CN103327502A - Self-adaption semi-dynamic clustering method containing service classification - Google Patents
Self-adaption semi-dynamic clustering method containing service classification Download PDFInfo
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- CN103327502A CN103327502A CN2013102192675A CN201310219267A CN103327502A CN 103327502 A CN103327502 A CN 103327502A CN 2013102192675 A CN2013102192675 A CN 2013102192675A CN 201310219267 A CN201310219267 A CN 201310219267A CN 103327502 A CN103327502 A CN 103327502A
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Abstract
An embodiment of the invention relates to a self-adaption semi-dynamic clustering method containing service classification. The method is applied to a wireless communication system and can achieve the purpose of reducing cost of the whole system in the situation of guaranteeing the throughput capacity of a base station. The method specifically comprises the steps of classifying according to users and serving the users according to user priority. Thus, the system can meet requirements of various kinds of performance of data rate, time delay and the like, then the base station can be chosen from a pre-cooperation set to participate cooperation according to the throughput capacity maximizing principle, and finally clustering can be finished according to the minimum overhead of the system. The self-adaption semi-dynamic clustering method containing service classification overcomes the defects of an existing clustering method, is convenient and effective, reduces system overhead and improves system capacity.
Description
Technical field
The present invention relates to the mobile communication technology field, especially, the present invention carries out sub-clustering for providing a kind of at future mobile communication system, reduces the expense of whole system in the situation that guarantee the base station throughput, can be used in Long Term Evolution of future generation (LTE) mobile communication system.
Background technology
The cooperative cluster division methods has three kinds at present:
Static cooperation: selecting regularly several base station collaborations according to certain criterion, generally is to select to disturb larger several base stations, is conducive to like this eliminate the strongest several presence of intercell interference.This cooperation mode is simple, but all UE in the same base station, its corresponding cooperative cluster is all the same, concerning the UE that is in diverse geographic location, not necessarily can eliminate the strongest presence of intercell interference like this, and fairness can not get ensureing; And along with the movement of UE, its strong interferers also can change thereupon, and static cooperation can't be satisfied this dynamic variation.
Dynamic cooperative: according to the interference source information of UE feedback, its main serving BS is dynamically selected the cooperative cluster to this UE service.Under this mode, cooperative cluster corresponding to the different UEs of same base station may be different, like this for each UE, all is farthest to have eliminated presence of intercell interference; But this mode more complicated implements cost high.
Half dynamic cooperative: under this cooperation mode, pre-determine a large Candidate Set (pre-collaboration set), then UE dynamically selects to participate in the base station of cooperation in pre-collaboration set, and the base station number of final cooperation is less than or equal to the base station number in the Candidate Set.This cooperation mode obviously adaptability than the static state cooperation is stronger, and the cooperation of complexity relative dynamic is little.
The document of research cluster-dividing method has much at present, all is to study respectively throughput and overhead mostly still, lacks comparative maturity and comprehensive achievement in research in the research aspect comprehensive the two characteristic.
Summary of the invention
The present invention is intended to the shortcoming for existing cluster-dividing method, propose a kind of easy, improve throughput of system and power system capacity, and can greatly eliminate the self adaptation that the comprises service classification half dynamic clustering algorithm of total system expense.The sub-clustering step of this algorithm:
Step 1. is at first carried out service classification to user (UE), according to User Priority it is served
Step 2. pre-determines a large pre-collaboration set U to each user (UE)
Step 3. is calculated the throughput of each base station in this collaboration set to each user (UE), and selects the base station C of throughput maximum to participate in cooperation;
Step 4. is scratched base station C in pre-collaboration set U, upgrades this pre-collaboration set, changes a user (UE);
Step 5. forms a cooperative cluster U ' with the base station of scratching, and calculates the total system expense after its associated treatment;
Step 6. compares the value of the total system expense of calculating in 5 in whole process, if the value of this moment is minimum, then the cooperative cluster U ' of this moment is the sub-clustering of finishing, the sub-clustering end, otherwise enter step 7;
Step 7. judges whether the number of base station among the U is 0, if then U is the sub-clustering of finishing, sub-clustering finishes, otherwise returns step 2.
Illustrate:
In the described sub-clustering step 1, user (UE) is carried out service classification, according to User Priority it is served, so just can make system satisfy the user to the requirement of the various performances such as time delay.
