CN104469834A - Service simulation-perception-evaluation method and system - Google Patents
Service simulation-perception-evaluation method and system Download PDFInfo
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Abstract
The invention provides a service simulation-perception-evaluation method and system. The method comprises the following steps: acquiring the levels of grids in each cell service area and the levels of adjacent cells in a network, segmenting the levels of the adjacent cells according to the upper threshold values and lower threshold values of the levels of the adjacent cells, and counting probabilities P1, P2 and P3 that the level of each adjacent cell is positioned in three intervals; calculating the service occurrence probability of each grid; calculating the service occurrence probability of the entire network; simulating telephone traffic scattering in a network area according to the service occurrence probability of the entire network by using Monte Carlo simulation; simulating a new site according to the telephone traffic simulation scattering of each cell in the network; and performing service perception and evaluation on the simulated new site. Compared with the prior art, the service simulation-perception-evaluation method and system have the advantages that comprehensive optimization of service perception can be performed by making full use of MR (Measurement Result) data; service scattering, the determination and evaluation of the new site and network optimizing flow can be greatly simplified; and manpower and material resources are greatly saved.
Description
Technical field
The present invention relates to mobile communication technology field, particularly relate to a kind of business simulating perception evaluation method and system.
Background technology
Along with the development of mobile communications network, for regular maintenance and the optimization of network, to improve user awareness and use, it is all the important process of mobile operator all the time.To in the maintenance optimization process of network, what use at most is exactly analysis and the Site Selection of newly-built base station, and this relates to traffic and spreads the various aspects such as point, new site total evaluation.
In prior art, usually utilizing TA(Timing Advance, Timing Advance) distribution carries out traffic and spreads a little.According to TA information along community radiation direction distributing traffic amount.Utilize TA accounting information, the telephone traffic of community is distributed in overlay area, and add up the traffic density in each TA fan ring.Traffic density in each fan ring is the total sampled point of sampled point/TA of Pn=(n-th TA) area of * total traffic/the n-th TA.Utilize MPS process overlapped information, vertical community radiation direction segments telephone traffic.According to TA accounting information, the traffic distribution situation of community along radiation direction roughly can only be described.Utilize the covering overlapped information of minizone, carry out the superposition of telephone traffic in overlapping region, the telephone traffic that each TA fans in ring can be segmented in the radiation direction of vertical community.Like this, the traffic density of certain sampled point, all communities just equaling to cover this point are distributed in the multiple stacking of the traffic density in this region.Carry out the superposition of telephone traffic according to the overlapping coverage condition of community, finally obtain the traffic distribution in each network, and then obtain the traffic model of the whole network.
In another scheme, utilizing the original MR(measurement result reported, measurement report) data carry out traffic and spread a little.Main flow thinking that traffic spreads a little is to utilize the original MR data reported to carry out: collect the original reported data of 480ms, by the unified normalization to initial data, obtain the average path loss of community and the relation of average distance, by this relation, the general distance of the sampled point that can at every turn be reported and community, with this distance in conjunction with nearby region measurement, carry out three-point fix, obtain with this and spread a distribution comparatively accurately.
The advantage of these two kinds of modes is: theoretical foundation is ripe, and has the feasibility of realization; Spread a precision high, more accurately can reflect actual network condition.Meanwhile, shortcoming also clearly: raw data acquisition is loaded down with trivial details, and data volume is huge, and data processing time is longer; The provider of current main flow is producer, decreases the right to choose of operator, is unfavorable for the development of network optimization service comparatively speaking; Do not have perfect theoretical foundation for isolated island community or edge cell, possible accuracy can decline to some extent.
The evaluation measures of existing new site, mainly by following 3 as foundation:
One, customer complaint: be by what reason caused according to customer complaint analysis, if weak covering, long-time heavy traffic, then in complaint newly-built base station, region;
Two, drive test analysis: test out weak overlay area according to drive test software, carries out newly-built base station and solves weak covering problem;
Three, KPI(Key Performance Indication, critical sales index) statistics: the community being found out long-term heavy traffic by the KPI statistics on backstage, near these communities, newly-built base station solves heavy traffic problem.
Can see, traditional new site, mainly by the information such as customer complaint, drive test analysis, KPI statistics, in conjunction with artificial experience, select multiple alternative website, then by manned surveys, finally determine the position of new site and work ginseng information etc.
Specifically, realizing in process of the present invention, inventor finds that existing scheme exists following shortcoming:
In existing business evaluation of programme, traffic spreads a little or owing to spreading a precision thick (distribution due to TA), or needs to drop into the factors such as a large amount of man power and materials, cannot meet the requirement of routine optimization and fine optimization; Meanwhile, lack objective assessment means to evaluate new site, for the network optimization brings puzzlement.
