CN104410978A - Method and device of evaluating site planning - Google Patents
Method and device of evaluating site planning Download PDFInfo
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- CN104410978A CN104410978A CN201410647454.8A CN201410647454A CN104410978A CN 104410978 A CN104410978 A CN 104410978A CN 201410647454 A CN201410647454 A CN 201410647454A CN 104410978 A CN104410978 A CN 104410978A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/08—Testing, supervising or monitoring using real traffic
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
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- H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
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Abstract
The invention provides a method and a device of evaluating site planning. The method comprises the following steps of computing a weak coverage rate of a planning site; determining coverage level grading parameters of the planning site according to the weak coverage rate of the planning site; determining at least one first grading parameter according to platform foundation information; computing a weighted average value of the coverage level grading parameters and all first grading parameters as a comprehensive grading parameter of the planning site; and evaluating the planning site according to the comprehensive grading parameter, wherein the first grading parameters consist of a portfolio grading parameter, an investment payoff period grading parameter, a market requirement grading parameter, a coverage scene grading parameter, a complaint grading parameter and a site distance grading parameter. An intelligent terminal both obtains the coverage level grading parameters and the first grading parameters, that is to say, the intelligent terminal carries out corresponding evaluation on site planning from multi-dimensional perspective, so that accuracy for planning is improved.
Description
Technical field
The present invention relates to network communication field, particularly relate to a kind of Bus stop planning appraisal procedure and device.
Background technology
Usually no matter be that macro station planing method or room point planing method mainly carry out Bus stop planning from angle of coverage in the field of communications, namely Consideration is comparatively single, and usually in planning process, bias toward single station analysis, artificial needs process collection and the analysis of a large amount of system data and measurement data, this method of carrying out Bus stop planning from angle of coverage is applied more at the networking initial stage, cover bad place just preferentially to build a station, belong to more extensive planning appraisal method.
Because Bus stop planning method mainly realizes planning from single angle of coverage in prior art, and be all collection and the analysis of manually carrying out mass data and measurement data by experience at present, manual operations workload is large, and human error is inevitable, simultaneously, after network planning construction completes, find that planning does not reach re-set target, occur the problem that network quality lifting, market development and the network planning disconnect.
Summary of the invention
The invention provides a kind of Bus stop planning appraisal procedure and device, thus avoid human error, improve planning precision.
First aspect, the embodiment of the present invention provides a kind of Bus stop planning appraisal procedure, comprising: the weak coverage rate calculating planning website; The horizontal grading parameters of covering of described planning website is determined according to the weak coverage rate of described planning website; At least one first grading parameters is determined according to platform base information; The weighted average calculating the horizontal grading parameters of described covering and all described first grading parameters is the comprehensive grading parameters of described planning website; According to described comprehensive grading parameters, described planning website is assessed; Wherein, described first grading parameters comprises: traffic carrying capacity grading parameters, investment payback time grading parameters, market demand grading parameters, covering scene grading parameters, complaint grading parameters, stop spacing grading parameters.
In conjunction with first aspect, in the first possible implementation of first aspect, before the weak coverage rate of described calculating planning website, also comprise: read measurement report (Measurement Report, MR) data and the first measurement data; Then the described weak coverage rate calculating planning website comprises: the weak coverage rate calculating described planning website according to described measurement report MR data and the first measurement data; Wherein, described first measurement data is drive test (Drive Test, DT) data or call quality test (Call Quality Test, CQT) data.
In conjunction with the first possible implementation of first aspect, in the implementation that the second of first aspect is possible, the described weak coverage rate calculating described planning website according to measurement report MR data and the first measurement data, specifically comprises: the first weak coverage rate calculating described planning website according to described measurement report MR data; The second weak coverage rate of described planning website is calculated according to described first measurement data; Determine that the maximum of described first weak coverage rate and described second weak coverage rate is the weak coverage rate of planning website.
In conjunction with the implementation that the second of first aspect is possible, in the third possible implementation of first aspect, if described first measurement data is drive test DT data, then described the second weak coverage rate calculating described planning website according to described first measurement data, specifically comprises: the covering radius calculating described planning website according to the link budget model corrected; Calculate described covering radius and drive test sampled point distance { d
1, d
2... d
ndifference, obtain difference set { D
1, D
2... D
n; Each element D in more described difference set
i(i=1,2 ... n) with zero relation, if D
ibe greater than zero, then i-th drive test sampled point is within the coverage of described planning website, otherwise, outside the coverage of described planning website; Determine the number m of the described drive test sampled point within the coverage of described planning website; The level value of described drive test sampled point within the coverage of more described planning website and the size of the second threshold value, determine that level value is less than the number p of the drive test sampled point of described second threshold value; Described second weak coverage rate equals the ratio of p and m.
In conjunction with the third possible implementation of first aspect, in the 4th kind of possible implementation of first aspect, correct according to the link budget model of different scene to described correction, the link budget model of described correction, for calculating the theoretical coverage radius d of the planning website of residing different scene, the link budget model of described correction is:
TX+G-L
path-L＝RX
min
a(h
r)＝(1.111lgf-0.7)h
r-1.56lgf+0.8
L
path＝46.3+33.9lgf-13.82lgh
t-a(h
r)+(44.9-6.55gh
t)lgd+C
x
Wherein L
pathfor path loss, f is system centre frequency, and usual Wideband Code Division Multiple Access (WCDMA) WCDMA network value is 2000, h
tfor antenna height and height above sea level drop sum, need determine according to actual antennas geographical position; h
rfor user terminal height above sea level, usual value 1.65 meters, a (h
r) be the correction factor of user terminal, d is covering radius, C
xfor the correction factor of different scene, RX
minfor receiving sensitivity and the cell-edge of user terminal, now be corrected to the difference of scene residing for planning website, choose different cell-edges, L is the loss of user terminal, now be corrected to the difference of scene residing for planning website, select different losses, the transmitting power of TX representative planning website side antenna, the gain of G representative antennas.
In conjunction with the 4th kind of possible implementation of first aspect, in the 5th kind of possible implementation of first aspect, described RX
minfor receiving sensitivity and the cell-edge of user terminal, be now corrected to the difference of scene residing for planning website, choose different cell-edges, comprising: in Computer Database, associate out scene residing for described planning website; Residing for described planning website, scene corrects described RX
min.
In conjunction with the 4th kind of possible implementation of first aspect, in the 6th kind of possible implementation of first aspect, described L is the loss of user terminal, now be corrected to the difference of scene residing for planning website, select different losses, comprising: in Computer Database, associate out scene residing for described planning website; Residing for described planning website, scene corrects described L.
