Communication method and communication device

Abstract

A communication method and a communication device are applied to a positioning scene. The method comprises: receiving configuration information from a core network element, processing a channel measurement result by using a first model to obtain probability distribution of a measurement result of a measurement quantity for terminal device positioning, the first model being determined based on a configuration parameter of a generation model. Wherein the configuration information is used for indicating configuration parameters of the generation model, and the measurement quantity corresponds to the channel measurement result. According to the scheme, the configuration information of the model is generated between the network element of the core network and the first equipment through alignment, so that the first model used for fitting / training of terminal equipment positioning is obtained, and improvement of the measurement precision of the terminal equipment is expected to be supported.

Description

Technical Field

[0001] This application relates to the field of communication technologies, and more particularly, to a communication method and a communication device. Background Art

[0002] In the positioning technology based on artificial intelligence (AI), the AI positioning model can be deployed in different devices or nodes. For example, it is usually deployed on the positioning device side, such as the location management function (LMF), or on the base station (gNodeB, gNB) side. The AI positioning model takes the channel measurement results reported by the channel measurement network element as input and the location of the terminal device (e.g., user equipment (UE)) as output. Therefore, the channel measurement network element usually needs to report the channel measurement report to the location management function network element.

[0003] In downlink positioning, the gNB sends a positioning reference signal (PRS) to the UE. The UE measures the PRS sent by the gNB to obtain a Gaussian mixture model of the downlink received reference signal time difference (DL-RSTD) distribution, and sends the relevant parameters of the Gaussian mixture model to the core network element (e.g., LMF) for the core network element to locate the UE. It is found that the current UE positioning accuracy is poor. Therefore, how to improve the positioning accuracy of the UE is an urgent problem to be solved. Summary of the Invention

[0004] This application provides a communication method and a communication device, aiming to support the improvement of the positioning accuracy of the terminal device.

[0005] In a first aspect, a communication method is provided. This method can be executed by a first device, or can also be executed by a chip or circuit of the first device. This application does not make any limitation in this regard. For the sake of description, the following takes the execution by the first device as an example for illustration.

[0006] The method includes: receiving configuration information from a core network element, where the configuration information is used to indicate configuration parameters for generating a model; processing the channel measurement results using a first model to obtain a probability distribution of the measurement results of the measurement quantities for positioning the terminal device; where the first model is determined based on the configuration parameters of the generation model, and the measurement quantities correspond to the channel measurement results.

[0007] According to the solution provided by this application, the terminal device receives configuration information from a core network element, enabling the core network element and the first device to know that the first model for fitting / training used for terminal device positioning is the same by aligning the configuration information of the generation model. Consequently, the analysis and application of the probability distribution of the measurement results of the measurement quantities based on this first model are more accurate, thereby improving the positioning accuracy of the terminal device.

[0008] Exemplarily, the channel measurement result is based on the measurement of a reference signal. For example, it can be the first device or other devices that measure the reference signal to obtain the channel measurement result.

[0009] Exemplarily, the first device can be a terminal device or an access network device. The first device can also be referred to as the network element that performs channel measurement, or the channel measurement network element, or the reference signal measurement node, etc. This application does not limit its name. The core network element can be a positioning node or a positioning device for managing the location of the terminal device, such as the Location Management Function (LMF).

[0010] In the embodiment of this application, the first model is determined based on the configuration parameters of the generation model. It can be understood that: the first device can fit or train the first model according to the obtained configuration parameters of the generation model. If the generation model is a Gaussian mixture model, it means that the first device can train or fit a specific Gaussian mixture model or a certain type of Gaussian mixture model according to the configuration parameters. That is, at this time, the first model is a trained Gaussian mixture model and can be used for the positioning of the terminal device.

[0011] In the embodiment of this application, the measurement quantity corresponds to the measurement result. It can be understood that: the first device measures one or more measurement quantities corresponding to the reference signal to obtain the measurement result, and this measurement result is the measurement result of the one or more measurement quantities. For example, taking the downlink positioning scenario as an example, assume the measurement quantity is the Time Difference of Arrival (TDoA). The first device is the terminal device. Network device #1 can send a reference signal, such as PRS#1, to the terminal device, and network device #2 can send PRS#2 to the terminal device. Correspondingly, the terminal device can measure the TDoA of PRS#1 and PRS#2, such as t2 - t1, which can be used as measurement result #1, where t1 represents the transmission time when network device #1 transmits a signal to the terminal device, and t2 represents the transmission time when network device #2 transmits a signal to the terminal device. Optionally, network device #1 and network device #2 can send PRS multiple times, or network device #3 can also send PRS#3 to the terminal device. Correspondingly, the terminal device can measure the TDoA of PRS#2 and PRS#3, such as t3 - t2, which can be used as measurement result #2, where t3 represents the transmission time when network device #3 transmits a signal to the terminal device, and so on.

[0012] In combination with the first aspect, in some implementations of the first aspect, the method further includes: sending first information to a core network element, where the first information is used to indicate a probability distribution.

[0013] Based on the above solution, by sending the first information, the terminal device can enable the core network element to learn the probability distribution of the measurement results of the measurement quantities used for terminal device positioning, and then accurately analyze the measurement quantities according to the probability distribution and the configuration information of the generation model, which is convenient for providing high-precision positioning for the terminal device.

[0014] In combination with the first aspect, in some implementations of the first aspect, the method further includes: obtaining the type and / or function of the generation model.

[0015] Optionally, the type and / or function of the generation model can be dynamically configured by the core network element to the first device through signaling or messages, or can also be pre-configured. For example, corresponding codes, tables or other ways that can be used to indicate the type and / or function of the generation model can be pre-saved in the first device. The present application does not limit its implementation manner. For example, through the type and / or function of the generation model, the first device can determine that the generation model to be trained or fitted is a GMM, and use the trained or fitted GMM (i.e., the first model) to position the terminal device.

[0016] In combination with the first aspect, in some implementations of the first aspect, the generation model is any one of the following: Gaussian mixture model; variational autoencoder; generative adversarial network.

[0017] In combination with the first aspect, in some implementations of the first aspect, the generation model is a Gaussian mixture model, and the configuration parameters of the generation model include one or more of the following: the generation method of the Gaussian mixture model; the convergence threshold of the Gaussian mixture model; the maximum number of iterations of the Gaussian mixture model; the model parameters of the Gaussian mixture model; the maximum value M of the number of single Gaussian models included in the Gaussian mixture model, where M is a positive integer; the maximum value N of the expected values of the single Gaussian models included in the Gaussian mixture model, where N is a positive number; the maximum value A of the variance or covariance of the single Gaussian models included in the Gaussian mixture model, where A is a positive number; the proportion of one or more single Gaussian models included in the Gaussian mixture model in the Gaussian mixture model.

[0018] In combination with the first aspect, in some implementations of the first aspect, the generative model is a variational autoencoder, and the configuration parameters of the generative model include one or more of the following: the structural parameters of the variational autoencoder; the type of neural network used by the variational autoencoder; the input and / or output dimensions of the variational autoencoder; the values of the model parameters of the variational autoencoder.

[0019] Among them, the structural parameters of the variational autoencoder include one or more of the following: the number of neural network layers used by the variational autoencoder, the number of neurons included in the neural network used by the variational autoencoder, the parameters related to the input layer of the variational autoencoder, the parameters related to the hidden layer, or the parameters related to the output layer.

[0020] In combination with the first aspect, in some implementations of the first aspect, the measurement quantities include one or more of the following: reference signal time difference (RSTD); time difference of arrival (TDoA); time of arrival (ToA); angle of arrival (AoA); line of sight (LOS) probability.

[0021] It should be understood that the above RSTD, TDoA, ToA, AoA, and LoS probability can be regarded as the measurement quantities of a channel measurement, and a channel can include one or more paths (such as a path set). For example, the LoS probability can be the average LoS probability of the LoS recognition results corresponding to all paths in a channel. The ToA estimation result can be the average of the time of arrival corresponding to all paths in a channel. The AoA estimation result can be the average of the angle of arrival corresponding to all paths in a channel, etc.

[0022] It should be noted that the measurement quantities in the embodiments of the present application can be one or more of the above parameters. Correspondingly, the measurement results of the measurement quantities can also be one or more. At the same time, the probability distribution of the measurement results of the measurement quantities used for terminal device positioning can also be one or more. The present application does not make any limitations in this regard.

[0023] In combination with the first aspect, in some implementations of the first aspect, the generative model is a Gaussian mixture model. The Gaussian mixture model includes k single Gaussian models, where k is an integer greater than or equal to 1. The first information includes one or more of the following: the values of k expected values; the values of k variances or covariances; the values of the proportions of k single Gaussian models in the Gaussian mixture model; among them, the k expected values, the k variances or covariances correspond one-to-one with the k single Gaussian models.

[0024] Exemplarily, the probability distribution of the Gaussian mixture model satisfies:

[0025]

[0026] Among them, That is, the expectation, variance (or covariance) of each single Gaussian model, and the probability (or proportion) that occurs in the Gaussian mixture model.

[0027] Combined with the first aspect, in some implementation manners of the first aspect, the generation model is a variational autoencoder, and the first information includes one or more of the following: the value of the model parameters of the variational autoencoder; the value of the probability distribution output by the variational autoencoder.

[0028] Combined with the first aspect, in some implementation manners of the first aspect, the channel measurement result is based on the measurement of the reference signal, including any one of the following: the first device is an access network device, and the channel measurement result is obtained based on the first channel measurement, and the first channel measurement includes: measuring the sounding reference signal from the terminal device; or, the first device is a terminal device, and the channel measurement result is obtained based on the second channel measurement, and the second channel measurement includes: measuring the positioning reference signal or the channel state information reference signal from the access network device; or, the first device is the first terminal device, and the channel measurement result is obtained based on the third channel measurement, and the third channel measurement includes: measuring the sidelink positioning reference signal from the second terminal device.

[0029] Combined with the first aspect, in some implementation manners of the first aspect, the configuration parameters include a first configuration parameter and a second configuration parameter, where the configuration information is used to indicate the first configuration parameter and the second configuration parameter; or, the configuration information is used to indicate the first configuration parameter, where the second configuration parameter is determined according to the mapping relationship, and the mapping relationship is used to indicate the corresponding relationship between the first configuration parameter and the second configuration parameter.

[0030] Combined with the first aspect, in some implementation manners of the first aspect, the configuration parameters include a first configuration parameter and a second configuration parameter, and receiving configuration information from a core network element includes: receiving configuration information from a core network element through a first signaling; where the configuration information of the first configuration parameter is carried in the first part of the first signaling, and the configuration information of the second configuration parameter is carried in the second part of the first signaling.

[0031] Combined with the first aspect, in some implementation manners of the first aspect, receiving configuration information from a core network element through a first signaling includes: receiving the first configuration parameter from the core network element through the first part of the first signaling at a first moment, and receiving the second configuration parameter from the core network element through the second part of the first signaling at a second moment; where the first moment and the second moment are the same, or the first moment and the second moment are different.

[0032] In a second aspect, a communication method is provided. This method can be executed by a core network element, or by a chip or circuit of a core network element. This application does not make any limitation in this regard. For the sake of convenience of description, the following takes the execution by a core network element as an example for illustration.

[0033] The method includes: sending configuration information to a first device, where the configuration information is used to indicate configuration parameters of a generation model; receiving first information from the first device, where the first information indicates a probability distribution of measurement results of measurement quantities for positioning a terminal device, and the probability distribution is related to the configuration parameters of the generation model.

[0034] It should be understood that the measurement quantity corresponds to a channel measurement result, and the channel measurement result is based on the measurement of a reference signal. For example, it can be the channel measurement result obtained by the first device or other devices measuring the reference signal.

[0035] According to the solution provided in this application, by sending configuration information to the terminal device, the core network element enables the configuration information of the generation model to be aligned between the core network element and the first device, so that the same first model for fitting / training for positioning the terminal device can be known. Furthermore, based on the analysis and application of the probability distribution of the measurement results of the measurement quantities by this first model, it can be more accurate, and thus the positioning accuracy of the terminal device can be improved.

[0036] In combination with the second aspect, in some implementation manners of the second aspect, the method further includes: determining the position of the terminal device according to the probability distribution of the measurement results of the measurement quantities and the configuration parameters of the generation model.

[0037] In combination with the second aspect, in some implementation manners of the second aspect, the configuration parameters include a first configuration parameter and a second configuration parameter. Among them, the configuration information is used to indicate the first configuration parameter and the second configuration parameter; or, the configuration information is used to indicate the first configuration parameter, and the second configuration parameter is determined according to a mapping relationship, and the mapping relationship is used to indicate the corresponding relationship between the first configuration parameter and the second configuration parameter.

[0038] In combination with the second aspect, in some implementation manners of the second aspect, the generation model is any one of the following: Gaussian mixture model; variational autoencoder; generative adversarial network.

[0039] In combination with the second aspect, in certain implementations of the second aspect, the generative model is a Gaussian mixture model, and the configuration parameters of the generative model include one or more of the following: the generation method of the Gaussian mixture model; the convergence threshold of the Gaussian mixture model; the maximum number of iterations of the Gaussian mixture model; the model parameters of the Gaussian mixture model; the maximum value M of the number of single Gaussian models included in the Gaussian mixture model, where M is a positive integer; the maximum value N of the expected values of the single Gaussian models included in the Gaussian mixture model, where N is a positive number; the maximum value A of the variance or covariance of the single Gaussian models included in the Gaussian mixture model, where A is a positive number; the proportion of one or more single Gaussian models included in the Gaussian mixture model in the Gaussian mixture model.

[0040] In combination with the second aspect, in certain implementations of the second aspect, the generative model is a variational autoencoder, and the configuration parameters of the generative model include one or more of the following: the structural parameters of the variational autoencoder; the type of neural network used by the variational autoencoder; the input and / or output dimensions of the variational autoencoder; the values of the model parameters of the variational autoencoder.

[0041] Among them, the structural parameters of the variational autoencoder include one or more of the following: the number of neural network layers used by the variational autoencoder, the number of neurons included in the neural network used by the variational autoencoder, the parameters related to the input layer of the variational autoencoder, the parameters related to the hidden layer, or the parameters related to the output layer.

[0042] In combination with the second aspect, in certain implementations of the second aspect, the measurement quantities include one or more of the following: reference signal time difference (RSTD); time difference of arrival (TDoA); time of arrival (ToA); angle of arrival (AoA); line-of-sight (LoS) probability.

[0043] In combination with the second aspect, in certain implementations of the second aspect, the generative model is a Gaussian mixture model, the Gaussian mixture model includes k single Gaussian models, where k is an integer greater than or equal to 1, and the first information includes one or more of the following: the values of the k expected values; the values of the k variances or covariances; the values of the proportions of the k single Gaussian models in the Gaussian mixture model; among them, the k expected values, the k variances or covariances correspond one-to-one with the k single Gaussian models.

[0044] In combination with the second aspect, in certain implementations of the second aspect, the generative model is a variational autoencoder, and the first information includes one or more of the following: the values of the model parameters of the variational autoencoder; the values of the probability distributions output by the variational autoencoder.

[0045] In combination with the second aspect, in some implementations of the second aspect, the channel measurement result is based on the measurement of a reference signal, including any one of the following: the first device is an access network device, and the channel measurement result is obtained based on a first channel measurement, where the first channel measurement includes: measuring the sounding reference signal from the terminal device; or, the first device is a terminal device, and the channel measurement result is obtained based on a second channel measurement, where the second channel measurement includes: measuring the positioning reference signal or the channel state information reference signal from the access network device; or, the first device is a first terminal device, and the channel measurement result is obtained based on a third channel measurement, where the third channel measurement includes: measuring the sidelink positioning reference signal from the second terminal device.

[0046] In combination with the second aspect, in some implementations of the second aspect, the configuration parameter includes a first configuration parameter and a second configuration parameter, where the configuration information is used to indicate the first configuration parameter and the second configuration parameter; or, the configuration information is used to indicate the first configuration parameter, where the second configuration parameter is determined according to a mapping relationship, and the mapping relationship is used to indicate the corresponding relationship between the first configuration parameter and the second configuration parameter.

[0047] In combination with the second aspect, in some implementations of the second aspect, the configuration parameter includes a first configuration parameter and a second configuration parameter, and receiving configuration information from a core network element includes: receiving configuration information from a core network element through a first signaling; where the configuration information of the first configuration parameter is carried in the first part of the first signaling, and the configuration information of the second configuration parameter is carried in the second part of the first signaling.

[0048] In combination with the second aspect, in some implementations of the second aspect, receiving configuration information from a core network element through a first signaling includes: receiving the first configuration parameter from the core network element through the first part of the first signaling at a first moment, and receiving the second configuration parameter from the core network element through the second part of the first signaling at a second moment; where the first moment and the second moment are the same, or the first moment and the second moment are different.

[0049] The beneficial effects of the above second aspect and some implementations of the second aspect can be correspondingly referred to the description related to the first aspect, and will not be elaborated here.

[0050] In a third aspect, a communication method is provided. This method can be executed by a first device, or can also be executed by a chip or circuit of the first device. This application does not make a limitation in this regard. For the sake of description, the following will take the execution by the first device as an example for illustration.

[0051] The method includes: obtaining configuration information, where the configuration information is used to indicate the configuration parameters of the generative model; processing the channel measurement result using a first model to obtain the probability distribution of the measurement result of the measurement quantity for terminal device positioning, where the first model is determined based on the configuration parameters of the generative model, and the measurement quantity corresponds to the channel measurement result.

[0052] Exemplarily, the channel measurement result is based on the measurement of a reference signal. For example, it can be the first device or other devices that measure the reference signal to obtain the channel measurement result.

[0053] According to the solution provided in this application, the terminal device sends the configuration information to the core network element, so that the core network element and the first device can know that the first model for fitting / training for terminal device positioning is the same by aligning the configuration information of the generative model. Furthermore, the analysis and application of the probability distribution of the measurement result of the measurement quantity based on the first model are more accurate, and thus the positioning accuracy of the terminal device can be improved.

[0054] Combined with the third aspect, in some implementation manners of the third aspect, the method further includes: sending all or part of the first information and the configuration information to the core network element, where the first information is used to indicate the probability distribution, and the configuration information is used to indicate the configuration parameters of the generative model.

[0055] Combined with the third aspect, in some implementation manners of the third aspect, the method further includes: obtaining the type and / or function of the generative model.

[0056] Combined with the third aspect, in some implementation manners of the third aspect, the generative model is any one of the following: Gaussian mixture model; variational autoencoder; generative adversarial network.

[0057] Combined with the third aspect, in some implementation manners of the third aspect, the generative model is a Gaussian mixture model, and the configuration parameters of the generative model include one or more of the following: the generation method of the Gaussian mixture model; the convergence threshold of the Gaussian mixture model; the maximum number of iterations of the Gaussian mixture model; the model parameters of the Gaussian mixture model; the maximum value M of the number of single Gaussian models included in the Gaussian mixture model, where M is a positive integer; the maximum value N of the expected value of the single Gaussian models included in the Gaussian mixture model, where N is a positive number; the maximum value A of the variance or covariance of the single Gaussian models included in the Gaussian mixture model, where A is a positive number; the proportion of one or more single Gaussian models included in the Gaussian mixture model in the Gaussian mixture model.

[0058] Combined with the third aspect, in some implementation manners of the third aspect, the generative model is a variational autoencoder, and the configuration parameters of the generative model include one or more of the following: the structural parameters of the variational autoencoder; the type of neural network used by the variational autoencoder; the input and / or output dimensions of the variational autoencoder; the value of the model parameters of the variational autoencoder.

