CN104113849A - Correction method of propagation model - Google Patents

Correction method of propagation model Download PDF

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Publication number
CN104113849A
CN104113849A CN201310132705.4A CN201310132705A CN104113849A CN 104113849 A CN104113849 A CN 104113849A CN 201310132705 A CN201310132705 A CN 201310132705A CN 104113849 A CN104113849 A CN 104113849A
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China
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propagation
base station
community
drive test
test point
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CN201310132705.4A
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Chinese (zh)
Inventor
杨尧
王秀梅
李晓坪
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普天信息技术研究院有限公司
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Priority to CN201310132705.4A priority Critical patent/CN104113849A/en
Publication of CN104113849A publication Critical patent/CN104113849A/en

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Abstract

A correction method of a propagation model disclosed by the application comprises the steps of for each cell, acquiring the latitude and longitude of each road test point in the cell and the signal intensity of the cell received by each road test point, calculating the distance between each road test point and a cell base station according to the latitude and longitude, and calculating the road loss between each road test point and the cell base station according to the signal intensity; for each cell, correcting the propagation model of the cell by the distance between each road test point and the cell base station, and the road loss between each road test point and the cell base station, to obtain a corrected propagation model of the cell, wherein the propagation model L=K1+K2*log10d, L is the signal transmission road loss between a user device and the base station, d is the distance between the user device and the base station, K1 is a first coefficient, and K2 is a second coefficient. The correction method of the propagation model of the present invention enables the algorithm complexity of the propagation model correction to be simplified and the correction accuracy to be improved, is easy to implement, and is suitable for a network optimization stage.

