User equipment positioning method and apparatus, electronic device, and storage medium

Abstract

The present disclosure provides a user equipment positioning method and device, electronic equipment and storage medium. The method comprises: obtaining K ranging results of K transmitting ends on a user equipment to be positioned; wherein K is an integer greater than or equal to 3; generating a predicted coordinate of the user equipment to be positioned according to the K ranging results; obtaining the coordinate of a verifying transmitting end; determining a first coordinate difference between the predicted coordinate and the coordinate of the verifying transmitting end; determining a second coordinate difference between the user equipment to be positioned and the verifying transmitting end according to the K ranging results and the coordinate of the verifying transmitting end; and determining the real coordinate of the user equipment to be positioned from the predicted coordinate of the user equipment to be positioned according to the first coordinate difference and the second coordinate difference. The method simplifies the ranging difficulty and the workload of ranging, saves communication resources, and uses a coordinate difference calculation method, which is simple to calculate and can save calculation resources.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a user equipment positioning method, apparatus, electronic device, and storage medium. Background Technology

[0002] User positioning is an important aspect of 5G operations, including indoor and outdoor positioning. Generally, satellite signals have difficulty penetrating walls, making them unsuitable for indoor positioning; therefore, indoor positioning primarily relies on base stations.

[0003] In the current 5G network architecture, user location can be calculated using various ranging results. However, multiple transmitters (remote radio units, RRUs) in an extended small cell base station share a single Physical Cell Identifier (PCI). In this case, the transmitters lack a unique identifier, making it impossible to distinguish the transmitter used for ranging based on the measurement reports (MRs) reported by the base station during the positioning process. Therefore, extended small cells cannot achieve user location using ordinary triangulation methods like satellite positioning.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this disclosure is to provide a user equipment positioning method, device, electronic device, and storage medium. This method simplifies the difficulty and workload of ranging and saves communication resources. The method of calculating coordinate differences is simple and can save computing resources.

[0006] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0007] This disclosure provides a user equipment (UE) positioning method, comprising: acquiring the coordinates of a verification transmitter corresponding to the UE to be located; acquiring K ranging results from K transmitters to the UE to be located, where K is an integer greater than or equal to 3; generating predicted coordinates of the UE to be located based on the K ranging results; determining a first coordinate difference between the predicted coordinates and the coordinates of the verification transmitter; determining a second coordinate difference between the UE to be located and the verification transmitter based on the K ranging results; and determining the true coordinates of the UE to be located from the predicted coordinates of the UE to be located based on the first coordinate difference and the second coordinate difference.

[0008] In an exemplary embodiment, generating the predicted coordinates of the user equipment to be located based on the K ranging results includes: inputting the K ranging results into N first neural network models to generate N predicted coordinates of the user equipment to be located, where N is an integer greater than or equal to 1, and N is the product of the number of transmitters contained in the K physical cells corresponding to the K PCIs.

[0009] In an exemplary embodiment, determining the second coordinate difference between the user equipment to be located and the verification transmitter based on the K ranging results includes: inputting the K ranging results into N second neural network models to obtain N second numerical differences between the user equipment to be located and the verification transmitter.

[0010] In an exemplary embodiment, the method further includes: obtaining the coordinates of a known user equipment when training the first neural network model; obtaining K groups of sample physical cells, wherein each group of sample physical cells corresponds to a PCI, and each group of sample physical cells includes one or more sample transmitters; randomly selecting a sample transmitter from each group of sample physical cells; performing ranging on the known user equipment through the K randomly selected sample transmitters to obtain K sample ranging results; and training the first neural network model based on the K sample ranging results and the coordinates of the known user equipment.

[0011] In an exemplary embodiment, the method further includes: determining a first training numerical difference between the coordinates of the known user equipment and the coordinates of the verification base station based on the coordinates of the known user equipment and the coordinates of the verification base station; and training a second neural network model based on the ranging results of the K samples and the first training numerical difference.

[0012] In an exemplary embodiment, determining a first coordinate difference between the predicted coordinates and the coordinates of the verification transmitter includes: determining N first coordinate differences between the N predicted coordinates and the coordinates of the verification transmitter; determining the true coordinates of the user equipment to be located from the predicted coordinates based on the first coordinate difference and the second coordinate difference includes: determining the true coordinates of the user equipment to be located from the N predicted coordinates based on the N first coordinate differences and the N second coordinate differences.

[0013] In an exemplary embodiment, determining the true coordinates of the user equipment to be located from the N predicted coordinates based on the N first coordinate differences and the N second coordinate differences includes: determining N gaps between the N first coordinate differences and the N second coordinate differences; and determining the predicted coordinates corresponding to the smallest gap among the N gaps as the true coordinates of the user equipment to be located.

