CN109831794A - Base station clustering method based on density and minimum range in a kind of super-intensive network - Google Patents
Base station clustering method based on density and minimum range in a kind of super-intensive network Download PDFInfo
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- CN109831794A CN109831794A CN201910222536.0A CN201910222536A CN109831794A CN 109831794 A CN109831794 A CN 109831794A CN 201910222536 A CN201910222536 A CN 201910222536A CN 109831794 A CN109831794 A CN 109831794A
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Abstract
The invention discloses the base station clustering methods based on density and minimum range in a kind of super-intensive network, the distribution density and sub-clustering density threshold for calculating each microcell base station in super-intensive network first, the microcell base station for making distribution density be greater than sub-clustering density threshold constitute initial cluster center pond；Calculate the minimum value of distance between each microcell base station and the microcell base station high compared with its distribution density in the pond of initial cluster center, the product of the distribution density and its minimum range that define microcell base station is weight distribution density, obtains cluster center to be selected pond by weight distribution density；Cluster center isolation distance is calculated, successively by the lesser cluster center of weight distribution density value is removed from the pond of cluster center to be selected in two cluster centers for being greater than cluster center isolation distance between any two in the pond of cluster center to be selected；Finally using the cluster center center Chi Zhongcu number to be selected and cluster center base station geographical location as the parameter of tradition K-means algorithm, executes K-means algorithm and obtain sub-clustering result；The present invention solves the problems, such as that sub-clustering is non-uniform.
Description
Technical field
The invention belongs to wireless communication technology fields specifically to relate to applied to the management of microcell base station in super-intensive network
And a kind of base station clustering method in super-intensive network based on density and minimum range.
Background technique
With the rapid development of mobile communication technology, the communication equipment in network is linked into explosive growth, so that former
Originally huge network structure becomes more complicated.Meanwhile the also sharp increase of demand of the user to data traffic, wireless network will
Face huge pressure and challenge.It, can be substantially by the way that microcell base station to be densely deployed in cell in super-intensive network
The power system capacity of the raising wireless network of degree.However due to the dense deployment of a large amount of base stations, the interference problem in network is also increasingly
Seriously, the unreasonable problem of resource allocation is urgently to be resolved.In super-intensive network, whole network is divided by cluster-based techniques first
At multiple sub-networks, interference management and resource allocation are then carried out in each sub-network can effectively simplify network topology knot
Structure, convenient for being managed to base station, so that interference management, reasonable distribution resource be effectively performed.Wherein, it is calculated using K-means
Method quickly can carry out effective sub-clustering to base station according to the position of microcell base station.But tradition K-means algorithm needs
The number of sub-clustering is artificially arranged, the algorithm is resulted in be unable to the variation of adaptive network topological structure in this way.In addition, should
Algorithm due to being random selection initial cluster center is easy that final sub-clustering result is made to fall into locally optimal solution.
It is, thus, sought for one can adapt to continually changing network topology structure, and preferably network is carried out
The cluster-dividing method of sub-clustering, so as to carry out practical application in super-intensive network.
Summary of the invention
The purpose of the present invention is to provide the base station clustering method based on density and minimum range in a kind of super-intensive network,
This method can carry out dynamic clustering to magnanimity base station according to the variation of network topology, avoid falling into office by screening cluster central point
The case where portion's optimal solution, improves the accuracy of sub-clustering, while also accelerating the convergence rate of sub-clustering, solves in traditional scheme
The non-uniform problem of sub-clustering result, the base station suitable for super-intensive network efficiently manage, and specific technical solution is as follows:
Base station clustering method based on density and minimum range in a kind of super-intensive network, the method includes the steps:
S1, record super-intensive network in N number of microcell base station geographical location, and calculate any two microcell base station it
Between Euclidean distance；
S2, the distribution density and sub-clustering density threshold for calculating each microcell base station in super-intensive network are more each small
The distribution density of area base station and the size of sub-clustering density threshold, and the microcell base station by distribution density greater than sub-clustering density threshold
As initial cluster center；
S3, all initial cluster centers are formed into initial cluster center pond, calculated each micro- in the pond of the initial cluster center
The minimum value of distance between cell base station and the microcell base station high compared with its distribution density；
S4, calculate in the pond of the initial cluster center distribution density of each microcell base station with compared with high small of its distribution density
Apart from the product of minimum value between area base station, it is denoted as weight distribution density θj, and microcell base station is corresponded into the weight distribution
Density θjDescending arranges to form cluster center to be selected pond from big to small；
S5, cluster center isolation distance is calculated, and according to the weight distribution density θjSize at the cluster center to be selected
Descending arrangement is carried out in pond, successively by distance is greater than two of cluster center isolation distance between any two in the pond of cluster center to be selected
The lesser cluster center of weight distribution density value is removed from the pond of cluster center to be selected in cluster center；
S6, statistics simultaneously record the number K's at the cluster center center Chi Zhongcu to be selected and every cluster center finally obtained
Geographical location, as in parameter input tradition K-means algorithm, execution K-means algorithm obtains all micro- in super-intensive network
The sub-clustering result of cell base station.
