A terminal positioning method and apparatus, an electronic device, and a storage medium

Abstract

The application discloses a terminal positioning method and device, electronic equipment and storage medium, and belongs to the technical field of communication. In the method, the relationship between fingerprint information and positioning information of each test terminal in the coverage range of a base station is learned in advance to obtain a positioning model. Then, the fingerprint information of each terminal is obtained every preset time period, and the fingerprint information of each terminal is input into the positioning model to obtain the positioning information of each terminal, wherein the fingerprint information of each terminal comprises CQI information and MAC layer resource scheduling information. The CQI information and the MAC layer resource scheduling information are naturally generated in a communication process, and the protocol does not need to be changed to obtain the CQI information and the MAC layer resource scheduling information. Before positioning, the terminal does not need to send positioning reference information, so that batch terminal positioning can be realized on the premise that the terminal is not aware, and air interface resources can be saved. Moreover, the CQI information and the MAC layer resource scheduling information of the physical layer are combined to position the terminal in multiple dimensions, so that accurate positioning can be realized.

Description

Technical Field

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

[0002] The fifth-generation mobile communication network has spawned a large number of new application scenarios, and obtaining accurate location information of terminals by base stations is a basic functional requirement in most application scenarios.

[0003] In related technologies, Channel State Information (CSI) is used as fingerprint information to locate terminals. However, since CSI is a set of physical layer information, it is generally unavailable to the terminal's wireless network card and the base station without additional operations that modify the protocol layer. Furthermore, when locating a terminal, the terminal must first send a location reference signal to the base station, which consumes air interface resources, thus wasting some of the base station's air interface resources.

[0004] It is evident that the terminal positioning methods in related technologies suffer from the problem of requiring modifications to the protocol layer and wasting air interface resources. Summary of the Invention

[0005] This application provides a terminal positioning method, device, electronic device, and storage medium to solve the problem that terminal positioning methods in related technologies require modification of the protocol layer and waste air interface resources.

[0006] In a first aspect, embodiments of this application provide a terminal positioning method, including:

[0007] At preset time intervals, fingerprint information of each terminal is acquired. The fingerprint information of each terminal includes channel quality indication information and media access control (MAC) layer resource scheduling information.

[0008] The fingerprint information of each terminal is input into the positioning model to obtain the positioning information of each terminal. The positioning model is obtained in advance by learning the relationship between the fingerprint information and positioning information of each test terminal within the coverage area of ​​the corresponding base station.

[0009] In some embodiments, the MAC layer resource scheduling information of each terminal includes any combination of the following information:

[0010] The assigned beam number, time slot, frequency band, and power level.

[0011] Scheduling time,

[0012] Lead time,

[0013] The confirmation character was transmitted correctly.

[0014] Terminal identification,

[0015] The base station number of the base station.

[0016] In some embodiments, before inputting the fingerprint information of each terminal into the positioning model to obtain the positioning information of each terminal, the method further includes:

[0017] The collection time of target information that meets preset conditions in the MAC layer resource scheduling information of each terminal is extended. The preset conditions include that the degree of information change at consecutive scheduling times is greater than a first preset value, and the degree of information correlation at consecutive scheduling times is greater than a second preset value.

[0018] In some embodiments, the collection time for target information that meets preset conditions in the MAC layer resource scheduling information of each terminal is extended, including:

[0019] Obtain the target information of each terminal at N historical scheduling times, where N is a preset integer;

[0020] The target information of the N historical scheduling times is integrated into the target information of the terminal at the scheduling time.

[0021] In some embodiments, after inputting the fingerprint information of each terminal into the positioning model to obtain the positioning information of each terminal, the method further includes:

[0022] Based on the location information of each terminal, the distribution of terminals within the coverage area of ​​the base station is determined;

[0023] Based on the terminal distribution, the air interface scheduling resources of the base station are optimized.

[0024] Secondly, embodiments of this application provide a terminal positioning device, comprising:

[0025] The acquisition module is used to acquire fingerprint information of each terminal at preset time intervals. The fingerprint information of each terminal includes channel quality indication information and media access control (MAC) layer resource scheduling information.

[0026] The positioning module is used to input the fingerprint information of each terminal into the positioning model to obtain the positioning information of each terminal. The positioning model is obtained in advance by learning the relationship between the fingerprint information and positioning information of each test terminal within the coverage area of ​​the corresponding base station.