The level distribution required in MR data is crossed " slightly ", cannot meet instructions for use.Equipment component is only had can indirectly to provide precision to be the level measurement of 1dB at present.Without the Joint Distribution (two-dimensional matrix) of level and distance → cannot path loss correction be carried out in MR data.TA precision due to GSM is 550 meters, suitable with the station spacing in real network, even if provide Two dimensional Distribution to be used for propagation model revision.The drive test data of multiple base station is changed into test data of singly standing, corrects for propagation model.The Joint Distribution (one-dimensional vector) of MR data Zhong Wuzhe community and adjacent area level → cannot three-point fix be carried out.
In conjunction with propagation model (calculating distance) and antenna pattern, also can only realize the geo-location of the measurement sampled point of " arc band " shape, precision is too poor, and represents poor effect.Simple dependence cannot realize the positioning precision of satisfying the demand from the MR data of webmaster.
New website networks cannot objective evaluation.The networking of new website can effectively solve weak covering and high load capacity, but must also cause the interference of regional area to increase simultaneously, cannot objective business assess, and obviously cannot determine whether new website networks suitable.
Summary of the invention
The object of the invention is to the shortcoming and defect overcoming prior art, a kind of business simulating perception evaluation method and system are provided.
A kind of business simulating perception evaluation method, described method comprises:
Obtain level and the adjacent area level of grid in each cell service area in network, according to the upper-lower door limit value of adjacent area level, segmentation is carried out to described adjacent area level, add up each adjacent area level and be positioned at three interval probability P 1, P2, P3;
According to formula Pp=Σ Ps*(P1, P2, P3) * Pii* user density, calculate the business probability of happening of each grid; Described Pp is grid service probability of happening, and Ps is the level distribution probability of grid, and Pii is the level distribution probability of each adjacent area;
According to formula P=Pp/(Σ Pp) calculate the business probability of happening of whole network;
Use Monte Carlo simulation, according to the business probability of happening of whole network, in network area, carry out emulation traffic spread a little, complete the call simulation of each community in network and spread a little;
Spread a little according to the call simulation of each community in described network, carry out new site emulation;
Service-aware assessment is carried out to the new site of described emulation.
Level and the adjacent area level of grid in each cell service area in described network is obtained by MR Data Collection.
The upper-lower door limit value of described adjacent area level sets as required;
The adjacent area level of described reception is according to following formulae discovery:
Pr=Pt – Lf+Ga – L θ – PL; Wherein, PL is airborne spread path loss values; Pt is the transmitting power of described community; Lf is the loss of feedback/wire jumper; Ga is antenna main lobe gain; L θ is the horizontal and vertical complete attenuation value of antenna to Signal reception point.
Described three interval probability P 1, P2, P3 are according to following formulae discovery:
P1=NBR_OF_SAMPLES_IN_CLASS_1/AVE_DL_SIG_STR_SERV_CELL_DEN；
P2=NBR_OF_SAMPLES_IN_CLASS_2/AVE_DL_SIG_STR_SERV_CEL L_DEN；
P3=NBR_OF_SAMPLES_IN_CLASS_3/AVE_DL_SIG_STR_SERV_CELL_DEN；
Wherein, described NBR_OF_SAMPLES_IN_CLASS_1, NBR_OF_SAMPLES_IN_CLASS_2, NBR_OF_SAMPLES_IN_CLASS_3, AVE_DL_SIG_STR_SERV_CELL_DEN obtain from described MR data.
The level distribution probability P s=of described each grid meets the probability of normal distyribution function at this level of Serving cell level; The probability at the level place, adjacent area that the normal distyribution function that the level distribution probability P ii=of each adjacent area meets adjacent area level counts in this grid.
In described network, the call simulation of each community spreads probability=Σ Σ (affected traffic accounting * grid user density accounting * neighbours level distribution accounting) a little; Wherein, the total sampling number in total number of sample points/Serving cell in a certain interval of affected traffic accounting=meet; Total number of users of the number of users/overlapping region of grid user density accounting=grid; The probability of adjacent area level distribution=adjacent area level Normal Distribution.
Described new site emulation, comprises the steps:
Determine to optimize region;
The comprehensive experience coefficient of computing service, determines whether to need new site according to service integration experience coefficient;
Determine new site parameter;
The field intensity in test new site region;
The antenna feeder of adjacent area around adjustment new site.
The described new site to described emulation carries out service-aware assessment, comprising:
The rationally distributed property assessment in basis;
The lifting amplitude assessment covered;
The improvement assessment of scenario of capacity;
The situation of change assessment of carrier interference ratio C/I;
The improvement assessment of scenario of cutting off rate.
