Method, device and computer readable storage medium for determining a transmitting end

Abstract

The present disclosure relates to a method and device for determining a transmitting end and a computer readable storage medium, and relates to the field of communication technology. The method comprises: obtaining physical cell identifiers (PCIs) of at least three transmitting ends to be determined which do not belong to the same cell, ranging results of the terminal to each of the transmitting ends to be determined, and position information of the terminal; determining all transmitting end groups corresponding to the PCIs, wherein each transmitting end group contains one transmitting end selected from a transmitting end set corresponding to each of the PCIs; for each transmitting end group, determining predicted position information of the terminal according to the ranging results of the terminal to each of the transmitting ends to be determined and a prediction model corresponding to the transmitting end group; and determining a transmitting end group to which the transmitting end to be determined belongs according to a difference between the position information of the terminal and each of the predicted position information.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a method, apparatus and computer-readable storage medium for determining a transmitter. Background Technology

[0002] The Remote Radio Unit (RRU) is a crucial component of 5G networks. As a transmitter, the RRU plays a vital role in various functions of the 5G operating system. In current 5G networks, transmitter RRUs within the same physical cell use the same PCI (Physical Cell Identifier), making it impossible to distinguish between transmitters within the same physical cell.

[0003] In 5G application scenarios, troubleshooting and power control at the transmitter end require accurate differentiation of the transmitter end. Summary of the Invention

[0004] The inventors discovered that when a base station or system automatically controls each transmitter, measurement reports (MRs) can be used to determine how to adjust the power of each transmitter or troubleshoot faults. However, in extended base station coverage scenarios, multiple transmitters located in the same physical cell use the same PCI, resulting in the absence of identifiers in the MRs to distinguish the transmitters. Similarly, terminals cannot distinguish transmitters using the same PCI.

[0005] One technical problem this disclosure aims to solve is: how to accurately distinguish between different transmitters when multiple transmitters in the same physical cell use the same PCI. According to some embodiments of this disclosure, a method for determining a transmitter is provided, comprising: obtaining the Physical Cell Identifiers (PCIs) of at least three transmitters to be determined that do not belong to the same cell, ranging results from the terminal to each transmitter to be determined, and the terminal's location information; determining all transmitter groups corresponding to the PCI, wherein each transmitter group includes one transmitter selected from the transmitter set corresponding to each PCI; for each transmitter group, determining the terminal's predicted location information based on the ranging results from the terminal to each transmitter to be determined and the prediction model corresponding to the transmitter group; and determining the transmitter group to which the transmitter to be determined belongs based on the differences between the terminal's location information and the predicted location information.

[0006] In some embodiments, obtaining the Physical Cell Identifiers (PCIs) of at least three transmitters to be determined that do not belong to the same cell includes: obtaining a measurement report corresponding to the terminal; and selecting the PCIs of at least three transmitters to be determined with different PCIs from the measurement report.

[0007] In some embodiments, determining the transmitter group to which the transmitter to be determined belongs based on the difference between the terminal's location information and each predicted location information includes: determining the transmitter group corresponding to the predicted location information with the smallest difference from the terminal's location information as the transmitter group to which the transmitter to be determined belongs.

[0008] In some embodiments, the method further includes: during training, selecting at least three PCIs each time, and selecting one transmitter from the transmitter set corresponding to each PCI to form a transmitter group, wherein the number of PCIs selected each time is the same, and the selected PCIs are not exactly the same each time; for each transmitter group, using the location information of the sample terminals marked in the training sample set and the ranging results from the sample terminals to each transmitter in the transmitter group, training the prediction model corresponding to the transmitter group.

[0009] In some embodiments, training the prediction model corresponding to the transmitter group using the location information of the sample terminals in the training sample set and the ranging results from the sample terminals to each transmitter in the transmitter group includes: inputting the ranging results from the sample terminals to each transmitter in the transmitter group into the prediction model corresponding to the transmitter group to obtain the output location information of the sample terminals; and adjusting the parameters of the prediction model according to the difference between the output location information of the sample terminals and the location information of the labeled sample terminals in order to train the prediction model corresponding to the transmitter group.