[0027] In some embodiments, the MAC layer resource scheduling information of each terminal includes any combination of the following information:

[0028] The assigned beam number, time slot, frequency band, and power level.

[0029] Scheduling time,

[0030] Lead time,

[0031] The confirmation character was transmitted correctly.

[0032] Terminal identification,

[0033] The base station number of the base station.

[0034] In some embodiments, a preprocessing module is further included, for:

[0035] Before inputting the fingerprint information of each terminal into the positioning model to obtain the positioning information of each terminal, the collection time of target information that meets the preset conditions in the MAC layer resource scheduling information of each terminal is extended. The preset conditions include that the degree of information change at continuous scheduling time is greater than a first preset value, and the degree of information correlation at continuous scheduling time is greater than a second preset value.

[0036] In some embodiments, the preprocessing module is specifically used for:

[0037] Obtain the target information of each terminal at N historical scheduling times, where N is a preset integer;

[0038] The target information of the N historical scheduling times is integrated into the target information of the terminal at the scheduling time.

[0039] In some embodiments, an optimization module is also included, for:

[0040] After inputting the fingerprint information of each terminal into the positioning model to obtain the positioning information of each terminal, the distribution of terminals within the coverage area of ​​the base station is determined based on the positioning information of each terminal.

[0041] Based on the terminal distribution, the air interface scheduling resources of the base station are optimized.

[0042] Thirdly, embodiments of this application provide an electronic device, including: at least one processor, and a memory communicatively connected to the at least one processor, wherein:

[0043] The memory stores a computer program that can be executed by at least one processor, which enables the at least one processor to perform any of the above-described terminal positioning methods.

[0044] Fourthly, embodiments of this application provide a storage medium in which, when a computer program in the storage medium is executed by a processor of an electronic device, the electronic device is able to execute any of the aforementioned terminal positioning methods.

[0045] In this embodiment, the relationship between the fingerprint information and location information of each test terminal within the base station coverage area is learned in advance to obtain a location model. Then, the fingerprint information of each terminal is acquired at preset time intervals, and the fingerprint information of each terminal is input into the location model to obtain the location information of each terminal. The fingerprint information of each terminal includes Channel Quality Indication (CQI) information and Medium Access Control (MAC) layer resource scheduling information. Since CQI is a channel quality indicator, it can be reported along with terminal measurements without requiring modification of the protocol layer to obtain. MAC layer resource scheduling information is naturally generated during communication establishment and does not require additional signaling to control its generation; it can also be obtained directly. Before location, the terminal does not need to send location reference information first, enabling batch location of terminals without the terminal's awareness and saving air interface resources required for sending location reference information. Furthermore, using multiple dimensions—combining physical layer CQI information and MAC layer resource scheduling information—to locate the terminal makes accurate location easier to achieve. Attached Figure Description

[0046] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0047] Figure 1 A schematic diagram of a terminal positioning process provided in an embodiment of this application;

[0048] Figure 2 A schematic diagram illustrating the deployment of an LMF network element entity provided in an embodiment of this application;

[0049] Figure 3 A flowchart illustrating a terminal positioning method provided in an embodiment of this application;

[0050] Figure 4 This is a schematic diagram of the structure of a terminal positioning device provided in an embodiment of this application;

[0051] Figure 5 This is a schematic diagram of the hardware structure of an electronic device for implementing a terminal positioning method, provided in an embodiment of this application. Detailed Implementation

[0052] To address the issues of requiring protocol layer modifications and wasting air interface resources in related technologies for terminal positioning, embodiments of this application provide a terminal positioning method, apparatus, electronic device, and storage medium.

[0053] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0054] This application proposes a scheme for implementing outdoor contactless batch positioning terminals by combining physical layer information and MAC layer information. See also... Figure 1 This is a schematic diagram of a terminal positioning process provided in an embodiment of this application. In a multi-user cellular mobile communication scenario, firstly, the fingerprint information and positioning information of the test terminal that can be directly obtained by the base station during the communication process are collected, and the fingerprint information of the test terminal is preprocessed to construct a fingerprint database that can be used for deep learning. Then, a deep neural network model is designed to train the fingerprint database offline to obtain a positioning model. Finally, the positioning model is used to locate the terminal online in real time.