A kind of business simulating perception evaluation system, described system comprises Monte Carlo simulation unit, grid service probability of happening computing unit, Network probability of happening computing unit, traffic spread dot element, new site simulation unit and service-aware assessment unit, wherein
Described Monte Carlo simulation unit, for obtaining level and the adjacent area level of grid in each cell service area in network, according to the upper-lower door limit value of adjacent area level, segmentation is carried out to described adjacent area level, add up each adjacent area level and be positioned at three interval probability P 1, P2, P3;
Described grid service probability of happening computing unit, for according to formula Pp=Σ Ps*(P1, P2, P3) * Pii* user density, calculate the business probability of happening of each grid; Described Pp is grid service probability of happening, and Ps is the level distribution probability of grid, and Pii is the level distribution probability of each adjacent area;
Described Network probability of happening computing unit, for according to formula P=Pp/(Σ Pp) calculate the business probability of happening of whole network;
Described traffic spreads dot element, for using Monte Carlo simulation, carrying out emulation traffic and spreading a little, completes the call simulation of each community in network and spreads a little;
Described new site simulation unit, for spreading a little according to the call simulation of each community in described network, carries out new site emulation;
Described service-aware assessment unit, for carrying out service-aware assessment to the new site of described emulation.
Described new site simulation unit comprises optimizes region subelement, service integration experience subelement, parameter determination subelement, field strength measurement subelement and adjacent area adjustment subelement, wherein,
Described optimization region subelement, optimizes region for determining;
Described service integration experiences subelement, for the comprehensive experience coefficient of computing service, determines whether to need new site according to service integration experience coefficient;
Described parameter determination subelement, for determining new site parameter;
Described field strength measurement subelement, for testing the field intensity in new site region;
Described adjacent area adjustment subelement, for adjusting the antenna feeder of adjacent area around new site.
Described service-aware assessment unit comprises basic layout subelement, covering promotes subelement, capacity improves subelement, carrier/interface ratio changes subelement and cutting off rate improves subelement, wherein,
Described basic layout subelement, for the rationally distributed property assessment in basis;
Described covering promotes subelement, for the lifting amplitude assessment covered;
Described capacity improves subelement, for the improvement assessment of scenario of capacity;
Described carrier/interface ratio change subelement, the situation of change for carrier interference ratio C/I is assessed;
Described cutting off rate improves subelement, for the improvement assessment of scenario of cutting off rate.
The present invention, by obtaining level and the adjacent area level of grid in each cell service area in network, carries out segmentation to adjacent area level, adds up each adjacent area level and is positioned at three interval probability; Calculate the business probability of happening of each grid, and calculate the business probability of happening of whole network; Use Monte Carlo simulation, according to the business probability of happening of whole network, in network area, carry out emulation traffic spread a little, complete the call simulation of each community in network and spread a little; Spread a little according to the call simulation of each community in network, carry out new site emulation; Service-aware assessment is carried out to the new site of emulation.Compared with prior art, the present invention can make full use of MR data and carry out service-aware complex optimum, greatly can simplify that business spreads point, new site is determined and assess, network optimization flow process, save a large amount of man power and materials, simultaneously, index by quantifying can objective assessment effect of optimization, possesses the value of large-scale promotion.
Accompanying drawing explanation
The business simulating perception evaluation method principle flow chart that Fig. 1 provides for the embodiment of the present invention 1;
The business simulating perception evaluation system structural representation that Fig. 2 provides for the embodiment of the present invention 2;
New site simulation unit 500 structural representation that Fig. 3 provides for the embodiment of the present invention 2;
Service-aware assessment unit 600 structural representation that Fig. 4 provides for the embodiment of the present invention 2.
Embodiment
Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in detail.But embodiments of the present invention are not limited thereto.
As shown in Figure 1, be the business simulating perception evaluation method principle flow chart that the embodiment of the present invention 1 provides, specific as follows:
Step 10, obtains level and the adjacent area level of grid in each cell service area in network, according to the upper-lower door limit value of adjacent area level, carries out segmentation to described adjacent area level, adds up each adjacent area level and is positioned at three interval probability P 1, P2, P3.
What first business simulating will solve is the problem that traffic spreads a little, also just needs the traffic using Monte Carlo to spread point methods.First, need clear and definite: the data utilizing MR to collect are statisticss, generally each statistical information that can report at most 6 adjacent areas the strongest, comprise adjacent area level, quality, BCCH(Broadcast Control Channel, Broadcast Control Channel), BCC(Base station Color Code, BCC), NCC(Network Color Code, Network Color Code) etc.From statistical theory, the adjacent area level counted on this meets normal distribution, and the level statistics of Serving cell also meets normal distribution.Table one is part nokia nearby region measurement static fields.
