CN108259097B

CN108259097B - Method and device for correcting wireless propagation model based on MR data

Info

Publication number: CN108259097B
Application number: CN201711485119.2A
Authority: CN
Inventors: 于仰源; 孟俊辉; 王磊; 王雅红; 铁小辉; 孙宜军; 朱运起; 罗海港; 吴海迁; 马涛; 马哲锐; 焦良全
Original assignee: China International Telecommunication Construction Group Design Institute Co ltd
Current assignee: China International Telecommunication Construction Group Design Institute Co ltd
Priority date: 2017-12-29
Filing date: 2017-12-29
Publication date: 2021-03-09
Anticipated expiration: 2037-12-29
Also published as: CN108259097A

Abstract

The invention discloses a method and a device for correcting a wireless propagation model based on MR data, wherein the method comprises the following steps: acquiring MR data of a target region; preprocessing the MR data of the target region, including: performing grid division on the target area; for any grid, respectively counting the number of MR data according to different donor cells, and only keeping the MR data of the donor cells with the number of the MR data being more than or equal to a preset number threshold; filtering the abnormal MR data; and correcting the wireless propagation model by using the preprocessed MR data. The invention utilizes MR data to correct the wireless transmission model, and can improve the accuracy of the wireless transmission model.

Description

Method and device for correcting wireless propagation model based on MR data

Technical Field

The present invention relates to the field of wireless communication, and more particularly, to a method and an apparatus for modifying a wireless propagation model based on MR data.

Background

The correction of the wireless propagation model is the premise of wireless network planning, and the accuracy of the propagation model directly influences the accuracy of scale estimation, site distribution, simulation and planning of the wireless network planning. The traditional correction method is to correct through CW (Continuous Wave) simulation test data, the accuracy of model correction depends on the quality of the CW simulation test data, and the high-quality simulation test data is often difficult to obtain.

The wireless propagation model is a mathematical model for describing slow fading effects of wireless signals in the spatial propagation process, and the statistical-based Okumura-Hata model and the Standard Propagation Model (SPM) are widely applied to practical engineering. The wireless signal is propagated in free space, and the fading of the wireless signal mainly consists of three parts: free space fading, slow fading, fast fading. Free space fading is characterized by the effect of signal dispersion in space; slow fading and fast fading are relatively complex, and slow fading is a shadow fading effect caused by the fact that objects such as mountains, trees, buildings and the like in a propagation space block the propagation of signals; fast fading is the jitter of the signal due to the superposition of multipath effects, and the variation scale of fast fading in the time domain is much faster than that of slow fading. The research object of the propagation model actually describes the slow fading effect of a specific area accurately, while the characteristics of the land features, the landforms, the land types, the human characters and the like in different areas and different cities have great differences, and theoretically, each area should be mapped with different propagation models (similar to human fingerprints).

Propagation models are broadly classified into 2 types: one is a theoretical analysis method based on radio propagation theory, such as a Volcano model, a WaveSight model, a WinProp model, and the like; one type is an actual measurement statistical method based on a large amount of test data and empirical formulas, such as an Okumura-Hata model, a COST231-Hata model, a Keenan-Motley model and the like. In a mobile communication system, since a mobile station is moving continuously, a propagation channel is influenced not only by the doppler effect but also by the terrain and the ground, and interference of the mobile system itself and external interference cannot be ignored. Based on the above characteristics of the mobile communication system, strict theoretical analysis is difficult to implement, and the propagation environment needs to be approximated and simplified, so that the theoretical model has a large error. Engineering applications typically use statistical models.

