CN100362889C - Mobile communication network propagation model correction method - Google Patents
Mobile communication network propagation model correction method Download PDFInfo
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- CN100362889C CN100362889C CNB200410053786XA CN200410053786A CN100362889C CN 100362889 C CN100362889 C CN 100362889C CN B200410053786X A CNB200410053786X A CN B200410053786XA CN 200410053786 A CN200410053786 A CN 200410053786A CN 100362889 C CN100362889 C CN 100362889C
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Abstract
The present invention relates to the plot layout of a mobile communication network and discloses a mobile communication network propagation model correction method which makes the treatment of the tested information of a propagation model more reasonable and the correction of the propagation model more precise. The mobile communication network propagation model correction method first adopts subparagraph fit according to different distances, and then uses an averaging method to a fit result to get a correcting parameter, or first adopts an interpolation method, etc. to add the tested information to get the purpose that the amounts of the tested information are uniformly distributed on the distances, and then uses a linear fit method to correct the parameter.
Description
Technical field
The present invention relates to the mobile communications network plot planning, particularly mobile communications network propagation model revision and test data processing method thereof.
Background technology
One of target of mobile communications network construction is how to improve the running quality and the service quality of network, for the mobile subscriber provides continuous, a high-quality network.Two of target is how to improve the returns of investment of network, and Internet resources are fully used, and avoids investment blindly, makes full use of Limited resources and obtains maximum benefit.The engineering construction of mobile radio communication is broadly divided into 6 step: 1-and drafts coverage goal and the traffic requirement that network need reach; 2. network pre-planning; 3. parameters such as base station location, configuration are determined in site, the base station inspection of the scene of a crime; 4. engineering design is finished in the network planning again; 5. the system comissioning and the network optimization; 6. according to optimizing result or network capacity extension requirement, return the first step.
For adapting to the rapid increase with number of users and traffic carrying capacity of developing rapidly of mobile communications network, operation enterprise network optimization department need adopt advanced technological means and rational method of testing, rationally determine the capacity ratio of each network element device, improve the utilance of the network equipment, realize planning of science activities, optimization and the appropriate design of network.Simultaneously, the drawback that exists in original network planning/optimal design work in the operation enterprise, need in addition perfect, to realize the quick deployment of new business.Drawback in existing planning/optimal design work generally has following several: can not fully understand present situations such as local society ﹠ culture's situation and customer consumption characteristic; Old numerical map makes simulation and forecast seriously deviate from local geomorphological features actual distribution characteristics, causes the inaccuracy and the uncertainty of prediction; The parameter correction of propagation model is left to be desired; Can not carry out sufficient drive test test to network coverage and speech quality situation, be difficult to analyze and sum up; Can not provide the detailed network traffic regularity of distribution; Can't carry out the essence analysis to the resource utilization of existing network, network design does not conform to the desired value of operator; Can't carry out the indoor CQT test in important place at aspects such as solid covering, indoor degree of depth coverings, therefore can not propose the detailed solution of the network coverage.
In wireless network design, planning and the optimization work of mobile communications network, the optimization personnel need utilize the emulation planning software that the wireless environment of system is carried out analogue simulation, so that on the whole system is analyzed, comprise the analysis on its rationality shared to the coverage Prediction of just deciding the site, to the peripheral base station traffic etc.
In the test optimization engineering of reality, the optimization personnel need test at each geographical environment from different places, by means such as analysis and calculating the parameter of propagation model are revised.Carry out CW (Continuous Wave by the actual transmitter that sets up, be called for short " CW ") test, the optimization personnel can obtain wireless signal path loss value the most accurately, revise repeatedly with the result of analogue simulation, finally draw and to reflect propagation model local wireless propagation environment, that have theoretical reliability most.And the wireless environment of ours at one's side is not unalterable, especially in the city, pile, intensive residential block increase the variation that all can cause wireless propagation environment, when this variation acquires a certain degree, just need revise, improve the authenticity of wireless analogue simulation propagation model parameter.
Wherein the general flow of propagation model revision as shown in Figure 1.