[0010] In some embodiments, the ranging results from the terminal to each transmitter to be determined include at least one of the following: the straight-line distance from the terminal to each transmitter to be determined calculated based on the tracking area, the straight-line distance from the terminal to each transmitter to be determined calculated based on the measurement signal transmitted by the terminal or the transmitter to be determined, and the azimuth angle, angle of arrival, and angle of departure calculated based on the field strength.

[0011] In some embodiments, the prediction model corresponding to each transmitter group is a neural network model with the same network structure.

[0012] According to some other embodiments of this disclosure, a transmitter determination apparatus is provided, comprising: an acquisition module, configured to acquire Physical Cell Identifiers (PCIs) of at least three transmitters to be determined that do not belong to the same cell, ranging results from the terminal to each transmitter to be determined, and the terminal's location information; a transmitter group determination module, configured to determine all transmitter groups corresponding to the PCIs, wherein each transmitter group includes a transmitter selected from the transmitter set corresponding to each PCI; a prediction module, configured to determine the predicted location information of the terminal for each transmitter group based on the ranging results from the terminal to each transmitter to be determined and the prediction model corresponding to the transmitter group; and a transmitter determination module, configured to determine the transmitter group to which the transmitter to be determined belongs based on the difference between the terminal's location information and each predicted location information.

[0013] In some embodiments, the acquisition module is used to acquire the measurement report corresponding to the terminal, and select at least three PCIs of the transmitter with different PCIs from the measurement report.

[0014] In some embodiments, the transmitter determination module is used to determine the transmitter group corresponding to the predicted location information with the smallest difference from the terminal's location information as the transmitter group to which the transmitter to be determined belongs.

[0015] In some embodiments, the device further includes: a training module, configured to select at least three PCIs each time during training, and select one transmitter from the transmitter set corresponding to each PCI to form a transmitter group, wherein the number of PCIs selected each time is the same, and the selected PCIs are not exactly the same each time; for each transmitter group, the prediction model corresponding to the transmitter group is trained using the location information of the sample terminals marked in the training sample set and the ranging results from the sample terminals to each transmitter in the transmitter group.

[0016] In some embodiments, the training module is used to input the ranging results from the sample terminal to each transmitter in the transmitter group into the prediction model corresponding to the transmitter group to obtain the output position information of the sample terminal; and adjust the parameters of the prediction model according to the difference between the output position information of the sample terminal and the labeled position information of the sample terminal so as to train the prediction model corresponding to the transmitter group.

[0017] According to some other embodiments of the present disclosure, a transmitter determination apparatus is provided, comprising: a processor; and a memory coupled to the processor for storing instructions, which, when executed by the processor, cause the processor to perform a transmitter determination method as described in any of the foregoing embodiments.

[0018] According to further embodiments of the present disclosure, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, wherein the program, when executed by a processor, implements the method for determining the transmitter of any of the foregoing embodiments.

[0019] In this disclosure, transmitters belonging to different cells are grouped into transmitter groups, and prediction models corresponding to each transmitter group are trained. For at least three transmitters not belonging to the same cell, the ranging results from the terminal to each transmitter can be obtained. Combined with the prediction models corresponding to each transmitter group, multiple predicted location information of the terminal is obtained. Then, based on the differences between the terminal's location information and each predicted location information, the transmitter group to which the transmitter belongs is determined. The method of this disclosure can solve the problem of multiple transmitters in the same physical cell using the same PCI, making it impossible to distinguish between multiple transmitters, and provides support for application scenarios in 5G that require transmitter differentiation.

[0020] Other features and advantages of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating a method for determining the transmitter in some embodiments of this disclosure is shown.

[0023] Figure 2 The diagram illustrates some application scenarios of embodiments of this disclosure.

[0024] Figure 3 A flowchart illustrating a method for determining the transmitter according to other embodiments of this disclosure is shown.

[0025] Figure 4 A schematic diagram of the structure of a transmitter determining device according to some embodiments of the present disclosure is shown.

[0026] Figure 5 A schematic diagram of the structure of a transmitter determining device according to other embodiments of this disclosure is shown.