In conjunction with first aspect or the first possible implementation of first aspect or the possible implementation of the second of first aspect or the third possible implementation of first aspect or the 4th kind of possible implementation of first aspect or the 5th kind of possible implementation or the 6th kind of possible implementation, in the 7th kind of possible implementation of first aspect, if described first grading parameters is traffic carrying capacity grading parameters, and described platform base information is speech business and the data service of N number of (N >=2) macro station nearest apart from planning website, then describedly determine at least one first grading parameters according to platform base information, specifically comprise:
The 2G speech business of described N number of macro station is averaged
The 2G data service of described N number of macro station is averaged
The 3G speech business of described N number of macro station is averaged
The 3G data service of described N number of macro station is averaged
To described T
2g_voice, T
2g_data, T
3g_voice, T
3g_dataask weighted average;
Relation according to described weighted average and the 3rd threshold value determines described traffic carrying capacity grading parameters;
Wherein, T
2g_voice_irepresent the 2G speech business of i-th macro station, T
2g_data_irepresent the 2G data service of i-th macro station, T
3g_voice_irepresent the 3G speech business of i-th macro station, T
3g_data_irepresent the 3G data service of i-th macro station, 1≤i≤N.
In conjunction with the 7th kind of possible implementation of first aspect, in the 8th kind of possible implementation of first aspect, if described first grading parameters is investment payback time grading parameters, and described platform base information is the unit price of 2G, 3G speech business every day and data service, then describedly determine at least one first grading parameters according to platform base information, specifically comprise: determine investment payback time IRC by following two formula;
P
average＝(T
2g_voice×P
2g_voice+T
2g_data×P
2g_data+T
3g_voice×P
3g_voice+T
3g_data×P
3g_data)；
IRC＝P
Base-station/(P
average×365)；
Wherein P
2g_voicerepresent 2G speech business unit price, P
2g_datarepresent 2G data service unit price, P
3g_voicerepresent 3G speech business unit price, P
3g_datarepresent 3G data service unit price, P
base-stationrepresent planning website unit price; Described investment payback time grading parameters is determined according to the relation of IRC and the 4th threshold value.
Second aspect, the embodiment of the present invention provides a kind of Bus stop planning apparatus for evaluating, comprising: computing module, for calculating the weak coverage rate of planning website; Determination module, for determining the horizontal grading parameters of covering of described planning website according to the weak coverage rate of described planning website; Described determination module, also for determining at least one first grading parameters according to platform base information; Described computing module, also for calculating the comprehensive grading parameters that the weighted average of the horizontal grading parameters of described covering and all described first grading parameters is described planning website; Evaluation module, for assessing described planning website according to described comprehensive grading parameters; Wherein, described first grading parameters comprises: traffic carrying capacity grading parameters, investment payback time grading parameters, market demand grading parameters, covering scene grading parameters, complaint grading parameters, stop spacing grading parameters.
In conjunction with second aspect, in the first possible implementation of second aspect, also comprise: read module, for reading measurement report MR data and the first measurement data; Then described computing module is specifically for the weak coverage rate that calculates described planning website according to described measurement report MR data and the first measurement data; Wherein, described first measurement data is drive test DT data or call quality test CQT data.
In conjunction with the first possible implementation of second aspect, in the implementation that the second of second aspect is possible, described computing module is specifically for the first weak coverage rate of calculating described planning website according to described measurement report MR data; The second weak coverage rate of described planning website is calculated according to described first measurement data; Determine that the maximum of described first weak coverage rate and described second weak coverage rate is the weak coverage rate of planning website.
In conjunction with the implementation that the second of second aspect is possible, in the third possible implementation of second aspect, if described first measurement data is drive test DT data, then described computing module specifically for: calculate the covering radius of described planning website according to the link budget model corrected; Calculate described covering radius and drive test sampled point distance { d
1, d
2... d
ndifference, obtain difference set { D
1, D
2... D
n; Each element D in more described difference set
i(i=1,2 ... n) with zero relation, if Di is greater than zero, then i-th drive test sampled point is within the coverage of described planning website, otherwise, outside the coverage of described planning website; Determine the number m of the described drive test sampled point within the coverage of described planning website; The level value of described drive test sampled point within the coverage of more described planning website and the size of the second threshold value, determine that level value is less than the number p of the drive test sampled point of described second threshold value; Described second weak coverage rate equals the ratio of p and m.
In conjunction with the third possible implementation of second aspect, in the 4th kind of possible implementation of second aspect, correct according to the link budget model of different scene to described correction, the link budget model of described correction, for calculating the theoretical coverage radius d of the planning website of residing different scene, the link budget model of described correction is:
TX+G-L
path-L＝RX
min
a(h
r)＝(1.111lgf-0.7)h
r-1.56lgf+0.8
L
path＝46.3+33.9lgf-13.82lgh
t-a(h
r)+(44.9-6.55gh
t)lgd+C
x
Wherein L
pathfor path loss, f is system centre frequency, and usual Wideband Code Division Multiple Access (WCDMA) WCDMA network value is 2000, h
tfor antenna height and height above sea level drop sum, need determine according to actual antennas geographical position; h
rfor user terminal height above sea level, usual value 1.65 meters, a (h
r) be the correction factor of user terminal, d is covering radius, C
xfor the correction factor of different scene, RX
minfor receiving sensitivity and the cell-edge of user terminal, now be corrected to the difference of scene residing for planning website, choose different cell-edges, L is the loss of user terminal, now be corrected to the difference of scene residing for planning website, select different losses, the transmitting power of TX representative planning website side antenna, the gain of G representative antennas.
In conjunction with the 4th kind of possible implementation of second aspect, in the 5th kind of possible implementation of second aspect, also comprise: relating module and correction module; Described relating module, for associating out scene residing for described planning website in Computer Database; Described correction module, corrects described RX for scene residing for described planning website
min.
In conjunction with the 5th kind of possible implementation of second aspect, in the 6th kind of possible implementation of second aspect, described correction module also corrects described L for scene residing for described planning website.