[0059] Among them, the structural parameters of the variational autoencoder include one or more of the following: the number of neural network layers used by the variational autoencoder, the number of neurons included in the neural network used by the variational autoencoder, the parameters related to the input layer of the variational autoencoder, the parameters related to the hidden layer, or the parameters related to the output layer.

[0060] Combined with the third aspect, in some implementation manners of the third aspect, the measurement quantity includes one or more of the following: reference signal time difference (RSTD); time difference of arrival (TDoA); time of arrival (ToA); angle of arrival (AoA); line-of-sight (LoS) probability.

[0061] Combined with the third aspect, in some implementation manners of the third aspect, the generative model is a Gaussian mixture model, the Gaussian mixture model includes k single Gaussian models, k is an integer greater than or equal to 1, and the first information includes one or more of the following: the values of the k expected values; the values of the k variances or covariances; the values of the proportions of the k single Gaussian models in the Gaussian mixture model; among them, the k expected values, the k variances or covariances correspond one-to-one to the k single Gaussian models.

[0062] Combined with the third aspect, in some implementation manners of the third aspect, the generative model is a variational autoencoder, and the first information includes one or more of the following: the values of the model parameters of the variational autoencoder; the values of the probability distributions output by the variational autoencoder.

[0063] Combined with the third aspect, in some implementation manners of the third aspect, the channel measurement result is based on the measurement of the reference signal and includes any one of the following: the first device is an access network device, and the channel measurement result is based on the first channel measurement, and the first channel measurement includes: measuring the sounding reference signal from the terminal device; or, the first device is a terminal device, and the channel measurement result is based on the second channel measurement, and the second channel measurement includes: measuring the positioning reference signal or the channel state information reference signal from the access network device; or, the first device is the first terminal device, and the channel measurement result is based on the third channel measurement, and the third channel measurement includes: measuring the sidelink positioning reference signal from the second terminal device.

[0064] Combined with the third aspect, in some implementation manners of the third aspect, the configuration parameters include a first configuration parameter and a second configuration parameter, where the configuration information is used to indicate the first configuration parameter and the second configuration parameter; or, the configuration information is used to indicate the first configuration parameter, where the second configuration parameter is determined according to the mapping relationship, and the mapping relationship is used to indicate the corresponding relationship between the first configuration parameter and the second configuration parameter.

[0065] In combination with the third aspect, in some implementation manners of the third aspect, the configuration parameters include a first configuration parameter and a second configuration parameter, and configuration information from a core network element is received, including: receiving configuration information from the core network element through a first signaling; wherein, the configuration information of the first configuration parameter is carried in a first part of the first signaling, and the configuration information of the second configuration parameter is carried in a second part of the first signaling.

[0066] In combination with the third aspect, in some implementation manners of the third aspect, receiving configuration information from a core network element through a first signaling includes: receiving a first configuration parameter from the core network element through a first part of the first signaling at a first moment, and receiving a second configuration parameter from the core network element through a second part of the first signaling at a second moment; wherein, the first moment and the second moment are the same, or the first moment and the second moment are different.

[0067] The beneficial effects of the above-mentioned third aspect and some implementation manners of the third aspect can be correspondingly referred to the description related to the first aspect, and will not be elaborated here.

[0068] In a fourth aspect, a communication method is provided. This method can be executed by a core network element, or can also be executed by a chip or circuit of the core network element. This application does not make a limitation on this. For the sake of description, the following will take the execution by the core network element as an example for illustration.

[0069] The method includes: receiving all or part of the first information and configuration information from a first device, where the first information indicates a probability distribution of measurement results of measurement quantities for terminal device positioning, and the configuration information is used to indicate configuration parameters of a generation model, and the probability distribution is related to the configuration parameters of the generation model.

[0070] It should be understood that the measurement quantities correspond to channel measurement results, and the channel measurement results are based on measurements of reference signals. For example, the channel measurement results can be obtained by the first device or other devices measuring the reference signals.

[0071] According to the solution provided by this application, by receiving configuration information from a terminal device, the core network element can make the configuration information of the generation model aligned between the core network element and the first device, so that the same first model for fitting / training for terminal device positioning can be known, and further, the analysis and application of the probability distribution of the measurement results of the measurement quantities based on the first model are more accurate, and thus the positioning accuracy of the terminal device can be improved.

[0072] In combination with the fourth aspect, in some implementation manners of the fourth aspect, the method further includes: determining the position of the terminal device according to the probability distribution of the measurement results of the measurement quantities and the configuration parameters of the generation model.

[0073] In combination with the fourth aspect, in some implementations of the fourth aspect, the configuration parameters include a first configuration parameter and a second configuration parameter, where the configuration information is used to indicate the first configuration parameter and the second configuration parameter; or the configuration information is used to indicate the first configuration parameter, where the second configuration parameter is determined according to a mapping relationship, and the mapping relationship is used to indicate the corresponding relationship between the first configuration parameter and the second configuration parameter.

[0074] In combination with the fourth aspect, in some implementations of the fourth aspect, the generative model is any one of the following: Gaussian mixture model; variational autoencoder; generative adversarial network.

[0075] In combination with the fourth aspect, in some implementations of the fourth aspect, the generative model is a Gaussian mixture model, and the configuration parameters of the generative model include one or more of the following: the generation method of the Gaussian mixture model; the convergence threshold of the Gaussian mixture model; the maximum number of iterations of the Gaussian mixture model; the model parameters of the Gaussian mixture model; the maximum value M of the number of single Gaussian models included in the Gaussian mixture model, where M is a positive integer; the maximum value N of the expected values of the single Gaussian models included in the Gaussian mixture model, where N is a positive number; the maximum value A of the variance or covariance of the single Gaussian models included in the Gaussian mixture model, where A is a positive number; the proportion of one or more single Gaussian models included in the Gaussian mixture model in the Gaussian mixture model.

[0076] In combination with the fourth aspect, in some implementations of the fourth aspect, the generative model is a variational autoencoder, and the configuration parameters of the generative model include one or more of the following: the structural parameters of the variational autoencoder; the type of neural network used by the variational autoencoder; the input and / or output dimensions of the variational autoencoder; the values of the model parameters of the variational autoencoder.

[0077] Among them, the structural parameters of the variational autoencoder include one or more of the following: the number of neural network layers used by the variational autoencoder, the number of neurons included in the neural network used by the variational autoencoder, the parameters related to the input layer of the variational autoencoder, the parameters related to the hidden layer, or the parameters related to the output layer.

[0078] In combination with the fourth aspect, in some implementations of the fourth aspect, the measurement quantities include one or more of the following: reference signal time difference (RSTD); time difference of arrival (TDoA); time of arrival (ToA); angle of arrival (AoA); line-of-sight (LoS) probability.

[0079] In combination with the fourth aspect, in some implementations of the fourth aspect, the generative model is a Gaussian mixture model, the Gaussian mixture model includes k single Gaussian models, where k is an integer greater than or equal to 1, and the first information includes one or more of the following: the values of k expected values; the values of k variances or covariances; the values of the proportions of k single Gaussian models in the Gaussian mixture model; among them, the k expected values, the k variances or covariances correspond one-to-one to the k single Gaussian models.

[0080] In combination with the fourth aspect, in some implementations of the fourth aspect, the generative model is a variational autoencoder, and the first information includes one or more of the following: the value of the model parameters of the variational autoencoder; the value of the probability distribution output by the variational autoencoder.

[0081] In combination with the fourth aspect, in some implementations of the fourth aspect, the channel measurement result is based on the measurement of a reference signal and includes any of the following: the first device is an access network device, and the channel measurement result is based on a first channel measurement, where the first channel measurement includes: measuring the sounding reference signal from the terminal device; or, the first device is a terminal device, and the channel measurement result is based on a second channel measurement, where the second channel measurement includes: measuring the positioning reference signal or the channel state information reference signal from the access network device; or, the first device is a first terminal device, and the channel measurement result is based on a third channel measurement, where the third channel measurement includes: measuring the sidelink positioning reference signal from the second terminal device.

[0082] In combination with the fourth aspect, in some implementations of the fourth aspect, the configuration parameters include a first configuration parameter and a second configuration parameter, where the configuration information is used to indicate the first configuration parameter and the second configuration parameter; or, the configuration information is used to indicate the first configuration parameter, where the second configuration parameter is determined according to a mapping relationship, and the mapping relationship is used to indicate the corresponding relationship between the first configuration parameter and the second configuration parameter.

[0083] In combination with the fourth aspect, in some implementations of the fourth aspect, the configuration parameters include a first configuration parameter and a second configuration parameter, and receiving configuration information from a core network element includes: receiving configuration information from a core network element through a first signaling; where the configuration information of the first configuration parameter is carried in the first part of the first signaling, and the configuration information of the second configuration parameter is carried in the second part of the first signaling.

[0084] In combination with the fourth aspect, in some implementations of the fourth aspect, receiving configuration information from a core network element through a first signaling includes: receiving the first configuration parameter from the core network element through the first part of the first signaling at a first moment, and receiving the second configuration parameter from the core network element through the second part of the first signaling at a second moment; where the first moment and the second moment are the same, or the first moment and the second moment are different.

[0085] The beneficial effects of the above fourth aspect and some implementations of the fourth aspect can be referred to the relevant description of the second aspect correspondingly, and will not be elaborated here.

[0086] Fifth aspect, a communication device is provided, which includes: a transceiver unit, configured to receive configuration information from a core network element, where the configuration information is used to indicate configuration parameters of a generation model; and a processing unit, configured to process channel measurement results using a first model to obtain a probability distribution of measurement results of measurement quantities for positioning a terminal device, where the first model is determined based on the configuration parameters of the generation model, and the measurement quantities correspond to the channel measurement results.

[0087] Exemplarily, the channel measurement results are based on measurements of reference signals. For example, the channel measurement results may be obtained by a first device or other devices measuring the reference signals.

[0088] The transceiver unit may perform the receiving and sending processes in the foregoing first aspect, and the processing unit of the communication device may perform other processes in the foregoing first aspect except for receiving and sending.

[0089] Sixth aspect, a communication device is provided, which includes: a transceiver unit, configured to send configuration information to a first device, where the configuration information is used to indicate configuration parameters of a generation model; and a processing unit, configured to receive first information from the first device, where the first information indicates a probability distribution of measurement results of measurement quantities for positioning a terminal device, and the probability distribution is related to the configuration parameters of the generation model.

[0090] The transceiver unit may perform the receiving and sending processes in the foregoing second aspect, and the processing unit of the communication device may perform other processes in the foregoing second aspect except for receiving and sending.

[0091] Seventh aspect, a communication device is provided, which includes: a processing unit, configured to obtain configuration information, where the configuration information is used to indicate configuration parameters of a generation model; and a transceiver unit, configured to process channel measurement results using a first model to obtain a probability distribution of measurement results of measurement quantities for positioning a terminal device, where the first model is determined based on the configuration parameters of the generation model, and the measurement quantities correspond to the channel measurement results.

[0092] Optionally, the channel measurement results are based on measurements of reference signals. For example, the channel measurement results may be obtained by a first device or other devices measuring the reference signals.

[0093] The transceiver unit may perform the receiving and sending processes in the foregoing third aspect, and the processing unit of the communication device may perform other processes in the foregoing third aspect except for receiving and sending.

[0094] In an eighth aspect, a communication device is provided. The device includes: a transceiver unit configured to receive all or part of first information and configuration information from a first device, where the first information indicates a probability distribution of measurement results of measurement quantities for positioning a terminal device, and the configuration information is used to indicate configuration parameters of a generation model, and the probability distribution is related to the configuration parameters of the generation model; and a processing unit configured to determine the location of the terminal device based on the probability distribution of the measurement results of the measurement quantities and the configuration parameters of the generation model.

[0095] The transceiver unit may perform the receiving and sending processes in the foregoing fourth aspect, and the processing unit of the communication device may perform other processes in the foregoing fourth aspect except for receiving and sending.

[0096] In a ninth aspect, a communication device is provided, including a processing circuit configured to execute a computer program, so that the device performs the methods in the foregoing first aspect to fourth aspect and any possible implementation manners thereof.

[0097] Optionally, the processing circuit is one or more processors, or all or part of one or more processors are circuits for processing functions.

[0098] Optionally, the communication device further includes a memory configured to store the computer program, and the memory is one or more.

[0099] Optionally, the memory may be integrated with the processor, or the memory is separately provided from the processor, or the memory is located within the processor.

[0100] Optionally, the communication device further includes a transceiver circuit, such as a transceiver or an input / output circuit.

[0101] In a tenth aspect, a communication system is provided, including: a first device and a core network element. The first device is configured to perform the methods in the foregoing first aspect or third aspect and any possible implementation manners thereof, and the core network element is configured to perform the methods in the foregoing second aspect or fourth aspect and any possible implementation manners thereof.

[0102] In an eleventh aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program or code, and when the computer program or code runs on a computer, the computer is caused to perform the methods in the foregoing first aspect or second aspect and any possible implementation manners thereof.

[0103] In a twelfth aspect, a chip is provided, including a processing circuit configured to run a computer program, so that a device installed with the chip performs the methods in the foregoing first aspect to fourth aspect and any possible implementation manners thereof.

[0104] Among them, the chip may include an output circuit or interface for sending information or data, and an input circuit or interface for receiving information or data.

[0105] In a thirteenth aspect, a computer program product is provided, which includes: computer program code that, when running on the computer, executes the methods in the above first aspect to fourth aspect and any possible implementation manner thereof. Description of the Drawings

[0106] Figure 1 is a schematic diagram of a wireless communication system 100 applicable to the embodiments of the present application;

[0107] Figure 2 is a schematic diagram of a wireless communication system 200 applicable to the embodiments of the present application;

[0108] Figure 3 is a schematic diagram of a wireless communication system 300 applicable to the embodiments of the present application;

[0109] Figure 4 is a schematic diagram of a network element involved in the embodiments of the present application;

[0110] Figure 5 is a schematic diagram of an AI / ML network element or module;

[0111] Figure 6 is a schematic diagram of an AI positioning model framework;

[0112] Figure 7 is a schematic diagram of TDoA positioning;

[0113] Figure 8 is a schematic diagram of LOS and NLOS;

[0114] Figure 9 shows a schematic diagram of the probability distribution of the Gaussian mixture model corresponding to different model fitting configurations;

[0115] Figure 10 is a schematic flowchart of a communication method 1000 provided by the embodiments of the present application;

[0116] Figure 11 is a schematic flowchart of a communication method 1100 provided by the embodiments of the present application;

[0117] Figure 12 is a schematic flowchart of a communication method 1200 provided by the embodiments of the present application;

[0118] Figure 13 is a schematic flowchart of a communication method 1300 provided by the embodiments of the present application;

[0119] Figure 14 is a schematic flowchart of the communication method 1400 provided by an embodiment of the present application;

[0120] Figure 15 is a schematic flowchart of the communication method 1500 provided by an embodiment of the present application;

[0121] Figure 16 is a schematic flowchart of the communication method 1600 provided by an embodiment of the present application;

[0122] Figure 17 is a schematic flowchart of the communication method 1700 provided by an embodiment of the present application;

[0123] Figure 18 is a schematic block diagram of the communication device 1800 provided by an embodiment of the present application;

[0124] Figure 19 is a schematic block diagram of the communication device 1900 provided by an embodiment of the present application. Detailed implementation manners

[0125] Next, the technical solutions in the present application will be described with reference to the accompanying drawings.

[0126] The technical solutions provided by the present application can be applied to various communication systems, such as: the fifth generation (5G) or new radio (NR) system, the long term evolution (LTE) system, the LTE frequency division duplex (FDD) system, the LTE time division duplex (TDD) system, the wireless local area network (WLAN) system, the satellite communication system, future communication systems, such as the sixth generation mobile communication system, or a fusion system of multiple systems, etc. The technical solutions provided by the present application can also be applied to device-to-device (D2D) communication, vehicle-to-everything (V2X) communication, machine-to-machine (M2M) communication, machine type communication (MTC), and the Internet of Things (IoT) communication system or other communication systems.

[0127] A device in a communication system can send a signal to another device or receive a signal from another device. The signal can include information, signaling, data, etc. Herein, the device can also be replaced by an entity, a network entity, a communication device, a communication module, a node, a communication node, etc. In this application, the description is made by taking the device as an example. For example, a communication system can include at least one terminal device and at least one network device. The network device can send a downlink signal to the terminal device, and / or the terminal device can send an uplink signal to the network device. It can be understood that the terminal device / network device in this application can be replaced by a first device, and execute the corresponding communication method in this application with a core network element (such as a positioning device, which can be a Location Management Function network element LMF).

[0128] The terminal devices in the embodiments of the present application include various devices with wireless communication functions, which can be used to connect people, objects, machines, etc. The terminal devices can be widely applied to various scenarios, such as: cellular communication, D2D, V2X, peer-to-peer (P2P), M2M, MTC, IoT, virtual reality (VR), augmented reality (AR), industrial control, autonomous driving, telemedicine, smart grid, smart home, smart office, smart wearables, smart transportation, smart city drones, robots, remote sensing, passive sensing, positioning, navigation and tracking, autonomous delivery, and other scenarios. The terminal device can be a terminal in any of the above scenarios, such as an MTC terminal, an IoT terminal, etc. The terminal device can be a user equipment (UE), terminal, fixed device, mobile station device or mobile device, subscriber unit, handheld device, in-vehicle device, wearable device, cellular phone, smart phone, session initialization protocol (SIP) phone, wireless data card, personal digital assistant (PDA), computer, tablet computer, laptop computer, wireless modem, handset, laptop computer, computer with wireless transceiver function, smart book, vehicle, satellite, global positioning system (GPS) device, target tracking device, aircraft (such as drones, helicopters, multi-rotor helicopters, quadcopters, or airplanes, etc.), ship, remote control device, smart home device, industrial device, or a device built into the above devices (such as a communication module, modem, or chip in the above devices), or other processing devices connected to a wireless modem. For the convenience of description, the terminal device will be described below by taking the terminal or UE as an example.

[0129] It should be understood that in some scenarios, the UE can also be used as a base station. For example, the UE can act as a scheduling entity, which provides sidelink signals between UEs in scenarios such as V2X, D2D, or P2P.

[0130] In the embodiments of the present application, the device for implementing the functions of the terminal device may be the terminal device itself, or a device capable of supporting the terminal device to implement such functions, such as a chip system or a chip, and this device may be installed in the terminal device. In the embodiments of the present application, the chip system may be composed of chips, or may include chips and other discrete devices.

[0131] The network device in the embodiments of the present application may be a device for communicating with the terminal device. This network device may also be referred to as an access network device or a radio access network device. For example, the network device may be a base station. The network device in the embodiments of the present application may refer to a radio access network (RAN) node (or device) that connects the terminal device to the wireless network. The base station may generally cover various names as follows, or be replaced with the following names, such as: Node B, evolved Node B (eNB), next generation Node B (gNB), relay station, access point, transmitting and receiving point (TRP), transmitting point (TP), master station, slave station, multi-mode radio (MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), radio unit (RU), positioning node, RAN intelligent controller (RIC), etc. The base station may be a macro base station, a micro base station, a relay node, a donor node or the like, or a combination thereof. The base station may also refer to a communication module, a modem or a chip disposed in the foregoing device or apparatus. The base station may also be a mobile switching center and a device that undertakes the base station function in D2D, V2X, M2M communications, a network-side device in a 6G network, a device that undertakes the base station function in a future communication system, etc. The base station may support networks with the same or different access technologies. The embodiments of the present application do not limit the specific technologies and specific device forms adopted by the network device.