[0027] Figure 6 A schematic diagram of the structure of a transmitter determining device is shown in some further embodiments of this disclosure. Detailed Implementation

[0028] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0029] This solution addresses the problem of inaccurate differentiation between transmitters using the same PCI within the same physical cell. The following section combines... Figures 1-3 Some embodiments of the method for determining the transmitter of this disclosure are described.

[0030] Figure 1Flowcharts are shown for some embodiments of the method for determining the transmitter of this disclosure. For example... Figure 1 As shown, the method of this embodiment includes steps S102 to S108.

[0031] In step S102, the PCI of at least three transmitters to be determined that do not belong to the same cell is obtained, along with the ranging results from the terminal to each transmitter to be determined and the terminal's location information.

[0032] In some embodiments, a measurement report (MR) corresponding to the terminal is obtained, and at least three transmitters with different PCIs are selected from the MR. The MR contains multiple measurement information items and the corresponding PCI for each measurement item, but it is not possible to distinguish which transmitter using the same PCI corresponds to the measurement information. Figure 2 As shown, multiple transmitters use the same PCI to serve users and report quality; the terminal can receive signals from several transmitters; transmitters cannot be distinguished by PCI or other obvious identifiers; the server backend and the terminal cannot determine which transmitter transmitted the signal, but can only determine that one or more transmitters with the same PCI sent a signal to the user.

[0033] For example, at least three measurement information items are selected sequentially from high to low signal quality, and the PCI corresponding to these three measurement information items is different. The transmitter corresponding to the selected measurement information item is the transmitter to be determined. Alternatively, the transmitter corresponding to each measurement information item can be used as the transmitter to be determined, and at least three measurement information items are randomly selected each time, with the PCI corresponding to these three measurement information items being different.

[0034] In some embodiments, the ranging results from the terminal to each transmitter to be determined include at least one of the following: a straight-line distance from the terminal to each transmitter to be determined calculated based on the tracking area; a straight-line distance from the terminal to each transmitter to be determined calculated based on the measurement signal transmitted by the terminal or the transmitter to be determined; and an azimuth angle, angle of arrival, and angle of departure calculated based on the field strength, not limited to the examples given. The ranging results are obtained after the terminal receives the signal from the transmitter to be determined and correspond to the transmitter to be determined.

[0035] In step S104, all transmitter groups corresponding to the acquired PCI are determined.

[0036] Each transmitter group contains one transmitter selected from the transmitter set corresponding to each PCI. Assume that in step S102, three different PCIs were obtained: PCI1, PCI2, and PCI3. The transmitter set corresponding to PCI1 contains n1 transmitters. RRU There are n2 transmitters, and the set of transmitters corresponding to PCI2 contains n2. RRUThere are n3 transmitters, and the set of transmitters corresponding to PCI3 contains n3. RRU If there are 1 transmitter, then the transmitter groups corresponding to the three different PCIs are: Each PCI has a set of transmitters that can be distinguished by geographic location information (e.g., geodetic coordinates or relative coordinates) as a unique identifier, and is not limited to the examples given.

[0037] In step S106, for each transmitter group, the predicted position information of the terminal is determined based on the ranging results of the terminal to each transmitter to be determined and the prediction model corresponding to the transmitter group.

[0038] Each transmitter group corresponds to a pre-trained prediction model. For each transmitter group, the ranging results from the terminal to each transmitter to be determined are input into the corresponding prediction model to obtain the predicted position information of the terminal. For example, three different PCIs correspond to the following transmitter groups: If there are , then the corresponding prediction models are . The predicted location information of the obtained terminal is also available. indivual.

[0039] In step S108, the transmitter group to which the transmitter to be determined belongs is determined based on the difference between the terminal's location information and each predicted location information.

[0040] In some embodiments, the transmitter group corresponding to the predicted location information with the smallest difference from the terminal's location information is determined as the transmitter group to which the transmitter to be determined belongs. For example, calculating... The predicted location information differs from the terminal's location information. The transmitter group corresponding to the predicted location information with the smallest difference is selected. Then, the transmitters to be determined are the three transmitters in that transmitter group.

