Positioning method and device, communication equipment, communication system and storage medium

Abstract

The disclosure proposes a positioning method and apparatus, a communication device, a communication system, and a storage medium, the method comprising: determining a training sample set, the training sample set comprising M groups of sample data, M being a positive integer, the sample data comprising: measurement data obtained after a third device processes positioning reference signals sent by S second devices, s is a positive integer and is smaller than N, and N is the total number of the second devices and is a positive integer; a first model is trained based on the training sample set, the first model is used for positioning, and the input of the first model is measurement data obtained after the third device processes the positioning purpose reference signals sent by the S second devices. The method can reduce the power overhead in the training application process of the first model, ensures the positioning accuracy of the first model, reduces the data transmission overhead, and is higher in practicability.

Description

Positioning method and device, communication equipment, communication system, and storage medium Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a positioning method and apparatus, a communication device, a communication system, and a storage medium. Background Art

[0002] In the New Radio (NR) system, artificial intelligence (AI)-based positioning is introduced to improve positioning accuracy.

[0003] Summary of the Invention

[0004] The present disclosure provides a positioning method and apparatus, a communication device, a communication system, and a storage medium.

[0005] According to a first aspect of an embodiment of the present disclosure, a positioning method is provided, which is performed by a first device. The method includes:

[0006] Determine a training sample set, where the training sample set includes M groups of sample data, where M is a positive integer, and the sample data includes: measurement data obtained by a third device processing positioning reference signals sent by S second devices, where S is a positive integer, S is less than N, and N is the total number of second devices, which is also a positive integer;

[0007] The first model is trained based on the training sample set; wherein the first model is used for positioning, and the input of the first model is: measurement data obtained after the third device processes S positioning reference signals sent by the second device.

[0008] According to a second aspect of an embodiment of the present disclosure, a positioning method is provided, which is performed by a fourth device, wherein the fourth device is a device on which a first model is deployed, and the first model is used for positioning. The method includes:

[0009] Determine first data, where the first data includes: measurement data obtained by a third device processing positioning reference signals sent by S second devices; wherein the second device is configured to send the positioning reference signal to the third device, and the third device is configured to process the positioning reference signal sent by the second device, and S is a positive integer, S is less than N, and N is the total number of second devices, which is also a positive integer;

[0010] The first data is input into the first model to output second information, where the second information is used to determine a positioning result.

[0011] According to a third aspect of an embodiment of the present disclosure, a positioning method is provided, which is performed by a third device. The method includes:

[0012] Determine a training sample set, where the training sample set includes M groups of sample data, where M is a positive integer, and the sample data includes: measurement data obtained after a third device processes positioning reference signals sent by S second devices, where S is a positive integer, S is less than N, where N is the total number of second devices, and N is a positive integer.

[0013] According to a fourth aspect of an embodiment of the present disclosure, a positioning method is provided, which is performed by a third device. The method includes:

[0014] Determine first data, where the first data includes: measurement data obtained after a third device processes positioning reference signals sent by S second devices; wherein the second device is used to send a positioning reference signal to the third device, and the third device is used to process the positioning reference signal sent by the second device, S is a positive integer, S is less than N, and N is the total number of second devices.

[0015] According to a fifth aspect of an embodiment of the present disclosure, a positioning method is provided, which is performed by a second device. The method includes:

[0016] A positioning reference signal is sent to a third device, where the third device is configured to process the positioning reference signal sent by the second device.

[0017] According to a sixth aspect of an embodiment of the present disclosure, a positioning method is provided for use in a communication system. The communication system includes a first device, a second device, and a third device. The first device is used to train a first model, the first model is used to perform positioning, the second device is used to send a positioning reference signal to the third device, and the third device is used to process the positioning reference signal sent by the second device. The method includes at least one of the following:

[0018] The second device sends a positioning reference signal to the third device;

[0019] The first device and the third device are the same device, and the first device determines a training sample set based on a positioning reference signal sent by the second device; or the first device and the third device are different devices, and the third device determines a training sample set based on a positioning reference signal sent by the second device, and sends the training sample set to the first device; wherein the training sample set includes M groups of sample data, where M is a positive integer, and the sample data includes: measurement data obtained by the third device after processing positioning reference signals sent by S second devices, where S is a positive integer, S is less than N, and N is the total number of second devices, which is also a positive integer;

[0020] The first device trains a first model based on the training sample set; wherein the input of the first model is: measurement data obtained after the third device processes S positioning reference signals sent by the second device.

[0021] According to a seventh aspect of an embodiment of the present disclosure, a positioning method is provided for use in a communication system. The communication system includes a fourth device, a second device, and a third device. The fourth device is a device deploying a first model, the first model is used for positioning, the second device is used to send a positioning reference signal to the third device, and the third device is used to process the positioning reference signal sent by the second device. The method includes at least one of the following:

[0022] The second device sends a positioning reference signal to the third device;

[0023] The fourth device and the third device are the same device, and the fourth device determines the first data based on the positioning reference signal sent by the second device; or the fourth device and the third device are different devices, and the third device determines the first data based on the positioning reference signal sent by the second device, and sends the first data to the fourth device; wherein the first data includes: measurement data obtained by the third device after processing the positioning reference signals sent by S second devices, where S is a positive integer, S is less than N, and N is the total number of second devices, which is also a positive integer;

[0024] The fourth device inputs the first data into a first model to output second information, where the second information is used to determine a positioning result.

[0025] According to an eighth aspect of an embodiment of the present disclosure, a communication device is provided, including:

[0026] a processing module, configured to determine a training sample set, the training sample set comprising M groups of sample data, where M is a positive integer, and the sample data comprising: measurement data obtained by a third device processing positioning reference signals sent by S second devices, where S is a positive integer, S is less than N, and N is the total number of second devices, which is also a positive integer;

[0027] The processing module is also used to train the first model based on the training sample set; wherein, the first model is used for positioning, and the input of the first model is: the measurement data obtained after the third device processes the positioning reference signals sent by S second devices.

[0028] According to a ninth aspect of an embodiment of the present disclosure, a communication device is provided, including:

[0029] a processing module, configured to determine first data, the first data comprising: measurement data obtained by a third device processing positioning reference signals sent by S second devices; wherein the second device is configured to send the positioning reference signal to the third device, and the third device is configured to process the positioning reference signal sent by the second device, S is a positive integer, S is less than N, N is the total number of second devices, and N is a positive integer;

[0030] The processing module is further configured to input the first data into a first model to output second information, where the second information is used to determine a positioning result.

[0031] According to a tenth aspect of an embodiment of the present disclosure, a communication device is provided, including:

[0032] A processing module is used to determine a training sample set, where the training sample set includes M groups of sample data, where M is a positive integer, and the sample data includes: measurement data obtained after a third device processes positioning reference signals sent by S second devices, where S is a positive integer, S is less than N, and N is the total number of second devices, which is a positive integer.

[0033] According to an eleventh aspect of the present disclosure, a communication device is provided, including:

[0034] A processing module is used to determine first data, where the first data includes: measurement data obtained after a third device processes positioning reference signals sent by S second devices; wherein the second device is used to send a positioning reference signal to the third device, and the third device is used to process the positioning reference signal sent by the second device, S is a positive integer, S is less than N, and N is the total number of second devices.

[0035] According to a twelfth aspect of an embodiment of the present disclosure, a communication device is provided, including:

[0036] The transceiver module is used to send a positioning reference signal to a third device, and the third device is used to process the positioning reference signal sent by the second device.

[0037] According to a thirteenth aspect of the embodiments of the present disclosure, a communication device is provided, including:

[0038] one or more processors;

[0039] A memory coupled to the processor stores instructions, which, when executed by the processor, cause the communication device to execute any one of the methods described in the first to fifth aspects.

[0040] According to the fourteenth aspect of the embodiment of the present disclosure, a storage medium is proposed, which stores instructions. When the instructions are executed on a communication device, the communication device executes any one of the methods described in the first to fifth aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The above and / or additional aspects and advantages of the present disclosure will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0042] FIG1 is a schematic diagram of the architecture of some communication systems provided by embodiments of the present disclosure;

[0043] 2A-9B are flowcharts of a positioning method provided in yet another embodiment of the present disclosure;

[0044] FIG9C is a schematic structural diagram of a model application method according to an embodiment of the present disclosure;

[0045] 10A-10E are schematic structural diagrams of a communication device provided by an embodiment of the present disclosure;

[0046] FIG11A is a schematic structural diagram of a communication device provided by an embodiment of the present disclosure;

[0047] FIG11B is a schematic structural diagram of a chip provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0048] The embodiments of the present disclosure provide a positioning method and apparatus, a communication device, a communication system, and a storage medium.

[0049] In a first aspect, an embodiment of the present disclosure provides a positioning method, which is performed by a first device. The method includes:

[0050] Determine a training sample set, where the training sample set includes M groups of sample data, where M is a positive integer, and the sample data includes: measurement data obtained by a third device processing positioning reference signals sent by S second devices, where S is a positive integer, S is less than N, and N is the total number of second devices, which is also a positive integer;

[0051] The first model is trained based on the training sample set; wherein the first model is used for positioning, and the input of the first model is: measurement data obtained after the third device processes S positioning reference signals sent by the second device.

[0052] In the above embodiment, the first device will first determine a training sample set when training the first model, wherein the training sample set includes M groups of sample data, where M is a positive integer, and the sample data includes: measurement data obtained after the third device processes the positioning reference signals sent by S second devices, where S is a positive integer, S is less than N, and N is the total number of second devices in the application scenario of the first model. Afterwards, the first device will train the first model based on the training sample set. Among them, since the sample data in the training sample set is: the measurement data obtained after the third device processes the positioning reference signals sent by S second devices, the input of the first model trained based on the sample data should also be: the measurement data obtained after the third device processes the positioning reference signals sent by S second devices (or simply referred to as: the measurement data corresponding to the S second devices). Among them, since S is less than N, it means that when the positioning method of the present invention trains the first model, the input dimension of the first model is: the measurement data corresponding to some second devices, rather than "the measurement data corresponding to all second devices", which greatly reduces the input dimension of the first model. Among them, since the complexity of the first model is positively correlated with the input dimension, therefore, when the input dimension of the first model is reduced, the complexity of the first model can be greatly reduced, thereby reducing the power overhead during the training and application of the first model.

[0053] Moreover, when the first model is trained with "the input dimension of the first model is: measurement data corresponding to some second devices", compared with the scheme of "the first model needs to input measurement data corresponding to all second devices", the method disclosed in the present invention can further reduce the input requirements of the first model on the basis of ensuring the positioning accuracy of the first model. Then, in the subsequent application of the first model, even if "the third device cannot simultaneously obtain the positioning reference signals sent by all or more second devices in the first model application scenario, but can only obtain the positioning reference signals sent by some or fewer second devices in the first model application scenario", the measurement data corresponding to the positioning reference signals sent by the some or fewer second devices can still be used as the input of the first model to determine the positioning result, and will not affect the positioning accuracy of the first model, and the practicality is high.

[0054] Also, under the premise that the input dimension of the first model is: the measurement data corresponding to part of the second device, if the first model is not deployed on the third device, the third device needs to send the input data required by the first model to the deployment device of the first model (hereinafter referred to as the fourth device). At this time, the third device only needs to send part of the measurement data corresponding to the second device to the fourth device, without sending all the measurement data corresponding to the second device, which can greatly reduce the data transmission overhead.

[0055] In combination with some embodiments of the first aspect, in some embodiments, S=1.

