Base station position determination method and device, electronic equipment, chip and storage medium

Abstract

The invention provides a base station position determination method and device, electronic equipment, a chip and a medium, and the method comprises the steps: obtaining measurement data of a terminal, the measurement data at least comprising one of measurement report (MR) data and minimization of drive test (MDT) data; determining the type of data contained in the measurement data, wherein the type of the data can be local network data or different network data; determining a propagation loss model according to the local network data; determining the distance from the terminal to the base station by using a propagation loss model based on the measurement data; and determining the position of the base station according to the distance from the terminal to the base station. The propagation loss model can be accurately determined according to the home network data in the measurement data, and the distance from the terminal to the base station can be determined based on the propagation loss model, so that the position of the base station is determined, and the accuracy of determining the position of the base station can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing, and in particular to a method, device, electronic device, chip and storage medium for determining a base station location. Background Art

[0002] With the large-scale construction of 5G networks around the world, the number of 5G network users is also growing, bringing new challenges to operators. As the core equipment of mobile communications, base stations play an indispensable supporting role in the construction of 5G networks. Operators can determine their own base station construction plans, network planning, optimization plans, etc. by understanding the base station layout and coverage of competitors. Summary of the invention

[0003] The present disclosure provides a method, device, electronic device, chip and storage medium for determining a base station location.

[0004] A first aspect embodiment of the present disclosure proposes a method for determining a base station position, the method comprising: acquiring measurement data of a terminal, the measurement data comprising at least one of measurement report MR data and minimization of drive test MDT data; determining a type of data contained in the measurement data, the type of data may be local network data or foreign network data; determining a propagation loss model based on the local network data; determining a distance from the terminal to the base station based on the measurement data using the propagation loss model; and determining a position of the base station based on the distance from the terminal to the base station.

[0005] In some embodiments of the present disclosure, determining a propagation loss model according to local network data includes: determining data characteristics of the local network data; and determining a propagation loss model corresponding to the local network data based on the data characteristics of the local network data.

[0006] In some embodiments of the present disclosure, based on the measurement data, the distance from the terminal to the base station of the foreign network is determined using a propagation loss model, including: based on the data of the local network, determining the environmental noise loss factor corresponding to the data of the local network; establishing a level model according to the environmental noise loss factor, the data of the foreign network, and the propagation loss model; based on the level model, determining the distance from the terminal to the base station of the foreign network.

[0007] In some embodiments of the present disclosure, determining the environmental noise loss factor corresponding to the data of the current network based on the data of the current network includes: determining the state prediction covariance, observation matrix and noise covariance corresponding to the data of the current network based on the data of the current network; determining the environmental noise loss factor corresponding to the data of the current network based on the state prediction covariance, observation matrix and noise covariance corresponding to the data of the current network.

[0008] In some embodiments of the present disclosure, determining the type of data contained in the measurement data includes: determining the frequency point and the physical cell identifier PCI of the data; when the frequency point and the physical cell identifier PCI of the data meet a first condition, determining that the type of the data is local network data; otherwise, determining that the type of the data is cross-network data.

[0009] In some embodiments of the present disclosure, determining the position of a base station based on the distance from a terminal to the base station includes: obtaining position information of a preset number of terminals; establishing a preset number of spherical equations with the position of the terminal as the center of the circle and the distance from the terminal to the base station as the radius; determining the intersection points between the preset number of spheres, and establishing an intersection point set based on the intersection points that satisfy the second condition; clustering the intersection points in the intersection point set to determine the center of mass of the intersection point set; and determining the center of mass coordinates of the intersection point set as the position of the base station.

[0010] The second aspect embodiment of the present disclosure proposes a base station position determination device, including an acquisition unit, used to acquire measurement data of a terminal, the measurement data including at least one of measurement report MR data and minimization of drive test MDT data; a determination unit, used to determine the type of data contained in the measurement data, the type of data may be local network data or foreign network data; a first processing unit, used to determine a propagation loss model based on the local network data; a second processing unit, used to determine the distance from the terminal to the base station using the propagation loss model based on the measurement data; and a third processing unit, used to determine the position of the base station based on the distance from the terminal to the base station.

[0011] The third aspect embodiment of the present disclosure proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the first aspect embodiment of the present disclosure.

[0012] The fourth aspect embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the method described in the first aspect embodiment of the present disclosure.

[0013] A fifth aspect embodiment of the present disclosure provides a computer program product, including a computer program, which executes the method described in the first aspect embodiment of the present disclosure when a processor is used to execute the computer program.

[0014] The sixth aspect embodiment of the present disclosure proposes a chip, which includes one or more interfaces and one or more processors; the interface is used to receive signals from a memory of an electronic device and send signals to the processor, the signals include computer instructions stored in the memory, and when the processor executes the computer instructions, the electronic device executes the method described in the first aspect embodiment of the present disclosure.

[0015] In summary, the base station location determination method, device, electronic device, chip and storage medium proposed in the present invention can determine the data type according to the measurement data of the terminal, and determine the corresponding propagation loss model based on the data of this network, so as to determine the propagation loss in the actual transmission environment. Determining the distance from the terminal to the base station based on the propagation loss model can improve the accuracy of the distance prediction, and determining the location of the base station based on the distance from the terminal to the base station can improve the accuracy of the base station location prediction.

