KR20230034103A - Base station coverage analysis method and apparatus - Google Patents

Abstract

A base station coverage analysis method, for effectively analyzing an entire coverage of a base station based on limited actual measurement values of wireless communication quality, is disclosed. The base station coverage analysis method according to the present invention comprises the steps of: collecting actual measurement values related to wireless communication quality of measurement points from user terminals measuring the wireless communication quality in a prediction target area; calculating primary prediction values related to a wireless communication coverage of a base station using base station information, environmental information, and a path loss model in the prediction target area; creating a collection area surrounding the measurement points; for a non-measurement point inside the collection area, calculating a secondary prediction value for the non-measurement point using a primary prediction value of the path loss model for the non-measurement point and actual measurement values around the non-measurement point; and for a prediction point outside the collection area, calculating a secondary prediction value for the prediction point using a primary prediction value of the path loss model for the prediction point and quality values inside the collection area.

Description

Base station coverage analysis method and apparatus {BASE STATION COVERAGE ANALYSIS METHOD AND APPARATUS}

The present invention relates to a method and apparatus for analyzing coverage of a base station, which can effectively analyze the entire coverage of a base station based on limited actually measured values of wireless communication quality.

Recently, as various mobile communication networks have been established, such as commercialization of 5G mobile communication through LTE (Long Term Evolution) and LTE-A (Long Term Evolution-Advanced), which are 4G mobile communication technologies, it is necessary to accurately predict the wireless communication coverage of the base station Interest and requests for technology are rapidly increasing.

For optimization of a mobile communication base station where services are provided by overlapping multiple cells, coverage analysis on how far each base station delivers a received signal capable of providing service to a user terminal and how much interference occurs to neighboring cells this should take precedence

Meanwhile, for coverage analysis, a network operator may directly collect RF quality parameters of a serving cell and a neighbor cell base station measured by a UE through a drive test. However, this method has a disadvantage in that it takes a lot of time and money because the network operator must move the entire coverage of the base station.

Meanwhile, in order to save manpower and time separately required for measuring wireless communication quality, mobile communication operators operate a system for collecting and analyzing wireless communication quality reported by user terminals while mobile communication subscribers actually use services. However, in order to provide meaningful information to the network operator, wireless communication quality must be collected from a sufficient number of user terminals, but collection parameters are often insufficient due to timing or geographical characteristics. In addition, compared to the method collected by a network operation manager in a professional manner through a drive test, the information collected by the user terminal is somewhat inferior in terms of type, accuracy, and reliability.

In addition, since both of the above two wireless communication quality collection methods can only collect wireless communication quality in a place where a terminal is actually located or can move (for example, a road), it is difficult to check the entire coverage of a plurality of base stations. .

In order to compensate for this, there is a method of theoretically predicting the wireless communication quality of the base station for each wireless signal reception position of the terminal through radio wave tracking or path loss model-based propagation prediction. However, propagation prediction using an analytical model such as ray-tracing has the disadvantage of requiring a lot of time for calculation instead of guaranteeing accuracy. In addition, propagation prediction using a statistical model such as a path loss model requires a small amount of computation but does not reflect local environmental characteristics, so the error is very large when using the model without separate correction.

In order to compensate for this, there is a method of reducing the prediction error of the path loss model by using wireless communication quality actually measured at measurement points within coverage.

However, as shown in FIG. 1, it is assumed that there is a base station built in the direction of the arrow.

For a cell corresponding to such a base station, a wide range representing a significant received signal strength is indicated by a solid line 1010. That is, the corresponding base station can transmit a radio signal of significant strength to areas within the solid line 1010 . However, since several cells overlap in an actual service environment, the range in which the user terminal can collect wireless communication quality for a corresponding base station is limited to an area within the dotted line 1020 located close to the base station. That is, there is a problem in that the radio communication quality related to the corresponding base station is reported from the user terminal located inside the dotted line 1020.

Accordingly, most of the area requiring coverage analysis (the area within the solid line 1010) is out of the range in which the wireless communication quality is reported from the user terminal (the area within the dotted line 1020). Therefore, it is necessary to appropriately utilize the wireless communication quality actually measured by the user terminal even for the area outside the dotted line 1020 and to increase the reliability of the prediction.

The present invention is to solve the above-described problems, and an object of the present invention is to provide a method and apparatus for analyzing coverage of a base station, which can effectively analyze the entire coverage of a base station based on limited measured values of wireless communication quality. It is for

A coverage analysis method of a base station according to the present invention includes the steps of collecting actual values of wireless communication quality of measurement points from user terminals measuring wireless communication quality in a prediction target area, base station information of the prediction target area, and environment. Calculating primary predicted values for wireless communication coverage of a corresponding base station using information and a pathloss model, generating a collection area surrounding the measurement points, and non-measurement points within the collection area , Calculating a secondary prediction value for the non-measurement point using the first prediction value of the path loss model for the non-measurement point and actual values around the non-measurement point, and prediction outside the collection area For a point, calculating a second prediction value for the prediction point using the first prediction value of the path loss model for the prediction point and the quality values inside the collection area.

In this case, the quality values inside the collection area include actual values of measurement points inside the collection area, secondary predicted values of non-measurement points inside the collection area, or one or more measurement points inside the collection area. It may include an actual measured value of and a secondary predicted value of one or more non-measurement points inside the collection area.