Forecast model formula among Fig. 1 can have a variety of, and for example the standard macrocell model (Standard MacroCell Model) of enterprise (Enterprise) the planning software support of Britain Aircom company making uses following formula:
Prx＝Ptx-Ploss
Wherein:
Prx＝Received?power(dBm)
Ptx＝Transmit?power(EiRP)(dBm)
Ploss＝Path?loss(dB)
And
Ploss＝K1+K2log(d)+K3(Hms)+K4log(Hms)+K5log(Heff)+K6log(Heff)log(d)+K7diffn+Clutter_Loss
Wherein:
D is that the base station is to the distance between the mobile radio station (km); Hms is the height (m) on the relative ground of mobile radio station, this numerical value or can be appointed as general numerical value, perhaps only corresponding single atural object classification; Heff is the effective depth (m) of antenna for base station; Diffn is to use Epstein, the diffraction loss that the equivalent tooth shape diffraction method of Peterson, Deygout or Bullington is calculated; K1﹠amp; K2 is intercept and slope, and these factors are corresponding to the multiplier factor of the log value of distance between a constant offset amount and base station and the mobile radio station; K3 is the height factors of mobile portable antennas, and this factor is used for revising the influence of the effective antenna height of travelling carriage; K4 is the increment factor of the Okumura Hata model of Hms; K5 is the effective antenna height gain in base station, and this is the increment factor of effective antenna height log value; K6 is the coefficient of Log (Heff) Log (d), and this is the increment factor of the Okumura Hata type of log (Heff) log (d) value; K7 is a diffraction coefficient, and this is the increment factor of Diffraction Calculation, and the user can select the method for diffraction; Clutter_Loss is the atural object specification, and for example, height and interval must be considered in computational process.
K1 in the above-mentioned formula, K2, K3, K4, K5, K6, K7 and Clutter_Loss can proofread and correct according to the CW test result, thereby can obtain to meet the radio transmission model of this area actual environment.
The formula that embodies and the correctable coefficient of different forecast model formula are different, but one of correctable coefficient that nearly all forecast model formula must provide is exactly the multiplier factor of the log value of distance between base station and the mobile radio station, the K2 in the standard macrocell model of supporting as the Enterprise planning software introduced above (Standard MacroCell Model).
Prior art is in the correction for propagation model parameter " the logarithm multiplier factor of the distance between base station and the travelling carriage ", directly with continuous wave (Continuous Wave, be called for short " CW; ;) data that obtain of test carry out the least square line match according to the distribution relation of the signal strength signal intensity that distance logarithm value and test obtain; and the slope that obtains straight line is " the logarithm multiplier factor of the distance between base station and the travelling carriage " value after the correction; perhaps elder generation obtains signal strength signal intensity by model prediction; the distribution relation according to the difference of distance logarithm value and test value and predicted value carries out fitting a straight line then, obtain slope and be correction value, proofread and correct original model parameter " the logarithm multiplier factor of the distance between base station and the travelling carriage " value with this correction value.
But because in the practical application, the area of sub-district increases and increases by radius, so for the near zone of distance, because so the little CW test data of area quantity is also few, and for the zone of distance, because signal quality relation, the data bulk that the CW test obtains is also few, therefore be uneven along with the different test datas that obtain of distance distribute, such test data is carried out in the corrected value that fitting a straight line obtains, the distance range influence that the tested person data volume is many is big, for the few zone of amount of test data and accurate inadequately.Fig. 4 has provided the distribution situation of the CW test data of the CW test acquisition of carrying out in a city with distance.As can be seen from the figure, the CW test data is uneven with the distribution of distance, and fewer less than the CW test data in the scope of 1km and big 2km, most of CW test data is to concentrate on 1km between the scope of 2km.
In actual applications, there is following problem in such scheme: the test data processing method precision of propagation model is not high, thereby causes the undercorrection of propagation model accurate.
Cause the main cause of this situation to be, in the existing propagation model revision method, in the correction for propagation model parameter " the logarithm multiplier factor of the distance between base station and the travelling carriage ", do not handle for range distribution is inhomogeneous according to actual test data.
Summary of the invention
In view of this, main purpose of the present invention is to provide a kind of mobile communications network propagation model revision method, makes that the test data processing of propagation model is more reasonable, and the correction of propagation model is more accurate.
For achieving the above object, the invention provides a kind of mobile communications network propagation model revision method, comprise following steps,
A tests practical radio communication environment, obtains test data;
B predicts with propagation model, obtains prediction data;
C, handles described test data and described prediction data with the inhomogeneous situation that " distance between base station and the travelling carriage " distributes according to the quantity of described test data, proofreaies and correct propagation model parameter.
Wherein, described propagation model parameter comprises " the logarithm multiplier factor of the distance between base station and the travelling carriage ";
Relation between the logarithm value of described " the logarithm multiplier factor of the distance between base station and the travelling carriage " reflection wireless signal strength and described " distance between base station and the travelling carriage ";
Described test data comprises the wireless signal strength test value of corresponding described " distance between base station and the travelling carriage ";
Described prediction data comprises the wireless signal strength predicted value of corresponding described " distance between base station and the travelling carriage ".