Further, in step S2, the distribution density is defined as:
Wherein, di,jIndicate super-intensive network in microcell base station i with it is micro-
Euclidean distance between cell base station j；
The sub-clustering density threshold is defined as:
Wherein, α be initial cluster center number control coefrficient, value range be α ∈ [0.5,
1]。
Further, in step S3, the minimum value is bigger, illustrates that Microcell micro-base station is distributed in the initial cluster center
It is more uniform, and the maximum microcell base station of distribution density apart from minimum value is farthest with its distance in the initial cluster center
The distance between microcell base station；
All initial cluster centers are according to the big of the distribution density for corresponding to microcell base station in the pond of the initial cluster center
Small descending arrangement.
Further, in step S4, the weight distribution density θjIt is bigger, illustrate and the weight distribution density θjIt is corresponding
Microcell base station distribution density it is bigger, and with the weight distribution density θjCorresponding microcell base station is away from other Microcell bases
The distance stood is remoter.
Further, in step S5, cluster center isolation distance is defined as:
Wherein, β is the control coefrficient of initial cluster center number, and value range is β ∈
[0.5,1]。
It further, will be between microcell base station i and any microcell base station j any in super-intensive network in step S5
Euclidean distance di,jWith the cluster center isolation distance RcCompare, if di,j> Rc, then by microcell base station j from the cluster to be selected
Center removes in pond, up to the distance d of any two microcell base station in the cluster center to be selected pondi,jIt is all larger than in the cluster
Heart isolation distance Rc。
Base station clustering method in super-intensive network of the invention based on density and minimum range, is calculated ultra dense first
Distribution density is greater than the Microcell of sub-clustering density threshold by the distribution density and sub-clustering density threshold for collecting microcell base station in network
The distance between base station constructs initial cluster center pond, and calculate initial cluster center pond any two microcell base station；Then, it calculates
The weight distribution density of each microcell base station in the pond of initial cluster center is for actual distribution density and apart from high density base station
The product of minimum range carries out descending arrangement to base station in pond by weight distribution density, forms cluster center to be selected pond；And calculate to
The cluster center isolation distance of scavenger；Compare the distance and cluster center isolation distance of initial cluster center pond any two microcell base station
Size, by be less than cluster center isolation distance microcell base station from cluster center to be selected pond remove；Finally, counting and recording to be selected
The number of microcell base station and geographical location in the pond of cluster center, number and geographical location is defeated as the parameter of tradition K-means
Enter and execute K-means algorithm, obtains the sub-clustering result of microcell base station in super-intensive network；Compared with prior art, this hair
The bright number that sub-clustering is set that can be adaptive according to the change in location of base station in network, so that super-intensive network be better achieved
Sub-clustering；Combine screening initial cluster center with minimum range by the distribution density of calculation base station, it can be to avoid falling into part
The case where optimal solution, to obtain the higher sub-clustering result of accuracy.
Detailed description of the invention
Fig. 1 is the base station clustering method based on density and minimum range in super-intensive network described in the embodiment of the present invention
Flow diagram signal.
Fig. 2 is the microcell base station distributing position simulation drawing of the embodiment of the present invention.
Fig. 3 is the sub-clustering result figure of the embodiment of the present invention.
Specific embodiment
In order to enable those skilled in the art to better understand the solution of the present invention, below in conjunction in the embodiment of the present invention
Attached drawing, technical scheme in the embodiment of the invention is clearly and completely described.
In embodiments of the present invention, the base station sub-clustering side based on density and minimum range in a kind of super-intensive network is provided
Method；Specifically, the present invention with the distributing position simulation that the region of 300m*300m carries out microcell base station come to the method for the present invention into
Row illustrates that the position distribution of all microcell base stations in region meets independent Poisson's point distributed process；Refering to fig. 1, it is assumed that area
In domain the number of microcell base station be N=50, then the detailed process of method comprising steps of
Step 1: recording the geographical location of N number of microcell base station in super-intensive network, i.e. N=50, referring to Fig.2, being illustrated as
The distributing position simulation drawing of microcell base station in the present embodiment, it can be seen that in super-intensive network each microcell base station actual bit
Distribution situation is set, so as to obtain the geographical location of each microcell base station；Then calculate any two microcell base station it
Between Euclidean distance；Base station i and base station j is 2 dimensional vectors in super-intensive network, then the Euclidean distance between base station i and base station j
It calculates are as follows:
L ∈ { 1,2 }, to obtain the Euclidean distance between any two microcell base station.