In conjunction with second aspect or second aspect the first may execution mode or the second may execution mode or the third may execution mode or the 4th kind may execution mode or the 5th kind of possible implementation or the 6th kind may execution mode, in the 7th kind of possible implementation of second aspect, if described first grading parameters is traffic carrying capacity grading parameters, and described platform base information is speech business and the data service of N number of (N >=2) macro station nearest apart from planning website, then described determination module, specifically for:
The 2G speech business of described N number of macro station is averaged
The 2G data service of described N number of macro station is averaged
The 3G speech business of described N number of macro station is averaged
The 3G data service of described N number of macro station is averaged
To described T
2g_voice, T
2g_data, T
3g_voice, T
3g_dataask weighted average;
Relation according to described weighted average and the 3rd threshold value determines described traffic carrying capacity grading parameters;
Wherein, T
2g_voice_irepresent the 2G speech business of i-th macro station, T
2g_data_irepresent the 2G data service of i-th macro station, T
3g_voice_irepresent the 3G speech business of i-th macro station, T
3g_data_irepresent the 3G data service of i-th macro station, 1≤i≤N.
In conjunction with the 7th kind of possibility execution mode of second aspect, in the 8th kind of possibility execution mode, if described first grading parameters is investment payback time grading parameters, and described platform base information is the unit price of 2G, 3G speech business every day and data service, then described determination module, specifically for:
Investment payback time IRC is determined by following two formula;
P
average＝(T
2g_voice×P
2g_voice+T
2g_data×P
2g_data+T
3g_voice×P
3g_voice+T
3g_data×P
3g_data)；
IRC＝P
Base-station/(P
average×365)；
Wherein P
2g_voicerepresent 2G speech business unit price, P
2g_datarepresent 2G data service unit price, P
3g_voicerepresent 3G speech business unit price, P
3g_datarepresent 3G data service unit price, P
base-stationrepresent planning website unit price;
Described investment payback time grading parameters is determined according to the relation of IRC and the 4th threshold value.
Embodiments provide a kind of Bus stop planning appraisal procedure, comprising: the weak coverage rate calculating planning website; The horizontal grading parameters of covering of described planning website is determined according to the weak coverage rate of described planning website; At least one first grading parameters is determined according to platform base information; The weighted average calculating the horizontal grading parameters of described covering and all described first grading parameters is the comprehensive grading parameters of described planning website; According to described comprehensive grading parameters, described planning website is assessed; Wherein, described first grading parameters comprises: traffic carrying capacity grading parameters, investment payback time grading parameters, market demand grading parameters, covering scene grading parameters, complaint grading parameters, stop spacing grading parameters.Also obtained the first grading parameters because intelligent terminal has both obtained the horizontal grading parameters of covering, that is, intelligent terminal has carried out corresponding assessment from multidimensional angle to Bus stop planning, thus improve the precision of planning.
Accompanying drawing explanation
The flow chart of a kind of Bus stop planning appraisal procedure that Fig. 1 provides for the embodiment of the present invention one;
The flow chart of a kind of Bus stop planning appraisal procedure that Fig. 2 provides for the embodiment of the present invention two;
The flow chart of a kind of Bus stop planning appraisal procedure that Fig. 3 provides for the embodiment of the present invention three;
The structural representation of a kind of Bus stop planning apparatus for evaluating that Fig. 4 provides for the embodiment of the present invention four;
The structural representation of a kind of Bus stop planning apparatus for evaluating that Fig. 5 provides for the embodiment of the present invention five;
The schematic diagram of a kind of Bus stop planning evaluating system that Fig. 6 provides for the embodiment of the present invention six.
Embodiment
For making the object of the embodiment of the present invention, technical scheme and advantage clearly, below in conjunction with the accompanying drawing in the embodiment of the present invention, technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
The flow chart of a kind of Bus stop planning appraisal procedure that Fig. 1 provides for the embodiment of the present invention one, wherein the method can be applicable to the scene determining site location in network communication field, the executive agent of the method is computer, and Bus stop planning appraisal procedure specifically comprises following flow process:
S101: the weak coverage rate calculating planning website.
Alternatively, before the weak coverage rate of described calculating planning website, also comprise: read measurement report MR data and the first measurement data; Then the described weak coverage rate calculating planning website comprises: the weak coverage rate calculating described planning website according to described measurement report MR data and the first measurement data; Wherein, described first measurement data is drive test DT data or call quality test CQT data.
S102: the horizontal grading parameters of covering determining described planning website according to the weak coverage rate of described planning website.
Particularly, usual weak coverage rate and the horizontal grading parameters of covering have certain corresponding relation, such as: weak coverage rate is at 0-10%, then the corresponding horizontal grading parameters of covering is 10, and weak coverage rate is at 10%-20%, the then corresponding horizontal grading parameters of covering is 20, the like, when weak coverage rate is at 90%-100%, then the corresponding horizontal grading parameters of covering is 100, certainly, corresponded manner is here not limited to time.
S103: determine at least one first grading parameters according to platform base information.
Wherein, platform base information can comprise: the speech business of N number of (N >=2) macro station that distance planning website is nearest and data service, the unit price of 2G, 3G speech business every day and data, market demand type, covering scene or brand importance, complain the distance of the complaint number of times that gets of platform and work order number and the nearest unobstructed website of distance planning website affiliated area.Described first grading parameters comprises: traffic carrying capacity grading parameters, investment payback time grading parameters, market demand grading parameters, covering scene grading parameters, complaint grading parameters, stop spacing grading parameters.Give an example, at least one first grading parameters is determined according to platform base information, specific as follows: if platform base information is speech business and the data service of N number of (N>=2) macro station nearest apart from planning website, first respectively the 2G speech business of N number of macro station to be averaged
in like manner the 2G data service of N number of macro station is averaged,
in like manner to the 3G speech business of N number of macro station
average, in like manner the 3G data service of N number of macro station averaged,
to described T
2g_voice, T
2g_data, T
3g_voice, T
3g_dataask weighted average; Relation according to described weighted average and the 3rd threshold value determines described traffic carrying capacity grading parameters; Wherein, T
2g_voice_irepresent the 2G speech business of i-th macro station, T
2g_data_irepresent the 2G data service of i-th macro station, T
3g_voice_irepresent the 3G speech business of i-th macro station, T
3g_data_irepresent the 3G data service of i-th macro station, 1≤i≤N.If platform base information is the unit price of 2G, 3G speech business every day and data service, then can determine investment payback time IRC by following two formula:
P
average＝(T
2g_voice×P
2g_voice+T
2g_data×P
2g_data+T
3g_voice×P
3g_voice+T
3g_data×P
3g_data)
IRC＝P
Base-station/(P
average×365)
Wherein P
2g_voicerepresent 2G speech business unit price, P
2g_datarepresent 2G data service unit price, P
3g_voicerepresent 3G speech business unit price, P
3g_datarepresent 3G data service unit price, P
base-stationrepresent website unit price.The business average gone out according to service computation is needed to carry out site type coupling when making an investment in macro station Investment calculation about website, according to antenna installation mode, main equipment investment, website attributed region determination website unit price P
base-station; When dividing Investment calculation in room, needs adopt the every square meter cost of point scene and building expection area coverage to calculate to invest.Then investment payback time grading parameters is determined according to the relation of IRC and the 4th threshold value.
