CN109600756A - A kind of physical area identification distribution method based on the preferential coloring algorithm of maximal degree - Google Patents
A kind of physical area identification distribution method based on the preferential coloring algorithm of maximal degree Download PDFInfo
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Abstract
The present invention relates to a kind of, and the physical area based on the preferential coloring algorithm of maximal degree identifies distribution method, solve the physical area identification (PCI for being used to identify cellular network base station in small cell network technology, Physical Cell Identification) it is extremely limited, small cell network is caused to can not be successfully deployment, the problem of network service quality (QoS, Quality of Service) of user receives extreme influence.This method uses K mean cluster algorithm to carry out clustering processing to network user's mobile data of magnanimity first, divides and determine the hot zones of user.It is then based on maximal degree preferential coloring algorithm and PCI distribution and multiplexing is carried out to the cellular base station of different hot zones.The method achieve distribution and multiplexing that PCI is fast and effeciently carried out to cellular base station, while reducing the conflict confusion probabilities of PCI, have ensured the QoS of hot zones user.
Description
Technical field
The present invention carries out clustering processing using network user mobile data of the K mean cluster algorithm to magnanimity, realizes heat
The determination and division in point area.Then a kind of PCI distribution method based on the preferential coloring algorithm of maximal degree is proposed, this method can
The distribution and multiplexing of PCI are carried out with the small cell base station fast and effeciently to hot zones, while the conflict for reducing PCI is obscured
Probability improves the network service quality of hot zones user.PCI distribution method based on the preferential coloring algorithm of maximal degree belongs to
Mobile communication network field.
Background technique
With the fast development of wireless communication and development of Mobile Internet technology, global mobile network data flow is past
20 times are increased in 5 years.Ended for the end of the year 2017, statistical result showed global network data traffic, which is averaged, to be reached every month
Surprising 7.2EB, wherein 4G data traffic accounting 69%.The explosive growth of data make big data become in recent years by
The concept of concern, it refers to by the way that the complicated data source in a large amount of, source is captured at high speed, finds and analyzed, and uses mould
Method in formula identification or statistics extracts the technical system or Technical Architecture of its value.
Big data early stage is mainly used in the fields such as business, finance, gradually expands to the neck such as traffic, medical treatment and energy later
Domain.Nowadays, wireless network is also considered one of the important technology field of big data application.On the one hand, with intelligent terminal
More new development, the network data services request of user is more frequent, and the network traffic data of generation is consequently increased, and is needed pair
These various structures, source complexity data effectively transmitted and handled.On the other hand, as user is for network data
Request increases, and the construction and development of wireless network encounter obstruction.The explosive growth of network data makes legacy cellular net
The actual demand of user is much not achieved in the capacity of network, and particularly with the hot zones in city, a large amount of network is in line service
It generates in a short time, the concurrent data traffic transmission also to increase sharply, these network requests concentrated may cause network resistance
Plug even interrupts, and causes immeasurable loss.
In order to cope with the problem of wireless network data flow rapid growth causes, existing solution generallys use increase
Network cell density come realize dense network deployment technology, thus promoted data traffic transmission rate and increase network capacity
And coverage area.The core of the program is by densely disposing the base station a large amount of small cell network (SC, Small Cells)
In traditional macrocellular network framework, isomery cellular network (HCN, Heterogeneous Cellular is formed
Network), more good network service quality is provided for the user of hot zones.It is extensive random in isomery cellular network
The small cell network base station of deployment needs physical area identification code (PCI, Physical Cell Identification) to come area
Divide each other.However, being limited by the limitation of physical spectrum resource, the quantity of PCI is extremely limited.Long evolving system (LTE,
Long Term Evolution) in available PCI quantity be 504.Therefore, it is necessary to magnanimity the network user and bee
The related data of nest base station is analyzed and is handled, and is designed rationally effective PCI allocation plan, is maximally utilized limited PCI
Resource completes the building of hot zones isomery cellular network, promotes network service quality (QoS, the Quality of of user
Service)。
Therefore, in order to solve the problems such as PCI resource is extremely limited, this method is higher to " liveness " using K mean cluster
Cellular base station area the network user carry out clustering processing, hot zones are determined according to obtained cluster result.Then to not
It is patterned modeling with the complicated cellular network in hot zones, and innovatively builds the PCI assignment problem of cellular base station
Mould is figure dyeing course.It finally proposes and uses the PCI allocation plan based on the preferential coloring algorithm of maximal degree.
