terminal only needs to measure and report the multiple target TRPs indicated by the network side device, the measurement overhead and reporting overhead are reduced.
  • the network side device generates target indication information based on the relevant information of model A. After the terminal side obtains the target indication information, it measures the reference signals associated with TRP1, TRP2, and TRP3 indicated by the target indication information, obtains the measurement results of TRP1, TRP2, and TRP3 respectively, and reports them to the network side device. The network side device inputs the measurement results reported by the terminal into model A to obtain the positioning result of the terminal.
  • the method further includes S450 and S460:
  • the terminal reports information related to the multiple target TRPs, where the information related to the multiple target TRPs includes at least one of the following: TRP pattern ID, TRP ID, and TRP quantity;
  • the terminal receives model configuration information, where the model configuration information is used to instruct the terminal to enable a positioning model associated with the multiple target TRPs.
  • the terminal side device has not determined the AI model or ML model used for positioning in advance.
  • the terminal determines multiple target TRPs from all configured TRPs for positioning according to the target indication information, and reports the information related to the multiple target TRPs to the network side device, so that the network side device determines the AI model or ML model associated with the multiple target TRPs, and sends the model configuration information of the AI model or ML model to the terminal.
  • the terminal uses the AI model or ML model indicated by the model configuration information and the measurement information of the multiple target TRPs for positioning to obtain the positioning result of the terminal.
  • the terminal uses the AI model or ML model indicated by the model configuration information and the multiple target TRPs for positioning, the terminal inputs the measurement information of the multiple target TRPs into the model configuration information.
  • the indicated AI model or ML model obtains the positioning result of the terminal.
  • multiple target TRPs can be selected by the terminal from all configured TRPs for positioning according to the TRP selection criteria, or TRP patterns selected from multiple TRP patterns according to the TRP pattern selection criteria (the multiple TRPs contained in the selected TRP pattern can be used as multiple target TRPs). That is to say, in the embodiment of the present application, multiple target TRPs are flexibly configured, not a fixed single TRP pattern, thereby avoiding the situation where the positioning accuracy of the AI model or ML model decreases or the model fails due to the deterioration of the channel environment of some TRPs or configuration changes, thereby improving the flexibility, reliability and accuracy of terminal positioning. .
  • FIG5 illustrates the positioning results of the terminal positioning method provided by the present application under different numbers of TRPs.
  • the horizontal axis represents the positioning error of the terminal
  • the vertical axis represents the cumulative probability density
  • the points on each curve represent the range of the positioning error.
  • the point (3, 0.85) on the curve when the number of TRPs is equal to 2 indicates that 85% of the positioning errors are 3 meters.
  • the positioning results shown in FIG5 show that reducing the number of TRPs will not significantly affect the positioning accuracy, and accurate terminal positioning can be achieved with fewer TRPs, which greatly reduces the measurement overhead and reporting overhead during the positioning process.
  • Figure 6 illustrates the positioning results of the same model of the terminal positioning method provided by the implementation of this application under different numbers of TRPs.
  • the same AI model or ML model can be applicable to TRPs in the same number of different TRP patterns and different numbers of different TRP patterns.
  • Figure 6 shows the positioning results of 4 TRPs or 9 TRPs randomly selected from 18 TRPs using the same AI model or ML model. It can be seen that when the number of TRPs is equal to 9, the 90% positioning error is 1.79 meters; and the positioning result using the fixed TRP pattern is 90% The positioning error is 1.26 meters.
  • the positioning results of the terminal positioning method provided by the implementation of this application using multiple target TRPs with flexible configurations are basically consistent with the positioning results of the existing fixed TRP pattern.
  • the terminal positioning method provided in the embodiment of the present application may be executed by a terminal positioning device.
  • the terminal positioning device provided in the embodiment of the present application is described by taking the terminal positioning method executed by the terminal positioning device as an example.
  • the terminal positioning device 700 may include the following modules:
  • An acquisition module 710 is used for the terminal to acquire target indication information, where the target indication information is indication information related to the TRP pattern;
  • the determination module 720 is used for the terminal to determine multiple target TRPs according to the target indication information, and the multiple target TRPs are used to locate the terminal based on the positioning model.
  • the network side device sends target indication information to the terminal, so that the terminal can determine multiple target TRPs that meet the positioning requirements based on the target indication information, and then the terminal positioning can be realized based on the AI model or ML model and multiple target TRPs. Since the multiple target TRPs used for positioning are flexibly configured based on the target indication information, and are not a fixed single TRP pattern, the situation where the positioning accuracy of the AI model or ML model decreases or the model fails due to the deterioration of the channel environment or configuration changes of some TRPs is avoided, thereby improving the flexibility, reliability and accuracy of terminal positioning.