[0014] This disclosure provides a user equipment positioning device, comprising: an acquisition module for acquiring the coordinates of a verification transmitter corresponding to a user equipment to be positioned; the acquisition module is further configured to acquire K ranging results from K transmitters to the user equipment to be positioned, wherein K is an integer greater than or equal to 3; a generation module for generating predicted coordinates of the user equipment to be positioned based on the K ranging results; a determination module for determining a first coordinate difference between the predicted coordinates and the coordinates of the verification transmitter; the determination module is further configured to determine a second coordinate difference between the user equipment to be positioned and the verification transmitter based on the K ranging results and the coordinates of the verification transmitter; and the determination module is further configured to determine the true coordinates of the user equipment to be positioned from the predicted coordinates of the user equipment to be positioned based on the first coordinate difference and the second coordinate difference.

[0015] This disclosure provides an electronic device, including: at least one processor; and a storage terminal device for storing at least one program, which, when executed by the at least one processor, causes the at least one processor to implement any of the user equipment positioning methods described above.

[0016] This disclosure provides a computer-readable storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements any of the above-described user equipment positioning methods.

[0017] The user equipment (UE) positioning method provided in this disclosure generates predicted coordinates of the UE based on K ranging results from K transmitters, determines a first coordinate difference between the predicted coordinates and the coordinates of the verification transmitter, and determines a second coordinate difference between the UE and the verification transmitter based on the K ranging results. Finally, it determines the true coordinates of the UE from the predicted coordinates based on the first and second coordinate differences. This method can be applied to scenarios where multiple transmitters share a Physical Cell Identifier (PCI). Based on a batch of ranging results, this method can simultaneously achieve positioning (i.e., obtaining the predicted coordinates of the UE) and verification (i.e., determining the true coordinates of the UE from the predicted coordinates). This method only requires the transmitter (or base station) to measure the distance to the UE, eliminating the need for transmitters (or base stations) to measure distances to each other, thus simplifying the ranging process and saving communication resources. Furthermore, the use of a coordinate difference calculation method simplifies the calculation, improves computational efficiency, and saves computational resources.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0020] Figure 1 This is a flowchart illustrating a user equipment positioning method according to an exemplary embodiment.

[0021] Figure 2 This is a schematic diagram illustrating the distribution of a transmitter, physical cell identifier, and user equipment, based on an example.

[0022] Figure 3 This is a flowchart illustrating another user equipment positioning method according to an exemplary embodiment.

[0023] Figure 4 This is a block diagram illustrating a user equipment positioning device according to an exemplary embodiment.

[0024] Figure 5 This is a schematic diagram of the structure of an electronic device according to an exemplary embodiment. Detailed Implementation

[0025] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0026] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor terminal devices and / or microcontroller terminal devices.

[0027] The steps of the user equipment positioning method in the exemplary embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings and examples.

[0028] Figure 1This is a flowchart illustrating a user equipment positioning method according to an exemplary embodiment. The method provided in this disclosure can be executed by a server, but this disclosure is not limited thereto.

[0029] like Figure 1 As shown, the user equipment positioning method provided in this embodiment may include the following steps.

[0030] In step S102, the coordinates of the verification transmitter corresponding to the user equipment to be located are obtained.

[0031] In this embodiment of the disclosure, a verification base station (the verification base station has only one transmitter) or a verification transmitter with known coordinates is required during the positioning process. In the following examples, the verification transmitter is used as an example. The PCI of the verification transmitter is different from the PCI of the following K transmitters, and the PCI of the verification transmitter is the unique identifier of the verification transmitter.

[0032] In extended base station positioning scenarios, at least three PCIs (PCI1, PCI2, PCI3) are required to range the user equipment, and an additional PCI (PCI4) is needed to verify the positioning results. Typical 5G network architectures combine macro and micro base stations; a mobile phone can receive information from multiple macro base stations and one micro base station. Since the macro base station locations are fixed, extended base stations in 5G networks can be used to locate user equipment.

[0033] Figure 2 This is a schematic diagram illustrating the distribution of a transmitter, physical cell identifier, and user equipment, based on an example.

[0034] refer to Figure 2 RRU10 is the verification transmitter, and its coordinates are known. The PCI corresponding to the verification transmitter is PCI4. This PCI4 identifier corresponds to only one transmitter.

[0035] In step S104, K ranging results from K transmitters to the user equipment to be located are obtained.

[0036] Where K is an integer greater than or equal to 3.

[0037] In this embodiment of the disclosure, at least three ranging results from three transmitters to the user equipment to be located can be obtained. The following is an example of obtaining three ranging results from three transmitters to the user equipment to be located. These three transmitters can correspond to three different PCIs.