Table one
| BTS_INT_ID | Community ID |
| NCC | Adjacent area NCC |
| BCC | Adjacent area BCC |
| BCCH | Adjacent area BCCH |
| DB_VALUE_LOW | Report thresholding 1 |
| DB_VALUE_HIGH | Working thresholding 2 |
| AVE_DL_SIG_STR_SERV_CELL_SUM | Cell receiver level summation is added up |
| AVE_DL_SIG_STR_SERV_CELL_DEN | The total sampling number in Serving cell |
| STD_DEV_OF_SERV_CELL | Cell receiver level variance statistic |
| AVE_DL_SIG_STR_ADJ_CELL | Adjacent area incoming level average |
| STD_DEV_OF_ADJ_CELL | Adjacent area incoming level variance |
| NBR_OF_SAMPLES_IN_CLASS_1 | Interval 1 statistical number |
| NBR_OF_SAMPLES_IN_CLASS_2 | Interval 2 statistical number |
| NBR_OF_SAMPLES_IN_CLASS_3 | Interval 3 statistical number |
Wherein collected parameters, can use successively in follow-up computational process.Data Source main in the present embodiment is all MR data, and the parameters needed for calculating is by obtaining in MR data acquisition mostly.
Traffic spreads in process a little, the first level of each grid and adjacent area level list in calculation services cell service area, and Neighboring Cell List arranges from big to small according to level, and retains front 6 neighboring BS information.Here the usual information according to front 6 adjacent areas calculates, and in real process, can set the adjacent area quantity of reservation as required.
According to the thresholding 1 defined in neighboring BS relationship and thresholding 2, segmentation is carried out to 6 adjacent areas, 3 interval statistics can be obtained.
The probability being in the first interval is:
P1=NBR_OF_SAMPLES_IN_CLASS_1/AVE_DL_SIG_STR_SERV_CELL_DEN；
The probability being in the second interval is:
P2=NBR_OF_SAMPLES_IN_CLASS_2/AVE_DL_SIG_STR_SERV_CELL_DEN；
The probability being in the 3rd interval is:
P3=NBR_OF_SAMPLES_IN_CLASS_3/AVE_DL_SIG_STR_SERV_CELL_DEN。
Here thresholding 1 and thresholding 2 can obtain from table one, and NBR_OF_SAMPLES_IN_CLASS_1, NBR_OF_SAMPLES_IN_CLASS_2, NBR_OF_SAMPLES_IN_CLASS_3, AVE_DL_SIG_STR_SERV_CELL_DEN also can obtain from table one.
In fact, in order to better describe sampling probability, we refine on each network that Serving cell covers, overlay area is and meets incoming level at (max(-110, Res_acc_level),-47) region between, the formula of incoming level, can adopt above-mentioned path loss formula to convert.Here Res_acc_level is the minimum access level in Serving cell.
Incoming level computing formula is as follows:
Pr=Pt–Lf+Ga–Lθ–PL；
PL is airborne spread path loss values, can be obtained the circuit loss value of Serving cell by correction above; Pt is transmitting power, i.e. cell parameter BSPWR value, can be known by introduction above, can from the transmitting power average taken in each period power control table; Lf is the loss of feedback/wire jumper, and Ga is antenna main lobe gain, can obtain from antenna model; L θ is the horizontal and vertical complete attenuation value of antenna to Signal reception point, obtains according to atural object and antenna view computation.The propagation model of Serving cell and the propagation model situation of the whole network community can be calculated with this.
Step 20, according to formula Pp=Σ Ps*(P1, P2, P3) * Pii* user density, calculate the business probability of happening of each grid.
Because each Serving cell statistics level meets normal distribution, and adjacent area level also meets normal distribution, so the level distribution probability P s=of each network meets the probability of normal distyribution function at this level of Serving cell level, the probability at the level place, adjacent area that the normal distyribution function that the level distribution probability P ii=of each adjacent area meets adjacent area level counts in this grid.So the business probability of happening of each network of COMPREHENSIVE CALCULATING is Pp=Σ Ps*(P1, P2, P3) * Pii* user density.
Step 30, according to formula P=Pp/(Σ Pp) calculate the business probability of happening of whole network.
The business probability of happening of each network can be calculated by above-mentioned steps, but be not the sampling probability in our statistics, for ensureing that total sampling probability is 1, and coincidence statistics is theoretical, need to be further processed business probability of happening, thus obtain the sampling probability meeting Monte Carlo simulation use.The computing formula of the sampling probability of each network is as follows:
P=Pp/（ΣPp）。
Step 40, uses Monte Carlo simulation, according to the business probability of happening of whole network, carries out emulation traffic and spread a little in network area, complete the call simulation of each community in network and spread a little.
How to know and carry out accurately spreading a little, Monte Carlo simulation is utilized to carry out spreading a little exactly, simulate actual sample distribution, and self level of the sampling situations of reality and Serving cell and adjacent area level statistics closely related, so how to utilize these information to be directly connected to accurately whether to spread a little to build sampling probability, thus consider each grid to spread a new probability formula as follows:
Spread a probability=Σ Σ (affected traffic accounting * grid user density accounting * neighbours level distribution accounting);
Wherein, the total sampling number in total number of sample points/Serving cell in a certain interval of affected traffic accounting=meet;
Total number of users of the number of users/overlapping region of grid user density accounting=grid;
The probability of adjacent area level distribution=adjacent area level Normal Distribution.
