Positioning method and communication device

Abstract

This application discloses a positioning method and a communication device, belonging to the field of wireless communication technology. The positioning method of this application includes: a first communication device receiving first positioning request information; the first communication device determining and / or sending an artificial intelligence network model and / or artificial intelligence network model parameters according to the first positioning request information, wherein the artificial intelligence network model is used to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal.

Description

Technical Field

[0001] This application belongs to the field of wireless communication technology, specifically relating to a positioning method and a communication device. Background Technology

[0002] New Radio (NR) positioning is based on signal measurements between the network and the User Equipment (UE, also known as the terminal). Currently, in the field of wireless communication networks, the terminal typically performs positioning directly based on positioning signal measurement information. However, in complex multipath or non-direct path environments (NLOS), the positioning results often contain errors and cannot meet the requirements. Summary of the Invention

[0003] This application provides a positioning method and a communication device that can solve the problem that existing positioning methods based directly on positioning signal measurement results have errors and cannot meet the requirements.

[0004] Firstly, a positioning method is provided, including:

[0005] The first communication device receives the first location request information;

[0006] The first communication device determines and / or sends a target artificial intelligence network model and / or target artificial intelligence network model parameters based on the first positioning request information. The target artificial intelligence network model is used to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal.

[0007] Secondly, a positioning method is provided, including:

[0008] The second communication device sends the first location request information;

[0009] The second communication device receives a target artificial intelligence network model and / or target artificial intelligence network model parameters, wherein the target artificial intelligence network model is used to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal.

[0010] Thirdly, a positioning device is provided, comprising:

[0011] The first receiving module is used to receive the first location request information;

[0012] The first determining module is used to determine and / or send a target artificial intelligence network model and / or target artificial intelligence network model parameters based on the first positioning request information. The target artificial intelligence network model is used to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal.

[0013] Fourthly, a positioning device is provided, comprising:

[0014] The first sending module is used to send the first location request information;

[0015] The first receiving module is used to receive the target artificial intelligence network model and / or the target artificial intelligence network model parameters, wherein the target artificial intelligence network model is used to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal.

[0016] Fifthly, a communication device is provided, the terminal including a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the method as described in the first or second aspect.

[0017] In a sixth aspect, a communication device is provided, including a processor and a communication interface, wherein the communication interface is used to receive first positioning request information; the processor is used to determine and / or send a target artificial intelligence network model and / or target artificial intelligence network model parameters according to the first positioning request information, wherein the target artificial intelligence network model is used to obtain or optimize positioning signal measurement information and / or location information of a target terminal.

[0018] In a seventh aspect, a communication device is provided, including a processor and a communication interface, wherein the communication interface is used to send a first positioning request information; and to receive a target artificial intelligence network model and / or target artificial intelligence network model parameters, wherein the target artificial intelligence network model is used to obtain or optimize positioning signal measurement information and / or location information of a target terminal.

[0019] Eighthly, a readable storage medium is provided, on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method as described in the first or second aspect.

[0020] In a ninth aspect, a chip is provided, the chip including a processor and a communication interface coupled to the processor, the processor being configured to run programs or instructions to implement the methods described in the first or second aspect.

[0021] In a tenth aspect, a computer program / program product is provided, the computer program / program product being stored in a storage medium, the computer program / program product being executed by at least one processor to implement the steps of the method as described in the first or second aspect.

[0022] In this embodiment of the application, the first communication device negotiates with the second communication device, based on the request of the second communication device, for obtaining or optimizing the positioning signal measurement information and / or location information of the target terminal, an artificial intelligence network model and / or artificial intelligence network model parameters, so as to use the negotiated artificial intelligence network model to obtain or optimize the positioning signal measurement information and / or location information of the target terminal, thereby reducing positioning errors and improving the accuracy of positioning results. Attached Figure Description

[0023] Figure 1 This is a block diagram of a wireless communication system applicable to embodiments of this application;

[0024] Figure 2 This is a schematic diagram of a neural network according to an embodiment of this application;

[0025] Figure 3 This is a schematic diagram of a neuron in an embodiment of this application;

[0026] Figure 4A This is a schematic flowchart of a positioning method according to an embodiment of this application;

[0027] Figure 4B This is a flowchart illustrating a positioning method according to another embodiment of this application;

[0028] Figure 5 This is a flowchart illustrating the positioning method of Embodiment 1 of this application;

[0029] Figure 6 This is a schematic diagram of the positioning device according to Embodiment 2 of this application;

[0030] Figure 7 This is a schematic diagram of the positioning device according to Embodiment 3 of this application.

[0031] Figure 8A This is a schematic diagram of the structure of a communication device according to an embodiment of this application;

[0032] Figure 8B This is a schematic diagram of the structure of a communication device according to another embodiment of this application;

[0033] Figure 9 This is a schematic diagram of the structure of a communication device according to another embodiment of this application;

[0034] Figure 10 This is a schematic diagram of the hardware structure of the terminal according to an embodiment of this application;

[0035] Figure 11 This is a schematic diagram of the hardware structure of a network-side device according to an embodiment of this application;

[0036] Figure 12 This is a schematic diagram of the hardware structure of a network-side device according to another embodiment of this application. Detailed Implementation

[0037] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0038] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same class, not limited in number; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0039] It is worth noting that the technologies described in this application are not limited to Long Term Evolution (LTE) / LTE-Advanced (LTE-A) systems, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency Division Multiple Access (SC-FDMA), and other systems. The terms "system" and "network" in this application are often used interchangeably, and the described technologies can be used with the systems and radio technologies mentioned above, as well as with other systems and radio technologies. The following description describes New Radio (NR) systems for illustrative purposes, and NR terminology is used in most of the following description; however, these technologies can also be applied to applications beyond NR systems, such as 6th generation (6G) radio systems. th Generation 6G communication system.

[0040] Figure 1This diagram illustrates a block diagram of a wireless communication system applicable to embodiments of this application. The wireless communication system includes a terminal 11 and a network-side device 12. Terminal 11 can be a mobile phone, tablet computer, laptop computer, personal digital assistant (PDA), handheld computer, netbook, ultra-mobile personal computer (UMPC), mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, vehicle-mounted device (VUE), pedestrian terminal (PUE), smart home (home devices with wireless communication capabilities, such as refrigerators, televisions, washing machines, or furniture), game console, personal computer (PC), ATM, or self-service machine, etc. Wearable devices include: smartwatches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart chains, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. It should be noted that the specific type of terminal 11 is not limited in this embodiment. Network-side equipment 12 may include access network equipment or core network equipment. Access network equipment 12 may also be referred to as radio access network equipment, radio access network (RAN), radio access network function, or radio access network unit. Access network equipment 12 may include base stations, WLAN access points, or WiFi nodes, etc. Base stations may be referred to as Node B, evolved Node B (eNB), access point, base transceiver station (BTS), radio base station, radio transceiver, Basic Service Set (BSS), Extended Service Set (ESS), home B node, home evolved B node, Transmitting Receiving Point (TRP), or any other suitable term in the field, as long as the same technical effect is achieved. The base station is not limited to specific technical terms. It should be noted that in this application embodiment, only a base station in an NR system is used as an example for description, and the specific type of base station is not limited.Core network equipment may include, but is not limited to, at least one of the following: core network node, core network function, Mobility Management Entity (MME), Access and Mobility Management Function (AMF), Session Management Function (SMF), User Plane Function (UPF), Policy Control Function (PCF), Policy and Charging Rules Function (PCRF), Edge Application Server Discovery Function (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), and Application Function. Functions include location management function (LMF), E-SMLC, and NWDAF (network data analytics function). It should be noted that this application embodiment only uses core network equipment in the NR system as an example for description, and does not limit the specific type of core network equipment.

[0041] The positioning method and communication device provided in this application will be described in detail below with reference to the accompanying drawings and through some embodiments and application scenarios.

[0042] The artificial intelligence (AI) network model involved in the embodiments of this application will be described first below.

[0043] Artificial intelligence network models have found widespread application across various fields. These models can be implemented in various ways, such as neural networks, decision trees, support vector machines, and Bayesian classifiers. This application uses neural networks as an example, but it does not limit the application of the other aforementioned artificial intelligence network models.

[0044] A schematic diagram of a neural network is shown below. Figure 2 As shown. The neural network is composed of neurons, such as... Figure 3 As shown in the diagram. Here, a1, a2, ..., aK are the inputs, w is the weight (multiplicative coefficient), b is the bias (additive coefficient), and σ(.) is the activation function. Common activation functions include Sigmoid, tanh, rectified linear function, and Rectified Linear Unit (ReLU).

[0045] The parameters of a neural network are optimized using optimization algorithms. Optimization algorithms are a class of algorithms that help us minimize or maximize an objective function (sometimes called a loss function). The objective function is often a mathematical combination of model parameters and data. For example, given data X and its corresponding label Y (i.e., the true value), we build a neural network model f(.). With the model, we can obtain the predicted output f(x) based on the input X, and calculate the difference between the predicted value and the true value (f(x) - Y), which is the loss function. Our goal is to find suitable values ​​w and b that minimize the value of the loss function. The smaller the loss value, the closer our model is to the reality.

[0046] Most common optimization algorithms are based on the error back propagation (BP) algorithm. The basic idea of ​​the BP algorithm is that the learning process consists of two parts: forward propagation of the signal and backward propagation of the error. During forward propagation, the input sample is introduced from the input layer, processed layer by layer by the hidden layers, and then propagated to the output layer. If the actual output of the output layer does not match the expected output, the process transitions to the error back propagation stage. Error back propagation involves propagating the output error back to the input layer layer by layer through the hidden layers, distributing the error to all units in each layer, thus obtaining the error signal of each unit. This error signal serves as the basis for adjusting the weights of each unit. This process of adjusting the weights through forward and backward propagation is repeated continuously. This 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 predetermined number of learning iterations is reached.

[0047] Common optimization algorithms include gradient descent, stochastic gradient descent (SGD), mini-batch gradient descent, momentum descent, Nesterov (named after the inventor, specifically stochastic gradient descent with momentum), Adagrad (Adaptive Gradient Descent), Adadelta, RMSprop (root mean square prop), and Adam (Adaptive Moment Estimation).

[0048] During error backpropagation, these optimization algorithms calculate the gradient based on the error / loss obtained from the loss function with respect to the current neuron, add the learning rate, previous gradients / derivatives / partial derivatives, etc., and then pass the gradient to the previous layer.

[0049] To address the issue that existing positioning methods based directly on positioning signal measurement results have errors and cannot meet requirements, please refer to... Figure 4A This application provides a positioning method, including:

[0050] Step 41A: The first communication device receives the first location request information;

[0051] Step 42A: The first communication device determines and / or sends the target artificial intelligence network model and / or target artificial intelligence network model parameters according to the first positioning request information. The target artificial intelligence network model is used to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal.

[0052] In this embodiment of the application, the first communication device negotiates with the second communication device, based on the request of the second communication device, for obtaining or optimizing the positioning signal measurement information and / or location information of the target terminal, an artificial intelligence network model and / or artificial intelligence network model parameters, so as to use the negotiated artificial intelligence network model to obtain or optimize the positioning signal measurement information and / or location information of the target terminal, thereby reducing positioning errors and improving the accuracy of positioning results.

[0053] In this embodiment of the application, the first communication device may be a network-side device, such as LMF, E-SMLC (Enhanced Service Mobile Location Center), or an artificial intelligence or machine learning processing function module, such as NWDAF.

[0054] In this embodiment of the application, the second communication device may be a terminal or a network-side device, such as an LMF, E-SMLC, or a base station.

[0055] In this embodiment of the application, optionally, the first communication device is a terminal or base station, or an LMF, and the second communication device is an artificial intelligence or machine learning processing function module, or an LMF.

[0056] In this embodiment of the application, optionally, the first location request information includes at least one of the following:

[0057] First measurement information;

[0058] The identifier ID of the artificial intelligence network model and / or the parameters of the artificial intelligence network model;

[0059] Artificial intelligence network models;

[0060] Some or all of the parameters of the artificial intelligence network model;

[0061] Complexity information of artificial intelligence network models;

[0062] Type information of artificial intelligence network models;

[0063] The first parameter information is used to determine the input information and / or output information of the artificial intelligence network model, or to determine the network structure and / or parameters of the artificial intelligence network model, or to determine the information reported or fed back for positioning.