Described step C comprises following substep,
Be divided into a plurality of with described " distance between base station and the travelling carriage " apart from segment;
Each described apart from segment on, carry out fitting a straight line according to the logarithm value of described " distance between base station and the travelling carriage " and the relation of described wireless signal strength test value, obtain slope value;
Will all described apart from segment on the described slope value that obtains of fitting a straight line average, obtain the corrected value of described " the logarithm multiplier factor of the distance between base station and the travelling carriage ".
Described step C comprises following substep,
Be divided into a plurality of described with described " distance between base station and the travelling carriage " apart from segment;
Each described apart from segment on, calculate the difference of described wireless signal strength test value and described wireless signal strength predicted value, logarithm value according to described " distance between base station and the travelling carriage " is carried out fitting a straight line with the relation of the described difference that calculates, and obtains the slope correction value;
Will all described apart from segment on the described slope correction value that obtains of fitting a straight line average, be used for described " the logarithm multiplier factor of the distance between base station and the travelling carriage " revised, obtain the corrected value of described " the logarithm multiplier factor of the distance between base station and the travelling carriage ".
Described step C comprises following substep,
According to the quantity of described test data the regularity of distribution by described " distance between base station and the travelling carriage ", insert the described test data that simulation produces, make quantity being evenly distributed of described test data by described " distance between base station and the travelling carriage ";
Carry out fitting a straight line according to the logarithm value of described " distance between base station and the travelling carriage " and the relation of described wireless signal strength test value, obtaining slope value is the corrected value of described " the logarithm multiplier factor of the distance between base station and the travelling carriage ".
Described step C comprises following substep,
According to the quantity of described test data the regularity of distribution by described " distance between base station and the travelling carriage ", insert the described test data that simulation produces, make quantity being evenly distributed of described test data by described " distance between base station and the travelling carriage ";
Calculate the described difference of described wireless signal strength test value and described wireless signal strength predicted value, logarithm value according to described " distance between base station and the travelling carriage " is carried out fitting a straight line with the relation of the described difference that calculates, obtain the slope correction value, be used for " the logarithm multiplier factor of the distance between base station and the travelling carriage " revised, obtain the corrected value of described " the logarithm multiplier factor of the distance between base station and the travelling carriage ".
Described number apart from segment is limited, and each described distributed number apart from the described test data in the segment is even.
Carry out curve fitting and interpolation is simulated and produced described test data by relation according to described " distance between base station and the travelling carriage " and described wireless signal strength test value.
Described test to practical radio communication environment is a continuous wave test.
By relatively finding, technical scheme difference with the prior art of the present invention is, adopt earlier and divide the segment match according to the distance difference, ask average method to obtain correction parameter to fitting result again, perhaps increase test data with methods such as interpolation earlier and on distance, be evenly distributed, use the method correction parameter of fitting a straight line then with the quantity that reaches test data.
Difference on this technical scheme, brought comparatively significantly beneficial effect, promptly owing to considered that test data is according to the uneven factor of range distribution, make propagation model revision for different distance scope equivalence, the correction result who obtains is effective to the different distance scope, thereby improved the propagation model revision accuracy, improved the reasonability and the high efficiency of mobile communication network planning.
Description of drawings
Fig. 1 is the general flow figure of propagation model revision;
Fig. 2 is a mobile communications network propagation model revision method flow diagram according to an embodiment of the invention;
Fig. 3 is a replacement scheme flow chart according to an embodiment of the invention;
Fig. 4 is the distribution situation figure of the CW test data in a city with distance.
Embodiment
For making the purpose, technical solutions and advantages of the present invention clearer, the present invention is described in further detail below in conjunction with accompanying drawing.
Consider in the actual conditions, the quantity of CW test data is inhomogeneous according to the distance different distributions, in order to make correction all of equal value for each distance range, the present invention adopts the match of branch segment to ask average method to come the calibration model parameter again, the quantity that perhaps adopts method such as interpolation to increase test data makes that the quantity of final test data is even according to range distribution, and then carry out match, so just guaranteed that any distance range is identical to the contribution of propagation model revision, the parameter that feasible correction obtains all is suitable for for each distance range, thereby improves the precision of propagation model revision.
Below with reference to Fig. 2, describe mobile communications network propagation model revision method flow according to an embodiment of the invention in detail.
At first, in step 110, the continuous wave test data are pressed apart from segmentation.Suppose branch for the N section, require the data volume of CW in each segment evenly approximate here.