Further, can spread and a little average by recycled for multiple times Monte Carlo simulation, reduce phantom error.
Step 50, spreads a little according to the call simulation of each community in network, carries out new site emulation.
In wireless network routine optimization process, usual respective regions also exists the problems such as weak covering, high load capacity, structure, the service quality of these problems to wireless network has tremendous influence, generally solve problems by antenna feeder optimization or newly-built station, thus quality of wireless network is got a promotion.
Weak overlay area: the information reported according to MR, add up each community BCCH Rxlev<-90(-94) the ratio reporting number of times, ratio is higher, then illustrate that the possibility of the weak covering in community is also higher, when being usually greater than a certain thresholding of specifying, think that community exists weak covering, generally also need to combine the covering problem covering accurate cell of origin and exist, for the community excessively covering the weak covering caused, preferentially solved by the means of coverage optimization.
High-load region: according to the statistical indicator of community, when every line telephone traffic, congestion ratio etc. (get rid of community and there is hidden failure) exceed certain thresholding, then think that community exists high load capacity, can be adjusted by antenna feeder or newly-built station shares the traffic of high load capacity community.
Structure problem region: for complicated network structure region, overlapping covering is serious, needs to carry out the means such as antenna feeder adjustment, cell splitting, traffic sinking and solves complex structure region, thus less system low noise, improve network quality.
The work of usual new site, comprises the steps:
1) determine to optimize region (capacity, covering, structure);
There is weak overlay area: Rxleve<-94dBm;
There is high-load region: congested >1%, SDCCH congestion ratio >5% of TCH, without line use ratio >80%, every line telephone traffic >0.8Erl etc.;
There is structure problem region: structure index >24, overlapping coverage >5 etc.
2) new site process optimization, whether comprise the information such as the covering according to problem area, capacity, calculating existing business experience satisfies the demands, and if it is solves problem by adjustment existing community antenna feeder, otherwise, then be optimized by new site mode.
Existing business experience is assessed by the comprehensive experience coefficient of computing service usually.This coefficient is by following formulae discovery:
Access sex determination criterion: descending level can not access lower than-94dBm.
Retentivity decision criteria: with the call drop of carrier interference ratio C/I<6dB or adjacent frequency rejection ratio C/A<-6 generation frequently.
Integrality decision criteria: extrapolate FER(Frame Error Rate according to comprehensive C/I, frame error rate).
Experience index by the integrated service calculated in next-door neighbour's cell area, carry out Multi simulation running for each simulated point, find integrated service to experience the highest new site work ginseng combination of index, as the alternative of new site.
Next-door neighbour community: the community that in cell cluster, in Neighboring Cell List, occurrence number is maximum.
Non-close community: other adjacent cells in cell cluster.
3) newly-built station parameter is determined:
Base station name: automatically distribute;
BSC(Base Station Controller, base station controller): automatically distribute, recommend the BSC at place, adjacent area recently around;
LAC(Location Area Code, Location Area Code): automatically distribute, recommend the LAC at place, adjacent area recently around;
Longitude and latitude: emulation obtains;
Frequency range type: user's definable, also provides by software that (given standard, according to surrounding environment: coverage requirement, urban district or suburb, whether there are 1800 base stations automatically; Under equal conditions, selection 1800 is preferential);
Azimuth: emulation obtains;
Mechanical tilt angle, electrical tilt angle: emulation obtains;
Stand high: emulation obtains;
Lobe width: lobe width acquiescence selection 65, also can user select;
Trx number: calculate, calculate the disappearance PDCH(Packet Data CHannel of selected zone, Packet Data Channel) number of channel (ERLANG B is determined), TCH(Traffic Channel, Traffic Channel) number of channel (ERLANG B is determined), SDCCH(Stand Alone Dedicated Channel, Separate Dedicated Control Channel) number of channel (generally determining with TCH number based on experience value), obtain total disappearance number of channel T, and then obtain the carrier number of needs;
BCCH(Broadcast Control Channel, Broadcast Control Channel) frequency, TCH frequency: according to the frequency of BCCH and TCH planning in existing network, calculate same, the adjacent frequency interference of each frequency, adopt the frequency disturbing into and disturb out coefficient minimum;
BSIC(Base Station Identity Code, base station identity code): according to homochromy designing requirement frequently, recommend BSIC farthest;
Base station transmitting power: emulation obtains;
Neighboring Cell List: by there is the community of covering as one-level adjacent area in newly-built station region, the adjacent area of one-level adjacent area is as secondary adjacent area, one-level adjacent area arranges by the interference value in region of building a station, determine that first 32 is the Neighboring Cell List (the equipment adjacent cell definitions had can be greater than 32, wouldn't be considered) of newly-built station; Usually according to practical experience, one-level neighbours can be less than 32, now just need secondary adjacent area, adding portion in Neighboring Cell List, and the interpolation of secondary adjacent area, by the principle of distance, is added into Neighboring Cell List, until 32 adjacent areas are extremely.