[0064] The first instruction information is used to indicate whether to request the artificial intelligence network model and / or artificial intelligence network model parameters.

[0065] Furthermore, in one embodiment, when the first communication device is a terminal, the terminal receives first indication information, indicating that the artificial intelligence network model is used for the terminal to perform positioning, such as for the terminal to acquire or optimize positioning signal measurement information and / or location information; and / or; the terminal receives an identifier ID of the artificial intelligence network model and / or artificial intelligence network model parameters, indicating that the artificial intelligence network model is used for the terminal to perform positioning, such as for the terminal to acquire or optimize positioning signal measurement information and / or location information; and / or; the terminal receives complexity information of the artificial intelligence network model, indicating the condition or complexity threshold for the terminal to end the iteration of the artificial intelligence network model; and / or; the terminal receives some or all of the artificial intelligence network model parameters, used to update the artificial intelligence network model and / or parameters used by the terminal for positioning, such as for the terminal to acquire or optimize positioning signal measurement information and / or location information; the terminal receives type information of the artificial intelligence network model, used to assist the terminal in selecting the artificial intelligence network model and / or parameters used for positioning.

[0066] Furthermore, in one embodiment, when the first communication device is a network-side device, the network-side device receives first indication information, indicating that the terminal expects to use an artificial intelligence network model for positioning, such as the terminal hoping to obtain or optimize positioning signal measurement information and / or location information through the artificial intelligence network model; and / or; the network-side device receives an identifier ID of the artificial intelligence network model and / or artificial intelligence network model parameters, indicating that the artificial intelligence network model is used by the terminal for positioning, or expects to be activated and configured with the artificial intelligence network model and / or artificial intelligence network model parameters corresponding to the ID; and / or; the network-side device receives complexity information of the artificial intelligence network model, used to indicate the conditions or complexity threshold for the iteration of the artificial intelligence network model expected by the terminal; and / or; the network-side device receives some or all of the artificial intelligence network model parameters, used to indicate the artificial intelligence network model and / or parameters expected by the terminal for positioning, such as for the terminal to obtain or optimize positioning signal measurement information and / or location information; the network-side device receives type information of the artificial intelligence network model, used to indicate the artificial intelligence network model and / or parameters expected by the terminal for positioning.

[0067] Furthermore, in one embodiment, when the first communication device is a first network-side device, the first network-side device receives first indication information, instructing the second network-side device to expect the first network-side device to use an artificial intelligence network model for positioning, such as the second network-side device wishing to obtain or optimize positioning signal measurement information and / or location information through the artificial intelligence network model; and / or; the first network-side device receives an identifier ID of the artificial intelligence network model and / or artificial intelligence network model parameters, instructing the second network-side device to indicate that the artificial intelligence network model is used by the first network-side device for positioning, or expecting the artificial intelligence network model and / or artificial intelligence network model parameters corresponding to the ID to be activated and configured; and / or; the first network-side device receives complexity information of the artificial intelligence network model, used as a condition or complexity threshold for the iteration of the artificial intelligence network model indicated by the second network-side device; and / or; the first network-side device receives some or all of the artificial intelligence network model parameters, used by the first network-side device to indicate the artificial intelligence network model and / or parameters used for positioning; the first network-side device receives type information of the artificial intelligence network model, used to indicate the artificial intelligence network model and / or parameters used for positioning.

[0068] The following sections will explain each of the first location request information.

[0069] In this embodiment of the application, optionally, the first measurement information is used to assist the first communication device in determining and / or sending the target artificial intelligence network model and / or the target artificial intelligence network model parameters, and the first measurement information includes at least one of the following:

[0070] Positioning signal measurement information of the target terminal;

[0071] The location information of the target terminal; the location information can be absolute location information (e.g., latitude and longitude information) or relative location information.

[0072] Error information, which includes at least one of the following: position error value, measurement error value, artificial intelligence network model error value, or parameter error value.

[0073] Optionally, the aforementioned positioning signal measurement information and / or position signal can be obtained through Observed Time Difference of Arrival (OTDOA), Global Navigation Satellite System (GNSS), downlink time difference of arrival (DL-TDOA), uplink time difference of arrival (UL-TDOA), uplink angle of arrival (AoA), angle of departure (AoD), round trip time (RTT), multi-station round trip time (Multi-RTT), Bluetooth, sensors, or Wi-Fi.

[0074] In this embodiment of the application, optionally, the positioning signal measurement information of the target terminal includes at least one of the following:

[0075] Location signal time difference (RSTD) measurement results;

[0076] Round Trip Time (RTT);

[0077] Angle of Arrival (AOA) measurement results;

[0078] Angle of Departure (AOD) measurement results;

[0079] Reference Signal Received Power (RSRP).

[0080] In this embodiment of the application, optionally, the positioning signal measurement information is associated with or includes at least one line of sight (LOS) indication information.

[0081] In this embodiment of the application, optionally, the positioning signal measurement information includes positioning signal measurement information for at least one path.

[0082] In this embodiment of the application, optionally, the positioning signal measurement information includes at least one of the following:

[0083] 1) Path angular information; for example, path AOA, path AoD;

[0084] 2) Time information of the path;

[0085] The time information includes, for example, the reference signal time difference (ReferenceSignal Time Difference, Additional Path RSTD, or Path RSTD) measurement results, the round-trip time (RTT) of the path, or the TOA or rx-tx (receive-transmit) measurement results of the path.

[0086] 3) Path energy information; for example, RSRPP (path RSRP);

[0087] 4) LOS indication information.

[0088] In this embodiment of the application, optionally, the positioning signal measurement information of the at least one path includes at least one LOS indication information. More optionally, the positioning signal measurement information of each path includes one LOS indication information.

[0089] Optionally, in one embodiment, the positioning signal measurement information of at least one path can be understood as the positioning signal measurement information corresponding to a timestamp including the positioning signal measurement information of at least two paths; or, in another embodiment, it can be understood as a positioning signal identification information associated with the positioning signal measurement information of at least one path.

[0090] Furthermore, in one embodiment, the positioning signal measurement information includes positioning signal measurement information for at least one path and positioning signal measurement information without path distinction, such as RSRP and RSRPP being reported together, path RSTD and RSRPP being reported together, path RSTD and RSTD being reported together, path rx-tx and RSRPP being reported together, etc.

[0091] In this embodiment of the application, optionally, the LOS indication information is used to indicate one of the following:

[0092] LOS status between the target terminal and the target transmit / receive point (TRP);

[0093] LOS status of the target terminal;

[0094] The LOS status between the target terminal and one or more positioning reference signal resources of the target TRP.

[0095] In this embodiment of the application, optionally, the LOS indication information includes at least one of the following:

[0096] 1) The first bit used to indicate whether it is LOS or non-line-of-sight NLOS;

[0097] For example, 0 or 1 can be used to represent LOS or NLOS.

[0098] 2) The second bit used to indicate the probability of LOS;

[0099] For example, {0, 0.X, 2*0.X, ..., 1}M bits are used to indicate the probability of LOS.

[0100] 3) The third bit used to indicate the confidence level as LOS.

[0101] In this embodiment of the application, optionally, the LOS indication information includes at least one of the following:

[0102] The first bit is used to indicate whether the positioning signal measurement is LOS or non-line of sight (NLOS);

[0103] The second bit is used to indicate the probability that the positioning signal measurement is LOS;

[0104] The third bit is used to indicate the confidence level of the positioning signal measurement as LOS.

[0105] The LOS (Low-Solution Time) situation between the terminal and the target transmit / receive point (TRP) can be understood as whether the relationship between the terminal and the TRP is LOS or NLOS, or whether it contains LOS, or the probability of containing LOS.

[0106] The LOS status of the terminal can be understood as the terminal having at least N LOS, or at most M LOS.

[0107] This refers to the LOS (Loss of Order) status between the terminal and one or more positioning reference signal resources of the target TRP. It can be understood that this indicates either the LOS status of positioning reference signal A between the terminal and the target TRP, or the LOS status of positioning reference signal B between the terminal and the target TRP, where positioning reference signals A and B are designated as selected positioning reference signals; and the number can be expanded to include A, B, C, D, E, F, G, H, etc.

[0108] In this embodiment of the application, optionally, the artificial intelligence network model parameters include at least one of the following:

[0109] 1) The structure of artificial intelligence network models;

[0110] The structure includes, for example, at least one of the following:

[0111] Fully connected neural networks, convolutional neural networks, recurrent neural networks, or residual networks;

[0112] Combinations of multiple small networks, such as fully connected + convolution, convolution + residual, etc.

[0113] The number of hidden layers;

[0114] The connection methods between the input layer and hidden layers, the connection methods between multiple hidden layers, and / or the connection methods between hidden layers and the output layer;

[0115] The number of neurons in each layer.

[0116] 2) The multiplicative coefficients, additive coefficients, and / or activation functions of each neuron in the artificial intelligence network model;

[0117] 3) Complexity information of artificial intelligence network models;

[0118] Examples include 1 flop, 100 iterations, hardware conditions, and computational conditions.

[0119] 4) The expected number of training iterations for the artificial intelligence network model;

[0120] 5) Application documentation of artificial intelligence network models;

[0121] In one embodiment, this can be understood as a general application of artificial intelligence network models;

[0122] 6) Input format for artificial intelligence network models;

[0123] In one embodiment, this can be understood as the information elements, information format, value range, etc., input to the artificial intelligence network model. For example, the input format of the first parameter information, the input format of the first measurement information, etc.

[0124] 7) Output format of artificial intelligence network models.

[0125] In one embodiment, this can be understood as the information elements, information format, and value range output by the artificial intelligence network model. Examples include the output format of second parameter information, and the output format of positioning signal measurement information and / or location information.

[0126] In this embodiment of the application, optionally, the type information of the artificial intelligence network model includes at least one of the following:

[0127] Fully connected model;

[0128] The first type is where the location information of the terminal is obtained by the first communication device based on an artificial intelligence network model;

[0129] The second type is the location signal measurement information of the terminal obtained by the first communication device based on the artificial intelligence network model;

[0130] The third type is where the second communication device assists the first communication device in obtaining the terminal's positioning signal measurement information based on an artificial intelligence network model;

[0131] The fourth type is where the second communication device assists the first communication device in obtaining the terminal's location information based on an artificial intelligence network model;

[0132] The sixth type is where the first communication device instructs the second communication device to obtain the terminal's location information based on an artificial intelligence network model;

[0133] The seventh type is where the first communication device instructs the second communication device to obtain the terminal's location information based on an artificial intelligence network model;

[0134] The eighth type is one of the terminal's positioning signal measurement information and the terminal's location information obtained by the first communication device according to the artificial intelligence network model, and the other of the terminal's positioning signal measurement information or the terminal's location information obtained by the second communication device according to the artificial intelligence network model.

[0135] Unsupervised or supervised models.

[0136] In this embodiment of the application, optionally, the first parameter information includes at least one of the following:

[0137] The length of the channel impulse response (CIR);

[0138] The start time of the channel impulse response (CIR);

[0139] The number of multipaths; e.g., a maximum of 20 paths.

[0140] Obtain the bandwidth of the CIR;

[0141] Frequency domain information of the signal, such as Frequency layer ID, band D, Point A, ARFCN, or Start PRB.

[0142] Time information; further optional information may include time information for at least one path.

[0143] Phase information; further optional, it may include phase information for at least one path;

[0144] Energy information: Further optional information may include energy information for at least one path;

[0145] Angle information; further optional features include the angle information of at least one path;

[0146] CIR information based on a third artificial intelligence model or parameters processed by a third artificial intelligence model;

[0147] Multipath information based on a third artificial intelligence model or parameters of a third artificial intelligence model, such as multipath delay, multipath energy, and multipath angle.

[0148] In this embodiment of the application, optionally, the first parameter information further includes at least one of the following:

[0149] The third artificial intelligence network model structure;

[0150] Third, the parameters of the artificial intelligence network model.

[0151] In this embodiment of the application, optionally, the first location request information further includes:

[0152] LOS confidence level;

[0153] The second piece of information may be related to the LOS indication information.