Further, can also computing market demand grading parameters according to market demand grade, market demand grade can be: city-level key project, clear and definite group customer demand, group customer demand, district level key project, the district level market demand, 2G/3G coordinating analysis, open market demand, without the market demand etc.Covering scene grading parameters can also be calculated according to covering scene or brand importance.Scene partitioning can be carried out according to website overlay area and brand importance, as urban district, county town, high ferro, motor-car, 3A/4A/5A level scenic spot, one/bis-/tri-class campuses, A/B/C class small towns during macro station planning; Room is divided during planning and is carried out scene partitioning according to building purposes, as residential quarters, independent Condom, transport hub, shopping mall and cluster market, office building, public place, school, Recreational places, hotel, shopping plaza, government, office, hospital etc.This region can also be obtained from customer complaint platform and complain number of times and work order number, calculate by complaining thresholding and complain grading parameters.Also all right, obtain the unscreened website distance that this region is nearest, by area type (comprising dense city, urban district, category-A small towns etc.) computer installation apart from grading parameters.Room is divided during planning and can not consider this dimension.
S104: the weighted average calculating the horizontal grading parameters of described covering and all described first grading parameters is the comprehensive grading parameters of described planning website.
S105: described planning website is assessed according to described comprehensive grading parameters.
Particularly, when comprehensive grading parameters is more than or equal to a certain preset value, then determine that assessment is passed through, otherwise the assessment of above-mentioned Bus stop planning is not passed through.Cite an actual example, when weak coverage rate is 45%, traffic carrying capacity is 4000, investment payback time is 1, market importance is open market demand, and brand is that complaint amount is 20 at a high speed, stop spacing is 456, scene be transprovincially high speed time, final grading parameters is respectively: the horizontal grading parameters of covering is 50, and traffic carrying capacity grading parameters is 60, investment payback time grading parameters is 100, market demand grading parameters is 20, and covering scene grading parameters is 80, complains grading parameters 80, stop spacing scoring ginseng 0, last comprehensive grading parameters is 63.
Present embodiments provide a kind of Bus stop planning appraisal procedure, comprising: the weak coverage rate calculating planning website; The horizontal grading parameters of covering of described planning website is determined according to the weak coverage rate of described planning website; At least one first grading parameters is determined according to platform base information; The weighted average calculating the horizontal grading parameters of described covering and all described first grading parameters is the comprehensive grading parameters of described planning website; According to described comprehensive grading parameters, described planning website is assessed; Also obtained the first grading parameters because computer has both obtained the horizontal grading parameters of covering, that is, computer has carried out corresponding assessment from multidimensional angle to Bus stop planning, thus improve the precision of planning.
The flow chart of a kind of Bus stop planning appraisal procedure that Fig. 2 provides for the embodiment of the present invention two, the further refinement of step S101 shown in Fig. 1.The weak coverage rate of described planning website comprises the weak coverage rate calculating described planning website according to measurement report MR data and the first measurement data, particularly, described first measurement data is drive test DT data or call quality test CQT data, when computer carry out be macro station planning time, then this first measurement data is DT data, when computer carry out be room divide planning time, then this first measurement data is CQT data, and MR data are derived by network optimization platform.
As shown in Figure 2, the described weak coverage rate calculating described planning website according to measurement report MR data and the first measurement data, specifically comprises:
S201: the first weak coverage rate calculating described planning website according to described measurement report MR data.
S202: the second weak coverage rate calculating described planning website according to described first measurement data.
S203: determine that the maximum of described first weak coverage rate and described second weak coverage rate is the weak coverage rate of planning website.
Such as: the MR data reading planning website region during macro station planning, calculate the first weak coverage rate RSCP according to MR data
mR, then obtain the DT data in this region, calculate the second weak coverage rate RSCP by the link budget model corrected
dT; The link budget model wherein corrected is generally the calibration model of COST231.Hata, then determines the weak coverage rate RSCP planning website
fINALfor RSCP
fINAL=min (RSCP
mR, RSCP
dT); When be room divide planning time, get this region MR data RSCP
mR, obtain the CQT data of these building, calculated the RSCP of planning website by the P.1238 model corrected
cQT; RSCP
fINAL=min (RSCP
mR, RSCP
cQT).
The flow chart of a kind of Bus stop planning appraisal procedure that Fig. 3 provides for the embodiment of the present invention three, as shown in Figure 3, it is the further refinement to S202 in Fig. 2, if described first measurement data is drive test DT data, then the described idiographic flow calculating the second weak coverage rate of described planning website according to described first measurement data is as follows:
S301: the covering radius calculating described planning website according to the link budget model corrected.
Particularly, correct, the link budget model of described correction according to the link budget model of different scene to described correction, for calculating the theoretical coverage radius d of the planning website of residing different scene, the link budget model of described correction is:
TX+G-L
path-L＝RX
min
a(h
r)＝(1.111lgf-0.7)h
r-1.56lgf+0.8
L
path＝46.3+33.9lgf-13.82lgh
t-a(h
r)+(44.9-6.55gh
t)lgd+C
x
Wherein L
pathfor path loss, f is system centre frequency, and usual Wideband Code Division Multiple Access (WCDMA) WCDMA network value is 2000, h
tfor antenna height and height above sea level drop sum, need determine according to actual antennas geographical position; h
rfor user terminal height above sea level, usual value 1.65 meters, a (h
r) be the correction factor of user terminal, d is covering radius, C
xfor the correction factor of different scene, RX
minfor receiving sensitivity and the cell-edge of user terminal, now be corrected to the difference of scene residing for planning website, choose different cell-edges, L is the loss of user terminal, now be corrected to the difference of scene residing for planning website, select different losses, the transmitting power of TX representative planning website side antenna, the gain of G representative antennas;
By the link budget model of described correction, the theoretical coverage radius d of the planning website of residing different scene just can be calculated.
Bearing calibration: (a) difference of scene residing for Target cell, chooses different cell-edges.The first step: associate out scene residing for described planning website in Computer Database; Second step: scene corrects described RX residing for described planning website
min.B () difference of scene residing for Target cell, selects different losses.The first step: associate out scene residing for described planning website in Computer Database; Second step: scene corrects described L residing for described planning website.Determine TX, G, L and RX
minthe theoretical coverage radius d of the planning website of residing different scene can be calculated after value.
