Positioning method and communication device

Abstract

The application discloses a positioning method and a communication device, and belongs to the technical field of wireless communication. The positioning method of the application comprises the following steps: a first communication device determines whether to use an artificial intelligence network model and / or artificial intelligence network model parameters according to first information, wherein the artificial intelligence network model is used to obtain or optimize positioning signal measurement information of a target terminal and / or position 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 determines, based on the first information, whether to use and / or use an artificial intelligence network model and / or artificial intelligence network model parameters, 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.

[0006] Secondly, a positioning method is provided, including:

[0007] The second communication network device receives third information, which includes at least one of the following:

[0008] Positioning signal measurement information of the target terminal;

[0009] Location information of the target terminal;

[0010] 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;

[0011] Indication information is used to indicate whether the positioning signal measurement information and / or location information reported by the target terminal is obtained or optimized using the artificial intelligence network model;

[0012] Artificial intelligence network model and / or artificial intelligence network model parameter information;

[0013] LOS indication information.

[0014] Thirdly, a positioning device is provided, comprising:

[0015] The first determining module is used to determine, based on the first information, whether to use and / or use an artificial intelligence network model and / or artificial intelligence network model parameters, 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.

[0016] Fourthly, a positioning device is provided, comprising:

[0017] A first receiving module is configured to receive third information, the third information including at least one of the following:

[0018] Positioning signal measurement information of the target terminal;

[0019] Location information of the target terminal;

[0020] 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;

[0021] Indication information is used to indicate whether the positioning signal measurement information and / or location information reported by the target terminal is obtained or optimized using the artificial intelligence network model;

[0022] Artificial intelligence network model and / or artificial intelligence network model parameter information;

[0023] LOS indication information.

[0024] 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 aspect.

[0025] In a sixth aspect, a communication device is provided, including a processor and a communication interface, wherein the processor is configured to determine, based on first information, whether to use and / or use an artificial intelligence network model and / or artificial intelligence network model parameters, wherein the artificial intelligence network model is configured to obtain or optimize positioning signal measurement information and / or location information of a target terminal.

[0026] In a seventh aspect, a communication device is provided, the communication device 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 second aspect.

[0027] Eighthly, a communication device is provided, including a processor and a communication interface, wherein the communication interface is used to receive third information, the third information including at least one of the following:

[0028] Positioning signal measurement information of the target terminal;

[0029] Location information of the target terminal;

[0030] 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;

[0031] Indication information is used to indicate whether the positioning signal measurement information and / or location information reported by the target terminal is obtained or optimized using the artificial intelligence network model;

[0032] Artificial intelligence network model and / or artificial intelligence network model parameter information;

[0033] LOS indication information.

[0034] A ninth aspect provides a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect, or implement the steps of the method described in the second aspect.

[0035] In a tenth 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 method as described in the first aspect, or to implement the method as described in the second aspect.

[0036] Eleventhly, a computer program / program product is provided, which is stored in a storage medium and is executed by at least one processor to implement the steps of the positioning method as described in the first aspect, or the computer program / program product is executed by at least one processor to implement the steps of the positioning method as described in the second aspect.

[0037] In the embodiments of this application, the communication device uses an 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. Attached Figure Description

[0038] Figure 1 This is a block diagram of a wireless communication system applicable to embodiments of this application;

[0039] Figure 2 This is a schematic diagram of a neural network according to an embodiment of this application;

[0040] Figure 3 This is a schematic diagram of a neuron in an embodiment of this application;

[0041] Figure 4AThis is a flowchart illustrating a positioning method according to an embodiment of this application;

[0042] Figure 4B This is a flowchart illustrating a positioning method according to an embodiment of this application;

[0043] Figure 5 This is a flowchart illustrating a positioning method according to another embodiment of this application;

[0044] Figure 6 This is a schematic diagram of the structure of a positioning device according to an embodiment of this application;

[0045] Figure 7 This is a schematic diagram of the positioning device according to another embodiment of this application.

[0046] Figure 8 This is a schematic diagram of the structure of a communication device according to an embodiment of this application;

[0047] Figure 9 This is a schematic diagram of the hardware structure of the terminal according to an embodiment of this application;

[0048] Figure 10 This is a schematic diagram of the hardware structure of a network-side device according to an embodiment of this application;

[0049] Figure 11 This is a schematic diagram of the hardware structure of a network-side device according to another embodiment of this application. Detailed Implementation

[0050] 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.

[0051] 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.

[0052] 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 in the systems and radio technologies mentioned above, as well as in 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.

[0053] 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 (also known as a notebook 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 this application embodiment only uses a base station in an NR system as an example for description and does not limit the specific type of base station.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 5G Network Data Analytics Function (NWDAF). 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.

[0054] 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.

[0055] The artificial intelligence (AI) network model involved in the embodiments of this application will be described below.

[0056] Artificial intelligence network models have found widespread application in various fields. There are multiple ways to implement artificial intelligence network models, 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 artificial intelligence network models mentioned above.

[0057] 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, and ReLU (Rectified Linear Unit).

[0058] 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.

[0059] 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 stages: 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.

[0060] 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).

[0061] 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.

[0062] 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:

[0063] Step 41A: The first communication device determines, based on the first information, whether to use and / or use an artificial intelligence network model and / or artificial intelligence network model parameters, 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.

[0064] In the embodiments of this application, the communication device uses an 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.

[0065] The first communication device may be a terminal or a network-side device, and the network-side device may be an LMF, NWDADF, or an artificial intelligence function module.

[0066] Optionally, the first information includes at least one of the following:

[0067] Line of sight (LOS) indication information;

[0068] Preset conditions;

[0069] Preset events;

[0070] Configuration information, wherein the configuration information is used to configure one or more artificial intelligence network models, and / or to configure one or more sets of artificial intelligence network model parameters, and / or to indicate whether to use artificial intelligence network models to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal;

[0071] Priority information, which is used to define the default or initial activation or priority use of an artificial intelligence network model and / or artificial intelligence network model parameters for events, conditions, or cells;

[0072] The environmental information of the target terminal;

[0073] Reference information sent by the reference terminal;

[0074] The positioning signal measurement information of the target terminal;

[0075] The location information of the target terminal.

[0076] Optionally, the positioning signal measurement information of the target terminal includes at least one of the following:

[0077] Channel response information of the positioning signal;

[0078] Positioning signal time difference (RSTD) measurement results;

[0079] Round-trip time (RTT)

[0080] Multi-station round-trip delay;

[0081] Angle of arrival (AOA) measurement results;

[0082] Departure Angle of Od (AOD) measurement results;

[0083] Positioning signal received power RSRP.

[0084] Optionally, the positioning signal measurement information may be associated with or include at least one LOS indication information.

[0085] Optionally, the positioning signal measurement information includes positioning signal measurement information for at least one path.

[0086] Optionally, the positioning signal measurement information includes at least one of the following:

[0087] Path angle information;

[0088] Path time information;

[0089] Energy information of the path;

[0090] LOS indication information.

[0091] Optionally, the positioning signal measurement information for the at least one path includes at least one LOS indication information.

[0092] Optionally, the positioning signal measurement information for each path includes a LOS indication.

[0093] Optionally, the LOS indication information is used to indicate one of the following:

[0094] The LOS status between the target terminal and the target transmitting / receiving point (TRP);

[0095] The LOS status of the target terminal;

[0096] The LOS status between the target terminal and one or more positioning reference signal resources of the target TRP.

[0097] Optionally, the LOS indication information includes at least one of the following:

[0098] The first bit is used to indicate whether it is LOS or non-line-of-sight NLOS;

[0099] The second bit is used to indicate the probability of LOS;

[0100] The third bit is used to indicate the confidence level for LOS.

[0101] 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 NLOS (non-line-of-sight);

[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] Optionally, the first communication network device, based on the first information, determines the artificial intelligence network model and / or artificial intelligence network model parameters, further including:

[0106] The terminal determines the LOS indication information based on the second artificial intelligence network model.

[0107] Optionally, the first communication network device determines the artificial intelligence network model and / or artificial intelligence network model parameters to be used based on the first information, and then further includes:

[0108] The first communication network device reports third information, which includes at least one of the following:

[0109] The positioning signal measurement information of the target terminal;

[0110] The location information of the target terminal;

[0111] 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;

[0112] Indication information is used to indicate whether the positioning signal measurement information and / or location information reported by the target terminal is obtained or optimized using the artificial intelligence network model;

[0113] The artificial intelligence network model and / or artificial intelligence network model parameter information;

[0114] LOS indication information.

[0115] Optionally, the positioning method further includes:

[0116] The associated information of the LOS indication information reported by the first communication network device includes at least one of the following:

[0117] LOS confidence level;

[0118] Secondary information used to determine LOS indication information.

[0119] Optionally, the second information includes at least one of the following:

[0120] A second artificial intelligence network model used to determine LOS indication information;

[0121] Channel impulse response (CIR);

[0122] Power of the first diameter;

[0123] Power of multipath propagation;

[0124] The time delay of the first diameter;

[0125] Time of arrival (TOA) of the first path;

[0126] Reference signal time difference (RSTD) of the first path;

[0127] Multipath delay;

[0128] Multipath TOA;

[0129] Multipath RSTD;

[0130] Angle of arrival of the first diameter;

[0131] Angle of arrival of multipath;

[0132] The phase difference between the antenna subcarriers in the first path;

[0133] Multipath antenna subcarrier phase difference;

[0134] Average excess latency;

[0135] Root mean square delay spread;

[0136] Coherent bandwidth.

[0137] Optionally, the parameters of the artificial intelligence network model include at least one of the following:

[0138] The structure of the artificial intelligence network model;

[0139] The multiplicative coefficients, additive coefficients, and / or activation functions of each neuron in the artificial intelligence network model;

[0140] Complexity information of the artificial intelligence network model;

[0141] The expected number of training iterations for the artificial intelligence network model;

[0142] Application documentation of the aforementioned artificial intelligence network model;

[0143] The input format of the artificial intelligence network model;

[0144] The output format of the artificial intelligence network model.

[0145] Optionally, the first communication network device determines the artificial intelligence network model and / or artificial intelligence network model parameters to be used based on the first information, including:

[0146] The first communication network device instructs, configures, or activates the target artificial intelligence network model and / or the target artificial intelligence network model parameters based on the first information.

[0147] Optionally, the first communication network device, based on the first information, instructs, configures, or activates the target artificial intelligence network model and / or the target artificial intelligence network model parameters, including one of the following:

[0148] If the LOS indication information indicates LOS, the first communication network device indicates, configures, or activates the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model.

[0149] If the LOS indication information indicates NLOS, the first communication network device indicates, configures, or activates the second target artificial intelligence network model and / or the second target artificial intelligence network model parameters.

[0150] If the probability of the LOS indication information being LOS is greater than or equal to the first threshold, the first communication network device indicates, configures, or activates the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model.

