CN106127332A - Base station resource configuration based on optimal spatial coupling and planing method - Google Patents
Base station resource configuration based on optimal spatial coupling and planing method Download PDFInfo
- Publication number
- CN106127332A CN106127332A CN201610444257.5A CN201610444257A CN106127332A CN 106127332 A CN106127332 A CN 106127332A CN 201610444257 A CN201610444257 A CN 201610444257A CN 106127332 A CN106127332 A CN 106127332A
- Authority
- CN
- China
- Prior art keywords
- population
- chromosome
- resource
- value
- optimum individual
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/043—Optimisation of two dimensional placement, e.g. cutting of clothes or wood
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Biophysics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Operations Research (AREA)
- Biomedical Technology (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Physiology (AREA)
- Genetics & Genomics (AREA)
- Artificial Intelligence (AREA)
- General Business, Economics & Management (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a kind of base station resource configuration based on optimal spatial coupling and planing method, including step 1: process resource allocation problem by genetic algorithm, introducing population concept, chromosomes multiple to stochastic generation after initialization of population, every item chromosome is equivalent to a kind of resource allocation proposal；Step 2: every chromosome in population is evaluated, obtains population optimum individual；Step 3: judge whether population optimum individual meets stopping criterion for iteration；If be unsatisfactory for, perform step 4；If it is satisfied, end algorithm, export population optimum individual；Step 4: population is implemented selection opertor operation；Step 5: population is implemented crossover operator operation；Step 6: population is implemented mutation operator operation, produces new generation population, return and perform step 2.The present invention highly shortened and calculates the time obtaining near-optimization allocation plan, and can stably obtain the solution that quality is higher.
Description
Technical field
The present invention relates to communication industry field, in particular it relates to a kind of base station resource configuration based on optimal spatial coupling
With planing method.
Background technology
In communication industry field, it is often necessary to find Optimal Signals tower to configure, realize given with minimum configuration expense
Quality of service requirement.Such as, how to increase the power of existing signal tower and how to select new signal tower to build a station position, with
Minimum cost realizes the communication connection demand of main residential block.Similar problem also has, and government department it is frequently necessary in city
The position of middle planning construction emergency center and receiving number of patients are to meet the medical demand in main residential area.
This type of planning problem above-mentioned can be concluded as spatial match problem, as it is shown in figure 1, wherein P1、P2、P3Represent
Resource provider, O1、O2、O3Represent resource consumption person, and in the numeral on line represents that the Europe between two connected points is several
Must be apart from (Fig. 2, Fig. 3, Fig. 4 be similar, hereafter will not be repeated again elaboration).Above-mentioned signal tower, emergency center etc. are considered as resource carry
Donor (Provider), residential block is considered as resource consumption person (Customer), spatial match problem can be described as: given
In the case of the position of each resource provider and total resources, between resource provider and resource consumption person, find optimum
Coupling (matching) (namely determining which resource consumption person which resource provider provides how many resources to) is to reach
Excellent quality of service goals (such as maximum service distance minimization).Problems is conducted extensive research by academia, according to
The difference of optimal service quality objective, mainly has three space-like matching problems: one is the space of maximum match distance minimization
Join problem (Spatial Matching for Minimizing Maximum Matching Distance Problem), should
The maximum match distance that the target of problem is desirable in all couplings is minimum, as shown in Figure 2.Two is that the spatial match of justiceization is asked
Topic (Fair Assignment), the target of this problem is for each resource consumption person, it is desirable to its resource provider
The least, as shown in Figure 3 from its distance.Three is the spatial match problem (Globally of global optimization
Optimized Assignment), the target of this problem is that the matching distance sum of all couplings is minimum, as shown in Figure 4.