[0056] In the above embodiment, by setting S = 1, the input dimension of the first model is reduced to the measurement data corresponding to one second device, thereby significantly reducing the input dimension of the first model and, in turn, the complexity of the first model. Furthermore, when the first model is subsequently applied, if the first model is not deployed on a third device, the third device only needs to send the measurement data corresponding to one second device to the device where the first model is deployed, rather than sending the measurement data corresponding to all second devices, thereby significantly reducing data transmission overhead. Furthermore, because the first model is trained using low-latitude data, the input requirements of the first model can be further reduced while ensuring the positioning accuracy of the first model. Therefore, even if the third device cannot simultaneously obtain positioning reference signals sent by all or more second devices in the first model application scenario, but can only obtain positioning reference signals sent by some or fewer second devices in the first model application scenario, the measurement data corresponding to the positioning reference signal sent by a second device can still be used as input to the first model to determine the positioning result without affecting the positioning accuracy of the first model, thus achieving high practicality.

[0057] In conjunction with some embodiments of the first aspect, in some embodiments, the first device and the third device are the same device;

[0058] Determining the training sample set includes:

[0059] receiving a positioning reference signal sent by at least one second device;

[0060] Determining M groups of positioning reference signals based on at least one positioning reference signal sent by the second device, where each group of positioning reference signals includes S positioning reference signals sent by the second device;

[0061] Processing M groups of positioning reference signals respectively to obtain the M groups of sample data;

[0062] The training sample set is determined based on the M groups of sample data.

[0063] In conjunction with some embodiments of the first aspect, in some embodiments, the first device and the third device are different devices;

[0064] Determining the training sample set includes:

[0065] Receive the training sample set sent by the third device.

[0066] In the above embodiment, a method for a first device to determine a training sample set is provided, so that the first device can successfully determine the training sample set, so that the first device can subsequently successfully train the first model based on the determined training sample set.

[0067] In conjunction with some embodiments of the first aspect, in some embodiments, the first device is different from a fourth device, and the fourth device is a device for deploying the first model; and the method further includes:

[0068] Send first information to the fourth device, where the first information is used to deploy the trained first model.

[0069] In conjunction with some embodiments of the first aspect, in some embodiments, the first device is the same as a fourth device, and the fourth device is a device for deploying the first model; and the method further includes:

[0070] Deploy the trained first model.

[0071] In the above embodiment, a method for deploying a trained first model is provided to facilitate the subsequent successful deployment of the trained first model, so that positioning can be successfully performed based on the first model to improve positioning accuracy.

[0072] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0073] The M is determined based on a first value and / or the positioning accuracy required for positioning by the first model; wherein the first value is: the number of the second devices that can successfully send a positioning reference signal to the third device.

[0074] In the above embodiment, a method for determining M is provided, and when determining M, the number of second devices that can successfully send positioning reference signals to the third device and the positioning accuracy required for positioning by the first model are comprehensively considered. The determined M value can take into account the number of positioning reference signals that can be successfully received by the third device and the positioning accuracy required for positioning. Therefore, when the first model is subsequently trained based on M groups of sample data, it can be ensured that the trained first model can be successfully applied, and the positioning accuracy of the first model can also be ensured.

[0075] In conjunction with some embodiments of the first aspect, in some embodiments, training the first model based on the training sample set includes:

[0076] The first model is trained M times based on M groups of sample data in the training sample set.

[0077] In the above embodiment, a method is provided for how a first device specifically trains a first model so that the first model can be successfully trained. In addition, the method disclosed herein can train the first model M times with high training accuracy, thereby making the positioning accuracy of the first model higher.

[0078] In combination with some embodiments of the first aspect, in some embodiments, the data dimension of the sample data is: S×F×2, and the input dimension of the first model is: S×F×2; wherein, F is the number of time domain sampling points when the third device processes the positioning reference signal, and 2 represents the real part and the imaginary part.

[0079] In the above embodiment, the input dimension of the first model is S×F×2, rather than N×F×2. Since S is smaller than N, the input dimension of the first model is significantly reduced, thereby significantly reducing the complexity of the first model. Furthermore, when the first model is subsequently applied, if the first model is not deployed on the third device, the third device needs to send the first model's input data to the first model. Since the input data of the first model has a lower dimension, this significantly reduces data transmission overhead. Furthermore, since the first model is trained using low-dimensional data, the input requirements of the first model can be further reduced while ensuring the positioning accuracy of the first model. Therefore, even if the third device cannot simultaneously obtain positioning reference signals sent by all or more second devices in the first model application scenario, but can only obtain positioning reference signals sent by some or fewer second devices in the first model application scenario, the measurement data corresponding to the positioning reference signals sent by S second devices can still be used as input to the first model to determine the positioning result without affecting the positioning accuracy of the first model, thus achieving high practicality.

[0080] In a second aspect, an embodiment of the present disclosure provides a positioning method, wherein the fourth device is a device deploying a first model, and the first model is used for positioning. The method includes:

[0081] Determine first data, where the first data includes: measurement data obtained by a third device processing positioning reference signals sent by S second devices; wherein the second device is configured to send the positioning reference signal to the third device, and the third device is configured to process the positioning reference signal sent by the second device, and S is a positive integer, S is less than N, and N is the total number of second devices, which is also a positive integer;

[0082] The first data is input into the first model to output second information, where the second information is used to determine a positioning result.

[0083] In the above embodiment, the fourth device determines the first data and inputs the first data into the first model to output second information, which is used to determine the positioning result. The first data includes: measurement data obtained by the third device after processing the positioning reference signals sent by S second devices; the second device is used to send the positioning reference signal to the third device, and the third device is used to measure the positioning reference signal sent by the second device, S is a positive integer, S is less than N, and N is the total number of second devices in the first model application scenario; it can be seen that during the application of the first model, the input dimension of the first model is: measurement data corresponding to S second devices (i.e., measurement data corresponding to some second devices), rather than "measurement data corresponding to all second devices", which greatly reduces the input dimension of the first model. Since the complexity of the first model is positively correlated with the input dimension, when the input dimension of the first model is reduced, the complexity of the first model can be greatly reduced, thereby reducing the power overhead during the training and application of the first model.

[0084] Moreover, when the input dimension of the first model is: measurement data corresponding to some second devices, compared with the solution that "the first model needs to input measurement data corresponding to all second devices", the method disclosed in the present invention can further reduce the input requirements of the first model. Based on this, even if the situation occurs that "the third device cannot simultaneously obtain the positioning reference signals sent by all or more second devices in the first model application scenario, but can only obtain the positioning reference signals sent by some or fewer second devices in the first model application scenario", the measurement data corresponding to the positioning reference signals sent by the some or fewer second devices can still be used as the input of the first model to determine the positioning results, and will not affect the positioning accuracy of the first model, and the practicality is high.

[0085] Also, under the premise that the input dimension of the first model is: the measurement data corresponding to part of the second device, if the first model is not deployed on the third device, the third device needs to send the input data required by the first model to the deployment device of the first model (i.e., the fourth device). At this time, the third device only needs to send part of the measurement data corresponding to the second device to the fourth device, without sending all the measurement data corresponding to the second device, thereby greatly reducing data transmission overhead.

[0086] With reference to some embodiments of the second aspect, in some embodiments, the fourth device and the third device are the same device;

[0087] The determining of the first data includes:

[0088] receiving a positioning reference signal sent by at least one second device;

[0089] Process the S positioning reference signals sent by the second devices to determine the first data.

[0090] In conjunction with some embodiments of the second aspect, in some embodiments, the fourth device and the third device are different devices;

[0091] The determining of the first data includes:

[0092] Receive the first data sent by the third device.

[0093] In the above embodiment, a method for the fourth device to determine the first data is provided, so that the fourth device can successfully determine the first data, so that the fourth device can subsequently successfully determine the positioning result using the first model based on the determined first data.

[0094] In combination with some embodiments of the second aspect, in some embodiments, the data dimension of the first data is: S×F×2, wherein F is the number of time domain sampling points when the third device processes the positioning reference signal, and 2 represents the real part and the imaginary part.

[0095] In the above embodiment, the data dimension of the first data input to the first model is: S×F×2, rather than N×F×2. Since S is less than N, the input dimension of the first model can be greatly reduced, thereby greatly reducing the complexity of the first model. In addition, if the fourth device and the third device are different devices (i.e., the first model is not deployed on the third device, but on the fourth device), the third device needs to send the input data of the first model to the fourth device. In this case, the third device only needs to send the measurement data corresponding to S second devices to the fourth device, without having to send the measurement data corresponding to all second devices, thereby greatly reducing data transmission overhead. At the same time, since the input dimension of the first model is small, the input requirements of the first model are reduced. Based on this, even if the situation occurs that "the third device cannot simultaneously obtain the positioning reference signals sent by all or more second devices in the first model application scenario, but can only obtain the positioning reference signals sent by some or fewer second devices in the first model application scenario", it is still possible to use only the measurement data corresponding to the positioning reference signals sent by S second devices as the input of the first model to determine the positioning result, and it will not affect the positioning accuracy of the first model, and it is highly practical.

[0096] In a third aspect, an embodiment of the present disclosure provides a positioning method, which is performed by a third device. The method includes:

[0097] Determine a training sample set, where the training sample set includes M groups of sample data, where M is a positive integer, and the sample data includes: measurement data obtained after a third device processes positioning reference signals sent by S second devices, where S is a positive integer, S is less than N, where N is the total number of second devices, and N is a positive integer.

[0098] In conjunction with some embodiments of the third aspect, in some embodiments, determining the training sample set includes:

[0099] receiving a positioning reference signal sent by at least one second device;

[0100] Determine M groups of positioning reference signals based on at least one positioning reference signal sent by the second device;

[0101] Processing M groups of positioning reference signals respectively to obtain the M groups of sample data;

[0102] The training sample set is determined based on the M groups of sample data.

[0103] In conjunction with some embodiments of the third aspect, in some embodiments, the method further includes:

[0104] The training sample set is sent to a first device, where the first device is used to train a first model based on the training sample set, where the first model is used for positioning.

[0105] In a fourth aspect, an embodiment of the present disclosure provides a positioning method, which is performed by a third device. The method includes:

[0106] Determine first data, where the first data includes: measurement data obtained after a third device processes positioning reference signals sent by S second devices; wherein the second device is used to send a positioning reference signal to the third device, and the third device is used to process the positioning reference signal sent by the second device, S is a positive integer, S is less than N, and N is the total number of second devices.

[0107] In conjunction with some embodiments of the fourth aspect, in some embodiments, determining the first data includes:

[0108] receiving a positioning reference signal sent by at least one second device;

[0109] Process the S positioning reference signals sent by the second devices to determine the first data.

[0110] In conjunction with some embodiments of the fourth aspect, in some embodiments, the method further includes:

[0111] The first data is sent to a fourth device, where the fourth device is a device deploying a first model, and the first model is used for positioning based on the first data.

[0112] In a fifth aspect, an embodiment of the present disclosure provides a positioning method, which is performed by a second device. The method includes:

[0113] A positioning reference signal is sent to a third device, where the third device is configured to process the positioning reference signal sent by the second device.

[0114] In a sixth aspect, an embodiment of the present disclosure provides a positioning method for a communication system, the communication system including a first device, a second device, and a third device, wherein the first device is used to train a first model, the first model is used to perform positioning, the second device is used to send a positioning reference signal to the third device, and the third device is used to process the positioning reference signal sent by the second device, the method including at least one of the following:

[0115] The second device sends a positioning reference signal to the third device;

[0116] The first device and the third device are the same device, and the first device determines a training sample set based on a positioning reference signal sent by the second device; or the first device and the third device are different devices, and the third device determines a training sample set based on a positioning reference signal sent by the second device, and sends the training sample set to the first device; wherein the training sample set includes M groups of sample data, where M is a positive integer, and the sample data includes: measurement data obtained by the third device after processing positioning reference signals sent by S second devices, where S is a positive integer, S is less than N, and N is the total number of second devices, which is also a positive integer;

[0117] The first device trains a first model based on the training sample set; wherein the input of the first model is: measurement data obtained after the third device processes S positioning reference signals sent by the second device.

