CN106131853A - Radio Spectrum Resource planing method based on conflict resolution iteration optimization algorithms - Google Patents

Abstract

The invention discloses a kind of Radio Spectrum Resource planing method based on conflict resolution iteration optimization algorithms, the method includes: plan contradiction according to frequency spectrum, the iteration optimization algorithms utilizing frequency spectrum conflict resolution builds Radio Spectrum Resource plan model, it is achieved the planning to the Radio Spectrum Resource of different business.Then utilize grey entropy fuzzy evaluation algorithm to construct Model for Comprehensive, described planning is carried out quantitative evaluation, obtain the programme of optimum.The inventive method considers the factor of each side, frequency spectrum resource planning is combined closely with business demand, can effectively clear up the collision problem in Radio Spectrum Resource planning, and programme is carried out quantitative evaluation, by constantly adjusting and optimizing, ultimately form the Radio Spectrum Resource programme of strong operability.

Description

Radio Spectrum Resource planing method based on conflict resolution iteration optimization algorithms

Technical field

The present invention relates to Radio Spectrum Resource planning, in particular to a kind of nothing based on conflict resolution iteration optimization algorithms Line electricity frequency spectrum resource planing method, belongs to radio control field.

Background technology

At present, in radio control field, there is Radio Spectrum Resource planning conflict and be difficult to coordinate and programme difficulty To verify its rational problem.In existing radio frequency spectrum planning technology, frequency resource common optimization is generally used to utilize Combine the schemes such as planning with frequency resource, wherein, in frequency resource common optimization Utilization plan, mainly have in mind and follow with for the moment Between, cover the principle that the frequency in same place do not collides and formulate, in conjunction with different communication conditional systems efficiency, interfere, real The comprehensive simulating of the aspect problems such as existing complexity and assessment, make every effort to while improving frequency resource utilization ratio, it is achieved many Channeling, the deficiency that the program exists shows as effectively realizing frequency time division multiplex based on multiple-object information, and In frequency resource associating programme, based on frequency resource common technology, the analysis of combining wireless electricity frequency spectrum resource demand, Set up plan model, and by spatial frequency resource information is carried out comprehensive descision and aid decision, to improve planning rationally While property, clear and definite one section of space frequency planning in period from now on, the plan model that the deficiency that the program exists shows as setting up leads to Not strong by property, the conclusion of comprehensive assessment also lacks certain specific aim and operability.

Summary of the invention

Present invention aim to overcome that above-mentioned the deficiencies in the prior art provide a kind of and calculate based on conflict resolution iteration optimization The Radio Spectrum Resource planing method of method, frequency spectrum resource planning is combined closely with business demand, is formed operability by the method Strong Radio Spectrum Resource programme.

Realize the object of the invention and employed technical scheme comprise that a kind of radio frequency line based on conflict resolution iteration optimization algorithms Spectrum resource planing method, the method includes: plans contradiction according to frequency spectrum, utilizes the iteration optimization algorithms of frequency spectrum conflict resolution Build Radio Spectrum Resource plan model, it is achieved the planning to the Radio Spectrum Resource of different business.

Further, grey entropy fuzzy evaluation algorithm is utilized to construct Model for Comprehensive, to the described planning amount of carrying out Change assessment, obtain the programme of optimum.

The inventive method considers the factor of each side, frequency spectrum resource planning is combined closely with business demand, can have Effect clears up the collision problem in Radio Spectrum Resource planning, and programme is carried out quantitative evaluation, by constantly adjusting With optimization, ultimately form the Radio Spectrum Resource programme of strong operability.

Accompanying drawing explanation

Fig. 1 is embodiment based on the conflict resolution iteration optimization algorithms flow chart to TD-LTE frequency spectrum resource planing method.

Fig. 2 is the process chart that frequency spectrum planning interval clashes.

Detailed description of the invention

The present invention is described in further detail with specific embodiment below in conjunction with the accompanying drawings.

TD-LTE frequency spectrum resource is planned, not only be unable to do without the demand analysis demonstration of TD-LTE frequency spectrum resource, it is also desirable to country, The local relevant support dividing regulation and programme.In terms of demand analysis demonstration, mainly follow the tracks of and grasp development of new techniques The TD-LTE frequency spectrum resource changes in demand caused with the factor such as technical system update and application mode are changed, and comprehensively analyze TD-LTE frequency spectrum resource development of demand trend and the mutual shadow possible with other communication technology system frequency spectrum resource demands both at home and abroad Ring, according to TD-LTE frequency spectrum resource Demand Forecast Model calculate estimate TD-LTE frequency spectrum resource demand and can support, with Form TD-LTE frequency spectrum resource demand analysis and prove conclusion；Specify relevant division and in terms of programme, state should be strictly observed Electricity Federation, border, country and local series of rules, standard, suggestion and the radio-frequency spectrum put into effect divide regulation, and implement mobile logical Communication service development programme.Factorial analysis in terms of the comprehensive two above of the present embodiment, uses the inventive method, in conjunction with About distributing the utilization of model, reach the predistribution of TD-LTE frequency spectrum resource, then by constantly adjusting and revising, ultimately form TD-LTE frequency spectrum resource programme, as in figure 2 it is shown, specifically comprise the following steps that