[0038] In extended base station coverage scenarios, multiple transmitters located in the same physical cell use the same PCI. The transmitter can be, for example, an RRU (Remote Radio Unit). (Reference) Figure 2RRU1, RRU2 and RRU3 share one PCI (i.e., PCI1), RRU4, RRU5 and RRU6 share one PCI (i.e., PCI2), and RRU7, RRU8 and RRU9 share one PCI (i.e., PCI3).

[0039] In this embodiment of the disclosure, three ranging results of the user equipment (UE) to be located can be obtained from three transmitters. These three transmitters include one transmitter identified by PCI1 (one of RRU1, RRU2 and RRU3), one transmitter identified by PCI2 (one of RRU4, RRU5 and RRU6), and one transmitter identified by PCI3 (one of RRU7, RRU8 and RRU9), that is, there are 27 possible permutations and combinations.

[0040] It should be noted that, in Figure 2 The scenario shown is illustrated using the example of 3 transmitters per PCI. In practical applications, one PCI can correspond to one or more transmitters, and the number of transmitters corresponding to different PCIs can be different. This disclosure does not limit this.

[0041] In step S106, the predicted coordinates of the user equipment to be located are generated based on the K ranging results.

[0042] In this embodiment of the disclosure, N predicted coordinates of the user equipment to be located can be generated based on K ranging results, where N is an integer greater than or equal to 1, and N is the product of the number of transmitters contained in the K physical cells corresponding to the K PCIs.

[0043] In extended small cell positioning scenarios, each PCI corresponds to several different transmitters, meaning there is no unique identifier for each transmitter. During positioning, the base station assumes that all transmitters using the same PCI share a logical location and are therefore equivalent. However, in reality, transmitters sharing the same PCI have different geographical locations, meaning they are not equivalent.

[0044] In this embodiment of the disclosure, N predicted coordinates of the user equipment to be located can be predicted based on K ranging results and their corresponding PCIs, one of which is the actual coordinate of the user equipment to be located.

[0045] Let's take K=3 as an example. For instance, the transmitter corresponding to the first PCI has n1. RRU There are n2 transmitters corresponding to the second PCI. RRU There are n3 transmitters corresponding to the third PCI. RRU If there are n, then N = n1RRU *n2 RRU *n3 RRU .

[0046] by Figure 2 Taking the scenario shown as an example, there are 3 transmitters corresponding to PCI1, 3 transmitters corresponding to PCI2, and 3 transmitters corresponding to PCI3. The product of the transmitters contained in the 3 physical cells corresponding to these 3 PCIs is 27, that is, N = 27.

[0047] Still with Figure 2 Taking the scenario shown as an example, 27 predicted coordinates can be generated based on these 3 ranging results. Among these 27 predicted coordinates, there is the actual coordinate of a user device.

[0048] In an exemplary embodiment, generating predicted coordinates of a user device to be located based on K ranging results includes: inputting the K ranging results into N first neural network models to generate N predicted coordinates of the user device to be located.

[0049] The first neural network model can also be called the localization model.

[0050] Neural network models can be used for classification, clustering, function fitting, etc. Specifically, they can be used to map input data to output data. Different numbers of neurons and network structures can fit different mapping relationships.

[0051] In this embodiment of the disclosure, N first neural network models can be pre-trained. The first neural network models are used to fit the mapping relationship between a set of ranging results and the coordinates of the user equipment. By inputting K ranging results into the N trained first neural network models, N predicted coordinates of the user equipment to be located can be obtained.

[0052] In this embodiment of the disclosure, the first neural network model can be trained using the following method.

[0053] In an exemplary embodiment, the coordinates of a known user equipment are obtained; K groups of sample physical cells are obtained, wherein each group of sample physical cells corresponds to a PCI, and each group of sample physical cells includes one or more sample transmitters; a sample transmitter is randomly selected from each group of sample physical cells; the known user equipment is ranged using the K randomly selected sample transmitters to obtain K sample ranging results; and a first neural network model is trained based on the K sample ranging results and the coordinates of the known user equipment.

[0054] In this embodiment of the disclosure, ranging results from different transmitters to different user coordinates can be obtained. Transmitters with the same PCI are grouped together, and a transmitter is randomly selected from the same group for ranging. The ranging results of three or more different transmitters are used to generate a training data set. Multiple training data sets are used to train the first neural network model.