  • a TRP with good channel conditions is dynamically selected for terminal positioning, and for TRPs that turn off the reference signal transmission for the purpose of energy saving, such TRPs are avoided from being selected for terminal positioning, thereby realizing flexible configuration of multiple target TRPs for terminal positioning in a dynamically changing environment.
  • the network side device has model-related information
  • the network side The device can directly indicate the TRP associated with the model to the terminal based on the model-related information. As multiple target TRPs are used for terminal positioning, the terminal only needs to measure or report on multiple target TRPs, reducing measurement overhead and reporting overhead.
  • the target indication information includes at least one of the following:
  • the number of cells used for terminal positioning is the number of cells used for terminal positioning
  • the number of TRPs in each cell used for terminal positioning is the number of TRPs in each cell used for terminal positioning
  • N TRP IDs where N is an integer greater than 1;
  • the minimum number of cells used for terminal positioning is the minimum number of cells used for terminal positioning
  • the terminal determines a plurality of target TRPs according to the target indication information, including:
  • the TRP selection index includes at least one of the following: reference signal received power RSRP, reference signal received quality RSRQ, interference signal-to-noise ratio SINR, signal-to-noise ratio SNR, synchronization error, Doppler spread, delay spread, channel estimation error, arrival time ToA, line-of-sight LoS information or non-line-of-sight NLoS information.
  • the TRP selection criteria are used to instruct the terminal to select M target TRPs from all TRPs included in the model training set of the positioning model, where M is not less than the number of TRPs in the target indication information or the minimum number of TRPs.
  • the method according to claim 2 is characterized in that before the terminal obtains the target indication information, the method further comprises:
  • the terminal obtains TRP pattern configuration information, where the TRP pattern configuration information is used to indicate multiple TRP pattern IDs and TRP patterns corresponding to each of the multiple TRP pattern IDs.
  • the target indication information further includes: IDs of multiple TRP patterns and at least one of the following parameters of each of the multiple TRP patterns:
  • the number of cells used for terminal positioning is the number of cells used for terminal positioning
  • the number of TRPs in each cell used for terminal positioning is the number of TRPs in each cell used for terminal positioning
  • T is an integer greater than 1;
  • the minimum number of cells used for terminal positioning is the minimum number of cells used for terminal positioning.
  • the target indication information further includes: TRP pattern priority information; when the target indication information further includes TRP pattern priority information, the terminal determines multiple target TRPs according to the target indication information, including:
  • the terminal determines a plurality of candidate TRP patterns according to the target indication information
  • the terminal determines a target TRP pattern from the multiple candidate TRP patterns according to the TRP pattern priority information, and each TRP included in the multiple target TRP patterns is the multiple target TRPs.
  • the target indication information further includes a TRP pattern selection criterion.
  • the terminal determines a plurality of target TRPs according to the target indication information, including:
  • the TRP pattern selection criterion includes at least one of the following selection parameters:
  • the TRP pattern selection indicator includes at least one of the following: reference signal received power RSRP, reference signal received quality RSRQ, interference signal-to-noise ratio SINR, signal-to-noise ratio SNR, synchronization error, Doppler spread, delay spread, channel estimation error, arrival time ToA, line-of-sight LoS information or non-line-of-sight NLoS information.
  • the terminal positioning device 700 further includes:
  • a measurement module configured for the terminal to obtain and report measurement information of a plurality of TRPs by measuring reference signals associated with the plurality of TRPs, wherein the plurality of TRPs at least include the plurality of target TRPs;
  • a positioning module is used for the terminal to obtain the positioning result of the terminal based on the measurement information of the multiple TRPs and the positioning model.
  • the positioning model is the target indication information deployed on the network side device and is used to indicate to the terminal the multiple target TRPs associated with the positioning model, and the positioning model and the multiple target TRPs are used to locate the terminal.
  • the terminal positioning device 700 further includes:
  • a reporting module configured for the terminal to report information related to the multiple target TRPs, wherein the information related to the multiple target TRPs includes at least one of the following: TRP pattern ID, TRP ID, and TRP quantity;
  • a model configuration acquisition module is used for the terminal to receive model configuration information, and the model configuration information is used to instruct the terminal to enable the positioning model associated with the multiple target TRPs.