Description

The bearing calibration of propagation model
Technical field
The present invention relates to mobile communication technology, particularly relate to a kind of bearing calibration of propagation model.
Background technology
In mobile communications network, need to utilize default propagation model to determine the path loss of signal propagation path.And for mobile communications network, path loss information is the important parameter index of carrying out plot planning and mobile management.Therefore, propagation model is the basis of carrying out plot planning, propagation model whether accurately whether be related to plot planning reasonable.In mobile communication system, because travelling carriage constantly moves, propagation channel is not only subject to the impact of Doppler effect, but also is subject to the impact of landform, atural object, and the interference of mobile system itself and external interference can not be ignored in addition.Based on the above-mentioned characteristic of mobile communication system, strict theory analysis is difficult to realize, and need be similar to communication environments, simplify, thereby make theoretical model error larger.Therefore need to test for each geographical environment from different places, by means such as analysis and calculating, the parameter of propagation model be revised.Finally drawing can propagation model reflect local wireless propagation environment, that have theoretical reliability most, thereby improves the accuracy of coverage prediction.
Due to each different regions, the city that each are different, it is quite large that its landforms differ, and the landform of various places is ever-changing, and this in the time that the parameter of a propagation model is applied to other city or area, certainly will will carry out corrected model parameter with regard to having determined.Model tuning, be exactly that the correction of radio transmission model is to obtaining field intensity prediction more accurately, that is to say, make to utilize path loss that propagation model calculates to approach most possibly the path loss of actual wireless communication environments by model tuning, this is also one of most important content of wireless network planning.Therefore, the accuracy of propagation model is to determine the whether believable key factor of wireless network planning, and whether this investment that is directly connected to operator is both economical rationally.
Current propagation model revision method is mainly continuous wave (CW) test of first not modulating for each geographical environment from different places, and then by means such as analysis and calculating, the parameter of propagation model is revised, common correcting process specifically comprises:
(1) start preparation model and proofread and correct, select to have the path that represents one's respective area;
(2) gather CW data;
(3) the CW data that collect are carried out to preliminary treatment, comprise data discrete, geographical average, data filtering etc.;
(4), based on preliminary treatment result, carry out model tuning calculating; The result that output model is proofreaied and correct, and the parameter of output is applied to proving correctness in certain engineering.
The general objective of proofreading and correct in above-mentioned model tuning method is for the error between propagation model prediction and CW test data is reduced to minimum.Being used for the tolerance of quantization error is average and the standard variance of error.In general, require error mean close to zero, standard variance is less than 8db.
Current model tuning has mainly adopted least square method and linear regression method.These two kinds of methods, all based on minimum variance and criterion, are identical at root; Emulation shows under identical initial conditions, and the result that these two kinds of methods are proofreaied and correct is duplicate.Linear regression method is divided into one-variable linear regression method and multiple linear regression analysis method according to the scope using, and is respectively used to the correction of a variable and multiple variablees in model.Least square method can be directly used in the correction of single variable and multiple variablees in model.
Current model tuning method is only applicable to the networking initial stage, is unsuitable for the network optimization stage.Specifically the reasons are as follows:
1, existing model tuning method is carried out model tuning based on CW test data, and CW test data is the data of obtaining by built non-directional transmitter before networking, its wireless environment propagation model revision to bulk zone can be predicted the outcome preferably, but the mobile communications network for different systems is not distinguished, and also the wireless environment prediction of certain concrete community is lacked to specific aim.
2, CW drive test data obtain also cumbersome, as previously mentioned, need to set up non-directional transmitter, also need the position of non-directional transmitter to carry out addressing before, CW drive test implement certain difficulty, the non-directional transmitter that can set up when enforcement is also very limited, so can cause CW drive test data can not reflect real wireless transmission environment comprehensively, and then the accuracy that causes the traffic model carrying out based on CW drive test data to be proofreaied and correct is not high.
3, obtaining of CW test data is to carry out taking the wireless environment of bulk zone as unit, bulk zone wireless environment has complexity, will cause like this variable parameter that need to consider in the time carrying out model tuning more, for example, measurement point is to distance, frequency, type of ground objects and the effective height of transmitting antenna etc. of base station.And no matter be to adopt least square method or linear regression method carrying out timing, the dimension that timing is set up equation can increase along with the increase of the variable quantity of needs consideration, thereby brings the complexity of equation solution.
As can be seen here, existing propagation model revision method due to exist complexity high, the problem such as be difficult to implement, accuracy is low, be not suitable for the network optimization stage.
Summary of the invention
In view of this, main purpose of the present invention is to provide a kind of bearing calibration of propagation model, and the method is applicable to the network optimization stage.
In order to achieve the above object, the technical scheme that the present invention proposes is:
A bearing calibration for propagation model, comprising:
For each community, gather the signal strength signal intensity that the longitude and latitude of each drive test point in this community and each drive test point receive Gai community, and according to described longitude and latitude, calculate each drive test and put the distance of this cell base station, according to described signal strength signal intensity, calculate the path loss between each drive test point and cell base station;
For each described community, utilize each drive test in community to put the path loss between distance and each drive test point and the cell base station of this cell base station, the propagation model of Dui Gai community is proofreaied and correct, and obtains the propagation model after proofread and correct this community, wherein, described propagation model is L=K 1+ K 2× log 10 d, wherein, described L is the signal transmission path loss between subscriber equipment and base station, described d is the distance of subscriber equipment to base station, described K 1be the first coefficient, described K 2it is the second coefficient.
In sum, the bearing calibration of the propagation model that the present invention proposes, utilize the drive test data obtaining based on real network, taking community as unit is proofreaied and correct propagation model, simplify propagation model, and then can effectively simplify the algorithm complex of propagation model revision, the omnidirectional transmitter antenna that improves calibration accuracy and do not need foundation to measure specially, and easy to implement, be therefore applicable to the network optimization stage.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of the embodiment of the present invention one.