S302: calculate covering radius and drive test sampled point distance { d
1, d
2d
ndifference, obtain difference set { D
1, D
2d
n.
S303: each element D in poor value set
i(i=1,2 ... n) with zero relation, if D
ibe greater than zero, then i-th drive test sampled point is within the coverage of planning website, otherwise, outside the coverage of planning website.
S304: the number m determining the described drive test sampled point within the coverage of described planning website.
S305: the level value of described drive test sampled point within the coverage of more described planning website and the size of the second threshold value, determines that level value is less than the number p of the drive test sampled point of described second threshold value.
S306: described second weak coverage rate equals the ratio of p and m.
In addition, when described first measurement data is call quality test CQT data, when namely planning is divided in room at that time, then the second weak coverage rate can be calculated by P.1238 model.
First the present embodiment calculates the covering radius of planning website according to the link budget model corrected, then determine path sampled point whether in the coverage of planning website, and the second weak coverage rate is calculated to the level value of all drive test sampled points belonged in planning site-bound and the relation of the second threshold value.Owing to adopting the link budget model corrected to obtain covering radius and the final second weak coverage rate, therefore make planning more accurate, meanwhile, the proposition of this algorithm avoids human error.
The structural representation of a kind of Bus stop planning apparatus for evaluating that Fig. 4 provides for the embodiment of the present invention four, this device can be applicable to the scene determining site location in network communication field, wherein Bus stop planning apparatus for evaluating can be computer, this device specifically comprises: computing module 401, for calculating the weak coverage rate of planning website; Determination module 402, for determining the horizontal grading parameters of covering of described planning website according to the weak coverage rate of described planning website; Described determination module 402, also for determining at least one first grading parameters according to platform base information; Described computing module 401, also for calculating the comprehensive grading parameters that the weighted average of the horizontal grading parameters of described covering and all described first grading parameters is described planning website; Evaluation module 403, for assessing described planning website according to described comprehensive grading parameters; Wherein, described first grading parameters comprises: traffic carrying capacity grading parameters, investment payback time grading parameters, market demand grading parameters, covering scene grading parameters, complaint grading parameters, stop spacing grading parameters.
The present embodiment Bus stop planning apparatus for evaluating, may be used for the enforcement technical scheme of the Bus stop planning appraisal procedure performed corresponding to Fig. 1, it realizes principle and technique effect is similar, repeats no more herein.
The structural representation of a kind of Bus stop planning apparatus for evaluating that Fig. 5 provides for the embodiment of the present invention five, composition graphs 4, on a upper embodiment basis, described device also comprises: read module 501, for reading measurement report MR data and the first measurement data; Then described computing module 401 is specifically for the weak coverage rate that calculates described planning website according to described measurement report MR data and the first measurement data; Wherein, described first measurement data is drive test DT data or call quality test CQT data.Further, described computing module 401 is specifically for the first weak coverage rate of calculating described planning website according to described measurement report MR data; The second weak coverage rate of described planning website is calculated according to described first measurement data; Determine that the maximum of described first weak coverage rate and described second weak coverage rate is the weak coverage rate of planning website.
Alternatively, if described first measurement data is drive test DT data, then described computing module 401 specifically for: calculate the covering radius of described planning website according to the link budget model corrected; Calculate described covering radius and drive test sampled point distance { d
1, d
2... d
ndifference, obtain difference set { D
1, D
2... D
n; Each element D in more described difference set
i(i=1,2 ... n) with zero relation, if Di is greater than zero, then i-th drive test sampled point is within the coverage of described planning website, otherwise, outside the coverage of described planning website; Determine the number m of the described drive test sampled point within the coverage of described planning website; The level value of described drive test sampled point within the coverage of more described planning website and the size of the second threshold value, determine that level value is less than the number p of the drive test sampled point of described second threshold value; Described second weak coverage rate equals the ratio of p and m.
Further, correct, the link budget model of described correction according to the link budget model of different scene to described correction, for calculating the theoretical coverage radius d of the planning website of residing different scene, the link budget model of described correction is:
TX+G-L
path-L＝RX
min
a(h
r)＝(1.111lgf-0.7)h
r-1.56lgf+0.8
L
path＝46.3+33.9lgf-13.82lgh
t-a(h
r)+(44.9-6.55gh
t)lgd+C
x
Wherein L
pathfor path loss, f is system centre frequency, and usual Wideband Code Division Multiple Access (WCDMA) WCDMA network value is 2000, h
tfor antenna height and height above sea level drop sum, need determine according to actual antennas geographical position; h
rfor user terminal height above sea level, usual value 1.65 meters, a (h
r) be the correction factor of user terminal, d is covering radius, C
xfor the correction factor of different scene, RX
minfor receiving sensitivity and the cell-edge of user terminal, now be corrected to the difference of scene residing for planning website, choose different cell-edges, L is the loss of user terminal, now be corrected to the difference of scene residing for planning website, select different losses, the transmitting power of TX representative planning website side antenna, the gain of G representative antennas.
Further, relating module 502 and correction module 503; Described relating module 502, for associating out scene residing for described planning website in Computer Database; Described correction module 503, corrects described RX for scene residing for described planning website
min.Described correction module 503 also corrects described L for scene residing for described planning website.
Alternatively, if described first grading parameters is traffic carrying capacity grading parameters, and described platform base information is speech business and the data service of N number of (N>=2) macro station nearest apart from planning website, then described determination module 402, specifically for: the 2G speech business of described N number of macro station is averaged
the 2G data service of described N number of macro station is averaged
the 3G speech business of described N number of macro station is averaged
the 3G data service of described N number of macro station is averaged
to described T
2g_voice, T
2g_data, T
3g_voice, T
3g_dataask weighted average; Relation according to described weighted average and the 3rd threshold value determines described traffic carrying capacity grading parameters.
Wherein, T
2g_voice_irepresent the 2G speech business of i-th macro station, T
2g_data_irepresent the 2G data service of i-th macro station, T
3g_voice_irepresent the 3G speech business of i-th macro station, T
3g_data_irepresent the 3G data service of i-th macro station, 1≤i≤N.
If described first grading parameters is investment payback time grading parameters, and described platform base information is the unit price of 2G, 3G speech business every day and data service, then described determination module 402, specifically for: determine investment payback time IRC by following two formula,
P
average＝(T
2g_voice×P
2g_voice+T
2g_data×P
2g_data+T
3g_voice×P
3g_voice+T
3g_data×P
3g_data)；
IRC＝P
Base-station/(P
average×365)；
Wherein P
2g_voicerepresent 2G speech business unit price, P
2g_datarepresent 2G data service unit price, P
3g_voicerepresent 3G speech business unit price, P
3g_datarepresent 3G data service unit price, P
base-stationrepresent planning website unit price; Described investment payback time grading parameters is determined according to the relation of IRC and the 4th threshold value.