[0118] In a seventh aspect, an embodiment of the present disclosure provides a positioning method for a communication system, the communication system including a fourth device, a second device, and a third device, the fourth device being a device deploying a first model, the first model being used for positioning, the second device being used to send a positioning reference signal to the third device, and the third device being used to process the positioning reference signal sent by the second device, the method including at least one of the following:

[0119] The second device sends a positioning reference signal to the third device;

[0120] The fourth device and the third device are the same device, and the fourth device determines the first data based on the positioning reference signal sent by the second device; or the fourth device and the third device are different devices, and the third device determines the first data based on the positioning reference signal sent by the second device, and sends the first data to the fourth device; wherein the first data includes: measurement data obtained by the third device after processing the positioning reference signals sent by S second devices, where S is a positive integer, S is less than N, and N is the total number of second devices, which is also a positive integer;

[0121] The fourth device inputs the first data into a first model to output second information, where the second information is used to determine a positioning result.

[0122] In an eighth aspect, an embodiment of the present disclosure provides a communication device, including:

[0123] a processing module, configured to determine a training sample set, the training sample set comprising M groups of sample data, where M is a positive integer, and the sample data comprising: measurement data obtained by a third device processing positioning reference signals sent by S second devices, where S is a positive integer, S is less than N, and N is the total number of second devices, which is also a positive integer;

[0124] The processing module is also used to train the first model based on the training sample set; wherein, the first model is used for positioning, and the input of the first model is: the measurement data obtained after the third device processes the positioning reference signals sent by S second devices.

[0125] In combination with some embodiments of the eighth aspect, in some embodiments, S=1.

[0126] With reference to some embodiments of the eighth aspect, in some embodiments, the first device and the third device are the same device;

[0127] Determining the training sample set includes:

[0128] receiving a positioning reference signal sent by at least one second device;

[0129] Determining M groups of positioning reference signals based on at least one positioning reference signal sent by the second device, where each group of positioning reference signals includes S positioning reference signals sent by the second device;

[0130] Processing M groups of positioning reference signals respectively to obtain the M groups of sample data;

[0131] The training sample set is determined based on the M groups of sample data.

[0132] With reference to some embodiments of the eighth aspect, in some embodiments, the first device and the third device are different devices;

[0133] The processing module is further configured to:

[0134] Receive the training sample set sent by the third device.

[0135] In conjunction with some embodiments of the eighth aspect, in some embodiments, the first device is different from the fourth device, and the fourth device is a device for deploying the first model; and the communication device is further configured to:

[0136] Send first information to the fourth device, where the first information is used to deploy the trained first model.

[0137] In conjunction with some embodiments of the eighth aspect, in some embodiments, the first device is the same as the fourth device, and the fourth device is a device for deploying the first model; and the communication device is further configured to:

[0138] Deploy the trained first model.

[0139] In conjunction with some embodiments of the eighth aspect, in some embodiments, the communication device is further configured to:

[0140] The M is determined based on a first value and / or the positioning accuracy required for positioning by the first model; wherein the first value is: the number of the second devices that can successfully send a positioning reference signal to the third device.

[0141] In conjunction with some embodiments of the eighth aspect, in some embodiments, the processing module is further configured to:

[0142] The first model is trained M times based on M groups of sample data in the training sample set.

[0143] In combination with some embodiments of the eighth aspect, in some embodiments, the data dimension of the sample data is: S×F×2, and the input dimension of the first model is: S×F×2; wherein, F is the number of time domain sampling points when the third device processes the positioning reference signal, and 2 represents the real part and the imaginary part.

[0144] In a ninth aspect, an embodiment of the present disclosure provides a communication device, including:

[0145] a processing module, configured to determine first data, the first data comprising: measurement data obtained by a third device processing positioning reference signals sent by S second devices; wherein the second device is configured to send the positioning reference signal to the third device, and the third device is configured to process the positioning reference signal sent by the second device, S is a positive integer, S is less than N, N is the total number of second devices, and N is a positive integer;

[0146] The processing module is further configured to input the first data into a first model to output second information, where the second information is used to determine a positioning result.

[0147] With reference to some embodiments of the ninth aspect, in some embodiments, the fourth device and the third device are the same device;

[0148] The processing module is further configured to:

[0149] receiving a positioning reference signal sent by at least one second device;

[0150] Process the S positioning reference signals sent by the second devices to determine the first data.

[0151] With reference to some embodiments of the ninth aspect, in some embodiments, the fourth device and the third device are different devices;

[0152] The processing module is further configured to:

[0153] Receive the first data sent by the third device.

[0154] In combination with some embodiments of the ninth aspect, in some embodiments, the data dimension of the first data is: S×F×2, wherein F is the number of time domain sampling points when the third device processes the positioning reference signal, and 2 represents the real part and the imaginary part.

[0155] In a tenth aspect, an embodiment of the present disclosure provides a communication device, including:

[0156] A processing module is used to determine a training sample set, where the training sample set includes M groups of sample data, where M is a positive integer, and the sample data includes: measurement data obtained after a third device processes positioning reference signals sent by S second devices, where S is a positive integer, S is less than N, and N is the total number of second devices, which is a positive integer.

[0157] In conjunction with some embodiments of the tenth aspect, in some embodiments, the processing module is further configured to:

[0158] receiving a positioning reference signal sent by at least one second device;

[0159] Determine M groups of positioning reference signals based on at least one positioning reference signal sent by the second device;

[0160] Processing M groups of positioning reference signals respectively to obtain the M groups of sample data;

[0161] The training sample set is determined based on the M groups of sample data.

[0162] In conjunction with some embodiments of the tenth aspect, in some embodiments, the communication device is further configured to:

[0163] The training sample set is sent to a first device, where the first device is used to train a first model based on the training sample set, where the first model is used for positioning.

[0164] In an eleventh aspect, an embodiment of the present disclosure provides a communication device, including:

[0165] A processing module is used to determine first data, where the first data includes: measurement data obtained after a third device processes positioning reference signals sent by S second devices; wherein the second device is used to send a positioning reference signal to the third device, and the third device is used to process the positioning reference signal sent by the second device, S is a positive integer, S is less than N, and N is the total number of second devices.

[0166] In conjunction with some embodiments of the eleventh aspect, in some embodiments, the processing module is further configured to:

[0167] receiving a positioning reference signal sent by at least one second device;

[0168] Process the S positioning reference signals sent by the second devices to determine the first data.

[0169] In conjunction with some embodiments of the eleventh aspect, in some embodiments, the device is further configured to:

[0170] The first data is sent to a fourth device, where the fourth device is a device deploying a first model, and the first model is used for positioning based on the first data.

[0171] In a twelfth aspect, an embodiment of the present disclosure provides a communication device, including:

[0172] The transceiver module is used to send a positioning reference signal to a third device, and the third device is used to process the positioning reference signal sent by the second device.

[0173] In the thirteenth aspect, an embodiment of the present disclosure proposes a communication device, which includes: one or more processors; one or more memories for storing instructions; wherein the processor is used to call the instructions so that the communication device executes the method described in the first aspect, the optional implementation of the first aspect, the second aspect, the optional implementation of the second aspect, the third aspect, the optional implementation of the third aspect, the fourth aspect, the optional implementation of the fourth aspect, the fifth aspect, and the optional implementation of the fifth aspect.

[0174] In the fourteenth aspect, an embodiment of the present disclosure proposes a communication system, which includes: a terminal and a network device; wherein the terminal is configured to execute the method described in the first aspect, the optional implementation of the first aspect, the second aspect, the optional implementation of the second aspect, the third aspect, the optional implementation of the third aspect, the fourth aspect, the optional implementation of the fourth aspect, the fifth aspect, and the optional implementation of the fifth aspect, and the network device is configured to execute the method described in the first aspect, the optional implementation of the first aspect, the second aspect, the optional implementation of the second aspect, the third aspect, the optional implementation of the third aspect, the fourth aspect, the optional implementation of the fourth aspect, the fifth aspect, and the optional implementation of the fifth aspect.

[0175] In the fifteenth aspect, an embodiment of the present disclosure proposes a storage medium, which stores instructions. When the instructions are executed on a communication device, the communication device executes the method described in the first aspect, the optional implementation of the first aspect, the second aspect, the optional implementation of the second aspect, the third aspect, the optional implementation of the third aspect, the fourth aspect, the optional implementation of the fourth aspect, the fifth aspect, and the optional implementation of the fifth aspect.

[0176] In the sixteenth aspect, an embodiment of the present disclosure proposes a program product. When the program product is executed by a communication device, the communication device executes the method described in the first aspect, the optional implementation of the first aspect, the second aspect, the optional implementation of the second aspect, the third aspect, the optional implementation of the third aspect, the fourth aspect, the optional implementation of the fourth aspect, the fifth aspect, and the optional implementation of the fifth aspect.

[0177] In the seventeenth aspect, an embodiment of the present disclosure proposes a computer program, which, when running on a computer, enables the computer to execute the method described in the first aspect, the optional implementation of the first aspect, the second aspect, the optional implementation of the second aspect, the third aspect, the optional implementation of the third aspect, the fourth aspect, the optional implementation of the fourth aspect, the fifth aspect, and the optional implementation of the fifth aspect.

[0178] It is understandable that the above-mentioned terminals, network devices, communication devices, communication systems, storage media, program products, and computer programs are all used to execute the methods proposed in the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding methods and will not be repeated here.

[0179] The present disclosure provides invention titles. In some embodiments, the terms "positioning method" and "information processing method," "information sending method," and "information receiving method" are interchangeable; the terms "communication device" and "information processing device," "information sending device," and "information receiving device" are interchangeable; and the terms "information processing system," "communication system," "information sending system," and "information receiving system" are interchangeable.

[0180] The embodiments of the present disclosure are not exhaustive and are merely illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined. For example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0181] In each embodiment of the present disclosure, unless otherwise specified or provided for by logic, the terms and / or descriptions between the embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their inherent logical relationships.

[0182] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0183] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular, such as "a", "an", "the", "above", "said", "the", "the", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun following the article may be understood as a singular expression or a plural expression.

[0184] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0185] In some embodiments, the terms "at least one of", "at least one of", "at least one of", "one or more", "a plurality of", "multiple", etc. can be used interchangeably.

[0186] In the embodiments of the present disclosure, descriptions such as “at least one of A, B, C…”, “A and / or B and / or C…”, etc. include the situation where any one of A, B, C… exists alone, and also include any combination of any multiple of A, B, C…, and each situation can exist alone; for example, “at least one of A, B, C” includes the situation where A exists alone, B exists alone, C exists alone, the combination of A and B, the combination of A and C, the combination of B and C, and the combination of A, B, and C; for example, A and / or B includes the situation where A exists alone, B exists alone, and the combination of A and B.

[0187] In some embodiments, descriptions such as "in one case A, in another case B," or "in response to one case A, in response to another case B," may include the following technical solutions depending on the situation: executing A independently of B (in some embodiments, A); executing B independently of A (in some embodiments, B); selectively executing A and B (in some embodiments, selecting between A and B); and executing both A and B (in some embodiments, A and B). The same applies when there are more branches, such as A, B, and C.

[0188] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restriction on the position, order, priority, quantity or content of the description objects. For the statement of the description object, please refer to the description in the context of the claims or embodiments, and no unnecessary restriction should be constituted due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields". "First" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and the "second device" can be the same device or different devices, and their types can be the same or different; for another example, if the description object is "information", then the "first information" and the "second information" can be the same information or different information, and their contents can be the same or different.

[0189] In some embodiments, “including A,” “comprising A,” “used to indicate A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0190] In some embodiments, terms such as "in response to...", "in response to determining...", "in the case of...", "at the time of...", "when...", "if...", "if...", etc. can be used interchangeably.