S100, generation programme

Set up frequency spectrum planning contradiction analysis rule, the iteration optimization algorithms of design frequency spectrum conflict resolution, build wireless Electricity frequency spectrum resource plan model, can reach the pre-planning of Radio Spectrum Resource based on different business.

S101, planning and designing

S1011, the design of conflict resolution iteration optimization algorithms

(1) determination that initial spectrum planning is interval

The determination in initial spectrum planning interval completes on the basis of spectrum requirement.

(a) model hypothesis

Assume that frequency spectrum planning reference interval is [a₁,b₁],[a₂,b₂],…,[a_n,b_n], it is now desired to obtain one with above-mentioned The interval that the reference interval goodness of fit is the highest, as final frequency spectrum planning interval.

Owing to the Data Source of above-mentioned reference interval is different, therefore its referring to property is different.According to different reference intervals can Referential, gives each reference interval one weights α_i(i=1,2 ..., n), α_iDescribe reference interval [a_i,b_i] referring to property Size.

Wherein

The foundation of (b) model

TakeThen reference interval

Length of an interval degree is the longest, and effectiveness is the strongest, and accuracy is the poorest；Siding-to-siding block length is the shortest, and effectiveness is the poorest, accurately Property is the strongest.

Consider the interval effectiveness of frequency spectrum planning and accuracy, take the half of planning interval a length of [a, b], if Distribution interval is [x, y], then

Definition frequency spectrum planning interval [x, y] and reference interval [a_i,b_i] (i=1,2 ..., goodness of fit n) is as follows:

Wherein, | | | | represent length of an interval degree.

(c) model solution

A) initial value x is made₀=a,Calculate the interval [x of frequency spectrum planning₀,y₀] and the goodness of fit of reference interval；

B) take step-length ε=0.1 (or 0.01, can determine the most voluntarily), calculate [x successively_i+1,y_i+1] and reference interval The goodness of fit；

Wherein x_i+1=x_i+ ε, y_i+1=y_i+ ε (i=1,2 ...)

If c) y_i+1≤ b the most constantly repeats step b), if y_i+1＞ b then stops circulation.

OrderThen η^*Corresponding interval [the x of frequency spectrum planning_*,y_*] it is required.

The interval process clashed of S1012, frequency spectrum planning

Initial spectrum planning interval will necessarily occur various conflict, carries out justice the most again according to importance Planning.

Again the planing method of (a) conflict frequency range

Assume the separate service x of certain operation system₁…x_nFrequency spectrum planning interval be respectively [a₁,b₁],[a₂,b₂],…,[a_n, b_n], without loss of generality, a might as well be assumed₁≤a₂≤…≤a_n。

If separate service x₁…x_nInterfere, then its frequency spectrum planning interval can not clash, i.e. to arbitrarilyMeet

If(i ＜ j might as well be assumed) so thatThen explanation separate service x_iAnd x_j Frequency spectrum planning interval there occurs conflict, need the fair planning principles in proportion of the frequency range to conflict again to plan.

Due to a_i≤a_j, thenWherein c=a_j, d=min{b_i,b_j}。

By separate service x_iAnd x_jThe significance level weight beta played a role in this operation system_iAnd β_jDescribe, then will punching Prominent frequency range [c, d] is planned in following ratio, willPlanning is to x_i, Planning is to x_j。

Now, separate service x_iFrequency spectrum planning interval be again entered as:

$\[{a_i}^{\′},{b_i}^{\′}\mathrm{\]}=(\[a_i,b_i\]-\[c,d\])\∪\[c,c+\frac{{\mathrm{\β}}_i}{{\mathrm{\β}}_i+{\mathrm{\β}}_j}(d-c)\]$

Separate service x_jFrequency spectrum planning interval be again entered as:

$\[{a_j}^{\′},{b_j}^{\′}\]=(\mathrm{\[}{a}_j,b_j\]-\[c,d\])\∪\[c+\frac{{\mathrm{\β}}_i}{{\mathrm{\β}}_i+{\mathrm{\β}}_j}(d-c),d\]$