[0055] The above process will be described in detail below.

[0056] The first step is to collect fingerprint and location information of test terminals from cellular mobile communication scenarios, and then use the fingerprint and location information to build a fingerprint database.

[0057] 1. Extract the fingerprint and location information of the test terminal that can be collected from the base station side within a certain period of time as raw data.

[0058] Generally, some terminals can be selected as test terminals. Staff take these test terminals to designated locations to collect fingerprint information. This manual method can obtain precise fingerprint and location information from the test terminals. However, due to the large coverage area of ​​base stations, it is difficult to manually obtain fingerprint information from all locations; only a small portion of precise location and fingerprint information can be obtained. To enable location tracking of terminals at any location within the base station's coverage area, a positioning model can be used to learn the relationship between this small portion of precise fingerprint and location information. Then, the positioning model can be used to locate terminals within the base station's coverage area.

[0059] Here, the fingerprint information collected for each test terminal includes CQI information and MAC layer resource scheduling information. Essentially, it combines physical layer CQI information and base station resource scheduling information at the MAC layer into a single fingerprint for the terminal. This information is naturally generated during communication, requiring no protocol modification and no additional processing on the terminal side. Furthermore, combining information from both layers as a fingerprint provides a more complete reflection of the terminal's location, which helps accelerate the convergence speed of the neural network and improve positioning accuracy.

[0060] In practical applications, the richer the MAC layer resource scheduling information, the more conducive it is to achieving accurate positioning. In some embodiments, the MAC layer resource scheduling information may include resource allocation result information and resource scheduling process information. The resource allocation result information can be denoted as RAR (the most important MAC layer resource scheduling information is the RAR, which is unique to each terminal and has uniqueness), including the allocated beam number, time slot, frequency band, power level, etc. The resource scheduling process information may include the time T when the base station performs scheduling, the time advance (TA), the acknowledgment character (ACK), the International Mobile Subscriber Identity (IMSI) reflecting the terminal identifier, and the base station number information reflecting the base station identifier, etc.

[0061] When the MAC layer resource scheduling information includes the above information, the fingerprint information of any test terminal obtained by the base station can be represented as: d = (T, CQI, TA, RAR, ACK, IMSI, eNB_Id), and the location information corresponding to the fingerprint information is the tag of d.

[0062] Where T is the scheduling time;

[0063] CQI is a vector, which can be represented as c = (c1, c2, ..., c n This reflects the channel quality of the test terminal on n channels, where c1, c2, ..., c n The value range is between 0 and 15;

[0064] TA stands for lead time.

[0065] RAR can be represented as (r beam ,r slot ,r fre ,p), and, This indicates the usage of m beams by the test terminal. The value is 1 when any beam is used and 0 when no beam is used. This indicates the occupancy status of the test terminal for k time slots. The value is 1 when any time slot is occupied and 0 when it is not occupied. This indicates the occupancy status of the test terminal on j frequency bands. The value is 1 when any frequency band is occupied and 0 when it is not occupied. p represents the power value allocated to the test terminal.

[0066] ACK is a confirmation character for successful transmission; its value is 1 when the transmission is successful and 0 when there is an error.

[0067] IMSI is the terminal identifier;

[0068] eNB_Id is the base station number.

[0069] It should be noted that the fingerprint information and location information reported by the test terminal at one time form a raw data set. A test terminal can report fingerprint information and location information multiple times within a certain period of time, so multiple raw data sets can be obtained for the same test terminal.

[0070] 2. Perform data preprocessing on the fingerprint information in the original data to remove some strong correlations between information and construct a fingerprint database suitable for training and prediction by a neural network model.

[0071] Generally, a fingerprint contains multiple pieces of information. Since base station scheduling occurs on a millisecond scale (meaning a fingerprint is generated every few milliseconds), some information within the fingerprint may exhibit significant but highly correlated changes across consecutive scheduling intervals. Directly using this information for model training increases training time and hinders the achievement of good training results. Therefore, extending the collection time of this information can reduce its correlation across consecutive scheduling intervals and improve its accuracy.

[0072] The following analysis examines whether each type of fingerprint information requires an extended collection time.

[0073] 1) RAR: The assigned beam number, time slot, frequency band, and power level.