Then enter step 120, the CW data of each segment are carried out fitting a straight line according to distance logarithm value and the distribution relation of testing the signal strength signal intensity that obtains, obtain the slope of each section respectively, be assumed to be K2
1, K2
2..., K2
NThe method of carrying out fitting a straight line here is a lot, for example arithmetic mean method, graphing method, by difference method, least square method, near optimal linear equation, best straight line equation or the like.Be that example is carried out fitting a straight line below with the least square method.Be implemented as follows: make that arbitrary mobile radio station is x to the logarithm value of the distance of base station, on this distance, survey the CW value be y, y and x are linear.Two variablees in research reality (x, during correlation between y), can obtain a series of paired data (x1, y1, x2, y2...xm, ym); Y becomes following relation with x, as follows:
Y=a0+a1 X (formula 2-1)
Wherein: a0, a1 are any real numbers.For setting up curvilinear equation, will determine a0, a1 value, with the quadratic sum (∑ (Yi-Y meter) of the deviation (Yi-Y meter) of measured value Yi and calculated value Y meter (Y meter=a0+a1 Xi)
2) be foundation:
Order: φ=∑ (Yi-Y meter)
2(formula 2-2)
Getting in (formula 2-1) substitution (formula 2-2):
φ=∑ (Yi-a0-a1 Xi)
2(formula 2-3)
Respectively a0, a1 are asked partial derivative with function phi, make these three partial derivatives equal zero promptly:
φ asks local derviation=-2 ∑s (Yi-a0-a1 Xi)=0 (formula 2-4) to a0
φ asks local derviation=-2 ∑ Xik (Yi-a0-a1 Xi)=0 (formula 2-5) to a1
Obtain two about a0, a1, be the equation with two unknowns group of unknown number, the group of solving an equation can obtain Mathematical Modeling.
Repeat the a1 that above method obtains and be corresponding K2
i
Then enter step 130, the slope of each section gained averaged,
The K2 of gained is travelling carriage after the correction of being asked to the increment factor values of base station distance logarithm and corresponding signal strength signal intensity.
An alternative method of above method is to utilize interpolation method to try to achieve rational correction parameter.Come this alternative method is elaborated below with reference to Fig. 3.
At first, in step 210, the continuous wave test data by apart from segmentation, are added up existing continuous wave test data of distribution law.Count counting of the interior existing CW data of every segment distance.CW number of data points in for example every 100m measuring distance.
Then enter step 220, draw counting that each segment need insert, and utilize correlation method to insert data.
In the number of data points that will insert in certain segment=all segments the CW test data count CW test data in maximum segments count-CW test data in this segment counts.
Can insert equably in this segment by the method for curve fit and interpolation needs the number of data points inserted in this segment, when wherein carrying out curve fitting, abscissa is the distance of CW test data from transmitter base station, and ordinate is a test signal intensity.
Arrive step 230 then, utilize the data of original data and insertion that " the logarithm multiplier factor of the distance between base station and the travelling carriage " proofreaied and correct.At this moment can adopt the whole bag of tricks of prior art to proofread and correct.
Though by reference some preferred embodiment of the present invention, the present invention is illustrated and describes, but those of ordinary skill in the art should be understood that, can do various changes to it in the form and details, and the spirit and scope of the present invention that do not depart from appended claims and limited.
Claims (9)
1. a mobile communications network propagation model revision method is characterized in that, comprises following steps,
A tests practical radio communication environment, obtains test data;
B predicts with propagation model, obtains prediction data, and wherein this propagation model comprises the propagation model parameter of the relation between the logarithm value that reflects wireless signal strength and " distance between base station and the travelling carriage ";
C, handles described test data and described prediction data with the inhomogeneous situation that " distance between base station and the travelling carriage " distributes according to the quantity of described test data, proofreaies and correct described propagation model parameter.
2. mobile communications network propagation model revision method according to claim 1, it is characterized in that the propagation model parameter of the relation between the logarithm value of described reflection wireless signal strength and " distance between base station and the travelling carriage " is " the logarithm multiplier factor of the distance between base station and the travelling carriage ";
Described test data comprises the wireless signal strength test value of corresponding described " distance between base station and the travelling carriage ";
Described prediction data comprises the wireless signal strength predicted value of corresponding described " distance between base station and the travelling carriage ".