B () implements step, algorithm flow is as in figure 2 it is shown, include:

If separate service x₁…x_nInitial spectrum planning sequence of intervals be [a₁,b₁],[a₂,b₂],…,[a_n,b_n], and a₁≤ a₂≤…≤a_n。

A) i=1 is made, j=i+1；

B) judge, if [a_i,b_i]∩[a_j,b_j] ≠ φ, then plan again by conflict frequency range, and by frequency spectrum planning region Between sequence assignment again, i.e. [a_i,b_i]=[a_i′,b_i'], [a_j,b_j]=[a_j′,b_j′].Perform the 2nd step again；IfThen perform c)；

C) make j=j+1, perform b)；Until j=n, terminate, perform d)；

D) make i=i+1, perform a)；Until i=n terminates.

S200, assessment programme

Owing to the evaluation index of programme had both existed ambiguity, there is again grey majorized model, this method design grey entropy mould Stick with paste assessment algorithm and it is carried out comprehensive assessment, specifically comprise the following steps that

S201, analyzing influence Factor system

The each factor affecting programme good and bad is classified by attribute, sets up the Recurison order hierarchy relation of influence factor. If set of factors is U, { U₁,U₂,…U_nConstitute the 1st layer of programme assessment factor, { U₁₁,U₁₂,U₁₃,U₂₁,…,U_nm-1,U_nm} Constitute the 2nd layer of programme assessment factor layer, the most sub-factor layer.Consider operability and the accuracy of assessment, comment for selected 5 grades Language collection, is divided into assessment result: outstanding (V₁), good (V₂), medium (V₃), qualified (V₄), defective (V₅) 5 grades, alternative Collection (or Comment gathers) is represented by: V={V₁,V₂,…,V₅}。

S202, simple element evaluation

A () sets up single factor judgment matrix

Single factor judgment matrix can be considered sub-set of factors { U_ijAnd alternative collection between Theory of Grey Fuzzy Relation.According to U_ijGive Go out to pass judgment on the object degree of membership to element each in Comment gathers, and provide corresponding gray scale according to the abundant degree of information, can use Expert judging method determines.

${\tilde{R}}_{\⊗i}=\left[\begin{array}{cccc}({\mathrm{\μ}}_{11},v_{11})& ({\mathrm{\μ}}_{12},v_{12})& ...& ({\mathrm{\μ}}_{1n},v_{1n})\\ ({\mathrm{\μ}}_{21},v_{21})& ({\mathrm{\μ}}_{22},v_{22})& ...& ({\mathrm{\μ}}_{2n},v_{2n})\\ \·& \·& \·& \·\\ \·& \·& \·& \·\\ \·& \·& \·& \·\\ ({\mathrm{\μ}}_{m1},v_{m1})& ({\mathrm{\μ}}_{m2},v_{m2})& ...& ({\mathrm{\μ}}_{mn},v_{mn})\end{array}\right]$

Owing to the very difficult numerical value of quantity of information is weighed, thus this project to use some illustrative language corresponding certain Tonal range, as the abundant degree of information being divided into following a few class: very abundant, the most fully, typically, the poorest, the leanest Weary }, difference corresponding grey scale value [0,0.2), [and 0.2,0.4), [0.4,0.6), [0.6,0.8), [0.8,1.0] }.

B () determines sub-set of factors { U_ijWeight

In view of every evaluation index is weighing the diversity of programme quality degree, for commenting more reasonably and accurately Estimate, need science to determine the weight of index at different levels.Determining that the method for evaluation index weight is a lot, native system mainly uses based on letter The index weights of breath entropy theory determines method.

If iotave evaluation data matrix X=(x_ij)_n×m, wherein x_ij>=0 (i=1,2 ..., n；J=1,2 ..., m).At letter Variable-value scope in breath entropy is between 0～1, it is therefore desirable to iotave evaluation data matrix X is carried out pretreatment, and this project is adopted Process by normalized method, obtain the matrix E=[e after normalized_ij], wherein

For certain index i in planning appraisal, its comentropy is:

The entropy weight of i-th index is:

In formula: 0≤w_i≤ 1, and

Weight sets can be considered the Theory of Grey Fuzzy Relation passed judgment between object and set of factors.Close according to influence factor's Recurison order hierarchy System, uses information entropy theory to provide each sub-factor { U in same level_ijAbout last layer criterion U_iWeight and put ash accordingly Degree, constitutes weight sets.

In formula, each weighted value requires normalization, i.e.