[0016] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0018] Figure 1 A flowchart of a method for determining a base station location provided by an embodiment of the present disclosure;

[0019] Figure 2 A flowchart of a method for determining a base station location provided by an embodiment of the present disclosure;

[0020] Figure 3 A flowchart of a method for determining a base station location provided by an embodiment of the present disclosure;

[0021] Figure 4 A schematic diagram of a flow chart of a method for identifying a location of a competing base station provided in an embodiment of the present disclosure;

[0022] Figure 5 A schematic diagram of a flow chart of a Kalman filter algorithm provided in an embodiment of the present disclosure;

[0023] Figure 6 A schematic diagram of the spatial distribution of spheres provided in an embodiment of the present disclosure;

[0024] Figure 7 A schematic diagram of the structure of a base station location determination device provided by an embodiment of the present disclosure;

[0025] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure;

[0026] Fig. 9 A schematic diagram of the chip structure provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] Embodiments of the present disclosure are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0028] Competing base stations refer to mobile communication base stations built by communication operators with market competition. Competing base stations can also be called base stations of other networks. The benefits of operators identifying competing base stations are as follows:

[0029] 1. In terms of market development, accurate understanding of competitors' base station layout and coverage will help companies formulate more effective market strategies and business decisions. For example, in areas where we have better coverage and competitors are weaker, we can increase marketing efforts and increase market share.

[0030] 2. In terms of network improvement, we can compare our network coverage with that of our competitors based on the location of their base stations, and provide a reference for our own network planning and optimization. For example, we can share the base stations of our competitors to open sites in areas where our competitors have base stations but we do not, thus avoiding unnecessary duplication of construction and reducing construction costs.

[0031] In the related art, the signal strength and time difference of a single competing base station can be measured at multiple locations at the same time, and the location of the competing base station can be determined using the triangulation method. For example, the signal power of the network and the distance from the base station of the network are tested through the measurement points, and the power and distance are linearly averaged; the distance of the competing base station is predicted based on the corresponding average value and the measured signal function of the competing base station; the intersection of multiple circles with the coverage distance of multiple measurement points and the competing base station as the radius is determined to determine the possible location of the predicted base station, and finally the approximate location of the competing base station is predicted using the directional triangulation algorithm through multiple prediction points. However, this solution has great limitations because the station height, azimuth angle, transmission power, and frequency used of the competing cell are inconsistent with those of the network cell, and as the user position changes, the path loss model of the wireless environment for receiving the signal of the same cell is inconsistent. As a result, the use of linear averaging of signal power has low prediction accuracy. This application proposal uses measurement reports (MR) to measure the network information and the competing information at the same location at the same time, and uses the similar characteristics of the signal propagation model to establish a Kalman model through the cell of the network, and calculate the Kalman gain to determine the wireless environment noise factor K. The Kalman gain is combined with the signal propagation loss model to establish a model of the competing signal level reception, and reversely estimate the distance between the terminal and the competing base station. Compared with the related technology, the advantage of this proposal is that it can establish a dynamic propagation model of each competing base station based on the characteristics of the received signal of the network, estimate the state of signal propagation in a recursive way, and continuously adjust and optimize to improve the accuracy of the location identification of the competing cell.

[0032] For another example, the relevant technology can use Bluetooth Low Energy (BLE) and Ultra Wide Band (UWB) two positioning technologies to realize the storage location of items, and determine the base station closest to the tag through the signal strength (Received Signal Strength Indication, RSSI) of the tag signal to each base station, so as to divide the area range of the time difference of arrival (TDOA) sub-unit, and lay the system base station according to the priority between the positions. This patent uses the unscented Kalman filter model based on the received signal strength and time arrival difference to predict the dynamic position model of material movement, divide the material warehouse into multiple positioning unit blocks, and determine the maximum coverage radius of each base station based on the propagation loss of UWB's own space and the lower limit of the signal loudness, and then determine the key positions for the laying of base stations. The UWB positioning algorithm used in this solution is limited to open wireless propagation environments, requiring that the propagation process is not blocked by buildings and other conditions, and is not suitable for complex indoor and outdoor environments. Compared with the related technology, the method of this proposal can predict the distance of the competing base station based on the environmental noise factor, effectively solve the problem of positioning in a complex wireless propagation environment, dynamically calculate the propagation loss factor based on the measurement characteristics of this network and the distance, and then infer the distance of the competing base station. Compared with the traditional method of using a fixed propagation loss factor, it can greatly improve the accuracy of position estimation.

[0033] Therefore, in order to solve the above technical problems, the present invention proposes a method for determining the location of a base station, which can be executed by an electronic device, for example, by a server. The method predicts the location of a competing base station based on the dynamic movement of a user, and uses simultaneous locations to simultaneously measure similar characteristics of the propagation types of the own network and other networks, calculates the environmental noise factor of the Kalman filter model, predicts the distance of the competing base station based on the environmental noise factor, dynamically calculates the propagation loss factor based on the measurement characteristics of the own network and the distance, and then infers the distance of the competing base station. Compared with the traditional method of using a fixed propagation loss factor, the accuracy of position estimation can be greatly improved.

[0034] Figure 1 A flow chart of a method for determining a base station location provided by an embodiment of the present disclosure. Figure 1 As shown, the method may include the following steps.

[0035] Step 101: Acquire measurement data of the terminal.

[0036] In some embodiments, the measurement data of the terminal is obtained by measuring the received signal by the terminal, and for example, the measurement data includes at least one of measurement report MR data and minimization drive test (MDT) data. Specifically, the measurement data of the terminal may include frequency information, physical cell identity (PCI) information, signal strength information, etc., which is not limited by the present disclosure.

[0037] In some embodiments, optionally, the measurement data of the terminal may also be information of the terminal itself. For example, the measurement data of the terminal may include location data of the terminal, such as longitude and latitude information of the terminal.

[0038] In some embodiments, the base station may enable a measurement reporting MR data or a minimization of drive test (MDT) data function to obtain the measurement data of the terminal.

[0039] Step 102: Determine the type of data contained in the measurement data.

[0040] In some embodiments, the type of data included in the measurement data may be determined, and the type of data may be local network data or foreign network data.

[0041] In some embodiments, the local network data may be measurement data of signals transmitted between the operator's own base station and the terminal, and the heterogeneous network data may be measurement data of signals transmitted between heterogeneous network base stations (i.e., competing base stations) of other operators and the terminal.