[0074] Generally, the RAR changes are relatively large during continuous scheduling (i.e., the degree of change is greater than the first preset value), and the correlation is also relatively high (i.e., the degree of correlation is greater than the second preset value). In order to cope with the strong correlation between RARs caused by the short scheduling time, the acquisition time of RAR can be extended.

[0075] 2) Scheduling time T.

[0076] The scheduling time T increases naturally with the number of scheduling times and is only used to mark the time corresponding to the fingerprint data. There is no need to extend the collection time.

[0077] 3) Lead time (TA).

[0078] Generally, the TA remains basically unchanged within three consecutive scheduling times, and there is no problem of significant changes. Therefore, there is no need to extend the data collection time.

[0079] 4) Correctly transmit the acknowledgment character ACK.

[0080] Generally, the ACK changes are relatively large during continuous scheduling (i.e., the degree of change is greater than the first preset value) but the correlation is also relatively high (i.e., the degree of correlation is greater than the second preset value). In order to cope with the strong correlation between ACKs caused by the short scheduling time, the ACK collection time can be extended.

[0081] 5) Terminal identification.

[0082] It will not change with the scheduling time, and there is no need to extend the collection time.

[0083] 6) Base station number.

[0084] It will not change with the scheduling time, and there is no need to extend the collection time.

[0085] The above analysis shows that the information that requires extended acquisition time processing includes RAR and ACK. The following is an introduction to the methods for extending acquisition time processing.

[0086] Assume the base station has 3 time slots, 3 frequency bands, and 3 beam numbers, and assume that the data collection time is extended by using the data from the two historical scheduling times before scheduling time T of the test terminal.

[0087] Further assume that the test terminal's RAR and ACK at scheduling time T are:

[0088]

[0089] The RAR and ACK at scheduling time T-1 are:

[0090]

[0091] The RAR and ACK at scheduling time T-2 are:

[0092]

[0093] Assuming the data from the three scheduling times are merged according to the maximum probability generation rule (i.e., the value is 1 if 1 appears most frequently in the three scheduling times, and 0 if 0 appears most frequently in the three scheduling times), then the RAR and ACK at scheduling time T after merging will be:

[0094]

[0095] It should be noted that the two historical scheduling moments mentioned here are just examples. In actual applications, the number N of historical scheduling moments can be adjusted according to the training effect of the localization model.

[0096] After preprocessing the fingerprint data in all the original data according to the above process, a fingerprint database suitable for model training is obtained.

[0097] The second step is to design a deep neural network model to train the fingerprint database offline, thereby obtaining a localization model.

[0098] First, the fingerprint database is divided into a training set and a validation set in an 8:2 ratio.

[0099] Then, a deep neural network model is designed for regression prediction of terminal location. The specific process is as follows:

[0100] Model pre-training phase: Network models with 1, 2, 3, 4, and 5 fully connected layers, each with 128, 256, 512, 1024, and 2048 neurons respectively, can be built. The training set is then input into each network model for training. A suitable network model is selected based on the training results. This phase can iterate up to 20,000 times, with a learning rate of 0.001 and a batch size of 4. After pre-training, the complexity and convergence of different network models are analyzed, and a network model with 4 fully connected layers and 1024 neurons is selected.

[0101] Model Validation Phase: After determining the network model structure, the training set is input into the network model with the corresponding structure. The convergence of the network is analyzed by adjusting hyperparameters such as the learning rate and batch size. The network model after every 1000 iterations of training can be saved, and the training effect of the network model is verified using the validation set. To determine the convergence performance of the network model, a terminal positioning error function can be set to quantitatively analyze the positioning accuracy of the network model. The hyperparameters of the network model are adjusted by analyzing the training loss function curve and the accuracy curve of the validation network model. Finally, the network model with the smallest average positioning error is tested in a real-world environment. The network model that passes the test is the successfully trained positioning model.

[0102] The third step is to use a positioning model to locate the terminal online in real time.

[0103] In cellular mobile communication networks, a Location Management Function (LMF) network element entity can be established to handle batch terminal location calculations, such as... Figure 2 As shown, the LMF network element entity can be deployed on each base station (or other feasible communication equipment). In practical applications, the trained positioning model is loaded into each LMF network element entity. Then, the LMF network element collects the fingerprint information of each terminal on the corresponding base station side. After preprocessing each fingerprint information according to the above preprocessing method, it is input into the loaded positioning model. The output is the positioning information of each terminal, thus realizing batch positioning of terminals without the terminals' awareness.