  • the positioning device in the embodiment of the present application can be an electronic device, such as an electronic device with an operating system, or a component in an electronic device, such as an integrated circuit or a chip.
  • the electronic device can be a terminal.
  • the terminal can include but is not limited to the types of terminals 11 listed above.
  • the positioning device provided in the embodiment of the present application can implement each process implemented by the positioning method embodiment shown in Figure 4 and achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the present application embodiment also provides a terminal positioning method, as shown in FIG8 , The method may include the following steps:
  • the network side device sends target indication information, where the target indication information is indication information related to the TRP pattern, and the target indication information is used to determine multiple target TRPs, and the multiple target TRPs are used to locate the terminal based on the positioning model.
  • the network side device sends target indication information to the terminal, so that the terminal can determine multiple target TRPs that meet the positioning requirements according to the target indication information, and then the terminal positioning can be realized based on the AI model or ML model and multiple target TRPs. Since the multiple target TRPs used for positioning are flexibly configured based on the target indication information, and are not fixed single TRP patterns, the situation where the positioning accuracy of the AI model or ML model decreases or the model fails due to the deterioration of the channel environment of some TRPs or configuration changes is avoided, and the flexibility, reliability and accuracy of terminal positioning are improved.
  • the TRP with good channel conditions is dynamically selected for terminal positioning, and for the TRP that turns off the reference signal transmission for the purpose of energy saving, such TRP is avoided from being selected for terminal positioning, thereby realizing the flexible configuration of multiple target TRPs for terminal positioning in a dynamically changing environment.
  • the network side device can directly indicate the TRP associated with the model to the terminal according to the model-related information.
  • the terminal only needs to measure or report multiple target TRPs, which reduces the measurement overhead and reporting overhead.
  • the target indication information includes at least one of the following:
  • the number of cells used for terminal positioning is the number of cells used for terminal positioning
  • the number of TRPs in each cell used for terminal positioning is the number of TRPs in each cell used for terminal positioning
  • N TRP IDs where N is an integer greater than 1;
  • the minimum number of cells used for terminal positioning is the minimum number of cells used for terminal positioning
  • the terminal determines multiple target TRPs according to the target indication information, including:
  • the TRP selection index includes at least one of the following: reference signal received power RSRP, reference signal received quality RSRQ, interference signal-to-noise ratio SINR, signal-to-noise ratio SNR, synchronization error, Doppler spread, delay spread, channel estimation error, arrival time ToA, line-of-sight LoS information or non-line-of-sight NLoS information.
  • the TRP selection criteria are used to instruct the terminal to select M target TRPs from all TRPs included in the model training set of the positioning model, where M is not less than the number of TRPs in the target indication information or the minimum number of TRPs.
  • the method before the network side device sends the target indication information, the method further includes:
  • the network side device sends TRP pattern configuration information, where the TRP pattern configuration information is used to indicate multiple TRP pattern IDs and TRP patterns corresponding to each of the multiple TRP pattern IDs.
  • the target indication information further includes: IDs of multiple TRP patterns and at least one of the following parameters of each of the multiple TRP patterns:
  • the number of cells used for terminal positioning is the number of cells used for terminal positioning
  • the number of TRPs in each cell used for terminal positioning is the number of TRPs in each cell used for terminal positioning
  • T is an integer greater than 1;
  • the minimum number of cells used for terminal positioning is the minimum number of cells used for terminal positioning.
  • the target indication information further includes: TRP pattern priority information; when the target indication information further includes TRP pattern priority information, the terminal determines multiple target TRPs according to the target indication information, including:
  • the terminal determines a plurality of candidate TRP patterns according to the target indication information
  • the target indication information further includes a TRP pattern selection criterion.
  • the terminal determines a plurality of target TRPs according to the target indication information, including:
  • the TRP pattern selection criterion includes at least one of the following selection parameters:
  • the TRP pattern selection indicator includes at least one of the following: reference signal received power RSRP, reference signal received quality RSRQ, interference signal-to-noise ratio SINR, signal-to-noise ratio SNR, synchronization error, Doppler spread, delay spread, channel estimation error, arrival time ToA, line-of-sight LoS information or non-line-of-sight NLoS information.
  • the method further includes:
  • the network side device receives measurement information associated with a plurality of TRPs sent by the terminal, where the plurality of TRPs include the plurality of target TRPs;
  • the network side device obtains the positioning of the terminal according to the measurement information of the multiple TRPs and the positioning model. bit result.