By the parameter request of above-mentioned newly-built station, wherein the parameter such as longitude and latitude, azimuth, angle of declination, high, the transmitting power of standing needs to be determined by emulation, and other parameters can require by software recommend according to user.
4) to build a station the field intensity prediction in region.
For each future position, if the intensity of the Received signal strength of this some emulation is greater than a certain thresholding of specifying, then think that newly-built station can reach at this signal.
5) after building a station, around the antenna feeder of adjacent area adjusts.
Gene: (azimuth, mechanical tilt angle, electrical tilt angle, high, transmitting power of standing);
Individual: emulation community;
Population: cell cluster;
Chromosome: one group of gene object;
Use the genetic algorithm problem that will solve to be modeled to the process of a biological evolution, by copying, intersecting, the operation such as sudden change produces follow-on solution, and progressively eliminates the low solution of fitness, increases the solution that fitness is high.Such evolution N is for the rear individuality very high with regard to fitness of probably evolving out.
Fitness function: direct the direction that Cutting experiments carries out, along with the change of the value of fitness function, genetic algorithm converges or disperse.
When the value of fitness function is 1, thinks and algorithmic statement obtain optimal solution.The data of fitness function are less, illustrate that the result of algorithm is more outstanding.
Rational structure fitness function is the prerequisite correctly applying Cutting experiments, and in the process of carrying out antenna feeder optimization Simulation, fitness function is exactly the intersection of all factors of influence in each community in fact.
∑ ∑ (call completing rate * (1-congestion ratio) * (1-cutting off rate) * (1-FER)->1) → 1;
Call completing rate: cell load too high (available channel distributes not enough); Cover not enough (rxlevel<-94dBm); Disturb excessive (adjacent cell exists with <-6dB when <12dB, adjacent frequency time frequently);
Cutting off rate: cover not enough (rxlevel<-94dBm); Disturb excessive (adjacent cell exists with <-6dB when <12dB, adjacent frequency time frequently).
Step 60, carries out service-aware assessment to the new site of emulation.
Newly-built station needs to estimate newly-built station before networking, thus judge the reasonability of newly-built station, according to the comprehensive examination thresholding of access sex determination criterion, retentivity decision criteria and integrality decision criteria three, the necessity of new site can be described, major embodiment the following aspects:
The rationally distributed property in basis: can reference;
The lifting amplitude covered: overall coverage rate in cell cluster;
The improvement situation of capacity: congested anticipation in cell cluster;
The situation of change of C/I: C/I change in cell cluster;
The improvement situation of cutting off rate: main consider that low covering (Rxlevel<-94dBm), low C/I(are with <12dB, adjacent frequency <-6dB frequently), high load capacity three kinds of call drops caused.
Network synthesis evaluation index=K1* accesses sex determination criterion score+K2* and accesses sex determination criterion score+K3* access sex determination criterion score, and wherein, K1, K2, K3 are respective weight, and total value is 1.If existing network overall target is 3.5 before building a station, after passing through new site, the network synthesis index of existing network emulation is greater than 4(and can determines according to wireless environment), then illustrate by new site to improve network quality, the lifting of introducing to network quality of new site is favourable, otherwise, then illustrate that the introducing of newly-built station can worsen the network quality of existing network, be preferentially optimized by other means.
The realization of the present embodiment, MR data can be made full use of and carry out service-aware complex optimum, relative to technology motion in the past, greatly can simplify Optimizing Flow, save a large amount of man power and materials, meanwhile, index by quantifying can objective assessment effect of optimization, possesses the value of large-scale promotion.
As shown in Figure 2, the embodiment of the present invention 2 also provides a kind of business simulating perception evaluation system, this system comprises Monte Carlo simulation unit 100, grid service probability of happening computing unit 200, Network probability of happening computing unit 300, traffic spread dot element 400, new site simulation unit 500 and service-aware assessment unit 600, specific as follows:
Monte Carlo simulation unit 100, for obtaining level and the adjacent area level of grid in each cell service area in network, according to the upper-lower door limit value of adjacent area level, segmentation is carried out to adjacent area level, add up each adjacent area level and be positioned at three interval probability P 1, P2, P3;
Grid service probability of happening computing unit 200, for according to formula Pp=Σ Ps*(P1, P2, P3) * Pii* user density, calculate the business probability of happening of each grid; Pp is grid service probability of happening, and Ps is the level distribution probability of grid, and Pii is the level distribution probability of each adjacent area;
Network probability of happening computing unit 300, for according to formula P=Pp/(Σ Pp) calculate the business probability of happening of whole network;
Traffic spreads dot element 400, for using Monte Carlo simulation, carrying out emulation traffic and spreading a little, completes the call simulation of each community in network and spreads a little;
New site simulation unit 500, for spreading a little according to the call simulation of each community in network, carries out new site emulation;
Service-aware assessment unit 600, for carrying out service-aware assessment to the new site of emulation.