[0104] In practical applications, after determining the location information of each terminal accessed by the base station, the distribution of terminals within the base station's coverage area can be analyzed based on the location information of each terminal, and then the air interface scheduling resources of the base station can be optimized based on the terminal distribution.

[0105] Generally, during the access process between a terminal and a base station, the terminal sends the estimated channel state quality information (CQI) between itself and the base station via the transmission control channel. The base station then uses this information to configure resource blocks for the service channels between the two. If the base station knows the distribution of terminals within its coverage area, it can roughly determine the service request density of terminals within that area. Subsequently, it can optimize air interface scheduling resources by rationally configuring each user resource block. Rational configuration of each user resource block includes reducing the frequency band resource reuse rate for users in hotspot areas, increasing the time slot allocation, adjusting the beam number aligned with the terminal in real time based on the terminal's location, and adjusting the base station's transmit power for users sharing the same frequency. This can avoid or suppress co-channel interference between users, improve the signal-to-interference-plus-noise-radio (SINR) ratio, and thus increase channel capacity.

[0106] In this embodiment, the raw data of physical layer information and MAC layer scheduling information collected regarding multi-terminal positioning are processed to construct a fingerprint database. Based on this fingerprint database, a pre-designed deep neural network model is trained offline and fine-tuned online to obtain a positioning model. This model enables batch positioning of terminals without the terminals being aware of the process. Furthermore, the locations of the batch-positioned terminals can be used to optimize the air interface scheduling resources of the base station, helping the base station to reasonably avoid or suppress interference during resource scheduling.

[0107] The terminal positioning proposed in this application will be described below with specific embodiments.

[0108] Figure 3 A flowchart of a terminal positioning method provided in an embodiment of this application is shown, the method including the following steps.

[0109] In step 301, fingerprint information of each terminal is acquired at preset time intervals. The fingerprint information of each terminal includes CQI information and MAC layer resource scheduling information.

[0110] In some embodiments, fingerprint information of each terminal can be obtained from a base station. The base station can be a terrestrial base station or an airborne base station (i.e., a satellite). Without conflict, the scheme of this application embodiment is applicable to both types of base stations.

[0111] Here, fingerprint information required for terminal positioning is actively acquired at preset time intervals, without the terminal needing to send positioning reference information first. This not only enables batch positioning of terminals without the terminal's awareness, but also saves air interface resources of the base station (sending positioning reference information requires air interface resources).

[0112] Furthermore, each terminal's fingerprint information includes both physical layer CQI information and MAC layer resource scheduling information. Essentially, it combines information from both the physical and MAC layers as the terminal's fingerprint. Both CQI and MAC layer resource scheduling information are naturally generated during communication, requiring no wireless protocol modification and no additional processing on the terminal side, making fingerprint acquisition relatively simple. No positioning reference signal needs to be sent before positioning, allowing for batch positioning of terminals without their awareness, and saving air interface resources. The combination of information from both layers allows the fingerprint information to more completely represent the terminal's location, and also helps to accelerate the convergence speed of the neural network and improve positioning accuracy.

[0113] In some embodiments, the MAC layer resource scheduling information of each terminal includes any combination of the following information:

[0114] RAR: The assigned beam number, time slot, frequency band, and power level.

[0115] Scheduling time T,

[0116] Lead time TA

[0117] The acknowledgment character ACK was transmitted correctly.

[0118] Terminal IMSI

[0119] The base station number eNB_Id of the base station to which each terminal is connected.

[0120] In step 302, the collection time of target information that meets the preset conditions in the MAC layer resource scheduling information of each terminal is extended.

[0121] The preset conditions include that the degree of information change during continuous scheduling is greater than a first preset value, and the degree of information correlation during continuous scheduling is greater than a second preset value. The values ​​of the first and second preset values ​​can be determined by technicians based on experience or experiments, and will not be elaborated here.

[0122] In practical applications, a terminal's fingerprint information may contain multiple types of information. However, the scheduling time of a base station is at the millisecond level, meaning that a terminal's fingerprint information is generated every few milliseconds. This may result in some information in the fingerprint information changing significantly over consecutive scheduling times, but with strong correlation. To reduce the data processing difficulty of the subsequent positioning model, the collection time of this information can be extended to reduce the correlation between these information over consecutive scheduling times.

