CN108632943A - Cluster-dividing method based on small base station deployment density in 5G super-intensive networks - Google Patents
Cluster-dividing method based on small base station deployment density in 5G super-intensive networks Download PDFInfo
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- CN108632943A CN108632943A CN201810293651.2A CN201810293651A CN108632943A CN 108632943 A CN108632943 A CN 108632943A CN 201810293651 A CN201810293651 A CN 201810293651A CN 108632943 A CN108632943 A CN 108632943A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/12—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality
- H04W40/16—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality based on interference
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/22—Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
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Abstract
The present invention relates to the cluster-dividing methods based on base station deployment density in 5G super-intensive networks, including establish interference topological diagram, and the approximate range of number of clusters is determined using the relationship of small number of base stations and sub-clustering number, k is selected using the centers k value-based algorithm0A cluster head；According to interference value carry out sub-clustering compared with the size of setting interference threshold between base station；The small base station deployment density in the small base station deployment density in macro region, each sub-clustering is calculated separately, judges whether the deployment density of each sub-clustering and macro regional deployment density are equal, is merged or splitting operation；Judge whether modification parameter, iteration are completed.The present invention utilizes small base station deployment density on the basis of the centers k value-based algorithm, is merged to sub-clustering or splitting operation, and reaching sub-clustering scheme can be according to the variation of network topology structure, and adaptive carries out dynamic clustering to small base station.
Description
Technical field
The present invention relates to wireless communication technology field, it is related specifically in 5G super-intensive nets based on small base station deployment density
Cluster-dividing method.
Background technology
It comes into operation from 3G (3G (Third Generation) Moblie) to the successful commercialization of 4G (forth generation mobile communication), wirelessly communicates skill
Art is in continuous development and advances.Along with the universal and broadband internet communication industry of mobile phone, tablet and wearable terminal equipment
The diversification of business, the following people show explosive growth for the demand of communication service.From the point of view of business scenario, mobile service
60%, data service 70% occurs indoors and hot spot region.Existing 4G mobile networks, solve user's access rate,
Cut-in quality, high-speed mobility support, throughput hoisting etc. are with great breakthrough.But for higher and higher user's body
The end-to-end time delay of rate, the equipment in future hundred billion connection number density, Millisecond is tested, the prior art also cannot be satisfied, next
In the research of the communication technology in generation, researcher proposes super-intensive network technology, for solving the business of populated area
Needs of problems.
The existing 4G network architectures are operated under the centralized control of macro base station, and macro base station is equivalent to the big of system
Brain.Super-intensive network is the small base station of dense deployment under classical macro-cellular covering, by the distance of the small base station of reduction to user, is closed
The means such as the resource allocation policy of reason promote the power system capacity of populated area, promote the service quality of user.However due to
It largely disposes small base station and also brings some new problems, such as interlayer interference, intraformational interference, system signaling expense etc..Wherein
With small number of base stations in the relationship for referring to quadratic power, system signaling expense can increase signaling overheads as the number of small base station increases
Add.The growth of signaling overheads can increase the computational load and resource consumption of whole network.Because of quantities great, existing network
Framework can't be changed completely into the network structure that control is detached with transmission within the short time, also be tieed up in long-term a period of time
Hold the network architecture of centralization.
It is reduced currently, being become using the method for small base station sub-clustering in signaling management scheme under macro base station focus control mode
The effective means of system signaling expense, pertinent literature have proposed a variety of different small base station clustering methods.By network topology into
Row sub-clustering is handled, then is managed as unit of cluster, on the one hand so that network structure waits until to degrade, enhances the pipe to node
Reason, on the other hand so that the local synchronization of network is also more easy to implement.
In signaling management project study, existing sub-clustering scheme has descends several types：1, point based on Graph Theory
Cluster scheme.The basic ideas of the type scheme are to be abstracted as small base station a little, and the line between node is abstracted as side, will be whole
A network decomposition becomes multiple subgraphs.2, the sub-clustering scheme based on similitude.By parameters such as path loss, interference thresholds in this type
It refers to as similitude, is grouped together similitude is larger or smaller, become a kind of or cluster.3, based on pattern-recognition
Sub-clustering scheme.Sub-clustering mainly is carried out according to the far and near of distance to small base station using k-means, k-means++ scheduling algorithm.