Especially, as shown in Figure 3, above-mentioned new site simulation unit 500 comprises optimizes region subelement 501, service integration experience subelement 502, parameter determination subelement 503, field strength measurement subelement 504 and adjacent area adjustment subelement 505, specific as follows:
Optimizing region subelement 501, optimizing region for determining;
Service integration experiences subelement 502, for the comprehensive experience coefficient of computing service, determines whether to need new site according to service integration experience coefficient;
Parameter determination subelement 503, for determining new site parameter;
Field strength measurement subelement 504, for testing the field intensity in new site region;
Adjacent area adjustment subelement 505, for adjusting the antenna feeder of adjacent area around new site.
Further, as shown in Figure 4, above-mentioned service-aware assessment unit 600 comprises basic layout subelement 601, covering promotes subelement 602, capacity improves subelement 603, carrier/interface ratio changes subelement 604 and cutting off rate improves subelement 605, specific as follows:
Basis layout subelement 601, for the rationally distributed property assessment in basis;
Cover and promote subelement 602, for the lifting amplitude assessment covered;
Capacity improves subelement 603, for the improvement assessment of scenario of capacity;
Carrier/interface ratio change subelement 604, the situation of change for carrier interference ratio C/I is assessed;
Cutting off rate improves subelement 605, for the improvement assessment of scenario of cutting off rate.
It should be noted that: the business simulating perception evaluation system that above-described embodiment provides is when business simulating perception is evaluated, only be illustrated with the division of above-mentioned each functional module, in practical application, can distribute as required and by above-mentioned functions and be completed by different functional modules, internal structure by system is divided into different functional modules, to complete all or part of function described above.In addition, the business simulating perception evaluation system that above-described embodiment provides and business simulating perception evaluation method embodiment belong to same design, and its specific implementation process refers to embodiment of the method, repeats no more here.
The invention described above embodiment sequence number, just to describing, does not represent the quality of embodiment.
To sum up, the present invention, by obtaining level and the adjacent area level of grid in each cell service area in network, carries out segmentation to adjacent area level, adds up each adjacent area level and is positioned at three interval probability; Calculate the business probability of happening of each grid, and calculate the business probability of happening of whole network; Use Monte Carlo simulation, according to the business probability of happening of whole network, in network area, carry out emulation traffic spread a little, complete the call simulation of each community in network and spread a little; Spread a little according to the call simulation of each community in network, carry out new site emulation; Service-aware assessment is carried out to the new site of emulation.Compared with prior art, the present invention can make full use of MR data and carry out service-aware complex optimum, greatly can simplify that business spreads point, new site is determined and assess, network optimization flow process, save a large amount of man power and materials, simultaneously, index by quantifying can objective assessment effect of optimization, possesses the value of large-scale promotion.
Above-described embodiment is the present invention's preferably execution mode; but embodiments of the present invention are not restricted to the described embodiments; change, the modification done under other any does not deviate from Spirit Essence of the present invention and principle, substitute, combine, simplify; all should be the substitute mode of equivalence, be included within protection scope of the present invention.
Claims (11)
1. a business simulating perception evaluation method, is characterized in that, described method comprises:
Obtain level and the adjacent area level of grid in each cell service area in network, according to the upper-lower door limit value of adjacent area level, segmentation is carried out to described adjacent area level, add up each adjacent area level and be positioned at three interval probability P 1, P2, P3;
According to formula Pp=Σ Ps*(P1, P2, P3) * Pii* user density, calculate the business probability of happening of each grid; Described Pp is grid service probability of happening, and Ps is the level distribution probability of grid, and Pii is the level distribution probability of each adjacent area;
According to formula P=Pp/(Σ Pp) calculate the business probability of happening of whole network;
Use Monte Carlo simulation, according to the business probability of happening of whole network, in network area, carry out emulation traffic spread a little, complete the call simulation of each community in network and spread a little;
Spread a little according to the call simulation of each community in described network, carry out new site emulation;
Service-aware assessment is carried out to the new site of described emulation.
2. the method for claim 1, is characterized in that, is obtained level and the adjacent area level of grid in each cell service area in described network by MR Data Collection.
3. the method for claim 1, is characterized in that, the upper-lower door limit value of described adjacent area level sets as required;
The adjacent area level of described reception is according to following formulae discovery:
Pr=Pt – Lf+Ga – L θ – PL; Wherein, PL is airborne spread path loss values; Pt is the transmitting power of described community; Lf is the loss of feedback/wire jumper; Ga is antenna main lobe gain; L θ is the horizontal and vertical complete attenuation value of antenna to Signal reception point.