[0151] If the probability of LOS indicated by the LOS indication information is less than or equal to the second threshold, the first communication network device indicates, configures or activates the second target artificial intelligence network model and / or the parameters of the second target artificial intelligence network model.

[0152] Optionally, the first communication network device may, according to the preset conditions, instruct, configure, or activate the target artificial intelligence network model and / or the target artificial intelligence network model parameters.

[0153] Optionally, the preset conditions include a first preset condition and a second preset condition. The first communication network device, based on the first information, instructs, configures, or activates the target AI network model and / or the target AI network model parameters, including:

[0154] If the first preset condition is met, the first communication network device indicates, configures or activates the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model.

[0155] If the second preset condition is met, the first communication network device indicates, configures, or activates the second target artificial intelligence network model and / or the parameters of the second target artificial intelligence network model.

[0156] Optionally, the first communication network device may, based on the plurality of preset conditions, instruct, configure, or activate the target artificial intelligence network model and / or the target artificial intelligence network model parameters.

[0157] Optionally, the preset conditions include at least one of the following:

[0158] The channel model is LOS;

[0159] The probability of LOS is greater than or equal to the first threshold;

[0160] The RSRP of the target cell is greater than or equal to the third threshold;

[0161] The Rx Timing or TOA of the target cell is less than or equal to the fourth threshold;

[0162] The difference between the Rx Timing or TOA of the target cell and that of the serving cell is less than or equal to the fifth threshold;

[0163] The multipath distribution satisfies the first condition;

[0164] The relevant bandwidth is greater than or equal to the sixth threshold;

[0165] The measurement results from the multiple antennas satisfy the second condition;

[0166] or,

[0167] The preset conditions include at least one of the following:

[0168] The channel model is NLOS;

[0169] The probability of LOS is less than or equal to the second threshold;

[0170] The RSRP of the target cell is less than or equal to the seventh threshold;

[0171] The Rx Timing or TOA of the target cell is greater than or equal to the eighth threshold;

[0172] The difference between the Rx Timing or TOA of the target cell and that of the serving cell is greater than or equal to the ninth threshold.

[0173] Multipath distribution does not satisfy the first condition;

[0174] The relevant bandwidth is less than or equal to the tenth threshold;

[0175] The measurement results from multiple antennas do not meet the second condition.

[0176] Optionally, the first communication network device may, according to the preset event, instruct, configure, or activate the target artificial intelligence network model and / or the target artificial intelligence network model parameters.

[0177] Optionally, the first communication network device may, based on the plurality of preset events, instruct, configure, or activate the target artificial intelligence network model and / or the target artificial intelligence network model parameters.

[0178] Optionally, the preset events include a first preset event and a second preset event. The first communication network device, based on the first information, instructs, configures, or activates the target AI network model and / or target AI network model parameters, including:

[0179] If the first preset event is triggered, the first communication network device indicates, configures or activates the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model;

[0180] If the second preset event is triggered, the first communication network device indicates, configures or activates the second target artificial intelligence network model and / or the parameters of the second target artificial intelligence network model.

[0181] Optionally, the preset event includes at least one of the following:

[0182] Quality of Service (QoS) events;

[0183] Periodic events;

[0184] Events whose absolute position variance is greater than or equal to the eleventh threshold;

[0185] An event in which the variance of multiple measurements is greater than or equal to the twelfth threshold;

[0186] Wireless connection failure RLF event;

[0187] Radio Resource Management (RRM) events;

[0188] Beam failure (BF) event;

[0189] Beam failure recovery BFR event;

[0190] Timed measurement;

[0191] TA measurement is scheduled in advance;

[0192] Events involving excessive round-trip time (RTT) measurement error or variance;

[0193] Observe events where the time difference of arrival (OTDOA) measurement error or excessive variance is observed.

[0194] Events involving excessive measurement error or variance in Time Difference of Arrival (TDOA) measurement;

[0195] RSRP measurement error or excessive variance events;

[0196] RSRP measures events below the thirteenth threshold;

[0197] The reference terminal's measurement error or variance is too large.

[0198] Reference terminal reported failure;

[0199] The positioning error or variance of the reference terminal is too large.

[0200] Optionally, the measurement error or variance of the reference terminal includes at least one of the following:

[0201] Based on timing or advance timing measurement error or variance;

[0202] Based on the measurement error or variance of round-trip events;

[0203] Based on OTDOA measurement error or variance;

[0204] Based on TDOA measurement error or variance;

[0205] Based on RSRP measurement error or variance.

[0206] Reference terminal error information.

[0207] Optionally, the reference information of the reference terminal includes at least one of the following:

[0208] Reference terminal identification information;

[0209] Reference terminal location information;

[0210] Reference terminal measurement information;

[0211] Reference terminal error information;

[0212] The reference terminal uses an artificial intelligence network model;

[0213] The parameters of the artificial intelligence network model used by the reference terminal.

[0214] Optionally, the first communication device may, based on the environmental information, instruct, configure, or activate the target artificial intelligence network model and / or the target artificial intelligence network model parameters.

[0215] Optionally, the first communication device, based on the first information, instructs, configures, or activates the target artificial intelligence network model and / or the target artificial intelligence network model parameters, further including:

[0216] If the environmental information is a first environment, the first communication device indicates, configures or activates the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model;

[0217] If the environmental information is a second environment, the first communication device indicates, configures, or activates the second target artificial intelligence network model and / or the parameters of the second target artificial intelligence network model.

[0218] Optionally, the first communication device may, based on the environmental information, instruct, configure, or activate the target artificial intelligence network model and / or the target artificial intelligence network model parameters.

[0219] Optionally, the priority information includes at least one of the following:

[0220] Prioritize using the AI ​​network models and / or AI network model parameters that are ranked higher.

[0221] Prioritize the use of specified AI network models and / or AI network model parameters;

[0222] Prioritize the use of associated AI network models and / or AI network model parameters;

[0223] Prioritize using AI network models and / or AI network model parameters with smaller IDs;

[0224] Prioritize using AI network models with large IDs and / or AI network model parameters;

[0225] Prioritize the use of AI network models and / or AI network model parameters with large amounts of data;

[0226] Prioritize the use of AI network models and / or AI network model parameters with smaller datasets;

[0227] Prioritize the use of AI network models with complex structures and / or AI network model parameters;

[0228] Prioritize the use of AI network models with simple model structures and / or AI network model parameters;

[0229] Prioritize the use of AI network models with more layers and / or AI network model parameters;

[0230] Prioritize the use of AI network models with fewer layers and / or AI network model parameters;

[0231] Prioritize the use of AI network models and / or AI network model parameters with high quantization levels;

[0232] Prioritize the use of AI network models and / or AI network model parameters with low quantization levels;

[0233] Prioritize the use of fully connected neural network architectures in artificial intelligence network models and / or their parameters.

[0234] Prioritize the use of AI network models and / or AI network model parameters with convolutional neural network structures.

[0235] Optionally, the positioning method further includes:

[0236] The first communication network device reports capability information, which includes at least one of the following:

[0237] Does it support artificial intelligence network models or artificial intelligence network model parameters?

[0238] Does it support multiple AI network models or multiple sets of AI network model parameters?

[0239] Does it support using artificial intelligence network models or artificial intelligence network model parameters to obtain or optimize positioning signal measurement information?

[0240] The aforementioned first communication device may be a terminal, an access network device, or a core network device.

[0241] The above positioning method will be explained below using the first communication device as an example.

[0242] Optionally, the first communication device may instruct, configure, or activate the target artificial intelligence network model and / or target artificial intelligence network model parameters based on one or more pieces of information in the first information.

[0243] The aforementioned "instruction" can be understood as instructing another communication device to use or adopt the target artificial intelligence network model and / or the target artificial intelligence network model parameters.

[0244] The term "configuration" as used above can be understood as indicating that the one or more target AI network models and / or target AI network model parameters are configured for the target device.

[0245] The term "activation" as used above can be understood as activating one or more target AI network models and / or having target AI network model parameters configured for the target device.

[0246] The target device may be a first communication network device or a second communication network device, and is a device that performs positioning using the target artificial intelligence network model and / or the target artificial intelligence network model parameters.

[0247] Please refer to Figure 4B This application provides a positioning method, including:

[0248] Step 41B: The terminal determines, based on the first information, whether to use and / or use an artificial intelligence network model and / or artificial intelligence network model parameters, wherein the artificial intelligence network model is used to obtain or optimize the terminal's positioning signal measurement information and / or the terminal's location information, wherein the location information is obtained based on the positioning signal measurement information or the optimized positioning signal measurement information.

[0249] In this embodiment, the terminal uses an artificial intelligence network model to obtain or optimize the terminal's positioning signal measurement information and / or the terminal's location information, thereby reducing positioning errors and improving the accuracy of positioning results.

[0250] In this embodiment of the application, optionally, the first information includes at least one of the following:

[0251] 1) Line of Sight (LOS) indication information;

[0252] 2) Preset conditions;

[0253] 3) Preset events;

[0254] 4) Configuration information, wherein the configuration information is used to configure one or more artificial intelligence network models, and / or to configure one or more sets of artificial intelligence network model parameters; and / or to indicate whether to use the artificial intelligence network model to obtain or optimize the positioning signal measurement information and / or the location information of the terminal;

[0255] Using a single AI network model or a set of AI network model parameters to handle all environments and scenarios lacks flexibility and makes it difficult to guarantee performance in complex environments. Therefore, in this embodiment, multiple AI network models or multiple sets of AI network model parameters can be configured, so that an AI network model or a set of AI network model parameters can be selected for use according to different environments or scenarios, thereby improving flexibility.

[0256] 5) Priority information, which is used to define the default or initial activation or priority use of the artificial intelligence network model and / or artificial intelligence network model parameters for events, conditions or cells;

[0257] Optionally, the event can be a current event or another event. The cell can be the current cell or another cell.

[0258] 6) Environmental information of the terminal;

[0259] The environmental information may be, for example, environmental classification information, which may include: indoor environment, outdoor environment, etc., or complex environment, or simple environment; or, for example, a predefined environmental type such as Inf-DH (dense clutter, high BS), Inf-SH (sparse clutter, high BS), Inf-DL (dense clutter, low BS), Inf-SL (sparse clutter, low BS), etc.

[0260] 7) Reference information sent by the reference terminal;

[0261] The reference terminal is, for example, a terminal with a fixed location, such as a fixed roadside device.

[0262] The reference terminal is, for example, a terminal with a predetermined trajectory, such as a patrol robot.

[0263] 8) Positioning signal measurement information of the terminal;

[0264] 9) The location information of the terminal.

[0265] The location information can be absolute location information (such as latitude and longitude information) or relative location information.

[0266] The positioning signal measurement information and the location information of the terminal are different from those in 1)-7) above, in that they are obtained by measurement.

[0267] Optionally, the aforementioned positioning signal measurement information and / or position signal may 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.