In the described sub-clustering step 2, under this cooperation mode, pre-determine a large pre-collaboration set, then UE dynamically selects to participate in the base station of cooperation in pre-collaboration set, and the base station number of final cooperation is less than or equal to the base station number in the Candidate Set.This cooperation mode obviously adaptability than the static state cooperation is stronger, and the cooperation of complexity relative dynamic is little.
In the described sub-clustering step 3, for each user (UE), all need to calculate the throughput of this cooperation centralized base-station, then select the base station C of throughput maximum to participate in cooperation.
In the described sub-clustering step 4, base station C in the collaboration set (U) is scratched, upgrade collaboration set, and change the process of next user (UE) repeating step 3.
In described sub-clustering step 5 and the step 6, the base station of scratching in the step 4 is formed a new cooperative cluster U ', and calculate the total system expense after its associated treatment, and the cooperative cluster will obtain this minimum total system expense the time is as the last sub-clustering of finishing.
In the described sub-clustering step 7, judge whether the number of base station among the collaboration set U is 0, if be not 0, then return step 2, otherwise sub-clustering finishes.
Below by the drawings and specific embodiments technical scheme of the present invention is further specified.
Description of drawings
In order more clearly to set forth embodiments of the invention and existing technical scheme, the below does simple introduction with the explanation accompanying drawing of using in technical scheme explanation accompanying drawing of the present invention and the description of the Prior Art, obviously, under the prerequisite of not paying creative work, those of ordinary skills can obtain by this accompanying drawing other accompanying drawing.
Fig. 1 comprises many base stations cooperative system scene graph in the embodiment of the invention.
Fig. 2 is the self adaptation half dynamic clustering flow chart that comprises service classification in the embodiment of the invention.
Embodiment
Clearer for what technical scheme advantage of the present invention was described, below in conjunction with accompanying drawing the specific embodiment of the present invention is described in further detail, obvious described embodiment is part embodiment of the present invention, rather than whole embodiment.Embodiments of the invention can be expanded on this basis, in the situation that overall architecture is consistent, be obtained more prioritization schemes.According to embodiments of the invention, those of ordinary skill in the art can realize every other embodiment of the present invention on without the basis of creative work, all belong to protection scope of the present invention.
Main thought of the present invention is: at first user (UE) is carried out service classification, according to User Priority it is served, so just can make system satisfy the user to the requirement of the various performances such as time delay, then pre-determine a large pre-collaboration set U for each user, then for each user, calculating is in this throughput with the centralized base-station that cooperates, and select the base station C of throughput maximum to participate in cooperation, in pre-collaboration set U, scratch base station C, upgrade collaboration set, change next user and repeat the appeal process.The base station of scratching is formed a new cooperative cluster U ', and calculate the total system expense after its associated treatment, and the cooperative cluster U ' will obtain minimum total system expense the time regards the sub-clustering of finishing as, sub-clustering this moment finishes.Whether the number of judging base station among the pre-collaboration set U is 0, if 0, then finish, otherwise turn back to beginning.
Fig. 2 is the self adaptation half dynamic clustering flow chart that comprises service classification in the embodiment of the invention.
Specifically describe as follows:
Step 1. is at first carried out service classification to user (UE), according to User Priority it is served
Step 2. pre-determines a large pre-collaboration set U to each user (UE)
Step 3. is calculated the throughput of each base station in this collaboration set to each user (UE), and selects the base station C of throughput maximum to participate in cooperation;
Step 4. is scratched base station C in pre-collaboration set U, upgrades this pre-collaboration set, changes a user (UE);
Step 5. forms a cooperative cluster U ' with the base station of scratching, and calculates the total system expense after its associated treatment;
Step 6. compares the value of the total system expense of calculating in 5 in whole process, if the value of this moment is minimum, then the cooperative cluster U ' of this moment is the sub-clustering of finishing, the sub-clustering end, otherwise enter step 7;
Step 7. judges whether the number of base station among the U is 0, if then U is the sub-clustering of finishing, sub-clustering finishes, otherwise returns step 2.
Claims (7)
1. self adaptation half dynamic clustering method that comprises service classification is characterized in that may further comprise the steps:
Step 1. is at first carried out service classification to user (UE), according to User Priority it is served
Step 2. pre-determines a large pre-collaboration set U to each user (UE)
Step 3. is calculated the throughput of each base station in this collaboration set to each user (UE), and selects the base station C of throughput maximum to participate in cooperation;
Step 4. is scratched base station C in pre-collaboration set U, upgrades this pre-collaboration set, changes a user (UE);
Step 5. forms a cooperative cluster U ' with the base station of scratching, and calculates the total system expense after its associated treatment;
Step 6. compares the value of the total system expense of calculating in 5 in whole process, if the value of this moment is minimum, then the cooperative cluster U ' of this moment is the sub-clustering of finishing, the sub-clustering end, otherwise enter step 7;
Step 7. judges whether the number of base station among the U is 0, if then U is the sub-clustering of finishing, sub-clustering finishes, otherwise returns step 2.