Embodiment
For making the object, technical solutions and advantages of the present invention clearer, the present invention is described in further detail below in conjunction with the accompanying drawings and the specific embodiments.
Core concept of the present invention is: utilize the drive test data obtaining based on real network, the path loss measurement data obtaining based on real network, carry out the correction of propagation model as unit taking community, like this, on the one hand because drive test data is to obtain based on real network, can reflect exactly actual wireless communication environment, and not need to set up specially again the non-directional transmitter for measuring, therefore can obtain higher calibration accuracy and easy to implement, on the other hand, be that unit carries out owing to proofreading and correct Shi Yi community, and wireless communications environment in cell range is comparatively simple, frequency, the parameter such as type of ground objects and effective height of transmitting antenna can be considered as constant in cell range, only having measurement point is variable to the distance of base station, so, propagation model will be comparatively simple, only exist an independent variable apart from variable, accordingly, the variable quantity that timing need to be considered will be reduced greatly, also only need to consider measurement point this variable of distance to base station, thereby can reduce largely the complexity of timing equation solution.
Fig. 1 is the schematic flow sheet of the embodiment of the present invention one, and as shown in Figure 1, this embodiment mainly comprises:
Step 101, for each community, gather the signal strength signal intensity that the longitude and latitude of each drive test point in this community and each drive test point receive Gai community, and according to described longitude and latitude, calculate each drive test and put the distance of this cell base station, according to described signal strength signal intensity, calculate the path loss between each drive test point and cell base station.
This step is for adding up the drive test data (being the signal strength signal intensity that the longitude and latitude of drive test point and drive test point receive Gai community) of each drive test point in community, and determine that according to drive test data drive test puts the distance of base station and signal transmission path loss, to after this utilize determined drive test distance and path loss, carry out the correction of propagation model.
Step 102, for each described community, utilize each drive test in community to put the path loss between distance and each drive test point and the cell base station of this cell base station, the propagation model of Dui Gai community is proofreaied and correct, and obtains the propagation model after proofread and correct this community, wherein, described propagation model is L=K 1+ K 2× log 10 d, wherein, described L is the signal transmission path loss between subscriber equipment and base station, described d is the distance of subscriber equipment to base station, described K 1be the first coefficient, described K 2it is the second coefficient.
In this step, utilize distance and the circuit loss value of each drive test point in the community obtaining in step 101, the propagation model of each community is proofreaied and correct.
Here it should be noted that, with traditional propagation model revision method difference be, this step Zhong Shiyi community is the correction that unit carries out the propagation model of community, as previously mentioned, because the communication environment school in community is simple, wireless communications environment is comparatively simple, only having measurement point is variable to the distance of base station, therefore, the propagation model of corresponding community is also simplified, i.e. L=K 1+ K 2× log 10 d, wherein, K 1and K 2for two coefficients of propagation model, be constant, diverse location is independent variable to the distance d of base station, that is to say, is worth L by the difference along with apart from d and difference as the road of dependent variable.In this step, to the correction of propagation model, obtain exactly the K matching with the communication environment of community reality 1and K 2, to guarantee that utilization is based on this K 1and K 2the community propagation model obtaining, can accurately obtain circuit loss value corresponding to diverse location in community.
Particularly, be L=K at known propagation model 1+ K 2× log 10 dsituation under, utilize each drive test in community to put distance and the path loss of this cell base station, the method that this propagation model is proofreaied and correct, by those skilled in the art are grasped, specifically can adopt following method to realize:
Step 2011, utilize each drive test in community to put the path loss between distance and each drive test point and the cell base station of this cell base station, according to described propagation model, generate corresponding data fitting matrix.
In this step, be L=K at known propagation model 1+ K 2× log 10 dsituation under, can adopt existing data fitting matrix generating method, utilize each drive test to put corresponding distance and circuit loss value, according to this propagation model with K 1and K 2for variable, generate each drive test and put corresponding equation, each drive test is put to corresponding equation group and be combined and obtain corresponding data fitting matrix.It should be noted that, owing to only there being an independent variable d in the propagation model in this step, the therefore relatively existing bearing calibration that need to consider multiple Variable Transmission models, the data fitting matrix constructing in this step is relatively simple.
Step 2012, described data fitting matrix is solved, obtain the first COEFFICIENT K of the propagation model after proofreading and correct 1with the second COEFFICIENT K 2.
This step can adopt existing data fitting Matrix Solving method to realize.Solve the K obtaining 1and K 2be the first COEFFICIENT K of the propagation model after correction 1with the second COEFFICIENT K 2.
Preferably, available following method solves described data fitting matrix:
Judge whether described data fitting matrix has unique solution, if had, calculate this unique solution, otherwise, according to default mean error desired value, standard variance desired value and default matching thresholding, the mode that adopts multiple linear regression and least square method to carry out matching, calculates the optimal solution of described data fitting matrix, the solving result using described optimal solution as data fitting matrix described in this.
Preferably, in the process of optimal solution of calculating described data fitting matrix, the condition that determines whether optimal solution can be: the standard variance that the mean error of the described propagation model after correction is less than or equal to the described propagation model after described mean error desired value and correction is less than or equal to described standard variance desired value, or matching number of times reaches described matching thresholding.
In actual applications, those skilled in the art can arrange described mean error desired value, standard variance desired value and matching thresholding according to actual needs, for example, can standard variance desired value be set to 6dB or 8dB etc.
Here, the mode that adopts multiple linear regression and least square method to carry out matching, utilize least square method to set up equation group, utilize multiple linear regression analysis method to solve, calculate the optimal solution of described data fitting matrix, concrete grammar, by those skilled in the art are grasped, does not repeat them here.
The first COEFFICIENT K of the propagation model after step 2013, utilization are proofreaied and correct 1with the second COEFFICIENT K 2, set up the propagation model after proofread and correct this community.
In sum, these are only preferred embodiment of the present invention, be not intended to limit protection scope of the present invention.Within the spirit and principles in the present invention all, any amendment of doing, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.