[0154] In this embodiment of the application, optionally, the second information includes at least one of the following:

[0155] 1) A second artificial intelligence network model used to determine LOS indication information;

[0156] This may include some key parameters of the artificial intelligence network model. If it is based on the neural network, it may be necessary to tell the network the composition of the training set, the specific parameters of the training, the hyperparameters of the neural network, etc. It may also be possible to directly tell the network the corresponding neural network parameters.

[0157] 2) Channel Impulse Response (CIR);

[0158] 3) Power of the first diameter;

[0159] 4) Power of multipath paths;

[0160] In this embodiment of the application, the power can be absolute power or relative power. Relative power is, for example, power relative to the signal RSRP, such as multipath relative to the first path, or multipath relative to the signal.

[0161] 5) Time delay of the first diameter;

[0162] 6) Time of Arrival (TOA) of the first path;

[0163] 7) Reference Signal Time Difference (RSTD) of the first path;

[0164] 8) Multipath delay;

[0165] In this embodiment of the application, the time delay can be an absolute time delay or a relative time delay. The relative time delay is, for example, relative to the signal time delay, such as multipath relative to the first path, or multipath relative to the signal.

[0166] 9) Multipath TOA;

[0167] 10) Multipath RSTD;

[0168] 11) Angle of arrival of the first diameter;

[0169] 12) Angle of arrival of multipaths;

[0170] 13) Antenna subcarrier phase difference of the first path;

[0171] 14) Multipath antenna subcarrier phase difference;

[0172] 15) Average excess delay;

[0173] 16) Root mean square delay extension;

[0174] 17) Coherence bandwidth.

[0175] In this embodiment of the application, optionally, the first communication device determines and / or sends the target artificial intelligence network model and / or target artificial intelligence network model parameters based on the first positioning request information, and further includes the following steps beforehand:

[0176] The first communication device sends or receives pre-configuration information, the pre-configuration information including at least one of the following:

[0177] One or more pre-configured artificial intelligence network models;

[0178] One or more sets of pre-configured artificial intelligence network model parameters.

[0179] In this embodiment, optionally, each pre-configured AI network model or AI network model parameter includes an ID. The ID can be described in two ways: The first way is a numerical value from 1 to N or from 0 to N-1, where N is the maximum number of pre-configured AI network models or parameters, and each value corresponds to a unique AI network model or parameter. The second way is to represent it with N bits, where N is the maximum number of pre-configured AI network models or parameters, and the i-th bit represents the i-th AI network model or parameter, where i belongs to (1 to N).

[0180] In this embodiment of the application, optionally, the first communication device determines and / or sends the target artificial intelligence network model and / or target artificial intelligence network model parameters according to the first positioning request information, and further includes:

[0181] The first communication device sends at least two target AI network models and / or target AI network model parameters. Optionally, the first communication device sends at least two target AI network models and / or target AI network model parameters to the second communication device that sent the first location request.

[0182] Optionally, the first communication device sends a target artificial intelligence network model and / or target artificial intelligence network model parameters to the second communication device and the third communication device respectively.

[0183] The optional at least two target AI network models and / or target AI network model parameters can be functionally decomposed into one AI network model and parameters. For example, target AI network model 1 performs function 1 (such as information preprocessing, such as obtaining the terminal's positioning signal measurement information, such as obtaining LOS indication information), and target AI network model 2 performs function 2 (such as information reverse preprocessing, such as information decoding, such as obtaining the terminal's location information).

[0184] Optionally, the at least two target AI network models and / or target AI network model parameters can be distributed AI network models and parameters, such as target AI network model 1 updating and iterating on target AI network model 2.

[0185] In this embodiment, optionally, if the first positioning request information includes first measurement information, the first measurement information is used to assist the first communication device in determining and / or sending the target AI network model and / or target AI network model parameters. That is, in one embodiment, the target AI network model and / or target AI network model parameters sent by the first communication network are selected based on the first measurement information.

[0186] In this embodiment, optionally, if the first location request information includes a target ID, the first communication device selects the target AI network model and / or target AI network model parameters corresponding to the target ID. That is, in one embodiment, the target AI network model and / or target AI network model parameters sent by the first communication network are the target AI network model and / or target AI network model parameters corresponding to the target ID.

[0187] Optionally, in this embodiment, if the first location request information includes an artificial intelligence network model, the first communication device selects a target artificial intelligence network model that matches the artificial intelligence network model in the first location request information. That is, in one embodiment, the target artificial intelligence network model and / or the target artificial intelligence network model parameters sent by the first communication network are associated with the artificial intelligence network model included in the first location request information.

[0188] In this embodiment of the application, optionally, if the first location request information includes some or all of the artificial intelligence network model parameters, the first communication device selects the target artificial intelligence network model parameters that match the artificial intelligence network model parameters in the first location request information.

[0189] Optionally, in this embodiment, if the first location request information includes complexity information of an artificial intelligence network model, the first communication device selects a target artificial intelligence network model that matches the complexity information in the first location request information. That is, in one embodiment, the target artificial intelligence network model and / or the target artificial intelligence network model parameters sent by the first communication network match the complexity information in the first location request information.

[0190] Optionally, in this embodiment, if the first location request information includes type information of an artificial intelligence network model, the first communication device selects a target artificial intelligence network model that matches the type information in the first location request information. That is, in one embodiment, the target artificial intelligence network model and / or the target artificial intelligence network model parameters sent by the first communication network match the type information in the first location request information.

[0191] In this embodiment of the application, optionally, if the first location request information includes first parameter information, the first communication device selects a target artificial intelligence network model or parameter that matches the first parameter information.

[0192] In this embodiment of the application, optionally, if the first location request information includes first indication information, and the first indication information indicates a request for an artificial intelligence network model and / or artificial intelligence network model parameters, the first communication device determines and / or sends the target artificial intelligence network model and / or target artificial intelligence network model parameters; if the first indication information indicates no request for an artificial intelligence network model and / or artificial intelligence network model parameters, the first communication device does not need to determine and / or send the target artificial intelligence network model and / or target artificial intelligence network model parameters.

[0193] In this embodiment of the application, optionally, the positioning method further includes:

[0194] The first communication network device sends second parameter information, which includes at least one of the following:

[0195] CIR;

[0196] Multipath measurement results of the first positioning reference signal;

[0197] Time information;

[0198] Phase information;

[0199] Angle information;

[0200] Energy information;

[0201] The second indication information is used to indicate whether to obtain or optimize other second parameter information through the fourth artificial intelligence network model structure or the fourth artificial intelligence network model parameters, or whether to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal using the target artificial intelligence network model;

[0202] Fourth artificial intelligence network model structure;

[0203] Fourth, the parameters of the artificial intelligence network model.

[0204] Optionally, the second parameter information is determined based on the first parameter information.

[0205] In one embodiment, the first communication device sends the CIR length information, expecting the second communication device to return a CIR length that matches the CIR length information in the first parameter.

[0206] In one embodiment, a first communication device sends the length information of the CIR and the structure and / or parameters of a fourth artificial intelligence network model, expecting a second communication device to return a CIR that satisfies the length information of the CIR in the first parameters, obtained according to the structure and / or parameters of the fourth artificial intelligence network model.

[0207] In one embodiment, a first communication device sends multipath number information, expecting a second communication device to return multipath number information that is the same as the multipath number information in the first parameter.

[0208] In one embodiment, a first communication device sends multipath number information and time information, expecting a second communication device to return multipath number information that includes the multipath number information in the first parameter and also includes multipath time information.

[0209] In one embodiment, a first communication device transmits multipath number information, time information, and energy information, expecting a second communication device to return multipath number information that is the multipath number information in the first parameter and includes multipath time information and energy information.

[0210] In one embodiment, a first communication device sends multipath number information, time information, energy information, and a fourth artificial intelligence network model structure and / or fourth artificial intelligence network model parameters, expecting a second communication device to return multipath number information that is the multipath number information in the first parameters and includes multipath time information and energy information.

[0211] In this embodiment of the application, optionally, the positioning method further includes:

[0212] The first communication device reports capability information, which includes at least one of the following:

[0213] Whether it supports an artificial intelligence network model or artificial intelligence network model parameters based on the first location request information;

[0214] Does it support multiple AI network models or multiple sets of AI network model parameters?

[0215] Does it support using artificial intelligence network models or artificial intelligence network model parameters to obtain or optimize positioning signal measurement information and / or location information?

[0216] The communication device in this application embodiment may be a terminal, an access network device, or a core network device.

[0217] The communication device in this application embodiment may be a terminal, an access network device, or a core network device.

[0218] Please refer to Figure 4B This application provides a positioning method, including:

[0219] Step 41B: The second communication device sends the first location request information;

[0220] Step 42B: The second communication device receives the target artificial intelligence network model and / or the target artificial intelligence network model parameters, wherein the target artificial intelligence network model is used to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal.

[0221] In this embodiment of the application, the first communication device negotiates with the second communication device, based on the request of the second communication device, for obtaining or optimizing the positioning signal measurement information and / or location information of the target terminal, an artificial intelligence network model and / or artificial intelligence network model parameters, so as to use the negotiated artificial intelligence network model to obtain or optimize the positioning signal measurement information and / or location information of the target terminal, thereby reducing positioning errors and improving the accuracy of positioning results.

[0222] In this embodiment of the application, optionally, the first location request information includes at least one of the following:

[0223] First measurement information;

[0224] The identifier ID of the artificial intelligence network model and / or the parameters of the artificial intelligence network model;

[0225] Artificial intelligence network models;

[0226] Some or all of the parameters of the artificial intelligence network model;

[0227] Complexity information of artificial intelligence network models;

[0228] Type information of artificial intelligence network models;

[0229] The first parameter information is used to determine the input information and / or output information of the artificial intelligence network model, or to determine the network structure and / or parameters of the artificial intelligence network model, or to determine the information reported or fed back for positioning.

[0230] The first instruction information is used to indicate whether to request the artificial intelligence network model and / or artificial intelligence network model parameters.

[0231] In this embodiment of the application, optionally, the first measurement information includes at least one of the following:

[0232] Positioning signal measurement information of the target terminal;

[0233] Location information of the target terminal;

[0234] Error information, which includes at least one of the following: position error value, measurement error value, artificial intelligence network model error value, or parameter error value.

[0235] In this embodiment of the application, optionally, the positioning signal measurement information of the target terminal includes at least one of the following:

[0236] Channel response information of the positioning signal;

[0237] Positioning signal time difference (RSTD) measurement results;

[0238] Round-trip time (RTT);

[0239] Angle of arrival (AOA) measurement results;

[0240] Departure Angle of Od (AOD) measurement results;

[0241] Positioning signal received power RSRP.

[0242] In this embodiment of the application, optionally, the positioning signal measurement information may be associated with or include at least one line-of-sight (LOS) indication information, or may include positioning signal measurement information for at least one path.

[0243] In this embodiment of the application, optionally, the LOS indication information includes at least one of the following:

[0244] The first bit is used to indicate whether it is LOS or non-line-of-sight NLOS;

[0245] The second bit is used to indicate the probability of LOS;

[0246] The third bit is used to indicate the confidence level for LOS.

[0247] In this embodiment of the application, optionally, the LOS indication information includes at least one of the following:

[0248] The first bit is used to indicate whether the positioning signal measurement is LOS or NLOS (non-line-of-sight);

[0249] The second bit is used to indicate the probability that the positioning signal measurement is LOS;

[0250] The third bit is used to indicate the confidence level of the positioning signal measurement as LOS.

[0251] In this embodiment of the application, optionally, the artificial intelligence network model parameters include at least one of the following:

[0252] The structure of artificial intelligence network models;

[0253] The multiplicative coefficients, additive coefficients, and / or activation functions of each neuron in an artificial intelligence network model;

[0254] Complexity information of artificial intelligence network models;

[0255] The expected number of training iterations for an artificial intelligence network model;

[0256] Application documentation of artificial intelligence network models;

[0257] Input format for artificial intelligence network models;

[0258] The output format of an artificial intelligence network model.