The present embodiment Bus stop planning apparatus for evaluating, may be used for the enforcement technical scheme of the Bus stop planning appraisal procedure performed corresponding to Fig. 2, it realizes principle and technique effect is similar, repeats no more herein.
The schematic diagram of a kind of Bus stop planning evaluating system that Fig. 6 provides for the embodiment of the present invention six, wherein this system comprises data acquisition computer by file transfer protocol (FTP) (File Transfer Protocol, FTP) interface, transmission control/Internet protocol agreement (Transfer Controln Protocol/InternetProtocol, TCP/IP) interface, database (Data Base, the interface data source such as DB), then data cleansing and filtration is carried out, namely data are screened, horizontal parameters is covered finally by the calculating provided in above-described embodiment, traffic carrying capacity grading parameters, investment payback time grading parameters, market demand grading parameters, covering scene grading parameters, complain grading parameters, the method determination above-mentioned parameter of stop spacing grading parameters and comprehensive grading parameters, by these parameters, planning website is assessed, certainly, also these parameters or assessment result can be exported to other clients.
Last it is noted that above embodiment is only in order to illustrate technical scheme of the present invention, be not intended to limit; Although with reference to previous embodiment to invention has been detailed description, those of ordinary skill in the art is to be understood that: it still can be modified to the technical scheme described in foregoing embodiments, or carries out equivalent replacement to wherein portion of techniques feature; And these amendments or replacement, do not make the essence of appropriate technical solution depart from the spirit and scope of various embodiments of the present invention technical scheme.
Claims (18)
1. a Bus stop planning appraisal procedure, is characterized in that, comprising:
Calculate the weak coverage rate of planning website;
The horizontal grading parameters of covering of described planning website is determined according to the weak coverage rate of described planning website;
At least one first grading parameters is determined according to platform base information;
The weighted average calculating the horizontal grading parameters of described covering and all described first grading parameters is the comprehensive grading parameters of described planning website;
According to described comprehensive grading parameters, described planning website is assessed;
Wherein, described first grading parameters comprises: traffic carrying capacity grading parameters, investment payback time grading parameters, market demand grading parameters, covering scene grading parameters, complaint grading parameters, stop spacing grading parameters.
2. method according to claim 1, is characterized in that, before the weak coverage rate of described calculating planning website, also comprises:
Read measurement report MR data and the first measurement data;
Then the described weak coverage rate calculating planning website comprises: the weak coverage rate calculating described planning website according to described measurement report MR data and the first measurement data;
Wherein, described first measurement data is drive test DT data or call quality test CQT data.
3. method according to claim 2, is characterized in that, the described weak coverage rate calculating described planning website according to measurement report MR data and the first measurement data, specifically comprises:
The first weak coverage rate of described planning website is calculated according to described measurement report MR data;
The second weak coverage rate of described planning website is calculated according to described first measurement data;
Determine that the maximum of described first weak coverage rate and described second weak coverage rate is the weak coverage rate of planning website.
4. method according to claim 3, is characterized in that, if described first measurement data is drive test DT data, then described the second weak coverage rate calculating described planning website according to described first measurement data, specifically comprises:
The covering radius of described planning website is calculated according to the link budget model corrected;
Calculate described covering radius and drive test sampled point distance { d
1, d
2... d
ndifference, obtain difference set { D
1, D
2... D
n;
Each element D in more described difference set
i(i=1,2 ... n) with zero relation, if D
ibe greater than zero, then i-th drive test sampled point is within the coverage of described planning website, otherwise, outside the coverage of described planning website;
Determine the number m of the described drive test sampled point within the coverage of described planning website;
The level value of described drive test sampled point within the coverage of more described planning website and the size of the second threshold value, determine that level value is less than the number p of the drive test sampled point of described second threshold value;
Described second weak coverage rate equals the ratio of p and m.
5. method according to claim 4, it is characterized in that, correct according to the link budget model of different scene to described correction, the link budget model of described correction, for calculating the theoretical coverage radius d of the planning website of residing different scene, the link budget model of described correction is:
TX+G-L
path-L＝RX
min
a(h
r)＝(1.111lg f-0.7)h
r-1.56lg f+0.8
L
path＝46.3+33.9lg f-13.82lg h
t-a(h
r)+(44.9-6.55g h
t)lg d+C
x
Wherein L
pathfor path loss, f is system centre frequency, and usual Wideband Code Division Multiple Access (WCDMA) WCDMA network value is 2000, h
tfor antenna height and height above sea level drop sum, need determine according to actual antennas geographical position; h
rfor user terminal height above sea level, usual value 1.65 meters, a (h
r) be the correction factor of user terminal, d is covering radius, C
xfor the correction factor of different scene, RX
minfor receiving sensitivity and the cell-edge of user terminal, now be corrected to the difference of scene residing for planning website, choose different cell-edges, L is the loss of user terminal, now be corrected to the difference of scene residing for planning website, select different losses, the transmitting power of TX representative planning website side antenna, the gain of G representative antennas.
6. method according to claim 5, is characterized in that, described RX
minfor receiving sensitivity and the cell-edge of user terminal, be now corrected to the difference of scene residing for planning website, choose different cell-edges, comprising:
Scene residing for described planning website is associated out in Computer Database;
Residing for described planning website, scene corrects described RX
min.
7. method according to claim 5, is characterized in that, described L is the loss of user terminal, is now corrected to the difference of scene residing for planning website, selects different losses, comprising:
Scene residing for described planning website is associated out in Computer Database;
Residing for described planning website, scene corrects described L.
8. the method according to any one of claim 1-7, it is characterized in that, if described first grading parameters is traffic carrying capacity grading parameters, and described platform base information is speech business and the data service of N number of (N >=2) macro station nearest apart from planning website, then describedly determine at least one first grading parameters according to platform base information, specifically comprise:
The 2G speech business of described N number of macro station is averaged
The 2G data service of described N number of macro station is averaged
The 3G speech business of described N number of macro station is averaged
The 3G data service of described N number of macro station is averaged
To described T
2g_voice, T
2g_data, T
3g_voice, T
3g_dataask weighted average;
Relation according to described weighted average and the 3rd threshold value determines described traffic carrying capacity grading parameters;
Wherein, T
2g_voice_irepresent the 2G speech business of i-th macro station, T
2g_data_irepresent the 2G data service of i-th macro station, T
3g_voice_irepresent the 3G speech business of i-th macro station, T
3g_data_irepresent the 3G data service of i-th macro station, 1≤i≤N.
