KR20230071405A - Apparatus, method and computer program for deriving prediction region which is required for quality analysis of wireless network - Google Patents

Abstract

A device for deriving a prediction area required for quality analysis of a wireless network comprises: a target area setting unit that sets a target area to perform optimization of a wireless network; a quality data collection unit that collects communication quality data for a base station existing within the set target area; and a prediction area deriving unit that selects first communication quality data generated at a first location furthest from the base station in the communication quality data and derives a prediction area to analyze quality of the wireless network based on the number of communication quality data collected within a radius of a preset range from the first location where the first communication quality data was generated.

Description

APPARATUS, METHOD AND COMPUTER PROGRAM FOR DERIVING PREDICTION REGION WHICH IS REQUIRED FOR QUALITY ANALYSIS OF WIRELESS NETWORK}

The present invention relates to an apparatus, method, and computer program for deriving a prediction area required for quality analysis of a wireless network.

The 5G mobile communication system means next-generation mobile communication with 1,000 times more data capacity and 200 times faster speed than 4G LTE. 5G mobile communication systems must be designed with essential consideration of four major mega trends: traffic increase, device increase, cloud computing dependence increase, and emergence of various 5G-based convergence services.

Recently, users want to experience natural interactions through various real-time interactive multimedia services such as augmented reality, virtual reality, and real-time online games.

In relation to such a 5G mobile communication system, Korean Patent Publication No. 2018-0049782, which is a prior art, discloses a 5G new radio ultra-wideband support method and apparatus.

Meanwhile, in order to analyze the quality of 5G mobile communication, it is necessary to collect and analyze communication quality data. The communication quality data includes outlier data. Such outlier data has a disadvantage in that reliability is lowered in the process of optimizing communication quality using communication quality data, and that it becomes an obstacle in deriving an optimal parameter related to an antenna.

An apparatus, method, and computer program for setting a target area for optimization of a wireless network and collecting communication quality data for base stations existing in the set target area are provided.

Among the communication quality data, first communication quality data generated at a first location farthest from the base station is selected, and based on the number of communication quality data collected within a radius of a predetermined range from the first location at which the first communication quality data is generated. Accordingly, an apparatus, method, and computer program for deriving a prediction area for analyzing the quality of a wireless network are provided.

However, the technical problem to be achieved by the present embodiment is not limited to the technical problems described above, and other technical problems may exist.

As a means for achieving the above technical problem, an embodiment of the present invention provides a target area setting unit for setting a target area for optimizing a wireless network, and communication quality data for a base station existing within the set target area. A first communication quality data generated at a first location farthest from the base station is selected from among the quality data collection unit and the communication quality data to be collected, and a radius within a predetermined range from the first location at which the first communication quality data is generated It is possible to provide a prediction area derivation device comprising a prediction area derivation unit for deriving a prediction area to analyze the quality of the wireless network based on the number of communication quality data collected within the wireless network.

Another embodiment of the present invention includes setting a target area for optimizing a wireless network, collecting communication quality data for a base station existing in the set target area, and farthest from the base station among the communication quality data. Selecting first communication quality data generated at a first location away from the wireless network based on the number of communication quality data collected within a radius of a preset range from the first location where the first communication quality data is generated It is possible to provide a prediction area derivation method comprising the step of deriving a prediction area to analyze quality.

In another embodiment of the present invention, a computer program, when executed by a computing device, sets a target area for optimization of a wireless network, collects communication quality data for base stations existing in the set target area, and Among the communication quality data, first communication quality data generated at a first location farthest from the base station is selected, and communication quality data collected within a radius of a predetermined range from the first location where the first communication quality data is generated It is possible to provide a computer program stored on a computer readable recording medium including a sequence of instructions for deriving a prediction region for analyzing the quality of the wireless network based on the number.

The above-described means for solving the problems is only illustrative and should not be construed as limiting the present invention. In addition to the exemplary embodiments described above, there may be additional embodiments described in the drawings and detailed description.

According to any one of the above-described problem solving means of the present invention, by setting a target area for optimizing a wireless network and deriving a predicted area to analyze the quality of the wireless network within the target area, wireless communication is performed through the predicted area. An apparatus, method, and computer program capable of deriving optimal parameters related to an antenna, such as a communication range of a system, an optimal antenna position, a direction, and a tilt, can be provided.