[0123] For example, the target information in a terminal's fingerprint information that needs to be extended in terms of collection time includes: RAR: the assigned beam number, time slot, frequency band, power level, and ACK.

[0124] In specific implementation, when extending the collection time of target information for each terminal, the target information of this terminal at N historical scheduling times can be obtained. Then, the target information of the N historical scheduling times is merged into the target information of the terminal at scheduling time T. Here, N is a preset integer, and these N historical scheduling times are scheduling times that are adjacent to scheduling time T, so as to ensure the accuracy and rationality of the fusion processing.

[0125] The following section details the methods for handling extended data collection times.

[0126] Assume the base station has 3 time slots, 3 frequency bands, and 3 beam numbers, and assume that the data collection time is extended by using the data from the two historical scheduling times before scheduling time T.

[0127] Further assume that the terminal's RAR and ACK at scheduling time T are:

[0128]

[0129] The RAR and ACK at scheduling time T-1 are:

[0130]

[0131] The RAR and ACK at scheduling time T-2 are:

[0132]

[0133] Therefore, if the data from the three scheduling times are merged according to the maximum probability generation rule (i.e., the value is 1 if 1 appears most frequently in the three scheduling times, and 0 if 0 appears most frequently in the three scheduling times), then the RAR and ACK at scheduling time T after merging will be:

[0134]

[0135] In this way, by extending the collection time, the correlation between target information within continuous scheduling time can be reduced, making the target information more accurate, thereby improving the accuracy of the final positioning.

[0136] In step 303, the fingerprint information of each terminal is input into the positioning model to obtain the positioning information of each terminal. The positioning model is obtained in advance by learning the relationship between the fingerprint information and positioning information of each test terminal within the coverage area of ​​the corresponding base station.

[0137] The corresponding base station refers to the base station that each terminal accesses, that is, the base station that provides communication services to each terminal.

[0138] In practical applications, the fingerprint information of each terminal is sequentially input into the positioning model to obtain the positioning information of the corresponding terminal. The training process of the positioning model has been described in detail above and will not be repeated here.

[0139] In step 304, the distribution of terminals within the base station coverage area is determined based on the location information of each terminal.

[0140] In step 305, the air interface scheduling resources of the base station are optimized based on the terminal distribution.

[0141] Generally, during the access process between a terminal and a base station, the terminal sends the estimated channel state quality information (CQI) between itself and the base station via the transmission control channel. Based on this, the base station configures resource blocks for the service channel between the two. If the base station knows the distribution of terminals within its coverage area, it can roughly determine the service request density of terminals within that area. Subsequently, it can optimize air interface scheduling resources by rationally configuring each user resource block. Rational configuration of each user resource block includes reducing the frequency band resource reuse rate for users in hotspot areas, increasing the time slot allocation, adjusting the beam number aligned with the terminal in real time based on the terminal's location, and adjusting the base station's transmit power for users sharing the same frequency. This can avoid or suppress co-channel interference between users, improve the user's SINR, and thus increase channel capacity.

[0142] In this embodiment, the combined information of MAC layer resource scheduling and physical layer CQI information is used as location fingerprint positioning information, and batch terminal positioning is achieved without the terminal needing to send a dedicated positioning reference signal. Furthermore, the batch-positioned terminal locations can help the base station avoid or suppress interference during subsequent MAC layer resource scheduling, thereby better allocating resources to terminals and improving the throughput of the communication system.

[0143] Based on the same technical concept, this application also provides a terminal positioning device. The principle of the terminal positioning device in solving the problem is similar to that of the terminal positioning method described above. Therefore, the implementation of the terminal positioning device can refer to the implementation of the terminal positioning method, and the repeated parts will not be described again.

[0144] Figure 4 This is a schematic diagram of the structure of a terminal positioning device provided in an embodiment of the present application, including an acquisition module 401 and a positioning module 402.

[0145] The acquisition module 401 is used to acquire fingerprint information of each terminal at preset time intervals. The fingerprint information of each terminal includes channel quality indication information and media access control (MAC) layer resource scheduling information.