[0055] In this embodiment of the disclosure, the coordinates of multiple known user devices can be randomly generated. Taking the coordinates of one of the known user devices as an example, Figure 2 Taking the scenario shown as an example, one transmitter (e.g., RRU2) can be randomly selected from the transmitters identified by PCI1 (RRU1, RRU2, and RRU3), one transmitter (e.g., RRU4) can be randomly selected from the transmitters identified by PCI1 (RRU4, RRU5, and RRU6), and one transmitter (e.g., RRU7) can be randomly selected from the transmitters identified by PCI1 (RRU7, RRU8, and RRU9). The distance of the known user equipment is measured by the randomly selected RRU2, RRU4, and RRU7 respectively, and three sample distance measurement results are obtained. The first neural network model is trained based on these three sample distance measurement results and the coordinates of the known user equipment. That is, the three sample distance measurement results are input into the neural network model to be trained, the coordinates of the known user equipment are used as labels, and the predicted coordinates of the known user equipment output by the neural network model to be trained are compared with the above labels. By adjusting the parameters of the neural network model, the difference between the predicted coordinates of the known user equipment output by the neural network model and the above labels is less than a preset condition, so as to obtain the first neural network model.

[0056] Using the training method described above, 27 groups of randomly selected transmitters were obtained, resulting in 27 first neural network models. These 27 models correspond to 27 combinations of RRUs. In application, the three ranging results are input into each of the 27 first neural network models to obtain 27 predicted coordinates, one of which is the actual coordinate of the user equipment. For example, if the ranging results are obtained from RRU2, RRU4, and RRU7, then inputting these results into the first neural network model corresponding to RRU2, RRU4, and RRU7 will yield the actual coordinates of the user equipment.

[0057] In step S108, the first coordinate difference between the predicted coordinates and the verified coordinates of the transmitter is determined.

[0058] In this embodiment of the disclosure, both the predicted coordinates and the coordinates of the verification transmitter can be three-dimensional coordinates. For example, if the predicted coordinates are (x1, y1, z1) and the coordinates of the verification transmitter are (x0, y0, z0), then the numerical difference can be (x1-x0, y1-y0, z1-z0).

[0059] In an exemplary embodiment, determining the first coordinate difference between the predicted coordinates and the coordinates of the verification transmitter includes: determining N first coordinate differences between N predicted coordinates and the coordinates of the verification transmitter.

[0060] In this embodiment of the disclosure, N first coordinate differences between N predicted coordinates and the coordinates of the verification transmitter can be calculated.

[0061] In step S110, the second coordinate difference between the user equipment to be located and the verification transmitter is determined based on K ranging results.

[0062] In this embodiment of the disclosure, the N second coordinate difference between the user equipment to be located and the verification transmitter can be determined based on K ranging results.

[0063] In an exemplary embodiment, determining the second coordinate difference between the user equipment to be located and the verification transmitter based on K ranging results includes: inputting the K ranging results into N second neural network models to obtain N second numerical differences between the user equipment to be located and the verification transmitter.

[0064] The second neural network model can also be called the validation model.

[0065] In this embodiment of the disclosure, N second neural network models can be pre-trained. The second neural network models are used to fit the mapping relationship between a set of ranging results and the corresponding numerical difference between (user equipment coordinates - verification transmitter coordinates). By inputting K ranging results and the coordinates of the verification transmitter into the N trained second neural network models, N second numerical differences between the user equipment to be located and the verification transmitter can be obtained.

[0066] In this embodiment of the disclosure, the second neural network model can be trained using the following method.

[0067] In an exemplary embodiment, a first training numerical difference between the coordinates of the known user equipment and the coordinates of the verification base station is determined based on the coordinates of the known user equipment and the coordinates of the verification base station; a second neural network model is trained based on the ranging results of K samples and the first training numerical difference.

[0068] In this embodiment of the disclosure, the coordinates of the known user equipment and the K sample ranging results used in the second neural network model are the same as or similar to those in the first neural network model, and will not be repeated here.

[0069] In this embodiment of the disclosure, the difference between the coordinates of the known user and the coordinates of the verification base station is used as the first training numerical difference (i.e., the label of the second neural network model), so as to... Figure 2Taking the scenario shown as an example, a second neural network model is trained based on the difference between the three sample ranging results and the first training numerical value. That is, the three sample ranging results are input into the neural network model to be trained, the difference between the coordinates of the known user equipment and the verification base station is used as the label, the predicted coordinate difference output by the neural network model to be trained is compared with the above label, and the parameters of the neural network model are adjusted so that the difference between the predicted coordinate difference output by the neural network model and the above label is less than a preset condition, so as to obtain the second neural network model.

[0070] In this embodiment of the disclosure, different second neural network models can be trained using different verification base stations.

[0071] Using the training method described above, 27 groups of randomly selected transmitters were obtained, thus 27 second neural network models could be trained. These 27 second neural network models correspond to 27 combinations of RRUs. In the application process, the three ranging results are input into the 27 second neural network models respectively, and 27 predicted coordinate differences (i.e., second coordinate differences) can be obtained.

[0072] In step S112, the actual coordinates of the user equipment to be located are determined from the predicted coordinates of the user equipment to be located based on the first coordinate difference and the second coordinate difference.