In this case, the step of calculating the secondary prediction value for the prediction point is to obtain the prediction point using the quality values within the collection area in a state in which the secondary prediction values for all non-measurement points within the collection area are calculated. Calculating a secondary prediction value for

Meanwhile, the step of calculating the secondary prediction value for the ratio measurement point may include interpolating the primary prediction value for the ratio measurement point and a preset number of measured values closest to the ratio measurement point, and then interpolating the ratio measurement point. Calculating a secondary predictive value for a point may be included.

Meanwhile, the step of calculating the secondary prediction value for the prediction point may include extrapolating the primary prediction value for the prediction point and a preset number of quality values in the collection area closest to the prediction point, Calculating a secondary predictive value for a point may be included.

In this case, the step of extrapolating the quality values within the collection area to calculate a secondary prediction value for the prediction point includes a predetermined number of prediction points closest to the prediction point while having the same propagation environment as the prediction point. Calculating a secondary prediction value for the prediction point using quality values within the collection area may be included.

Meanwhile, the collection area may be a convex hull that includes some of the measurement points for the corresponding base station as a boundary and includes all of the measurement points for the corresponding base station.

Meanwhile, the step of calculating the first predicted values for the wireless communication coverage of the corresponding base station includes generating a corrected path loss model based on the difference between the measured values of the wireless communication quality of the measurement points and the theoretical prediction values of the measurement points. and calculating a first prediction value for the non-measurement point and a first prediction value for the prediction point using the corrected path loss model.

Meanwhile, an apparatus for analyzing coverage of a base station according to the present invention includes a communication unit that collects actual values of wireless communication quality of measurement points from user terminals measuring wireless communication quality in a prediction target area, and a base station in the prediction target area. Using information, environment information, and a pathloss model, primary predicted values for wireless communication coverage of a corresponding base station are calculated, a collection area surrounding the measurement points is created, and non-measurement points within the collection area are calculated. , a secondary prediction value for the non-measurement point is calculated using the first predicted value of the path loss model for the non-measurement point and the actual values around the non-measurement point, and at a predicted point outside the collection area , a control unit for calculating a secondary prediction value for the prediction point using the first prediction value of the path loss model for the prediction point and the quality values within the collection area.

In this case, the quality values inside the collection area include actual values of measurement points inside the collection area, secondary predicted values of non-measurement points inside the collection area, or one or more measurement points inside the collection area. It may include an actual measured value of and a secondary predicted value of one or more non-measurement points inside the collection area.

Meanwhile, in a state in which secondary prediction values for all non-measurement points within the collection area are calculated, the controller may calculate a secondary prediction value for the prediction point using quality values within the collection area.

Meanwhile, the control unit may calculate a secondary prediction value for the non-measurement point by interpolating the primary prediction value for the non-measurement point and a preset number of measured values closest to the non-measurement point. .

Meanwhile, the control unit may calculate a secondary prediction value for the prediction point by extrapolating the primary prediction value for the prediction point and the quality values in the collection area of a predetermined number closest to the prediction point. there is.

In this case, the control unit may calculate a secondary prediction value for the prediction point using a predetermined number of quality values in the collection area closest to the prediction point while having the same propagation environment as the prediction point.

Meanwhile, the collection area may be a convex hull that includes some of the measurement points for the corresponding base station as a boundary and includes all of the measurement points for the corresponding base station.

Meanwhile, the control unit generates a corrected path loss model based on a difference between measured values of the wireless communication quality of the measurement points and theoretically predicted values of the measurement points, and measures the ratio using the corrected path loss model. A first prediction value for a point and a first prediction value for the prediction point may be calculated.

Meanwhile, a computer program stored in a medium according to the present invention includes the steps of collecting, from user terminals measuring wireless communication quality in the prediction target area, measured values for wireless communication quality of measurement points, and base station information of the prediction target area. , calculating primary predicted values for wireless communication coverage of a corresponding base station using environmental information and a pathloss model, generating a collection area surrounding the measurement points, and non-measurement points inside the collection area For , calculating a secondary prediction value for the non-measurement point using the first prediction value of the path loss model for the non-measurement point and actual values around the non-measurement point, and outside the collection area. Coverage analysis of a base station comprising calculating a secondary prediction value for a prediction point of , using a primary prediction value of the path loss model for the prediction point and quality values inside the collection area. do the way

According to the present invention, by quickly and accurately calculating the received signal strength in the entire coverage of the base station only with limited radio quality data collected by the user terminal in the vicinity of the base station, the quality of the overall coverage of the base station currently being built/operated and the quality of the neighboring cell and It has the advantage of being able to effectively perform analysis on the interference of

1 is a diagram for explaining problems of the prior art.
2 shows a communication network environment to which the present invention is applied.
3 is a block diagram illustrating an apparatus 100 for analyzing coverage of a base station according to the present invention.
4 is a flowchart illustrating a method for analyzing a coverage of a base station according to the present invention.
5 is a diagram for explaining a prediction target area and a collection area according to the present invention.
6 is a diagram for explaining a method of calculating a secondary prediction value for a non-measurement point inside a collection area according to the present invention.
7 is a diagram for explaining a method of calculating a secondary prediction value for a prediction point outside a collection area according to the present invention.
8 is a diagram for explaining a method of calculating a secondary prediction value for a prediction point using quality values having the same propagation environment as that of the prediction point.