[0268] In this embodiment of the application, optionally, the positioning signal measurement information of the target terminal includes at least one of the following:

[0269] Channel response information of the positioning signal;

[0270] Location signal time difference (RSTD) measurement results;

[0271] Round Trip Time (RTT);

[0272] Multi-RTT (Multi-station Round Trip);

[0273] Angle of Arrival (AOA) measurement results;

[0274] Angle of Departure (AOD) measurement results;

[0275] Reference Signal Received Power (RSRP).

[0276] In this embodiment of the application, optionally, the positioning signal measurement information may be associated with or include at least one LOS indication information.

[0277] In this embodiment of the application, optionally, the positioning signal measurement information includes positioning signal measurement information for at least one path.

[0278] In this embodiment of the application, optionally, the positioning signal measurement information includes at least one of the following:

[0279] 1) Path angular information; for example, path AOA, path AoD;

[0280] 2) Time information of the path;

[0281] 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 (Path RTT) of the path, or the TOA or rx-tx (receive-transmit) measurement results of the path.

[0282] 3) Path energy information; for example, RSRPP (path RSRP);

[0283] 4) LOS indication information.

[0284] 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.

[0285] 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.

[0286] 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.

[0287] In this embodiment of the application, optionally, the LOS indication information is used to indicate one of the following:

[0288] The LOS status between the terminal and the target transmit / receive point (TRP);

[0289] The LOS status of the terminal;

[0290] The LOS status between the terminal and one or more positioning reference signal resources of the target TRP.

[0291] In this embodiment of the application, optionally, the LOS indication information includes at least one of the following:

[0292] 1) The first bit used to indicate whether it is LOS or non-line-of-sight NLOS;

[0293] For example, 0 or 1 can be used to represent LOS or NLOS.

[0294] 2) The second bit used to indicate the probability of LOS;

[0295] For example, using {0, 0.X, 2} }0.X,…,1}M bits indicate the probability of LOS.

[0296] 3) The third bit used to indicate the confidence level as LOS.

[0297] In this embodiment of the application, optionally, the LOS indication information includes at least one of the following:

[0298] The first bit is used to indicate whether the positioning signal measurement is LOS or non-line-of-sight (NLOS);

[0299] The second bit is used to indicate the probability that the positioning signal measurement is LOS;

[0300] The third bit is used to indicate the confidence level of the positioning signal measurement as LOS.

[0301] 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.

[0302] The LOS status of the terminal can be understood as the terminal having at least N LOS, or at most M LOS.

[0303] This refers to the LOS (Lost in Terminate) status between the terminal and one or more positioning reference signal resources of the target TRP. It can be understood to indicate 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.

[0304] In this embodiment of the application, optionally, the terminal determines the artificial intelligence network model and / or artificial intelligence network model parameters based on the first information, and further includes:

[0305] The terminal determines the LOS indication information based on the second artificial intelligence network model.

[0306] The second artificial intelligence network model can be a pre-configured network model.

[0307] In the described embodiment, the terminal determines the LOS indication information based on an artificial intelligence network model. In one embodiment, the second artificial intelligence network model is an artificial intelligence network model stored in the UE or used by the UE implementation.

[0308] In this embodiment of the application, optionally, the artificial intelligence network model and / or artificial intelligence network model parameters are determined based on the first information, and then the process further includes:

[0309] The third information to be reported includes at least one of the following:

[0310] The positioning signal measurement information of the target terminal;

[0311] The location information of the target terminal;

[0312] 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;

[0313] Indication information is used to indicate whether the positioning signal measurement information and / or location information reported by the target terminal is obtained or optimized using the artificial intelligence network model;

[0314] The artificial intelligence network model and / or artificial intelligence network model parameter information;

[0315] LOS indication information.

[0316] In this embodiment of the application, optionally, the positioning method further includes: the terminal reporting LOS indication information related information, the related information including at least one of the following:

[0317] LOS confidence level;

[0318] Secondary information used to determine LOS indication information.

[0319] In this embodiment of the application, optionally, the second information includes at least one of the following:

[0320] 1) A second artificial intelligence network model used to determine LOS indication information;

[0321] 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.

[0322] 2) Channel Impulse Response (CIR);

[0323] 3) Power of the first diameter;

[0324] 4) Power of multipath components;

[0325] 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.

[0326] 5) Time delay of the first diameter;

[0327] 6) Time of Arrival (TOA);

[0328] 7) Reference Signal Time Difference (RSTD) of the first path;

[0329] 8) Multipath delay;

[0330] 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.

[0331] 9) Multipath TOA;

[0332] 10) Multipath RSTD;

[0333] 11) Angle of arrival of the first diameter;

[0334] 12) Angle of arrival of multipaths;

[0335] 13) Antenna subcarrier phase difference of the first diameter;

[0336] 14) Multipath antenna subcarrier phase difference;

[0337] 15) Average excess delay;

[0338] 16) Root mean square delay extension;

[0339] 17) Coherent bandwidth.

[0340] In this embodiment of the application, optionally, the artificial intelligence network model parameters include at least one of the following:

[0341] 1) The structure of the artificial intelligence network model;

[0342] The structure includes, for example, at least one of the following:

[0343] Fully connected neural networks, convolutional neural networks, recurrent neural networks, or residual networks;

[0344] Combinations of multiple small networks, such as fully connected + convolution, convolution + residual, etc.

[0345] The number of hidden layers;

[0346] 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;

[0347] The number of neurons in each layer.

[0348] 2) The multiplicative coefficients, additive coefficients, and / or activation functions of each neuron in the artificial intelligence network model.

[0349] 3) The complexity information of the artificial intelligence network model;

[0350] 4) The expected number of training iterations for the AI ​​network model;

[0351] 5) Application documentation of the aforementioned artificial intelligence network model;

[0352] 6) The input format of the artificial intelligence network model;

[0353] 7) The output format of the artificial intelligence network model.

[0354] In this embodiment of the application, optionally, the terminal determines the artificial intelligence network model and / or artificial intelligence network model parameters to be used based on the first information, including:

[0355] Based on the first information, instruct, configure, or activate the target artificial intelligence network model and / or the target artificial intelligence network model parameters.

[0356] In this embodiment of the application, optionally, according to the first information, instructing, configuring, or activating the target artificial intelligence network model and / or the target artificial intelligence network model parameters includes one of the following:

[0357] If the LOS indication information indicates LOS, it indicates, configures, or activates the first target AI network model and / or the parameters of the first target AI network model;

[0358] If the LOS indication information indicates NLOS, it indicates, configures, or activates the second target AI network model and / or the second target AI network model parameters.

[0359] If the probability of LOS indicated by the LOS indication information is greater than or equal to the first threshold, the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model are indicated, configured, or activated.

[0360] If the probability of LOS indicated by the LOS indication information is less than or equal to the second threshold, the second target AI network model and / or the parameters of the second target AI network model are indicated, configured, or activated.

[0361] In this embodiment of the application, optionally, instructing, configuring, or activating the target artificial intelligence network model and / or the target artificial intelligence network model parameters based on the first information includes:

[0362] If the preset conditions are met, instruct, configure or activate the target artificial intelligence network model and / or the target artificial intelligence network model parameters.

[0363] In this embodiment of the application, optionally, the preset conditions include a first preset condition and a second preset condition, and the first communication network device instructs, configures, or activates the target artificial intelligence network model and / or the target artificial intelligence network model parameters according to the first information, including:

[0364] If the first preset condition is met, instruct, configure or activate the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model;

[0365] If the second preset condition is met, instruct, configure or activate the second target artificial intelligence network model and / or the parameters of the second target artificial intelligence network model.

[0366] It is worth noting that the instruction, configuration, or activation may be a network device activating a terminal, or a terminal configuring and activating a network device. In one embodiment, it may be a network-side device updating the terminal's artificial intelligence network model and parameters, or a terminal updating the network-side device's artificial intelligence network model and parameters.

[0367] In another embodiment, instructing, configuring, or activating the target AI network model and / or target AI network model parameters based on the first information can be understood as updating the AI ​​network model and / or target AI network model parameters based on the first information.

[0368] In this embodiment of the application, optionally, the preset conditions include at least one of the following:

[0369] The channel model is LOS;

[0370] The probability of LOS is greater than or equal to the first threshold;

[0371] The RSRP of the target cell is greater than or equal to the third threshold;

[0372] The target cell's Rx Timing (reception timing) or TOA is less than or equal to the fourth threshold;

[0373] The difference between the Rx Timing or TOA of the target cell and that of the serving cell is less than or equal to the fifth threshold;

[0374] The multipath distribution satisfies the first condition;

[0375] The relevant bandwidth is greater than or equal to the sixth threshold;

[0376] The measurement results from the multiple antennas satisfy the second condition;

[0377] or,

[0378] The preset conditions include at least one of the following:

[0379] The channel model is NLOS;

[0380] The probability of LOS is less than or equal to the second threshold;

[0381] The RSRP of the target cell is less than or equal to the seventh threshold;

[0382] The Rx Timing or TOA of the target cell is greater than or equal to the eighth threshold;

[0383] The difference between the Rx Timing or TOA of the target cell and that of the serving cell is greater than or equal to the ninth threshold.

[0384] Multipath distribution does not satisfy the first condition;

[0385] The relevant bandwidth is less than or equal to the tenth threshold;

[0386] The measurement results from multiple antennas do not meet the second condition.

[0387] In this embodiment of the application, optionally, according to the first information, instructing, configuring, or activating the target artificial intelligence network model and / or the target artificial intelligence network model parameters includes:

[0388] If a preset event is triggered, it instructs, configures, or activates the target AI network model and / or the target AI network model parameters.

[0389] In this embodiment of the application, optionally, the preset event includes a first preset event and a second preset event. Based on the first information, instructing, configuring, or activating the target artificial intelligence network model and / or the target artificial intelligence network model parameters includes:

[0390] If the first preset event is triggered, instruct, configure or activate the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model;

[0391] If the second preset event is triggered, instruct, configure or activate the second target artificial intelligence network model and / or the parameters of the second target artificial intelligence network model.

[0392] In this embodiment of the application, optionally, the preset event includes at least one of the following:

[0393] 1) Quality of Service (QoS) events;

[0394] For example, different QoS requirements correspond to different artificial intelligence network models.

[0395] 2) Periodic events;

[0396] 3) Events whose absolute position variance is greater than or equal to the eleventh threshold;

[0397] 4) An event in which the variance of multiple measurements is greater than or equal to the twelfth threshold;

[0398] 5) Radio Link Failure (RLF) event;

[0399] 6) Radio Resource Management (RRM) events;

[0400] For example, events A1-A6.

[0401] 7) Beam Failure (BF) event;

[0402] For example, beam failure detection is an event.

[0403] 8) Beam Failure Recover (BFR) event;

[0404] 9) Timed measurement;

[0405] 10) Timing Advance (TA) Measurement;

[0406] 11) Round Trip Time (RTT) measurement error or excessive variance;

[0407] 12) Observed Time Difference of Arrival (OTDOA) events indicating excessive measurement error or variance;

[0408] For example, different OTDOA intervals correspond to different conditions.