The most well-known statistical model element Okumura model, which is a propagation model represented by a graph that Okumura is counted based on a large amount of test data thereof in japan; the COST231-Hata model was developed in the Okumura-Hata model, and the propagation model is an extended version of the Hata model developed by the COST working Committee consisting of EURO-COST. The SPM (Standard propagation model) propagation model is established on the basis of a COST231-Hata empirical model and is used for predicting the propagation loss of radio waves in the frequency band of 150-2000 MHz and predicting the path loss P_lossThe correlation expression of (1) is as follows:

P_loss＝K₁+K₂log(d)K₃log(H_eff)+K₄Diffractio n+K₅log(d)×log(H_eff)+K₆(H_meff)+K_clutterf(clluter_i)

the process of correcting the wireless propagation model by using the CW continuous wave test data is as follows:

(1) the continuous wave is used as a signal source and fixed at the higher position of the center of the area to be measured, so that the continuous wave signal can cover the whole area to be measured.

(2) A test route is then planned, the route being as extensive as possible over all terrain types within the area.

(3) And traversing the receiver provided with the GPS positioning device at a uniform speed according to a planned route, and recording the GPS position information and the continuous wave signal level value in the process according to a certain frequency.

(4) After the test is finished, all GPS position points and the corresponding level values thereof are imported into the propagation model correction software as correction data, and the software can output the corrected wireless propagation model.

It can be seen that the traditional CW test has complex work and long period, and the data quality depends on factors such as a test route, a test terminal, a test vehicle, a vehicle speed and the like, so that the method has great limitation, and the application of propagation model correction in practical engineering is limited. The propagation model is the basis of the cell planning of the mobile communication network, whether the propagation model is accurate or not is closely related to whether the cell planning is reasonable or not, and whether an operator can meet the requirements of users with relatively economic and reasonable investment or not. Therefore, in order to obtain a wireless propagation model conforming to the local actual environment, a foundation is laid for network planning, and a new propagation model modification scheme is urgently needed.

Disclosure of Invention

An object of the present invention is to provide a new technical solution for a method and an apparatus for correcting a wireless propagation model based on MR data, so as to improve the accuracy of the wireless propagation model.

According to a first aspect of the invention, there is provided a method of modifying a wireless propagation model based on MR data, comprising the steps of:

acquiring MR data of a target area, wherein the MR data at least comprises the following information: RSRP value, geographical location information, donor cell information, and frequency band information;

preprocessing the MR data of the target region, including:

performing grid division on the target area; for any grid, respectively counting the number of MR data according to different donor cells, and only keeping the MR data of the donor cells with the number of the MR data being more than or equal to a preset number threshold;

filtering the abnormal MR data, comprising any one or combination of the following steps:

filtering MR data of which the distance between the geographic position and a base station corresponding to the donor cell is greater than a first distance threshold;

filtering MR data of which the distance between the geographic position and a base station corresponding to the donor cell is smaller than a second distance threshold;

filtering the MR data for non-main beam directions;

filtering MR data for which the donor cell is inconsistent with the predicted donor cell for the geographic location;

and correcting the wireless propagation model by using the preprocessed MR data.

Preferably, the pre-treatment further comprises the steps of: calculating an average value of RSRP values of MR data belonging to the same donor cell at the same geographic position, and correcting the RSRP value of each MR data of the donor cell at the geographic position to the average value.

Preferably, the side length of the grid is less than or equal to 40 times the wavelength, and the number threshold is greater than or equal to 30.

Preferably, the first distance threshold is twice the inter-station distance; the second distance threshold is

And doubling the station spacing.

Optionally, the predicted donor cell is determined by: making a transmitter coverage range prediction to obtain the predicted coverage range of each transmitter; and inquiring a transmitter covering the geographic position of the MR data, wherein the predicted donor cell of the MR data is a cell to which the transmitter covering the geographic position of the MR data belongs.

Optionally, the MR data further includes report cycle information; the preprocessing the MR data of the target region further comprises: and screening the MR data with the reporting period of 120ms-1024 ms.

According to a second aspect of the present invention, there is provided an apparatus for modifying a wireless propagation model based on MR data, comprising a memory and a processor; the memory for storing a computer program which when executed by the processor performs the steps of:

preprocessing the MR data of the target region, including:

filtering the MR data for non-main beam directions;

And doubling the station spacing.