Hereinafter, the embodiments disclosed in this specification will be described in detail with reference to the accompanying drawings, but the same or similar elements are given the same reference numerals regardless of reference numerals, and redundant description thereof will be omitted. The suffixes "module" and "unit" for components used in the following description are given or used together in consideration of ease of writing the specification, and do not have meanings or roles that are distinct from each other by themselves. In addition, in describing the embodiments disclosed in this specification, if it is determined that a detailed description of a related known technology may obscure the gist of the embodiment disclosed in this specification, the detailed description thereof will be omitted. In addition, the accompanying drawings are only for easy understanding of the embodiments disclosed in this specification, the technical idea disclosed in this specification is not limited by the accompanying drawings, and all changes included in the spirit and technical scope of the present invention , it should be understood to include equivalents or substitutes.

Terms including ordinal numbers, such as first and second, may be used to describe various components, but the components are not limited by the terms. These terms are only used for the purpose of distinguishing one component from another.

It is understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, but other elements may exist in the middle. It should be. On the other hand, when an element is referred to as “directly connected” or “directly connected” to another element, it should be understood that no other element exists in the middle.

Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, terms such as "comprise" or "have" are intended to designate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other features It should be understood that the presence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded.

In implementing the present invention, components may be subdivided for convenience of description, but these components may be implemented in one device or module, or one component may be divided into multiple devices or modules may be implemented in

2 shows a communication network environment to which the present invention is applied.

As shown in FIG. 2 , a communication network environment to which the present invention is applied may include a network information management server 10 , an environment information management server 20 , and a plurality of user terminals 30 .

The network information management server 10 is, for example, a server operated by a network operator and is configured to store and manage base station information by region and base station setting information. In this case, the base station information may include at least one of a location where the base station is built (position of the base station antenna), a physical cell ID (PCI) as a cell identifier, an azimuth angle and physical tilt of the antenna, transmission power, a radiation pattern, and an electrical tilt. can

The environment information management server 20 may store and manage environment information about reception points. Here, the environmental information includes topography, building information (building location, building height, etc.), building penetration, number of building penetrations, building penetration distance, radio wave environment (LOS point) from individual base stations to the receiving point of the radio signal. or non-visible path (NLOS) points).

Meanwhile, each user terminal 30 is configured to measure wireless communication quality at a measurement point. In this case, the measured wireless communication quality is RSRP (Reference Signal Received Power), RSRQ (Reference Signal Received Quality), RSSI (Received Signal Strength Indicator), SINR (Signal to Interference Noise Ratio), SS (Synchronization Signal)-RSRP, It may include one or two or more of CSI (Channel State Info.)-RSRP, SS-RSRQ, CSI-RSRQ, SS-SINR, and CSI-SINR.

Also, the user terminal 30 is a terminal used by a user subscribed to a mobile communication service, and may be, for example, a smart phone. Accordingly, the user terminal 30 may transmit the measured wireless communication quality and cell identifier (PCI) while using the mobile communication service. In this case, the coverage analysis apparatus 100 of the base station may receive wireless communication quality and cell identifier (PCI) directly or through another device.

Also, the user terminal 30 may be a mobile terminal. And mobile terminals include mobile phones, smart phones, laptop computers, digital broadcasting terminals, personal digital assistants (PDAs), portable multimedia players (PMPs), navigation devices, slate PCs, and tablet PCs. (tablet PC), ultrabook, wearable device (eg, watch type terminal (smartwatch), glass type terminal (smart glass), head mounted display (HMD)), and the like may be included.

Meanwhile, in this specification, the wireless communication quality measured by the user terminal 30 is referred to as a measured value related to the wireless communication quality.

Meanwhile, the base station coverage analysis apparatus 100 is applied to such a communication network environment and is configured to analyze the wireless communication coverage of the base station based on the measured value of the wireless communication quality.

3 is a block diagram illustrating an apparatus 100 for analyzing coverage of a base station according to the present invention.

The base station coverage analysis apparatus 100 according to the present invention may include a communication unit 110, a control unit 120, and a memory 130.

The communication unit 110 may be configured to receive data transmitted from other communication devices through a wired/wireless communication network and forward the data to the control unit 120 or transmit data generated or processed by the control unit 120 to another device. can To this end, the communication unit 110 may include a communication modem that performs wired/wireless communication.

The controller 120 may control overall operations of the coverage analysis apparatus 100 of the base station.

In addition, the controller 120 may calculate a primary prediction value for the wireless communication coverage of the base station, calculate a secondary prediction value for a non-measurement point inside the collection area, and calculate a secondary prediction value for a prediction point outside the collection area. there is.

Meanwhile, the term control unit 120 may also be used as terms such as “processor,” “microprocessor,” “controller,” and “microcontroller.”

Meanwhile, the memory 130 may store instructions or other programs for operating the coverage analysis apparatus 100 of the base station. To this end, the memory 130 may selectively include various storage media such as ROM, RAM, EEPROM, registers, flash memory, CD-ROM, magnetic tape, hard disk, floppy disk, and optical data recording device.

Meanwhile, the base station coverage analysis apparatus 100 may further include an input unit (not shown) for receiving commands or data from an operator or manager and an output unit (not shown) for visually or audiovisually outputting data or information. .

4 is a flowchart illustrating a method for analyzing a coverage of a base station according to the present invention.