Summary of the invention
The arriving of big data era is so that explosive growth, existing cellular network is presented in hot zones network traffic data
Capacity is unable to satisfy the demand of user, and then cellulor technology is suggested and is widely applied.However, limited physics is small
Area's identification (PCI, Physical Cell Identification) is difficult to reasonable distribution to large number of small cell base station, leads
Cause cellular deployment difficult, the network service quality (QoS, Quality of Service) of user is affected.Therefore, originally
Method carries out clustering processing using network user mobile data of the K mean cluster algorithm to magnanimity, determines hot zones.Then it mentions
A kind of PCI distribution method based on the preferential coloring algorithm of maximal degree is gone out, the program can be fast and effeciently different to hot zones
Small cell base station in structure cellular network (HCN, Heterogeneous Cellular Network) carries out the distribution of PCI and answers
With, while the conflict confusion probabilities of PCI are reduced, improve the QoS of hot zones user.
Specific step is as follows for this method:
Step 1, the deployment model of small cell network and macrocellular network is constructed.
Traditional cellular deployment model uses Wrap-Around model, i.e., models the coverage area of cellular cell
At the regular hexagon of a regular length, and countless multiple regular hexagon cellular cells are embedded into the circle of a limited size
In plane, so as to form the deployment architecture of cellular network, the network topology schematic diagram of Wrap-Around model is as shown in Figure 1.
With the introducing of cellulor technology, existing network service quality is significantly improved, network cell is improved
Capacity.However accompanying problem is that cellulor substantial amounts, and meet random placement, traditional Wrap- can not be used
Around model simulates the deployment scenario of cellulor.This method carrys out mould using the homogeneous poisson process in random geometry theory
The deployment of quasi- emulation small cell base station.
Homogeneous poisson process meets following two condition:
A) assume to select an Euclidean space region B in space, while the region meets boundedness.So it is in region
The number N (B) of point in B then obeys Poisson distribution, and mean value is λ vd(B).Wherein λ > 0, vd(B) area of region B is represented.
Have for variable N (B), shown in the calculation formula such as formula (1) for occurring the probability P (N (B)=s) of s point in the B of region:
B) a series of mutually disjoint bounded domain B are randomly choosed in space1, B2..., Bn, then in that region
Point number it is mutually indepedent, i.e. variable N (B1), N (B2) ..., N (Bn) between it is mutually indepedent.
Above property substantially conforms to the actual deployment needs of small cell base station, therefore using poisson process model come mould
The deployment of quasi- emulation small cell base station.The coverage area of small cell base station then uses common Voronoi model.The model is
What mathematician Georgy Voronoi was proposed, it is all widely used in architecture, physics and computer network communication.
The model is assumed initially that plants some random points in area of space, divides the space into several areas according to the position of each point
Domain, the boundary for constituting these regions is then the perpendicular bisector of consecutive points line.Under such planing method and strategy, every piece of region
It is pertaining only to nearest point.According to research, such division mode also complies with the nearest communication principle in cellular network, so
This is a kind of at present using more extensive cellulor deployment model.The simulation deployment of small cell base station is as shown in Figure 2.
Step 2, it completes after being modeled in step 1 to the deployment model of cellular network base station, basis obtains this method in step 2
To deployment model establish the PCI distribution model of cellular network base station.
Due to the cellular base station quantity of hot zones and its huge, in order to simplify the PCI of hot zones cellular network base station
PCI assigning process under HCN scene is abstracted by assigning process, this method by patterned mode.Random distribution in HCN
A large amount of cellulors and macrocell base stations, this method the network topology structure of HCN is modeled as connected graph, with G=(V, E) table
Show, wherein vertex set V indicates to need to carry out the cellular base station of PCI distribution, and side collection E then indicates that there are PCI punchings between two base stations
It is prominent or obscure relationship.
If there are side collection relationships between two vertex in the figure of modeling, then it represents that corresponding two honeycomb bases in HCN
Station needs to be assigned different PCI, thus the generation for avoiding PCI from conflicting and obscure.So the dynamic allocation problem of PCI just turns
A given Connected undigraph and limited color category number are turned to, each vertex in figure is dyed, is existed in figure
Two vertex of side collection relationship need to dye different colors.Meanwhile in order to be further reduced present in PCI assigning process
Vertex in figure is not only connected with the vertex of its adjacent area by PCI confusion and conflict, this method, but also the adjacent area phase with all adjacent areas
Even, it can be further reduced the generation of PCI confusion and conflict in this way.