[0042] In other words, the data type of the data reported by the terminal can be determined, such as the data type of the reported measurement report MR data and / or minimization of drive test MDT data, where the data type can be local network data or cross-network data, that is, when the terminal reports measurement data, it can report local network data and cross-network data at the same time.

[0043] In other words, the measurement data reported by the terminal may include data of the local network and data of the foreign network. The measurement data may include measurement report MR data and / or minimized drive test MDT data of the local network, and may also include measurement report MR data and / or minimized drive test MDT data of the foreign network, or the measurement data may include measurement report MR data and / or minimized drive test MDT data of the local network, as well as the frequency point, physical cell identification code PCI and field strength data of the foreign network, etc., which is not limited by the present disclosure.

[0044] For example, after turning on the measurement report MR data or minimized drive test MDT data function, the base station can receive the MR data and / or MDT data of the local network reported by the terminal; after turning on the above function and heterofrequency measurement, the base station can also obtain the heteronetwork data reported by the terminal through neighboring cell measurement. In other words, after turning on the heterofrequency measurement, the terminal can measure the transmission signal between the terminal and the heteronetwork base station to obtain the corresponding measurement data, such as the measurement report MR data and / or minimized drive test MDT data of the transmission signal between the terminal and the heteronetwork, and report the heteronetwork data to the local network base station.

[0045] In some embodiments, determining the type of data included in the measurement data includes:

[0046] Determine the frequency point and the physical cell identification code PCI of the data; when the frequency point and the physical cell identification code PCI of the data meet the first condition, determine that the type of the data is local network data; otherwise, the type of the measured data is cross-network data.

[0047] In some embodiments, different operators have different frequencies and physical cell identifiers PCI, where the frequency may refer to the transmit power of a base station. For example, the first condition may be that the frequency and physical cell identifier of the measurement data are the frequency and physical cell identifier used by the operator.

[0048] In other words, after obtaining the measurement data of the terminal, the data type of the data contained in the measurement data can be determined. For example, the data contained in the measurement data may be measurement report MR data or minimization of drive test MDT data. Based on the frequency and PCI information contained in the MR data or the MDT data, it can be determined that the data is local network data, or that the data is cross-network data.

[0049] For example, for a piece of data in the measurement data, it is possible to determine whether the frequency and PCI are the frequency and PCI used by this network based on the frequency information and PCI information contained in the data, thereby determining whether the data is data of this network. It is possible to divide the data contained in the measurement data into data of this network and data of an external network, so as to determine the propagation loss model based on the data of this network, and determine the location of the base station of the external network based on the data of the external network.

[0050] In some embodiments, the heterogeneous network data can be optionally filtered. For example, only the heterogeneous network data with the strongest signal strength can be retained, and other heterogeneous network data with weaker signal strength can be discarded. The heterogeneous network base station can be determined only by the heterogeneous network data with the strongest signal strength. This can achieve the determination of the main heterogeneous network base station that interacts with the terminal, thereby improving the recognition accuracy of the heterogeneous network base stations.

[0051] Step 103: Determine a propagation loss model based on the data of the local network.

[0052] In some embodiments, determining the propagation loss model according to the network data includes:

[0053] Determine the data characteristics of the network data; and determine the propagation loss model corresponding to the network data based on the data characteristics of the network data.

[0054] In some embodiments, after determining the type of data contained in the measurement data, the propagation model corresponding to the data of this network can be determined based on the data of this network. For example, the propagation loss law of the signal when the base station of this network interacts with the terminal can be determined based on the historical data of this network, and the corresponding propagation loss model can be determined based on the law. For example, the corresponding propagation loss models under different wireless environments can be determined. Since the base station of this network and the base station of another network are in similar wireless environments, the propagation loss mode of their signals is similar to the propagation loss mode of the signal of this network. Therefore, it can be determined that the propagation loss model of the signal of this network is applied to the propagation loss determination of the signal of another network.

[0055] Step 104: Based on the measurement data, determine the distance from the terminal to the base station using a propagation loss model.

[0056] In some embodiments, for example, the distance from the terminal to the foreign network base station can be determined based on the local network data, foreign network data and a propagation loss model, so as to determine the location of the foreign network base station and identify the foreign network base station.

[0057] In some embodiments, optionally, the distance from the terminal to the base station of the local network may also be determined based on the local network data and a propagation loss model.

[0058] In some embodiments, the distance from the terminal to the base station may be the distance from the antenna of the base station to the terminal, the distance from the transmitting antenna position of the base station to the terminal, and so on.

[0059] Step 105: Determine the location of the base station according to the distance from the terminal to the base station.

[0060] In some embodiments, for example, determining the location of a base station may be determining the location of a base station of a local network or determining the location of a base station of a foreign network. For example, the location of the base station of the local network may be determined based on the data of the local network and a propagation loss model, and compared with the actual location of the base station of the local network, and the propagation loss model may be verified and updated, etc. For another example, the location of a base station of a foreign network may be determined based on the distances from multiple terminals to the base station of the foreign network, thereby realizing the identification of the base station of the foreign network and improving the accuracy of the identification of the base station of the foreign network.

[0061] In some embodiments, optionally, the location of the base station of the foreign network can be comprehensively determined based on the distances from multiple terminals to the base station of the foreign network. Therefore, it is necessary to obtain measurement data of multiple terminals within a certain range, and the measurement data of each of the multiple terminals are analyzed to determine the distance from each terminal to the base station of the foreign network. The location of the base station of the foreign network is determined based on the distances from multiple terminals to the base station. For example, the location of the base station can be determined based on the distances from at least three terminals to the base station of the foreign network.

[0062] In summary, the above-mentioned embodiments of the present application, by acquiring the measurement data of the terminal and determining the type of the measurement data, determine the propagation loss model based on the data of this network, and determine the distance from the terminal to the base station of the foreign network according to the measurement data and the propagation loss model, can realize the determination of an accurate propagation loss model according to the actual measurement data. The use of this model can improve the accuracy of the distance identification from the base station to the base station of the foreign network. According to the distance from the terminal to the base station of the foreign network, the location of the base station can be determined, and the base station of the foreign network can be identified, thereby improving the recognition rate of the base station of the foreign network, and facilitating the determination of the network planning and optimization plan based on the location of the base station of the foreign network.