Step 2: calculating the distribution density and sub-clustering density threshold of each microcell base station in super-intensive network, wherein point
Cloth density is defined as:In formula, di,jIndicate microcell base station i in super-intensive network
With the Euclidean distance between microcell base station j；Sub-clustering density threshold is defined as:In formula, α is the initial cluster
The control coefrficient of center number, value range is α ∈ [0.5,1], and α is bigger, is shown in super-intensive network containing in initial cluster
The number of the heart is fewer, and the present embodiment is specifically described with α=1 pair the method for the present invention；The each microcell base station that will be obtained
Distribution density with sub-clustering density threshold carry out numerical values recited compared with, if the corresponding distribution density of microcell base station be greater than sub-clustering it is close
Threshold value is spent, i.e.,Then using corresponding microcell base station j as initial cluster center.
Step 3: all initial cluster centers are formed initial cluster center pond, each Microcell in the pond of initial cluster center is calculated
The minimum value of distance between base station and the microcell base station high compared with its distribution density；In embodiment, institute in the pond of initial cluster center
There is initial cluster center to arrange according to the size descending of the distribution density of corresponding microcell base station；Specified microcell base station is corresponding minimum
Value is bigger, illustrates that micro-base station distribution in Microcell is more uniform in initial cluster center, and distribution density is maximum micro- in initial cluster center
Cell base station apart from minimum value be with it apart from farthest the distance between microcell base station.
Step 4: calculate in the pond of initial cluster center the distribution density of each microcell base station with compared with high small of its distribution density
Apart from the product of minimum value between area base station, it is denoted as weight distribution density θj, and microcell base station is corresponded into weight distribution density θj
Descending arranges to form cluster center to be selected pond from big to small；Specifically, by microcell base station j to the base station big compared with distribution density
Minimum value in the distance of (i.e. high density base station) is defined as δj, then weighted density θjIt can position are as follows: θj=δj·ρj；Specific real
It applies in example, weight distribution density θjIt is bigger, illustrate and weight distribution density θjThe distribution density of corresponding microcell base station is bigger,
And with weight distribution density θjCorresponding distance of the microcell base station away from other microcell base stations is remoter.
Step 5: calculating the cluster center isolation distance in cluster center to be selected pond, and according to cluster center in the pond of cluster center to be selected
In tandem sequence, successively by the pond of cluster center to be selected between any two distance be greater than cluster center isolation distance two cluster centers
The middle lesser cluster center of weight distribution density value is removed from the pond of cluster center to be selected；In the embodiment of the present invention, cluster center isolation away from
From is defined as:In formula, β be initial cluster center number control coefrficient, value range be β ∈ [0.5,
1], wherein if β is smaller, illustrate that cluster center isolation distance is smaller.
Obtaining the cluster center center Chi Zhongcu isolation distance R to be selectedcAfterwards, by microcell base station i any in super-intensive network
With the Euclidean distance d between any microcell base station ji,jWith cluster center isolation distance RcCompare, if di,j> Rc, then by Microcell
Base station j is removed from the pond of cluster center to be selected, up to the distance d of any two microcell base station in the pond of cluster center to be selectedi,jIt is all larger than
Cluster center isolation distance Rc。
Step 6: counting and recording the number K at the cluster center center Chi Zhongcu to be selected and every cluster center corresponds to Microcell base
The geographical location stood executes K-means algorithm as parameter input tradition K-means, obtains all small in super-intensive network
The sub-clustering result of area base station；Refering to Fig. 3, the super-intensive network containing 50 microcell base stations is divided by the method for the invention
After cluster, from the point of view of sub-clustering effect picture, it can be clearly seen that the sub-clustering result of the present invention program is more uniform；Wherein, 50 base stations
It is divided into 5 clusters altogether, the base station number of each cluster is essentially identical, and sub-clustering effect is ideal.
Base station clustering method in super-intensive network of the invention based on density and minimum range, is calculated ultra dense first
Distribution density is greater than the Microcell of sub-clustering density threshold by the distribution density and sub-clustering density threshold for collecting microcell base station in network
The distance between base station constructs initial cluster center pond, and calculate initial cluster center pond any two microcell base station；Then, it calculates
The weight distribution density of each microcell base station in the pond of initial cluster center is for actual distribution density and apart from high density base station
The product of minimum range carries out descending arrangement to base station in pond by weight distribution density, forms cluster center to be selected pond；And calculate to
The cluster center isolation distance of scavenger；Compare the distance and cluster center isolation distance of initial cluster center pond any two microcell base station
Size, by be less than cluster center isolation distance microcell base station from cluster center to be selected pond remove；Finally, counting and recording to be selected
The number of microcell base station and geographical location in the pond of cluster center, number and geographical location is defeated as the parameter of tradition K-means
Enter and execute K-means algorithm, obtains the sub-clustering result of microcell base station in super-intensive network；Compared with prior art, this hair
The bright number that sub-clustering is set that can be adaptive according to the change in location of base station in network, so that super-intensive network be better achieved
Sub-clustering；Combine screening initial cluster center with minimum range by the distribution density of calculation base station, it can be to avoid falling into part
The case where optimal solution, to obtain the higher sub-clustering result of accuracy.