Inventor has found, in some existing technologies, the signaling overheads Managed Solution under traditional macro base station centralized control
It is difficult to be fully applicable in super-intensive network environment, some gives sub-clustering number or progress before sub-clustering in traditional scheme
Fixed small base station sub-clustering.Because small base station deployment is flexible, dynamic change is presented in network topology structure, this needs sub-clustering scheme energy
It is enough directed to network structure variation and carries out dynamic sub-clustering, reduce the performance loss brought by signaling overheads, lifting system performance.
Invention content
For the above prior art the problem of, present invention discusses in 5G super-intensive networks be based on small base station deployment density
Cluster-dividing method, be directed to the network topology structure of dynamic change, effectively carry out small dynamic clustering of base station.
The present invention is a kind of cluster-dividing method based on small base station deployment density in 5G super-intensive networks, includes mainly
Following steps：
Step 101：Network interferences topological diagram is built, K is chosen using k central value methods0A cluster head.
Step 102：Interference threshold is set, calculates remaining small base station to the interference value of cluster head, descending arrangement.By small base station point
It is fitted in the sub-clustering where the larger cluster head of interference value, after being assigned, recalculates, chooses cluster head.
Step 103：On the basis of step 102 sub-clustering, the small base station deployment under entire macro base station coverage area is calculated
Density.
Step 104：The small base station deployment density in each sub-clustering is calculated, and it is close with the small base station deployment under macro region
Degree is compared, and judges whether to merge sub-clustering according to comparison result or splitting operation.
Step 105：Judge whether to change parameter.
Step 106：Judge whether iteration is completed.
The step 101 builds network topological diagram according to network interferences situation.According to sub-clustering number and small number of base stations
Relationship limits the range of choice of number of clusters, and K is chosen using k central value methods0A cluster head.
The step 102 calculates remaining small base station to K on the basis of step 1010The interference value of a cluster head will be done
It disturbs value and carries out descending arrangement, small base station is assigned in the sub-clustering where the maximum cluster head of interference value, after being assigned, again
It calculates, choose cluster head.
The step 103 calculates the small base station deployment density in macro base station coverage area.
The step 104 calculates the small base station deployment density of each sub-clustering, judges whether sub-clustering merges or divide
Operation.It is compared with the small base station deployment density under macro base station coverage area, is more than point of the deployment density in macro region
Cluster then carries out splitting operation, and recalculates, chooses the cluster head of sub-clustering.Less than macro region deployment density two sub-clusterings then into
Then row union operation recalculates, chooses the cluster head of sub-clustering.
The step 105 judges whether to change interference threshold parameter when there is small base station to enter or withdraw from system.
The step 106 judges whether iteration is completed.
The beneficial effects of the present invention are：The present invention is directed to small dynamic clustering of base station problem in super-intensive network, using base
It completes to carry out dynamic clustering to the small base station under macro base station coverage area in the cluster-dividing method of small base station deployment density, it will be numerous
The direct interaction message of small base station and macro base station is turned into concentration-centralization two-stage interactive structure, i.e., small base station and cluster head in cluster
Information exchange is carried out, then cluster head carries out information exchange with macro base station again.It, can be effective when network topology structure dynamic change
Carry out dynamic clustering, reduce signaling overheads, lifting system performance.
Description of the drawings
Fig. 1 5G super-intensive network system models
Cluster-dividing method flow example figure based on base station deployment density in Fig. 2 5G super-intensive networks
Specific implementation mode
It is below in conjunction with the accompanying drawings and specific real to make the purpose, technical scheme and advantage of invention express to be more clearly understood
Case is applied to be described in further details the present invention.
Cluster-dividing method flow example figure of Fig. 2 5G super-intensives networks based on small base station deployment density, this method include following
Step：
Step 101：Network interferences topological diagram is built according to network interferences situation, according to sub-clustering number and small base station sub-clustering number
Purpose relationship(K is number of clusters, and n is small number of base stations), provides expected sub-clustering number range [K/2,2K], in this range
Within select a number K0, K is selected using k central value methods0A small base station is as cluster head.