[0073] In this embodiment of the disclosure, the predicted coordinates of the user equipment to be located corresponding to the closest first coordinate difference and second coordinate difference can be determined as the true coordinates.

[0074] In an exemplary embodiment, determining the true coordinates of the user equipment to be located from the predicted coordinates based on the first coordinate difference and the second coordinate difference includes: determining the true coordinates of the user equipment to be located from the N predicted coordinates based on N first coordinate differences and N second coordinate differences.

[0075] In an exemplary embodiment, determining the true coordinates of the user equipment to be located from N predicted coordinates based on N first coordinate differences and N second coordinate differences includes: determining N gaps between the N first coordinate differences and N second coordinate differences respectively; and determining the predicted coordinates corresponding to the smallest gap among the N gaps as the true coordinates of the user equipment to be located.

[0076] As mentioned above Figure 2 Taking the scenario shown as an example, 27 first coordinate differences and 27 second coordinate differences are obtained through the above steps. The 27 first coordinate differences can be subtracted from the corresponding 27 second coordinate differences to obtain 27 gaps. The predicted coordinates corresponding to the smallest gap among these 27 gaps are determined as the true coordinates of the user device to be located.

[0077] For example, if the difference between the first coordinate difference and the second coordinate difference corresponding to the ranging results obtained by RRU2, RRU4 and RRU7 for ranging the user equipment is the smallest, then the ranging results obtained by RRU2, RRU4 and RRU7 for ranging the user equipment are the true coordinates of the user equipment.

[0078] The formula for calculating the difference can be: (abs(dif) 1x –dif 2x )+abs(dif 1y –dif 2y )+abs(dif 1z –dif 2z )) / 3,dif 1x The first coordinate difference in the X direction, dif 1y The first coordinate difference in the Y direction, dif 1z The first coordinate difference in the Z direction, dif 2x The second coordinate difference in the X direction, dif 2y The second coordinate difference in the Y direction, dif 2z denoted as the second coordinate difference in the Z direction, and abs is the absolute value.

[0079] The positioning principle in this scenario is as follows: if the coordinates of the transmitter are known, and the ranging result from the transmitter to the user equipment is known, then the user's coordinates can be calculated based on the principles of trilateration or trilateration. Therefore, in the application scenario of extended base stations with unknown transmitters, as long as the correct transmitter can be found, the coordinates of the user equipment can be calculated based on the ranging result.

[0080] In related technologies, a method for ranging a verification base station is verified using three positioning base stations. Since the coordinates of the verification base station are known, using three positioning base stations to range the verification base station allows the identification of the three corresponding transmitters based on the verification base station's actual and predicted coordinates. Because different transmitters are located in different geographical locations, their ranging results with the verification base station will also differ; transmitters in different geographical locations, using the same ranging result, will predict completely different verification base station coordinate values.

[0081] However, the verification process of the verification method in related technologies requires the location base station to simultaneously perform ranging between the verification base station and the user equipment. The first step requires calculating the ranging result from the transmitter to the unknown user equipment with all feasible transmitter coordinates to predict n1. RRU *n2 RRU *n3 RRU The second step involves calculating the distance measurement results from the transmitter to the verification base station, along with all feasible transmitters, to predict n1. RRU *n2 RRU *n3RRU The third step is to determine the coordinates of the corresponding verification base station based on the n1 of the verification base station. RRU *n2 RRU *n3 RRU Given the predicted and actual coordinates, select the three correct transmitters. Fourth step: Based on the three correct transmitters, start from n1... RRU *n2 RRU *n3 RRU From the predicted user coordinates, the correct user coordinates are selected. Therefore, the verification methods in related technologies are complex and involve a large amount of distance measurement work.

[0082] This embodiment of the disclosure uses a coordinate difference method for verification. Specifically, it subtracts the verification base station coordinates from the user coordinates to obtain a numerical difference. This numerical difference retains both the base station's coordinate and orientation information, equivalent to performing a coordinate transformation on the user coordinates with the verification base station as a reference point. Therefore, if a neural network is used to extract information and train parameters, the base station's coordinates and related orientation information can be obtained from the numerical difference.

[0083] Specifically, in this embodiment, the first neural network is used to train the mapping from the ranging result of the transmitter to the user coordinates, and the second neural network is used to train the mapping from the ranging result of the transmitter to the numerical difference between the user coordinates and the coordinates of the verification base station. Since the numerical difference is equivalent to a numerical transformation of the user coordinates with the verification base station as a reference point, the second neural network is equivalent to the mapping from the ranging result to the user coordinates, while retaining the base station coordinates and direction information. At this point, only the ranging result from the transmitter to the user equipment is needed to train two different neural networks, and the correct user coordinates can be predicted according to the above positioning principle. The method of calculating the numerical difference greatly simplifies the ranging difficulty and workload.