[0409] 13) Time Difference of Arrival (TDOA): An event where the measurement error or variance is too large;

[0410] 14) Events involving excessive RSRP measurement error or variance;

[0411] 15) Events where the RSRP measurement is below the thirteenth threshold;

[0412] 16) Events where the measurement error or variance of the reference terminal is too large;

[0413] 17) Reference terminal reporting failed;

[0414] 18) The positioning error or variance of the reference terminal is too large.

[0415] The positioning error can be an absolute position error or a relative position error.

[0416] In this embodiment of the application, optionally, the measurement error or variance of the reference terminal includes at least one of the following:

[0417] Based on timing or advance timing measurement error or variance;

[0418] Based on the measurement error or variance of round-trip events;

[0419] Based on OTDOA measurement error or variance; for example, different OTDOA intervals correspond to different conditions.

[0420] Based on TDOA measurement error or variance;

[0421] Based on RSRP measurement error or variance.

[0422] Error information from the reference terminal, such as the error between the calculated position and the actual position of the reference terminal.

[0423] In this embodiment of the application, optionally, the reference information of the reference terminal includes at least one of the following:

[0424] Reference terminal identification information;

[0425] Reference terminal location information;

[0426] Reference terminal measurement information;

[0427] Reference terminal error information;

[0428] The reference terminal uses an artificial intelligence network model;

[0429] The parameters of the artificial intelligence network model used by the reference terminal.

[0430] In this embodiment of the application, optionally, instructing, configuring, or activating the target artificial intelligence network model and / or the target artificial intelligence network model parameters based on the first information further includes:

[0431] If the environmental information is the first environment, instruct, configure or activate the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model;

[0432] If the environment information is a second environment, instruct, configure, or activate the second target artificial intelligence network model and / or the parameters of the second target artificial intelligence network model.

[0433] In this embodiment of the application, optionally, the priority information includes at least one of the following:

[0434] Prioritize using the AI ​​network models and / or AI network model parameters that are ranked higher.

[0435] Prioritize the use of specified AI network models and / or AI network model parameters;

[0436] Prioritize the use of associated AI network models and / or AI network model parameters;

[0437] Prioritize using AI network models and / or AI network model parameters with small identifiers (IDs);

[0438] Prioritize using AI network models with large IDs and / or AI network model parameters;

[0439] Prioritize the use of AI network models and / or AI network model parameters with large amounts of data;

[0440] Prioritize the use of AI network models and / or AI network model parameters with smaller datasets;

[0441] Prioritize the use of AI network models with complex structures and / or AI network model parameters;

[0442] Prioritize the use of AI network models with simple model structures and / or AI network model parameters;

[0443] Prioritize the use of AI network models with more layers and / or AI network model parameters;

[0444] Prioritize the use of AI network models with fewer layers and / or AI network model parameters;

[0445] Prioritize the use of AI network models and / or AI network model parameters with high quantization levels;

[0446] Prioritize the use of AI network models and / or AI network model parameters with low quantization levels;

[0447] Prioritize the use of fully connected neural network architectures in artificial intelligence network models and / or their parameters.

[0448] Prioritize the use of AI network models and / or AI network model parameters with convolutional neural network structures.

[0449] In this embodiment of the application, optionally, the positioning method further includes: the terminal reporting capability information, the capability information including at least one of the following:

[0450] Does it support artificial intelligence network models or artificial intelligence network model parameters?

[0451] Does it support multiple AI network models or multiple sets of AI network model parameters?

[0452] Does it support using artificial intelligence network models or artificial intelligence network model parameters to obtain or optimize positioning signal measurement information?

[0453] Please refer to Figure 5 This application also provides a positioning method, including:

[0454] Step 51: The second communication network device receives third information, the third information including at least one of the following:

[0455] Positioning signal measurement information of the target terminal;

[0456] Location information of the target terminal;

[0457] 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;

[0458] Indication information is used to indicate whether the positioning signal measurement information and / or location information reported by the target terminal is obtained or optimized using the artificial intelligence network model;

[0459] Artificial intelligence network model and / or artificial intelligence network model parameter information;

[0460] LOS indication information.

[0461] Optionally, the positioning signal measurement information of the target terminal includes at least one of the following:

[0462] Channel response information of the positioning signal;

[0463] Positioning signal time difference (RSTD) measurement results;

[0464] Round-trip time (RTT)

[0465] Multi-station round-trip delay;

[0466] Angle of arrival (AOA) measurement results;

[0467] Departure Angle of Od (AOD) measurement results;

[0468] Positioning signal received power RSRP.

[0469] Optionally, the positioning signal measurement information may be associated with or include at least one LOS indication information.

[0470] Optionally, the positioning signal measurement information includes at least one of the following:

[0471] Path angle information;

[0472] Path time information;

[0473] Energy information of the path;

[0474] LOS indication information.

[0475] Optionally, the LOS indication information is used to indicate one of the following:

[0476] The LOS status between the target terminal and the target transmitting / receiving point (TRP);

[0477] The LOS status of the target terminal;

[0478] The LOS status between the target terminal and one or more positioning reference signal resources of the target TRP.

[0479] Optionally, the LOS indication information includes at least one of the following:

[0480] The first bit is used to indicate whether it is LOS or non-line-of-sight NLOS;

[0481] The second bit is used to indicate the probability of LOS;

[0482] The third bit is used to indicate the confidence level for LOS.

[0483] Optionally, the LOS indication information includes at least one of the following:

[0484] The first bit is used to indicate whether the positioning signal measurement is LOS or NLOS (non-line-of-sight);

[0485] The second bit is used to indicate the probability that the positioning signal measurement is LOS;

[0486] The third bit is used to indicate the confidence level of the positioning signal measurement as LOS.

[0487] Optionally, the positioning method further includes: the second communication device receiving association information of the LOS indication information reported by the first communication device, the association information including at least one of the following:

[0488] LOS confidence level;

[0489] Secondary information used to determine LOS indication information.

[0490] Optionally, the second information includes at least one of the following:

[0491] A second artificial intelligence network model used to determine LOS indication information;

[0492] CIR;

[0493] Power of the first diameter;

[0494] Power of multipath propagation;

[0495] The time delay of the first diameter;

[0496] TOA of the first diameter;

[0497] RSTD of the first diameter;

[0498] Multipath delay;

[0499] Multipath TOA;

[0500] Multipath RSTD;

[0501] Angle of arrival of the first diameter;

[0502] Angle of arrival of multipath;

[0503] The phase difference between the antenna subcarriers in the first path;

[0504] Multipath antenna subcarrier phase difference;

[0505] Average excess latency;

[0506] Root mean square delay spread;

[0507] Coherent bandwidth.

[0508] Optionally, the positioning method further includes: the second communication device requesting to report the second information.

[0509] Optionally, the positioning method further includes: the second communication device determining a third artificial intelligence network model or parameters of a third artificial intelligence network model based on the third information and the second information;

[0510] The third artificial intelligence network model or its parameters are used by the network side to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal; or, sent to the target terminal to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal.

[0511] Alternatively, the second communication device may send an updated target AI network model or AI network model parameters to the terminal based on the third information, in order to adjust the network model stored in the terminal.

[0512] Alternatively, the second communication device may send the target AI network model or AI network model parameters for obtaining LOS indication information to the terminal based on the third information.

[0513] In this embodiment of the application, optionally, the positioning method further includes: the second communication device receiving capability information reported by the first communication device, the capability information including at least one of the following:

[0514] Does it support artificial intelligence network models or artificial intelligence network model parameters?

[0515] Does it support multiple AI network models or multiple sets of AI network model parameters?

[0516] Does it support using artificial intelligence network models or artificial intelligence network model parameters to obtain or optimize positioning signal measurement information?

[0517] The following is a supplementary explanation of the positioning method described in this application.

[0518] 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.

[0519] 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:

[0520] Convolutional Neural Networks (CNNs), such as GoogLeNet and AlexNet;

[0521] Recursive Neural Networks (RNNs) and Long Short-Term Memory (LSTMs).

[0522] Recursive Neural Tensor Network (RNTN);

[0523] Generative Adversarial Networks (GANs)

[0524] Deep Belief Networks (DBN);

[0525] Restricted Boltzmann Machine (RBM), etc.

[0526] 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.

[0527] In this embodiment of the application, optionally, the input information of the artificial intelligence network model includes at least one of the following:

[0528] Channel impulse response (CIR);

[0529] Power Delay Profile (PDP);

[0530] Reference Signal Time Difference (RSTD);

[0531] Round-trip time (RTT);

[0532] Angle of Arrival (AoA);

[0533] RSRP;

[0534] TOA;

[0535] Power of the first diameter;

[0536] Power of multipath propagation;

[0537] The time delay of the first diameter;

[0538] TOA of the first diameter;

[0539] RSTD of the first diameter;

[0540] Multipath delay;

[0541] Multipath TOA;

[0542] Multipath RSTD;

[0543] Angle of arrival of the first diameter;

[0544] Angle of arrival of multipath;

[0545] The phase difference between the antenna subcarriers in the first path;

[0546] Multipath antenna subcarrier phase difference;

[0547] LoS / NLoS identification information

[0548] Average excess latency;

[0549] Root mean square delay extension

[0550] Coherent bandwidth, etc.

[0551] 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.

[0552] The output information of the artificial intelligence network model includes at least one of the following:

[0553] Location coordinate information;

[0554] Reference Signal Time Difference (RSTD);

[0555] Round-trip time (RTT);

[0556] Angle of Arrival (AoA);

[0557] RSRP;

[0558] TOA;

[0559] Power of the first diameter;

[0560] Power of multipath propagation;

[0561] The time delay of the first diameter;

[0562] Time of arrival (TOA) of the first path;

[0563] Reference signal time difference (RSTD) of the first path;

[0564] Multipath delay;

[0565] Multipath TOA;

[0566] Multipath RSTD;

[0567] Angle of arrival of the first diameter;

[0568] Angle of arrival of multipath;

[0569] LoS / NLoS identification information.

[0570] 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:

[0571] 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;

[0572] 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.

[0573] 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:

[0574] Filter parameters or structure;

[0575] Convolutional layer parameters or structure;

[0576] Pooling layer parameters or structure;

[0577] Discrete Cosine Transform (DCT) parameters or structure;

[0578] Wavelet transform parameters or structure;

[0579] The parameters or structure of the positioning signal measurement information processing method (such as sampling, truncation, normalization, simultaneous merging, etc.).

[0580] Optionally, the positioning signal measurement information includes at least one of the following:

[0581] Channel impulse response (CIR);

[0582] Time-delay power spectrum;

[0583] Reference Signal Time Difference (RSTD);

[0584] Round-trip time (RTT);

[0585] Angle of Arrival (AoA);

[0586] RSRP;

[0587] TOA;

[0588] Power of the first diameter;

[0589] Power of multipath propagation;

[0590] The time delay of the first diameter;

[0591] Time of arrival (TOA) of the first path;

[0592] Reference signal time difference (RSTD) of the first path;

[0593] Multipath delay;

[0594] Multipath TOA;

[0595] Multipath RSTD;

[0596] Angle of arrival of the first diameter;

[0597] Angle of arrival of multipath;

[0598] The phase difference between the antenna subcarriers in the first path;

[0599] Multipath antenna subcarrier phase difference;

[0600] Reference signal waveform;

[0601] Correlation sequences of reference signals, etc.