Claims (4)

1. a bearing calibration for propagation model, is characterized in that, comprising:
For each community, gather the signal strength signal intensity that the longitude and latitude of each drive test point in this community and each drive test point receive Gai community, and according to described longitude and latitude, calculate each drive test and put the distance of this cell base station, according to described signal strength signal intensity, calculate the path loss between each drive test point and cell base station;
For each described community, utilize each drive test in community to put the path loss between distance and each drive test point and the cell base station of this cell base station, the propagation model of Dui Gai community is proofreaied and correct, and obtains the propagation model after proofread and correct this community, wherein, described propagation model is L=K 1+ K 2× log 10 d, wherein, described L is the signal transmission path loss between subscriber equipment and base station, described d is the distance of subscriber equipment to base station, described K 1be the first coefficient, described K 2it is the second coefficient.
2. method according to claim 1, is characterized in that, the described propagation model that utilizes described Dui Gai community is proofreaied and correct and comprised:
Utilize each drive test in community to put the path loss between distance and each drive test point and the cell base station of this cell base station, according to described propagation model, generate corresponding data fitting matrix;
Described data fitting matrix is solved, obtain the first COEFFICIENT K of the propagation model after proofreading and correct 1with the second COEFFICIENT K 2;
Utilize the first COEFFICIENT K of the propagation model after proofreading and correct 1with the second COEFFICIENT K 2, set up the propagation model after proofread and correct this community.
3. method according to claim 2, is characterized in that, described data fitting matrix is solved and comprised;
Judge whether described data fitting matrix has unique solution, if had, calculate this unique solution, otherwise, according to default mean error desired value, standard variance desired value and default matching thresholding, the mode that adopts multiple linear regression and least square method to carry out matching, calculates the optimal solution of described data fitting matrix, the solving result using described optimal solution as data fitting matrix described in this.
4. method according to claim 3, it is characterized in that, the Rule of judgment of described optimal solution is: the standard variance that the mean error of the described propagation model after correction is less than or equal to the described propagation model after described mean error desired value and correction is less than or equal to described standard variance desired value, or matching number of times reaches described matching thresholding.
CN201310132705.4A 2013-04-17 2013-04-17 Correction method of propagation model CN104113849A (en)

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Application publication date: 20141022