The foregoing is merely a prefered embodiment of the invention, is not intended to limit the scope of the patents of the invention, although referring to aforementioned reality
Applying example, invention is explained in detail, for a person skilled in the art, still can be to aforementioned each specific
Technical solution documented by embodiment is modified, or carries out equivalence replacement to part of technical characteristic.All utilizations
The equivalent structure that description of the invention and accompanying drawing content are done directly or indirectly is used in other related technical areas, together
Reason is within the invention patent protection scope.
Claims (6)
1. the base station clustering method in a kind of super-intensive network based on density and minimum range, which is characterized in that the method packet
Include step:
The geographical location of N number of microcell base station in S1, record super-intensive network, and calculate between any two microcell base station
Euclidean distance；
S2, the distribution density and sub-clustering density threshold for calculating each microcell base station in super-intensive network, more each Microcell base
The size of the distribution density stood and sub-clustering density threshold, and using distribution density be greater than the microcell base station of sub-clustering density threshold as
Initial cluster center；
S3, all initial cluster centers are formed into initial cluster center pond, calculates each Microcell in the pond of the initial cluster center
The minimum value of distance between base station and the microcell base station high compared with its distribution density；
S4, the distribution density of each microcell base station and the Microcell base high compared with its distribution density in the pond of the initial cluster center are calculated
Apart from the product of minimum value between standing, it is denoted as weight distribution density θj, and microcell base station is corresponded into the weight distribution density θj
Descending arranges to form cluster center to be selected pond from big to small；
S5, cluster center isolation distance is calculated, and according to the weight distribution density θjSize in the cluster center Chi Zhongjin to be selected
Row descending arrangement, successively by distance is greater than two cluster centers of cluster center isolation distance between any two in the pond of cluster center to be selected
The middle lesser cluster center of weight distribution density value is removed from the pond of cluster center to be selected；
S6, statistics simultaneously record the number K at the cluster center center Chi Zhongcu to be selected finally obtained and the geography at every cluster center
Position executes K-means algorithm and obtains all Microcells in super-intensive network as in parameter input tradition K-means algorithm
The sub-clustering result of base station.
2. the base station clustering method in super-intensive network as described in claim 1 based on density and minimum range, feature exist
In, in step S2, the distribution density is defined as:
Wherein, di,jIndicate microcell base station i and Microcell base in super-intensive network
The Euclidean distance stood between j；
The sub-clustering density threshold is defined as:
Wherein, α is the control coefrficient of initial cluster center number, and value range is α ∈ [0.5,1].
3. the base station clustering method in super-intensive network as described in claim 1 based on density and minimum range, feature exist
In in step S3, the minimum value is bigger, illustrates that micro-base station distribution in Microcell is more uniform and described in the initial cluster center
In initial cluster center the maximum microcell base station of distribution density apart from minimum value be with its apart from farthest microcell base station it
Between distance；
All initial cluster centers are dropped according to the size of the distribution density of corresponding microcell base station in the pond of the initial cluster center
Sequence arrangement.
4. the base station clustering method in super-intensive network as described in claim 1 based on density and minimum range, feature exist
In, in step S4, the weight distribution density θjIt is bigger, illustrate and the weight distribution density θjCorresponding microcell base station
Distribution density is bigger, and with the weight distribution density θjCorresponding distance of the microcell base station away from other microcell base stations is remoter.
5. the base station clustering method in super-intensive network as claimed in claim 2 based on density and minimum range, feature exist
In, in step S5, cluster center isolation distance is defined as:
Wherein, β be initial cluster center number control coefrficient, value range be β ∈ [0.5,
1]。
6. the base station clustering method in super-intensive network as claimed in claim 5 based on density and minimum range, feature exist
In in step S5, by the Euclidean distance d between microcell base station i and any microcell base station j any in super-intensive networki,jWith
The cluster center isolation distance RcCompare, if di,j> Rc, then microcell base station j is removed from the cluster center to be selected pond, directly
To the distance d of any two microcell base station in the cluster center to be selected pondi,jIt is all larger than the cluster center isolation distance Rc。
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