Above three spatial match problem, is all given resource provider position and resource offer amount thereof when
Accounting for, situation is the most preferable, departing from practical problem.In actual industrial scene, the position of resource provider
And resource offer amount (Capacity) is frequently not fixing, (Location) but provide the resource provider of some candidates
Position, (the namely resource provider of a candidate, it has one for corresponding different amounts of resource offer amount and construction cost thereof
The resource offer amount of a little candidates is available).Under this kind of scene, it is desirable to first pick out conjunction from the resource provider of candidate
Suitable resource provider and corresponding resource offer amount, try to achieve an Optimum Matching (Assignment), the most again with satisfied money
The demand of source consumer, target is so that total construction cost is minimum.This is also the new problem that the present invention proposes, the present invention
In be referred to as optimal spatial coupling under resource distribution and planning problem (Service Provider Configuration and
Planning with Optimal Spatial Matching).At present, also not for the solution of this problem.If
Solve this problem in actual applications, will greatly save construction cost under conditions of meeting demand.
Summary of the invention
For defect of the prior art, it is an object of the invention to provide a kind of base station resource based on optimal spatial coupling
Configuration and planing method.
The base station resource configuration based on optimal spatial coupling provided according to the present invention and planing method, including walking as follows
Rapid:
Step 1: process Optimal Allocation of Resources problem by genetic algorithm, introduces population concept, and random initializtion population generates
A plurality of chromosome, every item chromosome is equivalent to a kind of resource allocation proposal；
Step 2: every chromosome in population is evaluated, obtains population optimum individual；
Step 3: preserve optimum individual in current population, and judge whether population optimum individual meets stopping criterion for iteration；
If be unsatisfactory for, perform step 4；If it is satisfied, output population optimum individual, end step flow process；
Step 4: population is implemented selection opertor operation；
Step 5: population is implemented crossover operator operation；
Step 6: population is implemented mutation operator operation, produces new generation population, return and perform step 2.
Preferably, the population in described step 1 refers to the population in genetic algorithm, the colony being i.e. made up of several body；
Each individuality is item chromosome, and every chromosome is made up of a string gene order, therefore represents every by chromosome length
Gene number in bar chromosome；
Specifically, certain chromosome length N represents a total of N number of resource provider, described resource in this resource allocation proposal
Supplier waits the selection result determining resource capacity kind, one resource provider of each gene representation, and each base
The value of cause represents that the capacity kind of this resource provider selects result；Random ingrain colour solid refers to: the method using random integers
The value of the jth gene on generation item chromosome, and the integer value needs of each stochastic generation are [0, mjIn the range of], mj
Represent the capacity kind sum that jth resource provider has；One can be generated after generating all genic values according to preceding method
Bar chromosome, the resource allocation proposal of a kind of stochastic generation；Every chromosome all uses above-mentioned random integers method to generate.
Preferably, during described step 2 includes, the following principle of Appreciation gist to chromosome:
1) total resources that all resource providers are provided that is the aggregate demand that can meet all resource consumption persons；
2) any cost supplier is less than service restrictions distance D to the distance of the resource consumption person of corresponding with service；
3) the total cost implementing resource allocation proposal should be the least；
Evaluation principle design fitness function according to chromosome evaluates every item chromosome, the dyeing that fitness is the biggest
Body then represents that the individuality of this chromosome is the most excellent；Described fitness function is as follows:
Wherein, F represents the fitness value of current chromosome, and mmd represents that all resources carry under Current resource allocation plan
Donor is obtained to the maximum of the distance of the resource consumption person of corresponding with service, described maximum by exchanging chain technology Swap-Chain
Arrive；Total capacity represents that in Current resource allocation plan, resource provides total amount, and aggregate demand represents that the demand of all resource consumption persons is total
Amount, α, β are adjustable weight parameter.
Preferably, described step 3 includes: population optimum individual refers to the dyeing that in current population, fitness value is maximum
Body, if population optimum individual meets stopping criterion for iteration, then output population optimum individual, i.e. obtains optimal allocation scheme, knot
Bundle steps flow chart；If population optimum individual is unsatisfactory for stopping criterion for iteration, then perform step 4；
Described stopping criterion for iteration is as follows:
The absolute value of the difference of the fitness value of current population optimum individual and previous generation population optimum individual, if less than setting
Fixed real number threshold epsilon, then it is assumed that iteration tends towards stability, and meets end condition；Wherein ε is set as the arithmetic number close to zero.