[0132] A base station can be fixed or mobile. For example, a helicopter or a drone can be configured to act as a mobile base station, and one or more cells can move according to the position of the mobile base station. In other examples, a helicopter or a drone can be configured to be used as a device for communicating with another base station.

[0133] In some deployments, the network device mentioned in the embodiments of this application can be a device including a CU, or a DU, or a device including a CU and a DU, or a control plane CU node (Central Unit Control Plane (CU-CP)) and a user plane CU node (Central Unit User Plane (CU-UP)) and a DU node. For example, the network device can include a gNB-CU-CP, a gNB-CU-UP, and a gNB-DU.

[0134] In some deployments, multiple RAN nodes cooperate to assist a terminal in achieving wireless access, and different RAN nodes respectively implement partial functions of a base station. For example, the RAN node can be a CU, a DU, a CU-CP, a CU-UP, or an RU, etc. The CU and the DU can be separately provided, or can also be included in the same network element, such as a BBU. The RU can be included in a radio device or a radio unit, such as included in an RRU, an AAU, or an RRH.

[0135] RAN nodes can support one or more types of fronthaul interfaces. Different fronthaul interfaces respectively correspond to DUs and RUs with different functions. If the fronthaul interface between the DU and the RU is the Common Public Radio Interface (CPRI), the DU is configured to implement one or more of the baseband functions, and the RU is configured to implement one or more of the radio frequency functions. If the fronthaul interface between the DU and the RU is another interface, compared with the CPRI, some of the downlink and / or uplink baseband functions, for example, for the downlink, one or more of precoding, digital beamforming (BF), or inverse fast Fourier transform (IFFT) / cyclic prefix (CP) addition, are moved from the DU to the RU for implementation. For the uplink, one or more of digital beamforming (BF), or fast Fourier transform (FFT) / cyclic prefix (CP) removal, are moved from the DU to the RU for implementation. In a possible implementation, this interface can be the Enhanced Common Public Radio Interface (eCPRI). Under the eCPRI architecture, different splitting methods between the DU and the RU correspond to different categories (Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, F.

[0136] Taking eCPRI Cat A as an example, for downlink transmission, with layer mapping as the segmentation, the DU is configured to implement one or more functions before layer mapping (i.e., one or more of encoding, rate matching, scrambling, modulation, layer mapping), while other functions after layer mapping (such as one or more of resource element (RE) mapping, digital beamforming (BF), or inverse fast Fourier transform (IFFT) / adding cyclic prefix (CP)) are moved to the RU for implementation. For uplink transmission, with de-RE mapping as the segmentation, the DU is configured to implement one or more functions before demapping (i.e., one or more of decoding, derate matching, descrambling, demodulation, inverse discrete Fourier transform (IDFT), channel equalization, de-RE mapping), while other functions after demapping (such as one or more of digital BF or fast Fourier transform (FFT) / removing CP) are moved to the RU for implementation. It can be understood that for the function descriptions of DU and RU corresponding to various types of eCPRI, reference can be made to the eCPRI protocol, which will not be elaborated here.

[0137] In a possible design, the processing unit in the BBU for implementing baseband functions is called the baseband high (BBH) unit, and the processing unit in the RRU / AAU / RRH for implementing baseband functions is called the baseband low (BBL) unit.

[0138] In different systems, the CU (or CU-CP and CU-UP), DU, or RU may also have different names, but those skilled in the art can understand their meanings. For example, the radio access network may also be an open radio access network (O-RAN) architecture. In the ORAN system, the CU may also be called O-CU (open CU), the DU may also be called O-DU, the CU-CP may also be called O-CU-CP, the CU-UP may also be called O-CU-UP, and the RU may also be called O-RU. Any unit among the CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software modules and hardware modules.

[0139] In the embodiments of the present application, the device for implementing the functions of a network device may be the network device itself, or a device capable of supporting the network device to implement such functions, such as a chip system or a chip, and this device may be installed in the network device. In the embodiments of the present application, the chip system may be composed of chips, or may include chips and other discrete devices.

[0140] The network device and the terminal device may be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they may also be deployed on the water surface; or they may be deployed on aircraft, balloons, and satellites in the air. In the embodiments of the present application, the scenarios where the network device and the terminal device are located are not limited. In addition, the terminal device and the network device may be hardware devices, or software functions running on dedicated hardware or software functions running on general hardware. For example, they may be virtualized functions instantiated on a platform (such as a cloud platform), or entities including dedicated or general hardware devices and software functions. The present application does not limit the specific forms of the terminal device and the network device.

[0141] In the embodiments of the present application, the location management function network element may be a positioning node or a positioning device for positioning and managing the location of the terminal device, such as an LMF. Exemplarily, the device for positioning or managing the location of the terminal device may be the location management function network element, or the chip system, chip, or circuit of the location management function network element, and this chip system, chip, or circuit may be installed in the location management function network element. Among them, the chip system may be composed of chips, or may include chips and other discrete devices.

[0142] Optionally, the location management function network element may be a core network device, which refers to a device in the core network (CN) that provides service support for terminal devices. The core network device may include one or more core network elements. Taking the 5G core network as an example, the 5G core network includes an access and mobility management function (AMF) network element responsible for services such as mobility management and access management, a session management function (SMF) network element responsible for session management, a user plane function (UPF) network element responsible for packet routing and forwarding of the user plane and quality of service (QoS) control, a policy control function (PCF) network element, etc. The above core network elements can work independently or be combined together to implement certain control functions. For example, AMF, SMF, and PCF can be combined together as a core network device. All or part of the above core network elements may be independent in form or integrated in the same device, which is not limited herein.

[0143] In the embodiments of the present application, the first device may be a terminal device or a network device, or a component (such as a chip or a circuit) of a terminal device or a network device. Optionally, the network device may be a network device provided with one or more AI modules. For example, the network device may be one or more of a core network device, an access network node (RAN node), or an OAM. For instance, the AI module may be a RAN intelligent controller (RIC), such as a near real-time RIC or a non-real-time RIC. For example, the near real-time RIC is set in the RAN node (e.g., in the CU or DU), and the non-real-time RIC is set in the OAM, a cloud server, a core network device, or other network devices. The location management function network element is a network element for training the AI positioning model and / or storing the AI positioning model library, or is also an AI positioning model selection / inference network element at the same time. For example, the AI positioning model for positioning is configured in the location management function network element.

[0144] Exemplarily, the first device and the location management function network element may be logically separated and deployed. As different implementation manners, the first device and the location management function network element may be physically deployed in the same network element or different network elements, without limitation. For example, the first device is a terminal device, and the location management function network element is a server (also referred to as a host) or a cloud device in an over the top (OTT) system. The terminal device can communicate with the server or the cloud device of the OTT system through the Internet. For another example, the first device is a module in the terminal device (for example, a module in the physical layer), and the location management function network element is another module of the device (such as the LMF) (for example, a module in the application layer, such as an application module connected to the OTT server). It can be understood that in the embodiments of the present application, the module can be implemented by hardware, or by software, or by a combination of hardware and software, without limitation.

[0145] First, a communication system applicable to the embodiments of the present application is briefly introduced as follows.

[0146] Figure 1 is a schematic diagram of a wireless communication system 100 applicable to the embodiments of the present application. As Figure 1 shown, the wireless communication system includes a radio access network 100. The radio access network 100 may be a next-generation (such as 6G or a higher version) radio access network or a traditional (such as 5G, 4G, 3G, or 2G) radio access network. One or more terminal devices (120a - 120j, collectively referred to as 120) may be connected to each other or connected to one or more network devices (110a, 110b, collectively referred to as 110) in the radio access network 100. Figure 1 This is only a schematic diagram. The wireless communication system may further include other devices, such as a core network device, a wireless relay device, and / or a wireless backhaul device, etc., which are not drawn in Figure 1 here.

[0147] In practical applications, the wireless communication system may include multiple network devices at the same time, or may include multiple terminal devices at the same time, without limitation. One network device may serve one or more terminal devices at the same time. One terminal device may also access one or more network devices at the same time. The embodiments of the present application do not limit the number of terminal devices and network devices included in the wireless communication system.

[0148] Figure 2 is a schematic diagram of a wireless communication system 200 applicable to the embodiments of the present application. As Figure 2 shown, the wireless communication system 200 may include at least one network device, such as Figure 2 the network device 210 shown, and the wireless communication system 200 may further include at least one terminal device, such as Figure 2The terminal devices 220 and 230 shown, the wireless communication system 200 may further include a positioning device, such as Figure 2 the positioning device 240 shown. Exemplarily, the positioning device 240 is a positioning management function (LMF) network element, simply referred to as LMF.

[0149] Among them, the positioning device and the network device can communicate through interface messages. For example, taking the network device 210 as a gNB and the positioning device 240 as an LMF as an example, the gNB and the LMF can exchange information through NRPPa messages. Another example, taking the network device 210 as an eNB and the positioning device 240 as an LMF as an example, the eNB and the LMF can exchange information through LTE positioning protocol (LPP) messages.

[0150] Among them, the terminal device and the positioning device can communicate directly, or can also communicate through other devices. The other devices can be, for example, network devices and / or core network elements. As an example, as Figure 2 shown, the terminal device 220 and the positioning device 240 can communicate through the network device 210.

[0151] Optionally, the positioning device can be different modules of the same device as the network device, or can also be separate different devices.

[0152] Figure 3 is a schematic diagram of a wireless positioning system applicable to the embodiments of the present application. As Figure 3 shown, the wireless positioning system mainly includes an access network device, a terminal device, and a positioning device. Among them, the positioning device is mainly responsible for receiving positioning service requests, collecting positioning-related measurement results, calculating positioning results, and providing corresponding wireless positioning services, etc. Optionally, the positioning device can receive positioning service requests from the access network device or an upper-layer application. As an example, the positioning device can be a location management function network element, such as LMF. For the access network device and the terminal device, refer to the above description.

[0153] Figure 4 is a schematic diagram of the device involved in the embodiments of the present application. As Figure 4As shown, it includes a UE, a location management function network element, and an access network device. Optionally, it further includes an access and mobility management function (AMF) network element. As an example, the access network device can be an ng-eNB or a gNB. Among them, ng-eNB represents a 4G base station that can access the 5G core network, and gNB represents a 5G base station. Both are network elements of the NR-RAN. The radio access network device or base station mentioned in the embodiments of this application can be an ng-eNB or a gNB, without limitation. Communication between the UE and the radio access network device is carried out through corresponding interfaces. For example, communication between the UE and the gNB is carried out through the NR-Uu interface, and communication between the UE and the ng-eNB is carried out through the LTE-Uu interface. In the embodiments of this application, the NR-Uu interface and the LTE-Uu interface are used to transmit positioning-related signaling and / or data. In addition, communication between the gNB and the AMF, and between the ng-eNB and the AMF is carried out through the NG-C interface, such as transmitting positioning-related signaling. Communication between the AMF and the LMF is carried out through the NL1 interface, such as transmitting positioning-related signaling. Optionally, the interaction between the UE and the LMF is based on the LTE positioning protocol (LPP) protocol, and the interaction between the NG-RAN and the LMF is based on the NRPPa protocol. The NRPPa protocol is transparently transmitted across the AMF. It should be understood that the NG-RAN is only an example. When the technical solution of this application is applied to a future wireless communication system, such as a 6G system, the NG-RAN is correspondingly the access network device in the 6G system. Similarly, the names of each network element, the names of the interfaces between each network element, and the message names, etc. are only examples. In a future wireless communication system, network elements, interfaces, and interface messages with the same or similar functions can be used to implement the technical solution of this application.

[0154] In addition, in order to support machine learning functions in a wireless communication system, an AI node may also be introduced into the wireless communication system.

[0155] Optionally, the communication system further includes at least one AI node.

[0156] Optionally, the AI node is deployed in one or more of the following: network device, terminal device, core network, or positioning device; or, the AI node can also be deployed separately, such as at a location outside any of the above devices. The AI node can communicate with other devices in the communication system. Other devices can be, for example, one or more of the following: network device, terminal device, core network element, or positioning device.

[0157] Optionally, the AI node is used to perform AI-related operations. As an example, AI-related operations may include, for example, one or more of model failure testing, model performance testing, model training testing, or data collection, etc.

[0158] For example, the network device may forward the data related to the AI positioning model reported by the terminal device to the AI node, and the AI node performs AI-related operations. For another example, the access network device or the terminal device may forward the data related to the AI positioning model to the AI node, and the AI node performs AI-related operations. For another example, the AI node may send one or more of the outputs of AI-related operations, such as a trained neural network model, model evaluation, or test results, etc., to the network device and / or the terminal device. For example, the AI node may directly send the output of AI-related operations to the network device and the terminal device. For another example, the AI node may send the output of AI-related operations to the terminal device through the network device. For another example, the AI node may send the output of AI-related operations to the network device through the terminal device.

[0159] It can be understood that the number of AI nodes in this application is not limited. For example, when there are multiple AI nodes, the multiple AI nodes can be divided based on functions, such as different AI nodes being responsible for different functions.

[0160] It can also be understood that the AI node can be an independent device, or can be integrated into the same device to implement different functions, or can be a network element in a hardware device, or can be a software function running on dedicated hardware, or a virtualized function instantiated on a platform (such as a cloud platform). This application does not limit the specific form of the above AI node.

[0161] Exemplarily, the AI node can be an AI network element or an AI module.

[0162] Figure 5 It is a schematic diagram of an AI / ML network element or module. Among them, if an AI network element is introduced, it means that the AI network element corresponds to an independent network element; if an AI module is introduced, the AI module can be located inside a certain network element. As described above, the network elements involved in the embodiments of this application include UEs, radio access network devices, and LMFs. Optionally, an AMF may also be included. One or more of these UEs, radio access network devices, AMFs (if the network element is involved), and LMFs may be provided with an AI module inside, or one or more of the UEs, radio access network devices, AMFs, and LMFs may introduce corresponding AI network elements, or a combination of these two methods. This application does not limit this.

[0163] The AI module is used to implement corresponding AI functions. The AI modules deployed in different network elements can be the same or different. According to different parameter configurations of the model of the AI module, the AI module can implement different functions. The model of the AI module can be based on one or more of the following parameter configurations: structural parameters (such as at least one of the number of neural network layers, the width of the neural network, the connection relationship between layers, the weights of neurons, the activation function of neurons, or the bias in the activation function), input parameters (such as the type and / or dimension of the input parameters), hidden layer parameters (such as the type and / or dimension of the hidden layer parameters), or output parameters (such as the type and / or dimension of the output parameters).

[0164] An AI module can have one or more models. One model can infer an output, and the output includes one parameter or multiple parameters. The learning process, training process, or inference process of different models can be deployed in different nodes or devices, or can be deployed in the same node or device.

[0165] It should be understood that if one or more of the UE, radio access network device, AMF, and LMF introduce corresponding AI network elements, and the AI operation is executed by the corresponding AI network element, then the UE, radio access network device, AMF, or LMF needs to send information related to the AI operation to the corresponding AI network element. For example, if the LMF introduces a corresponding AI network element and this AI network element executes the inference operation of the AI model, after the LMF receives a channel measurement report from a first device (such as an access network device or UE), it sends the channel measurement result carried in the channel measurement report to the corresponding AI network element. Another example is in uplink positioning. If the access network device introduces a corresponding AI network element, assuming that the input of the AI model is the channel feature extracted from the channel measurement result, and the channel measurement result is obtained by the access network device measuring the sounding reference signal (SRS) of the UE. Therefore, after the access network device obtains the channel measurement result, it sends the channel measurement result to the corresponding AI network element. The AI network element extracts the channel feature from the channel measurement result through the AI model and then returns the extracted channel feature to the access network device. Then, the access network device sends the channel feature to the LMF.

[0166] It can also be understood that Figures 1 to 5 is a simplified schematic diagram for easy understanding. The wireless communication system may also include other network devices, or may also include other terminal devices, or may also include other AI nodes, Figures 1 to 5 which are not drawn in the figure.

[0167] To facilitate the understanding of the embodiments of the present application, the terms involved in the embodiments of the present application will be briefly described below.

[0168] (1) Artificial Intelligence (AI);

[0169] It enables machines to have the ability to learn, accumulate experience, and solve problems that humans can solve through experience, such as natural language understanding, image recognition, and playing chess. Artificial intelligence can be understood as the intelligence demonstrated by machines made by humans. Generally, artificial intelligence refers to the technology that presents human intelligence through computer programs. The goals of artificial intelligence include understanding intelligence by constructing computer programs with symbolic reasoning or inference.

[0170] (2) Machine Learning (ML);

[0171] ML is an implementation method of artificial intelligence. Machine learning is a method that can endow machines with the ability to learn, enabling machines to complete functions that cannot be achieved by direct programming. In a practical sense, machine learning is a method of using data to train a model and then using the model for prediction. There are many machine learning methods, such as neural network (NN), decision tree, support vector machine, etc. Machine learning theory mainly designs and analyzes algorithms that allow computers to learn automatically. Machine learning algorithms are a class of algorithms that automatically analyze and obtain rules from data and use the rules to predict unknown data.

[0172] (3) AI model;

[0173] An AI model is an algorithm or computer program that can implement AI functions. The AI model represents the mapping relationship between the input and output of the model. In other words, the AI model is a function model that maps inputs of a certain dimension to outputs of a certain dimension, and the parameters of the function model can be obtained through machine learning training. For example, f(x) = mx 2 + n is a quadratic function model, which can be regarded as an AI model. m and n are the parameters of this AI model, and m and n can be obtained through machine learning training. Exemplarily, the AI models mentioned in the embodiments below of this application are not limited to neural networks, linear regression models, decision tree models, support vector machines (SVM), Bayesian networks, Q - learning models, or other machine learning (ML) models.

[0174] The design of the AI model mainly includes a data collection phase (e.g., collecting training data and / or inference data), a model training phase, and a model inference phase. Further, it may also include an inference result application phase. In the aforementioned data collection phase, a data source is used to provide a training data set and inference data. In the model training phase, an AI model is obtained by analyzing or training the training data provided by the data source. Obtaining the AI model through learning at the model training node is equivalent to learning the mapping relationship between the input and output of the AI model using the training data. In the model inference phase, the AI model trained in the model training phase is used to perform inference based on the inference data provided by the data source to obtain an inference result. This phase can also be understood as: inputting the inference data into the AI model and obtaining an output through the AI model, and this output is the inference result. The inference result can indicate: configuration parameters used (executed) by an execution object, and / or operations executed by the execution object. In the inference result application phase, the inference result is published. For example, the inference result can be uniformly planned by an execution entity. For example, the execution entity can send the inference result to one or more execution objects (e.g., core network devices, access network devices, or terminal devices, etc.) for execution. Another example is that the execution entity can also feedback the performance of the AI model to the data source to facilitate subsequent implementation of updated training of the AI model.

[0175] It can be understood that the implementation of the AI model can be a hardware circuit, software, or a combination of software and hardware, without limitation. Non-limiting examples of software include: program code, program, subroutine, instruction, instruction set, code, code segment, software module, application program, or software application, etc.

[0176] (4) Neural network (NN);

[0177] The neural network is a specific implementation form of AI or machine learning. According to the universal approximation theorem, the neural network can theoretically approximate any continuous function, thus enabling the neural network to have the ability to learn any mapping.

[0178] The neural network can be composed of neural units. A neural unit can refer to an arithmetic unit with xs and an intercept of 1 as inputs. The neural network is a network formed by connecting many of the above single neural units together, that is, the output of one neural unit can be the input of another neural unit. The input of each neural unit can be connected to the local receptive field of the previous layer to extract the features of the local receptive field, and the local receptive field can be a region composed of several neural units.