[0259] In this embodiment of the application, optionally, the type information of the artificial intelligence network model includes at least one of the following:

[0260] Fully connected model;

[0261] The first type is where the location information of the terminal is obtained by the first communication device based on an artificial intelligence network model;

[0262] The second type is the location signal measurement information of the terminal obtained by the first communication device based on the artificial intelligence network model;

[0263] The third type is where the second communication device assists the first communication device in obtaining the terminal's positioning signal measurement information based on an artificial intelligence network model;

[0264] The fourth type is where the second communication device assists the first communication device in obtaining the terminal's location information based on an artificial intelligence network model;

[0265] The sixth type is where the first communication device instructs the second communication device to obtain the terminal's location information based on an artificial intelligence network model;

[0266] The seventh type is where the first communication device instructs the second communication device to obtain the terminal's location information based on an artificial intelligence network model;

[0267] The eighth type is one of the terminal's positioning signal measurement information and the terminal's location information obtained by the first communication device according to the artificial intelligence network model, and the other of the terminal's positioning signal measurement information or the terminal's location information obtained by the second communication device according to the artificial intelligence network model.

[0268] Unsupervised or supervised models.

[0269] In this embodiment of the application, optionally, the first location request information further includes:

[0270] LOS confidence level;

[0271] Second information.

[0272] In this embodiment of the application, optionally, the second information includes at least one of the following:

[0273] A second artificial intelligence network model used to determine LOS indication information;

[0274] Channel Impulse Response (CIR);

[0275] Power of the first diameter;

[0276] Power of multipath propagation;

[0277] The time delay of the first diameter;

[0278] Time of arrival (TOA) of the first path;

[0279] Reference signal time difference (RSTD) of the first path;

[0280] Multipath delay;

[0281] Multipath TOA;

[0282] Multipath RSTD;

[0283] Angle of arrival of the primary diameter;

[0284] Angle of arrival of multipath;

[0285] The phase difference between the antenna subcarriers in the first path;

[0286] Multipath antenna subcarrier phase difference;

[0287] Average excess latency;

[0288] Root mean square delay spread;

[0289] Coherent bandwidth.

[0290] In this embodiment of the application, optionally, the first parameter information includes at least one of the following:

[0291] The length of the CIR;

[0292] The number of multipaths;

[0293] CIR bandwidth;

[0294] Frequency domain information of the signal;

[0295] Time information;

[0296] Phase information;

[0297] Angle information;

[0298] Energy information;

[0299] CIR information based on a third artificial intelligence model or parameters processed by a third artificial intelligence model;

[0300] Multipath information based on a third artificial intelligence model or the parameters of a third artificial intelligence model.

[0301] In this embodiment of the application, optionally, the first parameter information further includes at least one of the following:

[0302] The third artificial intelligence network model structure;

[0303] Third, the parameters of the artificial intelligence network model.

[0304] In this embodiment of the application, optionally, the positioning method further includes: the second communication device sending second parameter information, the second parameter information including at least one of the following:

[0305] CIR;

[0306] Multipath measurement results of the first positioning reference signal;

[0307] Time information;

[0308] Phase information;

[0309] Angle information;

[0310] Energy information;

[0311] The second indication information is used to indicate whether to obtain or optimize other second parameter information through the fourth artificial intelligence network model structure or the fourth artificial intelligence network model parameters, or whether to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal using the target artificial intelligence network model;

[0312] Fourth artificial intelligence network model structure;

[0313] Fourth, the parameters of the artificial intelligence network model.

[0314] In this embodiment of the application, optionally, the second parameter information is determined based on the first parameter information.

[0315] The positioning method of this application will be explained below in conjunction with specific application scenarios.

[0316] Please refer to Figure 5 , Figure 5 This is a flowchart illustrating the positioning method according to Embodiment 1 of this application. The positioning method includes:

[0317] Step 51: The second communication device (terminal, access network device or local management function (LMF)) sends the first location request information to the first communication device (LMF or NWADF). The specific content of the first location request information can be found in the description in the above embodiments, and will not be repeated here.

[0318] Step 52: The first communication device determines the target artificial intelligence network model and / or the target artificial intelligence network model parameters based on the first positioning request information;

[0319] The method for determining the target AI network model and / or the target AI network model parameters based on the first location request information can be found in the description in the above embodiments, and will not be repeated here.

[0320] Step 53: The first communication device sends the target artificial intelligence network model and / or the target artificial intelligence network model parameters to the second communication device.

[0321] Please refer to Figure 6 , Figure 6 This is a flowchart illustrating the positioning method of Embodiment 2 of this application. The positioning method includes:

[0322] Step 61: The first communication device (LMF or NWADF) sends the pre-configured artificial intelligence network model and / or artificial intelligence network model parameters to the first communication device (terminal, access network device or LMF);

[0323] Step 62: The second communication device (terminal, access network device or LMF) sends a first location request information to the first communication device (LMF or NWADF), the first location request information including the ID of the pre-configured artificial intelligence network model and / or the parameters of the artificial intelligence network model;

[0324] Step 63: The first communication device determines the target artificial intelligence network model and / or target artificial intelligence network model parameters corresponding to the ID;

[0325] Step 64: The first communication device sends the target artificial intelligence network model and / or the target artificial intelligence network model parameters to the second communication device.

[0326] Please refer to Figure 7 , Figure 7 This is a flowchart illustrating the positioning method of Embodiment 3 of this application. The positioning method includes:

[0327] Step 71: The second communication device (LMF or NWADF) sends a first positioning request information to the first communication device (terminal, access network device or LMF). The first positioning request information includes first parameter information, such as CIR information based on a third artificial intelligence model or parameters processed by the third artificial intelligence model, or positioning signal measurement results based on a third artificial intelligence model or parameters processed by the third artificial intelligence model.

[0328] Step 72: The first communication device determines the target artificial intelligence network model and / or target artificial intelligence network model parameters according to the first positioning request information. Based on the determined target artificial intelligence network model and / or target artificial intelligence network model parameters, the positioning signal measurement result and / or location information corresponding to the first parameter information can be obtained.

[0329] The following is a supplementary explanation of the positioning method described in this application.

[0330] The artificial intelligence network model in this application includes one or more artificial intelligence network models, and / or one or more sets of artificial intelligence network model parameters.

[0331] The artificial intelligence network model in this application embodiment can be a machine learning model, a neural network model, or a deep neural network model, including but not limited to:

[0332] Convolutional Neural Networks (CNNs), such as GoogLeNet and AlexNet;

[0333] Recursive Neural Network (RNN) and Long Short-Term Memory (LSTM);

[0334] Recursive Neural Tensor Network (RNTN);

[0335] Generative Adversarial Networks (GANs);

[0336] Deep Belief Networks (DBN);

[0337] Restricted Boltzmann Machine (RBM), etc.

[0338] In this embodiment of the application, the parameters of the artificial intelligence network model include parameters of the machine learning model, the neural network model, or the deep neural network, including but not limited to at least one of the following: weights, step size, mean, and variance of each layer.

[0339] In this embodiment of the application, optionally, the input information of the artificial intelligence network model includes at least one of the following:

[0340] Channel impulse response (CIR);

[0341] Power Delay Profile (PDP);

[0342] Reference Signal Time Difference (RSTD);

[0343] Round-trip time (RTT);

[0344] Angle of Arrival (AoA);

[0345] RSRP;

[0346] TOA;

[0347] Power of the first diameter;

[0348] Power of multipath propagation;

[0349] The time delay of the first diameter;

[0350] TOA of the first diameter;

[0351] RSTD of the first diameter;

[0352] Multipath delay;

[0353] Multipath TOA;

[0354] Multipath RSTD;

[0355] Angle of arrival of the primary diameter;

[0356] Angle of arrival of multipath;

[0357] The phase difference between the antenna subcarriers in the first path;

[0358] Multipath antenna subcarrier phase difference;

[0359] LoS / NLoS identification information

[0360] Average excess latency;

[0361] Root mean square delay extension

[0362] Coherent bandwidth, etc.

[0363] In this embodiment of the application, the above input information can be single-site or multi-site. The single-site or multi-site information is determined by the number of base stations issued by the network side. The number of base stations includes 1-maxTRPNumber, where maxTRPNumber is the maximum number of TRPs in a specific scenario.

[0364] The output information of the artificial intelligence network model includes at least one of the following:

[0365] Location coordinate information;

[0366] Reference Signal Time Difference (RSTD);

[0367] Round-trip time (RTT);

[0368] Angle of Arrival (AoA);

[0369] RSRP;

[0370] TOA;

[0371] Power of the first diameter;

[0372] Power of multipath propagation;

[0373] The time delay of the first diameter;

[0374] Time of arrival (TOA) of the first path;

[0375] Reference signal time difference (RSTD) of the first path;

[0376] Multipath delay;

[0377] Multipath TOA;

[0378] Multipath RSTD;

[0379] Angle of arrival of the primary diameter;

[0380] Angle of arrival of multipath;

[0381] LoS / NLoS identification information.

[0382] The artificial intelligence network model in this application embodiment may further include: error model information, used to calibrate location, measurement, artificial intelligence network model and / or parameter errors, including at least one of the following:

[0383] 1) Error value estimated by the network side; further, the error value includes at least one of the following: position error value, measurement error value, artificial intelligence network model error value, or parameter error value;

[0384] 2) One or more network-side predicted error models; further, the error models include one of the following models: location error model, measurement error model, parameter error model.

[0385] The artificial intelligence network model in this application embodiment may further include: preprocessed model information for processing terminal positioning signal measurement information, including at least one of the following:

[0386] Filter parameters or structure;

[0387] Convolutional layer parameters or structure;

[0388] Pooling layer parameters or structure;

[0389] Discrete Cosine Transform (DCT) parameters or structure;

[0390] Wavelet transform parameters or structure;

[0391] The parameters or structure of the positioning signal measurement information processing method (such as sampling, truncation, normalization, simultaneous merging, etc.).

[0392] Optionally, the positioning signal measurement information includes at least one of the following:

[0393] Channel impulse response (CIR);

[0394] Time delay power spectrum;

[0395] Reference Signal Time Difference (RSTD);

[0396] Round-trip time (RTT);

[0397] Angle of Arrival (AoA);

[0398] RSRP;

[0399] TOA;

[0400] Power of the first diameter;

[0401] Power of multipath propagation;

[0402] The time delay of the first diameter;

[0403] Time of arrival (TOA) of the first path;

[0404] Reference signal time difference (RSTD) of the first path;

[0405] Multipath delay;

[0406] Multipath TOA;

[0407] Multipath RSTD;

[0408] Angle of arrival of the primary diameter;

[0409] Angle of arrival of multipath;

[0410] The phase difference between the antenna subcarriers in the first path;

[0411] Multipath antenna subcarrier phase difference;

[0412] Reference signal waveform;

[0413] Correlation sequences of reference signals, etc.

[0414] In this embodiment of the application, the error model information and / or preprocessing model information can be sent in association with an artificial intelligence network model used to optimize location information; each artificial intelligence network model corresponds to one error model information and / or preprocessing model information.

[0415] The positioning method provided in this application can be executed by a positioning device. This application uses the example of a positioning device executing the positioning method to illustrate the positioning device provided in this application.

[0416] Please refer to Figure 8A This application also provides a positioning device 80A, comprising:

[0417] The first receiving module 81A is used to receive the first positioning request information;

[0418] The first determining module 82A is used to determine and / or send a target artificial intelligence network model and / or target artificial intelligence network model parameters based on the first positioning request information. The target artificial intelligence network model is used to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal.

[0419] In the embodiments of this application, according to the request, an artificial intelligence network model and / or artificial intelligence network model parameters are negotiated to obtain or optimize the positioning signal measurement information and / or location information of the target terminal, so as to use the negotiated artificial intelligence network model to obtain or optimize the positioning signal measurement information and / or location information of the target terminal, thereby reducing positioning errors and improving the accuracy of positioning results.

[0420] In this embodiment of the application, optionally, the first location request information includes at least one of the following:

[0421] First measurement information;

[0422] The identifier ID of the artificial intelligence network model and / or the parameters of the artificial intelligence network model;

[0423] Artificial intelligence network models;

[0424] Some or all of the parameters of the artificial intelligence network model;

[0425] Complexity information of artificial intelligence network models;

[0426] Type information of artificial intelligence network models;

[0427] The first parameter information is used to determine the input information and / or output information of the artificial intelligence network model, or to determine the network structure and / or parameters of the artificial intelligence network model, or to determine the information reported or fed back for positioning.