Preferably, described step 4 includes:
Step 4.1: the fitness value of individualities all in current population is sued for peace, is designated as fs；
Step 4.2: set up wheel disc.The fitness value of i-th chromosome of note is fi, then the wheel disc scale value of this chromosome
Computing formula is as follows:
And
Wherein i=2 ..., M, M are Population Size；
In formula: piRepresent the wheel disc scale value of i-th chromosome, pi-1Represent the i-th-1 chromosome wheel disc scale value, need
It should be noted that
Step 4.3: the value making j is 1, stochastic generation real number r ∈ [0,1], if r falls at the interval [p of scale valuei,pi+1In],
Then selective staining body i replicates, and is saved in a new population；
Step 4.4: judge whether the value of j is less than or equal to M, if being less than, then stochastic generation real number r ∈ [0,1], when r falls
Interval [the p of scale valuei,pi+1In], then selective staining body i replicates, and is saved in the new population of step 4.3, enters step
4.5 continue executing with；If the value of j is more than M, then export described new population, end step flow process；
Step 4.5:j, from increasing 1, returns and performs step 4.4.
Preferably, described step 5 includes:
Step 5.1: the value making j is 1；
Step 5.2: select two chromosomes from current population randomly as parents；
Step 5.3: in above-mentioned two parental chromosomes, selects two positions as intersection in gene order randomly
Point；
Step 5.4: exchange being picked as two chromosomes of parents all genes between two cross points in order,
Obtain two child chromosome, and preserve；
Step 5.5:j is from increasing 1, if j > M/2, M represent total chromosome number in population, then it represents that created and contaminated by M strip
The new population that colour solid is constituted, end step flow process；Otherwise, step 5.2 is returned.
Preferably, described step 6 includes:
Step 6.1: make the value of p ∈ [0,1] be one close to zero real number；
Step 6.2: stochastic generation one real number r ∈ [0,1], such as r < p, then enters step 6.3.Otherwise, end step flow process.
Step 6.3: the item chromosome in the current population of random choose；
Step 6.4: in chromosome length N, selects a gene j randomly；
Step 6.5: the genic value of gene j in step 6.2 is made a variation, the integer that variation value is randomly generated, described
Integer is [0, mjIn], mjRepresent the capacity species number that jth resource provider is had.
Compared with prior art, the present invention has a following beneficial effect:
1, the genetic algorithm based on exchanging chain technology that the present invention proposes, highly shortened calculating and obtains near-optimization
The time of allocation plan.
2, the present invention introduces exchanging chain technology in genetic algorithm, can judge that each candidate resource configures the most efficiently
Whether the maximum match distance of scheme meets threshold restriction condition, has extremely strong fitness evaluation targetedly it is thus possible to design
Function.It addition, by the integer coding to resource allocation proposal so that the resource distribution under optimal spatial coupling and planning problem
Perfectly can be expressed in genetic algorithm, it is thus possible to stably obtain the solution that quality is higher.Test proves that, the present invention
In algorithm in precision and efficiency two aspect, really have well performance.
Accompanying drawing explanation
By the detailed description non-limiting example made with reference to the following drawings of reading, the further feature of the present invention,
Purpose and advantage will become more apparent upon:
Fig. 1 is spatial match (Spatial Matching) problem synoptic chart.
Fig. 2 is the illustration of the spatial match problem of maximum match distance minimization.
Fig. 3 is the illustration of the spatial match problem of justiceization.
Fig. 4 is the illustration of the spatial match problem of global optimization.
Fig. 5 is the workflow diagram of genetic algorithm based on exchanging chain technology.
Fig. 6 is for carry out integer coding schematic diagram to resource allocation proposal.
Fig. 7 is the design principle schematic diagram of two-point crossover operator in the present invention.
Fig. 8 is the design principle schematic diagram of mutation operator in the present invention.
Detailed description of the invention
Below in conjunction with specific embodiment, the present invention is described in detail.Following example will assist in the technology of this area
Personnel are further appreciated by the present invention, but limit the present invention the most in any form.It should be pointed out that, the ordinary skill to this area
For personnel, without departing from the inventive concept of the premise, it is also possible to make some changes and improvements.These broadly fall into the present invention
Protection domain.