A coverage analysis method of a base station according to the present invention includes the step of collecting base station information and environment information (S410), and obtains actually measured values of wireless communication quality of measurement points from user terminals measuring wireless communication quality in a prediction target area. Collecting (S420), calculating primary predicted values for the wireless communication coverage of the base station using base station information, environmental information, and a pathloss model of the prediction target area (S430), measuring points surrounding Creating a collection area (S440), for non-measurement points inside the collection area, using the first predicted value of the path loss model for the non-measurement point and the actual values around the non-measurement point, the secondary for the non-measurement point Calculating a prediction value (S450), for a prediction point outside the collection area, a secondary prediction value for the prediction point is calculated using the first prediction value of the path loss model for the prediction point and the quality values inside the collection area. A calculating step (S460) may be included.

In relation to S410, the control unit 120 may collect base station information and environment information through the communication unit 110. Specifically, the controller 120 may receive base station information and environment information from the network information management server 10 and the environment information management server 20 described in FIG. 2 or other external devices.

In relation to S420 , the controller 120 may receive radio quality data from user terminals measuring radio communication quality through the communication unit 110 . In this case, the radio quality data may include actually measured values of radio communication quality of measurement points.

In detail, an example of a table for radio quality data measured by user terminals and received by the coverage analysis apparatus 100 of a base station is as follows. Hereinafter, it will be described that the measured value included in the wireless communication quality data is Reference Signal Received Power (RSRP). That is, the measured value and predicted value described herein may mean the strength of a signal received by a user terminal such as RSRP, but are not limited thereto, and various indicators representing wireless communication quality may be used as the measured value and predicted value. there is.

ID UTMK_Y UTMK_Y ... PCI ... number of collections ... RSRP ... One 10XXXXXX 17XXXXXX ... 100 ... 9 ... -70 ... 10XXXXXX 17XXXXXX ... 200 ... 4 ... -80 ... 2 10XXXXXY 17XXXXXY ... 200 ... 19 ... -75 ... 10XXXXXY 17XXXXXY ... 300 ... 6 ... -90 ... 3 10XXXXXZ 17XXXXXZ ... 300 ... 21 ... -65 ... 10XXXXXZ 17XXXXXZ ... 100 ... 8 ... -85 ...

That is, the radio quality data may include an identifier (PCI) where the radio quality data was measured, location information about a measurement point of the radio quality data, and a RSRP for radio communication quality.

In addition, the control unit 120 may divide the area within the coverage into unit areas having an appropriate size (for example, a grid having a size of 25*25 m), and may perform analysis for each unit area.

Therefore, in this specification, “measurement point” may mean a point where radio quality data is measured or a unit area including a point where radio quality data is measured. Also, in the present specification, “non-measurement point” may refer to a unit area including a point where radio quality data is not measured or a point where radio quality data is not measured within the collection area. Also, in the present specification, “prediction point” may mean a point outside the collection area where radio quality data is not measured or a unit area including a point where radio quality data is not measured.

The table of radio quality data may further include information on the number of radio quality data collection cases at a corresponding measurement point.

Meanwhile, the controller 120 may collect actually measured values of wireless communication quality of measurement points from user terminals that measure wireless communication quality in the prediction target area.

The purpose of this specification is to analyze coverage for each base station. Therefore, the prediction target area may mean the coverage of a specific base station. For example, when coverage analysis of the first base station is performed, the controller 120 selects radio quality data including the cell identifier (PCI: 100) of the first base station, and determines the radio communication quality from the selected radio quality data. It is possible to obtain actual measured values for

In addition, the prediction target area may mean a range representing a significant received signal strength in the coverage of a specific base station.

In connection with S430, the control unit 120 may calculate first predicted values for the wireless communication coverage of a corresponding base station by using base station information, environment information, and a pathloss model of a prediction target region.

In detail, the control unit 120 may calculate a theoretically predicted value for the wireless communication coverage of the base station for each unit area of the prediction target area using the path loss model and the antenna gain information for each unit area.

More specifically, the control unit 120 may perform analysis for each unit area by reflecting environmental information (position, shape, height, etc. of a building) of the prediction target area. In addition, the control unit 120 performs a three-dimensional (3D) analysis based on the base station information and environment information, thereby calculating whether or not a straight path connecting each unit area in the base station antenna penetrates a building or terrain and the number of penetrations. .

In addition, the control unit 120 calculates a 3D distance (Three Dimension distance) between the base station antenna and each unit area based on base station information (base station location, height information, etc.) and environment information, and based on antenna direction and tilt angle information, The angle can be calculated for each unit area. Further, the controller 120 may calculate an antenna gain value using a radiation pattern of an antenna or a beam based on the angle calculated for each unit area, and may calculate a pathloss using a 3D distance or the like. In addition, the controller 120 may calculate a theoretical prediction value for each unit area using a path loss value for each unit area, such as a frequency band and power used by the base station. The calculated theoretical prediction value basically includes RSRP, and may optionally further include RSRQ, RSSI, SINR, and the like according to an embodiment.

Meanwhile, the control unit 120 may use the calculated theoretical prediction value for each unit area as a first prediction value of a path loss model for non-measurement points and a first prediction value of a path loss model for prediction points, which will be described later. However, it is not limited to this, and the control unit 120 corrects the path loss model using the correction value, and the value calculated using the corrected path loss model is the primary predicted value of the path loss model for non-measured points and the predicted point. can be used as the primary predictive value of the path loss model for

Specifically, the controller 120 may calculate a correction value to be applied to the path loss model and generate a corrected path loss model using the calculated correction value.