[0146] The positioning module 402 is used to input the fingerprint information of each terminal into the positioning model to obtain the positioning information of each terminal. The positioning model is obtained in advance by learning the relationship between the fingerprint information and positioning information of each test terminal within the coverage area of ​​the corresponding base station.

[0147] In some embodiments, the MAC layer resource scheduling information of each terminal includes any combination of the following information:

[0148] The assigned beam number, time slot, frequency band, and power level.

[0149] Scheduling time,

[0150] Lead time,

[0151] The confirmation character was transmitted correctly.

[0152] Terminal identifier,

[0153] The base station number of the base station.

[0154] In some embodiments, a preprocessing module 403 is further included, for:

[0155] Before inputting the fingerprint information of each terminal into the positioning model to obtain the positioning information of each terminal, the collection time of target information that meets the preset conditions in the MAC layer resource scheduling information of each terminal is extended. The preset conditions include that the degree of information change at continuous scheduling time is greater than a first preset value, and the degree of information correlation at continuous scheduling time is greater than a second preset value.

[0156] In some embodiments, the preprocessing module 403 is specifically used for:

[0157] Obtain the target information of each terminal at N historical scheduling times, where N is a preset integer;

[0158] The target information of the N historical scheduling times is integrated into the target information of the terminal at the scheduling time.

[0159] In some embodiments, an optimization module 404 is further included, for:

[0160] After inputting the fingerprint information of each terminal into the positioning model to obtain the positioning information of each terminal, the distribution of terminals within the coverage area of ​​the base station is determined based on the positioning information of each terminal.

[0161] Based on the terminal distribution, the air interface scheduling resources of the base station are optimized.

[0162] The module division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, other division methods are possible. Furthermore, the functional modules in each embodiment of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. Coupling between modules can be achieved through interfaces, typically electrical communication interfaces, but mechanical interfaces or other types of interfaces are also possible. Therefore, modules described as separate components may or may not be physically separate; they can be located in one place or distributed across different locations on the same or different devices. The integrated modules described above can be implemented in hardware or as software functional modules.

[0163] Having introduced the terminal positioning method and apparatus according to exemplary embodiments of this application, we will now introduce an electronic device according to another exemplary embodiment of this application.

[0164] The following reference Figure 5 To describe an electronic device 130 implemented according to this embodiment of the present application. Figure 5 The electronic device 130 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0165] like Figure 5 As shown, the electronic device 130 is presented in the form of a general-purpose electronic device. The components of the electronic device 130 may include, but are not limited to: at least one processor 131, at least one memory 132, and a bus 133 connecting different system components (including memory 132 and processor 131).

[0166] Bus 133 represents one or more of several bus structures, including a memory bus or memory controller, peripheral bus, processor, or local bus using any of the various bus structures.

[0167] The memory 132 may include a readable medium in the form of volatile memory, such as random access memory (RAM) 1321 and / or cache memory 1322, and may further include read-only memory (ROM) 1323.

[0168] The memory 132 may also include a program / utility 1325 having a set (at least one) of program modules 1324, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0169] Electronic device 130 can also communicate with one or more external devices 134 (e.g., keyboard, pointing device, etc.), and with one or more devices that enable a user to interact with electronic device 130, and / or with any device that enables electronic device 130 to communicate with one or more other electronic devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 135. Furthermore, electronic device 130 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 136. As shown, network adapter 136 communicates with other modules used in electronic device 130 via bus 133. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 130, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0170] In an exemplary embodiment, a storage medium is also provided, which enables the electronic device to perform the aforementioned terminal positioning method when a computer program in the storage medium is executed by a processor of the electronic device. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0171] In an exemplary embodiment, the electronic device of this application may include at least one processor and a memory communicatively connected to the at least one processor, wherein the memory stores a computer program that can be executed by the at least one processor, and when the computer program is executed by the at least one processor, it can cause the at least one processor to perform the steps of any terminal positioning method provided in the embodiments of this application.

[0172] In an exemplary embodiment, a computer program product is also provided, which, when executed by an electronic device, enables the electronic device to implement any of the exemplary methods provided in this application.

[0173] Furthermore, computer program products may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM), flash memory, optical fiber, compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0174] The program product for terminal positioning in this application embodiment may be a CD-ROM and include program code, and may run on a computing device. However, the program product of this application is not limited to this. In this document, the readable storage medium may be any tangible medium that contains or stores a program, which may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0175] A readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying readable program code. This propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0176] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency (RF), or any suitable combination thereof.