In predicting the quality of a wireless network, an apparatus, method, and computer program capable of deriving highly reliable optimization parameters using refined communication quality data by effectively removing outliers generated due to measurement error are provided. can

In the prior art, as the prediction area is derived by the field engineer's experience, the optimization period and cost increase, and it is possible to prevent a problem in which wireless network quality deviation occurs due to field engineer's capability deviation.

1 is a block diagram of an apparatus for deriving a predicted area according to an embodiment of the present invention.
2 is an exemplary diagram illustrating an environment analysis result for at least one piece of communication quality data according to an embodiment of the present invention.
3 is a diagram for explaining a process of deriving a predicted area when the number of communication quality data collected within a radius of a preset range from a specific location among first to nth locations is satisfied according to an embodiment of the present invention. It is an exemplary drawing.
4 is an example for explaining a process of deriving a prediction area when the number of communication quality data collected within a radius of a preset range from each of the first to nth locations is not satisfied according to an embodiment of the present invention. it is a figurative drawing.
5 is a flowchart of a method of deriving a prediction region required for quality analysis of a wireless network in a prediction region deriving apparatus according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail so that those skilled in the art can easily practice the present invention with reference to the accompanying drawings. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein. And in order to clearly explain the present invention in the drawings, parts irrelevant to the description are omitted, and similar reference numerals are attached to similar parts throughout the specification.

Throughout the specification, when a part is said to be "connected" to another part, this includes not only the case where it is "directly connected" but also the case where it is "electrically connected" with another element interposed therebetween. . In addition, when a part "includes" a certain component, this means that it may further include other components, not excluding other components, unless otherwise stated, and one or more other characteristics. However, it should be understood that it does not preclude the possibility of existence or addition of numbers, steps, operations, components, parts, or combinations thereof.

In this specification, a "unit" includes a unit realized by hardware, a unit realized by software, and a unit realized using both. Further, one unit may be realized using two or more hardware, and two or more units may be realized by one hardware.

In this specification, some of the operations or functions described as being performed by a terminal or device may be performed instead by a server connected to the terminal or device. Likewise, some of the operations or functions described as being performed by the server may also be performed in a terminal or device connected to the corresponding server.

Hereinafter, an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram of an apparatus for deriving a predicted area according to an embodiment of the present invention. Referring to FIG. 1 , the prediction area derivation apparatus 100 may include a target area setting unit 110 , a quality data collection unit 120 , an environment analysis unit 130 , and a prediction area derivation unit 140 .

The target region setting unit 110 may set a target region for optimization of a wireless network. For example, the target region setting unit 110 may set a target region for optimization of a wireless network such as Long Term Evolution (LTE) and 5G. In this case, the target area may be designated by a manager or may be designated using a vector map such as a shape file in which an administrative boundary map is configured.

The quality data collection unit 120 may collect communication quality data for base stations existing within a set target area. Here, the communication quality data is data periodically collected in relation to communication quality from the user terminal, for example, RSRI (Received Signal Strength Indicator), RSRP (Reference Signal Received Power), RSRQ (Reference Signal Received Quality), SINR (Signal to Noise Ratio) and the like.

The prediction area derivation unit 140 may select first communication quality data generated at a first location farthest from the base station from among the communication quality data.

The prediction region derivation unit 140 may further select at least one piece of communication quality data generated from the base station to the nearest n-th location based on a distance index set based on a degree of distance from the base station among the communication quality data. Here, the initial value of the distance index N may be 0. For example, the distance index corresponding to the first position may be '1' and the distance index corresponding to the nth position may be '5'.

The environment analyzer 130 may analyze the environment K of the first communication quality data. In addition, the environment analyzer 130 may further analyze the environment of at least one piece of communication quality data generated from the base station to the closest n-th location based on the distance index (N, for example, N is from 1 to 5). there is. Here, the number of communication quality data may be varied based on an analysis result of an environment of at least one communication quality data including the first communication quality data based on the distance index.