[0063] Figure 2 A flow chart of a method for determining a base station location provided by an embodiment of the present disclosure. Figure 2 As shown, the method may include the following steps.

[0064] Step 201: Based on the data of the current network, determine the environmental noise loss factor corresponding to the data of the current network.

[0065] In some embodiments, based on the local network data, determining the environmental noise loss factor corresponding to the local network data includes:

[0066] Determine the state prediction covariance, observation matrix and noise covariance corresponding to the data of this network according to the data of this network; determine the environmental noise loss factor corresponding to the data of this network based on the state prediction covariance, observation matrix and noise covariance corresponding to the data of this network.

[0067] In some embodiments, optionally, a Kalman algorithm may be used to determine a Kalman gain based on the local network data, and the Kalman gain may be determined as the above-mentioned environmental noise loss factor. For example, since the terminal has similar signal losses when transmitting signals with the local network base station and the foreign network base station in a similar wireless environment, the environmental noise loss factor determined based on the local network data may be applied to the foreign network data to accurately determine the location of the foreign network base station.

[0068] Specifically, the network data may include reference signal received power (Reference Signal Receiving Power, RSRP), reference signal received quality (Reference Signal Receiving Quality, RSRQ), power headroom report (Power Headroom Report, PHR), timing advance (Timing Advance, TA) or serving cell timing advance (Serving cell Timing Advance, SCTadv), longitude and latitude data of the terminal, etc.

[0069] In some embodiments, the latitude and longitude coordinates of the terminal can be expressed as p i =(x i ,y i ), specifically, the state vector of the terminal (i.e., the observation matrix of the terminal) can be determined according to the above-mentioned local network data, which can be expressed as the following formula 1:

[0070]

[0071] Where x and y are the longitude and latitude of the terminal's location, that is, the longitude is x (Longitude = x), the dimension is y (Latitude = y), R is the signal strength RSRP, T is the time advance SCTadv, and the state transfer matrix is ​​the following formula 2:

[0072]

[0073] Wherein Δt is the step length of the MR / MDT data collection cycle. Using the above state transfer matrix, the position of the terminal at the next time can be predicted.

[0074] In some embodiments, in the present application, the state of the terminal may include the location state of the terminal, the received signal strength measured by the terminal, the timing advance of the terminal, etc., that is, the state of the terminal may refer to the observation matrix of the above terminal.

[0075] In some embodiments, the Kalman algorithm can be used to update the state of the terminal. When there is no data of the terminal at the previous moment, the above observation value can be directly used as the state value of the terminal. The predicted covariance and the observed noise covariance of the terminal are directly determined according to the above observation value, and the Kalman gain is determined according to the predicted covariance, the observation matrix and the observed noise covariance. The Kalman gain can be expressed as the following formula 3:

[0076] K t =P t|t1 H T (HP t|t1 H T+N) 1 Formula 3

[0077] Among them, P t|t-1 is the prediction covariance, H is the observation matrix, and N is the observation noise covariance;

[0078] In some embodiments, when the terminal has data from the previous moment, the Kalman algorithm can be used to update the state of the terminal according to the data from the previous moment and the observation matrix at the current moment. The specific formula is as follows:

[0079] X t|t 1 =X t|t 1 K t (z t -K t X t|t 1 ) Formula 4

[0080] Among them, X t|t-1 is the predicted state, z t is the observation data.

[0081] In some embodiments, when the terminal has data from the previous moment, the prediction covariance at the current moment can be determined according to the prediction covariance at the previous moment. The specific formula is as follows: Formula 5:

[0082] P t|t-1 =FP t-1|t-1 F T Q Formula 5

[0083] Wherein, Q is the process noise covariance, which is the corresponding covariance calculated by analyzing historical data and performing statistical analysis on the process noise.

[0084] After the terminal status is updated as described above, the prediction covariance may be updated according to the updated terminal status. The specific formula is as shown in the following formula 6:

[0085] P t|t1 =FX t|t-1 Formula 6

[0086] Where F is the state transfer matrix.

[0087] Then, the corresponding Kalman gain can be determined according to the updated terminal state, the updated prediction covariance and the observation noise covariance. The formula is the same as the above formula 3:

[0088] K t =P t|t1 H T (HP t|t1 H T +N) 1

[0089] Step 202: Establish a level model based on the environmental noise loss factor, the different network data, and the propagation loss model.

[0090] In some embodiments, the cross-network data may include signal strength RSRP, frequency information, gain information, etc.

[0091] In some embodiments, a level model may be established based on an environmental noise loss factor, foreign network data, and a propagation loss model, wherein the established level model may be expressed as in the following formula 7:

[0092] P R =P t +G R +G t -PL Formula 7

[0093] In the above formula, P R is the signal receiving level value, P t is the power of the transmitting antenna, G R is the receiving antenna gain, G t is the transmitting antenna gain, PL is the free space propagation loss, where P R , P t , G R , G t is determined based on data from other networks, and PL is a propagation loss model determined based on data from this network.

[0094] In some embodiments, the environmental noise loss factor may be introduced into the above propagation loss model so that the free propagation loss may be closer to the loss of the terminal in the current wireless environment. Substituting the environmental noise factor into the above propagation loss model may obtain the following level model (Formula 8):

[0095]

[0096] Among them, K is the environmental noise coefficient, that is, the above-mentioned environmental noise factor (or path loss factor), r is the distance between the terminal and the base station (antenna signal source) (Km), f is the transmission frequency (MHz), and c is the speed of light. In the above parameters, K is the Karman gain obtained by the measurement data of this network, and f is the transmission power determined according to the frequency information of the data of the other network.