[0191] In some embodiments, terms such as "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not less than", and "above" can be replaced with each other, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", and "below" can be replaced with each other.

[0192] In some embodiments, devices, etc. can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. Terms such as "device", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", and "subject" can be used interchangeably.

[0193] In some embodiments, "network" can be interpreted as devices included in the network (eg, access network equipment, core network equipment, etc.).

[0194] In some embodiments, the terms "access network device (AN device)", "radio access network device (RAN device)", "base station (BS)", "radio base station" "fixed station", "node", "access point", "transmission point (TP)", "reception point (RP)", "transmission / reception point (TRP)", "panel", "antenna panel", "antenna array", "cell", "macro cell", "small cell", "femto cell", "pico cell", "sector", "cell group", "carrier", "component carrier", "bandwidth part (BWP)" and the like may be used interchangeably.

[0195] In some embodiments, the terms "terminal", "terminal device", "user equipment (UE)", "user terminal", "mobile station (MS)", "mobile terminal (MT)", subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, etc. can be used interchangeably.

[0196] In some embodiments, the access network device, the core network device, or the network device can be replaced by a terminal. For example, the various embodiments of the present disclosure can also be applied to a structure in which the communication between the access network device, the core network device, or the network device and the terminal is replaced by communication between multiple terminals (for example, it can also be called device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, it can also be set as a structure in which the terminal has all or part of the functions of the access network device. In addition, language such as "uplink" and "downlink" can also be replaced by language corresponding to communication between terminals (for example, "side"). For example, uplink channels, downlink channels, etc. can be replaced by side channels, and uplinks, downlinks, etc. can be replaced by side links.

[0197] In some embodiments, the terminal may be replaced by an access network device, a core network device, or a network device. In this case, the access network device, the core network device, or the network device may have a structure that has all or part of the functions of the terminal.

[0198] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0199] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0200] In addition, each element, each row, or each column in the table of the embodiment of the present disclosure can be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns can also be implemented as an independent embodiment.

[0201] The correspondences shown in the tables of the present disclosure can be configured or predefined. The values ​​of the information in each table are merely examples and can be configured to other values, which are not limited by the present disclosure. When configuring the correspondences between information and parameters, it is not necessarily required to configure all the correspondences shown in each table. For example, in the tables of the present disclosure, the correspondences shown in certain rows may not be configured. For another example, appropriate deformation adjustments can be made based on the above tables, such as splitting, merging, etc. The names of the parameters shown in the titles of the above tables may also adopt other names that can be understood by the communication device, and the values ​​or representations of the parameters may also adopt other values ​​or representations that can be understood by the communication device. When implementing the above tables, other data structures may also be used, such as arrays, queues, containers, stacks, linear lists, pointers, linked lists, trees, graphs, structures, classes, heaps, hash tables or hash tables, etc.

[0202] The predefined in the present disclosure may be understood as defined, predefined, stored, pre-stored, pre-negotiated, pre-configured, solidified, or pre-burned.

[0203] FIG1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure. As shown in FIG1 , the communication system 100 may include one or more of a signal measurement node, a signal sending node, and a positioning server. The signal sending node is used to send a positioning reference signal to the signal measurement node, and the signal measurement node is used to measure the positioning reference signal to obtain measurement data for positioning. Optionally, a positioning model (such as an AI positioning model) can be deployed on the signal measurement node. When the signal measurement node deploys the positioning model, the signal measurement node can input the measurement data used for positioning into the positioning model to obtain the output result of the positioning model. The output result of the positioning model may include: a positioning result or an intermediate positioning parameter, which can be used to determine the positioning result, and the signal measurement node can send the positioning result or the intermediate positioning parameter output by the positioning model to the positioning server, or the signal measurement node can determine the positioning result based on the intermediate positioning parameter output by the positioning model and send it to the positioning server. Optionally, the above-mentioned positioning model can also be deployed in the positioning server. In this case, the signal measurement node can send the measurement data for positioning to the positioning server so that the positioning server can input the measurement data for positioning into the positioning model to obtain the positioning result.

[0204] Optionally, the signal measurement node may be, for example, a network device or a terminal, and the signal sending node may be, for example, a network device or a terminal. When the signal measurement node is a network device, the signal sending node is a terminal; when the signal measurement node is a terminal, the signal sending node is a network device. The positioning server may be a positioning model inference node, for example, a core network device (such as a Location Management Function (LMF)). The network device may include at least one of an access network device and a core network device.

[0205] In some embodiments, the terminal includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and at least one of a wireless terminal device in a smart home, but is not limited thereto.

[0206] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network. The access network device may include an evolved NodeB (eNB), a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved nodeB (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, and at least one of an access node in a wireless fidelity (WiFi) system, but is not limited thereto.

[0207] In some embodiments, the technical solution of the present disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can be transformed into internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be implemented through software or programs.

[0208] In some embodiments, the access network device can be composed of a centralized unit (CU) and a distributed unit (DU), where the CU can also be called a control unit. The CU-DU structure can be used to split the protocol layer of the access network device, with the functions of some protocol layers centrally controlled by the CU, and the functions of the remaining part or all of the protocol layers distributed in the DU, which is centrally controlled by the CU, but is not limited to this.

[0209] In some embodiments, the core network device may be a device including one or more network elements, or may be multiple devices or a group of devices, each including all or part of one or more network elements. The network element may be virtual or physical. The core network includes, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC). Alternatively, the core network device may also be a location management function network element. Exemplarily, the location management function network element includes a location server (location server), which may be implemented as any one of the following: Location Management Function (LMF), Enhanced Serving Mobile Location Centre (ESMC), or a 5G Core Network (5GCN).

[0210] E-SMLC), Secure User Plane Location (SUPL) and Secure User Plane Location Platform (SUPLLP).

[0211] It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure. Ordinary technicians in this field can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.

[0212] The following embodiments of the present disclosure may be applied to the communication system 100 shown in Figure 1, or a portion thereof, but are not limited thereto. The entities shown in Figure 1 are illustrative only. The communication system may include all or part of the entities shown in Figure 1, or may include other entities outside of Figure 1. The number and form of the entities may be arbitrary. The connection relationship between the entities is illustrative only. The entities may be connected or disconnected, and the connection may be in any manner, including direct or indirect, wired or wireless.

[0213] The embodiments of the present disclosure can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), future radio access (FRA), new radio access technology (RAT), new radio (NR), new radio access (NX), future generation radio access (FX), Global System for Mobile Communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X), systems utilizing other positioning methods, and next-generation systems based on and extending these methods. Furthermore, a combination of multiple systems (e.g., a combination of LTE or LTE-A with 5G) may also be employed.

[0214] Optionally, the AI-based positioning method may include: a signal measurement node receives a positioning reference signal sent by a signal sending node, and performs positioning measurement on the positioning reference signal to obtain measurement data for positioning, for example, a channel impulse response (CIR), and then inputs the measurement data for positioning into a positioning model (for example, an AI positioning model) so that the positioning model outputs the required positioning results, or inputs the measurement data for positioning into the positioning model so that the positioning model outputs intermediate positioning parameters, such as time of arrival (ToA), angle of arrival (AoA), etc., and then uses a positioning method (such as time difference of arrival (TDOA) method, etc.) based on the intermediate positioning parameters to perform calculations to obtain the required positioning results. However, in some embodiments, the positioning model may not be deployed in the signal measurement node, but may be deployed in a positioning server (such as a location management function (LMF)). In this case, the signal measurement node usually needs to send the measurement data obtained for positioning to the positioning server, so that the positioning server can input the received measurement data for positioning into the positioning model to obtain the required positioning results.

[0215] Optionally, in AI-based positioning scenarios, multiple signal-sending nodes may each send a positioning reference signal to a signal-measuring node. The signal-measuring node needs to calculate the measurement data for positioning corresponding to the positioning reference signals sent by all signal-sending nodes, and input the measurement data corresponding to all signal-sending nodes into the positioning model to output the required positioning results. This can cause the following problems:

[0216] (1) When the positioning model is not deployed on the signal measurement node but on the positioning server, the signal measurement node usually needs to send measurement data for positioning to the positioning server. In this case, the signal measurement node needs to send the measurement data corresponding to all signal sending nodes to the positioning server. The data dimension is relatively large, and the data dimension is (N×F×2), where N is the total number of signal sending nodes in the positioning model application scenario, F is the number of time domain sampling points when the signal measurement node measures the reference signal for positioning purposes, and 2 represents the real part and the imaginary part. Among them, due to the large data dimension that needs to be transmitted, it will cause a large data transmission overhead, which is not conducive to the application of AI-based positioning technology in actual communication systems.

[0217] (2) The complexity of the positioning model is positively correlated with the input dimension of the positioning model input data. When the measurement data corresponding to all signal sending nodes are used as the input of the positioning model, the complexity of the positioning model will be higher due to the large input dimension, which will cause greater power overhead during the training and application of the positioning model.

[0218] (3) In a communication system, a signal measurement node may not be able to simultaneously obtain positioning reference signals sent by all or more signal sending nodes in a scene, resulting in the signal measurement node only being able to determine the measurement data corresponding to some signal sending nodes, but unable to obtain the measurement data corresponding to all signal sending nodes. In this case, only the measurement data corresponding to some signal sending nodes can be used as input to the positioning model to determine the required positioning results. However, since the positioning model is trained based on the measurement data corresponding to all signal sending nodes, using the measurement data corresponding to some signal sending nodes as input to the positioning model will result in a decrease in the positioning accuracy of the positioning model.

[0219] FIG2A is an interactive diagram of a positioning method according to an embodiment of the present disclosure. As shown in FIG2A , the present disclosure embodiment relates to a positioning method for a communication system 100 for training a first model for implementing positioning, the method comprising:

[0220] Step 2101: At least one second device sends a positioning reference signal.

[0221] Optionally, the first model may be an AI positioning model, and the first model may be used to implement AI positioning. For an introduction on how the first model implements AI positioning, reference may be made to the description before step 2A embodiment.

[0222] Optionally, the second device may be a signal sending node configured to send a positioning reference signal. Optionally, the second device may send a positioning reference signal to a third device. The third device may be a signal measuring node configured to process the positioning reference signal sent by the second device (e.g., perform positioning measurements) to obtain measurement data corresponding to the second device, so that positioning can be subsequently performed based on the measurement data.

[0223] Optionally, the second device may be, for example, a network device (such as a base station or a transmission / reception point (TRP)) or a terminal, and the third device may be, for example, a network device (such as a base station or a TRP) or a terminal. When the second device is a network device and the third device is a terminal, the positioning reference signal may be a positioning reference signal (PRS). When the second device is a terminal and the third device is a network device, the positioning reference signal may be a sounding reference signal (SRS-Pos).

[0224] Optionally, the measurement data obtained after processing the positioning reference signal may be, for example, a channel impulse response (CIR). Optionally, when the positioning reference signal is a PRS, the measurement data may be a downlink CIR; and when the positioning reference signal is an SRS-Pos, the measurement data may be an uplink CIR.

[0225] Step 2102: The third device determines a training sample set based on a positioning reference signal received from at least one second device.

[0226] Optionally, the third device may determine M groups of positioning reference signals based on positioning reference signals received from at least one second device, wherein each group of positioning reference signals may include positioning reference signals sent by S second devices. S is less than N, where N is the total number of second devices. For example, N may be the total number of second devices in the first model application scenario, and M, N, and S are all positive integers. The third device may process the M groups of positioning reference signals to obtain measurement data corresponding to the S second devices in each group of positioning reference signals. Afterwards, the third device may determine the measurement data corresponding to the S second devices in each group of positioning reference signals as a group of sample data, thereby determining M groups of sample data, and constructing the M groups of sample data as a training sample set.