[0428] The first instruction information is used to indicate whether to request the artificial intelligence network model and / or artificial intelligence network model parameters.

[0429] In this embodiment of the application, optionally, the first measurement information includes at least one of the following:

[0430] Positioning signal measurement information of the target terminal;

[0431] Location information of the target terminal;

[0432] Error information, which includes at least one of the following: position error value, measurement error value, artificial intelligence network model error value, or parameter error value.

[0433] In this embodiment of the application, optionally, the positioning signal measurement information of the target terminal includes at least one of the following:

[0434] Channel response information of the positioning signal;

[0435] Positioning signal time difference (RSTD) measurement results;

[0436] Round-trip time (RTT);

[0437] Angle of arrival (AOA) measurement results;

[0438] Departure Angle of Od (AOD) measurement results;

[0439] Positioning signal received power RSRP.

[0440] In this embodiment of the application, optionally, the positioning signal measurement information may be associated with or include at least one line-of-sight (LOS) indication information, or may include positioning signal measurement information for at least one path.

[0441] In this embodiment of the application, optionally, the LOS indication information includes at least one of the following:

[0442] The first bit is used to indicate whether it is LOS or non-line-of-sight NLOS;

[0443] The second bit is used to indicate the probability of LOS;

[0444] The third bit is used to indicate the confidence level for LOS.

[0445] In this embodiment of the application, optionally, the LOS indication information includes at least one of the following:

[0446] The first bit is used to indicate whether the positioning signal measurement is LOS or NLOS (non-line-of-sight);

[0447] The second bit is used to indicate the probability that the positioning signal measurement is LOS;

[0448] The third bit is used to indicate the confidence level of the positioning signal measurement as LOS.

[0449] In this embodiment of the application, optionally, the artificial intelligence network model parameters include at least one of the following:

[0450] The structure of artificial intelligence network models;

[0451] The multiplicative coefficients, additive coefficients, and / or activation functions of each neuron in an artificial intelligence network model;

[0452] Complexity information of artificial intelligence network models;

[0453] The expected number of training iterations for an artificial intelligence network model;

[0454] Application documentation of artificial intelligence network models;

[0455] Input format for artificial intelligence network models;

[0456] The output format of an artificial intelligence network model.

[0457] In this embodiment of the application, optionally, the type information of the artificial intelligence network model includes at least one of the following:

[0458] Fully connected model;

[0459] The first type is where the location information of the terminal is obtained by the first communication device based on an artificial intelligence network model;

[0460] The second type is the location signal measurement information of the terminal obtained by the first communication device based on the artificial intelligence network model;

[0461] The third type is where the second communication device assists the first communication device in obtaining the terminal's positioning signal measurement information based on an artificial intelligence network model;

[0462] The fourth type is where the second communication device assists the first communication device in obtaining the terminal's location information based on an artificial intelligence network model;

[0463] The sixth type is where the first communication device instructs the second communication device to obtain the terminal's location information based on an artificial intelligence network model;

[0464] The seventh type is where the first communication device instructs the second communication device to obtain the terminal's location information based on an artificial intelligence network model;

[0465] The eighth type is one of the terminal's positioning signal measurement information and the terminal's location information obtained by the first communication device according to the artificial intelligence network model, and the other of the terminal's positioning signal measurement information or the terminal's location information obtained by the second communication device according to the artificial intelligence network model.

[0466] Unsupervised or supervised models.

[0467] In this embodiment of the application, optionally, the positioning device 80 further includes:

[0468] A transmission module is configured to send or receive pre-configuration information, wherein the pre-configuration information includes at least one of the following:

[0469] One or more pre-configured artificial intelligence network models;

[0470] One or more sets of pre-configured artificial intelligence network model parameters.

[0471] In this embodiment of the application, optionally, each pre-configured artificial intelligence network model or artificial intelligence network model parameter includes an ID information.

[0472] In this embodiment of the application, optionally, the positioning device 80A further includes:

[0473] The first sending module is used to send at least two target artificial intelligence network models and / or target artificial intelligence network model parameters.

[0474] In this embodiment of the application, optionally, the first location request information further includes:

[0475] LOS confidence level;

[0476] Second information.

[0477] In this embodiment of the application, optionally, the second information includes at least one of the following:

[0478] A second artificial intelligence network model used to determine LOS indication information;

[0479] Channel Impulse Response (CIR);

[0480] Power of the first diameter;

[0481] Power of multipath propagation;

[0482] The time delay of the first diameter;

[0483] Time of arrival (TOA) of the first path;

[0484] Reference signal time difference (RSTD) of the first path;

[0485] Multipath delay;

[0486] Multipath TOA;

[0487] Multipath RSTD;

[0488] Angle of arrival of the primary diameter;

[0489] Angle of arrival of multipath;

[0490] The phase difference between the antenna subcarriers in the first path;

[0491] Multipath antenna subcarrier phase difference;

[0492] Average excess latency;

[0493] Root mean square delay spread;

[0494] Coherent bandwidth.

[0495] In this embodiment of the application, optionally, the first parameter information includes at least one of the following:

[0496] The length of the CIR;

[0497] The number of multipaths;

[0498] CIR bandwidth;

[0499] Frequency domain information of the signal;

[0500] Time information;

[0501] Phase information;

[0502] Angle information;

[0503] Energy information;

[0504] CIR information based on a third artificial intelligence model or parameters processed by a third artificial intelligence model;

[0505] Multipath information based on a third artificial intelligence model or the parameters of a third artificial intelligence model.

[0506] In this embodiment of the application, optionally, the first parameter information further includes at least one of the following:

[0507] The third artificial intelligence network model structure;

[0508] Third, the parameters of the artificial intelligence network model.

[0509] In this embodiment of the application, optionally, the positioning device 80A further includes:

[0510] The second sending module is configured to send second parameter information, the second parameter information including at least one of the following:

[0511] CIR;

[0512] Multipath measurement results of the first positioning reference signal;

[0513] Time information;

[0514] Phase information;

[0515] Angle information;

[0516] Energy information;

[0517] The second indication information is used to indicate whether to obtain or optimize other second parameter information through the fourth artificial intelligence network model structure or the fourth artificial intelligence network model parameters, or whether to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal using the target artificial intelligence network model;

[0518] Fourth artificial intelligence network model structure;

[0519] Fourth, the parameters of the artificial intelligence network model.

[0520] In this embodiment of the application, optionally, the second parameter information is determined based on the first parameter information.

[0521] In this embodiment of the application, optionally, the positioning device 80A further includes:

[0522] The reporting module is used to report capability information, which includes at least one of the following:

[0523] Whether it supports an artificial intelligence network model or artificial intelligence network model parameters based on the first location request information;

[0524] Does it support multiple AI network models or multiple sets of AI network model parameters?

[0525] Does it support using artificial intelligence network models or artificial intelligence network model parameters to obtain or optimize positioning signal measurement information and / or location information?

[0526] The positioning device in this application embodiment 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, or other devices besides a terminal. For example, the terminal can include, but is not limited to, the type of terminal 11 listed above; other devices can be servers, network attached storage (NAS), etc., and this application embodiment does not specifically limit the type.

[0527] The positioning device provided in this application embodiment can achieve... Figure 4A The various processes implemented in the method embodiments achieve the same technical effect, and will not be described again here to avoid repetition.

[0528] Please refer to Figure 8B This application embodiment also provides a positioning device 80B, including:

[0529] The first sending module 81B is used to send the first location request information;

[0530] The first receiving module 82B is used to receive the target artificial intelligence network model and / or the target artificial intelligence network model parameters, wherein the target artificial intelligence network model is used to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal.

[0531] In the embodiments of this application, according to the request, an artificial intelligence network model and / or artificial intelligence network model parameters are negotiated to obtain or optimize the positioning signal measurement information and / or location information of the target terminal, so as to use the negotiated artificial intelligence network model to obtain or optimize the positioning signal measurement information and / or location information of the target terminal, thereby reducing positioning errors and improving the accuracy of positioning results.

[0532] In this embodiment of the application, optionally, the first location request information includes at least one of the following:

[0533] First measurement information;

[0534] The identifier ID of the artificial intelligence network model and / or the parameters of the artificial intelligence network model;

[0535] Artificial intelligence network models;

[0536] Some or all of the parameters of the artificial intelligence network model;

[0537] Complexity information of artificial intelligence network models;

[0538] Type information of artificial intelligence network models;

[0539] The first parameter information is used to determine the input information and / or output information of the artificial intelligence network model, or to determine the network structure and / or parameters of the artificial intelligence network model, or to determine the information reported or fed back for positioning.

[0540] The first instruction information is used to indicate whether to request the artificial intelligence network model and / or artificial intelligence network model parameters.

[0541] In this embodiment of the application, optionally, the first measurement information includes at least one of the following:

[0542] Positioning signal measurement information of the target terminal;

[0543] Location information of the target terminal;

[0544] Error information, which includes at least one of the following: position error value, measurement error value, artificial intelligence network model error value, or parameter error value.

[0545] In this embodiment of the application, optionally, the positioning signal measurement information of the target terminal includes at least one of the following:

[0546] Channel response information of the positioning signal;

[0547] Positioning signal time difference (RSTD) measurement results;

[0548] Round-trip time (RTT);

[0549] Angle of arrival (AOA) measurement results;

[0550] Departure Angle of Od (AOD) measurement results;

[0551] Positioning signal received power RSRP.

[0552] In this embodiment of the application, optionally, the positioning signal measurement information may be associated with or include at least one line-of-sight (LOS) indication information, or may include positioning signal measurement information for at least one path.

[0553] In this embodiment of the application, optionally, the LOS indication information includes at least one of the following:

[0554] The first bit is used to indicate whether it is LOS or non-line-of-sight NLOS;

[0555] The second bit is used to indicate the probability of LOS;

[0556] The third bit is used to indicate the confidence level for LOS.

[0557] In this embodiment of the application, optionally, the LOS indication information includes at least one of the following:

[0558] The first bit is used to indicate whether the positioning signal measurement is LOS or NLOS (non-line-of-sight);

[0559] The second bit is used to indicate the probability that the positioning signal measurement is LOS;

[0560] The third bit is used to indicate the confidence level of the positioning signal measurement as LOS.

[0561] In this embodiment of the application, optionally, the artificial intelligence network model parameters include at least one of the following:

[0562] The structure of artificial intelligence network models;

[0563] The multiplicative coefficients, additive coefficients, and / or activation functions of each neuron in an artificial intelligence network model;

[0564] Complexity information of artificial intelligence network models;

[0565] The expected number of training iterations for an artificial intelligence network model;

[0566] Application documentation of artificial intelligence network models;

[0567] Input format for artificial intelligence network models;

[0568] The output format of an artificial intelligence network model.

[0569] In this embodiment of the application, optionally, the type information of the artificial intelligence network model includes at least one of the following:

[0570] Fully connected model;

[0571] The first type is where the location information of the terminal is obtained by the first communication device based on an artificial intelligence network model;

[0572] The second type is the location signal measurement information of the terminal obtained by the first communication device based on the artificial intelligence network model;

[0573] The third type is where the second communication device assists the first communication device in obtaining the terminal's positioning signal measurement information based on an artificial intelligence network model;

[0574] The fourth type is where the second communication device assists the first communication device in obtaining the terminal's location information based on an artificial intelligence network model;

[0575] The sixth type is where the first communication device instructs the second communication device to obtain the terminal's location information based on an artificial intelligence network model;

[0576] The seventh type is where the first communication device instructs the second communication device to obtain the terminal's location information based on an artificial intelligence network model;

[0577] The eighth type is one of the terminal's positioning signal measurement information and the terminal's location information obtained by the first communication device according to the artificial intelligence network model, and the other of the terminal's positioning signal measurement information or the terminal's location information obtained by the second communication device according to the artificial intelligence network model.

[0578] Unsupervised or supervised models.

[0579] In this embodiment of the application, optionally, the first location request information further includes:

[0580] LOS confidence level;

[0581] Second information.