More specifically, the controller 120 may generate a corrected path loss model based on a difference between measured values of wireless communication quality of measurement points and theoretically predicted values of measurement points.

For a base station with a cell identifier (PCI) of 100, the measured value at the measurement point (RSRP_terminal in Table 2), the theoretically predicted value at the measurement point (RSRP_Org_PLM in Table 2), and the measured value at the same measurement point And examples of the difference between theoretical prediction values (Delta_(actually-predicted) in Table 2) are as follows.

PCI ID UTMK_Y UTMK_Y radio environment RSRP_terminal RSRP_Org_PLM Delta_(actual-predicted) 100 One 10XXXXXX 17XXXXXX LOS -70 -60 -10 100 2 10XXXXXY 17XXXXXY LOS -75 -69 -6 100 5 10XXXXXA 17XXXXXXA NLOS -90 -105 -15 100 6 10XXXXXB 17XXXXXXB LOS -65 -66 +1 100 7 10XXXXXC 17XXXXXC NLOS -85 -95 -10 ... ... ... ... ... ... ... ...

The controller 120 may generate a correction value based on a difference between actually measured values of wireless communication quality of measurement points and theoretically predicted values of measurement points.

In this case, the control unit 120 may calculate a correction value for each propagation environment. Specifically, the control unit 120 calculates a first correction value corresponding to the visible path (LOS) by averaging differences between measured values and predicted values of radio quality data belonging to the visible path (LOS), and calculates a first correction value corresponding to the non-visible path (NLOS) ), a second correction value corresponding to the non-line-of-sight path (NLOS) may be calculated by averaging differences between measured values and predicted values of radio quality data belonging to . In the example of Table 2, the first correction value in the line of sight (LOS) propagation environment is calculated as (-10-6 + 1) / 3 = -5 dB, and the second correction value in the NLOS propagation environment is (- 15-10)/2 = -12.5 dB.

In addition, the controller 120 may generate a corrected path loss model by applying the correction value. In this case, a path loss model calibrated using the first correction value may be applied to radio quality data belonging to the visible path (LOS), and a second correction value may be used to radio quality data belonging to the non-linear path (LOS). Thus, a calibrated path loss model can be applied.

In addition, the control unit 120 may calculate first predicted values for wireless communication coverage of a corresponding base station by using the corrected path loss model. Specifically, the control unit 120 may calculate a first prediction value for a non-measurement point and a first prediction value for a prediction point by using the corrected path loss model.

Meanwhile, it has been described that the correction value is calculated by averaging the differences between the measured value and the predicted value of the radio quality data, but the present invention is not limited thereto. For example, the control unit 120 may calculate the correction value using other statistics such as the median value and standard deviation of differences between the measured value and the predicted value. In addition, in order to improve the reliability of the correction value, the control unit 120 calculates a correction value using only a certain number of wireless quality data in consideration of the number of collections for each coordinate, or calculates a correction value using only wireless quality data of a certain number or more If it is greater than or less than the threshold value, a correction value may be calculated after excluding it.

Next, the controller 120 may create a collection area surrounding the measurement points (S440). This will be described with reference to FIG. 5 .

5 is a diagram for explaining a prediction target area and a collection area according to the present invention.

First, the prediction target area 520 is an area subject to coverage analysis of a corresponding base station, and may be set as an area within a certain radius around the base station. However, it is not limited thereto, and the prediction target area 520 may be variably set in consideration of the beam angle of the antenna, morphology characteristics, and the like.

Meanwhile, black dots in FIG. 5 may mean measurement points at which actually measured values of wireless communication quality are collected.

In this case, the controller 120 may create a collection area 510 surrounding the measurement points.

Here, the collection area 510 may be a convex hull including all measurement points for a corresponding base station.

That is, the collection area 510 may include all measurement points from which radio quality data including the cell identifier (PCI) of the corresponding base station was collected. Also, the collection area 510 may include a minimum area that satisfies the convex (when two points in the collection area 510 are connected with a straight line, the straight line belongs to the collection area 510). Accordingly, some of the measurement points for the corresponding base station may be included in the boundary of the collection area 510 .

Meanwhile, in this specification, a point inside the collection area 510 may include a point inside the collection area 510 and at a boundary of the collection area 510 . In addition, points within the collection area 510 may include measurement points where radio quality data is collected and non-measurement points where radio quality data is not collected.

Also, in this specification, points outside the collection area 510 may include points outside the collection area 510 and inside the prediction target area 520 . In this specification, a point outside the collection area 510 is referred to as a prediction point. Meanwhile, since the collection area 510 includes all measurement points, all prediction points outside the collection area 510 may be points where radio quality data is not collected.

The reason why the collection area 510 is set in this way is that interpolation using measured values corresponding to the measurement points is possible inside the collection area 510, whereas the inside of the collection area 510 is a measurement point. This is because interpolation using measured values corresponding to s is impossible and only extrapolation is possible. That is, when comparing the secondary predicted value generated by interpolation and the secondary predicted value generated by extrapolation, the reliability of the secondary predicted value generated by interpolation is much higher. Therefore, in the present invention, secondary prediction values are calculated through interpolation for non-measurement points inside the collection area 510, and then secondary prediction values for prediction points outside the collection area 510. yields

In addition, when secondary prediction values are calculated for prediction points outside the collection area 510 by performing extrapolation using only measurement values corresponding to measurement points inside the collection area 510, the reliability will be lowered Therefore, in the present invention, after first calculating the secondary predicted values for all non-measurement points inside the collection area 510, the quality values inside the collection area 510 (measured values of measurement points and non-measurement points At least one of the secondary prediction values) is used to calculate secondary prediction values for prediction points outside the collection area 510 .