According to the scheme for correcting the wireless propagation model based on the MR data, provided by the invention, the MR data is used for replacing the traditional CW data to correct the wireless propagation model, and the MR data is subjected to preprocessing, rasterization, filtering and other processing, so that the effects of eliminating the randomness of the MR data and retaining the determinacy are achieved. Due to the real-time property, the comprehensiveness and the objectivity of the MR data, the working efficiency of correcting the wireless propagation model is greatly improved, and the method has important significance for 3/4G wireless network planning.

The scheme not only enables accurate prediction of a small-range wireless environment to be possible, but also can accurately predict propagation model differences of factors such as different weather, climate and human activities (only MR data of different weather time periods are selected).

For example, in rainy days, the influence of high-frequency signals is great, so that MR data in rainy days can be selected to correct a wireless propagation model in rainy days to obtain an accurate wireless propagation model in rainy days, and CW data is set experimental test data and cannot predict propagation models in different weather conditions. For the 5G era, the influence of rainy weather on high-frequency signals cannot be ignored, so that the MR data correction method also provides an effective means for correcting and predicting the propagation model of the 5G network. .

Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.

Fig. 1 is a flow chart of a method of modifying a wireless propagation model based on MR data according to an embodiment of the invention.

Fig. 2 is a block diagram of an apparatus for modifying a wireless propagation model based on MR data according to an embodiment of the present invention.

Fig. 3 is a comparison graph of a predicted RSRP value predicted by the modified wireless propagation model according to the embodiment of the present invention and an actual RSRP value.

Detailed Description

Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.

In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.

< method for modifying radio propagation model based on MR data >

FIG. 1 shows a flowchart of a method for modifying a wireless propagation model based on MR data according to an embodiment of the invention, comprising steps S1-S3.

Step S1, MR data of the target region is acquired.

MR data (Measurement Report) refers to a Report file that is reported by a base station or a terminal based on a certain period or event trigger, that is, MR data is generated during the daily operation of the base station or the terminal. The MR data reported by the base station or the terminal is stored in the wireless network management platform, and the terminal can be, for example, a mobile phone, a device with a mobile communication function, and the like. Because the MR data is generated in the daily operation process of the base station or the terminal, compared with CW simulation data, the MR data is closer to the real situation, and has the advantages of being more objective and more comprehensive.

Generally, the MR data includes: dozens of field information such as RSRP value, RSRQ value, geographical position information, donor cell information and frequency band information can really and comprehensively record the current situation of the network. Rsrp (reference single Received power) is the reference signal Received power for the MR data points. Rsrq (reference single Received quality) is the reference signal Received quality of the MR data point. The geographic location information of the MR data points generally includes longitude information and latitude information; the frequency band information may include the frequency band of the MR data points, and may also include a network standard adopted by the MR data points, for example, the LTE-4G network standard is used for the MR data points, and the frequency band of the MR data points can be determined through the network standard.

The MR data reporting period has 120ms, 480ms, 720ms … … 60min and the like, and is generally selected from 120ms to 1024ms in consideration of factors such as user moving rate, total data volume and the like. Preferably, the MR data with reporting periods of 120ms, 240ms and 480ms are selected. The longer the data recording time is, the richer the MR data is, and the data density which is required to meet the condition of being more than the Lee's theorem threshold in the subsequent steps is considered, so the data recording time is selected to be more than 1 week. For example, the estimated number of users in a 1-square-kilometer area is 30, the MR data reporting period is selected to be 480ms, and the data density threshold needs to satisfy 38/square meter, so that the data recording duration should be T38 × 1000000/(30 × 24 × 60 × 0.48) 7.03 days, and therefore the data recording duration is preferably more than 1 week.

The target area selected in embodiments of the present invention preferably satisfies the following characteristics: a) the wireless propagation environment is stable; b) the mobile network is covered sufficiently, and in order to verify the effect of correcting the wireless propagation model subsequently, the selected target area is covered with various region types such as a water area, a green space, a building and the like as much as possible; c) a certain amount of mobile network users exist in the region and are distributed uniformly, and the more users, the more MR data are acquired, so that the propagation model can be corrected accurately.