Step 102：Set interference threshold, calculate remaining small base station to these cluster heads interference value, and according to interference value
Size carries out descending arrangement, small base station is assigned in the sub-clustering where the larger cluster head of interference value, and again according to formula (1-1)
It calculates, choose cluster head.
Step 103：The small base station deployment density under macro base station coverage area is calculated according to formula (1-2).Wherein NSBSIt indicates
Small number of base stations in macro base station coverage area,Indicate that the areal calculation formula of regular hexagon, a indicate hexagon side
It is long.
Step 104：The small base station deployment density in the cluster region in above-mentioned sub-clustering is calculated according to formula (1-3), whereinIndicate sub-clustering CiIn small number of base stations, π r2The sub-clustering area of expression, r indicate sub-clustering radius.
Next judge whether it is whether equal with macro regional deployment density.If it is, going to step 105.If
It is no, judge whether deployment density in sub-clustering region is more than macro regional deployment density, if it is, by this sub-clustering into line splitting
Operation, recalculates according to formula (1-4,1-5), chooses cluster head.If it is not, then two sub-clusterings are merged into operation, according to
Formula (1-6) chooses cluster head again.
Step 105：Judge whether to change interference threshold, if it is, return to step 102.If it is not, then going to step
106。
Step 106：Judge whether to be last time iteration.If it is, terminating cluster-dividing method.If it is not, then going to step
Rapid 101.
Claims (7)
1. a kind of cluster-dividing method based on small base station deployment density in 5G super-intensive networks, which is characterized in that main packet
Include following steps：
Step 101：Network interferences topological diagram is built, K is chosen using k central value methods0A cluster head；
Step 102：Interference threshold is set, calculates remaining small base station to the interference value of cluster head, descending arrangement；Small base station is assigned to
In sub-clustering where the larger cluster head of interference value, after being assigned, recalculates, chooses cluster head；
Step 103：On the basis of step 102 sub-clustering, the small base station deployment density under entire macro base station coverage area is calculated；
Step 104：Calculate the small base station deployment density in each sub-clustering, and by it with the small base station deployment density under macro region into
Row compares, and judges whether to merge sub-clustering according to comparison result or splitting operation；
Step 105：When there is small base station to enter or withdraw from system, judge whether to change interference threshold parameter；
Step 106：Judge whether iteration is completed.
2. the base station clustering method according to claim 1 based on small base station deployment density, which is characterized in that the step
101, according to network interferences situation, build network topological diagram；The choosing of number of clusters is limited according to the relationship of sub-clustering number and small number of base stations
Range is selected, K is chosen using k central value methods0A cluster head.
3. the base station clustering method according to claim 1 based on small base station deployment density, which is characterized in that the step
102 on the basis of step 101, calculates remaining small base station to K0Interference value is carried out descending arrangement by the interference value of a cluster head,
Small base station is assigned in the sub-clustering where the maximum cluster head of interference value, after being assigned, recalculates, chooses cluster head.
4. the base station clustering method according to claim 1 based on small base station deployment density, which is characterized in that the step
103 calculate the small base station deployment density in macro base station coverage area.
5. the base station clustering method according to claim 1 based on small base station deployment density, which is characterized in that the step
104 calculate the small base station deployment density of each sub-clusterings, judge whether sub-clustering merges or splitting operation；By it and macro base station
Small base station deployment density under overlay area is compared, and the sub-clustering more than the deployment density in macro region then carries out splitting operation,
And recalculate, choose the cluster head of sub-clustering；Two sub-clusterings less than the deployment density in macro region then merge operation, then weigh
The new cluster head for calculating, choosing sub-clustering.
6. the base station clustering method according to claim 1 based on small base station deployment density, which is characterized in that the step
105 when there is small base station to enter or withdraw from system, judges whether to change interference threshold parameter.
7. the base station clustering method according to claim 1 based on small base station deployment density, which is characterized in that judge iteration
Whether complete.
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Application publication date: 20181009 |