[0097] Step 203: Determine the distance from the terminal to the base station based on the level model.

[0098] In some embodiments, the distance from the terminal to the base station of the foreign network can be determined by reverse deduction based on the above-mentioned level model, that is, the value of the above-mentioned parameter can be determined from the foreign network data, and the value of the parameter r can be determined according to the level model, that is, the distance from the terminal to the base station of the foreign network can be determined.

[0099] In summary, the above embodiments of the present application can determine the environmental noise loss factor based on the data of this network, and establish a level model based on the environmental noise loss factor, the data of the foreign network and the propagation loss model, so as to accurately determine the propagation loss of the terminal in the current wireless environment, and determine the distance from the terminal to the base station of the foreign network based on the level model, so as to improve the accuracy of predicting the location of the base station of the foreign network.

[0100] Figure 3 A flow chart of a method for determining a base station location provided by an embodiment of the present disclosure. Figure 3 As shown, the method may include the following steps.

[0101] Step 301: Acquire location information of a preset number of terminals.

[0102] In some embodiments, the location information of a preset number of terminals can be obtained, where the preset number can be determined based on actual applications and is not limited in comparison with the present disclosure. For example, the preset number can be determined to be 3, that is, the location information of 3 terminals needs to be obtained. By way of example, the location information of the terminal can be obtained from the measurement data reported by the terminal.

[0103] Step 302: Establish a preset number of spherical equations with the terminal position as the center and the distance from the terminal to the base station as the radius.

[0104] In some embodiments, after determining the location of the terminal, the distance from the terminal to the base station of the other network can be determined through the above embodiments. For a single terminal, a spherical equation can be established with the location of the terminal as the center and the distance from the terminal to the base station of the other network as the radius. That is, spherical equations are established for a preset number of terminals respectively, and a preset number of spherical equations can be obtained.

[0105] For example, when the preset number is 3, the positions of the three terminals are P1(x3, y3, z3), P2(x2, y2, z2), and P3(x3, y3, z3), and the distances from the three terminals to the base stations of the other networks are r1, r2, and r3, respectively, the three sphere equations established are as follows: Formula 9:

[0106]

[0107] Step 303: determine a preset number of intersection points between spherical surfaces, and establish an intersection point set based on the intersection points that meet the second condition.

[0108] In some embodiments, preferably, a preset number of sphere equations may be solved simultaneously to determine the intersection points of the preset number of spheres.

[0109] In some embodiments, optionally, a two-dimensional circle equation may be established based on the location of the terminal and the distance from the terminal to the base station of the other network to determine the intersection of the circles.

[0110] In some embodiments, after the intersection is obtained, the intersection can be screened. For example, the second condition may be that the height of the intersection is within a preset range, such as the height range of the intersection is between [0m, 60m]. The height range of the intersection can be determined according to the actual application scenario. For example, the height of the intersection can be limited to be higher than the height of the terminal and lower than the highest height of the base station, etc., which is not limited in the present disclosure.

[0111] In the above embodiment, the height of the intersection point may be determined based on the sea level, for example, it may be a vertical distance from the sea level, etc., which is not limited in the present disclosure.

[0112] In some embodiments, after the intersections are filtered based on the second condition, an intersection set may be established based on the filtered intersections.

[0113] Step 304: cluster the intersection points in the intersection point set to determine the centroid of the intersection point set.

[0114] In some embodiments, a clustering algorithm can be used to cluster the intersections in the intersection set, where the clustering algorithm can be a K-means clustering algorithm, a density clustering algorithm, an iterative self-organizing data analysis algorithm, a hierarchical clustering algorithm, or other clustering algorithms to achieve extraction of base station-level clusters.

[0115] In some embodiments, specifically, taking the K-means algorithm as an example, the intersection points in the intersection point set are clustered, and the process of determining the centroid of the intersection point set is as follows:

[0116] 1. First, the centers of K clusters can be randomly determined, where the center of the cluster can be the centroid that is initially determined. The intersection points are divided into corresponding clusters according to the distances from the intersection points to the centers of the K clusters. For example, the distance from the intersection points to the centers of the clusters can be determined by using the Euclidean distance. The specific formula is as follows: Formula 10:

[0117]

[0118] In the above formula, d is the coordinate of an intersection point in the intersection set, D is the intersection set, (x i ,y i ) are the coordinates of the randomly determined cluster center.

[0119] 2. Based on the above clustering results, the cluster center is updated. The cluster center is updated as shown in the following formula 11:

[0120]

[0121] Where K represents the number of clusters. When the distance J between the cluster centers obtained from two iterations is less than a certain threshold, that is, ΔJ<δ, the iteration is terminated and the cluster obtained at this time is the final clustering result.

[0122] In some embodiments, the specific threshold value can be determined according to the actual application scenario, which is not limited by the present disclosure.

[0123] 3. After determining the clustering results, determine the centroid of each cluster.

[0124] For example, in some embodiments, the centroid of a cluster may be determined using the Euclidean distance, and the centroid may be updated by calculating the total distance from the initial centroid to other intersections in the cluster until the total distance from the centroid to other intersections in the cluster is determined to be the minimum, and the current centroid is determined to be the centroid of the cluster, as shown in the following formula 12:

[0125]

[0126] Where: x represents one of the sample points (i.e., an intersection point in the cluster), μ represents the centroid of the cluster, n represents the number of features in each sample point (i.e., the total number of intersection points in the cluster), and i represents each feature of the constituent points (i.e., different intersection points in the cluster).

[0127] Step 305: determine the centroid coordinates of the intersection set as the location of the base station.

[0128] In some embodiments, after determining the centroid position, the centroid position can be determined as the position of the foreign network base station, that is, the coordinates of the centroid can be determined as the coordinates of the foreign network base station, thereby realizing the identification of the foreign network base station.