[0179] Taking the type of the AI model as a neural network as an example, the AI model involved in this application can be a deep neural network (DNN). According to the construction method of the network, DNN can include feedforward neural network (FNN), convolutional neural networks (CNN), recurrent neural network (RNN), etc.

[0180] (5) Training data set and inference data;

[0181] In the field of machine learning, ground truth usually refers to data that is considered accurate or real data.

[0182] The training data set is used for the training of the AI model. The training data set can include the input of the AI model, or include the input and target output of the AI model. Among them, the training data set includes one or more pieces of training data. The training data can include the training samples input to the AI model, or can also include the target output of the AI model. Among them, the target output can also be referred to as a label, sample label or labeled sample. The label is the ground truth.

[0183] In the field of communication, the training data set can include simulation data collected through a simulation platform, or can also include experimental data collected in an experimental scenario, or can also include measured data collected in an actual communication network. Due to differences in the geographical environment and channel conditions where the data is generated, for example, differences in indoor, outdoor, moving speed, frequency band or antenna configuration, etc., when obtaining data, the collected data can be classified. For example, data with the same channel propagation environment and antenna configuration can be grouped into one category.

[0184] Model training essentially means learning certain features from the training data. During the process of training an AI model (such as a neural network model), since we hope that the output of the AI model is as close as possible to the value we really want to predict, we can compare the predicted value of the current network with the real target value, and then update the weight vector of each layer of the AI model according to the difference between the two. (Of course, there is usually an initialization process before the first update, that is, pre-configuring parameters for each layer in the AI model). For example, if the predicted value of the network is too high, we adjust the weight vector to make it predict lower, and keep adjusting until the AI model can predict the real target value or a value very close to the real target value. Therefore, it is necessary to pre-define "how to compare the difference between the predicted value and the target value", which is the loss function or the objective function. They are important equations used to measure the difference between the predicted value and the target value. Among them, taking the loss function as an example, the higher the output value (loss) of the loss function, the greater the difference. Then the training of the AI model becomes a process of minimizing this loss as much as possible, making the value of the loss function less than the threshold, or making the value of the loss function meet the target requirements. For example, if the AI model is a neural network, adjusting the model parameters of the neural network includes adjusting at least one of the following parameters: the number of layers of the neural network, the width, the weights of the neurons, or the parameters in the activation function of the neurons.

[0185] The inference data can be used as the input of the trained AI model for inference of the AI model. During the model inference process, when the inference data is input into the AI model, the corresponding output can be obtained, which is the inference result.

[0186] Figure 6 is an AI application framework.

[0187] In the foregoing data collection phase, the data source is used to provide training datasets and inference data. In the model training phase, an AI model is obtained by analyzing or training the training data provided by the data source. Among them, the AI model represents the mapping relationship between the input and output of the model. Obtaining the AI model through the model training node is equivalent to learning the mapping relationship between the input and output of the model using the training data. In the model inference phase, the AI model trained in the model training phase is used to perform inference based on the inference data provided by the data source, and an inference result is obtained. This phase can also be understood as: inputting the inference data into the AI model and obtaining the output through the AI model, and this output is the inference result. The inference result can indicate: the configuration parameters used (executed) by the execution object, and / or the operations performed by the execution object. In the inference result application phase, the inference result is published. For example, the inference result can be uniformly planned by the execution entity. For example, the execution entity can send the inference result to one or more execution objects (such as access network devices or terminal devices, etc.) for execution. Another example is that the execution entity can also feedback the performance of the model to the data source to facilitate subsequent implementation of model update training.

[0188] It can be understood that network elements with artificial intelligence capabilities can be included in the communication system. The above-mentioned phases related to AI model design can be executed by one or more network elements with artificial intelligence capabilities. In one possible design, AI capabilities (such as AI modules or AI entities) can be configured in existing network elements in the communication system to implement AI-related operations, such as training and / or inference of AI models. For example, the existing network element can be an access network device or a terminal device, etc. Or in another possible design, independent network elements can also be introduced in the communication system to perform AI-related operations, such as training AI models. This independent network element can be called an AI network element or an AI node, etc., and the embodiments of the present application do not limit this name. Exemplarily, the AI network element can be directly connected to network devices in the communication system, or can be indirectly connected to network devices through a third-party network element. Among them, the third-party network element can be a core network element such as an authentication management function (AMF) network element, a user plane function (UPF) network element, operation administration and maintenance (OAM), a cloud server, or other network elements, without limitation. Exemplarily, the independent network element can be deployed on one or more of the network device side, the terminal device side, or the core network side. Optionally, it can be deployed on a cloud server.

[0189] The training processes of different models can be deployed in different devices or nodes, or can also be deployed in the same device or node. The inference processes of different models can be deployed in different devices or nodes, or can also be deployed in the same device or node. Exemplarily, the model parameters of an AI model can include one or more of the following: structural parameters of the model (such as the number of layers of the model, and / or weights, etc.), input parameters of the model (such as input dimension, number of input ports), or output parameters of the model (such as output dimension, number of output ports). It can be understood that the input dimension can refer to the size of an input data. For example, when the input data is a sequence, the input dimension corresponding to the sequence can indicate the length of the sequence. The number of input ports can refer to the number of input data. Similarly, the output dimension can refer to the size of an output data. For example, when the output data is a sequence, the output dimension corresponding to the sequence can indicate the length of the sequence. The number of output ports can refer to the number of output data.

[0190] (6) Generative model;

[0191] A generative model refers to a model that can randomly generate observed data, especially under the condition of given certain hidden parameters. In machine learning (ML), a generative model can be used to directly model data (for example, sampling data according to the probability density function of a certain variable), or can also be used to establish the conditional probability distribution between variables. Among them, the conditional probability distribution can be formed by the generative model according to Bayes' theorem.

[0192] For example, the data generation method of the generative model includes the following steps:

[0193] a) Obtain the probability distribution model of the training samples according to the training sample data and a specific generative learning method;

[0194] b) Sample the obtained probability distribution model to obtain newly generated data samples;

[0195] The generative model represents the distribution of data from a statistical perspective and can reflect the similarity of the same type of data itself.

[0196] For example, the generative model includes but is not limited to: Naive Bayes method, Markov model, Gaussian Mixture Model (GMM), which are generally based on statistics and Bayesian theory.

[0197] For another example, generative models based on deep learning ideas include, but are not limited to: Variational AutoEncoder (VAE) and Generative Adversarial Networks (GAN). For the sake of easy understanding and description, taking the generative models GMM and VAE as examples, the technical solutions of this application will be illustrated by examples.

[0198] (7) Gaussian Mixture Model (GMM);

[0199] GMM is a generative model, which can be regarded as a model composed of K single Gaussian models (which can also be called sub - distributions), where K is an integer greater than or equal to 1. These K single Gaussian models are the latent variables of the mixture model. Generally speaking, a mixture model can use any probability distribution. Here, the Gaussian mixture model GMM is used because the Gaussian distribution has good mathematical properties and good computational performance.

[0200] For example, the definition of a single Gaussian model can be: when the sample data X is one - dimensional data, the probability density function satisfied by the Gaussian distribution is as follows:

[0201]

[0202] where μ is the mean of the data, and σ is the standard deviation of the data;

[0203] When the sample data X is multi - dimensional data, the probability density function satisfied by the Gaussian distribution is as follows:

[0204]

[0205] where μ is the mean of the data, ∑ is the covariance of the data, and D is the dimension of the data.

[0206] For example, the probability distribution of the Gaussian mixture model GMM satisfies:

[0207]

[0208] where, generally, a complete mixture Gaussian model includes a covariance matrix, a parameter mean vector, and a mixing weight, which can be represented as θ, that is, θ=(r k ,σ k ,p k ), r k 、σ k 、p k respectively represent the expectation (or mean), variance (or covariance), and probability of occurrence in the mixture model (which can be called weight) of the k - th single Gaussian model.

[0209] (8) Variational AutoEncoder (VAE);

[0210] VAE is a generative model, a structure composed of an encoder and a decoder, which is trained to minimize the reconstruction error between the output data after passing through the encoder and decoder and the initial input data. Optionally, in order to introduce certain regularization in the latent space, VAE can modify the encoding-decoding process, that is, encode the input data into a probability distribution in the latent space instead of a single point in the latent space. The specific implementation methods include the following steps:

[0211] a) Encode the input into a distribution on the latent space;

[0212] b) Sample a point in the latent space from this distribution;

[0213] c) Decode the sampled point and calculate the reconstruction error;

[0214] d) The reconstruction error is backpropagated through the network.

[0215] It should be noted that after encoding by the variational autoencoder, the feature of each measurement result is no longer a single value in the variational autoencoder but a probability distribution. VAE can establish two probability density distribution models using two neural networks: one for variational inference of the original input data, that is, generating the variational probability distribution of the latent variable, called the inference network; the other restores and generates the approximate probability distribution of the original data according to the variational probability distribution of the generated latent variable, called the generation network.

[0216] (9) Generative Adversarial Networks (GAN);

[0217] GAN is a typical unsupervised learning method that can automatically extract features and complete data generation. GAN consists of two important parts. One is the generator (generating data through a neural network), which is used to generate data as similar as possible to the original data and deceive the discriminator; the other is the discriminator (judging whether the data is real or machine-generated through a neural network), which is used to find the "fake data" generated by the generator.

[0218] The essence of GAN is actually to utilize the powerful nonlinear fitting ability of the neural network to learn the nonlinear mapping from an arbitrary prior noise distribution to the real data distribution, so that the generator has the ability to generate realistic samples. Among them, the input of GAN is an arbitrary noise distribution, and through the supervision of the original training data, the final data generation is completed.

[0219] (10) Expectation Maximization (EM) algorithm;

[0220] The EM algorithm is an iterative optimization strategy that can solve the parameter estimation problem in the case of missing data. Its basic idea is as follows: First, based on the given observed data, estimate the values of the model parameters; then, based on the values of the model parameters, estimate the values of the missing data, and then, according to the values of the missing data plus the given observed data, re-estimate the values of the model parameters. Repeat the iteration until convergence, and the iteration ends.

[0221] (11) Time difference of arrival (TDoA): A positioning method that uses time differences.

[0222] Figure 7 It is a schematic diagram of TDoA positioning. As Figure 7 shown, assume that the distance between network device #1 and the terminal device is d1, and the transmission time when network device #1 transmits a signal to the terminal device is t1; the distance between network device #2 and the terminal device is d2, and the transmission time when network device #2 transmits a signal to the terminal device is t2; the distance between network device #3 and the terminal device is d3, and the transmission time when network device #3 transmits a signal to the terminal device is t3.

[0223] In TDoA, as an example, multiple network devices can send reference signals, such as positioning reference signals (PRS), to the terminal device. The terminal device determines its position by measuring the TDoA of the reference signal. Among them, the TDoA of the reference signal can also be called the reference signal time difference (RSTD). For Figure 7For example, assume that the reference signal sent by network device #1 to the terminal device is P1, the reference signal sent by network device #2 to the terminal device is P2, and the reference signal sent by network device #3 to the terminal device is P3. The terminal device measures the TDoA of P2 and P1, that is, t2 - t1. Using t2 - t1, the distance difference d2 - d1 between network device #2 and network device #1 can be inferred, and a curve is obtained. Each point on this curve satisfies that the distance difference to network device #2 and network device #1 is d2 - d1. Similarly, the terminal device measures the TDoA of P3 and P1, that is, t3 - t1. Using t3 - t1, the distance difference d3 - d1 between network device #3 and network device #1 can be inferred, and another curve is obtained. Each point on this curve satisfies that the distance difference to network device #3 and network device #1 is d3 - d1. Using the intersection point of the above two curves, the position of the terminal device can be determined, which can be represented by the mathematical model as shown in formula (4):

[0224]

[0225] where (a i , b i ) represents the position coordinates of network device #i. (a, b) represents the position coordinates of the terminal device to be determined. For example, a represents the position coordinate of the terminal device to be determined on the X-axis, b represents the position coordinate of the terminal device to be determined on the Y-axis, and c represents the speed of light.

[0226] Due to the existence of a certain synchronization error between different network devices, the corresponding measurement values also have a certain degree of uncertainty.

[0227] Combined with the above Figure 7 The method introduced is for positioning based on the network device sending a reference signal to the terminal device, which can also be called downlink TDoA (DL-TDoA) or observed time difference of arrival OTDoA. Similarly, positioning can also be performed according to the terminal device sending a reference signal, such as SRS, to the network device, and this positioning method can be called uplink TDoA (UL-TDoA).

[0228] It can be understood that in addition to measuring the time difference, positioning can also be performed by measuring the angle. The angle can be the angle of arrival (AoA), or it can also be the angle of departure (AoD). The angle of arrival is used to represent the angle between the direction of the received signal at the receiving end and the reference direction. The angle of departure is used to represent the angle between the direction of the transmitted signal at the transmitting end and the reference direction, where the reference direction can be determined according to the position and / or shape of the antenna.

[0229] In an actual communication scenario, due to the influence of noise and interference, there are certain measurement errors in the measured values of time or angle, and there will also be certain errors in the corresponding positioning results.

[0230] Figure 8 It is a schematic diagram of line of sight (LoS) and non-line of sight (non-LoS, NLoS). As Figure 8 shown, the LoS between the network device and the terminal device (such as Figure 8 the dotted line in it) is blocked by an obstacle. The reference signal transmitted between the network device and the terminal device is actually the reflected NLoS (such as Figure 8 the solid line in it). It can be seen from the figure that the distance of NLoS (i.e., d2 + d3) is greater than the distance of LoS (i.e., d1). If NLoS is regarded as LoS during position estimation, large measurement errors may occur. Therefore, the classification of LoS and NLoS is also very important for positioning accuracy.

[0231] In the AI-based positioning technology, the AI positioning model is usually deployed in the LMF. The AI positioning model takes the channel measurement results reported by the channel measurement network element as input and the position of the terminal device as output. Therefore, the channel measurement network element usually needs to report the channel measurement report to the location management function network element. For example, in downlink positioning, the gNB sends PRS to the UE, the UE measures the PRS sent by the gNB, obtains the GMM of the DL-RSTD distribution, and sends the relevant parameters of the GMM to the LMF for the LMF to position the UE.

[0232] Figure 9 It shows a schematic diagram of the probability distribution of the Gaussian mixture model (which can be called the Gaussian mixture distribution) corresponding to different model fitting configurations.

[0233] Among them, Figure 9 (a) of it represents the true value of the Gaussian mixture distribution of the data. This Gaussian mixture model includes 3 single Gaussian models. Specifically, Figure 9 (b) of it represents the Gaussian mixture distribution obtained by convergence with the number of single Gaussian models being 2 and the maximum number of iterations being 10, Figure 9 (c) of it represents the Gaussian mixture distribution obtained by convergence with the number of single Gaussian models being 2 and the maximum number of iterations being 50, Figure 9 (d) of it represents the Gaussian mixture distribution obtained by convergence with the number of single Gaussian models being 3 and the maximum number of iterations being 50. It can be seen that the more the number of single Gaussian models and the maximum number of iterations, the closer it is to the true value of the Gaussian mixture distribution. For the same Gaussian mixture distribution, the results obtained by convergence with different model fitting configurations may be different, that is, the GMMs fitted by the UE with different model fitting configurations are quite different, which may lead to poor positioning accuracy of the UE. Therefore, how to improve the positioning accuracy of the UE is an urgent problem to be solved.

[0234] Based on this, an embodiment of the present application provides a communication method and a communication device. By means of the configuration information of the alignment generation model between the core network element and the first device, it can be known that the first model for fitting / training used for terminal device positioning is the same. Furthermore, the analysis and application of the probability distribution of the measurement results of the measurement quantities based on this first model are more accurate, and thus the positioning accuracy of the terminal device can be improved.

[0235] The following will describe in detail the communication method provided by the embodiment of the present application with reference to the accompanying drawings. The embodiments provided by the present application can be applied to the Figure 1 or Figure 2 communication system shown above, without limitation.

[0236] Figure 10 is a schematic flowchart of a communication method 1000 provided by an embodiment of the present application. As Figure 10 shown, the method 1000 may include the following multiple steps. It should be understood that this method may be executed by the first device and the core network element, or may also be executed by the chip, chip system or circuit of the first device and the core network element. The present application does not limit this. For the sake of convenience of description, the following takes the first device and the core network element as the execution entities for example. It should be noted that the training / fitting of the model in this implementation manner occurs on the first device side, and the model inference / usage occurs on the core network element side, or the training / fitting of the model occurs on the OTT or third-party device or cloud device side, and the model inference / usage occurs on the OTT or third-party device or cloud device side. The present application does not limit this.

[0237] S1010, the core network element sends configuration information to the first device. Correspondingly, the first device receives the configuration information from the core network element.

[0238] Among them, the configuration information is used to indicate the configuration parameters of the generation model.

[0239] In one example, the first device may be a terminal device or an access network device, and the core network element may be a positioning node or a positioning device for managing the location of the terminal device, such as a location management function network element (such as LMF). Among them, the first device may also be referred to as the network element that performs channel measurement, or the channel measurement network element, or the reference signal measurement node, etc. The present application does not limit its name.

[0240] Exemplarily, the generation model is any one of the following:

[0241] (1) Gaussian mixture model GMM, and the specific interpretation can refer to the above relevant description.

[0242] (2) Variational autoencoder VAE, and the specific interpretation can refer to the above relevant description.

[0243] (3) Generative Adversarial Network (GAN), for specific interpretations, reference can be made to the relevant descriptions above.

[0244] In the first implementation manner, when the generative model is a Gaussian Mixture Model (GMM), the configuration parameters include one or more of the following:

[0245] (1) The maximum value M of the number of single Gaussian models included in the GMM, where M is a positive integer;

[0246] Exemplarily, if the upper limit of the number of single Gaussian models included in the GMM is 5, it means that the number of single Gaussian models included in the GMM is less than or equal to 5, that is, M ≤ 5. For example, the number of single Gaussian models included in the GMM can be 2 or 3.

[0247] (2) The generation method of the GMM;

[0248] Exemplarily, the generation method of the GMM can be the Expectation - Maximization (EM) algorithm. The EM algorithm is an iterative algorithm used for maximum likelihood estimation or maximum a posteriori probability estimation of probability parameter models containing hidden variables. For example, the first device first estimates the values of the model parameters based on the given observed data; then estimates the values of the missing data according to the values of the model parameters, and then re - estimates the values of the model parameters based on the values of the missing data plus the given observed data, and repeats the iteration until convergence, at which point the iteration ends.

[0249] (3) The convergence threshold of the GMM;

[0250] Exemplarily, assuming the convergence threshold is p, it can be understood that the convergence value of the GMM trained or fitted by the first device is less than or equal to p. For example, p = 0.01. For example, the convergence value of the GMM trained by the first device is q, and q is less than or equal to p, where both p and q are positive numbers.

[0251] (4) The maximum value of the number of iterations of the GMM;

[0252] Exemplarily, if the upper limit of the number of iterations is 50, it means that during the process of the first device training or fitting the GMM, the number of iterations can be less than or equal to 50. For example, the number of iterations can be 10, or 20, or 50. It should be understood that to a certain extent, the larger the number of iterations, the better the convergence effect.

[0253] (5) The model parameters of the GMM;

[0254] Exemplarily, the model parameters of the GMM include one or more of the following: the expectations r of the k single Gaussian models included in the GMM k and the variances or covariances σ of the k single Gaussian models k and the proportions p of the k single Gaussian models in the GMMk , for the specific interpretation, reference can be made to the relevant description of the above formula (3).