For example, the environment analyzer 130 analyzes at least one piece of communication quality data including the first communication quality data, and the at least one piece of communication quality data is LOS (α, Line of Sight), Near LOS (β), and It is possible to detect whether it corresponds to any one of NLOS (γ, None Line of Sight). Here, LOS means a rectilinear wave, and Near LOS and NLOS may mean a non-straight wave propagated by diffraction, reflection, etc. due to an obstacle. A process of analyzing the environment of at least one piece of communication quality data according to the distance index will be described in detail with reference to FIG. 2 .

2 is an exemplary diagram illustrating an environment analysis result for at least one piece of communication quality data according to an embodiment of the present invention. Referring to FIG. 2 , the environment analyzer 130 analyzes the environment for at least one communication quality data based on the distance index set according to the degree of distance from the base station 210, and the analyzed environment analysis result 200 can be saved.

For example, in the case of the first communication quality data generated at the first location farthest from the base station 210 corresponding to 'index: 1' 211, the environment analyzer 130 The distance to the location is 167 m, the environmental analysis value is analyzed and stored as γ (NLOS), and the first location generated at the second location (corresponding to 'index: 2' (202)) is second farthest from the base station 210. 2 In the case of communication quality data, the environment analyzer 130 analyzes and stores the environment analysis value as α(LOS) when the distance from the base station 210 to the second position is 85 m, and stores the third position from the base station 210. In the case of the third communication quality data generated at a third location (corresponding to 'index: 3' (203)) far away from , the environment analyzer 130 determines that the distance from the base station 210 to the third location is 84 m, The environmental analysis value is analyzed and stored as α(LOS), and in the case of the fourth communication quality data generated at the fourth position (corresponding to 'index: 4' 204) fourth farthest from the base station 210, the environment The analyzer 130 has a distance from the base station 210 to the fourth position of 82 m, analyzes and stores the environmental analysis value as α (LOS), and stores the fifth farthest from the base station 210, that is, the base station ( In the case of the fifth communication quality data generated at the fifth position (corresponding to 'index: 5' (205)) corresponding to the closest position from the base station 210), the environment analyzer 130 moves The distance of is 75m, and the environmental analysis value can be analyzed and stored as α(LOS).

Returning to FIG. 1, the prediction area derivation unit 140 predicts to analyze the quality of the wireless network based on the number of communication quality data collected within a radius of a predetermined range from the first location where the first communication quality data is generated. area can be derived.

The prediction area derivation unit 140 derives a prediction area to analyze the quality of the wireless network based on whether or not the number of communication quality data collected within a radius of a predetermined range from each of the first to nth locations is satisfied. can be repeated. For example, the radius (X) of the preset range is, for example, X(N, α) ≥ 12.5[m]x4 based on the BIN size of 12.5m at each location, and the number of communication quality data (A) may be set to A(N, α) ≥ 5 [pieces]. This is a condition set by considering that the communication quality data corresponds to LOS. If the communication quality data corresponds to Near LOS and NLOS in addition to LOS, the reliability of Near LOS and NLOS is higher when the number of collected communication quality data is greater than that of LOS. Therefore, for example, the radius X of the predetermined range is set to, for example, X(N, α) ≤ X(N, β) ≤ X(N, γ), and the number of communication quality data is It can be set as A(N, α) ≤ A(N, β) ≤ A(N, γ).

Here, the radius of the predetermined range and the number of communication quality data may be designated by the administrator in consideration of the environment analysis result of at least one communication quality data, for example, 12.5 m, which is the size of the BIN corresponding to each location. Based on , it can be set to about 50mx50m for each of 4 horizontally and vertically, and the number of communication quality data can be set to 5 or more, for example.

A process of determining whether the number of communication quality data collected within a radius of a predetermined range from each of the first to nth positions is satisfied will be described in detail with reference to FIGS. 3 and 4 .

3 is a diagram for explaining a process of deriving a predicted area when the number of communication quality data collected within a radius of a preset range from a specific location among first to nth locations is satisfied according to an embodiment of the present invention. It is an exemplary drawing. Referring to FIG. 3 , when the number of communication quality data collected within a radius of a predetermined range from a specific location among the first to n-th locations is satisfied, the predicted area derivation unit 140 determines a predicted area based on the specific location. can be derived.