[0129] In summary, the above-mentioned embodiments of the present application, through the location information reported by multiple terminals, establish the spherical equations jointly to determine the intersection points, and screen the intersection points to determine the intersection point set, so as to determine the possible locations of the foreign network base stations that meet the requirements, and cluster the intersection points in the intersection point set to determine the center of mass, and determine the coordinates of the center of mass as the location of the foreign network base station, so as to realize the identification of the foreign network base station.

[0130] For the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present disclosure is not limited by the described order of actions, because according to the present disclosure, certain steps can be performed in other orders or simultaneously.

[0131] Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0132] The technical solution of the present disclosure is further described in detail below in conjunction with specific application examples.

[0133] The embodiment of the present disclosure provides a method for identifying the location of a competing base station.

[0134] The method in this example uses the MR or MDT function of the 4 / 5G base station to obtain the terminal location, the frequency point, PCI, signal strength and other information of the competing base station, and establishes a model to reversely infer the location of the competing base station based on the similar characteristics of the propagation environment where the terminal receives the signal at the same location. Figure 4 As shown, the implementation steps of this method are as follows:

[0135] Step 1: Based on the local network signal characteristics and location information in the MR / MDT data reported by all terminals in the cell, the Kalman algorithm is used for all terminals to obtain the Kalman gain of each terminal in different wireless environments, which is equivalent to the environmental noise loss factor;

[0136] Step 2: Using the similar characteristics of the signal propagation model at the same location, the Kalman gain calculated in the first step is combined with the competing cell with the strongest signal strength to reversely deduce the distance between the competing cell antenna and the terminal location, and establish a three-dimensional spatial distribution function of the competing cell location.

[0137] Step 3: Through the MR / MDT data of the same competing cell reported by terminals at different locations, the coordinates of the intersections of multiple spheres are obtained using the distances between the competing cell and the different terminal locations obtained in the second step, and the top intersection is simulated as the possible location of the competing cell. Subsequently, a set of possible locations of the competing cell is obtained based on the combination of sampling data from different terminals.

[0138] Step 4: Use a clustering algorithm to cluster the possible locations of competing base stations obtained in the third step into clusters.

[0139] Step 5: Use the Euclidean distance calculation method to find the centroid of the competing cluster points.

[0140] Step 6: Output the list of competing base station locations.

[0141] The above steps are explained in detail below through specific examples.

[0142] Step 1: Estimate the source location

[0143] In the wireless environment of the terminal at a certain moment, the propagation loss of the network and the competing signal will be similar. Therefore, by knowing the location (latitude, longitude, altitude) information of the 4 / 5G site of the network and the terminal distribution, the Kalman filter algorithm is used to calculate the Kalman gain according to the signal characteristics (RSRP, RSRQ, PHR, SCTadv, etc.) to determine the wireless environment noise factor K. The Kalman gain is combined with the free space propagation loss model to establish the competing signal level reception model, and the competing signal strength information measured by different frequencies is used to reversely estimate the distance between the terminal and the competing base station (determine the distance r from the competing signal source to the terminal). Figure 5 As shown, the specific algorithm is as follows:

[0144] 1. Let p i =(x i ,y i ) is the coordinate of the i-th terminal at the current position. For each detected terminal p i ,The Kalman filter prediction module uses the Kalman filter for state prediction.

[0145] In this process, the state vector (i.e., observation matrix) of a single terminal is established based on the terminal latitude and longitude position data (e.g., [113.375279, 28.06215]) and signal strength (e.g., [-87dBm]) and time advance SCTadv in the MR / MDT measurement report, which is expressed as:

[0146]

[0147] Where x and y are the longitude and latitude of the terminal, that is, Longitude = x, Latitude = y, R is the signal strength RSRP, T is the time advance SCTadv, and the state transfer matrix is:

[0148]

[0149] Wherein Δt is the step length of the MR / MDT data collection cycle. The above state transition matrix is ​​used to predict the position of the terminal at the next time.

[0150] 2. Use the Kalman filter prediction module to update the state of each terminal using its associated observation data and its predicted state data through the Kalman filter. The specific update steps are as follows:

[0151] (1) Calibration steps

[0152] Calculate the Kalman gain: K t =P t|t-1 H T (HP t|t-1 H T +N) -1And the Kalman gain is used as the environmental noise loss factor in the process of wireless signal propagation.

[0153] Among them, P t|t-1 is the prediction covariance, H is the observation matrix, and N is the observation noise covariance;

[0154] Update estimate status: X t|t-1 =X t|t-1 K t (z t -K t X t|t-1 ),

[0155] Among them, X t|t-1 is the predicted state, z t is the observational data;

[0156] Update covariance: P t|t-1 =(IK t H)P t|t-1 H T , where I is the identity matrix.

[0157] (2) Prediction steps:

[0158] Predict the state at the next moment: P t|t-1 =FX t|t-1 ,

[0159] Where, F is the state transfer matrix;

[0160] Prediction covariance: P t+1|t =FP t|t F T Q,

[0161] Among them, Q is the process noise covariance, which is the corresponding covariance calculated by analyzing historical data and performing statistical analysis on process noise.

[0162] Next, the Kalman gain K and free space propagation loss are combined to form the terminal receiving signal level P according to the RSRP of the competing signal in the MR / MDT data, the frequency information and the longitude and latitude data of the terminal. R Model, reverse the distance r from the competing signal source to the terminal location. The terminal receiving signal level model is expressed as follows;

[0163]

[0164] Where: P R is the signal receiving level value, P t is the power of the transmitting antenna, G R is the receiving antenna gain, G tis the transmitting antenna gain, PL is the free space propagation loss (dB), K is the environmental noise coefficient, i.e. the path loss factor, r is the distance between the terminal and the antenna source (Km), f is the transmitting frequency (MHz), c is the speed of light, K is the Karman gain obtained from the measurement data of this network, and f has frequency information in the competitive measurement, from which the distance r can be calculated.