2. the self adaptation half dynamic clustering method that comprises service classification according to claim 1 is characterized in that:
In the described sub-clustering step 1, user (UE) is carried out service classification, according to User Priority it is served, so just can make system satisfy the user to the requirement of the various performances such as time delay.
3. the self adaptation half dynamic clustering method that comprises service classification according to claim 1 is characterized in that:
In the described sub-clustering step 2, under this cooperation mode, pre-determine a large pre-collaboration set, then UE dynamically selects to participate in the base station of cooperation in pre-collaboration set, and the base station number of final cooperation is less than or equal to the base station number in the Candidate Set.This cooperation mode obviously adaptability than the static state cooperation is stronger, and the cooperation of complexity relative dynamic is little.
4. the self adaptation half dynamic clustering method that comprises service classification according to claim 1 is characterized in that:
In the described sub-clustering step 3, for each user (UE), all need to calculate the throughput of this cooperation centralized base-station, then select the base station C of throughput maximum to participate in cooperation.
5. the self adaptation half dynamic clustering method that comprises service classification according to claim 1 is characterized in that:
In the described sub-clustering step 4, base station C in the collaboration set (U) is scratched, upgrade collaboration set, and change the process of next user (UE) repeating step 3.
6. the self adaptation half dynamic clustering method that comprises service classification according to claim 1 is characterized in that:
In described sub-clustering step 5 and the step 6, the base station of scratching in the step 4 is formed a new cooperative cluster U ', and calculate the total system expense after its associated treatment, and the cooperative cluster will obtain this minimum total system expense the time is as the last sub-clustering of finishing.
7. the self adaptation half dynamic clustering method that comprises service classification according to claim 1 is characterized in that:
In the described sub-clustering step 7, judge whether the number of base station among the collaboration set U is 0, if be not 0, then return step 2, otherwise sub-clustering finishes.
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| CN104918256A (en) * | 2014-03-14 | 2015-09-16 | 电信科学技术研究院 | Transmission scheduling method and device |
| WO2016101502A1 (en) * | 2014-12-25 | 2016-06-30 | 中兴通讯股份有限公司 | Method and device for splitting super-cell |
| CN108668325A (en) * | 2017-12-22 | 2018-10-16 | 航天恒星科技有限公司 | User oriented grade efficiency CoMP switching methods based on lte-a system |
| CN113395699A (en) * | 2021-05-26 | 2021-09-14 | 哈尔滨工业大学 | Clustering and frequency resource allocation method based on cooperation |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104918256A (en) * | 2014-03-14 | 2015-09-16 | 电信科学技术研究院 | Transmission scheduling method and device |
| CN104918256B (en) * | 2014-03-14 | 2018-09-11 | 电信科学技术研究院 | A kind of transmission dispatching method and device |
| WO2016101502A1 (en) * | 2014-12-25 | 2016-06-30 | 中兴通讯股份有限公司 | Method and device for splitting super-cell |
| CN105791196A (en) * | 2014-12-25 | 2016-07-20 | 中兴通讯股份有限公司 | Method and device for splitting super cell |
| CN105791196B (en) * | 2014-12-25 | 2020-04-03 | 中兴通讯股份有限公司 | Splitting method and device for super cell |
| CN108668325A (en) * | 2017-12-22 | 2018-10-16 | 航天恒星科技有限公司 | User oriented grade efficiency CoMP switching methods based on lte-a system |
| CN108668325B (en) * | 2017-12-22 | 2020-10-23 | 航天恒星科技有限公司 | User-level-energy-efficiency-oriented CoMP switching method based on LTE-A system |
| CN113395699A (en) * | 2021-05-26 | 2021-09-14 | 哈尔滨工业大学 | Clustering and frequency resource allocation method based on cooperation |
| CN113395699B (en) * | 2021-05-26 | 2023-05-12 | 哈尔滨工业大学 | Base station cooperation set generation method based on overlapped clusters |
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Application publication date: 20130925 |