9. method according to claim 8, it is characterized in that, if described first grading parameters is investment payback time grading parameters, and described platform base information is the unit price of 2G, 3G speech business every day and data service, then describedly determine at least one first grading parameters according to platform base information, specifically comprise:
Investment payback time IRC is determined by following two formula;
P
average＝(T
2g_voice×P
2g_voice+T
2g_data×P
2g_data+T
3g_voice×P
3g_voice+T
3g_data×P
3g_data)；
IRC＝P
Base-station/(P
average×365)；
Wherein P
2g_voicerepresent 2G speech business unit price, P
2g_datarepresent 2G data service unit price, P
3g_voicerepresent 3G speech business unit price, P
3g_datarepresent 3G data service unit price, P
base-stationrepresent planning website unit price;
Described investment payback time grading parameters is determined according to the relation of IRC and the 4th threshold value.
10. a Bus stop planning apparatus for evaluating, is characterized in that, comprising:
Computing module, for calculating the weak coverage rate of planning website;
Determination module, for determining the horizontal grading parameters of covering of described planning website according to the weak coverage rate of described planning website;
Described determination module, also for determining at least one first grading parameters according to platform base information;
Described computing module, also for calculating the comprehensive grading parameters that the weighted average of the horizontal grading parameters of described covering and all described first grading parameters is described planning website;
Evaluation module, for assessing described planning website according to described comprehensive grading parameters;
Wherein, described first grading parameters comprises: traffic carrying capacity grading parameters, investment payback time grading parameters, market demand grading parameters, covering scene grading parameters, complaint grading parameters, stop spacing grading parameters.
11. devices according to claim 7, is characterized in that, also comprise: read module, for reading measurement report MR data and the first measurement data;
Then described computing module is specifically for the weak coverage rate that calculates described planning website according to described measurement report MR data and the first measurement data;
Wherein, described first measurement data is drive test DT data or call quality test CQT data.
12. devices according to claim 11, is characterized in that, described computing module specifically for:
The first weak coverage rate of described planning website is calculated according to described measurement report MR data;
The second weak coverage rate of described planning website is calculated according to described first measurement data;
Determine that the maximum of described first weak coverage rate and described second weak coverage rate is the weak coverage rate of planning website.
13. devices according to claim 12, is characterized in that, if described first measurement data is drive test DT data, then described computing module specifically for:
The covering radius of described planning website is calculated according to the link budget model corrected;
Calculate described covering radius and drive test sampled point distance { d
1, d
2... d
ndifference, obtain difference set { D
1, D
2... D
n;
Each element D in more described difference set
i(i=1,2 ... n) with zero relation, if Di is greater than zero, then i-th drive test sampled point is within the coverage of described planning website, otherwise, outside the coverage of described planning website;
Determine the number m of the described drive test sampled point within the coverage of described planning website;
The level value of described drive test sampled point within the coverage of more described planning website and the size of the second threshold value, determine that level value is less than the number p of the drive test sampled point of described second threshold value;
Described second weak coverage rate equals the ratio of p and m.
14. devices according to claim 13, it is characterized in that, correct according to the link budget model of different scene to described correction, the link budget model of described correction, for calculating the theoretical coverage radius d of the planning website of residing different scene, the link budget model of described correction is:
TX+G-L
path-L＝RX
min
a(h
r)＝(1.111lg f-0.7)h
r-1.56lg f+0.8
L
path＝46.3+33.9lg f-13.82lg h
t-a(h
r)+(44.9-6.55g h
t)lg d+C
x
Wherein L
pathfor path loss, f is system centre frequency, and usual Wideband Code Division Multiple Access (WCDMA) WCDMA network value is 2000, h
tfor antenna height and height above sea level drop sum, need determine according to actual antennas geographical position; h
rfor user terminal height above sea level, usual value 1.65 meters, a (h
r) be the correction factor of user terminal, d is covering radius, C
xfor the correction factor of different scene, RX
minfor receiving sensitivity and the cell-edge of user terminal, now be corrected to the difference of scene residing for planning website, choose different cell-edges, L is the loss of user terminal, now be corrected to the difference of scene residing for planning website, select different losses, the transmitting power of TX representative planning website side antenna, the gain of G representative antennas.
15. devices according to claim 14, is characterized in that, also comprise: relating module and correction module;
Described relating module, for associating out scene residing for described planning website in Computer Database;
Described correction module, corrects described RX for scene residing for described planning website
min.
16. devices according to claim 15, is characterized in that, described correction module also corrects described L for scene residing for described planning website.
17. devices according to any one of claim 10-16, it is characterized in that, if described first grading parameters is traffic carrying capacity grading parameters, and described platform base information is speech business and the data service of N number of (N >=2) macro station nearest apart from planning website, then described determination module, specifically for:
The 2G speech business of described N number of macro station is averaged
The 2G data service of described N number of macro station is averaged
The 3G speech business of described N number of macro station is averaged
The 3G data service of described N number of macro station is averaged
To described T
2g_voice, T
2g_data, T
3g_voice, T
3g_dataask weighted average;
Relation according to described weighted average and the 3rd threshold value determines described traffic carrying capacity grading parameters;
Wherein, T
2g_voice_irepresent the 2G speech business of i-th macro station, T
2g_data_irepresent the 2G data service of i-th macro station, T
3g_voice_irepresent the 3G speech business of i-th macro station, T
3g_data_irepresent the 3G data service of i-th macro station, 1≤i≤N.