[0041] In the above embodiments, transmitters belonging to different cells form transmitter groups, and prediction models corresponding to each transmitter group are trained. For at least three transmitters not belonging to the same cell, the ranging results from the terminal to each transmitter can be obtained. Combined with the prediction models corresponding to each transmitter group, multiple predicted location information of the terminal is obtained. Then, based on the difference between the terminal's location information and each predicted location information, the transmitter group to which the transmitter belongs is determined. The method of the above embodiments can solve the problem that multiple transmitters in the same physical cell use the same PCI and cannot distinguish between multiple transmitters, providing support for application scenarios in 5G that require transmitter differentiation.

[0042] The following is combined with Figure 3 This describes a method for obtaining the prediction model corresponding to each transmitter group.

[0043] Figure 3 Flowcharts are shown for some other embodiments of the method for determining the transmitter of this disclosure. For example... Figure 3 As shown, the method of this embodiment includes steps S302 to S304.

[0044] In step S302, during training, at least three PCIs are selected each time, and one transmitter is selected from the transmitter set corresponding to each PCI to form a transmitter group.

[0045] The number of PCIs selected during training must be consistent with the number of PCIs obtained during actual application (i.e., in step S102). The number of PCIs selected each time is the same, and the selected PCIs are not identical each time; that is, two selections of the same combination of PCIs will not occur. Each transmitter can be distinguished using its geographical location information as a unique identifier.

[0046] In step S304, for each transmitter group, the prediction model corresponding to the transmitter group is trained using the location information of the sample terminals marked in the training sample set and the ranging results from the sample terminals to each transmitter in the transmitter group.

[0047] The location information (e.g., 3D coordinates) of a batch of sample terminals can be randomly generated, or the location information of a batch of sample terminals in the network can be randomly sampled and used as the location information of the labeled sample terminals. Different training samples are used for training so that the prediction model can extract sufficient features to fit the sample data.

[0048] In some embodiments, the ranging results from the sample terminal to each transmitter in the transmitter group are input into the prediction model corresponding to the transmitter group to obtain the output position information of the sample terminal. Based on the difference between the output position information of the sample terminal and the position information of the labeled sample terminal, the parameters of the prediction model are adjusted to train the prediction model corresponding to the transmitter group. For example, a loss function is determined based on the difference between the output position information of the sample terminal and the position information of the labeled sample terminal, and the parameters of the prediction model are adjusted based on the loss function until a preset convergence condition is met. The prediction model trained in this way can output the predicted position information of the terminal given the ranging results from the input terminal to each transmitter in the transmitter group.

[0049] In some embodiments, the prediction model corresponding to each transmitter group is a neural network model with the same network structure.

[0050] The principle behind the above scheme is as follows:

[0051] Assume the coordinates of the verification terminal are (x v ,y v ,z vThe correct transmitter is RRU1. 1 RRU2 1 RRU3 1 RRU i j If the j-th transmitter of the i-th PCI is used, then the three-dimensional coordinates from the transmitter RRU1 can be measured. 1 RRU2 1 RRU3 1 The straight-line distance is (d1) 1 ,d2 1 ,d3 1 ).

[0052] Because the transmitters have different geographical coordinates, the straight-line distance from the verification terminal to the other transmitters is (d1) j ,d2 j ,d3 j (j≠1), is equivalent to (d1) 1 +δ1,d2 1 +δ2,d3 1 +δ3). The values ​​of δ1, δ2, and δ3 are determined based on the straight-line distance from the verification terminal to different transmitters.

[0053] Assuming the transmitter is RRU1 1 RRU2 1 RRU3 1 The corresponding model is M 111 M j1j2j3 If the models trained at the j1, j2, and j3 transmitters are respectively, then (d1) 1 ,d2 1 ,d3 1 Using ) as input, it can approximate the correct prediction result (x) v ,y v ,z v ).