The present invention is directed to the resource distribution under optimal spatial coupling and planning problem, first analyze the rule of its solution space
Mould, finds that this is a np hard problem.Therefore, it is proposed to heredity (Genetic based on exchanging chain (Swap-Chain) technology
Algorithm) algorithm solves this problem.Exchanging chain technology is the sky of the solution maximum match distance minimization being currently known
Between the optimal case of matching problem, it can provide rapidly the coupling of maximum match distance minimization.And the problem that the present invention proposes
In, it is desirable to resource consumption person requires to provide the resource provider serviced to its distance less than given threshold value, Wo Menxu for it
Exchanging chain technology to be utilized calculates the maximum match distance under resource distribution candidate scheme, if maximum match distance is less than threshold
Value, it is legal for being considered as this scheme.Otherwise, it is believed that it is illegal scheme.And genetic algorithm be in computational mathematics for
Solve optimized searching algorithm, be the one of evolution algorithm.Genetic algorithm, as an Intelligent Computation Technology, can help to reduce
Search volume, obtains quality in the short period of time and preferably solves.But, for the problem of the present invention, algorithm needs some special
Different change.Firstly, it is necessary to the resource allocation proposal of candidate is encoded.We present invention uses and conventional genetic algorithm
Different coding techniques integer coding methods.Secondly, during implementing, also to crossover operator, choosing in genetic algorithm
Select operator and mutation operator is made that adaptive change.It addition, be also directed to optimization aim and restrictive condition, it is proposed that based on
The ideal adaptation degree evaluation function of exchanging chain technology.Finally, in order to improve the efficiency of algorithm, the present invention calculates platform at Spark
Our algorithm of upper parallelization.Genetic algorithm flow process based on exchanging chain technology is as shown in Figure 5.
Genetic algorithm job step based on exchanging chain technology is as follows:
Step S1: initialization of population, the resource allocation proposal of stochastic generation candidate, and each scheme is used integer coding
Method；
Step S2: every chromosome in population is evaluated by fitness function based on exchanging chain technology；
Step S3: judge whether population optimum individual meets stopping criterion for iteration；If be unsatisfactory for, perform step S4；As
Fruit meets, and terminates algorithm, exports optimum individual；
Step S4: population is implemented selection opertor operation；
Step S5: population is implemented crossover operator operation；
Step S6: population is implemented mutation operator operation, produces new generation population, return and perform step S2.
Population in genetic algorithm refers to the colony being made up of several body, and the most each individuality is item chromosome, often
Bar chromosome is made up of gene order.Generally represent quantity individual in population with Population Size.And for item chromosome,
Generally gene number in this chromosome is described, as shown in Figure 6 by chromosome length.
As shown in Figure 6, item chromosome is exactly a kind of resource allocation proposal.Chromosome length N represents in the program altogether
There is N number of resource provider to be determined.One resource provider of each gene representation, and the value of each gene represents this money
The Capacity Selection of source supplier, i.e. integer coding method.Illustrate as a example by Fig. 6, first gene representation first of arrow indication
Individual resource provider, this resource provider have selected oneself No. 0 volume solutions, and (each resource provider i's is all to be selected
Volume solutions start coding from 0, until mi-1。miRepresent that i-th resource provider has miPlant capacity available, and every kind
Capacity Selection scheme has the Capacity Selection cost that it is corresponding.It is pointed out that No. 0 volume solutions refer to is exactly capacity
It is 0, say, that do not select this resource provider).The resource allocation proposal of stochastic generation candidate refers to, for a dyeing
The value of the i-th gene on body, uses the method for random integers to generate it, and the integer value for each stochastic generation needs
In scope [0, mi-1In].The generation method of all genic values on this chromosome is the most as previously mentioned, then give birth to the most at random
Become item chromosome, i.e. one resource allocation proposal.In population, all chromosomes (resource allocation proposal) are all by above-mentioned
Method generates.
In the present embodiment, from accuracy in computation with the calculating time, the algorithm proposed in the present invention is tested.
Table 1 is the result the arrived comparing result with optimal solution of genetic algorithm based on exchanging chain technology
Specifically, in terms of accuracy in computation, generate small-scale simulation resource allocation problem according to setting rule
Data.Then, simple integral linear programming method is used to obtain the optimal solution of every group data set.To same data set, profit
The genetic algorithm based on exchanging chain technology proposed by the present invention calculates, it is thus achieved that result.By this result and employing simplicity
The optimal solution that integral linear programming method obtains compares, and experimental result is as shown in table 1.Wherein, obtained by inventive algorithm
The optimal solution gone out shows with adding boldface type.