18. devices according to claim 17, is characterized in that, if described first grading parameters is investment payback time grading parameters, and described platform base information is the unit price of 2G, 3G speech business every day and data service, then described determination module, specifically for:
Investment payback time IRC is determined by following two formula;
P
average＝(T
2g_voice×P
2g_voice+T
2g_data×P
2g_data+T
3g_voice×P
3g_voice+T
3g_data×P
3g_data)；
IRC＝P
Base-station/(P
average×365)；
Wherein P
2g_voicerepresent 2G speech business unit price, P
2g_datarepresent 2G data service unit price, P
3g_voicerepresent 3G speech business unit price, P
3g_datarepresent 3G data service unit price, P
base-stationrepresent planning website unit price;
Described investment payback time grading parameters is determined according to the relation of IRC and the 4th threshold value.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105072640A (en) * | 2015-07-02 | 2015-11-18 | 中国联合网络通信集团有限公司 | Method and device for determining antenna feeder parameters |
| CN105228164A (en) * | 2015-10-14 | 2016-01-06 | 中国联合网络通信集团有限公司 | A kind of Pre-Evaluation method that site is multiplexing and device |
| CN105682104A (en) * | 2016-02-29 | 2016-06-15 | 广州银禾网络通信有限公司 | Mobile communication site planning method and system |
| CN105682105A (en) * | 2016-03-15 | 2016-06-15 | 中国联合网络通信集团有限公司 | Planned site evaluation method and device |
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| CN113329411A (en) * | 2021-05-24 | 2021-08-31 | 中国联合网络通信集团有限公司 | Wireless network planning method and device |
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| CN114980138A (en) * | 2022-05-24 | 2022-08-30 | 四川通信科研规划设计有限责任公司 | Planning method, system and terminal of 5G wireless base station |
| CN115843039A (en) * | 2022-11-17 | 2023-03-24 | 中国联合网络通信集团有限公司 | Base station efficiency evaluation method, device, equipment and storage medium |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1132584A (en) * | 1993-09-30 | 1996-10-02 | 荷兰皇家.Ptt.有限公司 | Method for determining base station locations, and device for applying the method |
| EP1292162A1 (en) * | 2001-09-10 | 2003-03-12 | Koninklijke KPN N.V. | Method and system for planning and evaluation of CDMA radio networks |
| US20040014476A1 (en) * | 2000-10-27 | 2004-01-22 | Sergio Barberis | System and method for planning a telecommunications network for mobile terminals |
| CN101072121A (en) * | 2007-05-31 | 2007-11-14 | 中国移动通信集团广东有限公司 | System and method for estimating network optimized engineering requirements |
| CN101119573A (en) * | 2007-08-16 | 2008-02-06 | 中讯邮电咨询设计院 | 2G service data guiding method for 3G planning simulation software |
| CN101203018A (en) * | 2007-11-30 | 2008-06-18 | 中国移动通信集团重庆有限公司 | Method for distributing business density time and business in a mobile communication |
| CN101309009A (en) * | 2008-02-28 | 2008-11-19 | 江苏省电力试验研究院有限公司 | Urban electric grid overall evaluation system established based on layer analysis and Delphi |
| CN102123429A (en) * | 2011-03-24 | 2011-07-13 | 北京拓明科技有限公司 | Method for assessing coverage rationality of mobile communication base station |
| CN102149103A (en) * | 2011-04-11 | 2011-08-10 | 北京铭润创展科技有限公司 | Network optimizing system and method |
| CN102202330A (en) * | 2011-05-23 | 2011-09-28 | 北京邮电大学 | Coverage self-optimization method of cellular mobile communication system |
| CN102547579A (en) * | 2012-02-07 | 2012-07-04 | 大唐移动通信设备有限公司 | Method and system of road test |
| CN103178995A (en) * | 2013-02-05 | 2013-06-26 | 中国电子科技集团公司电子科学研究院 | Systematic multi-scale evaluation method for performance of communication network |
| CN103298024A (en) * | 2012-02-28 | 2013-09-11 | 温州大学 | Performance assessment method and device of wireless network |
| CN103473608A (en) * | 2013-09-02 | 2013-12-25 | 河海大学 | Method for processing high-efficiency evaluation indexes of smart distribution network |
| CN103686762A (en) * | 2013-12-18 | 2014-03-26 | 中国联合网络通信集团有限公司 | WCDMA system cell coverage evaluation method and device |
| CN103906104A (en) * | 2012-12-31 | 2014-07-02 | 中国移动通信集团内蒙古有限公司 | Method and device for positioning and covering hole |
-
2014
- 2014-11-14 CN CN201410647454.8A patent/CN104410978A/en active Pending
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1132584A (en) * | 1993-09-30 | 1996-10-02 | 荷兰皇家.Ptt.有限公司 | Method for determining base station locations, and device for applying the method |
| US20040014476A1 (en) * | 2000-10-27 | 2004-01-22 | Sergio Barberis | System and method for planning a telecommunications network for mobile terminals |
| EP1292162A1 (en) * | 2001-09-10 | 2003-03-12 | Koninklijke KPN N.V. | Method and system for planning and evaluation of CDMA radio networks |
| CN101072121A (en) * | 2007-05-31 | 2007-11-14 | 中国移动通信集团广东有限公司 | System and method for estimating network optimized engineering requirements |
| CN101119573A (en) * | 2007-08-16 | 2008-02-06 | 中讯邮电咨询设计院 | 2G service data guiding method for 3G planning simulation software |
| CN101203018A (en) * | 2007-11-30 | 2008-06-18 | 中国移动通信集团重庆有限公司 | Method for distributing business density time and business in a mobile communication |
| CN101309009A (en) * | 2008-02-28 | 2008-11-19 | 江苏省电力试验研究院有限公司 | Urban electric grid overall evaluation system established based on layer analysis and Delphi |
| CN102123429A (en) * | 2011-03-24 | 2011-07-13 | 北京拓明科技有限公司 | Method for assessing coverage rationality of mobile communication base station |
| CN102149103A (en) * | 2011-04-11 | 2011-08-10 | 北京铭润创展科技有限公司 | Network optimizing system and method |
| CN102202330A (en) * | 2011-05-23 | 2011-09-28 | 北京邮电大学 | Coverage self-optimization method of cellular mobile communication system |
| CN102547579A (en) * | 2012-02-07 | 2012-07-04 | 大唐移动通信设备有限公司 | Method and system of road test |
| CN103298024A (en) * | 2012-02-28 | 2013-09-11 | 温州大学 | Performance assessment method and device of wireless network |
| CN103906104A (en) * | 2012-12-31 | 2014-07-02 | 中国移动通信集团内蒙古有限公司 | Method and device for positioning and covering hole |
| CN103178995A (en) * | 2013-02-05 | 2013-06-26 | 中国电子科技集团公司电子科学研究院 | Systematic multi-scale evaluation method for performance of communication network |
| CN103473608A (en) * | 2013-09-02 | 2013-12-25 | 河海大学 | Method for processing high-efficiency evaluation indexes of smart distribution network |
| CN103686762A (en) * | 2013-12-18 | 2014-03-26 | 中国联合网络通信集团有限公司 | WCDMA system cell coverage evaluation method and device |
Non-Patent Citations (3)
| Title |
|---|
| 常静，张惠等: "多维度TD-SCDMA网络覆盖分析方法研究", 《互联网天地》 * |
| 杨靖: "TD-SCDMA无线网络覆盖优化研究", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
| 牛宪华，曾柏森: "基于用户感知的WCDMA网络深度覆盖评估研究", 《网规网优》 * |
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