C () obtains simple element evaluation vector

For retaining judge information as much as possible, the computing of mould portion uses M (,+) operator, and employing M in the computing of ash portion (⊙, +) operator, therefore, go out single factor test U according to the following formula_iJudge vector:

S203, Comprehensive Evaluation

By sub-set of factors U_iAs the element of its upper level set of factors U, by the simple element evaluation vector above obtained, can structure One-tenth comprehensive evaluation matrix:

Same utilization information entropy theory provides factor U_iAbout the weight of destination layer and put gray scale accordingly, constitute weight Collection.

Wherein, each weighted value requires normalization, i.e.

The Comprehensive Evaluation obtaining planning concept is vectorial:

S204, the process of evaluation result

In Grey Fuzzy Comprehensive Evaluation, gray scale is the description of degree insufficient to information, it is possible to be interpreted as information Insincere degree, corresponding whiteness is the credibility of information.Therefore, above-mentioned evaluation result can be handled as follows:

If b_i≥b_j, then b_i≥b_jCredibility be: p_ij=P (b_i≥b_j)=(1-v_i)(1-v_j) (i, j=1,2,3,4,5 And i ≠ j)；

Otherwise, b_i≥b_jCan not reliability, i.e. b_j＞ b_iCredibility be: p_ji=1-p_ij=1-(1-v_i)(1-v_j)(i,j =1,2,3,4,5 and i ≠ j)；

In above-mentioned evaluation result, degree of membership b_iFor maximum credibility it is:

According to maximum membership grade principle, choose the p that confidence value is maximum_iCorresponding comment V_iFinal review for programme Sentence result, thus determine the quality of programme.

Claims (7)

1. a Radio Spectrum Resource planing method based on conflict resolution iteration optimization algorithms, it is characterised in that: according to frequency Spectrum planning contradiction, utilizes the iteration optimization algorithms of frequency spectrum conflict resolution to build Radio Spectrum Resource plan model, it is achieved Planning to the Radio Spectrum Resource of different business.

The most according to claim 1, Radio Spectrum Resource planing method based on conflict resolution iteration optimization algorithms, it is special Levy and be: utilize grey entropy fuzzy evaluation algorithm to build Model for Comprehensive, described planning is carried out quantitative evaluation, obtains Excellent programme.

The most according to claim 1, Radio Spectrum Resource planing method based on conflict resolution iteration optimization algorithms, it is special Levy and be that the iteration optimization algorithms of described frequency spectrum conflict resolution builds Radio Spectrum Resource plan model and includes:

Determine frequency spectrum planning interval；

According to importance, the frequency range clashed in frequency spectrum planning interval is carried out justice the most again plan.

The most according to claim 3, Radio Spectrum Resource planing method based on conflict resolution iteration optimization algorithms, it is special Levy be described determine frequency spectrum planning interval include:

Taking frequency spectrum planning reference interval is [a_i,b_i], give each reference interval one weights α_i(i=1,2 ..., n), α_iFor ginseng [a between examination district_i,b_i] size of referring to property,

TakeThen reference intervalFrequency spectrum planning interval is [x_i,y_i], [x_i,y_i] a length of [a_i,b_i] half, then

According toCalculate the interval [x of frequency spectrum planning_i,y_i] and reference interval [a_i,b_i] coincide Degree, wherein, | | | | represent length of an interval degree；The spectrum planning interval calculating the gained goodness of fit the highest is final reference area Between.

The most according to claim 3, Radio Spectrum Resource planing method based on conflict resolution iteration optimization algorithms, it is special Levy to clash in being described frequency spectrum planning interval and judge according in the following manner:

The separate service x of operation system₁…x_nFrequency spectrum planning interval be respectively [a₁,b₁],[a₂,b₂],…,[a_n,b_n]；

If separate service x₁…x_nInterfere, then its frequency spectrum planning interval can not clash, i.e. to arbitrarilyMeet [a_i,b_i]∩[a_j,b_j]=φ；

IfMakeThen separate service x_iAnd x_jFrequency spectrum planning interval send out Give birth to conflict.