[0602] 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.

[0603] 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.

[0604] Please refer to Figure 6 This application embodiment also provides a positioning device 60, including:

[0605] The first determining module 61 is used to determine, based on the first information, whether to use and / or use an artificial intelligence network model and / or artificial intelligence network model parameters, 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.

[0606] In the embodiments of this application, an artificial intelligence network model is used 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.

[0607] Optionally, the first information includes at least one of the following:

[0608] Line of sight (LOS) indication information;

[0609] Preset conditions;

[0610] Preset events;

[0611] Configuration information, wherein the configuration information is used to configure one or more artificial intelligence network models, and / or to configure one or more sets of artificial intelligence network model parameters, and / or to indicate whether to use artificial intelligence network models to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal;

[0612] Priority information, which is used to define the default or initial activation or priority use of an artificial intelligence network model and / or artificial intelligence network model parameters for events, conditions, or cells;

[0613] The environmental information of the target terminal;

[0614] Reference information sent by the reference terminal;

[0615] The positioning signal measurement information of the target terminal;

[0616] The location information of the target terminal.

[0617] Optionally, the positioning signal measurement information of the target terminal includes at least one of the following:

[0618] Channel response information of the positioning signal;

[0619] Positioning signal time difference (RSTD) measurement results;

[0620] Round-trip time (RTT)

[0621] Multi-station round-trip delay;

[0622] Angle of arrival (AOA) measurement results;

[0623] Departure Angle of Od (AOD) measurement results;

[0624] Positioning signal received power RSRP.

[0625] Optionally, the positioning signal measurement information may be associated with or include at least one LOS indication information.

[0626] Optionally, the positioning signal measurement information includes positioning signal measurement information for at least one path.

[0627] Optionally, the positioning signal measurement information includes at least one of the following:

[0628] Path angle information;

[0629] Path time information;

[0630] Energy information of the path;

[0631] LOS indication information.

[0632] Optionally, the positioning signal measurement information for the at least one path includes at least one LOS indication information.

[0633] Optionally, the positioning signal measurement information for each path includes a LOS indication.

[0634] Optionally, the LOS indication information is used to indicate one of the following:

[0635] The LOS status between the target terminal and the target transmitting / receiving point (TRP);

[0636] The LOS status of the target terminal;

[0637] The LOS status between the target terminal and one or more positioning reference signal resources of the target TRP.

[0638] Optionally, the LOS indication information includes at least one of the following:

[0639] The first bit is used to indicate whether it is LOS or non-line-of-sight NLOS;

[0640] The second bit is used to indicate the probability of LOS;

[0641] The third bit is used to indicate the confidence level for LOS.

[0642] Optionally, the LOS indication information includes at least one of the following:

[0643] The first bit is used to indicate whether the positioning signal measurement is LOS or NLOS (non-line-of-sight);

[0644] The second bit is used to indicate the probability that the positioning signal measurement is LOS;

[0645] The third bit is used to indicate the confidence level of the positioning signal measurement as LOS.

[0646] Optionally, the positioning device 60 further includes:

[0647] The second determination module is used to determine LOS indication information based on the second artificial intelligence network model.

[0648] Optionally, the positioning device 60 further includes:

[0649] The first reporting module is used to report third information, which includes at least one of the following:

[0650] The positioning signal measurement information of the target terminal;

[0651] The location information of the target terminal;

[0652] 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;

[0653] Indication information is used to indicate whether the positioning signal measurement information and / or location information reported by the target terminal is obtained or optimized using the artificial intelligence network model;

[0654] The artificial intelligence network model and / or artificial intelligence network model parameter information;

[0655] LOS indication information.

[0656] Optionally, the positioning device 60 further includes:

[0657] The second reporting module is used to report the associated information of the LOS indication information, and the associated information includes at least one of the following:

[0658] LOS confidence level;

[0659] Secondary information used to determine LOS indication information.

[0660] Optionally, the second information includes at least one of the following:

[0661] A second artificial intelligence network model used to determine LOS indication information;

[0662] Channel impulse response (CIR);

[0663] Power of the first diameter;

[0664] Power of multipath propagation;

[0665] The time delay of the first diameter;

[0666] Time of arrival (TOA) of the first path;

[0667] Reference signal time difference (RSTD) of the first path;

[0668] Multipath delay;

[0669] Multipath TOA;

[0670] Multipath RSTD;

[0671] Angle of arrival of the first diameter;

[0672] Angle of arrival of multipath;

[0673] The phase difference between the antenna subcarriers in the first path;

[0674] Multipath antenna subcarrier phase difference;

[0675] Average excess latency;

[0676] Root mean square delay spread;

[0677] Coherent bandwidth.

[0678] Optionally, the parameters of the artificial intelligence network model include at least one of the following:

[0679] The structure of the artificial intelligence network model;

[0680] The multiplicative coefficients, additive coefficients, and / or activation functions of each neuron in the artificial intelligence network model;

[0681] Complexity information of the artificial intelligence network model;

[0682] The expected number of training iterations for the artificial intelligence network model;

[0683] Application documentation of the aforementioned artificial intelligence network model;

[0684] The input format of the artificial intelligence network model;

[0685] The output format of the artificial intelligence network model.

[0686] Optionally, the first determining module is used to instruct, configure, or activate the target artificial intelligence network model and / or target artificial intelligence network model parameters based on the first information.

[0687] Optionally, the first determining module is used to perform:

[0688] If the LOS indication information indicates LOS, it indicates, configures, or activates the first target AI network model and / or the parameters of the first target AI network model;

[0689] If the LOS indication information indicates NLOS, it indicates, configures, or activates the second target AI network model and / or the second target AI network model parameters.

[0690] If the probability of LOS indicated by the LOS indication information is greater than or equal to the first threshold, the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model are indicated, configured, or activated.

[0691] If the probability of LOS indicated by the LOS indication information is less than or equal to the second threshold, the second target AI network model and / or the parameters of the second target AI network model are indicated, configured, or activated.

[0692] Optionally, the first determining module is used to instruct, configure, or activate the target artificial intelligence network model and / or the target artificial intelligence network model parameters if preset conditions are met.

[0693] Optionally, the preset conditions include a first preset condition and a second preset condition, and the first determining module is used to perform:

[0694] If the first preset condition is met, instruct, configure or activate the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model;

[0695] If the second preset condition is met, instruct, configure or activate the second target artificial intelligence network model and / or the parameters of the second target artificial intelligence network model.

[0696] Optionally, the preset conditions include at least one of the following:

[0697] The channel model is LOS;

[0698] The probability of LOS is greater than or equal to the first threshold;

[0699] The RSRP of the target cell is greater than or equal to the third threshold;

[0700] The Rx Timing or TOA of the target cell is less than or equal to the fourth threshold;

[0701] The difference between the Rx Timing or TOA of the target cell and that of the serving cell is less than or equal to the fifth threshold;

[0702] The multipath distribution satisfies the first condition;

[0703] The relevant bandwidth is greater than or equal to the sixth threshold;

[0704] The measurement results from the multiple antennas satisfy the second condition;

[0705] or,

[0706] The preset conditions include at least one of the following:

[0707] The channel model is NLOS;

[0708] The probability of LOS is less than or equal to the second threshold;

[0709] The RSRP of the target cell is less than or equal to the seventh threshold;

[0710] The Rx Timing or TOA of the target cell is greater than or equal to the eighth threshold;

[0711] The difference between the Rx Timing or TOA of the target cell and that of the serving cell is greater than or equal to the ninth threshold.

[0712] Multipath distribution does not satisfy the first condition;

[0713] The relevant bandwidth is less than or equal to the tenth threshold;

[0714] The measurement results from multiple antennas do not meet the second condition.

[0715] Optionally, the first determining module is used to indicate, configure, or activate the target artificial intelligence network model and / or the target artificial intelligence network model parameters if a preset event is triggered.

[0716] Optionally, the preset events include a first preset event and a second preset event, and the first determining module is used to execute:

[0717] If the first preset event is triggered, instruct, configure or activate the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model;

[0718] If the second preset event is triggered, instruct, configure or activate the second target artificial intelligence network model and / or the parameters of the second target artificial intelligence network model.

[0719] Optionally, the preset event includes at least one of the following:

[0720] QoS events;

[0721] Periodic events;

[0722] Events whose absolute position variance is greater than or equal to the eleventh threshold;

[0723] An event in which the variance of multiple measurements is greater than or equal to the twelfth threshold;

[0724] Wireless connection failure RLF event;

[0725] Radio Resource Management (RRM) events;

[0726] Beam failure (BF) event;

[0727] Beam failure recovery BFR event;

[0728] Timed measurement;

[0729] TA measurement is scheduled in advance;

[0730] Events involving excessive round-trip time (RTT) measurement error or variance;

[0731] Observe events where the time difference of arrival (OTDOA) measurement error or excessive variance is observed.

[0732] Events involving excessive measurement error or variance in Time Difference of Arrival (TDOA) measurement;

[0733] RSRP measurement error or excessive variance events;

[0734] RSRP measures events below the thirteenth threshold;

[0735] The reference terminal's measurement error or variance is too large.

[0736] Reference terminal reported failure;

[0737] The positioning error or variance of the reference terminal is too large.

[0738] Optionally, the measurement error or variance of the reference terminal includes at least one of the following:

[0739] Based on timing or advance timing measurement error or variance;

[0740] Based on the measurement error or variance of round-trip events;

[0741] Based on OTDOA measurement error or variance;

[0742] Based on TDOA measurement error or variance;

[0743] Based on RSRP measurement error or variance.

[0744] Reference terminal error information.

[0745] Optionally, the reference information of the reference terminal includes at least one of the following:

[0746] Reference terminal identification information;

[0747] Reference terminal location information;

[0748] Reference terminal measurement information;

[0749] Reference terminal error information;

[0750] The reference terminal uses an artificial intelligence network model;

[0751] The parameters of the artificial intelligence network model used by the reference terminal.

[0752] Optionally, the first determining module is configured to perform:

[0753] If the environmental information is the first environment, instruct, configure or activate the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model;

[0754] If the environment information is a second environment, instruct, configure, or activate the second target artificial intelligence network model and / or the parameters of the second target artificial intelligence network model.

[0755] Optionally, the priority information includes at least one of the following:

[0756] Prioritize using the AI ​​network models and / or AI network model parameters that are ranked higher.