3. mobile communications network propagation model revision method according to claim 2 is characterized in that described step C comprises following substep,
Described " distance between base station and the travelling carriage " is divided into a plurality of apart from segment, each apart from amount of test data in the segment and each apart from the difference of the mean value of amount of test data in the segment all less than predetermined threshold;
Each described apart from segment on, carry out fitting a straight line according to the logarithm value of described " distance between base station and the travelling carriage " and the relation of described wireless signal strength test value, obtain slope value;
Will all described apart from segment on the described slope value that obtains of fitting a straight line average, obtain the corrected value of described " the logarithm multiplier factor of the distance between base station and the travelling carriage ".
4. mobile communications network propagation model revision method according to claim 2 is characterized in that described step C comprises following substep,
With described " distance between base station and the travelling carriage " be divided into a plurality of equal lengths apart from segment;
Each described apart from segment on, calculate the difference of described wireless signal strength test value and described wireless signal strength predicted value, logarithm value according to described " distance between base station and the travelling carriage " is carried out fitting a straight line with the relation of the described difference that calculates, and obtains the slope correction value;
Will all described apart from segment on the described slope correction value that obtains of fitting a straight line average, be used for described " the logarithm multiplier factor of the distance between base station and the travelling carriage " revised, obtain the corrected value of described " the logarithm multiplier factor of the distance between base station and the travelling carriage ".
5. mobile communications network propagation model revision method according to claim 2 is characterized in that described step C comprises following substep,
According to the quantity of described test data the regularity of distribution by described " distance between base station and the travelling carriage ", insert the described test data that simulation produces, make quantity being evenly distributed of described test data by described " distance between base station and the travelling carriage ";
Carry out fitting a straight line according to the logarithm value of described " distance between base station and the travelling carriage " and the relation of described wireless signal strength test value, obtaining slope value is the corrected value of described " the logarithm multiplier factor of the distance between base station and the travelling carriage ".
6. mobile communications network propagation model revision method according to claim 2 is characterized in that described step C comprises following substep,
According to the quantity of described test data the regularity of distribution by described " distance between base station and the travelling carriage ", insert the described test data that simulation produces, make quantity being evenly distributed of described test data by described " distance between base station and the travelling carriage ";
Calculate the described difference of described wireless signal strength test value and described wireless signal strength predicted value, logarithm value according to described " distance between base station and the travelling carriage " is carried out fitting a straight line with the relation of the described difference that calculates, obtain the slope correction value, be used for " the logarithm multiplier factor of the distance between base station and the travelling carriage " revised, obtain the corrected value of described " the logarithm multiplier factor of the distance between base station and the travelling carriage ".
7. according to claim 3 or 4 described mobile communications network propagation model revision methods, it is characterized in that described number apart from segment is limited, each described distributed number apart from the described test data in the segment is even.
8. according to claim 5 or 6 described mobile communications network propagation model revision methods, it is characterized in that, carry out curve fitting and interpolation is simulated and produced described test data by relation according to described " distance between base station and the travelling carriage " and described wireless signal strength test value.
9. according to each described mobile communications network propagation model revision method in the claim 1 to 6, it is characterized in that described test to practical radio communication environment is a continuous wave test.
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| CN101159962B (en) * | 2007-10-29 | 2010-04-21 | 中国移动通信集团设计院有限公司 | Data processing method and device for continuous wave test of propagation model revision |
| CN101959199A (en) * | 2009-07-17 | 2011-01-26 | 中兴通讯股份有限公司 | Transmission environment dividing method and device based on existing network test data |
| CN102595435B (en) * | 2011-01-04 | 2015-08-19 | 中国移动通信集团公司 | A kind of construction method of test environment of peer-to-peer external field and device |
| CN106162862A (en) * | 2015-03-24 | 2016-11-23 | 中兴通讯股份有限公司 | Calculate the mobile station method and device to community distance |
| US10415528B2 (en) * | 2017-06-30 | 2019-09-17 | GM Global Technology Operations LLC | Vehicle PEPS system calibration using a mobile device |
| CN113099464B (en) * | 2021-05-12 | 2022-11-08 | 国网河南省电力公司经济技术研究院 | Wireless sensor network deployment method and computer readable medium for power distribution network |
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| WO1998038756A1 (en) * | 1997-02-26 | 1998-09-03 | Nokia Telecommunications Oy | Method and apparatus for determining radio wave propagation attenuation |
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| WO1998038756A1 (en) * | 1997-02-26 | 1998-09-03 | Nokia Telecommunications Oy | Method and apparatus for determining radio wave propagation attenuation |
| CN1478361A (en) * | 2000-10-27 | 2004-02-25 | ���ȿ���ͨ�Źɷ�����˾ | System and method for planning telecommunications network for mobile terminals |
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