[0084] The user equipment (UE) positioning method provided in this disclosure generates predicted coordinates of the UE based on K ranging results from K transmitters. It then determines a first coordinate difference between the predicted coordinates and the coordinates of a verification transmitter. A second coordinate difference between the UE and the verification transmitter is determined based on the K ranging results and the verification transmitter's coordinates. Finally, the true coordinates of the UE are determined from the predicted coordinates based on the first and second coordinate differences. This method can be applied to scenarios where multiple transmitters share a Physical Cell Identifier (PCI). Based on a batch of ranging results, this method can simultaneously achieve positioning (i.e., obtaining the predicted coordinates of the UE) and verification (i.e., determining the true coordinates of the UE from the predicted coordinates). This method only requires the transmitter (or base station) to measure the distance to the UE, eliminating the need for transmitters (or base stations) to measure distances to each other, thus simplifying the ranging process and saving communication resources. Furthermore, the use of a coordinate difference calculation method simplifies the calculation, improves computational efficiency, and saves computational resources.

[0085] In some embodiments of this disclosure, a first neural network model and a second neural network model are used to solve the user positioning problem, which can achieve batch user positioning, has strong portability, and the system model is in line with the current 5G network mode. A neural network training method using the numerical difference between user coordinates and verification base station coordinates is designed to simplify the calculation steps and reduce the ranging workload, while retaining base station coordinate information and direction information, thereby achieving equivalent user positioning function.

[0086] Figure 3 This is a flowchart illustrating another user equipment positioning method according to an exemplary embodiment.

[0087] Figure 3 The method shown can be executed by 6 modules, namely: training data generation module 1, training data generation module 2, neural network generation module 1, neural network generation module 2, verification module 1, and verification module 2.

[0088] like Figure 3 As shown, the user equipment positioning method provided in this embodiment may include the following steps.

[0089] In step S301, the training data generation module 1 randomly generates a batch of three-dimensional coordinates of user equipment.

[0090] In step S302, the training data generation module 1 acquires ranging results from three or more transmitters (with different PCI values) and user equipment.

[0091] In step S303, the training data generation module 1 takes the ranging result as input and the three-dimensional coordinates of the user device as output to generate the first set of training data, and then passes the three-dimensional coordinates of the user device and the ranging result to the neural network generation module 1.

[0092] In step S304, the training data generation module 1 transmits the three-dimensional coordinates and ranging results of the user device to the training data generation module 2.

[0093] In step S305, the training data generation module 2 uses the data input from the training data generation module 1 to calculate the numerical difference between the user's three-dimensional coordinates and the verification base station's three-dimensional coordinates.

[0094] In step S306, the training data generation module 2 transmits the ranging result and the numerical difference between the user's three-dimensional coordinates and the verification base station's three-dimensional coordinates to the neural network generation module 2.

[0095] In step S307, the neural network training module 1 takes the ranging result as input and the three-dimensional coordinates of the user equipment as output to train the neural network 1 (also known as the positioning model).

[0096] Neural network generation module 1 can use the first set of training data, the same neural network structure and the same set of parameters, to train n1 RRU *n2 RRU *n3 RRU There are n localization models. i RRU It represents the number of transmitters using the i-th PCI.

[0097] In step S308, the neural network training module 1 outputs the localization model to the verification module 1.

[0098] In step S309, the neural network training module 2 takes the ranging result as input and the three-dimensional coordinate difference between the user equipment and the verification base station as output to train the neural network 2 (also known as the verification model).

[0099] Neural network generation module 2 can use a second set of training data, a different neural network structure, and a different set of parameters to train n1. RRU *n2 RRU *n3 RRU A validation model.

[0100] In step S310, the neural network training module 2 outputs the verification model to the verification module 2.

[0101] In step S311, the verification module 1 obtains the user request and randomly selects one transmitter from multiple transmitters of the same PCI for ranging. Multiple PCIs can obtain multiple ranging results.

[0102] In step S312, the verification module 1 uses different positioning models to predict the coordinates of multiple unknown users.

[0103] In step S313, the verification module 1 subtracts the three-dimensional coordinates of the verification base station from the predicted unknown user coordinates to obtain several numerical differences of 1.

[0104] In step S314, the verification module 1 transmits the ranging result to the verification module 2.

[0105] In step S315, the verification module 1 outputs the predicted numerical difference (i.e., the numerical difference 1 mentioned above) to the verification module 2.

[0106] In step S316, the verification module 2 uses the ranging results and different verification models to predict the numerical difference between the coordinates of the unknown user and the coordinates of the verification base station, and obtains the numerical difference 2.