Further, large-scale industry data set has carried out computational efficiency test.Use by 11 (1
Master server, 11 Slave servers) the Spark computing cluster that constitutes of machine accelerates the algorithm in the present invention parallel,
Wherein Master server memory is 30GB, 10 cores.Every Slave server memory 20G, 10 cores.Computational efficiency
Experimental result is as shown in table 2.
Table 2 is large-scale industry data set efficiency test result
Above the specific embodiment of the present invention is described.It is to be appreciated that the invention is not limited in above-mentioned
Particular implementation, those skilled in the art can make a variety of changes within the scope of the claims or revise, this not shadow
Ring the flesh and blood of the present invention.In the case of not conflicting, the feature in embodiments herein and embodiment can any phase
Combination mutually.
Claims (7)
1. a base station resource configuration based on optimal spatial coupling and planing method, it is characterised in that comprise the steps:
Step 1: process Optimal Allocation of Resources problem by genetic algorithm, introduces population concept, and random initializtion population generates a plurality of
Chromosome, every item chromosome is equivalent to a kind of resource allocation proposal；
Step 2: every chromosome in population is evaluated, obtains population optimum individual；
Step 3: preserve optimum individual in current population, and judge whether population optimum individual meets stopping criterion for iteration；If
It is unsatisfactory for, performs step 4；If it is satisfied, output population optimum individual, end step flow process；
Step 4: population is implemented selection opertor operation；
Step 5: population is implemented crossover operator operation；
Step 6: population is implemented mutation operator operation, produces new generation population, return and perform step 2.
Base station resource configuration based on optimal spatial coupling the most according to claim 1 and planing method, it is characterised in that
Population in described step 1 refers to the population in genetic algorithm, the colony being i.e. made up of several body；Each individuality is a dye
Colour solid, every chromosome is made up of a string gene order, therefore represents the gene in every chromosome by chromosome length
Number；
Specifically, certain chromosome length N represents a total of N number of resource provider in this resource allocation proposal, and described resource provides
Person waits the selection result determining resource capacity kind, each gene representation one resource provider, and each gene
Value represents that the capacity kind of this resource provider selects result；Random ingrain colour solid refers to: use the method for random integers to generate
The value of the jth gene on item chromosome, and the integer value needs of each stochastic generation are [0, mjIn the range of], mjRepresent
The capacity kind sum that jth resource provider has；A dye can be generated after generating all genic values according to preceding method
Colour solid, the resource allocation proposal of a kind of stochastic generation；Every chromosome all uses above-mentioned random integers method to generate.
Base station resource configuration based on optimal spatial coupling the most according to claim 1 and planing method, it is characterised in that
During described step 2 includes, the following principle of Appreciation gist to chromosome:
1) total resources that all resource providers are provided that is the aggregate demand that can meet all resource consumption persons；
2) any cost supplier is less than service restrictions distance D to the distance of the resource consumption person of corresponding with service；
3) the total cost implementing resource allocation proposal should be the least；
Evaluation principle design fitness function according to chromosome evaluates every item chromosome, and the biggest chromosome of fitness is then
Represent that the individuality of this chromosome is the most excellent；Described fitness function is as follows:
Wherein, F represents the fitness value of current chromosome, and mmd represents all resource providers under Current resource allocation plan
To the maximum of distance of the resource consumption person of corresponding with service, described maximum is obtained by exchanging chain technology Swap-Chain；Always
Capacity represents that in Current resource allocation plan, resource provides total amount, and aggregate demand represents the total demand of all resource consumption persons, α, β
For adjustable weight parameter.
Base station resource configuration based on optimal spatial coupling the most according to claim 1 and planing method, it is characterised in that
Described step 3 includes: population optimum individual refers to the chromosome that in current population, fitness value is maximum, if population optimum individual
Meet stopping criterion for iteration, then output population optimum individual, i.e. obtains optimal allocation scheme, end step flow process；If population
Optimum individual is unsatisfactory for stopping criterion for iteration, then perform step 4；
Described stopping criterion for iteration is as follows:
The absolute value of the difference of the fitness value of current population optimum individual and previous generation population optimum individual, if less than set
Real number threshold epsilon, then it is assumed that iteration tends towards stability, meets end condition；Wherein ε is set as the arithmetic number close to zero.