For example, at least one piece of communication quality data may be displayed according to a distance index based on a distance from the antenna RU of the base station 300 within the target area. For example, the first communication quality data generated at the first location 301 corresponding to 'index: 1' farthest from the base station 300, and the second location corresponding to 'index: 2' farthest from the base station 300 The second communication quality data generated at 302, the third communication quality data generated at the third position 303 corresponding to 'index: 3' that is the third farthest away, and the fourth farthest 'index: 4' The fourth communication quality data generated at the fourth location 304 and the fifth communication quality data generated at the fifth location 305 that is fifth farthest, that is, closest to the base station 300, may be displayed.

The prediction area derivation unit 140 determines the number (eg, 5) of communication quality data collected within a radius of a predetermined range (eg, 50mx50m) from each of the first to nth positions based on the distance index. Based on whether the above) is satisfied, it is possible to repeatedly derive a prediction region in which to analyze the quality of a wireless network. For example, the prediction area derivation unit 140 may determine whether the number of communication quality data collected within a radius of a preset range from the first location 301 farthest from the base station 300 is satisfied.

When the number of communication quality data collected within a radius of a preset range from the first location 301 is not satisfied, the prediction area derivation unit 140 obtains data collected within a radius of a preset range from the second location 302. It can be determined whether the number of communication quality data is satisfied.

When the number of communication quality data collected within a radius of a preset range from the second location 302 is not satisfied, the prediction area derivation unit 140 collects data collected within a radius of a preset range from the third location 303. It can be determined whether the number of communication quality data is satisfied. At this time, when the number of communication quality data collected within a radius of a predetermined range from the third location 303 is satisfied, the predicted area derivation unit 140 determines the third location 303 from the base station 300. The distance may be determined as the radius of the optimal prediction region of the antenna (RU) of the base station 300 .

4 is an example for explaining a process of deriving a prediction area when the number of communication quality data collected within a radius of a preset range from each of the first to nth locations is not satisfied according to an embodiment of the present invention. it is a schematic drawing.

Referring to FIG. 4, when the number of communication quality data collected within a radius of a predetermined range from each of the first to n-th positions is not satisfied, the predicted area derivation unit 140 determines an intermediate position between the base station and the first position. A prediction area may be derived based on the corresponding distance.

For example, the number of communication quality data collected within a radius of a predetermined range from each of the first location 301 farthest from the base station 300 to the fifth location 305 closest to the base station 300 is not satisfied. If not, the prediction area derivation unit 140 may determine a distance corresponding to the middle of the distance 306 between the base station 300 and the first location 301 farthest from the base station 300 as the radius of the prediction area. .

Through this process, by removing the outlier data included in the communication quality data, it is possible to perform an optimization analysis on the effective communication quality data and improve the communication processing speed. In addition, since the maximum measurement distance of the communication quality data for each antenna (RU) of the base station does not affect each other and is independent for each base station, a prediction region can be derived more quickly through parallelization.

Returning to FIG. 1 again, the parameter derivation unit 150 may derive a parameter for the antenna (RU) of the base station related to the quality of the radio network based on the predicted region.

For example, the parameter derivation unit 150 may derive a direction parameter and a tilt parameter of an antenna of a base station. Here, the tilt is a physical tilt (mechanical tilt) that is physically set when attaching the antenna to a pole or wall for fixing the antenna, and an electronic tilt (electrical tilt) that sets the tilt of the antenna through changing the parameters of the antenna without changing the physical tilt. tilt) may be included. Conventionally, in order to adjust the electronic tilt, the field manager had to go to the site where the base station is located and operate the electronic tilt adjustment switch located at the lower part of the antenna. The electronic tilt of the can be adjusted.

Also, the parameter derivation unit 150 may derive the antenna type (eg, beam pattern) of the base station. For example, the parameter derivation unit 150 may change the beam pattern of an antenna by changing an operating parameter of an integrated antenna (RU) of a 5G base station through a management system without replacing the antenna.

Also, the parameter derivation unit 150 may derive parameters related to the output of the base station.