[0054] However, if M is used j1j2j3 To make predictions for the model (j1≠1, j2≠1, j3≠1), we need to approximate the correct prediction result (x). v ,y v ,z v ), the corresponding (d1) must be used. 1 +δ1,d2 1 +δ2,d3 1 +δ3) is used as input.

[0055] Because (d1) 1 ,d2 1 ,d3 1 ) and (d1 1+δ1,d2 1 +δ2,d3 1 +δ3) are not equal. According to the correspondence between input and output values, its output value (x) v -,y v -,z v -) Unable to approximate the correct coordinates (x) v ,y v ,z v Therefore, the prediction accuracy is not as good as that of model M. 111 .

[0056] Therefore, by using the model M with the highest prediction accuracy... 111 The correct transmitter RRU1 can then be found. 1 RRU2 1 RRU3 1 .

[0057] The following describes some application scenarios of this disclosure.

[0058] In real-world applications, ranging results are not 100% accurate due to reflections and refractions from buildings / obstacles, as well as multipath effects during signal propagation. These ranging results include, but are not limited to, the straight-line distance from the terminal to the transmitter calculated using the TA (Transmitter Aspect Ratio), the straight-line distance calculated from the measurement signal sent by the terminal or transmitter, and the azimuth, angle of arrival, and angle of separation calculated using methods such as field strength.

[0059] Suppose there are four transmitters in an indoor setting (e.g., a shopping mall): (0,0), (0,1), (1,0), and (1,1). A terminal with known coordinates (0.75, 0.28) is also present. Ideally, the straight-line distances measured by the four transmitters would be 0.80056, 1.03966, 0.37536, and 0.76216, respectively. However, in reality, due to multipath effects, the actual distance measurement might be 0.78. The question then becomes: is the transmitter corresponding to the distance measurement of 0.78 (0,0) or (1,1)? Ideally, we should choose (1,1) because the straight-line distance is closer. However, if there is an obstacle between the terminal and the transmitter (0,0), causing inaccurate distance measurements, then (0,0) might be the correct transmitter.

[0060] In this case, it is necessary to design and collect sampling point data of the shopping mall, and construct a neural network model based on the actual distance measurement results of the sampling points and the actual terminal coordinates (at this time, the model corresponds to the actual scene features of the shopping mall, including but not limited to obstacle features, propagation path features, etc.).

[0061] Only by using this shopping mall feature model and based on the result with the highest accuracy can we determine whether the terminal with known coordinates (0.75, 0.28) and the distance measurement result 0.78 correspond to the transmitter (0, 0) or (1, 1).

[0062] The neural network used in the above embodiments can be trained once and used multiple times, has strong portability, and is suitable for real-world application scenarios that process large amounts of data in a short time.

[0063] The following is combined with Figure 4 Some embodiments of the determining device for the transmitter of this disclosure are described.

[0064] Figure 4 These are structural diagrams of some embodiments of the determining device for the transmitting end of this disclosure. For example... Figure 4 As shown, the apparatus 40 in this embodiment includes: an acquisition module 410, a transmitter group determination module 420, a prediction module 430, and a transmitter determination module 440.

[0065] The acquisition module 410 is used to acquire the physical cell identifiers (PCIs) of at least three transmitters to be determined that do not belong to the same cell, the ranging results of the terminal to each transmitter to be determined, and the location information of the terminal.

[0066] In some embodiments, the acquisition module 410 is used to acquire the measurement report corresponding to the terminal, and select at least three PCIs of the transmitter with different PCIs from the measurement report.

[0067] In some embodiments, the ranging results from the terminal to each transmitter to be determined include at least one of the following: the straight-line distance from the terminal to each transmitter to be determined calculated based on the tracking area, the straight-line distance from the terminal to each transmitter to be determined calculated based on the measurement signal transmitted by the terminal or the transmitter to be determined, and the azimuth angle, angle of arrival, and angle of departure calculated based on the field strength.

[0068] The transmitter group determination module 420 determines all transmitter groups corresponding to PCI, wherein each transmitter group contains a transmitter selected from the transmitter set corresponding to each PCI.

[0069] The prediction module 430 is used to determine the predicted position information of the terminal for each transmitter group based on the ranging results of the terminal to each transmitter to be determined and the prediction model corresponding to the transmitter group.