After completing mathematics library, the assigning process of PCI is conceptualized as the coloring problem of graphics vertex.Give a nothing
The color different with c kind to connected graph G=(V, E), is dyed, each vertex with vertex of the c kind color to Connected undigraph
Can only a kind of color, the final goal of the process be to find a kind of color method to make two of arbitrary neighborhood in figure G=(V, E)
A vertex different colors.
Step 3, after the foundation for completing PCI distribution model, in order to promote the efficiency of PCI distribution, it is thus necessary to determine that and divide heat
Point area, targetedly to distribute PCI to cellular network base station in different hot zones.
The number of network users of hot zones is numerous, by the analysis to hot zones user, finds the use of hot zones
Family has mobile group and activity centrality, i.e., user is often a large amount of in certain places and the time intensively initiates network
Data service request causes network congestion in short-term even to interrupt.Therefore it needs preferentially to carry out the cellular network of hot zones
The distribution of PCI ensures the hot zones network user's so that the building and deployment of this area's isomery cellular network be rapidly completed
QoS。
In order to quickly position hot zones, this method clusters the mobile data of user using K mean cluster algorithm
Processing, the mobile data point of user are divided into different class clusters, each class cluster indicates a hot zones.K-means is poly-
Class algorithm is a kind of simple, efficient clustering algorithm, and core concept is to randomly select initial cluster center first, is then calculated
Each sample point to initial cluster center Euclidean distance, it is maximum according to similarity is assigned these to apart from nearest criterion
Class representated by cluster centre.The mean value of each all sample points of class cluster is finally calculated, cluster centre is updated, until objective criteria
Until function convergence.
Step 4, after the deployment model foundation and the determination of hot zones of completing cellular network base station, this method is in step 4
The middle cellular network base station deployment model according under different hot zones is patterned the topological structure of isomery cellular network
Modeling.
It gives Connected undigraph G=(V, E), wherein vertex set V=(v1, v2..., vn), side collection E=(e1, e2..., en)。
If the degree of vertex v is the number for scheming side associated by vertex v in G, it is denoted as Δ G.It is learnt according to the theory of graph coloring algorithm, when
When Δ G+1≤c, certainly exist a kind of coloring algorithm make in G=(V, E) any two vertex different colors.
Before carrying out graph coloring algorithm, needing to be obtained according to the base station deployment situation of cellular network indicates that base station adjacent area is closed
The Connected undigraph G=(V, E) of system.If the network base station collection in cellular network is combined into BS={ bs1, bs2..., bsn, wherein n
Indicate the quantity of base station, it is assumed that base station bsiFor the network user us under the scenei, can be calculated according to signal-to-noise ratio formula (2)
User usiOther base stations bs under to the scenejSignal to Interference plus Noise Ratio (SINR, Signal to Interference
Plus Noise Ratio), to obtain SINRij.If obtained SINRij≥SINRthreshold, indicate base station bsiAnd bsj
Between there are neighboring BS relationship, wherein SINRthresholdWhat is indicated is the minimum SINR value that LTE system allows access base station.
Wherein σ indicates that ambient noise, M are cellular base station quantity all under cellular network scene,Indicate that user u connects
Receive the carrier signal strength of k-th of subchannel from cellular base station m.Wherein received signal strengthCalculation formula such as
Shown in formula (3):
WhereinIndicate the carrier power in k-th of subchannel of cellular base station m, AaddIndicate the antenna gain of sending and receiving end,
δM, uIndicate that carrier frequency is the path loss under 2.0GHz, ζM, uThe shade then represented between cellular network base station m and user u declines
It falls.Path loss δM, uCalculation formula such as formula (4) shown in:
δM, u=140.7+37.6-log10(dM, u) (4),
Wherein dM, uIndicate the distance between cellular base station m and user u, unit is km.Cellular network base station m and user u
Between shadow fading ζM, uObeying mean value is 0, and standard deviation is the log series model of noise floor value σ, ζM, uCalculation formula it is such as public
Shown in formula (5):
ζM, u=log10(0, σ2) (5)
Two base stations are calculated between the vertex in Connected undigraph there are after the collection relationship of side, indicate in side collection set
Element eij=1, illustrate that vertex i and vertex j need different colors when being dyed, to avoid cellular base station bsi
And bsjPCI conflict occurs and obscures.In order to be further reduced PCI confusion, for the element in the collection set of relationship of side, if deposited
In eij=1 and ejk=1, then there is eik=1, show base station bsiWith base station bskThere are second level neighboring BS relationship, are carrying out vertex coloring
When need different colors.