[0227] Optionally, in some embodiments, the value of the aforementioned “M” may be determined based on the first value P and / or the positioning accuracy required for positioning the first model.

[0228] Optionally, the first value P may be: the number of second devices that can successfully send positioning reference signals to the third device. Optionally, in some embodiments, the meaning of M may be: the number of groups of sample data that can be achieved by the total number of positioning reference signals that can be successfully received by the third device, and M is less than or equal to For example, assuming that the third device can successfully receive 12 positioning reference signals sent by the second device, and S=1, the third device can obtain a maximum of 12 sets of sample data based on the positioning reference signals sent by the 12 second devices. In this case, M can be less than or equal to Then the value range of M can be [1, 12].

[0229] Optionally, in some other embodiments, the value of M may be positively correlated with the positioning accuracy required for positioning the first model. For example, if the positioning accuracy required for positioning the first model is higher, the value of M may be larger.

[0230] For example, assuming that the number of second devices that can successfully send positioning reference signals to the third device is 12, and S=1, at this time, if the positioning accuracy required for positioning by the first model is higher, the value of M can be made the maximum value it can take, that is, M=12; or, if the positioning accuracy required for positioning by the first model is lower, the value of M can be made smaller, for example, M=9.

[0231] Optionally, in combination with the above content, a method for the third device to determine a training sample set is described below with a specific example:

[0232] For example, assuming that the value of N is 18 (i.e., the total number of second devices in the first model application scenario is 18), the value of M is 9, and the value of S is 1, if the third device can only receive positioning reference signals from all 12 second devices, then the third device can select 9 second devices from the 12 second devices. For example, 9 second devices can be selected at random from the 12 second devices, or the top 9 second devices with the best communication quality among the 12 second devices can be selected. After that, the third device can determine the positioning reference signals sent by each of the 9 selected second devices as a group of positioning reference signals to obtain 9 groups of positioning reference signals, and the third device can process the 9 groups of positioning reference signals separately (such as performing positioning measurements), and determine the measurement data corresponding to each group of positioning reference signals as a group of sample data to obtain 9 groups of sample data, and construct the 9 groups of sample data into a training sample set.

[0233] Furthermore, the specific processing method of the above-mentioned “the third device processes the positioning reference signal sent by the second device to obtain measurement data” is introduced in detail below.

[0234] Optionally, the third device may first determine the time domain sampling dimension G of the positioning reference signal, where G is a positive integer, wherein the time domain sampling dimension may be agreed upon by the protocol, or may be configured by the network device, and the third device may determine at least one time domain sampling position in the positioning reference signal received by the third device based on the time domain sampling dimension G, and determine the measurement data corresponding to the above-mentioned second device based on the data at each time domain sampling position. Optionally, assuming that the third device receives a symbol sent by the second device, the symbol carries the positioning reference signal, and the signal waveform of the positioning reference signal may be a superposition of multiple signal waveforms propagated through multipath. If the duration of the symbol is T, then when the time domain sampling dimension is G, the third device may use "the time interval between two adjacent time domain sampling positions is "This criterion determines at least one time domain sampling position in the symbol, wherein the data at each time domain sampling position includes amplitude and delay, and the third device can calculate the above-mentioned measurement data based on the data at each time domain sampling position. Optionally, the measurement data corresponding to each time domain sampling position of the positioning reference signal sent by the second device constitutes the measurement data corresponding to the second device. Based on this, the data dimension of the measurement data corresponding to a second device can be: 1×F×2, where F is the number of time domain sampling points when the third device measures the positioning reference signal, that is, the total number of time domain sampling positions determined by the third device for the positioning reference signal sent by the second device, for example, can be: 256, where 2 represents the real part and the imaginary part (that is, the measurement data is in complex form).

[0235] Optionally, based on the above description, since the sample data includes: measurement data obtained after the third device processes the positioning reference signals sent by S second devices, and since the data dimension of the measurement data corresponding to one second device is: 1×F×2, the data dimension of the sample data can be: S×F×2.

[0236] Step 2103: The third device sends a training sample set.

[0237] Optionally, the third device may send a training sample set to the first device, and the first device may receive the training sample set. The first device may be configured to train a first model based on the training sample set. Optionally, the first device may be, for example, a network device, such as a base station, a LMF, or a positioning server.

[0238] Step 2104: The first device trains the first model based on the training sample set.

[0239] Optionally, the third device can pre-determine the required positioning result, which can be, for example, the positioning position of the third device; then the third device can input the sample data into the first model to obtain the training output result of the first model, and calculate the loss value based on the training output result and the positioning result. The loss value can be used to reflect the gap between the training output result and the positioning result. When the loss value is greater than a preset threshold, the model parameters of the first model are adjusted, and the sample data is input again into the first model after the model parameters are adjusted to calculate the loss value; when the loss value is less than the preset threshold, the training is confirmed to be completed.

[0240] Optionally, the third device may train the first model M times based on M groups of sample data in the training sample set.

[0241] Optionally, from the foregoing, it can be seen that the data dimension of the sample data is: S×F×2, and the sample data serves as the input of the first model. Therefore, the input dimension of the first model is: S×F×2. Since S is less than N, the input dimension of the first model in the present disclosure is S×F×2, which is smaller than the input dimension of the first model in the related art, which is N×F×2. This can reduce the complexity of the first model in the present disclosure.

[0242] Step 2105: The first device sends the first information.

[0243] Optionally, the first information may be used to deploy a trained first model. For example, the first information may be a model parameter of the trained first model.

[0244] Optionally, the first device may send the first information to a fourth device, and the fourth device may receive the first information. The fourth device may be a device that deploys the first model, for example, the fourth device may be a device that needs to deploy the first model in an application scenario of the first model. Optionally, the fourth device may be a signal measurement node, or the fourth device may be a device other than a signal measurement node, for example, a positioning server.

[0245] Step 2106: The fourth device deploys the trained first model.

[0246] Optionally, the fourth device may deploy the trained first model based on the first information.

[0247] It should be noted that, in some embodiments, the first device may be the same device as the fourth device, that is, the training device of the first model and the deployment device of the first model are the same device. In this case, there is no need to execute the above steps 2105 and 2106. After the first device trains the first model based on the training sample set (that is, after executing step 2104), the first device can directly deploy the trained first model.

[0248] In other embodiments, the above-mentioned third device may be the same device as the fourth device, that is, the signal measurement node and the deployment device of the first model are the same device. In this case, there is no need to execute the above-mentioned steps 2105 and 2106. After the first device trains the first model based on the training sample set (that is, after executing step 2104), the first device can send the first information to the third device, and the third device deploys the trained first model based on the first information.

[0249] In another embodiment, the first device and the third device may be the same device, that is, the training device of the first model and the signal measurement node are the same device. In this case, there is no need to execute step 2103. After the third device determines the training sample set (that is, after executing step 2102), the third device can directly train the first model based on the training sample set, and then send the first information to the fourth device so that the fourth device deploys the trained first model based on the first information.

[0250] In another embodiment, the first device, the third device, and the fourth device may be the same device. In this case, there is no need to execute step 2103 and step 2105, and step 2102, step 2104, and step 2106 may be executed by the same device.

[0251] In another embodiment, the first device may be the same device as the second device, that is, the training device of the first model and the signal sending node are the same device. In this case, after executing the above step 2102, the third device may send a training sample set to the second device, and then the second device trains the first model based on the training sample set, and sends the first information to the fourth device so that the fourth device deploys the trained first model based on the first information.

[0252] In the above embodiment, the first device will first determine a training sample set when training the first model, wherein the training sample set includes M groups of sample data, where M is a positive integer, and the sample data includes: measurement data obtained after the third device processes the positioning reference signals sent by S second devices, where S is a positive integer, S is less than N, and N is the total number of second devices in the application scenario of the first model. Afterwards, the first device will train the first model based on the training sample set. Among them, since the sample data in the training sample set is: the measurement data obtained after the third device processes the positioning reference signals sent by S second devices, the input of the first model trained based on the sample data should also be: the measurement data obtained after the third device processes the positioning reference signals sent by S second devices (or simply referred to as: the measurement data corresponding to the S second devices). Among them, since S is less than N, it means that when the positioning method of the present invention trains the first model, the input dimension of the first model is: the measurement data corresponding to some second devices, rather than "the measurement data corresponding to all second devices", which greatly reduces the input dimension of the first model. Among them, since the complexity of the first model is positively correlated with the input dimension, therefore, when the input dimension of the first model is reduced, the complexity of the first model can be greatly reduced, thereby reducing the power overhead during the training and application of the first model.

[0253] Moreover, when the first model is trained with "the input dimension of the first model is: measurement data corresponding to some second devices", compared with the scheme of "the first model needs to input measurement data corresponding to all second devices", the method disclosed in the present invention can further reduce the input requirements of the first model on the basis of ensuring the positioning accuracy of the first model. Then, in the subsequent application of the first model, even if "the third device cannot simultaneously obtain the positioning reference signals sent by all or more second devices in the first model application scenario, but can only obtain the positioning reference signals sent by some or fewer second devices in the first model application scenario", the measurement data corresponding to the positioning reference signals sent by the some or fewer second devices can still be used as the input of the first model to determine the positioning result, and will not affect the positioning accuracy of the first model, and the practicality is high.

[0254] Also, under the premise that the input dimension of the first model is: the measurement data corresponding to part of the second device, if the first model is not deployed on the third device, the third device needs to send the input data required by the first model to the deployment device of the first model (hereinafter referred to as the fourth device). At this time, the third device only needs to send part of the measurement data corresponding to the second device to the fourth device, without sending all the measurement data corresponding to the second device, which can greatly reduce the data transmission overhead.

[0255] The method according to the embodiments of the present disclosure may include at least one of steps 2101 to 2105. For example, step 2101 may be implemented as an independent embodiment, step 2102 may be implemented as an independent embodiment, step 2103 may be implemented as an independent embodiment, and step 2101+step 2102 may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0256] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0257] FIG2B is an interactive diagram of a positioning method according to an embodiment of the present disclosure. As shown in FIG2B , the present disclosure embodiment relates to a positioning method for a communication system 100, which is used to implement positioning using a first model. The method includes:

[0258] Step 2201: At least one second device sends a positioning reference signal.

[0259] Step 2202: The third device determines first data based on a positioning reference signal received from at least one second device.

[0260] Optionally, the third device can select S second devices from at least one second device that has sent a positioning reference signal. For example, S second devices can be arbitrarily selected from at least one second device, or the first S second devices with the best communication quality can be selected. Thereafter, the positioning reference signals sent by the S second devices are processed to obtain first data. The first data may include: measurement data obtained after the third device processes the positioning reference signals sent by the S second devices.

[0261] Optionally, S is a positive integer, S is less than N, N is the total number of second devices, for example, N can be the total number of second devices in the first model application scenario, N is a positive integer, for example, N can be equal to 18, and S can be equal to 1.

[0262] Optionally, the data dimension of the first data may be: S×F×2, where F is the number of time-domain sampling points when the third device processes the positioning reference signal, and 2 represents the real part and the imaginary part. For a detailed description of this part, please refer to the above embodiment.

[0263] Step 2203: The third device sends the first data.

[0264] Optionally, the third device may send the first data to a fourth device, and the fourth device may be a device that deploys the first model, and the fourth device may be, for example, a positioning server.

[0265] Step 2204: The fourth device inputs the first data into the first model to output second information.

[0266] Optionally, the second information can be used to determine the positioning result. For example, the second information may be: the positioning result, or an intermediate positioning parameter. The intermediate positioning parameter is used to determine the positioning result. The intermediate parameter may be, for example, time of arrival (ToA), angle of arrival (AoA), etc., and, for example, the TDOA positioning method may be used to determine the positioning result based on the intermediate positioning parameter.