Step S2, preprocessing the MR data of the target region, including step S21 and step S22.

S21, performing grid division on the target area; for any grid, the number of MR data is respectively counted according to different donor cells, and only the MR data of the donor cells with the number of MR data being more than or equal to a preset number threshold value is reserved.

According to the Lee's theorem, when a test of wireless propagation model correction is performed, it is ensured that within an interval of 40 wavelengths, the number of recorded test points cannot be less than 30, so that fast fading is effectively eliminated, and slow fading is reserved. Therefore, when the grid is divided, the side length of the grid is equal to or less than 40 times the wavelength, and the number threshold is equal to or greater than 30.

In one embodiment, the wavelength may be determined by frequency band information of the MR data. Assuming that the user terminal uses a telecommunication LTE-4G network, the carrier frequency is 800MHz, the wavelength is 0.38m, and the 40 times wavelength is about 15 meters, the side length of the grid is less than or equal to 15 meters. In an embodiment of the present invention, the target area is selected to be an area of 3 km by 3 km, and the side length of the grid is set to be 15 m, so that the target area may be divided into 4 ten thousand grids.

After the grid division, for any grid, respectively counting the number of MR data according to different donor cells, and only keeping the MR data of the donor cells with the number of the MR data being more than or equal to 30, namely only keeping the MR data conforming to the Lee's theorem. That is, for MR data in any grid, if the number of MR data of a certain donor cell is less than 30, the MR data of that donor cell of that grid is discarded.

Step S22, filtering the abnormal MR data, including any one or combination of the following steps:

s221, MR data with too far distance between the geographic position and the base station corresponding to the donor cell is filtered. For MR data, if the distance between its geographical location and the base station corresponding to the donor cell is too far, it is said that the MR data is abnormal.

Generally, MR data with a distance 2 times the inter-station distance from the base station is considered as abnormal data with an excessively long distance and needs to be filtered out, and can also be set according to actual application requirements. For example, in the present embodiment, MR data with a distance greater than 700m from the base station may be filtered.

S222, filtering the MR data with the geographical position too close to the base station corresponding to the donor cell. When the terminal is particularly close to the base station, the random fluctuation of the RSRP value is relatively large, so if the distance between the geographical position of the MR data and the base station corresponding to the donor cell is too close, the MR data is regarded as abnormal data and needs to be filtered out.

The MR data within the second distance threshold from the base station is generally regarded as abnormal data to be filtered out, and may also be set according to the actual application requirements, and the second distance threshold is preferably set

And doubling the station spacing. For example, in the present embodiment, MR data with a distance of less than 10m from the base station may be filtered.

And S223, filtering the MR data in the non-main beam direction. Because the transmitter of the existing mobile communication network mostly uses directional antennas, the MR data in the non-main beam direction needs to be filtered out only if the measurement point in the main beam direction of the antenna corresponding to the current donor cell of the MR data is valid.

S224, filtering MR data of the donor cell inconsistent with the predicted donor cell of the geographic position;

for step S224, in an embodiment, coverage prediction may be performed on the transmitters in advance to obtain a predicted coverage of each transmitter, and then the transmitter covering the geographic location of the MR data is queried, where the predicted donor cell of the MR data is a cell to which the transmitter covering the geographic location of the MR data belongs. If the donor cell in the MR data does not coincide with the predicted donor cell, it is an indication that the MR data is abnormal and should be filtered out.

In a more specific example:

a. and (3) using Atoll software to predict the coverage area of the transmitter to obtain a coverage area prediction graph of each transmitter.

b. And importing the MR data and the coverage prediction graph into Maplnfo software, performing superposition query on the MR data point graph layer and the coverage prediction graph layer, and exporting a query result.

c. And comparing the donor cell in the MR data with the predicted donor cell in the query result, keeping the MR data consistent with the donor cell, and filtering out inconsistent data.

In another embodiment, the preprocessing of the MR data of the target region in step S2 may further include step S23.