[0255] (6) The maximum value N of the expected values of the single Gaussian models included in the GMM, where N is a positive number;

[0256] Exemplarily, assume that the GMM contains 3 single Gaussian models, then the expected values of these 3 single Gaussian models are all less than or equal to N.

[0257] (7) The maximum value A of the variances or covariances of the single Gaussian models included in the GMM, where A is a positive number;

[0258] Exemplarily, assume that the GMM contains 3 single Gaussian models, then the variances or covariances of these 3 single Gaussian models are all less than or equal to A.

[0259] (8) The proportion X of one or more single Gaussian models included in the GMM in the GMM.

[0260] Exemplarily, assume that the GMM contains 3 single Gaussian models, then the proportions of these 3 single Gaussian models in the entire GMM are all less than or equal to X.

[0261] In the second implementation manner, when the generative model is a VAE, the configuration parameters include one or more of the following:

[0262] (1) The values of the model parameters of the VAE;

[0263] Among them, the model parameters of the VAE include one or more of the following: the weights of the neurons, the activation functions of the neurons, or the biases in the activation functions of the neurons. Herein, the bias in the activation function can also be referred to as the bias of the neural network. It should be understood that the model parameters of the VAE refer to the trained model parameters, that is, the first device can determine the trained VAE based on the model parameters of the VAE.

[0264] Exemplarily, assume that the input of the neuron is x = [x 0 , x 1 , …, x n , and the corresponding weights are respectively w = [w, w 1 , …, w n , and the bias of the weighted sum is b. Among them, b can be an integer, a decimal, or a complex number, etc., with various possible values. The form of the activation function can be diversified. For example, assume that the activation function of the neuron is: y = f(z) = max(0, z), then the output of the neuron is: For another example, assume that the activation function of the neuron is: y = f(z) = z, then the output of the neuron is: The activation functions of different neurons in the neural network can be the same or different.

[0265] (2) Structural parameters of the VAE;

[0266] Exemplarily, the structural parameters of the VAE include one or more of the following: the number of neural network layers used by the VAE, the number of neurons included in the neural network used by the VAE, parameters related to the input layer of the VAE, i.e., input parameters, parameters related to the hidden layer of the VAE, or parameters related to the output layer of the VAE, i.e., output parameters. For specific interpretations, reference can be made to the descriptions related to the above AI model.

[0267] It should be understood that the neural network used by the VAE can include a multi-layer structure, and each layer can include one or more logical judgment units, which can be referred to as neurons. For example, the neural network includes an input layer and an output layer. After the input received by the input layer of the neural network is processed by the neurons, the result is passed to the output layer, and the output result of the neural network is obtained by the output layer. For another example, the neural network includes an input layer, a hidden layer, and an output layer. After the input received by the input layer of the neural network is processed by the neurons, the result is passed to the intermediate hidden layer, and the hidden layer then passes the calculation result to the output layer or the adjacent hidden layer, and finally the output result of the neural network is obtained by the output layer. A neural network can include one or more sequentially connected hidden layers, without limitation.

[0268] Exemplarily, the number of neural network layers used by the VAE can be referred to as the depth of the neural network. By increasing the depth of the neural network, the expression ability of the neural network can be improved, providing a more powerful information extraction and abstract modeling ability for complex systems.

[0269] Exemplarily, the neural network used by the VAE includes a multi-layer structure, and the number of neurons included in each layer can be referred to as the width of that layer. By increasing the width of the neural network, the expression ability of the neural network can be improved, providing a more powerful information extraction and abstract modeling ability for complex systems.

[0270] Exemplarily, the input dimension of the VAE can refer to the size of an input data. For example, when the input data is a sequence, the input dimension corresponding to the sequence can indicate the length of the sequence. The output dimension of the VAE can refer to the size of an output data. For example, when the output data is a sequence, the output dimension corresponding to the sequence can indicate the length of the sequence. Or rather, the VAE can represent the mapping relationship between the input and output of the model, or rather, the VAE is a function model that maps an input of a certain dimension to an output of a certain dimension.

[0271] (3) The type of neural network used by the VAE;

[0272] Exemplarily, the type of neural network used by the VAE can be a deep neural network DNN or other neural networks. Among them, the DNN can include one or more of the following: a feedforward neural network FNN, a convolutional neural network CNN, or a recurrent neural network RNN.

[0273] It should be noted that this application does not specifically limit the number of configuration parameters, the sending method, or the sending timing. In addition, it can be understood that the configuration parameters not indicated can be predefined by the protocol or obtained in other ways, which are not limited herein.

[0274] Optionally, the configuration parameters include a first configuration parameter and a second configuration parameter. Among them, the configuration information is used to indicate the first configuration parameter and the second configuration parameter; or, the configuration information is used to indicate a part of them (for example, the first configuration parameter or the second configuration parameter), and the other part of the configuration parameters can be determined according to the mapping relationship, where the mapping relationship is the corresponding relationship between the first configuration parameter and the second configuration parameter. This implementation method can save signaling overhead. That is to say, the configuration information in the embodiments of this application can indicate all the configuration parameters of the generation model or a part of the configuration parameters of the generation model. This application does not make any limitations in this regard.

[0275] In this application, the mapping relationship between the first configuration parameter and the second configuration parameter can be predefined. Predefinition can include pre - definition, such as protocol definition; or, the mapping relationship can be configured by signaling or pre - configured. Pre - configuration can be implemented by pre - saving the corresponding code, table, or other means that can be used to indicate relevant information in the device. This application does not limit its specific implementation method.

[0276] Optionally, the mapping relationship can exist in the form of a table, function, text, or string, such as for storage or transmission.

[0277] Next, an example of the mapping relationship between the first configuration parameter and the second configuration parameter is given in the form of a table. As shown in Table 1, taking GMM as an example, assuming that the configuration information is used to indicate the first configuration parameter, that is, the maximum value M of the number of single - Gaussian models included in GMM is 5, and the generation method of GMM is the EM algorithm, then according to the mapping relationship shown in Table 1, the convergence threshold p of GMM can also be determined to be 0.01, and the maximum number of iterations of GMM is 100. Based on these configuration parameters, a specific GMM, that is, the first model, can be fitted. Taking VAE as an example, assuming that the configuration information is used to indicate the second configuration parameter, that is, the number of neural network layers used by VAE is 5 layers, and the number of neurons included in the neural network used by VAE is 1, then according to the mapping relationship shown in Table 1, it can also be determined that the neural network used by VAE is DNN. Based on these configuration parameters, a specific VAE, that is, the first model, can be fitted.

[0278] Optionally, the present application does not limit the number of the first configuration parameters and the second configuration parameters corresponding to each generation model in Table 1.

[0279] Table 1

[0280]

[0281] It should be understood that the mapping relationship between the first configuration parameter and the second configuration parameter of the GMM shown in Table 1 above, and the mapping relationship between the first configuration parameter and the second configuration parameter of the VAE can be implemented independently or in combination. For example, one row corresponding to the GMM in Table 1 and one row corresponding to the VAE can be respectively shown in two tables, and the present application does not limit this.

[0282] It should be understood that Table 1 above is only an example given for easy understanding and should not constitute any limitation to the technical solution of the present application.

[0283] Optionally, the configuration parameter includes a first configuration parameter and a second configuration parameter. The first device receives configuration information from a core network element, including: the first device receives configuration information from the core network element through a first signaling; wherein, the configuration information of the first configuration parameter is carried in the first part of the first signaling, and the configuration information of the second configuration parameter is carried in the second part of the first signaling.

[0284] Optionally, the first device receives configuration information from the core network element through the first signaling, including: the first device receives the first configuration parameter from the core network element through the first part of the first signaling at a first moment, and the first device receives the second configuration parameter from the core network element through the second part of the first signaling at a second moment; wherein, the first moment and the second moment are the same, or the first moment and the second moment are different.

[0285] Optionally, the method further includes: the first device obtains the type and / or function of the generation model.

[0286] In one implementation, before performing the above step S1010, the first device obtains the type and / or function of the generation model. Wherein, the type and / or function of the generation model can be dynamically configured (dynamic configured) by the core network element to the first device through signaling or messages, or can also be pre-configured (pre-configured). For example, corresponding codes, tables or other ways that can be used to indicate the type and / or function of the generation model can be pre-saved in the first device, and the present application does not limit its implementation manner.

[0287] Optionally, the type of the generation model can be any of the following: GMM, VAE, GAN. For specific interpretations, please refer to the relevant descriptions above.

[0288] Optionally, the function of the generation model can be: for terminal device positioning or for image recognition, etc. For example, when positioning a terminal device, the function of the generation model can be to output the probability distribution of the position coordinate information of the terminal device, or to output the probability distribution of the measurement results of the measurement quantities for terminal device positioning, etc.

[0289] Exemplarily, based on the type and / or function of the generation model, the first device can determine a GMM for terminal device positioning. That is to say, the first device can determine through the type and / or function of the generation model that the generation model to be trained or fitted is a GMM, and use the trained or fitted GMM to position the terminal device.

[0290] S1020, the first device processes the channel measurement results using the first model to obtain the probability distribution of the measurement results of the measurement quantities for terminal device positioning.

[0291] Among them, the first model is determined based on the configuration parameters of the generation model. It can be understood that: the first device can fit or train the first model according to the obtained configuration parameters of the generation model. If the generation model is a GMM, it means that the first device can train or fit a specific GMM or a certain type of GMM according to the configuration parameters. That is, at this time, the first model is a trained GMM model and can be used for terminal device positioning. For example, the first model is a GMM obtained by iterating 50 times using the EM algorithm, and the trained GMM can include 3 single Gaussian models.

[0292] Exemplarily, the measurement quantity can include one or more of the following:

[0293] (1) RSTD;

[0294] Exemplarily, RSTD can also be referred to as TDoA. For specific interpretations and implementation methods, please refer to the relevant descriptions above Figure 7 above.

[0295] (2) TDoA;

[0296] Exemplarily, TDoA is a positioning method using time difference. For specific implementation methods, please refer to the relevant descriptions above Figure 7 above.

[0297] (3) ToA;

[0298] Exemplarily, ToA is a method that calculates the physical distance by using the transmission delay of a wireless signal between two nodes, that is, the position is determined by measuring the time interval from sending the signal to receiving the signal, and usually the receiving node needs to synchronize the timing.

[0299] (4) AoA;

[0300] Exemplarily, by using a single antenna to send a data packet with a direction finding function, a low energy (LE) device can make its direction available to a peer device. The peer device includes a radio frequency switch and an antenna array, switches the antenna when receiving a partial data packet, and acquires in-phase quadrature (IQ) signal samples. The IQ signal samples can be used to calculate the phase difference of the radio signals received by different elements of the antenna array, and further can be used to estimate the angle of arrival AoA.

[0301] (5) LoS probability;

[0302] Exemplarily, the LoS probability is used to judge the NLoS degree of the channel environment. Different NLoS degrees (that is, different channel conditions) have different influences on the inference accuracy of the generation model. Therefore, the first device can know the NLOS degree of the channel based on the channel measurement result, and thus know the current channel condition. For example, LOS(1) or NLOS(0) can be used to indicate that the LOS between the network device and the terminal device is not blocked by an obstacle, and the reference signal transmitted between the network device and the terminal device is not affected.

[0303] It should be noted that LOS means that the transmitting antenna and the receiving antenna transmit signals at a distance where they "can see each other". It can be understood that there are no obstacles affecting signal propagation between the two antennas, and the signal can be completely transmitted. Non-line-of-sight NLoS means that the transmitting antenna and the receiving antenna transmit signals at a distance where they "cannot see each other". It can be understood that there are obstacles affecting signal propagation between the two antennas, and the signal cannot be completely transmitted.

[0304] It should be understood that the above RSTD, TDoA, ToA, AoA, and LoS probability can be regarded as measurement quantities of a channel measurement. A channel can include one or more paths (for example, a path set). For example, the LoS probability can be the average LoS probability of the line-of-sight recognition results corresponding to all paths in a channel. The ToA estimation result can be the average of the arrival times corresponding to all paths in a channel. The AoA estimation result can be the average of the angles of arrival corresponding to all paths in a channel, etc.

[0305] It should be noted that the measurement quantity in the embodiments of the present application may be one or more of the above parameters (1)-(5). Correspondingly, the channel measurement result may also be one or more, and the probability distribution of the measurement results of the measurement quantities for terminal device positioning may also be one or more. The present application does not limit this.

[0306] In a possible implementation manner of the embodiments of the present application, the channel measurement result is based on the measurement of a reference signal.

[0307] In the first example, the channel measurement result may be obtained by a first device measuring a reference signal.

[0308] For example, in an uplink positioning scenario, when the first device is a network device, the channel measurement result is based on a first channel measurement, and the first channel measurement includes: the network device measures the sounding reference signal (such as SRS) from the terminal device.

[0309] For another example, in a downlink positioning scenario, when the first device is a terminal device, the channel measurement result is based on a second channel measurement, and the second channel measurement includes: the terminal device measures the positioning reference signal (such as PRS, or preamble) or the channel state information reference signal (CSI-RS) from the access network device;

[0310] For yet another example, in a sidelink positioning scenario, when the first device is a first terminal device, the channel measurement result is based on a third channel measurement, and the third channel measurement includes: the first terminal device measures the sidelink-positioning reference signal (SL-PRS) from the second terminal device.

[0311] In the second example, the channel measurement result may also be obtained by other devices (such as network elements performing channel measurement, or channel measurement network elements, or other devices such as reference signal measurement nodes) measuring the reference signal. The present application does not limit this.

[0312] It should be noted that the embodiments of the present application mainly take the channel measurement based on the reference signal to obtain the channel measurement result as an example for illustration, and this is not limited. For example, the channel measurement result may also include the result of the pedestrian dead-reckoning (PDR) measurement of the terminal; or, the channel measurement result may also include the camera environment monitoring and recognition result, such as the camera environment monitoring and recognition result of the indoor factory.

[0313] In the embodiments of the present application, the measurement quantity corresponds to the channel measurement result. It can be understood that: the first device measures one or more measurement quantities corresponding to the reference signal to obtain the channel measurement result, and this measurement result is the measurement result of the one or more measurement quantities. For example, taking the downlink positioning scenario as an example, assuming the measurement quantity is TDoA, the first device is a terminal device, the network device #1 can send a reference signal, such as PRS #1, to the terminal device, and the network device #2 can send PRS #2 to the terminal device. Correspondingly, the terminal device can measure the TDoA of PRS #1 and PRS #2, such as t2 - t1, which can be used as the channel measurement result #1, where t1 represents the transmission time when the network device #1 transmits a signal to the terminal device, and t2 represents the transmission time when the network device #2 transmits a signal to the terminal device. Optionally, the network device #1 and the network device #2 can send PRS multiple times, or the network device #3 can also send PRS #3 to the terminal device. Correspondingly, the terminal device can measure the TDoA of PRS #2 and PRS #3, such as t3 - t2, which can be used as the channel measurement result #2, where t3 represents the transmission time when the network device #3 transmits a signal to the terminal device, and so on.

[0314] Next, an example is given for the first device to process the channel measurement result using the first model to obtain the probability distribution of the measurement result of the measurement quantity for terminal device positioning.

[0315] In one example, the first device can use the channel measurement result (or the channel feature extracted from the channel measurement result, this application takes the channel measurement result as an example for illustration) as the input of the first model, and the output of the first model is used to directly or indirectly determine the position of the terminal device. For example, the output of the first model can be the probability distribution of the measurement result of the measurement quantity for terminal device positioning.

[0316] For example, when the generative model is GMM, the first model can be a GMM fitted or trained according to the configuration parameters of GMM. Then the first device can use the above-obtained channel measurement result #1 and channel measurement result #2 as the inputs of GMM respectively, and correspondingly obtain the probability distribution of GMM of TDoA for terminal device positioning.

[0317] For another example, when the generative model is VAE, the first model can be a VAE fitted or trained according to the configuration parameters of VAE. Then the first device can use the above-obtained channel measurement result #1 and channel measurement result #2 as the inputs of VAE respectively, and correspondingly obtain the variational probability distribution of TDoA for terminal device positioning.

[0318] It should be noted that the number of channel measurement results corresponding to a certain measurement quantity, which is the input of the first model in the embodiments of the present application, is not specifically limited. Optionally, generally, it can be understood that the more the number of channel measurement results, the more accurate the probability distribution of the measurement results of the corresponding measurement quantity, and thus the higher the positioning accuracy of the terminal device.

[0319] Exemplarily, taking the estimated value of the distance between two nodes (that is, the product of the transmission delay ToA between two nodes and the electromagnetic wave propagation speed) as an example, where the propagation speed of electromagnetic waves in free space is equal to the speed of light, with a speed of c = 299792458 m / s ≈ 3×10 8 m / s. The Gaussian Mixture Model (i.e., the first model) is used to describe the probability distribution of the distance estimate.

[0320] For example, a commonly used estimation algorithm is maximum likelihood (ML) estimation. A set of vector sequences of distance estimate values for training can be set as X = {x 1 , x 2 , …, x N}, which includes distance estimate values in LOS and NLOS environments, and N is an integer greater than 1. The probability density function of the distance estimate value under line-of-sight conditions satisfies: x = x 1 , x 2 , …, x N , where r LOS represents the true distance in the line-of-sight environment, is a fixed value in a stable indoor environment. In the case of no large deviation, this value is set to 0. represents the variance in the line-of-sight environment, that is, the distance estimate value x follows a Gaussian distribution. At this time, represents the mean of this Gaussian distribution, represents the variance of this Gaussian distribution. The probability density function of the distance estimate value x in the non-line-of-sight environment satisfies: where, that is, the distance estimate value x follows a Gaussian distribution. At this time, r NLOS represents the mean of this Gaussian distribution, represents the variance of this Gaussian distribution. From this, the probability density function of a K-order Gaussian mixture model can be obtained, expressed as:

[0321]

[0322] where, θ = (r k , σ k , p k ), represents the N-dimensional joint Gaussian probability distribution of the k-th single Gaussian model, σk is the variance or covariance matrix of the kth single Gaussian model, r k is the expected value of the kth single Gaussian model, indicating the estimated distance between two nodes, p k is the weight of the k-th single Gaussian model in the mixed Gaussian model,

[0323] It should be understood that both the probability distribution function and the probability density function are functions that describe the probability of a random variable within a certain interval. Assuming that F(x) is the probability distribution function of the random variable X, and f(x) is the probability density function of X, then F(x) = ∫f(x)dx. Among them, the probability distribution function represents the probability of a random variable taking a value within a certain interval, and the probability density function represents the probability density of a random variable taking a value at a certain point.

[0324] For example, using the EM algorithm to iterate the estimated values ​​of the weight, expectation, variance or covariance of the k-th single Gaussian model in GMM, we can get:

[0325] The iterative formula for the estimated value of the weight is:

[0326] The iterative formula for estimating the expected value is:

[0327] The iterative formula for estimating the variance or covariance is:

[0328] Among them, p(k丨n) in the above three formulas is the posterior probability, which can be expressed as:

[0329]

[0330] It should be understood that the EM algorithm can better solve the problem of estimating the parameters of the Gaussian mixture model of the training samples using the maximum likelihood algorithm. After collecting a large number of distance measurements, the distance estimation value is obtained through the EM algorithm to realize the location estimation of the terminal device.

[0331] Optionally, after executing the above step S1020, the method 1000 also includes step S1030.

[0332] S1030, the first device sends first information to a core network network element, and correspondingly, the core network network element receives the first information from the first device.

[0333] The first information is used to indicate the probability distribution of the measurement results of the measurement quantity used for terminal device positioning.