Through this process, by setting the parameters derived for the antenna of the base station, the reception strength and signal-to-interference ratio of the serving cell received by the user terminal change, so that the user can feel the quality of experience of the wireless network differently. can provide

The predicted area deriving apparatus 100 may be executed by a computer program stored in a medium including a sequence of instructions for deriving a predicted area necessary for quality analysis of a wireless network. When executed by a computing device, the computer program sets a target area for optimization of the wireless network, collects communication quality data for base stations existing in the set target area, and selects a first communication quality data farthest from the base station. First communication quality data generated at the location is selected, and based on the number of communication quality data collected within a radius of a predetermined range from the first location where the first communication quality data is generated, a prediction area in which the quality of the wireless network is to be analyzed is determined. It may contain a sequence of instructions that cause it to be derived.

5 is a flowchart of a method of deriving a prediction region required for quality analysis of a wireless network in a prediction region deriving apparatus according to an embodiment of the present invention. Referring to FIG. 5 , a method of deriving a prediction area required for quality analysis of a wireless network performed by the prediction area derivation apparatus 100 includes steps processed in time sequence according to the embodiment shown in FIGS. 1 to 4 . do. Therefore, even if the details are omitted below, they are also applied to the method of deriving a prediction region required for quality analysis of a wireless network performed by the prediction region deriving apparatus 100 according to the embodiments shown in FIGS. 1 to 4 .

The prediction area deriving apparatus 100 may set a target area for optimization of a wireless network (S501).

The predicted area deriving apparatus 100 may collect communication quality data for base stations existing within the set target area (S502).

The prediction region deriving apparatus 100 may select first communication quality data generated at a first location farthest from the base station from among the communication quality data (S503).

The prediction area derivation apparatus 100 may derive a prediction area to analyze the quality of a wireless network based on the number of communication quality data collected within a radius of a predetermined range from a first location where the first communication quality data is generated. (S504).

The predicted area deriving apparatus 100 may determine whether the number of communication quality data collected within a radius of a preset range from the first location is satisfied (S505). For example, when the number of communication quality data is satisfied (S506), the prediction area deriving apparatus 100 derives a prediction area based on the first position where the number of communication quality data is satisfied (S510), and If the number is not satisfied (S507), the prediction area deriving apparatus 100 includes at least one communication quality data generated from the base station to the closest n-th position based on a distance index set based on a distance from the base station among the communication quality data. can be further selected (S508).

The predicted area deriving apparatus 100 may determine whether the number of communication quality data collected within a radius of a predetermined range from each of the second to nth positions that are second farthest from the base station among the communication quality data is satisfied. (S509). For example, when the number of communication quality data is satisfied at a specific position among the second to nth positions (S510), the prediction region deriving apparatus 100 derives a prediction region based on the specific position at which the number of communication quality data is satisfied. (S511), and when the number of communication quality data is not satisfied at a specific position among the second to nth positions (S512), the prediction area deriving apparatus 100 calculates based on the distance corresponding to the middle between the base station and the first position A prediction area can be derived (S513).

In the above description, steps S501 to S513 may be further divided into additional steps or combined into fewer steps, depending on the implementation of the present invention. Also, some steps may be omitted as needed, and the order of steps may be switched.

A method for deriving a prediction area required for quality analysis of a wireless network performed by the prediction area derivation apparatus described with reference to FIGS. 1 to 5 includes a computer program stored in a medium executed by a computer or instructions executable by a computer. It may also be implemented in the form of a recording medium. In addition, the method for deriving a prediction area necessary for quality analysis of a wireless network performed by the prediction area derivation apparatus described with reference to FIGS. 1 to 5 may be implemented in the form of a computer program stored in a medium executed by a computer. .

Computer readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. Also, computer readable media may include computer storage media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.

The above description of the present invention is for illustrative purposes, and those skilled in the art can understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, the embodiments described above should be understood as illustrative in all respects and not limiting. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form.

The scope of the present invention is indicated by the following claims rather than the detailed description above, and all changes or modifications derived from the meaning and scope of the claims and equivalent concepts should be construed as being included in the scope of the present invention. do.