[0177] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0178] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0179] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0180] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0181] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will 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, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0182] 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.

[0183] 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.

[0184] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0185] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, then this application also includes such modifications and variations.

Claims

1. A terminal positioning method, characterized in that, The method is applied to a location management function network element entity, which is deployed on a base station. The method includes: At preset time intervals, fingerprint information of each terminal is obtained from the base station. The fingerprint information of each terminal includes channel quality indicator (CQI) information and media access control (MAC) layer resource scheduling information. The fingerprint information of each terminal is input into the positioning model to obtain the positioning information of each terminal, so as to realize the batch positioning of terminals without the terminals noticing. The positioning model is obtained in advance by learning the relationship between the fingerprint information and positioning information of each test terminal within the coverage area of ​​the base station. Before inputting the fingerprint information of each terminal into the positioning model to obtain the positioning information of each terminal, the following steps are also included: The collection time of target information that meets preset conditions in the MAC layer resource scheduling information of each terminal is extended. The preset conditions include that the degree of information change at consecutive scheduling times is greater than a first preset value, and the degree of information correlation at consecutive scheduling times is greater than a second preset value.

2. The method as described in claim 1, characterized in that, The MAC layer resource scheduling information for each terminal includes any combination of the following: The assigned beam number, time slot, frequency band, and power level. Scheduling time, Lead time, The confirmation character was transmitted correctly. Terminal identification, The base station number of the base station.

3. The method as described in claim 1, characterized in that, The collection time for target information that meets preset conditions in the MAC layer resource scheduling information of each terminal is extended, including: Obtain the target information of each terminal at N historical scheduling times, where N is a preset integer; The target information of the N historical scheduling times is integrated into the target information of the terminal at the scheduling time.

4. The method as described in claim 1, characterized in that, After inputting the fingerprint information of each terminal into the positioning model to obtain the positioning information of each terminal, the process also includes: Based on the location information of each terminal, the distribution of terminals within the coverage area of ​​the base station is determined; Based on the terminal distribution, the air interface scheduling resources of the base station are optimized.

5. A terminal positioning device, characterized in that, The device is applied to a location management function network element entity, which is deployed on a base station. The device includes: The acquisition module is used to acquire fingerprint information of each terminal from the base station at preset time intervals. The fingerprint information of each terminal includes channel quality indicator (CQI) information and media access control (MAC) layer resource scheduling information. The positioning module is used to input the fingerprint information of each terminal into the positioning model to obtain the positioning information of each terminal, so as to realize the batch positioning of terminals without the terminals noticing. The positioning model is obtained in advance by learning the relationship between the fingerprint information and positioning information of each test terminal within the coverage area of ​​the base station. It also includes a preprocessing module, which extends the collection time of target information that meets preset conditions in the MAC layer resource scheduling information of each terminal before inputting the fingerprint information of each terminal into the positioning model to obtain the positioning information of each terminal. The preset conditions include that the degree of information change at continuous scheduling time is greater than a first preset value and the degree of information correlation at continuous scheduling time is greater than a second preset value.

6. The apparatus as claimed in claim 5, characterized in that, The MAC layer resource scheduling information for each terminal includes any combination of the following: The assigned beam number, time slot, frequency band, and power level. Scheduling time, Lead time, The confirmation character was transmitted correctly. Terminal identification, The base station number of the base station.

7. The apparatus as claimed in claim 5, characterized in that, The preprocessing module is specifically used for: Obtain the target information of each terminal at N historical scheduling times, where N is a preset integer; The target information of the N historical scheduling times is integrated into the target information of the terminal at the scheduling time.

8. The apparatus as claimed in claim 5, characterized in that, It also includes an optimization module for: After inputting the fingerprint information of each terminal into the positioning model to obtain the positioning information of each terminal, the distribution of terminals within the coverage area of ​​the base station is determined based on the positioning information of each terminal. Based on the terminal distribution, the air interface scheduling resources of the base station are optimized.

9. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to said at least one processor, wherein: The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the method as described in any one of claims 1-4.

10. A storage medium, characterized in that, When the computer program in the storage medium is executed by the processor of the electronic device, the electronic device is able to perform the method as described in any one of claims 1-4.

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