[0334] In the first example, when the generative model is a GMM, the first information may indicate the GMM of the measurement quantities for terminal device positioning. For example, assuming that the GMM includes k single Gaussian models, where k is an integer greater than or equal to 1, the first information may include one or more of the following:

[0335] (1) The values of the k expected values ;

[0336] (2) The values of the k variances or covariances ;

[0337] (3) The proportions of the k single Gaussian models in the Gaussian mixture model ;

[0338] Among them, the k expected values, the k variances or covariances correspond one-to-one with the k single Gaussian models. For specific interpretations, reference can be made to the relevant descriptions of formula (3) above.

[0339] It should be understood that the above parameters (1)-(3) can be specific numerical values, where and are both positive numbers. Based on the parameters (1)-(3) included in the first information, the core network element can determine the first model fitted or trained by the first device, that is, the GMM. Furthermore, based on the analysis and application of the probability distribution of the measurement results of the measurement quantities by the first model, it can be more accurate, thereby improving the positioning accuracy of the terminal device.

[0340] In the second example, when the generative model is a VAE, the first information may indicate the variational probability distribution of the measurement quantities for terminal device positioning. At this time, the first information may include one or more of the following:

[0341] (1) The values of the model parameters of the VAE;

[0342] Exemplarily, the values of the model parameters of the VAE may include one or more of the following: the weights w = [w, w 1 , …, w n of the neurons, or the bias in the activation function of the neurons (or the bias of the neural network) b.

[0343] (2) The values of the probability distribution output by the VAE.

[0344] Exemplarily, the value taken by the probability distribution output by the VAE can be the value taken by the probability distribution of the measurement result of a certain measurement quantity. For example, when the measurement quantity is TDoA, the values taken by the probability distribution output by the VAE can include the values x, y, z of the probability distributions corresponding to t2 - t1, t3 - t2, t3 - t1. Among them, t2 - t1 can represent the channel measurement result #1 obtained by the terminal device measuring the TDoA of PRS#1 and PRS#2, t3 - t2 represents the channel measurement result #2 obtained by the terminal device measuring the TDoA of PRS#3 and PRS#2, t3 - t1 represents the channel measurement result #3 obtained by the terminal device measuring the TDoA of PRS#3 and PRS#1, and PRS#1, PRS#2, and PRS#3 can be the reference signals sent by network device #1, network device #2, and network device #3 to the terminal device respectively.

[0345] It should be understood that the above parameters (1)-(2) can be specific numerical values, where, w, w 1 ,…,w n , b, x, y, and z are all positive numbers. Based on the parameters (1)-(2) included in the first information, the core network element can determine the first model, that is, the VAE, fitted or trained by the first device. Furthermore, based on the analysis and application of the probability distribution of the measurement result of the measurement quantity by the first model, it can be more accurate, thereby improving the positioning accuracy of the terminal device.

[0346] Optionally, after performing the above step S1030, the method 1000 further includes step S1040.

[0347] S1040, the core network element determines the location of the terminal device according to the probability distribution of the measurement result of the measurement quantity and the configuration parameters of the generation model.

[0348] In the first example, assuming that the generation model is GMM, the core network element can determine the location of the terminal device according to the GMM distribution of the measurement quantity and the configuration information of the generation model.

[0349] Exemplarily, taking the uplink positioning scenario as an example, P first devices (such as network devices) can respectively send the GMM probability distributions of P measurement quantities for terminal device positioning to the core network element. Optionally, for the same measurement quantity (such as TDoA), the number of single Gaussian models included in each GMM probability distribution corresponding to the first model (i.e., GMM) among the P GMM probability distributions may be different, and the values of the expected value, variance, or covariance of each single Gaussian model, and the proportion values in the entire GMM may be different.

[0350] For example, assume that the number k of single Gaussian models included in each GMM corresponding to each GMM distribution is the same, i.e., k = 3, and the first information reported by each network device includes the values of the expected values and variances of the 3 single Gaussian models. Then, the core network element can obtain the average values of the expected values and variances of the 3 single Gaussian models by averaging the values of the expected values and variances of the 3 single Gaussian models in the P pieces of first information, and further obtain a more accurate estimated value of TDoA based on the average values of the expected values and variances. Alternatively, the core network element can also calculate the estimated value of TDoA corresponding to the first information reported by each network device respectively, and then average all the estimated values of TDoA to obtain the final estimated value of TDoA, and perform more accurate positioning of the terminal device according to the estimated value of TDoA. This application does not make any limitation in this regard.

[0351] As another example, assume that P = 2. The number of single Gaussian models corresponding to the GMM probability distribution sent by network device #1 to the core network element is 3, and the first information reported by network device #1 includes the values of the expected values and variances of these 3 single Gaussian models. The number of single Gaussian models corresponding to the GMM probability distribution sent by network device #2 to the core network element is 2, and the first information reported by network device #2 includes the values of the expected values and variances of these 2 single Gaussian models. Then, the core network element can obtain the estimated value of TDoA#1 based on the values of the expected values and variances of the 3 single Gaussian models reported by network device #1, and obtain TDoA#2 based on the values of the expected values and variances of the 2 single Gaussian models reported by network device #2. Furthermore, the average of TDoA#1 and TDoA#2 is taken to obtain the final estimated value of TDoA, and more accurate positioning of the terminal device is performed according to the estimated value of TDoA.

[0352] As yet another example, assume that P = 2. The number of single Gaussian models corresponding to the GMM probability distribution sent by network device #1 to the core network element is 3, and the first information reported by network device #1 includes the values of the expected values and proportions (or weights) of these 3 single Gaussian models. The number of single Gaussian models corresponding to the GMM probability distribution sent by network device #2 to the core network element is 3, and the first information reported by network device #2 includes the values of the expected values and proportions (or weights) of these 3 single Gaussian models. Then, the core network element can obtain the weighted average value of the expected values of the 3 single Gaussian models corresponding to the GMM probability distribution by averaging the products of the values of the expected values and proportions (or weights) of the 3 single Gaussian models in these two pieces of first information, and further obtain a more accurate estimated value of TDoA based on the weighted average value. This application does not make any limitation in this regard.

[0353] In the second example, assume that the generative model is VAE. The core network element can determine the location of the terminal device according to the variational probability distribution of the measurement quantity and the configuration information of the generative model.

[0354] For example, taking the above line positioning scenario as an example, Q first devices (such as network devices) can respectively send variational probability distributions of measurement quantities for terminal device positioning to a core network element. Optionally, for the same measurement quantity (such as TDoA), the values of the probability distributions corresponding to the Q variational probability distributions may be different. For example, for each variational probability distribution, the values x, y, z of the probability distributions corresponding to the measurement results of t2 - t1, t3 - t2, and t3 - t1 may be different. Optionally, the core network element can select the value of the higher probability distribution from the Q variational probability distributions, and then obtain a more accurate estimated value of TDoA based on the value of the higher probability distribution, and perform more accurate positioning of the terminal device according to the estimated value of TDoA.

[0355] In the embodiments of the present application, the transmission of information / or data between devices is not limited to direct transmission or indirect transmission (including transparent transmission), etc. Therefore, when device A sends information to device B, it includes that device A directly sends the information to device B through the interface between device A and device B, and it can also include that device A sends the message to device C, and device C sends the message to device B, and it is not limited to how many relays the message passes through from device A to device B. For example, when a UE sends a first piece of information to an LMF, it can include: the UE directly sends the first piece of information to the LMF through an LPP message; or the UE sends the first piece of information to the LMF through a gNB; or the UE sends the first piece of information to the LMF through a gNB and an AMF, and the UE sends the first piece of information to the LMF through an AMF, etc. Multiple specific implementations are not limited. The interactions between other devices are similar, and those skilled in the art can understand and will not be elaborated here. For the specific interface messages between devices, please refer to Figure 4 the description in

[0356] According to the above solution, by sending configuration information to the first device, the core network element can know that the first model for fitting / training for terminal device positioning is the same through the configuration information of the alignment generation model between the core network element and the first device. Furthermore, the analysis and application of the probability distribution of the measurement results of the measurement quantity by the core network element based on the first model can be more accurate, and thus the positioning accuracy of the terminal device can be further improved.

[0357] It should be understood that the above Figure 10In the solution shown, the core network element sends configuration information to the first device, enabling the first device to determine a first model for fitting / training for terminal device positioning, and further obtaining the probability distribution of the measurement results of the measurement quantities based on the first model. It should be noted that this application is also applicable to the terminal device reporting configuration information to the core network element to notify the first model for fitting / training for terminal device positioning, so that the core network element can analyze and apply the probability distribution of the measurement results of the measurement quantities based on the first model more accurately. For the specific implementation method, please refer to the relevant description below Figure 11 related description.

[0358] Figure 11 is a schematic flowchart of the communication method 1100 provided by an embodiment of this application. As Figure 11 shown, it includes the following multiple steps. It should be understood that this method can be executed by the first device and the core network element, or it can also be executed by the chips or circuits of the first device and the core network element. This application does not make any limitations in this regard. For the sake of convenience of description, the first device and the core network element are used as the execution entities for example below. For the sake of convenience of description, the first device and the core network element are used as the execution entities for example below. It should be noted that the model training / fitting in this implementation method occurs on the first device side, and the model inference / use occurs on the core network element side, or the training / fitting of the model occurs on the OTT or third-party device or cloud device side, and the model inference / use occurs on the OTT or third-party device or cloud device side. This application does not make any limitations in this regard.

[0359] S1110, the first device obtains configuration information, and the configuration information is used to indicate the configuration parameters for generating a model.

[0360] Exemplarily, the configuration information is used to indicate the configuration parameters for generating a model. Among them, the content of the configuration information, the generated model, and the configuration parameters can refer to the relevant description of step S1010 of the above method 1000.

[0361] In one example, the first device can be a terminal device or an access network device, and the core network element can be a positioning device, such as a location management function network element (such as LMF). Among them, the first device can also be referred to as the network element that performs channel measurement, or the channel measurement network element, or the reference signal measurement node, etc. This application does not make any limitations on its name.

[0362] Optionally, the configuration information can be dynamically configured through signaling or messages, or it can also be determined by the first device independently, or it can also be pre-configured. For example, corresponding codes, tables, or other ways that can be used to indicate the configuration information can be pre-saved in the first device to implement it. This application does not make any limitations in this regard.

[0363] Optionally, the configuration parameter includes a first configuration parameter and a second configuration parameter. For example, the configuration information is used to indicate the first configuration parameter and the second configuration parameter; alternatively, the configuration information is used to indicate a part of the configuration parameter (e.g., the first configuration parameter or the second configuration parameter), and the other part of the configuration parameter can be determined according to the mapping relationship. The specific implementation method can refer to the relevant description of the above method 900.

[0364] Optionally, the method further includes: the first device obtains the type and / or function of the generation model.

[0365] In one implementation, before performing the above step S1110, the first device obtains the type and / or function of the generation model. Among them, the type and / or function of the generation model can be dynamically configured for the first device through signaling or messages, or can also be pre-configured. For example, corresponding codes, tables or other ways that can be used to indicate the type and / or function of the generation model can be pre-saved in the first device. The present application does not limit it.

[0366] Among them, the content of the type and / or function of the generation model can refer to the relevant description of the above method 1000.

[0367] S1120, the first device processes the channel measurement result by using the first model to obtain the probability distribution of the measurement result of the measurement quantity for the positioning of the terminal device.

[0368] Among them, the first model is determined based on the configuration parameter of the generation model, the measurement quantity corresponds to the channel measurement result, and the channel measurement result is based on the measurement of the reference signal. The specific interpretation can refer to the relevant description of step S1020 of the above method 1000.

[0369] Exemplarily, the measurement quantity may include one or more of the following: RSTD, TDoA, ToA, AoA, LoS probability, LoS and NLoS identification results. The specific interpretation of the measurement quantity and the specific implementation method of this step can refer to the relevant description of step S1020 of the above method 1000.

[0370] Optionally, after performing the above step S1120, the method 1100 further includes step S1130.

[0371] S1130, the first device sends all or part of the first information and the configuration information to the core network element. Correspondingly, the core network element receives all or part of the first information and the configuration information from the first device.

[0372] Optionally, all or part of the first information and the configuration information may be sent through one signaling (or one data packet), or may be sent separately through two signals (or two data packets). This application does not make any limitation in this regard. Optionally, all or part of the first information and the configuration information may be sent simultaneously or separately. For example, the first device may first send the first information and then send the configuration information; or the first device may first send the configuration information and then send the first information. That is to say, this application does not make any limitation on the sending timing of all or part of the first information and the configuration information.

[0373] Exemplarily, the first information is used to indicate the probability distribution of the measurement results of the measurement quantities for terminal device positioning. Among them, the specific interpretation of the first information can refer to the relevant description in step S1030 of the above method 1000.

[0374] It should be understood that the configuration information in step S1130 may be the configuration information obtained in step S1110 above. This configuration information may indicate all the configuration parameters of the generation model or may indicate some of the configuration parameters of the generation model. The specific implementation method can be seen in the relevant description of the above method 1000.

[0375] Optionally, the configuration information in step S1130 may also be part of the configuration information obtained in step S1110 above. For example, if the configuration information obtained in step S1110 indicates all the configuration parameters of the generation model, the first device may send the first configuration parameter or the second configuration parameter to the core network element according to the mapping relationship in Table 1 above to save signaling overhead. Correspondingly, the core network element may determine all the configuration parameters of the generation model according to the mapping relationship in Table 1 or the predefined parameters in other protocols.

[0376] Optionally, the configuration information in step S1130 may also be the complete set of the configuration information obtained in step S1110 above. For example, if the configuration information obtained in step S1110 indicates the first configuration parameter of the generation model, the first device may also send the corresponding second configuration parameter to the core network element according to the mapping relationship in Table 1 above. This application does not make specific limitations in this regard. Optionally, after performing the above step S1130, the method 1100 further includes step S1140.

[0377] S1140. The core network element determines the location of the terminal device according to the probability distribution of the measurement results of the measurement quantities and the configuration parameters of the generation model.

[0378] Exemplarily, assuming that the generation model is a GMM, the core network element may determine the location of the terminal device according to the GMM distribution of the measurement quantities and the configuration information of the generation model.

[0379] For example, taking the uplink positioning scenario as an example, multiple first devices (such as network devices) can respectively send the GMM distributions of the measurement quantities for terminal device positioning to the core network element. Optionally, for the same measurement quantity (such as TDoA), the number of single Gaussian models included in each GMM distribution corresponding to the first model (i.e., GMM) can be different, and the number of iterations of the first model (i.e., GMM) can be different. Optionally, the core network element can select the GMM distribution with higher convergence accuracy, for example, select the GMM distribution with more single Gaussian models and more iterations of the first model, so as to obtain a more accurate estimated value of TDoA, and perform more accurate positioning of the terminal device based on the estimated value of TDoA.

[0380] Exemplarily, assuming that the generative model is VAE, the core network element can determine the location of the terminal device according to the variational probability distribution of the measurement quantity and the configuration information of the generative model. The specific implementation manner can refer to the relevant description in step S1040 of the above method 1000.

[0381] For example, taking the uplink positioning scenario as an example, multiple first devices (such as network devices) can respectively send the variational probability distributions of the measurement quantities for terminal device positioning to the core network element. Optionally, for the same measurement quantity (such as TDoA), the values of each variational probability distribution can be different. For example, the values x, y, z of the probability distributions corresponding to the measurement results t2 - t1, t3 - t2, t3 - t1 in each variational probability distribution can be different. Optionally, the core network element can select the variational probability distribution with a higher value of the probability distribution, so as to obtain a more accurate estimated value of TDoA, and perform more accurate positioning of the terminal device based on the estimated value of TDoA.

[0382] According to the above solution, by sending the configuration information to the core network element, the first device enables the alignment of the configuration information of the generative model between the core network element and the first device, and can know that the first models for fitting / training for terminal device positioning are the same. Furthermore, the analysis and application of the probability distribution of the measurement results of the measurement quantity based on the first model can be more accurate, and thus the positioning accuracy of the terminal device can be further improved.

[0383] The following combines Figures 12 to 17 , and gives examples of the application of the embodiments of the present application in the uplink positioning scenario, downlink positioning scenario, and sidelink positioning scenario respectively. It should be understood that Figures 10 to 17 the method embodiments shown can be combined with each other, Figures 10 to 17 and the steps in the method embodiments shown can be referenced to each other. For example, in the embodiments of the present application, Figures 12 to 17 the method embodiments shown can be regarded as possible implementation manners for implementing Figure 10 and Figure 11 the functions of the method embodiments shown, whereFigure 12 and Figure 15 mainly describes the uplink positioning scenario, Figure 13 and Figure 16 then mainly describes the downlink positioning scenario, Figure 14 and Figure 17 then mainly describes the sidelink positioning scenario.

[0384] Figure 12 is a schematic flowchart of the communication method 1200 provided by the embodiments of the present application. As Figure 12 shown, taking the LMF as the core network element, the first device as the gNB, and the second device as the UE as an example, the model training / fitting in this implementation occurs on the gNB side, and the model inference / usage occurs on the LMF side. It should be understood that the relevant descriptions in the above Figure 10 and Figure 11 shown embodiments are equally applicable to this implementation, Figures 10 to 12 there may be the same or similar technical means between them, Figure 12 and Figure 10 and Figure 11 the content already described in the embodiments shown will not be elaborated here.

[0385] It should be understood that this implementation takes the generation model as the GMM as an example. The gNB obtains the measurement results of the measurement quantities by measuring the SRS. Further, the gNB determines the GMM1 for the UE positioning based on the configuration information #1 sent by the LMF, and processes the channel measurement results using the GMM1 to obtain the GMM distribution (corresponding to Method 1), or the gNB reports the configuration information #1 and the parameters of the GMM1 to the LMF (corresponding to Method 2), so that the gNB and the LMF align the configuration information #1, ensuring that the GMM1 used for the fitting / training of the UE positioning is the same, and further ensuring that the analysis and application of the probability distribution of the measurement results of the measurement quantities based on the GMM1 are more accurate, improving the positioning accuracy of the UE.

[0386] Method 1:

[0387] S1210, the LMF sends the configuration information #1 to the gNB. Correspondingly, the gNB receives the configuration information #1 from the LMF.

[0388] Among them, the content and its interpretation included in the configuration information #1, as well as the specific implementation method, can refer to the relevant description of step S1010 of the above method 1000.

[0389] S1220, the UE sends the SRS to the gNB. Correspondingly, the gNB receives the SRS from the UE.

[0390] S1230, the gNB determines the GMM1 of the measurement quantities for the UE positioning according to the channel measurement result #1 and the configuration information #1.

[0391] Exemplarily, the gNB measures the SRS to obtain channel measurement result #1. According to the configuration information #1, GMM1 can be determined, and this channel measurement result #1 is used as the input of GMM1. The output of GMM1 is GMM1 itself.

[0392] Among them, the content and its interpretation included in the measurement quantity, the correlation between the measurement quantity and channel measurement result #1, and the specific implementation manner of this step can refer to the relevant description of step S1020 of the above method 1000.

[0393] S1240, the gNB sends the parameters of GMM1 to the LMF. Correspondingly, the LMF receives the parameters of GMM1 from the gNB.

[0394] Among them, the content and its interpretation included in the parameters of GMM1, and the specific implementation manner of this step can refer to the relevant description of step S1030 of the above method 1000.

[0395] S1250, the LMF determines the location of the UE according to the configuration information #1 and the parameters of GMM1.

[0396] Among them, the specific implementation manner can refer to the relevant description of step S1040 of the above method 1000.