Step 5, the nothing for constructing cellular network base station is connected after figure, and this method uses maximal degree preferential in steps of 5
Coloring algorithm colours the connected graph of modeling.
Step 5.1, each degree of vertex in vertex set V is calculated first and all vertex are sorted from large to small,
And indicate different serial numbers to distinguish on different vertex.
Step 5.2, workable color is stored in set C, and indicates different serial numbers to different types of color
To distinguish.
Step 5.3, first available color c that first in vertex set V is unstained in vertex v C1Coloring.
Step 5.4, other vertex of being unstained of orderly vertex set V are traversed, and by identical color assignment to not phase
Adjacent vertex of being unstained.
Step 5.5: step 5.3 and 5.4 are iteratively applied, until all vertex are all colored.
After the preferential colouring algorithm of maximal degree for completing step 5, all vertex of connected graph are completed coloring process, and
Meet have side collection relationship two vertex different colors, indicate have neighboring BS relationship cellular base stations be assigned with not
Same PCI conflicts and obscures to avoid PCI.
By the preferential coloring algorithm of maximal degree, unique PCI is all assigned in the cellular base station in HCN, and has in HCN
There is the cellular base station of neighboring BS relationship that different PCI is assigned, avoid PCI conflict and obscure, while PCI has also been obtained effectively
Multiplexing.The program not only saves valuable PCI resource, has also ensured the QoS of user.
Creativeness of the invention is mainly reflected in:
1) mobile group and activity centrality that the present invention has for the user of hot zones, using K mean cluster
Algorithm carries out clustering processing to user's mobile data of magnanimity, realizes the division and determination of user's hot zones；
2) PCI assignment problem is innovatively modeled as to the colouring problem of Connected undigraph, greatly simplifies PCI distribution
Process, while decreasing the conflict of PCI and obscuring；
3) vertex coloring is carried out using modeling figure of the preferential coloring algorithm of maximal degree to cellular base station, it is existing compared to other
Scheme has shorter dyeing time and less color usage quantity, the benefit with higher PCI distribution time efficiency and PCI
With rate.
Detailed description of the invention
Fig. 1 is the simulation deployment diagram of macrocellular network base station in the present invention
Fig. 2 is the simulation deployment diagram of the medium and small cellular network base station of the present invention
Fig. 3 is the mobile data visualization schematic diagram of user of the present invention
Fig. 4 is that the present invention uses K- mean cluster completion hot zones division schematic diagram
Fig. 5 is the figure dyeing schematic diagram of cellular network base station modeling of the present invention
Specific embodiment
The present invention carries out clustering processing using network user mobile data of the K mean cluster algorithm to magnanimity, realizes heat
The determination and division in point area.Then a kind of PCI distribution method based on the preferential coloring algorithm of maximal degree is proposed, this method can
The distribution and multiplexing of PCI are carried out with the small cell base station fast and effeciently to hot zones, while the conflict for reducing PCI is obscured
Probability improves the network service quality of hot zones user.
Present invention employs the following technical solution and realize step.
Step 1, the deployment model of cellular network base station is constructed, macrocellular network base station is using traditional Wrap-
Around deployment model, as shown in Figure 1, being 1500 × 1500m of scene2Under macrocellular network base station simulate deployment diagram.For
Small cell network, the present invention carry out analog simulation small cell base station using the homogeneous poisson process in random geometry theory
Deployment, when the distribution of Stochastic Poisson point is desired for 100, simulated scenario size is 1500 × 1500m2When, small cell network base station
Simulation deployment diagram it is as shown in Figure 2.
Step 2, visualization processing is carried out to the mobile data of user, it is found that the mobile data of user has apparent group
Property and centrality, user mobile data point visualization after as shown in Figure 1.Then using k- means clustering algorithm to mass users
Mobile data carry out clustering processing.Realize the division and determination of user's hot zones.The mobile data point of user visualizes
Afterwards as shown in Figure 3.
Carrying out the realization of K- mean cluster to the mobile data of user, steps are as follows:
Step 2.1, the number of cluster and the data point set of user are inputted, K- means clustering algorithm needs are opened in cluster process
The specific number of specified cluster before beginning, the data point set by observing user in Fig. 3 has apparent hot zones feature, in map
On certain areas can largely assemble, these regions be exactly need herein divide and determine hot zones.It is equal by adjusting K-
It is worth the division result of the adjustable hot zones of clusters number of clustering algorithm, complies with actual application effect.