[0070] The transmitter determination module 440 is used to determine the transmitter group to which the transmitter to be determined belongs based on the difference between the terminal's location information and the various predicted location information.

[0071] In some embodiments, the transmitter determination module 440 is used to determine the transmitter group corresponding to the predicted location information with the smallest difference from the terminal's location information as the transmitter group to which the transmitter to be determined belongs.

[0072] In some embodiments, the device 40 further includes: a training module 450, configured to select at least three PCIs each time during training, and select one transmitter from the transmitter set corresponding to each PCI to form a transmitter group, wherein the number of PCIs selected each time is the same, and the selected PCIs are not exactly the same each time; for each transmitter group, the prediction model corresponding to the transmitter group is trained using the location information of the sample terminals marked in the training sample set and the ranging results from the sample terminals to each transmitter in the transmitter group.

[0073] In some embodiments, the training module 450 is used to input the ranging results from the sample terminal to each transmitter in the transmitter group into the prediction model corresponding to the transmitter group to obtain the output position information of the sample terminal; and adjust the parameters of the prediction model according to the difference between the output position information of the sample terminal and the labeled position information of the sample terminal so as to train the prediction model corresponding to the transmitter group.

[0074] In some embodiments, the prediction model corresponding to each transmitter group is a neural network model with the same network structure.

[0075] The transmitter determination device in the embodiments of this disclosure can be implemented by various computing devices or computer systems, as described below. Figure 5 as well as Figure 6 Describe it.

[0076] Figure 5 These are structural diagrams of some embodiments of the determining device for the transmitting end of this disclosure. For example... Figure 5 As shown, the apparatus 50 of this embodiment includes a memory 510 and a processor 520 coupled to the memory 510. The processor 520 is configured to execute the transmitter determination method in any of the embodiments of this disclosure based on instructions stored in the memory 510.

[0077] The memory 510 may include, for example, system memory, fixed non-volatile storage media, etc. The system memory may store, for example, an operating system, application programs, a boot loader, a database, and other programs.

[0078] Figure 6 These are structural diagrams of other embodiments of the determining device for the transmitting end of this disclosure. For example... Figure 6As shown, the device 60 in this embodiment includes a memory 610 and a processor 620, which are similar to the memory 510 and processor 520, respectively. It may also include an input / output interface 630, a network interface 640, a storage interface 650, etc. These interfaces 630, 640, 650, and the memory 610 and processor 620 can be connected, for example, via a bus 660. The input / output interface 630 provides a connection interface for input / output devices such as a display, mouse, keyboard, and touchscreen. The network interface 640 provides a connection interface for various networked devices, such as connecting to a database server or cloud storage server. The storage interface 650 provides a connection interface for external storage devices such as SD cards and USB flash drives.

[0079] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0080] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0081] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0082] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0083] The above description is only a preferred embodiment of this disclosure and is not intended to limit this disclosure. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the protection scope of this disclosure.

Claims

1. A method for determining a transmitter, comprising: Obtain the Physical Cell Identifiers (PCIs) of at least three transmitters to be determined that do not belong to the same cell, the ranging results from the terminal to each transmitter to be determined, and the location information of the terminal; Determine all transmitter groups corresponding to the PCI, wherein each transmitter group contains one transmitter selected from the transmitter set corresponding to each PCI; For each transmitter group, the predicted position information of the terminal is determined based on the ranging results of the terminal to each transmitter to be determined and the prediction model corresponding to the transmitter group. Based on the difference between the terminal's location information and the various predicted location information, the transmitter group to which the transmitter to be determined belongs is determined.

2. The method for determining the transmitting end according to claim 1, wherein, The acquisition of the Physical Cell Identifiers (PCIs) of at least three transmitters to be determined that do not belong to the same cell includes: Obtain the measurement report corresponding to the terminal; Select at least three different PCIs from the measurement report for the transmitter to be determined.