[0397] Method 2:

[0398] S1260, the UE sends the SRS to the gNB. Correspondingly, the gNB receives the SRS from the UE.

[0399] S1270, the gNB determines GMM1 of the measurement quantity for UE positioning according to channel measurement result #1.

[0400] Exemplarily, the gNB measures the SRS to obtain channel measurement result #1, and uses this channel measurement result #1 as the input of GMM1. The output of GMM1 is GMM1 itself.

[0401] Among them, the content and its interpretation included in the measurement quantity, the correlation between the measurement quantity and channel measurement result #1, and the specific implementation manner can refer to the relevant description of step S1120 of the above method 1100.

[0402] S1280, the gNB sends the parameters of GMM1 and the configuration information #1 to the LMF. Correspondingly, the LMF receives the parameters of GMM1 and the configuration information #1 from the gNB.

[0403] Among them, the content and its interpretation of the parameters of GMM1 and the configuration information #1, and the specific implementation manner can refer to the relevant description of step S1130 of the above method 1100.

[0404] S1290, the LMF determines the location of the UE according to the parameters of GMM1 and the configuration information #1.

[0405] Among them, for the specific implementation method, reference can be made to the relevant description of step S1140 of the above method 1100.

[0406] In the embodiment of the present application, taking the generation model as GMM as an example, the gNB measures the measurement quantity of the SRS to obtain the measurement result of the measurement quantity. Further, the gNB and the LMF align the configuration parameters of GMM1 by sending the configuration information #1, so that the GMM1 used for fitting / training of UE positioning is the same, thereby ensuring that the analysis and application of the probability distribution of the measurement result of the measurement quantity based on this GMM1 are more accurate and improving the positioning accuracy of the UE.

[0407] Figure 13 is a schematic flowchart of the communication method 1300 provided by the embodiment of the present application. As Figure 13 shown, taking the LMF as a core network element, the first device as the UE, and the second device as the gNB as an example, the model training / fitting in this implementation method occurs on the UE side, and the model inference / use occurs on the LMF side. It should be understood that the relevant descriptions in the above Figure 10 、 Figure 11 shown embodiments are equally applicable to this implementation method, Figure 10 、 Figure 11 and Figure 13 there may be the same or similar technical means between Figure 13 and Figure 10 、 Figure 11 The content already described in the embodiments shown will not be repeated.

[0408] It should be understood that this implementation method takes the generation model as GMM as an example. The UE measures the PRS to obtain the measurement result of the measurement quantity. Further, the UE determines GMM2 based on the configuration information #2 sent by the LMF, and processes the channel measurement result using GMM2 to obtain the GMM distribution (corresponding to Method 1), or the UE reports the configuration information #2 and the parameters of GMM2 to the LMF (corresponding to Method 2), so that the UE and the LMF align the configuration information #2, ensuring that the GMM2 used for fitting / training of UE positioning is the same, thereby ensuring that the analysis and application of the probability distribution of the measurement result of the measurement quantity based on this GMM2 are more accurate and improving the positioning accuracy of the UE.

[0409] Method 1:

[0410] S1310, the LMF sends the configuration information #2 to the UE. Correspondingly, the UE receives the configuration information #2 from the LMF.

[0411] Among them, the content and interpretation of the configuration information #2, as well as the specific implementation method, can refer to the relevant description in step S1010 of the above method 1000.

[0412] S1320, the gNB sends the PRS to the UE. Correspondingly, the UE receives the PRS from the gNB.

[0413] S1330, the UE determines the GMM2 of the measurement quantity for UE positioning according to the channel measurement result #2 and the configuration information #2.

[0414] Exemplarily, the UE measures the PRS to obtain the channel measurement result #2, determines the GMM2 according to the configuration information #2, and uses the channel measurement result #2 as the input of the GMM2. The output of the GMM2 is the GMM1.

[0415] Among them, the content and interpretation of the measurement quantity, the correlation between the measurement quantity and the channel measurement result #2, as well as the specific implementation method, can refer to the relevant description in step S1020 of the above method 1000.

[0416] S1340, the UE sends the parameters of the GMM2 to the LMF. Correspondingly, the LMF receives the parameters of the GMM2 from the UE.

[0417] Among them, the content and interpretation of the parameters of the GMM2, as well as the specific implementation method, can refer to the relevant description in step S1030 of the above method 1000.

[0418] S1350, the LMF determines the location of the UE according to the parameters of the GMM2 and the configuration information #2.

[0419] Among them, the specific implementation method can refer to the relevant description in step S1040 of the above method 1000.

[0420] Method 2:

[0421] S1360, the gNB sends the PRS to the UE. Correspondingly, the UE receives the PRS from the gNB.

[0422] S1370, the UE determines the GMM2 of the measurement quantity for UE positioning according to the channel measurement result #2.

[0423] Exemplarily, the UE measures the PRS to obtain the channel measurement result #2, and uses the channel measurement result #2 as the input of the GMM2. The output of the GMM2 is the GMM1.

[0424] Among them, the content and interpretation of the measurement quantity, the correlation between the measurement quantity and the channel measurement result #2, as well as the specific implementation method, can refer to the relevant description in step S1120 of the above method 1100.

[0425] S1380, the UE sends the parameters and configuration information #2 of GMM2 to the LMF. Correspondingly, the LMF receives the parameters and configuration information #2 of GMM2 from the UE.

[0426] Among them, for the content and interpretation of the parameters and configuration information #2 of GMM2, and the specific implementation method, reference can be made to the relevant description in step S1130 of the above method 1100.

[0427] S1390, the LMF determines the location of the UE according to the parameters and configuration information #2 of GMM2.

[0428] Among them, for the specific implementation method, reference can be made to the relevant description in step S1140 of the above method 1100.

[0429] In the embodiment of the present application, taking the generation model as GMM as an example, the UE measures the measurement quantity of the PRS to obtain the measurement result of the measurement quantity. Further, the UE and the LMF align the configuration parameters of GMM2 by sending the configuration information #2, so that the GMM2 used for fitting / training of UE positioning is the same, thereby ensuring that the analysis and application of the probability distribution of the measurement result of the measurement quantity based on this GMM2 are more accurate and improving the positioning accuracy of the UE.

[0430] Figure 14 It is a schematic flowchart of the communication method 1400 provided by the embodiment of the present application. As Figure 14 shown, taking the LMF as the core network element, the first device as UE#1, and the second device as UE#2 as an example, the model training / fitting in this implementation method occurs on the UE#1 side, and the model inference / use occurs on the LMF side. It should be understood that the relevant descriptions in the above Figure 10 、 Figure 11 shown embodiments are equally applicable to this implementation method, Figure 10 、 Figure 11 and Figure 14 There may be the same or similar technical means between Figure 14 and Figure 10 、 Figure 11 The content already described in the embodiments shown will not be repeated.

[0431] It should be understood that in this implementation mode, taking the generation model as GMM as an example, UE1 obtains the measurement results of the measurement quantities by measuring the SL-PRS. Further, UE1 determines GMM3 based on the configuration information #3 sent by the LMF, and processes the channel measurement results using GMM3 to obtain the GMM distribution (corresponding to Method 1), or UE1 reports the configuration information #3 and the parameters of GMM3 to the LMF (corresponding to Method 2), so that UE1 and the LMF align the configuration information #3, ensuring that the GMM3 used for fitting / training of UE positioning is the same, and further ensuring that the analysis and application of the probability distribution of the measurement results of the measurement quantities based on this GMM3 are more accurate, thereby improving the positioning accuracy of the UE.

[0432] Method 1:

[0433] S1410. The LMF sends the configuration information #3 to UE#1. Correspondingly, UE#1 receives the configuration information #3 from the LMF.

[0434] Among them, the content and its interpretation included in the configuration information #3, as well as the specific implementation method, can refer to the relevant description in step S1010 of the above method 1000.

[0435] S1420. UE#2 sends the SL-PRS to UE#1. Correspondingly, UE#1 receives the SL-PRS from UE#2.

[0436] S1430. UE#1 determines GMM3 for the measurement quantities used for UE positioning according to the channel measurement result #3 and the configuration information #3.

[0437] Exemplarily, UE#1 measures the SL-PRS to obtain the channel measurement result #3, can determine GMM3 according to this configuration information #3, and uses this channel measurement result #3 as the input of GMM3. The output of GMM3 is GMM1.

[0438] Among them, the content and its interpretation included in the measurement quantities, the correlation relationship between the measurement quantities and the channel measurement result #3, as well as the specific implementation method, can refer to the relevant description in step S1020 of the above method 1000.

[0439] S1440. UE#1 sends the parameters of GMM3 to the LMF. Correspondingly, the LMF receives the parameters of GMM3 from UE#1.

[0440] Among them, the content and its interpretation included in the parameters of GMM3, as well as the specific implementation method, can refer to the relevant description in step S1030 of the above method 1000.

[0441] S1450. The LMF determines the location of the UE according to the parameters of GMM3 and the configuration information #3.

[0442] Among them, for the specific implementation method, reference can be made to the relevant description of step S1040 of the above method 1000.

[0443] Method 2:

[0444] S1460, UE#2 sends SL-PRS to UE#1. Correspondingly, UE#1 receives the SL-PRS from UE#2.

[0445] S1470, UE#1 determines GMM3 of the measurement quantity for UE positioning according to the channel measurement result #3.

[0446] Exemplarily, UE#1 measures the SL-PRS to obtain the channel measurement result #3, and uses the channel measurement result #3 as the input of GMM3. The output of GMM3 is GMM1.

[0447] Among them, for the content and interpretation of the measurement quantity, the correlation between the measurement quantity and the channel measurement result #3, and the specific implementation method, reference can be made to the relevant description of step S1120 of the above method 1100.

[0448] S1480, UE#1 sends the parameters and configuration information #3 of GMM3 to the LMF. Correspondingly, the LMF receives the parameters and configuration information #3 of GMM3 from UE#1.

[0449] Among them, for the content and interpretation of the parameters and configuration information #3 of GMM3, and the specific implementation method, reference can be made to the relevant description of step S1130 of the above method 1100.

[0450] S1490, the LMF determines the location of the UE according to the parameters and configuration information #3 of GMM3.

[0451] Among them, for the specific implementation method, reference can be made to the relevant description of step S1140 of the above method 1100.

[0452] In the embodiments of the present application, taking the generation model as GMM as an example, UE#1 measures the measurement quantity of the SL-PRS to obtain the measurement result of the measurement quantity. Further, UE#1 and the LMF align the configuration parameters of GMM3 by sending the configuration information #3, so that the GMM3 for fitting / training for UE positioning is the same, thereby ensuring more accurate analysis and application of the probability distribution of the measurement result of the measurement quantity based on this GMM3, and improving the positioning accuracy of the UE.

[0453] Figure 15 It is a schematic flowchart of the communication method 1500 provided by the embodiments of the present application. As Figure 15As shown, taking the LMF as the core network element, the first device as the gNB, and the second device as the UE as an example, the model training / fitting in this implementation method occurs on the gNB side, and the model inference / usage occurs on the LMF side. It should be understood that the relevant descriptions in the above Figure 10 and Figure 11 shown in the embodiments are equally applicable to this implementation method. Figure 10 and Figure 11 and Figure 15 There may be the same or similar technical means among them. Figure 15 and Figure 10 and Figure 11 The content already described in the embodiments shown will not be elaborated here.

[0454] It should be understood that this implementation method takes the VAE as the generative model as an example. The gNB measures the SRS to obtain the measurement result of the measurement quantity. Further, the gNB determines the VAE1 based on the configuration information #4 sent by the LMF, and processes the channel measurement result using the VAE1 to obtain the variational probability distribution (corresponding to Method 1), or the gNB reports the configuration information #1 and the parameters of the variational probability distribution to the LMF (corresponding to Method 2), so that the gNB and the LMF align the configuration information #1, ensuring that the VAE1 used for UE positioning fitting / training is the same, and further ensuring that the analysis and application of the probability distribution of the measurement result of the measurement quantity based on this VAE1 are more accurate, improving the positioning accuracy of the UE.

[0455] Method 1:

[0456] S1510. The LMF sends the configuration information #4 to the gNB. Correspondingly, the gNB receives the configuration information #4 from the LMF.

[0457] Among them, the content and interpretation of the configuration information #4, as well as the specific implementation method, can refer to the relevant description of step S1010 of the above method 1000.

[0458] S1520. The UE sends the SRS to the gNB. Correspondingly, the gNB receives the SRS from the UE.

[0459] S1530. The gNB determines the variational probability distribution #1 of the measurement quantity for UE positioning according to the channel measurement result #4 and the configuration information #4.

[0460] Exemplarily, the gNB measures the SRS to obtain the channel measurement result #4, determines the VAE1 according to this configuration information #4, and uses this channel measurement result #4 as the input of the VAE1. The output of the VAE1 is the variational probability distribution #1.

[0461] Among them, the content and interpretation of the measurement quantity, the correlation between the measurement quantity and the channel measurement result #4, and the specific implementation method can refer to the relevant description in step S1020 of the above method 1000.

[0462] S1540, the gNB sends the parameters of the variational probability distribution #1 to the LMF. Correspondingly, the LMF receives the parameters of the variational probability distribution #1 from the gNB.

[0463] Among them, the content and interpretation of the parameters of the variational probability distribution #1, and the specific implementation method can refer to the relevant description in step S1030 of the above method 1000.

[0464] S1550, the LMF determines the location of the UE according to the configuration information #4 and the parameters of the variational probability distribution #1.

[0465] Among them, the specific implementation method can refer to the relevant description in step S1040 of the above method 1000.

[0466] Method 2:

[0467] S1560, the UE sends the SRS to the gNB. Correspondingly, the gNB receives the SRS from the UE.

[0468] S1570, the gNB determines the variational probability distribution #1 of the measurement quantity for UE positioning according to the channel measurement result #4.

[0469] Exemplarily, the gNB measures the SRS to obtain the channel measurement result #4, and uses this channel measurement result #4 as the input of VAE1. The output of VAE1 is the variational probability distribution #1.

[0470] Among them, the content and interpretation of the measurement quantity, the correlation between the measurement quantity and the channel measurement result #4, and the specific implementation method can refer to the relevant description in step S1120 of the above method 1100.

[0471] S1580, the gNB sends the parameters of the variational probability distribution #1 and the configuration information #4 to the LMF. Correspondingly, the LMF receives the parameters of the variational probability distribution #1 and the configuration information #4 from the gNB.

[0472] Among them, the content and interpretation of the parameters of the variational probability distribution #1 and the configuration information #4, and the specific implementation method can refer to the relevant description in step S1130 of the above method 1100.

[0473] S1590, the LMF determines the location of the UE according to the parameters of the variational probability distribution #1 and the configuration information #4.

[0474] Among them, for the specific implementation method, reference can be made to the relevant description in step S1140 of the above method 1100.

[0475] In the embodiment of this application, taking the generative model as VAE as an example, the gNB measures the measurement quantity of the SRS to obtain the measurement result of the measurement quantity. Further, the gNB and the LMF align the configuration parameters of VAE1 by sending configuration information #4, so that the VAE1 used for fitting / training of UE positioning is the same, thereby ensuring more accurate analysis and application of the probability distribution of the measurement result of the measurement quantity based on this VAE1, and improving the positioning accuracy of the UE.

[0476] Figure 16 It is a schematic flowchart of the communication method 1600 provided by the embodiment of this application. As Figure 16 shown, taking the LMF as the core network element, the first device as the UE, and the second device as the gNB as an example, the model training / fitting in this implementation method occurs on the UE side, and the model inference / usage occurs on the LMF side. It should be understood that the relevant descriptions in the above Figure 10 、 Figure 11 shown embodiments are equally applicable to this implementation method, Figure 10 、 Figure 11 and Figure 16 there may be the same or similar technical means between Figure 16 and Figure 10 、 Figure 11 The content already described in the embodiments shown will not be elaborated here.

[0477] It should be understood that this implementation method takes the generative model as VAE as an example. The UE measures the PRS to obtain the measurement result of the measurement quantity. Further, the UE determines VAE2 based on the configuration information #5 sent by the LMF, and uses VAE2 to process the channel measurement result to obtain the variational probability distribution (corresponding to Method 1), or the UE reports the configuration information #5 and the parameters of the variational probability distribution to the LMF (corresponding to Method 2), so that the UE and the LMF align the configuration information #5, ensuring that the VAE2 used for fitting / training of UE positioning is the same, thereby ensuring more accurate analysis and application of the probability distribution of the measurement result of the measurement quantity based on this VAE2, and improving the positioning accuracy of the UE.

[0478] Method 1:

[0479] S1610. The LMF sends the configuration information #5 to the UE. Correspondingly, the UE receives the configuration information #5 from the LMF.

[0480] Among them, for the content and interpretation of the configuration information #5, and the specific implementation method, reference can be made to the relevant description in step S1010 of the above method 1000.

[0481] S1620, the gNB sends PRS to the UE. Correspondingly, the UE receives the PRS from the gNB.

[0482] S1630, the UE determines the variational probability distribution #2 of the measurement quantities for UE positioning according to the channel measurement result #5 and the configuration information #5.

[0483] Exemplarily, the UE measures the PRS to obtain the measurement result #5, and according to the configuration information #5, VAE2 can be determined. The channel measurement result #5 is used as the input of VAE2, and the output of VAE2 is the variational probability distribution #2.

[0484] Among them, the content and its interpretation included in the measurement quantities, the correlation between the measurement quantities and the channel measurement result #5, and the specific implementation method can refer to the relevant description of step S1020 of the above method 1000.

[0485] S1640, the UE sends the parameters of the variational probability distribution #2 to the LMF, and the LMF receives the parameters of the variational probability distribution #2 from the UE.

[0486] Among them, the content and its interpretation included in the parameters of the variational probability distribution #2, and the specific implementation method can refer to the relevant description of step S1030 of the above method 1000.

[0487] S1650, the LMF determines the location of the UE according to the configuration information #5 and the parameters of the variational probability distribution #2.

[0488] Among them, the specific implementation method can refer to the relevant description of step S1040 of the above method 1000.

[0489] Method 2:

[0490] S1660, the gNB sends PRS to the UE. Correspondingly, the UE receives the PRS from the gNB.

[0491] S1670, the UE determines the variational probability distribution #2 of the measurement quantities for UE positioning according to the channel measurement result #5.

[0492] Exemplarily, the UE measures the PRS to obtain the measurement result #5, and uses the channel measurement result #5 as the input of VAE2. The output of VAE2 is the variational probability distribution #2.

[0493] Among them, the content and its interpretation included in the measurement quantities, the correlation between the measurement quantities and the channel measurement result #5, and the specific implementation method can refer to the relevant description of step S1120 of the above method 1100.

[0494] S1680, The UE sends the parameters and configuration information #5 of variational probability distribution #2 to the LMF. Correspondingly, the LMF receives the parameters and configuration information #5 of variational probability distribution #2 from the UE.

[0495] Among them, for the content and interpretation of the parameters and configuration information #5 of variational probability distribution #2, and the specific implementation method, reference can be made to the relevant description in step S1130 of the above method 1100.

[0496] S1690, The LMF determines the location of the UE according to the parameters and configuration information #5 of variational probability distribution #2.

[0497] Among them, for the specific implementation method, reference can be made to the relevant description in step S1140 of the above method 1100.

[0498] In the embodiments of this application, taking the generative model as VAE as an example, the UE measures the measurement quantity of the PRS to obtain the measurement result of the measurement quantity. Further, the UE and the LMF align the configuration parameters of VAE2 by sending configuration information #5, so that the VAE2 used for fitting / training of UE positioning is the same, thereby ensuring more accurate analysis and application of the probability distribution of the measurement result of the measurement quantity based on this VAE2, and improving the positioning accuracy of the UE.