The most according to claim 5, Radio Spectrum Resource planing method based on conflict resolution iteration optimization algorithms, it is special Levy and be that the fair planning principles in proportion of the frequency range to conflict is planned again and include:

Due to a_i≤a_j, then [a_i,b_i]∩[a_j,b_j]=[c, d], wherein c=a_j, d=min{b_i,b_j}；

By separate service x_iAnd x_jThe significance level weight beta played a role in this operation system_iAnd β_jDescribe, then frequency range of conflicting [c, d] is planned in following ratio, willPlanning is to x_i,Rule Give x_j；

Now, separate service x_iFrequency spectrum planning interval be again entered as:

Separate service x_jFrequency spectrum planning interval be again entered as:

The most according to claim 2, Radio Spectrum Resource planing method based on conflict resolution iteration optimization algorithms, it is special Levy and be described to utilize grey entropy fuzzy evaluation algorithm to construct Model for Comprehensive, described planning is carried out quantitative evaluation bag Include:

(1) influencing factor system is set up

The each factor affecting programme good and bad is classified by attribute, sets up the Recurison order hierarchy relation of influence factor；Impact Set of factors is U, { U₁,U₂,…U_nConstitute the 1st layer of programme assessment factor, { U₁₁,U₁₂,U₁₃,U₂₁,…,U_nm-1,U_nmStructure Become the 2nd layer of planning scheme evaluation factor layer, the most sub-factor layer；Assessment result is divided into outstanding V₁, good V₂, medium V₃, qualified V₄, defective V₅5 grades, alternative set representations is: V={V₁,V₂,…,V₅}；

(2) simple element evaluation

A () sets up single factor judgment matrix

Single factor judgment matrix is sub-set of factors { U_ijAnd alternative collection between Theory of Grey Fuzzy Relation, according to U_ijProvide judge object Degree of membership to element each in Comment gathers, and provide corresponding gray scale according to the abundant degree of information, use Delphi method to determine:

${\tilde{R}}_{\⊗i}=\left[\begin{array}{cccc}({\mathrm{\μ}}_{11},v_{11})& ({\mathrm{\μ}}_{12},v_{12})& ...& ({\mathrm{\μ}}_{1n},v_{1n})\\ ({\mathrm{\μ}}_{21},v_{21})& ({\mathrm{\μ}}_{22},v_{22})& ...& ({\mathrm{\μ}}_{2n},v_{2n})\\ \·& \·& \·& \·\\ \·& \·& \·& \·\\ \·& \·& \·& \·\\ ({\mathrm{\μ}}_{m1},v_{m1})& ({\mathrm{\μ}}_{m2},v_{m2})& ...& ({\mathrm{\μ}}_{mn},v_{mn})\end{array}\right]$

The abundant degree of information is divided into following a few class: very abundant, the most fully, typically, the poorest, the poorest, respectively Corresponding grey scale value [0,0.2), [and 0.2,0.4), [0.4,0.6), [0.6,0.8), [0.8,1.0] }；

B () determines sub-set of factors { U_ijWeight

Index weights based on information entropy theory is used to determine method, if iotave evaluation data matrix X=(x_ij)_n×m, wherein x_ij >=0 (i=1,2 ..., n；J=1,2 ..., m).Variable-value scope in comentropy is between 0～1, it is therefore desirable to former Beginning evaluating data matrix X carries out pretreatment, and this project uses normalized method to process, and obtains the square after normalized Battle array E=[e_ij], wherein

For certain index i in planning appraisal, its comentropy is:

The entropy weight of i-th index is:In formula: 0≤w_i≤ 1, and

According to influence factor's Recurison order hierarchy relation, information entropy theory is used to provide each sub-factor { U in same level_ijAbout upper one Layer criterion U_iWeight and put gray scale accordingly, constitute weight sets；

In formula, each weighted value requires normalization, i.e.

C () obtains simple element evaluation vector

M (,+) operator is used in the computing of mould portion, and employing M in the computing of ash portion (⊙ ,+) operator, therefore, based on following formula Calculate single factor test U_iJudge vector:

(3) Comprehensive Evaluation

By sub-set of factors U_iAs the element of its upper level set of factors U, by the simple element evaluation vector above obtained, constitute comprehensive Judgement Matrix:

Same utilization information entropy theory provides factor U_iAbout the weight of destination layer and put gray scale accordingly, constitute weight sets:Wherein, each weighted value requires normalization, i.e.

The Comprehensive Evaluation obtaining planning concept is vectorial:

(4) process of evaluation result

Above-mentioned evaluation result is handled as follows:

If b_i≥b_j, then b_i≥b_jCredibility be: p_ij=P (b_i≥b_j)=(1-v_i)(1-v_j) (i, j=1,2,3,4,5 and i ≠ j)

Otherwise, b_i≥b_jCan not reliability, i.e. b_j＞ b_iCredibility be: p_ji=1-p_ij=1-(1-v_i)(1-v_j) (i, j=1,2, 3,4,5 and i ≠ j)；

In above-mentioned evaluation result, degree of membership b_iFor maximum credibility it is

According to maximum membership grade principle, choose the p that confidence value is maximum_iCorresponding comment V_iKnot is finally passed judgment on for programme Really.