[0582] In this embodiment of the application, optionally, the second information includes at least one of the following:

[0583] A second artificial intelligence network model used to determine LOS indication information;

[0584] Channel Impulse Response (CIR);

[0585] Power of the first diameter;

[0586] Power of multipath propagation;

[0587] The time delay of the first diameter;

[0588] Time of arrival (TOA) of the first path;

[0589] Reference signal time difference (RSTD) of the first path;

[0590] Multipath delay;

[0591] Multipath TOA;

[0592] Multipath RSTD;

[0593] Angle of arrival of the primary diameter;

[0594] Angle of arrival of multipath;

[0595] The phase difference between the antenna subcarriers in the first path;

[0596] Multipath antenna subcarrier phase difference;

[0597] Average excess latency;

[0598] Root mean square delay spread;

[0599] Coherent bandwidth.

[0600] In this embodiment of the application, optionally, the first parameter information includes at least one of the following: the length of the CIR;

[0601] The number of multipaths;

[0602] CIR bandwidth;

[0603] Frequency domain information of the signal;

[0604] Time information;

[0605] Phase information;

[0606] Angle information;

[0607] Energy information;

[0608] CIR information based on a third artificial intelligence model or parameters processed by a third artificial intelligence model;

[0609] Multipath information based on a third artificial intelligence model or the parameters of a third artificial intelligence model.

[0610] In this embodiment of the application, optionally, the first parameter information further includes at least one of the following:

[0611] The third artificial intelligence network model structure;

[0612] Third, the parameters of the artificial intelligence network model.

[0613] In this embodiment of the application, optionally, the positioning device 80B further includes:

[0614] The second sending module is configured to send second parameter information, the second parameter information including at least one of the following:

[0615] CIR;

[0616] Multipath measurement results of the first positioning reference signal;

[0617] Time information;

[0618] Phase information;

[0619] Angle information;

[0620] Energy information;

[0621] The second indication information is used to indicate whether to obtain or optimize other second parameter information through the fourth artificial intelligence network model structure or the fourth artificial intelligence network model parameters, or whether to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal using the target artificial intelligence network model;

[0622] Fourth artificial intelligence network model structure;

[0623] Fourth, the parameters of the artificial intelligence network model.

[0624] In this embodiment of the application, optionally, the second parameter information is determined based on the first parameter information.

[0625] Optional, such as Figure 9 As shown, this application embodiment also provides a communication device 90, including a processor 91 and a memory 92. The memory 92 stores a program or instructions that can run on the processor 91. When the program or instructions are executed by the processor 91, they implement the various steps of the above-described positioning method embodiment and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0626] This application also provides a communication device, including a processor and a communication interface. The communication interface is used to receive first positioning request information. The processor is used to determine and / or send a target artificial intelligence network model and / or target artificial intelligence network model parameters based on the first positioning request information. The target artificial intelligence network model is used to obtain or optimize positioning signal measurement information and / or location information of a target terminal. This embodiment corresponds to the positioning method embodiment executed by the first communication device described above. All implementation processes and methods of the above method embodiments can be applied to this embodiment and achieve the same technical effects.

[0627] This application also provides a communication device, including a processor and a communication interface. The communication interface is used to send a first positioning request information and receive a target artificial intelligence network model and / or target artificial intelligence network model parameters. The target artificial intelligence network model is used to obtain or optimize positioning signal measurement information and / or location information of a target terminal. This embodiment corresponds to the positioning method embodiment executed by the second communication device described above. All implementation processes and methods of the above method embodiments can be applied to this embodiment and can achieve the same technical effect.

[0628] Specifically, Figure 10 This is a schematic diagram of the hardware structure of a terminal according to an embodiment of this application. The terminal 100 includes, but is not limited to, at least some of the following components: radio frequency unit 101, network module 102, audio output unit 103, input unit 104, sensor 105, display unit 106, user input unit 107, interface unit 108, memory 109, and processor 1010.

[0629] Those skilled in the art will understand that the terminal 100 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 1010 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 10 The terminal structure shown does not constitute a limitation on the terminal. The terminal may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0630] It should be understood that, in this embodiment, the input unit 104 may include a graphics processing unit (GPU) 1041 and a microphone 1042. The GPU 1041 processes image data of still images or videos obtained by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 106 may include a display panel 1061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 107 includes at least one of a touch panel 1071 and other input devices 1072. The touch panel 1071 is also called a touch screen. The touch panel 1071 may include a touch detection device and a touch controller. Other input devices 1072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be described in detail here.

[0631] In this embodiment, after receiving downlink data from the network-side device, the radio frequency unit 101 can transmit it to the processor 1010 for processing; in addition, the radio frequency unit 101 can send uplink data to the network-side device. Typically, the radio frequency unit 101 includes, but is not limited to, antennas, amplifiers, transceivers, couplers, low-noise amplifiers, duplexers, etc.

[0632] The memory 109 can be used to store software programs or instructions, as well as various data. The memory 109 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 109 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 109 in the embodiments of this application includes, but is not limited to, these and any other suitable types of memory.

[0633] The processor 1010 may include one or more processing units; optionally, the processor 1010 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into the processor 1010.

[0634] The radio frequency unit 101 is used to receive the first positioning request information;

[0635] The processor 1010 is configured to determine and / or send a target artificial intelligence network model and / or target artificial intelligence network model parameters based on the first positioning request information, wherein the target artificial intelligence network model is used to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal.

[0636] In this embodiment of the application, the terminal negotiates with the second communication device, according to the request of the second communication device, for obtaining or optimizing the positioning signal measurement information and / or the location information of the target terminal, the artificial intelligence network model and / or the parameters of the artificial intelligence network model, so as to use the negotiated artificial intelligence network model to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal, thereby reducing positioning errors and improving the accuracy of positioning results.

[0637] In this embodiment of the application, optionally, the first location request information includes at least one of the following:

[0638] First measurement information;

[0639] The identifier ID of the artificial intelligence network model and / or the parameters of the artificial intelligence network model;

[0640] Artificial intelligence network models;

[0641] Some or all of the parameters of the artificial intelligence network model;

[0642] Complexity information of artificial intelligence network models;

[0643] Type information of artificial intelligence network models;

[0644] The first parameter information is used to determine the input information and / or output information of the artificial intelligence network model, or to determine the network structure and / or parameters of the artificial intelligence network model, or to determine the information reported or fed back for positioning.

[0645] The first instruction information is used to indicate whether to request the artificial intelligence network model and / or artificial intelligence network model parameters.

[0646] In this embodiment of the application, optionally, the first measurement information includes at least one of the following:

[0647] Positioning signal measurement information of the target terminal;

[0648] Location information of the target terminal;

[0649] Error information, which includes at least one of the following: position error value, measurement error value, artificial intelligence network model error value, or parameter error value.

[0650] In this embodiment of the application, optionally, the positioning signal measurement information of the target terminal includes at least one of the following:

[0651] Channel response information of the positioning signal;

[0652] Positioning signal time difference (RSTD) measurement results;

[0653] Round-trip time (RTT);

[0654] Angle of arrival (AOA) measurement results;

[0655] Departure Angle of Od (AOD) measurement results;

[0656] Positioning signal received power RSRP.

[0657] In this embodiment of the application, optionally, the positioning signal measurement information may be associated with or include at least one line-of-sight (LOS) indication information, or may include positioning signal measurement information for at least one path.

[0658] In this embodiment of the application, optionally, the LOS indication information includes at least one of the following:

[0659] The first bit is used to indicate whether it is LOS or non-line-of-sight NLOS;

[0660] The second bit is used to indicate the probability of LOS;

[0661] The third bit is used to indicate the confidence level for LOS.

[0662] In this embodiment of the application, optionally, the LOS indication information includes at least one of the following:

[0663] The first bit is used to indicate whether the positioning signal measurement is LOS or NLOS (non-line-of-sight);

[0664] The second bit is used to indicate the probability that the positioning signal measurement is LOS;

[0665] The third bit is used to indicate the confidence level of the positioning signal measurement as LOS.

[0666] In this embodiment of the application, optionally, the artificial intelligence network model parameters include at least one of the following:

[0667] The structure of artificial intelligence network models;

[0668] The multiplicative coefficients, additive coefficients, and / or activation functions of each neuron in an artificial intelligence network model;

[0669] Complexity information of artificial intelligence network models;

[0670] The expected number of training iterations for an artificial intelligence network model;

[0671] Application documentation of artificial intelligence network models;

[0672] Input format for artificial intelligence network models;

[0673] The output format of an artificial intelligence network model.

[0674] In this embodiment of the application, optionally, the type information of the artificial intelligence network model includes at least one of the following:

[0675] Fully connected model;

[0676] The first type is where the location information of the terminal is obtained by the first communication device based on an artificial intelligence network model;

[0677] The second type is the location signal measurement information of the terminal obtained by the first communication device based on the artificial intelligence network model;

[0678] The third type is where the second communication device assists the first communication device in obtaining the terminal's positioning signal measurement information based on an artificial intelligence network model;

[0679] The fourth type is where the second communication device assists the first communication device in obtaining the terminal's location information based on an artificial intelligence network model;

[0680] The sixth type is where the first communication device instructs the second communication device to obtain the terminal's location information based on an artificial intelligence network model;

[0681] The seventh type is where the first communication device instructs the second communication device to obtain the terminal's location information based on an artificial intelligence network model;

[0682] The eighth type is one of the terminal's positioning signal measurement information and the terminal's location information obtained by the first communication device according to the artificial intelligence network model, and the other of the terminal's positioning signal measurement information or the terminal's location information obtained by the second communication device according to the artificial intelligence network model.

[0683] Unsupervised or supervised models.

[0684] In this embodiment of the application, optionally, the processor 1010 is further configured to send or receive pre-configuration information, the pre-configuration information including at least one of the following:

[0685] One or more pre-configured artificial intelligence network models;

[0686] One or more sets of pre-configured artificial intelligence network model parameters.

[0687] In this embodiment of the application, optionally, each pre-configured artificial intelligence network model or artificial intelligence network model parameter includes an ID information.

[0688] In this embodiment of the application, optionally, the processor 1010 is further configured to send at least two target artificial intelligence network models and / or target artificial intelligence network model parameters.

[0689] In this embodiment of the application, optionally, the first location request information further includes:

[0690] LOS confidence level;

[0691] Second information.

[0692] In this embodiment of the application, optionally, the second information includes at least one of the following:

[0693] A second artificial intelligence network model used to determine LOS indication information;

[0694] Channel Impulse Response (CIR);

[0695] Power of the first diameter;

[0696] Power of multipath propagation;

[0697] The time delay of the first diameter;

[0698] Time of arrival (TOA) of the first path;

[0699] Reference signal time difference (RSTD) of the first path;

[0700] Multipath delay;

[0701] Multipath TOA;

[0702] Multipath RSTD;

[0703] Angle of arrival of the primary diameter;

[0704] Angle of arrival of multipath;

[0705] The phase difference between the antenna subcarriers in the first path;

[0706] Multipath antenna subcarrier phase difference;

[0707] Average excess latency;

[0708] Root mean square delay spread;

[0709] Coherent bandwidth.

[0710] In this embodiment of the application, optionally, the first parameter information includes at least one of the following:

[0711] The length of the CIR;

[0712] The number of multipaths;

[0713] CIR bandwidth;

[0714] Frequency domain information of the signal;

[0715] Time information;

[0716] Phase information;

[0717] Angle information;

[0718] Energy information;

[0719] CIR information based on a third artificial intelligence model or parameters processed by a third artificial intelligence model;

[0720] Multipath information based on a third artificial intelligence model or the parameters of a third artificial intelligence model.

[0721] In this embodiment of the application, optionally, the first parameter information further includes at least one of the following:

[0722] The third artificial intelligence network model structure;

[0723] Third, the parameters of the artificial intelligence network model.

[0724] In this embodiment of the application, optionally, the processor 1010 is further configured to send second parameter information, the second parameter information including at least one of the following:

[0725] CIR;

[0726] Multipath measurement results of the first positioning reference signal;

[0727] Time information;

[0728] Phase information;

[0729] Angle information;

[0730] Energy information;

[0731] The second indication information is used to indicate whether to obtain or optimize other second parameter information through the fourth artificial intelligence network model structure or the fourth artificial intelligence network model parameters, or whether to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal using the target artificial intelligence network model;

[0732] Fourth artificial intelligence network model structure;

[0733] Fourth, the parameters of the artificial intelligence network model.