  • the network side device when the positioning model is deployed on a network side device, the network side device sends target indication information, including:
  • the network side device determines a plurality of target TRPs associated with the positioning model of the network side device
  • the network side device sends target indication information based on the multiple target TRPs; the target indication information is used to indicate the multiple target TRPs associated with the positioning model to the terminal, and the positioning model and the multiple target TRPs are used to locate the terminal.
  • the method further includes:
  • the information related to the plurality of target TRPs includes at least one of the following: a TRP pattern ID, a TRP ID, and a number of TRPs;
  • the method further comprises:
  • the network side device receives measurement information of the multiple target TRPs
  • the network side device receives information related to the multiple target TRPs reported by the terminal, and the information related to the multiple target TRPs includes at least one of the following: TRP pattern ID, TRP ID, and TRP quantity;
  • the network side device determines an ML model associated with information related to the plurality of target TRPs
  • the network side device inputs the measurement information of the multiple target TRPs into the associated ML model to obtain the positioning result of the terminal.
  • the positioning device provided in the embodiment of the present application can implement each process implemented by the positioning method embodiment shown in Figure 4 and achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the terminal positioning method provided in the embodiment of the present application may be executed by a terminal positioning device.
  • the terminal positioning device provided in the embodiment of the present application is described by taking the terminal positioning method executed by the terminal positioning device as an example.
  • the terminal positioning device 900 may include the following modules:
  • the sending module 910 is used for the network side device to send target indication information, where the target indication information is indication information related to the TRP pattern, and the target indication information is used to determine multiple target TRPs, and the multiple target TRPs are used to locate the terminal based on the positioning model.
  • the network side device sends target indication information to the terminal, so that the terminal can determine multiple target TRPs that meet the positioning requirements based on the target indication information, and then the terminal positioning can be achieved based on the AI model or ML model and multiple target TRPs. Since the multiple target TRPs used for positioning are flexibly configured based on the target indication information, rather than a fixed single TRP pattern, the situation in which the positioning accuracy of the AI model or ML model decreases or the model fails due to the deterioration of the channel environment or configuration changes of some TRPs is avoided, thereby improving the flexibility, reliability and accuracy of terminal positioning.
  • a TRP with good channel conditions is dynamically selected for terminal positioning, and for TRPs that turn off reference signal transmission for the purpose of energy saving, such TRPs are avoided from being selected for terminal positioning, thereby achieving the positioning of the terminal in a dynamically changing environment.
  • Multiple target TRPs can be flexibly configured.
  • the network side device can directly indicate the TRP associated with the model to the terminal based on the model-related information. As multiple target TRPs for terminal positioning, the terminal only needs to measure or report multiple target TRPs, reducing measurement overhead and reporting overhead.
  • the target indication information includes at least one of the following:
  • the number of cells used for terminal positioning is the number of cells used for terminal positioning
  • the number of TRPs in each cell used for terminal positioning is the number of TRPs in each cell used for terminal positioning
  • N TRP IDs where N is an integer greater than 1;
  • the minimum number of cells used for terminal positioning is the minimum number of cells used for terminal positioning
  • the terminal determines a plurality of target TRPs according to the target indication information, including:
  • the TRP selection index includes at least one of the following: reference signal received power RSRP, reference signal received quality RSRQ, interference signal-to-noise ratio SINR, signal-to-noise ratio SNR, synchronization error, Doppler spread, delay spread, channel estimation error, arrival time ToA, line-of-sight LoS information or non-line-of-sight NLoS information.
  • the TRP selection criterion is used to instruct the terminal to select M target TRPs from all TRPs included in the model training set of the positioning model, where M is not less than the number of TRPs in the target indication information or the minimum number of TRPs.
  • the terminal positioning device 900 before the network side device sends the target indication information, the terminal positioning device 900 further includes:
  • the pattern configuration sending module is used for the network side device to send TRP pattern configuration information, wherein the TRP pattern configuration information is used to indicate a plurality of TRP pattern IDs and TRP patterns corresponding to the plurality of TRP pattern IDs.
  • the target indication information further includes: IDs of multiple TRP patterns and at least one of the following parameters of each of the multiple TRP patterns:
  • the number of cells used for terminal positioning is the number of cells used for terminal positioning
  • the number of TRPs in each cell used for terminal positioning is the number of TRPs in each cell used for terminal positioning
  • T is an integer greater than 1;
  • the minimum number of cells used for terminal positioning is the minimum number of cells used for terminal positioning.