Base station resource configuration based on optimal spatial coupling the most according to claim 1 and planing method, it is characterised in that
Described step 4 includes:
Step 4.1: the fitness value of individualities all in current population is sued for peace, is designated as fs；
Step 4.2: set up wheel disc.The fitness value of i-th chromosome of note is fi, then the calculating of the wheel disc scale value of this chromosome
Formula is as follows:
And
Wherein i=2 ..., M, M are Population Size；
In formula: piRepresent the wheel disc scale value of i-th chromosome, pi-1Represent the i-th-1 chromosome wheel disc scale value, should be noted that
Be
Step 4.3: the value making j is 1, stochastic generation real number r ∈ [0,1], if r falls at the interval [p of scale valuei,pi+1In], then select
Chromosome i replicates, and is saved in a new population；
Step 4.4: judge whether the value of j is less than or equal to M, if being less than, then stochastic generation real number r ∈ [0,1], when r falls at scale
Interval [the p of valuei,pi+1In], then selective staining body i replicates, and is saved in the new population of step 4.3, enters step 4.5
Continue executing with；If the value of j is more than M, then export described new population, end step flow process；
Step 4.5:j, from increasing 1, returns and performs step 4.4.
Base station resource configuration based on optimal spatial coupling the most according to claim 1 and planing method, it is characterised in that
Described step 5 includes:
Step 5.1: the value making j is 1；
Step 5.2: select two chromosomes from current population randomly as parents；
Step 5.3: in above-mentioned two parental chromosomes, selects two positions as cross point in gene order randomly；
Step 5.4: exchange being picked as two chromosomes of parents all genes between two cross points in order, obtain
Article two, child chromosome, and preserve；
Step 5.5:j is from increasing 1, if j > M/2, M represent total chromosome number in population, then it represents that created by M bar daughter chromosome
The new population constituted, end step flow process；Otherwise, step 5.2 is returned.
Base station resource configuration based on optimal spatial coupling the most according to claim 1 and planing method, it is characterised in that
Described step 6 includes:
Step 6.1: make the value of p ∈ [0,1] be one close to zero real number；
Step 6.2: stochastic generation one real number r ∈ [0,1], such as r < p, then enters step 6.3.Otherwise, end step flow process.
Step 6.3: the item chromosome in the current population of random choose；
Step 6.4: in chromosome length N, selects a gene j randomly；
Step 6.5: the genic value of gene j in step 6.2 is made a variation, the integer that variation value is randomly generated, described integer
[0, mjIn], mjRepresent the capacity species number that jth resource provider is had.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610444257.5A CN106127332A (en) | 2016-06-20 | 2016-06-20 | Base station resource configuration based on optimal spatial coupling and planing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610444257.5A CN106127332A (en) | 2016-06-20 | 2016-06-20 | Base station resource configuration based on optimal spatial coupling and planing method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106127332A true CN106127332A (en) | 2016-11-16 |
Family
ID=57471284
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610444257.5A Pending CN106127332A (en) | 2016-06-20 | 2016-06-20 | Base station resource configuration based on optimal spatial coupling and planing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106127332A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106845623A (en) * | 2016-12-13 | 2017-06-13 | 国网冀北电力有限公司信息通信分公司 | A kind of electric power wireless private network base station planning method based on artificial fish-swarm algorithm |
| CN110612767A (en) * | 2017-03-28 | 2019-12-24 | 瑞典爱立信有限公司 | Techniques for allocating radio resources in a radio access network |
| CN113709753A (en) * | 2021-08-24 | 2021-11-26 | 中国人民解放***箭军工程大学 | Wireless broadband communication system site laying networking method and system |
| CN115496246A (en) * | 2022-10-08 | 2022-12-20 | 卓思韦尔(北京)信息技术有限公司 | Group difference-based intelligent searching and agile distribution method for shared conference room |