[0734] In this embodiment of the application, optionally, the second parameter information is determined based on the first parameter information.

[0735] In this embodiment of the application, optionally, the processor 1010 is further configured to report capability information, the capability information including at least one of the following:

[0736] Whether it supports an artificial intelligence network model or artificial intelligence network model parameters based on the first location request information;

[0737] Does it support multiple AI network models or multiple sets of AI network model parameters?

[0738] Does it support using artificial intelligence network models or artificial intelligence network model parameters to obtain or optimize positioning signal measurement information and / or location information?

[0739] or

[0740] The radio frequency unit 101 is used to send a first positioning request information; receive a target artificial intelligence network model and / or target artificial intelligence network model parameters, wherein the target artificial intelligence network model is used to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal.

[0741] In this embodiment of the application, optionally, the first location request information includes at least one of the following:

[0742] First measurement information;

[0743] The identifier ID of the artificial intelligence network model and / or the parameters of the artificial intelligence network model;

[0744] Artificial intelligence network models;

[0745] Some or all of the parameters of the artificial intelligence network model;

[0746] Complexity information of artificial intelligence network models;

[0747] Type information of artificial intelligence network models;

[0748] The first parameter information is used to determine the input information and / or output information of the artificial intelligence network model, or to determine the network structure and / or parameters of the artificial intelligence network model, or to determine the information reported or fed back for positioning.

[0749] The first instruction information is used to indicate whether to request the artificial intelligence network model and / or artificial intelligence network model parameters.

[0750] In this embodiment of the application, optionally, the first measurement information includes at least one of the following:

[0751] Positioning signal measurement information of the target terminal;

[0752] Location information of the target terminal;

[0753] Error information, which includes at least one of the following: position error value, measurement error value, artificial intelligence network model error value, or parameter error value.

[0754] In this embodiment of the application, optionally, the positioning signal measurement information of the target terminal includes at least one of the following:

[0755] Channel response information of the positioning signal;

[0756] Positioning signal time difference (RSTD) measurement results;

[0757] Round-trip time (RTT);

[0758] Angle of arrival (AOA) measurement results;

[0759] Departure Angle of Od (AOD) measurement results;

[0760] Positioning signal received power RSRP.

[0761] In this embodiment of the application, optionally, the positioning signal measurement information may be associated with or include at least one line-of-sight (LOS) indication information, or may include positioning signal measurement information for at least one path.

[0762] In this embodiment of the application, optionally, the LOS indication information includes at least one of the following:

[0763] The first bit is used to indicate whether it is LOS or non-line-of-sight NLOS;

[0764] The second bit is used to indicate the probability of LOS;

[0765] The third bit is used to indicate the confidence level for LOS.

[0766] In this embodiment of the application, optionally, the LOS indication information includes at least one of the following:

[0767] The first bit is used to indicate whether the positioning signal measurement is LOS or NLOS (non-line-of-sight);

[0768] The second bit is used to indicate the probability that the positioning signal measurement is LOS;

[0769] The third bit is used to indicate the confidence level of the positioning signal measurement as LOS.

[0770] In this embodiment of the application, optionally, the artificial intelligence network model parameters include at least one of the following:

[0771] The structure of artificial intelligence network models;

[0772] The multiplicative coefficients, additive coefficients, and / or activation functions of each neuron in an artificial intelligence network model;

[0773] Complexity information of artificial intelligence network models;

[0774] The expected number of training iterations for an artificial intelligence network model;

[0775] Application documentation of artificial intelligence network models;

[0776] Input format for artificial intelligence network models;

[0777] The output format of an artificial intelligence network model.

[0778] In this embodiment of the application, optionally, the type information of the artificial intelligence network model includes at least one of the following:

[0779] Fully connected model;

[0780] The first type is where the location information of the terminal is obtained by the first communication device based on an artificial intelligence network model;

[0781] The second type is the location signal measurement information of the terminal obtained by the first communication device based on the artificial intelligence network model;

[0782] The third type is where the second communication device assists the first communication device in obtaining the terminal's positioning signal measurement information based on an artificial intelligence network model;

[0783] The fourth type is where the second communication device assists the first communication device in obtaining the terminal's location information based on an artificial intelligence network model;

[0784] The sixth type is where the first communication device instructs the second communication device to obtain the terminal's location information based on an artificial intelligence network model;

[0785] The seventh type is where the first communication device instructs the second communication device to obtain the terminal's location information based on an artificial intelligence network model;

[0786] The eighth type is one of the terminal's positioning signal measurement information and the terminal's location information obtained by the first communication device according to the artificial intelligence network model, and the other of the terminal's positioning signal measurement information or the terminal's location information obtained by the second communication device according to the artificial intelligence network model.

[0787] Unsupervised or supervised models.

[0788] In this embodiment of the application, optionally, the first location request information further includes:

[0789] LOS confidence level;

[0790] Second information.

[0791] In this embodiment of the application, optionally, the second information includes at least one of the following:

[0792] A second artificial intelligence network model used to determine LOS indication information;

[0793] Channel Impulse Response (CIR);

[0794] Power of the first diameter;

[0795] Power of multipath propagation;

[0796] The time delay of the first diameter;

[0797] Time of arrival (TOA) of the first path;

[0798] Reference signal time difference (RSTD) of the first path;

[0799] Multipath delay;

[0800] Multipath TOA;

[0801] Multipath RSTD;

[0802] Angle of arrival of the primary diameter;

[0803] Angle of arrival of multipath;

[0804] The phase difference between the antenna subcarriers in the first path;

[0805] Multipath antenna subcarrier phase difference;

[0806] Average excess latency;

[0807] Root mean square delay spread;

[0808] Coherent bandwidth.

[0809] In this embodiment of the application, optionally, the first parameter information includes at least one of the following:

[0810] The length of the CIR;

[0811] The number of multipaths;

[0812] CIR bandwidth;

[0813] Frequency domain information of the signal;

[0814] Time information;

[0815] Phase information;

[0816] Angle information;

[0817] Energy information;

[0818] CIR information based on a third artificial intelligence model or parameters processed by a third artificial intelligence model;

[0819] Multipath information based on a third artificial intelligence model or the parameters of a third artificial intelligence model.

[0820] In this embodiment of the application, optionally, the first parameter information further includes at least one of the following:

[0821] The third artificial intelligence network model structure;

[0822] Third, the parameters of the artificial intelligence network model.

[0823] In this embodiment of the application, optionally, the radio frequency unit 101 is further configured to transmit second parameter information, the second parameter information including at least one of the following:

[0824] CIR;

[0825] Multipath measurement results of the first positioning reference signal;

[0826] Time information;

[0827] Phase information;

[0828] Angle information;

[0829] Energy information;

[0830] The second indication information is used to indicate whether to obtain or optimize other second parameter information through the fourth artificial intelligence network model structure or the fourth artificial intelligence network model parameters, or whether to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal using the target artificial intelligence network model;

[0831] Fourth artificial intelligence network model structure;

[0832] Fourth, the parameters of the artificial intelligence network model.

[0833] In this embodiment of the application, optionally, the second parameter information is determined based on the first parameter information. Specifically, this embodiment of the application also provides a network-side device. Figure 11 As shown, the network-side device 110 includes: an antenna 111, a radio frequency (RF) device 112, a baseband device 113, a processor 114, and a memory 115. The antenna 111 is connected to the RF device 112. In the uplink direction, the RF device 112 receives information through the antenna 111 and transmits the received information to the baseband device 113 for processing. In the downlink direction, the baseband device 113 processes the information to be transmitted and sends it to the RF device 112. The RF device 112 processes the received information and transmits it through the antenna 111.

[0834] The method executed by the network-side device in the above embodiments can be implemented in the baseband device 113, which includes a baseband processor.

[0835] Baseband device 113 may include, for example, at least one baseband board on which multiple chips are disposed, such as Figure 11 As shown, one of the chips is, for example, a baseband processor, which is connected to the memory 115 via a bus interface to call the program in the memory 115 and execute the network device operation shown in the above method embodiment.

[0836] The network-side device may also include a network interface 116, such as a common public radio interface (CPRI).

[0837] Specifically, the network-side device 1100 of this embodiment of the invention further includes: instructions or programs stored in memory 115 and executable on processor 114. Processor 114 calls the instructions or programs in memory 115 to execute the methods executed by each module shown in FIG8 and achieve the same technical effect. To avoid repetition, it will not be described in detail here.

[0838] Specifically, embodiments of this application also provide a network-side device. For example... Figure 12 As shown, the network-side device 120 includes a processor 121, a network interface 122, and a memory 123. The network interface 122 is, for example, a common public radio interface (CPRI).

[0839] Specifically, the network-side device 120 of this embodiment of the invention further includes: instructions or programs stored in memory 123 and executable on processor 121. Processor 121 calls the instructions or programs in memory 123 to execute the methods executed by each module shown in FIG8 and achieve the same technical effect. To avoid repetition, it will not be described in detail here.

[0840] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described positioning method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0841] The processor is the processor in the terminal described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0842] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described positioning method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0843] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0844] This application also provides a computer program / program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the above-described positioning method embodiments, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0845] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0846] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0847] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A positioning method, characterized in that, include: The first communication device receives the first location request information; The first communication device determines and / or sends a target artificial intelligence network model and / or target artificial intelligence network model parameters based on the first positioning request information. The target artificial intelligence network model is used to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal. The first location request information includes first parameter information, which is used to determine the input and / or output information of the artificial intelligence network model, or to determine the network structure and / or parameter information of the artificial intelligence network model, or to determine the information to be reported or fed back for location; the first parameter information includes: the length of the channel impulse response (CIR); The method further includes: the first communication device sending second parameter information, the second parameter information being determined based on the first parameter information; the second parameter information including at least one of the following: CIR; time information; The first location request information further includes: type information of the artificial intelligence network model; the type information of the artificial intelligence network model includes at least one of the following: The first type is where the location information of the terminal is obtained by the first communication device based on an artificial intelligence network model; The second type is the location signal measurement information of the terminal obtained by the first communication device based on the artificial intelligence network model; The eighth type is where the first communication device obtains one of the terminal's positioning signal measurement information and the terminal's location information based on the artificial intelligence network model, and the second communication device obtains the other of the terminal's positioning signal measurement information or the terminal's location information based on the artificial intelligence network model.

2. The positioning method according to claim 1, characterized in that, The first location request information also includes at least one of the following: First measurement information; The identifier ID of the artificial intelligence network model and / or the parameters of the artificial intelligence network model; Artificial intelligence network models; Some or all of the parameters of the artificial intelligence network model; Complexity information of artificial intelligence network models; The first instruction information is used to indicate whether to request the artificial intelligence network model and / or artificial intelligence network model parameters.

3. The positioning method according to claim 2, characterized in that, The first measurement information includes at least one of the following: Positioning signal measurement information of the target terminal; Location information of the target terminal; Error information, which includes at least one of the following: position error value, measurement error value, artificial intelligence network model error value, or parameter error value.

4. The positioning method according to claim 3, characterized in that, The positioning signal measurement information of the target terminal includes at least one of the following: Channel response information of the positioning signal; Positioning signal time difference (RSTD) measurement results; Round-trip time (RTT); Angle of arrival (AOA) measurement results; Departure Angle of Od (AOD) measurement results; Positioning signal received power RSRP.

5. The positioning method according to claim 3, characterized in that, The positioning signal measurement information is associated with or includes at least one line-of-sight (LOS) indication, or includes positioning signal measurement information for at least one path.

6. The positioning method according to claim 5, characterized in that, The LOS indication information includes at least one of the following: The first bit is used to indicate whether it is LOS or non-line-of-sight NLOS; The second bit is used to indicate the probability of LOS; The third bit is used to indicate the confidence level for LOS.

7. The positioning method according to claim 6, characterized in that, The LOS indication information includes at least one of the following: The first bit is used to indicate whether the positioning signal measurement is LOS or NLOS (non-line-of-sight); The second bit is used to indicate the probability that the positioning signal measurement is LOS; The third bit is used to indicate the confidence level of the positioning signal measurement as LOS.