100: prediction area derivation device
110: target area setting unit
120: quality data collection unit
130: environment analysis unit
140: prediction area derivation unit

Claims (19)

An apparatus for deriving a prediction area necessary for quality analysis of a wireless network,
a target region setting unit configured to set a target region in which optimization of a wireless network is to be performed;
a quality data collection unit that collects communication quality data for base stations existing within the set target area; and
Among the communication quality data, first communication quality data generated at a first location farthest from the base station is selected, and communication quality data collected within a radius of a predetermined range from the first location at which the first communication quality data is generated. And a prediction area derivation unit for deriving a prediction area to analyze the quality of the wireless network based on the number of.
According to claim 1,
Further comprising an environment analyzer for analyzing an environment of the first communication quality data.
According to claim 2,
The environment analysis unit analyzes the first communication quality data to detect whether the first communication quality data corresponds to any one of LOS (Line of Sight), Near LOS, and NLOS (None Line of Sight). extraction device.
According to claim 2,
wherein the number of communication quality data is varied based on an analysis result of an environment of the first communication quality data.
According to claim 1,
Wherein the prediction region derivation unit further selects at least one communication quality data generated from the base station to the nearest n-th location based on a distance index set based on a distance from the base station from among the communication quality data. extraction device.
According to claim 5,
The prediction area derivation unit of the prediction area to analyze the quality of the wireless network based on whether or not the number of communication quality data collected within a radius of the predetermined range from each of the first to nth locations is satisfied. An apparatus for deriving a predicted area, which repeatedly performs derivation.
According to claim 6,
The prediction area derivation unit derives the prediction area based on the specific location when the number of communication quality data collected within a radius of the preset range from a specific location among the first to n-th locations is satisfied. That is, a predicted area derivation device.
According to claim 6,
The prediction area derivation unit calculates a distance corresponding to the middle between the base station and the first location when the number of the communication quality data collected within a radius of the preset range from each of the first to n-th locations is not satisfied. The prediction area derivation apparatus for deriving the prediction area based on.
According to claim 1,
The prediction area derivation apparatus further comprising a parameter derivation unit for deriving a parameter for an antenna of the base station related to the quality of the wireless network based on the prediction area.
A method for deriving a predicted area required for quality analysis of a wireless network in a predicted area deriving apparatus,
setting a target area for optimization of a wireless network;
collecting communication quality data for base stations existing within the set target area;
selecting first communication quality data generated at a first location farthest from the base station from among the communication quality data; and
Deriving a prediction area in which to analyze the quality of the wireless network based on the number of communication quality data collected within a radius of a preset range from the first location where the first communication quality data occurred. Method for deriving prediction domain.
According to claim 10,
Further comprising the step of analyzing the environment of the first communication quality data, the prediction area derivation method.
According to claim 11,
Analyzing the environment,
Analyzing the first communication quality data and detecting whether the first communication quality data corresponds to any one of Line of Sight (LOS), Near LOS, and None Line of Sight (NLOS). Derivation method.
According to claim 12,
Wherein the number of communication quality data is varied based on an analysis result of an environment of the first communication quality data.
According to claim 10,
The step of deriving the prediction area,
Further selecting at least one communication quality data generated from the base station to the closest n-th location based on a distance index set based on a distance from the base station from among the communication quality data, method.
15. The method of claim 14,
The step of deriving the prediction area,
Repeatedly deriving a prediction area for analyzing the quality of the wireless network based on whether or not the number of communication quality data collected within a radius of the preset range from each of the first to nth locations is satisfied A method of deriving a prediction area, comprising the step.
According to claim 15,
The step of deriving the prediction area,
Deriving the prediction area based on the specific location when the number of communication quality data collected within a radius of the preset range from a specific location among the first to n-th locations is satisfied , the method of deriving the predicted area.
According to claim 15,
The step of deriving the prediction area,
If the number of communication quality data collected within a radius of the predetermined range from each of the first to n-th locations is not satisfied, the prediction based on the distance corresponding to the middle between the base station and the first location A prediction area derivation method comprising deriving an area.
According to claim 10,
Further comprising deriving a parameter for the antenna of the base station related to the quality of the wireless network based on the prediction area.
A computer program stored in a computer readable recording medium comprising a sequence of instructions for deriving a prediction area necessary for quality analysis of a wireless network,
When the computer program is executed by a computing device,
Set the target area to perform optimization of the wireless network,
Collecting communication quality data for base stations existing within the set target area;
Among the communication quality data, first communication quality data generated at a first location farthest from the base station is selected, and communication quality data collected within a radius of a predetermined range from the first location at which the first communication quality data is generated. A computer program stored on a computer readable medium comprising a sequence of instructions for deriving a prediction region for analyzing the quality of the wireless network based on the number of.

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