  • the target indication information further includes: TRP pattern priority information; when the target indication information further includes TRP pattern priority information, the terminal determines multiple target TRPs according to the target indication information, including:
  • the terminal determines a plurality of candidate TRP patterns according to the target indication information
  • the terminal determines a target TRP pattern from the multiple candidate TRP patterns according to the TRP pattern priority information, and each TRP included in the multiple target TRP patterns is the multiple target TRPs.
  • the target indication information further includes a TRP pattern selection criterion.
  • the terminal determines a plurality of target TRPs according to the target indication information, including:
  • the TRP pattern selection criterion includes at least one of the following selection parameters:
  • the TRP pattern selection indicator includes at least one of the following: reference signal received power RSRP, reference signal received quality RSRQ, interference signal-to-noise ratio SINR, signal-to-noise ratio SNR, synchronization error, Doppler spread, delay spread, channel estimation error, arrival time ToA, line-of-sight LoS information or non-line-of-sight NLoS information.
  • the terminal positioning device 900 further includes:
  • a first measurement receiving module configured for the network side device to receive measurement information associated with a plurality of TRPs sent by the terminal, wherein the plurality of TRPs include the plurality of target TRPs;
  • the first positioning module is used by the network side device to obtain the positioning result of the terminal based on the measurement information of the multiple TRPs and the positioning model.
  • the network side device when the positioning model is deployed on a network side device, the network side device sends target indication information, including:
  • the network side device determines a plurality of target TRPs associated with the positioning model of the network side device
  • the network side device sends target indication information based on the multiple target TRPs; the target indication information is used to indicate the multiple target TRPs associated with the positioning model to the terminal, and the positioning model and the multiple target TRPs are used to locate the terminal.
  • the terminal positioning device 900 further includes:
  • a first reporting receiving module configured to receive information related to the plurality of target TRPs reported by the terminal, wherein the information related to the plurality of target TRPs includes at least one of the following: TRP pattern ID, TRP ID, and TRP quantity;
  • a model configuration sending module is used to send model configuration information, where the model configuration information is used to instruct the terminal to enable the positioning model associated with the multiple target TRPs.
  • the terminal positioning device 900 further includes:
  • a second measurement receiving module configured for the network side device to receive measurement information of the multiple target TRPs
  • the second reporting receiving module is used for the network side device to receive the multiple target TRPs reported by the terminal Related information, the information related to the plurality of target TRPs includes at least one of the following: TRP pattern ID, TRP ID, TRP quantity;
  • a model determination module used for the network side device to determine a positioning model associated with information related to the multiple target TRPs
  • the second positioning module is used for the network side device to input the measurement information of the multiple target TRPs into the associated positioning model to obtain the positioning result of the terminal.
  • the positioning device provided in the embodiment of the present application can implement each process implemented by the positioning method embodiment shown in Figure 8 and achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the embodiment of the present application further provides a communication device 1000, including a processor 1001 and a memory 1002, and the memory 1002 stores a program or instruction that can be run on the processor 1001.
  • the communication device 1000 is a terminal
  • the program or instruction is executed by the processor 1001 to implement the various steps of the method embodiment of FIG4, and can achieve the same technical effect.
  • the communication device 1000 is a network side device
  • the program or instruction is executed by the processor 1001 to implement the various steps of the method embodiment shown in FIG8 and FIG8, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the terminal 1100 includes but is not limited to: a radio frequency unit 1101, a network module 1102, an audio output unit 1103, an input unit 1104, a sensor 1105, a display unit 1106, a user input unit 1107, an interface unit 1108, a memory 1109 and at least some of the components of the processor 1110.
  • the terminal 1100 can also include a power supply (such as a battery) for supplying power to each component, and the power supply can be logically connected to the processor 1110 through a power management system, so as to implement functions such as charging, discharging, and power consumption management through the power management system.
  • a power supply such as a battery
  • the terminal structure shown in FIG11 does not constitute a limitation on the terminal, and the terminal can 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 1104 may include a graphics processing unit (GPU) 11041 and a microphone 11042, and the graphics processor 11041 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 1106 may include a display panel 11061, and the display panel 11061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc.
  • the user input unit 1107 includes a touch panel 11071 and at least one of other input devices 11072.
  • the touch panel 11071 is also called a touch screen.
  • the touch panel 11071 may include two parts: a touch detection device and a touch controller.
  • Other input devices 11072 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 1101 can transmit the data to the processor 1110 for processing; in addition, the RF unit 1101 can send uplink data to the network side device.