S23, calculating an average value of RSRP values of MR data belonging to the same donor cell at the same geographic location, and correcting the RSRP value of each MR data of the donor cell at the geographic location to the average value. That is, RSRP values of MR data belonging to the same donor cell at the same geographical location are unified into an average value, making the RSRP values more reliable.

The sequence of the steps S21-S23 can be interchanged, the sequence of the steps S221-S224 can be interchanged, and the invention is not limited to the sequence.

And step S3, correcting the wireless propagation model by using the preprocessed MR data.

In the conventional method, the SPM model is modified by CW data, in this embodiment, the preprocessed MR data is used to replace the conventional CW data, and the SPM model is modified as an example.

The SPM (Standard propagation model) model is established on the basis of a COST231-Hata empirical model and is used for predicting the propagation loss of radio waves in the frequency band of 150-2000 MHz and the path loss P_lossThe correlation expression and correlation parameter of (1) are as follows:

P_loss＝K₁+K₂log(d)+K₃log(H_eff)+K₄Diffractio n+K₅lod(d)×log(H_eff)+K₆(H_meff)+K_clutterf(clluter_i)

TABLE 1SPM model parameter Table

TABLE 2SPM model coefficient Table

Directly obtaining or calculating multiple sets of corresponding parameter values and path loss P in the table 1 through multiple pieces of MR data_lossSolving the path loss formula through the plurality of groups of parameter values to obtain a coefficient value K₁、K₂、K₃、K₄、K₅、K₆And K_clutterAnd realizing the correction of the SPM model.

The calculation formula of the path loss is as follows: p_lossBase station transmit power-RSRP.

Therefore, the wireless propagation model is modified, and the fact that K is solved₁、K₂、K₃、K₄、K₅、K₆And K_clutterIs the 7-element equation.

Since a total of 7 coefficient values need to be solved, at least 7 pieces of MR data are required, and it is preferable to select 1000 or more pieces of MR data for more accuracy of the obtained correction model, which is more effective.

In step S3, the modifying the wireless propagation model may include:

and S31, importing the base station and the transmitter working parameters into Atoll software, and creating a CW test correction project, wherein the CW test correction project comprises all network and geographical position information.

The parameters are determined according to the parameters input by Atoll software requirements, and comprise a coordinate system, map data, antenna parameters, transmitting power and the like.

Wherein the map data includes a digital ground model DTM, a feature type, a feature height, a vector map, a scanned image, etc.

And S32, importing the MR data into CW test sites one by distinguishing donor cells, wherein the geographic position information of each group of MR data is consistent with the geographic position information of the CW test correction project.

The CW test station is selected manually as appropriate.

S33, data correctnessAfter the SPM model is imported, the SPM model can be corrected, a base station and a transmitter corresponding to the MR data are selected for correction, and corrected K is obtained₁～K₆And K_clutterThe value of (c).

In particular, the MR data is imported into an SPM model

P_loss＝K₁+K₂log(d)+K₃log(H_eff)+K₄Diffractio n+K₅log(d)×log(H_eff)+K₆(H_meff)+K_clutterf(clluter_i)

N groups of K can be obtained₁、K₂、K₃、K₄、K₅、K₆And K_clutterSince at least 7 pieces of MR data are selected, N is greater than or equal to 7.

Solving the N groups of 7-element equations by using a least square method to obtain corrected K₁～K₆And K_clutterThe value of (c).

Correcting the above K₁～K₆And K_clutterSubstituting the value into the SPM model to obtain a modified wireless propagation model.

The foregoing is a method for correcting an SPM model, and it should be noted that the method for correcting a wireless propagation model can be implemented by various methods, and only one of them is described here, which is well known to those skilled in the art, so that redundant description is not repeated here.

In another embodiment of the present invention, the step S4 of verifying the accuracy of the modified wireless propagation model may be further included.

The verification accuracy can be compared with the RSRP values before and after correction, and the higher the coincidence degree of the RSRP values before and after correction is, the more accurate the corrected wireless propagation model is.