6 is a diagram for explaining a method of calculating a secondary prediction value for a non-measurement point inside a collection area according to the present invention.

With respect to the non-measurement point 610 inside the collection area 510, the control unit 120 determines the primary predicted value (RSRP ₀ ) of the path loss model for the non-measurement point 610 and the actual values around the non-measurement point ( RSRP ₁ , RSRP ₂ , RSRP ₃ , RSRP ₄ ) may be used to calculate secondary prediction values for non-measurement points (S450).

As described above, the primary predicted value (RSRP ₀ ) may mean a wireless communication quality value calculated using a corrected path loss model for the non-measurement point 610 .

In addition, the control unit 120 interpolates the primary prediction value for the non-measurement point 610 and the preset number of measured values closest to the non-measurement point 610 to obtain a secondary prediction value for the non-measurement point 610. A predicted value can be calculated.

6 illustrates that the preset number is 4 (k=4). That is, the control unit 120 selects four measurement points closest to the non-measurement point 610, and uses four measured values (RSRP ₁ , RSRP ₂ , RSRP ₃ , RSRP ₄ ) corresponding to the four measurement points, respectively. Thus, a secondary predicted value for the non-measurement point 610 may be calculated. In addition, the primary predicted value (RSRP ₀ ) for the non-measurement point 610 may be used together with four measured values (RSRP ₁ , RSRP ₂ , RSRP ₃ , RSRP ₄ ) to calculate the secondary predictive value.

In addition, the control unit 120 applies a weight to each of the first prediction value for the non-measurement point 610 and the preset number of measured values closest to the non-measurement point 610 to make a secondary prediction for the non-measurement point 610. value can be calculated. This can be expressed in the following equation.

(RSRP _i,est : 2nd prediction value (last received signal strength at non-measurement point), w _i : weight, RSRP _i : primary prediction value for non-measurement point and actual measurement of the preset number closest to non-measurement point value)

Meanwhile, various methods may be used for interpolation. As an example of an interpolation method, the control unit uses an inverse distance weighting (IDW) method to obtain a primary prediction value for non-measurement points 610 and a predetermined number closest to the non-measurement points 610 A weight can be assigned to each measured value of . This can be expressed in the following equation.

(RSRP _i,est : 2nd predictive value (last received signal strength at non-measurement point), d _i : distance between non-measurement point and measurement point, RSRP _i : primary predicted value for non-measurement point and the nearest preset number of measured values)

Meanwhile, the distance (d ₀ ) to the primary predicted value (RSRP ₀ ) of the non-measurement point is 0. Therefore, in order to prevent the weight (w ₀ ) corresponding to the primary predicted value (RSRP ₀ ) of the non-measurement point from becoming infinite, the distance (d ₀ ) to the primary predicted value (RSRP ₀ ) is set to an arbitrary number greater than 0. can be set.

Of course, the weight (w ₀ ) corresponding to the primary prediction value (RSRP ₀ ) must be greater than other weights. That is, the weight (w ₀ ) corresponding to the primary prediction value (RSRP ₀ ) is the largest, and the sizes of the remaining weights may be in inverse proportion to the distance between the non-measurement point 610 and the measurement point. That is, the smaller the distance between the non-measurement point 610 and the measurement point, the greater the weight applied to the measured value of the measurement point.

In the same way, the controller 120 may calculate secondary prediction values for all non-measurement points within the collection area 510 . Then, the controller 120 may calculate a secondary prediction value for the prediction point. This will be described with reference to FIG. 7 .

7 is a diagram for explaining a method of calculating a secondary prediction value for a prediction point outside a collection area according to the present invention.

Referring to FIG. 7 , all secondary prediction values of non-measurement points within the collection area 510 have been calculated. Accordingly, the inside of the collection area 510 may include a measurement point (eg, 710) having a measured value and a non-measurement point (eg, 720) having a secondary prediction value.

Meanwhile, the control unit 120, with respect to the prediction point 730 outside the collection area 510, the primary prediction value (RSRP ₀ ) of the path loss model for the prediction point 730 and the quality value inside the collection area 510 A secondary prediction value for the prediction point 730 may be calculated using (S460).

As described above, the primary prediction value (RSRP ₀ ) may mean a wireless communication quality value calculated using a corrected path loss model with respect to the prediction point 730 . In addition, the controller 120 may calculate secondary prediction values for prediction points using quality values of the collection area 510 in a state in which secondary prediction values for all non-measurement points inside the collection area 510 are calculated. can

In detail, the control unit 120 extrapolates the primary prediction value for the prediction point 730 and the quality values in a predetermined number of collection areas closest to the prediction point 730, thereby performing secondary prediction values for the prediction point. can be calculated.

7 illustrates that the preset number is 4 (k=4). That is, the controller 120 selects four points closest to the prediction point 730 among the points existing inside the collection area 510, and predicts using four quality values respectively corresponding to the four points. A secondary prediction value for point 730 can be calculated. In addition, the primary prediction value (RSRP ₀ ) for the prediction point 730 together with four quality values may be used to calculate the secondary prediction value.