[0107] Verification module 2 obtains all data from verification module 1, acquires the ranging results from the unknown user to the three unknown transmitters, and uses n1 RRU *n2 RRU *n3 RRU A validation model is generated, producing n1. RRU *n2 RRU *n3 RRU The difference between the predicted values ​​is denoted as value difference 2.

[0108] In step S317, the verification module compares numerical difference 1 and numerical difference 2, and the prediction result with the smallest difference is the user's accurate coordinates.

[0109] For specific implementation methods, please refer to the other embodiments described above.

[0110] It should also be understood that the above is only to help those skilled in the art better understand the embodiments of this disclosure, and is not intended to limit the scope of the embodiments of this disclosure. Those skilled in the art can obviously make various equivalent modifications or changes based on the examples given above. For example, some steps in the above methods may be unnecessary, or new steps may be added, etc. Alternatively, any combination of any two or more of the above embodiments may be used. Such modifications, changes, or combinations also fall within the scope of the embodiments of this disclosure.

[0111] It should also be understood that the above description of the embodiments of this disclosure focuses on highlighting the differences between the various embodiments. Similarities or differences not mentioned can be referred to each other, and for the sake of brevity, they will not be repeated here.

[0112] It should also be understood that the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this disclosure.

[0113] It should also be understood that, in the various embodiments of this disclosure, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions between different embodiments are consistent and can be referenced by each other, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0114] The foregoing has detailed examples of user equipment positioning methods provided in this disclosure. It is understood that, in order to implement the above functions, computer devices include corresponding hardware structures and / or software modules for performing each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this disclosure can be implemented in hardware or a combination of hardware and computer software. Whether a function is implemented in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0115] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0116] Figure 4 This is a block diagram illustrating a user equipment positioning device according to an exemplary embodiment.

[0117] like Figure 4 As shown, the user equipment positioning device 400 may include: an acquisition module 402, a generation module 404, and a determination module 406.

[0118] The acquisition module 402 is used to acquire the coordinates of the verification transmitter corresponding to the user equipment to be located; the acquisition module 402 is also used to acquire K ranging results of K transmitters for the user equipment to be located, wherein; the generation module 404 is used to generate the predicted coordinates of the user equipment to be located based on the K ranging results; the determination module 406 is used to determine a first coordinate difference between the predicted coordinates and the coordinates of the verification transmitter; the determination module 406 is also used to determine a second coordinate difference between the user equipment to be located and the verification transmitter based on the K ranging results; the determination module 406 is also used to determine the true coordinates of the user equipment to be located from the predicted coordinates of the user equipment to be located based on the first coordinate difference and the second coordinate difference.

[0119] In an exemplary embodiment, the generation module 404 is further configured to input the K ranging results into N first neural network models to generate N predicted coordinates of the user equipment to be located, wherein N is an integer greater than or equal to 1, and N is the product of the number of transmitters contained in the K physical cells corresponding to the K PCIs.

[0120] In an exemplary embodiment, the determining module 406 is further configured to input the K ranging results into N second neural network models to obtain N second numerical differences between the user equipment to be located and the verification transmitter.

[0121] In an exemplary embodiment, the user equipment positioning device 400 further includes: a ranging module and a training module; the acquisition module 402 is further configured to acquire the coordinates of a known user equipment when training the first neural network model; the acquisition module 402 is further configured to acquire K groups of sample physical cells, wherein each group of sample physical cells corresponds to a PCI, and each group of sample physical cells includes one or more sample transmitters; the acquisition module 402 is further configured to randomly select one sample transmitter from each group of sample physical cells; the ranging module is configured to perform ranging on the known user equipment through the K randomly selected sample transmitters respectively, and obtain K sample ranging results; the training module is configured to train the first neural network model based on the K sample ranging results and the coordinates of the known user equipment.

[0122] In an exemplary embodiment, the user equipment positioning device 400 further includes: a determining module 406 further configured to determine a first training numerical difference between the coordinates of the known user equipment and the coordinates of the verification base station based on the coordinates of the known user equipment and the coordinates of the verification base station; and a training module further configured to train a second neural network model based on the ranging results of the K samples and the first training numerical difference.

[0123] In an exemplary embodiment, the determining module 406 is further configured to determine N first coordinate differences between the N predicted coordinates and the coordinates of the verification transmitter; and to determine the true coordinates of the user equipment to be located from the predicted coordinates based on the first coordinate differences and the second coordinate differences, including: determining the true coordinates of the user equipment to be located from the N predicted coordinates based on the N first coordinate differences and the N second coordinate differences.

[0124] In an exemplary embodiment, the determining module 406 is further configured to determine N gaps between the N first coordinate differences and the N second coordinate differences; and to determine the predicted coordinates corresponding to the smallest gap among the N gaps as the true coordinates of the user equipment to be located.