Reference is made to fig. 3, wherein RSRP values are plotted on the vertical axis and different geographical locations are plotted on the horizontal axis. The solid line is the RSRP value in the MR data, i.e., the true RSRP value. The dotted line is the predicted RSRP value predicted by the radio propagation model corrected by the MR data, and as can be seen from fig. 3, the two values are basically consistent, which indicates that the method achieves a good effect, and the corrected radio propagation model accurately simulates the radio propagation characteristics of the region.

In addition, common indexes for verifying accuracy also comprise a mean value and a variance, wherein when the mean value deviation is 0-1dB and the variance is 0-8dB, the accuracy of the corrected wireless propagation model is high.

Table 3 shows the effect of comparing the RSRP value predicted by the null propagation model with the RSRP value in the MR data after the wireless propagation model is corrected by the CW continuous wave test data. Table 4 shows the comparison effect between the RSRP value predicted by the invalid propagation model and the RSRP value in the MR data after the MR data provided by the embodiment of the present invention is used to correct the wireless propagation model, and it can be seen from these two tables that the mean deviation obtained by correcting the wireless propagation model by the MR data provided by the embodiment of the present invention is 0.01dB and the variance is 7.77dB, which shows that the method accurately simulates the wireless propagation characteristic of this region.

TABLE 3

TABLE 4

Landform	Data volume	Mean deviation (dB)	Variance (dB)
				Water area	1	0.73	0
Sea area	0	0	0
				Wetland	0	0	0
Open ground in suburb	0	0	0
				Open ground of city	64	-0.14	7.37
Greenbelt	1.335	0.01	8.29
				Forest land	0	0	0
High building	0	0	0
				General building	0	0	0
Compared with common buildings	68	0.97	8.42
				Irregular large building	57	0.41	3.87
Irregular building	2.239	0	7.5
				Suburb area	0	0	0
Land for road	308	-0.14	7.77
				Total of	4072	0.01	7.77

As can be seen from the comparative data, the wireless propagation model corrected by using the method for correcting a wireless propagation model based on MR data of the embodiment of the present invention has extremely high accuracy because comprehensive and objective MR data is adopted and the MR data is subjected to processing such as screening and filtering. By using the wireless propagation model corrected by the correction method, the predicted mean deviation and variance of data and true values are smaller.

The embodiment of the invention provides a scheme for correcting a wireless propagation model based on MR data, the MR data replaces the traditional CW data to correct the wireless propagation model, and due to the real-time property, the comprehensiveness and the objectivity of the MR data, the working efficiency of correcting the wireless propagation model is greatly improved, and the method has important significance for 3/4G wireless network planning.

The method not only enables accurate prediction of a small-range wireless environment to be possible, but also can accurately predict propagation model differences of factors such as different weather, climate and human activities (only MR data of different weather time periods are selected).

For example, in rainy days, the influence of high-frequency signals is great, so that MR data in rainy days can be selected to correct a wireless propagation model in rainy days to obtain an accurate wireless propagation model in rainy days, and CW data is set experimental test data and cannot predict propagation models in different weather conditions. For the 5G era, the influence of rainy weather on high-frequency signals cannot be ignored, so that the MR data correction method also provides an effective means for correcting and predicting the propagation model of the 5G network.

Those skilled in the art will appreciate that the methods described above can be embodied in an article of manufacture by software, hardware, and a combination of software and hardware in the field of computer-related technology. The person skilled in the art will readily be able to produce a device for modifying the radio propagation model based on MR data based on the method disclosed above.

< apparatus for modifying radio propagation model based on MR data >

The apparatus 300 for modifying a wireless propagation model based on MR data includes a processor 3010, a memory 3020, an interface device 3030, a communication device 3040, a display device 3050, an input device 3060, a speaker 3070, a microphone 3080, and the like.

The processor 3010 may be, for example, a central processing unit CPU, a microprocessor MCU, or the like. The memory 3020 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface device 3030 includes, for example, a USB interface, a headphone interface, and the like. The communication device 3040 is capable of long-range wireless communication, such as with GSM/GPRS functionality, for example. The display device 3050 is, for example, a liquid crystal display panel, a touch panel, or the like. The input device 3060 may include, for example, a touch screen, a keyboard, and the like. A user can input/output voice information through the speaker 3070 and the microphone 3080.