Meanwhile, the controller 120 selects a preset number of points closest to the prediction point 730 among points existing inside the collection area 510 . Therefore, the preset number of points may consist of only measurement points, only non-measurement points, or may include one or more measurement points and one or more non-measurement points.

Therefore, the quality values inside the collection area 510 used for extrapolation include actual values of measurement points inside the collection area 510 or secondary predicted values of non-measurement points inside the collection area 510. or may include actual values of one or more measurement points within the collection area 510 and secondary prediction values of one or more non-measurement points within the collection area 510 .

In addition, the control unit 120 calculates a secondary prediction value for the prediction point 730 by applying a weight to each of the first prediction value for the prediction point 730 and the preset number of actually measured values closest to the prediction point 730. can do. For this, Equation 1 described above may be used again. In this case, RSRP _i,est in Equation 1 is the second prediction value (last received signal strength at the prediction point), w _i is the weight, RSRP _i is the primary prediction value for the prediction point and the preset number closest to the prediction point. It can mean the quality value of

On the other hand, as an example of an extrapolation method, the control unit 120 uses an inverse distance weighting (IDW) method to obtain a primary prediction value for the prediction point 730 and a base closest to the prediction point 730. A weight may be assigned to each of the set number of quality values. For this, Equation 2 described above can be used again. In this case, RSRP _i,est of Equation 2 is the secondary predicted value (final received signal strength at the predicted point, d _i is the distance between the predicted point and a selected point in the collection area, RSRP _i is the primary predicted value for the predicted point) and a preset number of actually measured values closest to the prediction point.

Meanwhile, the distance of the prediction point 730 to the primary prediction value RSRP0 is 0. Therefore, in order to prevent the weight w0 corresponding to the primary prediction value RSRP0 of the prediction point 730 from becoming infinite, a distance to the primary prediction value RSRP0 may be set to an arbitrary number greater than 0.

Of course, the weight (w ₀ ) corresponding to the primary prediction value (RSRP ₀ ) must be greater than other weights. That is, the weight (w ₀ ) corresponding to the primary prediction value (RSRP ₀ ) is the largest, and the sizes of the remaining weights may be in inverse proportion to the distance between the predicted point 730 and the selected point. As the distance between the prediction point 730 and the selected point within the collection area 510 is smaller, the weight applied to the quality value of the selected point may be larger.

In the same way, the controller 120 may calculate secondary prediction values for all prediction points outside the collection area 510 . Accordingly, measured values and predicted values for all points constituting the prediction target area (including measurement points, non-measurement points, and prediction points) may be calculated.

As described above, according to the present invention, by quickly and accurately calculating the received signal strength in the entire coverage of the base station only with limited radio quality data collected by the user terminal in the vicinity of the base station, the quality and neighboring There is an advantage in that an analysis of interference with a cell can be effectively performed.

In addition, based on this, it is possible to improve the quality of the wireless network by performing simulations such as antenna azimuth/tilt, base station output, electrical parameter change, and deriving optimal operating parameters based on the simulation results.

8 is a diagram for explaining a method of calculating a secondary prediction value for a prediction point using quality values having the same propagation environment as that of the prediction point.

It has been described above that the control unit 120 calculates a correction value for each propagation environment and generates a corrected path loss model by applying the correction value for each propagation environment.

Thus, the predicted point 730 may belong to a specific propagation environment. For example, the prediction point 730 may belong to a path-of-sight (LOS) propagation environment. In this case, the controller 120 may generate a first prediction value for the prediction point 730 based on a path loss model calibrated using the first correction value in the LOS propagation environment.

Meanwhile, the control unit 120 extrapolates the primary prediction value for the prediction point 730 and the quality values within a predetermined number of collection areas closest to the prediction point 730 to obtain a secondary prediction value for the prediction point. can be calculated

In this case, the control unit 120 has the same propagation environment as the prediction point 730 and uses the quality values in a predetermined number of collection areas closest to the prediction point 730 to obtain a secondary prediction value for the prediction point 730. can be calculated.

For example, a specific point 815 within the collection area 510 is closest to the prediction point 730 . However, when the specific point 815 belongs to a non-line-of-sight (NLOS) propagation environment, the controller 120 may not select the specific point 815.

Accordingly, the control unit 120 determines that all quality values within the predetermined number of collection areas 510 closest to the prediction point 730 are points 811 belonging to the same propagation environment (visible path propagation environment) as the prediction point 730. , 812, 813, 814).

The above-described present invention can be implemented as computer readable code on a medium on which a program is recorded. The computer-readable medium includes all types of recording devices in which data that can be read by a computer system is stored. Examples of computer-readable media include Hard Disk Drive (HDD), Solid State Disk (SSD), Silicon Disk Drive (SDD), ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. there is Also, the computer may include a processor 180 of a server. Accordingly, the above detailed description should not be construed as limiting in all respects and should be considered as illustrative. The scope of the present invention should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent scope of the present invention are included in the scope of the present invention.