Step 2.2, central point of the K data point as cluster is arbitrarily chosen, and successively calculates other data points to cluster
The Euclidean distance of central point, the data point with minimum Eustachian distance then belong to the class cluster of the cluster centre.
Step 2.3, cluster centre point is constantly adjusted, cluster centre point can be adjusted using mean value method, first complete
After cluster process, the average value of all data points of each class cluster is sought as new cluster centre point, then repeatedly step
1.2, until objective criteria function convergence.
As shown in figure 4, for the result schematic diagram after the mobile data point completion K- mean cluster of user, different color points
The different hot zones of set representations.
Step 3, cellular network base station is modeled as Connected undigraph.User can be calculated according to signal-to-noise ratio formula (2)
The Signal to Interference plus Noise Ratio of each base station into scene, to obtain SINRij.If obtained SINRij≥
SINRthreshold, indicate base station bsiAnd bsjBetween there are neighboring BS relationship.According to the neighboring BS relationship being calculated by cellular network base
Station is modeled as Connected undigraph.Wherein each vertex can only a kind of color, there are two vertex of side collection relationship to need not
Same color.The coloring schematic diagram of connected graph is as shown in Figure 5.
Step 4, the figure of modeling is coloured using maximal degree preferential colouring algorithm, completes maximal degree and preferentially dyes calculation
After method, all vertex of connected graph are all coloured, and have different colors between two vertex with side collection relationship,
Different colors then indicates different PCI, illustrates that the two cellular base stations have been allocated for different PCI, to avoid
PCI conflicts and obscures.
Simulation parameter in this method provides in simulation parameter table 1.
1 simulation parameter table of table
Claims (2)
1. a kind of physical area based on the preferential coloring algorithm of maximal degree identifies distribution method, it is characterised in that steps are as follows:
Step 1, the deployment model of macrocellular network and small cell network is constructed；
The deployment of macrocellular network is simulated using Wrap-Around model, i.e., models the coverage area of cellular network cell
It is embedded on the circular flat of a limited size at the regular hexagon of a regular length, and by countless multiple regular hexagons；
The deployment of small cell network base station is emulated and simulated using homogeneous poisson process model；
The coverage area of small cell base station uses Voronoi model；
Step 2, it completes after being modeled in step 1 to the deployment model of cellular network base station, is built according to the deployment model that step 1 obtains
The PCI distribution model of vertical cellular network base station；
PCI assigning process under cellular network scene is abstracted by patterned mode, by cellular network topologies structure
It is modeled as connected graph, is indicated with G=(V, E), wherein vertex set y indicates to need to carry out the cellular base station of PCI distribution, and side collection E is then
Indicate existing neighboring BS relationship between base station；If the two vertex side Jian You collection line in connected graph exists, then it represents that honeycomb
Need to be assigned different PCI there are neighboring BS relationship between corresponding two cellular base stations in network, thus avoid PCI conflict and
The generation obscured；
Therefore, given a Connected undigraph and limited color category number are converted by the dynamic allocation problem of PCI, to connection
Each vertex in figure is dyed, wherein two vertex with neighboring BS relationship need to be dyed to different colors；It will connection
Not only vertex is connected with its adjacent area on each vertex in figure, but also is connected with the adjacent area vertex on its adjacent area vertex；
After completing mathematics library, the assigning process of PCI is conceptualized as the Vertex Coloring Problem of connected graph；I.e. given one undirected
Colors connected graph G=(V, E) different with c kind is dyed with vertex of the c kind color to Connected undigraph, and each vertex is only
Can a kind of color；The final goal of the process is two for finding a kind of color method and making arbitrary neighborhood in figure G=(V, E)
Vertex different colors；
Step 3, after the foundation for completing PCI distribution model, in order to promote the efficiency of PCI distribution, it is thus necessary to determine that and with dividing hot spot
Area, targetedly to distribute PCI to cellular network base station in different hot zones；
Clustering processing is carried out using mobile data of the K mean cluster algorithm to user, the mobile data point of user is divided into not
Same class cluster, each class cluster indicate a hot zones；K-means clustering algorithm core concept is to randomly select first initially
Cluster centre, then calculate each sample point to initial cluster center Euclidean distance, according to apart from nearest criterion by they