[0125] It should be noted that the block diagrams shown in the above figures are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor terminal devices and / or microcontroller terminal devices.

[0126] Figure 5 This is a schematic diagram illustrating the structure of an electronic device according to an exemplary embodiment. It should be noted that... Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0127] like Figure 5 As shown, the electronic device 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage section 508 into a random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the system 500. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0128] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 510 as needed so that computer programs read from it can be installed into storage section 508 as needed.

[0129] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs the functions defined above in the system of this disclosure.

[0130] It should be noted that the computer-readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, terminal device, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, terminal device, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit programs for use by or in connection with an instruction execution system, terminal device, or apparatus. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0131] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0132] The units described in the embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor can be described as including a sending unit, an acquisition unit, a determining unit, and a first processing unit. The names of these units do not necessarily limit the specific unit; for example, a sending unit can also be described as "a unit that sends an image acquisition request to a connected server."

[0133] In another aspect, this disclosure also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to perform the methods described in the following embodiments. For example, the electronic device may perform... Figure 1 The steps shown.

[0134] According to one aspect of this disclosure, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in various optional implementations of the above embodiments.

[0135] It should be understood that any number of elements in the accompanying drawings is for illustrative purposes only and not for limitation, and any naming is for distinction only and has no limiting meaning.

[0136] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0137] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A user equipment positioning method, characterized in that, include: Obtain the coordinates of the verification transmitter corresponding to the user equipment to be located; Obtain K ranging results from K transmitters to the user equipment to be located, where K is an integer greater than or equal to 3; The K ranging results are input into N first neural network models to generate N predicted coordinates of the user equipment to be located, where N is an integer greater than or equal to 1, and N is the product of the number of transmitters contained in the K physical cells corresponding to the K PCIs. Determine a first coordinate difference between the predicted coordinates and the coordinates of the verification transmitter; The K ranging results are input into N second neural network models to obtain N second coordinate differences between the user equipment to be located and the verification transmitter. The true coordinates of the user equipment to be located are determined from the predicted coordinates of the user equipment to be located based on the first coordinate difference and the second coordinate difference.

2. The method according to claim 1, characterized in that, Also includes: When training the first neural network model, the coordinates of the known user device are obtained; Obtain K groups of sample physical cells, where each group of sample physical cells corresponds to one PCI, and each group of sample physical cells includes one or more sample transmitters; Randomly select one transmitter from each group of physical cells; Ranging is performed on the known user equipment by randomly selecting K sample transmitters, and K sample ranging results are obtained. The first neural network model is trained based on the ranging results of the K samples and the coordinates of the known user equipment.

3. The method according to claim 2, characterized in that, Also includes: Based on the coordinates of the known user equipment and the coordinates of the verification base station, determine the first training value difference between the coordinates of the known user equipment and the coordinates of the verification base station; The second neural network model is trained based on the distance measurement results of the K samples and the difference between the first training values.

4. The method according to claim 1, characterized in that, Determining the first coordinate difference between the predicted coordinates and the coordinates of the verification transmitter includes: determining N first coordinate differences between the N predicted coordinates and the coordinates of the verification transmitter; Determining the true coordinates of the user equipment to be located from the predicted coordinates based on the first coordinate difference and the second coordinate difference includes: determining the true coordinates of the user equipment to be located from the N predicted coordinates based on the N first coordinate differences and the N second coordinate differences.

5. The method according to claim 4, characterized in that, Determining the true coordinates of the user equipment to be located from the N predicted coordinates based on the N first coordinate differences and the N second coordinate differences includes: Determine the N differences between the N first coordinate differences and the N second coordinate differences respectively; The predicted coordinates corresponding to the smallest of the N differences are determined as the true coordinates of the user equipment to be located.

6. A user equipment positioning device, characterized in that, include: The acquisition module is used to acquire the coordinates of the verification transmitter corresponding to the user equipment to be located; The acquisition module is also used to acquire K ranging results from K transmitters to the user equipment to be located, where K is an integer greater than or equal to 3; The generation module is used to input the K ranging results into N first neural network models to generate N predicted coordinates of the user equipment to be located, where N is an integer greater than or equal to 1, and N is the product of the number of transmitters contained in the K physical cells corresponding to the K PCIs; A determining module is used to determine a first coordinate difference between the predicted coordinates and the coordinates of the verification transmitter; The determining module is further configured to input the K ranging results into N second neural network models to obtain N second coordinate differences between the user equipment to be located and the verification transmitter; The determining module is further configured to determine the actual coordinates of the user equipment to be located from the predicted coordinates of the user equipment to be located based on the first coordinate difference and the second coordinate difference.

7. An electronic device, characterized in that, include: At least one processor; A storage device for storing at least one program, which, when executed by the at least one processor, causes the at least one processor to implement the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 5.

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