[0267] It should be noted that, in some embodiments, the third device may be the same device as the fourth device, that is, the signal measurement node and the deployment device of the first model are the same device. In this case, there is no need to execute step 2203. After the third device determines the first data, it can directly input the first data into the first model to output the second information, and the third device can send the second information to the positioning server so that the positioning server can know the positioning result based on the second information and perform related scheduling management.

[0268] In the above embodiment, the fourth device determines the first data and inputs the first data into the first model to output the second information, which is used to determine the positioning result. The first data includes: the measurement data obtained by the third device after processing the positioning reference signals sent by S second devices; the second device is used to send the positioning reference signal to the third device, and the third device is used to process (such as measure) the positioning reference signal sent by the second device, S is a positive integer, S is less than N, and N is the total number of second devices in the first model application scenario; it can be seen that during the application of the first model, the input dimension of the first model is: the measurement data corresponding to the S second devices (i.e., the measurement data corresponding to some second devices), rather than "the measurement data corresponding to all second devices", which greatly reduces the input dimension of the first model. Since the complexity of the first model is positively correlated with the input dimension, when the input dimension of the first model is reduced, the complexity of the first model can be greatly reduced, thereby reducing the power overhead during the training and application of the first model.

[0269] Moreover, when the input dimension of the first model is: measurement data corresponding to some second devices, compared with the solution that "the first model needs to input measurement data corresponding to all second devices", the method disclosed in the present invention can further reduce the input requirements of the first model. Based on this, even if the situation occurs that "the third device cannot simultaneously obtain the positioning reference signals sent by all or more second devices in the first model application scenario, but can only obtain the positioning reference signals sent by some or fewer second devices in the first model application scenario", the measurement data corresponding to the positioning reference signals sent by the some or fewer second devices can still be used as the input of the first model to determine the positioning results, and will not affect the positioning accuracy of the first model, and the practicality is high.

[0270] Also, under the premise that the input dimension of the first model is: the measurement data corresponding to part of the second device, if the first model is not deployed on the third device, the third device needs to send the input data required by the first model to the deployment device of the first model (i.e., the fourth device). At this time, the third device only needs to send part of the measurement data corresponding to the second device to the fourth device, without sending all the measurement data corresponding to the second device, thereby greatly reducing data transmission overhead.

[0271] The method involved in the embodiments of the present disclosure may include at least one of steps 2201 to 2205. For example, step 2201 may be implemented as an independent embodiment, step 2202 may be implemented as an independent embodiment, step 2203 may be implemented as an independent embodiment, and step 2201+S2202 may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0272] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0273] FIG3A is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in FIG3A , the present disclosure embodiment relates to a positioning method for a first device, the method comprising:

[0274] Step 3101: Receive a training sample set.

[0275] Step 3102: Train the first model based on the training sample set.

[0276] Step 3103: Send the first message.

[0277] For a detailed description of steps 3101 - 3103 , please refer to the above embodiment description.

[0278] The method involved in the embodiments of the present disclosure may include at least one of steps 3101 to 3103. For example, step 3101 may be implemented as an independent embodiment, step 3102 may be implemented as an independent embodiment, and step 3101+S3102 may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0279] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0280] FIG3B is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in FIG3B , the present disclosure embodiment relates to a positioning method for a first device, the method comprising:

[0281] Step 3201: Determine a training sample set.

[0282] Step 3202: Train the first model based on the training sample set.

[0283] Optionally, the training sample set includes M groups of sample data, where M is a positive integer, and the sample data includes: measurement data obtained after the third device processes positioning reference signals sent by S second devices, where S is a positive integer, S is less than N, and N is the total number of second devices, which is also a positive integer;

[0284] Optionally, the first model is used for positioning, and the input of the first model is: measurement data obtained after the third device processes S positioning reference signals sent by the second devices.

[0285] Optionally, S=1.

[0286] Optionally, the first device and the third device are the same device;

[0287] Determining the training sample set includes:

[0288] receiving a positioning reference signal sent by at least one second device;

[0289] Determining M groups of positioning reference signals based on at least one positioning reference signal sent by the second device, where each group of positioning reference signals includes S positioning reference signals sent by the second device;

[0290] Processing M groups of positioning reference signals respectively to obtain the M groups of sample data;

[0291] The training sample set is determined based on the M groups of sample data.

[0292] Optionally, the first device and the third device are different devices;

[0293] Determining the training sample set includes:

[0294] Receive the training sample set sent by the third device.

[0295] Optionally, the first device is different from the fourth device, and the fourth device is a device for deploying the first model; the method further includes:

[0296] Send first information to the fourth device, where the first information is used to deploy the trained first model.

[0297] Optionally, the first device is the same as the fourth device, and the fourth device is a device for deploying the first model; the method further includes:

[0298] Deploy the trained first model.

[0299] Optionally, the method further includes:

[0300] The M is determined based on a first value and / or the positioning accuracy required for positioning by the first model; wherein the first value is: the number of the second devices that can successfully send a positioning reference signal to the third device.

[0301] Optionally, the training the first model based on the training sample set includes:

[0302] The first model is trained M times based on M groups of sample data in the training sample set.

[0303] Optionally, the data dimension of the sample data is: S×F×2, and the input dimension of the first model is: S×F×2; wherein, F is the number of time domain sampling points when the third device processes the positioning reference signal, and 2 represents the real part and the imaginary part.

[0304] For a detailed description of steps 3201 - 3202 , please refer to the above embodiment description.

[0305] The method involved in the embodiments of the present disclosure may include at least one of steps 3201 to 3203. For example, step 3201 may be implemented as an independent embodiment, step 3202 may be implemented as an independent embodiment, and step 3201+S3202 may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0306] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0307] FIG4A is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in FIG4A , the present disclosure embodiment relates to a positioning method for a third device, the method comprising:

[0308] Step 4101: Receive a positioning reference signal sent by at least one second device.

[0309] Step 4102: Determine a training sample set based on a positioning reference signal received from at least one second device.

[0310] Step 4103: Send the training sample set.

[0311] For a detailed description of steps 4101-4103, please refer to the above embodiment.

[0312] The method involved in the embodiment of the present disclosure may include at least one of steps 4101 to 4103. For example, step 4101 may be implemented as an independent embodiment, and step 4102 may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0313] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0314] FIG4B is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in FIG4B , the present disclosure embodiment relates to a positioning method for a third device, the method comprising:

[0315] Step 4201: Determine a training sample set.

[0316] Optionally, the training sample set includes M groups of sample data, where M is a positive integer, and the sample data includes: measurement data obtained after the third device processes the positioning reference signals sent by S second devices, where S is a positive integer, S is less than N, N is the total number of second devices, and N is a positive integer.

[0317] Optionally, determining the training sample set includes:

[0318] receiving a positioning reference signal sent by at least one second device;

[0319] Determine M groups of positioning reference signals based on at least one positioning reference signal sent by the second device;

[0320] Processing M groups of positioning reference signals respectively to obtain the M groups of sample data;

[0321] The training sample set is determined based on the M groups of sample data.

[0322] Optionally, the method further includes:

[0323] The training sample set is sent to a first device, where the first device is used to train a first model based on the training sample set, where the first model is used for positioning.

[0324] For a detailed introduction to step 4201, please refer to the content of the above embodiment.

[0325] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0326] FIG5 is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in FIG5 , the embodiment of the present disclosure relates to a positioning method for a fourth device, the method comprising:

[0327] Step 5101: Receive first information.

[0328] Step 5102: Deploy the trained first model.

[0329] For a detailed description of steps 5101 - 5102 , please refer to the above embodiment.

[0330] The method involved in the embodiment of the present disclosure may include at least one of step 5101 and step 5102. For example, step 5101 may be implemented as an independent embodiment, and step 5102 may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0331] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0332] FIG6A is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in FIG6A , the present disclosure embodiment relates to a positioning method for a third device, the method comprising:

[0333] Step 6101: Receive a positioning reference signal sent by at least one second device.

[0334] Step 6102: Determine first data based on a positioning reference signal received from at least one second device.

[0335] Step 6103: Send the first data.

[0336] For a detailed description of steps 6101-6102, please refer to the above embodiment.

[0337] The method involved in the embodiment of the present disclosure may include at least one of steps 6101 and 6102. For example, step 6101 may be implemented as an independent embodiment, and step 6102 may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0338] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0339] FIG6B is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in FIG6B , the embodiment of the present disclosure relates to a positioning method for a third device, the method comprising:

[0340] Step 6201: Determine the first data.

[0341] Optionally, the first data includes: measurement data obtained after the third device processes the positioning reference signals sent by S second devices; wherein, the second device is used to send a positioning reference signal to the third device, and the third device is used to process the positioning reference signal sent by the second device, S is a positive integer, S is less than N, and N is the total number of second devices.

[0342] Optionally, determining the first data includes:

[0343] receiving a positioning reference signal sent by at least one second device;

[0344] Process the S positioning reference signals sent by the second devices to determine the first data.

[0345] Optionally, the method further includes:

[0346] The first data is sent to a fourth device, where the fourth device is a device deploying a first model, and the first model is used for positioning based on the first data.

[0347] For a detailed introduction to step 6201, please refer to the contents of the above embodiment.

[0348] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0349] FIG7A is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in FIG7A , the present disclosure embodiment relates to a positioning method for a fourth device, the method comprising:

[0350] Step 7101: Receive first data.

[0351] Step 7102: Input the first data into the first model to output second information.

[0352] For a detailed description of steps 7101-7102, please refer to the above embodiment.

[0353] The method involved in the embodiment of the present disclosure may include at least one of steps 7101 and 7102. For example, step 7101 may be implemented as an independent embodiment, and step 7102 may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0354] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0355] FIG7B is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in FIG7B , the embodiment of the present disclosure relates to a positioning method for a fourth device, the method comprising:

[0356] Step 7201: Determine the first data.

[0357] Step 7202: Input the first data into the first model to output the second information.

[0358] Optionally, the first data includes: measurement data obtained after the third device processes positioning reference signals sent by S second devices; wherein the second device is used to send the positioning reference signal to the third device, and the third device is used to process the positioning reference signal sent by the second device, S is a positive integer, S is less than N, N is the total number of second devices, and N is a positive integer;

[0359] Optionally, the second information is used to determine a positioning result.

[0360] Optionally, the fourth device and the third device are the same device;

[0361] The determining of the first data includes:

[0362] receiving a positioning reference signal sent by at least one second device;

[0363] Process the S positioning reference signals sent by the second devices to determine the first data.

[0364] Optionally, the fourth device and the third device are different devices;

[0365] The determining of the first data includes:

[0366] Receive the first data sent by the third device.

[0367] Optionally, the data dimension of the first data is: S×F×2, where F is the number of time domain sampling points when the third device processes the positioning reference signal, and 2 represents the real part and the imaginary part.

[0368] For a detailed description of steps 7201-7202, please refer to the above embodiment.

[0369] The method involved in the embodiment of the present disclosure may include at least one of step 7201 and step 7202. For example, step 7201 may be implemented as an independent embodiment, and step 7202 may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0370] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0371] FIG8 is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in FIG8 , the embodiment of the present disclosure relates to a positioning method for a second device, the method comprising:

[0372] Step 8101: Send a positioning reference signal to a third device.

[0373] Optionally, the third device is used to process a positioning reference signal sent by the second device.

[0374] For a detailed introduction to step 8101, please refer to the content of the above embodiment.

[0375] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0376] Figure 9A is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in Figure 9A, the present disclosure relates to a positioning method for a communication system, wherein the communication system includes a first device, a second device, and a third device. The first device is used to train a first model, which is used for positioning. The second device is used to send a positioning reference signal to the third device, and the third device is used to process the positioning reference signal sent by the second device. The method includes at least one of the following:

[0377] Step 9101: The second device sends a positioning reference signal to the third device.