[0499] Figure 17 It is a schematic flowchart of the communication method 1700 provided by the embodiments of this application. As Figure 17 shown, taking the LMF as a core network element, the first device as UE#1, and the second device as UE#2 as an example, the model training / fitting in this implementation method occurs on the UE#1 side, and the model inference / use occurs on the LMF side. It should be understood that the relevant descriptions in the above Figure 10 、 Figure 11 shown embodiments are equally applicable to this implementation method, Figure 10 、 Figure 11 and Figure 17 there may be the same or similar technical means between Figure 17 and Figure 10 、 Figure 11 The content already described in the embodiments shown will not be repeated.

[0500] It should be understood that in this implementation method, taking the generative model as VAE as an example, UE#1 obtains the measurement results of the measurement quantities by measuring the SL-PRS. Further, UE#1 determines VAE3 based on the configuration information #6 sent by the LMF, and processes the channel measurement results using VAE3 to obtain the variational probability distribution (corresponding to Method 1), or UE#1 reports the configuration information #6 and the parameters of the variational probability distribution to the LMF (corresponding to Method 2), so that UE#1 and the LMF align the configuration information #6, ensuring that the VAE3 used for fitting / training of UE positioning is the same, thereby ensuring more accurate analysis and application of the probability distribution of the measurement results of the measurement quantities based on this VAE3, and improving the positioning accuracy of the UE.

[0501] Method 1:

[0502] S1710. The LMF sends the configuration information #6 to UE#1. Correspondingly, UE#1 receives the configuration information #6 from the LMF.

[0503] Among them, the content and its interpretation included in the configuration information #6, as well as the specific implementation method, can refer to the relevant description in step S1010 of the above method 1000.

[0504] S1720. UE#2 sends the SL-PRS to UE#1. Correspondingly, UE#1 receives the SL-PRS from UE#2.

[0505] S1730. UE#1 determines the variational probability distribution #3 of the measurement quantities for UE positioning according to the measurement results #6 and the configuration information #6.

[0506] Exemplarily, UE#1 measures the SL-PRS to obtain the channel measurement results #6, can determine VAE3 according to the measurement results #6, and uses the channel measurement results #6 as the input of VAE3. The output of VAE3 is the variational probability distribution #3.

[0507] Among them, the content and its interpretation included in the measurement quantities, the correlation between the measurement quantities and the channel measurement results #6, as well as the specific implementation method, can refer to the relevant description in step S1020 of the above method 1000.

[0508] S1740. UE#1 sends the parameters of the variational probability distribution #3 to the LMF, and the LMF receives the parameters of the variational probability distribution #3 from UE#1.

[0509] Among them, the content and its interpretation included in the parameters of the variational probability distribution #3, as well as the specific implementation method, can refer to the relevant description in step S1030 of the above method 1000.

[0510] S1750. The LMF determines the position of the UE according to the configuration information #6 and the parameters of the variational probability distribution #3.

[0511] Among them, for the specific implementation method, reference can be made to the relevant description in step S1040 of the above method 1000.

[0512] Method 2:

[0513] S1760, UE#2 sends SL-PRS to UE#1. Correspondingly, UE#1 receives the SL-PRS from UE#2.

[0514] S1770, UE#1 determines the variational probability distribution #3 of the measurement quantity for UE positioning according to the channel measurement result #6.

[0515] Exemplarily, UE#1 measures the SL-PRS to obtain the measurement result #6, and uses this channel measurement result #6 as the input of VAE3. The output of VAE3 is the variational probability distribution #3.

[0516] Among them, for the content and its interpretation included in the measurement quantity, the correlation between the measurement quantity and the channel measurement result #6, and the specific implementation method, reference can be made to the relevant description in step S1120 of the above method 1100.

[0517] S1780, UE#1 sends the parameters and configuration information #6 of the variational probability distribution #3 to the LMF. Correspondingly, the LMF receives the parameters and configuration information #6 of the variational probability distribution #3 from UE#1.

[0518] Among them, for the content and its interpretation of the parameters and configuration information #6 of the variational probability distribution #3, and the specific implementation method, reference can be made to the relevant description in step S1130 of the above method 1100.

[0519] S1790, the LMF determines the location of the UE according to the parameters and configuration information #6 of the variational probability distribution #3.

[0520] Among them, for the specific implementation method, reference can be made to the relevant description in step S1140 of the above method 1100.

[0521] In the embodiment of the present application, taking the generation model as VAE as an example, UE#1 measures the measurement quantity of the SL-PRS to obtain the measurement result of the measurement quantity. Further, UE#1 and the LMF align the configuration parameters of VAE3 by sending the configuration information #6, so that the VAE3 used for fitting / training of UE positioning is the same, thereby ensuring more accurate analysis and application of the probability distribution of the measurement result of the measurement quantity based on this VAE3, and improving the positioning accuracy of the UE.

[0522] It should be noted that the above Figures 15 to 17The method shown makes use of the method for fitting the measurement result distribution based on VAE. Optionally, the technical solution of the present application is also applicable to the method for fitting the measurement result distribution based on GAN. For the specific implementation manner, reference may be made to the relevant description above Figures 15 to 17 and will not be described again for the sake of brevity.

[0523] Above, in combination with Figures 1 to 17 the method provided in the embodiments of the present application has been described in detail. Below, in combination with Figures 17 to 18 the device provided in the embodiments of the present application will be described in detail. It should be understood that the description of the device embodiments corresponds to the description of the method embodiments. Therefore, the content not described in detail can be referred to the above method embodiments and will not be elaborated here for the sake of brevity.

[0524] Figure 18 FIG. 13 is a schematic diagram of a communication device 1800 provided in the embodiments of the present application. As Figure 18 shown, the communication device 1800 includes a processing module 1801 and a communication module 1802. The communication device 1800 may be a first device (such as an access network device or a terminal device), or a communication device that is applied to the first device or used in matching with the first device and can implement the method executed by the first device, such as a chip, a chip system or a circuit. Alternatively, the communication device 1800 may be a core network element (such as a location management function network element), or a communication device that is applied to the core network element or used in matching with the core network element and can implement the method executed by the core network element, such as a chip, a chip system or a circuit.

[0525] Among them, the communication module may also be referred to as a transceiver module, a transceiver, a transceiver, or a transceiver device, etc. The processing module may also be referred to as a processor, a processing board, a processing unit, or a processing device, etc. Optionally, the communication module is used to execute the sending operation and receiving operation of the first device (such as an access network device or a terminal device) or the core network element (such as a location management function network element) in the above method. The device for implementing the receiving function in the communication module may be regarded as a receiving unit, and the device for implementing the sending function in the communication module may be regarded as a sending unit, that is, the communication module includes a receiving unit and a sending unit.

[0526] When the communication device 1800 is applied to the first device, the processing module 1801 may be used to implement the processing function of the first device (such as an access network device or a terminal device) in the above embodiments, and the communication module 1802 may be used to implement the transceiver function of the first device in the above embodiments.

[0527] When the communication device 1800 is applied to the core network element, the processing module 1801 may be used to implement the processing function of the core network element (such as a location management function network element) in the above embodiments, and the communication module 1802 may be used to implement the transceiver function of the first device in the above embodiments.

[0528] In addition, it should be noted that the foregoing communication module and / or processing module may be implemented by a virtual module. For example, the processing module may be implemented by a software functional unit or a virtual device, and the communication module may be implemented by a software function or a virtual device. Alternatively, the processing module or the communication module may also be implemented by a physical device. For example, if the device is implemented by a chip / circuit (such as an integrated circuit or a logic circuit, etc.). The communication module may be an input / output circuit and / or a communication interface, and perform input operations (corresponding to the foregoing receiving operations) and output operations (corresponding to the foregoing sending operations); the processing module is an integrated processor, a microprocessor or a circuit (such as an integrated circuit or a logic circuit, etc.).

[0529] The division of modules in this application is illustrative, and is only a logical function division. In actual implementation, there may be other division methods. In addition, in each example of this application, each functional module may be integrated in a processor, may exist separately physically, or two or more modules may be integrated in one module. The above integrated module may be implemented in the form of hardware or in the form of a software functional module.

[0530] Figure 19 is a schematic diagram of another communication device 1900 provided by an embodiment of the present application. As Figure 19 shown, optionally, the communication device 1900 may be the foregoing first device or a core network element, or a chip or a chip system for the foregoing first device or core network element. Optionally, in the present application, the chip system may be composed of chips, or may include chips and other discrete devices.

[0531] The communication device 1900 may be used to implement the functions of any network element (such as a core network element or a first device in the communication system described in the foregoing examples. Optionally, the core network element is a location management function network element, and the first device is an access network device or a terminal device). The communication device 1900 may include a processing circuit 1910. Optionally, the processing circuit 1910 is coupled to a memory. The memory may be located inside the device, or the memory may be integrated with the processor, or the memory may also be located outside the device. For example, the communication device 1900 may further include at least one memory 1920. The memory 1920 stores the necessary computer programs, computer programs or instructions and / or data in implementing any of the foregoing examples; the processing circuit 1910 may execute the computer programs stored in the memory 1920 to complete the methods in any of the foregoing examples.

[0532] The communication device 1900 may further include a transceiver circuit 1930. The communication device 1900 can interact with other devices through the transceiver circuit 1930. Exemplarily, the transceiver circuit 1930 can be a transceiver, a circuit, a bus, a module, a pin, or other types of communication interfaces. When the communication device 1900 is a chip-type device or circuit, the transceiver circuit 1930 in the device 1900 can also be an input / output circuit, or an interface circuit, which can input information (or receive information) and output information (or send information). When the communication device 1900 is a core network element, a network device, or a terminal device, the transceiver circuit can be a transmitter, a receiver, or a transceiver, or a communication interface, which is not limited herein.

[0533] Among them, the processing circuit 1910 can be one or more processors, or all or part of the processing circuits in one or more processors. The processing circuit 1910 is an integrated processor, a microprocessor, an integrated circuit, or a logic circuit, etc. The processor can determine the output information according to the input information.

[0534] The coupling in this application is an indirect coupling or communication connection between devices, units, or modules, which can be electrical, mechanical, or other forms, and is used for information interaction between devices, units, or modules. The processing circuit 1910 may cooperate with the memory 1920 and the transceiver circuit 1930. The specific connection medium between the processing circuit 1910, the memory 1920, and the transceiver circuit 1930 is not limited in this application.

[0535] Optionally, as Figure 19 shown, the processing circuit 1910, the memory 1920, and the transceiver circuit 1930 are interconnected through a bus 1940. Optionally, the bus can include buses of types such as an address bus, a data bus, and a control bus. In addition, for the convenience of representation, Figure 19 one bus 1940 is shown, but it does not mean that there is only one bus or one type of bus.

[0536] It should be understood that the processor mentioned in the embodiments of the present application may be the following devices or a partial circuit for processing functions in the following devices: a central processing unit (CPU), and may also be other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0537] It should also be understood that the memory mentioned in the embodiments of the present application may be a volatile memory and / or a non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM). For example, the RAM may be used as an external cache. By way of example and not limitation, the RAM includes the following various forms: static random access memory (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0538] It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, the memory (storage module) may be integrated in the processor.

[0539] It should also be noted that the memories described herein are intended to include, but are not limited to, these and any other suitable types of memories.

[0540] In the embodiments of the present application, the methods described in the above embodiments may be executed by the first device and the core network element, or may be executed by the chips, chip systems or circuits of the first device and the core network element, and the chips, chip systems or circuits may be installed in the first device and the core network element.

[0541] The embodiments of the present application provide a computer-readable storage medium, on which computer instructions for implementing the methods executed by the device (such as the first device or the core network element) in the above method embodiments are stored.

[0542] For example, when the computer program is executed by a computer, the computer can implement the methods executed by the device (such as the first device or the core network element, etc.) in the above method embodiments.

[0543] The embodiments of the present application provide a computer program product, including instructions, which when executed by a computer, implement the methods executed by the device (such as the first device or the core network element (or positioning device), etc.) in the above method embodiments.

[0544] The embodiments of the present application provide a communication system, which includes the first device and / or the core network element in the above embodiments. For example, the system includes the first device and / or the core network element in the above embodiments. For another example, the system includes the first device and / or the core network element in the above embodiments.

[0545] For the explanations and beneficial effects of the relevant content in any of the above-mentioned devices, reference may be made to the corresponding method embodiments provided above, and details are not described herein again.

[0546] To facilitate the understanding of the above embodiments provided in the present application, the following points are explained:

[0547] In the present application, if there is no special description and logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0548] In this application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the relationship between related objects and indicates that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, or B exists alone, where A and B can be singular or plural. In the written description of this application, the character " / " generally represents an "or" relationship between the related objects before and after. "At least one (item)" or a similar expression refers to any combination of these items, including any combination of a single item or multiple items. For example, at least one (item) of a, b, and c can represent: a, or b, or c, or a and b, or a and c, or b and c, or a, b, and c. Where a, b, and c can each be single or multiple.

[0549] In this application, "first", "second", and various numerical designations are for the convenience of description and do not limit the scope of the embodiments of this application. For example, to distinguish different messages, etc., rather than for describing a specific order or sequence. It should be understood that the objects described in this way can be interchanged under appropriate circumstances so as to be able to describe the solutions other than the embodiments of this application.

[0550] In this application, the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0551] In this application, "for indicating" can include for directly indicating and for indirectly indicating. When describing that a certain indication information is used to indicate A, it can include that the indication information directly indicates A or indirectly indicates A, and does not mean that the indication information must carry A. Among them, directly indicating information A means including the information A; implicitly indicating information A means indicating information A through the correspondence between information A and information B and the directly indicating information B. Among them, the correspondence between information A and information B can be predefined, pre-stored, pre-fired, or pre-configured.

[0552] It can be understood that some optional features in the embodiments of this application can, in some scenarios, be independent of other features, and in some scenarios, can be combined with other features, without limitation.

[0553] It can also be understood that in some of the above embodiments, sending information is mentioned multiple times. For example, "network element A sends information A to network element B" can be understood as the destination of information A or an intermediate network element in the transmission path between the destination and the source of information A is network element B, which may include directly or indirectly sending information to network element B. "Network element B receives information A from network element A" can be understood as the source of information A or an intermediate network element in the transmission path between the source and the destination of information A is network element A, which may include directly or indirectly receiving information from network element A. Necessary processing may be performed on the information between the source and the destination of the information transmission, such as format change, etc., but the destination can be understood as the valid information from the source. Similar expressions in this application can be understood similarly and will not be elaborated here.

[0554] It can also be understood that in some of the above embodiments, the AI model for positioning is mainly used as an example for illustrative purposes. It can be understood that the above AI model can also be used for other purposes.

[0555] It can also be understood that the solutions in the embodiments of the present application can be reasonably combined and used, and the explanations or descriptions of each term appearing in the embodiments can be referred to or explained with each other in each embodiment, which is not limited herein.

[0556] It can also be understood that in each of the above method embodiments, the methods and operations implemented by the first device or the positioning device can also be implemented by components (such as chips or circuits) that can be part of the first device or the positioning device, which is not limited.

[0557] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0558] Those skilled in the art can 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 foregoing method embodiments and will not be elaborated here.

[0559] In several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0560] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0561] In addition, in each embodiment of this application, each functional unit can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0562] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art or part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0563] As described above, only the specific implementation manners of this application are provided, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A communication method, characterized in that, executed by a first device or a chip or circuit of the first device, the method comprising: receiving configuration information from a core network element, the configuration information being used to indicate configuration parameters of a generation model; processing channel measurement results using a first model to obtain a probability distribution of measurement results of measurement quantities for positioning a terminal device, the first model being determined based on the configuration parameters of the generation model, and the measurement quantities corresponding to the channel measurement results.

2. The method according to claim 1, characterized in that, the method further comprises: sending first information to the core network element, the first information being used to indicate the probability distribution.

3. The method according to claim 1 or 2, characterized in that, the method further comprises: obtaining the type and / or function of the generation model.

4. A communication method, characterized in that, executed by a first device or a chip or circuit of the first device, the method comprising: obtaining configuration information, the configuration information being used to indicate configuration parameters of a generation model; processing channel measurement results using a first model to obtain a probability distribution of measurement results of measurement quantities for positioning a terminal device, the first model being determined based on the configuration parameters of the generation model, and the measurement quantities corresponding to the channel measurement results.

5. The method according to claim 4, characterized in that, the method further comprises: sending all or part of the first information and the configuration information to a core network element, the first information being used to indicate the probability distribution.

6. The method according to any one of claims 1 to 5, characterized in that, the channel measurement results are based on measurements of reference signals.

7. The method according to any one of claims 1 to 6, characterized in that, the generation model is any one of the following: Gaussian mixture model; Variational autoencoder; Generative adversarial network.

8. The method according to any one of claims 1 to 7, characterized in that, the generation model is a Gaussian mixture model, and the configuration parameters of the generation model include one or more of the following: the generation method of the Gaussian mixture model; the convergence threshold of the Gaussian mixture model; the maximum value of the number of iterations of the Gaussian mixture model; the model parameters of the Gaussian mixture model; the maximum value M of the number of single Gaussian models included in the Gaussian mixture model, M being a positive integer; the maximum value N of the expected values of the single Gaussian models included in the Gaussian mixture model, N being a positive number; the maximum value A of the variance or covariance of the single Gaussian models included in the Gaussian mixture model, A being a positive number; the proportion of one or more single Gaussian models included in the Gaussian mixture model in the Gaussian mixture model.

9. The method according to any one of claims 1 to 8, characterized in that, the generation model is a variational autoencoder, and the configuration parameters of the generation model include one or more of the following: the structural parameters of the variational autoencoder; the type of neural network used by the variational autoencoder; the number of neural network layers used by the variational autoencoder; the number of neurons included in the neural network used by the variational autoencoder; The input and / or output dimensions of the variational autoencoder; The values of the model parameters of the variational autoencoder.

10. The method according to any one of claims 1 to 9, wherein, The measured quantity includes one or more of the following: Reference Signal Time Difference (RSTD); Time Difference of Arrival (TDoA); Time of Arrival (ToA); Angle of Arrival (AoA); Line of Sight (LoS) probability.

11. The method according to claim 2 or 5, wherein, The generative model is a Gaussian mixture model, the Gaussian mixture model includes k single Gaussian models, k is an integer greater than or equal to 1, and the first information includes one or more of the following: The values of k expected values; The values of k variances or covariances; The values of the proportions of the k single Gaussian models in the Gaussian mixture model; wherein, the k expected values, the k variances or covariances correspond one-to-one with the k single Gaussian models.

12. The method according to claim 2 or 5, wherein, The generative model is a variational autoencoder, and the first information includes one or more of the following: The values of the model parameters of the variational autoencoder; The values of the probability distribution output by the variational autoencoder.

13. The method according to any one of claims 6 to 12, wherein, The channel measurement result is based on the measurement of a reference signal, including any one of the following: The first device is an access network device, the channel measurement result is based on a first channel measurement, and the first channel measurement includes: measuring the sounding reference signal from the terminal device; or, The first device is a terminal device, the channel measurement result is based on a second channel measurement, and the second channel measurement includes: measuring the positioning reference signal or channel state information reference signal from the access network device; or, The first device is a first terminal device, the channel measurement result is based on a third channel measurement, and the third channel measurement includes: measuring the sidelink positioning reference signal from a second terminal device.

14. A communication device, wherein, It includes a module for executing the method according to any one of claims 1 - 13.

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