[0757] Prioritize the use of specified AI network models and / or AI network model parameters;

[0758] Prioritize the use of associated AI network models and / or AI network model parameters;

[0759] Prioritize using AI network models and / or AI network model parameters with smaller IDs;

[0760] Prioritize using AI network models with large IDs and / or AI network model parameters;

[0761] Prioritize the use of AI network models and / or AI network model parameters with large amounts of data;

[0762] Prioritize the use of AI network models and / or AI network model parameters with smaller datasets;

[0763] Prioritize the use of AI network models with complex structures and / or AI network model parameters;

[0764] Prioritize the use of AI network models with simple model structures and / or AI network model parameters;

[0765] Prioritize the use of AI network models with more layers and / or AI network model parameters;

[0766] Prioritize the use of AI network models with fewer layers and / or AI network model parameters;

[0767] Prioritize the use of AI network models and / or AI network model parameters with high quantization levels;

[0768] Prioritize the use of AI network models and / or AI network model parameters with low quantization levels;

[0769] Prioritize the use of fully connected neural network architectures in artificial intelligence network models and / or their parameters.

[0770] Prioritize the use of AI network models and / or AI network model parameters with convolutional neural network structures.

[0771] Optionally, the positioning device 60 further includes:

[0772] The third reporting module is used to report capability information, which includes at least one of the following:

[0773] Does it support artificial intelligence network models or artificial intelligence network model parameters?

[0774] Does it support multiple AI network models or multiple sets of AI network model parameters?

[0775] Does it support using artificial intelligence network models or artificial intelligence network model parameters to obtain or optimize positioning signal measurement information?

[0776] 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 be, but is not limited to, the type of terminal 11 listed above, and other devices can be servers, network attached storage (NAS), etc., and this application embodiment does not specifically limit the types.

[0777] The positioning device provided in this application embodiment can achieve... Figure 4A or Figure 4B The various processes implemented in the method embodiments achieve the same technical effect, and will not be described again here to avoid repetition.

[0778] Please refer to Figure 7 This application embodiment also provides a positioning device 70, including:

[0779] The first receiving module 71 is configured to receive third information, the third information including at least one of the following:

[0780] Positioning signal measurement information of the target terminal;

[0781] Location information of the target terminal;

[0782] 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;

[0783] Indication information is used to indicate whether the positioning signal measurement information and / or location information reported by the target terminal is obtained or optimized using the artificial intelligence network model;

[0784] Artificial intelligence network model and / or artificial intelligence network model parameter information;

[0785] LOS indication information.

[0786] Optionally, the positioning signal measurement information of the target terminal includes at least one of the following:

[0787] Channel response information of the positioning signal;

[0788] Positioning signal time difference (RSTD) measurement results;

[0789] Round-trip time (RTT)

[0790] Multi-station round-trip delay;

[0791] Angle of arrival (AOA) measurement results;

[0792] Departure Angle of Od (AOD) measurement results;

[0793] Positioning signal received power RSRP.

[0794] Optionally, the positioning signal measurement information may be associated with or include at least one LOS indication information.

[0795] Optionally, the positioning signal measurement information includes at least one of the following:

[0796] Path angle information;

[0797] Path time information;

[0798] Energy information of the path;

[0799] LOS indication information.

[0800] Optionally, the LOS indication information is used to indicate one of the following:

[0801] The LOS status between the target terminal and the target transmitting / receiving point (TRP);

[0802] The LOS status of the target terminal;

[0803] The LOS status between the target terminal and one or more positioning reference signal resources of the target TRP.

[0804] Optionally, the LOS indication information includes at least one of the following:

[0805] The first bit is used to indicate whether it is LOS or non-line-of-sight NLOS;

[0806] The second bit is used to indicate the probability of LOS;

[0807] The third bit is used to indicate the confidence level for LOS.

[0808] Optionally, the LOS indication information includes at least one of the following:

[0809] The first bit is used to indicate whether the positioning signal measurement is LOS or NLOS (non-line-of-sight);

[0810] The second bit is used to indicate the probability that the positioning signal measurement is LOS;

[0811] The third bit is used to indicate the confidence level of the positioning signal measurement as LOS.

[0812] Optionally, the positioning device 70 further includes:

[0813] The second receiving module is configured to receive association information of the LOS indication information reported by the first communication device, wherein the association information includes at least one of the following:

[0814] LOS confidence level;

[0815] Secondary information used to determine LOS indication information.

[0816] Optionally, the second information includes at least one of the following:

[0817] A second artificial intelligence network model used to determine LOS indication information;

[0818] Channel impulse response (CIR);

[0819] Power of the first diameter;

[0820] Power of multipath propagation;

[0821] The time delay of the first diameter;

[0822] Time of arrival (TOA) of the first path;

[0823] Reference signal time difference (RSTD) of the first path;

[0824] Multipath delay;

[0825] Multipath TOA;

[0826] Multipath RSTD;

[0827] Angle of arrival of the first diameter;

[0828] Angle of arrival of multipath;

[0829] The phase difference between the antenna subcarriers in the first path;

[0830] Multipath antenna subcarrier phase difference;

[0831] Average excess latency;

[0832] Root mean square delay spread;

[0833] Coherent bandwidth.

[0834] Optionally, the positioning device 70 further includes:

[0835] The request module is used to request the reporting of the second information.

[0836] Optionally, the positioning device 70 further includes:

[0837] The determining module is used to determine the third artificial intelligence network model or the parameters of the third artificial intelligence network model based on the third information and the second information.

[0838] The third artificial intelligence network model or its parameters are used by the network side to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal; or, sent to the target terminal to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal.

[0839] Optionally, the positioning device 70 further includes:

[0840] The third receiving module is configured to receive capability information reported by the first communication device, the capability information including at least one of the following:

[0841] Does it support artificial intelligence network models or artificial intelligence network model parameters?

[0842] Does it support multiple AI network models or multiple sets of AI network model parameters?

[0843] Does it support using artificial intelligence network models or artificial intelligence network model parameters to obtain or optimize positioning signal measurement information?

[0844] The positioning device provided in this application embodiment can achieve... Figure 5 The various processes implemented in the method embodiments achieve the same technical effect, and will not be described again here to avoid repetition.

[0845] Optional, such as Figure 8 As shown, this application embodiment also provides a communication device 80, including a processor 81 and a memory 82. The memory 82 stores programs or instructions that can run on the processor 81. For example, when the communication device 80 is a terminal, the program or instructions executed by the processor 81 implement the various steps of the positioning method embodiment executed by the terminal described above, and achieve the same technical effect. When the communication device 80 is a network-side device, the program or instructions executed by the processor 81 implement the various steps of the positioning method embodiment executed by the network-side device described above, and achieve the same technical effect. To avoid repetition, further details are omitted here.

[0846] This application embodiment also provides a terminal, including a processor and a communication interface. The processor is used to determine, based on first information, whether to use and / or use an artificial intelligence network model and / or artificial intelligence network model parameters. 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. This terminal embodiment corresponds to the above-described terminal-side method embodiment. All implementation processes and methods of the above-described method embodiments can be applied to this terminal embodiment and achieve the same technical effect. Specifically, Figure 9 A schematic diagram of the hardware structure of a terminal to implement an embodiment of this application.

[0847] The terminal 90 includes, but is not limited to, at least some of the following components: radio frequency unit 91, network module 92, audio output unit 93, input unit 94, sensor 95, display unit 96, user input unit 97, interface unit 98, memory 99, and processor 910.

[0848] Those skilled in the art will understand that the terminal 90 may also include a power supply (such as a battery) for supplying power to various components. The power supply can be logically connected to the processor 910 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 9 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.

[0849] It should be understood that, in this embodiment, the input unit 94 may include a graphics processing unit (GPU) 941 and a microphone 942. The GPU 941 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 96 may include a display panel 961, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 97 includes a touch panel 971 and at least one of other input devices 972. The touch panel 971 is also called a touch screen. The touch panel 971 may include a touch detection device and a touch controller. Other input devices 972 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.

[0850] In this embodiment, after receiving downlink data from the network-side device, the radio frequency unit 91 can transmit it to the processor 910 for processing; in addition, the radio frequency unit 91 can send uplink data to the network-side device. Typically, the radio frequency unit 91 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, etc.

[0851] The memory 99 can be used to store software programs or instructions, as well as various data. The memory 99 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 99 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 99 in the embodiments of this application includes, but is not limited to, these and any other suitable types of memory.

[0852] Processor 910 may include one or more processing units; optionally, processor 910 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 processor 910.

[0853] The processor 910 is configured to determine, based on the first information, whether to use and / or use an artificial intelligence network model and / or artificial intelligence network model parameters, 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.

[0854] In the embodiments of this application, the terminal uses an 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.

[0855] Optionally, the first information includes at least one of the following:

[0856] LOS indication information;

[0857] Preset conditions;

[0858] Preset events;

[0859] Configuration information, wherein the configuration information is used to configure one or more artificial intelligence network models, and / or to configure one or more sets of artificial intelligence network model parameters, and / or to indicate whether to use artificial intelligence network models to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal;

[0860] Priority information, which is used to define the default or initial activation or priority use of an artificial intelligence network model and / or artificial intelligence network model parameters for events, conditions, or cells;

[0861] The environmental information of the terminal;

[0862] Reference information sent by the reference terminal;

[0863] Positioning signal measurement information of the target terminal;

[0864] Location information of the target terminal.

[0865] Optionally, the positioning signal measurement information of the target terminal includes at least one of the following:

[0866] Channel response information of the positioning signal;

[0867] Positioning signal time difference (RSTD) measurement results;

[0868] Round-trip time (RTT);

[0869] Multi-station round-trip delay;

[0870] Angle of arrival (AOA) measurement results;

[0871] Departure Angle of Od (AOD) measurement results;

[0872] Positioning signal received power RSRP.

[0873] Optionally, the positioning signal measurement information may be associated with or include at least one LOS indication information.

[0874] Optionally, the positioning signal measurement information includes positioning signal measurement information for at least one path.

[0875] Optionally, the positioning signal measurement information includes at least one of the following:

[0876] Path angle information;

[0877] Path time information;

[0878] Energy information of the path;

[0879] LOS indication information.

[0880] Optionally, the positioning signal measurement information for the at least one path includes at least one LOS indication information.

[0881] Optionally, the positioning signal measurement information for each path includes a LOS indication.

[0882] Optionally, the LOS indication information is used to indicate one of the following:

[0883] The LOS status between the target terminal and the target transmitting / receiving point (TRP);

[0884] The LOS status of the target terminal;

[0885] The LOS status between the target terminal and one or more positioning reference signal resources of the target TRP.

[0886] Optionally, the LOS indication information includes at least one of the following:

[0887] The first bit is used to indicate whether it is LOS or non-line-of-sight NLOS;

[0888] The second bit is used to indicate the probability of LOS;

[0889] The third bit is used to indicate the confidence level for LOS.

[0890] Optionally, the LOS indication information includes at least one of the following:

[0891] The first bit is used to indicate whether the positioning signal measurement is LOS or NLOS (non-line-of-sight);

[0892] The second bit is used to indicate the probability that the positioning signal measurement is LOS;

[0893] The third bit is used to indicate the confidence level of the positioning signal measurement as LOS.