[0378] Step 9102: The first device and the third device are the same device, and the first device determines a training sample set based on a positioning reference signal sent by the second device; or the first device and the third device are different devices, and the third device determines a training sample set based on a positioning reference signal sent by the second device, and sends the training sample set to the first device.

[0379] Step 9103: The first device trains the first model based on the training sample set.

[0380] The optional implementation of steps 9101 to 9103 can be found in the above embodiment.

[0381] In some embodiments, the above method may include the method described in the above embodiments of the communication system side, terminal side, network device side, etc., which will not be repeated here.

[0382] The method involved in the embodiment of the present disclosure may include at least one of steps 9101 to 9103. For example, step 9101 may be implemented as an independent embodiment, and step 9102 may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0383] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0384] Figure 9B is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in Figure 9A, an embodiment of the present disclosure relates to a positioning method for a communication system, wherein the communication system includes a fourth device, a second device, and a third device, wherein the fourth device is a device deploying a first model, the first model is used for positioning, the second device is used to send a positioning reference signal to the third device, and the third device is used to process the positioning reference signal sent by the second device, and the method includes at least one of the following:

[0385] Step 9201: The second device sends a positioning reference signal to the third device.

[0386] Step 9202: The fourth device and the third device are the same device, and the fourth device determines first data based on the positioning reference signal sent by the second device; or the fourth device and the third device are different devices, and the third device determines first data based on the positioning reference signal sent by the second device, and sends the first data to the fourth device.

[0387] Step 9203: The fourth device inputs the first data into a first model to output second information.

[0388] The optional implementation of steps 9201 to 9203 can be found in the above embodiment.

[0389] In some embodiments, the above method may include the method described in the above embodiments of the communication system side, terminal side, network device side, etc., which will not be repeated here.

[0390] The method involved in the embodiment of the present disclosure may include at least one of steps 9201 to 9203. For example, step 9201 may be implemented as an independent embodiment, and step 9202 may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0391] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0392] The following is an exemplary introduction to the above method.

[0393] Optionally, for application scenarios where high-precision positioning is completed based on AI models, the AI ​​model used for positioning is deployed on the UE, BS or LMF, and the model input is channel measurement data such as CIR used for positioning, which is calculated by the positioning target based on the positioning reference signal it receives from the TRP.

[0394] Optionally, in actual system applications, the AI ​​positioning model (i.e., the aforementioned AI model) needs to be deployed and stored in actual communication equipment including UE, BS or LMF. The channel measurement data collection node calculates and obtains channel measurement data based on the positioning reference signals sent by multiple TPRs as the input of the AI ​​positioning model. There are situations such as high model calculation complexity, large reporting overhead of channel measurement data used for positioning, and inability to obtain channel measurement data corresponding to some TRPs, which have a certain impact on the effective operation of the AI ​​positioning model in the system.

[0395] Optionally, the present disclosure proposes a method for training and applying an AI positioning model with low data reporting overhead and low model complexity. The AI ​​positioning model trained using this method can use the channel measurement data obtained by calculating the positioning reference channel sent by any TRP to complete target positioning with high accuracy. This can significantly reduce the computational complexity of the AI ​​positioning model and the requirements for channel measurement data in the application of the AI ​​positioning model, thus having good practicality. At the same time, in application modes 3 and 5 where channel measurement data reporting is required, the channel measurement data reporting overhead used for positioning can be significantly reduced.

[0396] Optionally, the AI ​​positioning model training method proposed in this disclosure:

[0397] If there are N TRPs in the model application scenario, the channel data measurement node calculates and obtains the corresponding channel measurement data Data based on the positioning reference signal sent by the i-th TRP i , use N TRPs, or only use a mixed data set consisting of channel measurement data corresponding to N' TRPs in N TRPs to train an AI positioning model Positioning Model, the input of which is the channel measurement data Data corresponding to a TRP i , the output is the positioning result, i.e., the position coordinates. For example, the input data dimension of the AI ​​positioning model obtained by training using the model training method proposed in this disclosure is, where 1 is the number of TRPs, 256 is the number of time domain sampling points, and 2 is the real part and the imaginary part.

[0398] The number of TRPs during model training can be determined by the dataset conditions in the actual application and the positioning accuracy requirements of the AI ​​model.

[0399] Optionally, Figure 9C is a schematic diagram of a model application method according to an embodiment of the present disclosure. As shown in Figure 9C, a mixed data set consisting of channel measurement data corresponding to N TRPs is used to train the AI ​​positioning model.

[0400] Optionally, the AI ​​positioning model application method proposed in this disclosure:

[0401] a. The channel data measurement node calculates and obtains the channel measurement data based on the positioning reference signal sent by any TRP i ;

[0402] b. For application mode 3 and mode 5, the channel data measurement node will i Report to LMF;

[0403] c. AI positioning model inference node will be Data iInput the AI ​​positioning model and output the positioning result, which is the target location coordinates.

[0404] Optionally, the beneficial effects brought about by the technical solution of the present disclosure are:

[0405] The present disclosure is aimed at application scenarios in which high-precision positioning is completed based on AI models. In view of the problems existing in the application of AI positioning models when the channel measurement data corresponding to all or multiple model TPRs in the usage scenario are used as input for training, such as high model calculation complexity, large channel measurement data reporting overhead, and difficulty in obtaining data corresponding to some TRPs, the present disclosure proposes an AI positioning model training and application method with low data reporting and low model complexity, which can significantly reduce the calculation complexity of the AI ​​positioning model and the channel measurement data reporting overhead for positioning under some application models, while effectively reducing the requirements of the AI ​​positioning model for input data collection, which is conducive to promoting the application of AI-based high-precision positioning solutions in actual communication systems.

[0406] The embodiments of the present disclosure further provide an apparatus for implementing any of the above methods. For example, an apparatus is provided, comprising units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0407] It should be understood that the division of the various units or modules in the above device is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a physical entity, or they may be physically separated. In addition, the units or modules in the device may be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units or modules can be realized by designing the hardware circuits. The above-mentioned hardware circuits can be understood as one or more processors; for example, in one implementation, the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), which realizes the functions of some or all of the above units or modules by designing the logical relationship of the components in the circuit; for example, in another implementation, the above-mentioned hardware circuit can be realized by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by configuring the configuration file, thereby realizing the functions of some or all of the above units or modules. All units or modules of the above devices can be realized in the form of software called by the processor, or in the form of hardware circuits, or in part by the form of software called by the processor, and the rest by hardware circuits.

[0408] In the embodiments of the present disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationship of the hardware circuit. The logical relationship of the above-mentioned hardware circuit is fixed or reconfigurable. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and implementing the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc.

[0409] FIG10A is a schematic diagram of the structure of a communication device proposed in an embodiment of the present disclosure. As shown in FIG10A , it includes:

[0410] a processing module, configured to determine a training sample set, the training sample set comprising M groups of sample data, where M is a positive integer, and the sample data comprising: measurement data obtained by a third device processing positioning reference signals sent by S second devices, where S is a positive integer, S is less than N, and N is the total number of second devices, which is also a positive integer;

[0411] The processing module is also used to train the first model based on the training sample set; wherein, the first model is used for positioning, and the input of the first model is: the measurement data obtained after the third device processes the positioning reference signals sent by S second devices.

[0412] Optionally, the above-mentioned processing module is also used to execute the steps related to "processing" performed by the first device in any of the above-mentioned positioning methods. The above-mentioned communication device also includes a transceiver module, which is used to execute the steps related to "transmitting and receiving" performed by the first device in any of the above-mentioned positioning methods, which will not be repeated here.

[0413] FIG10B is a schematic diagram of the structure of the communication device proposed in an embodiment of the present disclosure. As shown in FIG10B , it includes:

[0414] a processing module, configured to determine first data, the first data comprising: measurement data obtained by a third device processing positioning reference signals sent by S second devices; wherein the second device is configured to send the positioning reference signal to the third device, and the third device is configured to process the positioning reference signal sent by the second device, S is a positive integer, S is less than N, N is the total number of second devices, and N is a positive integer;

[0415] The processing module is further configured to input the first data into a first model to output second information, where the second information is used to determine a positioning result.

[0416] Optionally, the above-mentioned processing module is also used to execute the steps related to "processing" performed by the fourth device in any of the above-mentioned positioning methods. The above-mentioned communication device also includes a transceiver module, which is used to execute the steps related to "transmitting and receiving" performed by the fourth device in any of the above-mentioned positioning methods, which will not be repeated here.

[0417] FIG10C is a schematic diagram of the structure of the communication device proposed in an embodiment of the present disclosure. As shown in FIG10C , it includes:

[0418] A processing module is used to determine a training sample set, where the training sample set includes M groups of sample data, where M is a positive integer, and the sample data includes: measurement data obtained after a third device processes positioning reference signals sent by S second devices, where S is a positive integer, S is less than N, and N is the total number of second devices, which is a positive integer.

[0419] Optionally, the above-mentioned processing module is also used to execute the steps related to "processing" performed by the third device in any of the above-mentioned positioning methods. The above-mentioned communication device also includes a transceiver module, which is used to execute the steps related to "transmitting and receiving" performed by the third device in any of the above-mentioned positioning methods, which will not be repeated here.

[0420] FIG10D is a schematic diagram of the structure of a communication device proposed in an embodiment of the present disclosure. As shown in FIG10D , it includes:

[0421] A processing module is used to determine first data, where the first data includes: measurement data obtained after a third device processes positioning reference signals sent by S second devices; wherein the second device is used to send a positioning reference signal to the third device, and the third device is used to process the positioning reference signal sent by the second device, S is a positive integer, S is less than N, and N is the total number of second devices.

[0422] Optionally, the above-mentioned processing module is also used to execute the steps related to "processing" performed by the third device in any of the above-mentioned positioning methods. The above-mentioned communication device also includes a transceiver module, which is used to execute the steps related to "transmitting and receiving" performed by the third device in any of the above-mentioned positioning methods, which will not be repeated here.

[0423] FIG10E is a schematic diagram of the structure of a communication device proposed in an embodiment of the present disclosure. As shown in FIG10E , it includes:

[0424] The transceiver module is used to send a positioning reference signal to a third device, and the third device is used to process the positioning reference signal sent by the second device.

[0425] Optionally, the above-mentioned transceiver module is also used to execute the steps related to "transmitting and receiving" performed by the second device in any of the above methods. The above-mentioned communication device also includes a processing module, which is used to execute the steps related to "processing" performed by the second device in any of the above methods, which will not be repeated here.

[0426] Figure 11A is a schematic diagram of the structure of a communication device 11100 proposed in an embodiment of the present disclosure. Communication device 11100 can be a network device (e.g., an access network device, a core network device, etc.), a terminal (e.g., a user equipment, etc.), a chip, a chip system, or a processor that supports a network device to implement any of the above methods, or a chip, a chip system, or a processor that supports a terminal to implement any of the above methods. Communication device 11100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

[0427] As shown in Figure 11A, the communication device 11100 includes one or more processors 11101. The processor 11101 can be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication device (such as a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute programs, and process program data. The processor 11101 is used to call instructions to enable the communication device 11100 to perform any of the above methods.

[0428] In some embodiments, the communication device 11100 further includes one or more memories 11102 for storing instructions. Optionally, all or part of the memories 11102 may be located outside the communication device 11100.

[0429] In some embodiments, the communication device 11100 further includes one or more transceivers 11103. When the communication device 11100 includes one or more transceivers 11103, the communication steps such as sending and receiving in the above method are performed by the transceiver 11103, and the other steps are performed by the processor 11101.

[0430] In some embodiments, a transceiver may include a receiver and a transmitter, which may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, and transceiver circuit may be used interchangeably; the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be used interchangeably; and the terms receiver, receiving unit, receiver, and receiving circuit may be used interchangeably.