| CN116975655A (en) * | 2023-08-29 | 2023-10-31 | 天栋智能科技(天津)有限公司 | Parameter generation method, signal compression and reconstruction method, system, equipment and medium |
-
2016
- 2016-06-20 CN CN201610444257.5A patent/CN106127332A/en active Pending
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106845623A (en) * | 2016-12-13 | 2017-06-13 | 国网冀北电力有限公司信息通信分公司 | A kind of electric power wireless private network base station planning method based on artificial fish-swarm algorithm |
| CN106845623B (en) * | 2016-12-13 | 2019-09-13 | 国网冀北电力有限公司信息通信分公司 | A kind of electric power wireless private network base station planning method based on artificial fish-swarm algorithm |
| CN110612767A (en) * | 2017-03-28 | 2019-12-24 | 瑞典爱立信有限公司 | Techniques for allocating radio resources in a radio access network |
| CN110612767B (en) * | 2017-03-28 | 2023-05-16 | 瑞典爱立信有限公司 | Techniques for allocating radio resources in a radio access network |
| CN113709753A (en) * | 2021-08-24 | 2021-11-26 | 中国人民解放***箭军工程大学 | Wireless broadband communication system site laying networking method and system |
| CN113709753B (en) * | 2021-08-24 | 2023-11-28 | 中国人民解放***箭军工程大学 | Wireless broadband communication system site layout networking method and system |
| CN115496246A (en) * | 2022-10-08 | 2022-12-20 | 卓思韦尔(北京)信息技术有限公司 | Group difference-based intelligent searching and agile distribution method for shared conference room |
| CN116975655A (en) * | 2023-08-29 | 2023-10-31 | 天栋智能科技(天津)有限公司 | Parameter generation method, signal compression and reconstruction method, system, equipment and medium |
| CN116975655B (en) * | 2023-08-29 | 2024-04-05 | 天栋智能科技(天津)有限公司 | Parameter generation method, signal compression and reconstruction method, system, equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106127332A (en) | Base station resource configuration based on optimal spatial coupling and planing method | |
| CN104036324B (en) | Optimal design method of communication network capacity based on genetic algorithm | |
| CN105373601B (en) | A kind of multi-model matching method based on keyword words-frequency feature | |
| CN107544251B (en) | Single machine scheduling method for minimizing total stall period based on distributed robust model | |
| CN102413029A (en) | Method for partitioning communities in complex dynamic network by virtue of multi-objective local search based on decomposition | |
| CN105335524B (en) | A kind of graph search method applied to extensive irregular eutectic data | |
| CN113422695B (en) | Optimization method for improving robustness of topological structure of Internet of things | |
| CN101777990A (en) | Method for selecting multi-objective immune optimization multicast router path | |
| CN108038339A (en) | A kind of multiple target mixed flow two-sided assembly line balance method based on migratory bird optimization algorithm | |
| CN104268629A (en) | Complex network community detecting method based on prior information and network inherent information | |
| CN105574541A (en) | Compactness sorting based network community discovery method | |
| CN105678401A (en) | Global optimization method based on strategy adaptability differential evolution | |
| CN104951668A (en) | Method for predicting protein association graphs on basis of cascade neural network structures | |
| Chen et al. | The Evolutionary Algorithm to Find Robust Pareto‐Optimal Solutions over Time | |
| CN105550208B (en) | Similitude design Storage method based on spectrum Hash | |
| CN105354264B (en) | A kind of quick adding method of theme label based on local sensitivity Hash | |
| CN108198084A (en) | A kind of complex network is overlapped community discovery method | |
| Ono et al. | Island model genetic programming based on frequent trees | |
| CN110189230B (en) | Construction method of analytic model of dynamic partition | |
| CN106055742B (en) | Product adaptive design based on design entropy changes method | |
| CN106296420A (en) | A kind of community discovery method | |
| Hua et al. | New knowledge-based genetic algorithm for excavator boom structural optimization | |
| Wang et al. | The combination forecasting of electricity price based on price spikes processing: a case study in south Australia | |
| CN111489035A (en) | Mold combination distribution method based on combination of machine learning and backtracking method | |
| Yijun et al. | GA-BP neural networks for environmental quality assessment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20161116 |