Distribute to class representated by the maximum cluster centre of similarity；The mean value of each all sample points of class cluster is finally calculated, is updated
Cluster centre, until objective criteria function convergence；
Step 4, after the deployment model foundation and the determination of hot zones of completing cellular network base station, according under different hot zones
Cellular network base station deployment model modeling is patterned to the topological structure of isomery cellular network；
It gives Connected undigraph G=(V, E), wherein vertex set V=(v1, v2..., vn), side collection E=(e1, e2..., en)；Arbitrarily
The degree of one vertex v is the number for scheming side associated by vertex v in G, is denoted as Δ G；It is learnt according to the theory of graph coloring algorithm,
As Δ G+1≤c, certainly exist a kind of coloring algorithm make any two are adjacent in G=(V, E) vertex different face
Color；
Before carrying out graph coloring algorithm, needing to be obtained according to the base station deployment situation of cellular network indicates base station neighboring BS relationship
Connected undigraph G=(V, E)；If the network base station collection in cellular network is combined into BS={ bs1, bs2..., bsn, wherein n is indicated
The quantity of base station, it is assumed that base station bsiFor the network user us under the scenei, user us is calculated according to signal-to-noise ratio formula (2)i
Other base stations bs under to the sceneiSignal to Interference plus Noise Ratio, to obtain SINRij；If obtained SINRij≥
SINRthreshold, indicate base station bsiAnd bsjBetween there are neighboring BS relationship, wherein SINRthresholdWhat is indicated is that LTE system allows
The minimum SINR value of access base station；
Wherein σ indicates that ambient noise, M are cellular base station quantity all under cellular network scene,Indicate that user u is received
The carrier signal strength of k-th of subchannel from cellular base station m；Wherein received signal strengthCalculation formula such as formula
(3) shown in:
WhereinIndicate the carrier power in k-th of subchannel of cellular base station m, AaddIndicate the antenna gain of sending and receiving end, δM, uTable
Show that carrier frequency is the path loss under 2.0GHz, ζM, uThen represent the shadow fading between cellular network base station m and user u；Road
δ is lost in diameterM, uShown in calculation formula such as formula (4):
δM, u=140.7+37.6log10(dM, u) (4),
Wherein dM, uIndicate the distance between cellular base station m and user u, unit is km；Between cellular network base station m and user u
Shadow fading ζM, uObeying mean value is 0, and standard deviation is the log series model of noise floor value σ, ζM, uCalculation formula such as formula (5)
It is shown:
ζM, u=log10(0, σ2) (5)
Two base stations are calculated between the vertex in Connected undigraph there are after the collection relationship of side, indicate the member in side collection set
Plain eij=1, illustrate that vertex i and vertex j need different colors when being dyed, to avoid cellular base station bsiAnd bsj
PCI conflict occurs and obscures；In order to be further reduced PCI confusion, for the element in the collection set of relationship of side, if there is eij=
1 and ejk=1, then there is eik=1, show base station bsiWith base station bskThere are second level neighboring BS relationship, the needs when carrying out vertex coloring
Different colors；
Step 5, the nothing for constructing cellular network base station is connected after figure, the connection using the preferential coloring algorithm of maximal degree to modeling
Figure is coloured, specific as follows:
Step 5.1, each degree of vertex in vertex set V is calculated first and all vertex are sorted from large to small, and right
Indicate different serial numbers to distinguish in different vertex；
Step 5.2, workable color is stored in set C, and indicates different serial numbers with area different types of color
Point；
Step 5.3, first available color c that first in vertex set V is unstained in vertex v C1Coloring；
Step 5.4, other vertex of being unstained of orderly vertex set V are traversed, and by identical color assignment to non-conterminous
It is unstained vertex；
Step 5.5: step 5.3 and 5.4 are iteratively applied, until all vertex are all colored.
2. according to the method described in claim 1, it is characterized by: homogeneous poisson process model meets following two property:
A) assume to select an Euclidean region B in a space, which meets boundedness；So in the B of Euclidean region
The number N (B) of point then obey Poisson distribution, distribution mean value is λ vd(B)；Wherein λ indicates the Poisson distribution expectation of these points
Value, and λ > 0, vd(B) area of Euclidean region B is then indicated；Occurs the probability P (N (B)=s) of s point in the B of Euclidean region
Shown in calculation formula such as formula (1):
B) a series of mutually disjoint bounded domain B are randomly choosed in space1, B2..., Bn, then occurring in that region
Point number it is mutually indepedent, i.e. variable N (B1), N (B2) ..., N (Bn) between it is mutually indepedent.
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