The memory 3020 is used for storing a computer program which, when executed by the processor 3010, performs the steps of:

preprocessing the MR data of the target region, including:

filtering the MR data for non-main beam directions;

filtering MR data of the donor cell inconsistent with the donor cell predicted according to the geographic position;

In another embodiment, the preprocessing of the MR data of the target region may further comprise the steps of: calculating an average value of RSRP values of MR data belonging to the same donor cell at the same geographic position, and correcting the RSRP value of each MR data of the donor cell at the geographic position to the average value.

In another embodiment, the grid has a side length equal to or less than 40 wavelengths and the number threshold is equal to or greater than 30.

In another embodiment, the first distance threshold is two times the inter-site distance; the second distance threshold is

The station distance, the first distance threshold and the second distance threshold may also be set by human.

In another embodiment, said predicting a donor cell based on geographical location comprises the steps of: making a coverage prediction of a transmitter to obtain a coverage prediction graph of the transmitter; and inquiring a transmitter covering the geographic position of the MR data, wherein the predicted donor cell of the MR data is a cell to which the transmitter covering the geographic position of the MR data belongs.

In another embodiment, the MR data further includes reporting period information; the preprocessing the MR data of the target region further comprises: and screening the MR data with the reporting period of 120ms-1024 ms.

It will be appreciated by those skilled in the art that although a plurality of devices are shown in fig. 2, devices of embodiments of the present invention may refer to only some of the devices therein, and other devices may be encompassed. Skilled artisans may design instructions that control the operation of the processor according to the disclosed aspects, and such instructions are well known in the art and will not be described in detail herein.

The foregoing method for modifying a wireless propagation model based on MR data has fully illustrated various aspects of the present invention, and the embodiments of the apparatus for modifying a wireless propagation model based on MR data can be referred to in the related contents of the foregoing method, and the contents of the foregoing method can be used to illustrate the aspects of the apparatus for modifying a wireless propagation model based on MR data.

The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.

Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.

Claims

1. A method for modifying a wireless propagation model based on MR data, comprising the steps of:

preprocessing the MR data of the target region, including:

filtering the MR data for non-main beam directions;

calculating an average value of RSRP values of MR data belonging to the same donor cell at the same geographic position, and correcting the RSRP value of each MR data of the donor cell at the geographic position into the average value;

2. The method of claim 1, wherein the grid has a side length of 40 wavelengths or less and the number threshold is 30 or more.

3. The method of claim 1, wherein the first distance threshold is two times the inter-station spacing; the second distance threshold is

And doubling the station spacing.

4. The method of claim 1, wherein the predicted donor cell is determined by:

making a transmitter coverage range prediction to obtain the predicted coverage range of each transmitter;

and inquiring a transmitter covering the geographic position of the MR data, wherein the predicted donor cell of the MR data is a cell to which the transmitter covering the geographic position of the MR data belongs.

5. The method of claim 1, wherein the MR data further includes reporting period information; the preprocessing the MR data of the target region further comprises: and screening the MR data with the reporting period of 120ms-1024 ms.

6. An apparatus for modifying a wireless propagation model based on MR data, comprising a memory and a processor; the memory for storing a computer program which when executed by the processor performs the steps of:

preprocessing the MR data of the target region, including:

filtering the MR data for non-main beam directions;

7. The apparatus of claim 6, wherein the grid has a side length of 40 wavelengths or less, and wherein the number threshold is 30 or more.

8. The apparatus of claim 6, wherein the first distance threshold is two times a station spacing; the second distance threshold is

And doubling the station spacing.

9. The apparatus of claim 6, wherein the predicted donor cell is determined by:

10. The apparatus of claim 6, wherein the MR data further includes reporting period information; the preprocessing the MR data of the target region further comprises: and screening the MR data with the reporting period of 120ms-1024 ms.