110: communication unit 120: control unit
130: memory

Claims (17)

collecting actual values of wireless communication quality of measurement points from user terminals that measure wireless communication quality in a prediction target area;
calculating first predicted values for wireless communication coverage of a corresponding base station using base station information, environment information, and a pathloss model of the prediction target region;
creating a collection area surrounding the measurement points;
With respect to the non-measurement point inside the collection area, using the first prediction value of the path loss model for the non-measurement point and the actual values around the non-measurement point, calculating a secondary prediction value for the non-measurement point step; and
For a prediction point outside the collection area, calculating a secondary prediction value for the prediction point using the first prediction value of the path loss model for the prediction point and the quality values inside the collection area; doing
Base station coverage analysis method. According to claim 1,
The quality values inside the collection area are,
contain actual values of measurement points inside the collection area, or
Contains secondary prediction values of non-measurement points inside the collection area, or
Including an actual value of one or more measurement points inside the collection area and a secondary predicted value of one or more non-measurement points inside the collection area
Base station coverage analysis method. According to claim 1,
Calculating a secondary prediction value for the prediction point,
Calculating a secondary prediction value for the prediction point using quality values inside the collection area in a state in which secondary prediction values for all non-measurement points inside the collection area are calculated;
Base station coverage analysis method. According to claim 1,
Calculating a secondary prediction value for the non-measurement point,
Calculating a secondary prediction value for the non-measurement point by interpolating the primary prediction value for the non-measurement point and a preset number of actual measurement values closest to the non-measurement point;
Base station coverage analysis method. According to claim 1,
Calculating a secondary prediction value for the prediction point,
Calculating a secondary prediction value for the prediction point by extrapolating the primary prediction value for the prediction point and a preset number of quality values in the collection area closest to the prediction point;
Base station coverage analysis method. According to claim 5,
Calculating a secondary prediction value for the prediction point by extrapolating quality values within the collection area,
Calculating a secondary prediction value for the prediction point using a predetermined number of quality values in the collection area closest to the prediction point while having the same propagation environment as the prediction point;
Base station coverage analysis method. According to claim 1,
The collection area is
A convex hull that includes some of the measurement points for the corresponding base station in the boundary and includes all of the measurement points for the corresponding base station.
Base station coverage analysis method. According to claim 1,
Calculating the first prediction values for the wireless communication coverage of the corresponding base station,
generating a calibrated path loss model based on a difference between measured values of wireless communication quality of the measurement points and theoretically predicted values of the measurement points; and
Calculating a first prediction value for the non-measurement point and a first prediction value for the prediction point using the corrected path loss model;
Base station coverage analysis method. a communication unit that collects actually measured values of wireless communication quality of measurement points from user terminals measuring wireless communication quality in a prediction target area; and
Using base station information, environmental information, and a pathloss model of the prediction target area, first prediction values for wireless communication coverage of the corresponding base station are calculated, a collection area surrounding the measurement points is created, and the collection area For an internal non-measurement point, a secondary predictive value for the non-measurement point is calculated using the first predicted value of the path loss model for the non-measurement point and the actual values around the non-measurement point, and the collection With respect to a prediction point outside the prediction point, a control unit for calculating a secondary prediction value for the prediction point using the first prediction value of the path loss model for the prediction point and the quality values inside the collection area;
Base station coverage analysis device. According to claim 9,
The quality values inside the collection area are,
contain actual values of measurement points inside the collection area, or
Contains secondary prediction values of non-measurement points inside the collection area, or
Including an actual value of one or more measurement points inside the collection area and a secondary predicted value of one or more non-measurement points inside the collection area
Base station coverage analysis device. According to claim 9,
The control unit,
Calculating a secondary prediction value for the prediction point using quality values inside the collection area in a state in which secondary prediction values for all non-measurement points inside the collection area are calculated
Base station coverage analysis device. According to claim 9,
The control unit,
Calculating a secondary prediction value for the non-measurement point by interpolating the primary prediction value for the non-measurement point and a preset number of actual measurement values closest to the non-measurement point
Base station coverage analysis device. According to claim 9,
The control unit,
Calculating a secondary prediction value for the prediction point by extrapolating the primary prediction value for the prediction point and a preset number of quality values in the collection area closest to the prediction point
Base station coverage analysis device. According to claim 13,
The control unit,
Calculating a secondary prediction value for the prediction point using a preset number of quality values in the collection area closest to the prediction point while having the same propagation environment as the prediction point
Base station coverage analysis device. According to claim 9,
The collection area is
A convex hull that includes some of the measurement points for the corresponding base station in the boundary and includes all of the measurement points for the corresponding base station.
Base station coverage analysis device. According to claim 9,
The control unit,
Creating a calibrated path loss model based on the difference between the measured values of the wireless communication quality of the measurement points and the theoretically predicted values of the measurement points;
Calculating a first prediction value for the non-measurement point and a first prediction value for the prediction point using the corrected path loss model
Base station coverage analysis device. collecting actual values of wireless communication quality of measurement points from user terminals that measure wireless communication quality in a prediction target area;
calculating first predicted values for wireless communication coverage of a corresponding base station using base station information, environment information, and a pathloss model of the prediction target region;
creating a collection area surrounding the measurement points;
With respect to the non-measurement point inside the collection area, using the first prediction value of the path loss model for the non-measurement point and the actual values around the non-measurement point, calculating a secondary prediction value for the non-measurement point step; and
For a prediction point outside the collection area, calculating a secondary prediction value for the prediction point using the first prediction value of the path loss model for the prediction point and the quality values inside the collection area; A computer program stored in a medium to perform a method of analyzing the coverage of a base station.

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