[0894] Optionally, the processor 910 is also configured to determine LOS indication information based on a second artificial intelligence network model.

[0895] Optionally, the radio frequency unit 91 is used to report third information, which includes at least one of the following:

[0896] The positioning signal measurement information of the target terminal;

[0897] The location information of the target terminal;

[0898] 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;

[0899] Indication information is used to indicate whether the positioning signal measurement information and / or location information reported by the target terminal is obtained or optimized using the artificial intelligence network model;

[0900] The artificial intelligence network model and / or artificial intelligence network model parameter information;

[0901] LOS indication information.

[0902] Optionally, the radio frequency unit 91 is used to report associated information of the LOS indication information, the associated information including at least one of the following:

[0903] LOS confidence level;

[0904] Secondary information used to determine LOS indication information.

[0905] Optionally, the second information includes at least one of the following:

[0906] A second artificial intelligence network model used to determine LOS indication information;

[0907] Channel impulse response (CIR);

[0908] Power of the first diameter;

[0909] Power of multipath propagation;

[0910] The time delay of the first diameter;

[0911] Time of arrival (TOA) of the first path;

[0912] Reference signal time difference (RSTD) of the first path;

[0913] Multipath delay;

[0914] Multipath TOA;

[0915] Multipath RSTD;

[0916] Angle of arrival of the first diameter;

[0917] Angle of arrival of multipath;

[0918] The phase difference between the antenna subcarriers in the first path;

[0919] Multipath antenna subcarrier phase difference;

[0920] Average excess latency;

[0921] Root mean square delay spread;

[0922] Coherent bandwidth.

[0923] Optionally, the parameters of the artificial intelligence network model include at least one of the following:

[0924] The structure of the artificial intelligence network model;

[0925] The multiplicative coefficients, additive coefficients, and / or activation functions of each neuron in the artificial intelligence network model;

[0926] Complexity information of the artificial intelligence network model;

[0927] The expected number of training iterations for the artificial intelligence network model;

[0928] Application documentation of the aforementioned artificial intelligence network model;

[0929] The input format of the artificial intelligence network model;

[0930] The output format of the artificial intelligence network model.

[0931] Optionally, the processor 910 is configured to instruct, configure, or activate the target artificial intelligence network model and / or target artificial intelligence network model parameters based on the first information.

[0932] Optionally, the processor 910 is used for:

[0933] If the LOS indication information indicates LOS, it indicates, configures, or activates the first target AI network model and / or the parameters of the first target AI network model;

[0934] If the LOS indication information indicates NLOS, it indicates, configures, or activates the second target AI network model and / or the second target AI network model parameters.

[0935] If the probability of LOS indicated by the LOS indication information is greater than or equal to the first threshold, the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model are indicated, configured, or activated.

[0936] If the probability of LOS indicated by the LOS indication information is less than or equal to the second threshold, the second target AI network model and / or the parameters of the second target AI network model are indicated, configured, or activated.

[0937] Optionally, the preset conditions include a first preset condition and a second preset condition, and the processor 910 is used to:

[0938] If the first preset condition is met, instruct, configure or activate the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model;

[0939] If the second preset condition is met, instruct, configure or activate the second target artificial intelligence network model and / or the parameters of the second target artificial intelligence network model.

[0940] Optionally, the preset conditions include at least one of the following:

[0941] The channel model is LOS;

[0942] The probability of LOS is greater than or equal to the first threshold;

[0943] The RSRP of the target cell is greater than or equal to the third threshold;

[0944] The Rx Timing or TOA of the target cell is less than or equal to the fourth threshold;

[0945] The difference between the Rx Timing or TOA of the target cell and that of the serving cell is less than or equal to the fifth threshold;

[0946] The multipath distribution satisfies the first condition;

[0947] The relevant bandwidth is greater than or equal to the sixth threshold;

[0948] The measurement results from the multiple antennas satisfy the second condition;

[0949] or,

[0950] The preset conditions include at least one of the following:

[0951] The channel model is NLOS;

[0952] The probability of LOS is less than or equal to the second threshold;

[0953] The RSRP of the target cell is less than or equal to the seventh threshold;

[0954] The Rx Timing or TOA of the target cell is greater than or equal to the eighth threshold;

[0955] The difference between the Rx Timing or TOA of the target cell and that of the serving cell is greater than or equal to the ninth threshold.

[0956] Multipath distribution does not satisfy the first condition;

[0957] The relevant bandwidth is less than or equal to the tenth threshold;

[0958] The measurement results from multiple antennas do not meet the second condition.

[0959] Optionally, the preset events include a first preset event and a second preset event, and the processor 910 is used to:

[0960] If the first preset event is triggered, instruct, configure or activate the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model;

[0961] If the second preset event is triggered, instruct, configure or activate the second target artificial intelligence network model and / or the parameters of the second target artificial intelligence network model.

[0962] Optionally, the preset event includes at least one of the following:

[0963] QoS events;

[0964] Periodic events;

[0965] Events whose absolute position variance is greater than or equal to the eleventh threshold;

[0966] An event in which the variance of multiple measurements is greater than or equal to the twelfth threshold;

[0967] Wireless connection failure RLF event;

[0968] Radio Resource Management (RRM) events;

[0969] Beam failure (BF) event;

[0970] Beam failure recovery BFR event;

[0971] Timed measurement;

[0972] TA measurement is scheduled in advance;

[0973] Events involving excessive round-trip time (RTT) measurement error or variance;

[0974] Observe events where the time difference of arrival (OTDOA) measurement error or excessive variance is observed.

[0975] Events involving excessive measurement error or variance in Time Difference of Arrival (TDOA) measurement;

[0976] RSRP measurement error or excessive variance events;

[0977] RSRP measures events below the thirteenth threshold;

[0978] The reference terminal's measurement error or variance is too large.

[0979] Reference terminal reported failure;

[0980] The positioning error or variance of the reference terminal is too large.

[0981] Optionally, the measurement error or variance of the reference terminal includes at least one of the following:

[0982] Based on timing or advance timing measurement error or variance;

[0983] Based on the measurement error or variance of round-trip events;

[0984] Based on OTDOA measurement error or variance;

[0985] Based on TDOA measurement error or variance;

[0986] Based on RSRP measurement error or variance.

[0987] Reference terminal error information.

[0988] Optionally, the reference information of the reference terminal includes at least one of the following:

[0989] Reference terminal identification information;

[0990] Reference terminal location information;

[0991] Reference terminal measurement information;

[0992] Reference terminal error information;

[0993] The reference terminal uses an artificial intelligence network model;

[0994] The parameters of the artificial intelligence network model used by the reference terminal.

[0995] Optionally, the processor 910 is used for:

[0996] If the environmental information is the first environment, instruct, configure or activate the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model;

[0997] If the environment information is a second environment, instruct, configure, or activate the second target artificial intelligence network model and / or the parameters of the second target artificial intelligence network model.

[0998] Optionally, the priority information includes at least one of the following:

[0999] Prioritize using the AI ​​network models and / or AI network model parameters that are ranked higher.

[1000] Prioritize the use of specified AI network models and / or AI network model parameters;

[1001] Prioritize the use of associated AI network models and / or AI network model parameters;

[1002] Prioritize using AI network models and / or AI network model parameters with smaller IDs;

[1003] Prioritize using AI network models with large IDs and / or AI network model parameters;

[1004] Prioritize the use of AI network models and / or AI network model parameters with large amounts of data;

[1005] Prioritize the use of AI network models and / or AI network model parameters with smaller datasets;

[1006] Prioritize the use of AI network models with complex structures and / or AI network model parameters;

[1007] Prioritize the use of AI network models with simple model structures and / or AI network model parameters;

[1008] Prioritize the use of AI network models with more layers and / or AI network model parameters;

[1009] Prioritize the use of AI network models with fewer layers and / or AI network model parameters;

[1010] Prioritize the use of AI network models and / or AI network model parameters with high quantization levels;

[1011] Prioritize the use of AI network models and / or AI network model parameters with low quantization levels;

[1012] Prioritize the use of fully connected neural network architectures in artificial intelligence network models and / or their parameters.

[1013] Prioritize the use of AI network models and / or AI network model parameters with convolutional neural network structures.

[1014] Optionally, the radio frequency unit 91 is used to report capability information, which includes at least one of the following:

[1015] Does it support artificial intelligence network models or artificial intelligence network model parameters?

[1016] Does it support multiple AI network models or multiple sets of AI network model parameters?

[1017] Does it support using artificial intelligence network models or artificial intelligence network model parameters to obtain or optimize positioning signal measurement information?

[1018] This application embodiment also provides a communication device, including a processor and a communication interface, wherein the communication interface is used to receive third information, the third information including at least one of the following:

[1019] The terminal's positioning signal measurement information;

[1020] Terminal location information;

[1021] 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;

[1022] Indication information is used to indicate whether the positioning signal measurement information and / or location information reported by the terminal is obtained or optimized using the artificial intelligence network model.

[1023] This communication device embodiment corresponds to the method embodiment executed by the second communication device described above. All implementation processes and methods of the above method embodiment can be applied to this communication device embodiment and can achieve the same technical effect.

[1024] Specifically, embodiments of this application also provide a network-side device. For example... Figure 10 As shown, the network-side device 1000 includes: an antenna 101, a radio frequency (RF) device 102, a baseband device 103, a processor 104, and a memory 105. The antenna 101 is connected to the RF device 102. In the uplink direction, the RF device 102 receives information through the antenna 101 and transmits the received information to the baseband device 103 for processing. In the downlink direction, the baseband device 103 processes the information to be transmitted and sends it to the RF device 102. The RF device 102 processes the received information and transmits it through the antenna 101.

[1025] The method executed by the network-side device in the above embodiments can be implemented in the baseband device 103, which includes a baseband processor.

[1026] The baseband device 103 may include, for example, at least one baseband board on which multiple chips are disposed, such as... Figure 10 As shown, one of the chips is, for example, a baseband processor, which is connected to the memory 105 via a bus interface to call the program in the memory 105 and execute the network device operation shown in the above method embodiment.

[1027] The network-side device may also include a network interface 106, such as a common public radio interface (CPRI).

[1028] Specifically, the network-side device 1000 of this embodiment further includes: instructions or programs stored in memory 105 and executable on processor 104, wherein processor 104 calls the instructions or programs in memory 105 to execute... Figure 7 The methods executed by each module shown achieve the same technical effect, and to avoid repetition, they will not be described in detail here.

[1029] Specifically, embodiments of this application also provide a network-side device. For example... Figure 11 As shown, the network-side device 110 includes a processor 111, a network interface 112, and a memory 113. The network interface 112 is, for example, a common public radio interface (CPRI).

[1030] Specifically, the network-side device 110 of this embodiment further includes: instructions or programs stored in memory 113 and executable on processor 111, wherein processor 111 calls the instructions or programs in memory 113 to execute. Figure 7 The methods executed by each module shown achieve the same technical effect, and to avoid repetition, they will not be described in detail here.