4. the method for claim 1, is characterized in that, described three interval probability P 1, P2, P3 are according to following formulae discovery:
P1=NBR_OF_SAMPLES_IN_CLASS_1/AVE_DL_SIG_STR_SERV_CELL_DEN；
P2=NBR_OF_SAMPLES_IN_CLASS_2/AVE_DL_SIG_STR_SERV_CELL_DEN；
P3=NBR_OF_SAMPLES_IN_CLASS_3/AVE_DL_SIG_STR_SERV_CELL_DEN；
Wherein, described NBR_OF_SAMPLES_IN_CLASS_1, NBR_OF_SAMPLES_IN_CLASS_2, NBR_OF_SAMPLES_IN_CLASS_3, AVE_DL_SIG_STR_SERV_CELL_DEN obtain from described MR data.
5. the method for claim 1, is characterized in that, the level distribution probability P s=of described each grid meets the probability of normal distyribution function at this level of Serving cell level; The probability at the level place, adjacent area that the normal distyribution function that the level distribution probability P ii=of each adjacent area meets adjacent area level counts in this grid.
6. the method for claim 1, is characterized in that, in described network, the call simulation of each community spreads probability=Σ Σ (affected traffic accounting * grid user density accounting * neighbours level distribution accounting) a little; Wherein, the total sampling number in total number of sample points/Serving cell in a certain interval of affected traffic accounting=meet; Total number of users of the number of users/overlapping region of grid user density accounting=grid; The probability of adjacent area level distribution=adjacent area level Normal Distribution.
7. the method for claim 1, is characterized in that, described new site emulation, comprises the steps:
Determine to optimize region;
The comprehensive experience coefficient of computing service, determines whether to need new site according to service integration experience coefficient;
Determine new site parameter;
The field intensity in test new site region;
The antenna feeder of adjacent area around adjustment new site.
8. the method for claim 1, is characterized in that, the described new site to described emulation carries out service-aware assessment, comprising:
The rationally distributed property assessment in basis;
The lifting amplitude assessment covered;
The improvement assessment of scenario of capacity;
The situation of change assessment of carrier interference ratio C/I;
The improvement assessment of scenario of cutting off rate.
9. a business simulating perception evaluation system, it is characterized in that, described system comprises Monte Carlo simulation unit, grid service probability of happening computing unit, Network probability of happening computing unit, traffic spread dot element, new site simulation unit and service-aware assessment unit, wherein
Described Monte Carlo simulation unit, for obtaining level and the adjacent area level of grid in each cell service area in network, according to the upper-lower door limit value of adjacent area level, segmentation is carried out to described adjacent area level, add up each adjacent area level and be positioned at three interval probability P 1, P2, P3;
Described grid service probability of happening computing unit, for according to formula Pp=Σ Ps*(P1, P2, P3) * Pii* user density, calculate the business probability of happening of each grid; Described Pp is grid service probability of happening, and Ps is the level distribution probability of grid, and Pii is the level distribution probability of each adjacent area;
Described Network probability of happening computing unit, for according to formula P=Pp/(Σ Pp) calculate the business probability of happening of whole network;
Described traffic spreads dot element, for using Monte Carlo simulation, carrying out emulation traffic and spreading a little, completes the call simulation of each community in network and spreads a little;
Described new site simulation unit, for spreading a little according to the call simulation of each community in described network, carries out new site emulation;
Described service-aware assessment unit, for carrying out service-aware assessment to the new site of described emulation.
10. system as claimed in claim 9, is characterized in that, described new site simulation unit comprises optimizes region subelement, service integration experience subelement, parameter determination subelement, field strength measurement subelement and adjacent area adjustment subelement, wherein,
Described optimization region subelement, optimizes region for determining;
Described service integration experiences subelement, for the comprehensive experience coefficient of computing service, determines whether to need new site according to service integration experience coefficient;
Described parameter determination subelement, for determining new site parameter;
Described field strength measurement subelement, for testing the field intensity in new site region;
Described adjacent area adjustment subelement, for adjusting the antenna feeder of adjacent area around new site.
11. systems as claimed in claim 9, is characterized in that, described service-aware assessment unit comprises basic layout subelement, covering promotes subelement, capacity improves subelement, carrier/interface ratio changes subelement and cutting off rate improves subelement, wherein,
Described basic layout subelement, for the rationally distributed property assessment in basis;
Described covering promotes subelement, for the lifting amplitude assessment covered;
Described capacity improves subelement, for the improvement assessment of scenario of capacity;
Described carrier/interface ratio change subelement, the situation of change for carrier interference ratio C/I is assessed;
Described cutting off rate improves subelement, for the improvement assessment of scenario of cutting off rate.
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