8. The positioning method according to claim 2, characterized in that, The parameters of the artificial intelligence network model include at least one of the following: The structure of artificial intelligence network models; The multiplicative coefficients, additive coefficients, and / or activation functions of each neuron in an artificial intelligence network model; Complexity information of artificial intelligence network models; The expected number of training iterations for an artificial intelligence network model; Application documentation of artificial intelligence network models; Input format for artificial intelligence network models; The output format of an artificial intelligence network model.

9. The positioning method according to claim 1, characterized in that, The type information of the artificial intelligence network model also includes at least one of the following: Fully connected model; The third type is where the second communication device assists the first communication device in obtaining the terminal's positioning signal measurement information based on an artificial intelligence network model; The fourth type is where the second communication device assists the first communication device in obtaining the terminal's location information based on an artificial intelligence network model; The sixth type is where the first communication device instructs the second communication device to obtain the terminal's location information based on an artificial intelligence network model; The seventh type is where the first communication device instructs the second communication device to obtain the terminal's location information based on an artificial intelligence network model; Unsupervised or supervised models.

10. The positioning method according to claim 1, characterized in that, The first communication device determines and / or sends the target artificial intelligence network model and / or target artificial intelligence network model parameters based on the first location request information, and before that, it further includes: The first communication device sends or receives pre-configuration information, the pre-configuration information including at least one of the following: One or more pre-configured artificial intelligence network models; One or more sets of pre-configured artificial intelligence network model parameters.

11. The positioning method according to claim 10, characterized in that, Each pre-configured AI network model or AI network model parameter includes an ID.

12. The positioning method according to claim 1, characterized in that, The first communication device, based on the first location request information, determines and / or sends the target artificial intelligence network model and / or target artificial intelligence network model parameters, and further includes: The first communication device sends at least two target artificial intelligence network models and / or target artificial intelligence network model parameters according to the first location request information.

13. The positioning method according to claim 2, characterized in that, The first location request information also includes: LOS confidence level; Second information.

14. The positioning method according to claim 13, characterized in that, The second information includes at least one of the following: A second artificial intelligence network model used to determine LOS indication information; Channel Impulse Response (CIR); Power of the first diameter; Power of multipath propagation; The time delay of the first diameter; Time of arrival (TOA) of the first path; Reference signal time difference (RSTD) of the first path; Multipath delay; Multipath TOA; Multipath RSTD; Angle of arrival of the first diameter; Angle of arrival of multipath; The phase difference between the antenna subcarriers in the first path; Multipath antenna subcarrier phase difference; Average excess latency; Root mean square delay spread; Coherent bandwidth.

15. The positioning method according to claim 2, characterized in that, The first parameter information also includes at least one of the following: The number of multipaths; CIR bandwidth; Frequency domain information of the signal; Time information; Phase information; Angle information; Energy information; CIR information based on a third artificial intelligence model or parameters processed by a third artificial intelligence model; Multipath information based on a third artificial intelligence model or the parameters of a third artificial intelligence model.

16. The positioning method according to claim 15, characterized in that, The first parameter information also includes at least one of the following: The third artificial intelligence network model structure; Third, the parameters of the artificial intelligence network model.

17. The positioning method according to claim 2, characterized in that, The second parameter information also includes at least one of the following: Multipath measurement results of the first positioning reference signal; Phase information; Angle information; Energy information; The second indication information is used to indicate whether to obtain or optimize other second parameter information through the fourth artificial intelligence network model structure or the fourth artificial intelligence network model parameters, or whether to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal using the target artificial intelligence network model; Fourth artificial intelligence network model structure; Fourth, the parameters of the artificial intelligence network model.

18. The positioning method according to claim 1, characterized in that, Also includes: The first communication device reports capability information, which includes at least one of the following: Whether it supports an artificial intelligence network model or artificial intelligence network model parameters based on the first location request information; Does it support multiple AI network models or multiple sets of AI network model parameters? Does it support using artificial intelligence network models or artificial intelligence network model parameters to obtain or optimize positioning signal measurement information and / or location information? 19. A positioning method, characterized in that, include: The second communication device sends the first location request information; The second communication device receives a target artificial intelligence network model and / or target artificial intelligence network model parameters, wherein the target artificial intelligence network model is used to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal; The first location request information includes first parameter information, which is used to determine the input and / or output information of the artificial intelligence network model, or to determine the network structure and / or parameter information of the artificial intelligence network model, or to determine the information to be reported or fed back for location; the first parameter information includes: the length of the channel impulse response (CIR); The method further includes: the second communication device receiving second parameter information, the second parameter information being determined based on the first parameter information; the second parameter information including at least one of the following: CIR; time information; The first location request information further includes: type information of the artificial intelligence network model; the type information of the artificial intelligence network model includes at least one of the following: The first type is where the location information of the terminal is obtained by the first communication device based on an artificial intelligence network model; The second type is the location signal measurement information of the terminal obtained by the first communication device based on the artificial intelligence network model; The eighth type is where the first communication device obtains one of the terminal's positioning signal measurement information and the terminal's location information based on the artificial intelligence network model, and the second communication device obtains the other of the terminal's positioning signal measurement information or the terminal's location information based on the artificial intelligence network model.

20. The positioning method according to claim 19, characterized in that, The first location request information also includes at least one of the following: First measurement information; The ID of the artificial intelligence network model and / or the parameters of the artificial intelligence network model; Artificial intelligence network models; Some or all of the parameters of the artificial intelligence network model; Complexity information of artificial intelligence network models; The first instruction information is used to indicate whether to request the artificial intelligence network model and / or artificial intelligence network model parameters.

21. The positioning method according to claim 20, characterized in that, The first measurement information includes at least one of the following: Positioning signal measurement information of the target terminal; Location information of the target terminal; Error information, which includes at least one of the following: position error value, measurement error value, artificial intelligence network model error value, or parameter error value.

22. The positioning method according to claim 21, characterized in that, The positioning signal measurement information of the target terminal includes at least one of the following: Channel response information of the positioning signal; Positioning signal time difference (RSTD) measurement results; Round-trip time (RTT); Angle of arrival (AOA) measurement results; Departure Angle of Od (AOD) measurement results; Positioning signal received power RSRP.

23. The positioning method according to claim 21, characterized in that, The positioning signal measurement information is associated with or includes at least one line-of-sight (LOS) indication, or includes positioning signal measurement information for at least one path.

24. The positioning method according to claim 23, characterized in that, The LOS indication information includes at least one of the following: The first bit is used to indicate whether it is LOS or non-line-of-sight NLOS; The second bit is used to indicate the probability of LOS; The third bit is used to indicate the confidence level for LOS.

25. The positioning method according to claim 20, characterized in that, The parameters of the artificial intelligence network model include at least one of the following: The structure of artificial intelligence network models; The multiplicative coefficients, additive coefficients, and / or activation functions of each neuron in an artificial intelligence network model; Complexity information of artificial intelligence network models; The expected number of training iterations for an artificial intelligence network model; Application documentation of artificial intelligence network models; Input format for artificial intelligence network models; The output format of an artificial intelligence network model.

26. The positioning method according to claim 19, characterized in that, The type information of the artificial intelligence network model also includes at least one of the following: Fully connected model; The third type is where the second communication device assists the first communication device in obtaining the terminal's positioning signal measurement information based on an artificial intelligence network model; The fourth type is where the second communication device assists the first communication device in obtaining the terminal's location information based on an artificial intelligence network model; The sixth type is where the first communication device instructs the second communication device to obtain the terminal's location information based on an artificial intelligence network model; The seventh type is where the first communication device instructs the second communication device to obtain the terminal's location information based on an artificial intelligence network model; Unsupervised or supervised models.

27. The positioning method according to claim 20, characterized in that, The first parameter information also includes at least one of the following: The number of multipaths; CIR bandwidth; Frequency domain information of the signal; Time information; Phase information; Angle information; Energy information; CIR information based on a third artificial intelligence model or parameters processed by a third artificial intelligence model; Multipath information based on a third artificial intelligence model or the parameters of a third artificial intelligence model.

28. The positioning method according to claim 27, characterized in that, The first parameter information also includes at least one of the following: The third artificial intelligence network model structure; Third, the parameters of the artificial intelligence network model.

29. The positioning method according to claim 20, characterized in that, The second parameter information also includes at least one of the following: Multipath measurement results of the first positioning reference signal; Phase information; Angle information; Energy information; The second indication information is used to indicate whether to obtain or optimize other second parameter information through the fourth artificial intelligence network model structure or the fourth artificial intelligence network model parameters, or whether to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal using the target artificial intelligence network model; Fourth artificial intelligence network model structure; Fourth, the parameters of the artificial intelligence network model.

30. A positioning device, characterized in that, include: The first receiving module is used to receive the first location request information; The first determining module is used to determine and / or send a target artificial intelligence network model and / or target artificial intelligence network model parameters based on the first positioning request information. The target artificial intelligence network model is used to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal. The first location request information includes first parameter information, which is used to determine the input and / or output information of the artificial intelligence network model, or to determine the network structure and / or parameter information of the artificial intelligence network model, or to determine the information to be reported or fed back for location; the first parameter information includes: the length of the channel impulse response (CIR); The positioning device further includes: a second transmitting module, used to transmit second parameter information, the second parameter information being determined based on the first parameter information; the second parameter information includes at least one of the following: CIR; time information; The first location request information further includes: type information of the artificial intelligence network model; the type information of the artificial intelligence network model includes at least one of the following: The first type is where the location information of the terminal is obtained by the first communication device based on an artificial intelligence network model; The second type is the location signal measurement information of the terminal obtained by the first communication device based on the artificial intelligence network model; The eighth type is where the first communication device obtains one of the terminal's positioning signal measurement information and the terminal's location information based on the artificial intelligence network model, and the second communication device obtains the other of the terminal's positioning signal measurement information or the terminal's location information based on the artificial intelligence network model.

31. The positioning device according to claim 30, characterized in that, The first location request information also includes at least one of the following: First measurement information; The identifier ID of the artificial intelligence network model and / or the parameters of the artificial intelligence network model; Artificial intelligence network models; Some or all of the parameters of the artificial intelligence network model; Complexity information of artificial intelligence network models; The first instruction information is used to indicate whether to request the artificial intelligence network model and / or artificial intelligence network model parameters.

32. A positioning device, characterized in that, include: The first sending module is used to send the first location request information; The first receiving module is used to receive the target artificial intelligence network model and / or the target artificial intelligence network model parameters, wherein the target artificial intelligence network model is used to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal; The first location request information includes first parameter information, which is used to determine the input and / or output information of the artificial intelligence network model, or to determine the network structure and / or parameter information of the artificial intelligence network model, or to determine the information to be reported or fed back for location; the first parameter information includes: the length of the channel impulse response (CIR); The positioning device further includes: a second receiving module, configured to receive second parameter information, the second parameter information being determined based on the first parameter information; the second parameter information includes at least one of the following: CIR; time information; The first location request information further includes: type information of the artificial intelligence network model; the type information of the artificial intelligence network model includes at least one of the following: The first type is where the location information of the terminal is obtained by the first communication device based on an artificial intelligence network model; The second type is the location signal measurement information of the terminal obtained by the first communication device based on the artificial intelligence network model; The eighth type is where the first communication device obtains one of the terminal's positioning signal measurement information and the terminal's location information based on the artificial intelligence network model, and the second communication device obtains the other of the terminal's positioning signal measurement information or the terminal's location information based on the artificial intelligence network model.

33. The positioning device according to claim 32, characterized in that, The first location request information also includes at least one of the following: First measurement information; The ID of the artificial intelligence network model and / or the parameters of the artificial intelligence network model; Artificial intelligence network models; Some or all of the parameters of the artificial intelligence network model; Complexity information of artificial intelligence network models; The first instruction information is used to indicate whether to request the artificial intelligence network model and / or artificial intelligence network model parameters.

34. A communication device, characterized in that, It includes a processor and a memory, the memory storing a program or instructions that can run on the processor, the program or instructions being executed by the processor to implement the steps of the positioning method as described in any one of claims 1 to 18, or the program or instructions being executed by the processor to implement the steps of the positioning method as described in any one of claims 19 to 29.

35. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the location method as described in any one of claims 1 to 18, or, when executed by a processor, implement the location method as described in any one of claims 19 to 29.

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