[0165] Step 2: Calculate the intersection point on the sphere surface

[0166] like Figure 6 As shown, according to the terminal's location (longitude and latitude data in MR / MDT data) and the distance r between the competing cell and the terminal obtained in the first step (the distance between the competing cell and different terminals), three sphere center coordinates (i.e., three locations continuously reported by three different terminals) are established, namely P1 (x1, y3, z3), P2 (x2, y2, z2), and P3 (x3, y3, z3). The sphere radius (the distance r between the terminal and the competing cell) is r1, r2, and r3 respectively.

[0167] The three sphere equations can be combined to form a nonlinear system of equations. Based on this linear system of equations, the coordinates of the intersection points A and B of the three spheres can be calculated:

[0168]

[0169] After determining the coordinates of intersections A and B, since the terminal is located at a low altitude and not higher than the altitude of the base station of the network, the location of the competing cell can exclude intersection B. According to this method, all possible location data sets of multiple users in the competing cell are obtained.

[0170] Step 3: Point Clustering

[0171] The K-means algorithm is used to cluster all possible location data sets of competing cells.

[0172] The specific implementation process of the K-means algorithm is as follows:

[0173] The K-means algorithm is a centroid-based clustering algorithm that can calculate the centroid position of a cluster (usually determined by Euclidean distance). The calculation formula is:

[0174]

[0175] Where d is one of the possible position data in the above set, (x i ,y i ) is the coordinate of the cluster center initially determined, dist(x i ,y i ) is the distance from other positions in the possible position data set to the cluster center, and the position data in the possible position data set can be clustered according to the calculated distance.

[0176] Define the center of the kth cluster as Center k , then the cluster center is updated as follows:

[0177]

[0178] Where K represents the number of clusters. When the difference between the distances J between the cluster centers determined by two iterations is less than a certain threshold, that is, ΔJ<δ, the iteration is terminated, and the cluster obtained at this time is the final clustering result.

[0179] Step 4: Cluster centroid calculation

[0180] The Euclidean distance calculation method is used to obtain the centroid of the competing cluster points, and the center position of the cluster is the centroid position.

[0181] The formula is as follows:

[0182]

[0183] Among them: x represents one of the sample points, μ represents the centroid of the cluster, n represents the number of features in each sample point (i.e. the total number of points in the cluster), i represents each feature of the constituent points (i.e. the different points in the cluster), that is, the distance from the different points in the cluster to the centroid can be calculated, and the centroid is updated to determine the centroid with the smallest distance to the different points in the cluster as the final cluster centroid.

[0184] Step 5: Identify the location of competing base stations

[0185] The centroid point of each competing base station calculated in the fourth step is defined as the position of the corresponding competing base station.

[0186] In summary, the above examples of the present disclosure accurately determine the propagation loss in the process of each user receiving the signal by calculating the Kalman gain K of the signal of this network. The competing base station will transmit the signal through the loss (Kalman gain) at the same sampling time, so the accuracy of the predicted position can be improved; starting from the wireless environment where the terminal is located, a spherical model of the possible location of the base station to be identified is established through the received / signal situation simulation. Then, the most likely location of the competing cell to be identified is estimated by using the position of the intersection point on the surface of the sphere to realize the identification of the competing position; the Kalman filter algorithm is used to establish a dynamic propagation model of each competing base station based on the competing hetero-frequency measurement data of this network, and the state of signal propagation is estimated in a recursive manner, and it is continuously adjusted and optimized. It can effectively handle noise and uncertainty in the communication process, provide optimal observation of signal propagation conditions; and provide a new way to calculate the path loss factor in the process of wireless signal propagation.

[0187] Figure 71 is a block diagram of a base station location determination device 700 provided in an embodiment of the present disclosure. Figure 7 As shown, the device 700 includes: an acquisition unit 710, used to acquire measurement data of the terminal, the measurement data includes at least one of measurement report MR data and minimization of drive test MDT data; a determination unit 720, used to determine the type of data contained in the measurement data, the type of data can be local network data or foreign network data; a first processing unit 730, used to determine a propagation loss model according to the local network data; a second processing unit 740, used to determine the distance from the terminal to the base station based on the measurement data using the propagation loss model; a third processing unit 750, used to determine the position of the base station according to the distance from the terminal to the base station.

[0188] In summary, the above-mentioned device disclosed in the present invention can determine the corresponding propagation loss model based on the measurement data of the terminal, so as to determine the propagation loss in the actual transmission environment. Determining the distance from the terminal to the base station based on the propagation loss model can improve the accuracy of the distance prediction. Determining the position of the base station based on the distance from the terminal to the base station can improve the accuracy of the base station position prediction.

[0189] In some embodiments, the first processing unit is further used to determine data characteristics of the local network data; and determine a propagation loss model corresponding to the local network data based on the data characteristics of the local network data.

[0190] In some embodiments, the second processing unit is also used to determine the environmental noise loss factor corresponding to the local network data based on the local network data; establish a level model according to the environmental noise loss factor, the foreign network data, and the propagation loss model; and determine the distance from the terminal to the base station based on the level model.

[0191] In some embodiments, the second processing unit is also used to determine the state prediction covariance, observation matrix and noise covariance corresponding to the current network data based on the current network data; based on the state prediction covariance, observation matrix and noise covariance corresponding to the current network data, determine the environmental noise loss factor corresponding to the current network data.

[0192] In some embodiments, the first processing unit can also be used to determine the frequency of the data and the physical cell identification code PCI; when the frequency of the data and the physical cell identification code PCI meet the first condition, the type of the data is determined to be local network data; otherwise, the type of the data is determined to be cross-network data.

[0193] In some embodiments, the third processing unit is also used to obtain the location information of a preset number of terminals; establish a preset number of spherical equations with the location of the terminal as the center and the distance from the terminal to the base station as the radius; determine the intersection points between the preset number of spheres, and establish an intersection point set based on the intersection points that meet the second condition; cluster the intersection points in the intersection point set to determine the center of mass of the intersection point set; and determine the center of mass coordinates of the intersection point set as the location of the base station.