[1031] 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.

[1032] 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.

[1033] 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.

[1034] 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.

[1035] 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.

[1036] 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.

[1037] 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.

[1038] 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 determines, based on the first information, whether to use and / or use an artificial intelligence network model and / or artificial intelligence network model parameters, 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; The first information includes at least one of the following: Line-of-sight (LOS) indication information; wherein the LOS indication information includes at least one of the following: a first bit for indicating whether it is LOS or non-line-of-sight (NLOS); a second bit for indicating the probability of LOS; Reference information from the reference terminal; The reference information of the reference terminal includes at least one of the following: Reference terminal location information; Reference terminal measurement information; The reference terminal uses an artificial intelligence network model; The parameters of the artificial intelligence network model used by the reference terminal.

2. The positioning method according to claim 1, characterized in that, The first information also includes at least one of the following: Preset conditions; Preset events; Configuration information, wherein the configuration information is used to configure one or more artificial intelligence network models, and / or to configure one or more sets of artificial intelligence network model parameters, and / or to indicate whether to use artificial intelligence network models to obtain or optimize the positioning signal measurement information and / or the location information of the target terminal; Priority information, which is used to define the default or initial activation or priority use of an artificial intelligence network model and / or artificial intelligence network model parameters for events, conditions, or cells; The environmental information of the target terminal; The positioning signal measurement information of the target terminal; The location information of the target terminal.

3. The positioning method according to claim 1 or 2, 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); Multi-station round-trip delay; Angle of arrival (AOA) measurement results; Departure Angle of Od (AOD) measurement results; Positioning signal received power RSRP.

4. The positioning method according to claim 2, characterized in that, The positioning signal measurement information is associated with or includes at least one LOS indication information.

5. The positioning method according to claim 2, characterized in that, The positioning signal measurement information includes positioning signal measurement information for at least one path.

6. The positioning method according to claim 5, characterized in that, The positioning signal measurement information includes at least one of the following: Path angle information; Path time information; Energy information of the path; LOS indication information.

7. The positioning method according to claim 6, characterized in that, The positioning signal measurement information for at least one path includes at least one LOS indication information.

8. The positioning method according to claim 7, characterized in that, The location signal measurement information for each path includes a LOS indication.

9. The positioning method according to any one of claims 2, 4, or 6, characterized in that, The LOS indication information is used to indicate one of the following: The LOS status between the target terminal and the target transmitting / receiving point (TRP); The LOS status of the target terminal; The LOS status between the target terminal and one or more positioning reference signal resources of the target TRP.

10. The positioning method according to any one of claims 2, 4, or 6, characterized in that, The LOS indication information also includes: The third bit is used to indicate the confidence level for LOS.

11. The positioning method according to claim 10, 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.

12. The positioning method according to any one of claims 2, 4, or 6, characterized in that, The first communication device determines the artificial intelligence network model and / or artificial intelligence network model parameters based on the first information, and further includes: The terminal determines the LOS indication information based on the second artificial intelligence network model.

13. The positioning method according to claim 1, characterized in that, The first communication device determines the artificial intelligence network model and / or artificial intelligence network model parameters to be used based on the first information, and then further includes: The first communication device reports third information, which includes at least one of the following: The positioning signal measurement information of the target terminal; The 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; Indication information is used to indicate whether the positioning signal measurement information and / or location information reported by the target terminal is obtained or optimized using the artificial intelligence network model; The artificial intelligence network model and / or artificial intelligence network model parameter information; LOS indication information.

14. The positioning method according to claim 13, characterized in that, Also includes: The associated information of the LOS indication information reported by the first communication device includes at least one of the following: LOS confidence level; Secondary information used to determine LOS indication information.

15. The positioning method according to claim 14, 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.

16. The positioning method according to claim 1, characterized in that, The parameters of the artificial intelligence network model include at least one of the following: The structure of the artificial intelligence network model; The multiplicative coefficients, additive coefficients, and / or activation functions of each neuron in the artificial intelligence network model; Complexity information of the artificial intelligence network model; The expected number of training iterations for the artificial intelligence network model; Application documentation of the aforementioned artificial intelligence network model; The input format of the artificial intelligence network model; The output format of the artificial intelligence network model.

17. The positioning method according to claim 2, characterized in that, The first communication device determines the artificial intelligence network model and / or artificial intelligence network model parameters to be used based on the first information, including: The first communication device instructs, configures, or activates the target artificial intelligence network model and / or the target artificial intelligence network model parameters based on the first information.

18. The positioning method according to claim 17, characterized in that, The first communication device, based on the first information, instructs, configures, or activates the target artificial intelligence network model and / or the target artificial intelligence network model parameters, including one of the following: If the LOS indication information indicates LOS, the first communication device indicates, configures, or activates the first target AI network model and / or the first target AI network model parameters. If the LOS indication information indicates NLOS, the first communication device indicates, configures, or activates the second target artificial intelligence network model and / or the second target artificial intelligence network model parameters. If the probability of LOS indicated by the LOS indication information is greater than or equal to the first threshold, the first communication device indicates, configures or activates the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model. If the probability of LOS indicated by the LOS indication information is less than or equal to the second threshold, the first communication device indicates, configures or activates the second target artificial intelligence network model and / or the parameters of the second target artificial intelligence network model.

19. The positioning method according to claim 17, characterized in that, The preset conditions include a first preset condition and a second preset condition. The first communication device, based on the first information, instructs, configures, or activates the target AI network model and / or the target AI network model parameters, including: If the first preset condition is met, the first communication device indicates, configures or activates the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model. If the second preset condition is met, the first communication device instructs, configures, or activates the second target artificial intelligence network model and / or the parameters of the second target artificial intelligence network model.

20. The positioning method according to claim 2 or 19, characterized in that, The preset conditions include at least one of the following: The channel model is LOS; The probability of LOS is greater than or equal to the first threshold; The RSRP of the target cell is greater than or equal to the third threshold; The Rx Timing or TOA of the target cell is less than or equal to the fourth threshold; The difference between the Rx Timing or TOA of the target cell and that of the serving cell is less than or equal to the fifth threshold; The multipath distribution satisfies the first condition; The relevant bandwidth is greater than or equal to the sixth threshold; The measurement results from the multiple antennas satisfy the second condition; or, The preset conditions include at least one of the following: The channel model is NLOS; The probability of LOS is less than or equal to the second threshold; The RSRP of the target cell is less than or equal to the seventh threshold; The Rx Timing or TOA of the target cell is greater than or equal to the eighth threshold; The difference between the Rx Timing or TOA of the target cell and that of the serving cell is greater than or equal to the ninth threshold. Multipath distribution does not satisfy the first condition; The relevant bandwidth is less than or equal to the tenth threshold; The measurement results from multiple antennas do not meet the second condition.

21. The positioning method according to claim 17, characterized in that, The preset events include a first preset event and a second preset event. The first communication device, based on the first information, instructs, configures, or activates the target AI network model and / or the target AI network model parameters, including: If the first preset event is triggered, the first communication device indicates, configures or activates the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model; If the second preset event is triggered, the first communication device instructs, configures, or activates the second target artificial intelligence network model and / or the parameters of the second target artificial intelligence network model.

22. The positioning method according to claim 2 or 21, characterized in that, The preset event includes at least one of the following: Quality of Service (QoS) events; Periodic events; Events whose absolute position variance is greater than or equal to the eleventh threshold; An event in which the variance of multiple measurements is greater than or equal to the twelfth threshold; Wireless connection failure RLF event; Radio Resource Management (RRM) events; Beam failure (BF) event; Beam failure recovery BFR event; Timed measurement; TA measurement is scheduled in advance; Events involving excessive round-trip time (RTT) measurement error or variance; Observe events where the time difference of arrival (OTDOA) measurement error or excessive variance is observed. Events involving excessive measurement error or variance in Time Difference of Arrival (TDOA) measurement; RSRP measurement error or excessive variance events; RSRP measures events below the thirteenth threshold; The reference terminal's measurement error or variance is too large. Reference terminal reported failure; The positioning error or variance of the reference terminal is too large.

23. The positioning method according to claim 22, characterized in that, The measurement error or variance of the reference terminal includes at least one of the following: Based on timing or advance timing measurement error or variance; Based on round-trip time delay measurement error or variance; Based on OTDOA measurement error or variance; Based on TDOA measurement error or variance; Based on RSRP measurement error or variance; Reference terminal error information.

24. The positioning method according to claim 1, characterized in that, The reference information of the reference terminal also includes at least one of the following: Reference terminal identification information; Reference terminal error information.

25. The positioning method according to claim 17, characterized in that, The first communication device, based on the first information, instructs, configures, or activates the target artificial intelligence network model and / or the target artificial intelligence network model parameters, and further includes: If the environmental information is a first environment, the first communication device indicates, configures or activates the first target artificial intelligence network model and / or the parameters of the first target artificial intelligence network model; If the environmental information is a second environment, the first communication device indicates, configures, or activates the second target artificial intelligence network model and / or the parameters of the second target artificial intelligence network model.

26. The positioning method according to claim 2, characterized in that, The priority information includes at least one of the following: Prioritize using the AI ​​network models and / or AI network model parameters that are ranked higher. Prioritize the use of specified AI network models and / or AI network model parameters; Prioritize the use of associated AI network models and / or AI network model parameters; Prioritize using AI network models and / or AI network model parameters with smaller IDs; Prioritize using AI network models with large IDs and / or AI network model parameters; Prioritize the use of AI network models and / or AI network model parameters with large amounts of data; Prioritize the use of AI network models and / or AI network model parameters with smaller datasets; Prioritize the use of AI network models with complex structures and / or AI network model parameters; Prioritize the use of AI network models with simple model structures and / or AI network model parameters; Prioritize the use of AI network models with more layers and / or AI network model parameters; Prioritize the use of AI network models with fewer layers and / or AI network model parameters; Prioritize the use of AI network models and / or AI network model parameters with high quantization levels; Prioritize the use of AI network models and / or AI network model parameters with low quantization levels; Prioritize the use of fully connected neural network architectures in artificial intelligence network models and / or their parameters. Prioritize the use of AI network models and / or AI network model parameters with convolutional neural network structures.

27. 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: Does it support artificial intelligence network models or artificial intelligence network model parameters? 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? 28. A positioning device, characterized in that, include: The first determining module is used to determine, based on the first information, whether to use and / or use an artificial intelligence network model and / or artificial intelligence network model parameters, 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; The first information includes at least one of the following: Line-of-sight (LOS) indication information; wherein the LOS indication information includes at least one of the following: a first bit for indicating whether it is LOS or non-line-of-sight (NLOS); a second bit for indicating the probability of LOS; Reference information from the reference terminal; The reference information of the reference terminal includes at least one of the following: Reference terminal location information; Reference terminal measurement information; The reference terminal uses an artificial intelligence network model; The parameters of the artificial intelligence network model used by the reference terminal.

29. 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 27.

30. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the positioning method as described in any one of claims 1 to 27.

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