[0431] Optionally, the communication device 11100 further includes one or more interface circuits 11104, which are connected to the memory 11102. The interface circuits 11104 may be configured to receive signals from the memory 11102 or other devices, and may be configured to send signals to the memory 11102 or other devices. For example, the interface circuits 11104 may read instructions stored in the memory 11102 and send the instructions to the processor 11101.

[0432] The communication device 11100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 11100 described in the present disclosure is not limited thereto, and the structure of the communication device 11100 may not be limited to FIG. 11a. The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data or programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0433] FIG11B is a schematic diagram of the structure of a chip 11200 according to an embodiment of the present disclosure. If the communication device 11100 can be a chip or a chip system, please refer to the schematic diagram of the structure of the chip 11200 shown in FIG11B , but the present disclosure is not limited thereto.

[0434] The chip 11200 includes one or more processors 11201 , and the processor 11201 is used to call instructions so that the chip 11200 executes any of the above methods.

[0435] In some embodiments, chip 11200 further includes one or more interface circuits 11202, which are connected to memory 11203. Interface circuit 11202 can be used to receive signals from memory 11203 or other devices, and can be used to send signals to memory 11203 or other devices. For example, interface circuit 11202 can read instructions stored in memory 11203 and send the instructions to processor 11201. Optionally, the terms interface circuit, interface, transceiver pin, and transceiver are interchangeable.

[0436] In some embodiments, the chip 11200 further includes one or more memories 11203 for storing instructions. Alternatively, all or part of the memories 11203 may be located outside the chip 11200.

[0437] The present disclosure also provides a storage medium having instructions stored thereon. When the instructions are executed on the communication device 11100, the communication device 11100 is caused to execute any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited thereto and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto and may also be a transient storage medium.

[0438] The present disclosure also provides a program product, which, when executed by the communication device 11100, enables the communication device 11100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0439] The present disclosure also proposes a computer program, which, when executed on a computer, causes the computer to perform any one of the above methods.

[0440] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer programs. When the computer program is loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present disclosure are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer program can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0441] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this disclosure can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0442] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0443] The above description is merely a specific embodiment of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this disclosure should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A positioning method, characterized in that, Performed by a first device, the method includes: Determine a training sample set, the training sample set including M sets of sample data, M being a positive integer, the sample data including: measurement data obtained after a third device processes positioning-purpose reference signals sent by S second devices, S being a positive integer, S being less than N, N being the total number of second devices, N being a positive integer; Train a first model based on the training sample set; wherein, the first model is used for positioning, and the input of the first model is: the measurement data obtained after the third device processes positioning-purpose reference signals sent by S of the second devices.

2. The method according to claim 1, characterized in that, The first device and the third device are the same device; The determining the training sample set includes: Receive positioning-purpose reference signals sent by at least one of the second devices; Determine M sets of positioning-purpose reference signals based on the positioning-purpose reference signals sent by at least one of the second devices, wherein each set of positioning-purpose reference signals includes positioning-purpose reference signals sent by S second devices; Process each of the M sets of positioning-purpose reference signals to obtain the M sets of sample data; Determine the training sample set based on the M sets of sample data.

3. The method according to claim 1, characterized in that, The first device and the third device are different devices; The determining the training sample set includes: Receive the training sample set sent by the third device.

4. The method according to any one of claims 1 - 3, characterized in that, The first device is different from a fourth device, the fourth device being the device that deploys the first model; the method further includes: Send first information to the fourth device, the first information being used to deploy the trained first model.

5. The method according to any one of claims 1 - 3, characterized in that, The first device is the same as the fourth device, the fourth device being the device that deploys the first model; the method further includes: Deploy the trained first model.

6. The method according to any one of claims 1 - 5, characterized in that, The method further includes: Determine the M based on a first value and / or the positioning accuracy required when the first model performs positioning; wherein, the first value is: the number of second devices that can successfully send positioning-purpose reference signals to the third device.

7. The method according to any one of claims 1 - 6, characterized in that, The training the first model based on the training sample set includes: Train the first model M times respectively based on the M sets of sample data in the training sample set.

8. The method according to any one of claims 1 - 7, characterized in that, The data dimension of the sample data is: S×F×2, and the input dimension of the first model is: S×F×2; wherein, F is the number of time-domain sampling points when the third device processes the positioning-purpose reference signals, and the 2 represents the real part and the imaginary part.

9. A positioning method, characterized in that, Performed by a fourth device, the fourth device being the device that deploys a first model, the first model being used for positioning, the method includes: Determine first data, the first data including: measurement data obtained after a third device processes positioning-purpose reference signals sent by S second devices; wherein, the second devices are used to send positioning-purpose reference signals to the third device, the third device is used to process the positioning-purpose reference signals sent by the second devices, S being a positive integer, S being less than N, N being the total number of second devices, N being a positive integer; Input the first data into the first model to output second information, where the second information is used to determine a positioning result.

10. The method according to claim 9, characterized in that, The fourth device and the third device are the same device; The determining of the first data includes: Receiving positioning-purpose reference signals sent by at least one of the second devices; Processing the positioning-purpose reference signals sent by S of the second devices to determine the first data.

11. The method according to claim 9, characterized in that, The fourth device and the third device are different devices; The determining of the first data includes: Receiving the first data sent by the third device.

12. The method according to any one of claims 9-11, characterized in that, The data dimension of the first data is: S×F×2, where F is the number of time-domain sampling points when the third device processes the positioning-purpose reference signals, and 2 represents the real part and the imaginary part.

13. A positioning method, characterized in that, Executed by a third device, the method includes: Determining a training sample set, where the training sample set includes M sets of sample data, M is a positive integer, and the sample data includes: measurement data obtained after the third device processes the positioning-purpose reference signals sent by S second devices, S is a positive integer, S is less than N, N is the total number of second devices, and N is a positive integer.

14. The method according to claim 13, characterized in that, The determining of the training sample set includes: Receiving positioning-purpose reference signals sent by at least one second device; Determining M sets of positioning-purpose reference signals based on the positioning-purpose reference signals sent by at least one of the second devices; Processing the M sets of positioning-purpose reference signals respectively to obtain the M sets of sample data; Determining the training sample set based on the M sets of sample data.

15. The method according to claim 13 or 14, characterized in that, The method further includes: Sending the training sample set to a first device, where the first device is used to train a first model based on the training sample set, and the first model is used for positioning.

16. A positioning method, characterized in that, Executed by a third device, the method includes: Determining first data, where the first data includes: measurement data obtained after the third device processes the positioning-purpose reference signals sent by S second devices; where the second device is used to send the positioning-purpose reference signals to the third device, and the third device is used to process the positioning-purpose reference signals sent by the second device, S is a positive integer, S is less than N, N is the total number of second devices, and N is a positive integer.

17. The method according to claim 16, characterized in that, The determining of the first data includes: Receiving positioning-purpose reference signals sent by at least one second device; Processing the positioning-purpose reference signals sent by S of the second devices to determine the first data.

18. The method according to claim 16 or 17, characterized in that, The method further includes: Sending the first data to a fourth device, where the fourth device is a device deploying the first model, and the first model is used to perform positioning based on the first data.

19. A positioning method, characterized in that, Executed by a second device, the method includes: Sending positioning-purpose reference signals to a third device, where the third device is used to process the positioning-purpose reference signals sent by the second device.

20. A positioning method for a communication system, the communication system comprising a first device, a second device, and a third device, the first device being configured to train a first model for positioning, the second device being configured to send a positioning-purpose reference signal to the third device, the third device being configured to process the positioning-purpose reference signal sent by the second device, the method comprising at least one of the following: The second device sends a positioning-purpose reference signal to the third device; The first device and the third device are the same device, and the first device determines a training sample set based on the positioning-purpose reference signal sent by the second device; or, the first device and the third device are different devices, the third device determines a training sample set based on the positioning-purpose reference signal sent by the second device, and sends the training sample set to the first device; where, The training sample set includes M sets of sample data, M is a positive integer, and the sample data includes: measurement data obtained after the third device processes the positioning-purpose reference signals sent by S second devices, S is a positive integer, S is less than N, N is the total number of second devices, and N is a positive integer; The first device trains a first model based on the training sample set; wherein, the input of the first model is: the measurement data obtained after the third device processes the positioning-purpose reference signals sent by S second devices.

21. A positioning method for a communication system, the communication system includes a fourth device, a second device, and a third device, the fourth device is a device deploying a first model for positioning, the second device is used to send a positioning-purpose reference signal to the third device, the third device is used to process the positioning-purpose reference signal sent by the second device, the method includes at least one of the following: The second device sends a positioning-purpose reference signal to the third device; The fourth device and the third device are the same device, and the fourth device determines first data based on the positioning-purpose reference signal sent by the second device; or, the fourth device and the third device are different devices, the third device determines first data based on the positioning-purpose reference signal sent by the second device, and sends the first data to the fourth device; where, The first data includes: the measurement data obtained after the third device processes the positioning-purpose reference signals sent by S second devices, where S is a positive integer, S is less than N, N is the total number of second devices, and N is a positive integer; The fourth device inputs the first data into the first model to output second information, and the second information is used to determine the positioning result.

22. A communication device, characterized in that, Comprising: A processing module, configured to determine a training sample set, the training sample set includes M groups of sample data, M is a positive integer, and the sample data includes: the measurement data obtained after the third device processes the positioning-purpose reference signals sent by S second devices, where S is a positive integer, S is less than N, N is the total number of second devices, and N is a positive integer; The processing module is further configured to train the first model based on the training sample set; wherein, the first model is used for positioning, and the input of the first model is: the measurement data obtained after the third device processes the positioning-purpose reference signals sent by S second devices.

23. A communication device, characterized in that, Comprising: A processing module, configured to determine first data, the first data includes: the measurement data obtained after the third device processes the positioning-purpose reference signals sent by S second devices; wherein, the second device is used to send positioning-purpose reference signals to the third device, and the third device is used to process the positioning-purpose reference signals sent by the second device, S is a positive integer, S is less than N, N is the total number of second devices, and N is a positive integer; The processing module is further configured to input the first data into the first model to output second information, and the second information is used to determine the positioning result.

24. A communication device, characterized in that, Comprising: A processing module, configured to determine a training sample set, the training sample set includes M groups of sample data, M is a positive integer, and the sample data includes: the measurement data obtained after the third device processes the positioning-purpose reference signals sent by S second devices, where S is a positive integer, S is less than N, N is the total number of second devices, and N is a positive integer.

25. A communication device, characterized in that, Comprising: A processing module, configured to determine first data, the first data includes: the measurement data obtained after the third device processes the positioning-purpose reference signals sent by S second devices; wherein, the second device is used to send positioning-purpose reference signals to the third device, and the third device is used to process the positioning-purpose reference signals sent by the second device, S is a positive integer, S is less than N, N is the total number of second devices, and N is a positive integer.

26. A communication device, characterized in that, Comprising: A transceiver module, configured to send positioning-purpose reference signals to a third device, and the third device is used to process the positioning-purpose reference signals sent by the second device.

27. A communication device, characterized in that, Comprising: One or more processors; A memory coupled to the processor, wherein instructions are stored on the memory, and when the instructions are executed by the processor, cause the communication device to perform the method according to any one of claims 1 to 8, 9 to 12, 13 to 15, 16 to 18, 19.

28. A communication system, characterized in that, Including a terminal and a network device, wherein the terminal is configured to implement the method according to any one of claims 1 to 8, 9 to 12, 13 to 15, 16 to 18, 19, and the network device is configured to implement the method according to any one of claims 1 to 8, 9 to 12, 13 to 15, 16 to 18, 19.

29. A storage medium storing instructions, characterized in that, When the instructions run on the communication device, cause the communication device to perform the method according to any one of claims 1 to 8, 9 to 12, 13 to 15, 16 to 18, 19.

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