[0194] Figure 8 A block diagram of an electronic device 800 for implementing the above method is provided in an embodiment of the present disclosure.

[0195] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiment of the present disclosure, the embodiment of the present disclosure also provides an electronic device, such as Figure 8 As shown, the electronic device 800 includes:

[0196] Communication interface 801, capable of exchanging information with other devices;

[0197] The processor 802 is connected to the communication interface 801 to implement information interaction with other devices, and is used to execute the method provided by one or more of the above technical solutions when running the computer program;

[0198] A memory 803 , on which the computer program is stored.

[0199] Specifically, the processor 802 may be configured to determine a first family to which the first object belongs based on the communication data of the first object; determine the first family as a target family when the first family satisfies a first preset condition; and push target information to the target family.

[0200] It should be noted that the specific processing process of the processor 802 can be understood by referring to the above method.

[0201] Of course, in actual application, the various components in the electronic device 800 are coupled together through the bus system 804. It can be understood that the bus system 804 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 804 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Figure 8 Various buses are labeled as bus system 804 .

[0202] The memory 803 in the embodiment of the present application is used to store various types of data to support the operation of the electronic device 800. Examples of such data include: any computer program used to operate on the electronic device 800.

[0203] The method disclosed in the above embodiment of the present application can be applied to the processor 802, or implemented by the processor 802. The processor 802 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 802. The above-mentioned first processor 802 can be a general-purpose processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 802 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiment of the present application, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in the memory 803, and the processor 802 reads the information in the memory 803 and completes the steps of the above method in combination with its hardware.

[0204] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0205] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, and the above instructions can be executed by the processor 820 of the electronic device 800 to complete the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0206] The embodiments of the present disclosure further provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the method described in the above embodiments of the present disclosure.

[0207] The embodiment of the present disclosure also provides a chip, such as Fig. 9 As shown, the chip includes a processor 910 and an interface 920. The number of the processor 910 may be one or more, the number of the interface 920 may be multiple, and the interface circuit is used to receive a signal from the memory of the electronic device and send a signal to the processor, the signal includes a computer instruction stored in the memory, and when the processor executes the computer instruction, the electronic device executes the method described in the above embodiment of the present disclosure.

[0208] It should be noted that, for the convenience of description, only the parts related to the relevant invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0209] It should be understood that the "system", "device", "unit" and / or "module" used in this application is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the word can be replaced by other expressions.

[0210] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "a kind" and / or "the" do not refer to the singular, but also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements. The elements defined by the sentence "includes a..." do not exclude the existence of other identical elements in the process, method, commodity or device that includes the elements.

[0211] In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B; "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, "multiple" means two or more than two.

[0212] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features.

[0213] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, the various steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0214] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0215] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0216] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processing module, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (control method), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0217] It should be understood that the various parts of the embodiments of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0218] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0219] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent commodity, it may also be stored in a computer-readable storage medium. The above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0220] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for determining a base station location, characterized in that: The method comprises: Acquire measurement data of the terminal, where the measurement data includes at least one of measurement report (MR) data and minimization of drive test (MDT) data; Determine the type of data included in the measurement data, where the type of data may be local network data or foreign network data; Determine a propagation loss model based on the local network data; Based on the measurement data, determining the distance from the terminal to the base station using the propagation loss model; The position of the base station is determined according to the distance from the terminal to the base station.

2. The method according to claim 1, characterized in that Determining the propagation loss model according to the local network data includes: Determine data characteristics of the data on the local network; A propagation loss model corresponding to the local network data is determined based on the data characteristics of the local network data.

3. The method according to claim 1, characterized in that The determining, based on the measurement data, the distance from the terminal to the base station by using the propagation loss model comprises: Based on the local network data, determining an environmental noise loss factor corresponding to the local network data; Establishing a level model according to the environmental noise loss factor, the foreign network data, and the propagation loss model; Based on the level model, a distance from the terminal to a base station is determined.

4. The method according to claim 3, characterized in that The determining, based on the local network data, an environmental noise loss factor corresponding to the local network data comprises: Determine the state prediction covariance, observation matrix and noise covariance corresponding to the local network data according to the local network data; Based on the state prediction covariance, observation matrix and noise covariance corresponding to the local network data, the environmental noise loss factor corresponding to the local network data is determined.

5. The method according to claim 1, characterized in that Determining the type of data included in the measurement data includes: Determine the frequency point and physical cell identity PCI of the data; When the frequency point and the physical cell identifier PCI of the data meet the first condition, determining that the type of the data is local network data; Otherwise, it is determined that the type of the data is cross-network data.

6. The method according to claim 1, characterized in that The determining the position of the base station according to the distance from the terminal to the base station comprises: Obtaining location information of a preset number of terminals; Establishing a preset number of spherical equations with the position of the terminal as the center and the distance from the terminal to the base station as the radius; Determine the intersection points between the preset number of spherical surfaces, and establish an intersection point set based on the intersection points that meet the second condition; Clustering the intersection points in the intersection point set to determine the centroid of the intersection point set; The centroid coordinates of the intersection set are determined as the position of the base station.

7. A base station location determination device, characterized in that: The device includes: An acquiring unit, configured to acquire measurement data of a terminal, wherein the measurement data includes at least one of measurement report MR data and minimization of drive test MDT data; A determination unit, used to determine the type of data included in the measurement data, where the type of data may be local network data or foreign network data; A first processing unit, configured to determine a propagation loss model according to the local network data; A second processing unit, configured to determine a distance from the terminal to a base station using the propagation loss model based on the measurement data; The third processing unit is used to determine the position of the base station according to the distance from the terminal to the base station.

8. An electronic device, characterized in that: include: one or more processors; A storage device communicatively connected to the one or more processors, on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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