  • the RF unit 1101 includes but is not limited to an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc.
  • the memory 1109 can be used to store software programs or instructions and various data.
  • the memory 1109 can mainly include storage A first storage area for 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 instructions required for at least one function (such as a sound playback function, an image playback function, etc.), etc.
  • the memory 1109 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 1110 may include one or more processing units; optionally, the processor 1110 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 1110.
  • the network side device 1200 includes: an antenna 1201, a radio frequency device 1202, a baseband device 1203, a processor 1204 and a memory 1205.
  • the antenna 1201 is connected to the radio frequency device 1202.
  • the radio frequency device 1202 receives information through the antenna 1201 and sends the received information to the baseband device 1203 for processing.
  • the baseband device 1203 processes the information to be sent and sends it to the radio frequency device 1202, and the radio frequency device 1202 processes the received information and sends it out through the antenna 1201.
  • the method executed by the network-side device in the above embodiment may be implemented in the baseband device 1203, which includes a baseband processor.
  • the baseband device 1203 may include, for example, at least one baseband board, on which multiple chips are arranged, one of which is, for example, a baseband processor, which is connected to the memory 1205 through a bus interface to call the program in the memory 1205 and execute the network device operations shown in the above method embodiment.
  • the network side device may also include a network interface 1206, which is, for example, a Common Public Radio Interface (CPRI).
  • CPRI Common Public Radio Interface
  • the network side device 1200 of the embodiment of the present invention also includes: instructions or programs stored in the memory 1205 and executable on the processor 1204.
  • the processor 1204 calls the instructions or programs in the memory 1205 to execute the methods executed by the modules shown in Figure 8 and achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the 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 program or instruction is executed by a processor, each process of the method embodiment shown in FIG. 4 and FIG. 8 is implemented, and the corresponding The same technical effects are achieved, and will not be described here to avoid repetition.
  • the processor is the processor in the terminal 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 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 shown in Figures 4 and 8 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 terminal and a network side device, wherein the terminal can be used to execute the steps of the method embodiment shown in FIG. 4 as described above, and the network side device can be used to execute the steps of the method embodiment shown in FIG. 8 as described above.

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Abstract

La présente demande appartient au domaine technique des communications. Un procédé et un appareil de positionnement de terminal, et un terminal, un dispositif côté réseau et un support sont divulgués. Le procédé de positionnement de terminal dans les modes de réalisation de la présente demande comprend les étapes suivantes : un terminal acquiert des informations d'indication cible, les informations d'indication cible étant des informations d'indication relatives à un motif de point de réception de transmission (TRP) ; et le terminal détermine une pluralité de TRP cibles en fonction des informations d'indication cible, la pluralité de TRP cibles étant utilisée pour positionner le terminal sur la base d'un modèle de positionnement.
PCT/CN2024/088012 2023-04-20 2024-04-16 Procédé et appareil de positionnement de terminal, terminal, dispositif côté réseau et support Pending WO2024217405A1 (fr)

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CN202310431550.8A CN118828344A (zh) 2023-04-20 2023-04-20 终端定位方法、装置、终端、网络侧设备及介质
CN202310431550.8 2023-04-20

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111972013A (zh) * 2020-07-07 2020-11-20 北京小米移动软件有限公司 定位方法、装置、通信设备及存储介质
CN113518301A (zh) * 2020-04-09 2021-10-19 大唐移动通信设备有限公司 一种定位参考信号配置方法、lmf、终端及基站
WO2022063319A1 (fr) * 2020-09-28 2022-03-31 维沃移动通信有限公司 Procédé et appareil de meure de positionnement, dispositif, et support de stockage lisible
US20220303079A1 (en) * 2020-10-15 2022-09-22 Apple Inc. Uplink Transmission Enhancements for Multi-TRP Operation

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113518301A (zh) * 2020-04-09 2021-10-19 大唐移动通信设备有限公司 一种定位参考信号配置方法、lmf、终端及基站
CN111972013A (zh) * 2020-07-07 2020-11-20 北京小米移动软件有限公司 定位方法、装置、通信设备及存储介质
WO2022063319A1 (fr) * 2020-09-28 2022-03-31 维沃移动通信有限公司 Procédé et appareil de meure de positionnement, dispositif, et support de stockage lisible
US20220303079A1 (en) * 2020-10-15 2022-09-22 Apple Inc. Uplink Transmission Enhancements for Multi-TRP Operation

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