3. The method for determining the transmitting end according to claim 1, wherein, The step of determining the transmitter group to which the transmitter to be determined belongs based on the difference between the terminal's location information and each predicted location information includes: The transmitter group corresponding to the predicted location information with the smallest difference from the terminal's location information is determined as the transmitter group to which the transmitter to be determined belongs.

4. The method for determining the transmitting end according to claim 1 further includes: During training, at least three PCIs are selected each time, and one transmitter is selected from the transmitter set corresponding to each PCI to form a transmitter group. The number of PCIs selected each time is the same, and the selected PCIs are not exactly the same each time. For each transmitter group, the prediction model corresponding to the transmitter group is trained using the location information of the sample terminals marked in the training sample set and the ranging results from the sample terminals to each transmitter in the transmitter group.

5. The method for determining the transmitting end according to claim 4, wherein, The step of training the prediction model corresponding to the transmitter group by using the location information of the sample terminals in the training sample set and the ranging results from the sample terminals to each transmitter in the transmitter group includes: The ranging results from the sample terminal to each transmitter in the transmitter group are input into the prediction model corresponding to the transmitter group to obtain the output position information of the sample terminal. Based on the difference between the output location information of the sample terminal and the labeled location information of the sample terminal, the parameters of the prediction model are adjusted in order to train the prediction model corresponding to the transmitter group.

6. The method for determining the transmitting end according to claim 1, wherein, The ranging results from the terminal to each transmitter to be determined include: the straight-line distance from the terminal to each transmitter to be determined calculated based on the tracking area, the straight-line distance from the terminal to each transmitter to be determined calculated based on the measurement signal sent by the terminal or the transmitter to be determined, and at least one of the azimuth angle, angle of arrival, and angle of departure calculated based on the field strength.

7. The method for determining the transmitting end according to any one of claims 1-6, wherein, The prediction model for each transmitter group is a neural network model with the same network structure.

8. A device for determining the transmitter, comprising: The acquisition module is used to acquire the Physical Cell Identifiers (PCIs) of at least three transmitters to be determined that do not belong to the same cell, the ranging results of the terminal to each transmitter to be determined, and the location information of the terminal. The transmitter group determination module determines all transmitter groups corresponding to the PCI, wherein each transmitter group contains a transmitter selected from the transmitter set corresponding to each PCI; The prediction module is used to determine the predicted position information of the terminal for each transmitter group based on the ranging results of the terminal to each transmitter to be determined and the prediction model corresponding to the transmitter group. The transmitter determination module is used to determine the transmitter group to which the transmitter to be determined belongs based on the difference between the location information of the terminal and the various predicted location information.

9. The determining device for the transmitting end according to claim 8, wherein, The acquisition module is used to acquire the measurement report corresponding to the terminal, and select at least three PCIs of the transmitter with different PCIs from the measurement report.

10. The determining device for the transmitting end according to claim 8, wherein, The transmitter determination module is used to determine the transmitter group corresponding to the predicted location information with the smallest difference from the location information of the terminal as the transmitter group to which the transmitter to be determined belongs.

11. The device for determining the transmitting end according to claim 8, further comprising: The training module is used to select at least three PCIs each time during training, and select one transmitter from the transmitter set corresponding to each PCI to form a transmitter group. The number of PCIs selected each time is the same, and the selected PCIs are not exactly the same each time. For each transmitter group, the prediction model corresponding to the transmitter group is trained using the location information of the sample terminals marked in the training sample set and the ranging results from the sample terminals to each transmitter in the transmitter group.

12. The determining device for the transmitting end according to claim 11, wherein, The training module is used to input the ranging results from the sample terminal to each transmitter in the transmitter group into the prediction model corresponding to the transmitter group to obtain the output position information of the sample terminal; and adjust the parameters of the prediction model according to the difference between the output position information of the sample terminal and the labeled position information of the sample terminal in order to train the prediction model corresponding to the transmitter group.

13. A transmitter determining device, comprising: processor; as well as A memory coupled to the processor is used to store instructions that, when executed by the processor, cause the processor to perform the transmitter determination method as described in any one of claims 1-7.

14. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein, When executed by a processor, the program implements the steps of the method according to any one of claims 1-7.

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